A Coordinated Voltage Control for Overvoltage Mitigation in LV Distribution Grids

Abstract

The design of intelligent strategies for grid management is a cost-effective solution to increase the hosting capacity of distribution grids without investing in the reinforcement of the grid assets. This paper presents a distributed voltage control algorithm to coordinate Energy Storage Systems (ESSs) and Distributed Generation (DG) in a scenario of high renewable penetration. The proposed control algorithm relies on a dual decomposition approach and aims at mitigating possible voltage rise events occurring in the Low Voltage (LV) grid by solving an optimization problem of power minimization. Instead of using local control strategies, in the proposed solution, the voltage control burden is distributed among all the available resources in the grid, which cooperate to resolve the existing voltage violations. The performance of the developed voltage control has been tested under realistic distribution grid scenarios, using stochastic load profiles together with photovoltaic generation profiles obtained in the presence of both clear sky and cloudy sky conditions. The algorithm is also compared to a strategy that considers only DG management, highlighting the benefits associated to the proposed coordination of DG and Energy Storage Systems (ESSs).

A Sensitivity Matrix Approach Using Two-Stage Optimization for Voltage Regulation of LV Networks with High PV Penetration

“…In order to find the optimum solution to the centralized control method in mitigating voltage violations, many optimization techniques have been researched. Among these, SQP [52], Nonlinear Programming (NLP) [62,63], the Evolutionary Algorithm [64], Lagrangian multipliers [65], the Multi-Objective Evolutionary Algorithm (MOEA) [66] and Particle Swarm Optimization (PSO) [55,67] have been widely used. In order to act as a viable near-real-time system, the accuracy and the computational time of the algorithm play a key role.…”

Analysis of a Multi-Timescale Framework for the Voltage Control of Active Distribution Grids

“…This section introduces the equations that are used throughout the paper to model the distribution grid and the impact of the power injections on the voltage profiles of the nodes. The final equation of the model is shown in Equation (1), which describes the impact of distributed power injections on the voltage profile of the grid in time, which was extensively analyzed in De Din et al and Farivar et al [23,30], and it can be linearly approximated around the slack bus voltage under the assumptions of negligible shunt impedances and voltage magnitudes near nominal. The resulting equation is described as:…”

“…where V is the N BUS × 1 vector of voltage magnitudes for the N BUS grid nodes; 1 is an N BUS × 1 vector of ones; V 0 is the voltage magnitude at the slack bus; p c and q c are the N BUS × 1 vectors of active and reactive power consumption at the nodes (defined as positive), respectively; p g and q g are the N BUS × 1 vectors of active and reactive power generation at the nodes (defined as positive), respectively; and R and X are the real and imaginary parts of the N BUS × N BUS impedance matrix of the grid [23]. Equation (1) shows that the voltage magnitude at each node of the grid differs from the voltage V 0 at the slack bus due to the existing power consumptions and generations, which are weighted according to the resistance and reactance terms in the grid impedance matrix that are responsible for the resulting voltage drops.…”

“…On the other hand, however, this prevents considering the future horizon and generally does not allow obtaining an optimal usage of the resources over time. As demonstrated in De Din et al [23], the ESSs could be used for implementing an online feedback-based voltage control without having a pre-defined schedule. However, the control solves short-term voltage violations without taking into account how the set-points can eventually affect the SoC throughout the day.…”

A Virtual Tool for Load Flow Analysis in a Micro-Grid

“…However, such solutions can be unsuitable for small island micro-grids, because power lines are short and/or the intrinsic costs of such instrumentation are high. To reduce the installation costs, some authors propose to use a few measurement points and to integrate them with load estimations [20][21][22][23][24][25][26][27]; however, when dealing with load estimations (or pseudo-measurements), higher uncertainty levels are generally expected and more sophisticated algorithms can be needed for the distribution system state's estimation, which also may entail higher computational costs. The integration of differently distributed measurement solutions have also been investigated, for example, considering the possibility of smart meter and power quality meter exploitation or SCADA-and PMU-enhanced integration, for a number of applications (load forecasting, optimization, demand side management, fault detection and so on) [28][29][30][31][32][33][34][35][36][37][38][39][40].…”