A practical approach to real time economic dispatch considering unit's prohibited operating zones

Abstract

This paper develops an efficient and practical approach for determining the feasible optimal solution of the economic dispatch problem when some of the on-line units have prohibited operating zones. When a unit has prohibited operating zones, its operating region will be broken into isolated sub-regions, which results in multiple decision spaces for the economic dispatch problem. For each of the decision spaces, it can be either feasible or infeasible with respect to the system demand. The feasible optimal solution for the dispatch problem can only reside in one of the feasible spaces, and can be found by performing the conventional A-6 iterative search based on the equal-incremental-cost criterion in each of the feasible spaces. However, for a system with a large number of decision spaces, such exhaustive search method would not be acceptable in real time operation due to high computational time requirement. In this paper, a novel approach is proposed. It defines a small and advantageous set of decision spaces, uses an efficient algorithm to determine the most advantageous space and then, utilizes the conventional A-8 iterative method to find the feasible optimal dispatch solution.

Artificial Immune Algorithm for Practical Power Economic Dispatch Problems

“…The operating limits are 120 MW < P i < 450MW for i =1, 2, …, 5. Units 1, 2 and 3 have POZ as defined in [2], these zones result in a non-convex decision space composed of 27 convex sub-spaces. The system load demand P D and spinning reserve S R are 1175 MW and 100 MW, respectively.…”

“…Lee et al [1] decomposed the non-convex decision space into a small number of subsets such that each of the associated dispatch problems, if feasible, was solved via the conventional Lagrangian relaxation approach. Fan et al [2] defined a small and advantageous set of decision space with respect to the system demand, used an algorithm to determine the most advantageous space, and then utilized the λ-δ iterative method to find the feasible optimal dispatch solution. For infeasible solutions, they re-dispatch the units using some heuristic rules to probe the neighborhood for feasibility.…”

Bee Hive Algorithm to Optimize Multi Constrained Piecewise Non-Linear Economic Power Dispatch Problem in Thermal Units

“…To overcome such difficulties many heuristic search algorithms, such as Genetic algorithm [1], [5], Differential Evolution [6], Tabu search [7], [19], etc., have been proposed to solve ELD problem. These techniques can be used to search the global optimum with any type of objective function and constraints [22].…”

“…However none of these methods may be able to provide an optimal solution and they usually get stuck at a local optimum. Normally the input-output characteristic of modern generating units are highly nonlinear in nature due to valve-point effect [ [19], etc., have been proposed to solve ELD problem. These techniques can be used to search the global optimum with any type of objective function and constraints [22].…”

“…However none of these methods may be able to provide an optimal solution and they usually get stuck at a local optimum. Normally the input-output characteristic of modern generating units are highly nonlinear in nature due to valve-point effect [1], [14], [15], [16], [18], [19] ramp-rate limits, Fuel switching [11], [20] etc, having multiple local minimum points in the cost function.…”