Optimal and simultaneous siting and sizing of distributed generators and capacitor banks in distribution systems have attracted a lot of attention from distribution companies. The placement and capacity of these devices have direct effects on the system’s performance. This paper presents a model for the simultaneous allocation of capacitor banks and distributed generation, which takes into account the stochastic nature of distributed generation. To solve the model presented, we propose an efficient hybrid method based on Tabu search and genetic algorithms. The hybrid method is applied to a well-known system in literature.

This study presents a multiobjective tabu search algorithm to solve the multistage planning problem of a distribution system formulated as a multiobjective dynamic mixed integer non‐linear programming problem. Multiobjective problems do not have a specific solution, but a set of solutions that allows us to observe the trade‐off among the analysed objectives. Taking into account this concept, the objective functions of the model proposed in this study are: costs (investment and operational) and reliability. The actions deemed in this model for each period of the planning horizon are: increase in the capacity of existing substations (or construction of new ones), exchange of cables in existing lines (and construction of new feeders), reconfiguration of the network, allocation of sectionalising switches and construction of tie lines. The system's reliability is evaluated by means of the non‐supplied energy under contingencies using the n − 1 criterion. By line switching and the use of tie lines, part of the loads affected by a contingency can be restored, thus, the non‐supplied energy can be evaluated by solving a distribution network restoration problem. Numerical results are presented for a 54‐bus system.

This paper presents a new methodology based on multiobjective optimization techniques to perform an optimized, coordinated, and selective allocation of control and protection devices in distribution networks with distributed generation (DG). The proposed mathematical model consists of two objective functions that consider economic issues and the network continuity index. Physical and operational constraints are taken into account, with emphasis on the set of constraints based on practical rules of distribution companies and international technical standards, which require the specification, coordination, and selectivity of the protection devices installed in the network. The possibility of load transfer from neighboring feeders and islanded DG operation is also considered. The proposed model is a mixed integer nonlinear programming, and we use NSGA-II (Nondominated Sorting Genetic Algorithm) to solve it. The proposed methodology is applied in a real 135-bus system found in the literature.

The new energy resources in distribution systems have led researchers to consider the operation of such elements during service restoration. Hence, the current state-of-the-art regarding the restoration problem considers the operation of distributed resources in both island and grid-connected modes. Few articles also address the system's restoration capacity changes throughout the time needed to repair the network after an outage, given the variations in the system's demand and availability of energy sources. However, the solutions proposed in the literature converge in proposing a single topology that is feasible throughout the entire repairing time. In this article, we propose addressing the mutable nature of the system's restoration capacity by splitting the repairing time into several time intervals and associating each of them with a specific configuration that minimizes the out-of-service load. Energy storage systems, inter-mittent and dispatchable generators, and voltage-control devices are also considered. The problem is formulated as a mixed-integer linear programming (MILP). The model and its scalability are tested using the 53-, 217-, and 404-node systems, considering different fault scenarios and reliability indices. The results show that the nonsupplied energy during the repairing time decreases when dynamic changes in the system topology are considered. Furthermore, the effects of storage systems operation and their integration with dynamic topology changes are highlighted.

This study proposes a new methodology to carry out distribution network planning considering medium‐voltage (MV) and low‐voltage (LV) systems. The integrated planning problem of a MV/LV distribution system is formulated as a mixed‐integer non‐linear program and aims at minimising investment (substations, transformers, conductors, poles, and structures), operating, and reliability costs. The coupling constraints between MV/LV networks are represented by MV/LV modelling transformers through the power flow equations. The mathematical model is solved by the general variable neighborhood search metaheuristic method, which allows us to explore the search space through intensification and diversification criteria. The proposed methodology is applied in a system with 50 buses at the MV level and 410 buses at the LV level.