Optimization of Voltage Regulation and Loss Reduction of Offshore Wind Power Transmission Based on Multi-agent Q-learning in Uncertain Environment
In order to achieve the purpose of reducing the loss of the offshore wind power transmission project,the paper deduces the transmission efficiency function of the submarine cable in the offshore wind power transmission project in detail,analyzes the influencing factors of the transmission efficiency of the submarine cable and the loss reduction principle of the optimization for the on-load transformer tap changer,and establishes the daily loss optimization model of the submarine cable transmission project.Then,given the time series auto-correlation analysis of random variables,a predictive data-driven approach to modeling a high-dimensional convex hull uncertain set is applied in this paper to consider the fluctuations in the wind farm output and the grid network voltage in the loss reduction optimization,which gets extreme scenarios and reduces the decision conservatism while improving the computational efficiency.In addition,the Q-learning algorithm is used for training based on extreme scenarios obtained and combined with the multi-agent system optimization method to solve the optimization model by the tap position optimization and adjustment time optimization alternately.The agent learns from the interaction between the action strategy and the wind farm state,so as to form the best tap changer setting and the best adjustment time strategy under various operating states of the offshore wind farm.Finally,the rationality of the proposed model and the validity of the method are verified through the simulation analysis of a 220 kV AC submarine cable transmission project in an offshore wind farm.
offshore wind power transmissionpower loss reductionon-load tap changer(OLTC)multi-agent Q-learning algorithmuncertainties
万方数据
维普
