Big data computing and other technologies can improve the effectiveness of photovoltaic power plant management optimization. This paper uses the traditional gray wolf optimization (GWO) algorithm to optimize and extract the five parameters of the single diode model of photovoltaic components. Considering the objective function of purchase cost and cost loss, the paper seeks the possibility of achieving the optimal configuration with the lowest total cost. A spatial recognition mechanism is introduced to optimize the gray wolf algorithm, and the degree of violation of multi-step calculation time constraints is calculated to iteratively complete the global solution through local optimization breakthroughs. Research shows: The Gray Wolf optimization algorithm achieved a 100.0% success rate in optimization across five test functions. The optimized power generation cost was only 1.20270 × 10 ⁴ yuan, and the optimal solution was obtained after 118 iterations. The improvement in photovoltaic power generation reached up to 51.5%, with voltage fluctuations under different operating conditions less than 0.01V, achieving efficient and stable power generation.
