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PV hosting capacity quantification is currently of importance for distribution system operators to manage upcoming PV installation requests, especially within low-voltage grids. The violation-mitigation-based (VMB) method presented a novel downward approach to quantify the PV hosting capacity in low-voltage grids, having the advantage of also quantifying possible expansions in the hosting capacity. The VMB method, however, requires typically hundreds of power flow simulations to quantify the hosting capacity in a low-voltage grid. In this paper, we address this issue by introducing sensitivity matrices to optimize the downward process of quantification. Evaluated in 79 low-voltage feeders from Austria, the enhanced VMB method reduces the number of iterations required from a median of 1764 to 7 while obtaining the same or higher hosting capacity values. The reduced computational effort permits the application of the method proposed at a large-scale. The application of the method proposed on the standard CIGRE residential low-voltage grid is also presented for possible comparison with other methods.
Violation-mitigation-based method for PV hosting capacity quantification in low voltage grids
(2022)
Hosting capacity knowledge is of great importance for distribution utilities to assess the amount of PV capacity possible to accommodate without troubling the operation of the grid. In this paper, a novel method to quantify the hosting capacity of low voltage grids is presented. The method starts considering a state of fully exploited building rooftop solar potential. A downward process is proposed - from the starting state with expected violations on the grid operation to a state with no violations. In this process, the installed PV capacity is progressively reduced. The reductions are made sequentially and selectively aiming to mitigate specific violations: nodes overvoltage, lines overcurrent and transformer overloading. Evaluated on real data of fourteen low voltage grids from Austria, the method proposed exhibits benefits in terms of higher hosting capacities and lower computational costs compared to stochastic methods. Furthermore, it also quantifies hosting capacity expansions achievable by overcoming the effect of the violations. The usage of a potential different from solar rooftops is also presented, demonstrating that a user-defined potential allows to quantify the hosting capacity in a more general setting with the method proposed.
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.
