Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem

Abstract

The unit commitment problem in power system is a highly nonlinear, non-convex, multi-constrained, complex, highly dimensional, mixed integer and combinatorial generation selection problem. The phenomenon of committing and de-committing represents a discrete problem that requires binary/discrete optimization techniques to tackle with unit commitment optimization problem. The key functions of the unit commitment optimization problem involve deciding which units to commit and then to decide their optimum power (economic dispatch). This paper confers a binary grasshopper optimization algorithm to solve the unit commitment optimization problem under multiple constraints. The grasshopper optimization algorithm is a metaheuristic, multiple solutions-based algorithm inspired by the natural swarming behavior of grasshopper towards food. For solving the binary unit commitment optimization problem, the real/continues value grasshopper optimization algorithm is mapped into binary/discrete search-space by using an Sshaped sigmoid function. The proposed algorithm is tested on IEEE benchmark systems of 4,

Unit Commitment Considering Electric Vehicles and Renewable Energy Integration—A CMAES Approach

“…Among them, worst is the value of the worst individual in the population of this generation. The value of the worst individual is found by (40), and thus the worstIndex (the index number of the individual position in the population) of the worst individual is found. Finally, the worst individual in the population can be directly replaced with the optimal individual as follows:…”

Multi–dimensional firefly algorithm based on local search for solving unit commitment problem

“…And it can be seen from the whisker diagram of the ordinate axis, that in the initial iterations of the optimization, convergence speed is quick, and most of the results are concentrated around the optimal solution, this phenomenon also confirms the improvement in both in the random movement factor α and the attractiveness β, namely in the initial iterations of the optimization needs larger random movement factor and enough stable attractiveness to find the optimal solution, and as the iteration goes on, the decrease of random movement factor α is beneficial to the optimization in a small range, and in this process, the attractiveness function is dynamic, which ensures the convergence speed and the diversity of the population and avoids premature convergence. Finally, from the enlarged image about the last period of the iteration, at 178th, the cost In order to further illustrate the advantages and practicability of LS-MFA, the clustering results of the other twenty-two methods of DPLR, ALR, ELR (Ongsakul and Petcharaks, 2004), LR, GA (Kazarlis et al, 1996),EP (Juste, 1999), LRGA (Cheng and Liu, 2000), GAUC (Yamashiro, 2001), DPSO (Gaing, 2004), ICGA, BCGA (Damousis et al, 2004), BF (Eslamian et al, 2009), PSO-LR (Balci and Valenzuela, 2004), SLFA (Ebrahimi et al, 2011), HPSO (Ting et al, 2006), BGOA (Shahid et al, 2021), ABC (Kokare and Tade, 2018), ABFMO (Pan et al, 2021), BCS(Reddy Surender, 2017), BDEr (Kamboj et al, 2017), BGWO (Panwar et al, 2018), BPSOGWO (Kamboj, 2016) which are shown in Table 5, meanwhile, the results are compared with the result of LS-MFA, obtaining the cost difference. Figure 8 visualizes the comparison, ranking several methods using operating costs as the primary axis (black) and cost differences (blue) as the secondary axis.…”

A Novel Gradient Based Optimizer for Solving Unit Commitment Problem

“…Diligence Algorithm [1] Two updated version of GWO for solving unit commitment GWO [2] Hybridization of GA with MILP for solving unit commitment in a microgrid environment Hybrid GA MILP [3] Unit commitment of a CHP plant having cogeneration in a multi-objective framework with total operating cost and net emission as objective functions PSO [4] Robust formulation of unit commitment problem considering hydropower and solar CSA [5] A novel BWA for analyzing profit-based the UC in competitive power market BWA [6] Novel SCA for solving the UC of thermal units SCA [7] Novel PVS algorithm for solving the UC of thermal units in attendance of PHEVs PVS [8] Novel BASA for solving the UC in attendance of pumped storage and renewable sources BASA [9] Hybrid DA PSO method for solving the UC problem DA PSO [10] Forceful generation scheduling of thermal power plant by SCA SCA [11] Novel BMFO-SIG for solving unit commitment of a conventional power plant with and without wind resources BMFO-SIG [12] Validation of the performance of BFMO on unit commitment problem BFMO [13] Novel PSA algorithm for unit commitment problem PSA [14] Improved version of PSO for solving unit commitment in a micro grid environment in presence of battery energy storage Improved PSO [15] Binary CSA for solving unit commitment of a 4 unit system Binary CSA [16] BGSA for solving constrained unit commitment problem BGSA [17] Hybrid HS random search for solving unit commitment HS Random search [18] Quantum inspired GSA for solving the UC problem Quantum Inspired GSA [19] Use of BWA for analyzing profit-based UC in a smart city platform BWA [20] Hybridization of GA and DE for solving basic UC problem GA DE [21] Modelling of unit commitment problem considering line outages in case of natural calamities and using machine learning approach for its solution Machine learning [22] Modelling of unit commitment for hydropower plants considering multiple hydraulic head by nested optimization approach Nested optimization approach [23] Modelling of unit commitment as Markov process and solving it by reinforcement learning Reinforcement learning [24] A novel Bayesian optimization approach for unit commitment Bayesian optimization [25] Modelling and solution of security constrained scenario based unit commitment by consi...…”

“…In [15], a binary CSA was performed for solving the UC of a standard 4-unit system. In [16], a novel BGSA was performed for solving constrained UC problem. In [17], authors have hybridized HS with a random search strategy in order to solve unit commitment problem for various IEEE test beds.…”