In the past decade, enhancing the reliability of distribution networks by means of optimal switch placement has attracted much attention. In the case of failures in a distribution feeder, such disconnect switches will isolate the faulted section, and the customers downstream of the faulted point can be supplied by neighboring feeders through tie lines. Nevertheless, such reserve branches not only might experience failures themselves but also may not even exist prior to the switch placement. Accordingly, this paper presents a mathematical-programming-based model for the concurrent placement of disconnect switches and tie lines in the distribution networks to enhance the service reliability, considering both practical benefits and drawbacks of such reserve branches. In the proposed model, installation of remote-controlled and manual switches at various locations of distribution feeders together with potential tie lines are considered. Also, practical operational constraints regarding the utilization of tie lines, and the impact of failures in such reserve branches on the reliability indices are meticulously modeled in the proposed formulation. Unreliability cost is estimated based on a reward-penalty scheme and the revenue lost due to the not supplied demand during the network contingencies. As an instance of mixed-integer linear programming, the proposed optimization model can be efficiently solved to the global optimality using commercially available software. Aiming at investigating the applicability of the proposed model, it is implemented on a test network, and the results are thoroughly analyzed through various case studies.

Highly stochastic loading in the emerging active distribution networks means that electric utilities need to use their assets to the fullest by deploying intelligent network management tools. Real-time thermal rating (RTTR) provides possibility for short term and even real-time active distribution network management enabling the network to run closer to an overload state without damage. In this study, a RTTR based active distribution network management framework is formulated giving hour-by-hour network capacity limits. Relationships of stochasticities in customer loads and DG output with thermal responses of underground cables, overhead lines and distribution transformers are explained. RTTR is applied on all distribution network components with simulated scenarios involving various levels of DG penetration. This study quantifies the potential for an increased DG utilization and an increased potential for new DG installations when RTTR is integrated with distribution management systems.Peer reviewe

The uptake of electric vehicles (EV) is increasing every year and will eventually replace the traditional transport system in the near future. This imminent increase is urging stakeholders to plan up-gradation in the electric power system infrastructure. However, for efficient planning to support an additional load, an accurate assessment of the electric vehicle load and power quality indices is required. Although several EV models to estimate the charging profile and additional electrical load are available, but they are not capable of providing a high-resolution evaluation of charging current, especially at a higher frequency. This paper presents a probabilistic approach capable of estimating the time-dependent charging and harmonic currents for the future EV load. The model is based on the detailed travel activities of the existing car owners reported in the travel survey. The probability distribution functions of departure time, distance, arrival time, and time span are calculated. The charging profiles are based on the measurements of several EVs.

Sectionalizing switches (SSs) and tie lines play essential roles in reducing the duration of customer interruptions in electricity distribution networks. The effectiveness of such assets is strongly influenced by their placement in the grid. Operation of SSs and tie lines is also inherently interdependent. Due to the structural complexities regarding the mathematical modeling of such dependencies, optimization of the planning and operation of switches and tie lines has typically required either leveraging heuristic and metaheuristic approaches or oversimplifying the network topology. To tackle such issues, this paper presents a computationally-efficient model for reliability-oriented concurrent switch and tie line placement in distribution networks with complex topologies. The proposed model can be applied to grids with several tie lines and laterals per feeder, and yields the optimal location of tie lines, type of tie switches, namely manual or remote-controlled, and the location and type of SSs. Being cast as a mixed integer linear programming (MILP) problem, the model can be efficiently solved with guaranteed convergence to global optimality using off-the-shelf optimization software. The efficiency and scalability of the proposed model are demonstrated through implementation on five networks and the outcomes are thoroughly discussed.

In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.

Installing sectionalizing switches and field circuit breakers (FCBs) is vital for the fast restoration of customer electricity supply in distribution systems. However, the high capital costs of these protection devices, especially remote-controlled switches (RCSs) and FCBs, necessitate finding a trade-off between their costs and financial benefits. In this study, we propose a mixed-integer linear programming (MILP) model for optimizing switch planning in distribution systems. The proposed model determines the optimal allocation of manual switches, RCSs, and FCBs to minimize the costs of switches and the reliability-oriented expenses. While the former includes the costs of installing and operating the switches, the latter consists of the distribution company’s lost revenue due to the undelivered energy and the regulatory incentives (or penalties) associated with service reliability indices. Two penalty-reward mechanisms are used to account for the financial benefits of increasing the service reliability through reducing the duration and frequency of interruptions. Proposing a novel reliability assessment model, we consider the possibility of malfunctions in both RCSs and FCBs in a highly efficient manner, which is a major contribution of this work. The proposed MILP model is applied to three test networks to validate its applicability and efficacy. The results show the importance of considering the possibility of switch malfunctions in distribution networks.