Estratégia de Contratação de Energia Elétrica no Mercado Regulado Brasileiro: Um Modelo Fuzzy (Energy Purchasing Strategy On The Brazilian Regulated Electricity Market: A Fuzzy Model)

Clodomiro Unsihuay Vila (clodomiro.unsihuay.vila@gmail.com)1, Gustavo Perondi (clodo10@hotmail.com)2


1Universidade Federal do Paraná
2COPEL

This paper appears in: Revista IEEE América Latina

Publication Date: Feb. 2016
Volume: 14,   Issue: 2 
ISSN: 1548-0992


Abstract:
Brazilian regulatory model of electricity imposes that distribution companies should acquire all your energy market related to captive customers on, regulated auctions, and distributed generation, whose energy is traded with public calls. An energy market forecast is designed by the utilities and this forecast has to be a 100% covered with contracts agreed from regulated trades. The challenge of utilities is to forecast their own energy market five years in advance, which brings to the process a high level of uncertainty. This paper presents a methodology for planning the purchase of electricity by distribution agents in the Brazilian regulated contracting environment (ACR). The contracting strategy covers a horizon of 5 years, with monthly discretization and the goal is to minimize the total cost of energy procurement strategy. The proposed model considers the current regulatory Brazilian Laws of energy contracting by distribution companies, i.e. energy purchases from auctions in the regulated environment, distributed generation - GD, Compensation Mechanism Surplus and Deficit - MCSD and short-term market. The uncertainties of future electric consumption are modeled following a fuzzy approach. Thus, the formulation if proposed model is a fuzzy non-linear programming problem. Results show the importance of the short-term modeling and the consideration of MCSD and GD in the Energy Purchase Strategy on the Brazilian Regulated Electricity Market.

Index Terms:
Energy Purchase Strategy, Distribution companies, Brazilian Electricity Market, Fuzzy non-linear programming, Short-Term Market   


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