Portfolio optimization of renewable energy assets: Hydro, wind, and photovoltaic energy in the regulated market in Brazil

Neto, Daywes Pinheiro; Domingues, Elder Geraldo; Coimbra, A. Paulo; Almeida, Anı́bal T. de; Alves, Aylton J.; Calixto, Wesley P.

doi:10.1016/j.eneco.2017.03.020

Cited by 55 publications

(9 citation statements)

References 29 publications

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“…where τ is the discount rate, the investment costs CI are discounted at t = 1, and the residual value VR is added at T. Therefore, the first considered objective function is the expected net present value of the portfolio [19,38], which can be calculated as follows:…”

Section: Objectives In Energy Investmentsmentioning

confidence: 99%

Robust Multiobjective Decision Making in the Acquisition of Energy Assets

Bambirra,

Schiavo,

Lima

et al. 2023

Energies

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In asset management for energy portfolios, quantitative methodologies are typically employed. In Brazil, the NEWAVE computational model is universally used to generate scenarios of hydraulic production and future prices, which result in revenue distributions. These distributions are then used to estimate the portfolio’s revenue and assess its risk. Although this is a well-established analysis, it has some shortcomings that are not always considered. The validity of the revenue series constructed by NEWAVE, especially in long-term analysis, is a real problem for agents concerning the acquisition of assets such as power plants. Another issue is the disregard for other objectives that are important for the operationality of the management task and are often ignored, such as operational risk. To address these limitations, this work combines the areas of multicriteria decision making under uncertainty and risk management and presents a methodology for evaluating the acquisition of long-term energy assets, as well as a practical application of the proposed method. Investment alternatives are evaluated in multiple developed scenarios, so it is possible to measure how robust a given option is. By analyzing several scenarios simultaneously, a larger region of uncertainties can be covered, and therefore, decision making becomes more secure. The proposed methodology includes six objectives, designed to address a wider range of stakeholder needs. This approach is applied to an illustrative portfolio, producing results that allow for a more comprehensive understanding of decision attributes. Therefore, this work not only addresses the current limitations in the field but also adds an original contribution by considering simultaneously several scenarios and integrating multiple objectives in a robust and secure decision-making framework.

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Section: Objectives In Energy Investmentsmentioning

confidence: 99%

Robust Multiobjective Decision Making in the Acquisition of Energy Assets

Bambirra,

Schiavo,

Lima

et al. 2023

Energies

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show abstract

“…In (Pinheiro Neto et al 2017), the authors propose a methodology for risk analysis and portfolio optimization of power assets with hydro, wind and solar power. In the case of study the authors considering the Regulated Contacting Environment and the Mechanism for Reallocation of Energy Brazil.…”

Section: Related Workmentioning

confidence: 99%

Hybrid approach based on particle swarm optimization for electricity markets participation

et al. 2019

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In many large-scale and time-consuming problems, the application of metaheuristics becomes essential, since these methods enable achieving very close solutions to the exact one in a much shorter time. In this work, we address the problem of portfolio optimization applied to electricity markets negotiation. As in a market environment, decision-making is carried out in very short times, the application of the metaheuristics is necessary. This work proposes a Hybrid model, combining a simplified exact resolution of the method, as a means to obtain the initial solution for a Particle Swarm Optimization (PSO) approach. Results show that the presented approach is able to obtain better results in the metaheuristic search process.

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“…Neto et al [48] propose a methodology for risk analysis and portfolio optimization of power generation assets in Brazil with hydro, wind, and solar power. Innovative stochastic models are used to generate synthetic time series for water inflow, wind speed, solar irradiance, temperature of the photovoltaic panel, and average generation capacity of energy.…”

Section: Literature Reviewmentioning

confidence: 99%

Moving Average Market Timing in European Energy Markets: Production Versus Emissions

et al. 2018

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This paper searches for stochastic trends and returns predictability in key energy asset markets in Europe over the last decade. The financial assets include Intercontinental Exchange Futures Europe (ICE-ECX) carbon emission allowances (the main driver of interest), European Energy Exchange (EEX) Coal ARA futures and ICE Brent oil futures (reflecting the two largest energy sources in Europe), Stoxx600 Europe Oil and Gas Index (the main energy stock index in Europe), EEX Power Futures (representing electricity), and Stoxx600 Europe Renewable Energy index (representing the sunrise energy industry). This paper finds that the Moving Average (MA) technique beats random timing for carbon emission allowances, coal, and renewable energy. In these asset markets, there seems to be significant returns predictability of stochastic trends in prices. The results are mixed for Brent oil, and there are no predictable trends for the Oil and Gas index. Stochastic trends are also missing in the electricity market as there is an ARFIMA-FIGARCH process in the day-ahead power prices. The empirical results are interesting for several reasons. We identified the data generating process in EU electricity prices as fractionally integrated (0.5), with a fractionally integrated Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) process in the residual. This is a novel finding. The order of integration of order 0.5 implies that the process is not stationary but less non-stationary than the non-stationary I(1) process, and that the process has long memory. This is probably because electricity cannot be stored. Returns predictability with MA rules requires stochastic trends in price series, indicating that the asset prices should obey the I(1) process, that is, to facilitate long run returns predictability. However, all the other price series tested in the paper are I(1)-processes, so that their returns series are stationary. The empirical results are important because they give a simple answer to the following question: When are MA rules useful? The answer is that, if significant stochastic trends develop in prices, long run returns are predictable, and market timing performs better than does random timing.

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Portfolio optimization of renewable energy assets: Hydro, wind, and photovoltaic energy in the regulated market in Brazil

Cited by 55 publications

References 29 publications

Robust Multiobjective Decision Making in the Acquisition of Energy Assets

Robust Multiobjective Decision Making in the Acquisition of Energy Assets

Hybrid approach based on particle swarm optimization for electricity markets participation

Moving Average Market Timing in European Energy Markets: Production Versus Emissions

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