Refine
Document Type
- Article (3)
- Conference Proceeding (2)
Institute
Is part of the Bibliography
- yes (5)
Keywords
- Demand side management (3)
- Distribution grids (2)
- Electric vehicles (2)
- Demand Side Management (1)
- Demand response (1)
- Electric vehicle charging (1)
- Elektromobilität (1)
- Lastmanagement (1)
- Optimum charging strategy (1)
- Peak demand (1)
Increasing electric vehicle penetration leads to undesirable peaks in power if no proper coordination in charging is implemented. We tested the feasibility of electric vehicles acting as flexible demands responding to power signals to minimize the system peaks. The proposed hierarchical autonomous demand side management algorithm is formulated as an optimal power tracking problem. The distribution grid operator determines a power signal for filling the valleys in the non-electric vehicle load profile using the electric vehicle demand flexibility and sends it to all electric vehicle controllers. After receiving the control signal, each electric vehicle controller re-scales it to the expected individual electric vehicle energy demand and determines the optimal charging schedule to track the re-scaled signal. No information concerning the electric vehicles are reported back to the utility, hence the approach can be implemented using unidirectional communication with reduced infrastructural requirements. The achieved results show that the optimal power tracking approach has the potential to eliminate additional peak demands induced by electric vehicle charging and performs comparably to its central implementation. The reduced complexity and computational overhead permits also convenient deployment in practice.
PV hosting capacity provides utilities the knowledge of the maximum amount of solar installations possible to accommodate in low voltage grids such that no operational problems arise. As the quantification of the hosting capacity requires data collection, grid modelling, and often time-consuming simulations, simplified estimations for large-scale applications are of interest. In this paper, Bayesian statistical inference is applied to estimate the hosting capacities of more than 5000 real feeders in Austria. The results show that the hosting capacity of 95% of the total feeders can be estimated with a mean error below 20% by only having knowledge of a random sample of 5%. Moreover, the hosting capacity estimation at a regional level shows a maximum error below 9%, also relying on a random sample of 5% of the total feeders. Furthermore, the approach proposed provides a methodology to assess new parameters aiming to improve the accuracy of the hosting capacity estimation at a feeder level.
If left uncontrolled, electric vehicle charging poses severe challenges to distribution grid operation. Resulting issues are expected to be mitigated by charging control. In particular, voltage-based charging control, by relying only on the local measurements of voltage at the point of connection, provides an autonomous communication-free solution. The controller, attached to the charging equipment, compares the measured voltage to a reference voltage and adapts the charging power using a droop control characteristic. We present a systematic study of the voltage-based droop control method for electric vehicles to establish the usability of the method for all the currently available residential electric vehicle charging possibilities considering a wide range of electric vehicle penetrations. Voltage limits are evaluated according to the international standard EN50160, using long-term load flow simulations based on a real distribution grid topology and real load profiles. The results achieved show that the voltage-based droop controller is able to mitigate the under voltage problems completely in distribution grids in cases either deploying low charging power levels or exhibiting low penetration rates. For high charging rates and high penetrations, the control mechanism improves the overall voltage profile, but it does not remedy the under voltage problems completely. The evaluation also shows the controller’s ability to reduce the peak power at the transformer and indicates the impact it has on users due to the reduction in the average charging rates. The outcomes of the paper provide the distribution grid operators an insight on the voltage-based droop control mechanism for the future grid planning and investments.
Verbraucherseitige Laststeuerung (Demand Side Management – DSM) wird als ein möglicher Ansatz betrachtet, um die Auswirkungen des Ausbaus von fluktuierenden Erneuerbaren im Stromnetz auszugleichen. Sollen viele verteilte Energiesysteme damit angesprochen werden, stellen zentralistische Ansätze dabei hohe Anforderungen an die Kommunikationsinfrastruktur. Als Alternative wird vielfach eine autonome Laststeuerung (ADSM) mit anreizbasierter Optimierung direkt auf dem Verbrauchergerät betrachtet. Dabei kann die Anreizfunktion mittels unidirektionaler Kommunikation übertragen werden.
Am Forschungszentrum Energie der Fachhochschule Vorarlberg wurden in den letzten Jahren Algorithmen und Prototypen für den Einsatz von ADSM auf verschiedensten verteilten Energiespeichern im elektrischen Stromnetz entwickelt. Dabei werden sowohl thermische Energiespeicher (z. B. Haushalts-Warmwasserspeicher) als auch elektrochemische Speicher (z. B. Batteriespeichersysteme oder Elektroautos) betrachtet. Außerdem werden die Auswirkungen solcher Systeme auf das elektrische Verteilnetz untersucht. Dieser Artikel gibt einen Überblick über die entwickelten Methoden und Ergebnisse aus diesem Forschungsfeld mit dem Ziel, ein weitreichendes Verständnis für die Chancen und Grenzen des ADSM zu schaffen.
The electricity demand due to the increasing number of EVs presents new challenges for the operation of the electricity network, especially for the distribution grids. The existing grid infrastructure may not be sufficient to meet the new demands imposed by the integration of EVs. Thus, EV charging may possibly lead to reliability and stability issues, especially during the peak demand periods. Demand side management (DSM) is a potential and promising approach for mitigation of the resulting impacts. In this work, we developed an autonomous DSM strategy for optimal charging of EVs to minimize the charging cost and we conducted a simulation study to evaluate the impacts to the grid operation. The proposed approach only requires a one way communicated incentive. Real profiles from an Austrian study on mobility behavior are used to simulate the usage of the EVs. Furthermore, real smart meter data are used to simulate the household base load profiles and a real low voltage grid topology is considered in the load flow simulation. Day-ahead electricity stock market prices are used as the incentive to drive the optimization. The results for the optimum charging strategy is determined and compared to uncontrolled EV charging. The results for the optimum charging strategy show a potential cost saving of about 30.8% compared to uncontrolled EV charging. Although autonomous DSM of EVs achieves a shift of load as pursued, distribution grid operation may be substantially affected by it. We show that in the case of real time price driven operation, voltage drops and elevated peak to average powers result from the coincident charging of vehicles during favourable time slots.
