Concurrent Design and Operation Optimization of a Multi-Generation Energy System by Using a SMO-MILP Hybrid Algorithm

Multi-generation energy systems, which include both fossil fuel technologies and renewable energy systems, are recognized as key players towards more sustainable energy production. To this aim, this paper proposes a methodology for the concurrent design and operation optimization of a multi-generation energy system by considering both life cycle energy consumption and economic aspects. The multi-generation energy system considered in this paper comprises renewable energy systems (photovoltaic panels, solar thermal collectors), heat pumps, fossil fuel energy systems (combined heat and power systems, gas boilers, absorption and compression chillers), and thermal storage systems. The design optimization problem is solved by means of a surrogate modeling approach, while the operation optimization problem is addressed by means of mixed-integer linear programming and by considering the minimization of fossil cumulative energy demand or total cost. A clustering approach is employed to simulate the yearly electrical, thermal and cooling energy demand by using selected representative days. The variability of natural gas and electricity price in year 2019 and 2022 is also investigated. The validity of the suggested approach is demonstrated by using the Campus of the University of Parma (Italy) as the case study. The proposed methodology provides an effective and flexible framework for the optimal design and operation of multi-generation energy systems, with a reduced computational time thanks to the clustering approach which allows the identification of a low number of representative days that simulate actual energy demand. The energy saving that can be achieved by means of the methodology developed in this paper can be up to 53%.