In this paper, a new objective function (OF) within the genetic algorithm (GA) approach is presented to solve the optimization problem of coordination of overcurrent and distance relays. The existing objective function of overcurrent (O/C) relays coordination is improved and extended to include the coordination of O/C and distance relays. Various O/C relays characteristics are considered within the approach to select the best of them by GA to fulfill optimal coordination. The proposed method is applied to two different power system networks. Simulation results demonstrate that the method can obtain feasible and effective solutions for optimal coordination in the practical power system networks.

This paper conducts a new study on the coupled dynamic expansion problem of electric vehicle parking lots (EVPLs) placement and distribution networks. To reach this goal, at first, electric vehicles (EVs) driving and charging/discharging behavior as some influential factors are modeled using an efficient probabilistic algorithm. An analytical model is then introduced to estimate the number of EVs in EVPLs at different times. To find out the optimal place for installing EVPLs and reinforcement of distribution network, a multi-objective (MO) optimization model is formulated considering main requirements of EVs users, EVPLs owners and distribution system operator. A linear model is also derived for energy monument of EVs in EVPLs to be used in the mentioned MO expansion problem. Numerical results with various cases are presented to illustrate main advantages of proposed coupled expansion model.

Traditionally, capacitor placement and conductor replacement have been considered as independent solutions for loss reduction in distribution networks. However, these two methods have an inherent coupling relationship, especially at harmonic frequencies. This paper presents a joint optimization algorithm for both conductor replacement of overhead lines and capacitor placement to minimize energy losses in the presence of harmonics throughout the distribution system, such that both individual and total harmonic distortion of bus voltages are kept within the acceptable levels. All actual conditions including time varying nature of loads, network extension over the planning horizon, annual load growth and different load patterns are taken into account based on the realistic data. By applying the proposed method, the economic cost and energy losses are remarkably reduced compared to that could be achieved by applying these two methods separately in the presence of harmonics. The proposed method is applied on a realistic distribution network of Sirjan city-center in Iran.

The global transport sector has a significant share of greenhouse gas emissions. Thus, plug-in electric vehicles (PEVs) can play a vital role in the reduction of pollution. However, high penetration of PEVs can pose severe challenges to power systems, such as an increase in energy losses and a decrease in the transformers expected life. In this paper, a new day-ahead co-optimization algorithm is proposed to reduce the unwanted effects of PEVs on the power system. The aim of the proposed algorithm is minimizing the cost of energy losses as well as transformer operating cost by the management of active and reactive powers simultaneously. Moreover, the effect of harmonics, which are produced by the charger of PEVs, are considered in the proposed algorithm. Also, the transformer operating cost is obtained from a method that contains the purchase price, loading, and losses cost of the transformer. Another advantage of the proposed algorithm is that it can improve power quality parameters, e.g., voltage and power factor of the distribution network by managing the reactive power. Afterward, the proposed algorithm is applied to a real distribution network. The results show that the proposed algorithm optimizes the daily operating cost of the distribution network efficiently. Finally, the robustness of the proposed algorithm to the number and distribution of PEVs is verified by simulation results.

One of the major challenges in reliability evaluation of smart grid is the direct cyber‐power interdependencies (DCPIs) impacts. The less effort has been devoted in the literature to investigate the DCPIs impacts, particularly in stochastic simulating space. This study proposed a novel methodology to determine the risk assessment of smart grid due to DCPIs under various distributed generation (DG) technology scenarios. The presented method is applied to a realistic distribution grid. The several sensitivity studies are performed to investigate the pattern of DCPIs impacts as DG penetration level in various scenarios. The test results show that regardless of the DG technology scenario, the DCPIs impacts gradually increase while the DG penetration increases, and it saturates when the penetration exceeds a certain level. The numerical result implies that the scenarios having more effective performance from the reliability improvement perspective such as those consist of dispatchable DG units such as microturbine are more affected due to DCPIs.