Optimal scheduling of large-scale hydrothermal power systems using the Lagrangian relaxation technique

Ngundam, J.M.; Kenfack, F.; Tatietsé, Thomas Tamo

doi:10.1016/s0142-0615(99)00054-x

Cited by 49 publications

(18 citation statements)

References 26 publications

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“…Only when the constraints are satisfied, the result of optimized scheduling become useful in a practical way. The optimal power system scheduling model based on green economy has a number of constraints [11]- [12].…”

Section: Constraintsmentioning

confidence: 99%

“…Linear programming [3] requires linear simplification of the problems to be solved, thereby reducing the accuracy of the calculation. The Lagrangian relaxation [4] method has oscillations, even singular points, in the solution process. The genetic algorithm [5] and the PSO [6] algorithm have weak global search capability, and may easily fall into a local optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Power System Scheduling Based on Shuffled Complex Evolution Metropolis Algorithm

Qiang

Dong

et al. 2015

TELKOMNIKA

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IntroductionSince the joint power system scheduling optimization is stochastic, dynamic, and involves time-delay, studies at home and abroad have been carried out on the development of power generation schemes and power system scheduling [1]. Commonly used methods include the equal incremental method, dynamic programming, linear programming, Lagrangian relaxation, the genetic algorithm, and the particle swarm optimization (PSO) algorithm. However, these algorithms all have their own limitations on solving the problem of scheduling optimization of hydrothermal power systems. The equal incremental method only satisfies the necessary conditions for the objective function to take the minimum value, not the sufficient conditions. Dynamic programming [2] suffers from the curse of dimensionality. Linear programming [3] requires linear simplification of the problems to be solved, thereby reducing the accuracy of the calculation. The Lagrangian relaxation [4] method has oscillations, even singular points, in the solution process. The genetic algorithm [5] and the PSO [6] algorithm have weak global search capability, and may easily fall into a local optimal solution. Consequently, none of these algorithms can accurately solve the problem of optimal power system scheduling.In order to overcome the shortcomings of power system scheduling optimization and its corresponding solutions of traditional models [7], this paper proposes a power system scheduling optimization model which takes the economic benefits, energy efficiency and environmental benefits into consideration. A new objective function, i.e. objective function of pollution emissions, is added to the objective function based on the conventional coal consumption costs. In this way, under the premise of effectively ensuring safe operation of the power system, the number of thermoelectric generator starts and stops can be minimized, and water resources can be used efficiently. This may also reduce pollutant emissions from electric power companies. This paper also uses the SCEM-UA [8] global optimization algorithm to solve the model. The SCEM-UA algorithm is a global optimization algorithm that combines the advantages of Shuffled Complex Evolution (SCE-UA) algorithm and Markov chain Monte Carlo (MCMC) method.With the SCEM-UA algorithm, in the process of evolution the complexes are not partitioned into multiple sub-complexes. Instead, a Markov chain is constructed so that parameters evolve toward the target posterior probability distribution [9]. SCEM-UA algorithm is

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Section: Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Power System Scheduling Based on Shuffled Complex Evolution Metropolis Algorithm

Qiang

Dong

et al. 2015

TELKOMNIKA

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“…Applied optimization methods can be classical calculus-based algorithms such as linear and *Address correspondence to this author at the Business School, Hohai University, Nanjing 210098, P.R. China; Tel: +86-25-83786327; Fax: +86-25-83786319; E-mail: chyzheng@hhu.edu.cn nonlinear programming [2], interior point [3,4] and dynamic programming [5], Lagrange relaxation method [6,7]. The other methods are the artificial intelligence techniques including network flow method [8], heuristic methods, expert systems and artificial neural networks [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…In order to reduce the dimension of the problem, aggregation of hydroelectric subsystem into an equivalent hydroplant is the common manipulation. Unfortunately, this practice is sometimes not adapted and unusable [13].…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Hydrothermal System with Network and Ramping via SCE-UA Method

Dong¹

2013

TOCSJ

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This paper describes a method for scheduling large-scale hydrothermal power systems based on the shuffled complex evolution (SCE-UA) method. A multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, net head and power generation is considered. The water transport delay between connected reservoirs is also taken into account. SCE-UA is a successfully proven method in global optimization for many situations. Benefiting from its unique global optimization strategies into the inverse procedure greatly enhances the performance of SCE-UA method since it can not only effectively locate the promising areas in the solution space for a global minimum but also avoid its wandering near the global minimum in the final stage of search. The efficiency of the SCE-UA method is analyzed in terms of the mean performance and computational time, in comparison with the particle swarm optimization (PSO) algorithm. The simulation results reveal that SCE-UA effectively overcomes the premature phenomenon and improves the global convergence and optimization searching capability. It is a relatively consistent, effective and efficient optimization method in solving the large scale hydrothermal scheduling problem.

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“…The MIP methods [3][4] for solving the unit commitment problems fail when the number of units increases because they require a large memory and suffer from great computational delay. The LR approach [5][6][7][8] to solve the short-term UC Problems was found that it provides faster solution but it will fail to obtain solution feasibility and solution quality problems and becomes complex if the number of units increased. The BB method [9] employs a linear function to represent fuel cost and start-up cost and obtains a lower and upper bounds.…”

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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Optimal scheduling of large-scale hydrothermal power systems using the Lagrangian relaxation technique

Cited by 49 publications

References 26 publications

Optimization of Power System Scheduling Based on Shuffled Complex Evolution Metropolis Algorithm

Optimization of Power System Scheduling Based on Shuffled Complex Evolution Metropolis Algorithm

Optimal Scheduling of Hydrothermal System with Network and Ramping via SCE-UA Method

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

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