A Multi-Step Reconfiguration Model for Active Distribution Network Restoration Integrating DG Start-Up Sequences

The ever-increasing penetration of Distributed Generators (DGs) in distribution networks suggests to enable their potentials in better fulfilling the restoration objective. The objective of the restoration problem is to resupply the maximum energy of loads considering their priorities using minimum switching operations. Basically, it is desired to provide a unique configuration that is valid regarding the load and generation profiles along the entire restorative period. However, this unique configuration may not satisfy at the same time: I) the DG start-up requirements at the beginning of the restoration plan and II) the topological conditions that would allow the DG to provide later on the most efficient support for the supply of loads. Therefore, it is proposed in this paper to allow a limited number of reconfiguration steps according to the DG start-up requirements. In addition, this paper presents a novel formulation for the reconfiguration problem that accounts for partial restoration scenarios where the whole unsupplied area cannot be restored. The decision variables of the proposed multi-step restoration problem are: I) the line switching actions at each step of the reconfiguration process, II) the load switching actions during the whole restorative period and, III) the active/reactive power dispatch of DGs during the whole restorative period. A relaxed AC power flow formulation is integrated to the optimization problem in order to ensure the feasibility of the solution concerning the operational safety constraints. The overall model is formulated in terms of a mixed-integer second-order cone programming. Two simulation scenarios are studied in order to illustrate different features of the proposed strategy and to demonstrate its effectiveness particularly in the case of large-scale outages in distribution networks

A Novel Decomposition Solution Approach for the Restoration Problem in Distribution Networks

The distribution network restoration problem is by nature a mixed integer and non-linear optimization problem due to the switching decisions and Optimal Power Flow (OPF) constraints, respectively. The link between these two parts involves logical implications modelled through big-M coefficients. The presence of these coefficients makes the relaxation of the mixed-integer problem using branch-and-bound method very poor in terms of computation burden. Moreover, this link inhibits the use of classical Benders algorithm in decomposing the problem because the resulting cuts will still depend on the big-M coefficients. In this paper, a novel decomposition approach is proposed for the restoration problem named Modified Combinatorial Benders (MCB). In this regard, the reconfiguration problem and the OPF problem are decomposed into master and sub problems, which are solved through successive iterations. In the case of a large outage area, the numerical results show that the MCB provides, within a short time (after a few iterations), a restoration solution with a quality that is close to the proven optimality when it can be exhibited

Analytical Approach for Active Distribution Network Restoration Including Optimal Voltage Regulation

The ever increasing utilization of sensitive loads in the industrial, commercial and residential areas in distribution networks requires enhanced reliability and quality of supply. This can be achieved thanks to self healing features of smart grids that already include the control technologies necessary for the restoration strategy in case of a fault. In this paper, an analytical and global optimization model is proposed for the restoration problem. A novel mathematical formulation is presented for the reconfiguration problem reducing the number of required binary variables while covering more practical scenarios compared to the existing models. The considered self healing actions besides the network reconfiguration are the nodal load rejection, the tap setting modification of voltage regulation devices (incl. OLTCs, SVR, and CBs), and the active or reactive power dispatch of DGs. The voltage dependency of loads is also considered. Thus, the proposed optimization problem determines the most efficient restoration plan minimizing the number of deenergized nodes with the minimum number of self healing actions. The problem is formulated as a Mixed Integer Second Order Cone Programming (MISOCP) and solved using the Gurobi solver via the MATLAB interface YALMIP. A real 83 node distribution network is used to test and verify the presented methodology

Distribution Network Restoration in a Multiagent Framework Using a Convex OPF Model

The ever-increasing requirement for reliability and quality of supply suggests to enable the self-healing features of modern distribution networks employing the intelligent measurement, communication, and control facilities of smart grids. In this paper, the concept of multiagent automation in smart grids is applied to build a self-healing framework to be used for restoration service. In this regard, an agent interaction mechanism is designed to build a reduced model of only those parts of the network that could participate in the restoration process. This reduced model is subject to a global optimization method, aiming at restoring a maximum of loads with minimum switching operations. This optimization problem, including power flow constraints is formulated as a convex second-order cone programming and solved using GUROBI solver. The proposed multi-agent systems-based strategy is completely scalable and leads to a global optimum solution (up to the desired accuracy) in a short time, without the need for powerful processors. The simulation studies are carried out on a 70-bus distribution network in case of multiple fault scenarios, using MATLAB/Yalmip toolbox

Analytical Approach for Active Distribution Network Restoration Including Optimal Voltage Regulation

The ever-increasing utilization of sensitive loads in the industrial, commercial, and residential areas in distribution networks requires enhanced reliability and quality of supply. This can be achieved, thanks to self-healing features of smart grids that already include the control technologies necessary for the restoration strategy in case of a fault. In this paper, an analytical and global optimization model is proposed for the restoration problem. A novel mathematical formulation is presented for the reconfiguration problem reducing the number of required binary variables while covering more practical scenarios compared to the existing models. The considered self-healing actions besides the network reconfiguration are the nodal load-rejection, the tap setting modification of voltage regulation devices (incl. OLTCs, SVR, and CBs), and the active/reactive power dispatch of DGs. The voltage dependency of loads is also considered. Thus, the proposed optimization problem determines the most efficient restoration plan minimizing the number of de-energized nodes with the minimum number of self-healing actions. The problem is formulated as a Mixed-Integer Second Order Cone Programming (MISOCP) and solved using the Gurobi solver via the MATLAB interface YALMIP. A real 83-node distribution network is used to test and verify the presented methodology