Smart bidding strategy of the demand-side loads based on the reinforcement learning

Zhou, Jing; Wang, Ke; Mao, Wenbo; Wang, Yong; Huang, Pengxiang

doi:10.1109/ei2.2017.8245286

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…The research on bidding strategies for energy trading can be divided into two categories: traditional modalbased optimization approaches (Fooladivanda et al, 2018;Herranz et al, 2012;Fang et al, 2016;Dou et al, 2020;Wu et al, 2016) and intelligent learning methods (Kim et al, 2016;Wang et al, 2019;Zhou et al, 2017;Xu et al, 2019). The former obtains the optimal bidding strategy of energy trading participants by constructing and solving mathematical optimization models.…”

Section: Related Workmentioning

confidence: 99%

A deep reinforcement learning-based bidding strategy for participants in a peer-to-peer energy trading scenario

Zhang

Yang

2023

Front. Energy Res.

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An efficient energy trading strategy is proven to have a vital role in reducing participants’ payment in the energy trading process of the power grid, which can greatly improve the operation efficiency of the power grid and the willingness of participants to take part in the energy trading. Nevertheless, with the increasing number of participants taking part in the energy trading, the stability and efficiency of the energy trading system are exposed to an extreme challenge. To address this issue, an actor-critic-based bidding strategy for energy trading participants is proposed in this paper. Specifically, we model the bidding strategy with sequential decision-making characteristics as a Markov decision process, which treats three elements, namely, total supply, total demand, and participants’ individual supply or demand, as the state and regards bidding price and volume as the action. In order to address the problem that the existing value-based reinforcement learning bidding strategy cannot be applied to the continuous action space environment, we propose an actor–critic architecture, which endows the actor the ability of learning the action execution and utilizes the critic to evaluate the long-term rewards conditioned by the current state–action pairs. Simulation results in energy trading scenarios with different numbers of participants indicate that the proposed method will obtain a higher cumulative reward than the traditional greedy method.

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Section: Related Workmentioning

confidence: 99%

A deep reinforcement learning-based bidding strategy for participants in a peer-to-peer energy trading scenario

Zhang

Yang

2023

Front. Energy Res.

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“…Ref. [21] proposed an intelligent bidding strategy based on an adaptive reinforcement learning model for prosumers within a local grid. In 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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“…Radhakrishnan et al [32] utilized techniques of agent-based computational to overcome the unpredictability in electricity prices. Zhou et al [33] established a piecewise regulation in order to simulate continuous and discrete regulation demand loads. In contrast to the above works, we introduce reinforcement learning to predict the charging behavior of electric vehicles and combine deep learning to solve the dimensional disaster problem of reinforcement learning.…”

Section: B Application Of Dqnmentioning

confidence: 99%

Deep Q-Network Based Energy Scheduling in Retail Energy Market

Lin

et al. 2020

IEEE Access

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As a significant component of electric energy trades, retail electric market (REM) can effectively alleviate the pressure of load demand from the power grid. However, the load demand uncertainty of customers becomes a nodus because retail electricity providers (REPs) should predict the load demand when trading with wholesaler electricity provider (WEP) based on the interaction. Therefore, in this paper, we propose an optimal energy scheduling scheme in REM with consideration of the influence of decisions made in pre-purchase stages to situations in real-time stages. Firstly, we present a trading framework to analyze the strategies of REPs in REM, in which REPs conduct both pre-purchase trading with WEP, and real-time trading with customers. Then, to solve the scheduling problem caused by the demand uncertainty of customers, we design a power allocation mechanism based on the charging demand degree, by which REPs can minimize the operating cost while ensuring that each electric vehicle can be charged with sufficient energy. Next, to minimize the cost of REPs in the pre-purchase stage, we adopt deep Q-network (DQN) algorithm to implement the pre-purchase schedule. The charging station adjusts the pre-purchased schedule for each period through Q-learning and utilizes the optimal strategy to design the electricity schedule. Finally, simulation experiments show that the proposal can obtain the optimal strategy to significantly reduce the operating costs of REP. INDEX TERMS Retail electricity market (REM), plug-in electric vehicles (PEVs), deep Q-network (DQN). I. INTRODUCTION

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Smart bidding strategy of the demand-side loads based on the reinforcement learning

Cited by 6 publications

References 5 publications

A deep reinforcement learning-based bidding strategy for participants in a peer-to-peer energy trading scenario

A deep reinforcement learning-based bidding strategy for participants in a peer-to-peer energy trading scenario

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

Deep Q-Network Based Energy Scheduling in Retail Energy Market

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