Optimal Power Flow Solution of Power Systems with Renewable Energy Sources Using White Sharks Algorithm

Abstract

Modern electrical power systems are becoming increasingly complex and are expanding at an accelerating pace. The power system’s transmission lines are under more strain than ever before. As a result, the power system is experiencing a wide range of issues, including rising power losses, voltage instability, line overloads, and so on. Losses can be minimized and the voltage profile can be improved when energy resources are installed on appropriate buses to optimize real and reactive power. This is especially true in densely congested networks. Optimal power flow (OPF) is a basic tool for the secure and economic operation of power systems. It is a mathematical tool used to find the instantaneous optimal operation of a power system under constraints meeting operation feasibility and security. In this study, a new application algorithm named white shark optimizer (WSO) is proposed to solve the optimal power flow (OPF) problems based on a single objective and considering the minimization of the generation cost. The WSO is used to find the optimal solution for an upgraded power system that includes both traditional thermal power units (TPG) and renewable energy units, including wind (WPG) and solar photovoltaic generators (SPG). Although renewable energy sources such as wind and solar energy represent environmentally friendly sources in line with the United Nations sustainable development goals (UN SDG), they appear as a major challenge for power flow systems due to the problems of discontinuous energy production. For overcoming this problem, probability density functions of Weibull and Lognormal (PDF) have been used to aid in forecasting uncertain output powers from WPG and SPG, respectively. Testing on modified IEEE-30 buses’ systems is used to evaluate the proposed method’s performance. The results of the suggested WSO algorithm are compared to the results of the Northern Goshawk Optimizer (NGO) and two other optimization methods to investigate its effectiveness. The simulation results reveal that WSO is more effective at finding the best solution to the OPF problem when considering total power cost minimization and solution convergence. Moreover, the results of the proposed technique are compared to the other existing method described in the literature, with the results indicating that the suggested method can find better optimal solutions, employ less generated solutions, and save computation time.

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].…”

“…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].…”

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.…”

Developing a strategy based on weighted mean of vectors (INFO) optimizer for optimal power flow considering uncertainty of renewable energy generation

“…In [ 3 ], the authors provided ant lion optimization (ALO) algorithm for solving the OPF with wind energy. White sharks algorithm has been employed in [ 4 ] for solving the OPF problem with solar and wind energies. The authors in [ 5 ] have provided an economical-environmental-technical dispatch (EETD) model that includes thermal and high penetration of RESs using the coronavirus herd immunity algorithm (CHIA).…”