ISO New England Inc. (ISO-NE) is an independent, non-profit Regional Transmission Organization (RTO), headquartered in Holyoke, Massachusetts, serving Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont.ISO-NE oversees the operation of New England's bulk electric power system and transmission lines, generated and transmitted by its member utilities, as well as Hydro-Québec, NB Power, the New York Power Authority and utilities in New York state, when the need arises. ISO-NE is responsible for reliably operating New England's 32,000 megawatt bulk electric power generation and transmission system. One of its major duties is to provide tariffs for the prices, terms, and conditions of the energy supply in New England.ISO New England's stated mission is to protect the health of New England's economy and the well-being of its people by ensuring the constant availability of electricity, today and for future generations. ISO New England ensures the day-to-day reliable operation of New England's bulk power generation and transmission system, oversees the administration of the region's wholesale electricity markets, and manages the regional planning processes.ISO-NE was created in 1997 by the Federal Energy Regulatory Commission, as a replacement for the New England Power Pool (NEPOOL), which was created in 1971.The ISO-NE grid does not extend to remote parts of eastern and northern Maine in Washington and Aroostook Counties. In these areas, residents receive their electricity from Canadian providers such as NB Power and Hydro-Québec.
Supercritical generation technologies have emerged as a promising pathway toward clean and flexible energy conversion. Advancing these technologies demands highly accurate mathematical models capable of supporting innovative control strategies and enhancing energy efficiency. The modeling of industrial systems has evolved from simple first-principle formulations to sophisticated frameworks that preserve their physical foundations while being enhanced through optimization algorithms. As these optimization problems increase in size, sensitivity analysis becomes essential for identifying the most influential parameters, thereby reducing computational complexity without sacrificing accuracy. These critical parameters are then refined using an adaptive Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization method. The results demonstrate notable gains in both predictive accuracy and computational efficiency. The proposed model holds significant potential for power generation applications, particularly in advanced control system design and real-time performance monitoring. This study developed a supercritical power plant (SCPP) model that achieved improved accuracy while maintaining simplicity compared to existing models. A model of a 600 MW SCPP is first constructed and systematically analyzed to capture its key characteristics. Subsequently, a sensitivity analysis is conducted to identify the parameters with the greatest influence on model performance. These critical parameters are then tuned using an adaptive Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization method. The results demonstrate notable gains in accuracy. The proposed model offers promising potential for power generation applications, particularly in the areas of advanced control system design and real-time performance monitoring.
The power grid is rapidly evolving, leading to unplanned operating conditions where the distribution system is no longer a passive consumer of electric energy. Most of the bulk system stability analytics assume that the distribution system is a passive consumer of electricity, which needs to be reevaluated with the evolving power grid. Large-scale integration of inverter-based technology, like distributed generation (DG) and flexible loads [(collectively called distributed energy resources (DERs)], in the distribution system is leading to an increase in transmission and distribution (T&D) system interactions. T&D interactions can be defined as the interdependence of system planning, performance, and operational decision making between T&D systems. T&D interactions impact several power grid aspects, including system stability, system planning, protection and controls, system resilience and reliability enhancement, etc. The California Independent System Operator (CAISO) has shed some light on the operational challenges observed in the real world and has highlighted the need to develop mechanisms to utilize T&D interactions for reliable grid operation (e.g., DER-based automatic generation control). For a stable future power grid, modeling, analyzing, and utilizing these T&D interactions effectively will be crucial. Aggregated models like the composite load models (CLMs) and other reduced order models (ROMs) can capture certain aggregated behavior of distribution system aspects, but they do not capture some features, like unbalanced nature and feeder voltage impacts on the distribution loads and DERs. T&D cosimulation can effectively capture the T&D interactions and their impact on power system stability.
The increasing penetration of wind and solar generation introduces significant uncertainties for the planning and operation of power systems. Thus, it is critical to generate reliable, realistic wind and solar energy scenarios to support risk assessment and identify potential extreme events. This paper develops an R-vine copula framework to capture the complex spatiotemporal dependence among wind and solar plants in large scale systems. Since the large number of random variables poses significant challenges for vine construction, a truncation strategy is proposed to reduce computational complexity while preserving the dominant dependence structure. The proposed approach is validated using real data from ISO New England. Comparison results with the multivariate Gaussian copula show that the truncated R-vine copula substantially reduces computational burden and accurately captures tail dependence, making it more suitable for risk-aware power system applications.
[Most recently, there have been significant efforts by power system operators in studying generation and transmission uncertainties in operation planning. With the increasing penetration of weather-dependent renewable generation and frequent geo-climate events, power system operators have begun to predict weather changes and preventively mitigate their impacts on power generation and transmission. Uncertainty evaluation and subsequent preventive mitigation actions have become important tasks in operating regions with significant renewable generation. Amid these efforts, artificial intelligence and machine learning are playing instrumental roles in the development of new tools. Based on a handful of public reports, this article discusses the emerging new problem of operational uncertainties for power system operators and outlines a systematic approach that involves risk evaluation and mitigation in both generation scheduling and transmission operation. It is envisioned that risk-based operation will be the new paradigm for power system security assessment.
Immigrants consume information from both their origin and destination countries. I examine how they adjust their behavior in response to new developments in their origin country by exploiting the timing of COVID-19 outbreak in each origin country. I find that as COVID-19 spread in various countries, immigrants from these countries were more likely to practice social distancing in the United States. Additionally, they reacted more strongly to new developments of COVID-19 in their origin countries after the declaration of a national emergency in the U.S., indicating immigrants’ actions are influenced by both information from their origin countries and the relevance of such information in their current country of residence. These behavioral changes are likely driven by real-time transmission of information through social media and other online channels.
