MILP Formulation for Energy Mix Optimization

Abstract

Energy mix (EM) is a term used to describe a share of different technologies used to meet the demand for electric power and energy. Development of EM is driven by many factors such as economic constraints, technical constraints, environmental requirements, and policy aspects. Therefore, the design of a proper EM is a demanding task, and requires adequate methodology and sophisticated modeling tools. The natural approach to defining the EM is application of optimization methodology. The majority of existing models is based on linear programming (LP). It enables the calculation of the total capacity by technology in a whole power system, but does not allow for estimation of the rated power of a particular power generating unit. Additionally, such an approach limits the development of model to include electrical grid constraints, detailed economic analyses, and consideration of rules of electricity market and power system operation. More sophisticated approaches are based on mixed integer linear programming (MILP). This paper deals with a problem of computational efficiency in MILP formulation of EM optimization. It presents three methods of formulation of binary variables for EM. The first method is based on unit commitment (UC) binary formulation. The second one implements an improvement on UC programming, while the third method introduces a novel approach for binary modeling of EM optimization. This paper delivers detailed mathematical formulations of the methods analyzed, and their evaluation in terms of computational performance and accuracy.