CVaR-based method for optimizing the contract bidding strategy of PV power stations

With the rapid development of the new power system, the proportion of renewable energy participating in the electricity market is constantly increasing, and the uncertainty of renewable energy power output and market clearing price brings risks to its participation in long-term contract bidding. Therefore, a method of contract bidding involving bidding amount and bidding price of PV power station is proposed. Firstly, the multi-order weighted Markov chain model is used to forecast the distribution of weather states in the trading cycle, and the fuzzy C-means (FCM) method is used to cluster the joint samples of power output indicators and market clearing price indicators, and then Latin hypercube sampling is used to generate power output scenarios and market clearing price scenarios. Then, an optimization method for long-term contract bidding strategy of PV power station considering conditional value at risk (CVaR) and transaction probability is established, and the model is divided into two stages and solved in an alternate iterative way. Finally, an example is used to verify that the constructed model enables PV power stations to effectively avoid risks and obtain more stable revenue.