Strategic gaming analysis for electric power systems: an MPEC approach

Hobbs, Benjamin F.; Metzler, Carolyn Burr; Pang, Jong-Shi

doi:10.1109/59.867153

Cited by 696 publications

(384 citation statements)

References 30 publications

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“…Following the methodology adopted in [2] and [3] for modeling the decision making problem of strategic producers and consumers respectively, the decision making problem of strategic ES is modeled through a bi-level optimization problem, which is formulated as:…”

Section: A Bi-level Optimization Modelmentioning

confidence: 99%

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Strategic capacity withholding by energy storage in electricity markets

Papadaskalopoulos

Moreira

et al. 2017

2017 IEEE Manchester PowerTech

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Abstract-Although previous work has demonstrated the ability of large energy storage (ES) units to exercise market power by withholding their capacity, it has adopted modeling approaches exhibiting certain limitations and has not analyzed the dependency of the extent of exercised market power on ES operating properties. In this paper, the decision making process of strategic ES is modeled through a bi-level optimization problem; the upper level determines the optimal extent of capacity withholding at different time periods, maximizing the ES profit, while the lower level represents endogenously the market clearing process. This problem is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC) and linearizing the latter through suitable techniques. Case studies on a test market quantitatively analyze the extent of capacity withholding and its impact on ES profit and social welfare for different scenarios regarding the power and energy capacity of ES.Index Terms-Electricity markets, energy storage, market power, mathematical program with equilibrium constraints. NOMENCLATURE A. Indices and SetsIndex of time periods running from 1 to Index of producers running from 1 to Index of generation blocks running from 1 to Index of demand blocks running from 1 to B. Parameters Length of market horizon (hours),

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Section: A Bi-level Optimization Modelmentioning

confidence: 99%

“…influence the electricity prices and increase their profits beyond the competitive equilibrium levels, through strategic bids and offers [1]. Previous work on imperfect electricity markets has mainly focused on optimizing market strategies of strategic electricity producers [2] and consumers [3].…”

Section: Introductionmentioning

confidence: 99%

Strategic capacity withholding by energy storage in electricity markets

Papadaskalopoulos

Moreira

et al. 2017

2017 IEEE Manchester PowerTech

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“…Thus, the original bilevel optimization problem is transformed to a single-level problem, which is a mathematical program with equilibrium constraints (MPEC) [13]. The MPEC approach has been widely used to obtain the optimal bidding strategies for conventional power producers [14]- [16]. Stochastic programming [17] provided a suitable tool to model the uncertainties faced by wind power producers in the short-term market.…”

Section: Introductionmentioning

confidence: 99%

Optimal bidding strategy of a strategic wind power producer in the short-term market

Dai

Wang

2016

2016 IEEE Power and Energy Society General Meeting (PESGM)

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Abstract-Wind energy is a clean and renewable energy source which is rapidly growing globally. As the penetration level of wind power grows, the system operators need to consider wind power producers as strategic producers whose bidding behaviors will have an impact on the locational marginal prices. This paper proposes a bilevel stochastic optimization model to obtain the optimal bidding strategy for a strategic wind power producer in the short-term electricity market. The upper level problem of the model maximizes the profit of the wind power producer, while the lower level problem represents the market clearing processes of both day-ahead and real-time markets. The uncertainties in the demand, the wind power production, and the bidding strategies of the strategic conventional power producers are represented by scenarios in the model. The conditional value at risk of the selected worst scenarios is included in the objective function for managing the risk due to uncertainties. Using the duality theory and Karush-Kuhn-Tucker condition, the bilevel model is transferred into a mixed-integer linear problem. Case studies are performed to show the effectiveness of the proposed model. Decision Variables: λ W D bjtOffer price of block b of the wind generating unit j in a period t in the day-ahead market. λ W R jtOffer price of the wind generating unit j in a period t in the real-time market. p W D bjtProduced power of block b of the wind generating unit j in a period t in the day-ahead market. P W R jtωRescheduled power of the wind generating unit j in a period t in the real-time market. p CD bitPower of block b produced by the conventional power producer i in a period t in the day-ahead market. P CR+ it /P CR− itIncreased/decreased power of the conventional power producer i in a period t in the real-time market. Voltage angle of bus m in a period t in the day-ahead/real-time market. ζ Auxiliary variable used to compute CVaR. η ω Auxiliary variable used to compute CVaR in a scenario. Random Variables: λ CD bitOffer price of block b of the conventional power producer i in a period t in the dayahead market

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“…Both are quite difficult to apply in a market with transmission constraints. One notable exception is the work of Hobbs et al (2000) who restrict themselves to linear supply functions. Most researchers therefore opt for some kind of Cournot market, while dropping some of the multi-good aspects of the actual market.…”

Section: Modeling Market Powermentioning

confidence: 99%

“…Such models are becoming more and more common in the simulation of the electricity markets, but often the timing of the game is reversed: generators bid in stage 1 and the network operator balances the network in stage 2. See for instance (Ehrenmann 2004;Gabriel and Leuthold 2010;Hobbs et al 2000 andNeuhoff et al 2005) In order to solve the numerical optimization problem, we relax the complementarity conditions of the second stage and use several starting values to be confident that we are close to the global optimum. For a comparison of different methods to solve MPEC problems see Fletcher and Leyffer (2002).…”

Section: Modeling Market Powermentioning

confidence: 99%

Cost Recovery in Congested Electricity Networks

Pepermans¹,

Willems

2010

Z Energiewirtsch

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Large scale investments in European electricity networks are foreseen in the next decade. Pricing the network at marginal cost will not be sufficient to pay for those investments as the network is a natural monopoly. This paper derives numerically the socially optimal transmission prices for cost recovery, taking into account that electricity networks are often congested, while allowing for market power in generation. The model is illustrated with a Stackelberg game for the Belgian electricity market.

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Strategic gaming analysis for electric power systems: an MPEC approach

Cited by 696 publications

References 30 publications

Strategic capacity withholding by energy storage in electricity markets

Strategic capacity withholding by energy storage in electricity markets

Optimal bidding strategy of a strategic wind power producer in the short-term market

Cost Recovery in Congested Electricity Networks

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