IEEE 123-node Test System Research Articles

Reliability Studies of Distribution Systems Integrated With Electric Vehicles Under Battery-Exchange Mode

Although the concept of vehicle-to-grid (V2G) has explicitly shown its benefits in improving grid efficiency, it has not yet been furnished with effective evaluation methods in power system studies. This paper aims to propose an analytical approach by which the reliability impacts of electric vehicles (EVs) in the distribution level can effectively be assessed. The studies are done for EVs integration under the battery-exchange (BE) operating mode. In this regard, at first, different behaviors of EV users under BE mode are extracted based on refueling patterns of conventional vehicles. Afterwards, an algorithm is presented to optimally break down the period of studies into a few time intervals during which fluctuations of the distribution system load can be neglected. Subsequently for each time interval, probabilistic model of available energy in batteries of BE station is extracted taking into account the battery-exchange patterns of EV users. Convolving the load model of system and available energy in batteries of BE station, calculation procedure of some commonly used reliability indices is introduced. A comprehensive study is also presented through implementing the proposed approach on the IEEE 34-node test system. The obtained results demonstrate the efficiency of the proposed method in extracting the main benefits of EVs with the goal of enhancing the reliability performance of distribution systems.

An Outage Risk Oriented Dynamic Distribution Network Reconfiguration Methodology Considering the Effects of Weather Conditions on Power Line Failure Rate

A dynamic network reconfiguration methodology considering time-varying weather conditions is proposed. The aim of the proposed methodology is to minimize outage risk. To fully reflect actual weather severity, a classification method for weather states based on weather variables analysis is presented. Considering time-varying nature of weather, an approach to determine the optimal time for reconfiguration by comparing reliability change between adjacent intervals is provided. Then, applying the Quantum Genetic Algorithm to solve reconfiguration optimization problem is introduced. Simulation results on the IEEE 33-node test systems show that the proposed dynamic reconfiguration method can reduce distribution network's outage risk. In addition, weather conditions have impacts on outage risk of distribution network and should be considered in reconfirmation problems.

Multi-objective service restoration of distribution systems using user-centered methodology

A multi-objective problem formulation of service restoration and a three-stage methodology tailored to user-centered service restoration for large-scale, unbalanced distribution systems are presented. One feature is that the experience and engineering judgment of operators is integrated into the three-stage methodology. Since solutions suggested by a method need to be compromised with engineering judgment, this methodology involves system users (at Stage II), meta-heuristics (at Stage I), and local heuristics in combination with restoration strategies (at Stage III) for efficient and robust performance in service restoration. An adaptive K-means method is incorporated in the proposed group-based PSO (Stage I) for global search. It also assists users in their decision making. Evaluation results of the proposed user-centered methodology on the IEEE 123-node test system and a 1101-node practical unbalanced network show its effectiveness and flexibility in restoring service after faults.

Multiobjective Optimization Algorithm for Switch Placement in Radial Power Distribution Networks

Customer satisfaction counts very much to the electric power distribution companies. The improvement of reliability indices is an effective way to achieve this goal. The optimal placement of power switches is a desirable procedure because while it reduces investment in network assets, it also reduces the number of customers not supplied by outages and, accordingly, improves system reliability. This paper proposes a multiobjective optimization approach for switch placement based on the particle swarm optimization method applied to radial distribution networks of power utilities. The proposed algorithm gives a set of solutions instead of a single one. It intends to be a valuable and flexible tool for planning modern and reliable power distribution networks. Simulation results when using the IEEE 123-node test system and the Roy Billinton Test System are presented as testbeds.

Optimized Probabilistic PHEVs Demand Management in the Context of Energy Hubs

This paper develops a new framework to coordinate the charging process of plug-in hybrid electric vehicles (PHEVs) in the context of energy hubs. Attempts are focused not only on the impact evaluation of PHEVs on the system technical performance, but also customers' preferences on the charging patterns. PHEVs coordinator agent (PCA) is proposed to run a multiobjective optimization (MO) framework for the optimal charging patterns of PHEVs from the vehicle owners' and system operator's viewpoint. Wind energy utilization for PHEVs charging, energy costs at the hub input layer, and customers' convenience are all taken into account as the optimization objectives. Due to its promising performance in dealing with MO problems, the multiobjective particle swarm optimization method is employed to evaluate the optimization problem. The 2-point estimation method is used to model the existence uncertainties. The proposed framework is applied to the modified IEEE 34-node test system, and the obtained results demonstrate the efficiency and applicability of the proposed approach in real-world scenarios.

Optimal power flow for distribution networks using gravitational search algorithm

This paper presents a gravitational search algorithm (GSA)-based approach to solve the optimal power flow (OPF) problem in a distribution network with distributed generation (DG) units. The OPF problem is formulated as a nonlinear optimization problem with equality and inequality constraints, where optimal control settings in case of fuel cost minimization of DG units, power loss minimization in the distribution network, and finally simultaneous minimization of the fuel cost and power loss are obtained. The proposed approach is tested on an 11-node test system and on a modified IEEE 34-node test system. Simulation results obtained from the proposed GSA approach are compared with that obtained using a genetic algorithm approach. The results show the effectiveness and robustness of the proposed GSA approach.

Point estimate method for probabilistic load flow of an unbalanced power distribution system with correlated wind and solar sources

Context: The electric parameters of the power networks are usually analysed through deterministic power flows; however, the variation in load demands and power fluctuation of renewable generators cannot be considered with the deterministic power flows because it uses specific power values. The probabilistic power flow methods are better for this purpose since they apply techniques to include and reflect the uncertainty of input variables on the results obtained.Objective: This paper extends the Point Estimate Method (PEM) applied to the probabilistic power flow of an unbalanced power distribution system with dispersed generation and variable power factors. This method is applied to include uncertainties of loads and power sources such as wind and solar. As PEM requires independent input random variables, but usually there is spatial correlation between loads or power sources; therefore, Cholesky decomposition is applied to deal with this situation.Method: In this paper are combined the scheme 2m+1 of the Point Estimate Method with the Cholesky decomposition and some approximation methodologies to estimate the cumulative distribution function of some electrical parameters.Results: The results obtained are the moments about the mean of the output variables, which are used in conjunction with some approximation methodologies to obtain an estimation of the Cumulative Distribution Function for nodes or branch parameters. The proposed methodology is tested on the three-phase unbalanced IEEE 123-node test system, and results are compared with those obtained from the benchmark Monte Carlo simulation.Conclusions: There are comments on some pertinent information about Point Estimate Method performance on this kind of power systems.