文章摘要
董雷,孟天骄,陈乃仕,等.基于马尔可夫链-多场景技术的交直流主动配电网优化调度[J].电力系统自动化. DOI: 10.7500/AEPS20170616002.
DONG Lei,MENG Tianjiao,CHEN Naishi, et al.Optimized Scheduling Based on Markov Chains-Multiple Scenarios Technique in AC/DC Hybrid Active Distribution Network[J].Automation of Electric Power Systems. DOI: 10.7500/AEPS20170616002.
基于马尔可夫链-多场景技术的交直流主动配电网优化调度
Optimized Scheduling Based on Markov Chains-Multiple Scenarios Technique in AC/DC Hybrid Active Distribution Network
DOI:10.7500/AEPS20170616002
关键词: 多场景技术  马尔可夫链  模糊C均值聚类  多时间尺度协调调度
KeyWords: multiple scenarios technique  Markov chain  fuzzy C-means clustering  multi-time-scale coordinated scheduling
上网日期:2018-01-12
基金项目:国家重点研发计划资助项目(2017YFB0903300),北京市自然科学基金重点资助项目(3161002)
作者单位E-mail
董雷 华北电力大学电气与电子工程学院 hbdldl@126.com 
孟天骄 华北电力大学电气与电子工程学院 853593803@qq.com 
陈乃仕 中国电力科学研究院 chennaishi@epri.sgcc.com.cn 
李烨 中国电力科学研究院 liye@epri.sgcc.com.cn 
蒲天骄 中国电力科学研究院 putianjiao@vip.sina.com 
摘要:
      针对含有柔性直流装置的交直流混合主动配电网,构建基于场景分析的随机优化调度模型。计及时间轴线上各点误差相关性,利用基于马尔可夫链的多场景技术模拟风电、光伏与负荷随时间变化的间歇性和波动性;通过模糊C均值聚类思想进行场景削减得典型场景。提出多时间尺度的协调优化调度策略:长时间尺度计及不确定性,以电网期望成本最小为目标,优化联络线出力和柔性直流装置出力;短时间尺度调整可控单元,使两级优化结果偏差最小。算例验证了采用马尔可夫链的多场景抽样能有效描述原始问题的不确定性,修正随时间推移逐渐增大的预测偏差,减轻短时间尺度调度压力;验证了所提调度策略能有效应对间歇式能源的不确定波动,提高分布式能源消纳能力。
Abstract:
      Aimed at the AC/DC hybrid active distribution network (ADN) containing voltage sourced converters (VSCs), a scenario-based stochastic optimal scheduling model is built. Considering the temporal correlation of the errors changes, a multiple scenarios technique combined with Markov chain is used to imitate the intermittency and volatility of the wind power, photovoltaic power and uncertain load. A large number of scenarios are clustered by fuzzy C-means algorithm to gain representative scenarios. A multi-time-scale coordinated scheduling strategy is proposed and the specific strategies are as follows: In the long-time scale, the outputs of the connecting line and flexible DC devices are optimized with the goal of minimizing the total cost of the power grid, while in the short-time scale, the output of adjustable resources is corrected on the basis of long-time scale results to minimize the difference. The results verify that multiple scenarios technique with the Markov chain can describe the uncertainty of original problem effectively and correct the prediction deviation which increases over time. It is also beneficial to reducing the pressure of short-time scale dispatch. The paper also illustrates that the model is contributed to deal with the uncertain fluctuations and improve the consumptive ability of intermittent resource.
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