考虑风电条件风险的水火风联合调度模型及求解

HYDRO-THERMAL-WIND CO-SCHEDULING MODELAND SOLUTION METHOD CONSIDERING CONDITIONAL RISK OF WIND POWER

在“双碳”战略和高比例可再生能源并网政策背景下,为准确量化风电等新能源消纳成本及其随机性造成的风险损失以支持电力调度决策,采用CVaR建模风电随机性造成的弃风和弃负荷条件风险值,并应用Copula函数计算连续马尔可夫链风速模型预测风电出力,建立风电不确定风险损失、发电成本和污染排放最小的水火风电短期多目标调度模型。并通过可变外部种群规模、增强局部搜索能力和基于K近邻距离的精英种群淘汰规则3方面改进SPEA2算法以对该模型进行高效求解。仿真结果显示CVaR能很好建模风电不确定风险,并通过改进SPEA2找到更好的Pareto最优解集。

Under the background of the“dual carbon”strategy and the high proportion of renewable energy grid-connected policy,in order to accurately quantify the consumption cost of new energy such as wind power and the risk loss caused by its randomness to support power dispatching decisions,the CVaR(conditional value-at-risk)is used to model the conditional risk value of abandoned wind and abandoned load caused by the randomness of wind power,and the Copula function is used to calculate the continuous Markov chain wind speed model to predict wind power output,and then a short-term multi-objective optimal scheduling model of hydro-thermal-wind system(MOS-HTW)with minimum of uncertain risk loss,power generation cost and pollution emission is established.Meanwhile,strength Pareto evolutionary algorithm 2(SPEA2)is improved to solve the model efficiently in three aspects:variable external population size,enhanced local search ability and elite population elimination rule based on K-nearest neighbor distance(KND).The simulation results show that CVaR can well model the uncertain risk of wind power,and find a better Pareto optimal solution set by improving SPEA2.