A Solution to the Unit-Commitment Problem Using Integer-Coded Genetic Algorithm

Damousis, I. G.; Bakirtzis, Anastasios G.; Dokopoulos, P.S.

doi:10.1109/tpwrs.2003.821625

Cited by 361 publications

(206 citation statements)

References 35 publications

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“…The UC simulation results of the proposed SSAS are compared against several UC solution methods taken from the previous publications in the following: Lagrangian Relaxation (LR) [13], Genetic Algorithms [13], Evolutionary Programming (EP) [6], Improved Cooperative Genetic Algorithms (ICGA) [14], Genetic Algorithms for Unit Commitment (GAUC) [15], Fast Genetic Algorithms (FGA) [16], and Enhanced Lagrangian Relaxation (ELR) [17]. For confirming the performance, the proposed SSAS is also compared to the results of AS based the diversity control approach (DAS) [9].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this section, we also propose a new method to adjust transition probability parameters of trail decision formula in (14) adaptively. These parameters, represented as alpha (α t,u  ) and beta (β t,u ), control the relative importance of the pheromone trail and the visibility, respectively.…”

Section: Selective Self-adaptive Transition Probability Parametersmentioning

confidence: 99%

“…As is shown in (19), we divide the second term by the bias parameter of β t,u-1 to delay the decreasing of α t,u  . When adaptive transition probability parameters are applied, the adjusting would result in the transition probability in (14). Too small α t,u  and large β t,u causes little chance of the diversity, while small β t,u and large α t,u  gives strong concern of the pheromone trail importance more than the visibility when SSAS needs to reinforce decision process of a local search, and to diverge UC solution far away form a local optima.…”

Section: Selective Self-adaptive Transition Probability Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Selective self-adaptive approach to ant system for solving unit commitment problem

Chusanapiputt

Nualhong

Jantarang

et al. 2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

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This paper presents a novel approach to solve the constrained unit commitment problem using Selective Self-Adaptive Ant System (SSAS) for improving search performance by automatically adapting ant populations and their transition probability parameters, which cooperates with Candidate Path Management Module (CPMM) and Effective Repairing Heuristic Module (ERHM) in reducing search space and recovering a feasible optimality region so that a high quality solution can be acquired in a very early iterative. The proposed SSAS algorithm not only enhances the convergence of search process, but also provides a suitable number of the population sharing which conducts a good guidance for trading-off between the importance of the visibility and the pheromone trail intensity. The proposed method has been performed on a test system up to 100 generating units with a scheduling time horizon of 24 hours. The numerical results show the most economical saving in the total operating cost when compared to the previous literature results. Moreover, the proposed SSAS topology can remarkably speed up the computation time of ant system algorithms, which is favorable for a large-scale unit commitment problem implementation.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Selective Self-adaptive Transition Probability Parametersmentioning

confidence: 99%

Section: Selective Self-adaptive Transition Probability Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Selective self-adaptive approach to ant system for solving unit commitment problem

Chusanapiputt

Nualhong

Jantarang

et al. 2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

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“…In (30), it is assumed that all p objective functions should be minimized. In order to properly apply the ε-constraint method, the ranges of at least p-1 objective functions are needed that will be used as the additional objective function constraints.…”

Section: Multiobjective Mathematical Programming (Mmp)mentioning

confidence: 99%

Uncertainty management in multiobjective hydro-thermal self-scheduling under emission considerations

Aghaei

Ahmadi

Rabiee

et al. 2015

Applied Soft Computing

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In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units' contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte Carlo simulation has been adopted to have a better coverage of the system uncertainty spectrum. Consequently, the resulting multiobjective optimization scenarios should concurrently optimize competing objective functions including GENeration COmpany's (GENCO's) profit maximization and thermal units' emission minimization. Accordingly, the ε-constraint method is used to solve the multiobjective optimization problem and generate the Pareto set. Then, a fuzzy satisfying method is employed to choose the most preferred solution among all Pareto optimal solutions. The performance of the presented method is verified in different case studies. The results obtained from ε-constraint method is compared with those reported by weighted sum method, evolutionary programming-based interactive Fuzzy satisfying method, differential evolution, quantumbehaved particle swarm optimization and hybrid multi-objective cultural algorithm, verifying the superiority of the proposed approach. Abstract-In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units' contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte Carlo simulation has been adopted to have a better coverage of the system uncertainty spectrum. Consequently, the resulting multiobjective optimization scenarios should concurrently optimize competing objective functions including GENeration COmpany's (GENCO's) profit maximization and thermal units' emission minimization. Accordingly, the ε-constraint method is used to solve the multiobjective optimization problem and generate the Pareto set. Then, a fuzzy satisfying method is employed to choose the most preferred solution among all Pareto optimal solutions. The perfo...

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“…It can characterize the on/off status of units and optimally handle the UC control variables. In this method, the binary variables are replaced by integer operation cycles without previous restrictions in [13]. This leads to a substantial drop down of the number of decision variables and better numerical results.…”

Section: Introductionmentioning

confidence: 99%

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Bavafa

Niknam

Azizipanah‐Abarghooee

et al. 2017

IEEE Trans. Ind. Inf.

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Abstract-Large penetration of wind generating units in power systems necessitates a flexible unit commitment tool to handle the intermittent nature of these units as well as demand. Moreover, power system operators face not only the risks of wind power curtailments, but also probable unit outages. Therefore, assessing a trade-off between operational costs and such risks is very important. In the proposed approach, the probability of the residual demand falling within the up-and-down spinning reserve imposed by n-1 security criterion is converted into a risk index. A new bi-objective probabilistic risk/cost based unit commitment model is proposed to simultaneously minimize both the operational costs and risk. The novel formulation presented provides a new power re-dispatch process to satisfy up-and-down ramp rate constraints. A new operational cycles based unit commitment algorithm is developed. The approach profits from a new non-dominated sorting backtracking search optimization algorithm for extracting the Pareto optimal set. The proposed approach is shown to provide superior results when applied to two test systems: a) 10-unit and b) IEEE 118-bus, 54-unit system.

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A Solution to the Unit-Commitment Problem Using Integer-Coded Genetic Algorithm

Cited by 361 publications

References 35 publications

Selective self-adaptive approach to ant system for solving unit commitment problem

Selective self-adaptive approach to ant system for solving unit commitment problem

Uncertainty management in multiobjective hydro-thermal self-scheduling under emission considerations

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

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