A solution method of unit commitment by artificial neural networks

Sasaki, Hiroshi; Watanabe, Masahiro; Kubokawa, Junji; Yorino, Naoto; Yokoyama, Ryuichi

doi:10.1109/59.207310

Cited by 188 publications

(64 citation statements)

References 11 publications

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“…In the FT method [11], [13], [24] using fuzzy set solves the forecasted load schedules error but it will also suffer from complexity. The H neural network technique [12] considers more constraints but it may suffer from numerical convergence due to its training process. SA [14][15][16][17], [23][24] is a powerful, general-purpose stochastic optimisation technique, which can theoretically converge asymptotically to a global optimum solution with probability one.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

Christober¹,

Rajan²

2011

Journal of Electrical Engineering

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Section: Introductionmentioning

confidence: 99%

An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

Christober¹,

Rajan²

2011

Journal of Electrical Engineering

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“…In the LDWPSO approach, particle's fitness is best until the preceding iteration, its velocity is kept unchanged in the next iteration, otherwise, the particle's velocity and position are changed according to (12) and (13), which does not truly reflect the search process to find the optimum.…”

Section: Overview Of the Psomentioning

confidence: 99%

“…A number of methods have been proposed for solving the hydrothermal scheduling problem. Some examples of these methods are nonlinear programming (NLP) [1][2][3], Dynamic Programming (DP) [4][5][6], Lagrangian Relaxation (LR) [7][8][9], Tabu Search [10], Expert Systems [11], Artificial Neural Networks (ANN) [12], Genetic Algorithms (GA) [13][14][15]. Among these methods, DP would provide a good framework for optimizing the decisions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Particle Swarm Optimization for Optimal Scheduling of Hydrothermal System

Chang¹

2010

EPE

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A fuzzy adaptive particle swarm optimization (FAPSO) is presented to determine the optimal operation of hydrothermal power system. In order to solve the shortcoming premature and easily local optimum of the standard particle swarm optimization (PSO), the fuzzy adaptive criterion is applied for inertia weight based on the evolution speed factor and square deviation of fitness for the swarm, in each iteration process, the inertia weight is dynamically changed using the fuzzy rules to adapt to nonlinear optimization process. The performance of FAPSO is demonstrated on hydrothermal system comprising 1 thermal unit and 4 hydro plants, the comparison is drawn in PSO, FAPSO and genetic algorithms (GA) in terms of the solution quality and computational efficiency. The experiment showed that the proposed approach has higher quality solutions and strong ability in global search.

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“…Generate initial frog population by (23) where , is initialized with 0 (shutdown) or 1 (startup) at each period using the rand( ) function.…”

Section: The Coupled External and Internal Model For Sthgsmentioning

confidence: 99%

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

Yang

et al. 2018

Mathematical Problems in Engineering

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The short-term hydro generation scheduling (STHGS) decomposed into unit commitment (UC) and economic load dispatch (ELD) subproblems is complicated problem with integer optimization, which has characteristics of high dimension, nonlinear and complex hydraulic and electrical constraints. In this study, the improved binary-real coded shuffled frog leaping algorithm (IBR-SFLA) is proposed to effectively solve UC and ELD subproblems, respectively. For IB-SFLA, the new grouping strategy is applied to overcome the grouping shortage of SFLA, and modified search strategies for each type of frog subpopulation based on normal cloud model (NCM) and chaotic theory are introduced to enhance search performance. The initialization strategy with chaos theory and adaptive frog activation mechanism are presented to strengthen performance of IR-SFLA on ELD subproblem. Furthermore, to solve ELD subproblem, the optimal economic operation table is formed using IR-SFLA and invoked from database. Moreover, reserve capacity supplement and repair, and minimum on and off time repairing strategies are applied to handle complex constraints in STHGS. Finally, the coupled external and internal model corresponding to UC and ELD subproblems is established and applied to solve STHGS problem in Three Gorges hydropower station. Simulation results obtained from IBR-SFLA are better than other compared algorithms with less water consumption. In conclusion, to solve STHGS optimization problem, the proposed IBR-SFLA presents outstanding performance on solution precision and convergence speed compared to traditional SFLA effectively and outperforms the rivals to get higher precision solution with improving the utilization rate of waterpower resources.

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A solution method of unit commitment by artificial neural networks

Cited by 188 publications

References 11 publications

An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

A Novel Particle Swarm Optimization for Optimal Scheduling of Hydrothermal System

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

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