Demand Response with Volt/Var Optimization for unbalanced active distribution systems

被引:0
|
作者
Vijayan V. [1 ]
Mohapatra A. [1 ]
Singh S.N. [1 ]
机构
[1] Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur
关键词
Active distribution systems; Demand Response; Smart inverter; Unbalance minimization; Volt/Var Optimization;
D O I
10.1016/j.apenergy.2021.117361
中图分类号
学科分类号
摘要
The strong mutual influence of Demand Response (DR) and Volt/Var Optimization (VVO) on each other has largely been ignored in most of the previous schemes. Those research studies, which collectively consider DR and VVO, deal with minimal devices. Further, rescheduling of loads participating in DR and phase-specific operations of voltage control devices, such as Capacitor Banks (CBs), Voltage Regulators (VRs), On-Load Tap Changer (OLTC), and inverters of the solar photovoltaic sources, in an active three-phase distribution system significantly cause the unbalance levels. Hence, a comprehensive formulation, which considers DR to reduce peak load, VVO to minimize loss, and unbalance minimization using elastic loads, inverters, OLTC, VRs, and CBs, is proposed in this paper for three-phase unbalanced active distribution systems. Most previous studies deal with the cost while integrating DR and VVO, favoring real power rescheduling. In this research, rescheduling of kVA loads, affecting VVO devices’ operations, is adopted. A load factor-based load shifting index is proposed and utilized for this purpose. The proposed formulation aims to minimize the substation transformer's current unbalance levels that seem to increase while limiting bus voltage unbalance factors. The proposed scheme is solved using the multi-objective particle swarm optimization and is tested on the modified IEEE 13-bus and IEEE 123-bus test systems. Compared with the Conservation Voltage Reduction (CVR) based method, the obtained results reveal that the proposed formulation minimizes peak load, loss, and unbalances more effectively while managing voltage rise or drop issues more efficiently without source or load curtailments. © 2021 Elsevier Ltd
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