摘要
为充分发挥区域内小水电等分布式电源支撑潜力,提高弱区域配电网抵御极端天气的能力,文章提出了弱电网极端天气风险运行模式。首先通过内部分布式小水电出力、主网联络线输送功率和负荷的多时间尺度的联合优化调度,在系统发生风险时确保区内重要负荷可靠持续供电;然后,根据不同时间尺度下风险来临概率和水电站来水预测,自适应调整各水电站蓄水量可接受的最小目标值,提高了小水电站的经济性。最后以海南乐东电网为算例,验证了所提模式的可行性和有效性。
In extreme weather,the weak regional distribution network is easily disconnected from the power grid,resulting in loss of power throughout the city.The novel extreme weather risk model with its dispatch method is proposed to make full use of the potential of regional distributed generators such as small hydropower station and to improve the ability of resisting extreme weather.First of all,the dispatch is divided into a long-time scale dispatch,a day-ahead scale dispatch and a real-time scale dispatch,through the long time scale effect analysis of small hydropower capacity adjustment and according to the characteristic that the accuracy of extreme weather forecast information increases continuously over time.Moreover,important loads can be sustained and reliably powered through the joint optimization scheduling of distributed small hydropower output,the main tie line transmission power and loads.Then,the risk model according to the risk approaching probability and the water inflow forecasting of hydropower station at different time scales,the acceptable minimum target of hydropower station water storage capacity is automatically adjusted,improving the economy of small hydropower station.Finally,Hainan Ledong regional distribution grid example is used to prove the feasibility and validity of this model.
作者
蔡渊
方连航
杨钦臣
孟春旅
朱望诚
庞松岭
程相金
Cai Yuan;Fang Lianhang;Yang Qinchen;Meng Chunlv;Zhu Wangcheng;Pang Songling;Cheng Xiangjin(Hainan Electric Power Research Institute, Haikou 570125, University, Nanjing 210096, China;3.Hainan Power Grid 572599, China） China;2.School of Electrical Engineering,Southeast Co., Ltd LeDong Power Supply Bureau, Ledon)
出处
《可再生能源》
CAS
北大核心
2018年第4期550-556,共7页
Renewable Energy Resources
基金
国家自然科学基金项目(51277029)
关键词
弱区域配电网
水电站
多时间尺度
风险模式
极端天气
weak regional distribution network
hydropower station
multi-time scale
riskmodel
extreme weather
