Optimal placement ofFACTSdevices and power‐flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques

Abstract

Summary Flexible AC transmission systems (FACTS) and optimal power‐flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the power generation profiles from renewable energy sources, solar and wind systems, and determining the optimal locations and sizes of FACTS devices increase the complexity of the OPF problems in modern power network models, such as transmission power loss, power generation operation cost and voltage deviation, as a highly nonlinear‐nonconvex optimization problem. Therefore, this article introduces and employs four new independent, reliable and efficient optimization algorithms inspired by nature and biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem‐based Optimization (AEO), Marine Predators Algorithm (MPA) and Jellyfish Search (JS), for solving both multi‐ and single‐OPF objective problems for a power network incorporating FACTS and stochastic renewable energy sources. The proposed new metaheuristic optimization techniques are compared to the common and available alternatives in the literature, Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO) and Grey Wolf Optimizer (GWO), using IEEE 30‐bus test system. To consider and address the challenges of the OPF in modern power network models, the proposed optimization techniques tested under different operation cases such as an increasing in the load, with and without FCTAS and renewable energy sources, different renewable energy sources locations on the network. The result showed that the MPA, SMA, JS and AEO algorithms are more effective solvers for the OPF problems cases compared to the PSO, GWO and MFO algorithms. For example, the AEO obtained 0.0844 p.u. in case of minimizing the voltage deviation compared to 0.1155 p.u. for PSO, which means that the AEO algorithm improved the voltage deviation term by 27% compared to the PSO algorithm.

Highly Fast Innovative Overcurrent Protection Scheme for Microgrid Using Metaheuristic Optimization Algorithms and Nonstandard Tripping Characteristics

“…In traditional power networks, power is centrally generated and transferred to the end-users by using high, medium and low voltage networks with a unidirectional power flow. Nowadays, the rapid implementation of Distributed Generation (DG) such as renewable energy resources in modern power systems or microgrids has introduced a new trend of power generation and distribution 2 schemes to achieve a more sustainable electrical grid and meet the yearly increase in electrical demand [1]. However, the incorporation of DGs into power networks brings along several new protections challenges due to the new behaviour of power flow and fault current distributions [2], [3].…”

“…Then, the 𝑂𝐹 𝑟𝑒𝑙𝑎𝑦𝑠 , in Equation 4, will be solved by using four modern metaheuristic optimization algorithms (MPSO, TL, GWO and MFO) to find the optimal TMS for OC relays based on standard and nonstandard curves. In the literature, the MPSO [28], [29], TL [19], [30], GWO [1], [31] and MFO [1], [32] algorithms showed a powerful performance in solving complex engineering problems and stochastic behaviour by considering a different number of possible solutions at each iteration [1], [7], [19]. Therefore, these modern metaheuristic optimization algorithms can be beneficial in improving the OC relays performance and solving the relays coordination problems in a microgrid.…”

“…Grey Wolf Optimizer (GWO): A new bio-inspired metaheuristic optimization algorithm called Grey Wolf Optimizer (GWO) has been designed and introduced by Mirjalili et al [1], [31]. The GWO is presented by [31] as a new metaheuristic algorithm for solving stochastic optimization problems and the results of [31] showed that the GWO outperformed common metaheuristic algorithms, such as differential evolution and evolutionary programming algorithms, in 29 well-known test functions.…”

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

“…Like many metaheuristic optimization algorithms, SMA suffers from premature convergence during solving complex nonlinear problems. It also suffers from the insufficient precision of optimization and slow optimization speed, which may lead to easily falling into a local optima [15][16][17][18].…”

Strategic Placement of Solar Power Plant and Interline Power Flow Controllers for Prevention of Blackouts

“…It can be very effective in improving the performance of the integrated system [18]. Nusair et al [19] have performed optimal power flow of a power system with renewable systems in the presence of TCSC. Authors have performed OPF for reduction of cost in presence of FACTS devices [20,21].…”