Optimal Hydrothermal Scheduling With Cascaded Plants Using Progressive Optimality Algorithm

Nanda,; Bijwe,

doi:10.1109/mper.1981.5511434

Cited by 5 publications

(7 citation statements)

References 1 publication

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“…Step1: Setting parameter in PSO: particle population size, the solution hyperspace dimensionality, the maximum iterations, other parameters of the PSO algorithm respectively 1 c , 2 c , 1 r , 2 r , et al Then initializing particles with random positions and velocities in the search space;…”

Section: B Particle Swarm Optimization Algorithmmentioning

confidence: 99%

A particle swarm optimization algorithm based collaborative optimal scheduling for multi-level water basin in non-flood season

Lü

Wang

et al. 2011

Proceedings of 2011 International Conference on Modelling, Identification and Control

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Water resource optimization has become an important issue due to water shortage in recent years. In the paper, we build a collaborative model for optimal water scheduling. It considers many real factors in scheduling reasonably, including domestic water, ecological water, production water, agricultural water and their backwater et al.In the model , all the factors determine the minimum required water and control section flow in river. When a water control system monitors the fact that water flow in control section is lower than the minimum required water flow. The multi-reservoirs water dispatching which based on collaborative particle swarm optimization is activated to solve the contradiction among different water demanding , especially in the peak of agricultural water in non-flood. The experiment result shows that the model in practice is reliable.

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Section: B Particle Swarm Optimization Algorithmmentioning

confidence: 99%

A particle swarm optimization algorithm based collaborative optimal scheduling for multi-level water basin in non-flood season

Lü

Wang

et al. 2011

Proceedings of 2011 International Conference on Modelling, Identification and Control

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“…based scheduling algorithm which resembles that outlined in sub-section 11.2 were discussed in [3,4,5]. In this section, the additional computational requirements associated with applying the proposed method to the P.O.P.…”

Section: Iii3 Computational Requirementsmentioning

confidence: 99%

“…based approach to short-term hydrothermal scheduling. The advantages of this application were discussed in [3 and [4]. Unfortunately, it sociated with this application.…”

Section: Introductionmentioning

confidence: 99%

A method to eliminate solution trapping in applying progressive optimality principle to short-term hydrothermal scheduling

Lee

1989

IEEE Trans. Power Syst.

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This paper presents a method to eliminate solution "trapping" for the P.O.P. (Progressive Optimality Principle) based short-term hydrothermal scheduling algorithm. In a P.O.P. based scheduling algorithm, the "trapping" phenomenon can severely hinder solution optimality, and thus limits its applicability. The "trapping" phenomenon is caused by the "weak" links which break the optimization path into isolated segments, and thus destroy the path continuity required by the P.O.P. based decomposition approach. In order to ensure path continuity, the proposed method identifies and bypasses the potential "weak" l i s , such that global optimality can be achieved progressively by successively solving a sequence of two-hour sub-problem. In this paper, examples are used to illustrate the "trapping" phenomenon and the proposed method.

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“…MOCTU is a multi-stage decision problem that involves a highly nonlinear and computationally expensive objective function with a large number of constraints. The progressive optimality algorithm (POA) has been shown to be an effective method for solving multi-stage optimization problems by decomposing a multi-stage decision problem into a series of non-linear programming two-stage problems [24][25][26][27][28], and it is suitable for solving MOCTU. However, it is a difficult task to find feasible solutions for a large-scale MOCTU problem using POA due to its drawback, namely the easily encountered local optimum for complex problems.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of the Medium-Term Units Commitment of Thermal Power

Liao

et al. 2015

Energies

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Coal-fired thermal power plants, which represent the largest proportion of China's electric power system, are very sluggish in responding to power system load demands. Thus, a reasonable and feasible scheme for the medium-term optimal commitment of thermal units (MOCTU) can ensure that the generation process runs smoothly and minimizes the start-up and shut-down times of thermal units. In this paper, based on the real-world and practical demands of power dispatch centers in China, a flexible mathematical model for MOCTU that uses equal utilization hours for the installed capacity of all thermal power plants as the optimization goal and that considers the award hours for MOCTU is developed. MOCTU is a unit commitment (UC) problem with characteristics of large-scale, high dimensions and nonlinearity. For optimization, an improved progressive optimality algorithm (IPOA) offering the advantages of POA is adopted to overcome the drawback of POA of easily falling into the local optima. In the optimization process, strategies of system operating capacity equalization and single station operating peak combination are introduced to move the target solution from the boundary constraints along the target isopleths into the feasible solution's interior to guarantee the global optima. The results of a case study consisting of nine thermal power plants with 27 units show that the presented algorithm can obtain an optimal solution and is competent in solving the MOCTU with high efficiency and accuracy as well as that the developed simulation model can be applied to practical engineering needs.

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Optimal Hydrothermal Scheduling With Cascaded Plants Using Progressive Optimality Algorithm

Cited by 5 publications

References 1 publication

A particle swarm optimization algorithm based collaborative optimal scheduling for multi-level water basin in non-flood season

A particle swarm optimization algorithm based collaborative optimal scheduling for multi-level water basin in non-flood season

A method to eliminate solution trapping in applying progressive optimality principle to short-term hydrothermal scheduling

Modeling and Optimization of the Medium-Term Units Commitment of Thermal Power

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