Multi-Objective Optimization Model to Minimize Upgrade and Utility Costs of Large Existing Buildings

Abstract

Energy and water efficiency measures can offer great reduction in utility costs of large existing buildings. However, decision-makers are often faced with a challenging task to identify the most cost effective measures within available budgets due to wide variety of energy and water efficiency measures. This paper presents the development of a new model that is capable of identifying optimum trade-offs between (1) minimizing upgrade cost, and (2) minimizing utility cost of existing buildings simultaneously while maintaining building operational performance. To this end, a multi-objective model is developed in three main steps: (1) formulation step where decision variables, objective function, and constraints are identified and formulated; (2) implementation step that performs the model computations and specifies the model input and output data; and (3) performance evaluation step where a case study is analyzed to evaluate the performance of the model. The primary contributions that this research adds to existing are (1) development of a new optimization model that can identify optimal building upgrades to strike a balance between upgrade cost and utility cost, and (2) implementing the model computations using epsilon-constraint method and binary linear programming to guarantee the optimality of the generated solution in short computational time. The case study results illustrated that the developed model was able to identify pareto-optimal solutions of the two optimization objectives and optimal set of upgrade measures for each of the pareto-optimal solutions for a study period of 20 years that reduced utility costs up to 39.5%.