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dD1.2 Report of development for flexible MV-network operation
Advanced MV network operations using a multi agent system
dD1.2 Report of development for flexible MV-network operation
30 October 2013 2/92
ID & Title : dD1.2 Report of development for flexible MV-network operation
Version : V1.0 Number of pages : 92
Short Description
1) Introduction and scope of the document 2) Update deliverable dD1.1 3) Description of requirements and functionality of the overall system 4) Autonomous multi agent system 5) RTU communication
6) Risk management year 2
Revision history
Version Date Modifications’ nature Author
V0.1 23/04/2013 First draft Anton Shapovalov
V0.2 11/07/2013 Internal Review René Lorenz
V0.10 22/08/2013 Ready for review René Lorenz
V1.0 18/10/2013 Final version René Lorenz
Accessibility
Public Consortium + EC Restricted to a specific Group + EC Confidential + EC
If restricted, please specify here the group
Owner / Main responsible
Name (s) Function Company Visa
Dr. Lars Jendernalik Technical Manager DEMO1 Westnetz GmbH Dr. Lars Jendernalik
Author (s) / Contributor (s) : Company name (s)
RWE Deutschland AG Westnetz GmbH (RWE DSO) ABB Deutschland AG TU Dortmund
Reviewer (s) : Company name (s)
Company Visa
ENEL Review validated by Technical Committee on 18/10/2013
Approver (s) : Company name (s)
Company Visa
CEZ, ENEL, ERDF, IBERDROLA, VATTENFALL Approved by Steering Committee on 18/10/2013
Work Package ID: DEMO1 Task ID: DEMO1.2.1, 1.2.2, 1.2.3, 1.2.4
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Executive summary
This document describes the activities of the demonstration project DEMO1 “Advanced MV
network operations using a multi agent system” in the period of the second project year.
The high share and still massive increasing amount of distributed generation, predominantly wind
and photo-voltaic set new challenges to the DSO‟s. In order to provide hosting capacity to integrate
these resources huge investments in grid infrastructure are required. Grid operation and grid
observation becomes more complex since power flows become less predictable. At present in
Germany there are hardly any surveillance facilities or grid automation in place in medium voltage
networks.
DEMO1 addresses these challenges with the demonstrator to be built up in the area of “Reken”,
located in North-Rhine-Westphalia. The considered grid is well selected since it shows already
today a balance between installed generation power and maximum demand. Further increase in
renewables to be connected is forecasted.
The grid area of Reken consists of around 120 secondary substations. In the first year a total
number of about 15 stations was selected to be equipped with switching facilities – so called
switching agents.
The following objectives are targeted:
– Integrating an increasing number of decentralized energy resources (DER) in the medium-
voltage (MV) network and underlying low-voltage (LV) networks
– Achieving higher reliability, shorter recovery times after grid failures
– Avoiding unknown overloads and voltage violations
– Fulfilling the needs of surveillance and remote-control in MV-networks
– Reducing network losses
These objectives led to the question of a minimum number of necessary switching operations per
year. The standard load switching gear is not capable of the estimated number. Therefore the
primary switching hardware also has to be replaced leading to a significant increase of the specific
costs of this solution.
Due to the principle project idea of innovative but also economical grid solutions this necessary
equipment was again discussed at the beginning of the second project year regarding terms of
cost-benefit-analysis. This led to a solution where the most expensive parts of the equipment – the
switching agents with their replaced switching gear – are only installed in those grid areas with the
most benefit in the context of the above shown objectives. The final analysis now shows the
installation of 7 switching agents gaining the most benefit of this innovative switching solution.
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Regarding the demonstrative character of this project the main activity of this period was the
preparation of all necessary requirements to ensure the construction of an automated multi agent
system in the field in the next project period. To fulfil this objective, several subtasks were solved.
The principle idea of a multi agent system is based on an autonomous interaction between these
agents and their responsibility for a defined part of the MV network. The agents are divided into the
two groups: Switching Agents and Measuring Agents. Switching Agents can use the switch gear of
their secondary substation whereas Measuring Agents provide measured values to the Switching
Agents. The possibility of autonomous switching provides dynamic topology reconfiguration which
is a new concept of operation.
Based on the systematic results of the first project year the principles of the multi agent system
were finalized. The project discussions showed that the completely decentralized approach (where
the system intelligence is divided between the switching agents) will be very complex. Therefore a
two-step approach was chosen: in the first step the system intelligence will be concentrated in the
control centre steering the switching agents. The second step will be an implementation of the
above shown decentralized approach. This two-step approach has several advantages: the central
approach is still a decentralized solution regarding from the central grid control centre. Furthermore
the first step will be possible without larger implementation risks. The change between both steps
will be a change of the multi agent system software, the hardware will remain the same.
The tasks of the second project year finalized the principle central approach and the underlying
communication structure. The system structure and their related algorithms were basically
implemented in the RTUs and tested in laboratory tests. The primary hardware equipment was fully
specified and finally ordered. The risk management was detailed regarding the upcoming field
construction subtasks. The field implementation of the hardware components will be one of the
main tasks of the first half of the third project year. In parallel the completely decentralized
approach will be further elaborated and developed. First experiences gained from the field- and
lab-tests of the centralized approach will be considered in order to eventually identify the most
promising approach for the communication scheme of a multi-agent-system.
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Table of Contents
EXECUTIVE SUMMARY ............................................................................. 3
LIST OF FIGURES & TABLES ..................................................................... 7
1 INTRODUCTION AND SCOPE OF THE DOCUMENT .............................. 10
1.1 Scope of the document ........................................................................................................ 10
1.2 Structure of the document ................................................................................................... 10
1.3 Notations, abbreviations and acronyms .............................................................................. 11
1.4 Definitions and Explanations ............................................................................................... 12
2 UPDATE DELIVERABLE DD1.1 .............................................................. 13
2.1 Focus on most benefical area ............................................................................................. 13
2.2 Two step approach of implementation ................................................................................ 17
2.3 Use Case - Grid losses ........................................................................................................ 17
2.3.1 Concept of loss measurement .............................................................................................. 17
2.3.2 Approach of simulations ....................................................................................................... 20
3 DESCRIPTION OF REQUIREMENTS AND FUNCTIONALITY OF THE
OVERALL SYSTEM ................................................................................ 24
3.1 Hardware specification of the multi agent system ............................................................... 24
3.1.1 Concept of the switching agent ............................................................................................ 24
3.1.2 Market analysis of primary equipment .................................................................................. 28
3.1.3 Concept of the measurement part of s-agents and m-agents............................................... 28
4 AUTONOMOUS MULTI AGENT SYSTEM ............................................... 34
4.1 Concept ............................................................................................................................... 34
4.1.1 Architecture of the autonomous system ............................................................................... 34
4.1.2 Control and Decision module ............................................................................................... 35
4.1.3 Post-fault operation .............................................................................................................. 42
4.1.4 Topology Optimization .......................................................................................................... 44
4.1.5 Time series forecasting ........................................................................................................ 49
4.1.6 Reduced Network model ...................................................................................................... 53
4.1.7 Data Storage ........................................................................................................................ 58
4.2 Laboratory model ................................................................................................................. 59
4.2.1 RTU Hardware ..................................................................................................................... 60
4.2.2 Grid Model ............................................................................................................................ 60
4.2.3 Model coupling ..................................................................................................................... 70
4.3 Current status and outlook .................................................................................................. 72
5 RTU COMMUNICATION ........................................................................ 74
5.1 Communication overview .................................................................................................... 74
5.2 VPN communication ............................................................................................................ 76
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5.2.1 Central communication ......................................................................................................... 76
5.2.2 Peer to Peer communication ................................................................................................ 76
5.3 IEC 60870-5-104 ................................................................................................................. 77
5.3.1 Overview 77
5.3.2 Central approach .................................................................................................................. 79
5.3.3 Peer 2 Peer approach .......................................................................................................... 80
5.3.4 Peer2Peer Database ............................................................................................................ 82
6 RISK MANAGEMENT YEAR 2 ................................................................ 84
7 REFERENCES ........................................................................................ 86
7.1 Project Documents .............................................................................................................. 86
7.2 External documents ............................................................................................................. 86
8 APPENDIX ............................................................................................. 88
8.1 Fault detection ..................................................................................................................... 88
8.2 Load/Generation models ..................................................................................................... 89
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List of figures & tables
Figure 1: Final selection of separation points.............................................................................. 13
Figure 2: Actual state of separation points ................................................................................... 14
Figure 3: Procedure of calculation ................................................................................................... 15
Figure 4: Number of switching actions .......................................................................................... 15
Figure 5: Actual extension plan ......................................................................................................... 16
Figure 6: Chosen feeder in the selected grid area ...................................................................... 18
Figure 7: Example of a m-agent for grid losses ........................................................................... 19
Figure 8: Example of an installation position in a secondary substation ......................... 20
Figure 9: Effective feed-in capacity of a wind reference unit in Reken ............................. 21
Figure 10: Projected feed-in capacity of wind energy in Reken ........................................... 21
Figure 11: Diagram of wind ................................................................................................................ 22
Figure 12: Diagram of PV ..................................................................................................................... 22
Figure 13: Diagram of load .................................................................................................................. 22
Figure 14: Circuit diagram of the new switchgear in walk-in substations ....................... 25
Figure 15: Circuit diagram of a new MV/LV substation .......................................................... 26
Figure 16: Circuit diagram of the switchboard solution .......................................................... 28
Figure 17: Voltage taps and current transducer for m-agent or s-agent .......................... 29
Figure 18: Principle structure of a m-agent and s-agent ......................................................... 30
Figure 19: Top view of a secondary substation with installation space for a s-agent . 31
Figure 20: Picture of a control cabinet for a m-agent ............................................................... 32
Figure 21: 560CVD11 multimeter .................................................................................................... 33
Figure 22: Drawing of the measurement concept in the primary substation ................. 33
Figure 23: Architecture of the centralized autonomous system .......................................... 35
Figure 24: State machine representation of the system internal states ............................ 36
Figure 25: State limits for the medium-voltage network defined by the DSO ................ 37
Figure 26: Secure state and its transitions ................................................................................... 38
Figure 27: Algorithm for handling the endangered state level 1 ......................................... 39
Figure 28: Qualitative voltage behaviour in the endangered state level 1 and the system reaction ....................................................................................................................................... 40
Figure 29: internal logic of the endangered state level 2 ........................................................ 41
Figure 30: Qualitative behaviour of a voltage measurement when reaching the endangered state level 2 and the consequence of the control actions ............................... 41
Figure 31: Algorithm of the FDIR module ..................................................................................... 42
Figure 32: Short circuit case and the corresponding indication .......................................... 43
Figure 33: Flowchart of the SEM algorithm .................................................................................. 47
Figure 34: Dynamic optimization for an examplary network ............................................... 49
Figure 35: Principle of observations weighting .......................................................................... 50
Figure 36: Measurement and forecast of an active power time series (Exponential Smoothing) ................................................................................................................................................ 52
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Figure 37: Measurement and forecast of an active power time series (Multiple Regression + ARIMA) ............................................................................................................................ 53
Figure 38: Basic principle of the network reduction ................................................................ 56
Figure 39: Complete and reduced Reken topology ................................................................... 57
Figure 40: Reduced model precision for the 130 node Reken grid ..................................... 57
Figure 41: Concept of the laboratory model ................................................................................ 59
Figure 42: Basic principle of the grid modelling with aggregated LV grid ....................... 61
Figure 43: Exemplary sector of Reken MV grid modelled in MATLAB®/Simulink® .... 62
Figure 44: Principle of the phasor simulation method [16] ................................................... 63
Figure 45: Model of a secondary substation in Simulink® ...................................................... 63
Figure 46: Model of the controlled current source .................................................................... 64
Figure 47: Typical household load profiles .................................................................................. 65
Figure 48: Biogas plant time series. Top: five different units; Bottom: cumulated feed-in .................................................................................................................................................................... 66
Figure 49: Day-long elevation angle course ................................................................................. 67
Figure 50: Day-long photovoltaic feed-in ...................................................................................... 68
Figure 51: Converted wind speed measurements in the relevant region ........................ 69
Figure 52: Wind power coefficient curve ...................................................................................... 69
Figure 53: Active power output of a wind turbine model for the given time period ... 70
Figure 54: Architecture of the laboratory model ....................................................................... 71
Figure 55: Picture of the laboratory set-up .................................................................................. 72
Figure 56: Communication layer ...................................................................................................... 74
Figure 57: Central communication .................................................................................................. 76
Figure 58: Peer to Peer communication ........................................................................................ 77
Figure 59: Hierarchical communication ........................................................................................ 79
Figure 60: Peer2Peer Communication to neighbors ................................................................. 80
Figure 61 : Data routing ....................................................................................................................... 81
Figure 62: Risk Matrix Year 1 ............................................................................................................. 85
Figure 63: Risk Matrix Year 2 ............................................................................................................. 85
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Table 1: List of deliverable dD1.2 ..................................................................................................... 10
Table 2: Scenario matrix ...................................................................................................................... 23
Table 3: Overview of the existing optimization techniques ................................................... 45
Table 4: Key properties of both forecasting methods .............................................................. 53
Table 5: Exemplary line data .............................................................................................................. 59
Table 6: RWE ASDU/IOA address scheme .................................................................................... 78
Table 7: RWE Data model .................................................................................................................... 79
Table 8: RTU connection matrix ....................................................................................................... 81
Table 9: Actual list of risks .................................................................................................................. 84
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1 Introduction and scope of the document
1.1 Scope of the document The scope of the document is the description of all DEMO1-activities in the second project year.
The delivery date of the document is M24 (end of month 24).
Task
number
Deliverable
number Deliverable Deliverable description and responsibilities
Delivery
date
1.2.1
dD1.2
Report of development for
flexible MV-network operation
1) Introduction and scope of the document
2) Update of the technical description of the
deliverable dD1.1
3) Description of requirements and functionality of the overall system
4) Autonomous multi agent system
5) RTU communication 6) Risk management year 2
M24
1.2.2
1.2.3
1.2.4
1.2.5
1.2.6
Table 1: List of deliverable dD1.2
Table 1 shows that the deliverable dD1.2 “Report of development for flexible MV-network
operation” consists of different tasks. All the tasks are described separately in the document.
1.2 Structure of the document
The structure of the document is according to the tasks listed in Table 1: List of deliverable dD1.2
1) Introduction and scope of the document
2) Update of the technical description of the deliverable dD1.1
3) Description of requirements and functionality of the overall system
4) Autonomous multi agent system
5) RTU communication
6) Risk management year 2
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1.3 Notations, abbreviations and acronyms
CC-Agent Control Centre-Agent
DER Distributed Energy Resources
DG Distributed Generators
DSO Distribution System Operator
EU European Union
IAF Infrastructure Agent Function
KPI Key Performance Indicator
MAF MV-Agent Function
M-Agent Measuring-Agent
MAS Multi Agent System
PC Project Coordinator
PLC Programmable Logic Controller
PLC Power Line Carrier
PV Photovoltaic
RES Renewables
RTU Remote Terminal Unit
S-Agent Switching-Agent
SCADA Supervisory Control and Data Acquisition
SCC Special Contract Customer
SEM Switch Exchange Method
SGAM Smart Grid Architecture Model
SGCG Smart Grid Coordination Group
SSOM Sequential Switch Opening Method
SSS Secondary Substation
TM Technical Manager
UPS Uninterrupted Power Supply
VPN Virtual Private Network
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1.4 Definitions and Explanations
Generic Networks – Generic Networks are theoretically assumed structures, which are
common in MV-networks of a Distribution System Operator (DSO). The generic network
consists of typically used equipment. The network is tested with various scenarios of load
and feed-in, especially the possible location of the circuit breakers has to be analysed.
Scenario – A certain constellation of different parameters of load and feed-in. Different
scenarios have to be mastered by the tested network. Also a development of load and
feed-in over several years give multiple scenarios.
Switch state – This state represents the actual topology of the grid, depending on the
actual circuit breaker state. For example a ring structure with three breakers could have
three practical switch states, which give a radial structure and not a meshed structure
(closed ring topology and / or “islanding” needs to be avoided).
Limit value violation – under/over crossing of an allowed operative state value. Allowed
state values are:
voltage limits: +/- 10% from the nominal voltage
current limits: the current in a line must not exceed 100% of the nominal
current
Primary hardware – Equipment that is directly used for the energy transport within a
substation, e.g. the electromechanical components
Secondary hardware – Equipment that is used for safety, monitoring, control, automation
and communication tasks, e.g. the RTU or communication modules
Circuit Breaker - A circuit breaker is an automatically operated electrical switch designed
to protect an electrical circuit from damage caused by overload or short circuit.
Walk-in substation – a secondary substation where the primary and secondary hardware
equipment is located inside a small building. Maintenance and manual operation are done
within this building in contrast to typical compact substations where the personal is
working from outside of the station housing.
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2 Update deliverable dD1.1
2.1 Focus on most benefical area
In the deliverable dD1.1 we have described two different methods to place the agents: the heuristic
approach by the Technical University of Dortmund and the operational approach for finding an
optimal location of the circuit breakers by RWE. At the end the operational approach by RWE was
chosen. As you can see in Figure 1, seventeen separation points were planned in the first
deliverable dD1.1.
Figure 1: Final selection of separation points
One important issue of the first year was to estimate the number of expected switching operations.
Analysis and simulations have shown a higher needed number of switching operations as planned
before. Approximately 700 switching operations per year were expected for a single separation
point. So far, only circuit breakers with motor drives were considered.
The implemented circuit breakers have a M1000-classification. This means that 1000 mechanical
switching operations, but only 100 switching operations under load are possible per lifetime. At the
moment there is no technical solution to solve the problem with the restricted number of switching
operations. The result is the need of power circuit breakers and significant additional expenses for
reinforcement of primary hardware.
SP Method 2
SP Method 1
Chosen SP
Shift to near SP
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Due to the higher costs for reinforcement of primary hardware, the focus will be on the most
beneficial areas to get a positive business case (with use of power circuit breakers). Network
calculations and simulations by the Technical University of Dortmund have shown, that a great
effect will be reached with a few separation points (s-agents).
This subchapter describes the procedure to identify the most beneficial area in the demonstration
grid of Reken. In this regard the changes in the current topology compared to the previously
planned allocation of agents are necessary. Due to operational reasons four separation points
were rejected and one separation point was complemented. Operational reasons are for example
static separation points with no considerable effects or separation points close to special contract
customers that should only be used for failure management (minimizing the impacts on this
sensible customer group). These minor changes lead to the following data base.
Figure 2: Actual state of separation points
A few scenarios (load and feed-in) for one year were defined, corresponding to the actual state.
These data consists of
o 15-minutes intervals
o reference time series of real measured generators
o mean load curves, created from standard load curves
o real installed decentralised generators.
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The whole year is analysed. For each time interval the following steps are performed (Figure 3).
Figure 3: Procedure of calculation
After these steps are performed for every time interval, the switching activities are analysed. For
each switching agent the state change (closed->opened OR opened->close) is counted.
Figure 4: Number of switching actions
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Based on this evaluation the most beneficial area in the demonstration grid was identified. The
result is shown in Figure 5. This picture shows seven most important separation points. Four
separation points will be realized as a switchboard solutions close to the MV/LV substation, two by
the replacement of the complete MV/LV substation and one by the replacement of the MV
switchgear in walk-in substations. These three different concepts for station reinforcement of
primary hardware (s-agents) will be tested in DEMO1.
Figure 5: Actual extension plan
The red circle in Figure 5 symbolises the replacement of the MV switchgear in walk-in substations
or the replacement of the complete MV/LV substation. The drawing x symbolises the switchboard
solution close to the MV/LV substation. The different concepts will be descripted separately in
another chapter (chapter 3.1.1).
In the first year (see deliverable dD1.1) we planned to implement seventeen s-agents. Now one
year later we have to correct the number of separation points (s-agents) due to the previous
explanations. The aim is, that we can reach round about 80 per cent of the benefit with the reduced
number of separation points (s-agents). The business case becomes better, because of less
station reinforcement of primary hardware as planned before. And furthermore no changes on
testing the algorithm and the communication structure or on topics like scalability and replicability
are necessary.
The number and location of m-agents is not changed in comparison to the first year deliverable.
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2.2 Two step approach of implementation
The initial idea stated in dD1.1 was to develop a fully decentralized autonomous control system.
This would exhibit decentralized logic and decision making as well as peer-to-peer communication.
In the course of the second project year some new insights have been made:
o Traditionally DSOs rely on centralized logics placed in the network control centres, so it is
hard to migrate to a fully decentralized alternative
o Chosen control action (switching) causes non-local changes in the systems state
o More global system insight is probably needed to guarantee secure operation
o Power flow computation is needed
These points made clear that a reliable fully decentralized concept would be very challenging and
an alternative approach could enable an easier and faster access to the implementation phase.
The decentralized implementation should not be completely disregarded, but postponed. In the
meanwhile a centralized approach, which seems to be more distinct, should be implemented.
The proposed approach has some similarities with a classical SCADA system like centralized data
acquisition and centralized decision making. Though, some features make it innovative. The
system should work in an autonomous way and make decisions based on manageable models.
Some interesting and not foreseen aspects have been identified in the second project year:
o Load flow model for a not fully monitored power system is required
o Optimization techniques require insight in a specific time horizon
System state forecast should be applied
In the following, we plan to proceed in a two-step manner. Currently the centralized system
implementation is being carried out. Also a laboratory test set-up with real RTU units is being
realized. In the second step, which will be by October 2013, we start to develop and implement
some decentralized functions. It has been decided that not all system functionalities have to be
necessarily decentralized. The most important and interesting aspects are peer-to-peer
communication, decentralized post-fault operation, decentralized topology detection and state
identification. We plan to combine these new aspects with the results from the central
implementation, so that the system will have both kinds of functionalities – centralized as well as
decentralized. One of the most interesting research questions will be: what approach can show the
best performance?
2.3 Use Case - Grid losses
2.3.1 Concept of loss measurement
In this chapter the loss measurement concept is described. Reconstruction work and additional
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measurements are necessary in the primary substation. The objective is to recognize the grid
losses in the distribution grid of Groß Reken. Additionally it is planned to identify the grid losses in
detail for one selected MV feeder. Figure 6 shows the selected feeder (pink colour).
Figure 6: Chosen feeder in the selected grid area
The transformation losses of the MV/LV transformers and the line losses in the MV network will be
measured with the help of the m-agents, which are located in each secondary substation. The m-
agents for loss detection are different to the m-agents planned for detection of unknown overloads
and voltage violations (see 3.1.3). Furthermore the line losses in the underlying LV network will be
identified in a few secondary substations of the selected feeder.
Based on these collected data the expected grid losses will be estimate for the distribution grid of
Reken. The results of the estimation can be used for a comparison with the results by simulation.
The simulation and the results will be described in the next chapter (2.3.2).
The voltage will be measured with an accuracy of 1 %, the current with an accuracy of 0.5 %.
These values are quite small, this means that really high-end equipment is deployed for the
measurement concept.
2.3.1.1 Concept of the measurement in the primary substation
All switching bays of the primary substation of Groß Reken have to be upgraded in the same way.
An additional current transducer and the multimeter 560CVD11 have to be implemented in each
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switching bay. This multimeter is also integrated in the RS485-bus, so that the values can be
concentrated in the control centre (RTU) and transmitted to the central SCADA system for
analysing and archiving data. An expected accuracy of 1 % can be achieved with this
methodology. The same procedure is also described in chapter 3.1.3.1.
2.3.1.2 Concept of the measurement agent for grid losses
The structure of the loss measurement agent leans heavily on the structure of the m-agent. The
structure of the m-agent is described separately in section 3.1.3. Therefore, here are only the
essential differences characterised.
The objective is to measure the power (in detail voltage and current) on the secondary side of the
transformer. So all measurements in the MV network and the associated measuring components
(transmitter, transducer) will be obsolete. The measurement on the LV side is performed as
described later in the report (chapter 3.1.3). The power values will not be measured in the
secondary substations in case of failure. In this way, an additional battery supply is not necessary.
Binary I/O´s are also not necessary. In total less components are responsible for a reduction of the
installation dimensions. This allows, at least partly, to install the m-agent directly in the secondary
substation. A maximum dimension of (W x H x D) = (360 mm x 254 mm x 165 mm) is provided.
Figure 7 and Figure 8 are showing the different variants of installation in the secondary substation.
The loss measurement agent will be realized with components of ABB (FIONA) in this project. It is
possible to achieve an accuracy of 1 % with the implemented current transducers.
Figure 7: Example of a m-agent for grid losses
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Figure 8: Example of an installation position in a secondary substation
2.3.2 Approach of simulations
This chapter describes the procedure to determine the grid losses in a distribution grid. In order to
recreate the supply over the course of a year, it is necessary to evaluate reference units of
renewable energy resources (PV, wind, biomass). The reference values are normed. These
normed factors are used to calculate the actual amount of feed-in in 15-minute values, with the
installed feed-in capacity. The load is calculated with the help of standard load profiles, also in 15-
minute values.
Figure 9 shows the effective feed-in capacity of a selected wind reference unit in Reken. The feed-
in capacity is measured in 15-minute-values.
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Figure 9: Effective feed-in capacity of a wind reference unit in Reken
The data of the reference unit are used to extrapolate the full feed-in capacity of wind energy in
Reken. The result of a selected day is shown in Figure 10.
Figure 10: Projected feed-in capacity of wind energy in Reken
All possible scenarios are simulated in NEPLAN (NEPLAN is the standard grid planning tool at
RWE). To minimize the number of simulated scenarios, the renewable energy resources (PV, wind,
biomass) and load are divided in different clusters. The values of biomass are not considered here
and expected durable at 100 %, because they remain nearly constant during the year. The
following pictures (Figure 11, Figure 12, Figure 13) are showing the defined four clusters for PV,
wind and load.
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Figure 11: Diagram of wind
Figure 12: Diagram of PV
Figure 13: Diagram of load
Thus, a number of 64 (4x4x4) different scenarios is possible. With the help of a scenario matrix
(Table 2), the frequency of each scenario is counted. Each scenario with a frequency of more than
100 is simulated twice in NEPLAN.
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Table 2: Scenario matrix
The first simulation is a determination of grid losses with an load optimized grid plan (given factors
for load and feed-in). The second simulation is a optimization of the separation points with the
same factors and a subsequently determination of the grid losses.
The comparison of these two simulations shows the savings potential. The savings potential
means the reduction of grid losses and at the end of the day the monetary gain: Especially the
scenarios with strong supply and low load resulting in very low total grid losses.
Subsequently, with the help of the determined reference topology (optimized load case), an
increase of the power supply will be simulated under attention of voltage violations and current
limits. By the automated switching operation of the grid topology the voltage violations and
overloads should be adhered.
The resulting grid situation has to be checked with the factors of the load case. Therefore the
voltage violations, unknown overloads and additionally the potential of savings has to be
considered in detail.
The factors to simulate the feed-in scenario will be increased as long as no voltage violation or
unknown overload is located. Thus, the time period up to a necessary grid expansion and the
associated costs has to be determined.
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3 Description of requirements and functionality of the overall system
3.1 Hardware specification of the multi agent
system
3.1.1 Concept of the switching agent
The following chapter describes the three different concepts for station reinforcement of primary
hardware. As mentioned before, these concepts are:
o the replacement of the MV switchgear in walk-in substations
o the replacement of the complete MV/LV substation
o a switchboard solution close to the MV/LV substation
The requirements for the switching technology concerning the expected number of switching
operations per year and the useful life time are the same for all three concepts. The power circuit
breakers have to be designed for 1.000 switching operations per year (under load) and a useful life
time of 20 years. These two points were advertised by the invitation.
The manufacturers were encouraged to name their maximum number of switching operations for
the different types of the MV switchgear. The result was, that no manufacturer could guarantee the
expected number of 20.000 switching operations over 20 years. Furthermore the manufacturers
have to guarantee that their components fulfil the RWE-standards.
3.1.1.1 Replacement of the MV switchgear in walk-in substations
The first concept describes the replacement of the MV switchgear in walk-in substations. The
actual existing air-insulated switchgear will be replaced by a new sulphur hexafluoride (SF6)-
insulated switchgear in the MV/LV substation.
Following steps have to be conducted for a technical implementation:
o Dismantling and a proper disposal of the existing 10 kV air-insulated switchgear.
o Creating a temporary 10 kV solution to ensure the local power supply.
o The new switchgear consists of three switching bays and a transformer. The switches of
the s-agents will be equipped with motor drives. Additionally status signals will be used to
transmit the currently switching state.
o Connection of the motor drives and feedback of the switching state to the SCADA system.
o In every switching bay the measured values (for the actual current and voltage level) have
to be acquired for the data transmission with an accuracy of 1 %. Also the acquisition and
announcement of short and earth faults will be required.
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o Possibility to connect 10 kV lines (cables) with a size of 500 mm² in a walk-in substation.
o Delivery and installation of an uninterrupted power supply (UPS) with a rectifier to supply
the motor drives of switches in case of a grid failure. The two components are involved in
the monitoring process. The UPS shall buffer 3 switching operations for a time period of 3
hours.
o Installation of a box (600 mm x 600 mm) to connect the remote control technology to the
switchgear and power supply.
o Pressure calculation for the secondary substation housing.
Figure 14: Circuit diagram of the new switchgear in walk-in substations
3.1.1.2 Replacement of the complete MV/LV substation
The second concept describes the replacement of the complete MV/LV substation. The actual
existing secondary substation will be replaced by a new MV/LV compact substation.
Following steps have to be conducted for a technical implementation:
o Delivery and installation of a complete 10-kV secondary substation (including a 400-kVA
transformer)
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o 10 kV switchgear will be equipped with two switching bays for cables and one for the
transformer. The MV switches have also motor drives and status signals to transmit the
actual switching state.
o Connection of the motor drives and feedback of the switching state to the SCADA system.
o In every switching bay the measured values (for the actual current and voltage level) have
to be acquired for the data transmission with an accuracy of 1 %. Also the acquisition and
announcement of short and earth faults will be required.
o Possibility to connect 10 kV lines (cables) with a size of 500 mm² in a substation.
Introduction of the cables into the substation by sealing packing HIS 150.
o Delivery and installation of an uninterrupted power supply (UPS) with a rectifier to supply
the motor drives of switches in case of a grid failure. The two components are involved in
the monitoring process. The UPS shall buffer 3 switching operations for a time period of 3
hours.
o Installation of a box (600 mm x 600 mm) to connect the remote control technology to the
switchgear and power supply.
Figure 15: Circuit diagram of a new MV/LV substation
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3.1.1.3 Switchboard solution close to the MV/LV substation
The third concept describes the switchboard solution close to the MV/LV substation. The actual
existing secondary substation will be untouched. An additional switchboard will be placed close to
the station. The function is to operate (open/close) the line in one direction.
Following steps have to be conducted for a technical implementation:
o Delivery and installation of complete 10-kV switchboards
o The switchboard housing is made of concrete, plastic or comparable materials for the
switching equipment.
o The switchgear will be equipped with one switching bay for a cable and one recital bay with
earth switch. The switch is equipped with a motor drive and status signal to transmit the
actual switching state.
o Connection of the motor drives and feedback to the transmission technique.
o In the switching bay the measured values (for the actual current and voltage level) have to
be acquired for the data transmission with an accuracy of 1 %. Also the acquisition and
announcement of short and earth faults will be required.
o Possibility to connect 10 kV lines (cables) with diameter of 500 mm² in a substation.
Introduction of the cables into the substation by sealing packing HIS 150.
o Delivery and installation of an uninterrupted power supply (UPS) with a rectifier to supply
the motor drives of switches in case of a grid failure. The two components are involved in
the monitoring process. The UPS shall buffer 3 switching operations for a time period of 3
hours.
o Installation of a box (600 mm x 600 mm) to connect the remote control technology to the
switchgear and power supply.
o Installation of a LV distribution panel in the switchgear for grid connection
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Figure 16: Circuit diagram of the switchboard solution
3.1.2 Market analysis of primary equipment
The idea was to modify the circuit breakers in the MV network to round about 10.000 electrical
switching operations. Due to this topic we had a lot of discussions with several manufacturers.
Currently available circuit breakers have a M1000-classification.
As mentioned before, all circuit breakers in the demonstration grid of Groß Reken have also a
M1000-classification. If these types of circuit breakers should be used for 10.000 switching
operations for example, it would be necessary to upgrade the mechanical system. In this case, it
was even a new development of a circuit breaker. Additionally these circuit breakers are not
designed for 10.000 electrical switching operations. An adjustment of the chambers and
extinguishing system would raise a great effort and means also a new development.
In summary one can say that an adjustment or modification of the circuit breakers is neither
technically nor economically feasible within this project scope. For applications with a high
switching frequency, furthermore the existing power circuit breakers have to be used.
3.1.3 Concept of the measurement part of s-agents
and m-agents
Basically active and reactive power values of all feeders are needed in a secondary substation.
This means that the voltage level of the MV busbar and the current of each feeder of the selected
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secondary substations must be measured. Additionally the switching agents must be equipped with
a control option. These requirements are responsible for the secondary technical structure of the s-
agents and m-agents.
The secondary hardware of the s-agents and m-agents is basically constructed similarly. It consists
essentially of the following components:
o battery
o power supply unit
o safety devices (fuses)
o Remote Terminal Unit (RTU) with binary I/Os
o GPRS modem
o measurement inputs for current from each feeder
o measurement inputs for current and voltage on LV side
o current transducer in each feeder
o current transducer on LV side of the (secondary substation) transformer
o voltage taps to measure voltage level on LV side and providing the supply voltage
Figure 17: Voltage taps and current transducer for m-agent or s-agent
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Figure 18 shows an example of the standard product of ABB (type FIONA) with the principle
structure of the m-agent and s-agent.
Figure 18: Principle structure of a m-agent and s-agent
The power supply of components is secured by voltage taps on the LV side in the secondary
substation. Therefore, the voltage is 230 V (AC). The power supply unit converts 230 V (AC) into
24 V (DC). This voltage is necessary, because all secondary technical components are operating
with 24 V (DC).
Depending on the number of feeders in a secondary substation, the corresponding number of
measurement components and current transducers will be deployed.
The voltage values will be directly ascertained in the switching bays of the s-agents with the use of
capacitive voltage taps. So the power values can be determined directly in the feeders and
transmitted to the RTU.
In secondary substations, where only m-agents are planned, therefore no capacitive voltage taps
are existing. A upgrade of these substations with voltage transducers would be very expensive and
because of that another methodology will be used. The measured voltage value on the LV side will
be used to determine the power values on the MV side. This LV value is used in consideration of
the transmission ratio and the losses of the MV/LV transformer to calculate the voltage value which
exists on the primary side of the transformer.
Another difference between m-agents and s-agents is the dimension of the battery. In error-free
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operation can be assumed that the supply voltage for the components of secondary equipment is
available. If the supply is interrupted in case of error, it may be necessary to perform switching
operations in the local distribution grid. Due to these switching operations, the battery in the s-
agents is designed in a way, that there is sufficient capacity for at least two hours of operation and
three switching operations.
It has to be ensured that after the interruption of the supply voltage a message about the short
circuit or ground fault is transmitted by the m-agent securely. From previous experience with the
GSM transmission a further supply of the system of maximum 10 minutes is sufficient. A
transmission of measured values is no longer necessary, because all measured values are zero.
For reasons of IT-security the RTU sets up an encrypted connection via IPSec encryption. The
necessary certificates are predefined by the respective DSO, in case of DEMO1 by RWE. The
GPRS modem transmits the encrypted data. Based on this methodology, there are no special
requirements for the functionality of the modems.
For data transfer the communication protocol in accordance with IEC 60870-5-104 is used in the
project. Additionally RWE requires the observation of a special RWE-profile.
The installation slot for the secondary equipment has to be chosen for easily maintenance access
in the secondary substation. A space requirement with a maximum dimension of (W x H x D) =
(600mm x 600mm x 400mm) is provided for the s-agents. Separate cabinets will be placed next to
the secondary substations by the m-agents. The internal dimension of these cabinets are (W x H x
D) = (600mm x 600mm x 300mm) at minimum.
Figure 19: Top view of a secondary substation with installation space for a s-agent
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Figure 20: Picture of a control cabinet for a m-agent
3.1.3.1 Concept of the measurement in the primary substation
Currently there is only a single-phase current measurement located in the primary substation. In
addition, protective cores are inserted that are connected only to the protective devices at the
moment. For the algorithms that are used in the project, the active and reactive power values are
essential. Therefore the measurements must be upgraded in the affected switching bays of the
primary substation. That‟s why current transducers (type RITZ ZKSW 70 1/1A Kl.05 10 VA) are
deployed in the existing protection cores of the switching bays. The voltage will be measured at the
MV busbar and transmitted via an existing ring line to each switching bay. Direct-measurement-
facilities are employed in the affected switching bays, which are supplied with the voltage value of
the ring line and the current measurement of the transducer. The ABB unit 560CVD11 is used for
the measurement. This multimeter is supplied with 220 V (DC), because it is the only secured
auxiliary voltage supply in the primary substation.
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Figure 21: 560CVD11 multimeter
This ABB unit will be implemented in each switching bay and connected via RS485-interface and
modbus protocol to the control centre (RTU). The following picture (Figure 22) shows the
measurement concept in the primary substation.
Figure 22: Drawing of the measurement concept in the primary substation
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4 Autonomous multi agent system
This chapter introduces the concept and the implementation of the autonomous control and
operation system for use in electrical medium-voltage networks. First, the overall system
architecture is described and the essential functional modules are explained in detail. In the
following part, the laboratory implementation and the concept of the hardware-in-the-loop simulator
are presented.
4.1 Concept
4.1.1 Architecture of the autonomous system
The overall system is based on the central architecture approach, which means that the system
logic and decision making entity is placed at the so called master agent or control centre (CC)
placed in the Reken primary substation. The control centre receives measurements from the slave
agents and is able to give switching commands in order to reconfigure the network topology.
Furthermore CC is connected to the SCADA system for the reason of information transparency and
in case of the need to control the system remotely. Also, occasionally some network topology
information update from SCADA is needed.
One major benefit of the central approach is the security of switching actions: because of the
central supervised switching process and the underlying optimization algorithm (see chapter 4.1.4)
no illegal topology configurations can be achieved. This proceeding corresponds to the current
manually operation philosophy: when switching actions are planned, the executing personal has to
receive a switching permission from the network control centre.
As illustrated in Figure 23, the structure of the control centre logic is subdivided in following entities:
o Control and decision module
o Forecast module
o Execution module
o Topology optimization module
o Post-fault operation module (FDIR)
o Data storage/SCADA Interface
All of these blocks are interconnected through communication or data channels.
For better understanding of the logical organization the secure operation case is considered. Slave
agents acquire the current measurements and transmit them to the CC. At the CC side the control
and decision module analyses incoming data. No state violations are detected and the
measurements are passed over to the forecast module.
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Figure 23: Architecture of the centralized autonomous system
The latest forecast is updated and the deviation from the last forecast is calculated. The forecast
update is given back to the decision module. The decision module initializes a new optimization
process in order to update the switching action plan for the current day and the day ahead. The
topology optimization requires a new nodal power forecast for the optimization horizon. After
computing the new switching plan, it is forwarded to the control and decision module. The old
switching schedule is now updated and passed to the execution module. This module is
responsible for the proper switching procedure and its supervision. In the meanwhile the
measurements and the switch state updates are transmitted to the SCADA system. Also if some
topology updates, e.g. due to network reinforcement, occur – the SCADA system sends this
information to the CC.
In the following sections, a more detailed explanation of every single logical module is given.
4.1.2 Control and Decision module
The control and decision module (CD) contains the overall system logic and is responsible for the
control actions organization. The system differentiates between different internal states, which are
always given by the present measurements and by the short circuit indication signals. There are
two types of internal states: non-faulty and faulty operation. The first one is subdivided into secure
Grid
Slave agent
Control and Decision
Forecast
Optimization
FDIR
Execution
Data storage
Control centre
SCADA
‚Actor’
‚Storage’
Data exchange
Communication channel
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state, endangered state level 1 and endangered state level 2. These states are defined in 4.1.2.2.
After a short circuit appeared, the system is at the faulty operation state. Failure isolation and
system restoration have to be carried out. So the state is set back to the most common secure
mode. Figure 24 illustrates all possible system states and their transitions. Some of the states are
looped, which means that the system remains for a longer period of time in secure or endangered
state level 1. It is assumed that it is always a possibility to avoid critical overloads or over-voltages
due to the load flow situation. So the endangered state level 2 is not looped.
Figure 24: State machine representation of the system internal states
Every internal state requires some specific actions. Typically measurements acquisition and
analysis of the measurements is performed continuously. Based on that, a transition between two
states can be decided.
For better understanding the non-faulty internal states, an overview about the state variables limits
is given.
4.1.2.1 State limits
A state of an electrical network is defined by its branch currents (or in following line loadings) and
nodal voltages. The described control system acts at the medium-voltage level. That means that
voltage changes at the substations affect the low-voltage customers. By the norm DIN EN 50160
the voltage level at the end customer‟s node has to be held in the ±10% range in respect to the
nominal voltage (0.4 kV for the low-voltage level). Depending on this requirement, RWE defined
viable limits for the MV-network operation (see Figure 25). These limits should not be violated while
operating the network.
The state values beyond the allowed limits belong to the endangered state level 2 (red areas in
Figure 25). This state has to be left very fast for preventing possible violations in the underlying
secure state
endangeredstate level 1
endangeredstate level 2
fault detection
restoration
isolation
faulty operationnon-faulty operation
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low-voltage network. Also the line loading of over 100% of line‟s thermal rating leads to the
endangered state level 2.
Basically all the states below the limits of the endangered state level 2 are considered as not
critical. However, it is proper to define some security margin which has to be passed through
before causing critical violations. The voltage margin of 1% from the critical limits and the line
loading between 80% and 100% is defined as endangered state 1. This state is allowed to be
driven accidently. The remaining voltage and current range are building the secure state, which is
preferred to be driven all the time.
Figure 25: State limits for the medium-voltage network defined by the DSO
4.1.2.2 Non-faulty operation
Now the main principles of the non-faulty state transitions and the inner logic of every state are
described. In chapter 4.1.3 the post-fault FDIR module is explained more in detail.
Secure State
The secure state can be changed to every other state (Figure 26). Also it is possible to get from
other states into secure state. Basically continuous measurements supervision is carried out. In the
meanwhile the losses optimal network configuration is calculated and a switching job plan is set.
So if nothing extraordinary happens, the switching jobs are performed.
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Figure 26: Secure state and its transitions
Endangered State Level 1
As described by the state limits, the endangered state level 1 is taken, when the system state
variables are not yet hard violated, but may run into the not permitted range. That‟s why a
mechanism which considers the staying time in this state and the next forecast value is
established. The flow chart of the algorithm is given in Figure 27.
measurement
Secure
regular optimization
switching actions
Endangered lvl 1
Faulty operation
Endangered lvl 2
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Figure 27: Algorithm for handling the endangered state level 1
As soon as one of the measured values access the endangered level 1, an integration process is
started. A threshold is defined for remaining in the state. When the integrated threshold is reached
two possible decisions can be made: whether to let the system remain for a while in the
endangered state level 1 or to leave this state by means of topology reconfiguration. The decision
is supported by the information provided from the forecast tool. It gives voltages and current
forecasts for the next 15 minutes interval. If the forecast indicates that the system would remain in
the same endangered state, an optimization process is started and a new topology is evaluated in
order to prevent a longer staying in this state. Otherwise no actions are done and the integrator is
reset. Also some planned switching job should be considered in order not to perform extra
switching. State transitions to and from all other states are possible. State supervision is carried out
by measurements acquisition.
Integrator limit crossed?
Is forecast secure?
switching operation
yes
yes
Is another switching operation planned during
the next 5 minutes?
no
no
start integrator
yes
Endangered lvl 1
no
Secure
Faulty operation
Endangered lvl 2measurement
reset integrator
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The principle of the variable integration and the usage of the forecast are illustrated in Figure 28.
Figure 28: Qualitative voltage behaviour in the endangered state level 1 and the system reaction
Every measurement needs its own integration process and though the state supervision is carried
out in parallel for every state variable. The most critical state of one of the variables corresponds to
the state of the entire system.
Endangered State Level 2
As already mentioned, as soon as the endangered level 2 state is reached a control action has to
be applied immediately. The flowchart for the endangered state level 2 is given in Figure 29.
This state assumed to be reached only from the secure state or from the endangered state level 1.
A transition from the post-fault state to the endangered state level 2 is unlikely, because the logic of
the post-fault state doesn‟t allow leaving the state before not guaranteed the transition to at least
endangered state level 1 or at the best to the secure state. After performing the topology
reconfiguration, the system has to be set to the one of the allowed states.
In the following a qualitative example of the time dependent system behaviour is shown in Figure
30. After a limit violation is detected the autonomous control system requires some time to
compute the new optimal topology. When the switching job is generated a sequence of topology
changes is performed. As the new optimal topology is applied, the system is driven out from the not
permitted state.
integrator trips, noneed for actionbecause of forecast
endangered lvl 2
endangered lvl 1
secure
Limit violation end lvl 1 -> integrator starts
V
t
forecast
measured voltage
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Figure 29: internal logic of the endangered state level 2
Figure 30: Qualitative behaviour of a voltage measurement when reaching the endangered state level 2 and the consequence of the control actions
Endangered lvl 1
Faulty operation
Secure
optimization
Endangered lvl 2
switching operation
Short circuit appeared?
no
yes
forecast
measured voltage
V
t
endangered lvl 2
endangered lvl 1
secure
Limit violation end lvl1 -> need for action
switchingsequence
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4.1.3 Post-fault operation
The FDIR module defines the system behaviour in case of the occurred short circuit fault (SC). The
autonomous multi agent system does not replace the protection system, which acts in the
traditional way by opening circuit breakers at the primary substation when detecting overcurrent.
So the entire interconnected faulty network region is disconnected. The autonomous system has
the task to identify the fault location between two measuring agents and to restore the part of the
affected area in a short time. The process is typically subdivided in three phases (FDIR):
1. fault detection
2. fault isolation
3. restoration
In Figure 31 the overall algorithm of the FDIR module is given. The transition to the faulty state
exists from all the other states. The transition from the faulty state is only given to the secure and
endangered state level 1.
Figure 31: Algorithm of the FDIR module
Short circuit
Isolating SC-area,lock switch for safety reason
Possible to restore existing topology without sc-area
LF-calculation
yes
Is topology secure?
Execute switching algorithm
yes
Optimizationno
no
Is new topology in endangered lvl 1 or
secure?
Execute switching algorithm
yes
Delay-block
SC-detection
Endangered lvl 2
Secure
Endangered lvl 1
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FDIR is a well-known procedure, already applied in some other autonomous systems [1]. The
essential difference to the other approaches is considering an amount of installed decentralized
generation in the grid. Due to this fact, the restoration procedure becomes more complicated. Thus
it requires a better understanding of the system state after performing switching actions. So a load
flow and the topology optimisation are involved. In following the single steps of the algorithm are
demonstrated for the exemplary topology in Figure 32.
Figure 32: Short circuit case and the corresponding indication
4.1.3.1 Fault Detection
Only a line segment between two switching agents can be disconnected in order to restore the rest
of the system. Because of that the SC indicators of the switching agents have to be analysed (see
Figure 32).
The fault detection algorithm (flow chart in Appendix 1: Flow chart of the algorithm for fault
detection) starts at the primary substation bus bar and goes through the path of the positive short
circuit indications. As soon as the SC flags don‟t appear anymore the faulty segment is found. In
the given example it would be the segment E-D. The algorithm uses neighbourhood relations of the
switching agents.
4.1.3.2 Isolation
To the faulty segment belong a couple of switching agents. These agents and their switches are
identified in the fault detection phase. A direct commando to disconnect the switches performs the
isolation of the faulty line.
A
B
C
D E
F
G
Feeder 2 Feeder 1
0
1
0
0
0
0
0
0 0
1
1
0
1 0
1
0
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4.1.3.3 Restoration
The steps of the restoration process are described in Figure 31. Beside the optimization based
restoration a fast variant is given: this consists in closing the circuit breaker at the primary
substation. In the given example this would be probably the easiest way to restore the most part of
the network without involving any additional switching. Nevertheless this method needs a validation
of the system state after performing the restoration. This is carried out by means of a simple load
flow computation and analysing the resulting state variables. If the supposed system state is
secure, this fast restoration is applied.
Otherwise an optimization based variant is chosen. The restoration process has two requirements
– to find a new valid topology after the fault isolation and to avoid over-voltages and over-currents
in the restored area. This is a typical task for the topology optimization routine. In case of not
finding an appropriate solution, the system may wait and perform the optimization again. As soon
as the expected restored state is secure or endangered level 1, the switching execution is initiated.
4.1.4 Topology Optimization
Network topology optimization by reconfiguration is a well-known approach at the distribution
network level. A typical application field for this optimization is network planning. Assuming a
specific load and a generation structure an optimal open switch configuration for a radial network
can be found in order to minimize a certain objective function.
Another topology optimization application is carried out indirectly while operating the network and
conducting some maintenance works. In such a case a part of the network has to be reconnected
so that the system state values are still within valid limits. So an operating engineer proposes a
reconfiguration scheme for the duration of the maintenance. Usually the new topology is either
calculated with a load flow tool or estimated by the operational experience.
In GRID4EU DEMO1-project the autonomous control system acts only by closing or opening circuit
breakers1. This special control action affects the topology directly. A reliable optimization tool for
evaluating an optimal topology and a switching sequence is thus needed to provide an analytical
basis for the control actions, which are by their nature strongly non-linear and affect plenty of nodal
voltages and line currents.
The boundary conditions of the optimization problem are given by the operational requirements:
o The network topology has to remain unmeshed or radial
o Islanding through switching actions has to be avoided
o The voltages and currents are to be hold in the given limits (see chapter 4.1.2.1)
o The number of switching actions should remain low
The objective function assumed for the optimization problem is the network total losses. Even if the
exact losses cannot be obtained because of the lack of measurements a qualitative losses figure
obtained by the reduced model still can be used. The reduced network model is used in order to
1 In following, for the sake of simplicity, called “switches”
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provide a mathematical base for load flow computations on a not fully observed network. The
detailed description of this concept is given in the chapter 4.1.6.
In following an overview about the state of the art optimization techniques is given and the selected
method for the implementation in the project is described. This method refers to the so called static
optimization for a given system state snapshot. In order to compute a quasi-optimal switching plan
for a day, a dynamic optimization has to be carried out.
4.1.4.1 Review and selection of the optimization technique
Finding an optimal switch configuration in order to minimize a given objective function may be a
computational intensive task. The more switches are involved in the optimization the more complex
is the search of an optimal solution. In general, it is not possible to prove the absolute optimality of
a found topology configuration. Though, optimal topology reconfiguration techniques for radial
distribution networks are being developed since 80‟s. A good literature overview is given in [2] and
also in [3].
In Table 3 a summarized overview of the different techniques is given. The developed approaches
can be subdivided in mathematical programming, heuristics and methods from artificial intelligence
(AI). Also sometimes a possibility exists to list all possible topologies and to search over the entire
set. This naïve method is only applicable for small optimization problems and gets more
problematic yet for more than 15 switches2.
Considering the future implementation of the selected method on the RTU in PLC code the
complexity and needed time of the existing approaches have been ranked in a qualitative way.
Table 3: Overview of the existing optimization techniques
AI methods like genetic algorithms and particle swarm optimization are rather not applicable
because of their computational intensive nature. They are based on thousands of load flow
computations and require much virtual memory and time. The methods of mathematical
2 This reference number is evaluated by own experience
Naïve approachMathematical
programmingheuristics
'intelligent'
methods
some typical
methods
try all solutions dynamic programming
Branch exchange, heuristic rules
Genetic
algorithms,
Particle Swarm
Optimization
complexity + ++ + +++
quality of
solution + ++ + ++
needed time + ++ ++ +++
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programming are applied in the field of feeder planning and the formulation is not always directly
transferable to the problem of the reconfiguration. The quality of solution of heuristic methods
seems to be slightly inferior to the other methods. Thus their simplicity and easiness of the
implementation make these methods attractive. For the use in the project a method from the
category of the heuristics has been chosen.
Heuristics in general doesn‟t deliver an optimal solution. They are often based on intuitive rules,
like opening and closing switches and detecting lowest currents. One of such techniques is being
used in the network planning by RWE (see the first year deliverable dD1). In the literature it is
known as sequential switch opening method (SSOM) [4]. The approach chosen for the current
implementation is of the switch exchange type (SEM) and bases on [5]. This technique shows often
similar results like SSOM, though in some cases slightly better results are provided. The main
benefit towards the SSOM is the ability to understand the sequence of switching. Both approaches
make use of load flow computation.
4.1.4.2 Load flow computation
Load flow computation is a basic tool used for the topology optimization or system state
computation. Typically this mathematical method requires some input data:
o Network topology
o Nodal active and reactive power
The complete network topology is known by the DSO and can be directly used. However a
fundamental problem is the fact that the given distribution network is not fully observed. Thus the
information about the nodal power values at the unobserved nodes is missing and the load flow
computation can‟t be directly applied to the complete network topology in operation.
There are some few approaches in the literature to handle this typical problem of less
measurement. [6] uses an assumption that the total load of the unobserved network segment is
distributed either equally at all the secondary substations or in an increasing manner. In this way it
is possible to generate complete input data for the load flow computation. Another approach given
by [7] consists in performing a generalized state estimation for an underdetermined system. Also
here the output from the state computation contains deviations to the real state due to
uncertainties. Although this approach seems to be generally applicable, its scalability to the
systems with several more nodes is not clear.
The approach used here is built on the usage of the reduced network model which is described in
detail in chapter 4.1.6. The two main benefits of this approach concerning the implementation on
the RTU are the reduction of the system complicity3 and thus faster computation and the
mathematical reproduction of the system behaviour at the measured nodes.
The suggested load flow algorithm is the Gauß-Seidel [8] load flow. Its main benefit towards the
most common used Newton-Raphson algorithm is the absence of the matrix inversion which would
involve extra computational burden when implemented on the RTU. The convergence behaviour
and the fastness of the algorithm implementation show good results.
3 Which means reducing the number of nodes and branches of the network model
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4.1.4.3 Static optimization
The problem of static optimization consists of finding a switch configuration for a given static load
flow case so that an objective function is minimized. The applied method is the SEM based on [5].
The flowchart of the algorithm is presented in Figure 33.
Given a radial operated network with N open switches a random opened switch is virtually closed.
This causes a closed looped which contains several closed switches. A load flow computation is
performed to determine the closed switch leading the minimal current. This switch is the candidate
for exchanging the previously opened switch in the loop. After it is opened an additional load flow is
computed. When no violations are detected the objective function of this new topology is compared
with the older value of the objective function. If the objective function has been reduced by the
switch exchange, the procedure should be repeated again. This time another random switch is
chosen. Usually about 2N are sufficient until no more objective function reduction happens. Thus
the final „optimal‟ topology is found.
Some additional checks are needed. They are not described in detail here. E.g. one of them would
be avoiding the successive closing of the same switch.
It is notable that the random choosing of the switch to close is fully sufficient. Whereas other
supposed criteria like voltage difference at the opened switch would lead to the same solution [5].
Figure 33: Flowchart of the SEM algorithm
close a random opened
switch
find the corresponding
loop
open a new optimal
switch (Imin)
objective fucntion
reduced?
limit violations? cancelY
N
Y
N
END
START
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While performing virtually the closing and opening of the switches the sequence of the switch
exchange is noted. When optimizing the network topology for different load flow situations a
switching sequence is developed. In following it is called switching function. A network with N open
switches will have N time-dependent switching functions.
4.1.4.4 Dynamic optimization
In the context of the preventive and losses optimal network reconfiguration a horizon for optimizing
of the switching function is defined as 24 hours. The optimizing horizon is subdivided in 15 minutes
intervals. That means 96 static optimizations are carried out. Input data for the static optimization of
future time steps is provided by the forecast (see chapter 4.1.5). Due to volatile behaviour of nodal
active and reactive power time series and also due to the forecast errors too many switching
actions can be suggested when performing optimization for every 15 minutes interval. For that
reason the 24 hours switching functions have to be smoothened to some extent.
One effective way to minimize switching actions is the application of the slightly modified moving
average filter to the switching function. In Figure 34 an example for the network topology with two
opened switches is given. The reference topology corresponds to opened switches 4 and 16. The
non-optimized switching functions (blue and green in Figure 35) show different behaviour:
o Switching function 1 remains constant. This means the initially opened switch 4 is optimal
for the entire horizon
o Switching function 2 has some relatively short steps which let the switch number 16 be
closed and reopened plenty of times
Applying the moving average filter means substituting every value with the median value of a
certain interval. The length of this interval is essential for the smoothening strength. For the
„window length‟ of > 10 all the switching actions except of the {switch16-switch9} at the time
intervals 80 to 90 are neglected (compare with the red curve). Hence the number of switching
actions is strongly reduced and the lifetime of the circuit breakers is been extended.
Figure 34: Schematic switch configuration of the exemplary network
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Figure 35: Dynamic optimization for an examplary network
The main disadvantage of this approach is neglecting of the losses reduction potential
corresponding to the switching actions. As seen from Figure 35 below the losses reduction effect in
the interval 0-70 is being lost after performing dynamic optimization.
This issue is being currently considered. The method will be extended by involving the loss
reduction information.
4.1.5 Time series forecasting
A wanted and essential property of the multi agent system is its non-reactive behaviour. This
means, that the system does not react immediately to any limit value violation of the
measurements with a switching operation, but foresees a possible return of the system in the
secure state by its own.
To gain knowledge of the future system state4 a forecasting tool is necessary. Within the scope of
the project two different forecasting methods are considered:
1. Double Seasonal Exponential Smoothing
2. Multiple Regression + ARIMA (Auto Regressive Integrated Moving Average)
The double seasonal exponential smoothing method is purely based on measured values and
4 For the power flow computation a forecast of the nodal power values and also line power flow forecasts are needed. From the power flow computation nodal voltages and branch current forecasts are evaluated.
0 10 20 30 40 50 60 70 80 900
0.5
1
1.5
2x 10
5
15 minutes time intervals
loss
es
red
ucti
on
in W
0 10 20 30 40 50 60 70 80 900
5
10
15
20
15 minutes time intervals
ope
n s
wit
ch #
switching function 1
switching function 2
optimized switching function 2
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combines the advantages of low computing power demand and low memory space requirements,
so that it is suitable for running on RTUs.
On the other hand the Regression + ARIMA forecasting method has got the benefit that additional
information like weather data can be involved, so that a high forecasting quality can be achieved.
In the following sections these two concepts are briefly introduced and their advantages and
disadvantages are described. Finally both methods are compared with respect to their forecasting
accuracy by using real measured data.
4.1.5.1 Double Seasonal Exponential Smoothing
The Exponential Smoothing method is purely based on measured values. It is based on the
assumption, that the Time Series contains all necessary information.
In the Exponential Smoothing method forecasts are weighted averages of past observations. The
weights decay exponentially as the observations get older, so that the most recent observations
get the highest associated weights. Formula (1) describes the principle of the simple exponential
smoothing method [9]:
𝑦 𝑡+ℎ |𝑡 = 𝛼 ∙ 𝑦𝑡 + 1 − 𝛼 ∙ 𝑦 𝑡|𝑡−ℎ
(1)
According to the formula the forecast for time t+h given all information up to t is equal to a weighted
average between the most recent observation yt and the forecast for time t given all information up
to t-h. In Figure 36 the weights assigned to the observations are shown exemplarily. The more
recent the single observation the higher is its influence on the next forecast.
Figure 36: Principle of observations weighting
In order to be able to consider seasonal patterns in time series, the simple exponential smoothing
method needs to be extended. Within the scope of the project the double seasonal exponential
smoothing method is considered, which is suitable for series with two seasonal patterns.
In this method the three time series components – Level St, within-day seasonality Dt and within-
week seasonality Wt – are smoothed separately. The forecast for time t+h is obtained by
multiplying these three components:
(2)
Weights assigned to observations
t t-16 t-36 t-56 t-76 t-96
time axis
weig
ht
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𝑦 𝑡+ℎ |𝑡 = 𝑆𝑡|𝑡 ∙ 𝐷𝑡−𝑠1+ℎ|𝑡 ∙ 𝑊𝑡−𝑠2+ℎ|𝑡
The components are calculated using formulas (3)-(5) [10]
𝑆𝑡 = 𝛽 ∙ 𝑦𝑡
𝐷𝑡−𝑠1∙ 𝑊𝑡−𝑠2
+ 1 − 𝛽 𝑆𝑡−1 (3)
𝐷𝑡 = 𝛿 ∙ 𝑦𝑡
𝑆𝑡 ∙ 𝑊𝑡−𝑠2
+ 1 − 𝛿 𝐷𝑡−𝑠1 (4)
𝑊𝑡 = 𝜔 ∙ 𝑦𝑡
𝑆𝑡 ∙ 𝐷𝑡−𝑠1
+ 1 −𝜔 𝑊𝑡−𝑠2 (5)
Where β, δ and ω are the smoothing parameters. Applying the method to a quarter-hourly time
series, the seasonal indices s1 and s2 of the two seasonalities would be set to s1 = 96 and s2 = 7*96
= 672.
4.1.5.2 Multiple Regression + ARIMA
In contrast to the Exponential Smoothing method the Multiple Regression (MR) considers external
factors, so that e.g. weather data can be taken into account when calculating the forecasts. The
multiple regression method is based on the assumption, that the forecast variable has a linear
relationship with several explanatory variables. The forecast variable Y is calculated from a
functional relation between the explanatory variables Ai and a residual error ε:
𝑌 = 𝑓 𝐴1,𝐴2,… ,𝐴𝑝 , 휀 (6)
The explanatory variables Ai are weighted with regression coefficients γi. The aim of the method is
to determine the regression coefficients γi using the method of the smallest error squares. The
solution is given by the linear model:
𝑌 = 𝛾0 + 𝛾1 ∙ 𝐴1 + 𝛾2 ∙ 𝐴2+. . + 𝛾𝑝 ∙ 𝐴𝑝 + 휀 (7)
Within the scope of the project the explanatory variables day, time, combination of day and time,
solar radiation, wind speed (quadratic) and temperature are considered.
Due to the fact, that the forecast variable is calculated considering only external factors and
neglecting time dependencies within the time series, an ARIMA-model is used subsequently to
analyse the MR‟s residual error.
An Auto Regressive Integrated Moving Average (ARIMA) method aims to describe the
autocorrelations in the time series data [9]. The Auto Regressive (AR) part forecasts the variable of
interest using a linear combination of past values of the variable (instead of explanatory variables):
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𝑌𝑡 = 𝜙0 + 𝜙1 ∙ 𝑌𝑡−1 + 𝜙2 ∙ 𝑌𝑡−2+. . +𝜙𝑝 ∙ 𝑌𝑡−𝑝 + 휀𝑡 (8)
In contrast to the AR part the Moving Average (MA) method predicts the variable of interest using a
linear combination of past forecast errors:
𝑌𝑡 = 𝜃0 + 휀𝑡 + 𝜃1 ∙ 휀𝑡−1 + 𝜃2 ∙ 휀𝑡−2+. . +𝜃𝑝 ∙ 휀𝑡−𝑝 (9)
The ARIMA method is applied to the residual error ε of the multiple regression model to search for
structures resp. dependencies in the time series. With the help of the AR method the current
residual error is explained by previous residual errors. Due to the MA method the current residual
error is explained by errors, which were made when forecasting the previous residual errors.
4.1.5.3 Comparison of both forecasting methods
The Double Seasonal Exponential Smoothing and the Multiple Regression + ARIMA forecasting
methods were compared with respect to their accuracy using real 15-minutes active nodal power
time series of secondary substations with corresponding weather data. Three weeks of the
measurement data were used for training and the fourth week was forecasted.
In Figure 37 and Figure 38 the measured time series and the predicted time series (one-step-
forecast) of each method are shown for one secondary substation.
Figure 37: Measurement and forecast of an active power time series (Exponential Smoothing)
forecast
measurement
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Figure 38: Measurement and forecast of an active power time series (Multiple Regression + ARIMA)
It can be seen that both forecasting methods provide a comparable accuracy. The following Table
4 summarises the key properties of both concepts.
Double Seasonal ES Multiple Regression + ARIMA
low data demand
high data demand
(as much historical data as possible)
forecast only from running measurements possibility to involve additional information
(weather forecast...)
simple implementation sophisticated implementation
low hardware requirements high hardware requirements
Table 4: Key properties of both forecasting methods
Due to the low computing power demand and the low memory space requirements the Double
Seasonal Exponential Smoothing method is suitable for running on RTUs. In contrast the Multiple
Regression + ARIMA forecasting method requires a PC system, because of its sophisticated
hardware demands. Based on the simulation results it was decided to use the Double Seasonal
Exponential Smoothing method within the laboratory model.
4.1.6 Reduced Network model
As mentioned before, a viable network model for the state identification is needed. The state
identification is based on the load flow computation (LFC). LFC requires all nodal power values. On
forecast
measurement
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the distribution network level there are often only few measurements due to the high number of
nodes. Hence to perform a LFC some assumptions about the unobserved network areas are
needed. One possibility of dealing with this problem is to reduce these areas. The resulting network
model has to reproduce the same electrical behaviour in the measured areas.
From the literature different methods for network reduction are known. In [11] and [12] approaches
are suggested, which imply complete knowledge of the current network state. Thus, such methods
are not applicable for not fully observed systems.
Also in [13] some ideas of network simplification are described, which are yet based on grouping of
loads and pruning of unmeasured laterals5. Contrariwise, in [6] the aggregated load of the
unobserved area is distributed to the complete topology. This approach has been already
successful applied for an online control system. However, it is assumed that the unobserved areas
mainly consist of loads.
A more general approach, which is not bound on a particular load flow case, is described in [14].
As well as neglecting laterals, aggregating of nodal power is applied. For the calculation of the
equivalent line parameters a simple topology reduction principle is suggested.
In the scope of the GRID4EU DEMO1-project a systematic approach for reducing distribution
network topologies based on the ideas in [14] has been developed. An important extension is done
by assuming measurements at the laterals and considering the interconnections between different
feeders. A detailed illustration and an accuracy discussion are given in [15].
The application of the reduced model takes place in several areas of the autonomous control
system. It is always used in combination with the load flow computation and occurs in:
o Topology optimization module
o FDIR module
o For the computation of the system state from the power forecast
The reduced network model is computed once offline and is used as long as no network topology
changes, e.g. network extension, occur. When the topology has been changed, an updated model
has to be provided to the system.
In following the basic principle of the network reduction is described. Then the algorithm accuracy
is evaluated. Finally, the idea of the topology data exchange interface with the SCADA system is
illustrated.
4.1.6.1 Basic principle of the network reduction
The aim of the network reduction is to produce an equivalent behaviour at the interface to the non-
reduced part of the network, while simplifying the network topology. The detailed system state in
the reduced network areas is not regarded. For the network reduction some measurements at
critical nodes are necessary. In practice, the number and placement of measurements can be
carried out by the expertise of the DSO.
5 Laterals are shorter lines connected to the main feeder and supplying few secondary substations
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A typical constellation in distribution networks is a feeder line with several secondary substations
(see Figure 39), which are interpreted as nodes. At the nodes 1, 2 and 5 the voltages, active and
reactive power, line currents and the corresponding line power flows are measured. Due to the two
flow measurements at both ends of the unobserved area, between the nodes 2 and 5, the total
power feed-in of the nodes 3 and 4 can be calculated. The influence of network losses is
neglected.
The unobserved area is replaced by an equivalent node (3‟) and two lines (2-3‟ and 3‟-4‟). At the
equivalent node the total measured power S is cumulated. Generally, the -equivalent circuit
parameters of the equivalent lines can be calculated as weighted mean values of the original
lines [14]:
i
itotal,redtotal,red
i
i'i
'red
total,red
i
i'i
'red
total,red
i
i'i
'red
lll
lB
B
l
lX
Xl
lR
R
(10)
Where 'redR , '
redX and 'redB are respectively equivalent line resistance, reactance, susceptance
and total,redl is the total line length. is the set of original lines, which are being reduced.
The position of the equivalent node can be determined with two practical approaches [14]
o equidistant positioning, where both equivalent lines are of the same length total,redl2
1
o terminal current dependent positioning can be applied regarding the ratio of the
measured currents:
|I|
|I|
l
l
right
left
left,red
right,red (11)
Where “left” and “right” means the relative position of both reduced lines and of the measurements.
The first approach has the advantage of its simplicity and can be applied directly. The second
approach requires continuous model adaption through measurements. Thus, the model accuracy
increases.
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Figure 39: Basic principle of the network reduction
4.1.6.2 Accuracy results
For testing the concept of the reduced network the considered part of Reken medium-voltage grid
has been chosen. The original complete topology consists of 130 nodes and 128 lines. In contrast,
the reduced model has only 29 nodes and 27 lines. This is a remarkable complexity reduction,
when considering online power flow computations (see Figure 40).
To determine the accuracy of the presented method 10000 power flow calculations are performed
by varying the nodal powers. The nodal powers are generated by uniformly distributed random
numbers, which are scaled to the typical range of the installed capacities and loads. The reactive
power is assumed to a 90.cos by the load. The distributed energy resources have no reactive
power injection with 1cos . After every power flow computation, voltage and current deviations
to the complete network model are calculated. The deviation statistics are presented in a box plot
(Figure 41):
o The “box” includes 50% of all deviation values
o The median is the mark in the middle of the box
The whiskers on both ends of the box mark the entire value range of the deviations
1 2 4‟S1 S2 SΣ S5
3‟
~
1 2 3 4 5
~~
1 2 3‟ 4‟
~~
measurement
~
~ loadgenerator
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Figure 40: Complete and reduced Reken topology
Figure 41: Reduced model precision for the 130 node Reken grid
-1000 -500 0 500 1000-600
-400
-200
0
200
400
600
800
1000
A
B
C
D
E
F
G
H
I J
K
L
MN
measured
nodes
unmeasured
nodes
equivalent
nodes
complete topology reduced topology
equivalent
lines
original
lines
0
0.005
equidistant positioning
A B C D E F G H I J K L M N0
0.005
current dependent positioning
abso
lute
volt
age
devia
tio
ns
in p
.u.
node index
0
0.04
0.08
measured lines
abso
lute
curr
ent
devia
tio
n in
p.u
.
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Both described equivalent node positioning approaches are applied and compared. In the case of
equidistant positioning (Figure 41 top), the voltage deviations reach nearly 0.7% in some seldom
cases. This is a relatively high deviation, which occurs on the distant lateral node “N”. It can be
explained due to wrong placement of the connection point of the reduced lateral to the reduced
main feeder. The precision can be improved significantly by applying the current dependent
positioning of the equivalent nodes. In this case all the median values of the voltage deviations do
not exceed 0.5%.
Current deviations are almost not affected by the positioning approach. Most deviations tend to be
less than 8%.
Considering voltage violations as the most common and critical, the accuracy of the esteemed
voltages has to be good. If the accuracy can be held under 0.5%, this is supposed to be not much
worse than the typical measurement equipment accuracy.
4.1.6.3 Integration into the autonomous system
Network reduction is a process which doesn‟t have to be carried out within the autonomous
system. It is assumed that the number of agents, the line parameters and the number of secondary
substations stay constant for a longer time. So the corresponding reduced model used by the
autonomous system hasn‟t to be changed. As soon as any external changes appear – e.g. a new
secondary substation has been built – the model has to be adapted as well.
The up to date network topology is documented by RWE in form of a NEPLAN file. This NEPLAN
file is provided for both DSO control centre and the planning department. All the changes in the file
are synchronised via a data management system. In future every file changing should trigger a
software routine which would perform an offline network model reduction. This routine is not a part
of the autonomous system and could be integrated into the interface between the SCADA system
and the autonomous multi agent control system. Currently ABB investigates the possibility of
importing text files with updated topology into the PLC code placed at the RTU.
4.1.7 Data Storage
In order to guarantee proper interaction of different logical modules a concept of electrical network
data and state representation is needed.
The entire data can be classified as dynamic and static data: Static data refers to the topology
information (line parameters, nodal admittance matrix, etc.) and to the state limits. Dynamic data
are measurements, indications and system‟s topology state (status of the switches). It is assumed
that the static data can be only changed from the superior SCADA system. A typical example
would be an extension by an additional measuring agent. The dynamical data is being changed
permanently through measurements and switching operations.
One of the most important data structures is the line table with the line parameters (Table 5).
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node i node j switch i switch j R/Ohm/km X/Ohm/km B/µS/km L/km Imax/A
7 5 1 1 0.169 0.111 127.5 0.75 316
… … … … … … … … …
Table 5: Exemplary line data
In the line table the line interconnections between nodes are given. Of great importance is the
information about the electrical parameters of the line (R, X, B). This is necessary to build the nodal
admittance matrix of the system in order to perform load flow computation. In PLC language, used
at RTU560, such table is represented by a structure object.
4.2 Laboratory model
Another challenge of the DEMO1-project year two has been a development of the laboratory test
model. While the above described concept of the autonomous system has been modelled to a
great extent in MATLAB® environment, the actual system will run on the RTUs. Thus the
functionalities have to be implemented in PLC (programmable logic controller) language, real
communication has to be considered and some test data has to be provided to the system. For this
aim a testing environment framework is needed. Such a framework would integrate the RTU
network, which simulate the agent network of the equipped substations. Also a source of
measurements for every simulated substation is needed - this task is carried out by a network
simulator.
The idea behind the laboratory setup is illustrated in Figure 42.
Figure 42: Concept of the laboratory model
hardware layer
Smart Grid applications
in PLC* programming language
software layer
network model
MATLAB/Simulink
measurements
control
signals
* programmable logic controller
control
centre
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The introduced network consists of two logical layers: hardware and software. Both of them are
coupled by communication links and can affect each other. Hence a so called Hardware-in-the-loop
simulation is performed.
The Hardware layer is given by real automation equipment from ABB. The task of these Remote
Terminal Units is to receive measurements from the grid and to transmit signals to installed circuit
breakers. In the simulation real grid is represented by a software based grid model in
MATLAB®/Simulink
®. Based on the load and generator structure of the simulated network,
synthetic time series are created offline. These nodal power time series represent the network
behaviour. The grid model produces quasi real time signals.
In the following chapters the hardware and the software layer as well as the communication
between them are described more detailed.
4.2.1 RTU Hardware
Complete agent software will run on the ABB-RTU560 platform. A remote terminal unit is a
microprocessor-controlled electronic device, which can be programmed in the PLC-Language
(programmable logic controller). A RTU560 has digital and analog inputs and binary outputs. For
the given application of the multi agent control system, the input signals are corresponding to the
measurements from the field and the output signals are the obtained switching commands.
In order to analyse the performance, four devices of the RTU560 are used in the laboratory model.
One of the RTUs is used as the control centre, which collects all the measurements from the slave
RTUs. An alternative option is the direct coupling of the master RTU to the grid model.
The power supply of 24 VDC is provided for all RTUs. Typically the measurements in a secondary
substation are provided by an ABB CVD device (multimeter). In the laboratory set-up the
measurements are transmitted via Ethernet as IEC 60870-5-104 or DNP3 messages (depending
on the measurement acquisition concept).
Measurement values are initially provided by a MATLAB®/Simulink
® model. They are then sent to
the RTUs via different software interfaces that are described in the later chapter. The control output
of the autonomous system is sent back from the RTU to the MATLAB®/Simulink
® model.
4.2.2 Grid Model
In the laboratory environment the real power system is represented by software grid model. The
task of the grid model is to produce measuring signals and to react to switching actions calculated
by RTU agents.
The model is implemented in the MATLAB®/Simulink
® environment by using the SimPowerSystems
library in addition of some self-developed models. The phasor simulation method is applied. It
enables time series simulation and provides fast computation. The coupling with the hardware
layer is provided by the OPC software interface (see chapter 4.2.3). Necessary OPC blocks are
connected with power system model blocks.
Since the autonomous multi agent system is acting on the medium-voltage level, the modelling is
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focused on that level. Underlying low-voltage networks are not modelled explicitly. Every MV
substation features a MV/LV transformer and aggregated load and generation units (see Figure
43).
Figure 43: Basic principle of the grid modelling with aggregated LV grid
The single generator and load time series are computed offline, previous to the simulation. The
underlying models use weather data and standard load curves as input. A detailed descriptions of
the models is given in chapter 4.2.2.3.
In Figure 44 a sector from the Reken MV grid is illustrated. The essential model components are -
lines (cable type as well as overhead lines), secondary substations and breakers. For controlling
the breakers at simulation runtime a connection to the OPC interface is needed. In Figure 44
constants blocks are used for test purpose. Secondary substation block is a subsystem with
individual input data. Detailed information about its modelling follows in chapter 4.2.2.2.
MV grid
LV grid
~
...
~
...
...~
...
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Figure 44: Exemplary sector of Reken MV grid modelled in MATLAB®/Simulink
®
4.2.2.1 Phasor simulation
Because of the focus on the time dependent behaviour of the modelled power system, the phasor
solution method is chosen. Simulink® environment provides this method for computation of
magnitudes and phases of all steady state voltages and currents, without considering transient
system dynamics. Thus a fix frequency is considered.
Figure 45 illustrates the relation between continuous time series signal and its phasor
representation. While the differential equations describing the system behaviour are substituted by
a set of algebraic equations, the sinusoidal voltages and currents are replaced by complex
numbers [16]. This property leads to a much faster solution.
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Figure 45: Principle of the phasor simulation method [16]
4.2.2.2 Model of the secondary substation
As mentioned before, the model of a secondary substation is a slef-made Simulink® block. The
idea behind the model is to simulate power consumption or generation by applying a controlled
current source (CCS). This element is set by a load or generation profile from a predefined vector.
In Figure 46 the circuit of a secondary substation is depicted. The time series vectors are given by
“P_load” and “P_PV”. In every simulation step power values in Watt are read from these vectors.
Figure 46: Model of a secondary substation in Simulink®
Two CCS, corresponding to a load and a PV generator, are connected in parallel. A high rated RC
Element is adjusted to simulate the inner impedance of the current source. A MV/LV transformer
connects the low-voltage sources with the medium-voltage level.
The principle of CCS is depicted in Figure 47. A current source is connected between the neutral
phase N and the phase P. “Set point” input defines the power value. With the gain factor K a set
point current is calculated. Thereby the assumption about the constant voltage of 230 V is made.
This type of modelling refers to the constant current type load [17].
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Figure 47: Model of the controlled current source
With the block “U_load_1” complex voltage measurement is fulfilled. The voltage angle information
is extracted for the setting of the predefined power angle. The angle measurement isn‟t allowed to
be directly forwarded to the current source input because of their mutual dependency (so called
algebraic loop). Hence this signal is delayed with a unit delay block.
After building the difference between the predefined angle “Phi_load” and the measured angle, the new complex current is set.
4.2.2.3 Load and Generator models
In this subchapter models of load and generation units used in the network simulator are
presented. The synthetic generated data bear on historical weather time series or standard load
profiles. This raw data with different time resolution is used as input for generating active power
time series. These are applied at simulation runtime to CCS.
All introduced concepts are implemented as functions in MATLAB®. They can be called
automatically before starting a simulation. A detailed overview of the algorithms, including
flowcharts and input/output parameter, is given in Appendix. In following, main ideas of modelling
load, biogas plant, photovoltaic generator and wind turbine active power time series are presented.
The developed load model is based on the accumulation of the standard load profiles, which are
often used by DSOs. The standard load profiles (SLP) define typical energy consumption of private
household loads as well of industrial or rural loads. Here private household models are used. For
every season and every typical day of the week a SLP is given in 15 minutes intervals. To provide
a needed time resolution the raw data is being interpolated.
The number of households belonging to a substation has to be specified. For every household a
SLP is assumed so that the number of inhabitants is randomly set and also some normal
distributed noise added. All the household profiles are accumulated to the substation profile. In
Figure 48 some load time series for different numbers of households are presented.
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Figure 48: Typical household load profiles
Biogas plant model
The biogas plant model used for the network simulator is based on the yearlong measurements
from a reference biogas plant, provided by RWE. Multiple plants connected at one substation can
be also modelled.
The main idea is to provide quasi-random plant behaviour by randomly choosing an extract from
the yearly reference measurement. For multiple plants random extracts are chosen. Usually the
power feed-in of a biogas plant is nearly constant. Though, since the dataset contains days with
different feed-in behaviour due to variable methane production, the superposed feed-in of multiple
units gets more realistic in this way.
In the upper plot of Figure 49 five day-long biogas plants time series are presented. Two of the
units are part time disconnected from grid, e.g. due to maintenance work. The others have
constant power outputs of about 250 kW all day long. The lower plot of Figure 49 shows the total
active power of all biogas plants. The part, where two plants are disconnected, depicts a big power
drop.
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Figure 49: Biogas plant time series. Top: five different units; Bottom: cumulated feed-in
Photovoltaic feed-in model
The suggested model provides the electrical output power of solar power systems. Required input
parameters are the number of households with photovoltaic systems and their corresponding
surface areas, the start and stop time as well as the step size (for more details see Appendix 4:
Flowchart of PV time series generation).
First the geographic coordinates have to be set. For the assumed grid model the coordinates for
Reken (latitude 𝜙 ≈ 51.8° and longitude 𝜆 ≈ 7.0°) are predefined. After that, solar power system
specifications like deviation from the south axis, work angle and efficiency are initialized. Efficiency
can be separated into two parts, where the first part describes the efficiency of the photovoltaic-
module (e.g. for polycrystalline silicon: 𝜂𝑝𝑣 ≈ 15%) and the second one the efficiency of the inverter
(e.g. 𝜂𝑖𝑛𝑣 ,𝑚𝑎𝑥 ≈ 95%).
To compute the insolation onto an arbitrary aligned surface, in a first step, a mathematical
description of the course of the sun is needed. For that the following two equations taken from [18]
are used, where 𝛽𝑆 is the elevation and 𝛼𝑆 the azimuth angle of the sun:
𝛽𝑆 = arcsin 𝑠𝑖𝑛 𝜙 ⋅ 𝑠𝑖𝑛 𝛿 + 𝑐𝑜𝑠 𝜙 ⋅ 𝑐𝑜𝑠 𝛿 ⋅ 𝑐𝑜𝑠 𝜔 (12)
𝛼𝑆 = 𝐶1 ⋅ arctan
sin𝜔
sin𝜙 ⋅ cos𝜔 − cos𝜙 ⋅ tan 𝛿 + 𝐶2 1 − 𝐶1𝐶3 ⋅ 90° (13)
The position of the sun is determined by the declination angle 𝛿, which describes the angle
between the sun and the equatorial plane and the hour angle 𝜔, which is 15° per hour and
dependent on the solar time. The solar time is calculated by means of longitude 𝜆 and the number
of the day within a year. 𝐶1, 𝐶2 and 𝐶3 are constants, which are dependent on the latitude 𝜙, the
declination angle 𝛿 and the hour angle 𝜔. In Figure 50 you can see curve shape of the elevation
angle, which describes the altitude of the sun from sunrise to sunset.
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Figure 50: Day-long elevation angle course
In order to calculate the incident angle 𝜃 of the direct insolation on an arbitrary aligned surface the
following trigonometric relationships (eq. (15) and (15) [18]) are used:
cos 𝜃 𝛼,𝛽 = cos𝛽 sin𝜙 − sin𝛽 cos𝜙 cos𝛼 sin 𝛿
+ cos𝛽 cos𝜙 + sin𝛽 sin𝜙 cos𝛼 cos 𝛿 cos𝜔
+ sin𝛼 sin𝛽 cos𝛿 sin𝜔 (14)
cos 𝜃 𝛼,𝛽 = cos𝛽 sin𝛽𝑆 + sin𝛽 cos𝛽𝑆 cos 𝛼𝑆 − 𝛼 (15)
The incident angle 𝜃 describes the angle between the surface normal of the PV-Module and the
position of the sun.
Finally the direct insolation on an arbitrary aligned surface can be calculated with eq. (16) [18]:
𝐼 𝛼,𝛽 = 𝐼0 ⋅ cos 𝜃 ⋅ exp −
𝑇𝐿 ⋅ 𝑚
0.9 ⋅ 𝑚 + 9.4 (16)
Eq. (16) contains the extraterrestrial radiation intensity 𝐼0 (calculated from the solar constant
𝐸0 = 1367 𝑊/𝑚2), the Linke turbidity factor 𝑇𝐿 of the atmosphere and the air mass coefficient 𝑚.
However the curves generated with eq. (16) are ideal, because realistic influences like particles,
steam (clouds) and other outside influences, which are responsible for insolation drops, are not
considered. These influences are implemented by subtracting a random amount from the direct
insolation.
By multiplying the insolation 𝐼 calculated above with the surface 𝐴 of the solar power system and
the efficiency 𝜂 the electrical power output 𝑃𝑜𝑢𝑡 of the system is obtained with eq. (17):
𝑃𝑜𝑢𝑡 = 𝐴 ⋅ 𝜂 ⋅ 𝐼 (17)
To straighten the curve shape in the area of drops, the data is interpolated.
Figure 51 shows an example of three substations that are fed by different numbers of solar power
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systems with different collector areas. Before sunrise and after sunset the output power is set to
zero. The figure also shows the mentioned power drops caused by shadowing due to clouds and
other outer influences. Furthermore the peaks of the curves are not completely cantered. The
reason for these small displacements is the different alignment of the surfaces of the PV modules.
Figure 51: Day-long photovoltaic feed-in
Wind-Power-Plant-Model
For the evaluation of wind power feed-in a weather data input is needed. Thus wind speeds for a
requested measuring site and time range, provided by "Deutscher Wetterdienst (DWD)" [21], are
used. It contains the wind speeds metered in 10 m height for different locations. To obtain the wind
speeds at hub height, data have to be converted with equation (18) [18]:
vwind =log
z2
z0
log z1
z0 ⋅ windspeed10m (18)
Where vwind is the wind speed at hub height z2 and windspeed10m the wind speed at 10 m height
(z1). Furthermore the roughness class (or roughness length z0) has to be specified. For example
the roughness length for a landscape with some houses and hedges, bushes and trees or at least
250 m open space is z0 = 0.2 m [19]. The converted wind speed data can be further applied to the
mathematical wind turbine model.
In Figure 52 you can see the wind speeds measured in Münster-Osnabrück on 17th to 20th May
2012. On May the 18th a top speed of nearly 8 m/s (that means ca. 28 km/h) is reached.
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Figure 52: Converted wind speed measurements in the relevant region
The computation of the active power output of a wind turbine is done with a deposited power
coefficient curve (cp over wind speed) (see Figure 53). The power coefficient cp describes the ratio
between the wind turbine output power P and the total wind power P0. The ideal power coefficient
cp,max defines that part, which can maximally be drawn from the wind with an ideal wind turbine. It
will amount to cp,max =16
27≈ 0.59, if the wind speed ratio between the wind speed behind (v2) the
turbine and in front of it (v1) is v2/v1 = 1/3. Since these are theoretical figures, the ideal value is
never reached (see Figure 53).
Figure 53: Wind power coefficient curve
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Each occurring wind speed is compared with the cp-curve, so that one cp-value is obtained. With
that value, the corresponding wind speed v, the density of air ρair and the rotor sweep A, the wind
turbine output power is computed with equation (19):
Pw = 0.5 ⋅ ρ ⋅ A ⋅ cp value⋅ v3
(19)
In Figure 54 you can see the results of electrical output power for a wind turbine with a rotor sweep
of A = 4000 m² for the wind speeds presented above. It can be stated that wind speeds below 2
m/s are insufficient to generate active power output.
Figure 54: Active power output of a wind turbine model for the given time period
4.2.3 Model coupling
In this project, in order to simulate, monitor and control real world grid application by RTU 560s, we
have a program chain. This chain is in the following,
o MATLAB®/Simulink® is used to simulate the real world like grid application.
o OPC server is used to establish connection between MATLAB®/Simulink® and PCU400
server.
o PCU400 server is used to establish connection between Matrikon OPC server and
RTU560s.
o RTU560s are hardware modules to observe and control grid.
An overview of the particular interconnections is illustrated in Figure 55.
The grid model part of the simulation is carried out on a PC. This part introduces the data
exchange via OPC interface (OPC = Object Linking and Embedding (OLE) for Process
Control) [20]. The grid model acts as an OPC client and communicates with the software OPC
Server. OPC has Analog Measured value Input (AMI) tags (voltage, current, real and reactive
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power) which are used to observe grid simulation in MATLAB®/Simulink
®. One can configure OPC
AMI tags on CSV excel sheet. An important point here is that, tags are supposed to be consistent
on each part of OPC-PCU400-RTU560 chain.
PCU400 is a software application by ABB which establishes the connection between OPC server
and Rtu560. PCU400 has also web interface which lets users to observe indicators and AMI`s by
local network and IP connections. Alarms and Events can also be observed.
For simulating the measurements of a single RTU, PCU400 provides DNP3 communication lines
(Distributed Network Protocol). This imitates local measurements acquisition of an RTU.
Figure 55: Architecture of the laboratory model
Excel sheets are used to configure tags in PCU400. Those tags are short circuit indicators and
AMIs. One specific excel tool converts these excel sheets to XML files which include all relevant
information belonging to particular communication line. Generated XML files are supposed to be
copied to configuration files under PCU400 folder.
PCU400 has clock server which enables PCU400 to connect general system clock (if available) in
order to increase system consistency.
RTU RTU RTU
simulation PC
OPC client
network model (Matlab/Simulink)
OPC Server (Matrikon)
PCU 400
OPC client
DN
P 3
DN
P 3
DN
P 3
DN
P 3
104
104
IEC 6
0870-5-1
04
RTU
(control center)
104
local signals
remote signals
software
hardware
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Last part of the communication chain is RTUs. Slave RTUs forward their local data via IEC 60870-
5-104 protocol to the master RTU placed in the control centre. This is performed automatically due
to the slave RTU‟s configuration. As mentioned in the RTU560 configuration, RTU web-interface
enables us to observe all measurements and connections from Master and Slave RTUs. As in the
program demo, AMI or circuit indicator changes in MATLAB®/Simulink
® are supposed to be
observable through the OPC server, PCU400 web-interface, and RTU560 web-interface.
In Figure 56 an insight in the laboratory set-up is depicted. On the left, the RTU network can be
recognized. On the right, the Simulink® grid model running on a PC system is visible. Both
simulation domains are connected via Ethernet switch.
Figure 56: Picture of the laboratory set-up
4.3 Current status and outlook
By the middle of August 2013 different implementation works are still in progress. Some of the
concepts like topology optimization are approved in MATLAB®
environment and have to be
transferred into PLC code partially. Some other modules are being directly developed in PLC. By
the end of September it is planned to test the whole simulation chain including the RTU logic.
Currently the data transition from the network model up to the RTU is already established. Also the
backward path – switching command from RTU to the Simulink® grid model is already working. The
most time consuming part of the work, the completion of all software modules in PLC, still remains.
Though the following essential packages are already implemented and fulfil their functions:
o State machine
o Load flow computation
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o Static topology optimization
The next steps of the laboratory implementation and test phase will be:
o Finalizing basis RTU software implementation and testing
o Involving real measured data for more realistic scenarios
o Developing and implementing of decentralized concepts
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5 RTU communication
5.1 Communication overview
A stable communication between the agents is essential for the correct working of the complete
MAS. Beside the pure communication stability some other points must also be taken in account:
o High availability
o Standard components, availability in the market
o Cyber security
o Easy maintainability
o Reasonable initial costs
o Predictable operating costs
In addition to this, one very important item is that the communication technology should be well
known and already used within RWE. Finally it was decided to use GSM as basis for the
communication infrastructure. The used services are GPRS and UMTS.
To meet security polices it is necessary to introduce an encrypted communication. This is done
with a dedicated VPN within the RWE network. To avoid any potential hole in the security concept
the encryption is already done in the RTU.
In such complex system it is important to distinguish between different layers of communication
relationship, see Figure 57.
Figure 57: Communication layer
Agent Agent
VPN
TCP/IP TCP/IP
VPN
Network
VPN-Network
Agent-Network RTU RTU SCADA
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As basic layer the traditional TCP/IP communication will be used. The main objective is to
guarantee a stable, reliable communication infrastructure. The second layer is the VPN which
spans a closed network between all partners. This is a vital part of the communication to ensure
that the agent-system cannot be attacked from outside.
The communication between the agents can be seen as a communication over a dedicated
network, in this case called the “Agent-Network”. Over this virtual network only agent information
will be exchanged.
Also the RTU information, like indications, will go through all these levels. From the RTU point of
view everything works as a transparent network. Moreover the communication towards SCADA is
using this infrastructure.
As well as for the basic communication layers, there must be a decision done, which protocol will
be used for agent to agent communication. This decision follows almost the same rules as for
choosing the communication technology itself. In addition the technical capabilities of the SCADA
system must be taken in account. As described earlier in chapter 3.1.3 RWE is using IEC 60870-5-
104 for communication between SCADA and RTUs. Due to this restriction no other communication
protocol can be used between master agent in the primary substation of Groß Reken and the
SCADA system. To ensure consistency it was decided to use IEC 60870-5-104 in all other
communication links as well.
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5.2 VPN communication
As described in chapter 2.2 there are two possible approaches for the MAS:
o Central, with communication towards one single “Master” Agent
o Decentral, with peer to peer communication between neighbours
The next chapters will show the differences between the two structures.
5.2.1 Central communication
Figure 58 shows the communication structure of a hierarchic network using VPN. As described in
5.1 we have as basic level the TCP/IP data flow which is always over the VPN-router in the
communication centre. The second layer, the encrypted VPN, is always established from the RTU
towards the VPN-router. After the RTU is connected to the VPN the logical IEC 60870-5-104
connections can be established between the agents and master agent and also SCADA system.
This is always initiated by the master or SCADA (controlling station).
Primary substation Groß-Reken
SCADA
IEC 104 Agent n...
Connection request from A to B
(A = Controlling station)
Monitoring direction from B to A
(B = Controlled station)
Communication Center
Firewall Firewall
VPN-Router
Legend:Logic IEC 60870-5-104 ConnectionIPsec VPN tunnelPhysical data flow (TCP/IP)
RTU560, control centre
AgentMappingSCADA
Information
A B
GPRS-
Network
Agent 1 Agent n
Figure 58: Central communication
5.2.2 Peer to Peer communication
Figure 59 shows the communication structure of a P2P-network using VPN. Again we have the
TCP/IP and VPN connections. The difference to the standard approach is now, that in principle all
agents can talk to each other.
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Figure 59: Peer to Peer communication
Physically all traffic is still going over the VPN-router. As well as in the centralized concept, always
the controlling station is initiating the IEC 60870-5-104 connection to the so called controlled
station. We will later on see, that this does not mean, that only the controlled station is allowed to
send information to the controlling station.
5.3 IEC 60870-5-104
5.3.1 Overview
The information between partner RTU‟s within this system is exchanged via IEC 60870-5-104. This
exchange of data is not limited to the measured information, like status of a switch or the voltage,
also all information about the status of the agents will be transferred by using this protocol.
Again we have to distinguish between different communication links:
o Communication between Agents (Peer to Peer)
o Communication between Agents and Master Agent
o Communication between Master Agent and SCADA
The simplest way of exchanging the data is of course the classical approach of a data concentrator
in the substation and connecting all agents (RTUs) in the field to this master agent. In that case all
monitoring data is send to the master agent, and only control data from master agent to the
Primary substation Groß-Reken
Agent n...
GPRS – Network
Connection request from A to B
(A = Controlling station)
Monitoring direction from B to A
(B = Controlled station)
Communication Center
Firewall Firewall
VPN-Router
Legend:Logic IEC 60870-5-104 ConnectionIPsec VPN tunnelPhysical data flow (TCP/IP)
RTU560, Control Centre
AgentMappingSCADA
Information
A B
Agent 1 Agent n
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“normal” agents. The consolidated data is then transferred to the SCADA system. This is the
standard hierarchical approach (see chapter 5.2.1).
A little bit more complicated is the peer to peer (P2P) communication, where in principle all agents
can talk among each other. Also the monitoring and control direction is no longer fixed. All peers
can be controlled and controlling station at the same time. Obviously it is not possible to handle this
naïve approach in regards of configuration and maintenance, because RTUs have not the
capabilities and power like standard PC. The configuration effort is, even if only a few agents are in
the system, immense. It is for example necessary to configure each possible link between the
agents. Also maintaining such a system is a mess, because adding one simple agent causes
configuration of a new link in all other. To reduce complexity it is necessary to reduce the number
of communication links. This is done by limiting the communication links to links between
neighbours in the MV-grid. This method is described more detailed in chapter 5.2.2.
Another issue is the addressing inside the IEC 60870-5-104. The address scheme between master
agent and SCADA system is fixed by the RWE profile:
The address scheme used by RWE is using the structured address mode. This means, that every
field has a meaning, for example the 12 MSB of the common address of ASDU contain the station
number. This is a unique number within RWE. The advantage of this structured address is, that it is
very easy to assign a telegram to a station, and at the end to an object within the station. The
disadvantage could be, that in some cases it is not possible to assemble several message in an
optimal way in one telegram. This increases the transferred data volume and could lead in higher
costs.
The following table shows the used objects and their addresses.
Table 6: RWE ASDU/IOA address scheme
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5.3.2 Central approach
The central approach is a hierarchical design where we have a SCADA system at the top, a data
concentrator in the middle and the agents (RTUs) at the bottom (see Figure 60).
SCADA
Data concentrator
RTU
Control Centre
Agents
Figure 60: Hierarchical communication
The communication between all levels is independent of each other. The instance on the higher
level is always the controlling station of the lower level (SCADA->Control Centre->Agents), where
the instance on the lower level is the controlled station.
Table 7: RWE Data model
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This approach is an absolute standard and will not be discussed further.
5.3.3 Peer 2 Peer approach
A peer-to-peer (P2P) network is a decentralized and distributed network architecture, with nodes
which can act as both suppliers and consumers of a service, in contrast to the centralized
approach which was described in chapter 5.2.1. As already discussed it is not possible to run a real
classical peer to peer approach, where every agent can talk to every other agent. Due to
restrictions in sense of configuration and manageability the number of connections must be
reduced. This is done by allowing connection only to the neighbors on the MV-line.
Figure 61: Peer2Peer Communication to neighbors
In Figure 61 there is an example of a simple MV-network with some few nodes. In this picture we
can see, that RTU 11 is talking to RTU 21, RTU 22, RTU 23 and RTU 24, but has no connection to
RTU 12 and the RTU 25. For RTU 22 the things are much easier, here we have only one
connection between RTU 11 and RTU 22. In sum the number of connections is reduced, and the
configuration of one RTU is much simpler, because it is enough to configure only the neighbors
and not all existing RTUs in the networks.
Another point is the definition, who is controlling station and who is controlled station. This is a
configuration issue and must be done very carefully even though it seems to be very simple.
One way to ensure that all connections are correct configured is to create a matrix with all RTUs
and their connections (see Table 8).
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RTU 11 RTU 12 RTU 21 RTU 22 RTU 23 RTU 24 RTU 25
RTU 11 RTU 21 RTU 22 RTU 23 RTU 24
RTU 12
RTU 23 RTU 24 RTU 25
RTU 21 RTU 11
RTU 25
RTU 22 RTU 11
RTU 23 RT11 RTU 12
RTU 24
RTU 24 RTU 11 RTU 12
RTU 23
RTU 25
RTU 12 RTU 21
Table 8: RTU connection matrix
For the RTU configuration we need only the red part of the matrix. In detail it means, that for
example RTU 11 is controlling station of RTU21, 22, 23 and 24. RTU21, 22, 23 and 24 are in this
case the controlled stations. These configurations must now be stored in all agents. Obviously this
relationship is critical because, if for example for the connection between RTU11 and RTU22 both
RTUs configured as controlled station, there will be no connection between both RTUs established.
The next step in this approach is do define, how the data transfer should be done. Under the
premise that all agents should have all information about the whole MV-network it is not enough to
transfer only their own data between the agents, but also all other information from the network. To
implement this, the so called IEC 60870-5-104 “reverse direction” is used. Usually the information
flow is always from the controlled to the controlling station. All measurements, indications and so
on are “created” by the controlled station and transferred to the controlling station, usually a
SCADA system. This is called the monitoring direction. In the control direction, this is the direction
from controlling to controlled station, only commands are transferred. The relationship between
controlled and controlling station is fixed and cannot be changed without changing configuration.
But it is possible to use the reverse direction, which means that also the controlling station is
allowed to send monitoring information. Using this feature we will have a bidirectional data
exchange between two agents.
Figure 62 : Data routing
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Finally the data must be transferred between all agents. This is done by a transparent routing
through all agents. The idea is very simple: all received data is sending out to all direct neighbors,
except that one from where the data was received. The challenge here is to avoid, that data is
circulation endless in the system (see Figure 62).
As an example we will look what will be happen after RTU 22 sends out an analog value to his
neighbor agent RTU11:
RTU11 sends this message to RTU 21, RTU23 and RTU 24. RTU 23 forwards the same message
also to RTU24. In this situation we have the first conflict: RTU 24 receives the same message
twice. The solution, and the first rule, is that, it is not allowed to forward any message which was
already received from another agent. To ensure that, it is necessary that all information is time
stamped and it is not allowed to modify the time information by any agent. All received information
is compared against the latest value and time, and only if the time is newer than the stored the
message will be forwarded.
The second problem area is that in huge agent networks with a high number of agents the
messages will be also received by agents which are far away from the source in the grid and not
interested on this information. To avoid this two approaches are in discussion:
o Counting the number of hops, and stop forwarding after a number of hops is exceeded.
o Clustering agents in groups, where it is not allowed to forward the information to other
groups.
The final decision, which strategy will be used is not yet done. This will be done in the test phase,
after we have better knowledge about the real quantity of data which will be exchange.
5.3.4 Peer2Peer Database
Up to know only the communication itself was considered. The idea was to minimize the
configuration effort and the number of communication links. The engineering should be as simple
as possible. But engineering of a RTU is not only to configure the communication links, it usually
also includes the definition of all data points. It is clear that this cannot be done in our MAS,
because the simple adding of one signal would lead in changing configuration of all agents. This is
not a feasible solution.
To avoid this, a dynamic database will be developed within this project. The idea behind that is
very simple and robust:
o Every received monitoring information creates a database entry in the RTU database.
o Objects which are not updated for a while (e.g. one day) will be deleted from the database
(garbage collection).
The algorithm is also very simple:
o After start-up of an agent and establishing of all connections the standard IEC 60870-5-104
General Interrogation starts.
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o The information received by the all other agents is stored if necessary and forwarded to the
next agent(s).
o Information which was already received will not be forwarded.
After this sequence all agents have the same database, and the same view of the MV-network.
This data base can then be used in the PLC-programs described in chapter 4.
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6 Risk Management Year 2
After the first year of the GRID4EU-project, the project team comes to the conclusion, that all
DEMOs have to use the same methodology for the documentation of the risk management. All
DEMOs have to fill uniform templates with the same procedure. The main issue is to reach a
common understanding in the project. Another aspect is to get a better comparability between the
different DEMOs. Table 9 shows the actual risk list for DEMO1, with one additional risk in
comparison to the previous year (see in deliverable dD1.1).
Table 9: Actual list of risks
Figure 63 shows the risk matrix of the first year. The matrix represents an overview of the risk
management in DEMO1. The matrix is built up with the risks of the first year of the project (see
deliverable dD1.1). The entire methodology is described separately in GWP1 deliverable gD1.1
and in ENEL‟s deliverable dD4.1 of DEMO4. The three different colours are standing for several
risk levels (green (low), yellow (medium), red (high)). A risk is the product of probability, impact and
residual risk.
Nb Description
DEMO1 - 001 Implementation of the agent intelligence
DEMO1 - 002 Software development
DEMO1 - 003 Communication structure
DEMO1 - 004 Scalability and replication
DEMO1 - 005 Hardware upgrade of substations
DEMO1 - 006 Substation space difficulties
DEMO1 - 007 Heat conditions in substations
DEMO1 - 008 RWE security standards
DEMO1 - 009 Network penetration within the project run time
DEMO1 - 010 Real network operation failures
DEMO1 - 011 Technical requirements of switching agents
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Figure 63: Risk Matrix Year 1
Figure 64 shows the risk matrix of the second year. This risk matrix is built up with the actual risk
list (Table 9). As described before, one additional risk compared to the first year of the project was
identified. Nevertheless, in comparison to the matrix of the first year (Figure 63), you can see a
decreasing risk level.
Figure 64: Risk Matrix Year 2
For more details, please refer to the document “GRID4EU Year 2 Detailed Risk Reports”, where all
reported risks are detailed by Work Package (DEMO and GWP) in a common risk report template.
A decreasing risk level is also the expectation for the upcoming years. The result should be no
remaining risk at the end of the project in 2015.
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7 References
7.1 Project Documents
List of reference document produced in the project or part of the grant agreement
[DOW] – Description of Work
[GA] – Grant Agreement
[CA] – Consortium Agreement
7.2 External documents
[1] D. M. Staszesky, “Use of virtual agents to effect intelligent distribution automation,” in Proc.
IEEE Power Eng. Soc. General Meeting, 2006, pp. 1–6.
[2] K. Nara, Y. Mishima and T. Satoh, “Network Reconfiguration for Loss Minimization and Load
Balancing,” IEEE Power Engineering Society General Meeting, v. 4, Jul. 2003
[3] T. Gönen and I. J. Ramírez-Rosado, “Review of distribution system planning models: A model
for optimal multistage planning,” Proc. Inst. Elect. Eng., vol. 133, no. 7, pp. 397–408, Nov.
1986
[4] D. Shirmohammadi, H. W. Hong; “Reconfiguration of electric distribution networks for resistive
line losses reduction”, IEEE Trans. on PWRD, Vo1.4. Na.2, 1492-1498, April 1989
[5] S. K. Goswami; “Distribution system planning using branch exchange technique”, IEEE Trans.
on PWRS, V01.12, Na.2.718 -723, May 1997
[6] N. Neusel-Lange, C. Oerter, M. Zdralek, “State Identification and Automatic Control of Smart
Low Voltage Grids,” 3rd IEEE PES ISGT Europe, 2012, Berlin
[7] O. Krause, S. Lehnhoff, „Generalized static-state estimation“, 22nd AUPEC, 2012 Bali,
Indonesia
[8] A. F. Glimn, G. W. Stagg., “Automatic Calculation of Load Flows”, Ibid., vol. 76, Oct. 1957, pp.
817-28.
[9] Hyndman, R. J.; Athanasopoulos, G.: Forecasting: principles and practice, An online textbook,
August 2013
[10] Taylor, J. W.: Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential
Smoothing, The Journal of the Operational Research Society, Vol. 54, No. 8 (Aug., 2003), pp.
799-805
[11] J. B. Ward, “Equivalent circuits for power-flow studies,” AIEE Trans., Vol. 68, pp. 373–382,
1949
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[12] A. C. Neto, A. B. Rodrigues, R. B. Prada, M. da Guia da Silva, “External Equivalent for Electric
Power Distribution Networks With Radial Topology,” Power Systems, IEEE Transactions on,
Vol. 23, No. 3, pp. 889-895, August 2008
[13] M. E. Baran, A. W. Kelley, “A branch-current-based state estimation method for distribution
systems,” Power Systems, IEEE Transactions on, Vol. 10, No. 1, pp. 483-491, February 1995
[14] M. Wolter, “Grid State Identification of Distribution Grids,” Ph.D. thesis, Shaker Verlag, 2008
[15] A. Shapovalov, C. Spieker, Ch. Rehtanz “Network Reduction Algorithm for Smart Grid
Applications”, 23nd AUPEC, 2013 Hobart, Australia
[16] MATLAB® Help documentation, The MathWhorks, Inc.
[17] V. Crastan, „Elektrische Energieversorgung“, Springer Verlag, 2000
[18] V. Wesselak, T. Schabbach, „Regenerative Energietechnik“, Springer Verlag, 2009
[19] http://www.renewable-energy-concepts.com/german/windenergie/wind-
basiswissen/rauhigkeitsklassen.html
[20] http://www.matrikonopc.com/resources/dictionary.aspx
[21] German Meteorological Service, www.werdis.dwd.de, 2013
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8 Appendix
8.1 Fault detection
Appendix 1: Flow chart of the algorithm for fault detection
Start
Does the outgoing line n have a sc-flag
Does the outgoing line n have a
neighbour agent
yes
Short-circuit foundno
n := n +1 no
m := 1
yes
Does the neighbour agent m have a sc-flag
yes
Are there further neighbour agents
no
m := m + 1
yes
n := 1
Short-circuit found
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8.2 Load/Generation models
Algorithm for household load accumulation
[data] = load_data(nr_of_houses, nr_of_stations, day, month, starttime, t, t_step)
Appendix 2: Flowchart of load accumulation
Read load data for the chosen dates
Calculate load power for each household h connected to substation s:
define number of persons of household h
randomly choose one load profile for household h
modify each power value with a standard deviation of 0.5%
multiply the power curve with the number of inhabitants
sum up the power of every household to the total power of substation s
Start
Output a matrix with a time-vector and the load power of all substations
End
For each substation s
For each household h
For each date
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Biogas plant model
[P_out] = biogas(nr_of_plants, starttime, stoptime, stepsize)
Appendix 3: Flowchart of biogas plant time series generation
Read data from one reference plant and save it into workspace
Calculate power for the given number of biogas plants:
the required data for every biogas plant is chosen randomly within every iteration
Start
Interpolate data
End
Output data
For each biogas plant
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Photovoltaic power plant model
[t, data_solar] = solar_power(hh, A, starttime, stoptime, stepsize)
Appendix 4: Flowchart of PV time series generation
Initialize:
Geographic coordinates
Specifications of the solar power system (deviation from the south axis, work angle, efficiency)
Start
Calculate the course of the sun over one day of a requested date for Reken
Calculate the angle 𝜃 (or 𝑐𝑜𝑠(𝜃)) between the surface normal of the
PV-Module (with its alignment) and the position of the sun.
Simulate the influence of clouds, by subtracting a random amount from the direct insolation.
Calculate the direct insolation onto the currently considered PV-Module
Interpolate data
End
Output data
Multiply the surface of the solar power system with the direct insolation and the efficiency to obtain the power output
For each hour angle
For each solar power system
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Wind power plant model
[timestep windspeed] = wind_data(date_input_beg,date_input_end)
Appendix 5: Flowchart for wind speed conversion
[Pw] = wind_power_timeseries(c_p, v)
Appendix 6: Flowchart for wind power time series generation
With the deposited power coefficient curve (𝑐𝑝 vs.
wind speed) and the inputted wind speed the corresponding 𝑐𝑝 -values is obtained.
Start
Pw = 0.5 ⋅ ρair ⋅ A ⋅ cp value⋅ v3
The output power of the wind turbine is computed with the power equation:
End
Output data
For each wind speed
Load data from file: DWD_Wind_Data, which contains the
wind speeds measured at 10 m height provided by "Deutscher Wetterdienst (DWD)"
Start
Convert wind speeds to hub height
End
Output data