نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری دانشکده اقتصاد و مدیریت دانشگاه باهنرکرمان، کرمان، ایران
2 دانشیار اقتصاد دانشکده مدیریت واقتصاد، دانشگاه شهید باهنر کرمان، کرمان، ایران
3 استاد دانشکده اقتصاد و مدیریت دانشگاه باهنر کرمان،کرمان، ایران
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
This study proposes a structural optimization framework for simulating electricity capacity markets, incorporating both fossil-fuel and renewable energy sources. The proposed model integrates market economic behavior with intelligent computational methods through the Grey Wolf Optimizer algorithm. In the first stage, the inverse demand function is estimated using real market data, including short-term stochastic fluctuations to capture demand uncertainty. Next, the cost structure of electricity generation units is derived by separating fixed and variable components, based on observed capacity and production patterns. Finally, the GWO algorithm is applied to determine the optimal allocation of generation capacity under technical and market constraints. The hierarchical design of the algorithm enables efficient exploration of the decision space, rapid convergence, and preservation of economic consistency. Simulation results indicate inefficiencies in the utilization of fossil-fuel units, while renewable energy units, with near-zero marginal costs, demonstrate superior economic performance. The proposed framework serves as a comprehensive tool for market equilibrium analysis and capacity planning and can inform energy policy evaluation and strategic decision-making in electricity markets and capital-intensive industries.
کلیدواژهها [English]
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