CAJ | 학술논문

现有风电功率预测方法只提供功率的单点预测值,但在电力市场的决策过程中却需要更多的信息。文中提出一种基于离散时间Markov链理论的新功率预测模型。针对功率数据的无规律性,采用等分法划分了4种状态空间,并对每种状态空间都建立1阶和2步混合Markov模型,模型权重系数采用加速遗传算法求解。该模型直接对风电功率数据进行数值分析,有效避免通过风速预测再转换为功率时带来的误差累积。给出4种混合模型和最新的评价误差公式。分析和算例表明,N为102时混合模型预测精度高于持续法模型,并给出了单点预测值和概率分布值。
현유풍전공솔예측방법지제공공솔적단점예측치,단재전력시장적결책과정중각수요경다적신식。문중제출일충기우리산시간Markov련이론적신공솔예측모형。침대공솔수거적무규률성,채용등분법화분료4충상태공간,병대매충상태공간도건립1계화2보혼합Markov모형,모형권중계수채용가속유전산법구해。해모형직접대풍전공솔수거진행수치분석,유효피면통과풍속예측재전환위공솔시대래적오차루적。급출4충혼합모형화최신적평개오차공식。분석화산례표명,N위102시혼합모형예측정도고우지속법모형,병급출료단점예측치화개솔분포치。
A wind power forecasting method generally provides estimation of future wind power as a single point forecast,while most of the decision-making processes in the electric power systems management require more information than a single value.A new wind power forecasting method is proposed on the basis of discrete time Markov chain models.Aiming at the randomness of power data,a 4-state space is divided on the equal length,and a one-order and two-step hybrid model is built in each state space.The coefficient weights of the hybrid model are obtained by using accelerating genetic algorithm.Since the model analyzes power data directly,it efficiently avoids amplifying errors in converting wind speed forecasts into power forecasts.The hybrid models of four types and the new prediction error formula are presented.Analysis and numerical examples show that the prediction accuracy of hybrid models（N=102） is better than that of persistence method（PM） model,and the corresponding point prediction and probability distribution estimation are also presented.