Development of a GIS-based platform for the allocation and optimisation of distributed storage in urban energy systems

Abstract

As the world is already highly urbanised, energy systems in cities are already responsible for significant amount of the global Greenhouse Gas (GHG) emissions. Therefore, climate change mitigation demands a fundamental transformation in the Urban Energy Systems (UES), energy markets and energy policies. In this context, the large shift to micro-generation from renewable energy sources and their integration in the current energy system are a technical challenge for future energy systems design and operation. This will be further exacerbated if flexibilisation technologies such as storage are not efficiently integrated. For this purpose, an accurate modelling and representation of UES requires the characterisation of different urban energy requirements. These requirements, along with the urban fabric of cities, should be adequately incorporated in a spatial-temporal framework including both static and dynamic datasets. In this context, urban energy models provide policymakers with qualitative and quantitative insights for the planning of future UES. Within this framework, urban energy models integrated in Geographic Information Systems (GIS) will play an important role due to their multi-layer approach.This study introduces an open source GIS-based platform called FlexiGIS for the optimisation of energy systems in cities. FlexiGIS is used in this contribution to optimally allocate distributed battery storage in urban areas. The FlexiGIS platform provides the urban energy infrastructure (spatial dimension), simulates electricity consumption and generation (spatial and temporal dimension) and performs a linear optimisation for the economic deployment of micro-generation and decentralised storage under different energy scenarios. The first case study considers the city as a single system or ‘energy cell’, while the second one assumes that the city is divided into connected subsystems or districts. The total UES costs and required storage capacities for the investigated scenarios are obtained using optimisation. A key finding is that, for the investigated scenarios, investing in local electricity storage and renewable power generation can significantly reduce the total system costs and increase urban self-sufficiency. This study also highlights that the off-grid scenario (isolated city) is not an optimal choice.