Multi-objective investment and operating optimization of energy systems with integer cut constraints and evolutionary algorithm
The design and operating of energy systems are key issues for matching the energy supply and consumption. Several optimization methods based on the Mixed Integer Linear Programming (MILP) have been developed for this purpose. However, due to the uncertainty, analyzing only one optimum solution with mono objective function is not sufficient for sizing the energy system. In this study, first a multi periods MILP model with Integer Cut Constraints (ICC) is developed. The goal is to systematically generate a set of good solutions rather than one optimum solution. In this step the effect of CO2 emission is studied by doing the parametric optimization. In the second step, in order to study the economical and environmental targets simultaneously, the problem is reformulated as a multi-objective optimization model with evolutionary algorithm (QMOO). In this step the model is decomposed into master and slave optimization. Finally both developed models are demonstrated by means of a case study comprises six types of conversion technologies, namely heat pump, boiler, photovoltaics, as well as gas turbine, fuel cell and gas engine. Results shown, QMOO is particularly suited for doing a multi-objective optimization because it works with a population of potential solutions, each presenting a different trade-off between objectives. However MILP with ICC is more suited for generating a set of ordered solutions with a short resolution time. It is not computationally expensive.