Optimal joint demand and virtual bidding for a strategic retailer in the short-term electricity market

•A market decision model for strategic retailer using virtual bidding is developed.•The model is developed by using stochastic optimization technique.•The retailer's joint demand/virtual bidding strategies are generated by the model.•The retailer's market power is considered by using bilev...

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Vydané v:Electric power systems research Ročník 190; s. 106855
Hlavní autori: Xiao, Dongliang, do Prado, Josue Campos, Qiao, Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 01.01.2021
Elsevier Science Ltd
Predmet:
Bilevel optimization
Demand
Electricity consumption
Electricity market
Electricity pricing
Integer programming
Kuhn-Tucker method
Linear programming
Market power
Mixed integer
Optimization
Risk management
Stochastic models
Strategic retailer
Strategy
Virtual bidding
Virtual reality
Market power
Bilevel optimization
Virtual bidding
Risk management
Electricity market
Strategic retailer
ISSN:0378-7796, 1873-2046
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Shrnutí:•A market decision model for strategic retailer using virtual bidding is developed.•The model is developed by using stochastic optimization technique.•The retailer's joint demand/virtual bidding strategies are generated by the model.•The retailer's market power is considered by using bilevel programming technique.•The potential risks of the retailer are managed by using CVaR. This paper proposes a bilevel stochastic optimization model for generating the optimal joint demand and virtual bidding strategy for a strategic retailer in the short-term electricity market, where virtual bidding is used to improve the retailer's market power in the day-ahead (DA) electricity market. In the proposed model, virtual bidding can be used at multiple buses, which are not limited to the locations of the demands of the strategic retailer. In the bilevel stochastic optimization model, the upper level problem maximizes the total profit of demand and virtual bidding, while the lower level problem represents the DA electricity market clearing process; the uncertain demands of the strategic retailer and real-time (RT) electricity prices in the market are represented by scenarios; and the Conditional Value at Risk (CVaR) is used for risk management. By using the duality theory, Karush–Kuhn–Tucker (KKT) conditions and big M method, the proposed bilevel nonlinear optimization model is converted into a single-level mixed-integer linear programming (MILP) problem, which can be solved efficiently by existing commercial solvers. Case studies are performed to validate the proposed model and study the impacts of various model parameters on the strategic retailer's joint demand and virtual bidding strategy.
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ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2020.106855