A framework for regional smart energy planning using volunteered geographic information

Abstract. This study presents a framework for regional smart energy planning for the optimal location and sizing of small hybrid systems. By using an optimization model – in combination with weather data – various local energy systems are simulated using the Calliope and PyPSA energy system simulation tools. The optimization and simulation models are fed with GIS data from different volunteered geographic information projects, including OpenStreetMap. These allow automatic allocation of specific demand profiles to diverse OpenStreetMap building categories. Moreover, based on the characteristics of the OpenStreetMap data, a set of possible distributed energy resources, including renewables and fossil-fueled generators, is defined for each building category. The optimization model can be applied for a set of scenarios based on different assumptions on electricity prices and technologies. Moreover, to assess the impact of the scenarios on the current distribution infrastructure, a simulation model of the low- and medium-voltage network is conducted. Finally, to facilitate their dissemination, the results of the simulation are stored in a PostgreSQL database, before they are delivered by a RESTful Laravel Server and displayed in an angular web application.

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