An Effective Algorithm for MAED Problems with a New Reliability Model at the Microgrid

Josep M Guerrero,Amirreza Naderipour,Akhtar Kalam,Zulkurnain Abdul-Malek,Mohd Wazir Bin Mustafa,Iraj Faraji Davoudkhani

doi:10.3390/electronics10030257

Abstract

This paper proposes a new framework for multi-area economic dispatch (MAED) in which the cost associated with the reliability consideration is taken into account together with the common operational and emission costs using expected energy not supplied (EENS) index. To improve the reliability level, the spinning reserve capacity is considered in the model as well. Furthermore, the MAED optimization problem and non-smooth cost functions are taken into account as well as other technical limitations such as tie-line capacity restriction, ramp rate limits, and prohibited operating zones at the microgrid. Considering all the above practical issues increases the complexity in terms of optimization, which, in turn, necessitates the use of a powerful optimization tool. A new successful algorithm inspired by phasor theory in mathematics, called phasor particle swarm optimization (PPSO), is used in this paper to address this problem. In PPSO, the particles’ update rules are driven by phase angles to essentially ensure a spread of variants across the population so that exploitation and exploration can be balanced. The optimal results obtained via simulations confirmed the capability of the proposed PPSO algorithm to find suitable optimal solutions for the proposed model.

Highlights

Thermal generating units constitute a large fraction of electricity production; the optimal management of such units in the power system is of high importance [1].Operation of the power system, usually from one hour to one week, mainly belongs to the short-term scheduling problems such as economic load dispatch (ELD) [2] and unit commitment (UC) [3], in which the focus is on the minimization of the operational cost
particle swarm optimization (PSO) (CLPSO), fully informed particle swarm (FIPS), Frankenstein’s PSO (FPSO), and the improved standard PSO 2011 (SPSO2011) as well as several algorithms selected from recently-published papers, e.g., Hopfield neural network (HNN) [33], direct search method (DSM) [32], PSO-TVAC [32], PSO [9], hybrid harmony search (HHS) [12], network flow programming (NFP) [37], classical evolutionary programming (CEP) [36], pattern search (PS) [38], hybridizing sum-local search optimizer (HSLSO) [9], and DEC2 [7], it was concluded that the proposed method can be effectively applied to different problems in the field of energy and engineering optimization
Multi-area economic dispatch (MAED) is a very important issue in power systems, which affects the transmission of electrical energy

Summary

Introduction

Thermal generating units constitute a large fraction of electricity production; the optimal management of such units in the power system is of high importance [1].Operation of the power system, usually from one hour to one week, mainly belongs to the short-term scheduling problems such as economic load dispatch (ELD) [2] and unit commitment (UC) [3], in which the focus is on the minimization of the operational cost. An expansion of the ELD optimization issue is the functional multi-area economic dispatch (MAED) optimization problem [5], whose main goal is to evaluate the power generation of generators in various areas and the power exchange between regions [6]. Following many operating and network constraints [7], taking into account reserve limits in MAED contributes to the problem of reserve constrained multi-area economic dispatch (RCMAED). The current position vector of the ith particle, for instance, is [23]. With the use of its current position and velocity vectors, the position and velocity of the ith particle are updated. The optimization phase is carried out in each iteration of the algorithm based on design knowledge to maximize the objective function (f).

Results

Discussion

Conclusion