This document is a summary of a report prepared by the IEEE PES Task Force (TF) on Microgrid Stability Definitions, Analysis, and Modeling \cite{task}, which defines concepts and identifies relevant issues related to stability in microgrids. In this paper, definitions and classification of microgrid stability are presented and discussed, considering pertinent microgrid features such as voltage-frequency dependency, unbalancing, low inertia, and generation intermittency. A few examples are also presented, highlighting some of the stability classes defined in the paper. Further examples, along with discussions on microgrid components modeling and stability analysis tools can be found in the TF report.

The COVID-19 related shutdowns have made significant impacts on the electric grid operation worldwide. The global electrical demand plummeted around the planet in 2020 continuing into 2021. Moreover, demand shape has been profoundly altered as a result of industry shutdowns, business closures, and people working from home. In view of such massive electric demand changes, energy forecasting systems struggle to provide an accurate demand prediction, exposing operators to technical and financial risks, and further reinforcing the adverse economic impacts of the pandemic. In this context, the “IEEE DataPort Day-Ahead Electricity Demand Forecasting Competition: Post-COVID Paradigm” was organized to support the development and dissemination state-of-the-art load forecasting techniques that can mitigate the adverse impact of pandemic-related demand uncertainties. This paper presents the findings of this competition from the technical and organizational perspectives. The competition structure and participation statistics are provided, and the winning methods are summarized. Furthermore, the competition dataset and problem formulation is discussed in detail. Finally, the dataset is published along with this paper for reproducibility and further research.

The increasing frequency and magnitude of weather-related extreme events in recent years have severely impacted power grids. Examples of these extreme events include wildfires, heat waves, hurricanes, tornadoes, storms, flooding, etc. Extreme weather disturbances have rendered this critical infrastructure susceptible to disruption, risking essential services from health care to transportation. An important objective for many power utilities is to improve resilience and avoid widespread outages when faced with extreme events. The resilience goal of utility companies is to minimize the duration and magnitude of power outages and enable the rapid recovery of service after an outage event. For this purpose, various preventive and restorative resilience actions are required that can focus on adaptation plans as well as restoration plans that utilities can adopt to restore power to customers in an optimized fashion. However, one of the significant challenges that utilities face is to effectively handle large amounts of data from different sectors and utilize the data in an effective and optimized fashion for making resilience decisions and actions. This article discusses the data management practices required by electric power utilities to improve grid resilience and elaborates the applications of artificial intelligence (AI) for the enhanced resilience of electric power grids. The data management and AI applications are discussed from the perspective of preventive and mitigative actions on different power grid sectors, like generation, transmission, and distribution. The article concludes by summarizing the gaps in grid resilience research and technologies.

This paper presents findings on mitigating the negative impact of renewable energy resources variability on the energy scheduling problem, in particular for island grids and microgrids. The methods and findings presented in this paper are twofold. First, data obtained from the City of Summerside in the province of Prince Edward Island, Canada, is leveraged to demonstrate the effectiveness of state-of-the-art time series predictors in mitigating energy scheduling inaccuracy. Second, the outcome of the time series prediction analysis is used to propose a novel data-driven battery energy storage system (BESS) sizing study for energy scheduling purposes. The proposed probabilistic method accounts for intra-interval variations of generation and demand, thus mitigating the trade-off between time resolution of the problem formulation and the solution accuracy. In addition, as part of the sizing study, a BESS management strategy is proposed to minimize energy scheduling inaccuracies, and is then used to obtain the optimal BESS size. Finally, the paper presents quantitative analyses of the impact of both the energy predictors and the BESS on the supplied energy cost using the actual data of the Summerside Electric grid. The paper reveals the significant potential for reducing energy cost in renewable-penetrated grids and microgrids through state-of-the-art predictors combined with applications of properly-sized energy storage systems.

HighlightsA Perspective on Modular Battery Management System Integrating Digital Twins and Learning Methods A modular BMS framework integrating PINNs, RL, and Digital Twin is presented.Digital twins enable safety-aware RL training and PINN calibration for BMS control.PINNs embed battery physics to inform safe RL control by battery state estimation.Cloud-based hierarchical BMS coordinates global optimization with local control.The framework transforms BMS from reactive monitoring to self-optimizing control.

The global solar photovoltaic (PV) capacity has grown significantly over the last few decades. On the other hand, in recent years, wildfires have increased noticeably in some regions of the planet, leading to a surge in transported wildfire smoke in urban areas. Wildfire smoke attenuates solar irradiance and reduces the energy yield of solar PV, a phenomenon that is underexplored, specifically for distributed solar PV systems. Thus, this research develops a practical framework for estimating the impact of wildfire smoke on distributed solar PV generation using accessible data. The estimation models are developed using real-world distributed PV generation data from the city of Calgary over a nine-year period. A novel approach to classify smoke-affected days in historical data is proposed and used to isolate the impact of smoke. The analysis demonstrates the effectiveness of accessible inputs in estimating the wildfire smoke-related solar PV loss. For example, using the City of Calgary, AB as a case study, it is estimated that wildfire smoke can cause energy losses exceeding 50% of potential generation during extreme smoke events. This paper's findings can support utilities, planners, and solar PV owners in both planning and operation studies.