Evolutionary Algorithms for Energy Scheduling under uncertainty considering Multiple Aggregators

Almeida, José; Soares, João; Canizes, Bruno; Lezama, Fernando; Fotouhi Ghazvini, Mohammad Ali; Vale, Zita

http://hdl.handle.net/10400.22/22468

Utilize este identificador para referenciar este registo.

Nome:	Descrição:	Tamanho:	Formato:
COM_GECAD_IEEE_CEC_2021.pdf		1.74 MB	Adobe PDF	Ver/Abrir

Contacte-nos

Autores

Almeida, José

Soares, João

Canizes, Bruno

Lezama, Fernando

Fotouhi Ghazvini, Mohammad Ali

Vale, Zita

Resumo(s)

The ever-increasing number of electric vehicles (EVs) circulating on the roads and renewable energy production to achieve carbon footprint reduction targets has brought many challenges to the electrical grid. The increasing integration of distributed energy resources (DER) in the grid is causing severe operational challenges, such as congestion and overloading for the grid. Active management of distribution network using the smart grid (SG) technologies and artificial intelligence (AI) techniques can support the grid's operation under such situations. Implementing evolutionary computational algorithms has become possible using SG technologies. This paper proposes an optimal day-ahead resource scheduling to minimize multiple aggregators' operational costs in a SG, considering a high DER penetration. The optimization is achieved considering three metaheuristics (DE, HyDE-DF, CUMDANCauchy++). Results show that CUMDANCauchy++ and HyDE-DF present the best overall results in comparison to the standard DE.