Due to the low dispatchability of wind power,the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as...Due to the low dispatchability of wind power,the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as possible.A study is conducted in the present paper of potential improvements to the performance of artificial neural network(ANN)models in terms of efficiency and stability.Generally,current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station,in addition to selecting a fixed number of time periods prior to the forecasting.In this respect,new ANN models are proposed in this paper,which are developed by:varying the number of prior 1-h periods(periods prior to the forecasting hour)chosen for the input layer parameters;and/or incorporating in the input layer data from a second weather station in addition to the wind farm reference station.It has been found that the model performance is always improved when data from a second weather station are incorporated.The mean absolute relative error(MARE)of the new models is reduced by up to 7.5%.Furthermore,the longer the forecasting horizon,the greater the degree of improvement.展开更多
为进一步提高风电并网后电力系统运行的安全稳定性,提出一种针对风电波动性的新型储能参与电网调频控制策略。首先分析电力系统频率恢复不同阶段运行特性,并基于此提出储能参与电网调频的改进虚拟惯性响应和虚拟下垂控制,用于平抑风电...为进一步提高风电并网后电力系统运行的安全稳定性,提出一种针对风电波动性的新型储能参与电网调频控制策略。首先分析电力系统频率恢复不同阶段运行特性,并基于此提出储能参与电网调频的改进虚拟惯性响应和虚拟下垂控制,用于平抑风电并网的波动性;以储能电池剩余电量及负荷扰动量作为双输入,采用模糊控制策略,推导出虚拟下垂系数与双输入之间的平滑曲线关系;利用负荷扰动时的变化率进行负荷预测,预测超短下一时刻的负荷变化情况,计及风电波动功率,优化虚拟惯性控制和虚拟下垂控制的出力系数,以达到最优化调频效果,并提出三项评价指标对控制策略进行评价。最后以某区域电网模型为例,在试验台中进行仿真验证分析。仿真结果显示所提控制策略能够有效改善风电并网波动性带来的电网扰动,且对维持储能电池SOC(State of charge)具有积极意义。展开更多
基金co-funded with ERDF fundsthe INTERREG MAC 2014-2020 programme,within the ENERMAC project(No.MAC/1.1a/117)。
文摘Due to the low dispatchability of wind power,the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as possible.A study is conducted in the present paper of potential improvements to the performance of artificial neural network(ANN)models in terms of efficiency and stability.Generally,current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station,in addition to selecting a fixed number of time periods prior to the forecasting.In this respect,new ANN models are proposed in this paper,which are developed by:varying the number of prior 1-h periods(periods prior to the forecasting hour)chosen for the input layer parameters;and/or incorporating in the input layer data from a second weather station in addition to the wind farm reference station.It has been found that the model performance is always improved when data from a second weather station are incorporated.The mean absolute relative error(MARE)of the new models is reduced by up to 7.5%.Furthermore,the longer the forecasting horizon,the greater the degree of improvement.
文摘为进一步提高风电并网后电力系统运行的安全稳定性,提出一种针对风电波动性的新型储能参与电网调频控制策略。首先分析电力系统频率恢复不同阶段运行特性,并基于此提出储能参与电网调频的改进虚拟惯性响应和虚拟下垂控制,用于平抑风电并网的波动性;以储能电池剩余电量及负荷扰动量作为双输入,采用模糊控制策略,推导出虚拟下垂系数与双输入之间的平滑曲线关系;利用负荷扰动时的变化率进行负荷预测,预测超短下一时刻的负荷变化情况,计及风电波动功率,优化虚拟惯性控制和虚拟下垂控制的出力系数,以达到最优化调频效果,并提出三项评价指标对控制策略进行评价。最后以某区域电网模型为例,在试验台中进行仿真验证分析。仿真结果显示所提控制策略能够有效改善风电并网波动性带来的电网扰动,且对维持储能电池SOC(State of charge)具有积极意义。
