A New Heuristic Algorithm for Unit Commitment Problem

Najafi, S.; Pourjamal, Y.

doi:10.1016/j.egypro.2011.12.1201

Cited by 21 publications

(11 citation statements)

References 24 publications

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“…2 and 4 are linear in the action variable. However, in real word applications, it happens that these assumptions are not satisfied [9], [19], [11]. With many algorithms, there is no solution for using non-linear transitions and costs.…”

Section: B Non-linear Settingmentioning

confidence: 99%

Direct Model Predictive Control: A Theoretical and Numerical Analysis

Cauwet

Decock

Liu

et al. 2018

2018 Power Systems Computation Conference (PSCC)

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This paper focuses on online control policies applied to power systems management. In this study, the power system problem is formulated as a stochastic decision process with large constrained action space, high stochasticity and dozens of state variables. Direct Model Predictive Control has previously been proposed to encompass a large class of stochastic decision making problems. It is a hybrid model which merges the properties of two different dynamic optimization methods, Model Predictive Control and Stochastic Dual Dynamic Programming. In this paper, we prove that Direct Model Predictive Control reaches an optimal policy for a wider class of decision processes than those solved by Model Predictive Control (suboptimal by nature), Stochastic Dynamic Programming (which needs a moderate size of state space) or Stochastic Dual Dynamic Programming (which requires convexity of Bellman values and a moderate complexity of the random value state). The algorithm is tested on a multiple-battery management problem and two hydroelectric problems. Direct Model Predictive Control clearly outperforms Model Predictive Control on the tested problems.

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Section: B Non-linear Settingmentioning

confidence: 99%

Direct Model Predictive Control: A Theoretical and Numerical Analysis

Cauwet

Decock

Liu

et al. 2018

2018 Power Systems Computation Conference (PSCC)

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“…Fukao et al [4] applied dynamic programming to power system optimization operations. Najafi [5] proposed a new harmonic search algorithm for unit combination problem, which greatly improved the computational efficiency; Ren et al [6] proposed a dynamic programming method based on the priority order method, which is suitable for the systems of small units. However, for large-scale systems with a large number of units, the calculation amount is very large, and the optimal solution may not be found.…”

Section: Introductionmentioning

confidence: 99%

Modified Dynamic Programming Algorithm and Its Application in Distribution of Power Plant Load

Wang

Dong

2019

E3S Web Conf.

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Dynamic programming algorithm is the classic method in distribution of plant loads. The traditional calculation method of dynamic programming is easy to make the calculation take too long time. This paper proposes two modiefied methods of dynamic programming, namely, dynamic programming in progressive structure and dynamic programming in progressive step-size. What is more, these two methods are combined to a new hybrid method. And these three methods and conventional dynamic programming are applied to the distribution of thermal power plant load to verify. The results of modiefied methods contrast with the conventional method show the modiefied methods reduce the calculation time and alleviate the curse of dimensionality effectively, ensuring the accuracy of calculation.

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“…UC is considered as a complex optimization problem that determines the load of each power generation unit satisfying the load demand of the generation system in a specified planning horizon. Its complexity is further enhanced by special constraints that conform to some system and physical conditions [2] such as meeting emission limits, addressing market requirements, and incorporating renewable energy sources. The main objective of the basic UC is to minimize total generation costs while meeting system requirements.…”

Section: Introductionmentioning

confidence: 99%

Application of Unit Commitment with Market Pool in a Peaking Power Generation Firm in the Philippines for a Cost Reduction Case Study

Santillan

Senen

Wamilda

et al. 2016

Journal of Engineering

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This paper attempts to provide an optimum loading schedule of power generating units with the least cost by solving a unit commitment (UC) problem and to present good estimates of cost differences when UC problem is not applied. UC is a fundamental optimization problem of power generation systems which determines the optimum schedule of generating units which minimizes generation costs. However, for small power generation firms which are situated in developing countries, UC-based problems are poorly understood if not implemented and the scheduling of generating units is based on some methodologies which may provide results that are not optimal. Thus, a case study in a small power generation firm in central Philippines is carried out to elucidate these objectives. The case requires a solution of the mixed-integer nonlinear programming (MINLP) problem. Results show that the proposed UC-based problem yields optimal costs and the cost disparity from the current scheduling scheme is approximately at 27% which may be considered as potential cost savings. This shows that UC-based problem provides a reliable platform in achieving minimal generation costs. These results are significant to decision-makers particularly in small power generation firms and to engineering practitioners in the Philippines and in some developing countries as these provide an overview of the disparity of cost figures of not implementing UC.

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A New Heuristic Algorithm for Unit Commitment Problem

Cited by 21 publications

References 24 publications

Direct Model Predictive Control: A Theoretical and Numerical Analysis

Direct Model Predictive Control: A Theoretical and Numerical Analysis

Modified Dynamic Programming Algorithm and Its Application in Distribution of Power Plant Load

Application of Unit Commitment with Market Pool in a Peaking Power Generation Firm in the Philippines for a Cost Reduction Case Study

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