Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting


Autoria(s): Melo, Joel D.; Carreno, Edgar Manuel; Padilha-Feltrin, Antonio
Contribuinte(s)

Universidade Estadual Paulista (UNESP)

Data(s)

20/05/2014

20/05/2014

01/11/2012

Resumo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A multi-agent system for spatial electric load forecasting, especially suited to simulating the different social dynamics involved in distribution systems, is presented. This approach improves the spatial forecasting techniques that usually consider the service zone as a static entity to model or simulate the spatial electric load forecasting in a city. This paper aims to determine how the electric load will be distributed among the sub-zones in the city. For this, the service zone is divided into several subzones, each subzone considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented through development probabilities. These probabilities are considered as input data for the simulation. Given this setting, different kinds of agents can be developed to simulate the growth pattern of the loads in distribution systems in parallel. The approach is tested with data from a real distribution system in a mid-size city; the results show a low spatial error when compared to real data. Less than 6% of the load growth was identified 0.71 km outside of its correct location on the test system.

Formato

1870-1878

Identificador

http://dx.doi.org/10.1109/TPWRS.2012.2190109

IEEE Transactions on Power Systems. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc, v. 27, n. 4, p. 1870-1878, 2012.

0885-8950

http://hdl.handle.net/11449/9803

10.1109/TPWRS.2012.2190109

WOS:000310389000016

Idioma(s)

eng

Publicador

Institute of Electrical and Electronics Engineers (IEEE)

Relação

IEEE Transactions on Power Systems

Direitos

closedAccess

Palavras-Chave #Agent #distribution planning #knowledge extraction #land use #multi-agent #spatial electric load forecasting #spatial error
Tipo

info:eu-repo/semantics/article