roy_emmerich-eurec_dissertation-final

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 penetration
of geographically distributed, controllable generators, loads and storage units, also known as
controllable distributed energy (CDE’s) units. This policy shift challenges the status quo in
the electricity industry on many fronts, particularly in the areas of communication, power flow
and grid stability. In the medium term it will become a critical requirement to control large
numbers of CDE’s in a way that will substitute services currently provided by large, centralised
fossil and nuclear powered generators. This dissertation investigates one approach, namely
the hierarchically independent, agent based model as a possible solution. The main objective
is to create an open, software based framework capable of allowing the flexible, multi-tiered
aggregation of CDE’s as well as being able to incorporate or interface with other applicable
software1
that could aid research in this field. The final result is a successful laboratory based
demonstration of the aggregation capabilities of this framework utilising existing CDE hardware
in the Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) Design
Centre for Modular Supply Technology (DeMoTec) laboratory.
1
e.g. Powerfactory

I would like to make it known that it is my faith in God and his son Jesus Christ which has
brought me to Europe from South Africa for the EUREC Renewable Energy Masters degree
programme. I hope my humble eﬀorts during this time, and after, will contribute in some way
to improving this beautiful and remarkable earth we live on. I dedicate this work to my wife
Joanne. Her unfailing faith in me and the sharing of my quest has pulled me through. With
this dissertation I have achieved a personal goal by completing all the new work contained
herein using only open source software. I salute all those who promote this ideology through
the selﬂess giving of their most precious resource, time.
3

Contents
1 Introduction 5
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 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
4

1 Introduction
1.1 Background
The electricity distribution grid was previously designed to accommodate a one way flow of
active 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 transmission
level, 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 to
customers. 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 and
voltage 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 relatively
new legislation, compared to the age of electricity distribution infrastructure, challenges the
traditional grid design ideology in the following ways.
All small scale, roof mounted, domestic photovoltaic installations in Germany are connected to
the distribution grid. At times of high solar radiation levels, it is possible for active power to
flow from the distribution level, up to the transmission voltage level and then back down to a
consumer on the distribution level at some other point on the grid. This is contrary to the one
way 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 of
the grid. Originally deemed to be the most critical concerning stability, it was designed to be
actively managed. However at the distribution level the operator has almost no knowledge of
or influence over the current grid status except at certain strategic nodes. It was never meant
for any significant amounts of active power to be injected into the grid at the distribution level
and hence very little monitoring infrastructure exists here. Not knowing the real time power
flow metrics within large sections of the grid makes it more difficult for the operator to plan
the efficient operation and further development thereof.
As the number of distributed generators increases, the contribution of the large scale, centralised
generators will naturally diminish. Therefore as the fraction of renewable energy generators
grows, it is obvious that they will have to play an increasing role in maintaining the stability
of 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
5

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 power
supply 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 and
tertiary control. When a sufficiently large disturbance is detected, primary control is activated
within seconds, secondary control within minutes and tertiary control within tens of minutes,
should the disturbance endure that long. Each successive control category relieves the previous
one of its responsibilities to await the next request. It is the task of the TSO to send the
secondary control (SC) signal to generators requesting either an increase or decrease in active
power output. The problem for CDE’s is the minimum generating capacity required to partake
in this market. This dissertation specifically focuses on the SC market which, in Germany, has
an entry level bid of 10 MW with 1 MW 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 it
both possible and profitable for even the smallest grid connected CDE to trade on the existing
electricity markets.
Various methods have been considered to integrate large numbers of controllable distributed
energy units into the existing grid topology. These include, among other approaches, distributed
energy management systems, micro grids, virtual power plants and cells [2][3][4][6][7][10]. The
principle idea behind all of them is the aggregation of CDE’s in order to behave like conventional
power plants so as to more easily fit into the existing technical and economic models that
constitute 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 software
has 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 experimental
procedure that will create the platform to collect the data and finally analyse it in section 6.
6

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. Practically
this means multiple levels of aggregation as depicted in figure 2.1 and is very similar to the
Powermatcher concept described in [10]. For this study the DEMS software was only required
to control the active power consumption and generation of four existing generators and loads
which 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 an
aggregator 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 a
communication conduit for receiving control signals from above.
aggregator
... CDE aggregator
... CDE aggregator
... ...
Figure 2.1: An illustration of the multi-tiered aggregation of
CDE’s
Allowing multiple levels of ag-
gregation on the communica-
tion side opens possibilities of
new business models taking
root. For example, a small
group of CDE’s such as a few
electric vehicle charging sta-
tions in a certain area may, as
a collective, still not satisfy the
minimum bid requirement for
the German SC market. It
would then be required to fur-
ther aggregate the already ag-
gregated charging stations by
entering into a contract with a
larger aggregator.
Other examples to substantiate this approach would be to reduce congestion by optimising
power flow or to reduce active power line losses through real time simulation techniques. The
agent, coupled to an electrical simulation software package, could then make the decision on
how best to engage the CDE’s based on the simulation results. Using a multi-tiered approach
the 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 the
DEMS 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 rewards
CDE’s feeding active power into the grid. Generators taking part in the secondary control
1
as opposed to the electrical interfaces
7

market are paid for being on standby should their services be required by the TSO as well
as for the amount of active power produced [8]. It was assumed that the revenue from active
power 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 study
must be to generate maximum active power. For the loads the default operating state must be
to consume as much active power as possible. In the context of this study the two loads are an
electric vehicle charging station and an industrial load of some sort. In the case of the charging
station, profit is only generated when charging vehicles. It is therefore in the interests of the
DEMS 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, is
contracted 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 prefer
a constant supply. The contract binds the DEMS operator to a service level agreement that
rewards 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 order
to satisfy the TSO’s secondary control request but limiting the impact on the profit earned from
the feed-in tariff.
It should be noted that the secondary control signal is a request by the TSO for a relative
change 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’s
described 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 software
written 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 simulate
a large scale real world situation.
• although real hardware is being used, it is still only simulating actual CDE’s.
8

3 Software Design
The developed distributed energy management system (DEMS) is a software based solution
which 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 following
reasons:
• 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 other
languages, 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 with
respect 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 which
are connected to each other in a hierarchical tree structure as shown in figure 5.1. Each agent
within the DEMS is represented by an instance of a single Python class which is designed to
run on physically separate hardware. Inter-agent communication is via the internet protocol
suite (TCP/IP) using the Python Remote Objects package [12]. Agents are only allowed to
have one superior agent but can theoretically be connected to an infinite number of sub-agents
and 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 measurement
hardware via various OPC servers in the DeMoTec laboratory.
The use of a standardised application programming interface (API) promotes flexibility by
allowing agents and CDE’s to be connected in virtually any configuration, thereby allowing
many 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 incoming
secondary control request. Figure 3.1 shows the income, expenditure and resultant profit curves
for each CDE used in this experiment. Note the axis values for the generator plots are positive
while those for the loads are negative. The reason for this was to ensure the slopes of all profit
curves were greater than or equal to zero.
Notice how the expenditure curve always intersects the y axis above or below zero, but never at
zero. Even when CDE’s are not in operation they still incur operational costs such as interest
1
for generators
2
for loads
9

0
2000
4000
6000
8000
10000
12000
14000
16000
Active Power [W]
0
200
400
600
800
1000
1200
1400
1600
Euro/h
slope = 0.069
16 kW CHP Plant (G1 )
Income
Expenditure
Profit
16000
14000
12000
10000
8000
6000
4000
2000
0
Active Power [W]
3000
2500
2000
1500
1000
500
0
Euro/h
slope = 0.01
14 kW Electric Vehicle Charging Station (L1 )
16000
14000
12000
10000
8000
6000
4000
2000
0
Active Power [W]
3000
2500
2000
1500
1000
500
0
Euro/h
slope = 0.022
11 kW Industrial Load (L2 )
0
2000
4000
6000
8000
10000
12000
14000
16000
Active Power [W]
0
200
400
600
800
1000
1200
1400
1600
Euro/h
slope = 0.063
12 kW Wind Turbine (G2 )
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 curve
always intersects the origin. If no active power is produced then no income is generated. The
profit curve is simply the difference between income and expenditure. Notice that the profit
curve always intersects the x axis away from the origin. This means there is an active power
range extending from zero to this intersection point in which it is not financially viable to
operate a CDE as income is less than expenditure. Using the slopes of the profit curves and the
simulated active power working range of each CDE, the DEMS is able to make the decision to
simultaneously meet the secondary control signal and generate active power from the available
CDE’s to maximise profit. The values chosen to represent income and expenditure were only
meant to be indicative and don’t accurately represent actual operating costs of the real world
equivalent units. However, what is important to understand is the concept of using the slope of
the 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 the
situation varies depending on how much active and reactive power a CDE is required to produce
as well as the associated grid losses [11]. It is however sufficient as a first order approach to
10

demonstrate the concept.
Two aptly named helper applications, setter.py and logger.py, were also written to support
this experiment. The task of setter.py is to extract the profiles from a comma separated
variable (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 values
of each CDE from the central OPC server at a fixed interval and log them to a CSV file for
offline processing. Figure 5.3 shows the actual laboratory configuration.
11

4 Laboratory Equipment
The CDE hardware used in this experiment consisted of three controllable generators and one
controllable load. They can be seen in figure 4.1.
Each of these units were used to simulate a real world, distributed, renewable energy source or
sink 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 via
100 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 acquisition
and control hardware solutions. These RTU’s updated control setting and measurement values
to, and monitored requests for control setting value changes from a central Object-Linking and
Embedding (OLE) for Process Control (OPC) server. The DEMS software, described in detail
below, was then able to control each CDE by changing control setting values on the central OPC
server. Measured values were also read from the central OPC server. The actual laboratory
communication configuration can be seen in figure 5.3.
In order to perform the experiment, each CDE had to have an active power generation or
consumption profile applied to it in order to simulate a real world CDE. The time step resolution
for each profile was 15 minutes in actual time representing 15 seconds in the laboratory. The
overall duration of each profile was 24 hours in actual time which totalled 24 minutes in the
laboratory. Below are descriptions of how each profile was created:
• Wind profile
An actual wind turbine power output profile was scaled to match the capacity of the
laboratory generator used to simulate this CDE. This profile represented the maximum
possible 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 indicated
in red in figure 4.3.
1
Written by Rodrigo Estrella, a EUREC alumnus from the 2007 intake
12

(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 profile
As combined heat and power (CHP) units are most efficient when running at or near their
rated power output, an operator decision was made to maintain active power output at
or above 80% of the assumed nominal rated power of 16 kW. This operating range is
indicated 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 electric
vehicle charging station is proportional to the number of connected vehicles as well as the
state of charge of the batteries within these vehicles. This charging station was assumed
to be located in a parking lot of a large commercial bakery. Employees begin arriving
for work at 06h00. By 09h00 everybody is at work. At 14h00 the early shift leaves work
followed by the rest of the employees at 16h00. At this factory most employees remain
at work all day. Because of this user behaviour it is not critical for the batteries to be
recharged in the early part of the day, hence the wide active power operating range in the
morning, indicated in blue in figure 4.3. It is however imperative to make sure all vehicles
are sufficiently charged for the drive home after work. This results in the progressively
narrower 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 incorporate
the concept of people movement which results in electric vehicle charging stations having
their own unique challenges concerning the provision of electricity services [1]. For this
experiment, 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
13

G1 L1 G2L2
0.4 kV 0.4 kV 0.4 kV
10 kV
Public Grid
Legend:
= 100 kVA Transformer
G1 = 16 kW CHP
G2 = 12 kW Wind turbine
L1 = 14 kW Electric vehicle charging station
L2 = 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% from
the 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 area
for 3 January 2008. As each CDE has only a limited active power operating window at
any particular time2
, the SC signal had to be scaled to fit the combined capacity of the
CDE’s. The default operating state for this study is for generators to produce as much
active 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 business
model. To arrive at a properly scaled SC signal for this experiment a calculation had
to be made to determine how much positive and negative SC capacity this portfolio of
CDE’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 power
operating 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 shaded
regions. These operating ranges indicated at cross-section XX in figure 4.3 can be seen
transcribed 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 the
two 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 be
obtained 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 not
make sense for the DEMS operator to offer SC capacity on the balancing power market
which falls outside the combined colour shaded regions.
2
Indicated by the colour shaded areas in figure 4.3
3
Indicated using the notation δPxx
14

0123456789101112131415161718192021222324
Timeofday[h]
12000
8000
4000
0
4000
8000
12000
16000
ActivePower[W]
X
X
δPG1
δPG2
δPL1
δPL2
Windmax
Windmin
CHPmax
CHPmin
EV
Chargemax
EV
Chargemin
Ind.Load
max
Ind.Load
min
DEMSConfiguration1
CHP(G1)
WindTurbine(G2)
ElectricVehicle(EV)ChargingStation(L1)
IndustrialLoad(L2)
Figure 4.3: Plot showing the CDE operating ranges resulting from the simulated proﬁles
15

0123456789101112131415161718192021222324
Timeofday[h]
6000
4000
2000
0
2000
4000
6000
ActivePower[W]
X
X
δPG2
δPG1
δPL1
δPL2
EVCharging
Station
IndustrialLoad
CHP
WindTurbine
SetSC
ScalingtheSCSignal
IndustrialLoad(L2)
SecondaryControl(SC)Signal
CHP(G1)
WindTurbine(G2)
Figure 4.4: A graphical description of how the SC signal was scaled to ﬁt the combined
capacity of the CDE’s
16

5 Experimental Procedure
The idea being explored is that of using a hierarchically independent, agent based, distributed
energy management system approach to control the active power generation of CDE’s in a
flexible fashion. This study is divided into two scenarios which will be compared. Each scenario
is based on a different communication configuration. All agents were identically programmed to
satisfy 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 represents
the layout used in part 2. Please note that these diagrams are simplified layout configurations
to assist understanding. The actual laboratory configuration can be seen in figure 5.3. The
only difference between part 1 and 2 is the point of connection for the wind turbine (G2). The
intention is to prove the flexibility of this aggregation approach by investigating the combined
active power output from each layout, while using the same decision making process in each
agent.
A0
A2A1
G1 L1 G2L2
Legend:
= TCP/IP connection
A0 = Root agent
A1 = Agent 1
A2 = Agent 2
G1 = 16 kW CHP
G2 = 12 kW Wind turbine
L1 = 14 kW Electric vehicle charging station
L2 = 11 kW Industrial load
Figure 5.1: Simplified DEMS communication configuration for part 1
17

A0
A2A1
G1 L1
G2
L2
Figure 5.2: Simplified DEMS communication configuration for part 2
A0
A2A1
OPC Server
setter.py
set SC
signal
set CDE’s min,
max power
logger.py
log set and
measured
values to
csv file
G1 L1 G2L2
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 operating
ranges for each CDE as the decision making criteria. The goal of the software is to fulfil a
secondary control request entering the system at the root agent, represented by A0 in figure
5.1, as well as to produce as much active power as possible to feed into the grid, thereby
maximising profits from the generating CDE’s. In the case of the electric vehicle charging
station and industrial load, it is assumed that a profit is generated by fulfilling contractually
bound services. For the charging station this service is charging cars and for the industrial load
it is driving an industrial process of some sort. The provision of these services is the incentive
to 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 each
agent upon receiving the secondary control signal. This should be studied in conjunction with
cross-section XX indicated in figures 4.3 and 6.6.
Notice how the incoming secondary control request (SCtotal), calling for a reduction in active
power being produced, is split between the two generators. The wind turbine (G2) has a
18

shallower profit slope than the CHP unit (G1), is therefore less profit sensitive with respect to
a change in active power, and so is chosen first. Based on this criterion, its active power output
is reduced by SCG2. If SCtotal was less than or equal to the available active power operating
range for G2 at this time (δPG2), then it would have been the only generator used to fulfil
the secondary control active power request. However, this is not the case so the more profit
sensitive 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 same
conclusion as the root agent concerning the distribution of active power.
A0
A2A1
G1 L1 G2L2
AC
h
δP(W)
SCG1
δPG1
AC
h
δP[W]
δPL1
AC
h
δP[W]
SCG2
δPG2
AC
h
δP[W]
δPL2
AC
h
δP[W]
SCG1
AC
h
δP[W]
SCG2
AC
h
δP[W]
SCtotal
SCG2 SCG1
Figure 5.4: DEMS communication configuration for part 1 showing the distribution of the
incoming secondary control signal across the portfolio of CDE’s. The profit slope graphs shown
in this figure are not drawn to scale. They are merely intended to be indicative. This should
be studied in conjunction with figures 4.3 and 6.6
19

6 Results and Analysis
In order to promote a better understanding it would be wise to first examine the expected
performance of the CDE’s without the effects of the secondary control (SC) signal. Figure 6.1
shows the colour shaded operating ranges of the four CDE’s. It also shows, in the form of dark
dotted lines, the expected active power output for each CDE if there was no SC request from
the TSO. This CDE behaviour corresponds to the default operating state for this experiment
as described in section 2. The solid brown line in figure 6.1 is the sum of the active power
outputs from all the CDE’s.
Now we move on to figure 6.2. In this plot we can see the same information as shown in
figure 6.1 but this time the influence of the secondary control is introduced. This can be see
by the change in shape of the set active power curve shown with the brown dotted line. Note
that 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 power
output from all the CDE’s. Notice, for example, the time between 0-7.5 hours. During this
time the TSO is requesting a reduction in active power output as the black dotted SC curve
passes below the x axis. We now know from default operating state and the decision making
criteria employed that the output of the wind turbine and possibly the CHP will be curtailed
to reduce the total active power output between 0-7.5 hours. When compared with figure 6.1
in the same time window it can be seen that the total active power output is reduced. Looking
at the individual CDE active power profiles between 0-7.5 hours, the wind turbine has been
curtailed right down to the minimum allowable setting. During the same time the combined
heat and power (CHP) plant is only partially curtailed. It, in fact, never reaches its minimum
active power setting. This is due to the CHP plant having a steeper profit curve than the wind
turbine. It is said to be more sensitive to profit with respect to a change in active power and
is therefore only curtailed if the wind turbine has insufficient capacity to fulfil the requested
SC signal reduction. Just near end of this time window at the 7th
hour mark, the CHP returns
to its maximum active power output while the wind turbine only returns to maximum active
power output around the 7.5 hour mark when the SC is above zero. This is once again due to
the different profit slopes for the two CDE’s. The CHP has a steeper slope and hence will be
the first of the two CDE’s to return to full power output if the wind turbine is able to fulfil the
SC requirements alone. It will effectively relieve the CHP to continue producing active power
as 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 is
the electric vehicle charging station which fulfils the SC control signal first due to its shallower
profit curve compared with the industrial load. Only if there is no longer sufficient capacity from
the charging station is the industrial load curtailed to make up the shortfall. The first of these
shortfalls occurs between 10.7-11.5 hours when the SC signal rises above the available capacity
of the charging station. The second shortfall begins at the 13.7 hour mark when the active
power consumption capacity of the electric vehicle charging station falls away dramatically
due to a simulated loss of connected vehicles. This shortfall is further aggravated at the 16
hour mark when all employees leave work resulting in no more vehicles being connected to the
charging station.
20

Figure 6.3 shows the same information as figure 6.2 but now includes the actual measured active
power values obtained for the DEMS configuration 1. These measured values are depicted with
solid colour lines in each of the CDE colours, green, red, blue and magenta. The sum of these
measured CDE active power outputs, namely the total active power output, is shown using
the solid brown line. Here we can see the differences between the set and measured active
power values. Although not identical, the measured plots track the set values very closely. If
you look carefully at the magenta plot which represents the industrial load you will notice a
continuous, constant offset between set and measured values. This was due to the controllable
load in the laboratory being faulty and reporting the incorrect value. For reasons unknown a
similar problem was occurring with the CHP unit.
Figure 6.4 shows the same information as figure 6.3 but this time corresponds to the actual
measured values for the DEMS configuration 2. The two sets of graphs from the different
configurations 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 is
the difference between the set and measured values of the wind turbine data. A change in the
set value is not immediately followed by the CDE’s actual measured active power output. The
reason for this delay is due to the performance characteristics of the synchronous generator
used to simulate the wind turbine. As each time step shown in figure 6.5 equates to 15 seconds
of lab time, the generator settling time can be roughly measured by eye to be between 10-12
seconds. 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 from
each of the CDE’s. This graph gives a clear indication of the contributions that should be made
by the various CDE’s to fulfil the SC control signal. When the TSO stipulates a decrease in
active power via an SC signal, it is the wind turbine which is first to react due to its shallower
profit curve. Hence it is located directly below the zero y axis line to indicate this fact. If the
required reduction is greater than the wind turbine is able to provide then the combined heat
and power (CHP) unit is used to make up the shortfall. Consider cross-section XX in figure
6.6. At this point the SC signal is requesting an active power reduction of SCtotal. The wind
turbine is only able to provide a reduction SCG2so the CHP unit is curtailed by SCG1to make
up the shortfall. Similarly when the SC stipulates an increase in combined active power output
it is the electric vehicle charging station which is first to react with the industrial load making
up the shortfall if necessary.
21

0123456789101112131415161718192021222324
Timeofday[h]
15000
10000
5000
0
5000
10000
15000
ActivePower[W]
Wind
max&set
Wind
min
Ind.Load
max
Ind.Load
min&set
EV
max
EV
min&set
CHPmax&set
CHPmin
TotalPset
DEMSConfiguration1&2
CHP(G1)
WindTurbine(G2)
IndustrialLoad(L2)
TotalSetActivePower(P)
Figure 6.1: CDE set values including total P set. Valid for both DEMS conﬁgurations
22

0123456789101112131415161718192021222324
Timeofday[h]
15000
10000
5000
0
5000
10000
15000
ActivePower[W]
Wind
max&set
Wind
min
Ind.Load
max
Ind.Load
min&set
EV
max
EV
min&set
CHPmax&set
CHPmin
TotalPset+SCset
SCset
CHP(G1)
WindTurbine(G2)
IndustrialLoad(L2)
TotalSetActivePower(P)+SCset
Figure 6.2: CDE set values including total P set + SC set. Valid for both DEMS conﬁgurations
23

0123456789101112131415161718192021222324
Timeofday[h]
15000
10000
5000
0
5000
10000
15000
ActivePower[W]
Wind
max&set
Wind
min
Windmeas
Ind.Load
max
Ind.Load
min&set
EV
max
EVmin
meas&set
CHPmax&set
CHPmin
CHPmeas
TotalPset+SCset
TotalPmeas+SCset
SCset
measurmentsystemfailuremeasurement
system
failures
DEMSConfiguration1
CHP(G1)
WindTurbine(G2)
IndustrialLoad(L2)
TotalSet&Meas.ActivePower(P)+SCset
Figure 6.3: Set and measured values from the four CDE’s including the SC signal. Valid for
DEMS conﬁguration 1
24

0123456789101112131415161718192021222324
Timeofday[h]
15000
10000
5000
0
5000
10000
15000
ActivePower[W]
SCset
measurmentsystem
failuresmeasurement
system
failures
DEMSConfiguration2
CHP(G1)
WindTurbine(G2)
IndustrialLoad(L2)
TotalSet&Meas.ActivePower(P)+SCset
Figure 6.4: Set and measured values from the four CDE’s including the SC signal. Valid for
25

10 11 12 13
Time of day [h]
4000
5000
6000
7000
8000
9000
10000
ActivePower[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 the
26

0123456789101112131415161718192021222324
Timeofday[h]
4000
3000
2000
1000
0
1000
2000
3000
4000
ActivePower[W]
X
X
SCtotal
SCG2
SCG1
EVChargingStation
IndustrialLoad
WindTurbineWindTurbine
CHP
CHP
CHP(G1)
WindTurbine(G2)
SecondaryControl(SC)SetSignal
IndustrialLoad(L2)
Figure 6.6: The requested or set secondary control signal showing the expected contribution of
each 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 building
block. 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 hierarchical
independence being the core aims. The software was then implemented and finally a small
scale laboratory test was successfully completed. Two different communication configurations
were explored in the laboratory. Within a matter of minutes it was possible to change from
configuration 1 to 2 and continue testing. It can therefore be concluded, from a flexibility
and ease of use standpoint, that it is possible to aggregate CDE’s in any configuration in
order to reach the required generating capacity to partake in the German secondary control
regulating power market and that the software framework has proven itself to be flexible and
easily configurable.
Due to the similarity between figures 6.3 and 6.4 we can conclude that from the active power
output point of view the hypothesis is indeed correct.
This dissertation therefore concludes a successful demonstration of the multi-tiered, multi-agent
approach to CDE aggregation in the DeMoTec laboratory.
In addition this study has laid the groundwork for the future inclusion of and interfacing with
other optimisation algorithms and simulation packages. Further improvements should include
reactive power control of CDE’s, integrating realtime active and reactive power optimisations
based on soon to be completed Powerfactory simulations. A graphical user interface for better
realtime visualisation would be another worthwhile addition. The final aim should then be to
scale 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 that
is 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 secondary
control 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 slopes
delta_p_W_list = self.get_delta_p_W()
# Create a list to hold the applicable delta P’s
modified_delta_p_W_list = []
# Sort delta_p_W_list according to the profit slope
delta_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 >= 0
resultant_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 client
remaining_delta_p_W = delta_p_W
for client in modified_delta_p_W_list:
29

if client_delta_p_W >= remaining_delta_p_W:
resultant_client_delta_p_W[client_reference]+=client_delta_p_W
remaining_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_W
remaining_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_list
non_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:
# Just set the client delta_p_W by their respective values in
# the resultant_client_delta_p_W dictionary
for 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 <= 0
resultant_client_delta_p_W = {}
for client in delta_p_W_list:
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 client
remaining_delta_p_W = delta_p_W
for client in modified_delta_p_W_list:
if client_delta_p_W <= remaining_delta_p_W:
resultant_client_delta_p_W[client_reference]+=client_delta_p_W
remaining_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_W
remaining_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_list
non_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:
# Just set the client delta_p_W by their respective values in
# the resultant_client_delta_p_W dictionary
for 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 two
minutes of data obtained from the 15 kVA synchronous generator which was used to simulate
a 12 kW wind turbine.
32

TableB.1:Adatasamplefromthe12kWwindturbineforDEMSconﬁguration1
HoursTimestamp
Time
Slot
12kWWindTurbine
PSet
(W)
12kWWindTurbine
PMeasure
(W)
12kWWindTurbine
PMin
(W)
12kWWindTurbine
PMax
(W)
002/11/0915:28067441853956744
0.0202/11/0915:280674410753956744
0.0302/11/0915:2806744209153956744
0.0502/11/0915:2806744345453956744
0.0702/11/0915:2806744425453956744
0.0802/11/0915:2806744487653956744
0.102/11/0915:2806744535053956744
0.1202/11/0915:2906744570553956744
0.1302/11/0915:2906744606053956744
0.1502/11/0915:2906744623853956744
0.1702/11/0915:2906744635753956744
0.1802/11/0915:2906744644553956744
0.202/11/0915:2906744650553956744
0.2202/11/0915:2906744659453956744
0.2302/11/0915:2906744662353956744
0.2511/02/0915:2915578671255786972
0.2711/02/0915:2915578644555786972
0.2811/02/0915:2915578623855786972
0.311/02/0915:2915578603155786972
0.3211/02/0915:2915578591255786972
0.3311/02/0915:2915578582355786972
0.3511/02/0915:2915578579455786972
0.3711/02/0915:2915578570555786972
0.3811/02/0915:2915578567555786972
0.411/02/0915:2915578567555786972
0.4211/02/0915:2915578561655786972
0.4311/02/0915:2915578561655786972
0.4511/02/0915:2915578558655786972
0.4711/02/0915:2915578561655786972
0.4811/02/0915:2915578561655786972
33

C Bibliography
[1] C. Guille and G. Gross, “A conceptual framework for the vehicle-to-grid (V2G) implemen-
tation,” Energy Policy, 2009, doi:10.1016/j.enpol.2009.05.053.
[2] M. Braun and P. Strauss, “A review on aggregation approaches of controllable distributed
energy units in electrical power systems,” International Journal of Distributed Energy
Resources, vol. 4, 2008, pp. 297-319.
[3] G. Schaeffer and H. Akkermand, “CRISP - Distributed Intelligence in Critical Infras-
tructures for Sustainable Power,” Petten, Netherlands: Energy Research Centre of the
Netherlands, 20061
.
[4] T. Degner, J. Schmid, and P. Strauss, “DISPOWER - Distributed Generation with High
Penetration of Renewable Energy Sources,” Kassel, Germany: Institut für Solare Energiev-
ersorgungstechnik e.V., 20062
.
[5] “ENTSO-E Policy 1: Load-Frequency Control and Performance,” Operation Handbook,
04-20093
.
[6] “EUDEEP - The birth of a EUropean Distributed EnErgy Partnership,” Final Reports,
20094
.
[7] “FENIX Project - FENIX Deliverable 4.1.1: Specification of laboratory tests,” Technical
Report, Kassel, Germany: Institut für Solare Energieversorgungstechnik e.V., 20085
.
[8] S. Riedel and H. Weigt, “German Electricity Reserve Markets,” Electricity Markets Work-
ing Papers, 20076
.
[9] ”OpenOPC for Python - OPC for the Python programming language,” Website7
, Accessed
On: 22-11-2009
[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 and
Economic 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
.
1
http://crisp.ecn.nl/deliverables/D5.3.pdf
2
http://www.iset.uni-kassel.de/dispower_static/documents/fpr.pdf
3
http://www.entsoe.eu/fileadmin/user_upload/_library/publications/ce/oh/Policy1_final.pdf
4
http://www.eu-deep.org
5
Available soon here:http://www.fenix-project.org
6
http://ssrn.com/abstract=1137282
7
http://openopc.sourceforge.net
8
http://www.powermatcher.net/fileadmin/..../AAMAS_Article_PowerMatcher_DistributionVersion.pdf
9
http://www.upress.uni-kassel.de/publik/978-3-89958-638-1.volltext.frei.pdf
10
http://pyro.sourceforge.net
11
http://www.python.org
34

roy_emmerich-eurec_dissertation-final

More Related Content

What's hot

Similar to roy_emmerich-eurec_dissertation-final

roy_emmerich-eurec_dissertation-final