A Game Theoretical Approach Based Bidding Strategy Optimization for Power Producers in Power Markets with Renewable Electricity

Tang, Yi; Ling, Jing; Ma, Tingting; Chen, Ning; Liu, Xiaofeng; Gao, Bingtuan

doi:10.3390/en10050627

Cited by 15 publications

(11 citation statements)

References 39 publications

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“…For the solution of bidding problems, game theory is widely used, including evolutionary games [21], non-cooperative games [22], Nash equilibrium [23,24] etc. The bidding problem in the power auxiliary service market studied in this paper pursues an "evolutionary stable strategy," so the evolutionary game method is selected for research.…”

Section: Introductionmentioning

confidence: 99%

Bidding strategy of thermal power compound differential evolution game under the market mechanism of peak regulation auxiliary service

Dong

et al. 2021

IET Generation Trans & Dist

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As the main provider of peak regulation auxiliary services, thermal power units are particularly important to ensure their revenue in an uncertain environment. To obtain the optimal bidding strategy for thermal power units, a thermal power peak regulation bidding model based on the Northeast Power Grid's auxiliary service market bidding mechanism is established. Secondly, the evolutionary game theory is introduced into the bidding strategy of thermal power units. Meanwhile, in order to solve the multi-party game problem, a compound differential evolution game algorithm is constructed to solve the two-tiers bidding. Finally, based on the actual operating data of a typical day, the efficiency of the compound differential evolution game algorithm bidding strategy is verified, and the bidding strategy of thermal power units in different situations is discussed. The results show that when all units participate in peak regulation, the first-tier quotation is an optimal strategy at 0.19 yuan kWh −1 and below, and the second-tier quotation is distributed between 0.6-0.8 yuan kWh −1. When only some units are involved in peak regulation, the first-tier quotation of thermal power units adopts a high price strategy, mostly at 0.3 yuan kWh −1 , and the second-tier quotation is still based on a high price strategy.

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Section: Introductionmentioning

confidence: 99%

Bidding strategy of thermal power compound differential evolution game under the market mechanism of peak regulation auxiliary service

Dong

et al. 2021

IET Generation Trans & Dist

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“…Approaches for producing generation schedules have been suggested [2][3][4]. Reference [2] proposed a bidding strategy where wind farms offer energy jointly compared with bidding strategies that consider each wind farm separately.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [2] proposed a bidding strategy where wind farms offer energy jointly compared with bidding strategies that consider each wind farm separately. Reference [3] proposed a bidding strategy by using a non-cooperative game approach based on bi-level optimization with utility companies and power producers of thermal, solar, and wind power. Reference [4] proposed a bidding strategy based on a stochastic linear mathematical programming to maximize profit.…”

Section: Introductionmentioning

confidence: 99%

Scheduling method of wind power generation for electricity market using state-of-charge transition and forecast error

Kikuchi

Ito

Hayashi

2019

Journal of International Council on Electrical Engineering

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This study focuses on the trade of power from wind power plants (WPPs) at an electricity market. To trade power, operators should produce generation schedules for bids and then supply the same amount of energy as the schedules. However, power from WPPs tends to fluctuate, which complicates scheduling. This paper proposes three scheduling methods with an energy storage system (ESS) to solve this problem. The first method considers state-of-charge (SOC) transition to maintain the appropriate SOC and minimize the imbalance between supplied and scheduled energy. The second and third methods consider SOC transition and forecast errors. The second method uses a linear regression model to estimate forecast errors. The third method adopts a bagged trees model, which is a machine learning method, to directly estimate the adjusted forecast data considering errors. Five patterns of the rated power of the ESS are assumed, and these three methods are simulated on each power. In comparison with the basic method, whose schedules are the same as the forecast, the third method can reduce 84% of the imbalance from the schedules when the rated power of the ESS is the minimum. The proposed methods help develop further correct and practical scheduling methods.

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“…Since the significant document 'Several Opinions on Further Deepening the Reform of Electric Power System (No.9 document)' was issued in March 2015, China's electric power market is gradually introducing competition and establishing a market-oriented power trading platform, allowing new kinds of participants to emerge [2]. Consumers exhibit higher sensitivity for price and market demand varies from fixed demand to uncertain elastic demand with the participation of large bargaining customers [3]. On the supply side, many small and medium-sized electricity generators are involved in the competition, entailing fierce competitions and frequent auctions among them [4].…”

Section: Introductionmentioning

confidence: 99%

How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach

Fang

Ren

2018

Energies

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The deepening of electricity reform results in increasingly frequent auctions and the surge of generators, making it difficult to analyze generators’ behaviors. With the difficulties to find analytical market equilibriums, approximate equilibriums were obtained instead in previous studies by market simulations, where in some cases the results are strictly bound to the initial estimations and the results are chaotic. In this paper, a multi-unit power bidding model is proposed to reveal the bidding mechanism under clearing pricing rules by employing an auction approach, for which initial estimations are non-essential. Normalized bidding price is introduced to construct generators’ price-related bidding strategy. Nash equilibriums are derived depending on the marginal cost and the winning probability which are computed from bidding quantity, transmission cost and demand distribution. Furthermore, we propose a comparative analysis to explore the impact of uncertain elastic demand on the performance of the electricity market. The result indicates that, there exists market power among generators, which lead to social welfare decreases even under competitive conditions but elastic demand is an effective way to restrain generators’ market power. The feasibility of the models is verified by a case study. Our work provides decision support for generators and a direction for improving market efficiency.

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A Game Theoretical Approach Based Bidding Strategy Optimization for Power Producers in Power Markets with Renewable Electricity

Cited by 15 publications

References 39 publications

Bidding strategy of thermal power compound differential evolution game under the market mechanism of peak regulation auxiliary service

Bidding strategy of thermal power compound differential evolution game under the market mechanism of peak regulation auxiliary service

Scheduling method of wind power generation for electricity market using state-of-charge transition and forecast error

How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach

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