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Table 1 (a) Assessment of several heuristics and meta-heuristics search optimization techniques. (b) Literature survey of unit commitment. (c) Literature survey of wind power uncertainty. (d) Literature survey of solar uncertainty

From: A meliorated Harris Hawks optimizer for combinatorial unit commitment problem with photovoltaic applications

Reference Nos. Year of publication Main findings or conclusion relevant to proposed research work Algorithm name
(a)
[77] 2020 mGWO optimizer was designed to get suitable balance between exploration and exploitation phases. It was tested on IEEE CEC 2017 and IEEE CEC 2014 standard functions. And it is also verified on engineering design problem in real-world and multilevel thresholding problems Memory-based Gray Wolf Optimizer [mGWO]
[1] 2020 MG-SCA optimizer was implemented to solve optimization problems. It was verified on standard IEEE CEC 2014 benchmark functions to check the efficiency of this algorithm Memory-Guided Sine–Cosine Algorithm [MG-SCA]
[78] 2020 Orthogonally designed Adapted Grasshopper Optimization was designed to solve optimization problem. It was tested on 30 IEEE CEC2017 benchmarks to find effectiveness of the meta-heuristic algorithm Orthogonally designed Adapted Grasshopper Optimization [OAGO]
[79] 2020 A smart meta-heuristic algorithm was implemented to solve engineering design problems and it was tested on 15 benchmark on CEC 2015 by Wilcoxon’s test and statistical analysis. Smart Flower Optimization Algorithm [SFOA]
[80] 2020 Hybrid PSO and GWO algorithm was designed to solve global optimization problem Hybrid Crossover-Oriented PSO and GWO [HC-PSOGWO]
[81] 2020 Imperialist Competitive Learner-Based Optimization was implemented to solve engineering design problem Imperialist Competitive Learner-Based Optimization [ICLBO]
[82] 2020 Barnacles Mating Optimizer was designed to solve the problem related to engineering optimization Barnacles Mating Optimizer [BMO]
[83] 2020 Equilibrium Optimizer was created to solve optimization problems and it was test on 58 unimodal, multimodal, and composition functions and three engineering problems Equilibrium Optimizer [EO]
[84] 2020 Improved Fitness-Dependent Optimizer Algorithm was designed and tested on CEC2019 to validate its feasibility to real-world problem Improved Fitness-Dependent Optimizer Algorithm [IFDOA]
[85] 2020 Spotted Hyena Optimizer based on swarm based optimization in the area of meta-heuristic research to improve the exploratory search Spotted Hyena Optimizer [SHO]
[86] 2020 Improved Whale Optimization Algorithm was designed with using the mechanism of joint search to solve the global optimization problems Improved Whale Optimization Algorithm [IWOA]
[87] 2020 Multi-Strategy Enhanced Sine–Cosine Algorithm was calculated to engineering design problem in real world and improve the global optimization MSESCA
[88] 2020 Refined Selfish Herd Optimizer was designed to solve global optimization problem Refined Selfish Herd Optimizer [RSHO]
[89] 2020 Hybrid Harris Hawks optimizer combined with SCA was implemented to get solutions of numerical and engineering optimization problems Intensify Harris Hawks Optimizer [IHHO]
[75] 2019 GLF–GWO was implemented with leadership-based quality to solve the global optimization problem. The leadership quality was improved by Levy flight (LF) searching techniques. It was tested on standard benchmark functions including IEEE CEC 2006 and IEEE CEC 2014. leadership quality was improved by Levy flight (LF) search and Gray Wolf Optimizer [GLF–GWO]
[76] 2019 GWO optimizer had been modified with DE to avoid trapped in local optima and solve optimization problems. It was verified on 23 standard benchmarks Greedy differential evolution—Gray Wolf Optimizer [gDE-GWO]
[90] 2019 A novel meta-heuristic optimizer, Artificial Ecosystem-Based Optimization was implemented to resolve the problem related with unidentified search space Artificial Ecosystem-Based Optimization [AEBO]
[91] 2019 Incremental Gray Wolf Optimizer and Expanded Gray Wolf Optimizer were the improved version of GWO which used to get solution for the global optimization problem Incremental Gray Wolf Optimizer and Expanded Gray Wolf Optimizer [I-GWO and Ex-GWO]
[92] 2019 Life Choice-Based Optimizer was considered to resolve optimization problems and it was tested on six CEC-2005 functions life Choice-Based Optimizer [LCBO]
[93] 2019 Multi-objective technique was invented to get solutions of the problem related to truss method Multi-objective Heat Transfer Search Algorithm [MHTSA]
[94] 2019 Simplified Salp Swarm Algorithm was created to resolve the optimization problem and it was verified on 23 common benchmark to check the feasibility of this technique Simplified Salp Swarm Algorithm [SSSA]
[95] 2019 New method was designed and tested on 28 numbers of standard benchmark problem to solve global Optimization problems Self-adaptive differential artificial bee colony algorithm [SA-DABC]
[73] 2018 Modified SCA technique was developed by opposition-based learning and added the self-adaptive factor to solve the global optimization problem in real world. It was verified on 23 standard benchmarks and IEEE CEC 2014 standard test functions Modified Sine–Cosine Algorithm [m-SCA]
[74] 2018 SCA algorithm was improved with crossover scheme to develop the capability of exploitation to real-world solve optimization problem. It was tested on standard IEEE CEC 2014 and IEEE CEC 2017 test functions Improved Sine–Cosine Algorithm [ISCA]
References Year of publication Indexing of journal (Scopus/SCI index etc.) Main findings or conclusion relevant to proposed research work Remarks
(b)
[96] 2019 Science Citation Index expanded Comparative presentations on some benchmark instances were analyzed Optimization of UCP were solved including wrap-around scheduling and ramp-rate constraint
[97] 2019 Science Citation Index IEEE-9 bus system was applied to experiment the capability of technique considering different objectives Resolution of optimum electric power flow based on mutual reactive and active cost by particle swarm optimization [PSO]
[98] 2019 Science Citation Index 4th, 5th, 6th, 7th, 10th, 19th, 20th and 40th gen. units test systems were used to solve UCP optimization problem. An optimum forceful generation scheduling by sine cosine algorithm [SCA]
[99] 2019 Science Citation Index Framework: Quantum Inspired Binary Gray Wolf Optimizer was designed to solve UCP FQIBGWO
[100] 2019 Scopus An Improved DA-PSO Optimization used to solve UCP and the 5th, 6th, 10th, and 26th gen. units test systems were applied to check the efficacy of the suggested research work Dragonfly algorithm was joined with particle swarm Optimization [DA-PSO]
[101] 2018 Science Citation Index Hybrid GWO combined with RES technique was designed to solve UCP and it had been tested on standard 23 benchmark problems and 7th, 10th, 19th, 20th and 40th systems taken to validate the effectiveness of the planned method Hybrid Gray wolf optimizer combined with random exploratory search technique [hGWO-RES]
[102] 2017 Scopus Gravitational Search Algorithm was designed to solve UCP and the viability of the suggested method was tested on tested on 10-generating unit system later extended up to 40-generating unit test system with 24 -h time horizon Gravitational Search Algorithm [GSA]
[103] 2016 Scopus SFLA was created for short duration optimum schedule of thermal power generation units including prohibited Shuffled Frog Leaping Algorithm [SFLA]
Operational zone (poz) constraints and emission limitation
[104] 2016 Scopus 4-, 10-, 20-, 40-, 80-, 100-unit systems were applied to check effectiveness of research work Advanced 3-stage method to solve the UCP
[105] 2016 Scopus 10 generating units considering 24-h test system was used to check the effectiveness of the research work Fireworks Algorithm [FA]
[106] 2016 Science Citation Index The suggested memetic algorithm was verified for standard IEEE benchmark containing of 4th, 10th, 20th and 40th power generating unit Harmony Search [HS]
[107] 2016 Scopus WIC-PSO was designed to solve UCP and efficacy and viability of the suggested technique were verified on system considering and not including additional pumped storage plant. Weighted-Improved Crazy Particle Swarm Optimization [WIC-PSO]
[108] 2015 Scopus PSO was useful to reduce total operating price and exploit total benefit. Here 12 scenarios had been measured in the existence of battery banks and without them in 2 operating modes: grid-connected mode and stand-alone mode Here-and-Now [HN] approach in battery banks (BBs)
[106] 2015 Science Citation Index Hybrid HS–random search technique was invented to resolve single-area UCP and the suggested method had been verified on standard IEEE systems containing of 4th, 10th, 20th and 40th units to check the efficacy of the method. Hybrid Harmony Search–Random Search algorithm [hHS-RES]
[109] 2015 Science Citation Index expanded Demand Response Based approach including ramp rate constraints was designed to solve large scale UCP Demand response [DR]
[110] 2015 Science Citation Index A hybrid DE–RS optimization technique was designed to solve unit commitment problem and it was tested on IEEE benchmark systems consisting of 4 unit, 10 unit, 20th and 40th test systems hybrid DE–Random Search [hDE-RS]
[111] 2015 Science Citation Index A new hybrid PSO–GWO method was implemented to solve UCP and it was tested on 30-bus system, 14-bus system and 10th power generation model hybrid PSO–GWO
[112] 2015 Science Citation Index expanded 56 MW 1 gas turbine and 1 steam turbine, 2L 2 gas turbines, 530 MW and 1 steam turbine and 530 MW, 1 steam turbine and 3LR—2 gas turbines were considered to examination the viability of the research The proposed research work analyzed the significance of certain design including construction.
[113] 2015 Science Citation Index 10th power gen. unit was considered to checked the efficacy of the research Distributed power systems (DPSs) with IRESs
[114] 2015 Scopus 3rd and 8th gen. units were considered to check the efficacy of the research as taken for the wind power predicting errors. Fuzzy Chance-Constrained Program [FCCP]
[115] 2015 Science Citation Index expanded 10, 20 ,30, 40, 60, 80,100 unit system were applied to check effectiveness of research work Binary Artificial Bee Colony Algorithm [BABCA]
[116] 2014 Scopus Improved Shuffled Frog Leaping procedure was considered to solve UCP considering a constrained including multi objective combined emission Improved Shuffled Frog Leaping Algorithm [ISFLA]
[117] 2014 Science Citation Index To authenticate the viability and efficacy of the submitted method (BGSA) to solve UCP, the suggested BGSA was verified on dissimilar systems size created on basic systems of 10th gen. unit, 20th, 40th, 60th, 80th and 100th gen. unit BGSA with the Lambda-Iteration technique was applied and the data regarding system load and wind power prediction were collected
[117] 2014 Science Citation Index Model of thermal UCP with wind power addition was recognized and constrain programming was useful to mimic the special belongings of wind power variation. Founds TUCPW model
[118] 2014 Science Citation Index expanded Validate the ability of used the algorithm to solve the UCP, it was applied on a 10-, 20-, 40-, 60-, 80- and 100 unit systems Dynamic technique for probabilistic charge of power generator inaccessibility was planned
[119] 2013 Science Citation Index Cuckoo Search Algorithm was implemented to solve UCP and model power system including 10 power plant with generating units had been used in this study Cuckoo Search Algorithm [CSA]
[120] 2013 Science Citation Index Classical model of the Dynamic Combined Economic–environmental was implemented for optimum power generation scheduling in the electricity market with consideration of availability of power generation units Multi-objective-based Genetic Algorithm
[121] 2012 Scopus 10,20,40,60,80,100 unit test system were used Variable Neighborhood Search [VNS]
[122] 2012 Science Citation Index Shuffled Frog Leaping Algorithm was designed to solve UCP. To validate the enactment of the suggested method was useful for standard IEEE 14-, 30-, 56-, 118-slandered bus and 10th gen. test unit, 20th gen. test unit for 1-day forecast period Shuffled Frog Leaping Algorithm [SFL]
(c)
[123] 2018 Scopus Multi-objective GA method was invented to find optimal solution for UCP including lowest emission. Multi-objective GA was used and data regarding load demand considering renewable energy schedule are collected from the proposed research work.
[124] 2018 Science Citation Index FCUCP technique was designed to solve UCP considering wind power generation including ramp limit Frequency-Constrained Unit Commitment Problem [FCUCP] was used to solve UCP and Forecast wind power data are collected for day ahead
[125] 2018 Science Citation Index expanded ABC-CSA for cost assessment considering wind power were implemented and the effectiveness had been tested in IEEE 30 buses of six generator test systems with 10-generating unit test systems Artificial Bee Colony and Cuckoo Search Algorithm [ABC-CSA] were applied and cost estimated data were collected
[126] 2016 Scopus MTLBO technique was invented to solve UCP by using standard IEEE ten-unit test system and 26-unit reliability test system Modified Teaching–Learning-Based Optimization algorithm [MTLBO]
[127] 2016 Science Citation Index expanded MDE method was useful to solve unit commitment problematic considering impact of plug-in EVs Modified Differential Evolution [MDE]
[128] 2016 Science Citation Index BASA technique was implemented to solve unit commitment problem including renewable energy sources and hydro electric energy pump storage BASA was used and data of forecasted wind power and photovoltaic power has been collected from the proposed research work
[129] 2016 Scopus IEEE 118-bus test system with 54 power generating units used to validate the proposed method Artificial Computational Intelligence [ACI] was used
[130] 2015 Science Citation Index The proposed research work had been implemented about the collective and individual impact of 3 DERs, including generation for wind power, EDRP and PEV on unit commitment. Data regarding energy price and hourly electricity demand considering hourly electric vehicle power in charging and discharging mode were collected
[114] 2015 Scopus A fuzzy technique was used to solve UCP has taken load demand retort, EVs and wind power Data collection for load demand considering wind power
[131] 2015 Science Citation Index The proposed research work was implemented to find out the PDF of a resolute commitment of power generators or not. Priority List (PL) method
[132] 2014 Science Citation Index The proposed research method was used to solve UCP including pumped hydro energy storage and wind power Constraints of pumped storage power plant were collected
[117] 2014 Science Citation Index To authenticate the viability and efficacy of the submitted method (BGSA) to solve UCP, the recommended method was verified on various system BGSA with the Lambda-Iteration method was applied and the data regarding system load and wind power prediction were collected
[133] 2013 Scopus LR-PSO Method was designed to solve scheduling of power generation problem for thermal, wind-solar system for deregulated electrical power system LR-PSO Method
(d)
[134] 2018 Science Citation Index Optimum scheduling for unit commitment problem considering photovoltaic insecurity and suitable power of EVs and output showed the reduction of production cost and improved load flow. Collection of data regarding hourly evidence of solar power on the day of summer and winter day. Also collected data for UC without PV, UC with PV and PEV, PEV and UC with PEV.
[135] 2018 Science Citation Index Priority-based method was designed to solve stochastic UCP considering parking lot cooperation and renewable energy sources Priority-based method
[136] 2018 Scopus Dynamic programming technique was used to discover realistic conditions of power generating units, while consecutive quadratic programming algorithm was applied for ELD of committed gen. units Energy storage facilities [ESF]
[137] 2017 Science Citation Index Cooperative Multi-Swarm PSO was used to solve UCP under Photovoltaic Generation including day-ahead prices Cooperative Multi-Swarm PSO
[138] 2016 Science Citation Index Addition of renewable energy, power generation indecisions into stochastic nature of unit commitment considering risk and reserve SCUC
[139] 2016 Science Citation Index The proposed method was invented to solve UCP considering presence of discontinuous renewable energy resources Proposed research work helps to gain knowledge about the benefits of the present methodologies avoiding the obtainable weaknesses
[140] 2016 Science Citation Index expanded Proposed research work was designed to solve UCP considering solar power system. IEEE 39 bus system and forecasted solar radiation with 24 h load demand had been taken to validate Collection of data regarding solar irradiance data for 150 MW power plant
[141] 2015 Scopus The proposed research work was based on function approximation methodology of reinforcement learning to solve UCP with photovoltaic energy sources The research work proposed a Neural Network based Reinforcement Learning method [NNRL]
[113] 2015 Science Citation Index 10th generating power systems unit was applied to check the efficacy of the research A whole computational outline of addition considering quantification of vacillations in DPSs with IRESs
[142] 2014 Science Citation Index BRCFF technique was implemented to solve security-constrained UCP considering solar power Binary Real Coded Firefly (BRCFF)
[143] 2008 Scopus GA functioned PSO method was designed to solve UCP considering wind and solar Energy Systems [GA-PSO] Genetic Algorithm operated Particle Swarm Optimization