Renewable energies currently represent a significant portion of the sources of heat and electricity all over the world. However, their intermittent nature results in a major challenge for the design and management of the systems which exploit these energy sources. Indeed, the residential and industrial energy demand has to be fulfilled without any interruption. To face the aforementioned problem, this paper suggests to adopt polygeneration energy systems. These systems are distinguished by several sources of electrical and heating power, both renewable and traditional one, which interact to fulfill the thermal and electricity hourly demand considering the geographical location where the system is located. Indeed, the atmospheric condition evolution during the day and over the months significantly affect the energy availability from renewable sources. Considering the presented framework, this paper proposes a genetic algorithm for the economic optimization of the design and operation of polygeneration energy systems. The considered system fulfills the electric load through photovoltaic modules integrated with battery energy storage, whereas a natural gas boiler supplies the required thermal power. Furthermore, a cogenerative engine contributes to the demand of both energy types. An original genetic algorithm is developed to determine the optimal size of each polygeneration system module to minimize the levelized cost of energy, determined by the expenditures of both the system installation and operation phases. The energy flows between the polygeneration modules and the demand load are defined considering the hourly evolution of the external environment, as the atmospheric conditions and the electricity price. Finally, the developed genetic algorithm is tested and validated with a real case study represented by the thermal and electricity loads of an Italian manufacturing plant. The design and operation configuration proposed by the algorithm for the installed polygeneration energy system enables to save 9.2% of the costs of traditional energy source exploitation.

Economic optimization of the design and operation of polygeneration energy systems by genetic algorithms / Pilati, F.; Bortolini, M.; Gamberi, M.; Margelli, S.. - In: ...SUMMER SCHOOL FRANCESCO TURCO. PROCEEDINGS. - ISSN 2283-8996. - ELETTRONICO. - 2018:(2018), pp. 15-21. ((Intervento presentato al convegno 23rd Summer School "Francesco Turco" - Industrial Systems Engineering 2018 tenutosi a Grand Hotel et des Palmes, Palermo, Italy nel 12-14 Settembre 2018.

Economic optimization of the design and operation of polygeneration energy systems by genetic algorithms

Pilati, F.;
2018

Abstract

Renewable energies currently represent a significant portion of the sources of heat and electricity all over the world. However, their intermittent nature results in a major challenge for the design and management of the systems which exploit these energy sources. Indeed, the residential and industrial energy demand has to be fulfilled without any interruption. To face the aforementioned problem, this paper suggests to adopt polygeneration energy systems. These systems are distinguished by several sources of electrical and heating power, both renewable and traditional one, which interact to fulfill the thermal and electricity hourly demand considering the geographical location where the system is located. Indeed, the atmospheric condition evolution during the day and over the months significantly affect the energy availability from renewable sources. Considering the presented framework, this paper proposes a genetic algorithm for the economic optimization of the design and operation of polygeneration energy systems. The considered system fulfills the electric load through photovoltaic modules integrated with battery energy storage, whereas a natural gas boiler supplies the required thermal power. Furthermore, a cogenerative engine contributes to the demand of both energy types. An original genetic algorithm is developed to determine the optimal size of each polygeneration system module to minimize the levelized cost of energy, determined by the expenditures of both the system installation and operation phases. The energy flows between the polygeneration modules and the demand load are defined considering the hourly evolution of the external environment, as the atmospheric conditions and the electricity price. Finally, the developed genetic algorithm is tested and validated with a real case study represented by the thermal and electricity loads of an Italian manufacturing plant. The design and operation configuration proposed by the algorithm for the installed polygeneration energy system enables to save 9.2% of the costs of traditional energy source exploitation.
XXIII Summer School “Francesco Turco” – Industrial Systems Engineering
italia
AIDI - Italian Association of Industrial Operations Professors
Pilati, F.; Bortolini, M.; Gamberi, M.; Margelli, S.
Economic optimization of the design and operation of polygeneration energy systems by genetic algorithms / Pilati, F.; Bortolini, M.; Gamberi, M.; Margelli, S.. - In: ...SUMMER SCHOOL FRANCESCO TURCO. PROCEEDINGS. - ISSN 2283-8996. - ELETTRONICO. - 2018:(2018), pp. 15-21. ((Intervento presentato al convegno 23rd Summer School "Francesco Turco" - Industrial Systems Engineering 2018 tenutosi a Grand Hotel et des Palmes, Palermo, Italy nel 12-14 Settembre 2018.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/247864
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