This python package includes a toolset develop and utilized to assess the impact of a controlled plug-in electric vehicle (PEV) charging and variables renewable energy source (VRES) curtailment policy on distribution system cost.
It has been developped for research purposes of a research project titles "How much smart charging is smart?" by Christoph Heilmann and David Wozabal. The following abstract provides and overview on the content and findings:
The threat of climate change motivates the expansion of variable renewable energy sources (VRES) as well as the transition to plug-in electric vehicles (PEV). Both technologies present unique challenges to electricity systems, potentially leading to high grid expansion cost. In this paper, we present a detailed assessment on how smart-charging of PEVs can mitigate these problems and reduce cost on the distribution grid level. To this end, we propose a heuristic policy that dynamically decides on charging of PEVs, curtailment of VRES, and subsequently on infrastructure investments trading off fixed investments with variable operational costs. The main inputs are modeled as stochastic and the proposed policy is non-anticipative. We conduct a comprehensive case study for Germany in the year 2035 using detailed descriptions of existing distribution grids, realistic driving patterns, and real-world VRES feed-in data. Potential savings of EUR 6.2 billion in investment costs lead to a reduction in total distributio grid cost of around 19%. This result is achieved by upgrading 7 million (21%) PEV chargers all over Germany. Upgrading 100% of chargers to smart-chargers as is proposed in the extant literature is clearly sub-optimal as it leads to significantly higher total cost. A closer investigation for single grids reveals that savings as well as the optimal share of smart chargers varies widely between grids. In particular, the potential of smart charging is much greater in rural areas than in urban centers. Furthermore, the results suggest that curtailment of VRES production is economically only in rare circumstances.
We use the Open Electricity Grid Optimization (open_eGo - https://github.com/openego) toolbox to generate synthetic models of German grids as well as load and demand curves and perform the distribution grid expansion planning. We use the electricity Distribution Grid optimization (eDisGo - https://github.com/openego/eDisGo) to calculate the distribution grid expansion needed to avoid overloading and voltage issues in the system.
We optimize the parameters of our smart charging strategy using the Python implementation of the covariance matrix adaptation evolution strategy (CMAES - https://github.com/CMA-ES/pycma).
Our modeling of random PEV driving behaviour, specifically trip duration, trip times, and driving distance are based on real-world driving data from the German Mobility Panel (GMP) which supplies trip data from a survey in 2015/2016 and 2016/2017 [1]. As this data is not open source, we can not provide it as part of this package.
The package is run from "Run_files/run_file.py".
[1] German Federal Ministry of Transport and Digital Infrastructure, German
mobility panel(Deutsches Mobilit ¨atspanel): Time series 2015/2016 and 2016/17 (2017).
URLhttps://mobilitaetspanel.ifv.kit.edu/34
Christoph Heilmann – Christoph.Heilmann@tum.de David Wozabal - David.Wozabal@tum.de
Copyright (C) 2020 Christoph Heilmann
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.