Method and Case Study of Multiobjective Optimization-Based Energy System Design to Minimize the Primary Energy Use and Initial Investment Cost

Abstract

This study aimed to develop a building energy system design method to minimize the initial investment cost and primary energy use. As for the energy system, various combinations were generated depending on the type and capacity of the device used as well as the number of units, energy consumption, and efficiency of the building. Because the design process of energy systems is a critical step in determining the performance of the building throughout the lifecycle, an effective design method is necessary. The proposed method determines the energy system that can minimize the primary energy use and initial investment cost through a multiobjective optimization by calculating the cooling and heating energy consumptions of the building and initial investment cost of the energy system using the load profile by the design-day, and the information in the design phase of the building. This method can support the decision-making process by providing engineers with an alternative proposal for minimizing the initial investment cost and primary energy use by the Pareto analysis after reviewing the design combinations of various energy systems with limited information in the initial design phase. To verify the effectiveness of the methodology, a case study of the two buildings was performed, and the analysis results were compared to the conventional design alternatives. As shown in the case study results, using a method developed in comparison with the conventional result can provide the efficient alternative selection with 80% of initial investment cost and 86% of primary energy use, respectively. OPEN ACCESSEnergies 2015, 8 6115The results confirmed that the proposed methodology can provide various optimum results more effectively compared to the conventional design methods.

Energy Consumption Analysis and Optimization of the Deep-Sea Self-Sustaining Profile Buoy

“…The Figure 8 and can be calculated as formula (14). The seawater's temperature in the southern hemisphere in summer T h_ss can be calculated as formula (15) and data in winter T h_sw can be expressed as formula (16). The related parameters are shown in Tables 3 and 4.…”

“…The non-dominated sorted genetic algorithm-II (NSGA-II) method has been widely used in multi-objective optimization problems and has been proved to be one of the most effective algorithms at present [9,10]. The NSGA-II method has been applied in different fields such as power management in HEVs [11], embedded real-time system [12], energy system [13][14][15], power system reconstruction [16], generation expansion planning (GEP) problem [17][18][19], thermal generating optimization problem [20,21], land use scenario [22], machine and engine efficiency problem [23,24].…”