ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem

Abstract

In this work, an enhanced slime mould algorithm (ESMA) based on neighborhood dimension learning (NDL) search strategy is proposed for solving the optimal power flow (OPF) problem. Before using the proposed ESMA for solving the OPF problem, its validity is verified by an experiment using 23 benchmark functions and compared with the original SMA, and three other recent optimization algorithms. Consequently, the ESMA is used to solve a modified power flow model including both conventional energy, represented by thermal power generators (TPGs), and renewable energy represented by wind power generators (WPGs) and solar photovoltaic generators (SPGs). Despite the important role of WPGs and SPGs in reducing CO2 emissions, they represent a big challenge for the OPF problem due to their intermittent output powers. To forecast the intermittent output powers from SPGs and WPGs, Lognormal and Weibull probability density functions (PDFs) are used, respectively. The objective function of the OPF has two extra costs, penalty cost and reserve cost. The penalty cost is added to formulate the underestimation of the produced power from the WPGs and SPGs, while the reserve cost is added to formulate the case of overestimation. Moreover, to decrease CO2 emissions from TPGs, a direct carbon tax is added to the objective function in some cases. The uncertainty of load demand represents also another challenge for the OPF that must be taken into consideration while solving it. In this study, the uncertainty of load demand is represented by the normal PDF. Simulation results of ESMA for solving the OPF are compared with the results of the conventional SMA and two further optimization methods. The simulation results obtained in this research show that ESMA is more effective in finding the optimal solution of the OPF problem with regard to minimizing the total power cost and the convergence of solution.

Golden Search Optimization Algorithm

“…There are other algorithms that are a combination or modification of algorithms in these four categories. Some of them are: modified particle swarm optimization [36], [37], modified harmony search algorithm [38], modified gravitational search algorithm [39], [40], modified ant colony optimization [41], modified sine cosine algorithm [42], [43], modified wild horse optimization [44], modified slime mould algorithm [45], hybrid genetic algorithm and particle swarm optimization [46], hybrid firefly algorithm [47], hybrid sperm swarm optimization and gravitational search algorithm [48], hybrid tunicate swarm algorithm and pattern search [49], hybrid arithmetic optimization algorithm and sine cosine algorithm [50].…”

Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

“…Researchers have applied the slime mould algorithm and its variants to engineering optimization problems and other research fields. For example, solving single- and du-al-objective economic and emission scheduling (EED) problems considering valve point effects [ 34 ]; determining the best operating rules for complex hydropower multi-reservoir prediction problems [ 38 ]; distributed generation (DG) solution of distribution network reconfiguration (DNR) problem [ 39 ]; photovoltaic model optimization design (Lin, 2022); demand estimation of urban water resources problem [ 40 ]; feature selection [ 41 ]; Reliability optimization of micro-milling cutting parameters [ 42 ]; Opti-mal Power Flow Problem [ 43 ]; A Cost-Effective Solution for Non-Convex Economic Load Dispatch Problems in Power Systems [ 44 ]; path planning and obstacle avoidance problem in mobile robots [ 45 ], optimal load-shedding in distribution system problem [ 30 ], etc.…”

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

“…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].…”