Aiming at integrating different energy sectors and exploiting the synergies coming from the interaction of different energy carriers, sector coupling allows for a greater flexibility of the energy system, by increasing renewables’ penetration and reducing carbon emissions. At the local level, sector coupling fits well in the concept of an integrated local energy community (ILEC), where active consumers make common choices for satisfying their energy needs through the optimal management of a set of multi-carrier energy technologies, by achieving better economic and environmental benefits compared to the business-as-usual scenario. This paper discusses the stochastic operation optimization of the smart Savona Campus of the University of Genoa, according to economic and environmental criteria. The campus is treated as an ILEC with two electrically interconnected multienergy hubs involving technologies such as PV, solar thermal, combined heat and power systems, electric and geothermal heat pumps, absorption chillers, electric and thermal storage. Under this prism, the ILEC can participate in the day-ahead market (DAM) with proper bidding strategies. To assess the renewables’ uncertainties, the roulette wheel method is used to generate an initial set of scenarios for solar irradiance, and the fast forward selection algorithm is then applied to preserve the most representative scenarios, while reducing the computational load of the next optimization phase. A stochastic optimization model is thus formulated through mixed-integer linear programming (MILP), with the aim to optimize the operation strategies of the various technologies in the ILEC, as well as the bidding strategies of the ILECs in the DAM, considering both energy costs and carbon emissions through a multi-objective approach. Case study results show how the optimal bidding strategies of the ILEC on the DAM allow minimizing of the users’ net daily cost, and, as in the case of environmental optimization, the ILEC operates in self-consumption mode. Moreover, in comparison to the current operation strategies, the optimized case allows reduction of the daily net energy cost in a range from 5 to 14%, and the net daily carbon emissions in a range from 6 to 18%.

Stochastic Operation Optimization of the Smart Savona Campus as an Integrated Local Energy Community Considering Energy Costs and Carbon Emissions

Piazza G.;Bracco S.;Delfino F.
2022-01-01

Abstract

Aiming at integrating different energy sectors and exploiting the synergies coming from the interaction of different energy carriers, sector coupling allows for a greater flexibility of the energy system, by increasing renewables’ penetration and reducing carbon emissions. At the local level, sector coupling fits well in the concept of an integrated local energy community (ILEC), where active consumers make common choices for satisfying their energy needs through the optimal management of a set of multi-carrier energy technologies, by achieving better economic and environmental benefits compared to the business-as-usual scenario. This paper discusses the stochastic operation optimization of the smart Savona Campus of the University of Genoa, according to economic and environmental criteria. The campus is treated as an ILEC with two electrically interconnected multienergy hubs involving technologies such as PV, solar thermal, combined heat and power systems, electric and geothermal heat pumps, absorption chillers, electric and thermal storage. Under this prism, the ILEC can participate in the day-ahead market (DAM) with proper bidding strategies. To assess the renewables’ uncertainties, the roulette wheel method is used to generate an initial set of scenarios for solar irradiance, and the fast forward selection algorithm is then applied to preserve the most representative scenarios, while reducing the computational load of the next optimization phase. A stochastic optimization model is thus formulated through mixed-integer linear programming (MILP), with the aim to optimize the operation strategies of the various technologies in the ILEC, as well as the bidding strategies of the ILECs in the DAM, considering both energy costs and carbon emissions through a multi-objective approach. Case study results show how the optimal bidding strategies of the ILEC on the DAM allow minimizing of the users’ net daily cost, and, as in the case of environmental optimization, the ILEC operates in self-consumption mode. Moreover, in comparison to the current operation strategies, the optimized case allows reduction of the daily net energy cost in a range from 5 to 14%, and the net daily carbon emissions in a range from 6 to 18%.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1102879
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