Title :

Comparison of evolutionary algorithms for solving risk-based energy resource management considering conditional value-at-risk analysis

Author(s) :

Almeida, José [Auteur]
Soares, Joao [Auteur]
Lezama, Fernando [Auteur]
Vale, Zita [Auteur]
Francois, Bruno [Auteur]
Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697

Journal title :

Mathematics and Computers in Simulation

Abbreviated title :

Mathematics and Computers in Simulation

Publisher :

Elsevier BV

Publication date :

2023-07-23

ISSN :

0378-4754

English keyword(s) :

Aggregator
Computational intelligence
Energy resource management
Evolutionary algorithms
Risk analysis
Smart grid

HAL domain(s) :

Physique [physics]
Mathématiques [math]
Sciences de l'ingénieur [physics]

English abstract : [en]

Energy management systems must evolve due to the widespread use of distributed energy resources in modern society. In fact, with the current high penetration of renewables and other resources like electric vehicles, the ...
Show more >Energy management systems must evolve due to the widespread use of distributed energy resources in modern society. In fact, with the current high penetration of renewables and other resources like electric vehicles, the challenge of managing energy resources becomes more difficult. Uncertainty and unpredictability from distributed resources open the door for unique undesirable situations, often known as extreme events. Despite the low likelihood of occurrence, such severe events represent a significant risk to an aggregator’s resource management, for example. In this paper, we propose a day-ahead energy resource management model for an aggregator in a 13-bus distribution network with high penetration of distributed energy resources. In the proposed model, we consider a risk-based mechanism through the conditional value-at-risk method for risk measurement of these extreme events. Due to the complexity of the model, we also propose the use of evolutionary algorithms, a set of stochastic search algorithms, to find near-optimal solutions to the problem. Results show that implementing risk-averse strategies reduces the cost of the worst scenario and scheduling. From the tested algorithms, ReSaDE provides the solutions with the lowest cost, which is an improvement from previous work, and a reduction of around 13% in the worst-scenario costs comparing a risk-neutral approach to a risk-averse approach.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
Centrale Lille
Arts et Métiers Sciences et Technologies
Junia HEI

Collections :

• Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697

Research team(s) :

Équipe Réseaux

Submission date :

2024-01-05T17:29:10Z
2024-02-06T11:06:57Z