article · Energy Reports
Accurate extraction of electrical parameters is essential for modelling the behaviour of photovoltaic devices. The Supply-Demand-Based Optimization algorithm applies principles from market economy mechanisms, specifically the stability and instability of supply and demand, to determine these parameters across different solar models. Tested on three commercial photovoltaic modules, including STM6_40_36, STP6_120_36, and Photowatt-PWP 201, the method aims to minimise discrepancy with experimental current-voltage measurements. The technique was also applied to online parameter extraction across multiple solar irradiance levels ranging from 200 to 1000 watts per square metre and operating temperatures between 25 and 75 degrees Celsius. When compared against several established optimization methods across thirty runs, the approach demonstrated low standard deviations of fitness values and strong correlation with measured data, showing consistent convergence characteristics.
Solar energy systems require precise mathematical models to predict energy yield, assess degradation, and optimise performance in variable operating conditions. Using algorithms that closely match real physical data under shifting temperature and sunlight levels helps engineers simulate module behaviour more reliably. This improves the fidelity of solar power planning and monitoring tools used in renewable energy generation.
This method is an applied computational tool tested against commercial photovoltaic modules and measured operating data. Potential users include solar panel manufacturers, system integrators, and software developers building digital monitoring and control tools for solar installations. Given that tests evaluated online parameter extraction under varying field conditions, the technique appears close to deployment within photovoltaic simulation software or operational performance-monitoring systems.
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This article illustrates a new application of the Supply-Demand-Based Optimization (SDO) algorithm to accurately extract the electrical parameters of different PV models. The key goal is to minimize the total error between the experimental data and the proposed approach by optimizing the electrical parameter of three different models. The SDO simulates the stability and instability modes of the supply–demand mechanism in market economy, where the quantity and price converge and diverge from the equilibrium point, respectively. Three different marketable PV modules were provided, and the findings correlate with the experimental data and other well-known optimization techniques denoting the superiority of SDO. These modules are STM6_40_36, STP6_120_36, and Photowatt-PWP 201 are adopted. The validation is introduced, and the findings are correlated with experimental data and other recently well-known optimization techniques which are grey wolf optimization (GWO), Crow search Optimizer (CSO), Bernstein–Levy Search Differential Evolution Algorithm (BSDE), and Manta Ray Foraging Optimizer (MRFO), Backtracking Search Algorithm (BSA). In the article a deep analysis has been carried out for the best parameter extraction in a PV module. An online parameter extraction is extended based on the SDO algorithm via SDM under different sunshine irradiation of 200, 400, 600, 800 and 1000 W/m2 and temperature of 25, 50 and 75 °C. The standard deviations of the fitness values, over 30 runs, for three models for the above-mentioned modules are less than 1 × 10−18, 10−17 and 10−6, respectively which denotes the superiority of the SDO. Also, a unique set of the extracted parameters is provided with significant validations of the proposed SDO using measured I–V curves for different irradiance and temperature values. Also, the obtained results were compared to different algorithms with a detailed statistic characterizations for convergence, achieving good fitting correlations. Therefore, the SDO are extremely consistent, competitive one among other algorithms.
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DOI: 10.1016/j.egyr.2021.08.188
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