The energy transition poses a challenge for the electricity distribution network design as new energy technologies cause increasing and uncertain network loads. Traditional static load models cannot cope with the stochastic nature of this new technology adoption. Furthermore, traditional nonlinear power methods have difficulty evaluating very large networks with millions of cables, because they are computationally expensive. This paper proposes a method which uses copulas for modeling the uncertainty of technology adoption and load profiles, and combines it with a fast linear load flow model. The copulas are able to accurately model the stochastic behavior of solar irradiance, load measurements, and mobility data, converting them into electricity load profiles. The linear load flow model has better scalability and stability compared to traditional load flow models. The models are applied to a case study which uses a real-world dataset consisting of a realistic technology adoption scenario and a low-voltage network with millions of cables, which considers both voltage and current problems. Results show that risk profiles can be generated for all cables in the network, resulting in a valuable map for the district network operator as to where to focus their efforts.

By installing a battery storage system in the power grid, Distribution Network Operators (DNOs) can solve congestion problems caused by decentralized renewable generation. This paper provides the necessary theory to use such a community battery for grid congestion reduction, backed up by experimental results. A simple network model was constructed by linearizing the load flow equations using a constant impedance load model. Using this model, an accurate estimate of voltage and overload problems is fed into a receding horizon charge path optimizer. The charge path optimization problem is posed as a linear problem and subsequently solved by an LP solver. The algorithms have been applied and validated on a real-world community battery installation. It was found that the voltages and currents can be controlled to a great degree, increasing the grid capacity significantly. The proposed control framework can be used to safeguard network constraints and is compatible with other battery control goals, such as energy trading or energy independence. Network design formulas are described with which a DNO can quickly estimate the potential (de) stabilization of a community battery on the steady-state voltages and currents in the grid.

In this paper, we propose a fast linear power flow method using a constant impedance load model to simulate both the entire Low Voltage (LV) and Medium Voltage (MV) networks in a single simulation. Accuracy and efficiency of this linear approach are validated by comparing it with the Newton power flow algorithm and a commercial network design tool Vision on various distribution networks including real network data. Results show that our method can be as accurate as classical Nonlinear Power Flow (NPF) methods using a constant power load model and additionally, it is much faster than NPF computations. In our research, it is shown that voltage problems can be identified more efficiently when MV and LV are integrally evaluated. Moreover, Numerical Analysis (NA) techniques are applied to the Large Linear Power Flow (LLPF) problem with 27 million nonzeros in order to improve the computation time by studying the properties of the linear system. Finally, the original computation times of LLPF problems with real and complex components are reduced by 2.8 times and 5.7 times, respectively.

Since the volatility of the power load is expected to keep increasing due to new energy technologies, modelling the stochastic properties of the power loads becomes increasingly important for distribution network operators. Due to limited measurements in these grids, often bottom-up methods are used to create load estimations with which the peak load of the power customers is calculated. However, in average electricity consumption profiles, as used in most bottom-up methods, the stochastic behaviour of the customers energy consumption is neglected. In this study, the effect of neglecting the stochastic behaviour is investigated and is shown to be particularly strong in situations with a low number of consumers. To cope with this problem, several efficient methods to quantify the uncertainty and to determine peak loads have been evaluated. These methods were applied and validated on a data set with nearly thousand consumer measurement series, measured over 3.5 years on a 15 min resolution. In low-voltage networks with <10 power consumers, the conventional methods are shown to be at least a factor 2 too low. The suggested 'individual rescaling method' is accurate within 10%.

This paper assesses the power capacity overload problem caused by new energy techniques on a large portion of the Alliander power distribution grid. Over 15,000 km of power cables and 7,000 transformers have been evaluated as part of this study. Many data sources have been combined to construct several detailed energy scenarios for predicting the energy demand in 2030. These scenarios have been converted into power load time series with a 15 minute interval. Using these detailed load profiles various emergent energy management techniques, such as power storage and load control, have been assessed for their reducing the impact of peak loads. In the worst case scenario in 2030 only 6% of the total number of transformers is overloaded. While the energy management techniques can significantly reduce these overload problems, the financial benefits of applying these techniques to reduce overload problems the next decade are limited.