Combined hydro-wind generation bids in a pool-based electricity market

Angarita, Jorge L.; Usaola, Julio; Martínez-Crespo, Jorge

doi:10.1016/j.epsr.2009.01.002

Cited by 95 publications

(49 citation statements)

References 20 publications

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“…The stochastic models were proven outperforming the deterministic ones generated by using forecasted values directly [6]. Another risk mitigation approach is based on a combined and coordinated use of wind power and energy storage or thermal units [7]- [10].…”

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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“…These Uncer have to be expediently considered, i.e., treated into the variables of the problems [17] to be addressed by a WPP in order to know how much to produce and the price for bidding. The technical literature presents methods for WP bidding strategies solving using different approaches: the first one is the use of WP with technologies of storage of energy [18]; the use of economic options as a tool for WPP to hedge against WP Uncer [19]; another approach is the design of Stoc models in order to obtain OBS for WPP participating in an EMar [20], without the aforementioned policies. The 3rd line of action is a Stoc formulation explicitly modelling the Uncer faced by a WPP [21], using indeterminate measures and an established of scenarios built by WP forecast and market-clearing EPr forecast [22] requests.…”

Section: State Of the Artmentioning

confidence: 99%

Self-scheduling of Wind-Thermal Systems Using a Stochastic MILP Approach

Laia

Gomes

Pousinho

et al. 2017

IFIP Advances in Information and Communication Technology

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Abstract. In this work a stochastic (Stoc) mixed-integer linear programming (MILP) approach for the coordinated trading of a price-taker thermal (Ther) and wind power (WP) producer taking part in a day-ahead market (DAM) electricity market (EMar) is presented. Uncertainty (Uncer) on electricity price (EPr) and WP is considered through established scenarios. Thermal units (TU) are modelled by variable costs, start-up (ST-UP) technical operating constraints and costs, such as: forbidden operating zones, minimum (Min) up/down time limits and ramp up/down limits. The goal is to obtain the optimal bidding strategy (OBS) and the maximization of profit (MPro). The wind-Ther coordinated configuration (CoConf) is modelled and compared with the unCoConf. The CoConf and unCoConf are compared and relevant conclusions are drawn from a case study.

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“…A WiPP in a deregulated market can benefit without depending on third-parties from: a coordination of wind power production with energy storage technology [16]; a financial options as a tool for WiPP to hedge against wind power Unc [17]; a stochastic model envisioned to determine optimal offer strategies for WiPP participating in a day-ahead (DaH) electricity market [18]. The stochastic model is a formulation explicitly taking into account the uncertainties tackled by the ScP of a WiPP [19], using multiple scenarios obtained by computer applications for wind power and market price forecasts [20].…”

Section: State Of the Artmentioning

confidence: 99%

Optimal Bidding Strategies of Wind-Thermal Power Producers

Laia

Pousinho

Melício

et al. 2016

Technological Innovation for Cyber-Physical Systems

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Abstract. This paper addresses a stochastic mixed-integer linear programming model for solving the self-scheduling problem of a thermal and wind power producer acting in an electricity market. Uncertainty on market prices and on wind power is modelled via a scenarios set. The mathematical formulation of thermal units takes into account variable and start-up costs and operational constraints like: ramp up/down limits and minimum up/down time limits. A mixed-integer linear formulation is used to obtain the offering strategies of the coordinated production of thermal and wind energy generation, aiming the profit maximization. Finally, a case study is presented and results are discussed.

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Combined hydro-wind generation bids in a pool-based electricity market

Cited by 95 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

Self-scheduling of Wind-Thermal Systems Using a Stochastic MILP Approach

Optimal Bidding Strategies of Wind-Thermal Power Producers

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