Tabu search-enhanced artificial bee colony algorithm to solve profit-based unit commitment problem with emission limitations in deregulated electricity market

Sundaram, C. Shanmuga; Sudhakaran, M.; Raj, P. Ajay-D-Vimal

doi:10.1504/ijmheur.2017.083099

Cited by 13 publications

(7 citation statements)

References 32 publications

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“…Dynamic programming [15] 565,825 Genetic algorithm [15] 570,032 Advanced PSO [19] 563,942 Proposed method 554,637 vehicles occurs due to parking the vehicle in parking lot and at a home garage about 90-95% time a day. Therefore, probability distribution of solar, wind and Electric vehicles is modeled and included in this paper.…”

Section: Methodsmentioning

confidence: 99%

“…Earlier, the PBUC problems were solved by various conventional methods such as Lagrangian Relaxation (LR) method [14], Priority List (PL) method [14] and Dynamic Programming (DP) method [15]. LR method provides fast solution but suffers from numerical divergence problem and PL method provides high speed of convergence but it leads to high operating cost for large scale problems.…”

Section: Introductionmentioning

confidence: 99%

“…The maximum number of discharging vehicles at each hour, 10% of total vehicles.. Due to stochastic nature of solar and wind, the output power from solar and wind farms cannot be predicted accurately and uncertainty in Electric Fig. 3 PBUC-profit comparison of proposed method with different methods 100000 105000 110000 115000 120000 125000 130000 135000 Tabu Search-RP [15] Muller method [20] LR-EP method [14] Improved PSO [19] Memory Mgnt method [23] Proposed method…”

Section: Introduction Of Renewable Energy Sources and Electric Vehicle Systemmentioning

confidence: 99%

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Profit Based Unit Commitment of Thermal Units with Renewable Energy and Electric Vehicles in Power Market

Vasiyullah¹,

Bharathidasan²

2020

J. Electr. Eng. Technol.

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In restructured power system, Generation Companies (GENCOs) has an opportunity to sell power and reserve in power market to earn profit by market clearing process. Defining unit commitment problem in a competitive environment to maximize the profit of GENCOs while satisfying all the network constraints is called Profit Based Unit Commitment problem (PBUC). The main contribution of this paper is modeling and inclusion of Market Clearing Price (MCP) in PBUC problem. In Day market, MCP is determined by market operator which provides maximum social welfare for both GENCOs and Consumers.On other hand this paper proposes a novel combination of solution methodology: Improved Pre-prepared power demand (IPPD) table and Analytical Hierarchy method (AHP) for solving the optimal day ahead scheduling problem as an another contribution. In this method, the status of unit commitment is obtained by IPPD table and AHP provides an optimal solution to PBUC problem. Minimizing total operating cost of thermal units to provide maximum profit to GENCOs is called an optimal day ahead scheduling problem. Also it will be more realistic to redefine this problem to include multiple distributed resources and Electric vehicles with energy storage. Because of any uncertainties or fluctuation of renewable energy resources (RESs), Electric vehicles (EV) can be used as load, energy sources and energy storage. This would reduce cost, emission and to improve system power quality and reliability. So output power of solar (PS), wind output power (PW) and Electric Vehicles power (PEV) are modeled and included into day ahead scheduling problem.The proposed methodology is tested on a standard thermal unit system with or without RESs and EVs. Cost and emission reduction in a smart grid by maximum utilization of EVs and RESs are presented in this literature. It is indicated that the proposed method provides maximum profit to GENCOs when compared to other methodologies such as Memory Management Algorithm, Improved Particle Swarm Optimization (PSO), Muller method, Gravitational search algorithm etc.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introduction Of Renewable Energy Sources and Electric Vehicle Systemmentioning

confidence: 99%

See 1 more Smart Citation

Profit Based Unit Commitment of Thermal Units with Renewable Energy and Electric Vehicles in Power Market

Vasiyullah¹,

Bharathidasan²

2020

J. Electr. Eng. Technol.

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“…One of the most effective methods to reduce environmental emissions is through GENCOs' optimal operation planning. While numerous algorithms, such as the ICA [40], the shuffled frog leaping algorithm (SFLA) [22], the hybrid PSO (HPSO) [41], the TS-enhanced ABC algorithm (TSEABC) [42], civilized swarm optimization (CSO) [43], and double benders decomposition (DBD) [44], have all been used to solve the PBUC problem, a review of the studies [22,[39][40][41][42] shows that none of the studies included contamination as an objective function, and they could have included the constraints of the optimization problem. In other words, the profit function is maximized according to the contagion condition, but both functions are not optimized together.…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…Simopoulos et al use simulated annealing in unit commitment considering reliability [18]. Sundaram et al integrate artificial bee colony algorithm and tabu search to solve the profitbased unit commitment problem [19]. Marrouchi et al apply fuzzy logic approaches in unit commitment compared with gradient-genetic algorithm to test their performance [20].…”

Section: Introductionmentioning

confidence: 99%

Low Carbon Multi-Objective Unit Commitment Integrating Renewable Generations

et al. 2020

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Unit commitment is an intractable issue aiming to reduce the overall economic cost of power system operation while maintaining the system constraints. Due to the emerging scenario of global warming, many countries are vigorously developing renewable energy to replace the traditional fossil power plant, in order to reduce the environmental and carbon emission. The increasing penetration of renewable generation significantly challenge the economic and security of power system operation. In this paper, a low carbon multi-objective objective unit commitment model considering economic cost, environmental cost and, more importantly, the carbon emission is established, integrating wind and solar power and therefore generating a multi-objective, high-dimensional, strong non-linear, multi-constraint and mixed integer optimization problem. The non-dominated sorting genetic algorithm-III is tailored and adopted for solving the proposed challenging task, where the decision-making scheme is designed according to the normalization method and weighted sum function. Numerical results show that the proposed complex many-objective low carbon unit commitment model can be successfully solved by the proposed algorithm and the carbon emission is effectively reduced by the integration of renewable generations.

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Tabu search-enhanced artificial bee colony algorithm to solve profit-based unit commitment problem with emission limitations in deregulated electricity market

Cited by 13 publications

References 32 publications

Profit Based Unit Commitment of Thermal Units with Renewable Energy and Electric Vehicles in Power Market

Profit Based Unit Commitment of Thermal Units with Renewable Energy and Electric Vehicles in Power Market

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

Low Carbon Multi-Objective Unit Commitment Integrating Renewable Generations

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