研究目的
To establish an optimal allocation model of DSTATCOM that considers active power loss, voltage deviation, and total allocation cost as objective functions, addressing the impact of high penetration of photovoltaic (PV) systems on an active distribution network.
研究成果
The paper concludes that the proposed optimization method integrating MCS, opportunity constraint, and MODEGWO effectively solves the location and capacity problem of DSTATCOM with PV accessed in a distributed network. The simulation results confirm the validity and feasibility of the proposed method, providing a framework for future studies on the optimal allocation of DSTATCOM with renewable energy accessed in a distributed network.
研究不足
The study primarily focuses on the random output of PV systems and does not consider other renewable energy sources like wind turbine generators, micro-turbine generators, and electric vehicles, which could be included in future research.
1：Experimental Design and Method Selection:
The study employs Monte Carlo simulation (MCS) to simulate the random output of PV and introduces an opportunity constraint into the optimal model to evaluate the reliability of the allocation scheme. Multiobjective differential evolution gray wolf optimization (MODEGWO) is proposed to determine the optimal location and capacity of the DSTATCOM.
2：Sample Selection and Data Sources:
The IEEE 33-bus system is used as the test system, with total active power and reactive power being 3715 kW and 2300 kVar, respectively.
3：List of Experimental Equipment and Materials:
The study involves the use of DSTATCOM and PV systems within the IEEE 33-bus system.
4：Experimental Procedures and Operational Workflow:
The methodology includes coding for the allocation of DSTATCOM, integer programming for position variables, cross-border punishment for configuration results that do not satisfy the opportunity constraint, and the implementation of the improved gray wolf optimization algorithm.
5：Data Analysis Methods:
The study uses probabilistic power flow calculation to analyze the mean of each objective function and the probability of events that satisfy the opportunity constraints during the sampling period.
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