Paper Info
Title
Bi-level particle swarm optimization and evolutionary algorithm approaches for residential demand response with different user profiles.
Abstract
The deregulation of electricity retail markets requires the development of new modeling approaches for the optimal setting of dynamic tariffs, in which consumers’ responses according to their flexibility to schedule demand are considered. Retailers and consumers have conflicting goals: the former aim to maximize profits and the latter aim to reduce electricity bills. Also, there is a hierarchical relation between them, as retailers (upper-level decision makers) determine the pricing strategy and consumers (lower-level decision makers) react by scheduling their loads according to price signals and comfort requirements. This is a bi-level optimization problem. In this paper, typical residential loads are considered and three scenarios of feasible windows of appliance operation are established. Two new population-based approaches, an evolutionary algorithm and a particle swarm optimization algorithm, are developed to solve the bi-level problem. The results obtained are then compared with a hybrid algorithm that solves the lower-level problem exactly.
Year
DOI
Venue
2017
10.1016/j.ins.2017.08.019
Information Sciences
Keywords
Field
DocType
Bi-level optimization,Particle swarm optimization,Evolutionary algorithms,Demand response,Electricity retail markets
Particle swarm optimization,Mathematical optimization,Hybrid algorithm,Evolutionary algorithm,Scheduling (computing),Electricity,Demand response,Multi-swarm optimization,Optimization problem,Mathematics
Journal
Volume
Issue
ISSN
418
C
0020-0255
Citations 
PageRank 
References 
2
0.41
6
Authors
3
Name
Order
Citations
PageRank
Pedro Carrasqueira181.95
Maria João Alves217117.88
Carlos Henggeler Antunes326134.75