MF-APSO-Based Multiobjective Optimization for PV System Reactive Power Regulation

Abstract

This paper proposes a reactive power-regulation strategy for a distribution system connected with high-penetration photovoltaic (PV) generation. The PV reactive power regulation is formulated as a multiobjective optimization problem to relieve the overvoltage caused by high PV penetration and to minimize total line loss. With integrated power-flow analysis, a new mutation fuzzy adaptive particle swarm optimization (MF-APSO) algorithm is proposed to solve the multiobjective optimization problem. The proposed reactive power-regulation strategy and MF-APSO algorithm are, respectively, compared with conventional methods for overvoltage mitigation and total line-loss reduction, as well as with the referenced optimization algorithms for the problems of concern. Numerical results verify that the proposed multiobjective optimization method can more effectively mitigate the overvoltage issue and greatly reduce the total line loss as compared to other methods. Utilization of high-penetration PV systems can thus be further enhanced with reduced power curtailment owing to the added functions of voltage regulation and line-loss minimization.Index Terms-Distributed power generation, Pareto-optimal set, particle swarm optimization (PSO), photovoltaic (PV) systems, reactive power control.

Impact of optimal integration of renewable energy sources and electric vehicles in practical distribution feeder with uncertain load demand

“…Here, s indicates the solar radiation (kW/m 2 ). α and β represents the parameters of beta distribution function f b (s) and they are computed via (23) and (24), respectively.…”

“…These data are adopted from Kakdwip region distribution system site in the province of West Bengal, India. The EOP of PVGS at different levels of s is determined through (22)(23)(24)(25)(26)(27)(28)(29)(30). The PDF and EOP of solar irradiance for complete 24 hours with 20 states are illustrated in Figure 3.…”

“…13 As of now from the literature, it is realized that many authors have applied various approaches to solve DER integration problem in a multiobjective framework. These are analytical-based approach, 17 AI techniques such as particle swarm optimization (PSO), 18 multiobjective artificial bee colony (MOABC) optimization algorithm, 19 multiobjective particle swarm optimization (MOPSO) algorithm, 20,21 GA, 22 mutation fuzzy adaptive particle swarm optimization (MF-PSO) algorithm, 23 Fuzzy-based ant colony optimization (ACO), 24 hybrid MOPSO, 25 fuzzy-based PSO, 26 modified PSO algorithm, 27 modified honey bee mating optimization (MHBMO) algorithm, 28,29 nondominated sorting genetic algorithm-II (NSGA-II), 30 and hybrid algorithm formed by combining ACO and ABC algorithm. 31 Furthermore, some recently published articles related to DER integration-based distribution systems are discussed here.…”

A Sensitivity Matrix Approach Using Two-Stage Optimization for Voltage Regulation of LV Networks with High PV Penetration

“…However, these methods suffer from a high computation time due to varying reasons such as the need to solve load flows within the optimization algorithm and the integration of VAR compensation equipment. Most of the referenced centralized methods related to power systems control use load flow analysis to calculate voltage variation [55][56][57]. Since these methods achieve accurate results at the expense of time, a voltage and PV power sensitivity approach is used to calculate the voltage variations [27,[58][59][60][61].…”

Comprehensive improvement of artificial fish swarm algorithm for global MPPT in PV system under partial shading conditions

“…Renaudineau et al (2015) presented a global optimization strategy applied to a distributed PV generation system, in which the real-time constrained optimization problem is solved by using the PSO method, which needs knowledge of the actual current vs voltage curve of each PV generator. By combining the Pareto-search method and a mutation process based on the existing fuzzy adaptive PSO (FAPSO) algorithm, a new mutation FAPSO (MF-APSO) approach is proposed to achieve the Pareto-front of the formulated multi-objective optimization problem and overcome the problem of trapping at a local-optimal point for PV system reactive power regulation (Yang and Liao, 2015). The authors in (Phimmasone et al, 2010) have updated the traditional PSO equations by adding various coefficients to improve the searching accuracy of the algorithm, although this has increased its computational burden.…”