A Metaheuristic Approach to the Multi-Objective Unit Commitment Problem Combining Economic and Environmental Criteria

Roque, Luís A. C.; Fontes, Dalila B.M.M.; Fontes, Fernando A. C. C.

doi:10.3390/en10122029

Cited by 14 publications

(5 citation statements)

References 55 publications

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“…In these works, the UC covers ED and EmD problems as discussed in [6], [12]- [14] and integrated to become an economic and emission dispatch (EED) problem under operational limitations [14], [25], [39]- [41]. Furthermore, many works have used the EED to describe some economical measurements on the desirable targets [4], [14], [38], [42], [43]. In these studies, the EED is formulated using mathematical statements for defining the objective function and technical constraints [6], [10], [26], [39] considering the IEEE-62 bus system as a sample model of the power system.…”

Section: Methods and Approachmentioning

confidence: 99%

Combined Computational Intelligence Approach for the Power System Optimization Problem

Afandi¹,

Fadlika²,

Gumilar³

et al. 2018

EECSI

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A power system optimization is one of the major problems in the operating performances of the existing system. This problem takes place on the various technical cases to get optimal conditions of the system. Moreover, many approaches have been implemented to carry out the solution under operational constraints. This paper presents Thunderstorm Algorithm (TA) and Artificial Salmon Tracking Algorithm (ASTA) for defining the optimal strategy of the power system optimization based on the unit commitment. Both algorithms are tested on the IEEE-62 bus system, whereas, results show that ASTA and TA can be combined together to solve the power system problem. These algorithms have been applied to predict the power consumption and it has good performances while searching for the optimal solution. These results also show that the economic dispatch problem is conducted to the power production while the algorithm is performed in good characteristics.

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Section: Methods and Approachmentioning

confidence: 99%

Combined Computational Intelligence Approach for the Power System Optimization Problem

Afandi¹,

Fadlika²,

Gumilar³

et al. 2018

EECSI

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“…GAs are a powerful stochastic global search technique that due to their flexibility and good performance have been proposed to solve numerous optimization problems in many fields (Snyder and Daskin, 2006;Gonçalves, 2007, 2013;Roque et al, 2011Roque et al, , 2014Roque et al, , 2017Homayouni et al, 2014;Fontes et al, 2018).…”

Section: The Multistart Biased Random Key Genetic Algorithmmentioning

confidence: 99%

A multistart biased random key genetic algorithm for the flexible job shop scheduling problem with transportation

Homayouni

Fontes

Gonçalves

2020

Int Trans Operational Res

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This work addresses the flexible job shop scheduling problem with transportation (FJSPT), which can be seen as an extension of both the flexible job shop scheduling problem (FJSP) and the job shop scheduling problem with transportation (JSPT). Regarding the former case, the FJSPT additionally considers that the jobs need to be transported to the machines on which they are processed on, while in the latter, the specific machine processing each operation also needs to be decided. The FJSPT is NP‐hard since it extends NP‐hard problems. Good‐quality solutions are efficiently found by an operation‐based multistart biased random key genetic algorithm (BRKGA) coupled with greedy heuristics to select the machine processing each operation and the vehicles transporting the jobs to operations. The proposed approach outperforms state‐of‐the‐art solution approaches since it finds very good quality solutions in a short time. Such solutions are optimal for most problem instances. In addition, the approach is robust, which is a very important characteristic in practical applications. Finally, due to its modular structure, the multistart BRKGA can be easily adapted to solve other similar scheduling problems, as shown in the computational experiments reported in this paper.

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“…Microgrids are complex power systems and their efficient operation demands the study on multiple interlocking factors [1], such as power losses, electricity cost, renewable resources, component size and the like. To date, the state of the art in the microgrid topic has shown promising results in the power flow amelioration and performance enhancement [2].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Control Approach for Power Loss Minimization and Optimal Power Flow within a Meshed DC Microgrid

2021

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This work considers the DC part of a hybrid AC/DC microgrid with a meshed topology. We address cost minimization, battery scheduling and the power loss minimization within the power distribution network through constrained optimization. The novelty comes from applying differential flatness properties to the microgrid components and formulating the cost and constraints in terms of the associated B-splines parametrization of the flat outputs (the voltages and currents of the system). This allows us to obtain optimal power profiles to minimize the power dissipation and the cost of the electricity purchase from the external grid. These profiles are tracked by a model predictive controller at the higher level, while at a a lower level a controller deals with the operation of the switches within the DC/DC converters. Extensive simulations under nominal and fault-affected scenarios using realistic data validate the proposed approach.

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A Metaheuristic Approach to the Multi-Objective Unit Commitment Problem Combining Economic and Environmental Criteria

Cited by 14 publications

References 55 publications

Combined Computational Intelligence Approach for the Power System Optimization Problem

Combined Computational Intelligence Approach for the Power System Optimization Problem

A multistart biased random key genetic algorithm for the flexible job shop scheduling problem with transportation

A Hierarchical Control Approach for Power Loss Minimization and Optimal Power Flow within a Meshed DC Microgrid

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