引用本文: | 姚 刚,熊国江,张 靖,等.区域风光集群断面裕度分配策略研究[J].电力系统保护与控制,2021,49(19):48-55.[点击复制] |
YAO Gang,XIONG Guojiang,ZHANG Jing,et al.Section margin allocation strategy for regional landscape cluster[J].Power System Protection and Control,2021,49(19):48-55[点击复制] |
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摘要: |
为适应高占比可再生能源接入后的电网形态,从装机容量、开机容量、超短期功率预测、电量完成率、最小弃风弃光等角度给出了多种不同的断面裕度分配方法,并比较分析了各种方法的优缺点。提出了一种断面裕度最小弃风弃光加权分配法,综合考虑了新能源场站的考核打分指标和电量完成指标,并提出了相应的基于正交学习差分进化算法的求解方法。通过对新英格兰39节点系统仿真,分析了该断面裕度分配方法的合理性和有效性。在确保电网安全稳定的前提下,该方法最大程度地利用电网输送通道资源,最大限度地提高电网对新能源发电的接纳能力,实现了对新能源场站实时出力的动态优化分配,保证了新能源场站出力最大化。 |
关键词: 断面裕度 正交学习差分进化 新能源 最小弃风弃光 |
DOI:DOI: 10.19783/j.cnki.pspc.201412 |
投稿时间:2020-11-24修订日期:2021-05-04 |
基金项目:国家自然科学基金资助项目(51867005);贵州电网有限责任公司科技项目(066500KK52170037) |
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Section margin allocation strategy for regional landscape cluster |
YAO Gang1,XIONG Guojiang2,ZHANG Jing2,SHI Ke1,WU Shihong1 |
(1. Power Dispatch Control Center of Guizhou Power Grid Co., Ltd., Guiyang 550002, China;
2. School of Electrical Engineering, Guizhou University, Guiyang 550025, China) |
Abstract: |
In order to adapt to the grid form after a high-proportion of renewable energy is connected, this paper gives a variety of different power flow margin allocations from the perspective of installed capacity, start-up capacity, ultra-short-term power prediction, power completion rate and minimum abandoned wind and light. Also it compares and analyses their advantages and disadvantages. A power flow margin-weighted distribution method with the smallest abandonment of wind and light is proposed. This comprehensively considers the assessment and scoring indicator of new energy stations and power completion indicators, and proposes a corresponding solution based on an orthogonal learning differential evolution algorithm. The rationality and effectiveness of this power flow margin allocation method are analyzed by simulation of the New England 39-bus system. To help ensure the safety and stability of the power grid, the transmission channel resources of the power grid are used to the greatest extent, and the power grid's ability to accept new energy is maximized. This realizes the dynamic and optimal distribution of real-time output of new energy plants, and ensures the maximum output of new energy plants.
This work is supported the National Natural Science Foundation of China (No. 51867005) and the Science and Technology Foundation of Guizhou Power Grid Co., Ltd. (No. 066500KK52170037). |
Key words: section margin orthogonal learning differential evolution new energy minimum abandon wind and light |