Optimizing Unit Scheduling with Fuzzy Logic: A Strategic Approach for Efficient Power Network Operations

Abstract

This study delves into addressing the challenge of resolving the Unit Commitment (UC) problem, which focuses on enhancing the efficiency of production units and devising their operational schedules to accommodate fluctuations in consumption spanning from a day to a month. Given the intricate, combinatorial, and nonlinear constraints associated with each production unit, this study advocates an optimization approach rooted in fuzzy logic. A Langrangian function was established to simplify the UCP and to transform the different inequality into a linear unconstrained problem. The choice of fuzzy inputs was established using the partial derivatives of a Lagrangian function as a function of the powers injected into each node of the electrical network. This combination of the Lagrangian function and the input of the fuzzy regulator made it possible to control the different constraints in the total production cost function and to improve the operating efficiency of the different production units. This method was effectively applied to a 14-bus IEEE power network encompassing 5 generating units, to address the UC problem by optimizing generator load capacity (LCG) and minimizing Incremental Losses (IL). The numerical processing of the fuzzy linguistic variables was implemented using Mamdani-type fuzzy rules. This strategy stands out for its robust exploratory capability, facilitating the identification of optimal solutions to reduce production costs while ensuring optimal planning of production units.