Goal programming models with interval coefficients for the sustainable selection of marine renewable energy projects in the UK

• Published in: European journal of operational research Vol. 293; no. 2; pp. 748 - 760
• Main Authors: Akbari, Negar, Jones, Dylan, Arabikhan, Farzad
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2021
• Subjects: Clustering; Goal programming; Interval analysis; Marine renewable energy; Offshore wind Energy; Uncertainty; Interval analysis; Uncertainty; Marine renewable energy; Clustering; Goal programming; Offshore wind Energy
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2020.12.038

•Adoption and development of interval coefficient goal programming models for marine renewable energy project selection.•Addressing the uncertainty associated with the coefficients and goals in the model.•Identification of potential marine renewable energy zones in the UK using the K-means clustering method. In this paper, a strategic decision making model for the sustainable development of marine renewable energy is proposed, and a specific application to the United Kingdom (UK) is demonstrated. As an island nation the UK benefits from significant marine energy potential which is providing an increasing contribution to UK's renewable energy portfolio. The paper investigates the question of how decision makers can be aided to reach a decision on which types of marine renewable energy projects should be chosen for development given that strategic energy planning is subject to a number of uncertain parameters and multiple sustainability objectives. In this context, the contribution of this paper lies in the combination of renewable energy portfolio selection and the application of multi-objective methods. Interval coefficient goal programming models are adopted in order to address the impreciseness and uncertainty associated with the goals and coefficients of the models in the context of renewable energy selection. The potential renewable energy projects are clustered in order to aid the decision making process and preferential weight sensitivity methods are employed. Conclusions are drawn for optimistic and pessimistic scenarios in the context of UK marine renewable energy planning.