Hybrid Robust/Stochastic Unit Commitment With Iterative Partitions of the Continuous Uncertainty Set

Gogler, Patrick; Dorfner, Magdalena; Hamacher, Thomas

doi:10.3389/fenrg.2018.00071

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…The overall cost of the system gets minimized by applying various algorithms in different domains [79,80]. Unit commitment problem is solved by Benders' decomposition algorithm [81,82], column and constraints generation algorithm [83,84] and Lagrangian decomposition method [85]. Integrated electricity and heating system is scheduled by column and constraints generation algorithm [86].…”

Section: Unit Commitmentmentioning

confidence: 99%

Probabilistic Optimization Techniques in Smart Power System

et al. 2022

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Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented.

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Section: Unit Commitmentmentioning

confidence: 99%

Probabilistic Optimization Techniques in Smart Power System

et al. 2022

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“…UC is accountable in the selection of the units which can be set to operate economically [38]. UC also contributes to the power calculation of each unit based on total demand [39]. In power systems, it is important to create a table of optimum generating units with minimum fuel and transaction costs corresponding to the load requirements [40].…”

Section: Introductionmentioning

confidence: 99%

A New “Doctor and Patient” Optimization Algorithm: An Application to Energy Commitment Problem

et al. 2020

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Regular assessments of events taking place around the globe can be a conduit for the development of new ideas, contributing to the research world. In this study, the authors present a new optimization algorithm named doctor and patient optimization (DPO). DPO is designed by simulating the process of treating patients by a physician. The treatment process has three phases, including vaccination, drug administration, and surgery. The efficiency of the proposed algorithm in solving optimization problems compared to eight other optimization algorithms on a benchmark standard test function with 23 objective functions is been evaluated. The results obtained from this comparison indicate the superiority and quality of DPO in solving optimization problems in various sciences. The proposed algorithm is successfully applied to solve the energy commitment problem for a power system supplied by a multiple energy carriers system.

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“…1 Unit commitment (UC) is responsible for the selection of the generation units that can work economically. 2,3 UC is also involved in calculating the power for each unit on the basis of the total power demand. 1,2 The differences in features of generating units make UC a considerable problem.…”

Section: Introductionmentioning

confidence: 99%

Unit commitment based on modified firefly algorithm

Hussein

Jaber

2020

Measurement and Control

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Optimization technologies have drawn considerable interest in power system research. The success of an optimization process depends on the efficient selection of method and its parameters based on the problem to be solved. Firefly algorithm is a suitable method for power system operation scheduling. This paper presents a modified firefly algorithm to address unit commitment issues. Generally, two steps are involved in solving unit commitment problems. The first step determines the generating units to be operated, and the second step calculates the amount of demand-sharing among the units (obtained from the first step) to minimize the cost that corresponds to the load demand and constraints. In this work, the priority list method was used in the first step and the second step adopted the modified firefly algorithm. Ten generators were selected to test the proposed method, while the values of the cost function were regarded as criteria to gauge and compare the modified firefly algorithm with the classical firefly algorithm and particle swarm optimization algorithms. Results show that the proposed approach is more efficient than the other methods in terms of generator and error selections between load and generation.

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Hybrid Robust/Stochastic Unit Commitment With Iterative Partitions of the Continuous Uncertainty Set

Cited by 4 publications

References 28 publications

Probabilistic Optimization Techniques in Smart Power System

Probabilistic Optimization Techniques in Smart Power System

A New “Doctor and Patient” Optimization Algorithm: An Application to Energy Commitment Problem

Unit commitment based on modified firefly algorithm

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