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

Guojiang Xiong,Jing Zhang,Dongyuan Shi,Xufeng Yuan

doi:10.1155/2019/3923691

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.

Highlights

Rising energy costs, losses in the present-day electricity grid, risks from nuclear power generation, and global environmental changes highlight the increasing signi cance of renewable energy resources for electricity generation [1]
To validate the performance of supplydemand-based optimization (SDO) in solving the parameter extraction problem of PV models, SDO is applied to the following four different PV models with diverse characteristics: (i) RTC France silicon solar cell [48]: contains 26 pairs of I-V data points measured at 33°C under 1000 W/m2 irradiance (ii) PVM 752 GaAs thin film cell [17]: contains 44 pairs of I-V data points measured at 25°C under 1000 W/m2 irradiance (iii) STM6-40/36 monocrystalline module [30, 49]: composed of 36 cells in series with 20 pairs of I-V data points measured at 51°C
SDO beats both self-adaptive teaching-learning-based optimization (SATLBO) and teaching-learning-based artificial bee colony (TLABC) for the diode model (DDM), indicating SDO is more effective in solving this complex model. e test results show SDO is better than the nine algorithms except improved JAYA optimization algorithm (IJAYA). e convergence curves in Figure 7 show that IJAYA is the fastest while SDO maintains a relatively fast speed even at the later stage

Summary

Introduction

Losses in the present-day electricity grid, risks from nuclear power generation, and global environmental changes highlight the increasing signi cance of renewable energy resources for electricity generation [1]. Xiong et al solved the parameter extraction problem of different PV models by using several metaheuristics including symbiotic organisms search (SOS) algorithm [24], improved WOA based on two modified prey searching strategies [25], and hybrid DE with WOA [26]. There is no “one size fits all” metaheuristic for extracting accurate parameters for all PV models, which highly motivates the authors to attempt new ones for the purpose of achieving optimal or suboptimal solutions for the problem considered here. A very young and effective metaheuristic named supply-demand-based optimization (SDO) [42] developed in 2019 is first applied to the parameter extraction problem of PV models.

Problem Formulation

Experimental Results

Conclusions and Future Work