Data, code and results behind the journal article : "Leveraging Location-Unconstrained Vehicle to Grid to Reduce Energy Community Costs" (under review). Alvaro Menendez Agudin , Nikolaos Damianakis , Gautham Ram Chandra Mouli , Pavol Bauer
Wat erin zit
This dataset supports a simulation-based optimisation study that assesses whether allowing EVs to charge and discharge at any location they visit (home, workplace and intermediate stops), rather than only at home, reduces the operating costs and peak power of an energy community. The community consists of five residential nodes, one workplace node and 50 EVs and is optimised with a mixed-integer linear programme that includes a linearised battery-ageing model. Stationary and “locational” V2G are compared for summer and winter, two commuting shares, two shares of bidirectional chargers, and cost-based and peak-based objectives (33 scenarios plus sensitivity runs on prices, trips and ageing). The dataset contains 5-minute EV mobility profiles (synthetic trip chains from an EV mobility model), community load, PV and electricity price series (based on open data from ENTSO-E, Liander/Zonnedael and NEDU), EV battery data, the Python/Pyomo code, and the scenario outputs and summary files behind the article.
Bestanden en diensten
- DataCite REST API (JSON)api.datacite.orgOpenen