OPTIMIZATION METHODS FOR ALTERNATIVE ENERGY SYSTEM DESIGN

摘要

Although in reality most decision variables in the designs of alternative energy systems are generally discrete (e.g., numbers of photovoltaic modules, solar thermal panels, layers of glazing in windows), an extensive review of the literature shows that historically the optimization methods used for design utilize continuous decision variables. The design optimization tools of linear programming and integer programming were compared using an electric vehicle heating system case study. Linear programming and integer programming were each applied to find the optimal investment in conservation and passive solar design measures as a function of the heating system life cycle cost. The results demonstrate the importance of accounting for the discrete nature of design variables. It was also shown that passive solar design methods similar to those used in buildings, reduced the overall UA of a 22 ft. electric shuttle bus from 488 to 202 (Btu/hr-F), eliminating the need for fossil fuel heating when operating in the northeast United States. In general, optimization methods that treat decision variables as discrete, such as integer programming, seem more appropriate for alternative energy system design than methods used historically. Integer programming software is widely available and is not difficult to use.