This study proposes an optimized design method for wind-solar-storage microgrid systems in the Gobi Desert region of northwest China. The core innovation is the development of a Modified Dragonfly Algorithm (MDA) to address the challenges of optimal system sizing and operation under complex desert conditions characterized by high renewable volatility and demanding environmental constraints. To strengthen the algorithm’s global search capability and convergence speed, three key enhancements are introduced: optimal point set initialization for even population distribution, cosine similarity guidance for balanced exploration-exploitation, and a nonlinear convergence factor for adaptive adjustment. The multi-objective optimization model is evaluated using a comprehensive set of technical, economic, and environmental metrics. Simulation results for a case study demonstrate the effectiveness of the proposed approach. The optimized microgrid configuration achieves a total net present cost of 40.062 million CNY, a competitive levelized cost of energy of 0.452 CNY/kWh, and a high renewable energy penetration rate of 88.73%. Environmentally, the system significantly reduces carbon dioxide emissions by approximately 1403.35 t annually compared to conventional power supply. A detailed sensitivity analysis reveals that energy storage capacity, local wind speed variability, and load fluctuations are the most critical factors influencing system economy and operational stability. Furthermore, a financial feasibility assessment yields a positive net present value of 9.237 million CNY and an investment payback period of approximately 8.7 years. These results collectively confirm the proposed MDA-optimized microgrid design offers strong economic viability, technical reliability, and substantial environmental benefits for sustainable development in arid and remote desert regions.
Nie et al. (Thu,) studied this question.