The development of communications and telecommunications infrastructure, followed by the extension of a new generation of smart Distribution grids, has brought realtime control of Distribution Systems to electrical industry professionals’ attention. Also, the increasing use of distributed generation (DG) resources and the need for participation in the System voltage control, which is possible only with central control of the Distribution System, has increased the importance of the real-time operation of Distribution Systems. In real-time operation of a power System, what is important is that since the grid information is limited, the overall grid status such as the voltage phasor in the buses, current in branches, the values of loads, etc. are specified to the grid operators. This can occur with an Active Distribution System state estimation (ADSSE) method. The conventional method in the state estimation of an Active Distribution System is the weighted least squares (WLS) method. This paper presents a new method to modify the error modeling in the WLS method and improve the accuracy SVs estimations by including load variations (LVs) during measurement intervals, transmission time of data to the information collection center, and calculation time of the state variables (SVs), as well as by adjusting the variance in the smart meters (SM). The proposed method is tested on an IEEE 34-bus standard Distribution System, and the results are compared with the conventional method. The simulation results reveal that the proposed approach is robust and reduces the estimation error, thereby improving ADSSE accuracy compared with the conventional methods.

Today the application of Distributed Energy Resources (DERs) in Distribution System expansion planning (DNEP) problems is more crucial than before. Despite of advantages, the presence of the resources considering Renewable Energy Sources (RESs) and Dispatchable Generation (DG) units in the Distribution System expansion planning problems, brings more challenges, especially in reliability characteristics. This paper proposes a new Distribution network expansion planning model embedded with a novel reliability assessment approach for Active Distribution Networks (ADNs). The proposed method aims to determine the optimal location and capacity of the new generation and Distribution assets, responsible for providing power, in both the normal operation and contingency conditions. The load forecast significantly affects the results of the Distribution network expansion planning. The K-means clustering method is used to address the uncertainty of load growth in the planning horizon which is coordinated with a Mixed Integer Linear Programming (MILP) optimization model. The proposed model is applied to the IEEE 33 bus test case, to guarantee its technical and economical effectiveness. The results verify that this model is cost-effective and can increase the robustness of the Distribution network compared with recent similar works.

After extreme events such as floods, thunderstorms, blizzards and hurricanes there will be devastating effects in the Distribution networks which may cause a partial or complete blackout. Then, the major concern for the System operators is to restore the maximum critical loads as soon as possible by available generation units. In order to solve this problem, this paper provides a restoration strategy by using Distributed Generations (DGs). In this strategy, first, the shortest paths between DGs and critical loads are identified. Then, the best paths are determined by using a decision-making method, named PROMOTHEE-II to achieve the goals. The uncertainties for the output power of DGs are also considered in different scenarios. The IEEE 123-node Distribution network is used to show the performance of the suggested method. The simulation results clearly show the efficiency of the proposed strategy for critical loads restoration in Distribution networks.