Applications of Simulated Annealing-Based Approaches to Electric Power Systems

Huang, Yann-Chang; Sun, Huo-Ching; Huang, Kun-Yuan

doi:10.5772/50388

Cited by 3 publications

(3 citation statements)

References 86 publications

(61 reference statements)

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“…The distribution is executed in order of lowest to highest incremental rate of objective function, which refers to the change rate of formula (3) as unit output increases. The concept of objective function incremental rate is extended from the increment rate of fuel consumption [7].…”

Section: Phased Optimizationmentioning

confidence: 99%

A modified artificial fish swarm algorithm for unit commitment optimization

Jin,

Zhang,

Zhang

2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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Section: Phased Optimizationmentioning

confidence: 99%

A modified artificial fish swarm algorithm for unit commitment optimization

Jin,

Zhang,

Zhang

2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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“…This way, the computation time of exact methods becomes impractical calling for some heuristic approaches for which optimality is not given such a high priority, but the emphasis is on producing near optimal solutions in a lower computational burden [12]. The dominant methods extensively presented in the literature are genetic algorithms [13], simulated annealing [14], particle swarm optimisation [15], fuzzy LOGIC [16], expert systems [17], ant colony [18], Tabu search [17], evolutionary programming [19] and artificial neural networks [17]. Owing to their main disadvantages of local optimum trapping, untraceable transition rules and unreliable findings, hybrid metaheuristic approaches have been proposed in order to employ more rigorous mechanisms.…”

Section: Contribution and Motivationmentioning

confidence: 99%

Enhanced Lagrange relaxation for the optimal unit commitment of identical generating units

Nikolaidis¹,

Poullikkas

2020

IET Generation, Transmission & Distribution

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“…As discussed in [38], simulated annealing should understand and make use of properties of the problem to be effective. Accordingly, the following simulated annealing moves are defined, with an equal probability of being applied during each annealing round:…”

Section: Simulated Annealingmentioning

confidence: 99%

Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing

Crossland

Jones

Wade

2014

International Journal of Electrical Power & Energy Systems

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Newcastle University ePrintsCrossland AF, Jones D, Wade NS. Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing.This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License ePrints -Newcastle University ePrints http://eprint.ncl.ac.ukPlanning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing International Journal of Electrical Power & Energy Systems http://dx.AbstractIn light of the expansion of domestic photovoltaic (PV) systems in the UK, there are concerns of voltage rise within LV networks. Consequently, network operators are interested in the costs and benefits of different technologies to manage their assets. This paper examines the particular case for distributed energy storage. A heuristic planning tool is developed using a genetic algorithm with simulated annealing to investigate the problem of locating and sizing energy storage within LV networks. This is applied to investigate the configuration and topologies of storage to solve voltage rise problems as a result of increased penetration of PV. Under a threshold PV penetration, it is shown that distributed storage offers a financially viable alternative to reconductoring the LV network. Further, it is shown that a configuration of single phase storage located within the customer home can solve the voltage problem using less energy than a three phase system located on the street. NomenclatureRandom number in the interval zero to one, applied in simulated annealing Capital cost of storage unit [£] Installation cost of each storage unit [£] Power cost of particular energy storage technology [£/kW] Energy cost of particular energy storage technology [£/kWh] Cost of reconductoring the network [V] Permissible depth of discharge [%] Capacity of energy storage unit [kWh] Roulette wheel fitness of solution during algorithm round Probability that solution is selected during algorithm round Rating of energy storage unit [kW] Length of time that storage operates at full power [h] Temperature, applied in simulated annealing Highest voltage in LV network when storage solution is implemented [V] Round trip efficiency of particular energy storage technology [%]Please cite this article as: Crossland, A.F., et al., Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing.

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Applications of Simulated Annealing-Based Approaches to Electric Power Systems

Cited by 3 publications

References 86 publications

A modified artificial fish swarm algorithm for unit commitment optimization

A modified artificial fish swarm algorithm for unit commitment optimization

Enhanced Lagrange relaxation for the optimal unit commitment of identical generating units

Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing

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