A framework for regional smart energy planning using volunteered geographic information

Abstract

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.

Energy Cell Simulation for Sector Coupling with Power-to-Methane: A Case Study in Lower Bavaria

“…In the literature on regional energy system simulations, weather data has proven to be an important issue in various contexts [19]. There is also great concern about the quality of the data used to develop power system simulation models that serve as the basis for decision-making [20]. In this regard, much effort has been devoted to the improvement and validation of new climate data collections [19].…”

“…The working hypothesis is that the use of real demand profiles (RLP) combined with multiple time series of weather data should have an impact on the economic results of the optimization model compared to models using only a one-year data series and SLP. Therefore, we optimize the size of the systems using SLP [20], a demand that is characterized by no large peaks of use and very regular behavior, and compare them to the results of RLP, which contains a larger amount of variation. Our analysis focuses on runtime and size capacity because the use of RESs and energy storage technologies depend highly on peak demand and production.…”

“…In this paper, we continued the mixed-integer linear optimization (MILP) model from Valdes et al [20] with hourly resolution and analysis of the hourly operation of generation and storage, such that the electricity demand in different periods was met. In this article, we considered two case study regions with a PtM system coupled to its renewable and other existing non-electricity supply technologies.…”

“…These data are presented in Section 2.1. The methodology consisted of three phases, following Valdes [20] and Alhamwi [15], as shown in Figure 1.…”

Techno-economic modelling and optimisation of excess heat and cold recovery for industries: A review

“…Valdes et al created a soft link between Calliope and the power grid simulation tool PyPSA to develop a framework for regional smart energy planning considering geographical data [145]. Calliope is used to determine the least-cost investment in decentralised power generation and the optimal operation of the system, while PyPSA is used to conduct the grid calculations and simulate Alternating current (AC) power flow in the distribution grid.…”