Short-term scheduling of hydro-based power plants considering application of heuristic algorithms: A comprehensive review

Nazari-Heris, Morteza; Mohammadi‐Ivatloo, Behnam; Gharehpetian, Gevork B.

doi:10.1016/j.rser.2017.02.043

Cited by 84 publications

(27 citation statements)

References 102 publications

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“…Power flow calculations are made using PowerWorld simulator [45]. Power flow calculations results show possible congestion on one 110-kV transmission line (see Figure 7) in hours 10,11,12,13,14,17,18,19, and 23, as shown in Figure 12. In the next step, it is necessary to calculate the ATC values of the critical line in the case without the WPP production.…”

Section: Results Of the Second Optimization Stagementioning

confidence: 99%

Optimal Re-Dispatching of Cascaded Hydropower Plants Using Quadratic Programming and Chance-Constrained Programming

et al. 2019

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Stochastic production from wind power plants imposes additional uncertainty in power system operation. It can cause problems in load and generation balancing in the power system and can also cause congestion in the transmission network. This paper deals with the problems of congestion in the transmission network, which are caused by the production of wind power plants. An optimization model for corrective congestion management is developed. Congestions are relieved by re-dispatching several cascaded hydropower plants. Optimization methodology covers the optimization period of one day divided into the 24 segments for each hour. The developed optimization methodology consists of two optimization stages. The objective of the first optimization stage is to obtain an optimal day-ahead dispatch plan of the hydropower plants that maximizes profit from selling energy to the day-ahead electricity market. If such a dispatch plan, together with the wind power plant production, causes congestion in the transmission network, the second optimization stage is started. The objective of the second optimization stage is the minimization of the re-dispatching of cascaded hydropower plants in order to avoid possible congestion. The concept of chance-constrained programming is used in order to consider uncertain wind power production. The first optimization stage is defined as a mixed-integer linear programming problem and the second optimization stage is defined as a quadratic programming (QP) problem, in combination with chance-constrained programming. The developed optimization model is tested and verified using the model of a real-life power system.

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Section: Results Of the Second Optimization Stagementioning

confidence: 99%

Optimal Re-Dispatching of Cascaded Hydropower Plants Using Quadratic Programming and Chance-Constrained Programming

et al. 2019

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“…2 Whereas a review on heuristics-like approaches with a focus on hydro-dominated systems is provided in Ref. 25 Additionally, techniques and strategies for improving the convergence and general performance of the BD are discussed in Ref. 5 In this work, we exploit the structure of the two-stage stochastic hydrothermal UC firstly by applying the BD.…”

Section: Referencesmentioning

confidence: 99%

Combining Lagrangian relaxation, benders decomposition, and the level bundle method in the stochastic hydrothermal unit‐commitment problem

Colonetti

Finardi

2020

Int Trans Electr Energ Syst

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Summary In recent years, stochastic programming has gained increasing attention as a tool to support the scheduling of generating units in the face of uncertain information. One approach that has a long‐standing history in stochastic programming is the Benders decomposition (BD). However, BD is known to suffer from a series of shortcomings, for example, oscillation and tailing‐off effect. To reduce these drawbacks, regularization techniques are appealing options. However, even if regularized, BD may still struggle to converge due to the growing computational burden of its master problem (MP) over the iterations — this is especially noticeable in mixed‐integer programming models. Thus, to tackle this growing MP, we propose decomposing it using dual decomposition. We test our methodology on a testbed comprised of 108 cases from a system with 46 buses. Our results show that our methodology is effective both in terms of running times as well as the optimality gap.

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“…The probability function operates to solve the optimal value of the compact pigeon-inspired optimization (CPIO) algorithm, and uses a real-valued prototype vector to generate each candidate solution. The algorithm has been tested on multiple continuous multi-modal functions as well as the short-term power generation of cascade hydropower stations [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station

et al. 2020

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Pigeon-inspired optimization (PIO) is a new type of intelligent algorithm. It is proposed that the algorithm simulates the movement of pigeons going home. In this paper, a new pigeon herding algorithm called compact pigeon-inspired optimization (CPIO) is proposed. The challenging task for multiple algorithms is not only combining operations, but also constraining existing devices. The proposed algorithm aims to solve complex scientific and industrial problems with many data packets, including the use of classical optimization problems and the ability to find optimal solutions in many solution spaces with limited hardware resources. A real-valued prototype vector performs probability and statistical calculations, and then generates optimal candidate solutions for CPIO optimization algorithms. The CPIO algorithm was used to evaluate a variety of continuous multi-model functions and the largest model of hydropower short-term generation. The experimental results show that the proposed algorithm is a more effective way to produce competitive results in the case of limited memory devices.

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Short-term scheduling of hydro-based power plants considering application of heuristic algorithms: A comprehensive review

Cited by 84 publications

References 102 publications

Optimal Re-Dispatching of Cascaded Hydropower Plants Using Quadratic Programming and Chance-Constrained Programming

Optimal Re-Dispatching of Cascaded Hydropower Plants Using Quadratic Programming and Chance-Constrained Programming

Combining Lagrangian relaxation, benders decomposition, and the level bundle method in the stochastic hydrothermal unit‐commitment problem

A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station

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