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Three-level day-ahead optimal scheduling framework considering multi-stakeholders in active distribution networks: Up-to-down approach

机译:考虑主动配送网络中的多利益相关者:上下方法考虑三级日前最佳调度框架

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摘要

Distribution systems are transforming from passive networks to active ones with the development and deployment of renewable energy sources, microgrids (MGs) and virtual plants. Accompanied by the emergence of multiple stakeholders owning personal distributed generators (DGs), energy storage systems and even MGs, the optimal dispatch problem in active distribution networks (ADNs) is challenged. Basing on three-level day-ahead optimal scheduling framework, an up-to-down interaction mechanism is proposed firstly for the optimization in ADNs considering multi-stakeholders. The optimization mechanism starts from DN-layer with minimizing total active power loss of the DN, and then it implements the optimizations of all MGs within the optimizations of all users in the corresponding MG. The optimizing information of connection nodes will be fed back to upper layers in turn and this three-level optimization will be repeatedly implemented until the maximal unbalance power generation returned from all MGs satisfying the convergence condition. Additionally, an elasticity coefficient-based demand response program using time-of-use (TOU) pricing is integrated into the optimizations of MG-layer and User-Layer to guide peak load cutting. Moreover, a power flow rebalancing strategy is also integrated into the MG-layer optimization to accelerate the convergence of the whole solution. Finally, an actual 47-bus distribution system is employed to verify this proposed three-level optimization and the results show the effectiveness and applicability of the proposed method. (C) 2020 Elsevier Ltd. All rights reserved.
机译:随着可再生能源,微电网(MGS)和虚拟工厂的开发和部署,分配系统正在从被动网络转换为活动网络。伴随着拥有个人分布式发电机(DGS)的多个利益攸关方的出现,能量存储系统甚至MGS,主动分配网络(ADNS)中的最佳调度问题受到挑战。基于三级日前最佳调度框架,提出了一种上下的交互机制,首先提出了考虑多利益相关者的ADNS优化。优化机制从DN层开始,最小化DN的总有效功率损耗,然后它在相应MG中所有用户的优化内实现所有MG的优化。连接节点的优化信息依次反馈到上层,并且将重复实现这三级优化,直到满足收敛条件的所有MGS返回的最大不平衡发电。另外,基于弹性系数的需求响应程序使用使用时间(TOU)定价进行集成到MG层和用户层的优化中以引导峰值负载切割。此外,电流重新平衡策略还集成到MG层优化中,以加速整个解决方案的收敛。最后,采用实际的47母线分配系统来验证这一提出的三级优化,结果表明了该方法的有效性和适用性。 (c)2020 elestvier有限公司保留所有权利。

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