Coordination and Monitoring C e Leve ServiCeS BaSed on ...835042/FULLTEXT01.pdfSchool of Computing Coordination and Monitoring ServiCeS BaSed on ServiCe LeveL ... The thesis concludes

Blekinge Institute of Technology

Licentiate Dissertation Series No. 2012:01

School of Computing

Coordination and Monitoring ServiCeS BaSed on ServiCe LeveL agreeMentS in SMart gridS

Co

or

din

at

ion

an

d M

on

ito

rin

g S

er

viC

eS

B

aS

ed

on

Se

rv

iCe

Le

ve

L a

gr

ee

Me

nt

S in

SM

ar

t g

rid

S Shahid H

ussain

ISSN 1650-2140

ISBN: 978-91-7295-224-9

The EU Climate and Energy package, setting

the 20-20-20 targets of future energy systems by

2020 will change the landscape of future energy

system in Europe and worldwide. A transition

from monopolised controlled Power network to

customer oriented Smart Grids operating in dere-

gulated energy markets poses several regulatory,

organizational and technical challenges. To that

end several international Smart Grid projects

have been launched worldwide in EU, the US

and China. To cope with the inherent complexity

of Smart grid systems the systemic property of

Interoperability has been proposed by organisa-

tions such as NIST and GridWise in the US and

is also adopted by EU.

Interoperability of smart grids entails design,

implementation, validation and maintenance of

systems ensuring technical, information, and or-

ganizational interoperability. In order to address

Quality of Service (QoS) in this setting, the tool

of Service Level Agreements (SLAs) has been

proposed. A SLA set up the coordination bet-

ween stakeholders in a business case and rele-

vant services with set-points and agreements to

be monitored. A challenge is to identify relevant

(new) stakeholders, their competences and roles

in the business case.

In the thesis we specifically address the follo-

wing issues:

• Empowerment of end-users

• Trustworthy integration of DER- Distributed

Energy Resources in delivered services

• Validation (Interoperability) of SLAs

To those ends, we have implemented an experi-

mental test bed based on Multi-Agent systems

and sensor technologies.

The thesis concludes with assessments of our

findings and some pointers to future work. Our

work is validated scientifically and industrially

by participating in the two EU project INTE-

GRAL and SEESGEN-ICT, both ended in late

spring 2011.

aBStraCt

2012:01

2012:01

Shahid Hussain

Coordination and Monitoring Services Based on Service

Level Agreements in Smart GridsShahid Hussain

Coordination and Monitoring Services Based on Service

Level Agreements in Smart Grids

Shahid Hussain

Licentiate Dissertation in Computer Science

Blekinge Institute of Technology licentiate dissertation seriesNo 2012:01

School of ComputingBlekinge Institute of Technology

SWEDEN

2012 Shahid Hussain School of ComputingPublisher: Blekinge Institute of Technology,SE-371 79 Karlskrona, SwedenPrinted by Printfabriken, Karlskrona, Sweden 2012ISBN: 978-91-7295-224-9 ISSN 1650-2140urn:nbn:se:bth-00518

Abstract

The EU Climate and Energy package, setting the 20‐20‐20 targets of future energy systems by 2020 will change the landscape of future energy system in Europe and worldwide. A transition from monopolised controlled power network to customer oriented Smart Grids operating in deregulated energy markets poses several regulatory, organizational and technical challenges. To that end several international Smart Grids projects have been launched worldwide in EU, US and China. To cope with the inherent complexity of Smart grids the systemic property of Interoperability has been proposed by organisations such as NIST and GridWise in the USA and is also adopted by EU. Interoperability of smart grids entails design, implementation, validation and maintenance of systems ensuring technical, information, and organizational interoperability. In order to address Quality of Service (QoS) in this setting, the tool of Service Level Agreements (SLAs) has been proposed. A SLA set up the coordination between stakeholders in a business case and relevant services with set‐points and agreements to be monitored. A challenge is to identify relevant (new) stakeholders, their competences and roles in the business case. In the thesis we specifically address the following issues:

• Empowerment of end‐users • Trustworthy integration of DER‐ Distributed Energy Resources in delivered services • Validation (Interoperability) of SLAs

To those ends, we have implemented an experimental test bed based on Multi‐Agent systems and sensor technologies. The thesis concludes with assessments of our findings and some pointers to future work. Our work is validated scientifically and industrially by participating in the two EU project INTEGRAL and SEESGEN‐ICT, both ended in late spring 2011.

V

Acknowledgement

I would like to express my sincere gratitude to my main supervisor Prof. Rune Gustavsson for his invaluable guidance and help in my ongoing studies. It is really a wonderful experience I gained from him about understanding not just education but about life.

Thanks to Prof. Lars Lundberg and Dr. Martin Fredriksson for their useful guidance in understanding PhD education. None the less the moral support from Jenny Lundberg and the news about flaws and breaches in ICT from Bjorn Stål has really motivated me to go forward. Thanks to my fellow PhD students that provided me a wonderful experience in life during the stay at BTH. Thanks to all the other co‐workers at BTH for their encouragement that keep me fresh and energetic. I gratefully acknowledge the contributions of Lars Nordström, Arshad Saleem, Nicholas Honeth and Claes Sandels for their Ideas and contribution to this thesis.

Last, I would like to thank my Father and Family for all their patience they have shown during my studies.

This work has been partially funded by the EC grants: FP6‐038576, INTEGRAL FP7‐238868, SEESGEN‐ICT

VI

Dedicated to

My father & family

VII

List of Publications

The following publications are included in this thesis

Published Papers

• Hussain, S., Gustavsson, R.: Coordinating Energy Business Models and Customer Empowerment in Future Smart Grids. ICST Conference on E‐Energy. E‐Energy, 2010. October 14‐15, 2010, Athens, Greece.

• Hussain, S., Honeth, N., Gustavsson, R., Sandels, C., Saleem, A.: Trustworthy Injection/Curtailment of DER in Distribution Network maintaining quality of Service. Intelligent System Application to Power Systems (ISAP), 2011 16th International Conference on. pp. 1–6 (2011).

• Gustavsson, R., Hussain, S. and Nordström, L: Engineering of Trustworthy Smart Grids Implementing Service Level Agreements. In Proceedings of 16th International Conference on Intelligent System Applications to Power Systems. September 25 – 28, 2011, Greece, 2011 (2011).

The following reports were published for EU SEESGEN‐ICT project; some material from the reports has been adapted in this thesis.

EU Project Published Reports

• SEESGEN‐ICT D3.2 ICT for Data Management and Inter‐Stakeholders Services Monitoring in Smart Grids, July 2010 • SEESGEN‐ICT D3.3 Report on Technical and Non‐Technical Barriers and Solutions for Inter Stakeholders Service Monitoring in Smart Grids with DER, September 2010 • SEESGEN‐ICT D3.4 Policy Actions and Recommendations for Coordination and Monitoring of Processes in Smart Grids, March 2011

VIII

IX

List of Abbreviations

BMS Business Management Systems DER Distributed Energy Resources DG Distributed Generation DGO Distributed Generation Operator DN Distribution Network DS Distributed System DSO Distribution System Operator CVPP Commercial Virtual Power Plant EC2 Elastic Cloud Computing EMS Energy Management Systems IaaS Infrastructure as a Service ICT Information Communication Technologies IKT Information and Knowledge Technologies OPC OLE for Process Control PaaS Platform as a Service PHEV Plug‐in Hybrid Electric Vehicles PLC Programmable Logic Controller RES Renewable Energy Sources RTU Remote Terminal Unit SaaS Software as a Service SCADA Supervisory Control and Data Acquisition SLA Service Level Agreements SLO Service Level Objectives TSO Transmission System Operator VPN Virtual Private Network VPP Virtual Power Plant XML Extensible Markup Language

Table of Contents

PART I ‐ PREAMBLE

1. INTRODUCTION ..................................................................................................... 15

1.1 POWER GRID ............................................................................................................ 15 1.2 SMART GRIDS ........................................................................................................... 16 1.3 STAKEHOLDERS & CHALLENGES OF SMART GRIDS ............................................................ 17 1.4 MONITOR/CONTROL – COORDINATION IN SMART GRIDS .................................................. 20 1.5 CHAPTER SUMMARY .................................................................................................. 23 1.6 THESIS STRUCTURE .................................................................................................... 24

2 RESEARCH METHODOLOGY ................................................................................... 25

2.1 SERVICE LEVEL AGREEMENTS (SLAS) ............................................................................. 26 2.2 OBJECTIVES OF THE THESIS ........................................................................................... 26 2.3 CONFIGURATION OF METHODOLOGY ............................................................................. 27 2.4 IDENTIFIED GAPS ....................................................................................................... 27 2.5 RESEARCH QUESTIONS ................................................................................................ 28 2.6 CONTRIBUTIONS ........................................................................................................ 29

3 COORDINATION ‐ INTEROPERABILITY AND TRUSTWORTHINESS ............................. 31

3.1 INTEROPERABILITY ..................................................................................................... 32 3.2 TRUSTWORTHINESS .................................................................................................... 34 3.3 CHAPTER SUMMARY .................................................................................................. 37

4 SLA – A MECHANISM FOR TRUSTWORTHY COORDINATION ................................... 41

4.1 SLA DESIGN ............................................................................................................. 43 4.2 SLA FRAMEWORK ..................................................................................................... 44 4.3 SLA NEGOTIATING TOOL ............................................................................................. 47 4.4 CLUSTERS OF SLAS .................................................................................................... 48 4.5 MONITORING SLA ..................................................................................................... 49 4.6 ARCHITECTURAL DESIGN ............................................................................................. 50

5 CONCLUSION ......................................................................................................... 53

5.1 RESEARCH QUESTION ANSWERS ................................................................................... 53 5.2 RELEVANCE .............................................................................................................. 54 5.3 VALIDATION ............................................................................................................. 54 5.4 CONCLUSION ............................................................................................................ 55 5.5 FUTURE WORK .......................................................................................................... 55

6 REFERENCES .......................................................................................................... 57

XI

XII

PART II ‐ PUBLISHED PAPERS

COORDINATING ENERGY BASED BUSINESS MODELS AND CUSTOMER EMPOWERMENT IN FUTURE SMART GRIDS ................................................................................................... 63

TRUSTWORTHY INJECTION/CURTAILMENT OF DER IN DISTRIBUTION NETWORK MAINTAINING QUALITY OF SERVICE ................................................................................ 75

ENGINEERING OF TRUSTWORTHY SMART GRIDS IMPLEMENTING SERVICE LEVEL AGREEMENTS ................................................................................................................ 87

APPENDIX A

SLA AGENT IMPLEMENTATION ...................................................................................... 100

PART I PREAMBLE

Introduction P a g e | 15

1. Introduction

During the last decade there has been a significant increase in the consumption of energy due to increase usage and reliance on electrical devices. The usage of smart devices makes life comfortable during travelling, reading, listening, jogging and sleeping. These devices create energy demand. Such energy is in the form of electricity produced by large power plants at distant location then transports that electricity to the home power outlet with the help of electric wires. The distributed nature of this electric wires network poses numerous challenges for the safe and reliable delivery of the energy. Numerous incidents have demonstrated the frequency and serious consequences of disruption of energy delivery on the broader scales like the 2003 blackouts in USA, where millions of people suffered for a 24 hours outrage costing billions of US dollars. Scandinavia also suffered similar incident in 2003 and the estimated economy loss was around 4000MSEK [1]. These events raise concerns in public opinion about the reliability of the power systems. The noticeable fact is that the above mentioned blackouts happened due to malfunctioning in SCADA (Supervisory Control and Data Access) system or the staff was not trained enough to verify what was the cause of the alarm, so information was not properly managed [2]. Therefore a need is noticeable for ICT (Information Communication Technology) systems to measure, analyze and control such information. Traditional power systems are composed of BMS (Business Management System) and EMS (Energy Management System).

• Power Systems = EMS + BMS The EMS system is in turn composed of tools supporting the operator to manage daily energy flow activities; therefore, these tools help the operators to make effective decision. A subsystem of EMS called SCADA system provides Control and Monitoring of electrical devices to EMS system. The BMS handles business management functions like CRM (Customer Relationship Management) and Billing system.

1.1 PowerGridTraditional Power grid consists of multiple functions like generation, transmission and distribution of electricity. Conventional grid stakeholders are based on these predefined functions. Hence the stakeholders (like DG, TSO and DSO) describe the functionalities of the stakeholders. The old power systems primarily focus was to generate, transmit and distribute energy, with predefined quality to support demands. Furthermore the business should be as cost efficient as possible. Here are some of the key points in Power Systems

• Focuses on generation and distribution of power, the customer is traditionally

regarded as a passive load.

P a g e | 16 Introduction

• Emphasis on hierarchical monitoring and control of power production and distribution manifested stovepipe SCADA system.

• Emphasis on subsystem related to reliability and billing. • Generation Operators and TSOs are the main key players that control the market

and the pricing strategy. • Tariff based billing

Power Distribution

Generation

Information flow Energy flow

Consumer

Business Fig 1.1, Architecture view of information/energy flow in power grid

Fig 1.1, presents the architecture view of energy flow and information flow in the power grid, the power link between the power generation and distribution are supported by long transmission lines. Today SCADA systems monitor different set point for energy flow in generation, transmission and to some extent in distribution as shown by dotted lines in the Fig 1.1. SCADA system relays back the information to a control centre operator that takes necessary action to keep demand and supply in electric balance. From Fig 1.1, the information about consumption is in the form of meter data to generate the bills for the customer.

1.2 SmartGridsThe concept of “smart” in Smart Grids refer mostly to “Smart distribution grids” utilizing smart energy system components, empowered and active customers, and flexible and resilient systems [3]. This is enabled by a transition of today’s hierarchical and mostly proprietary systems to open, loosely coupled and flexible service oriented systems. These flexible pattern oriented interaction models are key enabler for smart grids as evolving system.

P a g e | 17

Future Smart Grid goals are presented in number of articles; however a standard and generic definition does not exist. In this thesis, we follow the Smart Grids European Technology platform1 definition as presented “Smart grids as electricity networks that can intelligently integrate the behaviour and actions of all users connected to it ‐ generators, consumers and those that do both – in order to efficiently deliver sustainable, economic and secure electricity supplies. “ As presented further, a smart grid employs innovative products and services together with intelligent monitoring, control, communication, and self‐healing technologies in order to:

• Better facilitate the connection and operation of generators of all sizes and technologies.

• Allow consumers to play a part in optimizing the operation of the system. • Provide consumers with greater information and options for choice of supply. • Significantly reduce the environmental impact of the whole electricity supply

system. • Maintain or even improve the existing high levels of system reliability, quality and

security of supply. • Maintain and improve the existing services efficiently. • Foster market integration towards European integrated market.

1.3 Stakeholders&ChallengesofSmartGridsStakeholders plays important role in defining the business and organization aspects in power grids. There are number of challenges associated with the increase of these stakeholders. In this section we will discuss the old and new stakeholders as well as challenges involved in the current infrastructure.

1.3.1 TraditionalStakeholdersIn traditional power systems, the stakeholders have names based on their roles. The stakeholders are:

• Generation Operator • Transmission System Operator (TSO) • Distributed System Operator (DSO) • Consumers/End Users (Loads)

Usually, the TSO monitors and controls the high‐voltage energy flow from generation to lower‐voltage levels via the transmission networks. The energy flow at lower voltage levels to end users is managed by DSO.

1 EU Smart Grid Home Page http://www.smartgrids.eu/


Generation Operator The estimated household electricity consumption of EU countries was approximate 765TWh2 in 2004 [4].Vast amount of resources are required to balance this needs. The large segment of energy is produced by big power plants that generates huge amount of electricity like Dams, Gas/Coal power plants and nuclear power plant.

There are two categories of generation operator’s • Large scale

Hydro Generation (DAM), Coal Power Plants, Nuclear power plants

• Small Scale Small scale Hydro power Generation, Wind farms, Gas Power plants

The future role of Large generation plant remain the same, however the small scale plants will consider to take more active part in smart grid (referred in section heading DERs).

TSO Electricity as fundamental energy resource, it is important to transport generated energy from generation to consumer locations. Transmission System Operator manages large networks of electric wires. These electric wires provide a medium to carry electricity from generation to near location of the consumers. Additionally TSO is also responsible to maintain energy balance by incorporating small power plants that functions in

• Increasing/decreasing the amount of energy • Controlling frequency and harmonics levels • Maintaining voltage level (reliability/protection)

DSO DSO (Distribution System Operator) is responsible for distribution of electricity to the consumer. Typically the distribution network works at medium (less than 50kV3) and lower (less than 1 kV) voltage levels. Due to the deregulation in EU, the DSO is responsible for running the distribution network maintaining power quality in the network. To maintain the power quality it has to consider either injection or curtailment of DER. The role entails the DSO to act as a facilitator in energy flow and not to participate in actual energy market. The DSO main concern is to optimize the usage of its grid network. Consumers/End Users (Loads) Consumers are one of the key stakeholders that use electricity. They are divided into two main categories

• Residential • Industrial

2 TWh ‐ Terawatt hours 3 kV ‐ Kilovolts


From the power grid perspective they are termed as loads and these loads are divided into two categories

• Deferrable loads The transition from simpler loads to Demand Side Management (DSM) programs has provided means for DSO to control customer’s energy usage according to agreement. Few Industrial units categorize as deferrable loads, where the DSO/consumer if desired can cut, decrease or increase the loads.

• Non deferrable loads Consumers who do not participate to reduce or increase loads in DSM

programs referred as non deferrable loads.

1.3.2 Transition:NewstakeholdersThe transition from old power grid to future smart grid introduces new stakeholders and also there are additional roles for DG and DSO. As a result, it creates new business cases and the demand for additional ICT infrastructure to manage information flow. Distributed Energy Resources (DERs) Operator These are the small distributed power sources that are used to generate power and typically incorporated at the distribution level in the Grid. DER injects energy at the horizontal level in the Grid. Normally DER generate small amount of electricity in comparison to large power plant. DSO injects or curtails energy from DER into the distribution grid in a short amount of time to balance demand and supply gap resulting in optimally distribution system management. These are broadly categorize into two types

• Schedulable resources CHP, Small Gas/Diesel Generator, Bio Gas plant

• Non‐Schedulable resources Wind Power plant, Solar panels

DER sells its energy in the day‐ahead market, although their production is dependent on the DSO. However their aim is to provide efficient substitute to central generation of energy. Commercial Aggregator The role of the commercial aggregator is to manage the information flow and act as the service broker. It acts as a market, the sellers publish their service offerings and the buyers buy those service offerings using the brokerage services. Empowered Customer In view of the new use cases the need of empowered customer is highly emphasis in recent EU Smart Grid report [4]. As due to the transition and also due to the influx of PHEVs, the consumer has a new role of producing energy also, thus sometime refer as prosumer.


According to Webster dictionary Empower means “to give official authority or legal power”. In this thesis we argue that this transition from loads to empowered customer needs support of additional tools for empowerment. These tools require ICT platform to facilitate in providing services to the customer. These tools provide sufficient interfaces for adding and monitoring services and other information based on negotiated SLAs.

1.3.3 ChallengesinSmartGridDuring the last century, the State manages and operates the Power Grid from Generation, Transmission till Distribution. In the last decennium of 20th century, it was demanded by the EU to de‐regulate the power grid. Due to these de‐regulation new players emerged on the scene resulting additional business perspective with new business offerings along with competitive prices. However, as the old infrastructure has been running almost for 100 years in a controlled manner, less effort has been done for new business opportunities and services offering. Hence, legacy system like SCADA shall be needed to monitor and control the existing components [5]. This tight coupling entails the SCADA system to work in a vertical manner. Furthermore, the control and monitoring mechanism works in the same bidirectional way i.e. measurements collected at the distribution level and transmission level relay to an operator to manage the generation and distribution accordingly. Future Smart Grids highlight new stakeholders and future needs of individuals as well as of the society at large to save the resources of the planet. It enhances the importance of adding more renewable resources to the current infrastructure and manages the existing resources in an effective and efficient manner. The basic services offered by the current infrastructure needs an improved measuring and monitoring mechanism that will provide a way forward to achieve the goals emphasized in the future Smart Grid agenda that are mentioned as:

• Consumer empowerment • Interoperability between SCADA and ICT • Managing horizontal integration by incorporating DER and RES • New services offering like Green energy and V2G integration • Security of the Grid • Self healing and resilience

Solutions for alternative energy sources deem important to manage demand and the supply gap. It has been highlighted in many USA and EU projects to develop new ways for energy sources and enhance methods that helps not only in restoring the energy balance but also preserve the natural resources to keep the planet clean for future generations.

1.4 Monitor/Control–CoordinationinSmartGridsAs already discussed in introduction section, currently power grids are monitored and controlled by SCADA system. SCADA system is a vital component in today’s energy sector; it provides monitoring and controlling interfaces between the human and the machine. It


helps the operator to read sensory data or to change state in actuators to ensure stream flow of energy.

1.4.1 SCADASystemTraditional SCADA systems evolve from telemetry and telecomm solutions. In the context of power management, SCADA is used to monitor the generation, transmission and distribution of energy flows. Characteristics of SCADA systems are:

• Hierarchical • Highly integrated • Supporting simple data models (set points)

SCADA system has evolved through three generations:

• Mainframe (monolithic) systems • Distributed systems (Mostly proprietary solutions) • Networked systems (Open architectures and protocols)

SCADA system monitors sensors to measure the amount of some specific energy flow or state of a device. These measurements are affixed to a static, typically hard coded, hierarchical representation of the grid. The energy flow process consists of thousands of devices from large scale transformers to small switches and sensors that relay measurement to a specific terminal for showing specific information. Common SCADA system components:

• Human‐Machine Interface • A supervisory (computer) system • Remote Terminal Units (RTUs) • Programmable Logic Controller (PLCs) • Communication infrastructure (Connecting the supervisory system with RTUs)

Control actions are performed automatically by (local loops) RTUs and PLCs. The Host control functions mostly on supervisory level interventions. Furthermore, it is developed to operate in a closed, controlled networked environment [6, 7]. This is relevant in part due to the sensitive nature of the governed infrastructure, but also because the components that make out the system span considerably larger period of time and area than other corporate ICT solutions. Implies that a larger span of attack techniques are viable and more likely to succeed than in a more dynamic open or semi‐open network such as the internet or an intranet [8]. Moreover, as the SCADA system is a key component in the retrieval of measurements on the current state of the governed infrastructure, information somehow need to move between the closed SCADA networks to other parts of the corporate network [7]. Thus, it is no longer feasible to operate under the conditions of the SCADA network being closed to the outside world.


As a matter of fact, without added communication systems, a transmission or distribution operator has no way of determining whether customers are connected or a problem has occurred somewhere in the field [8]. The SCADA offers no intrinsic way of informing from one location; what is happening at another location. Furthermore, the tight coupling and integration of signals make it hard to “interpret”, “reuse” or “manage” data from a SCADA system [8]. Of course, SCADA systems have also been complemented by tools for data management (predictions, performance analysis) by system administrators. But, in summary, present SCADA systems have to be supplemented by other ICT systems supporting new energy based business processes (energy efficiency) and customer empowerment of future Smart Grids.

1.4.2 ChallengesforICTsupportedSCADAsystemsSCADA system is a vital component in today’s utility sector; it provides monitoring and controlling interfaces between the human and the machine. There are number of factors to support the need of new SCADA systems:

• The technology is getting older as it can only manage the energy flow, while there is a huge demand for managing information flow caused by the emergence of Smart Grids concepts.

• Future Smart Grids introduces new stakeholders that require information about

the energy flow in real time that is arguably not being provided by the current SCADA systems.

• In Smart Grids the consumer is also an active participant (new type of stakeholder)

that makes optimized decision based on his/her usage of energy. There are known vulnerabilities, at different voltage levels, in currently used SCADA technologies as presented in [2, 6, 8]. However, due to the large installed base of SCADA systems, we have to deal with several legacy problems during transition from current grids to future Smart grids. One challenge for SCADA systems in near future will be the increasing amount of data. There will be more sensory measurements in the network. These measurements will be located on low voltage level at the distribution level or at end user premises. For instance distribution transformers are increasingly equipped with on‐line monitoring systems and protection relays include measurement functionalities. Present communication systems are becoming more and more reliable for managing these data masses. The key function will be to find the important data and to use it efficiently. This means that data processing will be a vital function and development will be needed also on this area.

Another challenge for SCADA systems is the increasing amount of generation on all voltage levels [9, 10]. As DG can be present practically anywhere, the real‐time network


management becomes more important. For instance the switching state of the DG units can be crucial information for the network operator. From the network reliability point of view, network becomes more dependent on the generators and needs thereby to control and monitor them efficiently. Also power quality monitoring and management will increase and it needs to be integrated into distribution automation and SCADA.

Generally the protection system will be using much more communication. Increase in communication will also occur between individual relays, not only between SCADA and the relay. Standardized protocols such as IEC 61850 enable effective communication. For instance, typical protection problems caused by DG in distribution network could be solved by using communication between feeder relay and DG interconnection relay. Generally, it can also be said that distribution system protection will start to share similarities with transmission system protection as the amount of DG increases. This is mainly because distribution network becomes more meshed with generators connected. Thus some protection solutions could be imported quite simply from transmission networks.

1.5 ChapterSummaryThe inherit properties of Power System are

• Distributed Systems • Multiple Stakeholders • Business Cases

Based on above properties, the challenge is to make a smooth transition from classical monitoring and control stovepipe system to flexible, well‐coordinated and reliable systems of systems. A stovepipe system is a legacy system that is an assemblage of inter‐related elements that are so tightly bound together that the individual elements cannot be differentiated, upgraded or refactored. Due to the fact that future Smart Grids will incorporate new stakeholders with new business cases, eventually results in huge amount of additional information flow. The current system tools like SCADA system is unable to handle such transition, hence there is a need to have additional ICT infrastructure to handle coordination between components. A requirement for new ICT systems with loosely coupled objects enables flexibility. The decoupling of systems is enabled by a service oriented approach; hence, web service applications realized by developing small component with flexible configuration yet having strong coordination orchestrated as a service. A decoupled (and hence more flexible) SCADA system could in fact be realized by coordinated set of services offered by coordinated SLA. Keeping in view the benefits of ICT in individual’s life it is worthy to open the stovepipe energy system and couple it with ICT system to build future Smart Grids platform. The gap between the current power grids infrastructure and future smart grids provides researchers the opportunity to open up the traditional grid and coordinate information using ICT systems.


1.6 ThesisStructureThis thesis is divided into two parts, Part 1 covers chapter I – V. In chapter I, we introduce background knowledge, stakeholder information, future perspective and challenges involved in the future smart grids. Chapter II presents research focus, aim and objectives identified gaps and research questions. In Chapter III we address the interoperability framework and trustworthiness. Chapter IV describes SLA mechanism for coordinating information flow. In Chapter V we concluded the thesis with SLA as promising approach towards trustworthy coordination. Part II present selected publications related to the research.

P a g e | 25 Research Methodology

2 ResearchMethodology EU commitment towards 2020 Goals is driving Power System players to integrate ICT platform to achieve their goals. We have also noticed high emphasis in EU 6th and 7th Framework program to upgrade the traditional Power Systems and couple it with ICT system to create future smart grids platform. As mentioned in several EU smart grid reports [3, 11], there will be a huge increase in information flow within the power components. To manage such huge amount of information, there is a need of additional explicit ICT platform to manage and monitor the system. Due to the distributed nature of power systems each stakeholder knows only a small part of systems information i.e. at local level. Any malfunction at a local level can create cascading effects [2, 10]. Hence, a global view is required to make critical decisions if anything happens at a local level. Currently, ICT tools deploy at more local level, however to have a global view a strong ICT infrastructure is desired as presented by NIST [12] in Fig 2.1.

Fig 2.1, NIST Smart Grid Conceptual Model – Top Level [12]

As presented in Fig 2.1, a step towards future Smart Grid is to have strong coordination mechanism among all the stakeholders. Coordination binds two systems to manage and share information [13]. The key mechanism for trustworthy coordination is to manage and monitor the information exchange as agreed by all the stakeholders. Successful Information exchange can be achieved with interoperable systems [14]. Protocols and standards create interoperable system. Adherence to these protocols ensures interoperability. Protocols provide horizontal level interoperability, whereas for vertical interoperability data models are required for information exchange between the network and business layer.

The other important barrier is the tight integration of Price with Energy (KW/h) as a product. The approach we recommend is to look at the system from ICT perspective, to

Research Methodology P a g e | 26

take advantage of services and allow new stakeholders and business cases. We argue to decouple energy with price and consider more added values to customer by not just providing energy but COMFORT as a service. Consequently, we highlight strong coordination between ICT and SCADA system facilitated by Service Level Agreements as a middleware solution towards trustworthy Smart Grids.

2.1 ServiceLevelAgreements(SLAs)SLA (Service Level Agreement) is a formal agreement between the service providers and the customers in the context of service provisioning. It plays significant role in ensuring trustworthiness among stakeholders. The agreement mostly covers the quality aspect of service like performance, availability and responsibility. It also holds the information about the roles and the responsibilities between the service provider and the customer and within the other stakeholders involved in the provisioning of services.

The future SMART GRIDS requires new stakeholders and the empowerment of the customers that are not supported by the current EMS and BMS systems. To incorporate these stakeholders and new business cases, a strong coordination and monitoring mechanism is required to ensure the provisioning of services in a trusted way. Implementation of systems taking into account functional and non‐functional requirements as well as issues of flexibility and maintainability take into models and methods of Service Oriented System engineering. Applications are then designed as configuration of services. This transition from closely coupled system to loosely coupled systems with strong coordination emphasis the need of SLA as coordination mechanism. Moreover, SLAs provides tools for monitoring critical state parameters that are important to measure breakdowns and recover the system from critical situations.

In this thesis, we argue that Monitoring set points using Service Level Agreements (SLAs) as a tool provide trustworthy information exchange among the stakeholders. Further, we presented use case scenarios and simulation using agent systems to provide the proof of concept of our approach.

2.2 ObjectivesofthethesisThe aim of the study is to provide proof of concept that SLAs offers a mechanism for trustworthy coordination within Smart Grid systems.

The following objectives represent the direction of the study

• SLA governance and procurement. • Modelling and evaluation of information flows across boundaries provided by SLA

concerning interoperability. • Benefit of adding ICT infrastructure by promoting additional business perspectives

like o Empowerment of the customer o Trustworthy Management of Distributed Energy Resources (DERs)

P a g e | 27 Research Methodology

This study will further help in understanding the maintenance aspect of the system in order to make it resilient. Future viewpoint is to develop a trustworthy coordination mechanism between horizontal and vertical components to maintain quality of service in critical and emergency situations in SMART GRIDS.

2.3 ConfigurationofmethodologyThe methodology adopted is based on the resources we have at our disposal. We study reports from the relevant EU projects like EU‐DEEP, CRISP and FENIX as part of literature review. To address the gaps mentioned in EU 20‐20‐20 goals we select appropriate use cases, filtered and refined it. We model the use cases into SLAs using agent based simulations. The purpose of this method is to explore and generate ideas and awareness about the usage of SLAs in critical infrastructure. We develop agent based simulation to provide proof of concept. On the basis of these studies, the second phase will be to develop further an instrument to analyze economical and technical (ICT) impact of business cases bounded SLAs in critical infrastructure.

2.4 IdentifiedGapsThe transition of stakeholders from current grid and future smart grids is presented in Fig 2.2.

Fig 2.2, Transition of Stakeholders and the requirement of Information flow

Generation Operator

TSO

DSO

Loads

DER/DG

TSO

DSO

Empowered Users

Service Broker

SCAD

A

Inform

ation Flow

Transition of StakeholdersCurrent Power Grid Future Smart Grid

SCAD

A

Service Provider


As presented in Fig 2.2, the new stakeholders emphasise a need for additional ICT system. That will also support the need of coordination mechanism between the stakeholders as well as with the SCADA system. Hence, the following gaps between the current grid and future smart grids are identified as.

• New stakeholders (mostly related to information flow) • Enhanced coordination mechanism • Interoperability issues • Trustworthy information flow

On a broader scale the requirement of ICT platform is non‐trivial for future smart grids, however, in order to have such a platform there is a need to address and solve the following identified questions.

2.5 ResearchquestionsThe following research questions addressed in this thesis:

RQ1 ‐ Describe mechanisms supporting coordination of stakeholders in Smart Grids?

To answer this research question, we looked at the methods for coordination. In distributed system the two methods provide coordination between systems.

• Data Driven (e.g., SCADA) • Control Driven

Both methods have their own pros and cons, however given the nature of heterogeneous system we adopt for Control Driven method. However to ensure the semantic translation we argue that end to end SLA based coordination is a promising approach towards interoperability [15].

RQ2 ‐ Identify the mechanism supporting trustworthy inclusion of DERs.

To address this question, we looked at the concerns in the management of DERs. Reviewing through the literature we identify the Gaps that concern the relevant stakeholders. These concerns inform us to look at trustworthy information exchange. For that we review reputation and concern based trust models. As trustworthiness provide certain degree of trust that can be quantified based on user concerns. We identify concerns based trust model is more appropriate for trustworthy management of DERs in smart grid [16].

RQ3 – Exemplify a proper model supporting system quality?

The interoperability model provided by NIST is very comprehensive; it provides a framework for all the systems providers to have a common understanding. However it is unable to provide common common understanding between the


systems. To ensure this common common understanding we propose that procurement and management of SLA is a promising approach [17].

2.6 ContributionsThe main contributions are in the form of papers and the tool we developed for the management of SLA. Paper I: In this paper we identified that there will be an increase in information flow in energy systems. ICT systems are required to manage it properly; however the integration of the systems requires strong coordination. Coordination between these systems should also consider other factors like mentioned Gaps in SCADA system. Paper II: This paper contribution is about curtailment and injection of DERs; moreover paper II presents use case and certain rule set modelled in the form of SLAs for trustworthy management of DERs. Paper III: Contribution in this paper is to understand the need of syntax and semantic while designing a system. Specifically, we argue that interoperability at vertical levels requires data models that can be address by SLAs. The contribution to the research questions are as under Research Questions Paper I Paper II Paper III RQ1 X RQ2 X RQ3 X X

Coordination ‐ Interoperability and Trustworthiness P a g e | 31

3 Coordination‐InteroperabilityandTrustworthiness

A transition from monopolised hierarchically controlled power networks to customer oriented Smart Grids operating in deregulated energy markets poses several regulatory, organizational and technical challenges [3]. To that end several international Smart Grids projects have been launched worldwide in EU, USA and China. The core of present day power systems is the EMS – Energy Management System monitoring and controlling the performance of production, transport and distribution of power. The EMS is supported by SCADA systems to monitor and control as well as support systems for protection, optimization and billing. The important factor is coordination for effective communication among stakeholders. Coordination is defined as

“Coordination is managing dependencies between activities” [13, 18].

Coordination is required where two or more systems communicate to do a distributed task. Due to the distributed nature of smart grid the tasks needs to be distributed. According to [13], coordination is classified into two models.

• Data Driven • Control Driven

In data driven coordination model, the actors exchange data with predefined structural information. A static shared space is used to represent, store and access data. In agent system “Black board” coordination mechanism exemplifies the data driven coordination.

Whereas, in control driven coordination models the system components interact through events or functions. In this type of coordination the exchange of information is about states and data. The change in state or events may modify the control structure of components.

Why Coordination With the advent of Smart Grid new stakeholders emerges that are not directly involved in the power flow, but have influence over services based infrastructure. Most of these services are in the form of information that provides add‐on values to other stakeholders. The range of the services can vary from business to technical aspects. These services require coordination between components. Due to these services the increase in information exchange is advent, hence requires proper management and monitoring of coordinated tasks and the data involved in the system. A few concerns like interoperability, trustworthy and reliability raise questions in the current infrastructures [6].

A number of smart grids pilots in USA and EU have shown progress, however the promises made by incorporating ICT with energy systems are yet to be proven at large [3] . The problems faced by the engineers are manifold further research and development needs collaboration from multi disciplinary sciences. The current consideration about the impact of future smart grids is set by EU in it 20‐20‐20 goals. These EU goals drive the researchers and the stakeholders to change the century old legacy systems to more customer focus service system. The previous Energy Efficiency (EE) programmes like Demand Side


Management (DSM) reduces the amount of energy usage to 7% for large commercial and industrial customer [19], however the lack of participation of residential and small scale commercial customer in such programs poses a challenge [20, 21]. The future smart grid needs to have proper tools to incorporate those residential and small scale commercial customers. However, new challenges faced by future smart grids require empowerment of the customers: A subject in social sciences. That makes the grid a social‐technical system [11]; hence a need of multi disciplinary research emerges. Moreover, recent emphasis drawn from EU Smart Grids reports also promotes “Empowered/Active Customer”. Customer empowerment provides market flexibility and the acceptance of change from the customer’s point of view. It also provides opportunities to develop new third party tools which help the customer to make intelligent decision based on the information provided by the service providers. This information requires management of data to be stored, measured and monitored in a proper way. Eventually, provides opportunities for third party service providers to introduce value added services. These services involve interaction with other services and components of the grid: raise demand for additional coordination. The coordination need among systems need to have the following properties for managing future smart grids platform.

• Interoperability • Trustworthiness

3.1 InteroperabilityThe scope and purpose of monitoring has lately changed towards ensuring interoperability of systems due to increased complexity of the systems at hand [5]. Analysis of larger blackouts, such as the August 14, 2003 blackout in northeast United Stated and Ontario, has shown that this kind of event can be attributed to sequences of interoperability failures4 of related systems [2]. The systemic property of Interoperability has been proposed by organisations such as NIST5 and GridWise6 in the US and is also adopted by EU.

NIST has the following definition of Interoperability:

“The capability of two or more networks, systems, devices, applications or components to exchange and readily use information, securely, effectively and with little or no inconvenience to the user. The system will share a common meaning of the exchanged information and this information will elicit agreed‐upon types of response.” [NIST7]

The following additional requirements are put forward by GridWise Architecture Council (GWAC8):

4 GridWise Architecture Council Report: Reliability Benefits of Interoperability, 2009, pp. 7 – 9. 5 Home page: http://www.nist.gov/smartgrid/ 6 Home page: http://www.gridwiseac.org/ 7 Home page: http://www.nist.gov/smartgrid/ 8 Home page: http://www.gridwiseac.org/

P a g e | 33 Coordination ‐ Interoperability and Trustworthiness

• “an agreed expectation for the response of the information exchange” • “requisite quality of service in information exchange: reliability, fidelity, security” • “the results of such interactions enables a larger system capability that transcends

the local perspective of each participating subsystem”

GWAC proposed the following Interoperability Framework consisting of three Interoperability Categories (Technical, Informational and Organizational) and Crosscutting Issues related to non‐functional requirements, such as Energy Efficiency (EE). The Technical interoperability is enabled by proper open protocols and network technologies. In order to verify or validate interoperability of Smart grid systems we have to identify suitable views of those systems. That is, also take into account the Informational and the Organizational categories of the GWAC Framework.

Fig. 3.1, GWAC Interoperability Framework with a layered set of Categories and non‐functional Cross‐cutting Issues

3.1.1 SharedSituationawarenessInteroperability of systems assures and entails shared situation awareness among stakeholders in a given context. This situation can be regarded as a slice connecting those stakeholders within and across categories (Fig. 3.1).

Since decades representation and processing of information have been at the core of technologies underpinning knowledge based systems such as AI and MAS (Multi‐agent systems) as well as systems supporting information sharing such as Semantic web9. Formal models of semantics, e.g., ontology’s and reasoning, has supported digital representation and processing of semantic content10. However, there is a fundamental gap between

9 Home page: http://semanticweb.org/wiki/Main_Page 10 W3C home page: http://www.w3.org/


formal semantics and shared understanding between people. In this context there have been some groundbreaking work done by Barwise and Perry at Stanford University related to language understanding based on situation theory [22]. The work has later been extended by Devlin K. et al in several directions [23].

The following relation captures the main ideas:

Information = Representation + Interpretation (1)

The relation captures the idea that in a given situation the information message conveyed by a representation (text, language, picture, and video) has to be interpreted to give meaning (semantics). The meaning of the Information given to an agent (machine or human) is totally dependent on the agent’s capabilities to interpret the representation. Representations in machine readable format can be processed and interpreted as formal syntactic constructs that could be interpreted by humans to give contextual meaning. Agent based information processing systems process machine readable representations implementing selected reasoning models (rule sets). The strengths and weaknesses of MAS approaches are presented by Wooldridge M. et al [39]. Moreover, further complications and challenges arise when the information sharing should support common awareness in groups of people as typically in Smart Grids.

From GWAC Interoperability Framework (Fig. 3.1) it follows that the non‐syntactic semantic challenges appear at the Informational and Organizational Categories. The different Categories have the following specifications:

Organizational ‐ Economic/regulatory Policy (Pragmatics) ‐ Business objectives ‐ Business procedures Informational ‐ Business contracts (Semantics) ‐ Semantic understanding Technical ‐ Syntactic Interoperability (Syntax) ‐ Network Interoperability

‐ Basic Connectivity

From the discussion above it follows that present day syntactic representation models from, e.g., semantic web will not enable machine readable representations supporting full system interoperability regarding shared understanding (in groups) of organizational or business contexts. The proposed solution is to establish a shared ontology with agreed upon interpretations (folksonomies [24]) when setting up the relevant SLAs. Furthermore additional agreed upon information could be provided using sensor networks [25].

3.2 TrustworthinessTrust represents a relation between two actors, whereas, trustworthiness is the mechanism developed to solve trust concerns by the solution provider. Trust is hard to engineer in the


automated systems as presented in [26]. However, we can attain certain degree of trustworthiness in the system using engineering approach by addressing concerns [26] .

Modelling of trust is important from the requirement engineering perspective. Requirement engineering is formal way of gathering and managing the data [27]. Often these requirements are gathered at the primary stage of the software development cycle. At times, failing to perceive the operational requirements provides inadequate results. The operation results are highly dependent upon the following segments

• Environment in which the hardware/software is running • Communication infrastructure • Data structures

System failures and performance issues are mostly reported due to lack of requirements gathering at initial stages. Mostly the requirement gathering fails to understand the need of non‐functional requirement, hence compromising on the quality of service aspect.

3.2.1 WhatisTrustAccording to Barber B (1983), trust is defined as

“The meanings actors attribute to themselves and others as they make choices about which actions and reactions are rationally effective and emotionally and morally appropriate.”

As we look at the definition, trust is a subjective assessment of individual and this assessment can change depends on environment and other conditions influencing the individual [28]. Today’s shift of social media in ICT demand more engineering approaches to model social aspect like trust that are not thought of deemed important before. The existing model of trust like reputation management is unable to cater the need of new requirements of trust management. These systems mostly depend on user judgement or expert opinion that could suffers from individual biasness.

3.2.2 ModelofTrustIn this thesis we studied two trust models.

• Reputation Based Trust Model • Engineering Trustworthiness based on Concerns

Reputation Management System Reputation based trust model is very commonly used for online services. This model represents the experience of other users and how they judge the item/service. The model is elaborated in the example below.


EBay and Amazon have reputation management system that provides a certain degree of trust in buyer’s mind based on the judgement of previous seller’s point of view [29, 30]. Reputation management systems provide a mechanism to increase the level of trust perception in buyer’s mind. After perceiving that trust we access the trustworthiness of the rating mechanism, to add another level of trust we further read the comments of the existing buyers to get a clear picture. Although these reputation systems are very subjective to individual biasness, however, we accept the system as trustworthy while purchasing items online [29]. As discussed before the existing trust model based on reputation is very subjective assessment of individual that can not be mapped to other individuals need/values. Moreover, new product or services with no rating initially suffers unless someone use or acquire those specific object/services without trust.

Engineering Trustworthiness based on Concerns We presents engineering approach to model trust based on concerns. The figure represents our model, “An engineering approach supporting analysis of concerns” that would further provide a base for defining and monitoring appropriate QoS values.

Input Trust Concerns

Perceived Trustworthiness

Trust Aspects

Fig 3.2, Engineering Trustworthiness ‐ Supporting analysis of concerns [26]

Fig 3.2, represent a model in which concerns are filtered from stakeholder’s input and on that concerns we drive multiple aspects of it. Each aspect is than addressed and a mechanism is developed to match that aspect. The output mechanism presented in the form of sign which is shown to the concerned stakeholder in the form of signs. After seeing and interpreting the signs, the stakeholder accesses the trustworthiness and this process iterates again until the stakeholder is fully satisfied. Our trust model provides a pragmatic solution to the problems having trust concerns. It might be difficult to comprehend all the concerns with multiple aspects at a same time; however, the iterative process helps in understanding different aspect of involved

Trust Mechanism

Trust Sign

Assessed Trust


stakeholders, eventually improving the system. We can further verify flexible emerging patterns with the use of agent technologies.

In order to design and validate coordination aspect like interoperability and trustworthiness, we configure and monitor SLAs based on agent‐oriented services. Design, implementation and validation are utilizing aggregation tools and experimental environments. We implemented the concern based trust model to ensure trust between stakeholders and provide SLA as an interface to implement technical aspects in machine readable form. We describe the modelling of SLA in next chapter.

3.3 ChapterSummaryTo meet the 20‐20‐20 challenges we need to develop means and tools for improving Energy Efficiency (EE) as well as addressing other interoperability issues. More efforts are required by all stakeholders to enable improved future energy production, distribution and usage [31]. Furthermore, novel business models are required to support the transition from today’s situation to Smart grid based on markets of energy‐based services [32, 33]. Providing novel services based on setting of customer comfort is one identified area by the EU projects FENIX11 and SEESGEN‐ICT12.

Future energy systems will become robust and efficient with a careful supplement of the SCADA systems with specifically designed and implemented ICT systems ensuring Smart grid Interoperability (Fig. 3.1). We expand some novel ideas introduced in SEESGEN‐ICT [34], deliverables D3‐2, D3‐3 and [15] to assess identified barriers and implement relevant ICT solutions for future pilots of Smart Grids.

The following Fig. 3.3 captures some of the challenges identified in [34] by different Work Packages (WPs) addressed in SEESGEN‐ICT project. That is WP5 (Business Models), WP2 (Grid Infrastructures), WP4 (Demand Side Integration) and WP3 (Monitoring of SLAs).

11 http://www.fenix‐project.org/ 12 http://seesgen‐ict.erse‐web.it/


Selected EU projects and documents

Selected new components

Grid Infrastructures WP2 and WP4

SLAs WP3

Drivers and Opportunities

Drivers and Opportunities

ICT Infrastructure

Barrier

Requirements

High level (web Services)

Constraints

From Grids to Smart Grids

Business Models WP5

Stake‐holders

Low‐level (OPC)

Pilots

Solutions

‐Configuring Infrastructures and components

‐Technical ‐Non Technical Methodologies

Affordances

Fig 3.3, Views and challenges of future Smart Grids

In Fig. 3.3 the requirements from Business models have to be matched by the affordances from the grid infrastructures and the equipment and the demands at customer premises. The high‐level demands have to meet the low‐level affordances and constraints. Compare with Fig. 3.1, where the organizational and information categories have to meet the technical category as well as relevant cross cutting Issues while meeting interoperability goals. The Service Level Agreements involves concerned stakeholders as well as Key Performance Indicators (KPI) to be monitored to ensure interoperability and Quality of Service

It is worth to mentioning that in the classical grid the information processing system is by and large proprietary SCADA systems. The SCADA system integrates information bottom‐up from the grid to the system operators and allows sending control signals top‐down to the grid components: a stove‐pipe system [5]. In Smart Grids, we also need ICT systems providing horizontal as well as vertical interoperable information exchange between stakeholders (Fig. 3.1).

Identified challenges include coordination of sets of stakeholders and monitoring of processes related to new energy based business processes. To that end we have advocated introduction and use of mechanisms based on Service Level Agreements (SLAs). Introduction of SLAs also enables a principled structuring of Smart Grid systems and related data flows. In Fig. 3.3, the interaction between low‐level and high‐level SLAs is indicated. We have addressed this challenge in our publication Paper I. Our approach towards modelling and implementing Smart Grid is utilizing carefully chosen infrastructures in flexible couplings and integrations (configurations) of system components


[34–36]. The configurations support monitoring of processes by clusters of SLAs implementing selected scenarios of Smart Grids.

SLA – A Mechanism for Trustworthy Coordination P a g e | 41

4 SLA–AMechanismforTrustworthyCoordination

In Service Oriented Computing (SOC), service providers provides multiple solutions package by combining services that enhances the capability to provide off the shelf service packages. These types of offering initiated the concept of cloud computing where one can buy and sell the services from hardware resources to software systems. Such an example platforms are iPhone Apps, Amazon and eBay that are highly dependent on Service Oriented Computing to utilize their ICT infrastructure and offers the same platform for third party buyers and sellers to start their own business in the form of services.

In these scenarios, the customer interacts with the main service provider and the internal integration of sub‐contractual services provided is transparent to the end user. However, delay or failure in any internal service component might produce cascading effects that can damage the integrity of the main service provider. These dynamic integrations of services possess a threat on the trustworthy delivery service mechanism. To ensure proper service delivery, ensuring SLA provides such mechanism that enhances the trust by defining threshold values in the non‐functional specification of services. It would be plausible to monitor those quality aspects to ensure the service performance acknowledge certain threshold values in multi‐level SLAs. However typical SLA platform only ensure SLA in one to one service provisioning and unable to manage multi‐level service provisioning

The Service level agreements monitoring set points ensure certain degree of trust in the provisioning of services, providing substantial control over the dynamic behaviour of services. In service oriented computing SLA is presented in the Figure 4.1. SLAs represent an agreement between two parties, one is the producer and the other is consumer to exchange services in the presence of Publisher that can act as a market. In order to facilitate negotiations different parameters or Service Level objectives (SLOs) defines the measurement and monitoring criteria for effective and efficient delivery of the services. In our presented use case (See paper I, II) we extend the SLAs to typically involve more than two stakeholders. Occasionally the stakeholders can be grouped as classes of consumers or providers.

Fig 4.1, Models of Service Level Agreements (SLAs)


A SLA template presents stakeholders, roles and responsibilities, action points and the flow of information within the specified entities. The following general set of parameters includes the SLA template design.

• Descriptions of the activities involved in the business processes (Goals/Objectives) • Actors involved: (roles and responsibilities) • Coordination of tasks and responsibilities to achieve a goal • Conflict resolution

SLAs store information in the form of parameters and data types. Monitor set points (allowed intervals) and Key Performance Indicators (KPI). Actively monitoring of KPI threshold provides information about breaches in the contract.

Setting up and validating SLAs need to be supported by tools enabling translation and validation of concerns from the Interoperability Categories and Cross Cutting Issues into SLA parameters and KPIs to monitor (ensuring proper semantics).

Fig 4.2, illustrates a typical footprint of SLAs on the GWAC Interoperability Framework. The red rectangle captures the slice of categories and cross cutting issues related to the SLA at hand. The methodology outlined above aims at assuring proper behaviour of the SLA and hence validation of Interoperability of system slices crossing the footprint of Fig 4.2.

SLA Coverage

Fig 4.2, SLA footprint on the GWAC Interoperability Framework

The service bundles enable vertical integration of data flows (traditional bottom‐up aggregation from sensors to control centres or top‐down control signal down to actuators) or horizontal integration and monitoring of services on the same level. For instance, using


horizontal integration we can coordinate local Demand‐side Integration to a global view addressing specific sets of aggregators by flexible overlay architectures, such as peer‐to‐peer, on top of the underlying distribution networks and ICT networks.

In future Smart Grids, we argue to implement different trustworthy ICT solutions supported by partly the same physical infrastructure (Fig. 2.1). This is likely to increase the overall brittleness of the larger system and adding costs for further development as a larger number of combinations will need to be tested for both integration and regression, both in regards to the infrastructure and to other services this infrastructure supports. To address these issues, solutions involved need to be separated and protected from unwanted interactions and be resilient against disturbances at all supporting layers.

Principally, this can be achieved through virtualization; the act of encapsulating a subsystem by forcibly establishing an intermediate, formal border between the solution and its supporting infrastructure. In principle, this works recursively.

For a solution to be considered virtualized, the following requirements need to be fulfilled:

• One or more defined interfaces for service exchange. • Protocols governing service exchange across each defined interface. • Solutions adapted to conform to the specifics of each protocol.

Each paired interface and protocol defines a tractable border between solutions. This border is enforced by the use of monitoring to detect and prevent non‐conforming information exchange from cascading to other parts of the system. In full, these regulations ensure that different ICT solutions are logically separated and protected against indirect, unintended, interactions between solutions (Fig. 4.3).

Fig 4.3, Layered separation using virtualization

4.1 SLADesignWe model our SLA agreements as coordination of agent based services in multi‐agent systems. The core of a multi‐agent systems approach is the explicit rule‐sets of the agent’s behaviour capturing the following agent capabilities:


• Problem solving • Social competence in groups • Articulation competence in setting up and closing sessions

The behaviour of the multi‐agent system is governed by controlled execution of the rule sets given an explicit distributed World model hold by the agents. Updating of the World model can be either by explicit messages between agents or by explicit input from the external environment. In our setting, the World model captures the context at hand. In our simulations, we develop and test different rule sets and communication patterns. We model these inputs/outputs as sensor/actuator networks managed by a group of SLAs [See Paper I, II].

Setting up proper SLAs has to take the following considerations into account [Deliverable D3.2]. Basically SLAs coordinate and monitor a selected set of services and stakeholders, e.g., a service bundle.

Template: Dynamic Service Agreements. Supports and empower active users:

• Identification of stake holders and their roles • Identification of services belonging to a service bundle (task) • Identify service description terms • Guarantee Terms: defined as ranges or as functions (allows flexibility and changes) • Service Level Objective: defined as ranges or functions (allows flexibility and

changes) • Key Performance Indicators (KPIs). Metrics • Data management criteria • Privacy criteria • Policies and actions in detection of SLA violations

4.2 SLAFrameworkOur suggestions to cope with challenges related to having different sets of stakeholders involved in setting up and providing new kinds of energy‐based services. From figure 4.1, setting up and management of SLAs involves the following activities:

• Requirement engineering based on business case and stakeholders • Design and implementation of SLAs including mechanisms of monitoring and

exception handling • Validation of SLAs • Maintenance of SLAs

We address issues related to rule sets enabling control and monitoring, including exception handling. The framework includes the following objects as presented in Figure 4.4. In the figure the boxes represent the objects and the lines represent the relation between the objects. Some details about individual objects are presented further:


• Domain • Roles • Actor(s) • SLA and exception handling • Parameters • Data formats and protocols • Log

Fig 4.4, SLA‐Agent framework

Domain: The domain defines the area of application. It provides the organizational context for the framework. The contractual purpose for the SLA is identified and acknowledged by each actor involved (contracts).

Actor(s): Identifies stakeholder(s) within the domain of SLA. Attributes of actors include roles, capabilities and responsibilities within the SLA. The organisational affiliation for each actor is identified. Each actor has a well defined state space with allowed state’s transitions and a local data model. Roles: Roles identifies the actions, with outcomes, each actor is allowed to perform in a given situation.

SLA: SLA holds the description about SLA and its states with state transitions (Rule sets). Each state transition automatically informs relevant actors of outcomes. The SLA component also provides exception handling at breakdowns of SLA contracts.

Parameters: Each SLA state has parameters describing Key Performance Indicators (KPI); each parameter has threshold limits and a tolerance value that signifies the degree of freedom each SLA agreement can uphold with respect to that parameter. By monitoring the parameters, we detect eventual violations of thresholds and act accordingly (Exception


handling). We can also evaluate given metrics of parameters in order to evaluate performance (SLA contracts) and service usage (billing).

Data format and protocols: Data exchange between actors has to be in right format and following the right protocol (data integrity and system security). A smart (agent‐based) tool supporting monitoring of data conversion and proper connectivity can enhance system integrity and security.

Log: The Log component maintains a log of events in detail to support monitoring of performance and service usage as stated in the SLA.

4.2.1 RuleSetSLA Rule sets define the behaviour of SLA execution. Some interesting approaches of SLA management are presented by Fakhfakh et al [37], BREIN13 and SLA@SOI14 EU funded projects. Our presented solution for SLA management take those into consideration and provide rule sets for individual SLA defining and implementing dynamic relations between the different set point for individual stakeholders. A generic rule in a rule set has the following format:

If <pattern in data base > then <action and updating>

The rule engine reads an input and tries to have a match in the relevant data model according to the given invocation model. If a match is found, the corresponding action is performed followed by communication. The next step in the invocation is then performed

In our design each SLA Rule set is composed of following parameters

SLA Rule set Information

• Info o Date created o Date Modified o Invocation model

• Stakeholders involved o Data ownership (Actor ID) o Data responsible (Actor ID) o SLA responsible (Actor ID) o Exception handling (Type, documentation, Responsible Stakeholder ID))

• Parameters o Metrics to be used o Values; data format and protocols o Threshold to monitor

13 http://www.eu‐brein.com/ 14 http://sla‐at‐soi.eu/


4.2.2 ProcurementProcurement: Procurement is defined as “Process of obtaining things/Services” [Oxford dictionary]. Regarding SLA procurement is termed as the process to

• Express SLA (How the SLA is expressed) • Negotiated (What are the negotiation mechanisms used) • Stored (How it is stored)

Procurement is a process to think about the requirements and the other important task is the management of SLA life cycle that includes

• Creation and Negotiation o Identify producer o Define and Negotiate SLA

• Deployment and fulfilment o Delivery involvement of functional and non functional properties of

service • Monitoring and Evaluation

o Breakups o Roles, rights and responsibilities o Maintenance

• Termination o Rewards and Penalties

In our presented use case in the next section of the thesis, the set points for Smart Home sensor network (low‐level SLAs) assures that customers perceived comfort level is met by the sensors settings and offers from DSOs. It is also part of the high‐level SLA to ensure that the customer preferences are within the defined threshold values of the SLA and to provide quality of service as agreed in the SLA.

4.3 SLANegotiatingtoolThe SLA states Negotiation tool support stakeholders to set up and agree upon suitable SLAs. The outcome SLA agreement is a service accessible by related stakeholders during the whole lifetime of the SLA. The states of SLA Negotiation tool are presented in the Figure 4.5.


Fig 4.5, SLA‐ State diagram

Each SLA Negotiation process is associated with a state that describes the current status of the negotiation. The initial state of all negotiation is incomplete, meaning that it is still not finished and the coordinator must instantiate all stakeholders and parameters with parameter settings. Once ready, the coordinator enables the SLA, causing it to change state to pending. A pending SLA is waiting for additional data, start‐date and signing. When all stakeholders have signed the SLA, the state goes to valid. The state breached is reached when an exception sate is reached in the SLA or if we have a breakdown of the SLA agreements.

4.3.1 ExceptionhandlingandbreakdownsofSLAagreementsWe experiment by event monitoring of the SLA and identifying and rectifying the problem in service provisioning. In case of breakdown, a proper action of the SLA is identified and executed. Articulation work such as proper tracing and notification is also planned for future work along with prediction calculation as proactive approach.

4.4 ClustersofSLAsAs mentioned in SEESGEN‐ICT and Other EU Smart Grid projects, Smart Grid processes create and use vast amounts of data. Proper addressing of complexity issues related to trustworthy management of data is therefore of high concern for all stakeholders. And, indeed, is an identified barrier of Smart grid acceptance and uptake.


Further, we suggested that SLAs approach provides structured solutions to the data management challenges (i.e. protection, distribution, ownership and responsibility of tasks) in Smart Grids. From Figure 4.1, we can derive that SLAs can provide a vertical as well as horizontal grid structure of the data sets.

We can use cluster of SLAs to enable a principled structure of data and data flows. Given this structure we address, e.g., ownership in a structured way.

• Ownership of Data: The ownership and responsibility of the data is part of the SLA. Protection measures thus have clear ownerships.

• Shared ownership of data and information between stakeholders: Again, SLAs gives a framework for setting up and imposing collective responsibilities.

Intelligent Metering information is one example of SLA clustered approach. It is suggested that metering data is owned by the customer, as he/she is the one who generated it. But it should be shared with the DSO to calculate and predict the energy consumption for future needs, whereas, the same data might also be required by third parties for generating the bills and data reconciliation purposes. Again a SLA based approach could provide solutions to this complex issue of proper ownership and responsibility issues.

The suggested use of agent technologies at the boundaries between components might support intelligent data flow monitoring across the SLA data grid and thus enable support for trustworthy data management and interoperability of Smart Grids.

4.5 MonitoringSLAThe monitoring of SLA is divided into three types of monitoring:

• Predictive monitoring (Proactive) • Real time monitoring • Post monitoring (audit/trial)

Predictive monitoring: The predictive monitoring provides the prejudgment by looking at the current system logs/information and based on historical data and probability try to predict the future loads and usage. That provides a safe measure for the operators to act before anything happens.

Real time monitoring: It is the monitoring of the system in real time, in case of system breakdowns it notifies the operator immediately.

Post monitoring (audit/trial): Audit trial is the post checking of the logs and looking for any faults/quality metrics and recalculating of the quality factors based on daily/weekly/monthly logs. It is used mostly where the situation is not critical and the service provider and customer can have time to renegotiate and have settlement after the provisioning of service amicably.


Each monitoring type has its own pros and cons, but based on the situation one can select any combination of the three monitoring aspects depends on the use case. Monitoring SLAs comes down to three sub‐asks:

• Validation of stakeholders and their roles and the conditions of the SLA, including monitoring the rules and policies of data management and privacy

• Monitoring the value set of parameters of Guarantee Terms and Service Level Objectives, e.g., monitoring metrics and

• Taking actions in cases of violations of SLAs.

4.6 ArchitecturalDesignWe use JACK‐AOS multi agent platform to model our concepts in the form of agents. The overall architecture diagram is presented as in Fig. 4.6. In these experiments we model four stakeholders, DSO, DER, consumer/loads and Commercial Aggregator.

Fig 4.6, Architecture diagram of SLA‐Agent platform

To ensure trustworthiness, we introduce Commercial Aggregator as mediator actor between actors. The role of the mediator is to provide monitoring facilities of SLA and mitigate rewards and penalties if anything goes wrong. In the above figure, the DER and DSO are exchanging information via Commercial aggregator. It acts a Service Broker, where


third parties join‐in and provides different services like prediction based on weather forecast or historical data. Each DER is modelled as an individual entity and the loads are represented by accumulating residential loads and industrial loads by an agent.

Conclusion P a g e | 53

5 Conclusion

In this section we revisit the research questions to find the relevant answer in the thesis. Before addressing questions, we identified the following points in this thesis:

• New Stakeholders: New stakeholder to be enrolled in Smart Grid that creates new business cases. These new business cases need methods and tools to address, analyse and measure information.

• Requirement of ICT: We analysed and presented that the greater challenge is the management of information flow that is foreseen by the industry and is also mentioned in many report from EU and USA (NIST). In this thesis we argue that future Smart Grids should composed of multiple heterogeneous systems.

• Coordination: Coordination is required for the better management of information flow and energy flow. Standardization is one aspect to improve coordination at horizontal level, however, we propose SLA as promising approach towards trustworthy coordination.

• Interoperability: Interoperability is the key towards coordination, future Smart Grid requires interoperability at every level from lower layer like network to upper layer like organization layer as presented by NIST. It is important to have end to end interoperability between all the three layers as specified by NIST.

5.1 ResearchQuestionAnswersRQ1 ‐ Describe mechanism supporting coordination of stakeholders in Smart Grid?

We address this question by introducing SLA as coordination mechanism between the stakeholders. The motivation presented for using SLA is flexibility and empowerment of the stakeholders. To answer this question, we introduced another stakeholder/actor called Commercial Aggregator. The commercial aggregator acts as information exchanger/mediator. The role of this mediator is to ensure the ATOMIC properties of transaction and to secure both the interest of the DSO and the customer. It facilitates in binding both the parties to digitally sign SLAs before the provisioning of service. The general template for SLA has been defined and presented in Paper I and II.

RQ2 ‐ Identify the mechanism supporting trustworthy inclusion of DERs.

The management of DERs is a challenge with current limitation of monitoring and control system i.e. SCADA. This question is answered in paper II and partially addressed in paper III. In paper II the presented use case is relevant in the domain of trust between the stakeholders. To ensure this trust, we presented engineering approach for trust management. The requirements are presented in the form of concerns and each stakeholder has its own perspective about the same concerns mentioned as aspects. Further we modelled these aspects in the form of SLOs with threshold values in the procurement process of SLAs. Negotiatating and monitoring these SLAs represent the


mechanism and the information presented in the form of display or warning/error messages represent the signs that is perceived and accessed by concerned stakeholders.

We have implemented the system using simulation and provided a proof of concept where the decision of Inject/curtail DER is based on business and technical SLAs. SLA provides assurances that the system is working in accordance to the negotiated rules. This approach of validating information via SLA provides a trustworthy solution to incorporate ICT in future smart grid.

RQ3 ‐ Exemplify proper models supporting system quality in Smart Grid?

We have addressed this issue in paper III, however this topic is relatively new and we have highlighted it for our future goals.

5.2 RelevanceThe relevance of this thesis is supported by our contribution in two EU project (INTEGRAL and SEESGEN‐ICT) and the publication in Smart Grid related conferences.

5.3 ValidationWe validated our approach by the following methods:

• Presented the SLA approach in EU project SEESGEN‐ICT, which is thematic Smart Grid project and have 25 partners from EU. The work is acknowledged by the experts.

• The other approach adapted by modelling use case as agent based simulation. The agents represent actors (stakeholder/sensors) and by running the simulation we provided proof of concept about the application of automated SLA as trustworthy mechanisms for coordination in Smart Grids.

Validity Threats We simulate using agent technology that provide us the validation part mentioned above, however the real infrastructure need middleware such as OPC or directly using IEC 61850 to communicate with the real IEDs (Intelligent Electronic Devices) and RTUs (Remote Terminal Units). Integration to such interfaces requires implementation and assessments of Pilots, that will be addressed in future work.


5.4 ConclusionThe picture of the smart grids seems reality as the emphasis from the industry is now more focused on the end user and the distribution side. As this is the other side of the coin neglected before by the industry. After the deregulation and the demand from EU, the research entails to consider them as an empowered customer not just two holes in the wall.

Considering the fact EU 20‐20‐20 goals, the industry has to promote ICT and empowered end users with methods and tools. Our research focuses more on empowerment of customer that may lead to improve the utilization of energy. The current research needs to consider distribution and customer information to manage the grid in an optimal way. In this thesis, we argue a lot to consider end user as the other end of the network that can help in reducing the energy foot print, less emission of Co2 and also encouraged to help in advocating the real need of energy.

For DSO perspective, the data from the customer is helpful in network planning, schedule load shading and quick recovery of the Grid by getting system alert during breakdowns. However, the issues considering privacy and security can be monitored to some extent by SLA. We have not addressed such issues in this thesis, however by defining, negotiating and monitoring SLA, provides customer a tool to manage concerns and provide trustworthy information flow that seems to be “Good enough security”. Moreover, the SLA as a tool provides opportunity as well as responsibility on the customer like non compliance to SLA may lead to penalties.

That will further encourage the industry to invest in more tools that empowered the customers and make them responsible for their acts.

5.5 FutureworkIn this thesis we have identified some gaps between the Current Grid and Future Smart Grids. The major challenges identified are related to Interoperability and Trustworthy information exchange between ICT Systems with SCADA system. We propose to work in the same dimension using the framework provided by NIST and the challenges involved in incorporating ICT with SCADA system.

• SLA tool: We will further expand our research on SLA models and the SLA tool we developed in our group. Our focus will be more on real use cases in collaboration with the industry and the academia. We want to improve our tool to be generic so that it can be incorporated with Web Services.

• Maintenance and Repair: We have not analysed much into the maintenance or damage control scenarios of SLA. Insight to this information will further provide us about the breaches in the contracts. That additional information poses new challenges in defining business cases; hence a gap exists for researchers to implement prediction and approximation based algorithms which eventually help the stakeholders to make intelligent decision.

References P a g e | 57

6 References

1. Gustavsson, R., Mellstrand, P., Törnqvist, B., Akkermans, H., EnerSearch, A.: Dependable ICT Support of Power Grid Operations. CRISP Deliverable D. 2, (2005).

2. Andersson, G., Donalek, P., Farmer, R., Hatziargyriou, N., Kamwa, I., Kundur, P., Martins, N., Paserba, J., Pourbeik, P., Sanchez‐Gasca, J., others: Causes of the 2003 major grid blackouts in North America and Europe, and recommended means to improve system dynamic performance. Power Systems, IEEE Transactions on. 20, 1922–1928 (2005).

3. Gustavsson, R., Hussain, S., BTH, B.S., KUL, G.D., Hommelberg, M., Racovitan, I., Rikos, E., Destree, K., Grande, O.S., Franchioni, G., others: SEESGEN‐ICT. (2010).

4. Bertoldi, P., Atanasiu, B.: Electricity consumption and efficiency trends in the enlarged European Union. IES–JRC. European Union. (2007).

5. Commission, E., others: Energy Efficiency Plan 2011. Brussels, Belgium. (2011). 6. Bakken, D.E., Schantz, R.E., Tucker, R.D.: Smart Grid Communications: QoS Stovepipes

or QoS Interoperability. Proceedings of Grid‐Interop 2009. 17–19. 7. Fernandez, J.D., Fernandez, A.E.: SCADA systems: vulnerabilities and remediation.

Journal of Computing Sciences in Colleges. 20, 160–168 (2005). 8. Motta Pires, P., Oliveira, L.A.H.G.: Security aspects of SCADA and corporate network

interconnection: An Overview. Dependability of Computer Systems, 2006. DepCos‐RELCOMEX’06. International Conference on. pp. 127–134 (2006).

9. Clements, S., Kirkham, H.: Cyber‐security considerations for the smart grid. Power and Energy Society General Meeting, 2010 IEEE. pp. 1–5 (2010).

10. Hidalgo, R., Abbey, C., Joos, G.: Integrating distributed generation with Smart Grid enabling technologies. Innovative Smart Grid Technologies (ISGT Latin America), 2011 IEEE PES Conference on. pp. 1–7 (2011).

11. Ilic, M.D.: Dynamic monitoring and decision systems for enabling sustainable energy services. Proceedings of the IEEE. 99, 58–79 (2011).

12. Houwing, M., Heijnen, P., Bouwmans, I.: Socio‐Technical Complexity in Energy Infrastructures Conceptual Framework to Study the Impact of Domestic Level Energy Generation, Storage and Exchange. Systems, Man and Cybernetics, 2006. SMC’06. IEEE International Conference on. pp. 906–911 (2007).

13. Von Dollen, D.: Report to NIST on the smart grid interoperability standards roadmap. EPRI, Contract No. SB1341‐09‐CN‐0031–Deliverable. 7, (2009).

14. Papadopoulos, G.A., Arbab, F.: Coordination models and languages. Advances in Computers. 46, 329–400 (1998).

15. Vanfretti, L., Van Hertem, D., Nordstrom, L., Gjerde, J.O.: A smart transmission grid for Europe: Research challenges in developing grid enabling technologies. Power and Energy Society General Meeting, 2011 IEEE. pp. 1–8 (2011).

16. Hussain, S., Gustavsson, R.: Coordinating Energy Business Models and Customer Empowerment in Future Smart Grids. ICST Conference on E‐Energy. E‐Energy, 2010. October 14‐15, 2010, Athens, Greece.

17. Hussain, S., Honeth, N., Gustavsson, R., Sandels, C., Saleem, A.: Trustworthy Injection/Curtailment of DER in Distribution Network maintaining quality of Service. Intelligent System Application to Power Systems (ISAP), 2011 16th International Conference on. pp. 1–6 (2011).


18. Gustavsson, R., Hussain, S. and Nordström, L: Engineering of Trustworthy Smart Grids Implementing Service Level Agreements. In Proceedings of 16th International Conference on Intelligent System Applications to Power Systems (ISAP). September 25 – 28, 2011, Greece, 2011 (2011).

19. Ciancarini, P., Omicini, A., Zambonelli, F.: Multiagent system engineering: the coordination viewpoint. Intelligent Agents VI. Agent Theories, Architectures, and Languages. 250–259 (2000).

20. Nadel, S., Geller, H.: Utility DSM: What have we learned? Where are we going? Energy Policy. 24, 289–302 (1996).

21. Eberhard, A., Lazarus, M., Bernow, S., Rajan, C., Lefevre, T., Cabrera, M., O’Leary, D., Peters, R., Svensson, B., Wilkinson, R.: Electricity supply and demand side management options. Thematic review IV. 1, (2000).

22. Sarkar, A., Singh, J.: Financing energy efficiency in developing countries–lessons learned and remaining challenges. Energy Policy. 38, 5560–5571 (2010).

23. Barwise, J., Perry, J.: Situations and attitudes. CSLI Publications (1999). 24. Devlin, K.: Infosense: Turning Information Into Knowledge. W.H. Freeman (2001). 25. Malik, K., Choudhary, P.: Business Organizations and Collaborative Web: Practices,

Strategies and Patterns. Igi Global (2011). 26. Lundberg, J., Gustavsson, R.: Challenges and opportunities of sensor based User

empowerment. Engineering principles for open socio‐technical systems. 47. 27. Rindebäck, C., Gustavsson, R.: Why trust is hard–Challenges in e‐mediated services.

Trusting Agents for Trusting Electronic Societies. 180–199 (2005). 28. Zou, J., Pavlovski, C.J.: Modeling architectural non functional requirements: from use

case to control case. e‐Business Engineering, 2006. ICEBE’06. IEEE International Conference on. pp. 315–322 (2006).

29. Gambetta, D.: Can we trust trust. Trust: Making and breaking cooperative relations. 213–237 (2000).

30. Malaga, R.A.: Web‐based reputation management systems: Problems and suggested solutions. Electronic Commerce Research. 1, 403–417 (2001).

31. Li, H., Singhal, M.: Trust management in distributed systems. Computer. 40, 45–53 (2007).

32. Kok, K., Karnouskos, S., Nestle, D., Dimeas, A., Weidlich, A., Warmer, C., Strauss, P., Buchholz, B., Drenkard, S., Hatziargyriou, N., others: Smart houses for a smart grid. Electricity Distribution‐Part 1, 2009. CIRED 2009. 20th International Conference and Exhibition on. pp. 1–4 (2009).

33. Gustavsson, R., Fredriksson, M., Meilstrand, P.: The proper role of agent technologies in design and implementation of dependable network enabled systems. MULTIAGENT SYSTEMS AND SOFTWARE ARCHITECTURE. 49.

34. Gustavsson, R., Ståhl, B.: Self‐healing and resilient critical infrastructures. Critical Information Infrastructure Security. 84–94 (2009).

35. Deliverable D8.5: SEESGEN‐ICT: Recommendations for Policy Actions to Support Solutions Towards Energy Efficiency in SmartGrids, Download from http://seesgen‐ict.rse‐web.it.

36. Gustavsson, R.: Ensuring dependability in service oriented computing. Proceedings of The 2006 International Conference on Security & Management (SAM06) at The 2006 World Congress in Computer Science, Computer Engineering, and Applied Computing, Las Vegas (2006).


37. Mellstrand, P., Gustavsson, R.: An Experiment Driven Approach Towards Dependable and Sustainable Future Energy Systems. Proceedings of the 3rd International Conference on Critical Infrastructures (2006).

38. Fakhfakh, K., Chaari, T., others: A Comprehensive Ontology‐Based Approach for SLA Obligations Monitoring. The Second International Conference on Advanced Engineering Computing and Applications in Sciences. pp. 217–222 (2008).

39. Wooldridge and N. R. Jennings, “Pitfalls of agent‐oriented development,” in Proceedings of the second international conference on Autonomous agents, 1998, pp. 385–391.

P a g e | 61

PART II PUBLISHED PAPERS

P a g e | 62

P a g e | 63

CoordinatingEnergyBasedBusinessModelsandCustomerEmpowermentinFutureSmartGrids

Shahid Hussain, Rune Gustavsson School of Computing (COM)

Blekinge Institute of Technology Soft Center, Box 520, 372 25 Ronneby, Sweden

{shahid.hussain, rune.gustavsson}@bth.se

Abstract. Future sustainable energy systems are in focus of several national and international R&D programs. The transition from today’s tariff‐based energy systems towards future sustainable energy markets has to be supported by addressing and solving a range of challenges. Among the identified barriers are doubts of user acceptance of future Smart Grids due to lack of experiences, opportunities and possibilities: hence lack of experimental validations. Our suggestion of SLA‐Agents experimental facility is aiming at filling some of those shortcomings, not the least issues related to trust by stakeholders.

Keywords: Smart Grids, Service Level Agreements (SLAs), Energy efficiency, Business models, Customer Empowerment

1 Setting the Scene

The following Figure 1 adopted from deliverables from the EU funded TN SEESGEN‐ICT (Supporting Energy Efficiency in Smart Generation grids through ICT) illustrates the main characteristics behind the transition of electric grids from today to tomorrow.

Figure 1. Main drivers behind the transition towards Smart Grids

In Figure 1 the main stakeholders and roles are depicted as well as the path of transformation from Today to Tomorrow, related to effects due to the unbundling of the energy market. The deregulations and increased intelligence in the Transmission and

P a g e | 64

Distribution networks enabled by smart programmable electronic components and smart ICT information management systems are the two main drivers of this transition. Figure 1 depicts the main architectural components related to the energy flow of the future Smart Grid. The following Figure 2 outlines the information flows between groups of stakeholders to enable and support new business models as well as empowerment of the customers.

Figure 2 Information flows in future Smart Grids (adapted from SEESGEN‐ICT1 deliverables)

As it is indicated in Figure 1 and Figure 2 the role of new stakeholders, i.e. Aggregators and Retailers will interact in Smart Grids between Distribution System Operator (DSOs) and Customers, and secondly, we will have flexible configuration of stakeholders, such as Virtual Power Plants (VPP). Finally, we will need communication networks that must support the energy flow as well as the customer based business information flows. In short, present Supervisory Control and Data Acquisition (SCADA) systems have to be supported by novel ICT based information systems to meet the requirements of future Smart Grids for empowerment of customers [1].

As a consequence, the monitoring task of present day energy systems has to be re‐assessed and re‐designed. To that end we propose to extend the monitoring task by introducing the concept and mechanism of Service Level Agreements (SLAs) to:

1 SEESGEN‐ICT home page: http://seesgen‐ict.erse‐web.it/

P a g e | 65

• Allow flexible grouping of stakeholders • Allow flexible empowerment of users

1.1 Identified Barriers

Several international assessments of the transition from present day energy systems towards future Smart Grids have identified a set of barriers that have to be resolved, for instance2:

• Regulatory barriers. New types of stake holders and new kinds of business processes3

• Technical barriers. Architectures and technologies supporting new kinds of ICT systems complementing and enforcing SCADA systems4

• Customer acceptance. Trust in value‐added services provided5 • Lack of experiences. Today, there is a lack of experience from large‐scale field

tests or demonstrators addressing key challenges on the road towards smart grids. For instance, the roles and amounts of DER or RES that can be utilized and trustworthy managed6.

In this paper we describe a configurable agent‐based platform addressing coordination in future smart grids in the form of monitoring SLAs (Section 2). The initial focus is on customer acceptance and to gain experiences of possible new business processes in Smart Grids.

2 Service Level Agreements as a Basis for Coordination in Smart Grids

Classical SCADA systems are tailored to monitor the energy flow processes in energy systems. The need of supplementary ICT support for information management related to business cases and customer support is indicated in figure 1 and figure 2.

Of course, there are interdependencies between monitoring energy flows and information flows [2]. For example, increasing the amounts of Distributed Energy Resource (DER) and Renewable Energy Systems (RES) requires additional voltage control/frequency control to maintain the quality of service. We also have to address several aspects of data protection and data integrity [2, 3], not the least since we have different (potentially competing)

2 Technology Action Plan: Smart Grids. Report to the Major Economies Forum (MEF) on Energy and Climate by Italy and Korea, December 2009.

3 SEESGEN‐ICT home page: http://seesgen‐ict.erse‐web.it/ 4 INTEGRAL homepage: http://www.integral‐eu.com/ 5 Smart Grids home page: http://www.smartgrids.eu/ 6 The EUROPEAN future INTERNET initiative: http://www.future‐internet.eu/news/view/article/the‐european‐future‐internet‐initiative‐effi.html

P a g e | 66

stakeholders and customers (prosumers) in each Virtual Power Plant configuration (Figure 2). Identification of and harnessing such interdependencies are key challenges in future Smart Grids [4].

To attain system flexibility, a good approach is to virtualize the physical system components and groups of stakeholders into different non‐overlapping virtual infrastructures. We propose that the coordination in those virtual infrastructures can be modelled as bundles of services under SLAs related to given business processes [4].

Our starting point in setting up SLAs is thus:

• Business process • Stakeholders • Services • Contract (Key indicators) • Monitoring parameters • Assessments of contract • Billing • Non‐compliance of SLAs

Tight coupling of components provides stable platforms like current SCADA systems; however, there is lack of flexibility to add more stakeholder and business needs. A new approach towards improvement in Smart Grid is to restructure controlling and monitoring mechanisms accordingly to the present day need of customer empowerment from change in tariff based system to service based system. It is desirable for Smart Grid to have a flexible ICT platform by loose coupling the component to achieve the objectives. The ICT infrastructure provides more abstraction layers where components can collaborate and coordinate in a trustworthy and flexible way [4]. .

In such complex system the internal and external dependencies create a global phenomenon that is unable to comprehend without actually running of the system. Simulation is a viable alternative for examining these types of complex systems, which will help the researchers to learn more about the occurring problems and to provide solutions. To cater for that, the best available practices are to use Service Oriented Architecture [5] or Agent Systems to model the information processing systems as needed. The change of system control component from physical to more logical and distributed emphasis that the quality aspects must to be redesigned. We argue to manage such a complex system a better approach is to define and use Service Level Agreements (SLA). SLAs are mutually agreed contract between the service providers and the service users for the quality aspect in provisioning of services.

2.1 Business Cases as high‐level goals

The business case sets the goals, constraints, pre‐ and post conditions of the SLAs. In service oriented computing SLA is presented in the Figure 3 below. Normally the SLAs represent an agreement between two parties, one is the producer and the other is consumer/client to

P a g e | 67

exchange values/services in the presence of Publisher that can act as a market. In order to facilitate negotiations/transactions different parameters or Service Level objectives (SLOs) defines the measurement and monitoring criteria for effective and efficient delivery of the services. In our case we will extend the SLAs to typically involve more than two stakeholders. Occasionally the stakeholders can be grouped as classes of consumers or providers.

Figure 3. Management Model of Service Level Agreements (SLAs)

A business use case can be presented on a template identifying stakeholders, their roles and responsibilities, action points and the flow of information within the specified entities. In the next section we have included a business model from the EU project FENIX and indicate a translation based on that business model into a SLA. A business use case has the following general sets of parameters in the corresponding SLA.

• Descriptions of the activities involved in the business processes (Goals/Objectives)

• The actors involved: (roles and responsibilities) • Coordination of tasks and responsibilities to achieve a goal • Billing • Conflict resolution

The SLAs also defines the types and parameters to be monitored such as set points (allowed intervals) and Key Performance Indicators (KPI). Furthermore, it may have rules for SLA violation criteria’s to apply at breakdowns.

P a g e | 68

3 Case study: Customer Empowerment Enabling Increased Energy Efficiency in Smart Grids

Business case from the FENIX7 project is stated below

Business Case: Access to the Market through commercial aggregator, in absence of strong pressure to integrate DER.

Short description: “A Commercial Virtual Power Plant (CVPP) is a competitive market actor that aggregates DER units (not necessarily constrained by location). This kind of aggregator helps the DER to gain market access with the optimal returns prospective and market visibility. It carries out the economical transactions between the market and the DER and so it looks to the market like an imaginary single physical plant. The DER units, through this kind of aggregation, are enabled to participate not only in the wholesale market but also in the TSO‐organized balancing market and in the Guarantees of Origin (GO) market. Note that, in this business model, the CVPP does not absorb the balancing risks but shifts them to his clients. So, in this scenario, there will be only a financial aggregation of DER units without an operational integration. It is policy scenario that assumes the absence of strong societal pressures to really integrate DER into the electrical grid. Under these conditions, the current “fit and forget” practices will endure in the European operational network management. So distributed generation will penetrate fast, but it will not change the passive network operating philosophy.”

Further details can be viewed in the FENIX documentation

3.1 Translation of Extended FENIX Business Case into SLAs

Adjusted Business Case: The case study addresses customer empowerment and constraints of DER inclusion. Customer empowerment will derive the future energy business by giving the customer more control of use of energy and also the type of energy to be utilized. The energy flows and economic/information flows between actors are as in Figure 1 and Figure 2.

Stakeholders

• CVPP (Aggregator): Commercial Virtual Power Plant • DER units: Distributed Energy Resources might include RES Renewable Energy

Sources • TSO: Transmission System Operator • DSO: Distribution System Operator

7 FENIX Project Homepage: http://www.fenix‐project.org/

P a g e | 69

• Consumer/Prosumer The following adjustments of the FENIX business case are assumed:

• The CVPP will have the role of Aggregator coordinating the energy between producers and the energy consumers by smart Demand Side Management (DSM).

• The CVPP will empower the active consumer to adjust their profile to meet Energy Efficiency criteria and/or other consumer related services.

• The CVPP will balance incorporating of (vast amounts) of DER while maintaining electrical constraints such as voltage/frequency control in the grid ensuring quality of power service.

The Product/Services related Transaction and Contracts from the FENIX case are replaced by several Service Level Agreements (SLAs):

• SLA_Consumer_CVPP: Coordinating services between the Consumer and the Aggregator CVPP. Supports empowerment of the consumer (active consumer)

• SLA_CVPP_DSO_DSO: Coordinating services between the CVPP and TSO/DSO mainly related to energy balancing (Voltage control/ frequency control)

Synopsis

Each CVPP is coordinating Group of Energy providers including a set of DERs and associated TSOs and DSOs, together with a set of Consumers. The associated set of services is coordinated with two reciprocal sets of Service Level Agreements. On one side we have the coordination of energy providers on the other side is the coordination of the corresponding consumers.

In the SLAs the energy profile of each consumer is specified: type and amount of energy per unit interval. The lower and upper bounds of allowed change of DER (ΔDER) per unit interval and other constraints are also specified.

At given time point’s t0, t1, ., the following control cycle is performed:

1. Establishing energy balance of the CVPPs asserting Quality of power 2. Collecting DERs from empowered Consumers related to CVPPs 3. Checking that the proposed changes in DER are in accordance with the SLAs 4. If YES, updating of databases. 5. Go to 1. 6. If NO, try to reconfigure the grid (eventually including load shedding) to

achieve compliance with the SLAs. 7. Updating of databases 8. Go to 1.

P a g e | 70

Key parameters

• DERj resource j: Geographic position in the network (Posj, t0) , Energy production (KWh (j, Δt(t0), Constraints (C, j, Δt(t0)) during time interval Δt(t0),

• ΔDERj: Amount of DER resources that could be changed by the Consumer j during a time interval Δt(t0) starting at time t0.

Obviously, there are several ancillary services to be provided by different stake‐holders in order to perform the tasks of the control cycle given above. The empowerment of the consumer could be provided by a support tool based on, e.g., a Smart meter. This support tool should then also include a SCADA system controlling smart equipment in the home and visualizing important status parameters of the equipment and networks. Of specific concerns for the empowered prosumer are:

• Information security and protection • Reliable and traceable consumption and billing

4 The SLA-Agents experimental environment

The SLA‐Agent tool is an effort towards trustworthy coordination between the stakeholders and especially focusing on empowerment of the customers. Our SLA‐Agents platform is based on the JADE agent platform. However, we have improved the performance and scalability [6] by introducing and implementing distributed shared memory mechanisms in the Jade Directory component. Our SLA‐Agents platform can be implemented as a distributed system, which allows us to perform experiments on a distributed agent environment where we can model and evaluate communication and connectivity models [7]. Having validated architectures and mechanisms of SLA‐based coordination on SLA‐Agents we can in a structured way deploy some of the virtualized components into physically grounded components of a virtual infrastructure. The environment itself provides the following functionalities:

• Support for dynamically changing of role of stakeholders • Measure the effects of customer empowerment on aggregator role and impact on

DERS accordingly. • Monitoring of information on business layers and effects on network configuration • Support of dynamic change of the Meta‐Data information during run time and measure

the impact. • Produce alerts based on the threshold and penalty/reward the concern stakeholders. • Multi‐level coordination mechanism with feedback and calibration support.

P a g e | 71

5 Setting up SLA Experiments

The above business case present a scenario where increased customer based demand for DER/RES integration in the energy sector is sustainable supported. This will eventually leads to higher energy efficiency and lower CO2 emission partly due to empowering the customer. This in turn will increase customer awareness and acceptance of potentials of Smart Grids.

The SLA‐Agents environment for experiments and exploring possibilities and challenges of future Smart Grids is based on extensions of the JADE agent platform8. The agents implemented are firstly, agents corresponding to stake holders, secondly, ancillary support agents. We thus have the following agents and databases in our SLA‐Agents environment:

Agents:

• Controller: Configures and executes experiments • Setup SLA: An ancillary service to the Controller • Change profile: An ancillary service to the Consumer • Aggregator (CVPP). Trusted third party between producers and consumers of

energy • SLA management: Collects, processes and distributes data related to SLAs • TSO: Transmission System Operator • DSO: Distribution System Operator • Consumer/Prosumer: Active end user • Monitor: Collects data of delegated monitoring tasks by Aggregator, TSO or DSO • Billing: Collect and validate data related to billing • Evaluator: Evaluates the conformance of SLAs to business processes

Databases:

• SLA Database: • Experiments: Configurations and data • DER/RES: Capacities and positions • Billing data: Verified against SLAs • Network configurations: Position and distribution of network resources

The following Figure 4 depicts the main architecture of SLA‐Agents. The main access points to the environment are by the Controller or Customer. The Controller sets up the preconditions for an experiment. That is, configures the experiment and sets up the SLA that is going to be tested. The customer initiates experiments based on profile changes by first invoking the agent/service change profile.

8 http://jade.tilab.com/

P a g e | 72

Energy Resources

Customer(s) SLA‐Management

Aggregator (C

VPP)

Profile(s) DER/DG Tasks/SLO SLA Controller

Figure 4: Empowerment of customer by active participation in energy utilization based on SLAs

The request is sent to the Aggregator to verify the amount of resources required by this profile change, if the profile requirement is like more Green Energy than the Aggregator will calculate the existing amount of Green Energy and either allows the profile change requirement or put it on hold depending on the calculations. If more customers want to change their profiles due to some business incentive provided by the Aggregator or DSO then it is more feasible to allow that change based on the energy resources, instead of business incentives. With our flexible architecture design we can dynamically implement the changes and get the results by running the simulation using multiple time scales.

6 Conclusions and future work

We have proposed SLAs as a flexible approach to model and monitor inter‐stakeholder coordination between different actors of future Smart Grids. Furthermore, we have presented a real case scenario from the FENIX project giving emphasis on customer empowerment. We present work in progress, specifying tools under development supporting identified models and methods of experimentation. Specifically, we will address traceability and trustworthy challenges of information exchange. Our emphasis is the necessity of real time experimentation in large scale is necessary for proper design and implementation of the future Smart Grid.

7 References

1. Peeters, E., Belhomme, R., Batlle, C., Bouffard, F., Karkkainen, S., Six, D., Hommelberg, M.: ADDRESS: scenarios and architecture for active demand development in the smart grids of the future. (2009).

2. Gustavsson, R., Ståhl, B.: Self‐healing and Resilient Critical Infrastructures. Critical Information Infrastructure Security. 84–94 (2009).

3. Brown, G., Carlyle, M., Salmerón, J., Wood, K., others: Defending critical infrastructure. Interfaces. 36, 530–544 (2006).

4. Törnqvist, B., Gustavsson, R., Canal, C., Murillo, J., Poizat, P.: On adaptive aspect‐oriented coordination for critical infrastructures. Proceedings of the First International Workshop on Coordination and Adaptation Techniques for Software Entities. (2004).

Databases

MonitoringDSO Parameters

S‐Meters TSO Metrics

P a g e | 73

5. Andersen, P., Poulsen, B., Traeholt, C., Ostergaard, J.: Using service oriented architecture in a generic virtual power plant. 2009 Sixth International Conference on Information Technology: New Generations (ITNG 2009), 27‐29 April 2009. pp. 1621‐2 IEEE, Piscataway, NJ, USA (2009).

6. Mengistu, D., Tröger, P., Lundberg, L., Davidsson, P.: Scalability in Distributed Multi‐Agent Based Simulations: The JADE Case. Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking Symposia‐Volume 05. pp. 93–99 (2008).

7. Hägg, S., Gustavsson, R., Ygge, F., Ottosson, H.: Distribution Systems. (2007).

P a g e | 75

TrustworthyInjection/CurtailmentofDERinDistributionNetworkMaintainingQualityofService

Shahid Hussain, Nicholas Honeth, Rune Gustavsson, Claes Sandels, Arshad Saleem

Abstract‐‐ Future powers system is considering huge flux of information flow due to increase in Renewable Energy Sources. Due to the limited monitoring and management of SCADA systems, inclusion of DER at local distribution level is a challenging task for the Smart Grid. We discuss the non‐functional requirement aspects and their implication in trustworthy systems. The paper also illustrates an engineering approach towards trustworthy ICT systems. We present a use case about Injection/Curtailment of DER in distributed network. Further, we argue the importance of Service Level Agreements as coordination tool for information exchange between DSO and DER for the provisioning of trustworthy services. Finally conclude that modelling of SLA using Multi Agent Systems is a viable approach towards Trustworthy future Smart Grid applications.

Index Terms‐‐ Curtailment, DER, DSO, Injection, Multi Agent Systems, Services, Service Level Agreements, SLA, Smart Grid, Trust, Trustworthy

Introduction

ewnewable energy source (RES) demands more inclusion of Distributed Energy Resources (DER) into the power system. It is not easy to have participation of small DER into the Distribution Network (DN) due to technical dependencies. On the contrary, it

might be feasible to have an aggregation of DER into a Virtual Power Plant (VPP) that can support and provides the energy driven by specific business models. However, a real time monitoring and control of these devices poses a challenge for the Distribution Service Operator (DSO) and Transmission Service Operator (TSO) as presented by [1], [2].

R

We consider the trustworthiness between stakeholders which we define as the accuracy, authenticity and reliable exchange of data and messages. In order to coordinate monitoring and control of DER the trustworthiness of the ICT and the SCADA system presents a real challenge to operation and control of the smart grid. To make system transparent and trustworthy, we propose that an actor is required to monitor and provide information exchange to all the stakeholders. We name this actor as Commercial Aggregator or mediator. The role of Commercial Aggregator is to collect measuring data and monitor critical states information of all active stakeholders involved in the provision of energy. Delegating and monitoring these tasks will provide smooth and trustworthy coordination behaviour. Coordination is the key enabler to delegate and perform ICT task related to all the stakeholder requirements that should be managed and monitored properly in the presence of a flexible, scalable and resilient framework. Service Level Agreements (SLAs) assist this process; hence, automation of SLA can facilitate such coordination by monitoring

P a g e | 76

and measuring the critical values in the information processing systems [3], [4]. As presented in Fig. 1, the Perceived conceptual model of future smart grid by NIST23. This figure represents stakeholders’ involved in the future smart grid as well as information flow and energy flow. This figure depicts the need of ICT platform and we further argue that there will be additional business cases and more actors to provide information services to all the existing stakeholders.

Fig. 1, NIST Smart Grid Conceptual Model

Quality of Service (QoS) is the non‐functional attribute of a service (like delay, throughput) [1]. Satisfying QoS is important towards building sustainable and reliable future Smart Grid [1]. For example the decision required at substation level to control an Intelligent Electronic Device (IED) is in milliseconds to keep the balancing of power flow in the power lines. However, business actors can wait for pricing information that can take minutes to decide. Hence information has different temporal requirement based on the urgency of data. However, at the power grid level the data flow activity is in the levels of seconds or milliseconds to provide evidence to argue that localized decision making should be there at the component level to make decision based on the limited but critical information each component has at that specific time. In this paper we presented a use case about injection/curtailment of DER in DN to illustrate some of the trust concerns raised by the involved stakeholder. We propose a QoS based SLA to monitor and manage the information flow between the involved actors and we

23 NIST Framework for Smart Grid http://www.nist.gov/smartgrid

P a g e | 77

argue that there is a need of a third party that provides the assurances of trust by active monitoring of offered services. The remaining part of this paper is organized as follows. In section II we discuss about engineering approach to trust and how we can achieve trustworthiness in ICT using SLAs. Section III presents a business case about Injection/Curtailment of DER and the trust concerns of individual actors. In section IV we present SLA based modelling of the use case and further agent based simulation modelling. We conclude the paper in section V and give directions for future work.

Trustworthiness- An engineering approach towards Trust Trust represents an attribute of relation among two actors, whereas, trustworthiness is the mechanism developed to solve trust concerns by the solution provider [5]. Trust is hard to engineer in the automated systems as presented in [5]. However, we can attain certain degree of trustworthiness in the system using engineering approach to address some of the concerns and develop a mechanism to handle some concerns in trust, which makes the system more trustworthy as presented in Fig. 2.

This figure represents Engineering approach supporting analysis of concerns that would further provide a base for defining and monitoring appropriate QoS values.


Perceived Trust Trust Sign Trust Aspects

Accessed Trustworthiness

Trust Mechanism

Fig. 2, Engineering approach supporting analysis of concerns [5]

For example, EBay and Amazon have reputation management system that provides a certain degree of trust in buyer’s mind based on the judgement of previous seller’s point of view [6], [7]. Reputation management systems provide a mechanism to increase the level of trust perception in buyer’s mind. After perceiving that trust we access the trustworthiness of the rating mechanism, to add another level of trust we further read the comments of the existing buyers to get a clear picture. This cycle of trust model repeats until we attained certain degree of trustworthiness about the seller in our mind. Although these reputation systems are very subjective to individual biasness, however, we accept the system as trustworthy while purchasing items online [6].

P a g e | 78

Requirement engineering is formal way of gathering and managing the data [8]. Often these requirements are only gathered at the primary stage of the software development cycle. At times, failing to perceive the operational requirements provides inadequate results. The operation results are highly dependent upon the following segments

• Environment in which the hardware/software is running • Communication infrastructure • Data structures

System failures and performance issues are mostly reported due to lack of requirements gathering at initial stages.

Run Time Monitoring of Non Functional Requirements

In Smart Grid the functional aspect are more related to energy flow and the services provided by ICT infrastructure. The critical nature of power systems arguably require non functional aspect tightly incorporated into services for reliable solutions. Monitoring critical set points using sensory network enable the power systems to measure and monitor huge amount of data. However, to handle such a large amount of data an overlay ICT infrastructure is required. This additional infrastructure provides a new insight into future smart grid as service based industry; hence, materializing additional actors and new business cases [9]. We already have discussed this approach in EU project SEESGEN‐ICT24 and in our previous paper [9], where we unbundled the price and energy (kW/h) and start looking at business perspective like selling COMFORT and Green Energy.

SLA as Trust Mechanism

Traditional power grid automation systems are closed systems that provide inflexible but secure and reliable monitoring and control. Business ICT system are usually open systems and thus prone to security, and possibly performance problems.

In multi‐infrastructure like telecom, cloud computing rely on the usage of SLA. SLA as a tool facilitates such functionalities to monitor and manage information flow between multiple actors [10], [11]. Defining actors roles and data access provide certain degree of data security in the system which will increase trustworthiness in the system. Hence, we can argue that SLA can increase the level of trust if managed properly. In Smart Grid this will provides a mechanism to build a platform with horizontal and vertical integration of services in a trusted way.

We use SLA as an engineering approach towards trustworthiness. Using SLA provides the integrity and separation of data concerns for individual stakeholders. Each SLA is divided into multiple objectives that are measureable and monitored by ICT system like system bandwidth, CPU load, Round Trip Time (RTT) termed as SLO (Service Level Objectives). All SLO must be defined with parameters and predefined units [10]. The reliability and security

24 SEESGEN‐ICT Home Page http://seesgen‐ict.rse‐web.it/

P a g e | 79

of the data within certain limit can be measured and monitored at runtime by the automated SLA mechanism. It allows involve party’s flexibility to change the SLO values depending on their profit maximization strategies as negotiated by actors involved. Overall it facilitates significant assurances within involved actors by providing logging of data to ensure traceability.

In the future Smart Grid the ICT infrastructure manages information flow. Incorporating another infrastructure can raise some Trust concerns such a security, privacy, reliability, performance and scalability issues.

Each concern has different aspect based on the intention of stakeholder. For example, a consumer is more concerned about the privacy and billing issues. The DSO and TSO have more concern about security, reliability and performance issues.

The Commercial Aggregator and retail service provider are more concerned about scalability, privacy and security issues. Based on the runtime measuring and monitoring mechanism SLA can bridge that gap.

It will assist the stakeholders to define and negotiate on different concerns with involved actors. It will empower each actor to define, act and provide services according to a predefined limits that would be monitored by the actor himself and also by third parties to mitigate the issues, if need arises.

Business Case – Inclusion of DER

Motivation

Consider the 20/20/20 goals set by European Union for 2020. The 20% represent 20% more renewable energy sources next 20% less CO2 foot print and last 20 represent 20% less energy consumption [12]. To integrate 20% of DER as renewable energy sources is a challenging task considering the hierarchical current energy SCADA systems.

SCADA system is a stovepipe system and is built to integrate the components in a vertical manner [1]. It is not easy to integrate DER at the horizontal layer. The major barrier is the coordination mechanism between the stakeholders for the provisioning of services. It is plausible to have a stack of coordination tools to manage such horizontal integration of actors. Keeping in view of the European Commission (EC) goals and other advantages to have additional ICT platform, among other some are as follows:

• Use DER to generate and provide localized energy resources near to the

consumers. • This localization provides a certain degree of resilient mechanism for the DN in

case of critical/emergency situation like blackouts/outrages.

P a g e | 80

Use Case – Injection / Curtailment of DER

In this use case we limit our scope to DER that inject currents of above 63A into the DN and are located at the Medium Voltage (MV). We assume a minimum measurement time resolution of 1h. The DER class that we consider cannot schedule when it can generate electricity but is able to adjust set points to curtail production if required. Curtailing production represents an economic loss for the wind operator. The DER operator aims to always sell everything that it has produced. We assume a future scenario where losses due to curtailment are not compensated by subsidies and therefore need to define another method for ensuring that the DER operator is provided with incentives to curtail production but still ensure fair trade between the DSO and DER operators.

On the other hand we have the DSO who carries the power flow between the local DER and loads. By law the DSO must satisfy certain power quality criteria as determined and specified by the regulators. For the DSO carrying local generation by DER represents more dynamic power flows and thus higher requirements on the DN control and monitoring systems. This in turn requires the DSO to make increased investments on secondary equipment to support this functionality.

The obvious method to deal with power quality degradation that can be caused by DER integration is that the DSO demands that the DER operator curtails production. In this case the DER should be financially compensated by the DSO or by government subsidy. This method may function during a transition period it is not sustainable in future distribution grids with massive integration of DER. A solution that may work in such a future scenario is that the DSO calculates a “carrier fee” that is charged per MW capacity for particular time intervals. This carrier fee will have a fixed and variable component. The fixed component should reflect the depreciation of investment on the infrastructure required to carry the DER production. The variable component would vary according to DN loading and consequent power quality degradation or other important factors.

The carrier fee will vary according to the placement of the DER in the DN; guidelines for this calculation would have to be stipulated in detail by the regulator. The parameters used in the calculation of the variable component of the fee could be based on the physical topology and equipment types and configurations (line impedance, power rating etc.). This cost calculation based on the topology should be based on a central reference point used by all DER in the DN guarantee that it is fair for all connected DG (this may be a bit tricky for non‐radial DN(s). The fixed component could be similar to the billing model used in some countries today and as specified above, based on depreciation of the infrastructural investments.

In order to setup and monitor SLA between the DSO and DER a mediator is required to specify the cost of the infrastructural investments and depreciation rate associated with carrying the DER production capacity. The DN topology and power quality constraints and associated financial risks for the DSO are used to parameterise the function used to automatically calculate the variable component of the carrier fee. The SLA must also specify

P a g e | 81

the requirements for the rate and quality of the process status and measurements required to calculate the variable component as well as the interfaces by which this data is communicated.

In operation a system as described above would allow the DSO to receive financial compensation for the costs (close to the true cost + a profit margin) of carrying capacity for the DER. The DER then has a cost signal to react to and can make own decision on curtailment based on carries fees and market price of supply. An automated system that updates the values with a high time resolution (as in 10 min intervals or less) would allow the DER/DSO generation and distribution cluster to operate more closely to their maximum carrying capacity and thus more efficiently and fairly.

The following Technical concerns are important to monitor from DSO perspective as presented by [13]

• Voltage increase due to more wind generation DER • Protection of the grid from unmanageable bilateral power flow

To provide the trustworthy management of DER, the DSO needs to maintain the energy balance based on SLA between DERs and Loads. The following quality parameters are significant to monitor and measure mediated by clusters of SLA.

• Voltage Level • Power produced at specific time period • Downtime of DER

We also consider the business incentives for loads, but that might disrupt the technical feasibility of the network. To prevent the technical failures caused by business cases, a priority is given to the technical QoS. We implement the QoS based Services in different conditions like normal and critical.

• Normal condition: where the energy balance is maintained: i.e. energy supply

provided by DER is consumed by energy loads. • Critical condition: where there is imbalance in energy supply and we have to trade

off by either incorporating more generation or curtail of DER units.

We divided the SLA into multiple clusters of SLA(s) depending upon the use case. The role of DER is to provide energy to the DSO based on a predefined schedule. Both parties i.e., the DER and the DSO agrees on a schedule for the delivery of energy based on historical data, weather data and other constraints like curtailment.

Maximization of profit for DER The DSO takes most of the input from the DERs to provide with renewable energy, if there is wind and substantial loads to utilize that energy. In case of curtailment of energy: the DSO needs to protect the infrastructure e.g., in the case of:

• The wind power production is high but the demand is low.

P a g e | 82

Calculating the Quality metrics If i represent the number of DERs in the DN, V represents the voltage, and D is the density of DER at some specific node in the network. Then the equation for calculating the quality is Quality Metric = f(∑ Vi, D ) (1) The density D is defined by DERl = Number of DER in that specific location DERt = Total number of DER in the whole DN D = DERl / DERt (2) Curtailment Case The DSO can Inject or Curtail energy from DER, if imbalance occurs at the distribution grid or due to a business incentive. If Vi <> Defined_Level If Vi > Defined_Level then Curtail DER If Vi < Defined_Level then Inject DER

As already argued, the technical SLA has precedence over the business SLA to protect the infrastructure. To ensure this, technical SLA is evaluated before considering any business decision at time t.

Experimental Design

Simulation is one way to model the problem and analyze the results. We use agent systems to develop simulation and derived some approximate answer that will provide a quantitative validation for the hypothesis. Agent systems facilitate such simulations by using coordinated effort of multiple agents. The emergent behavior of such simulation is regarded as complex system due to the feedback mechanism. Future smart grid is one application of complex system that should be carefully crafted to validate the results.

Architectural Design

We use JACK‐AOS multi agent platform to model our concepts in the form of agents. The overall architecture diagram is presented as in Fig. 3.

P a g e | 83

Fig. 3, Architecture diagram of SLA-Agent platform

To ensure trustworthiness we introduce Commercial Aggregator as mediator actor between actors. The role of the mediator is to provide monitoring facilities of SLA and mitigate rewards and penalties if anything goes wrong. In the above figure, the DER and DSO are exchanging information via Commercial aggregator. It acts as a Service Broker, where third parties join‐in and provides different services like prediction based on weather forecast or historical data. Each DER is modelled as an individual entity and the loads are represented by accumulating residential loads and industrial loads by an agent.

Methodological issues:

• We did not consider the voltage drop due to resistance. • Our focus is more on the proof of concept that SLA is key enabler mechanism in

future power systems.

Agent Modelling

An agent is an encapsulated computer system that is situated in some environment and can act flexibly and autonomously in that environment to meet its design objectives [14]. Agents are defined by a metaphor commonly known as BDI (Belief, Desire, Intention). The beliefs represent knowledge of an agent about its environment. The Beliefs are captured through sensors of the agent and stored in an internal data base. Multi‐Agent systems (MAS) are systems consisting of more than one agent. MAS are useful to implement in

P a g e | 84

application areas that are naturally distributed, decentralized and are easy to be decomposed in their design. A system architecture based upon MAS provides a natural way of decomposing a software system into subsystems and to model interactions between these subsystems and individual components (agents) within the subsystems. In our approach the system is composed of four different kinds of agents. Following section describes these agents and their functionality.

Monitoring Agent This agent will act after the SLA has been negotiated and running. It monitors information at predefined interval or on events to update other agents or database. The QoS parameters are defined in the SLA with threshold values for generating alerts or violations depending on the service level objectives.

Load Agent The load agent is providing the information about energy flow to the DSO. For this simulation we combine the residential loads and industrial loads. Hence, there is no distinction between deferrable or non deferrable loads. We assume that these loads are all non deferrable.

DER Agent The DER agent is providing the information about the DER operation as well as receiving requests for curtailment of production. It implements the DER operator side of the SLA for the technical control of the DER.

Commercial Aggregator Agent This agent represents a market entity and is acting as a mediator between business actors facilitating services and a platform to have a transparent mechanism for information sharing. The business levels SLA are managed by this agent. It acts as a trustworthy actor that collects, and monitors the logs to make sure the smooth provision of services.

DSO Agent DSO agents represent the distribution station and all the loads agents and DER agent are coordinating information via Aggregator agent with DER. SLA about technical infrastructure are monitored by DSO agent and a log is sent to Aggregator. All the information is coordinated using SLA between the DSO agent and services provided i.e. the Aggregator

Conclusion and Future Work

In this paper we provided theoretical background about trustworthiness and the importance of SLA in future Smart Grid. We have identified problem cause by large‐scale DER integration into DNs and presented a use case for DER curtailment by the DSO in order to maintain power quality levels. We present and motivate SLA(s) as a coordination mechanism for management of the information flow between stakeholders in a smooth and flexible way. Our Multi Agent System (MAS) platform running SLA is a positive approach

P a g e | 85

toward coordination, flexibility and QoS monitoring. It also provides a degree of trustworthiness into the system by incorporating feedback mechanism by SLA.

In this experiment we have limited number of DER and loads. We do not even considering bidirectional flow of energy in the system, hence the loads are loads and producers are producing the energy. Further, we will look into the possibility of having real sensory network attached with our multi agent based SLA platform as a demonstrator in real test environment

For future experimentation, we are considering to evaluate performance issues integrating the system with SCADA system using OPC. Our focus is to provide active monitoring of energy flow and information flow for all stakeholders. We also achieve some degree of security within components facilitated by monitoring the data at application level. Implementing alerts generated in case of noise in the data to protect the system from security breaches.

Acknowledgment The authors gratefully acknowledge the contributions of Lars Nordstrom, David Sveningsson for their Ideas and contribution to this document.

References [1] D. E. Bakken, R. E. Schantz, and R. D. Tucker, “Smart Grid Communications: QoS

Stovepipes or QoS Interoperability?,” Proceedings of Grid‐Interop 2009, pp. 17–19.

[2] D. E. Bakken, D. E. Whitehead, and G. C. Zweigle, “Smart Generation and Transmission with Coherent, Real‐Time Data,” 2010.

[3] R. Gustavsson, “Ensuring dependability in service oriented computing,” in Proceedings of The 2006 International Conference on Security & Management (SAM06) at The 2006 World Congress in Computer Science, Computer Engineering, and Applied Computing, Las Vegas, 2006.

[4] T. Van Craenenbroeck and B. De Wispelaere Vreg, “Service level agreements and regulatory aspects of data communication between DGO's and suppliers,” in Electricity Distribution, 2005. CIRED 2005. 18th International Conference and Exhibition on, pp. 1–4, 2010.

[5] C. Rindebäck and R. Gustavsson, “Why trust is hard–Challenges in e‐mediated services,” Trusting Agents for Trusting Electronic Societies, pp. 180–199, 2005.

[6] R. A. Malaga, “Web‐based reputation management systems: Problems and suggested solutions,” Electronic Commerce Research, vol. 1, no. 4, pp. 403–417, 2001.

P a g e | 86

[7] H. Li and M. Singhal, “Trust management in distributed systems,” Computer, vol. 40, no. 2, pp. 45–53, 2007.

[8] J. Zou and C. J. Pavlovski, “Modelling architectural non functional requirements: from use case to control case,” in e‐Business Engineering, 2006. ICEBE'06. IEEE International Conference on, pp. 315–322, 2006.

[9] S. Hussain and R. Gustavsson, “Coordinating Energy Business Models and Customer Empowerment in Future Smart Grids,” in ICST Conference on E‐Energy. E‐Energy, 2010. October 14‐15, 2010, Athens, Greece.

[10] K. Fakhfakh, T. Chaari, and others, “A Comprehensive Ontology‐Based Approach for SLA Obligations Monitoring,” in The Second International Conference on Advanced Engineering Computing and Applications in Sciences, pp. 217–222, 2008.

[11] D. D. Lamanna, J. Skene, and W. Emmerich, “Slang: A language for defining service level agreements,” in Proc. of the 9th IEEE Workshop on Future Trends in Distributed Computing Systems‐FTDCS, pp. 100–106, 2003.

[12] R. Duan and G. Deconinck, “Future electricity market interoperability of a multi‐agent model of the Smart Grid,” in Networking, Sensing and Control (ICNSC), 2010 International Conference on, pp. 625–630, 2010.

[13] E. Haesen, A. D. Alarcon‐Rodriguez, J. Driesen, R. Belmans, and G. Ault, “Opportunities for active DER management in deferral of distribution system reinforcements,” in Power Systems Conference and Exposition, 2009. PSCE'09. IEEE/PES, pp. 1–8, 2009.

[14] M. J. Wooldridge, An introduction to multiagent systems. Wiley, 2009.

P a g e | 87

EngineeringofTrustworthySmartGridsImplementingServiceLevelAgreements

Rune Gustavsson, Member IEEE, Shahid Hussain, Lars Nordström, Senior Member IEEE

Abstract—Future Smart Grids have to meet high expectations from our societies on

improved energy efficiency and sustainability. However, system uptake and acceptance will to a large extent depend on the perceived trust by different stakeholders. In the paper we address issues related to engineering of trustworthy systems. This type of Engineering will be grounded in reliable models of interoperability. A key issue is here interoperability of information exchange and sharing. Based on analysis of trust concerns, we propose a model of coordinating and monitoring Services by Service Level Agreements (SLAs) between stakeholders to ensure trustworthiness of system performance and behavior. We illustrate our methods on case related to trustworthy inclusion of DERs in Smart Grids.

Index Terms‐‐ DER, DSO Multiagent Systems, Service Level Agreements, SLA, Smart Grid, Trust, Trustworthy

Introduction

In the EU Climate and Energy package it is stated that by 2020 EU should meet the targets of:

• At least 20% decrease in EU greenhouse gas emissions related to 1990 levels. • 20% of EU energy consumption to come from renewable resources. • A 20% reduction in primary energy use compared with projected levels, to be achieved

by improving energy efficiency. A roadmap to meet those targets includes a transition from grids to Smart Grids. Such a roadmap is NIST Framework and Roadmap for Smart Grid Interoperability Standards, issued by NIST25.

Some identified characteristics of Smart Grids:

• New stakeholders will complement existing stakeholders in future energy markets.

Gustavsson, Hussain are with the School of Computing, Blekinge Institute of Technology, Karlskrona, Sweden, email: (rune.gustavsson, shahid.hussain) @bth.se. Gustavsson, Nordström are with the Department of Industrial Information and Control Systems, Royal Institute of Technology, Stockholm, Sweden, e‐mail: (rune.gustavsson, larsn)@ics.kth.se 25 NIST Framework for Smart Grid: http://www.nist.gov/smartgrid

P a g e | 88

• New regulatory frameworks regulating the roles and responsibilities of stakeholders and the roles of the markets.

• Controlled massive inclusion of Distributed Energy Resources (DER) in the grid while maintaining high Quality of Service (QoS).

• Empowerment of end users to enable increased energy efficiency and new energy based services

• Implementation of distributed intelligence in the grid to enable trustworthy, secure and resilient operations.

The EU target of 2020 demands more inclusion of DER in the Distribution Network (DN). It might be promising to have an aggregation of DER into a Virtual Power Plant (VPP) that can support and provide the energy depending upon the business requirements. However, a real time monitoring and control of these devices poses a challenge for the Distribution Service Operator (DSO) and Transmission Service Operator (TSO) as presented by [1], [2].

From the following Conceptual model by NIST in Fig. 1, we firmly believe that in future Smart Grid, there will be additional roles of Information Communication Technology (ICT) infrastructure that will provide information services to all the existing stakeholders.

Fig. 1, NIST Smart Grid Conceptual Model – Top Level

From Fig. 1, it follows that Smart Grids can be modelled as involving seven domains; Bulk Generation, Transmission, Distribution, Customer, Markets, Operations and Service Provider with actors. Each of the seven domains is a high level grouping of organizations buildings, individuals, systems devices and other actors that have similar objectives and that they rely on – or participate in – similar types of applications.

P a g e | 89

An actor is a device, computer system, software program, or the individual or organization that participates in the Smart Grid. Actors have the capability to make decisions and to exchange information with other actors.

Organizations may have actors in more than one domain. The actors illustrated here are representative examples but are by no means all of the actors in a Smart Grid. Each actor may exist in several different varieties and may actually contain other actors within them.

We can distinguish two processes from Fig.1 Power System Management (Generation, Transmission and Distribution) and Information System Management (Collection, Trans‐formation, Processing and Distribution). Information systems include Supervisory Control and Data Acquisition (SCADA) systems and supplementary ICT systems.

Communication between actors in the same domain may have similar characteristics and requirements. Domains may contain sub‐domains. Moreover, domains have much overlapping functionality, as in the case of the transmission and distribution domains. Transmission and distribution often share networks, and, therefore, are represented as overlapping domains.

Interoperability is a key requirement for data communication and information sharing in the smart grid. This topic has been articulated at great length by the GridWise Architecture Council (GWAC26). However, the interoperability issues addressed to date include only syntactic interoperability of data exchange. We outline in this paper some aspects of higher order (semantic) interoperability as well (Figure 2).

In a recent paper [1] it is argued that middleware is a key enabling technology for meeting interoperability requirements of smart grids. A complementary paper [3] addresses the importance of significant interfaces of smart grids. Both papers have been presented at Grid‐Interop Forum 2009 organized by GWAC.

The remaining part of the paper is organized as follows. In Section II Interoperability we introduce The GWAC interoperability model together with definitions of Functional interoperability, Non‐functional interoperability, and Quality of Service (QoS). Furthermore, we introduce Middleware as a mean to implement Functional and Non‐functional interoperability meeting desired QoS.

In Section III Trustworthiness we outline an engineering model based on identifying concerns by different stakeholders to enable implementation of suitable mechanisms or signs to be interpreted as tokens of trustworthiness. Section IV Service Level Agreements (SLAs) introduces SLAs as a coordination and monitoring mechanism taking into account QoS requirements related to Smart Grids. We also outline the implementation of SLAs using proper middleware and taking into account stakeholders concerns related to trustworthiness. The concerns are examples of Cross cutting Issues at different Interoperability Categories. We argue that SLAs indeed supports assessments of trustworthiness by stakeholders in functions and behavior of Smart Grids. In Section V

2 Home page: http://www.gridwiseac.org/

P a g e | 90

Business cases – Inclusions of DER, we address criteria related to trustworthy inclusion of DER. The paper concludes with Section VI Conclusion and Future Work and Section VII References.

INTEROPERABILITY We must distinguish between two kinds of interoperability; Functional interoperability and Non‐functional interoperability.

Functional interoperability involves the traditional inter‐operability of the application or business logic. Functional interoperability requires some kind of agreement on the interface: an API or contract. Non‐functional interoperability involves interoperability cross‐behavioural issues such as delay and security.

Implementation of systems taking into account functional and non‐functional requirements as well as issues of flexibility and maintainability take into models and methods of Service Oriented System engineering. Applications are then not designed and implemented as stand‐alone stove‐pipe systems, but as configurations of services. The transition from closely coupled systems to loosely coupled systems puts issues of coordination and communication in focus.

Introducing agent‐based services or implementing Service Oriented Multi‐agent Systems (MAS) facilitates taking into account intelligence or smartness of future Smart Grids. Agent technologies allow modelling systems as configurations of smart flexible components, e.g., Active Network Management (ANM) of Distribution Grids [8]. The IEEE Power and Energy Society Multiagent Systems Working Group27 aims to promote the openness of agent architectures within the power domain. In this paper we argue that Service Level Agreements (SLAs) provides a control and monitoring structure of MAS assuring interoperability and QoS.

We also need to use Middleware for implementation of Service Oriented Systems. Middleware is a layer of software and services above the operating system but below the application program providing a common programming abstraction and system model across a distributed system.

Middleware exists in part to help manage the complexity and heterogeneity inherent in distribute systems. Network researchers have of course developed layers of network protocols, where each layer builds on the one below and offers higher level of abstractions or service. Similarly, middleware researchers have developed multiple layers of middleware that build on the layer below it. Note that middleware typically overlays and enhances OSI layers above the transport (level 4) layer. The alternative to handle these layers in middleware is to hard code these layers in the application program. However, this is very time consuming and error prone; the best practices are very hard to recreate.

The following figures articulate some of the issues supporting cross cutting security and privacy, as well as QoS aspects in the GWAC framework of network and syntactic categories. Interoperability is driven by the need of businesses (or business automation 27 Home page: http://ewh.ieee.org/mu/pes‐mas/

P a g e | 91

components) to share information between others. Business processes enable the necessary information exchange. At the organizational layers, inter‐operability requires agreement on the business process interaction that is expected to take place across an interface. Such an agreement would describe the service requests and responses that need to support a larger process picture that is shared by the collaborating parties. These processes must also be consistent with the tactical aspects of running the interacting businesses, the strategic aspects shared by the parties of the exchange, and the political environment embodied in economic and regulatory policy that governs such business. Fig. 2 depicts these categories of interoperability. The framework pertains to an electricity plus information (E+I) infrastructure. At the organizational layers, the pragmatic drivers revolve around the management of electricity markets. At the technical layers, the communications networking and syntax issues are information technology oriented. In the middle, we transform information technology into knowledge that supports the organization aspects of the electricity related business.

Fig. 2 GWAC Interoperability Framework Categories

Within each horizontal category the information exchange can be implemented using the same technologies. However, information exchange across categories typically requires other technologies and/or transformation of data between data models. Particular challenges arise when we have a vertical shift in the E+I column. This reflects a transformation of the semantics of information between Energy Systems and Information Systems.

P a g e | 92

Fig. 3 GWAC Interoperability Context – Setting Framework Diagram

Cross cutting issues are areas that need to be addressed and agreed upon to achieve interoperation. They usually are relevant to more than one interoperability category of the framework. Fig. 3 from GWAC proposes to organize interoperability issues into a series of topics. These topics are introduced in this formative stage of developing the framework with the realization that each topic needs to be articulated in future developments and captured in detailed technical papers. These topics would then help organize specific work items for soliciting proposals to resolve issues where their impact to interoperability can be prioritized and where establishing agreement on specific directions for resolution can advance the cause. In fact, cross cutting issues can be regarded as constraints on the functional solutions. This entails that the set of cross cutting issues might be bounded in order to have acceptable functional solutions. A technique to handle this situation might be using constraint programming.

Fig. 3 depicts the cross cutting issues spanning all categories. Deciding precisely which interoperability categories are relevant to each cross cutting issue requires more review. Though a matrix of issues for each interoperability category would arguably be desirable, further clarification and analysis of the issues will be necessary.

In Section IV we propose implementation of SLAs taking into cross cutting concerns of trustworthiness in vertical slices of Interoperability Categories. Furthermore, the information exchange across Categories is enabled by service exchange between SLA service bundles. The GWAC interoperability model suggests that any implementation supporting interoperability must also support the following principles:

Principle I09: An interoperability framework must be practical and achievable: • Meets performance requirements • Is reliable • Is scalable • Has sufficient breadth to meet the range of business needs

P a g e | 93

Principle I10: An interoperability strategy must accommodate the coexistence of and evolvement through several generations of IT standards and technologies that will reside at any point in time on the Grid.

Two major points are support of Principle I09 and Principle I10: In support of Principle I09, which essentially states that any solutions need to be effective from a variety of operational perspectives, it is essential that syntactic interoperability is incorporated across the board to its individual elements. We believe that having a comprehensive middleware architectural framework to deliver these services is the most effective way to ensure this in a comprehensive way, instead of a large collection of individual but narrow approaches, mechanisms, and evaluations. In order to support interoperability across organizations and in support of the "future proofing" articulated in Principle I10, it is essential that APIs for Quality of Service (including security) should be expressed at a middleware layer, which maps down onto the lower level mechanisms for providing a given property, in order to extend life cycle management across the evolution of these mechanisms. In order to support multiple non‐functional/QoS properties (delay, rate, confidentiality, criticality/availability, and so on), it is essential that APIs be expressed in middleware so that they can be integrated and co‐managed. Resource allocation is an important part of resource management and is essential for providing non‐functional properties. A given lower‐level mechanism enables one or more non‐functional properties that may be optimized (or, at minimum, appropriate) for some operating conditions and inappropriate or even considered “not working” under other conditions. At runtime, a given mechanism may utilize different levels of underlying resources (CPU, bandwidth, and memory/storage). Different mechanisms providing the same property can provide different levels of non‐functional service for given operating conditions; they also typically offer different tradeoffs between the level of non‐functional properties provided and resources consumed. Examples of typical ways that non‐functional properties can be supported include the following:

• Latency mechanisms: a chain of network level “reservations” for performance (see below for a more detailed view).

• Confidentiality mechanisms: encryption

• Integrity mechanisms: higher level algorithms built on top of encryption (e.g., digital signatures).

• Availability mechanisms: replication (spatial, temporal, value) and end‐to‐end latency mechanisms per above.

Best practices dictate that the abstraction level for non‐functional properties offered to the programmer be established as high as possible, rather than encouraging developers to bind directly into lower level mechanisms, for a number of reasons: • It is less error prone. Very few application programmers are expert in low level, non‐

P a g e | 94

functional property mechanisms.

• Different lower‐level mechanisms are available in different configurations in different deployments. The APIs of the lower‐level mechanisms will change over time and perhaps with situation.

• New lower‐level mechanisms providing the same property or properties will become available over the lifetime of an application (which often can span many decades). Such new mechanisms will often be better than existing ones in one or more ways, including offering a higher level of a non‐functional property or being useable across a wider range of operating conditions

A stovepipe system is a legacy system that is an assemblage of inter‐related elements that are so tightly bound together that the individual elements cannot be differentiated, upgraded or refactored. The stovepipe system must be maintained until it can be entirely replaced by a new system. From this, we propose the following new definition: QoS Stovepipe System (QSS): a system of systems whose subsystems are locked into low level mechanisms for QoS and security such that

a) It cannot be replaced in many reasonable configurations, or b) Some programs cannot be combined because they use different lower level QoS

mechanisms for the same property (e.g., latency) that cannot be directly composed, or

c) It cannot be upgraded to “ride the technology” curve as better low level QoS and security mechanisms become available

It is essential that any Smart Grid avoid enabling or perhaps even allowing QSS. This can be achieved using a proper set of Middleware components.

Trustworthiness Trust in Smart Grids entails for some stakeholders, e.g., TSOs and DSOs, trustworthy monitoring and control of inclusion of DERs. To make Smart Grids transparent and trustworthy, we propose that an actor is empowered to monitor (invoke services) and provide information exchange with all relevant stakeholders. We name this authority Commercial Aggregator or Mediator.

Trust and trustworthiness

Trust is an attribute of a relation between two actors or between an actor and an artefact. Trust is hard to design and implement in the automated systems as presented in [4]. However, we can attain certain degree of trustworthiness in the system using engineering approach to address some of the trust concerns articulated by a stakeholder. Eventually, we then can design and implement a mechanism (sign – brand‐name) to be assessed by the stakeholder. This mechanism provides certain degree of trust and the stakeholder then might judge the system (person) to be trustworthy! Fig. 4 gives a simplified process model of engineering trustworthiness along this line.

P a g e | 95


Trust Sign Perceived Trust Trust

Aspects

Accessed Trustworthiness

Trust Mechanism

Fig. 4 Engineering approach supporting analysis of concerns [4]

It should be noted that stakeholders concerns might involve all or a selection of the Crosscutting Issues mentioned in Fig. 3. It might also happen that some of the expressed concerns might be contradictory or have different weight among stakeholders. In order to support the engineering of trustworthy systems we thus need the support of regulatory frameworks of stakeholder responsibilities and obligations as well of market rules of future Smart Grids.

In fact these frameworks correspond to concerns by stakeholders at Higher Interoperability Categories (5‐8) of Fig. 3. The identification of Trust aspects and design and implementation of Trust mechanisms or Signs has to be modelled and assessed in cooperation with relevant stakeholders. We address some of these aspects in Section IV.

Service Level Agreements (SLAs) Service Level Agreements (SLAs) are specifications of Functional and Non‐functional Interoperability Categories taking into account Cross Cutting Issues by stakeholders to be taken into account in order to address trustworthiness of Smart Grid functions and behaviours [Fig. 3 and Fig.4].

The SLA life cycle includes the following steps:

• Requirement engineering, including addressing concerns • Setting up and agreeing on SLAs and Key Performance Indicators (KPIs) to be

monitored • Implementing SLAs as a coordinating mechanisms of Service Bundles involving agents

and services • Monitoring processes controlled by SLAs • Assessing performance • Addressing proper maintenance actions

P a g e | 96

These activities have to be supported by proper (agent based) tools. We have addressed some of those issues in [7].

The criticalities involved in these types of activities are highly dependent by satisfying the non‐functional requirements or quality of service (QoS) parameters. For example the decision required at substation level to control an IED is in milliseconds to keep the balancing of energy in the power lines; however business actors can wait for pricing information that can take minutes to decide. Hence information requires different temporal requirement based on the urgency of data. At the power grid level, the data flow activity is in second or in milliseconds provide evidence to argue that local decision making should be present at the component level. Those concerns belong to the Technical Interoperability Category of Fig.3. The eventual dependencies, or influence, on the higher Organizational Category depends on the relevant regulatory frameworks and business processes. In multi‐infrastructure like telecom, cloud computing rely on the usage of SLA. Automation of SLA provides such functionalities to define monitor and manage information flow between multiple actors [5], [6]. Defining actors roles and data access provide certain degree of data security in the system that will increase trustworthiness in the system. Hence, we can argue that SLA can increase the level of trust if managed properly. In Smart Grid this will provides a mechanism to build a platform with horizontal and vertical integration of services in a trusted way. In Smart Grid the functional aspect are more related to energy flow and the services provided by ICT infrastructure. The critical nature of power systems arguably require non‐functional aspect tightly incorporated into services for reliable solutions. Monitoring critical set points using sensory network enable the power systems to measure and monitor huge amount of data. However, an overlay ICT infrastructure is required to handle such a large amount of data. This additional infrastructure promotes service oriented perspective in the power grid; hence, materialize additional actors and new business cases. To increase profits, Smart Grid people have to think about unbundling of price from energy units and start looking at the other business perspective like selling COMFORT and Green Energy promoting both higher energy efficiency and higher profit by service providers as presented in SEESGEN‐ICT28 and in [7].

Business Case – Inclusion of DER From the EU 20‐20‐20 goal for 2020 we infer that integration of DERs in Smart Grids is regarded as a key enabler to obtain the stated objectives. To integrate 20% of DER as renewable energy sources is a challenging task considering the current hierarchical energy systems. Concerns from TSOs and DSOs include: • Inclusion of DERs while maintaining QoS related to voltage and frequency control • Inclusion of DERs while keeping trust by stakeholders

28 SEESGEN‐ICT Home Page http://seesgen‐ict.rse‐web.it/

P a g e | 97

• Empowerment of customers while keeping trust and profitability Those concerns can be mapped on suitable vertical slices (of Categories) covering horizontal areas of Cross‐cutting Issues of Fig. 3. The relevant SLAs with KPIs can then be deduced and monitored. We have modelled those and similar concerns as sets of SLAs that can be monitored. Furthermore we have performed some promising simulations using techniques of Multi‐Agent systems [7, 9].

Conclusion and Future Work We have introduced Service Level Agreements (SLAs) as a mean to capture Cross‐cutting Issues related to trust by stakeholders of Smart Grids over Interoperability Categories in the framework provided by GridWise. We argue that proper implementations of those SLAs will enable design and implementation of trustworthy future Smart Grids. The paper is a report of promising work in progress. The main ideas and findings will be further investigated in ongoing international and national Smart Grid projects.

References [1] D. E. Bakken, R. E. Schantz, and R. D. Tucker, “Smart Grid Communications: QoS

Stovepipes or QoS Interoperability?,” Proceedings of Grid‐Interop 2009, pp. 17–19.

[2] D. E. Bakken, D. E. Whitehead, and G. C. Zweigle, “Smart Generation and Transmission with Coherent, Real‐Time Data,” 2010.

[3] W. T. Cox, T. Considine, and T. C. Principal, “Architecturally Significant Interfaces for the Smart Grid.”

[4] C. Rindebäck and R. Gustavsson, “Why trust is hard–Challenges in e‐mediated services,” Trusting Agents for Trusting Electronic Societies, pp. 180–199, 2005.

[5] K. Fakhfakh, T. Chaari, and others, “A Comprehensive Ontology‐Based Approach for SLA Obligations Monitoring,” in The Second International Conference on Advanced Engineering Computing and Applications in Sciences, pp. 217–222, 2008.

[6] D. D. Lamanna, J. Skene, and W. Emmerich, “Slang: A language for defining service level agreements,” in Proc. of the 9th IEEE Workshop on Future Trends in Distributed Computing Systems‐FTDCS, pp. 100–106, 2003.

[7] S. Hussain and R. Gustavsson, “Coordinating Energy Business Models and Customer Empowerment in Future Smart Grids,” in ICST Conference on E‐Energy. E‐Energy, 2010. October 14‐15, 2010, Athens, Greece.

[8] V.M. Catterson, E.U. Davidson and S.D.J. McArthur, "Embedded Intelligence for Electrical Network Operation and Control," Special Issue "AI In Power Systems & Energy Markets", IEEE Intelligent Systems, March/April, 2011, pp. 38 ‐ 45.

P a g e | 98

[9] R. Gustavsson and B. Ståhl, “The empowered user ‐ The critical interface to critical infrastructures,” In Proceedings of The Fifth International CRIS conference on Critical Infrastructures – Interacting Critical Infrastructures for the 21st Century. Beijing 20‐22 September, 2010.

P a g e | 99

Appendix A

P a g e | 100

SLAAgentImplementation Class Diagram The libsla framework manages Service Level Agreements as presented in Fig 4.7; the figure describes the implementation of libsla. In the next paragraph we discuss the implementation detail of it.

Fig 4.7, libsla Implementation – Class diagram

Roles, parties and peers Roles are defined within the domain, and a party is a peer with an assigned role for the given SLA. For instance, a role might be “Service provider” and a peer acting as a service provider can have that role for a given SLA, then the peer would be the party. A peer can be as many parties as necessary, using any number of roles, even for the same SLA. A role declares what permission the parties will have and on what events they will be notified.

Data is pushed, not pulled All data within the libsla framework is pushed forward, it does not poll for anything. This has both pros and cons, the later mostly being that in order to monitor the integrity of the system you need to actively query it and parameters has to be updated when modified.

P a g e | 101

Events The framework will automatically notify involved parties when different events occur in the system, depending on what role the party has. For instance, if an SLA is breached by one of the parties all parties with relevant roles will be notified. The role definition decides which parties receive what events.

Metrics and parameters Each SLA has parameters describing the rules for the parties and if a parameter is not within the legal boundary the SLA is automatically breached. Parameters have a single metric and input, where the metric describes the kind of data it handles. Simple parameters have a valid range for its input.

States Each SLA has an associated state, most of the time valid. States does not change automatically but only when the SLA is validated. This is to gain some performance as the validation is a relatively expensive operation. First all parameters should be updated, then the validation can proceed.

The initial state of all SLA’s is incomplete, meaning that it is still not finished and the creator must assign all the parties and parameters. Once ready, the creator enables the SLA causing it to change state to pending. A pending SLA is waiting for additional data, start‐date and signing. When all parties have signed and the date is between the start‐ and end‐date the state goes either to valid or breached depending on the parameters.

Glue (adaptor) By itself the framework cannot influence the system it is running within, so glue is needed. The glue comes mostly in two parts, the host and the peers. A host is an instance which is able to connect and pass messages to peers, and a peer (from libsla’s POV) is the data needed to connect to a remote host.

When an event occurs in libsla the call backs implemented by the host will be used to pass messages to the relevant peers. It is very important that the peer is serializable and can be used by any host anywhere at any time.

SLA Editor An editor is included to modify domain‐specific settings (roles, metrics, etc) and to view the opened SLA’s. The following sample file is included generated by SLA editor.

P a g e | 102

SLA‐Agent generated Sample File

final DataLayer db = new DataLayer(){}; final Host host = new SampleHost(); db.initialize(dbhost, dbport, dbname, dbuser, dbpass); final Domain domain = db.domain_from_shortname("test"); System.out.println("Opened domain: " + domain); final SLA sla = domain.sla_create(new SamplePeer("example@test")); System.out.println("Created SLA: " + sla); Party self = sla.creator(); /* who peer role id */ Party a = sla.party_add(self, new SamplePeer("fred@test"), 1001); Party b = sla.party_add(self, new SamplePeer("barney@test"), 1002); Party c = sla.party_add(self, new SamplePeer("wilma@test"), 1003); /* who short‐ metric initial */ /* name id value */ Parameter x = sla.parameter_add(self, "test", 1, 0.0f); x.set_range(self, 0.0f, 5.0f); sla.enable(host, self); sla.sign_request(host, self); sla.validate(host, self); /* SLA is now valid */ sla.paramter_update("test", 6.2f); sla.validate(host, self); /* At this point the SLA would be breached because the parameter is out‐of‐range */

Blekinge Institute of Technology

Licentiate Dissertation Series No. 2012:01

School of Computing

Coordination and Monitoring ServiCeS BaSed on ServiCe LeveL agreeMentS in SMart gridS

Co

or

din

at

ion

an

d M

on

ito

rin

g S

er

viC

eS

B

aS

ed

on

Se

rv

iCe

Le

ve

L a

gr

ee

Me

nt

S in

SM

ar

t g

rid

S Shahid H

ussain

ISSN 1650-2140

ISBN: 978-91-7295-224-9

The EU Climate and Energy package, setting

the 20-20-20 targets of future energy systems by

2020 will change the landscape of future energy

system in Europe and worldwide. A transition

from monopolised controlled Power network to

customer oriented Smart Grids operating in dere-

gulated energy markets poses several regulatory,

organizational and technical challenges. To that

end several international Smart Grid projects

have been launched worldwide in EU, the US

and China. To cope with the inherent complexity

of Smart grid systems the systemic property of

Interoperability has been proposed by organisa-

tions such as NIST and GridWise in the US and

is also adopted by EU.

Interoperability of smart grids entails design,

implementation, validation and maintenance of

systems ensuring technical, information, and or-

ganizational interoperability. In order to address

Quality of Service (QoS) in this setting, the tool

of Service Level Agreements (SLAs) has been

proposed. A SLA set up the coordination bet-

ween stakeholders in a business case and rele-

vant services with set-points and agreements to

be monitored. A challenge is to identify relevant

(new) stakeholders, their competences and roles

in the business case.

In the thesis we specifically address the follo-

wing issues:

• Empowerment of end-users

• Trustworthy integration of DER- Distributed

Energy Resources in delivered services

• Validation (Interoperability) of SLAs

To those ends, we have implemented an experi-

mental test bed based on Multi-Agent systems

and sensor technologies.

The thesis concludes with assessments of our

findings and some pointers to future work. Our

work is validated scientifically and industrially

by participating in the two EU project INTE-

GRAL and SEESGEN-ICT, both ended in late

spring 2011.

aBStraCt

2012:01

2012:01

Shahid Hussain

Documents

Coordination and Monitoring C e Leve ServiCeS BaSed on ...835042/FULLTEXT01.pdfSchool of Computing Coordination and Monitoring ServiCeS BaSed on ServiCe LeveL ... The thesis concludes