A Reinforcement-learning-based Bidding Strategy for Power Suppliers with Limited Information

Jia, Qiangang; Li, Yiyan; Yan, Zheng; Xu, Chengke; Chen, Sijie

doi:10.35833/mpce.2020.000495

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…Game theory offers a quantifiable and understandable foundation for implementing active defensive strategies under uncertainty in several fields such as optimal energy demand analysis and Internet of Things networks [30]- [32]. In addition, game-theoretical research toward CPS security has been conducted.…”

Section: Related Work In Terms Of Cps Securitymentioning

confidence: 99%

Game-theoretical Model for Dynamic Defense Resource Allocation in Cyber-physical Power Systems Under Distributed Denial of Service Attacks

Yan,

Yao,

Yang

et al. 2024

Journal of Modern Power Systems and Clean Energy

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Electric power grids are evolving into complex cyber-physical power systems (CPPSs) that integrate advanced information and communication technologies (ICTs) but face increasing cyberspace threats and attacks. This study considers CPPS cyberspace security under distributed denial of service (DDoS) attacks and proposes a nonzero-sum game-theoretical model with incomplete information for appropriate allocation of defense resources based on the availability of limited resources. Task time delay is applied to quantify the expected utility as CPPSs have high time requirements and incur massive damage DDoS attacks. Different resource allocation strategies are adopted by attackers and defenders under the three cases of attackfree, failed attack, and successful attack, which lead to a corresponding consumption of resources. A multidimensional node value analysis is designed to introduce physical and cybersecurity indices. Simulation experiments and numerical results demonstrate the effectiveness of the proposed model for the appropriate allocation of defense resources in CPPSs under limited resource availability.

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Section: Related Work In Terms Of Cps Securitymentioning

confidence: 99%

Game-theoretical Model for Dynamic Defense Resource Allocation in Cyber-physical Power Systems Under Distributed Denial of Service Attacks

Yan,

Yao,

Yang

et al. 2024

Journal of Modern Power Systems and Clean Energy

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“…Before the introduction of LEM in the last two-decades and in recent years, reinforcement learning is used by electricity producers for making decision on their offering price in a competitive electricity market. In [14], a modified continuous action reinforcement learning automata algorithm was proposed to help power suppliers bid with the limited information in an electricity market. Ref.…”

Section: B Literature Reviewmentioning

confidence: 99%

Intelligent Bidding Strategies for Prosumers in Local Energy Markets Based on Reinforcement Learning

Okwuibe

Bhalodia²,

Gazafroudi³

et al. 2022

IEEE Access

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Local energy markets (LEMs) are proposed in recent years as a way to enable local prosumers and community to trade their electricity and have control over their electrical related resources by ensuring that electricity is traded closer to where it is produced. However, literature is still scarce with the most optimal and effective trading strategies for LEM design. In this work, we propose two reinforcement learning based intelligent bidding strategies for prosumers and consumers trading within an LEM. Our proposed models were evaluated of their performance by testing them in a German real case scenario. The simulation results show that intelligent bidding strategies create additional self sufficiency and market savings to the local community compared to the baseline strategy where the agents make their trading decision randomly without an intelligent agent. Moreover, modelling the intelligent agents to perform towards a common goal creates more share of individual savings for the prosumers and consumers compared to the classical intelligent bidding strategies employed in this work.INDEX TERMS Bidding strategy, energy community, local energy markets, Markov decision process, peerto-peer, reinforcement learning.

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“…The electricity market is a typical market with imperfect competition, where electricity suppliers earn higher profits through strategic bidding behavior. To help electricity suppliers bid with limited information, a modified continuous automata algorithm with reinforcement learning is proposed in [2]. Briefly, during this process, electricity suppliers and consumers submit their bids, and the independent system operator (ISO) solves the economic dispatch problem to calculate the volume of electricity production completing a round of the market clearing process.…”

Section: Author Abstractmentioning

confidence: 99%

A Brief Analysis of the 2017-2021 IEEE Energy Internet and Energy Systems Integration Conference Topics

Chigarev¹

2022

Preprint

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This article is devoted to a bibliometric analysis of IEEE Energy Internet and Energy Systems Integration Conference proceedings posted in IEEE Xplore in 2017-2021. The main objective of the study was to identify the actual research issues on the Energy Internet topic. Author Keywords, INSPEC Controlled Terms, and terms compiled from n-grams derived from conference papers title and abstract texts were used to describe the actual issues. The terms were clustered using VOSviewer, and the terms included in each cluster were treated as a description of the actual issues. Brief descriptions of several publications relevant to the issue in question were given for task detail. The actual issue, which in my opinion deserves further, more detailed analysis, is proposed.

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A Reinforcement-learning-based Bidding Strategy for Power Suppliers with Limited Information

Cited by 12 publications

References 22 publications

Game-theoretical Model for Dynamic Defense Resource Allocation in Cyber-physical Power Systems Under Distributed Denial of Service Attacks

Game-theoretical Model for Dynamic Defense Resource Allocation in Cyber-physical Power Systems Under Distributed Denial of Service Attacks

Intelligent Bidding Strategies for Prosumers in Local Energy Markets Based on Reinforcement Learning

A Brief Analysis of the 2017-2021 IEEE Energy Internet and Energy Systems Integration Conference Topics

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