1

AbstractThe future share of distributed generation units is

likely to increase due to political requests as well as to politically

driven subsidies in European and international energy supply

systems. This increase implies a rising impact of the distributed

generation units as well as of distributed storages and concepts of

load management for supply companies. Most of the units are

currently subsidized by governmental feed-in tariffs. As these

subsidies are retrogressive, the necessity of evaluating small

generators as part of the supply system arises. One option of

market participation for dispersed generation is the aggregation

in terms of a "virtual power plant", thus representing one single

generation or demand unit for the market. In this paper, differ-

ent distributed generation, storage and load management options

are presented and an optimization method for simulating a vir-

tual power plant in different markets for electrical energy is

introduced in order to demonstrate the benefits of such a con-

cept. The optimization method is then applied to an exemplary

scenario of a local utility company by aggregating the existing

distributed units in one virtual power plant.

Index TermsDistributed Power Generation, Virtual Power Plant, Power Generation Planning, Load Management, Cogener-

ation, Thermal Storage Systems, Smart Grids

I. MOTIVATION AND STATE OF RESEARCH

ISTRIBUTED generation units have been proven to be

highly relevant in future energy supply systems, as can be

deduced from various capacity forecasts. According to [1] the

expected capacity of the distributed generation will rise to

more than 40 GW in Europe until 2020.1 Distributed genera-

tion in this context includes regenerative energy sources as

well as small combined heat and power (CHP) generation

units ranging up to a typical capacity of 20 MW. Due to the

increasing interconnection with information technology, that

is, using so called smart metering technologies, the demand for energy is also an integral part of distributed generation

systems and can be controlled by using demand side manage-

ment (DSM). Additionally, small energy storages whether for

electricity or for thermal energy are to be included in the sys-

tem, thus providing additional flexibility in distributed genera-

tion systems. Most of these technologies are subsidized by

governmental laws with a feed-in premium, especially in Eu-

rope (see [2], [3]). As most of these premiums are decreasing

over time, distributed units have to participate in the markets

for electrical energy in the future. For this participation, dif-

ferent options can be considered. The combined optimization

of small generation units and storages by a central entity, the

so called energy management system (EMS), at different mar-

kets forms a virtual power plant (VPP) which offers the oppor-

1The given values do not include renewable generation yet.

tunity of reaching necessary minimum capacities for market

participation in e.g. day ahead or system reserve mar-

kets (Fig. 1). Transaction costs for market participation can

also be reduced.

Fig. 1: Principle of a virtual power plant with EMS

Distributed units and VPPs already have been subject of re-

search in different works with varying contexts. Numerous

researches, such as [4] or [5], focus on the implementation and

real-time operation of EMS for a short time frame. The opera-

tion of single generation technologies or marketing alterna-

tives respectively (see [6]) and grid integration of dispersed

generation (see [7] or [8]) have been widely analyzed as well.

Evaluation of integrating a VPP into an existing portfolio and

simulation of the achievable economic benefits arising from

combined marketing alternatives in a longer time frame has

not been treated sufficiently yet. With this in mind, the present

paper analyzes different technologies of distributed generation

as well as small energy storage systems and application of

DSM (Section II.B). Moreover, marketing options for VPPs

are presented (Section II.C). Following, an optimization meth-

od for the energy generation and trading planning of conven-

tional and distributed units, participating in different markets

for electrical energy is introduced in Section III. In Section IV,

the proposed method is applied to a realistic scenario of a

regional utility company in exemplary investigations. Finally,

the main findings of the paper are summarized (Section V.).

II. SYSTEM MODEL AND ANALYSIS

A. System Components

In this work focus is set on the portfolio of regional supply

or public service companies. Portfolios of such utility compa-

nies usually comprise conventional, large generation units as

well as distributed generators and energy storage options

which are operated in order to cover the aggregated custom-

ers demand for electricity and heat respectively. The system components considered in this paper are shown in Fig. 2.

Hence, the field of observation includes electrical as well as

thermal loads, wherein the thermal load may be differentiated

in physically independent thermal nodes. Moreover, conven-

tional generation as well as cogeneration units, renewable

energy sources and heat generators are considered. Addition-

ally, the system components are completed by storage systems

for electricity or heat. Restrictions regarding the electrical grid

Virtual Power Plant Market

EMS

Dispatch Optimization and Economic Evaluation of Distributed Generation in a Virtual Power Plant

A. Schfer, Graduate Student Member, IEEE and A. Moser, Member, IEEE

RWTH Aachen University

Institute of Power Systems and Power Economics

D

2

are not considered, neither are limitations arising from for

example district heating networks .

Fig. 2: System Components

B. Marketing Alternatives of Virtual Power Plants

In general, a variety of different marketing options for VPPs

are possible. In this work, focus is set on the following three

applications. The marketing alternatives are described in the

context of the German regulatory regime, but can also be ap-

plied to other countries with slight modifications.

1) Optimized Load Coverage - Participation in Day Ahead

Markets

The main task of the considered system of a regional supply

company is the coverage of the given load in terms of both

electricity and thermal energy. In order to do so, a cost-effi-

cient coverage is obviously worthwhile, reducing generation

costs and finally end-customers prices. The VPP consisting of different distributed generators, storages and load management

can be used in order to reduce generation costs. Therefore, the

VPP is to be integrated with the total generation system and to

be operated with the objective of efficient load coverage.

Moreover, by aggregating the different units, necessary mini-

mum capacities for participation in spot markets, especially

the day ahead market exhibited by the power exchange, can be

reached. Hence, additional revenues can be obtained by taking

part in the market for electrical energy. This mode of opera-

tion is especially feasible for units, such as older regenerative

generation units, which cannot participate in fixed feed-in

tariff systems subsidized by the government. In order to allow

incorporation of this marketing alternative, day ahead markets

must be modeled in the optimization method as well as the

participation of the VPP.

2) Participation in Markets for System Reserve

In addition to the day ahead markets, the possibility of par-

ticipating in markets for system reserve is given. System re-

serve is necessary in order to maintain frequency control in a

given control area. Thus, the transmission system opera-

tor (TSO) is responsible for contracting system reserve prod-

ucts. The four German TSOs offer participation in three dif-

ferent markets for system reserve [9].2 Due to the requirements

regarding the minimum capacity to be provided and minimum

provision time, only the market for tertiary reserve is taken

into account as a marketing alternative for VPPs.

The tertiary reserve market in Germany is organized by six

different products, each representing four subsequent hours of

the day, for which the reserve capacity has to be provided [9].

Minimum capacity is 5 MW. This capacity may also be

2 The markets differ regarding the technical requirements of the participat-

ing units as well as the time within which the reserve energy has to be provid-

ed and for how long the reserve energy must be provided at most.

reached by pooling several small units so that the concept of

the VPP gives distributed generation units the possibility of

participating in this market. In conclusion, markets for system

reserve offer additional revenues for distributed generation

units, but due to product and technical limitations, only the

market for tertiary reserve is to be considered in the optimiza-

tion method.

3) Avoiding of Load Peaks Reduction of Capacity Fees All units of the VPP as well as the other components in-

cluded in the considered system are connected to the electrical

grid. Usage of the grid is remunerated by grid fees for the

corresponding grid operator. This fee consists of a so called

energy fee and a capacity fee. While the energy fee is paid for

each kWh transmitted by the grid, the capacity fee complies

with the highest load in one single hour extracted by the utility

company from the grid. Therefore, it is paid in /kW and amounts to 30 /kW [10]. Local generation units as well as storages and load steering can be used in order to lower the

maximum load and thus reduce the capacity fee. The main

principle of this operation mode is shown in Fig. 3.

Fig. 3: Reduction of load peaks by using virtual power plants

It is essential that all of the participating units are locally

concentrated and not geographically widespread, as they have

to be connected to the same grid, in order to reduce the refer-

ring fee. In order to achieve the most efficient operation of the

portfolio, all of the marketing alternatives must be considered

in the optimization of the contribution margin at the same

time.

C. Modeling of System Components

1) Thermal and hydraulic generation units

Conventional thermal and hydraulic units might also be part

of the portfolio considered. Modeling of these components is

standard procedure in energy generation and trading planning

whilst the respective models using dynamic programming and

network flows / successive linear programming are also ap-

plied in this work [11].

2) Cogeneration Units

The system considered has an electrical, but also a thermal

load to be covered. CHP units can produce electrical energy

and heat at the same time by using either natural gas or an-

other primary energy source with a high efficiency of up

to 90% [12]. However, the ratio of heat to power is restricted

by the operating diagram of each unit. A typical operating

diagram for a modulating CHP unit is given in Fig. 4 with

and representing the minimum and maximum elec-trical power output and and the referring minimum and maximum thermal output. It is obvious that due to the

operating diagram a constant ratio of heat and electrical power

must be maintained. Hence, the model for conventional ther-

mal generation units has to be extended by a two-dimensional

operating diagram in order to allow representation of CHP

Capacity

Fees

Therm. Storage

Ele

ctric

ity

Th

erm

al

Nod

e1

Renewable

GenerationDay Ahead

Market

Reserve

Markets Conventional

Generation

Storage

Electrical Load

CHPCHP CHP

Thermal Storage Therm. Generation

Thermal

LoadThermal

Load

Th

erm

al

Nod

eN

Minimum

Power of

the VPPMWhh

Load Curve without Virtual Power Plant

Load Curve using Virtual Power Plant for Peak Load Reduction

Gri

dL

oad

Billing Period

Reduction of load peak }

3

units. Additionally, each CHP has to be connected to one

single thermal node and the referring thermal load.

Fig. 4: Exemplary operating diagram of a CHP unit

3) Renewable Generation Units

Renewable generation units comprise several generation

technologies. In this work, only photovoltaic cells, wind tur-

bines, biomass plants and run-of-river generation are consid-

ered. Herein, biomass plants are modeled as conventional

generation units with a limited number of full-load hours of

5000 h/a due to primary energy restrictions. Run-of-river gen-

eration is modeled analogically to large scale hydraulic stor-

ages, with consideration of the fluctuating inflows and the

limited storage capacity. Photovoltaic cells and wind turbines

represent intermittent energy generators. Hence, the intermit-

tent character of these technologies has to be considered as

well as the minimum power output of these units. Dynamic

programming is applied with the adjusted maximum power

output of each unit for each single hour depending on the solar

radiation or the wind speed, respectively. For all of the renew-

able generation units, the ability of participating in the market

for system reserve is assumed, when integrated in a VPP.

4) Thermal Energy Storages

As each CHP unit is connected to a certain thermal load, the

units would be in a heat-operated mode with only very limited

flexibility without additional arrangements. In order to in-

crease the flexibility and allow participation of

CHP units in the day ahead or system reserve market, thermal

energy storages are introduced into the model. Each thermal

energy storage system is connected to one thermal node and

provides the ability of decoupling heat generation and con-

sumption, thus increasing the units degrees of freedom in operation. Technically speaking there is a number of options

possible with regards to storing thermal energy [13].

Fig. 5: 3-layer model for thermal energy storages

However, in terms of feasibility and for small-scale applica-

tions, only sensible heat storages, especially hot water storag-

es, are considered. Hence, one focus of this work is set on hot

water storages. In contrast to hydraulic storages, losses are not

negligible in thermal energy storages and can amount to

7%/day and must be modeled accordingly [14]. A layer model

is chosen for simulating the energy flows and losses in the

storage [14], [15] (see Fig. 5). The model represents a linear

optimization problem, in which thermal energy can be stored

by shifting it along the arcs from one layer to another and

between the time intervals. This model can be solved by ap-

plying adjusted network flow programming [16], [17].

5) Demand Side Management

DSM describes the adjustment of the load by reducing, in-

creasing or shifting consumption from one time interval to

another. Thus, the load curve can be flattened in order to gain

a more steady utilization of generation units or decrease sup-

ply costs. The potential for DSM has been analyzed in several

papers and could be determined to up to 14% of the total

load [18], [19]. In Fig. 6 a run-of-day schedule of the potential

load management depending on the actual load is given.

Fig. 6: Potential of DSM in different economy sectors

Load management cannot only be applied for reducing sup-

ply costs, but is also possible to provide additional system

reserve. Moreover, DSM can contribute to reducing load

peaks by shifting consumption to adjacent time intervals.

Thus, DSM can participate in all three marketing alternatives

of VPPs. For modeling DSM it is assumed that load can be

influenced continuously. Hence, a linear optimization model

according to [20] is chosen.

III. OPTIMIZATION METHOD

Generally, planning of energy generation and trading is a

highly complex optimization problem including not only non-

linear and integer decisions, such as minimum power output of

power plants, but also inter-temporal time dependencies, that

is, under consideration of storage systems or energy con-

straints.

Objective function of the optimization is the minimization

of the total costs. Costs occur due to generation costs of ther-

mal or CHP units buying at the day ahead market or capacity

fees, whereas revenues can be realized by selling at the day

ahead or system reserve market or by feed-in tariffs. Con-

straints are the technical ones from the single sub-systems as

well as the coverage of both electrical and thermal consump-

tion for each thermal node and system reserve, if applicable.

Closed-loop solving by application of Mixed Integer Quad-

ratic Programming with commercial solvers to large problems

like energy generation and trading planning usually leads to

very long, hardly manageable computing times and demands

regarding main memory [21]. In contrast, decomposition

methods are practically approved and offer manageable times

of computing and good precision by dividing the actual main

problem into smaller sub-problems, which can be solved iter-

atively by coordinating the individual solutions [22]. All non-

Equation of Continuity

Xt,in

Xt+1,Layer

Losses

Xt,Layer+1

Time

2

T

1

n

1

Lay

er

Drain

Source

0%

5%

10%

15%

1 3 5 7 9 11 13 15 17 19 21 23

Haushalte Gewerbe Industrie

DS

M P

ote

nti

al

Time

h

Households IndustryCommerce

Ele

ctri

cal

Po

wer

Thermal Power

4

linear characteristics and integer decisions of each of the dif-

ferent components and markets can also be considered in this

approach. For the decomposition approach, a Lagrange Relax-

ation is applied, splitting the problem into sub problems of the

referring system components, such as thermal and hydraulic

plants. The method described is based on the tool presented

in [11] and [23]. In order to model distributed units and VPPs,

the method is extended by the demand for thermal energy and

CHP units as well as by the explicit modeling of intermittent

generation units. Moreover, the DSM is added to the method

as well as the consideration of capacity fees (cf. Fig. 7).

Fig. 7: Optimization Method for Energy Generation and Trading

3The Lagrange Relaxation is applied as an iterative optimi-

zation. Herein, the whole problem is divided into sub-

problems of the different system components according

to Fig. 7. Hence, each sub-system can be optimized separately

with the best fitting optimization algorithm, such as dynamic

or quadratic programming. Capacity constraints can be con-

sidered implicitly by limiting the maximum supply from the

day ahead market in this stage of the optimization.

In order to gain compliance with the cross-system con-

straints of the electrical and thermal loads and system reserve,

these constraints are coordinated by Lagrange multipliers.

Electric load and system reserve are coordinated by the La-

grange-coordinators and in each time interval t. For each thermal node i compliance with the load constraint is ensured

by the multiplier . The referring constraints are relaxed into the objective function and multiplied with the Lagrange-coor-

dinator. Thus, non-compliance of the constraint is penalized

and deteriorates the modified objective function. After each

iteration the load and reserve constraints are balanced and the

referring Lagrange-coordinators are updated using a sub gra-

dient method in order to gain convergence towards the opti-

mum [22].4 After the methods stage of decomposition of the

whole optimization problem, it cannot be ensured that all

cross-system constraints are in compliance for each time inter-

val of the given period. Hence, in a final closed-loop formula-

tion (Hydrothermal energy dispatch) the start-up decisions

from the decomposition are taken into account, thus eliminat-

ing all integer decisions and transforming the problem into a

continuous quadratic problem. Following this, the entire sys-

tem with all markets and components is solved by quadratic

programming. All of the distributed components have been

3 With LP for Linear, QP for Quadratic and DP for Dynamic Programming 4 I.e., is increased if

exceeds the current power output of all gen-

eration units in time interval t in order to give a higher incentive for pro-

ducing electrical energy. and are updated accordingly.

considered at this stage whilst the optimization of the capacity

fees is also formulated explicitly as an additional constraint.

Results of the optimization method are the total costs of the

system. Moreover, the hourly dispatch for each generation and

storage unit as well as the commitment of DSM can be deter-

mined.

IV. EXEMPLARY INVESTIGATIONS

A. Data Model

In order to show the additional value of integrating distrib-

uted units into an existing portfolio, different investigations on

the realistic portfolio of a close to reality regional utility com-

pany are conducted. The chosen portfolio represents a typical

German local supply company which is suitable for the im-

plementation of a virtual power plant. Moreover, the genera-

tion stack includes conventional plants as well as CHP and

renewable units. This variety provides the opportunity of ap-

plying all of the described marketing alternatives and allows

deduction of benefits arising from different combinations of

distributed units. The year 2010 is simulated in an hourly time

frame. Hence, all of the market prices as well as feed-in time

series refer to this year based on the data given in [9], [24].

The main task is to cover the given hourly consumer load

which amounts to a total of 1,200 GWh/a with a peak load of

225 MW [25]. The conventional generation stack as well as

the distributed units are shown in Table I. For the renewable

energy sources an immediate participation in the day ahead

market without additional feed-in premium is applied.

TABLE I SUMMARY OF THE SYSTEM CONSIDERED IN THE INVESTIGATIONS [26] [28]

Units Key factors Conven-

tional Units

- Combined-Cycle Gas Turbine with - Two gas turbines with and

CHP - Local district heating, two thermal nodes each covered by a CHP unit with

- Micro-CHP with an aggregated capacity of - Aggregated thermal consumption

Renewable

Energy

Sources

(RES)

- 12 biomass power stations, aggr. capacity of - 3 small run-of-river power stations, aggr. capacity of

- Photovoltaic units, modeled aggr. with a capacity of

- 10 wind turbine power stations, modeled individually, aggre-

gated capacity DSM - Consideration of load steering with daily and seasonal pro-

file, max. shifting capacity of 14% of the load, cf. [18]

Besides covering the load with the existing generation units,

it is possible to buy or sell electrical energy at the day ahead

market. Moreover, participation in the market for tertiary

reserve is possible. Feed-in of CHP units is not only remuner-

ated by the revenues from the market, in addition a govern-

mental premium (5.11 ct/kWh) is paid for each kWh [29].

B. Schedule of Investigation

In order to show the additional value of a VPP in a portfolio

and the benefit of different control strategies, several investi-

gations are conducted. The investigations successively in-

crease the units participating in the VPP and cover different

marketing options. A total of nine simulations are accom-

plished. The first investigations only consider participation in

the day ahead market (Investigation 1 4). Within this part, the VPP initially (Investigation 1) only consists of the conven-

Hydrothermal energy dispatch

Determination of start-up decisions

Lagrange-coordinators ,

Renewables

DP,

Network Flow

Thermal

storages

Network Flow

Hydraulic

units

Network Flow

Reserve

markets

QP

Day ahead

market

Analytical

Thermal

units

DP

CHP

DP

DSM

LP

Input data- Generation units and storages: Technical, economic parameters, feed-in time series for

renewables, parameters for remuneration model of renewables

- Market data: Hourly prices for day ahead and tertiary reserve market

- Load: Time series of electrical load and thermal load for each node,

parameters for DSM

Output data

5

tional portfolio, whereas the CHP and renewable energy

sources are optimized individually. Moreover, CHP are opti-

mized in a heat-operated mode, directly following the given

hourly demand for heat in the referring thermal node with no

thermal storages considered. The renewable energy sources

are simulated under a direct marketing regime, obtaining the

revenues from the spot market. In the second step, the CHP

are included in the VPP and optimized with a power-operated

mode (Investigation 2). Subsequently, the VPP is again in-

creased by also including the renewable energy sources in the

VPP and the combined optimization so that all generation

units are participating in the VPP and covering the demand

cost-efficient (Investigation 3). In Investigation 4, additionally

DSM is included in the portfolio, leading to an increased de-

gree of flexibility. In order to show the potential for additional

revenues of the combined market participation, the same in-

vestigations are conducted including the tertiary reserve mar-

ket (Investigations 5 8). Obviously reserve marketing is not possible for the units not included in the VPP in each stage of

the simulation. Finally, in Investigation 9, the additional value

of including capacity fees of 28.9 /kW in the objective func-tion of the optimization is evaluated [10].

C. Results

In order to determine the additional value of VPPs as well

as of combined participation in different markets, the total

costs for covering the given load are evaluated. For reasons of

comparability, the costs of all units of the portfolio are consid-

ered rather than only the costs of the units included in the

optimization of the VPP. The resulting costs for Investiga-

tions 1 - 4 are shown in Fig. 8. In the first investigation, not

including any of the distributed units in the VPP, total costs

for load coverage of 40.84 million Euros per year (Mio. /a) are calculated. It can be observed that the costs for generation

are the main component of the total costs. In Investigation 2

the advantage of a power-operated mode of CHP including

thermal storages becomes obvious, as the total costs can be

decreased by 0.34 Mio. /a. The cost reduction is mainly driv-en by an increased operation of the CHP resulting in higher

CHP premiums and reduced day ahead market costs, whereas

the generation costs rise slightly.

Fig. 8: Resulting cost components in Investigations 1 - 4

Integration of renewable energy sources in sole day ahead

marketing has no significant impact on the total costs. This is

obvious, as direct marketing of the renewable energy sources

is considered. Thus, the day ahead market decouples the dif-

ferent system components, as no system-coupling constraints

can be observed. Hence, the integration of the renewable units

does not lead to a reduction in the overall costs, but different

operation points of the plants can be observed, resulting in a

changed cost structure with higher participation in the day

ahead market. The increased flexibility of the load, provided

by a consideration of additional DSM, leads to an additional

cost reduction of approximately 1 Mio. /a. Thus, the total cost reduction of integrating the distributed units in the portfo-

lio as a VPP amounts to 1.4 Mio. /a or 3.5 %/a, respectively. The total costs resulting from the simulation of additional

participation in the tertiary reserve market for the VPP are

pictured in Fig. 9, with the total costs of Investigation 1 in

comparison. The positive effect of additional reserve market

participation is obvious even in the case of single optimiza-

tion, in which none of the additional distributed units can

participate in the market for system reserve.

Fig. 9: Resulting cost components in Investigations 5 8

Two major effects can be observed. At first, it can be

shown that all stages of integration of distributed units now

lead to reduced total costs. Moreover, the integration of re-

newable energy sources has an impact, since the tertiary re-

serve market provides a system coupling and the units can

give a contribution to the total portfolio. In addition, the gen-

eration costs are increased, mainly due to participation in

negative reserve markets, but hardly any energy has to be

bought from the day ahead market thus resulting in negative

cost components (equaling earnings) in Investigations 5 8. The total savings amount to 2.5 Mio. /a or 6.1 %/a in Investi-gation 8 compared to the base case of Investigation 1. Hence,

the VPP proves to be worthwhile in cases of combined spot

and reserve market participation.

Fig. 10: Resulting cost components in Investigations 8 - 9

Investigation 9 is conducted in order to show the benefits

arising from the consideration of capacity fees in the optimiza-

tion. Apart from this fee, Investigation 9 resembles Investiga-

tion 8. In Fig 10 the resulting costs are shown in comparison

-5

0

5

10

Investigation 1 Investigation 2 Investigation 3 Investigation 4

Generation Costs Day Ahead Market

CHP Premium Total Costs

Mio.

a

Tota

l C

ost

s

40

45

-5

0

5

10

Investigation 1 Investigation 5 Investigation 6 Investigation 7 Investigation 8

Generation Costs Day Ahead Market CHP Premium

Tertiary Reserve Total Costs

Mio.

a

Tota

l C

ost

s

40

45

-5

0

5

10

15

Investigation 8 Investigation 9

Generation Costs Day Ahead Market CHP Premium

Tertiary Reserve Capacity Fee Total Costs

Mio.

a

Tota

l C

ost

s

50

40

6

to the values obtained in Investigation 8. For reasons of com-

parability, the capacity fee has been calculated ex-post for

Investigation 8, thus not constituting part of the optimization

itself. The results show that the highest load peak occurring

can be reduced significantly from 219.2 MW to 141.6 MW by

including the referring fee into the optimization. Here, not

only the generation units but also DSM has a major contribu-

tion to the reduction. The savings over compensate the de-

creased earnings from CHP premium, day ahead market par-

ticipation and tertiary reserve marketing by far, leading to a

saving of 1.77 Mio. /a compared to Investigation 8 with ex post calculated capacity fees. Compared to Investiga-

tion 1, 9.1%/a can be saved.

V. CONCLUSION

Distributed generation will become a significant component

of energy systems in the future. This increase, in combination

with reducing feed-in tariffs, implies the need for new mar-

keting options of distributed units. In this paper, the aggrega-

tion of small generation and storage units as well as concepts

for DSM to one single entity, a VPP, have been proposed. For

marketing options, the participation in day ahead or system

reserve markets and the avoidance of load peaks could be

identified. In order to show the benefits of integrating a VPP

in the portfolio of a local utility company, an optimization

method for energy generation and trading planning has been

extended by the consideration of small generation units, stor-

ages, DSM and the referring marketing options.

Exemplary investigations show the additional benefit of

considering small generation and storage units as VPPs. Cost

reductions of up to 9.1%/a can be observed when applying all

of the proposed market participations. Of particular note is the

fact that the combination of different marketing options turns

out to be promising, whereas single spot marketing has only

minor effects on the total costs. Moreover, the change from

heat-operated to power-operated CHP units and the integration

of the load as an active part by means of DSM have a signifi-

cant impact on the costs. Hence, VPPs can contribute to an

efficient and sustainable future energy supply.

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VII. BIOGRAPHIES

Andreas Schfer was born in Darmstadt, Germany in

1983. He studied Electrical Engineering and Management at RWTH Aachen where he graduated in 2009 (Dipl.-

Wirt.-Ing.). He gained practical work experience in in-

ternships in Germany and Canada. Since November 2009 he is member of the academic staff of the Institute of

Power Systems and Power Economics at RWTH Aachen,

Germany. He is head of the research group Power Generation and Energy Trading. Andreas Schfer is graduate student mem-ber of the IEEE.

Albert Moser was born in Linz am Rhein, Germany. He received Dipl.-Ing. degree in electrical power engineering

and Ph.D. degree from RWTH Aachen University, in

1991 and 1995 respectively. From 1997 to 2000 he was product developer for TSO applications with Siemens AG

in Nuremberg, Germany, and Minneapolis, USA. From

2000 to 2009 he was head of business development and clearing & settlement at European Energy Exchange in

Leipzig, Germany. Since 2009 he is full professor and head of the Institute of Power Systems and Power Economics at RWTH Aachen University. Prof.

Moser is member of the IEEE.