Abstract
The cross-entropy based hybrid membrane computing method is proposed in this paper to solve the power system unit commitment problem. The traditional unit commitment problem can be usually decomposed into a bi-level optimization problem including unit start-stop scheduling problem and dynamic economic dispatch problem. In this paper, the genetic algorithm-based P system is proposed to schedule the unit start-stop plan, and the biomimetic membrane computing method combined with the cross-entropy is proposed to solve the dynamic economic dispatch problem with a unit start-stop plan given. The simulation results of 10–100 unit systems for 24 h day-ahead dispatching show that the unit commitment problem can be solved effectively by the proposed cross-entropy based hybrid membrane computing method and obtain a good and stable solution.
Highlights
The unit commitment (UC) problem is a typical optimization problem for power systems
A cross-entropy-based hybrid membrane computing method is proposed to solve the UC problem which is inspired by living cells and their organization in tissues and other higher the UC problem which is inspired by living cells and their organization in tissues and other higher order structures
The genetic algorithm-based P system is applied for the unit start-stop plan with embedded generic rules, which can transmit the outer optima into the unit start-stop plan with embedded generic rules, which can transmit the outer optima into the inner inner membranes
Summary
The unit commitment (UC) problem is a typical optimization problem for power systems. The main goal of UC is to schedule the start-stop state of units and generate power according to the load forecasting curve during the dispatch period, with the corresponding constraints so that the cost is minimized [1]. The UC problem can be broken down into two sub-problems: the unit start-stop plan and economic dispatch [2]. The UC problem is a high-dimensional, non-convex and mixed-integer nonlinear programming problem. Its discrete and continuous variables, non-convex objective function and network constraints enhance its non-convexity and complex [3]. With the increase in unit and calculation scale, it is difficult to obtain an accurate feasible solution in a reasonable time frame
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