Bidding Strategy Based On Data-driven Electricity Wholesale Market

This study aims to explore the best practices of bidding strategies in the electricity wholesale market through the application of data-driven and reinforcement learning algorithms.Power generation companies no longer merely perform their primary task of generating electricity in the electricity market environment;instead,they need to bid on their power load to maximize their profits.This means that the purpose and behavior of power generation companies have undergone fundamental changes.To achieve maximum profit,power generation companies need to declare information such as electricity prices and auxiliary service prices in advance.This information will be used by the power operations center to arrange the power generation of each company,which will be allocated according to the principles of fairness,impartiality,and openness.In the bidding decision,power generation companies need to solve the problem of how to use the information they possess reasonably to declare prices to obtain maximum profits.Game theory is an effective tool widely applied in this regard.Through game theory analysis,power generation companies can consider their interests and the behavior of other companies to formulate the best bidding strategy.Therefore,in the electricity market environment,the role and behavior of power generation companies have undergone significant changes,requiring them to obtain maximum profits by declaring prices.Game theory is an effective tool for analyzing the competition between power generation companies and formulating the best bidding strategy.However,classical game theory methods are based on the basic assumption that power generators participating in transactions are fully rational and have a thorough understanding of the market information.However,there are many uncertain factors in the market,such as weather,supply and demand situations,and the behavior of competitors,which make it challenging to formulate the best bidding strategy.When declaring prices,power generation companies face two difficult-to-overcome problems.On the one hand,they cannot fully grasp all the information on the market,making it difficult for them to make optimized decisions.On the other hand,there are various uncertain factors in real life,such as policy changes,natural disasters,etc.,which also affect the decision-making of power generation companies but are difficult to fully consider in prices.Therefore,when formulating prices,power generation companies must weigh various factors and make the most reasonable decisions as much as possible.Therefore,we need to use data-driven and reinforcement learning algorithms to optimize bidding strategies,which allows decision-makers to simplify the environment,avoid choosing the wrong strategy,and rely on seemingly irrelevant but related issues to construct detailed things:Firstly,a method of using Bayesian reinforcement learning algorithms to explore unknown market environments is proposed for optimizing the best bidding strategy for power generation companies pursuing economics.Based on the economics of power generators participating in market quotations,combined with classical reinforcement learning algorithm Q-Learning and Bayesian reinforcement learning algorithm methods,a dynamic exploration model of power generator quotations is constructed.Considering the behavior of individual competitors,each participating power generation unit is equivalently represented as a competitor.This makes the strategy of power generators to formulate the best bidding strategy based on market conditions and electricity prices.In the training process,we mainly consider the revenue of power generation companies and use some techniques to improve the convergence speed and stability of the algorithm.Experimental results show that the Bayesian reinforcement learning algorithm can obtain better results than the classical reinforcement learning algorithm on electricity market data.Secondly,in response to the risks present in market transactions,the study combines risk-constrained CVaR with Actor-Critic reinforcement learning algorithms to construct an adaptive learning pricing strategy for power generation companies.First,historical data is used to establish a pricing model for the power plant group,predicting future market conditions and the range of opponent bids.Power generation companies are treated as agents,and by analyzing historical transaction data,we can obtain the current state of the electricity market.Based on this state,we can adopt pricing strategies that are favorable to our own interests.At the same time,we can use Bayesian neural networks to estimate the behavior of competitors and predict the strategies they may adopt in the current state.This helps us better understand the behavior of competitors,thereby better responding to market changes and competitive pressures.This is important for improving the market competitiveness and economic benefits of power generation companies.Finally,we discuss the research results and propose some suggestions and future directions.Overall,this study demonstrates that data-driven and reinforcement learning algorithms are effective tools for optimizing bidding strategies in the power wholesale market.Our method not only increases the revenue of power companies but also reduces risks and uncertainties.Future research can further explore how to combine more market factors and more complex algorithms to formulate more refined and optimized bidding strategies.