Energy Infrastructures are perceived continuously vulnerable to a range of high-impact low-probability (HILP) incidents-e.g., earthquakes, tsunamis, floods, windstorms, etc.-the resilience to which is high on demand. Specifically suited to battery energy storage system (BESS) solutions, this paper presents a new resilience-driven algorithm for hardening power distribution systems against earthquakes. The concept of fragility curve is applied to characterize an earthquake hazard, assess its impact on power distribution systems, and estimate the unavailability of the network elements when exposed to extreme earthquakes. A new metric is defined to quantify the network resilience taking into account the uncertain nature of such HILP events. A linear programming (LP) optimization problem is formulated to determine the capacity and location of the BESSs for enhanced resilience against earthquakes. Efficacy of the proposed framework is numerically analyzed and verified through application to a real-world distribution power grid.

This paper develops a general framework for reliability assessment of multi-microgrid (MMG) distribution systems. It also investigates reliability impacts of coordinated outage management strategies in a MMG distribution network. According to the proposed reliability evaluation framework, which is based on sequential Monte Carlo simulation method, distribution system is divided into smaller sections/microgrids based on protection system configuration and operating measures are efficiently simulated considering different operation modes. In order to demonstrate the role of outage management strategy in reliability performance of MMG distribution systems, at first, the required features of an outage management strategy are identified. Then, suitable centralized and hierarchical schemes are introduced for operation of such systems during outage events. The proposed schemes, which are based on model predictive control approach, minimize total load curtailments in the system. Moreover, they are flexible and can effectively deal with multiple contingencies as well as uncertainties of outage duration. The developed reliability assessment framework is applied to a test system and performance of the presented outage management schemes are explored through extensive case studies. Obtained results suggest that implementation of an appropriate coordinated scheme is crucial to reliable operation of MMG distribution systems.

With the rapid advancement of phasor measurement units (PMUs) technology, system operators in different level of power systems have access to new and abundant measurements. Taking into account these measurements in active distribution systems (ADNs), a new algorithm for short-circuit fault detection and identification based on state estimation (SE) is introduced in this paper. In this regard, as the first step, traditional SE process is revised to be compatible with fault conditions. Then, a fault location algorithm (FLA) based on the revised SE (RDSSE) is presented which attends to detect the location of fault after diagnosing faulted zone. For this purpose, current and voltage synchrophasors captured by PMUs as well as pre-fault SE results are used and according to calculated measurement residual indexes, the correct location of fault is diagnosed. The performance of RDSSE and SE based fault location method are tested by applying on an ADN, considering different fault scenarios in the network. The results proved that the proposed method is more accurate and reliable than traditional SE based methods in fault conditions and can precisely determine the real location of fault at lower SE execution times.

The reliable design, planning, and operation of power systems are of paramount importance for providing reliable services to customers. This article reviews the different aspects of power system reliability, ranging from planning to operation. Standard benchmarks employed for power system studies are reviewed according to nearly 2,500 IEEE journal papers from 1986 to early 2019. Our overview provides the pros and cons of existing test systems, implying the lack of appropriate benchmarks for future power system studies, including renewable resources and modern technologies. Furthermore, this article presents requirements for updating and modifying the benchmarks for modern power systems analysis.

Regarding the potentials of activating commercial consumers in demand response (DR) programs, this paper proposes a new framework for energy scheduling of an active distribution network based on the concept of technical virtual power plant (TVPP), considering operational constraints of distribution network. The TVPP enables presence of commercial buildings and other distributed energy resources (DERs) in day-ahead (DA) electricity market, as a price maker agent. In this regard, a bi-level optimization framework is designed to optimize the bidding strategy of TVPP in DA market with the main goal of maximizing TVPP profit. The upper level problem maximizes the TVPP profit, while in the lower level, market is cleared from independent system operator (ISO) viewpoint. Using Karush-Kuhn-Tucker (KKT) optimality conditions and strong duality theory, proposed bi-level problem is transformed into mixed integer linear programming problem (MILP). Implementing the model on the RBTS test system demonstrates the applicability of the proposed model.

An adaptive approach for distribution system reconfiguration and charging management of plug-in electric vehicles (PEV) is presented in this study. A stochastic model predictive control is applied to stochastically, adaptively, and dynamically reconfigure the system, manage the incidental charging pattern of PEVs, and deal with the variable and uncertain power of renewable energy sources. The objective function of problem is minimizing daily operation cost of system. Herein, the geography of area is considered and the behavior of PEVs' drivers (based on their income level) is modeled with respect to the value of incentive and their hourly distance from each charging station. It is shown that behavioral model of drivers is able to affect the optimal results of problem. The simulation results demonstrate the competence of the proposed approach for cost reduction and making the problem outputs robust with respect to prediction errors.