Achieving the Dispatchability of Distribution Feeders through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage

We propose and experimentally validate a control strategy to dispatch the operation of a distribution feeder interfacing heterogeneous prosumers by using a grid-connected battery energy storage system (BESS) as a controllable element coupled with a minimally invasive monitoring infrastructure. It consists in a two-stage procedure: day-ahead dispatch planning, where the feeder 5-minute average power consumption trajectory for the next day of operation (called \emph{dispatch plan}) is determined, and intra-day/real-time operation, where the mismatch with respect to the \emph{dispatch plan} is corrected by applying receding horizon model predictive control (MPC) to decide the BESS charging/discharging profile while accounting for operational constraints. The consumption forecast necessary to compute the \emph{dispatch plan} and the battery model for the MPC algorithm are built by applying adaptive data driven methodologies. The discussed control framework currently operates on a daily basis to dispatch the operation of a 20~kV feeder of the EPFL university campus using a 750~kW/500~kWh lithium titanate BESS.Comment: Submitted for publication, 201

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

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

Modeling and control of double star induction machine by active disturbance rejection control

This paper aims to contribute to the modeling and control of the double star induction machine (DSIM) by a robust method called active disturbance rejection control (ADRC). The ADRC has become in the last decade one of the most important techniques of regulation. This method is based on the use of an ESO (Extended State Observer) which estimates in real-time and at the same time the external disturbances and the errors due to the variations of the parameters of the machine and to the uncertainties of modeling. The two stators of DSIM are powered by three-phase inverters based on transistors and MLI control and the entire system is modeled in Park's reference. We analyze in the Matlab/Simulink environment the dynamic behavior of the system and the different ADRC controllers under different operating conditions. The result has demonstrated the performance and effectiveness of the ADRC

Algorithmes de dénombrement d'extensions linéaires d'un ordre partiel et application aux problèmes d'ordonnancement disjonctif

RÉSUMÉ En programmation par contraintes, une contrainte de ressource unaire est un ensemble de permutations valides des activités chacune avec une fenêtre de temps et une durée. Cette contrainte est généralisée si on considère des préséances entre activités données sous la forme d’un ensemble partiellement ordonné. Un problème d’ordonnancement disjonctif peut être modélisé par une ou plusieurs contraintes de ressource unaire auxquelles s’ajoutent des contraintes supplémentaires telles que des disjonctions entre activités de différentes ressources ou des contraintes de séquences. La recherche d’une solution au problème se fait par une série de décisions de la position relative d’une paire d’activités associées à une contrainte dont l’ordre n’est pas encore connu. L’algorithme utilisé dans le choix de la paire ainsi que la position relative est appelé heuristique de branchement. Dans le contexte de l’heuristique maxSD, il s’agit de calculer les densités de solutions de toutes les assignations de paires d’activités à un ordre et ensuite de brancher sur celle de densité maximum. Pour adapter cette heuristique aux problèmes d’ordonnancement avec contraintes de ressource unaire, on considérera les densités de permutations dans lesquelles une activité est placée avant l’autre dans l’ordre partiel associé à chaque contrainte. Pour ce faire, on propose deux algorithmes exact et heuristique pour le calcul des densités de permutations dans un ensemble partiellement ordonné. Ces algorithmes sont utilisés dans l’heuristique de branchement pour résoudre la version de satisfaction de contraintes du problème Job-Shop, un cas typique d’ordonnancement avec ressources unaires.----------ABSTRACT In constraint programming a unary resource constraint is a set of valid permutations of activities each with a time window and a duration. This constraint is generalized if we consider precedence constraints between activities given by a partially ordered set. A disjunctive scheduling problem can be stated as a combination of one or more such constraints for which some additional constraints such as disjunction or sequence of activities on different resources may be added. In this model, a solution is found by a series of decisions on the relative po- sition of a pair of activities on a same resource and for which the order is unknown. The algorithm used to select the pair and the order is called a branching heuristic. In the context of maxSD, densities of all assignments of pairs and order are computed and the assignment of maximum density is selected. In order to adapt this heuristic for scheduling problems with unary resources, we will consider the permutations of the partial order in which the rank of an activity is superior to another. For that, we propose exact and heuristic algorithms that compute the density of permutations in a partially ordered set. These algorithms are then used in branching to solve the constraint satisfaction version of the Job-Shop scheduling problem, a typical use case of scheduling with unary resource constraints