Simulated annealing algorithm for combined economic and emission power dispatch using max/max price penalty factor

Ziane, Ismail; Benhamida, Farid; Graa, Amel

doi:10.1007/s00521-016-2335-3

Cited by 58 publications

(43 citation statements)

References 21 publications

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“…WPCost dir.j + WPCost oe.j + WPCost ue.j (18) where the values of P min , P max and cost coefficients vectors a, b, c, e and f are given in Table A1 of Appendix A, while the parameter of wind generating units is given in Table A2 of Appendix B.…”

Section: Resultsmentioning

confidence: 99%

“…The constraints associated with the problem are given in Equation (16). The design nine variants of GA were applied to solve ELD, ELD-VPLE and ELD-VPLE-SW problems using the cost function given in Equations (15), (17) and (18), respectively, while satisfying the constraints given in Equation (16). The learning curves of GA-1 along its fitness value and output power are shown graphically in Figure 2a,c,e for ELD, ELD-VPLE and ELD-VPLE-SW problems, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Normally, these environmental goals are conflicting with the economical nature of the systems, i.e., the decline in emission from generating units (GUs) results 2 of 23 in the increased rate of electricity generation and vice versa. In such circumstances, multi-objective optimization techniques are exploited for combined ELD with emission problems, such as the symbiotic organisms search optimization method [17], simulated annealing algorithms [18], multi-objective evolutionary computing [19], multi-objective biogeography-based optimization [20], flower pollination algorithms [21], modulated particle swarm optimization [22] and chaotic bat algorithms [23].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems Involving Stochastic Wind

et al. 2019

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In this research work, bio-inspired computational heuristic algorithms (BCHAs) integrated with active-set algorithms (ASA) were designed to study integrated economics load dispatch problems with valve point effects involving stochastic wind power. These BCHAs are developed through variants of genetic algorithms based on a different set of routines for reproduction operators in order to make exploration and exploitation in the entire search space for finding the global optima, while the ASA is used for rapid local refinements of the results. The designed schemes are estimated on different load dispatch systems consisting of a combination of thermal generating units and wind power plants with and without valve point loading effects. The accuracy, convergence, robustness and complexity of the proposed schemes has been examined through comparative studies based on a sufficiently large number of independent trails and their statistical observations in terms of different performance indices.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems Involving Stochastic Wind

et al. 2019

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“…It is obvious that, except the 150MW generator, the larger the rated capacity of the generator, the higher the load rate, and the lower the coal consumption. For the 150MW generator, the load rate equals the allowed minimum 9.50x10 3 1.90x10 4 2.85x10 4 3.80x10 4 0.0 limit, meaning that its operation performance is poor. In addition, although its load rate is greater than those of the 300MW and 600MW generators, its coal consumption is significantly higher, which indicates that its energy conservation is inefficient.…”

Section: Application To a Practical Provincial Power Grid Of Chinamentioning

confidence: 99%

“…They have no strict requirements on the form of optimization problems and can avoid the influences of the initial condition sensitivity and gradient information. Up to now, the successfully implemented metaheuristic methods include simulated annealing [3], genetic algorithm [4,5], particle swarm optimization [6][7][8], differential evolution [9,10], artificial bee colony [11,12], harmony search [13][14][15][16][17], biogeography-based optimization [18][19][20][21][22], teaching-learning-based optimization [23][24][25], firefly algorithm [26], crisscross optimization algorithm [27,28], bat algorithm [29], grey wolf optimizer [30,31], cuckoo search [32][33][34], ant lion optimizer [35], exchange market algorithm [36], symbiotic organisms search [37,38], backtracking search algorithm [39,40], interior search algorithm [41], whale optimization algorithm [42], mine blast algorithm [43], and hybrid methods [44][45]…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China

et al. 2018

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Economic dispatch (ED) is of cardinal significance for the power system operation. It is mathematically a typical complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering the valve-point effects. In order to effectively solve the problem, a simple yet very young and efficient population-based algorithm named across neighborhood search (ANS) is implemented in this paper. In ANS, a group of individuals collaboratively navigate through the search space for obtaining the optimal solution by simultaneously searching the neighborhoods of multiple superior solutions. Four benchmark test cases with diverse complexities and characteristics are firstly employed to comprehensively verify the feasibility and effectiveness of ANS. The experimental and comparison results fully demonstrate the superiority of ANS in terms of the final solution quality, convergence speed, robustness, and statistics. In addition, the sensitivities of ANS to variations of population size and across-search degree are studied. Furthermore, ANS is applied to a practical provincial power grid of China. All the comparison results consistently indicate that ANS is highly competitive and can be used as a promising alternative for ED problems.

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A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

Mahdi

Vasant

Abdullah-Al-Wadud

et al. 2017

Int Trans Electr Energ Syst

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Summary In electrical power dispatch problem, economic dispatch (ED) and environmental dispatch problems play a crucial part. Economic dispatch problem refers to the minimization of generation cost, where environmental dispatch problem refers to the minimization of emission of pollutants like CO2, SO2, and NOx from the power generation system. A quantum‐inspired particle swarm optimization (QPSO) technique is presented in this paper to solve many‐objective environmental economic dispatch (EED) problems. Emissions of CO2, SO2, and NOx are considered 3 different objectives, thus making it a 4‐objective problem considering ED. Many‐objective EED problems are defined by using a cubic criterion function, and a max/max price penalty factor is considered to convert all the objectives into a single objective to compare the final results with other well‐known methods found in the literature like Lagrangian relaxation, particle swarm optimization, simulated annealing, and quantum‐behaved bat algorithm. Quantum‐inspired particle swarm optimization is implemented on a 6‐unit system to solve many‐objective EED problems, and at the same time, to show the effectiveness of QPSO in large systems, it is also implemented in a 26‐unit power generation system for ED problem. The obtained results demonstrate and verify the effectiveness and robustness of QPSO to solve many‐objective EED problems. This also shows that QPSO can effectively be implemented in such power dispatch problems.

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Simulated annealing algorithm for combined economic and emission power dispatch using max/max price penalty factor

Cited by 58 publications

References 21 publications

Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems Involving Stochastic Wind

Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems Involving Stochastic Wind

A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China

A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

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