Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics

Multi-objective design and operation optimization of energy supply systems with respect to costs, energy efficiency and environmental indicators is a very important, yet difficult and challenging task. The assumption of cost-optimal operation may impose the necessity for bi-level problems. This paper presents a methodology to formulate and solve multi-objective bi-level optimization problems where upper-level decision makers decide on design and policy, while plant operation is defined at the lower level. It can be used to assess energy consumption or emission reduction potential corresponding to cost-optimal operation. The methodology applies conceptual decomposition to the outer design- and inner operation-optimization problems. The first is solved with the scatter search method and the second using mixed integer linear programming. Scatter search is a metaheuristic method convenient to integrate problem-specific heuristics into the process and systematically take advantage of the designer's knowledge. The fuzzy approach considers multiple design objectives and helps formulate heuristic rules based on the decision criteria satisfaction rates. The case study illustrates the design of a trigeneration system for an urban settlement. The design objectives are: total costs, primary energy and greenhouse gases emission. Cost-optimal operation is presumed. The optimal plant design and operation are realistic and mostly consistent with the literature. Compared to the cost-optimal solution, other objectives might lead to reduced primary energy consumption for 7.21% or lower emission for 91.85% with 43.35% and 83.1% larger costs. The results are compared with the ones obtained with customary methods. It is demonstrated that the methodology is applicable and convenient for the considered class of problems.