Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market

Zhang, Haifeng; Gao, Feng; Wu, Jiang; Liu, Kun; Liu, Xiaolin

doi:10.3390/en5114804

Cited by 18 publications

(10 citation statements)

References 27 publications

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“…A wind farm, as an economic agent, participates in the electricity market to make profit [8]. In order to maximize its profit, it adopts bidding strategies.…”

Section: Problem Statementmentioning

confidence: 99%

Long Term Expected Revenue of Wind Farms Considering the Bidding Admission Uncertainty

et al. 2016

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Abstract:As a long term bidding behavior, bid shading is exhibited by wind farms participating in real Uniform Price (UP) markets. This signifies that the wind farm owners bid far below their true long run marginal cost. In this paper, a method is proposed to consider the uncertainty of bidding admission in the long term expected revenue of wind farms. We show that this consideration could perfectly explain the observed bid shading behavior of wind farm owners. We use a novel market price model with a stochastic model of a wind farm to derive indices describing the uncertainty of bidding admission. The optimal behavior of the wind farm is then obtained by establishing a multi objective optimization problem and subsequently solved using genetic algorithm. The method is applied to the analysis of long term bidding behavior of a wind farm participating in a Pay-as-Bid (PAB) auction such as Iran Electricity Market (IEM). The results demonstrate that wind farm owners change their bid shading behavior in a PAB Auction. However, the expected revenue of the wind farm will also decrease in a PAB auction. As a result, it is not recommended to make an obligation for the wind farms to participate in a PAB auction as a normal market player.

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“…A wind farm, as an economic agent, participates in the electricity market to make profit [8]. In order to maximize its profit, it adopts bidding strategies.…”

Section: Problem Statementmentioning

confidence: 99%

Long Term Expected Revenue of Wind Farms Considering the Bidding Admission Uncertainty

et al. 2016

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“…This was the case in this research. A lot of work has been done in a field of bidding curve optimization models and a review of these models can be found in [11], with some early works in [12][13][14].…”

Section: Open Accessmentioning

confidence: 99%

Pricing Energy and Ancillary Services in a Day-Ahead Market for a Price-Taker Hydro Generating Company Using a Risk-Constrained Approach

et al. 2014

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This paper analyzes a price-taker hydro generating company which participates simultaneously in day-ahead energy and ancillary services markets. An approach for deriving marginal cost curves for energy and ancillary services is proposed, taking into consideration price uncertainty and opportunity cost of water, which can later be used to determine hourly bid curves. The proposed approach combines an hourly conditional value-at-risk, probability of occurrence of automatic generation control states and an opportunity cost of water to determine energy and ancillary services marginal cost curves. The proposed approach is in a linear constraint form and is easy to implement in optimization problems. A stochastic model of the hydro-economic river basin is presented, based on the actual Vinodol hydropower system in Croatia, with a complex three-dimensional relationship between the power produced, the discharged water, and the head of associated reservoir.

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“…With the increasing penetration of wind energy into power grids, the negative influences of wind farms are non-negligible [1,2]. The variability and intermittence of wind power cause fluctuation of the voltage and frequency of the grid, affecting the safety of the power grid [3,4].…”

Section: Introductionmentioning

confidence: 99%

Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

et al. 2015

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Abstract:Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC) method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long-and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.

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Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market

Cited by 18 publications

References 27 publications

Long Term Expected Revenue of Wind Farms Considering the Bidding Admission Uncertainty

Long Term Expected Revenue of Wind Farms Considering the Bidding Admission Uncertainty

Pricing Energy and Ancillary Services in a Day-Ahead Market for a Price-Taker Hydro Generating Company Using a Risk-Constrained Approach

Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control

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