This paper summarizes the report prepared by an IEEE PES Task Force. Resilience is a fairly new technical concept for power systems, and it is important to precisely delineate this concept for actual applications. As a critical infrastructure, power systems have to be prepared to survive rare but extreme incidents (natural catastrophes, extreme weather events, physical/cyber-attacks, equipment failure cascades, etc.) to guarantee power supply to the electricity-dependent economy and society. Thus, resilience needs to be integrated into planning and operational assessment to design and operate adequately resilient power systems. Quantification of resilience as a key performance indicator is important, together with costs and reliability. Quantification can analyze existing power systems and identify resilience improvements in future power systems. Given that a 100% resilient system is not economic (or even technically achievable), the degree of resilience should be transparent and comprehensible. Several gaps are identified to indicate further needs for research and development.

This paper presents a method for quantifying and enabling the resiliency of a power distribution system using analytical hierarchical process and percolation theory. Using this metric, quantitative analysis can be done to analyze the impact of possible control decisions to pro-actively enable the resilient operation of distribution system with multiple microgrids and other resources. Developed resiliency metric can also be used in short term distribution system planning. The benefits of being able to quantify resiliency can help distribution system planning engineers and operators to justify control actions, compare different reconfiguration algorithms, and develop proactive control actions to avert power system outage due to impending catastrophic weather situations or other adverse events. Validation of the proposed method is done using modified CERTS microgrids and a modified industrial distribution system. Simulation results show topological and composite metric considering power system characteristics to quantify the resiliency of a distribution system with the proposed methodology, and improvements in resiliency using two-stage reconfiguration algorithm and multiple microgrids.

The relative share of renewable energy, specifically the solar photovoltaic (PV), is increasing exponentially in the world electric energy sector. This is a cumulative result of reduction in the cost of solar panels, improvement in the panel efficiency, and advancement in the associated power electronics. Among different types of PV plants, installation of small-scale rooftop PV is growing rapidly due to direct end-user benefits and lucrative governmental incentives. There are various standards developed in regards to grid integration of PVs and other distributed generations (DGs). Different power converter topologies are developed to interface the PV panel with the utility grid. To keep up with the stringent regulations imposed by the standards, various control strategies and grid synchronization methods have been developed. This review article amalgamates and summarizes all of the aforementioned aspects of a grid-integrated PV system including various standards, power stage architectures, grid synchronization methods, operation under extreme events, and control methodologies, pertaining to small-scale PV plants. This article will help freshman researchers to gain some familiarity with the topic and introduce them to some of the key issues encountered in this field.

Price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk, and it also plays a key role in the economic optimization of the electric energy industry. This paper explores a technique of artificial neural network (ANN) model based on similar days (SD) method in order to forecast day-ahead electricity price in the PJM market. To demonstrate the superiority of the proposed model, publicly available data acquired from the PJM Interconnection were used for training and testing the ANN. The factors impacting the electricity price forecasting, including time factors, load factors, and historical price factors, are discussed. Comparison of forecasting performance of the proposed ANN model with that of forecasts obtained from similar days method is presented. Daily and weekly mean absolute percentage error (MAPE) of reasonably small value and forecast mean square error (FMSE) of less than 7$/MWh were obtained for the PJM data, which has correlation coefficient of determination of 0.6744 between load and electricity price. Simulation results show that the proposed ANN model based on similar days method is capable of forecasting locational marginal price (LMP) in the PJM market efficiently and accurately.

Network operators and utilities are challenged with increasing extreme weather conditions, resulting in interrupted power supply to critical loads. Resiliency metrics, which can capture the level of preparedness to resist adverse impact of extreme conditions on a distribution system, can be leveraged in multiple ways to provide better operation of the network and design of the future systems. In this paper, a methodology to quantify resiliency and maintain power supply to critical loads (CLs) during extreme contingencies has been proposed. Resiliency evaluation of power distribution system has been defined as a multi-criteria decision making problem and quantified using graph theoretic approach and Choquet integral. The algorithm proposed in this paper to calculate the resiliency for all feasible network configurations supplying CLs in a network is useful in planning as well as operation of the distribution network. The application of the proposed algorithm is demonstrated through several case studies using two proximal CERTS microgrids and IEEE 123 node distribution system. Simulation studies are also provided for planning of resilient network, by placing additional switches in the considered distribution systems with microgrid.