Methods for multi-objective investment and operating optimization of complex energy systems

摘要

The design and operations of energy systems are key issues for matching energy supply and consumption. Several optimization methods based on the mixed integer linear programming (MILP) have been developed for this purpose. However, due to uncertainty of some parameters like market conditions and resource availability, analyzing only one optimal solution with mono objective function is not sufficient for sizing the energy system. In this study, a multi-period energy system optimization (ESO) model with a mono objective function is first explained. The model is then developed in a multi-objective optimization perspective to systematically generate a good set of solutions by using integer cut constraints (ICC) algorithm and e constraint. These two methods are discussed and compared. In the next step, the ESO model is reformulated as a multi-objective optimization model with an evolutionary algorithm (EMOO). In this step the model is decomposed into master and slave optimization. Finally developed models are demonstrated by means of a case study comprising six types of conversion technologies, namely, a heat pump, boiler, photovoltaics, as well as a gas turbine, fuel cell and gas engine. Results show that, EMOO is particularly suited for multi-objective optimizations, working with a population of potential solutions, each presenting a different trade-off between objectives. However, MILP with ICC and e constraint is more suited for generating a small set of ordered solutions with shorter resolution time.