Table 1.
Literature contribution.
| Year | References | Method | Description |
|---|---|---|---|
| 2021 | 28 | Multi-objective Quasi-Reflected Jellyfish Search Optimizer (MOQRJFS) | MOQRJFS was developed for solving multi-dimensional Optimal Power Flow (MDOPF) issues with diverse objectives that display the minimization of economic fuel cost, total emissions, and active power loss while satisfying operational constraints |
| 2020 | 27 | Adaptive grasshopper optimization (AGO) algorithm | As part of the economic dispatch issue, an AGO algorithm had been devised to the optimal power flow (OPF) problem with the optimal incorporation of a center-node unified power flow controller (C-UPFC) |
| 2021 | 35 | Modified crow search optimizer (MCSO) | A modified CSO applies in IEEE 30 bus, IEEE 118-bus and West Delta power grid (WDPG) systems to solve various OPF issues |
| 2017 | 30 | Incorporation of OPF with stochastic wind and solar power | The OPF issue was solved by considering a differential evolution algorithm in a small IEEE-30 bus system. A successful adaptation technique based on the algorithm's history was employed to incorporate intermittent solar and wind power generation |
| 2019 | 36 | Improved moth flame optimization (IMFO) | Based on the results of this study, an improved moth flame optimization (IMFO)approach was introduced as a strategy for determining the OPF on 15 case studies in terms of different single and multi‐objective functioninto in the IEEE 30-bus, 57 bus and 118 bus systems |
| 2017 | 37 | Biogeography-based optimization (BBO) and grey wolf optimization (GWO) | There were two algorithms presented, BBO and GWO, that were used to solve multi-constrained OPF problems in the power system. Different conditions were used to test the algorithms' performance on both the IEEE 30-bus and the 9-bus systems |
| 2018 | 38 | Differential evolution algorithm integrated with effective constraint-handling techniques (ECHT-DE) | ECHT-DE was utilized to address the OPF issue. As part of the validation process, the approach was applied to the OPF in IEEE 30 bus , IEEE 57 bus and IEEE 118 bus systems while considering objective functions based on operational and economic indicators for the power system |
| 2018 | 39 | Stud krill herd optimizer (SKH) | The SKH optimizer solved OPF issues in IEEE 14, 30, and 57-bus networks. Several objective functions were considered in the proposed algorithm, including minimizing total production cost with and without the effect of valve point loading, active power loss, L-index, and emission pollution |
| 2018 | 21 | Developed Grey Wolf Optimizer (DGWO) | DGWO was utilized to address the OPF issue. As part of the validation process, the approach was applied to the OPF in IEEE 30 bus systems while considering objective functions based on operational and economic indicators for the power system |
| 2019 | 40 | Hybrid Firefly and krill herd method (FKH) | To address the OPF issue, the researchers utilized a revised version of the FKH optimizer and considered different types of single-objective and multi-objective functions: reducing fuel costs, reducing emissions, reducing transmission power losses, and improving voltage profiles. The FKH has been applied to IEEE 30 bus systems |
| 2020 | 31 | GWO Optimizer | The OPF issue was solved using the GWO Optimizer, which integrated intermittent solar and wind power generation without utilizing actual wind speed data |
| 2020 | 32 | Krill Herd Algorithm (KHA) | The OPF issue with FACTS devices and stochastic wind power generation was solved considering the KHA optimizer for one scenario where wind generation costs were overestimated or underestimated |
| 2020 | 41 | Modified Artificial Bee Colony (MABC) | The OPF has been addressed using MABC. With this method, four distinct objective functions have been minimized within the IEEE 30-bus system. These functions include total fuel cost for thermal units, total transmission losses, total fossil fuel emissions, and total voltage deviation on load nodes |
| 2021 | 29 | Moth-Flame Optimizer (MFO) | Three objective functions were solved simultaneously deemed minimizing fuel cost, transmission loss, and voltage deviation minimization using a weighted factor |
| 2021 | 33 | Barnacles mating optimizer (BMO) | The OPF issue has been achieved by utilizing the BMO that incorporated FACTS devices and stochastic wind power generation in a one-scenario. This technique also considered the costs associated with overestimating and underestimating wind power generation |
| 2021 | 42 | Rao Algorithm | Using the Rao algorithm, OPF problems with both technical and economic objectives can be addressed within the standard IEEE 30-bus, 57-bus, and 118-bus networks |
| 2021 | 43 | Multi-Objective Backtracking Search Algorithm (MOBSA) | The OPF issue in power systems was addressed using MOBSA technique. Multi-objective functions, such as fuel cost, power loss, and voltage deviation, are considered in this technique. As part of the standard BSA methodology, a fuzzy membership technique was utilized to identify the most likely compromise results among the derived Pareto optimal solutions. Three IEEE power systems were employed to determine and verify the effectiveness of the MOBSA approach: the small network 30-bus, the medium network 57-bus, and the large network 118-bus test systems |
| 2021 | 44 | Firefly Algorithm (FA) | The OPF issue was addressed using the FA technique. Newton–Raphson was used to calculate the real power loss when performing the load flow analysis. To optimize the control variables, including the magnitudes of generator bus voltages, transformer tap settings, and generator output active power, the FA methodology was applied. As a result, real power losses were minimized in the transmission system. In the context of IEEE 14-bus and 30-bus systems, MATLAB software was used to evaluate the proposed approach |
| 2021 | 45 | Multi Objective Particle Swarm Optimizer (MOPSO) | To address the constrained multi-objective OPF issue in power systems with conflicting objectives, the MOPSO technique has been implemented. The best optimal solution from the Pareto optimal set was extracted using fuzzy set theory and presented to the operator. The effectiveness and applicability of the introduced methodology were evaluated considering the IEEE 30-bus network |
| 2021 | 46 | Jellyfish Search Optimizer (JSO) | On the modified IEEE 30-bus grid, the JSO technique has been proposed to overcome the OPF problems |
| 2022 | 47 | Jellyfish Optimizer (JFO) | The JFO optimizer was implemented to solve the OPF considering fuel costs, emissions and losses. A Quasi-Reflection (QR) is integrated with JFOA in solving the OPF problem |
| 2022 | 48 | gorilla troops optimization technique (GTOT) | In order to solve OPF problems that contain single and multi-objective objectives, GTOT methodology was developed. In order to evaluate the algorithm, the IEEE 30-bus system was used |
| 2022 | 49 | Archimedes optimization algorithm (AOA) | An AOA algorithm using non-dominated sorting and a constraint handling technique is designed to solve the OPF issue renewable energy sources (RES). The efficacy of this approach is demonstrated by using it to solve problems on the standard and modified IEEE 30 bus networks. These tests also confirm the approach's effectiveness in handling significant dimensional problems |
| 2023 | 50 | Improved Cross-Entropy Method (CGSCE) | An Improved Cross-Entropy (CE) approach integrated with a chaotic operator (CGSCE) was introduced to tackle the OPF issue. Different target functions were evaluated on the IEEE-30 bus and IEEE 57 bus test system |