Search Space Reduction for the Thermal Unit Commitment Problem through a Relevance Matrix

Abstract

Given the combinatorial explosion related to the operation decisions in the thermal unit commitment problem, this paper presents a new strategy to reduce the search space and to start the multi-modal optimization process. To achieve such goals, a relevance matrix is obtained to indicate how important each generating unit is at each hour of the operational planning. This matrix is generated through the results of a constructive heuristic based on sensitivity indexes that account for operational and economic characteristics of the generating units and of the system under planning. The proposed method is shown to reduce the complexity of the problem, thus decreasing the combinatorial explosion and, consequently, the computational burden. Its effectiveness is verified by performing optimizations with and without its utilization. The results achieved with the proposed space-reduction approach enable solutions that present good quality. Furthermore, these solutions are retrieved with significantly reduced processing time.

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

“…The objective function for MOPS is usually formulated by combining the separate cost and emission functions in Equations ( 3) and (6), respectively, into one. Traditionally, various methods have been widely employed, such as emission constraints, weighted-sum approaches, and cost-penalty factors.…”

“…First, it involves a combinatorial dimensionality explosion, which makes it extraordinarily difficult as the number of generating units increases [4,5]. Each generating unit's unique characteristics and constraints add another layer of intricacy [6,7]. Ramp rate constraints additionally complicate the decision-making process since they contribute to the non-smooth and discontinuous nature of the input-output characteristics of generating units [8][9][10].…”

A Space Reduction Heuristic for Thermal Unit Commitment Considering Ramp Constraints and Large-Scale Generation Systems

“…In previous research [19], we presented a method of reducing the search space of the TUC problem. The proposed constructive heuristic based on sensitivity indices exploits system information to reduce the number of decision variables.…”

“…An adaptation of the method from [19] is presented to significantly reduce the search space for the addressed TUC problem considering ramp constraints. Despite the space reduction, the methodology preserves high quality solutions and enables feasibility for cases in which the non-reduced problem does not converge within the established time limit.…”

“…It should be emphasized that the method proposed in [19] has proven able to greatly reduce the search space for small-scale TUC problems. The results from [19] motivated the study presented herein concerning larger systems subjected to ramp constraints.…”