Large solar photovoltaic (PV) penetration using inverters in low voltage (LV) distribution networks may pose several challenges, such as reverse power flow and voltage rise situations. These challenges will eventually force grid operators to carry out grid reinforcement to ensure continued safe and reliable operations. However, smart inverters with reactive power control capability enable PV systems to support voltage quality in the distribution network better. This paper gives an overview of the current state-of-the-art control strategies for handling voltage problems through PV inverters and other devices. In addition, the (control) technical issues of PV systems integrated into the LV distribution network are considered from a control point of view. By comparing the control issues of PV integration into the grid, the paper aims to help distribution system operators to expand the volume of PV generation in the distribution system in an efficient and safe manner. Additionally, it will help control engineers and researchers select proper control strategies for PV systems as well as other distributed renewable sources.

In this paper, a model-based predictive control method is proposed for utilization of flexible resources such as battery energy storage systems and heating systems effectively to provide demand response in low-voltage distribution networks with solar PV. The contributions of this paper are twofold. Firstly, a linear power flow method based on relaxation of branch power losses applicable to radial distribution networks is proposed and formulated. Secondly, a flexible resources controller which solves a multi-objective linear optimization problem in recedinghorizon fashion is formulated taking into account system states, forecasts of generation and loads. Using the proposed control algorithm, flexibility from network resources can be utilized for low-voltage network management with assurance of quality of service to the customers. Simulations are conducted for summer and winter cases on a simplified Danish low-voltage network using Matlab/Simulink to study the performance of the proposed control method. Compared to the methods in state of the art, the proposed linear power flow method is proven to be accurate for the calculation of network power flows. Simulation results also show that proposed flexible resources controller can meet the network control objectives while satisfying the network constraints and operation limits of the flexible resources.

Data integration from heterogeneous data sources in low-voltage (LV) power distribution grids will be valuable to distribution system operators (DSOs). The power measurements from customer premises need to be processed with other data such as grid topology, line parameters etc., to deploy smart grid applications (SGA) such as real-time grid monitoring and voltage regulation. The most challenging task for DSOs is to collect and integrate data from several sources as several entities are involved in the data management and access to databases are restricted. This paper presents an op E n common information ${M}$ odel (CIM) BA sed sma ${R}$ t grid application framewor ${K}$ ( EMBARK ) to address the above-mentioned challenge. EMBARK is developed to be an efficient, modular and scalable architecture for extracting relevant grid related information from various asset management databases. A novel data management functionality is a part of EMBARK that enables data-driven update of settings and parameters of the algorithms behind smart grid applications. The proposed approach is demonstrated and numerically validated using grid data from a medium-sized distribution grid operator in Denmark. The architecture developed and presented in this paper can support all the phases from planning to the actual smart grid operation, i.e., automatically building the models to perform load flows, grid impact studies, planning, asset management etc.

The installation of smart meters at customer premises provides opportunities for the monitoring of distribution grids. This paper addresses the problem of improving the observability of low-voltage distribution grids using smart metering infrastructure. In particular, this paper deals with the application of state estimation algorithm using smart meter measurements for near-real-time monitoring of low-voltage distribution grids. This application is proposed to use a nonlinear weighted least squares method-based algorithm for estimating the node voltages from minimum number of smart meter measurements. This paper mainly deals with sensitivity analysis of the state estimation algorithm with respect to multiple uncertainties for, e.g., measurements errors, line parameter errors, and pseudo-measurements. Simulation studies are conducted to estimate the accuracy of the DSSE under various operating scenarios of a real-life low-voltage grid, and cost-effective ways to improve the accuracy of the state estimation algorithm are also evaluated. The paper concludes that by using smart meter measurements from few locations, voltage profiles of the low-voltage grid can be estimated with reasonable accuracy in near-real-time.

This paper reviews and analyzes the existing voltage control methods of distributed solar PV inverters to improve the voltage regulation and thereby the hosting capacity of a low-voltage distribution network. A novel coordinated voltage control method is proposed based on voltage sensitivity analysis. The proposed method is simple for computation and does not require closed loop communication if the network infrastructure does not change. This method is suitable for a hierarchical control structure where a supervisory controller has the provision to adapt the settings of local PV inverter controllers for overall system optimization. In the proposed method, the voltage bands and droop settings of PV inverters are calculated by the supervisory control, assuming equal power rating of inverters at each node. The local controllers of each PV inverter implement the volt/var control and if necessary, the active power curtailment based on measured local voltages and the predetermined settings calculated by the supervisory control. Advantage of the proposed method is that the calculated reactive power and active power droop settings enable fair contribution of PV inverters at each node to the voltage regulation. Simulation studies are conducted using DigSilent Power factory software on a simplified form of the benchmark LV network provided by CIGRE. The simulation results show that the proposed control method is effective for overvoltage prevention compared to the conventional volt/var method, which uses identical droop settings for all the PV inverters.

Future distribution grids will be subjected to fluctuations in voltages and power flows due to the presence of renewable sources with intermittent power generation. The advanced smart metering infrastructure (AMI) enables the distribution system operators (DSOs) to measure and analyze electrical quantities such as voltages, currents and power at each customer connection point. Various smart grid applications can make use of the AMI data either in offline or close to real-time mode to assess the grid voltage conditions and estimate losses in the lines/cables. The outputs of these applications can enable DSOs to take corrective action and make a proper plan for grid upgrades. In this paper, the process of development and deployment of applications for improving the observability of distributions grids is described, which consists of the novel deployment framework that encompasses the proposition of data collection, communication to the servers, data storage, and data visualization. This paper discussed the development of two observability applications for grid monitoring and loss calculation, their validation in a laboratory setup, and their field deployment. A representative distribution grid in Denmark is chosen for the study using an OPAL-RT real-time simulator. The results of the experimental studies show that the proposed applications have high accuracy in estimating grid voltage magnitudes and active energy losses. Further, the field deployment of the applications prove that DSOs can gain insightful information about their grids and use them for planning purposes.