Stochastic model for energy commercialisation of small hydro plants in the Brazilian energy market

This paper presents a stochastic model for energy commercialisation strategies of small hydro plants (SHPs) in the Brazilian electricity market. The model aims to find the maximum expected revenue of the generation company, considering the main energy market regulations in Brazil, such as the penalty for insufficient energy certificates, the seasonality of energy certificates and the stochastic processes of future energy prices and plant generation. The problem is formulated as a multi-stage linear stochastic programming model, where the stochastic variables are the energy future prices, the system hydro generation and the SHP generation in the portfolio. Because of the large number of time steps in this model, methods with sampling strategies are necessary to identify a good solution. Therefore, we apply the Stochastic Dual Dynamic Programming algorithm. A case example is presented to analyse certain results of the model, which considers a generator company with a set of SHPs that can sell energy through contracts with periods of 6–24 months.

[1]  Chuanwen Jiang,et al.  Portfolio management of hydropower producer via stochastic programming , 2009 .

[2]  Uerj Cepel Cepel,et al.  Chain of Optimization Models for Setting the Energy Dispatch and Spot Price in the Brazilian System M.E.P.Maceira L.A.Terry F.S.Costa J.M.Damázio A.C.G.Melo , 2002 .

[3]  G. Shrestha,et al.  Medium term power planning with bilateral contracts , 2005, IEEE Transactions on Power Systems.

[4]  Birger Mo,et al.  Integrated risk management of hydro power scheduling and contract management , 2001 .

[5]  Erlon Cristian Finardi,et al.  A computational study of a stochastic optimization model for long term hydrothermal scheduling , 2012 .

[6]  Gerd Infanger,et al.  Cut sharing for multistage stochastic linear programs with interstage dependency , 1996, Math. Program..

[7]  R. Souza,et al.  An approach for portfolio optimization of energy contracts in the Brazilian electric sector , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[8]  Vitor L. de Matos,et al.  Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion , 2012, Eur. J. Oper. Res..

[9]  Alexander Shapiro,et al.  Analysis of stochastic dual dynamic programming method , 2011, Eur. J. Oper. Res..

[10]  S. Granville,et al.  Benchmarking of hydroelectric stochastic risk management models using financial indicators , 2006, 2006 IEEE Power Engineering Society General Meeting.

[11]  Tito Homem-de-Mello,et al.  Sampling strategies and stopping criteria for stochastic dual dynamic programming: a case study in long-term hydrothermal scheduling , 2011 .

[12]  R.C.G. Teive,et al.  Risk Management in the Commercialization Activity in Brazil - An Approach by Using Markowitz, VaR and CVaR , 2006, 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America.

[13]  Asgeir Tomasgard,et al.  Multi-Stage Stochastic Programming for Natural Gas Infrastructure Design with a Production Perspective , 2013 .

[14]  M. V. F. Pereira,et al.  Multi-stage stochastic optimization applied to energy planning , 1991, Math. Program..

[15]  B. Flach,et al.  Risk Constrained Portfolio Selection of Renewable Sources in Hydrothermal Electricity Markets , 2009, IEEE Transactions on Power Systems.

[16]  J. F. Benders.  Partitioning procedures for solving mixed-variables programming problems , 1962 .