2. Dimitris Labridis (AUTH) - Presentation of the Theoretical Concepts and Modelling Aspects of the Cassandra Platform

Theoretical Concepts

and Modelling

Aspects of the

Cassandra Platform

Dimitris Labridis

Georgios Andreou

Apostolos Milioudis

Aristotle

University of

Thessaloniki

(AUTh)

Power Distribution Network

Cassandra Project 1st Workshop September 11, 2013

Small Industry

MV/LV Power

Transformer

Commercial

MV/LV Power

Transformer

MV/LV Power

Transformer

Residential

Residential

Commercial

Commercial

Overhead or Underground

Power Lines

HV/MV Power

Substation

Residential

Power Distribution Network

• Consumer Side (Demand Side)

• Demand Side Management

– Consumption optimization

– Energy efficiency

– Profit

• Problems

– Which is the profile of each consumer?

– How to cluster/categorize consumers?

– Which DSM program/incentive to offer to each consumer?


Cassandra as a solution

• A helping hand in order to:

– Simulate the consumption of consumer populations,

– Understand behaviors that drive consumption,

– Cluster consumers into groups for more efficient incentivisation,

– Simulate consumer response to specific Demand Response programs

• Two modeling approaches:

– Input based on demographic data → aggregate results for a consumer

population

– Input based on consumer level measurements → bottom-up approach

with results regarding individual consumers and/or consumer

populations


Basic modeling concepts

• Reality:

– Installation (the aggregation of all the appliances installed within the

premises of a consumer)

– Person(s) performing activities by the use of appliances according to

their utility

– Person(s) have habits (recurrent behaviour)

– Person(s) receive incentives to modify consumption, and choose either

to respond or not according to their utility


Basic modeling concepts

• Modeling:

– An Installation consists of its Appliances

– Measurements of the installation are used in order to determine its Appliances, as well as the consumer Activities (Disaggregationprocedure)

– Appliances are described by Consumption Models

– Consumer utility is modelled through Utility Models

– Consumer response to specific incentives is modelled through Response Models

– Auxiliary Models:

• Pricing Schemes Models

• CO2 Emission Calculation

• Prosumer Generation


Functionality according to quality of input

• Basic simulation rule: The more information you offer as input,

the more accurate results you will obtain

• Demographic data input: The simulation will be based on statistics,

thus it will not produce accurate individual consumer results

• Measurement data input:

– The Cassandra modeling is based on per minute measurements of

active and reactive power for an installation (loss of information)

– Accuracy of results depends on information compensation


Appliance Consumption Models

• They define mathematically the active and reactive power

demand of an appliance for a specified time of operation

• Example: Refrigerator active power


𝑐𝑝 𝑡 =

𝑘=1

𝑁

99.8 ∙ 𝑢 𝑡 − 𝑘2520 − 𝑢 𝑡 − 620 − 𝑘2520 , 𝑡 < 𝑑0

Activity Models

• They describe mathematically the occurrence, duration and

composition of an activity

𝐴 → 𝒂, 𝑓𝑠 𝑡 , 𝑓𝑑 𝑡 , 𝑓𝑁 𝑛A set of appliances

associated with the

activity

The probability density

function (pdf) of the activity

start time(s) within a day

The pdf of the number of

times the activity is

performed during a day

The pdf of the activity

duration

𝑓𝑎𝑖 𝑡 𝐴 : The probability that an appliance will be triggered at a

time t after the activity initiation (may differ according to

day type and/or season)


Activity Models

• Procedure:

– Correlation of Appliances and Activities (installation modelling phase):

Each Appliance may be correlated with a single Activity

– Determination of Appliances and respective Activities within

measurements (Disaggregation phase)

– Training of the Activity Modelling procedure


Response Models

• The aim of the CASSANDRA platform in this case is to estimate

whether a consumer will respond to a specific incentive or not

• Factors affecting behavioural change are:

– Pricing

– Environmental impact

– Awareness and Sensitivity

• Time shifting models describe the changes of the probability

density function fs(t) of the activity start time(s) within a day, as a

result of the change of price

• Consumers may also respond by reducing the number of times

they perform some activities


Disaggregation Methodology

• Aims to provide information regarding the appliances that

produce a given set of active and reactive power curves.

• Step 1: Determination of background demand


Disaggregation Methodology (cont.)

• Step 2: Determination of general events


Disaggregation Methodology (cont.)

• Step 3: Determination of repeated events


Disaggregation Methodology (cont.)• Step 4: Determination of individual appliances

– Slope Percentage Vector (SPV) calculation from active power

measurements vector Px

– Combination of Points of Interest (POIs) leads to POI vector

– Aggregated consumption curved is decomposed into events


B. Disaggregation Methodology (cont.)

• Step 5: Identification (clustering) of individual appliances

– Classification according to specific parameters:

• Active power

• Reactive power (power factor)

• Differentiation of active/reactive power during operation

• Typical (average) time of use for each appliance

– Matching in appliance library


Results in Pilot Cases (Lulea – 40 days)

Background 45,86 %

Refrigeration 9,79%

Cooking 18,79 %

Entertainment 6,67 %

Cleaning 9,31 %

Lighting 0,65 %

Unidentified 8,93 %

Percentage Energy Per Activity for Installation A


Results in Pilot Cases (Lulea – 40 days)

Background6,36 %

Refrigeration13,76 %

Cooking16,17 %

Entertainment3,78 %

Cleaning48,08 %

Lighting0,32 %

Unidentified11,53%

Percentage Energy Per Activity for Installation B


Load identification - Lulea installation B


0

200

400

600

800

1000

1200

1400

1600

1800

2000

TV1 TV2 Refrigerator Freezer Extra Freezer PC Cooker MicrowaveOven

VacuumCleaner

Tim

es o

f U

Se

Installed Appliance

Related Conference Papers

1. A Framework for the Implementation of Large Scale Demand ResponseG. T. Andreou, A. L. Symeonidis, C. Diou, P. A. Mitkas and D. P. Labridis, IEEE International Conference on Smart Grid Technology, Economics and Policies (SG-TEP 2012), December 3-4, 2012, Nürnberg, Germany.

2. Redefining the Market Power of Small-Scale Electricity Consumers through Consumer Social Networks K. C. Chatzidimitriou, K. N. Vavliakis, A. L. Symeonidis and P. A. Mitkas, 10th IEEE International Conference on e-Business Engineering (ICEBE 2013), September 11-13, 2013, Coventry, UK.

3. Event Detection for Load Disaggregation in Smart MeteringA. N. Milioudis, G. T. Andreou, V. N. Katsanou, K. I. Sgouras and D. P. Labridis, 4th IEEE European Innovative Smart Grid Technologies (ISGT 2013) Conference, October 6 - 9, 2013, Copenhagen, Denmark.

4. Agent-based Small-Scale Energy Consumer Models for Energy Portfolio ManagementA. Chrysopoulos, C. Diou, A. L. Symeonidis and P. A. Mitkas, 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT-13), November 17-20, 2013, Atlanta, Georgia, USA.


Professor Dimitris P. Labridis

Director of Power Systems Laboratory (PSL)

Department of Electrical and Computer Engineering

Aristotle University of Thessaloniki (AUTh)

P.O. Box 486

54124 Thessaloniki, Greece

tel: +302310996374

fax: +302310996302

e-mail: [email protected]

Skype: labridis

URL: http://users.auth.gr/labridis/index_en.htm

PSL: http://power.ee.auth.gr/index.php?page=intro&action=lnchange&lnid=124&lnsym=EN

AUTh: http://www.auth.gr/


Authors affiliations

mailto:[email protected]


http://users.auth.gr/labridis/index_en.htm

http://power.ee.auth.gr/index.php?page=intro&action=lnchange&lnid=124&lnsym=EN

http://www.auth.gr/

Lecturer Georgios T. Andreou

Power Systems Laboratory (PSL)



P.O. Box 486


tel: +302310996118

fax: +302310996302


URL: http://power.ee.auth.gr/index.php?page=andreou


Authors affiliations (cont.)


http://power.ee.auth.gr/index.php?page=andreou

Dr. Apostolos N. Milioudis

Power Systems Laboratory (PSL)



P.O. Box 486


tel: +302310996379

fax: +302310996302


URL: http://power.ee.auth.gr/index.php?page=milioudis


Authors affiliations (cont.)


http://power.ee.auth.gr/index.php?page=milioudis