Continuity in power supply for the consumers is a permanent concern from the utilities, pursued with the development of technological solutions in order to improve the performance of network restoration conditions. Using remotely controlled switches corresponds to one possible approach to reach such an improvement and gives some convenient remote resources, such as the fault detect, isolation, and transfer loads. This paper presents a methodology implemented in a computer programming language for allocation of these devices in electric distribution systems based on the analytic hierarchical process (AHP) method. The main contributions focus on considering the impact of installing remotely controlled switches in the reliability indices and the AHP decision-making algorithm for the switches allocation. The effectiveness of the proposed algorithm is demonstrated with case studies involving actual systems of the AES Sul utility located in Southern Brazil.

As there are different types of energy storage systems (ESS), it is important to have a tool that determines the best alternative to be installed in the distribution systems. This study presents a methodology for optimal determination of size, type and site of ESS in distribution systems with distributed generation (DG). The methodology uses a genetic algorithm and a power flow calculated by the Electric Power Research Institute software OPENDSS. For the optimal ESS charge and discharge management uses a non‐linear optimisation model that has as objective an energy cost and loss reduction. In addition, the methodology makes an energy management of the system by limiting the reverse flow with a load shifting process and a cost penalty. Solar and wind power are considered as DG modelled in Homer. Then, a genetic algorithm is used to select the size, type and site of the ESS, considering different lifecycles and costs. This methodology is tested in the IEEE 123 nodes system simulated and adapted in the OPENDSS and the formulation is implemented in MATLAB via COM interface. Finally, results of the methodology are presented and discussed.

This paper presents a heuristic reconfiguration algorithm, considering the locational economic signals of the tariffs for the use of the transmission system. Among other aspects, the economic signaling of locational tariffs encourages the rationalization of the use of transmission assets, as well as the reduction of operating costs. Therefore, the proposed algorithm is presented as an optimization problem with a focus on minimizing the transmission system usage. In order to validate the efficiency of the proposed reconfiguration algorithm, the IEEE 33 and 69 nodes test systems are evaluated. In addition, the algorithm is tested for different transmission cost allocation techniques and transmission system capacity scenarios. The results achieved demonstrate the systemic rationalization of the use of assets, which characterize the proposed algorithm as an important tool to avoid the increase in the share of energy transport in the tariff for the final consumer.