An enhanced simulated annealing approach to unit commitment

Wong, Suzannah Yin Wa

doi:10.1016/s0142-0615(97)00062-8

Cited by 49 publications

(6 citation statements)

References 13 publications

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“…Frangioni, Gentile and Lacalandra [88] implemented this sequential approach and found that it was more effective as compared to standard mixed integer linear programming. The enhanced simulated annealing improved the speed by using parallel version, Wong [99] used this technique and results obtained were satisfactory. Zheng et al [94] used benders decomposition and found that results obtained were efficient but the convergence was not faster.…”

Section: Summary Of Solution Techniquesmentioning

confidence: 95%

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Modeling and Solution Techniques Used for Hydro Generation Scheduling

Parvez

Shen

Khan

et al. 2019

Water

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The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

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Section: Summary Of Solution Techniquesmentioning

confidence: 95%

“…Wong [99] solved the unit commitment problem by using enhanced simulated annealing solution method as shown in Figure 1 because this technique was easy to implement and did not need large memory. In this method, iteration number was equal to temperature level.…”

Section: Enhanced Simulated Annealingmentioning

confidence: 99%

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Parvez

Shen

Khan

et al. 2019

Water

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“…When solving the UC problem with local search based meta‐heuristics, a single neighbourhood movement is usually considered (Zhuang and Galiana, 1990; Yin Wa Wong, 1998; Mantawy et al , 1998b; Purushothama and Jenkins, 2003; Mantawy et al , 1998a). Basically, it consists in the following steps.…”

Section: Uc With Constraint Oriented Neighbourhoodsmentioning

confidence: 99%

A meta‐heuristic approach to the unit commitment problem under network constraints

Pereira

Viana

Lucus

et al. 2008

International Journal of Energy Sector Management

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PurposeThe purpose of this paper is to solve the problem of committing electric power generators (unit commitment, UC), considering network constraints.Design/methodology/approachThe UC is first solved with a local search based meta‐heuristic, following the assumption that all generators and loads are connected to a single network node. For evaluation purposes, the economical production levels of the units committed are computed by running a pre‐dispatch algorithm where network constraints are not included. If a good quality solution is reached, an economic dispatch (ED) with network constraints is performed, where the geographic location of generators and loads are considered. Therefore, the production level of each committed generator is performed that leads to the global lowest solution cost, regarding both the generators' costs and constraints and the power system network constraints.FindingsThe algorithm proposed is computationally efficient, given the time available for decision making. In addition, the solution for this algorithm, in terms of minimization of total costs, is generally better than the solution of the two phases approach. Some contractual and legal aspects related with the injection in network connections can also be included in the model.Practical implicationsUC with network constraints has a large potential of use, especially for small and medium size power systems. It reflects reality in a closer way and provides a more complete and realistic knowledge about the system in operation.Originality/valueThe paper presents an approach where the ED with network constraints is integrated with the UC procedure. The model described is currently implemented in an EMS package offered in the market – making it a case of successful transfer from science to industry.

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“…Several numerical techniques have been applied to the UC problem, such as fuzzy logic [19][20][21][22], artificial neural networks [23], simulated annealing [24][25][26][27], Tabu search [26,28], and the genetic algorithm [29][30]. These methods can take into account more complex constraints and are claimed to have a better solution quality.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Unit Scheduling with Fuzzy Logic: A Strategic Approach for Efficient Power Network Operations

Marrouchi,

Ben Hessine,

Chebbi

2024

Eng. Technol. Appl. Sci. Res.

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This study delves into addressing the challenge of resolving the Unit Commitment (UC) problem, which focuses on enhancing the efficiency of production units and devising their operational schedules to accommodate fluctuations in consumption spanning from a day to a month. Given the intricate, combinatorial, and nonlinear constraints associated with each production unit, this study advocates an optimization approach rooted in fuzzy logic. A Langrangian function was established to simplify the UCP and to transform the different inequality into a linear unconstrained problem. The choice of fuzzy inputs was established using the partial derivatives of a Lagrangian function as a function of the powers injected into each node of the electrical network. This combination of the Lagrangian function and the input of the fuzzy regulator made it possible to control the different constraints in the total production cost function and to improve the operating efficiency of the different production units. This method was effectively applied to a 14-bus IEEE power network encompassing 5 generating units, to address the UC problem by optimizing generator load capacity (LCG) and minimizing Incremental Losses (IL). The numerical processing of the fuzzy linguistic variables was implemented using Mamdani-type fuzzy rules. This strategy stands out for its robust exploratory capability, facilitating the identification of optimal solutions to reduce production costs while ensuring optimal planning of production units.

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An enhanced simulated annealing approach to unit commitment

Cited by 49 publications

References 13 publications

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Modeling and Solution Techniques Used for Hydro Generation Scheduling

A meta‐heuristic approach to the unit commitment problem under network constraints

Optimizing Unit Scheduling with Fuzzy Logic: A Strategic Approach for Efficient Power Network Operations

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