Evolutionary Algorithms for Energy Scheduling under uncertainty considering Multiple Aggregators
Paper in proceeding, 2021

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.

uncertainty

aggregator

electric vehicles

smart grid

energy resource management

evolutionary algorithms

Author

Jose Almeida

Polytechnic Institute of Porto

Joao Soares

Polytechnic Institute of Porto

Bruno Canizes

Polytechnic Institute of Porto

Fernando Lezama

Polytechnic Institute of Porto

Ali Fotouhi

Chalmers, Electrical Engineering, Electric Power Engineering

Zita Vale

Polytechnic Institute of Porto

2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021)

225-232
978-1-7281-8392-3 (ISBN)

IEEE Congress on Evolutionary Computation (IEEE CEC)
, ,

Subject Categories

Other Engineering and Technologies not elsewhere specified

Energy Systems

Computer Systems

DOI

10.1109/CEC45853.2021.9504942

More information

Latest update

12/6/2021