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Optimizing offshore renewable portfolios under resource variability

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  • de Faria, Victor A.D.
  • de Queiroz, Anderson R.
  • DeCarolis, Joseph F.

Abstract

The deployment of offshore wind, wave, and ocean current technologies can be coordinated to provide maximum economic benefit. We develop a model formulation based on Mean-Variance portfolio theory to identify the optimal site locations for a given number of wind, wave, and ocean current turbines subject to constraints on their energy collection system and the maximum number of turbines per site location. A model relaxation is also developed to improve the computational efficiency of the optimization process, allowing the inclusion of more than 5000 candidate generation sites. The model is tested using renewable resource estimates from the coast of North Carolina, along the eastern US coast. Different combinations of technology-specific offshore technologies are compared in terms of their levelized cost of electricity and energy variability. The optimal portfolio results are then included in a capacity expansion model to derive economic targets that make the offshore portfolios cost-competitive with other generating technologies. Results of this work indicate that the integration of different offshore technologies can help to decrease the energy variability associated with marine energy resources. Furthermore, this research shows that substantial cost reductions are still necessary to realize the deployment of these technologies in the region investigated.

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  • de Faria, Victor A.D. & de Queiroz, Anderson R. & DeCarolis, Joseph F., 2022. "Optimizing offshore renewable portfolios under resource variability," Applied Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012697
    DOI: 10.1016/j.apenergy.2022.120012
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    References listed on IDEAS

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    1. Faria, Victor A.D. & Rodrigo de Queiroz, Anderson & DeCarolis, Joseph F., 2023. "Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies," Energy, Elsevier, vol. 270(C).
    2. Neshat, Mehdi & Sergiienko, Nataliia Y. & Nezhad, Meysam Majidi & da Silva, Leandro S.P. & Amini, Erfan & Marsooli, Reza & Astiaso Garcia, Davide & Mirjalili, Seyedali, 2024. "Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method," Applied Energy, Elsevier, vol. 362(C).

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