Optimal design and operation of energy systems serving clusters of buildings interconnected by energy microgrids is a scientific challenge for the engineering community, with many interdisciplinary aspects involved. In the paper, the optimization problem is tackled in terms of simulation-based design of the energy system for a typical year of operation. The methodology has been applied to a Campus in Trieste, Italy, involving locally available renewable energy sources, a centralized cogeneration system, and decentralized heat pumps. The nominal powers of cogeneration plant, photovoltaic modules and wind turbine have been optimized by a population-based evolutionary optimization algorithm, previously proposed by the authors. We have also found the optimal scheduling of energy generators by means of a greedy approach. The solution with maximum Annualized Cost Saving Percentage is discussed, highlighting how a configuration involving decentralized heat pumps is cost effective, integrates more renewable energy sources, and reduces environmental impact and grid exchange compared to a benchmark solution, with fully centralized generators.

Optimal synthesis, design and operation of smart microgrids serving a cluster of buildings in a campus with centralized and decentralized hybrid renewable energy systems

Daniele Testi
Primo
;
Luca Urbanucci;Chiara Giola;Davide Aloini;Nunzia Squicciarini;Mauro Tucci;Marco Raugi
2019-01-01

Abstract

Optimal design and operation of energy systems serving clusters of buildings interconnected by energy microgrids is a scientific challenge for the engineering community, with many interdisciplinary aspects involved. In the paper, the optimization problem is tackled in terms of simulation-based design of the energy system for a typical year of operation. The methodology has been applied to a Campus in Trieste, Italy, involving locally available renewable energy sources, a centralized cogeneration system, and decentralized heat pumps. The nominal powers of cogeneration plant, photovoltaic modules and wind turbine have been optimized by a population-based evolutionary optimization algorithm, previously proposed by the authors. We have also found the optimal scheduling of energy generators by means of a greedy approach. The solution with maximum Annualized Cost Saving Percentage is discussed, highlighting how a configuration involving decentralized heat pumps is cost effective, integrates more renewable energy sources, and reduces environmental impact and grid exchange compared to a benchmark solution, with fully centralized generators.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1023300
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