1

Abstract -- In this paper the authors propose a market

strategy of a microgrid incorporating a virtual power plant. For exemplification, a configuration consisting of different types of distributed generators and a lumped load are considered. The aim of the virtual power plant is to maximize the profit by minimization of the total cost involved for electrical energy generation by the distributed generators that are part of the virtual power plant.

Index Terms--virtual power plant, distributed generation, smart grids

I. INTRODUCTION N the last two decades the power systems have been undergoing major changes. The first major change was the decentralization of the generation, transmission and

distribution sectors, and introduction of the power market, respectively. The power market was created aiming to increase the competition among all involved entities in directly commercializing electrical energy, and finally reduce the electrical energy price. The next changes have been focusing on re-innovating renewable energy sources and therefore distributed generation, which are now the priority as a measure for protecting the environment. This major change have already entered into force and aims to stimulate for smart solutions needed to help overcoming problems which push the power systems to their limits and lead to a better comfort to the network users [1]. However, any new change in the power system structure may be challenging for the system operators.

II. IMPACT OF THE SMALL SIZE SOURCES ON THE POWER GRID OPERATION

The expected increase in the number of units and the installed power in the distributed generators (DG) will affect the power system operation following the change from a radial configuration, supplied from only one source (the higher voltage network), to a configuration with several sources. Furthermore, the lack in monitoring possibilities of the distributed generators would introduce higher uncertainty in the active powers balancing process for frequency control performed by the system operator and the need for more power reserves as ancillary service, which may lead to increased price of the electrical energy [2]. On this line, the network operators must take into account the intermittency of some renewable energy sources. Because of their small size,

The authors are with the Department of Electrical Power Systems, Power Engineering Faculty, from University “Politehnica” of Bucharest, Bucharest, Romania (e-mail: lucian@ieee.org)

the distributed generators cannot participate individually to the power market.

Various solutions for designing a virtual power plant are present in literature. The authors of [3] propose a solution for aggregation of distributed generators in order to reduce the imbalance risk in the market, by the means of an existing methodology based on stochastic programming. The authors of [4] and [5] propose a bidding strategy on the electricity market, as a non-equilibrium model based on the deterministic price-based unit commitment which takes the supply-demand balancing constraint and security constraints of VPP itself into account. Various other strategies are proposed in [6, 7, 8]

III. THE VIRTUAL POWER PLANT CONCEPT

A. Technical issues The idea of a virtual power plant is today possible due to

the technological progress in the telecommunications, automation, metering and computation sectors [9, 10]. The backbone of the virtual power plant is the communication infrastructure, which allow remote control of the distributed generators according to a predefined methodology [11].

As illustrated in Figure 1, the distributed generators are connected to the control system located at the local operator through the communication infrastructure. Using forecast tools, optimization tools, information from the power market, etc., the local operator decide the strategy for participation to the power market.

Hydro

μTurbine

Fuel cells

Bio-CHP

Storage

PV systemsWind-generation

Controllable loads

Diesel

LOCALCONTROLLER

Power Market

Hydro

μTurbine

Fuel cells

Bio-CHP

Storage

PV systemsWind-generation

Controllable loads

Diesel

LOCALCONTROLLER

Power Market

Fig. 1. Aggregation of distributed generators.

The active powers balancing process is strongly related to the frequency control [12]. A VPP could, for instance, participate to the frequency control by provision of fast tertiary reserve, both for the upward regulation and downward

Coordination of Distributed Generators Through the Virtual Power Plant Concept

Lucian Toma, IEEE Member, Bogdan Otomega, Constantin Bulac, IEEE Member, Ion Tristiu, IEEE Member

I

2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Berlin

978-1-4673-2597-4/12/$31.00 ©2012 IEEE

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regulation, in the latter case also with the contribution from the controllable loads [1, 13].

B. Economical Virtual Power Plant The idea: Several generation entities are aggregated to form

a single entity able to behave on the power market similar to a classical power plant. Participation on the market as independent entity is restricted by a minimum available power. For instance, in Romania the minimum power quantity that may be tendered on the market is 10 MW. However, smaller values may encourage the use of small generation units in the power market activity.

Besides the technical issues, the algorithm governing the local controller can include an objective function, which aims minimization of the global costs. In terms of availability of the primary resource (water, wind, sun), the total generation cost and the electrical energy price on the power market, the local operator, with the help of the algorithm implemented in the local controller, can decide for using mainly the generation sources from his portfolio or can choose to cover the electrical energy demand from the main grid.

C. Example of Virtual Power Plant Installing DG units in the distribution network avoids

transmission of electrical energy over long distances, from the big power plants through the transmission network and the distribution network. Higher efficiency is also obtained by supporting the distributed generators to produce more power, as the conversion efficiency from one type of conventional energy to electrical energy is higher as compared to the classical power plants.

DieselEngine

Hydro powerplant

Wind powerplant

PV powerplant

Controllableload

Load

Load

MV

HV

Powersystem

Controllableload

DistributionNetwork

Gas Engine

Load

Fig. 2. Configuration of the virtual power plant.

In this paper it is assumed that all distributed generators are located in the same distribution network. Also, the Virtual Power Plant includes all distributed generators, i.e. one diesel engine unit, two gas engine units, one PV power plant, one wind power plant (consisting of 4 units) and one hydro unit. On the other hand, the VPP may include a controllable load,

which is eligible for disconnection as stimulation for demand response.

D. Market strategy The supplier, using the VPP facilities, may choose to

participate on the electricity market, if he is able to provide at least 2 MW using its DGs, or it can inject power into the distribution network based on an agreement with the distribution company, supplying the consumers from its portfolio.

The supplier can choose between using the DGs to normally provide electrical energy or/and to provide power reserves as ancillary service. While injection of electrical energy into the distribution network ensures a constant income for the DGs’ owners, provision of power reserves increases the risk in the market strategy. Power balancing in the distribution network is possible, independent of the frequency regulation performed by the system operator, but this issue is not the purpose of the present work.

The diesel engine and the gas engine can operate over long period at constant outputs as the liquid fuel can be stored for longer period operation and the gas can be supplied at constant pressure.

If appropriate water flow is available, the hydro-generator can operate also at any output value, as they are the most flexible generation units.

However, the PV systems are dependent on the sun, and the wind generators are dependent on the wind, which are both variable in time. For the shake of simplicity, we will consider that the fluctuations during a dispatching interval do not vary much, and the available energies are represented as energy profiles.

The power flowing through the link with the transmission system (distribution substation) is considered flexible, allowing both imports and exports to the distribution network, although only the power specified in the bilateral contract is scheduled to flow from the transmission network to the distribution network.

Assumes that the supplier has a bilateral contract for consumption with a generation entity located in the transmission network. The energy is absorbed from the transmission network through the HV/MV substation.

IV. THE OPTIMIZATION PROBLEM The objective function An objective function of the virtual power plant can be the

maximization of the benefits after participation in various market arrangements for selling electrical energy and power reserves, i.e.:

BenefitMAX where

Benefit Incomes Costs= − Incomes

The VPP can have incomes from participation on various power markets:

24 24 24 24

, , , , , ,1 1 1 1

DAM t DAM t BC t BC t L VPP t VPP g t regt t t t

Incomes E c E c E c R c−= = = =

= + + +∑ ∑ ∑ ∑ (1)

3

,DAM tE is the energy traded by the VPP on the Day-Ahead Market in the dispatching interval t, in kWh;

,DAM tc – the DAM clearing price in the dispatching interval t, in m.u./kWh;

,BC tE – the energy traded by the VPP through Bilateral Contracts in the dispatching interval t, in kWh;

,BC tc – the energy price negotiated on the bilateral contracts market, in m.u./kWh;

,L VPP tE − – the energy provided to the costumers that are part of the VPP, in kWh;

VPPc – the supply energy price for the costumers that are part of the VPP, in m.u./kWh;

,g tR – the total power reserve provided by the VPP as ancillary service for regulation services, in kWh;

regc – the power reserve price provided by the VPP as ancillary services for regulation services, in m.u./kWh/h;

When sending bids on the DAM, the VPP manager can only forecast the clearing price. But, after market clearing, once the DAM clearing prince is known, the VPP can perform the internal dispatching, in terms of the available capacity of all distributed generators and VPP loads so that to maximize its benefits. Expenses

The total costs necessary for all distributed generators from the VPP to provide the electrical energy traded through various power markets, during 24 dispatching intervals (one day), is:

24

,1

g tt

Costs C=

=∑ (2)

where: ,g tC is the total cost of the energy generated by all

generators in the dispatching interval t, in m.u., with

, , , , ,1

n

g t g i t g i i ti

C E c I=

=∑ (3)

, ,g i tE is the energy produced by the generator i, in the dispatching interval t, in kWh;

,g ic – the marginal costs of the generator i, in m.u./kWh;

,i tI – a binary variable denoting the operation state of the generator i in the dispatching interval t: “1” shows that the generator is on and “0” shows that the generator is off;

The maximization of the objective function is subjected to the following equality and inequality constraints:

a) The power reserve The total power reserve that was traded for the dispatching

interval t and that must be kept available at every instant of time is the sum of all reserves that can ne provided by the distributed generators, i.e.:

, , , ,1

n

g t g i t i ti

R R I=

=∑ (4)

where , ,g i tR is the power reserve ensured by distributed generator i for the dispatching interval t; b) The total load

The total load that must be supplied by the VPP, consisting on energy sold through the day-ahead market, energy sold by bilateral contracts and the energy sold for the VPP consumers, is: , , ,load DAM t BC t L VPP tE E E E −= + + (5)

Considering that the VPP is involved in ancillary services provision, the total power capacity that must be dispatched is:

, ,load r load L tE E R= + (6)

The distributed generators are too small to participate in the secondary frequency control. However, they can participate in the tertiary frequency control either with fast reserved. c) Capability constraints of generators

It is assumed that all distributed generators are capable to provide power reserve for both the upward and downward regulation, except for the wind power plants and solar power plants, of which generated power is subjected to fluctuations of the wind and light, respectively. However, it is not compulsory that a distributed generator will be dispatched by the VPP coordinator to provide power reserve as ancillary service. If a distributed generator i is not taken into account when building up the power reserve, the generated power is subject to the generation capability constraints, i.e.: , ,min , , , , ,maxg i g i t i t g iP P I P≤ ≤ (7)

However, if the distributed generator i is taken into account when building up the power reserve, the total amount of the generated power and the power reserve is subject to the generation capability constraints, i.e.: , ,min , , , , , , , ,maxg i g i t i t g i t i t g iP P I R I P≤ + ≤ (8)

However, the PV panels and wind turbine systems are used at their actual available output, so that the limits are fixed to the available output, i.e. min,i availP P= and max.i availP P= .

d) Capability constraints of the loads It is assumed that the consumers can participate in the

upward regulation by disconnecting a certain part of the total load that is part of the VPP. The load that can be disconnected can take any value between 0 and maximum load, i.e.: , ,0 L t L VPP tR E −≤ ≤ (9)

The total load consists of the energy exported

V. STUDY CASE A group of 6 types of distributed generators is aggregated to

form a virtual power plant. The minimum, maximum and the generation costs for each type of distributed generator are given in Table 1. Note that the generation costs are given only for simulation purposes and might not necessarily reflect real costs, as these can vary from one unit to another and from one country to another. The generation costs for the wind and PV units may be lower then the real ones because financial

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incentives may be considered for their real incomes. The minimum limit for the classical generators is not zero because, for instance, the diesel and gas units may be involved in a cogeneration contract and need to supply hot water in the neighborhood [3]. However, low loading of cogeneration units results in lower efficiency.

TABLE I. ACTIVE POWER LIMITS AND GENERATION COSTS

Pmin Pmax Cost kW kW m.u./kWh DG1 (Wind power plant) 0 300 4.1 DG2 (PV power plant) 0 400 8.0 DG3 (Diesel Unit) 50 4000 5.1 DG4 (Gas Engine Unit) 50 5000 4.9 DG5 (Gas Engine Unit) 50 2500 4.6 DG6 (Hydro Unit) 50 3000 3.0

m.u. – Monetary Unit The diesel engine, gas engine and micro-hydro power plant may operate for an output power between the minimum and maximum limits, while for the PV power plant and the wind power plants the operation limits are restricted to the available power. Renewable energy sources are considered prioritizable generation units and they are dispatched according to their available power. Under the classical electricity market conditions, no matter of the generation costs (where the capital costs are the most important), the renewable energy units must accept the market price. This is because these units generate energy when the primary energy is available. In our case, because these units are part of the virtual power plant, similar to the others units, which are be located in a microgrid, they are remunerated according to their costs. Let us consider the generation capacity of the 6 distributed generators of the VPP for a 24 hours window as shown in Figure 3. The generation capacities of the DG1 (wind power plant) and the DG2 (solar power plant) are restricted to the profile provided in Figure 3 due to the availability of the primary energy source, whereas the generation capacities of the other generators can take any value between the minimum and the maximum limits.

2 4 6 8 10 12 14 16 18 20 22 240

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Hour

Cap

acity

[kW

]

DG3DG4

DG6

DG5

DG1

DG2

Fig. 3. Generation capacity.

A constant active power of 1000 kW maintained available as ancillary service reserve for upward regulation is added to the hourly generation profile. It is assumed that the availability costs of the power reserve was traded for the amount of 1.0 m.u./kWh.

Figure 4 shows the energy profile traded through different arrangements, i.e. energy traded on the day-ahead market (E-DAM), energy traded by bilateral contracts (E-BC) and the energy sold to the consumers that are part of the VPP (E-VPP). The total energy (E-Total) is the sum of the above-mentioned energies. Note that the total produced energy as shown in Figure 4 does not include the power reserve maintained available for regulation.

2 4 6 8 10 12 14 16 18 20 22 24

2000

4000

6000

8000

10000

12000

14000

Hour

Ene

rgy

prov

ided

[kW

h/h]

E-Total

E-VPP

E-BC E-DAM

Fig. 4. Energy sold by various contracts.

In order to have an image of the differences between the generation capacity and the total generation and reserve, Figure 5 shows their profile for a 24 hours time window.

2 4 6 8 10 12 14 16 18 20 22 241.1

1.2

1.3

1.4

1.5

1.6

1.7x 10

4

Hour

Tota

l cap

acity

/gen

erat

ion

[kW

]

Capacity

Generation + Regulation reserve

Fig. 5. The total capacity vs. the total generation. It is easy to observe that, during the peak load, when also the solar energy is not available and the wind may not be so strong, the reserve remained between the available capacity and the total contracted energy is very small. This is a normal situation for a correct bidding strategy, in which the use of DGs is maximized as much as possible. Three price values are considered for the three type of contract for energy sell, as shown in Figure 6. The bilateral contracts and the energy sold to local load are considered at fixed price, while the price of energy traded on the day ahead market reflects the hourly competition.

2 4 6 8 10 12 14 16 18 20 22 241

2

3

4

5

6

7

8

Hour

Pric

e [m

.u./k

Wh]

c DAM

cVPP

cBC

Fig. 6. Energy prices.

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The objective of the VPP is to maximize the profit. Considering that the prices cannot be influenced on short term, the optimal commitment of the distributed generators is considered. Therefore, the aim is to minimize the generation costs.

Case A: No load disconnection procedure is considered We consider first that no load disconnection is considered for the optimization problem. The unit commitment obtained for the 24 dispatching intervals after applying the optimization problem, considering only the generation units, is presented in Figure 7.

2 4 6 8 10 12 14 16 18 20 22 240

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Hour

Gen

erat

ion

[kW

]

DG3

DG5

DG6

DG4

DG1

DG2

Fig. 7. Individual generation of the DGs. It can be seen that, as agreed, the DG1 (wind power plant) and the DG2 (solar power plant) are dispatched with their available power, while the others generators are dispatched in terms of their marginal costs. Therefore, DG5 and DG6 are dispatched at their maximum capacity, while DG3 is mostly maintained at low loadings because it has the highest generation costs. Figure 8 illustrates the Incomes, the Expenses as well as the Profit resulted for the 24 dispatching intervals. We can see that profit peaks occur during peaks of the DAM clearing price, i.e. during the morning and during the load peak.

2 4 6 8 10 12 14 16 18 20 22 24-1

0

1

2

3

4

5

6

7

8x 10

4

Incomes

Costs (Expenses)

Profit

Fig. 8. The VPP profit, in monetary units.

Case B: Loads are voluntarily disconnected On of the actual preoccupations under the power market and smart grid concepts is the volunteer participation of the consumers to energy trade process. Figure 9 shows the offers for load disconnection on the 24 hours. We consider that the consumers are remunerated with

1 m.u./kWh during off-peak hours and 1.5 m.u./kWh during the day period, involving the peak period.

2 4 6 8 10 12 14 16 18 20 22 240

100

200

300

400

500

600

700

800

900

1000

Hour

Load

dis

conn

ectio

n [k

W]

Fig. 9. Controllable load. Figure 10 shows the new optimal unit commitment. We can se that during 4 dispatching intervals, the DG3, which is the most expensive, operates at minimum capacity, and the DG4 is unloaded during the same period.

2 4 6 8 10 12 14 16 18 20 220

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Hour

Gen

erat

ion

[kW

] DG6

DG5

DG1 DG3

DG2

DG4

Fig. 10. Optimal unit commitment considering load disconnection. As shown in Figure 10, the VPP profit has increased as compared to Case A because the revenue from selling energy to the local load is less than the financial effort of the distributed generators DG3 and DG4 to produce energy.

2 4 6 8 10 12 14 16 18 20 22 240

1

2

3

4

5

6

7

8x 10

4

Incomes

Costs (Expenses)

Profit

Fig. 11. The VPP profit, in monetary units, considering load disconnection.

VI. CONCLUSIONS In a microgrid where the consumers may own distributed

generators, aggregation of all DG units for establishing the

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market strategy may minimize the total costs. From technical point of view, aggregated behavior of DG units can help the supplier to balance its own contracts (portfolio) achieving a balance between the generated powers and the load demand, avoiding penalties that may be caused by imbalances.

VII. ACKNOWLEDGEMENTS The work has been co-funded by the Sectoral Operational

Programme Human Resources Development 2007-2013 of the Romanian Ministry of Labour, Family and Social Protection through the Financial Agreement POSDRU/89/1.5/S/62557 and by the project no. PN-II-ID-PCE-2011-3-0693 developed through CNCS.

VIII. REFERENCES [1] L. Toma, “Management of ancillary services under power market

conditions”, PhD thesis, University “Politehnica” of Bucharest, 2010. [2] ANRE, “Commercial Code of Electrical Energy En-gross Market” (in

Romanian), 2004 [Online]. Available at www.anre.ro. [3] G. Foggia, B. Delinchant, N. Hadjsaid, F. Wurtz, “Optimization by

stochastic programming for the aggregation of a commercial virtual power plant”, XI-th International Workshop Optimization and Inverse Problems in Electromagnetism, Sofia, Bulgaria, 14-18 September 2010.

[4] E. Mashhour, S.M. Maghaddas-Tafreshi, “Bidding strategy of Virtual Power Plant for participating in energy and spinning reserve markets? Part I: Problem Formulation”, IEEE Transactions on Power Systems, Vol. 26, No. 2, pp. 949-956, May 2011.

[5] E. Mashhour, S.M. Maghaddas-Tafreshi, “Bidding strategy of Virtual Power Plant for participating in energy and spinning reserve markets? Part I: Numerical Analysis”, IEEE Transactions on Power Systems, Vol. 26, No. 2, pp. 957-964, May 2011.

[6] L. Toma, B. Otomega, I. Triştiu, “Market strategy of distributed generation through the Virtual Power Plant concept”, 13th International Conference on Optimization of Electrical and Electronic Equipment, OPTIM-2012, Brasov, Romania, 24-26 May 2012.

[7] L. Toma, L. Urluescu, M. Eremia, J.-M. Revaz, “Trading ancillary services for frequency regulation in competitive electricity markets”, IEEE Lausanne PowerTech Conference, Lausanne, Switzerland, 1-5 July, 2007.

[8] L. Toma, M. Eremia, C. Bulac, I. Triştiu, “Optimizing the costs of reactive power for the coordinated voltage control service”, Proceedings of 2011 IEEE Trondheim PowerTech, Trondheim, Norway, 19-23 June 2011.

[9] N. Hadjsaid, L. Le- Thanh, R. Caire, B. Raison, F. Blache, B. Ståhl, R. Gustavsson, “Integrated ICT framework for Distribution Network with Decentralized Energy Resources: Prototype, Design and Development”, 2010 IEEE General Meeting, Minneapolis, USA, 25-29 July 2010.

[10] N. Hatziargyriou, H. Asano, R. Iravani, C. Marnay, “Microgrids: An overview of ongoing research, development and demonstration project”, Power & Energy Magazine, July-August 2007.

[11] K.E. Bakari, W.L. Kling, “Virtual power plants: An answer to increasing distributed generation”, Proceedings of IEEE Innovative Smart Grid Technology, Gothenburg, Sweden, 11-13 October 2010.

[12] M. Sanduleac, M. Eremia, L. Toma, P. Borza, “Integrating the Electrical Vehicles in the Smart Grid through Unbundled Smart Metering and multi-objective Virtual Power Plants”, IEEE PES Innovative Smart Grid Technology Europe, ISGT-2011, Manchester, UK, 5-7 December 2011.

[13] L. Toma, L. Urluescu, M. Eremia, J.-M. Revaz, “Trading ancillary services for frequency regulation in competitive electricity markets”, Proceedings of 2007 IEEE Lausanne PowerTech Conference, Lausanne, Switzerland, 1-5 July, 2007

[14] L. Tao, M. Eremia, M. Shahidehpour, “Interdependency of Natural Gas Network and Power System Security”, IEEE Transactions on Power Systems, Vol. 23, No. 4, pp. 1817-1824, November 2008.

IX. BIOGRAPHIES Lucian Toma received the B.Sc. and Ph.D. degree in electrical power engineering from the University “Politehnica” of Bucharest in 2002 and 2010 respectively. Currently he is lecturer at University “Politehnica” of Bucharest,

Power Engineering Faculty, Department of Electrical Power Systems. His fields of interest include power market, ancillary services, power systems dynamics, computer modeling of power systems, steady-state and transient stability assessment. Bogdan Otomega received the B.Sc. degree in power engineering from the University “Politehnica” of Bucharest in 2002 and the Ph.D title from Universite de Liege in 2007. Currently he is lecturer at University “Politehnica” of Bucharest, Power Engineering Faculty, Department of Electrical Power Systems. Constantin Bulac graduated at the University “Politehnica” of Bucharest in 1982 and received the Ph.D. degree in electrical engineering from same university in 1998. He is Professor within the Department of Electrical Power Systems from University “Politehnica” of Bucharest. His research interests include stability of power systems, FACTS devices and artificial intelligence applications in power systems. Ion Tristiu received the B.Sc. and Ph.D. degree in electrical engineering from the Polytechnic Institute of Bucharest in 1990 and 1998 respectively. He is currently Associate Professor at the Department of Electrical Power Systems from University “Politehnica” of Bucharest. His fields of interest includes transmission and distribution of electrical energy, distribution networks reconfiguration and distributed generation.