Unit commitment using a stochastic extended neighbourhood search

Purushothama, G K; Narendranath, UA; Jenkins, Lawrence

doi:10.1049/ip-gtd:20020743

Cited by 13 publications

(8 citation statements)

References 14 publications

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“…Thus the main features of the SA process are: the transition mechanism; and the cooling scheme. The transition mechanism consists of three components [11]:…”

Section: Simulated Annealingmentioning

confidence: 99%

“…The procedure by which the control temperature parameter is reduced is called the cooling (annealing) schedule. It is very critical to the success of the SA [11,17]. This article also uses the same cooling schedule suggested in [10].…”

Section: Simulated Annealingmentioning

confidence: 99%

“…The UCP to minimize production costs (mainly fuel cost) and transition costs (startup/shut-down costs) is traditionally referred as cost-based UCP [2][3][4][5][6][7][8][9][10][11][12]. The problem is broken down into a combinatorial sub-problem in unit status variables and non-linear programming sub-problem in unit power output variables [11]. In any UCP, the load demand to be met by the committed generating units will usually be a forecasted (crisp) one.…”

Section: Introductionmentioning

confidence: 99%

“…The combinatorial part of the UCP is solved using the SA method. The SA method has already proved itself as a powerful technique for solving combinatorial optimization problems [10,11,14]. A modified approach for generating the trial schedule as the neighbourhood of the current feasible schedule and a simple procedure to generate the initial feasible schedule for the SA method is presented [17].…”

Section: Introductionmentioning

confidence: 99%

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A Simulated Annealing Based Hybrid Optimization Approach for a Fuzzy Modelled Unit Commitment Problem

Victoire¹,

Jeyakumar²

2005

International Journal of Emerging Electric Power Systems

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This article presents a solution model for the unit commitment problem (UCP) using fuzzy logic to address uncertainties in the problem. Hybrid simulated annealing (SA), particle swarm optimization (PSO) and sequential quadratic programming (SQP) technique (hybrid SA-PSO-SQP) is used to schedule the generating units based on the fuzzy logic decisions. The fitness function for the hybrid SA-PSO-SQP is formulated by combining the objective function of UCP and a penalty calculated from the fuzzy logic decisions. Fuzzy decisions are made based on the statistics of the load demand error and spinning reserve maintained at each hour. SA is used to solve the combinatorial sub-problem of the UCP. An improved random perturbation scheme and a simple method for generating initial feasible commitment schedule are proposed for the SA method. The non-linear programming sub-problem of the UCP is solved using the hybrid PSO-SQP technique. Simulation results on a practical Neyveli Thermal Power Station system (NTPS) in India and several example systems validate, the presented UCP model is reasonable by ensuring quality solution with sufficient level of spinning reserve throughout the scheduling horizon for secure operation of the system.

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“…Thus the main features of the SA process are: the transition mechanism; and the cooling scheme. The transition mechanism consists of three components [11]:…”

Section: Simulated Annealingmentioning

confidence: 99%

Section: Simulated Annealingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Simulated Annealing Based Hybrid Optimization Approach for a Fuzzy Modelled Unit Commitment Problem

Victoire¹,

Jeyakumar²

2005

International Journal of Emerging Electric Power Systems

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“…Many deterministic methods have been utilized for the solution of the UC problem, such as Lagrangian rylaxation [ 11-[4], mixed-integer programming [5] and dynamic programming [6]. In addition to the above, there are meta-heuristic methods, such as genetic algorithms [7], simulated annealing (SA) [SI- [ lo], hybrid algorithms, which combine SA with other methods [11]- [13], and the ant colony search algorithm [14]. The main advantage of these methods is their ability to come up with very good solutions, while they can easily deal with difficult nonlinear constraints.…”

mentioning

confidence: 99%

Unit commitment with ramp rate constraints using the simulated annealing algorithm

Simopoulos

Contaxis

2004

Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521)

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A Simulated Annealing (SA) algorithm has been developed for solving the unit commitment (UC) problem. The algorithm is used for the scheduling of the generating units, while a quadratic programming routine carries out the economic dispatch. New rules concerning the cooling schedule and the random generation of the initial solution of the system are presented. A new approach for generating feasible neighboring solutions contributes to the reduction of the required CPU time. Ramp rate constraints have also been added in order to obtain more realistic results. Numerical simulations have proved the effectiveness of the proposed model.

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Modern Heuristic Optimization Approaches for Electrical Power System Applications

Huang

Liu

2013

Intelligent Technologies and Engineering Systems

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Unit commitment using a stochastic extended neighbourhood search

Cited by 13 publications

References 14 publications

A Simulated Annealing Based Hybrid Optimization Approach for a Fuzzy Modelled Unit Commitment Problem

A Simulated Annealing Based Hybrid Optimization Approach for a Fuzzy Modelled Unit Commitment Problem

Unit commitment with ramp rate constraints using the simulated annealing algorithm

Modern Heuristic Optimization Approaches for Electrical Power System Applications

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