Review on application and comparison of metaheuristic techniques to multi-area economic dispatch problem

Abstract

This paper presents both application and comparison of the metaheuristic techniques to multi-area economic dispatch (MAED) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. The metaheuristic techniques such as differential evolution, evolutionary programming, genetic algorithm and simulated annealing are applied to solve MAED problem. These metaheuristic techniques for MAED problem are evaluated on three different test systems, both small and large, involving varying degree of complexity and the results are compared against each other.

Single and Multi-Area Optimal Dispatch by Modified Salp Swarm Algorithm

“…Pioneer wok for related to MAED problem includes [18][19][20] [21], artificial bee colony (ABC) [22], flower pollination algorithm (FPA) [23], evolutionary programming (EP) [24,25], water wave optimization (WWO) [26], and teaching learning based optimization (TLBO) [27], were employed to solve MAED problems with different optional constraints. Attempt have been made for solution of two area problem with six generator unit system in ref [22], [23], [25][26][27][28][29], two area problem with four generator unit system in ref [29]. solution of MAED problem with three area system with three generator unit presented in ref [18] and with three area system with ten generator unit presented in ref.…”

“…There are also issues related to environmental pollution and global warming, minimization of harmful emission from power plant considered as other objective. The MAED problem has become much complex to solve due additional tie line capacity constraints along with the above mentioned operational constraints [18][19][20][21][22][23][24][25][26][27][28][29]. Considering new regulation for excessive generated greenhouse gases, a combination of MAED and constraints on emission has come in to picture called multi-objective multi-area economic dispatch (MOMAED).…”

Non-Convex Economic Dispatch of a Virtual Power Plant via a Distributed Randomized Gradient-Free Algorithm

“…If valve-point loading effects, simulated by sinusoidal terms, and multiple fuel options are taken into account, the cost function can be described as [4,12]: …”

“…When P i exceeds the value of P (q-1),i , unit i chooses the fuel q and its cost function C(P i ) uses coefficients of a Because the power output is usually concentrated in some areas, the operational efficiency can be greatly promoted by prohibiting units from running in low productivity areas. The power output constraints considering DGs' prohibited operating zones can be expressed as [4,12]:…”

“…The centralized dispatch methods adopt lambda iterative algorithms [2] and interior point lambda iterative algorithms [3], both of which need the objective functions to be smooth and derivable. Actually, the cost functions of DGs are non-convex because of their features of valve-point loading effects, prohibited operating zones, and multiple fuel options [4]. The charging and discharging of ESs also aggravates the complexity of economic dispatch problems [5].…”