The rising awareness of environmental issues and the increase of renewable energy sources(RESs)has led to a shift in energy production toward RES,such as photovoltaic(PV)systems,and toward a distributed generation(DG)...The rising awareness of environmental issues and the increase of renewable energy sources(RESs)has led to a shift in energy production toward RES,such as photovoltaic(PV)systems,and toward a distributed generation(DG)model of energy production that requires systems in which energy is generated,stored,and consumed locally.In this work,we present a methodology that integrates geographic information system(GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles.In particular,we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’electricity demand.Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation.It integrates methodologies to estimate energy demand with a high temporal resolution,accounting for realistic populations with realistic consumption profiles.Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities.The proposed methodology is tested and validated within the municipality of Turin,Italy.For the whole municipality,we estimate both the electricity absorbed from the residential sector(simulating a realistic population)and the electrical energy that could be produced by installing PV systems on buildings’rooftops(considering two different scenarios,with the former using only the rooftops of residential buildings and the latter using all available rooftops).The capabilities of the platform are explored through an in-depth analysis of the obtained results.Generated power and energy profiles are presented,emphasizing the flexibility of the resolution of the spatial and temporal results.Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO_(2) emissions.展开更多
文摘The rising awareness of environmental issues and the increase of renewable energy sources(RESs)has led to a shift in energy production toward RES,such as photovoltaic(PV)systems,and toward a distributed generation(DG)model of energy production that requires systems in which energy is generated,stored,and consumed locally.In this work,we present a methodology that integrates geographic information system(GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles.In particular,we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’electricity demand.Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation.It integrates methodologies to estimate energy demand with a high temporal resolution,accounting for realistic populations with realistic consumption profiles.Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities.The proposed methodology is tested and validated within the municipality of Turin,Italy.For the whole municipality,we estimate both the electricity absorbed from the residential sector(simulating a realistic population)and the electrical energy that could be produced by installing PV systems on buildings’rooftops(considering two different scenarios,with the former using only the rooftops of residential buildings and the latter using all available rooftops).The capabilities of the platform are explored through an in-depth analysis of the obtained results.Generated power and energy profiles are presented,emphasizing the flexibility of the resolution of the spatial and temporal results.Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO_(2) emissions.
