A b s t r a c t

In the complex and dynamic landscape of electricity markets, producers’ decision making regarding pricing strategies and technology selection for power generation is profoundly influenced by prevailing uncertainties, accumulated experience, and their understanding of present and future power system conditions. This study aims to model the decision making behavior of electricity producers by integrating the Gray Wolf Optimization (GWO) algorithm with a fuzzy inference system (FIS), in order to simulate the simultaneous effects of historical experience, market power, and evolving structural conditions on strategic behavior. Within this hybrid framework, the fuzzy system plays a pivotal role in determining risk taking strategies, while market power is analyzed in a dynamic, real time manner. Beyond behavioral modeling, the study also conducts a comparative evaluation of electricity generation technologies under uncertainty, assessing conventional and renewable alternatives—such as fossil fuel plants and photovoltaic systems—based on criteria including cost efficiency, supply reliability, and stakeholder preferences. By combining the GWO optimizer with stakeholder insights from the electricity industry, the proposed model incorporates qualitative, customer oriented factors into the optimization process, thereby enhancing the validity, adaptability, and practical effectiveness of decisions. Results from sensitivity analysis and technology categorization into three distinct groups yield more precise and comparable conclusions, offering a novel analytical framework for optimal technology selection under uncertainty.