General Three-Population Multi-Strategy Evolutionary Games for Long-Term On-Grid Bidding of Generation-Side Electricity Market

Cheng, Lefeng; Zhang, Jie; Yin, Linfei; Chen, Yang; Wang, Jianhui; Liu, Guiyun; Wang, Xiaogang; Zhang, Dongxing

doi:10.1109/access.2020.3046327

Cited by 12 publications

(2 citation statements)

References 43 publications

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“…A dynamic system of rewards and fines helps the system to reach equilibrium more than a static one [25,26], and a higher ceiling on unit fines can facilitate the completion of quota targets by power producers. When considering government regulation as an influencing factor in the third-party games, Cheng et al [27] revealed that effective government supervision can promote new energy accommodation. However, strict regulation by the central government has a more significant effect only in scenarios where high excess quotas are achieved [28]; meanwhile, thermal power generators will adopt the low-price strategy to ensure the profit space [29].…”

Section: Introductionmentioning

confidence: 99%

Green Power Trade Behavior in China’s Renewable Portfolio Standard: An Evolutionary Game-Based System Dynamics Approach

Chen

Pan

et al. 2022

Sustainability

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To promote the consumption of renewable energy (RE), in 2019, China introduced the Renewable Portfolio Standard (RPS) and adopted the weight of consumption responsibility to replace the quota index. This paper investigates the behavioral decisions of green power plants and power sales companies using evolutionary game theory. The results show that RPS becomes effective based on two rules: (1) the unit fine is higher than the transaction cost for power sales companies; and (2) the transaction prices of RE and Trade of Green Certificate (TGC) are greater than the unit transaction cost for green power plants. Taking Ningxia Hui Autonomous Region as an example, the scenario analysis method and a simple system dynamics model are employed to investigate the impact of fines on the green power trade behavior. With different fines, power sales companies are more sensitive than green power plants. These results provide some policy recommendations to promote the consumption of RE.

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Section: Introductionmentioning

confidence: 99%

Green Power Trade Behavior in China’s Renewable Portfolio Standard: An Evolutionary Game-Based System Dynamics Approach

Chen

Pan

et al. 2022

Sustainability

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“…In order to solve the optimal quotation problem of thermal power companies under the multi-agent incomplete information game, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm based on the multiagent reinforcement learning method was proposed [28][29][30][31] . The neural network parameters are updated to simulate the bounded rational process of the game to ensure that the game process is close to reality.…”

Section: Introductionmentioning

confidence: 99%

Research on Bidding Strategy of Thermal Power Companies in Electricity Market Based on Multi-Agent Deep Deterministic Policy Gradient

et al. 2021

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With the continuous improvement of new energy penetration in the power system, the price of the spot market of power frequently fluctuates greatly, which damages the income of a large number of thermal power enterprises. In order to lock in the profit, thermal power enterprises should turn the main target of profit to the medium and long-term power market. With the continuous advancement of the reform in China's power system, major changes have taken place in the medium and long-term power transactions, including the transaction target, organization method, clearing method and so on, so it is urgent to explore the quotation strategy of thermal power enterprises under the medium and long term market changes. Based on the theory of game equilibrium, this paper establishes non-cooperative game and cooperative game models between thermal power companies. Considering that the traditional reinforcement learning method is difficult to solve the multi-agent incomplete information game model, this paper uses the Multi-Agent Deep Deterministic Policy Gradient(MADDPG) algorithm to solve the above model. Finally, the validity of the proposed model is proved by a numerical example. The results show that, compared with other reinforcement learning algorithms, when solving the multi-agent incomplete information game model, the quotation obtained by MADDPG is more accurate, the revenue is increased by 5.2%, and the convergence time is reduced by 50%.In addition, this paper finds that in the medium and long-term power market, thermal power companies are more inclined to use physical retention methods to make profits. The greater the market power of thermal power companies, the greater the probability of physical retention. When low-cost thermal power companies retain more power, they will increase market clearing electricity prices and harm market efficiency. Regulators should focus on the market behavior of such thermal power companies.

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Strategic guidance of carbon emission reduction in industrial parks based on dynamic evolution game

Sun,

Fan,

Liu

et al. 2024

Computers and Electrical Engineering

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General Three-Population Multi-Strategy Evolutionary Games for Long-Term On-Grid Bidding of Generation-Side Electricity Market

Cited by 12 publications

References 43 publications

Green Power Trade Behavior in China’s Renewable Portfolio Standard: An Evolutionary Game-Based System Dynamics Approach

Green Power Trade Behavior in China’s Renewable Portfolio Standard: An Evolutionary Game-Based System Dynamics Approach

Research on Bidding Strategy of Thermal Power Companies in Electricity Market Based on Multi-Agent Deep Deterministic Policy Gradient

Strategic guidance of carbon emission reduction in industrial parks based on dynamic evolution game

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