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1.国网浙江省电力有限公司电力科学研究院,浙江省杭州市 310014
2.浙江大学电气工程学院,浙江省杭州市 310027
Received:20 March 2021,
Revised:2021-05-12,
Published Online:07 July 2021,
Published:10 December 2021
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陈哲,王橹裕,郭创新等.基于风险的多区互联电力系统分布式鲁棒动态经济调度[J].电力系统自动化,2021,45(23):113-122.
CHEN Zhe,WANG Luyu,GUO Chuangxin,et al.Risk-based Distributed Robust Dynamic Economic Dispatch for Interconnected Multi-regional Power Systems[J].Automation of Electric Power Systems,2021,45(23):113-122.
陈哲,王橹裕,郭创新等.基于风险的多区互联电力系统分布式鲁棒动态经济调度[J].电力系统自动化,2021,45(23):113-122. DOI: 10.7500/AEPS20210320002.
CHEN Zhe,WANG Luyu,GUO Chuangxin,et al.Risk-based Distributed Robust Dynamic Economic Dispatch for Interconnected Multi-regional Power Systems[J].Automation of Electric Power Systems,2021,45(23):113-122. DOI: 10.7500/AEPS20210320002.
为应对高比例可再生能源并网给调度运行带来的高不确定性,提出一种基于风险的两阶段鲁棒动态经济调度模型,可用于优化多区互联电力系统的联络线功率与区域发电及备用计划。在鲁棒优化模型中对所采用不确定集的边界进行优化,以评估弃风与失负荷风险。对联络线功率的优化包括两个方面:一是针对可再生能源预测出力制定基态联络线计划;二是针对实际可能出现的可再生能源出力制定功率调整计划,以实现区域备用共享。建立一种分布式调度体系,以实现各区域电力系统分散自治运行。采用动态乘子更新策略,避免了分布式优化算法参数选择的问题。在3区354节点系统上的算例分析验证了所提模型与算法的有效性。
In order to cope with the high uncertainty brought by the grid connection of high proportion of renewable energy to the dispatch operation
a risk-based two-stage robust dynamic economic dispatch model is proposed
which can optimize the tie-line power and regional power generation and backup plan for interconnected multi-regional power systems. The boundaries of the used uncertainty set in the robust optimization model are optimized to evaluate the risk of wind curtailment and loss of load. The optimization of tie line power includes two aspects. One is to formulate a base-state tie line plan for the forecasted power output of renewable energy. The other is to formulate a power adjustment plan for the actual possible renewable energy output to achieve regional backup sharing. A distributed dispatch system is established to realize the decentralized and autonomous operation of each regional power system. A dynamic multiplier update strategy is adopted to avoid the problem of parameter selection in the distributed optimization algorithm. Case studies on a 3-area 354-bus system demonstrate the effectiveness of the proposed model and algorithm.
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