Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies

Victor A.D. Faria, Anderson Rodrigo de Queiroz and Joseph F. DeCarolis

Energy, 2023, vol. 270, issue C

Abstract: This work proposes an analytical decision-making framework considering scenario generation using artificial neural networks and risk-averse stochastic programming to define renewable offshore portfolios of wind, wave, and ocean current technologies. For the scenario generation, a generative adversarial neural network is developed to generate synthetic energy scenarios considering resources distributed over large geographic regions. These scenarios are then fed to a stochastic model, which objective to determine the optimal location and number of turbines for each technology. In the stochastic model formulation, a representation of the limits in the portfolio Levelized Cost of Energy and the maximization of the five percent lower energy generation conditions, also known as Conditional Value at Risk, is presented. The framework proposed here is tested considering data from a portion of the U.S. East coast, where the generative model was successful in creating energy scenarios statistically consistent with the historical data for wind, wave, and ocean current resources at more than 500 sites. Furthermore, the Conditional Value at Risk portfolio optimization model was used to construct efficient frontiers for a combination of different technologies, showing the significance of resource diversification as a tool to improve system security.

Keywords: Generative adversarial neural networks; Portfolio optimization; Risk assessment; Optimal site selection; Renewable energy (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223003407
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:270:y:2023:i:c:s0360544223003407

DOI: 10.1016/j.energy.2023.126946

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().