Stefan Übermasser

Dynamic simulation of power system interaction with large electric vehicle fleet activities

Detailed analysis of the impact of electric vehicles on the distribution grid needs a dynamic model of the charging and discharging process of the battery. The energy demand depends on the driving behavior, e.g., departure/arrival times, distances and routes traveled, and are normally not modeled in power system simulation. Charging algorithms which control the charging power may adversely influence the range. A realistic simulation setup of the electric vehicles energy demand needs to incorporate transportation simulation, where the discharging is also modeled. To overcome the shortcomings of traditional power system simulation, the dynamically coupled simulation environment presented is able to model the charging and discharging process. Examples show the applications of dynamic simulation: utilization of charging points, intelligent charging management, impact on voltage symmetry and large scale EV activity.

Agent-Based Impact Analysis of Electric Vehicles on a Rural Medium Voltage Distribution Network Using Traffic Survey Data

Based on trips of more than 10,000 cars, the impact of charging electric vehicles on a rural medium voltage network is analysed and presented. Traffic is simulated by micro-simulation where each of the electric vehicles is represented by an agent. The total demand for charging battery electric vehicles and plug-in hybrid vehicles for one day in summer and winter is determined. Two different charging scenarios (end-of-travel-day and opportunity charging) as well as temperature influence are compared. Results show that due to concurrency the impact on the voltage is significant in terms of increasing penetration levels, especially with the end-of-travel-day charging scenario.

Dynamic Co-simulation of agent-based controlled charging electric vehicles and their impacts on low-voltage networks

During the last decades the electric vehicles (EVs) technologies are developing rapidly which may lead to a significant penetration level in the mobility market in the near future. As a consequence of these high penetration levels, power grids, especially on the low voltage level, might face a number of technical problems. These can be transformer and cables overloads, feeder congestions and especially voltage limits violations. Renewables may introduce a solution for part of these problems but not all of them. In order to minimize peak loads caused by charging EVs, intelligent control algorithms for EV charging are needed. This paper focuses on a dynamic co-simulation environment which enables the realistic evaluation of the impact of charging EVs on electrical networks. This co-simulation environment also simulates the behavior of the EVs and estimates the required charging power. In order to test the co-simulation environment a study case is analyzed for a rural residential network with a high penetration of photovoltaic systems.

Gap analysis of future energy grids

Based on existing low and medium voltage grids from distribution system operators and future scenarios of PV and EV penetration, critical load scenarios which lead to violations of relevant KPIs are to be developed in the scope of PlanGridEV. In the course of the gap analysis the expected limits of DER hosting capacities were identified. The method for developing and simulating the different scenarios is following a state-of-the-art approach. Results for each power grid and scenario are addressing specific KPIs. The main goal of this analysis was to assess the current EV and PV hosting capacity of existing low voltage and medium voltage grids and identify the factors which limit this hosting capacity. It is shown that European DSOs share similar structures. Depending on voltage level and topology type, specific KPIs were violated by increasing numbers of RES or EVs. Indications for synergies between PV and EVs could be identified due to an increased hosting capacity in the extra PV scenario.

Concept for Intra-Hour PV Generation Forecast based on Distributed PV Inverter Data - An Approach Considering Machine Learning Techniques and Distributed Data.

The mass-introduction of small scale power generation units like photovoltaic systems at household levels increase the risk for system unbalances, due to their stochastic generation profile. Additionally, upcoming technologies such as electric vehicles, battery storage systems and energy management systems lead to a change from consumer households to prosumers with a significant different residual load profile. For optimizing the profile of future prosumers, especially the forecast for PV generation is crucial. Whilst traditional weather forecasts are based on a few hundred metering locations in the case of Austria, more than 55000 PV systems are currently connected to the Austrian Power grid. Due to the low areal coverage of common metering locations, weather forecasts do not take local phenomena like shadows from clouds into account. An approach using generation data from neighbouring PV systems together with machine learning methods provides a promising alternative for individual location based intra-hour forecasts. This paper describes the requirements and methods of such a concept and concludes with a first proof of concept.

Rollout e-mobility – the next big challenge for network operations and network planning

The mass roll-out of electro-mobility, in conjunction with the increased production of electricity derived from renewable sources, is one of the major opportunities to reduce CO2 over the next decade. The efficiency of electric engines is superior to that of combustion engines and the renewable energy sources used to power them are naturally CO2-free. Additionally, the fact that electric cars use batteries takes care of the issue of energy storage, helping to overcome the problem of renewable sources only being available when the sun is shining or the wind is blowing. The battery in the car, in combination with an energy management system that can monitor the availability of renewable energy, is a major opportunity.

Optimized and enhanced grid architecture for electric vehicles in Europe

For an optimized large-scale roll-out of EVs in Europe whilst at the same time maximizing the potential of DER integration, an optimized and enhanced grid architecture for EVs in Europe has to be considered. The work in this paper is addressing this topic and summarizing the corresponding project findings. The aim of this approach is to provide a framework for the further investigation of selected use cases which allows implementing and comparing scenarios of different DSOs. Following a Smart Grid approach, the developed grid architecture implements energy grid entities and ICT components. The general framework was described including all its relevant clusters and indicating related entities. The network types used for this architecture are following the SGAM and Smart Grid Standards Map approach. A so-called “Smart Grid Connection Point”, which is a generic system interface, is used in this work to allow a more simplified graphical architecture model and increase its readability. Similar to the concept and purpose of the Smart Grid Connection Point, also the principle of an integration bus for entity clusters was introduced. From the Integration bus, the information from/to external systems passes through the Smart Grid Connection Point using one of a range of possible technological options. The position of EVs charging infrastructure within the framework is defined at the border between the domains DERs (generation) and consumption, which takes into account future V2G scenarios, where EVs may act as consumption and generation devices. EVSEs and DERs may be connected as standalone systems directly to the grid, or indirectly as part of one of the clusters at the customer premises domain which refers to the three location-wise types of charging, public, semi-public and private charging. Regarding controlled charging of EVs this optimized architecture allows a variety of different local, distributed or aggregated options which may involve different types of actors.

Linking statistical mobility data with electrical distribution network infrastructure for generating an agent population for multi-agent simulation of electric vehicles with Markov chains

To create realistic travel chains Markov Chains and Monte Carlo method are used. This includes different travel purposes in different locations, information about departure/arrival times and the distance driven. Individual agents are then generated which represent the EV population of a certain area. Depending on the defined charging infrastructure in the specific grid and the chosen EV model, this agent population can be calibrated for low- and medium voltage grids of different areas as there are rural or urban areas. Different charging strategies are simulated and their effect to the power grid analyzed.