A novel multi-objective approach based on improved electromagnetism-like algorithm to solve optimal power flow problem considering the detailed model of thermal generators

Abstract

Summary This paper formulates a new multi‐objective model for the optimal power flow (OPF) problem considering 3 non‐commensurable and contradictory objectives, namely cost, emission, and loss. The proposed multi‐objective OPF problem takes a complete model for power generators including valve‐point effects, ramp rate limits, prohibited operating zones, spinning reserve, and multi‐fuel operation. Considering these constraints results in a non‐convex and non‐linear optimization problem and requires powerful methods to cope with. In this regard, the present paper proposes a multi‐objective electromagnetism‐like algorithm (EMA) based on the Pareto dominance concept and external archive strategy to solve this problem. The original EMA has poor search space exploration ability; hence, this paper proposes an improved EMA with modified local search process, which increases both exploration and intensification capabilities of the algorithm. The feasibility and effectiveness of the proposed method are tested on the IEEE 30‐ and 118‐bus test systems and the obtained numerical results are compared with the results of the original EMA and other heuristic methods reported in the recent literature.

Enhanced Kepler Optimization Method for Nonlinear Multi-Dimensional Optimal Power Flow

“…Table 3 presents a comparison of the outcomes obtained from minimizing the FC of Sc. 1 in relation to various other optimization approaches, which are the Black-Hole-Based Optimizing Method (BHBOM) [40], DE versions [52], Symbiotic Organisms Search (SOS) [60], Crow Search Optimization (CSO) [61], Modified CSO (MCSO) [62], Improved Moth-Flame Optimization (IMFO) [63], Developed Grey Wolf Optimization (GWO) [64], Improved Electromagnetism-like Optimization Algorithm (IEOA) [65], Moth Swarm Algorithm (MSA) [66], Ensemble Constraint Handling Technique with DE (ECHT-DE) [67], Evolutionary Algorithm (EA) [68], Grasshopper Optimizer (GO) [69], Genetic Algorithm (GA) [70], Differential Harmony Search Approach (DHSA) [71], Imperialist Competitive Approach (ICA) [72], Teaching-Learning Algorithm (TLA) [73], Novel Bat Optimization (NBO) [74], and adapted GA [75]. As can be illustrated from Table 3, the proposed IKOT obtained the minimum FC among the original KOT and other approaches.…”

“…FCs (USD/h) Method FCs (USD/h) Proposed IKOT 799.0824 IMFO [63] 800.3848 KOT 799.0835 SOS [60] 801.5733 MSA [66] 800.5099 ICA [72] 801.843 NBO [74] 799.7516 CSO [61] 799.8266 Developed GWO [64] 800.433 TLA [73] 800.4212 GO [69] 800.9728 Adaptive GO [69] 800.0212 JFS [76] 799.1065 ECHT-DE [67] 800.4148 Improved EOA [65] 799.688 DHSA [40] 802.2966 MCSO [62] 799.3332 GA [70] 802.1962 BHBOA [40] 799.9217 Adaptive constraint DE [77] 800.4113 pbest-DE [52] 800.4115 Self-adaptive penalty-DE [52] 800.4293 Ensemble constraint handling-DE [52] 800.4148 Self-adaptive feasibility-DE [52] 800.4131…”

Robust optimization framework for dynamic distributed energy resources planning in distribution networks

“…pf to be within their bounds during the optimization process. For constraints (11), ( 12), ( 15) and ( 16), the penalty function approach is used to transform the constrained problem into an unconstrained one [53,54]. In this approach, a simple way to penalize infeasible solutions is to apply a constant penalty, consists of a penalty parameter multiplied by a measure of violation of the constraints, to those solutions that violate feasibility in any way.…”

“…This means that fewer iterations are required for the proposed algorithm, and therefore it is faster than the original one. Moreover, in order to compare the solution quality and robustness of the CHSFA with that of the HSA, deviation of the best and worst solutions from the corresponding average result are calculated by the following equations [53]:…”

Multiobjective Home Appliances Scheduling Considering Customer Thermal Discomfort: A Multistep Look-ahead ADP-Based Approach

“…Instead, a set of Pareto solutions can be found. None of the solutions dominates others and each of them is a compromise between the two competing objectives [16].…”