Simultaneous optimization of the design and operation of multi-generation energy systems based on life cycle energy and economic assessment

• Published in: Energy conversion and management Vol. 249; p. 114883
• Main Authors: Bahlawan, Hilal, Morini, Mirko, Pinelli, Michele, Spina, Pier Ruggero, Venturini, Mauro
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-12-2021; Elsevier Science Ltd
• Subjects: Algorithms; Design; Design optimization; Dynamic programming; Economic impact; Economics; Emissions; Energy; Energy conservation; Energy consumption; Energy costs; Energy demand; Energy storage; Fossil fuels; Life cycle assessment; Life cycles; Methodology; Multi-generation energy systems; Multiple objective analysis; Operating costs; Operation optimization; Particle swarm optimization; Renewable energy; Life cycle assessment; Operation optimization; Design optimization; Multi-generation energy systems
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2021.114883

•Methodology for the simultaneous optimization of the design and operation of MESs.•Multi-objective optimization based on life cycle energy and economic assessment.•Life cycle energy saving of 17%, compared to a conventional plant.•Total cost reduction of about 18%, compared to a conventional plant.•The proposed methodology significantly reduces the computational time.
Multi-generation energy systems could mitigate global energy consumption, carbon emissions and economic costs. The promising energy, environmental and economic advantages of these systems are greatly dependent on their design and operational strategy. Consequently, methods and guidelines for the optimal design and operation of such systems, also taking into consideration their life cycle, are needed to fully exploit their potential. This paper proposes a general methodology for the simultaneous optimization of the design and operation of multi-generation energy systems by considering life cycle energy and economic assessment. The design optimization problem is solved by means of surrogate modeling and the operation optimization problem by means of dynamic programming. The multi-generation energy system considered in this paper comprises renewable energy systems, fossil fuel energy systems and energy storage technologies. Different multi-objective optimizations were performed by considering the minimization of fossil cumulative energy demand, and total investment and operational costs. The validity of the proposed methodology is demonstrated by using the campus of the University of Parma (Italy) as a case study. Compared to a conventional plant, the optimal solution allows a life cycle energy saving of about 17% and total cost reduction of about 18%. Moreover, compared to an optimization method based on particle swarm optimization and dynamic programming, the proposed methodology provides comparable results, but the computation time is 78% lower. The proposed methodology outperforms commonly used optimization algorithms and provides an effective and flexible framework for the optimal design and operation of multi-generation energy systems.