Energy planning based on Vision-2023 of Turkey with a goal programming under fuzzy multi-objectives

• Published in: Energy (Oxford) Vol. 261; p. 124956
• Main Authors: Gezen, Mesliha, Karaaslan, Abdulkerim
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15-12-2022
• Subjects: Energy planning; Mathematical modeling; Multi objective goal programming; Mathematical modeling; Multi objective goal programming; Energy planning
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2022.124956

In this study, the realizability of the MENR's Vision-2023 goals is questioned under the contradictory objectives of minimum cost, minimum emission, maximum capacity factor and maximum resource potential. This is a goal-oriented programming problem with uncertain objectives. While the decision maker (DM) can determine the goals he wants to attain in this problem, he also has undefined goals (such as cost minimization) that constrain his ability to fulfill these goals. The GP-FMO model was proposed to model such a problem. The resulting model was solved by classical LP and non-dominated genetic algorithm II (NSGA II). The solution found using the classical LP is only one of the feasible solution alternatives. The non-dominated points on the Pareto efficient set obtained by the NSGA II Algorithm were presented to the decision makers as solution possibilities based on the common satisfaction levels of the uncertain goals. Prioritization of energy resources, regional allocation, and capacity increase planning were all combined in this study. All resources in Turkey's energy supply systems were discussed. The findings provide a multidimensional perspective for energy investments.
•Energy planning is a multi-objective decision-making problem in which the objectives conflict with one another.•Proposing a goal programming model under fuzzy conflicting objectives.•Integrating space allocation and assignment problems.•Offering flexible solutions to uncertain goals at a common degree of satisfaction.•Analysis of the cost of planning and carbon emissions.