Pool Strategy of a Price-Maker Wind Power Producer

Zugno, Marco; Morales, Juan M.; Pinson, Pierre; Madsen, Henrik

doi:10.1109/tpwrs.2013.2252633

Cited by 143 publications

(82 citation statements)

References 20 publications

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“…Neither [22] nor [23] considered risk management that is important for optimization involving uncertainties. Furthermore, neither [22] nor [23] considered the uncertainty of the bidding strategies of other strategic conventional power producers in the 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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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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“…Hence, in many of the previous studies, network effects which may be of less importance, have not been explicitly included (e.g., in the European context as discussed in [21] and [22]). Other cases without modeling network effects include short-term models [23,24] and longterm models [10]. In addition, regardless of the network, the overall average electricity price has a negative relationship with the wind penetration [10].…”

Section: Model Assumptionsmentioning

confidence: 99%

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Zhan

Zheng

Wang

et al. 2017

IEEE Trans. Power Syst.

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Abstract-Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets' planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage, decisiondependent stochastic optimization model for long-term, largescale generation expansion planning where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming (MILP) model. The wind penetration, investment decisions, and the optimality of the decisiondependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.

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“…However, as the cost of wind power production is low and the competitiveness of wind power increases, wind power producers are forced to participate in the electricity markets under full competition and following the same rules as conventional producers [3]. Under this context, [20] considers that wind power producers strategically offer their power in the balancing market. It is anticipated that wind producer acts as a price-taker in DA market, due to the large volume of traded energy.…”

Section: B Literature Review and Contributionsmentioning

confidence: 99%

“…It is anticipated that wind producer acts as a price-taker in DA market, due to the large volume of traded energy. In addition, [20] investigates how the shape of the forecast distribution impacts the offering strategy of producer. The problem of a price-maker wind power producer in DA market, being a deviator in the balancing market, is addressed in [21].…”

Section: B Literature Review and Contributionsmentioning

confidence: 99%

Impact of Public Aggregate Wind Forecasts on Electricity Market Outcomes

Exizidis

Kazempour

Grève

et al. 2017

IEEE Trans. Sustain. Energy

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Abstract-Following a call to foster a transparent and more competitive market, member states of the European transmission system operator are required to publish, among other information, aggregate wind power forecasts. The publication of the latter information is expected to benefit market participants by offering better knowledge of the market operation, leading subsequently to a more competitive energy market. Driven by the above regulation, we consider an equilibrium study to address how public information of aggregate wind power forecasts can potentially affect market results, social welfare as well as the profits of participating power producers. We investigate, therefore, a joint day-ahead energy and reserve auction, where producers offer their conventional power strategically based on a complementarity approach and their wind power at generation cost based on a forecast. In parallel, an iterative game-theoretic approach (diagonalization) is incorporated in order to investigate the existence of an equilibrium for various values of aggregate forecast. As anticipated, variations in public forecasts will affect market results and, more precisely, under-forecasts can mislead power producers to make decisions that favor social welfare, while over-forecasts will cause the opposite effect. Furthermore, energy and reserve market prices can also be affected by deviations in aggregate wind forecasts altering, inevitably, the profits of all power producers.

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Pool Strategy of a Price-Maker Wind Power Producer

Cited by 143 publications

References 20 publications

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

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

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Impact of Public Aggregate Wind Forecasts on Electricity Market Outcomes

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