Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models

Abstract

Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

“…In the original WOA, the random parameter p is generated for each individual, indicating that all dimensions would perform the same search strategy. For example, on the premise of |A| ≥ 1, if p < 0.5, then the current individual would perform Equation (15). According to Equation (15), WOA updates the current individual around a random individual x t r , which is beneficial for the exploration but harmful to the exploitation.…”

“…For example, on the premise of |A| ≥ 1, if p < 0.5, then the current individual would perform Equation (15). According to Equation (15), WOA updates the current individual around a random individual x t r , which is beneficial for the exploration but harmful to the exploitation. In fact, different dimensions of an individual have different performance requirements for exploration and exploitation.…”

“…In this work, the maximum number of fitness evaluations (Max_FEs) setting as 50,000 [15,17,24,33] was employed as the stopping condition. All involved algorithms used the same population size with the value ps = 50 [14,24].…”

“…The performance of MCSWOA was further verified by some advanced non−WOA variants. Thirteen algorithms consisting of BLPSO [43], CLPSO [44], CSO [45], DBBO [46], DE/BBO [47], GOTLBO [14], IJAYA [17], LETLBO [48], MABC [49], ODE [50], SATLBO [15], SLPSO [51], and TLABC [24] were employed for comparison in this subsection. The result of Wilcoxon's rank sum test tabulated in Table 12 shows that MCSWOA performed very competitively and outperformed all of the other 13 algorithms on 9 cases except Case 4, on which MCSWOA was surpassed by ODE and DBBO, and tied by TLABC.…”

“…Therefore, they can hedge the problems of deterministic methods and have attracted growing attention recently. Many metaheuristic methods concluding particle swarm optimization (PSO) [9][10][11], differential evolution (DE) [12], teaching-learning-based optimization (TLBO) [13,14], supply-demand-based optimization (SDO) [15], symbiotic organisms search algorithm (SOS) [16], JAYA algorithm [17], artificial bee colony (ABC) [18], imperialist competitive algorithm [19], flower pollination algorithm (FPA) [20], hybrid algorithms [21][22][23][24], etc., have been applied to the parameter extraction problem of PV models.…”