Energy system modelling supports decision-makers in the development of short and long-term energy strategies. In the field of bottom-up short-term energy system models, high resolution in time and space, the implementation of sector coupling and the adoption of a multi-objective investment optimization have never been achieved simultaneously because of the high computational effort. Within this paper, such a bottom-up short-term model which simultaneously implements (i) hourly temporal resolution, (ii) multi-node approach thus high spatial resolution, (iii) integrates the electric, thermal and transport sectors and (iv) implements a multi-objective investment optimization method is proposed. The developed method is applied to the Italian energy system at 2050 to test and show its main features. The model allows the evaluation of the hourly curtailments for each node. The optimization highlights that the cheapest solutions work towards high curtailments and low investments in flexibility options. In order to further reduce the CO2 emissions the investments in flexibility options like electric storage batteries and reinforcement and enlargement of the transmission grid become relevant.

Multi-objective investment optimization for energy system models in high temporal and spatial resolution

Prina M. G.;Casalicchio V.;Manzolini G.;
2020-01-01

Abstract

Energy system modelling supports decision-makers in the development of short and long-term energy strategies. In the field of bottom-up short-term energy system models, high resolution in time and space, the implementation of sector coupling and the adoption of a multi-objective investment optimization have never been achieved simultaneously because of the high computational effort. Within this paper, such a bottom-up short-term model which simultaneously implements (i) hourly temporal resolution, (ii) multi-node approach thus high spatial resolution, (iii) integrates the electric, thermal and transport sectors and (iv) implements a multi-objective investment optimization method is proposed. The developed method is applied to the Italian energy system at 2050 to test and show its main features. The model allows the evaluation of the hourly curtailments for each node. The optimization highlights that the cheapest solutions work towards high curtailments and low investments in flexibility options. In order to further reduce the CO2 emissions the investments in flexibility options like electric storage batteries and reinforcement and enlargement of the transmission grid become relevant.
2020
Energy scenarios
Evolutionary algorithms
Linear programming
Multi-objective optimization
Oemof
Pareto
Photovoltaics
Wind
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1161263
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