Modeling Market Power by Natural Gas Producers and Its Impact on the Power System

IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 28, NO. 4, NOVEMBER 2013 3737

Modeling Market Power by Natural GasProducers and Its Impact on the Power System

Stephan Spiecker, Member, IEEE

Abstract—Strategic behavior by natural gas producers mayinfluence the operation as well as the development of an electricitysystem. Within this paper a computational game theoretic invest-ment model is presented which allows assessing market powerby natural gas producers and its effects on the electricity marketunder consideration of emission markets. The model is formulatedas a mixed complementarity problem. It uses typical time seg-ments to represent both seasonal load fluctuations on the naturalgas market and load fluctuations within a day on the electricitymarket. Investment is possible in natural gas production and LNGinfrastructure as well as power plants. A test case is presentedcovering three regions and simultaneously optimizing powerplant dispatch and utilization of transmission lines on the powermarket as well as supply, transport and storage on the natural gasmarket. We compute prices, production volumes and power plantutilization for two different market power specifications to showthe impact of oligopolistic market behavior.

Index Terms—Complementarity modeling, electricity system,energy economics, gaming, natural gas system.

NOMENCLATURE

Parameter:

Annualized fix cost.

Losses.

Cost.

Reservoir capacity.

Duration of time segment.

Start-up cost.

Emission.

Emission factor.

Input price.

Elasticity.

Hydro inflow.

Efficiency.

Capacity.

Availability.

Manuscript received June 17, 2012; revised January 04, 2013 and March 01,2013; acceptedApril 28, 2013. Date of publicationMay 22, 2013; date of currentversion October 17, 2013. Paper no. TPWRS-00682-2012.The author is with the Chair for Management Sciences and Energy Eco-

nomics, University of Duisburg-Essen, Duisburg, Germany (e-mail: [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TPWRS.2013.2262006

Inverse demand function.

Variables:

Demand.

Pumping volume.

Injection.

Withdrawal.

Electricity/gas production.

Flows.

Load gradient.

Capacity price.

Sales.

Price.

Superscripts and indices:

Consumer.

Trader.

.

Index fuel.

Electricity.

Index day.

Natural gas.

Index trader.

Hydro storage unit.

Index sector.

Liquefier gas.

Index load segment.

Natural gas peak production.

Index region.

Producer.

Index producer.

Pump storage unit.

LNG supply chain.

Grid based transmission.

Efficiency slope within technology group.

Regasifier gas.

Index season.

Gas storage unit.

Index year.

Index technology.

0885-8950 © 2013 IEEE

3738 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 28, NO. 4, NOVEMBER 2013

I. INTRODUCTION

T HE Europe wide expansion of renewable energies, the in-troduction of emission trading as well as deregulation of

the electricity and natural gas markets have profoundlymodifiedthe context for the energy industry in the EU and will influenceit in the future. At the same time, Europe remains dependenton a limited number of gas suppliers, the largest of those beingRussia. In this context, the purpose of this paper is to present anintegrated model of electricity markets, emission markets andnatural gas markets for analyzing interdependencies betweenthose. Strategic behavior is modeled explicitly with natural gasconsumers facing only a small number of producers. Invest-ments are modeled endogenously given the long-term scope ofthe analysis.The usage of models for analyzing various problems related

to the energy industry has a long history [1]. In general thesemodels can be differentiated by simulation models and opti-mization models which are in the focus of this paper. Specificfundamental optimization models for the electricity market areamong others described in [2] and [3]. These models are charac-terized by a detailed temporal and technical resolution of energysupply and demand, which allows an in-depth analysis of powerplant dispatch and price formation. Corresponding models forthe gas market are illustrated in [4]–[6]. Iterative linking be-tween models of electricity markets and gas markets has beendescribed by [7]. References [8] and [9] introduced an integratednatural gas and electricity market model. An integrated modelwith focus on physical constraints can be found in [10].Due to their cost minimizing approach all these models as-

sume perfect markets. Oligopolistic market structures may bemodeled by applying game theory, and thus one can analyze theimpact of strategic behavior. An overview of different types ofstrategic interaction in equilibrium models can be found in [11].Thereby Cournot Nash equilibria as applied subsequently in thispaper and Supply Function Equilibria are mainly used. A com-parison of both categories can be found in [12]. In the literature,oligopoly models for the electricity market can be found in [13],as well as [14]. One of the first models for the European gasmarket regarding oligopolistic market structures was developedby [15] and was further developed in [16]. In these models pro-ducers/suppliers are forming an oligopoly, each with own costfunctions. The model equilibrium (Nash-Cournot-equilibrium)is obtained by considering production costs, transportation costsand demand elasticity as well as the players’ market behavior.Currently, there are further models, often with a higher degreeof detail especially in operational aspects [17].Besides problems of discretization and chronological succes-

sion of investment decisions, examples for endogenous invest-ment in mixed complementarity problem (MCP) models canbe found in [18], [19] and [20]. Reference [18] use a recursiveapproach with myopic expectations while [20] consider an in-tertemporal approach over the whole lifetime with perfect fore-sight.Existing models (cf. [18], [21]) are however not able to de-

scribe interactions between natural gas, emission and electricitymarkets comprehensively, having—in the case of electricity

markets—a given fuel and—in the case of gas markets—agiven gas demand by the power plant sector. The model usedin this paper therefore builds on a short term model of aninterconnected natural gas and electricity market system [22].The original approach is expanded by modeling investment indifferent technologies. On the natural gas market gas producersinstead of traders exert market power. And on the electricitymarket start-up costs for power plants are considered. In addi-tion, reduction objectives are implemented and therefore

prices are modeled endogenously.Subsequent to the introduction, the applied model is de-

scribed in Section II. The description of the investigated testsystem in Section III is followed by model results. They arepresented in Section IV and their implications are discussed.The paper ends with brief conclusions on the presented model.A companion paper provides a large-scale study of the Euro-pean gas and power markets [23].

II. FORMULATION OF THE MODEL

A. Theoretical Background and Model Framework

For the quantification of economic effects of market powera computational game theoretic model is used. Assumingoligopolistic natural gas markets, the model determines marketoutcomes for gas and electricity markets in different regionsand different time steps (typical seasons and typical hours) from a Nash-Cournot oligopoly. Each gas producing playerchooses his strategy under the assumption that other playersfollow their optimal strategy in order to maximize their profit.The strategic variables are the sales of traders on the marketthrough which producers and their related traders can affectthe market price. The model is implemented in the GeneralAlgebraic Modeling System (GAMS) [24].The market structure is assumed as follows (cf. Fig. 1):

Natural gas producers decide on operation and investmentof production facilities and offer their product to their relatedtraders. Traders sell the gas at demand hubs where demandis aggregated to three different consuming sectors (resi-

dential, industrial and power sector) differing in their demandbehavior, notably with regard to their demand profile and theirwillingness to pay. Transport via pipeline and LNG betweenlocal markets is organized by TSOs and shippers who havethe possibility of further investments in LNG infrastructure.Storage operators optimize their business facing marketprices. Hence, storage injection and withdrawal occurs as afunction of economic incentives.The power sector is modeled explicitly for selected coun-

tries. Companies produce electricity with several powerplant technologies differing in fuel and technology and decideon investments. Fuel usage of natural gas fired power plantsis considered on the related regional natural gas market. Inaddition, an overall emission bound has impact on decisions.On the wholesale electricity market demand and supply arematched. Demand curves are aggregated for industrial andresidential demand each.Within this model, the optimization problem for each of these

players and the connecting market-clearing conditions are for-mulated as follows.

SPIECKER: MODELING MARKET POWER BY NATURAL GAS PRODUCERS AND ITS IMPACT ON THE POWER SYSTEM 3739

Fig. 1. Market structure.

B. Natural Gas Model

1) Natural Gas Producer: Each producer located in re-gion chooses production volumes and sells them to his re-lated trader to maximize his profits (1). Production costsare divided in base and more expensive peak production

in line with [15]. The peak plateau stands for 10% oftotal production capacity (2) while base capacities contain90% of total production capacity (3) and their costs are approx-imated by a logarithmic function.1 Sales in each season may notexceed aggregated base and peak production (4).

(1)

(2)

(3)

(4)

2) Natural Gas Trader: Traders work as an agent of theirrespective producer and in the competitive case they optimizetheir profits by supplying natural gas markets earning whole-sale price . On the other hand they have procurement costsfrom producer f and transport cost via pipeline ,respectively, LNG (5).

1Costs are described by

with , and being country specific parameters of this function, Thecosts of the peak production are specified by a constant cost parameter(cf. [15]). This split is done to improve convergence of the model solution[cf. [18]]. In addition, the logarithmic function does not have a solution at fullutilization which is a further reason for using this plateau.

In the analyzed setting, dominating natural gas producersexert market power via their related trader according to thechosen Nash-Cournot oligopoly model. The extend of marketpower is given by the parameter .means no market power and means that the traderis a full Cournot player. Parameter settings between these

pure theoretical concepts of perfect competition and Cournotbehavior are possible (cf. [17]).The derived KKT conditions show that market price and

marginal cost are equal for traders without market power. Forthe oligopolistic case, marginal costs equal marginal revenues(32). The difference between these two cases is described bythe mark-up . The mark-updepends on the slope of the inverse demand function andsales of the respective trader. The inverse demand functionconsiders final demand of consumers as well as storageinjections and withdrawals . The mark-up shows theimpact of additional output on profitability of inframarginaloutput. With additional output market price decreases andalong with that the profitability of all other output [25]. Thedemand curve is defined as a linear function using an originalprice-demand-point and demand elasticity atthat point.

(5)

(6)

In addition, inflows and outflows at each market place haveto be balanced for each trader at every point in time (6). In-flows are purchases from producers and imports via

pipeline and LNG . Outflows are sales to the

market and exports via pipeline as well asLNG . For transport between two regions lossesare considered depending on technology and distance.3) Natural Gas TSO and Shipper: Pricing of gas transporta-

tion is a crucial point. In Europe, governmental regulations fornetwork charges of gas and electricity transportation grids existdue to the monopolistic character of the grid. In Germany andseveral other countries grid fees are mostly based on full costs,which are equally allocated towards all periods and users. Thus,it is assumed that transportation via pipeline is priced efficientlybased on some coherent regulation. The price of transport thenequals long run marginal costs and only in the case of restrictedcapacity a congestion premium is introduced to clear themarket.


Natural gas transport is differentiated between transport viapipeline and transport via LNG . For pipeline trans-port (7) this congestion premium is tied to pipeline ca-

pacity (8), for LNG transport (9) the congestion premiumsand are tied to liquefaction capacity (10) and re-

gasification capacity (11).

(7)

(8)

(9)

(10)

(11)

4) Natural Gas Storage: Storage facilities are optimizedagainst the wholesale market (12). During a storage period,injections and withdrawal have to be balanced (13)and filling levels must not exceed storage capacity . Forcomputational reasons we assume as a simplification that totalinflows during a storage season must not exceed capacity (14).This simplification is possible because the focus is on yearlystorage together with only a small amount of typical timesegments which makes short term storage impossible.

(12)

(13)

(14)

5) Natural Gas Market Clearing: The market clearing con-dition connects supply and demand on each market place andfor every season (18). On the supply side are sales from traders

and storage withdrawal from storage facilities. On the de-mand side are storage injection into storage facilities anddemand . Demand is divided in different sectors withdifferent elasticity and depends on the wholesale price .

(15)

C. Electricity Model

1) Electricity Producer: Electricity production is repre-sented by different producers . Electricity producers choosetheir output to maximize their profit (16). See (16)–(18) atthe bottom of the page. Power plants of a single producer areaggregated to technology classes depending on fuel type andtechnical properties. For power production, fuel cost, costand other variable cost are relevant. Fuel costs are derived fromthe fuel price and power plant efficiency . Therebywe assume that the most efficient power plants of a technologyclass are used with priority. Changes in efficiency within atechnology class are described by a linear approximation.Via the slope parameter the average efficiency withina technology class depends on current production . Asimilar approach is chosen for cost where a fuel related

emission factor is used in addition. Costs forpumping are considered for the case of pump storage.In addition, start-up costs are considered (cf. [26]). Variablestart-up costs consist of fuel consumption multiplied bycosts for fuel and emissions as well as other variable costs

and are weighted with the production gradient.

As an additional restriction, production in a load segment hasto be lower than available capacity (17). Availability de-pends in general on technical characteristics of the power plant,but additionally on wind speed for wind power plants, solar ra-diation for photovoltaic cells and water inflow for run of riverpower plants. Available capacity is also used for the calculationof the load gradient (18). The production gradient de-scribes the relative change in capacity online and is used to ap-proximate start-up costs. It is limited to positive values [26]. Ad-ditional constraints for the operation of different hydro powerplant technologies are implemented as in [22].

(16)

(17)

(18)


2) Emission Market: Future -emission targetsare provided by the EU. If available -emission certificatesform a binding restriction, the price for equals the mar-ginal abatement costs (19). emissions depend on the outputof a power plant group , the average efficiency of thepower plants within a power plant group and the fuel dependentemission factor . Emissions per time segment are weightedwith the duration of the time segment.

(19)

3) Power TSO: In the European electricity system TSOs areregulated. Therefore it is assumed that transport is priced effi-ciently as described previously for gas TSOs considering trans-port capacities .

(20)

(21)

4) Power Market Clearing: The power market clearing con-dition equilibrates supply and demand in every region (22). Onthe one hand there are productions from different pro-

ducers and technology groups as well as imports . Onthe other hand are demands in different sectors and re-

gions, exports and energy used for pump storage powerplants .

(22)

D. Investment

In this paper we consider endogenous investment in produc-tion, both for natural gas and electricity as well as transport byLNG. Investments are determined in a dynamic-recursive ap-proach and are possible in every modeled year. This myopicapproach accommodates the non-predictability of future devel-opments and reflects the uncertainty surrounding investment de-cisions.E.g., for electricity producers, the objective function (16)

is extended by including investment costs for addi-tional power plant capacity . Simultaneously the newcapacity is included in the capacity restriction (23).

(23)For investment decisions annualized investment and other fix

costs are compared with the corresponding congestion or ca-pacity rents. If the marginal value exceeds the annualized fixcost , an investment is profitable [27]. Investments arerealized as long as the congestion value of a facility equals in-vestment costs (24).

(24)

A similar procedure is chosen for the other facilities includingadditional upper bounds reflecting detailed political, technical,geographical and reserve restrictions. For natural gas produc-tion, a production profile describing the output of natural gaswells over time is used. Typical life times of facilities are neededfor calculation of annualized costs as well as available capacity.

E. Mixed Complementarity Problem

Basically, the optimization problem at hand is a Cournot-Nash equilibrium with further restrictions. According to [28],a Nash equilibrium where each player solves a convex programcan be formulated as a variational inequality (VI). Under cer-tain assumptions this VI is equivalent to a mixed complemen-tarity problem (MCP) [29]. Hence, in our case the profit maxi-mization functions of all agents are concave and smooth. Con-vexity of natural gas producers’ optimization function is explic-itly shown by [30]. In addition all side constraints are convexand smooth. Thus all feasible regions of the constrained opti-mization problem are specified by convex inequalities and affineequality conditions. For such problems, Karush-Kuhn-Tucker(KKT) conditions are necessary and sufficient for optimal solu-tions under the above assumptions [31]. However, such a solu-tion is not necessarily unique. Especially without strictly mono-tone increasing cost functions several optima might exist [32]and intertemporal restrictions as they are used for storages makeit even more unlikely to have a unique solution.In the following the KKTs are summarized which represent

the MCP implemented in GAMS (cf. [33]).:Natural gas model1) Natural gas producer:

(25)

(26)

(27)

(28)

(29)

(30)

2) Natural gas trader:

(31)

(32)

(33)

(34)


(35)

3) Natural gas shipper:

(36)

(37)

(38)

(39)

(40)

(41)

4) Natural gas TSO:

(42)

(43)

(44)

5) Natural gas storage:

(45)

(46)

(47)

(48)

6) Natural gas market clearing:

(49)

Electricity model1) Electricity producer:

(50)

(51)

(52)

(53)

(54)

(55)

2) emission market:

(56)

3) Power TSO:

(57)

(58)

4) Power market clearing:

(59)

III. TEST SYSTEM

The objective of this section is to show the interest of the pro-posed methodology to analyze the impact of strategic behaviorby natural gas producers on electricity markets.

A. Temporal Scope

For the sake of clarity the model is limited to four time steps.For the natural gas market two seasons are considered whichreflect high demand in the winter half year and lower demandin the summer half year. For the electricity market these sea-sons are again subdivided into two load segments which repre-sent peak and off-peak electricity demand according to tradedproducts at the power exchange. In total there are 5640 off-peakhours and 3120 peak hours.

B. Geographical Scope and Data

The illustrative example is as depicted in Fig. 1 and con-sists of three regions. A dominant natural gas player is placedin region 1, smaller players can be found in region 2 and 3 (cf.Table I). In reality, region 1 might correspond to a gas exportingcountry like Algeria or Russia while the remaining regions canbe compared to countries like Germany or France which arehighly dependent on natural gas imports. In region 2 and 3natural gas markets as well as electricity markets exist wheresupply and demand are matched. Exchange between markets is


TABLE IGAS PRODUCTION DATA, LNG, AND STORAGE CAPACITIES

TABLE IIEXISTING GENERATION SYSTEM

TABLE IIITRANSPORT INFRASTRUCTURE FOR NATURAL GAS AND ELECTRICITY

limited by terminal and pipeline capacity as well as transmis-sion capacity, respectively (Table III).For electricity markets the generation system is modeled in

detail according to Table II. Players on the electricity market canbe aggregated to one single player per country assuming marketpower only for natural gas producers. Both markets differ intheir structure but have significant shares of renewable ener-gies. Region 2 is dominated by fossil fuels while large nuclearpower plant capacities exist in region 3. Investments in the styl-ized model are possible in different base and peak technologies(Table V).Demand on both markets is price sensitive. To represent

demand behavior elasticity, prices and volumes are needed

TABLE IVFUEL PRICE ASSUMPTIONS AND INTENSITY

TABLE VNEW TECHNOLOGIES

TABLE VIMONTHLY GAS DEMAND AND ELASTICITY

TABLE VIIHOURLY ELECTRICITY DEMAND [GW] AND ELASTICITY

(Tables VI and VII). For this long term investment modelwe choose long term demand elasticities. Elasticity values fordifferent consumer groups of electricity and natural gas marketsare assumed according to [34] and [35]. The emission marketis considered with an upper bound of 300 Gt, fuel prices areassumed as given in Table IV together with their relatedintensity.

IV. RESULTS

Two different specifications for market power are analyzed.A Cournot oligopoly of natural gas producers on the one hand(olig) and pure competition on the other hand (comp) span thewhole range of market power. This is done by setting the marketpower factor to one and zero, respectively.Interdependencies between the analyzed markets and the im-

pact of strategic behavior by natural gas producers are analyzedin the following. Implications for natural gas markets are pre-sented first. Then interactions with regional electricity marketsare considered in more detail.


TABLE VIIIANNUAL NATURAL GAS PRODUCTION, INVESTMENTS, AND PROFITS

A. Natural Gas Market

The outcome of the model is highly influenced by the biddingbehavior of natural gas producers. In the competitive case, nat-ural gas producers bid with their marginal cost on the markets.That means that existing capacities have to cover their short-runmarginal costs while new capacities have to cover their annual-ized capital costs and fix costs in addition. In the oligopolisticcase a mark-up is added to the respective bids of the competi-tive case which depends on the price responsiveness of the de-mand curve as well as the market share of the player. As a re-sult supplied volumes are lower. This can be seen in the results(Table VIII). Production in the competitive case is considerablyhigher than in the oligopoly case.In the competitive case production in region 1 reaches the

capacity bounds and under the prevailing limited transport ca-pacities and resulting congestion rents investments in new pro-duction capacities are profitable. This leads to low profits be-cause capacities are automatically added if profits are sufficientto cover investment costs. This behavior is limited by case spe-cific restrictions. Transport capacities between regions are a bot-tleneck and avoid unlimited natural gas exchange.Congestions caused by these restrictions enable higher profits

for natural gas producer. Here, profits are defined as revenues onthe wholesale market minus production costs and (if applicable)transportation costs. Transportation costs include the assumedtariffs as well as congestion rents if full utilization of the trans-port capacity is reached.In the oligopoly case, prices are increasing (Table IX) because

strategic players set a price which includes a mark-up in addi-tion to marginal costs as described before. Hence, global nat-ural gas demand is declining and overall natural gas productionis decreasing in line with this development. This overall trendholds for region 1 while production in region 2 and region 3 isstrongly increasing. The rising output comes along with a boostof capacity investments.In summary the market share of large strategic players is re-

duced in line with the Cournot model when they act strategi-cally and prices are increasing. On the other side small playersincrease their output in order to benefit from a higher price level.Table VIII clearly shows the profit gain which is caused bystrategic behavior. Region 1 benefits most with nearly quadru-pled profits. But also minor players can boost their profits.In this context it has to be considered that all producers have

access to the market in region 3 while only producers of region1 and region 2 have access to the market of region 2. This ac-cess restriction exists because of missing transportation capaci-ties from region 3 to region 2. As a result, the respective marketshare of producer 1 and 2 in region 2 is higher which leads

TABLE IXPRICES FOR DIFFERENT CASES

TABLE XRESULTING ANNUAL DEMAND

to a higher strategic mark-up. The difference in the strategicmark-up even exceeds transportation costs. Hence, price differ-ence between region 2 and region 3 is inverted and region 3 hasthe lower price level. Nevertheless, natural gas transports fromregion 2 to region 3 still occur in both seasons.Changes in demand are quite different for particular demand

sectors due to the related elasticity assumptions (Table X).Changes are more obvious in the residential sector compared tothe industrial sector with lower elasticity values. Consumptiondata for the power sector indicate a strong price responsivenessespecially for region 3, but more detail is given on interactionswith the power sector in the following section.

B. Electricity Markets

In the following section the effects of different natural gasprices on the electricity market and its interaction with the emis-sion market are analyzed.In general, an increase of natural gas prices due to strategic

behavior leads to a decline of production from natural gas firedpower plants (Table XI).One might expect that with an increase of natural gas prices

other technologies become more advantageous. But this is onlypartly the case. If production of natural gas fired power plantsis reduced this has to be compensated by other technologies. Ifthey are fired with fossil fuels this results in higher emis-sions because of higher fuel related intensities (Table V)


TABLE XIANNUAL ELECTRICITY PRODUCTION FOR DIFFERENT SCENARIOS AND

REGIONS [TWh]

and the system wide price increases (Table IX). Thus,power plants with intensive fuel consumption are underfurther stress. Finally, a small increase in production of oil firedpower plants can be observed. Especially in region 2 natural gasfired power production is replaced by coal power plants. Thus,production of coal fired power plants in region 2 with a higher

intensity compared to oil is decreased in spite of increasednatural gas prices because of the before mentioned impact on

prices. The utility of existing lignite power plants in re-gion 2 remains unchanged due to low fuel prices. priceshave to become extraordinary high before their utilization is in-fluenced.In both markets existing capacities are not sufficient to cover

demand. Hence, investments in new capacities are necessary(Table XIII). However, investments in lignite capacities are lim-ited to region 2 and cannot occur in region 3. While naturalgas combined cycle power plants are dominating the invest-ment decision of region 3 in the competitive case this changesin the oligopolistic case. Here the effect of increased natural gasprices dominates the effect of increasing prices. Hence,coal power plants are economically efficient. For region 2, in-vestments in lignite power plants are stable as long as utilizationis sufficient.In both cases nuclear and lignite are base technologies

(Table XII). They are followed by new combined cycle powerplants which are placed on the left side of existing coal powerplants in the merit order. Other gas fired power plants haveeven less full load hours and oil fired power plants represent atypical peak technology.The increase of natural gas prices as well as the increase of theprice leads to higher electricity prices (Table IX). Thereby

the price difference between region 2 and region 3 has oppositesign in the comp and the olig case in the off-peak segment ofseason 2. In contrast to the natural gas market the electricityexchange is inverted together with the reversal of the electricityprice differential because markets are organized competitively.The different structure of the power plant systems has only

limited influence on the price level because price levels aremainly set by similar technologies in this stylized example.Withhigher electricity prices for the oligopoly case electricity de-mand is reduced. That decreases the price level and stimulates

TABLE XIIUTILIZATION FOR DIFFERENT TECHNOLOGIES [FULL LOAD HOURS]

TABLE XIIICAPACITY INVESTMENT FOR DIFFERENT SCENARIOS [GW]

demand in turn. This illustrates the interdependencies betweenprices and demand on the way to an overall equilibrium.

V. CONCLUSION

This paper presents an oligopolistic investment model for theevaluation of market power by natural gas producers and its im-pact on a power market with competitive structures. The impactof emission markets is considered in addition.In a stylized test case the interdependencies between different

markets and players are depicted. Especially the strategic be-havior of selected players has far reaching effect not only ontheir own market but also on connected markets. That is illus-trated by the reversal of price differentials due to changed be-havior. But also the complexity between decisions on the elec-tricity market and responses from the emission market has beenhighlighted.The proposed approach has a broad scope of applications that

might be of interest to system and market operators as well asregulators and economists. Further model runs have shown thata model extension up to 45 regions and 42 typical load segmentsfor the electricity market and three seasons for the natural gasmarket is possible [23]. Here it has to be considered that thecomputational burden is increasing overproportionally with anincrease of regions and time steps and that especially intertem-poral and interregional constraints stress the system.


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Stephan Spiecker (M’11) studied business administration at the University ofDuisburg-Essen, Germany and at the University of Skövde, Sweden. He re-ceived the Diploma degree in 2008. Then, he was a Ph.D. student at the chairfor management sciences and energy economics at the University of Duisburg-Essen.Currently, he works as a market analyst. His research interest is in the mod-

eling of energy markets.

Documents

Modeling Market Power by Natural Gas Producers and Its Impact on the Power System