dispatch optimization and economic evaluation of distributed generation in a virtual power plant

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

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  • 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

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    Losses

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    Time

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

    Haushalte Gewerbe Industrie

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    Households IndustryCommerce

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    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.

    VI. REFERENCES

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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.