Adaptive and Robust Cross-Voltage-Level Power Flow Control of Active Distribution Networks

The large-scale integration of Distributed Energy Resources (DERs) into the electric power system offers new opportunities to ensure stability. For example, Active Distribution Networks (ADNs) can be used in (sub-)transmission systems in the emergency state, as far as high robustness and performance of the ADN control are guaranteed. This paper presents an adaptive control system for ADN's cross-voltage-level power flow control. For this purpose, the gain scheduling approach is used. Furthermore, this work introduces a method for control parameter tuning. In order to validate the control parameter tuning, the adaptive control system is analyzed regarding robustness and performance using an exemplary medium voltage grid. In addition, the influence of uncertainties is examined. Finally, the operation of the adaptive control system is demonstrated by performing time-domain simulations.Comment: In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canad

Probabilistic load flow for uncertainty based grid operation

Traditional algorithms used in grid operation and planning only evaluate one deterministic state. Uncertainties introduced by the increasing utilization of renewable energy sources have to be dealt with when determining the operational state of a grid. From this perspective the probability of certain operational states and of possible bottlenecks is important information to support the grid operator or planner in their daily work. From this special need the field of application for Probabilistic Load Flow methods evolved. Uncertain influences like power plant outages, deviations from the forecasted injected wind power and load have to be considered by their corresponding probability. With the help of probability density functions an integrated consideration of the partly stochastic behaviour of power plants und loads is possible

Modeling and Contribution of Flexible Heating Systems for Transmission Grid Congestion Management

The large-scale integration of flexible heating systems in the European electricity market leads to a substantial increase of transportation requirements and consecutively grid congestions in the continental transmission grid. Novel model formulations for the grid-aware operation of both individual small-scale heat pumps and large-scale power-to-heat (PtH) units located in district heating networks are presented. The functionality of the models and the contribution of flexible heating systems for transmission grid congestion management is evaluated by running simulations for the target year 2035 for the German transmission grid. The findings show a decrease in annual conventional redispatch volumes and renewable energy sources (RES) curtailment resulting in cost savings of approximately 6 % through the integration of flexible heating systems in the grid congestion management scheme. The analysis suggests that especially large-scale PtH units in combination with thermal energy storages can contribute significantly to the alleviation of grid congestion and foster RES integration

Short-term load forecasting at electric vehicle charging sites using a multivariate multi-step long short-term memory

This study assesses the performance of a multivariate multi-step charging load prediction approach based on the long short-term memory (LSTM) and commercial charging data. The major contribution of this study is to provide a comparison of load prediction between various types of charging sites. Real charging data from shopping centres, residential, public, and workplace charging sites are gathered. Altogether, the data consists of 50,504 charging events measured at 37 different charging sites in Finland between January 2019 and January 2020. A forecast of the aggregated charging load is performed in 15-min resolution for each type of charging site. The second contribution of the work is the extended short-term forecast horizon. A multi-step prediction of either four (i.e., one hour) or 96 (i.e., 24 h) time steps is carried out, enabling a comparison of both horizons. The findings reveal that all charging sites exhibit distinct charging characteristics, which affects the forecasting accuracy and suggests a differentiated analysis of the different charging categories. Furthermore, the results indicate that the forecasting accuracy strongly correlates with the forecast horizon. The 4-time step prediction yields considerably superior results compared with the 96-time step forecast

Optimizing the Generation and Transmission Capacity of Offshore Wind Parks under Weather Uncertainty

Offshore wind power in the North Sea is considered a main pillar in Europe's future energy system. A key challenge lies in determining the optimal spatial capacity allocation of offshore wind parks in combination with the dimensioning and layout of the connecting high-voltage direct current grid infrastructure. To determine economically cost optimal configurations, we apply an integrated capacity and transmission expansion problem within a pan-European electricity market and transmission grid model with a high spatial and temporal granularity. By conducting scenario analysis for the year 2030 with a gradually increasing CO2 price, possible offshore expansion paths are derived and presented. Special emphasis is laid on the effects of weather uncertainty by incorporating data from 21 historical weather years in the analysis. Two key findings are (i) an expansion in addition to the existing offshore wind capacity of 0 GW (136 EUR/tCO2), 12 GW (159 EUR/tCO2) and 30 GW (186 EUR/tCO2) dependent on the underlying CO2 price. (ii) A strong sensitivity of the results towards the underlying weather data highlighting the importance of incorporating multiple weather years

Bottom-up self-organization of unpredictable demand and supply under decentralized power management

In the DEZENT1 project we had established a distributed base model for negotiating electric power from widely distributed (renewable) power sources on multiple levels in succession. Negotiation strategies would be intelligently adjusted by the agents, through (distributed) Reinforcement Learning procedures. The distribution of the negotiated power quantities (under distributed control as well) occurs such that the grid stability is guaranteed, under 0.5 sec. The major objective in this paper was to deal, on the same level of granularity, with short-term power balance fluctuation, in terms of a peak demand and supply management exhibiting highly dynamic, self-organizing, autonomous yet coordinated algorithms under fine-grained distributed control. Our extensive experiments show very clearly that these short-term fluctuations could be leveled down by 70 - 75 %. In this way we have tackled, for the quickly increasing renewable power systems, a crucial problem of its stability, in a novel way that scales very easily due to the completely decentralized control

Impact of ICT latency, data loss and data corruption on active distribution network control

The ongoing changes in modern power systems towards increasingly decentralized systems render the coordination of generation assets and the corresponding dependency on Information and Communication Technology highly relevant. This work demonstrates the impact of three types of ICT errors, namely delayed data, data loss and data corruption, on the control of distributed energy resources in an active distribution network. The settling time of the active power response at the interconnection point between the distribution and transmission system is investigated in the simulations. Additionally, two fallback strategies to mitigate the impact of data loss are proposed and evaluated with regard to their impact on the controller’s response. Finally, a generalized, aggregated service state description is proposed in order to capture the performance of the active distribution network service. It is meant to improve the interpretability of the results, which can be used to compare service designs and setups

Network-adaptive and capacity-efficient electric vehicle charging site

The adaptive charging algorithms of today divide the available charging capacity of a charging site between the electric vehicles without knowing how much current each vehicle draws in reality. Thus, they are not able to detect deviations between the current set point at the charging station and the real charging current. This leads to a situation where the charging capacity of the charging site is not used optimally. This paper presents an algorithm including a novel feature, Expected Characteristic Expectation and tested under realistic circumstances. It is demonstrated that the proposed algorithm enhances the adaptability of the charging site, increasing the efficiency of the used network capacity up to about 2 kWh per charging point per day in comparison with the previous benchmark algorithm. The algorithm is able to increase the average monetary benefits of the charging operators by up to around 5.8%, that is 0.6 € per charging point per day. No input, such as departure time, is required from the user. The proposed algorithm has been tested with real electric vehicles and charging stations and is compatible with the IEC 61851 charging standard. The charging algorithm is applicable in practice as it is described in this paper