Hydrothermal systems generation scheduling using cultural algorithm

Abstract

This paper proposes an enhanced cultural algorithm to solve the short-term generation scheduling of hydrothermal systems problem, in which differential evolution is embedded into a cultural algorithm and applies two knowledge sources to influence the variation operator of differential evolution and couples with simple selection criteria based on feasibility rules and heuristic search strategies to handle constraints in the cultural algorithm effectively. A test hydrothermal system is used to verify the feasibility and effectiveness of the proposed method.Results are compared with those of other optimization methods reported in the literature. It is shown that the proposed method is capable of yielding higher quality solutions.

Optimization Methods and Algorithms for Solving Of HydroThermal Scheduling Problems.

“…Considering an analysis horizon period of a week, the model obtains hourly generation schedules for each of the hydro and thermal units [45]. Figure 3 Scheduling model using a Genetic algorithm [45] Yuan et al [2] proposed a new enhanced cultural algorithm (ECA) to solve the short-term generation scheduling of hydrothermal systems. Cultural algorithm proposed by Reynolds in 1994 [70] is a technique that incorporates domain knowledge obtained during the evolutionary process so as to make the search process more efficient.…”

“…It is based on the assumption that domain knowledge can be extracted during the evolutionary process by means of the evaluation of each point generated. This process of extraction and use of information has been shown to be effective in decreasing computational cost while approximating global optima in optimization problems [2]. It has been successfully applied to solve optimization problems [71] The procedure of the proposed ECA [2] for solving the short term generation scheduling of hydrothermal systems is described as follows.…”

“…Convergence property of the ECA method is better than that of DE and GA for the solving short-term generation scheduling of hydrothermal systems. The main reason is that the ECA method has a belief space and it can utilize sufficiently the problem-based domain knowledge obtained during the evolutionary process to make the search process more efficient, while DE and GA are lacking this mechanism and thus make its search performance inferior to ECA [2].…”

“…The importance of hydrothermal generation scheduling is well recognized. An efficient generation schedule not only reduces the production costs but also increases the system reliability and maximizes the energy capability of the reservoirs [2].Therefore, many methods have been developed to solve this problem over the past decades. The major methods include variational calculus [3], maximum principle [4], functional analysis [5], dynamic programming [6,7,8], network flow and mixed-integer linear programming [9,10,11,12], nonlinearprogramming [13], progressive optimality algorithm [14,15], Lagrangian relaxation method [16][17][18] and modern heuristics algorithms such as artificial neuralnetworks [19], evolutionary algorithm [20][21][22], chaotic optimization [23], ant colony [24], Tabusearch [25], Expert Systems [26] and simulated annealing [27].…”

A short review study on problems during hydro-thermal optimal scheduling

“…Weekly maintenance scheduling regarding the large scale Spanish power grid with regard to year time period demonstrates the proposed methodology. The Spanish Power System includes about 16 thermal plants and 130 hydro units [7]. An innovative new solution to fixing the unit determination issue simply by simulation of a typical demanding marketplace exactly where electrical power is bought and sold through an electrical exchange is actually presented here.…”