A robust unit commitment based on GA-PL strategy by applying TOAT and considering emission costs and energy storage systems

Nikzad, Hamid Reza; Abdi, Hamdi

doi:10.1016/j.epsr.2019.106154

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…In general, there is no proven and recommended method for determining the optimal values of parameters due to the random nature of the algorithms. Therefore, in this paper, in order to determine the control parameters, an iteration-based method (30 repetitions for each algorithm) and the Taguchi method [56] were applied.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Metaheuristics and Transmission Expansion Planning: A Comparative Case Study

2021

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Transmission expansion planning (TEP), the determination of new transmission lines to be added to an existing power network, is a key element in power system planning. Using classical optimization to define the most suitable reinforcements is the most desirable alternative. However, the extent of the under-study problems is growing, because of the uncertainties introduced by renewable generation or electric vehicles (EVs) and the larger sizes under consideration given the trends for higher renewable shares and stronger market integration. This means that classical optimization, even using efficient techniques, such as stochastic decomposition, can have issues when solving large-sized problems. This is compounded by the fact that, in many cases, it is necessary to solve a large number of instances of a problem in order to incorporate further considerations. Thus, it can be interesting to resort to metaheuristics, which can offer quick solutions at the expense of an optimality guarantee. Metaheuristics can even be combined with classical optimization to try to extract the best of both worlds. There is a vast literature that tests individual metaheuristics on specific case studies, but wide comparisons are missing. In this paper, a genetic algorithm (GA), orthogonal crossover based differential evolution (OXDE), grey wolf optimizer (GWO), moth–flame optimization (MFO), exchange market algorithm (EMA), sine cosine algorithm (SCA) optimization and imperialistic competitive algorithm (ICA) are tested and compared. The algorithms are applied to the standard test systems of IEEE 24, and 118 buses. Results indicate that, although all metaheuristics are effective, they have diverging profiles in terms of computational time and finding optimal plans for TEP.

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Section: Simulation and Resultsmentioning

confidence: 99%

Metaheuristics and Transmission Expansion Planning: A Comparative Case Study

2021

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“…Unit commitment (UC), one of the most efficient tools for running and controlling power systems, is used by power system operators to carry out a number of tasks, one of which is figuring out the proper scheduling of generation units, particularly in the short-term planning time spanning from one day to one week [3]. In other words, UC prioritizes attaining the best scheduling of both in-service and off-service generation units while adhering to a number of limitations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since UC is a challenging mixed-integer quadratic programming problem, a reliable and effective optimization algorithm is required to solve it. Various mathematical and heuristic techniques, such as Lagrangian relaxation (LR) [4], mixed integer programming (MIP) [5], improved priority list (IPL) [6], particle swarm optimization (PSO) [7], hybrid binary successive approximation-civilized PSO [8], binary flame (BF) [9], modified binary flame (MBF) [10], the hybrid genetic algorithm-priority list-based (GA-PLB) strategy [3], improved particle swarm optimization (IPSO) [11], hybrid PSO-grey wolf optimization (GWO) [12], parallel social learning particle swarm optimization (PSLPSO) [13], the coyote optimization algorithm (COA) [14], and the imperialism competitive algorithm (ICA) [15,16] have been used to address the UC problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The optimal day-ahead scheduling issue in the presence of electric vehicles was resolved using an improved pre-prepared power demand table and analytical hierarchy approach [45]. A hybrid genetic algorithm-priority list-based (GA-PLB) strategy was proposed to solve conventional UC by considering ES units to reduce generation costs [3]. An enhanced particle swarm optimization for UC in microgrids with battery energy storage systems taking into account battery deterioration was proposed by the authors in [11].…”

Section: Literature Reviewmentioning

confidence: 99%

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Multi-Objective Profit-Based Unit Commitment with Renewable Energy and Energy Storage Units Using a Modified Optimization Method

Lotfi,

Nikkhah

2024

Sustainability

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The unit commitment (UC) problem aims to reduce the power generation costs of power generation units in the traditional power system structure. However, under the current arrangement, the problem of cutting the cost of producing electricity has turned into an opportunity to boost power generation units’ profits. Emission concerns are now given considerable weight when talking about the performance planning of power generation units, in addition to economic objectives. Because emissions are viewed as a limitation rather than an objective function in the majority of recent research that has been published in the literature, this paper solves the multi-objective profit-based unit commitment (PBUC) problem while taking into account energy storage systems (ESSs) and renewable energy systems (RESs) in the presence of uncertainty sources, such as demand and energy prices, in order to minimize generated emissions and maximize profits by power generation units in the fiercely competitive energy market. Owing to the intricacy of the optimization problem, a novel mutation-based modified version of the shuffled frog leaping algorithm (SFLA) is suggested as a way to get around the PBUC problem’s difficulty. A 10-unit test system is used for the simulation, which is run for a whole day to demonstrate the effectiveness of the suggested approach. The proposed algorithm’s output is compared with the best-known approaches from various references. The simulated results generated by the suggested algorithms and the previously reported algorithms to solve the PBUC problem show that the proposed method is better than other evolutionary methods utilized in this study and prior investigations. For example, the overall profit from the suggested MSFLA is around 4% and 5.5% higher than that from other algorithms like the ICA and Muller methods in the presence and absence of reserve allocation, respectively. Furthermore, the MSFLA emissions value is approximately 2% and 8% lower than the optimum emissions values obtained using the PSO and ICA approaches, respectively.

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“…The memetic binary differential evolution algorithm (MDPE) has been proposed to solve a profit-based UC problem [44]. An uncertain UC problem study is suggested in the presence of energy storage systems using list-based genetic algorithm-priority [45]. Quantum binary particle swarm optimization (QBPSO) algorithms are proposed to reduce operation cost in the UC problem [46].…”

Section: Introductionmentioning

confidence: 99%

A New “Doctor and Patient” Optimization Algorithm: An Application to Energy Commitment Problem

et al. 2020

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Regular assessments of events taking place around the globe can be a conduit for the development of new ideas, contributing to the research world. In this study, the authors present a new optimization algorithm named doctor and patient optimization (DPO). DPO is designed by simulating the process of treating patients by a physician. The treatment process has three phases, including vaccination, drug administration, and surgery. The efficiency of the proposed algorithm in solving optimization problems compared to eight other optimization algorithms on a benchmark standard test function with 23 objective functions is been evaluated. The results obtained from this comparison indicate the superiority and quality of DPO in solving optimization problems in various sciences. The proposed algorithm is successfully applied to solve the energy commitment problem for a power system supplied by a multiple energy carriers system.

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A robust unit commitment based on GA-PL strategy by applying TOAT and considering emission costs and energy storage systems

Cited by 14 publications

References 25 publications

Metaheuristics and Transmission Expansion Planning: A Comparative Case Study

Metaheuristics and Transmission Expansion Planning: A Comparative Case Study

Multi-Objective Profit-Based Unit Commitment with Renewable Energy and Energy Storage Units Using a Modified Optimization Method

A New “Doctor and Patient” Optimization Algorithm: An Application to Energy Commitment Problem

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