In increasingly digitalized and metered distribution networks, state estimation is generally recognized as a key enabler of advanced network management functionalities. However, despite decades of research, the real-life adoption of state estimation in distribution systems remains sporadic. This systematization of knowledge paper discusses the cause for this while comparing industrial and academic experiences and reviewing well- and less-established research directions. We argue that to make distribution system state estimation more practical and applicable in the field, new perspectives are needed. In particular, research should move away from conventional approaches and embrace generalized problem specifications and more comprehensive workflows. These, in turn, require algorithm advancements and more general mathematical formulations. We discuss lines of work to enable the delivery of tangible research.

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.

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.

Implementing state estimation in low and mediumvoltage power distribution is still challenging given the scale of many networks and the reliance of traditional methods on a large number of measurements. This paper proposes a method to improve voltage predictions in real-time by leveraging a limited set of real-time measurements. The method relies on Bayesian estimation formulated as a linear least squares estimation problem, which resembles the classical weighted least-squares (WLS) approach for scenarios where full network observability is not available. We build on recently developed linear approximations for unbalanced three-phase power flow to construct voltage predictions as a linear mapping of load predictions constructed with Gaussian processes. The estimation step to update the voltage forecasts in real-time is a linear computation allowing fast high-resolution state estimate updates. The uncertainty in forecasts can be determined a priori and smoothed a posteriori, making the method useful for both planning, operation and post-hoc analysis. The method outperforms conventional WLS and is applied to different test feeders and validated on a real test feeder with the utility Alliander in The Netherlands.

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.

Distribution Network Operators (DNOs) traditionally treat Low Voltage (LV) and Medium Voltage (MV) networks as two separate entities. Both voltage levels have their own set of assumptions and design policies. This paper proposes a fast load flow algorithm suitable to simulate both the LV and MV network in a single simulation. The algorithm is applied to the grid of Alliander DNO. Using this method, congestion problems in both the LV and MV grid can be determined with greater detail. Using a case study it was shown that identical customer load scenarios produce vastly differently results if the MV network is taken into account. While the absolute number of voltage problems was in the same order of magnitude, the location of these problems overlapped only 20\%. The lack of overlap has a severe implication, namely that searching for congestion by only simulating LV networks yields the wrong voltage problems. This conclusion calls for network design using integral MV/LV simulations.

State Estimation is an essential technique to provide observability in power systems. Traditionally developed for high-voltage transmission networks, state estimation requires equipping networks with many real-time sensors, which remains a challenge at the scale of distribution networks. This paper proposes a method to complement a limited set of real-time measurements with voltage predictions from forecast models. The method differs from the classical weighted least-squares approach, and instead relies on Bayesian estimation formulated as a linear least squares estimation problem. We integrate recently developed linear models for unbalanced 3-phase power flow to construct voltage predictions as a linear mapping of load predictions. The estimation step is a linear computation allowing high resolution state estimate updates, for instance by exploiting a small set of phasor measurement units. Uncertainties can be determined a priori and smoothed a posteriori, making the method useful for both planning, operation and post hoc analysis. The method is applied to an IEEE benchmark and on a real network testbed at the Dutch utility Alliander.

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 applies the distribution network reconfiguration problem to existing networks. The medium voltage distribution network of the Dutch DSO Alliander is operated using a radial topology. By optimizing this topology it is possible to reduce the energy losses caused by the cable impedances. Various solutions algorithms have been compared for this distribution network reconfiguration problem, while taking into account network capacity and voltage levels. A Genetic algorithm combined with a Greedy demeshing starting condition yields the best results. Applying the algorithm on real life distribution networks shows with 226 buses and 406 buses yield a reduction in power losses of 15% and 27% respectively.

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.

A novel method is proposed to interpret sensor data by applying a gas distribution network model based on the steady-state Weymouth equation. The proposed data interpretation method merges data gathered from sensors and models by comparing variances, so that more information is extracted than by applying the methods available in literature. The data interpretation method is subsequently used to define an objective function for finding the optimal sensor locations to minimize the uncertainty of the flows in a gas network. This method is then applied on the real high-pressure network of the island of Texel, for which gas consumption data is created using a Monte Carlo approach. The resulting optimization problem is solved by using a genetic and a greedy algorithm. The greedy algorithm performs best and yields a realistic set of sensor locations.

A gas network model has been constructed based on the steady-state Weymouth equation. A fast and robust solution algorithm is proposed and subsequently used to calculate all flows and pressures in a gas network with over 40,000 pipes. The obtained result is mathematically accurate within 0.1% and has been obtained within 0.2 seconds. Using basic physical principles, a simple gas consumption model has been constructed to feed the gas network model with necessary input data. A method has been developed for minimizing leaks in gas distribution networks by changing the network operating pressure. This strategy has the potential to reduce gas leakage with over 40% as concluded from two case studies.