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Demand Response Management in Power Systems Using a Particle Swarm Optimization Approach
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ISSN: 1541-1672
ASCII Text
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Pedro Faria, Zita Vale, João Soares, Judite Ferreira, "Demand Response Management in Power Systems Using a Particle Swarm Optimization Approach," IEEE Intelligent Systems, vol. 99, no. 1, pp. , , 5555.
 
 
BibTex
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@article{ 10.1109/MIS.2011.35,
author = {Pedro Faria and Zita Vale and João Soares and Judite Ferreira},
title = {Demand Response Management in Power Systems Using a Particle Swarm Optimization Approach},
journal ={IEEE Intelligent Systems},
volume = {99},
number = {1},
issn = {1541-1672},
year = {5555},
doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2011.35},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Intelligent Systems
TI - Demand Response Management in Power Systems Using a Particle Swarm Optimization Approach
IS - 1
SN - 1541-1672
SP
EP
EPD -
A1 - Pedro Faria,
A1 - Zita Vale,
A1 - João Soares,
A1 - Judite Ferreira,
PY - 5555
KW - H.1.2.a Human factors
KW - H.4.2.a Decision support
KW - H.5.2.p Training
KW - help
KW - and documentation
KW - I.2.3.l Uncertainty
KW - “fuzzy
KW - ” and probabilistic reasoning
KW - I.1.4 Applications
KW - I.2 Artificial Intelligence
KW - I.2.1 Applications and Expert Knowledge-Intensive Systems
KW - I.2.1.c Decision support
KW - I.2.1.d Education
KW - I.2.11.b Intelligent agents
KW - V2G
KW - Artificial Intelligence
KW - Demand Response
KW - Virtual Power Producers
KW - Power Plants
KW - Storage Systems
KW - Swarm Intelligence
KW - Non Linear Programing
KW - Mixed Integer Linear Programing
KW - SiPSO
KW - Locational marginal price
KW - Zonal Pricing
KW - Intelligent Control
KW - Social Computing
KW - I.2 Artificial Intelligence
KW - I.2.1.c Decision support
KW - I.2.11.b Intelligent agents
KW - I.2.11.d Multiagent systems
KW - I.2.13.b Knowledge engineering methodologies
KW - I.2.6.g Machine learning
KW - I.5.1.b Fuzzy set
KW - I.5.1.d Neural nets
KW - demand response
KW - distribution network
KW - electricity markets
KW - particle swarm optimization
KW - power systems
KW - simulation
KW -
VL - 99
JA - IEEE Intelligent Systems
ER -
 
Pedro Faria, Polytechnic of Porto, Porto
Zita Vale, Polytechnic of Porto, Porto
João Soares, Polytechnic of Porto, Porto
Judite Ferreira, Polytechnic of Porto, Porto
Demand response (DR) is not a new concept but it is gaining a growing focus of attention in nowadays electric power systems operation and planning, with several advantages for the reliable power system functioning and for electricity prices. In this paper, price-based DR is applied to electricity consumers through the management of electricity prices. This management is based on demand elasticity and consumers are expected to react enabling to accomplish the required load reduction. The methodology is implemented in a developed DR simulator – DemSi - that uses PSCAD® for technical validation of solutions and Particle Swarm Optimization (PSO) for solution optimization. The performance of PSO is evaluated in terms of running time and obtained solutions in comparison with the Non-Linear Programming (NLP) solutions obtained in GAMS™. Case studies involving 32 and 320 consumers are used to illustrate the proposed methodology and to discuss its performance.
Index Terms:
H.1.2.a Human factors, H.4.2.a Decision support, H.5.2.p Training, help, and documentation, I.2.3.l Uncertainty, “fuzzy,” and probabilistic reasoning, I.1.4 Applications, I.2 Artificial Intelligence, I.2.1 Applications and Expert Knowledge-Intensive Systems, I.2.1.c Decision support, I.2.1.d Education, I.2.11.b Intelligent agents, V2G, Artificial Intelligence, Demand Response, Virtual Power Producers, Power Plants, Storage Systems, Swarm Intelligence, Non Linear Programing, Mixed Integer Linear Programing, SiPSO, Locational marginal price, Zonal Pricing, Intelligent Control , Social Computing, I.2 Artificial Intelligence, I.2.1.c Decision support, I.2.11.b Intelligent agents, I.2.11.d Multiagent systems, I.2.13.b Knowledge engineering methodologies, I.2.6.g Machine learning, I.5.1.b Fuzzy set, I.5.1.d Neural nets, demand response, distribution network, electricity markets, particle swarm optimization, power systems, simulation,
Citation:
Pedro Faria, Zita Vale, João Soares, Judite Ferreira, "Demand Response Management in Power Systems Using a Particle Swarm Optimization Approach," IEEE Intelligent Systems, 04 April 2011. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/MIS.2011.35>
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