Academic year 2008­2009

Design and Implementation of a Flexible Distributed Energy Management System to Investigate the Grid Integration of Controllable Distributed Energy Units

Full Name of Student: Roy Martin Emmerich

Core Provider: Oldenburg

Specialisation: Kassel

Host Organisation: Fraunhofer Institute for Wind and Energy System Technology (IWES)

Academic Supervisor: Dr. Konrad Blum

Specialist Supervisor: Prof. Dr. Jürgen Schmid

On-site supervisor: Dr. Martin Braun

Submission Date: 30 November 2009

Abstract

The German Renewable Energy Sources Act is setting a trend towards a high penetrationof geographically distributed, controllable generators, loads and storage units, also known ascontrollable distributed energy (CDE’s) units. This policy shift challenges the status quo inthe electricity industry on many fronts, particularly in the areas of communication, power flowand grid stability. In the medium term it will become a critical requirement to control largenumbers of CDE’s in a way that will substitute services currently provided by large, centralisedfossil and nuclear powered generators. This dissertation investigates one approach, namelythe hierarchically independent, agent based model as a possible solution. The main objectiveis to create an open, software based framework capable of allowing the flexible, multi-tieredaggregation of CDE’s as well as being able to incorporate or interface with other applicablesoftware1 that could aid research in this field. The final result is a successful laboratory baseddemonstration of the aggregation capabilities of this framework utilising existing CDE hardwarein the Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) DesignCentre for Modular Supply Technology (DeMoTec) laboratory.

1e.g. Powerfactory

I would like to make it known that it is my faith in God and his son Jesus Christ which hasbrought me to Europe from South Africa for the EUREC Renewable Energy Masters degreeprogramme. I hope my humble efforts during this time, and after, will contribute in some wayto improving this beautiful and remarkable earth we live on. I dedicate this work to my wifeJoanne. Her unfailing faith in me and the sharing of my quest has pulled me through. Withthis dissertation I have achieved a personal goal by completing all the new work containedherein using only open source software. I salute all those who promote this ideology throughthe selfless giving of their most precious resource, time.

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Contents

1 Introduction 51.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Approach 7

3 Software Design 9

4 Laboratory Equipment 12

5 Experimental Procedure 17

6 Results and Analysis 20

7 Conclusion 28

A Source Code Extract 29

B Data Sample 32

C Bibliography 34

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1 Introduction

1.1 Background

The electricity distribution grid was previously designed to accommodate a one way flow ofactive power from the transmission level down to the consumer in the distribution level. Tra-ditionally, large scale, centralised, fossil fuel driven generators connected at the transmissionlevel, produced the required active power. The transmission grid was designed for bulk electric-ity transport over long distances while the distribution grid was only meant for distribution tocustomers. The stability of the grid was designed to be maintained by, among other approaches,dedicated generators, sometimes referred to as spinning reserves, tasked to keep frequency andvoltage disturbances within certain limits.

The German Renewable Energy Sources Act (EEG) gives priority to geographically distributed,grid connected, renewable energy sources to inject active power into the grid. This relativelynew legislation, compared to the age of electricity distribution infrastructure, challenges thetraditional grid design ideology in the following ways.

All small scale, roof mounted, domestic photovoltaic installations in Germany are connected tothe distribution grid. At times of high solar radiation levels, it is possible for active power toflow from the distribution level, up to the transmission voltage level and then back down to aconsumer on the distribution level at some other point on the grid. This is contrary to the oneway flow of active power intended in the original design of the grid.

At the transmission level the operator is easily able to monitor and influence the status ofthe grid. Originally deemed to be the most critical concerning stability, it was designed to beactively managed. However at the distribution level the operator has almost no knowledge ofor influence over the current grid status except at certain strategic nodes. It was never meantfor any significant amounts of active power to be injected into the grid at the distribution leveland hence very little monitoring infrastructure exists here. Not knowing the real time powerflow metrics within large sections of the grid makes it more difficult for the operator to planthe efficient operation and further development thereof.

As the number of distributed generators increases, the contribution of the large scale, centralisedgenerators will naturally diminish. Therefore as the fraction of renewable energy generatorsgrows, it is obvious that they will have to play an increasing role in maintaining the stabilityof the grid as well as satisfying consumer’s active power demands.

1.2 Motivation

The European Network of Transmission System Operators (ENTSOE) is the body which repre-sents all transmission system operators (TSO’s) in the European Union (EU). Among its many

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regulatory tasks it is responsible for the definition of the load-frequency control standard [5].The main functions of load-frequency control are to maintain a balance between active powersupply and demand as well as maintaining the frequency of the grid within all grid control ar-eas. This type of control is divided into three main categories, namely primary, secondary andtertiary control. When a sufficiently large disturbance is detected, primary control is activatedwithin seconds, secondary control within minutes and tertiary control within tens of minutes,should the disturbance endure that long. Each successive control category relieves the previousone of its responsibilities to await the next request. It is the task of the TSO to send thesecondary control (SC) signal to generators requesting either an increase or decrease in activepower output. The problem for CDE’s is the minimum generating capacity required to partakein this market. This dissertation specifically focuses on the SC market which, in Germany, hasan entry level bid of 10MW with 1MW increments [8].

The main motivating factors for this project therefore include:

• enabling CDE’s to overcome the minimum bid trade barrier for the SC market in Germany,• the need for a fully flexible and modifiable software framework which can easily aggregate

CDE’s in a variety of configurations,• the desire to easily incorporate algorithms and software from other projects,• the need to interface with other software and data sources such as Powerfactory, the

German Energy Exchange (EEX), weather forecast data providers etc.

These main points provide the motivation to seek ways to flexibly aggregate CDE’s, making itboth possible and profitable for even the smallest grid connected CDE to trade on the existingelectricity markets.

Various methods have been considered to integrate large numbers of controllable distributedenergy units into the existing grid topology. These include, among other approaches, distributedenergy management systems, micro grids, virtual power plants and cells [2][3][4][6][7][10]. Theprinciple idea behind all of them is the aggregation of CDE’s in order to behave like conventionalpower plants so as to more easily fit into the existing technical and economic models thatconstitute the current electricity industry.

The objective of this dissertation was to design and implement a flexible, software based dis-tributed energy management system (DEMS), based on ideas from the approaches listed above,for experimenting with aggregation approaches in a laboratory environment.

In section 2 the concept of multi-tiered aggregation will be explored further. With this foun-dation in place, section 3 will go into detail around the topic of software design. The softwarehas to be able to control hardware CDE’s so section 4 considers the laboratory equipment,computing infrastructure and how to control the CDE’s. Section 5 lays out the experimentalprocedure that will create the platform to collect the data and finally analyse it in section 6.

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2 Approach

The FENIX project[7] created the platform on which this dissertation is based.

The main concept which the software had to support was the ability to connect the com-munication interfaces1 of CDE’s together in a hierarchically independent manner. Practicallythis means multiple levels of aggregation as depicted in figure 2.1 and is very similar to thePowermatcher concept described in [10]. For this study the DEMS software was only requiredto control the active power consumption and generation of four existing generators and loadswhich simulated real world CDE’s as described in section 4.

The main building block of this approach is known as a software based agent. It acts as anaggregator for the CDE’s connected directly beneath it and contains logic aimed at control-ling them. Agents are also able to connect to a single superior agent thereby providing acommunication conduit for receiving control signals from above.

aggregator

... CDE aggregator

... CDE aggregator

... ...

Figure 2.1: An illustration of the multi-tiered aggregation ofCDE’s

Allowing multiple levels of ag-gregation on the communica-tion side opens possibilities ofnew business models takingroot. For example, a smallgroup of CDE’s such as a fewelectric vehicle charging sta-tions in a certain area may, asa collective, still not satisfy theminimum bid requirement forthe German SC market. Itwould then be required to fur-ther aggregate the already ag-gregated charging stations byentering into a contract with alarger aggregator.

Other examples to substantiate this approach would be to reduce congestion by optimisingpower flow or to reduce active power line losses through real time simulation techniques. Theagent, coupled to an electrical simulation software package, could then make the decision onhow best to engage the CDE’s based on the simulation results. Using a multi-tiered approachthe simulations could be tailored for each agent based on unique local conditions.

The electricity legislation was assumed to be sufficiently flexible to allow the operator of theDEMS to simultaneously benefit from the German Renewable Energy Sources Act (EEG) feed-in tariff as well as the German secondary control balancing power market. The EEG rewardsCDE’s feeding active power into the grid. Generators taking part in the secondary control

1as opposed to the electrical interfaces

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market are paid for being on standby should their services be required by the TSO as wellas for the amount of active power produced [8]. It was assumed that the revenue from activepower generated for the feed-in tariff would be substantially higher.

In order to generate maximum profit, the default operating mode of the generators in this studymust be to generate maximum active power. For the loads the default operating state must beto consume as much active power as possible. In the context of this study the two loads are anelectric vehicle charging station and an industrial load of some sort. In the case of the chargingstation, profit is only generated when charging vehicles. It is therefore in the interests of theDEMS operator to always aim for maximum active power consumption by the charging station.In the case of the industrial load it was assumed the owner, namely the DEMS operator, iscontracted to drive a certain industrial process that consumes a constant 11 kW of active power.The consumer of this power is able to tolerate a certain amount of variation but would prefera constant supply. The contract binds the DEMS operator to a service level agreement thatrewards the continuous supply of power.

The role of the DEMS in this study is to control the active power settings of the CDE’s in orderto satisfy the TSO’s secondary control request but limiting the impact on the profit earned fromthe feed-in tariff.

It should be noted that the secondary control signal is a request by the TSO for a relativechange in the active power output from a generator or active power consumption by a load.In the context of this study, every time a secondary control request is received by the DEMS,it is taken to be a relative change using the combined default operating states of all CDE’sdescribed above as the reference point.

The strengths of a laboratory based approach such as this are:

• it can be tested using real hardware with actual results obtained.• having full control over the software platform provides many opportunities to incorporate

new algorithms and perform real time optimisations either by incorporating softwarewritten by others or by interfacing with commercial packages such as Powerfactory.• allows virtually any CDE communication configuration to be tested.• the flexibility and ability to incorporate and/or interface with other software allows the

optimisation of each agent to be customised based on aspects such as electrical configu-ration, the types of CDE’s connected or any other item requiring optimisation.

While the weaknesses are:

• the limited number of available loads and generators which makes it impossible to simulatea large scale real world situation.• although real hardware is being used, it is still only simulating actual CDE’s.

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3 Software Design

The developed distributed energy management system (DEMS) is a software based solutionwhich was written in the Python programming language [13]. Python is an interpreted, inter-active, object-oriented programming language. It was chosen for this project for the followingreasons:

• its ability to easily incorporate existing code written in a number of other languages (e.g.Fortran, C, C++, Java). At the outset it was envisaged that code, written in otherlanguages, from other IWES projects would be utilised at a later stage.• it is open source and therefore freely available to anybody with an internet connection.• it runs on a number of operating systems (e.g. Windows, Linux, Apple Macintosh).• it is feature rich and easy to learn.• it has a large user base within the research community in many fields such as physics,

astronomy and bio-informatics.

When designing the DEMS, specific emphasis was given to allowing hierarchical flexibility withrespect to the communication connections as well as the interaction with different applications,systems, hardware and software. The DEMS consists of a number of nodes or agents whichare connected to each other in a hierarchical tree structure as shown in figure 5.1. Each agentwithin the DEMS is represented by an instance of a single Python class which is designed torun on physically separate hardware. Inter-agent communication is via the internet protocolsuite (TCP/IP) using the Python Remote Objects package [12]. Agents are only allowed tohave one superior agent but can theoretically be connected to an infinite number of sub-agentsand CDE’s. Each agent is only aware of sub-agents and CDE’s connected one level below itself.The OpenOPC package[9] was used to communicate with the CDE’s and other measurementhardware via various OPC servers in the DeMoTec laboratory.

The use of a standardised application programming interface (API) promotes flexibility byallowing agents and CDE’s to be connected in virtually any configuration, thereby allowingmany different scenarios to be easily tested.

Using profit as the main decision making criterion, the active power output1 or consumption2

of each CDE was adjusted from its default operating state by the DEMS to fulfil the incomingsecondary control request. Figure 3.1 shows the income, expenditure and resultant profit curvesfor each CDE used in this experiment. Note the axis values for the generator plots are positivewhile those for the loads are negative. The reason for this was to ensure the slopes of all profitcurves were greater than or equal to zero.

Notice how the expenditure curve always intersects the y axis above or below zero, but never atzero. Even when CDE’s are not in operation they still incur operational costs such as interest

1for generators2for loads

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Figure 3.1: Income, expenditure and profit curves for all CDE’s

rate repayments on bank loans. This is the reason for this offset. In contrast, the income curvealways intersects the origin. If no active power is produced then no income is generated. Theprofit curve is simply the difference between income and expenditure. Notice that the profitcurve always intersects the x axis away from the origin. This means there is an active powerrange extending from zero to this intersection point in which it is not financially viable tooperate a CDE as income is less than expenditure. Using the slopes of the profit curves and thesimulated active power working range of each CDE, the DEMS is able to make the decision tosimultaneously meet the secondary control signal and generate active power from the availableCDE’s to maximise profit. The values chosen to represent income and expenditure were onlymeant to be indicative and don’t accurately represent actual operating costs of the real worldequivalent units. However, what is important to understand is the concept of using the slope ofthe profit curve and the active power operating ranges as the critical decision making criteria.

It must be stated that this profit calculation approach is somewhat static. In reality thesituation varies depending on how much active and reactive power a CDE is required to produceas well as the associated grid losses [11]. It is however sufficient as a first order approach to

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demonstrate the concept.

Two aptly named helper applications, setter.py and logger.py, were also written to supportthis experiment. The task of setter.py is to extract the profiles from a comma separatedvariable (CSV) text file used to set the CDE minimum and maximum values for each timestep.Once the CDE’s are set up, it sends the relevant SC value, also extracted from the CSV file,to the root agent (A0). The logger.py class was written to read the set and measured valuesof each CDE from the central OPC server at a fixed interval and log them to a CSV file foroffline processing. Figure 5.3 shows the actual laboratory configuration.

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4 Laboratory Equipment

The CDE hardware used in this experiment consisted of three controllable generators and onecontrollable load. They can be seen in figure 4.1.

Each of these units were used to simulate a real world, distributed, renewable energy source orsink as described below:

• a 12 kW wind turbine represented by a 15 kVA controllable synchronous generator (SG).• a 16 kW CHP (combined heat and power) plant represented by a 20 kVA inverter coupled,

variable speed, controllable generator set.• a 14 kW electric vehicle charging station represented by an 80 kVA controllable SG oper-

ating in motor mode.• an 11 kW industrial load represented by a 12 kVA controllable load.

Figure 4.2 shows how these CDE’s were connected to the DeMoTec electric grid infrastructure.All units were connected to the 0.4 kV grid which were in turn coupled to the external grid via100 kV transformers.

Each CDE was configured with a dedicated control computer or remote terminal unit (RTU).Custom software1 on each RTU was used to control the CDE’s via a variety of data acquisitionand control hardware solutions. These RTU’s updated control setting and measurement valuesto, and monitored requests for control setting value changes from a central Object-Linking andEmbedding (OLE) for Process Control (OPC) server. The DEMS software, described in detailbelow, was then able to control each CDE by changing control setting values on the central OPCserver. Measured values were also read from the central OPC server. The actual laboratorycommunication configuration can be seen in figure 5.3.

In order to perform the experiment, each CDE had to have an active power generation orconsumption profile applied to it in order to simulate a real world CDE. The time step resolutionfor each profile was 15 minutes in actual time representing 15 seconds in the laboratory. Theoverall duration of each profile was 24 hours in actual time which totalled 24 minutes in thelaboratory. Below are descriptions of how each profile was created:

• Wind profileAn actual wind turbine power output profile was scaled to match the capacity of thelaboratory generator used to simulate this CDE. This profile represented the maximumpossible active power output for a certain 24 hour period. It was assumed that a contrac-tual requirement prevented the active power output from being curtailed to less than 80%of the maximum forecast available power. This resulted in the operating range indicatedin red in figure 4.3.

1Written by Rodrigo Estrella, a EUREC alumnus from the 2007 intake

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(a) 15kVA SG (b) 80kVA SG

(c) 20kVA generator set (d) 12kVA load

Figure 4.1: Portfolio of CDE’s used in this study

• CHP profileAs combined heat and power (CHP) units are most efficient when running at or near theirrated power output, an operator decision was made to maintain active power output ator above 80% of the assumed nominal rated power of 16 kW. This operating range isindicated in green in figure 4.3.• Electric vehicle charging station profile

Firstly, it is important to note that the active power consumption capacity of an electricvehicle charging station is proportional to the number of connected vehicles as well as thestate of charge of the batteries within these vehicles. This charging station was assumedto be located in a parking lot of a large commercial bakery. Employees begin arrivingfor work at 06h00. By 09h00 everybody is at work. At 14h00 the early shift leaves workfollowed by the rest of the employees at 16h00. At this factory most employees remainat work all day. Because of this user behaviour it is not critical for the batteries to berecharged in the early part of the day, hence the wide active power operating range in themorning, indicated in blue in figure 4.3. It is however imperative to make sure all vehiclesare sufficiently charged for the drive home after work. This results in the progressivelynarrower active power operating range towards the end of the day.The exact hours used in this study are not important, however it is essential to incorporatethe concept of people movement which results in electric vehicle charging stations havingtheir own unique challenges concerning the provision of electricity services [1]. For thisexperiment, feeding power into the grid was not considered.• Industrial load profile

It was assumed the owner of this industrial load was contracted to provide a certain

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G1 L1 G2L2

0.4 kV 0.4 kV 0.4 kV

10 kV

Public Grid

Legend:= 100 kVA Transformer

G1 = 16 kW CHPG2 = 12 kW Wind turbineL1 = 14 kW Electric vehicle charging stationL2 = 11 kW Industrial load

Figure 4.2: Electrical configuration of CDE’s

service. This allowed for a maximum reduction of active power consumption of 10% fromthe rated 11 kW and is represented by the magenta shaded area in figure 4.3.• Secondary control profile

A secondary control request signal, which is normally generated by the TSO, was sim-ulated by means of a real world profile obtained from the E.ON German control areafor 3 January 2008. As each CDE has only a limited active power operating window atany particular time2, the SC signal had to be scaled to fit the combined capacity of theCDE’s. The default operating state for this study is for generators to produce as muchactive power as possible and loads to consume as much active power as possible. By op-erating in this state maximum profit would be generated within this simulated businessmodel. To arrive at a properly scaled SC signal for this experiment a calculation hadto be made to determine how much positive and negative SC capacity this portfolio ofCDE’s is capable of supplying at a particular point in time.The cross-section XX shown in figure 4.3 passes through the colour shaded, active poweroperating ranges3 for the four CDE’s. Based on the operating paradigm for this exper-iment the active power settings for the CDE’s must at all times be within the shadedregions. These operating ranges indicated at cross-section XX in figure 4.3 can be seentranscribed onto cross-section XX shown in figure 4.4. At this point in time the maxi-mum possible reduction in active power would be obtained by reducing the output of thetwo generators to the lowest values within each of their shaded regions (i.e. δPG1 +δPG2).Similarly, at the same point, the maximum possible increase in active power would beobtained by reducing the consumption of active power of both loads to their minimum al-lowed values (i.e. δPL1 +δPL2). From figure 4.4 we can therefore deduce that it would notmake sense for the DEMS operator to offer SC capacity on the balancing power marketwhich falls outside the combined colour shaded regions.

2Indicated by the colour shaded areas in figure 4.33Indicated using the notation δPxx

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5 Experimental Procedure

The idea being explored is that of using a hierarchically independent, agent based, distributedenergy management system approach to control the active power generation of CDE’s in aflexible fashion. This study is divided into two scenarios which will be compared. Each scenariois based on a different communication configuration. All agents were identically programmed tosatisfy the secondary control signal requirements entering the DEMS, through the root agent,by choosing the least profit sensitive CDE’s first and thereby maximising net profit.

From this point onwards the two scenarios will be referred to as part 1 and part 2. The com-munication configuration used in part 1 is represented by figure 5.1 while figure 5.2 representsthe layout used in part 2. Please note that these diagrams are simplified layout configurationsto assist understanding. The actual laboratory configuration can be seen in figure 5.3. Theonly difference between part 1 and 2 is the point of connection for the wind turbine (G2). Theintention is to prove the flexibility of this aggregation approach by investigating the combinedactive power output from each layout, while using the same decision making process in eachagent.

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Legend:= TCP/IP connection

A0 = Root agentA1 = Agent 1A2 = Agent 2G1 = 16 kW CHPG2 = 12 kW Wind turbineL1 = 14 kW Electric vehicle charging stationL2 = 11 kW Industrial load

Figure 5.1: Simplified DEMS communication configuration for part 1

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Figure 5.2: Simplified DEMS communication configuration for part 2

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Figure 5.3: The actual DEMS communication configuration for part 1

CDE control is performed using the slope of the profit curves and the active power operatingranges for each CDE as the decision making criteria. The goal of the software is to fulfil asecondary control request entering the system at the root agent, represented by A0 in figure5.1, as well as to produce as much active power as possible to feed into the grid, therebymaximising profits from the generating CDE’s. In the case of the electric vehicle chargingstation and industrial load, it is assumed that a profit is generated by fulfilling contractuallybound services. For the charging station this service is charging cars and for the industrial loadit is driving an industrial process of some sort. The provision of these services is the incentiveto keep within the designated active power operating ranges for these loads.

Figure 5.4 provides a graphical representation of the decision making process used in eachagent upon receiving the secondary control signal. This should be studied in conjunction withcross-section XX indicated in figures 4.3 and 6.6.

Notice how the incoming secondary control request (SCtotal), calling for a reduction in activepower being produced, is split between the two generators. The wind turbine (G2) has a

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shallower profit slope than the CHP unit (G1), is therefore less profit sensitive with respect toa change in active power, and so is chosen first. Based on this criterion, its active power outputis reduced by SCG2. If SCtotal was less than or equal to the available active power operatingrange for G2 at this time (δPG2), then it would have been the only generator used to fulfilthe secondary control active power request. However, this is not the case so the more profitsensitive G1, is employed to make up the shortfall by reducing its active power output by SCG1.As the decision making process is the same in all three agents, the sub-agents come to the sameconclusion as the root agent concerning the distribution of active power.

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Figure 5.4: DEMS communication configuration for part 1 showing the distribution of theincoming secondary control signal across the portfolio of CDE’s. The profit slope graphs shownin this figure are not drawn to scale. They are merely intended to be indicative. This shouldbe studied in conjunction with figures 4.3 and 6.6

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6 Results and Analysis

In order to promote a better understanding it would be wise to first examine the expectedperformance of the CDE’s without the effects of the secondary control (SC) signal. Figure 6.1shows the colour shaded operating ranges of the four CDE’s. It also shows, in the form of darkdotted lines, the expected active power output for each CDE if there was no SC request fromthe TSO. This CDE behaviour corresponds to the default operating state for this experimentas described in section 2. The solid brown line in figure 6.1 is the sum of the active poweroutputs from all the CDE’s.

Now we move on to figure 6.2. In this plot we can see the same information as shown infigure 6.1 but this time the influence of the secondary control is introduced. This can be seeby the change in shape of the set active power curve shown with the brown dotted line. Notethat the data shown in the plots so far still only include the desired CDE and SC set values.Remember the affect of the SC is to alter, either positively or negatively, the total active poweroutput from all the CDE’s. Notice, for example, the time between 0-7.5 hours. During thistime the TSO is requesting a reduction in active power output as the black dotted SC curvepasses below the x axis. We now know from default operating state and the decision makingcriteria employed that the output of the wind turbine and possibly the CHP will be curtailedto reduce the total active power output between 0-7.5 hours. When compared with figure 6.1in the same time window it can be seen that the total active power output is reduced. Lookingat the individual CDE active power profiles between 0-7.5 hours, the wind turbine has beencurtailed right down to the minimum allowable setting. During the same time the combinedheat and power (CHP) plant is only partially curtailed. It, in fact, never reaches its minimumactive power setting. This is due to the CHP plant having a steeper profit curve than the windturbine. It is said to be more sensitive to profit with respect to a change in active power andis therefore only curtailed if the wind turbine has insufficient capacity to fulfil the requestedSC signal reduction. Just near end of this time window at the 7th hour mark, the CHP returnsto its maximum active power output while the wind turbine only returns to maximum activepower output around the 7.5 hour mark when the SC is above zero. This is once again due tothe different profit slopes for the two CDE’s. The CHP has a steeper slope and hence will bethe first of the two CDE’s to return to full power output if the wind turbine is able to fulfil theSC requirements alone. It will effectively relieve the CHP to continue producing active poweras efficiently as possible, which is at its rated full power setting of 16 kW.

Similarly when the SC is above the zero mark in figure 6.2 (i.e. between 7.5-18.75 hours) it isthe electric vehicle charging station which fulfils the SC control signal first due to its shallowerprofit curve compared with the industrial load. Only if there is no longer sufficient capacity fromthe charging station is the industrial load curtailed to make up the shortfall. The first of theseshortfalls occurs between 10.7-11.5 hours when the SC signal rises above the available capacityof the charging station. The second shortfall begins at the 13.7 hour mark when the activepower consumption capacity of the electric vehicle charging station falls away dramaticallydue to a simulated loss of connected vehicles. This shortfall is further aggravated at the 16hour mark when all employees leave work resulting in no more vehicles being connected to thecharging station.

20

Figure 6.3 shows the same information as figure 6.2 but now includes the actual measured activepower values obtained for the DEMS configuration 1. These measured values are depicted withsolid colour lines in each of the CDE colours, green, red, blue and magenta. The sum of thesemeasured CDE active power outputs, namely the total active power output, is shown usingthe solid brown line. Here we can see the differences between the set and measured activepower values. Although not identical, the measured plots track the set values very closely. Ifyou look carefully at the magenta plot which represents the industrial load you will notice acontinuous, constant offset between set and measured values. This was due to the controllableload in the laboratory being faulty and reporting the incorrect value. For reasons unknown asimilar problem was occurring with the CHP unit.

Figure 6.4 shows the same information as figure 6.3 but this time corresponds to the actualmeasured values for the DEMS configuration 2. The two sets of graphs from the differentconfigurations are almost identical which suggests that the hypothesis of this study is correct.

Figure 6.5 shows an enlarged section of the upper graph in figure 6.3. Of particular interest isthe difference between the set and measured values of the wind turbine data. A change in theset value is not immediately followed by the CDE’s actual measured active power output. Thereason for this delay is due to the performance characteristics of the synchronous generatorused to simulate the wind turbine. As each time step shown in figure 6.5 equates to 15 secondsof lab time, the generator settling time can be roughly measured by eye to be between 10-12seconds. The settling time increases the larger the change in set power.

Figure 6.6 shows the SC signal and the coloured infill indicates the expected contributions fromeach of the CDE’s. This graph gives a clear indication of the contributions that should be madeby the various CDE’s to fulfil the SC control signal. When the TSO stipulates a decrease inactive power via an SC signal, it is the wind turbine which is first to react due to its shallowerprofit curve. Hence it is located directly below the zero y axis line to indicate this fact. If therequired reduction is greater than the wind turbine is able to provide then the combined heatand power (CHP) unit is used to make up the shortfall. Consider cross-section XX in figure6.6. At this point the SC signal is requesting an active power reduction of SCtotal. The windturbine is only able to provide a reduction SCG2so the CHP unit is curtailed by SCG1to makeup the shortfall. Similarly when the SC stipulates an increase in combined active power outputit is the electric vehicle charging station which is first to react with the industrial load makingup the shortfall if necessary.

21

01

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

Tim

e o

f day [

h]

15000

10000

50000

5000

10000

15000

Active Power [W]

Win

dm

ax &

set

Win

dm

in

Ind. Lo

ad

max

Ind. Lo

ad

min

& s

et

EV

max

EV

min

& s

et

CH

P m

ax &

set

CH

P m

in

Tota

l P s

et

DEM

S C

onfigura

tion 1

& 2

CH

P (

G1)

Win

d T

urb

ine (

G2)

Ele

ctri

c V

ehic

le (

EV

) C

harg

ing S

tati

on (

L 1)

Indust

rial Lo

ad (

L 2)

Tota

l Set

Act

ive P

ow

er

(P)

Figure 6.1: CDE set values including total P set. Valid for both DEMS configurations

22

01

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

Tim

e o

f day [

h]

15000

10000

50000

5000

10000

15000

Active Power [W]

Win

dm

ax &

set

Win

dm

in

Ind. Lo

ad

max

Ind. Lo

ad

min

& s

et

EV

max

EV

min

& s

et

CH

P m

ax &

set

CH

P m

in

Tota

l P s

et

+ S

C s

et

SC

set

DEM

S C

onfigura

tion 1

& 2

CH

P (

G1)

Win

d T

urb

ine (

G2)

Ele

ctri

c V

ehic

le (

EV

) C

harg

ing S

tati

on (

L 1)

Indust

rial Lo

ad (

L 2)

Tota

l Set

Act

ive P

ow

er

(P)

+ S

C s

et

Seco

ndary

Contr

ol (S

C)

Sig

nal

Figure 6.2: CDE set values including total P set + SC set. Valid for both DEMS configurations

23

01

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

Tim

e o

f day [

h]

15000

10000

50000

5000

10000

15000

Active Power [W]

Win

dm

ax &

set

Win

dm

in

Win

d m

eas

Ind. Lo

ad

max

Ind. Lo

ad

min

& s

et

EV

max

EV

min

meas

& s

et

CH

P m

ax &

set

CH

P m

in

CH

P m

eas

Tota

l P s

et

+ S

C s

et

Tota

l P m

eas

+ S

C s

et

SC

set

measu

rment

syst

em

failu

rem

easu

rem

ent

syst

em

failu

res

DEM

S C

onfigura

tion 1

CH

P (

G1)

Win

d T

urb

ine (

G2)

Ele

ctri

c V

ehic

le (

EV

) C

harg

ing S

tati

on (

L 1)

Indust

rial Lo

ad (

L 2)

Tota

l Set

& M

eas.

Act

ive P

ow

er

(P)

+ S

C s

et

Seco

ndary

Contr

ol (S

C)

Sig

nal

Figure 6.3: Set and measured values from the four CDE’s including the SC signal. Valid forDEMS configuration 1

24

01

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

Tim

e o

f day [

h]

15000

10000

50000

5000

10000

15000

Active Power [W]

SC

set

measu

rment

syst

em

failu

res

measu

rem

ent

syst

em

failu

res

DEM

S C

onfigura

tion 2

CH

P (

G1)

Win

d T

urb

ine (

G2)

Ele

ctri

c V

ehic

le (

EV

) C

harg

ing S

tati

on (

L 1)

Indust

rial Lo

ad (

L 2)

Tota

l Set

& M

eas.

Act

ive P

ow

er

(P)

+ S

C s

et

Seco

ndary

Contr

ol (S

C)

Sig

nal

Figure 6.4: Set and measured values from the four CDE’s including the SC signal. Valid forDEMS configuration 2

25

10 11 12 13Time of day [h]

4000

5000

6000

7000

8000

9000

10000

Act

ive P

ow

er

[W]

max & set

min

measured

DEMS Configuration 1

Wind Turbine (G2 )

Figure 6.5: Enlarged version of figure 6.3 showing only the wind turbine data. Valid for theDEMS configuration 1

26

01

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

Tim

e o

f day [

h]

4000

3000

2000

10000

1000

2000

3000

4000

Active Power [W]

X X

SC

tota

l

SC

G2

SC

G1

EV

Charg

ing S

tati

on

Indust

rial Lo

ad

Win

d T

urb

ine

Win

d T

urb

ine

CH

P

CH

P

DEM

S C

onfigura

tion 1

& 2

CH

P (

G1)

Win

d T

urb

ine (

G2)

Seco

ndary

Contr

ol (S

C)

Set

Sig

nal

Ele

ctri

c V

ehic

le (

EV

) C

harg

ing S

tati

on (

L 1)

Indust

rial Lo

ad (

L 2)

Figure 6.6: The requested or set secondary control signal showing the expected contribution ofeach CDE. Valid for both DEMS configurations

27

7 Conclusion

The hypothesis of this study states that it is possible to aggregate CDE’s by using the multi-tiered, hierarchically independent approach, with the agent being the aggregator and buildingblock. In addition, this approach should make it possible to connect the communication inter-faces of CDE’s in any possible configuration.

Using this as the starting point, a software design was drawn up with flexibility and hierarchicalindependence being the core aims. The software was then implemented and finally a smallscale laboratory test was successfully completed. Two different communication configurationswere explored in the laboratory. Within a matter of minutes it was possible to change fromconfiguration 1 to 2 and continue testing. It can therefore be concluded, from a flexibilityand ease of use standpoint, that it is possible to aggregate CDE’s in any configuration inorder to reach the required generating capacity to partake in the German secondary controlregulating power market and that the software framework has proven itself to be flexible andeasily configurable.

Due to the similarity between figures 6.3 and 6.4 we can conclude that from the active poweroutput point of view the hypothesis is indeed correct.

This dissertation therefore concludes a successful demonstration of the multi-tiered, multi-agentapproach to CDE aggregation in the DeMoTec laboratory.

In addition this study has laid the groundwork for the future inclusion of and interfacing withother optimisation algorithms and simulation packages. Further improvements should includereactive power control of CDE’s, integrating realtime active and reactive power optimisationsbased on soon to be completed Powerfactory simulations. A graphical user interface for betterrealtime visualisation would be another worthwhile addition. The final aim should then be toscale up the experiment to include hundreds of CDE’s.

28

A Source Code Extract

This code extract is from the heart of the DEMS. It is the central decision making routine thatis called every time an agent receives a secondary control signal from its superior agent.

def set_delta_p_W(self, delta_p_W):’’’delta_p_W - The amount by which you want to change the resultant power(in Watts) that achieves maximum profit in order to satisfy a secondarycontrol signal.’’’print ’\nset_delta_p_W =’, delta_p_W

if delta_p_W == 0:for client in self.client_list:

client.set_delta_p_W(0)return

# Get all the available delta P’s with their profit slopesdelta_p_W_list = self.get_delta_p_W()

# Create a list to hold the applicable delta P’smodified_delta_p_W_list = []

# Sort delta_p_W_list according to the profit slopedelta_p_W_list = sorted(delta_p_W_list, key=operator.itemgetter(1))if delta_p_W < 0:

# First discard all the clients with a delta >= 0resultant_client_delta_p_W = {}for client in delta_p_W_list:

client_delta_p_W = client[0]client_reference = client[2]if client_delta_p_W < 0:

modified_delta_p_W_list.append(client)# Add an item to the dictionary which will be used later.# Python dictionaries can’t have duplicate client_reference# keys which is the desired effect.resultant_client_delta_p_W[client_reference] = 0

# Now work out the delta P for each clientremaining_delta_p_W = delta_p_Wfor client in modified_delta_p_W_list:

client_delta_p_W = client[0]client_reference = client[2]

29

if client_delta_p_W >= remaining_delta_p_W:resultant_client_delta_p_W[client_reference]+=client_delta_p_Wremaining_delta_p_W -= client_delta_p_W

elif client_delta_p_W < remaining_delta_p_W:resultant_client_delta_p_W[client_reference]+=remaining_delta_p_Wremaining_delta_p_W = 0

if remaining_delta_p_W == 0:# Don’t process any more ’cause we’ve got our delta P quota

# Now find the clients which aren’t going to contribute to this# SC round and set their delta_p_W to zero so they can operate# at max profit.# Make a copy of self.client_listnon_sc_contributors = list(self.client_list)sc_contributors = resultant_client_delta_p_W.keys()for client in sc_contributors:

non_sc_contributors.remove(client)

for client in non_sc_contributors:client.set_delta_p_W(0)

# Just set the client delta_p_W by their respective values in# the resultant_client_delta_p_W dictionaryfor sub_client in resultant_client_delta_p_W.items():

sub_client_ref = sub_client[0]sub_client_delta_p_W = sub_client[1]sub_client_ref.set_delta_p_W(sub_client_delta_p_W)

return

if delta_p_W > 0:# First discard all the clients with a delta <= 0resultant_client_delta_p_W = {}for client in delta_p_W_list:

client_delta_p_W = client[0]client_reference = client[2]if client_delta_p_W > 0:

modified_delta_p_W_list.append(client)# Add an item to the dictionary which will be used later.# Python dictionaries can’t have duplicate client_reference# keys which is the desired effect.resultant_client_delta_p_W[client_reference] = 0

# Now work out the delta P for each clientremaining_delta_p_W = delta_p_Wfor client in modified_delta_p_W_list:

client_delta_p_W = client[0]client_reference = client[2]if client_delta_p_W <= remaining_delta_p_W:

resultant_client_delta_p_W[client_reference]+=client_delta_p_Wremaining_delta_p_W -= client_delta_p_W

30

elif client_delta_p_W > remaining_delta_p_W:resultant_client_delta_p_W[client_reference]+=remaining_delta_p_Wremaining_delta_p_W = 0

if remaining_delta_p_W == 0:# Don’t process any more ’cause we’ve got our delta P quota

# Now find the clients which aren’t going to contribute to this# SC round and set their delta_p_W to zero so they can operate# at max profit.# Make a copy of self.client_listnon_sc_contributors = list(self.client_list)sc_contributors = resultant_client_delta_p_W.keys()for client in sc_contributors:

non_sc_contributors.remove(client)

for client in non_sc_contributors:client.set_delta_p_W(0)

# Just set the client delta_p_W by their respective values in# the resultant_client_delta_p_W dictionaryfor sub_client in resultant_client_delta_p_W.items():

sub_client_ref = sub_client[0]sub_client_delta_p_W = sub_client[1]sub_client_ref.set_delta_p_W(sub_client_delta_p_W)

return

31

B Data Sample

Below is as small sample of the raw data for this experiment. It includes only the first twominutes of data obtained from the 15 kVA synchronous generator which was used to simulatea 12 kW wind turbine.

32

TableB.1:

Ada

tasamplefro

mthe12

kWwindturbineforDEM

Sconfi

guratio

n1

Hou

rsTim

estamp

Tim

eSlot

12kW

WindTu

rbine

PSe

t(W

)

12kW

WindTu

rbine

PMeasure

(W)

12kW

WindTu

rbine

PMin

(W)

12kW

WindTu

rbine

PMax

(W)

002/11/09

15:28

06744

185395

6744

0.02

02/11/09

15:28

06744

107

5395

6744

0.03

02/11/09

15:28

06744

2091

5395

6744

0.05

02/11/09

15:28

06744

3454

5395

6744

0.07

02/11/09

15:28

06744

4254

5395

6744

0.08

02/11/09

15:28

06744

4876

5395

6744

0.1

02/11/09

15:28

06744

5350

5395

6744

0.12

02/11/09

15:29

06744

5705

5395

6744

0.13

02/11/09

15:29

06744

6060

5395

6744

0.15

02/11/09

15:29

06744

6238

5395

6744

0.17

02/11/09

15:29

06744

6357

5395

6744

0.18

02/11/09

15:29

06744

6445

5395

6744

0.2

02/11/09

15:29

06744

6505

5395

6744

0.22

02/11/09

15:29

06744

6594

5395

6744

0.23

02/11/09

15:29

06744

6623

5395

6744

0.25

11/02/09

15:29

15578

6712

5578

6972

0.27

11/02/09

15:29

15578

6445

5578

6972

0.28

11/02/09

15:29

15578

6238

5578

6972

0.3

11/02/09

15:29

15578

6031

5578

6972

0.32

11/02/09

15:29

15578

5912

5578

6972

0.33

11/02/09

15:29

15578

5823

5578

6972

0.35

11/02/09

15:29

15578

5794

5578

6972

0.37

11/02/09

15:29

15578

5705

5578

6972

0.38

11/02/09

15:29

15578

5675

5578

6972

0.4

11/02/09

15:29

15578

5675

5578

6972

0.42

11/02/09

15:29

15578

5616

5578

6972

0.43

11/02/09

15:29

15578

5616

5578

6972

0.45

11/02/09

15:29

15578

5586

5578

6972

0.47

11/02/09

15:29

15578

5616

5578

6972

0.48

11/02/09

15:29

15578

5616

5578

6972

33

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[8] S. Riedel and H. Weigt, “German Electricity Reserve Markets,” Electricity Markets Work-ing Papers, 20076.

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[10] J. Kok, C. Warmer, and I. Kamphuis, “PowerMatcher: Multiagent Control in the Elec-tricity Infrastructure,” Utrecht, Netherlands: Energy Research Centre of the Netherlands,20058.

[11] M. Braun, “Provision of Ancillary Services by Distributed Generators - Technological andEconomic Perspective,” University of Kassel, Germany, 20089.

[12] “Pyro - Python Remote Objects,” Website10, Accessed On: 22-11-2009.[13] “Python Programming Language – Official Website,” Website, Accessed On: 22-11-200911.

1http://crisp.ecn.nl/deliverables/D5.3.pdf2http://www.iset.uni-kassel.de/dispower_static/documents/fpr.pdf3http://www.entsoe.eu/fileadmin/user_upload/_library/publications/ce/oh/Policy1_final.pdf4http://www.eu-deep.org5Available soon here:http://www.fenix-project.org6http://ssrn.com/abstract=11372827http://openopc.sourceforge.net8http://www.powermatcher.net/fileadmin/..../AAMAS_Article_PowerMatcher_DistributionVersion.pdf9http://www.upress.uni-kassel.de/publik/978-3-89958-638-1.volltext.frei.pdf

10http://pyro.sourceforge.net11http://www.python.org

34