摘要
随着风光并网规模不断增加,仅在电源侧利用水电等调峰资源难以满足大规模风光并网需求。为此,本文在负荷侧引入价格型需求响应技术优化日负荷曲线,以源荷匹配度最高,弃电量最小为目标,建立了考虑价格型需求响应的水风光多能互补短期优化调度模型。同时为降低模型求解难度,将其解耦为两个单目标优化问题分阶段求解,最后将模型应用于金沙江下游乌东德水电站和昆明、玉溪两地风光电站互补联合发电研究中。结果表明:价格型需求响应技术对负荷曲线具有明显的削峰填谷效果,不仅可以提高水风光多能互补系统的源荷匹配程度,还能促进风光消纳,增加水风光联合总发电量。该结果论证了模型的有效性,为提高电网大规模消纳新能源提供了新思路。
As the scale of wind and photovoltaic energy integration increases,it is more difficult to satisfy the needs of large-scale new energy integration by only using hydropower and other peak-shaving resources on the power source side.To address this issue,we develop a short-term optimization model for hydro-wind-solar power complementary scheduling considering participation of the price-based demand response(PDR),which adjusts optimally the next-day load curve using PDR on the load side.And this model is decomposed into two sub-models of single target optimization,and solved in stages to reduce solution difficulty.Finally,it is applied to the system of the Wudongde hydropower station and the wind and photovoltaic power stations in Kunming and Yuxi.The results show that PDR has an obvious peak-load shifting effect on the load curve,so it not only can improve the source-load matching,but also can enhance the accommodation capacity of wind and solar power and increase the system’s total power generation.This demonstrates the effectiveness of the model and a new idea for the large-scale accommodation of new energy in the power grid.
作者
蒋光梓
彭杨
纪昌明
罗诗琦
于显亮
JIANG Guangzi;PENG Yang;JI Changming;LUO Shiqi;YU Xianliang(School of Water Resources and Hydropower Engineering,North China Electric Power University,Beijing 102206,China)
出处
《水力发电学报》
CSCD
北大核心
2023年第10期1-12,共12页
Journal of Hydroelectric Engineering
基金
国家重点研发计划项目(2016YFC0402308)
国家自然科学基金项目(51679088)。
关键词
价格型需求响应
多能互补
短期优化调度
源荷匹配
风光消纳
price-based demand response
multi-energy complementary
short-term optimal scheduling
source-load matching
accommodation of wind and solar power
作者简介
蒋光梓(1998-),男,硕士生.E-mail:120202238008@ncepu.edu.cn;通信作者:彭杨(1975-),女,教授.E-mail:pengyang@ncepu.edu.cn。
