Scenario Reduction for Futures Market Trading in Electricity Markets

Morales, Juan M.; Pineda, Salvador; Conejo, Antonio J.; Carrión, Miguel

doi:10.1109/tpwrs.2009.2016072

Cited by 246 publications

(159 citation statements)

References 21 publications

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“…Then, the scenario reduction method [26] is applied to reduce the number of scenarios for the wind power and real-time demand. In order to determine the best number of scenarios for the case studies, the objective value of (3a) and the CVaR (when β = 0.1) of the wind power producer are calculated using the MILP model for different number of scenarios.…”

Section: Case Studies and Resultsmentioning

confidence: 99%

“…The method to generate a large number of scenarios for random variables and then reduce them to a sufficiently small number of scenarios has been explained in [26]. That method will be applied in this paper for scenario generation and reduction of the forecasted bidding prices of other selected strategic conventional power producers, real-time demand, and wind power production.…”

Section: Imentioning

confidence: 99%

“…That method will be applied in this paper for scenario generation and reduction of the forecasted bidding prices of other selected strategic conventional power producers, real-time demand, and wind power production. The correlation between real-time demand and wind power production is considered in the scenario generating process and then the fast-forward scenario reduction technique [26] is applied to reduce the scenarios to a sufficiently low number. If Ω L reduced scenarios are selected for the real-time demand and Ω W j reduced scenarios are selected for the power production of each wind generating unit j, the total number of scenarios in the real-time market…”

Section: Imentioning

confidence: 99%

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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: Case Studies and Resultsmentioning

confidence: 99%

Section: Imentioning

confidence: 99%

Section: Imentioning

confidence: 99%

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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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“…The purpose is to select a small set, Ω S , with the cardinality of N Ω S , from the original set, Ω J [13]. A reasonable trade off must be respected between the loss of the information and decreasing the computational burden [2].…”

Section: Scenario Based Decision Makingmentioning

confidence: 99%

Decision making under uncertainty in energy systems: State of the art

Soroudi¹,

Amraee²

2013

Renewable and Sustainable Energy Reviews

416

226

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The energy system studies include a wide range of issues from short term (e.g. real-time, hourly, daily and weekly operating decisions) to long term horizons (e.g. planning or policy making). The decision making chain is fed by input parameters which are usually subject to uncertainties. The art of dealing with uncertainties has been developed in various directions and has recently become a focal point of interest. In this paper, a new standard classification of uncertainty modeling techniques for decision making process is proposed. These methods are introduced and compared along with demonstrating their strengths and weaknesses. The promising lines of future researches are explored in the shadow of a comprehensive overview of the past and present applications. The possibility of using the novel concept of Z-numbers is introduced for the first time.

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“…Ω S , with the cardinality of N Ω S , from the original set, i.e. Ω J [30]. This procedure should be done in a way that makes a trade off between the loss of the information and decreasing the computational burden [31].…”

Section: Appendix-ii:scenario Reduction Techniquementioning

confidence: 99%

Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty

Soroudi

Afrasiab

2012

IET Renew. Power Gener.

147

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A scenario reduction technique is used to reduce the computational burden of the model. The Pareto optimal solutions of the problem are found using a binary PSO algorithm and finally a fuzzy satisfying method is applied to select the optimal solution considering the desires of the planner. The effectiveness of the proposed model is demonstrated by applying it to a realistic 201-node distribution network. Index TermsDistributed generation, PSO, Dynamic planning, scenario reduction, Pareto optimal front. NOMENCLATURE Constants C ℓCost of reinforcement of feeder ℓ C

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Scenario Reduction for Futures Market Trading in Electricity Markets

Cited by 246 publications

References 21 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

Decision making under uncertainty in energy systems: State of the art

Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty

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