Risk-Based Optimization of Renewable Energy Investment Portfolios: A Multi-Stage Stochastic Approach to Address Uncertainty
Source abstract
Abstract
This work presents a multi-stage stochastic optimization model intended to improve investment decision-making for energy projects by incorporating uncertainty in contexts and changes in market pricing. In contrast to conventional deterministic models, which generally concentrate on a singular stage while neglecting the intricacies associated with policy and market uncertainties, our methodology incorporates Conditional Value at Risk as a pivotal risk metric. Across a span of five years, the model predicts how investments will be distributed among three types of electricity projects: Solar Farm, Wind Farm, and Hydro Plant. The stochastic model strategically allocates an investment of USD 16.5 million to achieve an expansion in the capacity of 925 megawatts and an expected portfolio return of USD 1,822,500. Notably, the model maintains a Conditional Value at Risk of USD 100,000 and an impressive Sharpe Ratio of 18.2250, demonstrating its ability to offer improved risk-adjusted returns. This study illustrates the effectiveness of stage stochastic optimization in enhancing diverse and robust renewable energy portfolios.
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Research context
OpenAlex topics: Capital Investment and Risk Analysis · Market Dynamics and Volatility · Energy Load and Power Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Agricultural Engineering, LAUTECH, Ogbomosho 210214, Nigeria
Department of Industrial Engineering, Durban University of Technology, Durban 4001, South Africa
Department of Electrical Electronics Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria
Department of Mechanical Engineering, Bells University of Technology, Ota 112104, Nigeria
Department of Systems Engineering, University of Lagos, Akoka 100213, Nigeria
References (56)
Show source-provided references
1. Energy Information Administration (2011). Annual Energy Review.
2. Strategic investment in renewable energy sources: The effect of supply intermittency10.1287/msom.2017.0621 ↗
3. Anukwonke, C.C., and Abazu, C.I. (2022). Green Business through Carbon Credits. Climate Change Alleviation for Sustainable Progression, CRC Press.10.1201/9781003106982-16 ↗
4. Asmelash, E., Prakash, G., Gorini, R., and Gielen, D. (2020). Role of IRENA for global transition to 100% renewable energy. Accelerating the Transition to a 100% Renewable Energy Era, Springer International Publishing.10.1007/978-3-030-40738-4_2 ↗
5. Renewable energy economics: Achieving harmony between environmental protection and economic goals
6. Hedging strategies in energy markets: The case of electricity retailers10.1016/j.eneco.2015.06.021 ↗
7. Carbon pricing: Effectiveness and equity10.1016/j.ecolecon.2018.03.030 ↗
8. From investment optimization to fair benefit distribution in renewable energy community modelling10.1016/j.apenergy.2021.118447 ↗
9. Risk hedging for VaR-constrained newsvendors10.1016/j.tre.2023.103365 ↗
10. Strategies to achieve a carbon neutral society: A review10.1007/s10311-022-01435-8 ↗
11. Côté, E. (2023). Financial Models and Incentives for Clean Energy Investment. [Doctoral dissertation, University of St. Gallen].
12. Investments and cleaner energy production: A portfolio analysis in the Italian electricity market10.1016/j.jclepro.2016.07.190 ↗
13. Delbeke, J. (2024). Delivering a Climate Neutral Europe, Taylor & Francis.10.4324/9781003493730 ↗
14. Renewable Energy Statistics
15. Dreżewski, R., and Doroz, K. (2017). An agent-based co-evolutionary multi-objective algorithm for portfolio optimization. Symmetry, 9.10.3390/sym9090168 ↗
16. Renewable Energy in Developing Countries: Challenges, Opportunities, and Policy Recommendations for Innovative Funding
17. The unstudied barriers to widespread renewable energy deployment: Fossil fuel price responses10.1016/j.enpol.2016.12.050 ↗
18. Optimization of power plant investments under uncertain renewable energy deployment paths: A multi-stage stochastic programming approach10.1007/s12667-013-0094-0 ↗
19. Proper conditioning for coherent VaR in portfolio management10.1287/mnsc.1060.0632 ↗
20. Portfolio optimization with irreversible long-term investments in renewable energy under policy risk: A mixed-integer multi-stage stochastic model and a moving-horizon approach10.1016/j.ejor.2020.08.033 ↗
21. Hilario-Caballero, A., Garcia-Bernabeu, A., Salcedo, J.V., and Vercher, M. (2020). Tri-criterion model for constructing low-carbon mutual fund portfolios: A preference-based multi-objective genetic algorithm approach. Int. J. Environ. Res. Public Health, 17.10.3390/ijerph17176324 ↗
22. International support for feed-in tariffs in developing countries—A review and analysis of proposed mechanisms10.1016/j.rser.2014.07.124 ↗
23. Navigating challenges in large-scale renewable energy storage: Barriers, solutions, and innovations10.1016/j.egyr.2024.08.019 ↗
24. James, T. (2008). Energy Markets: Price Risk Management and Trading, John Wiley & Sons.
25. Portfolio optimization with conditional value-at-risk objective and constraints10.21314/JOR.2002.057 ↗
26. A comprehensive review on large-scale photovoltaic system with applications of electrical energy storage10.1016/j.rser.2017.04.078 ↗
27. Lannoo, K., and Thomadakis, A. (2020). Derivatives in sustainable finance. CEPS-ECMI Study, Centre for European Policy Studies.
28. Stochastic dominance and risk measure: A decision-theoretic foundation for VaR and C-VaR10.1016/j.ejor.2010.05.043 ↗
29. The influence of perceived uncertainty on entrepreneurial action in emerging renewable energy technology; biomass gasification projects in the Netherlands10.1016/j.enpol.2007.07.009 ↗
30. Mhango, C. (2024). An Investigation into the Drivers and Barriers Affecting the Implementation of Renewable Energy Technologies in Zambia, The University of Zambia.
31. Mun, J. (2010). Modeling Risk: Applying Monte Carlo Risk Simulation, Strategic Real Options, Stochastic Forecasting, and Portfolio Optimization, John Wiley & Sons.
32. The effectiveness of feed-in-tariff policy in promoting power generation from renewable energy in Kenya10.1016/j.renene.2020.07.082 ↗
33. A review of strategic cash flow management models and their role in driving financial growth and sustainability for energy companies in developing markets10.30574/gscarr.2024.21.1.0378 ↗
34. Cost and technical assessment of integrated Photovoltaic, wind turbine, battery, and hydrogen storage system design for household use
35. Challenges and Opportunities in Nigeria’s Renewable Energy Policy and Legislation10.30574/wjarr.2024.23.2.2391 ↗
36. Contribution of renewable energy (hydro, wind, solar and biomass) to decarbonization and transformation of the electricity generation sector for sustainable development10.1016/j.egyr.2023.07.024 ↗
37. A dynamic real option-based investment model for renewable energy portfolios
38. Financing Green Energy for Enhancing Energy Security in Ukraine
39. Plagowski, P. (2022). Energy Transition–Risk and Uncertainty Quantification of Electric Infrastructure and Spot Market Prices, ETH Zurich.
40. Preference-based evolutionary multi-objective optimization for portfolio selection: A new credibilistic model under investor preferences10.1007/s10898-019-00782-1 ↗
41. Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection10.1016/j.asoc.2015.11.005 ↗
42. Energy sector vulnerability to climate change: A review10.1016/j.energy.2011.11.056 ↗
43. Financing renewable energy in Africa–Key challenge of the sustainable development goals10.1016/j.rser.2016.11.004 ↗
44. Opportunities, barriers and issues with renewable energy development–A discussion10.1016/j.rser.2016.09.137 ↗
45. A multi-objective evolutionary algorithm for a class of mean-variance portfolio selection problems10.1016/j.eswa.2019.05.018 ↗
46. Price risk and hedging strategies in Nord Pool electricity market evidence with sector indexes10.1016/j.eneco.2019.02.001 ↗
47. Sovacool, B.K., and Cooper, C.J. (2013). The Governance of Energy Megaprojects: Politics, Hubris and Energy Security, Edward Elgar Publishing.10.4337/9781781952542 ↗
48. Stewart, S.D., Piros, C.D., and Heisler, J.C. (2019). Portfolio Management: Theory and Practice, John Wiley & Sons.
49. Multi-objective genetic algorithms for solving portfolio optimization problems in the electricity market10.1016/j.ijepes.2014.01.014 ↗
50. Stochastic modelling of renewable energy sources from operators’ point-of-view: A survey10.1016/j.rser.2017.06.006 ↗
51. Hierarchical multi-objective optimization of India’s energy strategy portfolios for sustainable development10.1108/17506221211259691 ↗
52. Coping with Uncertainties in the Electricity Sector-Methods for Decisions of Different Scope10.5547/2160-5890.10.1.cweb ↗
53. A conditional value-at-risk based planning model for integrated energy system with energy storage and renewables10.1016/j.apenergy.2021.116971 ↗
54. Value-at-risk versus expected shortfall: A practical perspective10.1016/j.jbankfin.2004.08.010 ↗
55. Causality relationships between climate policy uncertainty, renewable energy stocks, and oil prices: A mixed-frequency causality analysis10.1186/s43093-024-00399-1 ↗
56. Uncertainty models for stochastic optimization in renewable energy applications10.1016/j.renene.2019.07.081 ↗