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Risk-averse portfolio selection of renewable electricity generator investments in Brazil: An optimised multi-market commercialisation strategy

Sebastian Maier (sebastian.maier***at***imperial.ac.uk)
Alexandre Street (street***at***ele.puc-rio.br)
Ken McKinnon (K.McKinnon***at***ed.ac.uk)

Abstract: Investment decisions in renewable energy sources such as small hydro, wind power, biomass and solar are frequently made in the context of enormous uncertainty surrounding both intermittent generation and the highly volatile electricity spot prices that are used for clearing of trades. This paper presents a new portfolio-based approach for selecting long-term investments in small-scale renewable energy projects and matching contracts for the sale of the resulting electricity. Using this approach, we have formulated a stochastic optimisation model that maximises a holding company's risk-averse measure of value. Using an illustrative example representative of investment decisions within the Brazilian electricity system, we investigate the sensitivity of the optimised portfolio composition and commercialisation strategy to contract prices in the free contracting environment and to the decision maker's attitude towards risk. The numerical results demonstrate it is possible to reduce significantly financial risks, such as the price-quantity risk, not only by exploiting the complementarity of the considered renewable sourcesí generation profiles, but also by selecting the optimal mix of commercialisation contracts from different markets. We find that the multi-market strategy generally results in appreciably higher optimal value than single-market strategies and can be applied to a wide range of renewable generators and contracts.

Keywords: Renewable energy investments, Electricity markets, Stochastic portfolio optimisation, Financial risk management, Decision support system

Category 1: Applications -- OR and Management Sciences (Finance and Economics )

Category 2: Applications -- Science and Engineering (Other )

Category 3: Stochastic Programming


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Entry Submitted: 01/22/2016
Entry Accepted: 01/22/2016
Entry Last Modified: 09/08/2016

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