摘要
随着多微网新能源渗透率不断提高,风电、光伏等新能源的随机性及不同运营主体间的利益博弈给传统经济调度模式带来挑战。为提高多微网系统安全性及经济性,提出了一种考虑风光互补特征的多微网系统自治经济调度方法。以多微网系统经济运行成本最优为目标,同时考虑多场景下的机组调节费用,基于Copula理论形成风电、光伏典型场景生成方法,运用目标级联分析法建立双层优化模型,实现微网、配电网自主独立优化。以IEEE 33节点系统为例,比较考虑风光相关性与仅考虑随机性情景的多微网系统经济性,并与集中式调度方法进行对比,验证了考虑风光互补特征的调度方法可提高系统经济性,并在极端场景时提高系统鲁棒性。
With the continuous increase of renewable energy permeability,the randomness of new energy and the benefit game between different operators bring a great challenge to the economic dispatch of power system. For safety and economy improvement of microgrid system,the paper proposes an autonomous economic dispatch method for multi-microgird systems considering the complementarity between wind solar power. With the goal of optimal cost for operation economy of multi-microgrid and taking account of unit regulation cost under multiple scenarios,a typical scenario generation method of wind and photovoltaic power based on Copula theory is proposed. Analytical target cascading(ATC)is used to establish a two-layer optimization model to optimize microgrid and distribution networks autonomously and independently. The IEEE 33 bus system is taken as an example to compare the economic efficiencies of multi-microgrid systems that take account of wind-solar power correlation and multi-microgrid systems that take account of random scenarios only. Furthermore,the economic efficiencies are compared with that of the centralized dispatching method. It is verified that the dispatching method taking account of the complementarity between wind and solar power can improve system economy and enhance system robustness under extreme scenarios.
作者
吉祥
谢敏
曾东
张昕
吴伟
尹起
付世杰
吴子杰
JI Xiang;XIE Min;ZENG Dong;ZHANG Xin;WU Wei;YIN Qi;FU Shijie;WU Zijie(State Grid Jiaxing Power Supply Company,Jiaxing Zhejiang 314000,China;School of Electric Power,South China University of Technology,Guangzhou 510640,China)
出处
《浙江电力》
2022年第10期97-105,共9页
Zhejiang Electric Power
基金
广东省自然科学基金项目(2021A1515012245)。
关键词
多微网系统
风光互补
场景生成
自治优化
multi-microgrid systems
wind-solar power complementarity
scenario generation
autonomous optimization
