The planning of an energy system with high penetration of renewables is becoming increasingly important to face environmental and energy security issues. Within bottom-up energy system modelling, two different approaches exist: one optimizes the energy mix of a selected future year, while a second optimizes the transition pathway between the current baseline and a future year. Markal/TIMES and OSeMOSYS are examples of valid modelling tools for both approaches. Due to computational issues, these models usually adopt low time resolutions and follow a time slice approach. The latter approximation is questionable given that renewables are intermittent and storage, stationary and in electric vehicles, is needed. To overcome the accuracy issue, a modelling approach based on a multi-objective evolutionary algorithm and the EnergyPLAN software, which allows for year by year simulations with an hourly time-step, was introduced. The method that includes cost decrease of technologies year by year and decommissioning of old plants is applied to the Italian energy system. The results show the importance of timing in renewables capacity expansion planning. The deepest cumulated CO2 emission reduction of the energy system operating only on residential photovoltaic, wind power and batteries is 24% while introducing electric mobility lower this value at almost 30%.

Transition pathways optimization methodology through EnergyPLAN software for long-term energy planning

Manzolini G.;
2019

Abstract

The planning of an energy system with high penetration of renewables is becoming increasingly important to face environmental and energy security issues. Within bottom-up energy system modelling, two different approaches exist: one optimizes the energy mix of a selected future year, while a second optimizes the transition pathway between the current baseline and a future year. Markal/TIMES and OSeMOSYS are examples of valid modelling tools for both approaches. Due to computational issues, these models usually adopt low time resolutions and follow a time slice approach. The latter approximation is questionable given that renewables are intermittent and storage, stationary and in electric vehicles, is needed. To overcome the accuracy issue, a modelling approach based on a multi-objective evolutionary algorithm and the EnergyPLAN software, which allows for year by year simulations with an hourly time-step, was introduced. The method that includes cost decrease of technologies year by year and decommissioning of old plants is applied to the Italian energy system. The results show the importance of timing in renewables capacity expansion planning. The deepest cumulated CO2 emission reduction of the energy system operating only on residential photovoltaic, wind power and batteries is 24% while introducing electric mobility lower this value at almost 30%.
Energy planning; Energy system modelling; EnergyPLAN; Long-term model; Transition pathways
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1124473
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