A reliable optimization framework using ensembled successive history adaptive differential evolutionary algorithm for optimal power flow problems

Abstract

The Optimal Power Flow (OPF) is a primary tool in planning and installing power systems. It attempts to minimize the operating costs associated with generating and transmitting electrical power by modifying control parameters to satisfy environmental, economic, and operational constraints. Implementing an efficient and robust optimization algorithm for the above-said problem is critical to achieving such a typical objective. Therefore, this paper introduces and evaluates new variants of the Successive History-based Adaptive Differential Evolutionary (SHADE) algorithm called ESHADE, SHADE-SFS, and SHADE-SAP to solve the OPF problems with equality and inequality constraints. Generally, the static penalty approach is widely used for eliminating infeasible solutions discovered during the search phase when searching for feasible solutions. This approach requires the accurate selection of penalty coefficients, accomplished through the trial-and-error method. The proposed ESHADE algorithm is formulated using Self-Adaptive Penalty (SAP) and Superiority of Feasible Solution (SFS) mechanisms to obtain feasible solutions for OPF problems. Two IEEE bus systems are used to demonstrate the effectiveness of the proposed algorithm in handling OPF problems. The fuel cost and active power loss obtained by the proposed algorithm are better than other state-of-the-art algorithms. The results reveal that the proposed framework offers significant advantages over other algorithms.

An enhanced hunter‐prey optimization for optimal power flow with FACTS devices and wind power integration

“…Meta-heuristic approaches have gained widespread popularity as effective strategies for solving real-world problems and finding optimal solutions. Some of the well-known metaheuristic algorithms include the genetic algorithm (GA) and its variants [3][4][5][6][7], Sine Cosine Algorithm (SCA) [8], the Orchard algorithm (OA) [9], Snake Optimizer (SO) [10], Jellyfish Optimizer (JS) [11], Fick's Law Algorithm (FLA) [12], the successive history-based adaptive differential evolutionary (SHADE) [13] algorithm, the enhanced chaotic grasshopper optimization algorithm (ECGOA) [14], the Aphid-Ant Mutualism (AAM) [15], pattern search algorithms (PSA) and their variants [16][17][18][19][20][21][22], among others. In the field of power systems, the optimal power flow (OPF) problem using thermal power generators has been extensively studied, with several meta-heuristic algorithms being applied.…”

“…In the field of power systems, the optimal power flow (OPF) problem using thermal power generators has been extensively studied, with several meta-heuristic algorithms being applied. These include the moth swarm algorithm (MSA) [23], white sharks algorithm (WSA) [24], an improved Remora Optimization Algorithm (IROA) [25], an enhanced Remora Optimization Algorithm (ROA) [26], an enhanced multi-objective Quasi-reflected Jellyfish search algorithm (MOQRJFS) [27], an improved adaptive differential evolution (DE) [28], enhanced equilibrium optimizer (EEO) [29], a hybrid evolutionary algorithm combining particle swarm optimization (PSO) and crow search algorithm (CSA) applied to the distribution portion of IEEE 30-bus ring and IEEE 69-bus distribution network [30], successive history-based adaptive differential evolutionary (SHADE) algorithm [31], Enhanced slime mould algorithm (ESMA) [32], a new version of the salp swarm algorithm (SSA) [33], a multi-regional OPF considering load and generation variability using marine predators algorithm (MPA) [34], a multi-objective evolutionary algorithm with constraint handling technique based on non-dominated sorting [35], Hybrid Gradient-Based Optimizer with Moth Flame Optimization Algorithm (GBOMFO) [36], an enhanced MSA (EMSA) based on quasi-opposition based learning [37], an orthogonal learning FIGURE 3 Basic structure and model of TCSC [58]. [58].…”

“…In the field of power systems, the optimal power flow (OPF) problem using thermal power generators has been extensively studied, with several meta‐heuristic algorithms being applied. These include the moth swarm algorithm (MSA) [23], white sharks algorithm (WSA) [24], an improved Remora Optimization Algorithm (IROA) [25], an enhanced Remora Optimization Algorithm (ROA) [26], an enhanced multi‐objective Quasi‐reflected Jellyfish search algorithm (MOQRJFS) [27], an improved adaptive differential evolution (DE) [28], enhanced equilibrium optimizer (EEO) [29], a hybrid evolutionary algorithm combining particle swarm optimization (PSO) and crow search algorithm (CSA) applied to the distribution portion of IEEE 30‐bus ring and IEEE 69‐bus distribution network [30], successive history‐based adaptive differential evolutionary (SHADE) algorithm [31], Enhanced slime mould algorithm (ESMA) [32], a new version of the salp swarm algorithm (SSA) [33], a multi‐regional OPF considering load and generation variability using marine predators algorithm (MPA) [34], a multi‐objective evolutionary algorithm with constraint handling technique based on non‐dominated sorting [35], Hybrid Gradient‐Based Optimizer with Moth Flame Optimization Algorithm (GBOMFO) [36], an enhanced MSA (EMSA) based on quasi‐opposition based learning [37], an orthogonal learning Bird swarm algorithm [38], a chaotic equilibrium optimization (CEO) [39], modified Rao‐2 algorithm [40], the delicate flower pollination algorithm (DFPA) [41], the levy spiral flight equilibrium optimizer (LSFEO) [42], the teaching‐learning‐studying‐based optimizer (TLSBO) [43], the boundary assigned animal migration optimization (BA‐AMO) [44], an adaptive Quasi‐oppositional differential migrated biogeography‐based optimization (AQODMBBO) [45], the chaotic Bonobo optimizer (CBO) [46], and the social spider optimization (SSO) [47]. Furthermore, a gravitational search algorithm (GSA) was used in a study to compare the optimal performance of static VAR compensator (SVC) in voltage stability enhancement problems [48].…”

An Enhanced Sparrow Search Algorithm for Solving Optimal Power Flow Problem Considering Renewable Energy Systems

“…Recently, some researchers have attempted OPF with RE using meta-heuristics. A modified Rao-2 algorithm (MRao-2) [16], gorilla troops algorithm (GTA) [17], manta ray foraging optimisation (MRFO) [18], an adaptive differential evolutionary algorithm (ADEA) [19], slime mould algorithm (SMA) [20], mixed-integer nonlinear programming (MINLP) [21], an improved grey wolf algorithm [22], an adaptive grasshopper optimization algorithm (AGOA) [23] and modified honey badger algorithm (MHBA) [24] are such recent works. However, according to the no-free-lunch (NFL) theorem [25], most heuristic algorithms suffer from preconvergence problems owing to their poor exploration and/or exploitation characteristics.…”

Optimal power flow analysis considering renewable energy resources uncertainty based on an improved wild horse optimizer

“…To address these issues, numerous heuristic optimization techniques were proposed through the last years. These intended to reach a best solution for the power network without adjusting the standard cost function such as Teaching-Learning-Based Optimization (TLBO) algorithm [10], chaotic Bonobo optimizer (CBO) [4], Modified Jaya Algorithm (MJYAY) [11], enhanced equilibrium optimizer (EEO) [12], Hybrid Firefly Bat technique with the constraints-prior object-fuzzy sorting (HFBA-COFS) algorithm [13], Rao-3 algorithm [14], Enhanced Slime Mould Algorithm (ESMA) [15], Mathematical distribution coyote optimization algorithm with crossover operator (MDCOA) [16], ensembled successive history adaptive differential evolutionary algorithm [17], White Sharks Algorithm (WSA) [18], and a Gaussian Bare-Bones Levy-Flight Firefly Algorithm [19]. Research in this domain indicates that each study has attempted to propose an algorithm that can outperform prior techniques.…”