The growing use of direct current (DC) load demands and distribution generations (DGs) has led to changes in the distribution network. Due to DC and alternating current (AC) loads and generators, switching to an AC/DC distribution network is an effective solution. In this paper, the AC/DC distribution network planning problem is discussed. Uncertainties in the load demand and power generation of renewable sources cause probabilistic behavior of the distribution network, which leads to risk in the network. Therefore, the technical risks related to the node voltage and the line loading constraints dominate the problem. By modeling the cost of damage due to technical risks, these risks have become economic risks. Using the conditional value at risk (CVaR) method, the economic risk assessment of planning has been addressed. In addition, the modeling of hard and soft constraints for technical constraints has been discussed. The K-means algorithm has been used to model the uncertainties in the problem. The goals of planning are: minimizing planning costs and reducing economic risk. The proposed mathematical model has been solved in MATLAB and general algebraic modeling system (GAMS) hybrid space using a non-dominated sorting genetic algorithm (NSGA-II). Numerical results are presented for a 13-node distribution network.

In this paper, the impact of residential photovoltaic (PV) battery systems in a real test system with the goal of system peak load shaving is investigated. In order to encourage residential investors, a Levelized feed-in tariff (LFiT) scheme is introduced. Accordingly, two proposed cases and relevant suggestions are presented to reach a better performance. The profitability of the project is apprised via several economic criteria such as net present value (NPV), payback period years (PBY), internal rate of return (IRR), benefit to cost ratio (BCR), net cash flow (NCF), and Levelized cost of energy (LCOE). Moreover, different levels of peak shaving subject to customers’ participation and the size of the PV-battery system are also obtained. An actual test system regarding one-year recorded data is employed to elevate the precise of results. Furthermore, results are economically compared with the current situation in the test system to assess the effectiveness of the suggested system. Obtained results determine the best size of PV and battery to attain the preferable profitability. Meanwhile, the LFiT with respect to the economic criteria is specified as well. Finally, a sensitivity analysis on systems’ parameters is performed to identify the impact of these parameters on economic indices.

With the increasing demand for electricity, power distribution utilities must provide an efficient and appropriate Service Restoration (SR) strategy to restore customers as soon as possible after power outages. The heuristic SR algorithm presented in this article is a bi-stage algorithm that initially re-energizes some loads quickly by remote-controlled switches in the first stage and then proceeds to restore the rest of the network in the second stage. In addition to solving the restoration problem in a bi-stage algorithm, determining the optimal Switching Sequences (SSs) and applying the Energy Not Supplied (EENS) as objective function included in this study. Also, taking into account the time of occurrence of the failure and the daily load curve, the position of the maintenance team, load transfer capability and the traffic conditions of the network has increased the attractiveness and practicality of the proposed method. The heuristic SR algorithm was applied on a standard IEEE 69-bus system in various scenarios. The results indicated a significant difference in the solutions of the problem and the ENS in different scenarios. Finally, it was found that using this heuristic method would lead to optimal, accurate, and applicable solutions for SR in distribution networks.