摘要
高比例可再生能源并网将成为未来电力系统的基本特征,可再生能源发电技术的持续发展和可再生能源价格补贴机制与市场化消纳方法结合,为主动负荷的时段转移特性提供技术支持与利益驱动。双重推动下使得负荷需求与可再生能源出力间的相关性增强,提出了考虑可再生能源出力因素的短期负荷组合预测方法,引入负荷需求与可再生能源出力的相关性以改善高比例可再生能源电力系统短期负荷的预测效果。应用交叉谱方法以凝聚谱值刻画负荷需求与可再生能源出力的相关性,将负荷与可再生能源出力数据序列离散化后形成2条马尔科夫链,结合马尔科夫误差修正建立了高比例可再生能源电力系统下考虑可再生能源出力因素的短期负荷组合预测模型。将预测模型应用于某实际电网,预测结果表明,所述方法提高了预测精度,能较好地解决高比例可再生能源电力系统的短期负荷预测问题。
High proportion of renewable energy will become basic characteristics of future power system. Sustainable development of renewable power generation technology and renewable energy price subsidy mechanism, combined with market accommodation method for active loads with time transfer characteristics, provide technical support and interest-driving. This paper proposes a combined short-term load forecasting method considering factor of renewable energy output, and introduces correlation between load demand and renewable energy output to improve short-term load predicting effect for power systems with high proportion of renewable energy. The correlation between load demand and renewable energy output is characterized with cross spectrum method. Two Markov chains are formed after discretization of load and renewable energy output data sequence, and a combined short-term load forecasting model considering renewable energy output factor in renewable power system is established with Markov error correction. The forecasting model is applied to an actual grid. Prediction results show that the proposed method can improve prediction accuracy and solve short-term load forecasting problem of power system with high proportion renewable energy.
作者
张涛
顾洁
ZHANG Tao, GU Jie(School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Minhang District, Shanghai 200240, Chin)
出处
《电网技术》
EI
CSCD
北大核心
2018年第4期1071-1078,共8页
Power System Technology
基金
国家重点研发计划项目(2016YFB0900100)~~
关键词
短期负荷预测
高比例可再生能源电力系统
可再生能源出力
交叉谱分析
双链马尔科夫
误差修正
short-term load forecasting
power system with high proportion renewable energy
renewable energy output
cross-spectrum analysis
double-chain Markov
error correction
