Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling

P. Faria, J. Soares, Z. Vale, H. Morais, T. Sousa

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.
Original languageEnglish
JournalI E E E Transactions on Smart Grid
Volume4
Issue number1
Pages (from-to)606-616
ISSN1949-3053
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • cost reduction
  • distributed power generation
  • Gaussian processes
  • nonlinear programming
  • particle swarm optimisation
  • power distribution economics
  • power generation economics
  • power generation scheduling
  • smart power grids

Fingerprint Dive into the research topics of 'Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling'. Together they form a unique fingerprint.

Cite this