Abstract
The paper presents a novel computationally efficient Quasi-Static-Time-Series (QSTS) approach for the sizing and sitting of photovoltaic arrays in a distribution network, which uses historical hourly demand and irradiance data. Additionally, the proposed approach combines a battery energy storage system (BESS) with the PV array for smoothing the array output and integrates inverter reactive power control for voltage support. An optimized search method based on the combined Non-dominated Sorting Genetic Algorithm-II and Fuzzy Decision-Making Tool (NSGAII/FDMT) is employed for the sizing and sitting problem along with three objectives: (a) minimization of total cost; (b) minimization of distribution network power losses; and (c) maximization of system security. The distribution network-PV array-BESS system along with its control functions is solved in hourly steps for a yearly 8760-h horizon obtaining an accurate estimate of the system performance. In order to decrease execution time, a robust formulation of the direct power flow method is developed in the paper yielding a fast 8760-solution algorithm. The performance of the proposed method is tested through different case studies on a modified IEEE 123-bus and 33-bus radial distribution systems. The results demonstrate that detailed system analysis using historical data produces more efficient options than conventional methods based on system analysis at peak load only.
Published Version
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