Genetic algorithm applied to the specialization of neural networks for the forecast of wind and solar generation

Vallejo, Damian; Cornalino, E.; Chaer, Ruben

doi:10.1109/epim.2018.8756397

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…In order to avoid the energy abandonment, the techniques for forecasting renewable energy such as wind energy and solar energy has developed greatly in recent years [9]. In [10], a short-term forecast method based on cuckoo search and differential evolution algorithms was proposed.…”

Section: Introductionmentioning

confidence: 99%

Optimal Evolutionary Dispatch for Integrated Community Energy Systems Considering Uncertainties of Renewable Energy Sources and Internal Loads

et al. 2021

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For the future development of integrated energy systems with high penetration of renewable energy, an integrated community energy systems (ICES) dispatch model is proposed including various renewable energy sources and energy conversion units. Energy coupling matrices of ICES based on traditional energy hub (EH) models are constructed. Uncertainties of long-term forecast data of renewable energy sources and internal loads are depicted by multi-interval uncertainty sets (MIUS). To cope with the impacts caused by uncertainties of renewable energy sources and internal loads, the whole dispatch process is divided into two stages. Considering various constraints of ICES, we solved the dispatch model through the improved particle swarm optimization (IPSO) algorithm in the first stage. The optimal evolutionary dispatch is then proposed in the second stage to overcome the evolution and errors of short-term forecast data and obtain the optimal dispatch plan. The effectiveness of the proposed dispatch method is demonstrated using an example considering dramatic uncertainties. Compared with the traditional methods, the proposed dispatch method effectively reduces system operating costs and improves the environmental benefits, which helps to achieve a win-win situation for both energy companies and users.

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Section: Introductionmentioning

confidence: 99%