This document describes a flexible software-based distributed energy management system (DEMS) designed to investigate how controllable distributed energy units (CDEs) can be aggregated and integrated into the electric grid. The DEMS uses a hierarchical agent-based model to control different CDEs, including a wind turbine, combined heat and power plant, electric vehicle charging station, and industrial load. An experiment was conducted using the DEMS to demonstrate how it can aggregate these CDEs in different communication configurations to meet a secondary frequency control signal while maximizing profit from energy generation. Results showed the DEMS was able to successfully control the CDEs to closely track the required active power output.

1. Design and Implementation of a Flexible
Distributed Energy Management System
to Investigate the Grid Integration of
Controllable Distributed Energy Units
Roy Emmerich
Carl von Ossietzky Universität, 114-118 Ammerländer Heerstraße, D-26129, Oldenburg,
Germany
Abstract: The German Renewable Energy
Sources Act is setting a trend towards a high
penetration of geographically distributed, con-
trollable 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 communi-
cation, power flow and grid stability. In the
medium term it will become a critical require-
ment to control large numbers of CDE’s in a
way that will substitute services currently pro-
vided by large, centralised fossil and nuclear
powered generators. This dissertation investi-
gates 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 al-
lowing 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 frame-
work utilising existing CDE hardware in the
Fraunhofer Institute for Wind Energy and En-
ergy System Technology (IWES) Design Cen-
tre for Modular Supply Technology (DeMoTec)
laboratory.
Keywords: distributed energy manage-
ment systems, controllable distributed energy
units, grid integration, ancillary services, elec-
tric vehicles, 100% renewable energy.
1 Introduction
1.1 Background
The electricity distribution grid was previously
designed to accommodate a one way flow of ac-
tive 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 transmis-
sion level, produced the required active power.
The German Renewable Energy Sources Act
gives priority to geographically distributed,
grid connected, renewable energy sources to in-
ject active power into the grid. As the number
of distributed generators increases, the contri-
bution of the large scale, centralised generators
will naturally diminish. Therefore as the frac-
tion of renewable energy generators grows, it
is obvious they will have to play an increas-
ing role in maintaining the stability of the grid
1
e.g. Powerfactory
1

2. as well as satisfying consumer’s active power
demands.
1.2 Motivation
The European Network of Transmission Sys-
tem Operators (ENTSOE) is the body which
represents all transmission system operators
(TSO’s) in the European Union (EU). It is
responsible for the definition of the load-
frequency control standard [5], the main func-
tions of which are to maintain a balance be-
tween active power supply and demand as well
as maintaining the frequency of the grid within
all grid control areas. This type of control is
divided into three main categories, namely pri-
mary, secondary and tertiary control. It is the
task of the TSO to send the secondary con-
trol (SC) signal to generators requesting either
an increase or decrease in active power output.
The problem for CDE’s is the minimum gener-
ating capacity required to partake in this mar-
ket. This dissertation specifically focuses on
the SC market which, in Germany, has an en-
try 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 mini-
mum bid trade barrier for the SC market
in Germany,
• the need for a fully flexible and modifi-
able software framework which can easily
aggregate CDE’s in a variety of configu-
rations,
• the desire to easily incorporate algo-
rithms 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.
2 Approach
Various approaches have been considered to
integrate large numbers of controllable dis-
tributed energy units into the existing grid
topology [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 distributed energy management system
(DEMS), based on ideas from the approaches
above, for experimenting with aggregation ap-
proaches in a laboratory environment. The
main concept which the software had to sup-
port was the ability to connect the communi-
cation interfaces2
of CDE’s together in a hier-
archically independent manner.
The main building block of this approach is
known as the agent. It acts as a software based
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 com-
munication conduit for receiving control sig-
nals from above.
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 market are paid for being on standby
should their services be required by the TSO
as well as for the amount of active power pro-
duced [8]. It was assumed that the revenue
from active power generated for the feed-in tar-
iff 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 ac-
tive 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 gener-
ated when charging vehicles. It is therefore in
2
as opposed to the electrical interfaces
2

3. the interests of the DEMS operator to always
aim for maximum active power consumption
by the charging station. In the case of the in-
dustrial load it was assumed the owner, namely
the DEMS operator, is contracted to drive a
certain industrial process that consumes a con-
stant 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 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 con-
trol signal is a request by the TSO for a rel-
ative 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 us-
ing the combined default operating states of all
CDE’s described above as the reference point.
3 Software Design
The developed distributed energy management
system (DEMS) is a software based solution
which was written in the Python programming
language [13]. When designing the DEMS,
specific emphasis was given to allowing hier-
archical flexibility with respect to the commu-
nication connections as well as the interaction
with different applications, systems, hardware
and software. The DEMS consists of a number
of agents which are connected to each other in
a hierarchical tree structure as shown in fig-
ure 2. Agents are only allowed to have one
superior agent but can theoretically be con-
nected 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 use of a standardised application pro-
gramming 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 cri-
terion, the active power output3
or consump-
tion4
of each CDE was adjusted from its de-
fault operating state by the DEMS to fulfil the
incoming secondary control request. Figure 1
shows the income, expenditure and resultant
profit curve for one CDE used in this experi-
ment.
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
Figure 1: Income, expenditure and profit
curves for the CHP plant
Even when CDE’s are not in operation they
still incur operational costs such as interest
rate repayments on bank loans. The income
curve always intersects the origin. If no active
power is produced then no income is gener-
ated. The profit curve is simply the difference
between income and 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 simulta-
neously meet the secondary control signal and
generate active power to maximise profit.
4 Laboratory Equipment
The CDE hardware used in this experiment
consisted of the following:
3
for generators
4
for loads
3

4. Wind turbine: a 12 kW wind turbine rep-
resented by a 15 kVA controllable synchronous
generator (SG).
CHP: a 16 kW CHP (combined heat and
power) plant represented by a 20 kVA inverter
coupled, variable speed, controllable generator
set.
Electric vehicle charging station: a
14 kW electric vehicle charging station repre-
sented by an 80 kVA controllable SG operating
in motor mode.
Industrial load: an 11 kW industrial load
represented by an 12 kVA controllable load.
Figure 2 shows how these CDE’s were con-
nected to the DeMoTec electric grid infrastruc-
ture.
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. Below are descrip-
tions of how each profile was created:
Wind turbine 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 rep-
resented the maximum possible active power
output for a certain 24 hour period. It was
assumed that a contractual requirement pre-
vented the active power output from being cur-
tailed to less than 80% of the maximum fore-
cast available power. This resulted in the op-
erating range shaded in red in figure 3.
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 nomi-
nal rated power of 16 kW. This operating range
is shaded in green in figure 3.
Electric vehicle charging station pro-
file: This charging station was assumed to be
located in a parking lot of a large commercial
bakery. Because of the daily staff routines 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, shaded
in blue in figure 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 oper-
ating range towards the end of the day. 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 service. This allowed for a
maximum reduction of active power consump-
tion of 10% from the rated 11 kW and is rep-
resented by the magenta shaded area in figure
3.
Secondary control profile: A secondary
control request signal was simulated 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 op-
erating window at any particular time, the SC
signal had to be scaled to fit the combined ca-
pacity of the CDE’s. This is shown by the
black dotted line in figure 3.
5 Experimental Procedure
The intention is to demonstrate the flexibility
of this aggregation approach by investigating
the combined active power output from two
different communication configurations. The
communication configurations used in parts 1
and 2 is shown in figure 2. The difference
between the two configurations is the point
of connection for the wind turbine (G2). All
agents were identically programmed to satisfy
the secondary control signal requirements en-
tering the DEMS, through the root agent, by
choosing the least profit sensitive CDE’s first
and thereby maximising net profit. The inten-
tion is to prove the flexibility of this aggrega-
tion approach by investigating the combined
active power output from each layout, while
using the same decision making process in each
agent.
4

5. A0
A2A1 Communication
config.
G1 L1 G2
G2
L2
Config. 2
Config. 1
0.4 kV 0.4 kV0.4 kVElectrical
config.
10 kV
Public Grid
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
= 100 kVA Transformer
= Electrical connection
Figure 2: Diagram showing the DEMS com-
munication configuration for parts 1 and 2 in
the top half and the electrical configuration in
the bottom half
6 Results and Analysis
Figure 3 shows the colour shaded operating
ranges of the four CDE’s. It also shows, in
the form of dark dotted lines, the expected ac-
tive power output for each CDE which includes
the impact of the SC request from the TSO.
In addition it includes the actual measured ac-
tive power values obtained for the DEMS con-
figuration 1. These measured values are de-
picted with solid colour lines in each of the
CDE colours, green, red, blue and magenta.
The solid brown line in figure 3 is the sum
of the measured active power outputs from all
the CDE’s while the dotted brown line rep-
resents the sum of the set active power CDE
settings. Although not identical, the measured
plots track the set values very closely.
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. Dur-
ing 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. Looking at the
individual CDE active power profiles between
0-7.5 hours, the wind turbine has been cur-
tailed 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 mini-
mum 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 sen-
sitive to profit with respect to a change in ac-
tive 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 tur-
bine is able to fulfil the SC requirements alone.
It will effectively relieve the CHP to continue
producing active power as efficiently as possi-
ble, which is at its rated full power setting of
16 kW.
Similarly when the SC is above the zero
mark in figure 3 (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 shal-
lower profit curve compared with the industrial
load. Only if there is no longer sufficient capac-
ity from the charging station is the industrial
load curtailed to make up the shortfall. The
first of these shortfalls occurs between 10.7-
5

6. 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 con-
nected vehicles. This shortfall is further aggra-
vated at the 16 hour mark when all employees
leave work resulting in no more vehicles being
connected to the charging station. For com-
parative purposes figure 4 has been included
in appendix A.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time of day [h]
15000
10000
5000
0
5000
10000
15000
ActivePower[W]
Wind
max & set
Wind
min
Wind meas
Ind. Load
max
Ind. Load
min & set
EV
max
EV min
meas & set
CHP max & set
CHP min
CHP meas
Total P set + SC set
Total P meas + SC set
SC set
measurment system failuremeasurement
system
failures
DEMS Configuration 1
CHP (G1 )
Wind Turbine (G2 )
Electric Vehicle (EV) Charging Station (L1 )
Industrial Load (L2 )
Total Set & Meas. Active Power (P) + SC set
Secondary Control (SC) Signal
Figure 3: Set and measured values from the four CDE’s including the SC signal. Valid for
DEMS configuration 1
7 Conclusion
The hypothesis of this study states that it
is possible to aggregate CDE’s by using the
multi-tiered, hierarchically independent ap-
proach, with the agent being the aggregator
and building block. In addition, this approach
should make it possible to connect the com-
munication interfaces of CDE’s in any possi-
ble configuration. Then as long as the decision
making criteria in each agent, and at each level
are the same, the combined active power out-
put from all the CDE’s should be the same.
Due to the similarity between figures 3 and 4
we can conclude that the hypothesis is indeed
correct from the active power output point of
view.
Two different communication configurations
have been explored. Within a matter of min-
utes it was possible, in the laboratory, 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 configura-
tion 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 flex-
ible and easily configured.
In addition this study has laid the ground-
work for the future inclusion of and interfacing
with other optimisation algorithms and simu-
6

7. lation packages.
This dissertation therefore concludes a suc-
cessful demonstration of the multi-tiered,
multi-agent approach to CDE aggregation in
the DeMoTec laboratory.
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7

8. A Additional Graphs
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time of day [h]
15000
10000
5000
0
5000
10000
15000
ActivePower[W]
SC set
measurment system
failuresmeasurement
system
failures
DEMS Configuration 2
CHP (G1 )
Wind Turbine (G2 )
Electric Vehicle (EV) Charging Station (L1 )
Industrial Load (L2 )
Total Set & Meas. Active Power (P) + SC set
Secondary Control (SC) Signal
Figure 4: Set and measured values from the four CDE’s including the SC signal. Valid for
DEMS configuration 2
8