Consolidated Edison (United States)

ABSTRACT In this study, we introduce a novel auto-compensation method designed to enhance the accuracy of pulsed eddy current (PEC) measurements, crucial for non-destructive testing (NDT) in assessing the integrity of high-voltage feeder pipes. We explicitly target the unique challenges posed by strong electromagnetic interference (EMI) from internal power lines carrying three-phase alternating current. To address them, we propose a compensation algorithm that accounts for spatially varying magnetic permeability in the piping materials. The proposed method integrates multi-physics simulations of the interactions between the power lines, the pipe, and the PEC probe. Two finite element method (FEM) simulation models are developed: the Cable-Pipe Model, simulating the magnetic flux density distribution around the pipe due to internal power lines, and the PEC-Pipe Model, simulating the PEC sensing response considering the circumferentially varying magnetic permeability. To bridge the gap between computational accuracy and field deployment requirements, novel surrogate models are developed based on parametric FEM simulation datasets, allowing rapid approximation of characteristic decay time constants and nominal wall thickness estimations. Field tests validated our method’s effectiveness, demonstrating a measurable reduction in false-positive indications after the physics-based compensation. This work challenges the conventional assumption of constant magnetic permeability in PEC inspections and extends the applicability of PEC for flaw detection in high-voltage feeder pipes and other challenging inspection scenarios, contributing to improved safety and infrastructure longevity. 1

Self-determination plays an important role in outcomes in autism and shows intersectional disparities. Yet, little is known about the role of individual differences or social drivers of health in the development of self-determination. Understanding these factors is key for developing effective supports. This mixed-methods convergent study examined self-determination in racially and ethnically minoritized autistic individuals and caregivers. Participants ages 13 to 30 ( N = 73) varying widely in language and cognitive ability and caregivers ( n =52) completed the Self-Determination Inventory. Autism traits and sense of community predicted caregiver report of self-determination, and autism traits and language predicted self-report of self-determination, consistent with DisCrit and Diversity Science. Self-Determination Inventory interviews of a subset of participants ( n = 13) and caregivers ( n = 9) were analyzed using inductive thematic analysis. Themes pointed to the role of the intersection of race and disability in shaping self-determination. Altogether, findings point to the importance of these frameworks, environmental influences, and multi-informant perspectives in characterizing self-determination. Future work should focus on the impact of environmental factors in self-determination in minoritized autistic individuals during the transition to adulthood.

The external forces of urbanization and environmental sustainability are driving the internal dynamics of electricity power systems and markets towards the undesirable operational conditions of lower system reliability and higher electricity prices. Due its unique capability to provide supply and consumption on demand, Energy Storage Systems (ESSs) are considered the primary solution by Federal and State agencies overseeing the dynamic. These agencies have provided orders to Independent System Operators (ISOs) increase the penetration of these technologies, open market access that recognize their unique dual capability, and include their functions as part of ISOs Operational Planning. This paper presents a new power system dynamic inclusive of ESS, analyzes its Consumption Income for magnitude and growth using New York ISO (NYISO) data, and develops scheduling schemes for ESS optimization using the Consolidated Edison Company of New York (CECONY) request for proposal. The proposed operational dynamic optimizes day-to-day storage capacity allocation for supply and consumption in cooperation with the ESS State-of-Charge (SOC), thereby maximizing total ESS income generated and minimizing electricity costs to consumers.

We conducted a study to assess whether testosterone therapy (TT) alters prostate cancer risk using a large U.S. commercial insurance research database. From the HealthCore Integrated Research Database (HIRD), we selected men ages 30 years or greater who were new users of TT during 2007 to 2015. We selected two comparison groups: (i) unexposed (matched 10:1) and (ii) new users of phosphodiesterase type 5 inhibitor (PDE5i). Incident prostate cancer was defined as diagnosis of prostate cancer within 4 weeks following prostate biopsy. Propensity scores and inverse probability of treatment weights were used in Poisson regression models to estimate adjusted incidence rates, incidence rate ratios (IRR), and 95% confidence intervals (CI). Subgroup analyses included stratification by prostate cancer screening, hypogonadism, and follow-up time. The adjusted prostate cancer IRR was 0.77 (95% CI, 0.68-0.86) when comparing TT with the unexposed group and 0.85 (95% CI, 0.79-0.91) in comparison with the PDE5i group. Inverse associations between TT and prostate cancer were observed in a majority of subgroup analyses, although in both comparisons estimates generally attenuated with increasing time following initial exposure. Among TT users, duration of exposure was not associated with prostate cancer. Men who received TT did not have a higher rate of prostate cancer compared with the unexposed or PDE5i comparison groups. The inverse association between TT and prostate cancer could be the result of residual confounding, contraindication bias, or undefined biological effect. This study suggests that limited TT exposure does not increase risk of prostate cancer in the short term.

Transformer testing is an indispensable procedure to guarantee that transformers are fulfilling all requirements and guaranteed values. After completed FAT (Factory Acceptance Test), to proof the transformer design, site commissioning test shall be performed to detect potential transport damages or faults at site installation before energizing the transformer.

Sustainability is achieved when a company is able to balance its financial, natural, and social capital, leveraging them to derive value for its stakeholders. An appreciation of how these forces work together to positively influence both the development and success of a business, as well as the business’ long-term societal goals, can mean the difference between simply surviving as a company, and thriving. The utility industry is often referred to as being on the verge of a revolution, as technology develops at a quicker pace and customers become more deeply engaged in their energy choices. Customers are evaluating their opportunities for on-site generation, as they now have the option to incorporate cost-effective, cleaner renewable technologies while potentially providing themselves a level of self-sufficiency and resiliency. Distributed generation (DG) can offer a truly sustainable solution—empowering customers with new energy sources, allowing for the development of cleaner technologies such as solar power and energy storage, and providing businesses with new opportunities to improve resiliency and defer or completely eliminate long-term infrastructure investment through targeted reductions in customer load. Con Edison is looking at how DG can be better integrated into its system planning process and long-term strategy. While the technology can present challenges to the utility business model and system design, Con Edison also acknowledges that DG brings benefits to its system and to its customers. Where Con Edison once developed energy forecasts and infrastructure plans based on the unidirectional flow of energy from large-scale generating facilities through to the customer meter, it now considers how a two-way flow of energy, changing load needs, and individual customer impact on the power system are driving the evolution of the modern utility.

This paper describes a methodology and an automated process to implement node-breaker capabilities for representing major stations within Con Edison's footprint. This process creates a “hybrid” power flow model of Con Edison's system such that the user-defined stations are represented using the node-breaker model and the rest of the system is modeled using bus-branch representation. The process is implemented within the Physical and Operational Margins (POM) Suite software, which automatically expands the existing planning load flow case to incorporate nodebreaker modeling of the stations, and automatically generate breaker-oriented contingency lists. For those stations, which are represented using the node-breaker model, a breakeroriented contingency list is automatically generated. For the rest of the system, a contingency list consists of transmission line, transformer and generator outages. The breaker -oriented contingency list and the list of single element outages are automatically combined into one master contingency list. The “hybrid” model allows Con Edison to more accurately model contingencies for NERC-compliance studies.

With the deployment of advanced metering infrastructure (AMI), an avalanche of new energy-use information became available. Better understanding of the actual power consumption patterns of customers is critical for improving load forecasting and efficient deployment of smart grid technologies to enhance operation, energy management, and planning of electric power systems. Unlike traditional aggregated system-level load forecasting, the AMI data introduces a fresh perspective to the way load forecasting is performed, ranging from very short-term load forecasting to long-term load forecasting at the system level, regional level, feeder level, or even down to the consumer level. This paper addresses the efforts involved in improving the system level intraday load forecasting by applying clustering to identify groups of customers with similar load consumption patterns from smart meters prior to performing load forecasting.

We summarize the first major effort to use analytics for preemptive maintenance and repair of an electrical distribution network. This is a large-scale multiyear effort between scientists and students at Columbia University and the Massachusetts Institute of Technology and engineers from the Consolidated Edison Company of New York (Con Edison), which operates the world’s oldest and largest underground electrical system. Con Edison’s preemptive maintenance programs are less than a decade old and are made more effective with the use of analytics developing alongside them. Some of the data we used for our projects are historical records dating as far back as the 1880s, and some of the data are free-text documents typed by Con Edison dispatchers. The operational goals of this work are to assist with Con Edison’s preemptive inspection and repair program and its vented-cover replacement program. This has a continuing impact on the public safety, operating costs, and reliability of electrical service in New York City.