A “faster curing” ethylene-silane copolymer has been designed as an alternative to existing ethylene-silane copolymers or silane grafted polyethylenes. Rapid crosslinking at ambient conditions has been demonstrated with the “faster curing” co-polymer. Even with its enhanced cure characteristics, the “faster curing” co-polymer still exhibits excellent processability during melt extrusion. For applications where blends with non-silane functionalized polymers are desirable (or required), dilution with up to 50% LLDPE has been demonstrated while maintaining acceptable cure rate and extent of crosslinking, and even greater dilution is possible. The “faster curing” copolymer also maintains superior shelf-life stability over Sioplas silane grafted polyethylene. The “faster curing” copolymer is also a suitable alternative to Monosil silane grafted polyethylene and can be provided for use in bulk in addition to traditional boxes and bags.
While efforts continue to build a more robust, efficient, and reliable power grid to access more renewable generation resources and support increased electrification, this paper describes the numerous benefits of leveraging advanced conductor technology to further support decarbonization.
High penetration of solar power introduces new challenges in the operation of distribution systems. Considering the highly volatile nature of solar power output due to changes in cloud coverage, maintaining the power balance and operating within ramp rate limits can be an issue. Great benefits can be brought to the grid by smoothing solar power output at individual sites equipped with flexible resources such as electrical vehicles and battery storage systems. This paper proposes several approaches to a solar smoothing application by utilizing battery storage and EV charging control in a "Nanogrid" testbed located at a utility in Florida. The control algorithms focus on both real-time application and predictive control depending on forecasts. The solar smoothing models are then compared using real data from the Nanogrid site to present the effectiveness of the proposed models and compare their results. Furthermore, the control methods are applied to the Orlando Utilities Commission (OUC) Nanogrid to confirm the simulation results.
The ongoing electrification of warfare intensifies the U.S. military's reliance on electrical energy. Military nanogrids are small, permanently islanded, highly mobile, electrical systems, with a generation capacity less than 25 kW. This paper proposes a resilience evaluation concept with metrics for mobile military nanogrids. The proposed metrics evaluate resilience against a disruption in primary generation, loss of a distribution component, loss of fuel supply, and electrical faults. A method for classifying distribution networks into radial, loop, or mesh topologies is also presented. The intent is to quantitatively evaluate a military nanogrid's resilience and drive the design of future systems with enhanced resilience.
High voltage (HV) electrical installations require appropriately designed grounding systems to provide the necessary levels of safety and operational security. Grounding system installers and HV asset owners face continual pressure to lower cost, and the projectbased nature of construction work leads to a focus primarily on component cost rather than whole of life cost. However, where this pressure leads to the use of inferior components or installation methods, the safety outcomes will be impaired, and the whole of life cost may even be higher. This paper will explore the cost of various component groups in grounding systems of HV substations and lines, and present these in the context of both the asset and grounding system installation cost; and in proportion to the life-long cost of owning and managing the grounding system for a 30-year asset life. The costs from failure of components will also be explored.
We propose a two-stage scenario-based stochastic optimization problem to determine investments that enhance power system resilience. The proposed optimization problem minimizes the Conditional Value at Risk (CVaR) of load loss to target low-probability high-impact events. We provide results in the context of generator winterization investments in Texas using winter storm scenarios generated from historical data collected from Winter Storm Uri. Results illustrate how the CVaR metric can be used to minimize the tail of the distribution of load loss and illustrate how risk-aversity impacts investment decisions.
Providing frequency response, especially fast frequency response such as Red D in the Pennsylvania-New Jersey-Maryland Interconnection (PJM) market, is challenging for many generation plants to deliver on their own. If they have adequate flexibility, hydropower plants are typically able to provide slower regulation support (e.g., Reg A in PJM), but do not respond fast enough to provide Reg D. The ability to provide Reg D would improve their revenue because this service is typically more valuable. This work presents a control approach to use hydropower, battery, and ultracapacitor systems to provide fast regulation in a way that uses the response contribution of each asset. In particular, the proposed control architecture applies a variational mode decomposition (VMD) technique on the incoming Reg D signal to extract multiple dynamic-regulation components with non-overlapping frequencies. With the response-speed dependent alignment, these regulation components are fed to the hydrogenator and hybrid energy storage system (HESS). The paper evaluates the proposed approach on a direct-current (DC)-coupled active system by computing several performance measures and analyzing sensitivity based on HESS component proportional capacities. The results reveal that the proposed VMD-based signal conditioning performs well and that optimizing the sizing of the battery and ultracapactor components further enhances performance.
This paper investigates various voltage issues at a rural manufacturing facility connected to a 115 kV transmission system. Low voltage issues during and following the tripping of an on-site generator and high and low voltages during the switching of a nearby mechanically-switched shunt capacitor bank have caused challenges to the operations of the facility. Through dynamic simulation for scenarios from past events combined with some potential transmission configurations, the voltage issues are presented and a possible solution is proposed to mitigate these issues.
In large power system simulations with positive-sequence root-mean-square (RMS) dynamic programs, bridge thyristor converters of conventional HVDC transmissions are usually represented by equations describing steady-state operation. The use of electromagnetic transient (EMT) bridge converter models reflecting commutations of the thyristor valves can enhance the accuracy of RMS simulations of large power systems with HVDC transmissions. The development of efficient EMT models suitable for multirate simulation with commercial RMS dynamic programs is a challenging task. The paper focuses on EMT models of the single-bridge (Graetz) converter and multi-bridge converters. The thyristor valve is represented by a variable resistor in parallel with a resistor-capacitor branch (the R-RC representation). R-RC bridge models are presented for line-commutated and capacitor-commutated converters. The paper discusses numerical solution of the differential and algebraic equations of the R-RC bridge models and their interface with the main RMS dynamic program.
This paper demonstrates the use of transactive energy (TE) to maximize the profitability and flexibility of a nuclear power plant. Reductions in the price of natural gas, and the concurrent influx of variable and distributed energy resources in the electricity market of the U.S. have impacted the economic viability of traditional nuclear power generation, which is currently untenable for adapting to dynamic pricing trends. This can be resolved by using TE concepts to control and coordinate an integrated nuclear energy system. This system can recuperate by using the excess nuclear thermal energy at times when the electricity prices are low to produce hydrogen and participate in the hydrogen market. A nuclear-renewable integrated energy system is demonstrated here with renewable sources, electrolyzers, a power conversion system, and storage systems along with the nuclear power plant. Deep reinforcement learning (DRL) methodology has been used to control, coordinate, and optimize the system based on TE concepts. The proposed framework demonstrates how future nuclear generation can flexibly participate in electric power markets.
Inverter-based resources (IBRs) are proliferating in distribution and transmission systems. These resources have been driving changes in industry paradigms, grid codes, standards, and recommended practices. One established utility system design is effective grounding for ground fault temporary overvoltages (GFTOV). This was derived entirely on the premise of a traditional grid, with utilities conforming their practices over the decades with the expectation of this topology to continue. As IBRs proliferate, traditional practices are becoming ineffective in depicting expected behavior. As a result, management of GFTOV becomes critically dependent on IBR control algorithms and on the negative sequence impedance of the aggregate load of a distribution feeder. This requires a shift in direction for transmission and distribution operators. This paper includes an electric utility's lessons learned in the interconnection of a range of large scale IBRs to its distribution grid. Two case studies are presented to illustrate the drawbacks of conventional practices. It also exemplifies how the utility manages the problem and presents a forward-looking perspective on the evolution of rules, standards, and practices.
Market splitting occurs when the energy flow between different market zones is higher than the cross-border capacity, separating the markets and bringing economic losses to market participants. The cross-border capacity is computed by Transmission System Operators using a seasonal steady-state line rating (SLR). SLR considers fixed conservative meteorological conditions throughout the year except for the ambient temperature that can have a fixed seasonal and spatial variation. Dynamic line rating (DLR) analysis using near real-time meteorological data allows to effectively computing the capacity of the lines while SLR, by usually underestimating it, may lead to market splitting. This work presents a case study where DLR is applied to reduce the number of market splitting occurrences in the Iberian market of electricity. For the different scenarios analyzed, the reduction of market splitting occurrences can range from 16% to 57%, being lower than 1% in case of exporting from Portugal to Spain.
Long-term drought and elevated environmental temperatures are contributing to a significant increase in the number and severity of wildfires in United States. One major west coast utility attributes 8-10% of wildfires to failures and faults on electric utility distribution circuits. Ignition mechanisms that can cause fires may develop over weeks or months before ignition occurs. Existing monitoring and protection systems cannot detect many incipient failure conditions before a catastrophic event causes fire ignition. This paper presents a real-time diagnostic technique capable of detecting developing fire ignition mechanisms. Operators can utilize actionable information, dispatching crews to find and fix certain developing ignition conditions prior to fire start. Actual case studies from utility trials are presented.
Wildfires caused by electric equipment have become a challenge for electricity distribution operators and utilities in vulnerable regions, as witnessed by the recent catastrophic cases. Part of the challenge in preventing such events is lack of effective ways for monitoring equipment condition that may produce arcing and sparks. In the meantime, high-resolution, high-fidelity sensor measurements can be used to detect unique signatures of equipment malfunction and anomalies such as arcing faults that can potentially cause outages and wildfires. However, even with high-speed measurement data, low-current arcing events are notoriously difficult to detect due to their short bursts of duration and low amplitudes. In this paper, we propose a combination of unsupervised and supervised classification framework to detect anomalous events such as voltage regulation, overcurrent, fuse open etc., along with more instantaneous low-amplitude current arcing events, using phasor measurements. While the difficulty the proposed supervised learning algorithm evaluates the probability of detected low-current arcing events based on an existing library of labeled arcing signatures.
The need to accurately measure high-frequency content in power system voltage and current phenomena is increasingly becoming more of a priority. As the amount of distributed energy resources (DER) and nonlinear loads penetrating the grid increases, so do challenges associated with traditional measurement and metering applications. In this paper, three commercially-available medium-voltage-level current sensors are characterized in terms of their harmonic amplitude and phase performance against reference signals that are “played back” through the sensors through the use of an arbitrary waveform generator. It is shown that none of the three sensors studied are able to faithfully replicate all of the input signals completely, though there are advantages and disadvantages to each in terms of noise, resonance, and induced phase drift. Additionally, the Goodness-of-Fit metric, typically used for PMU model validation, is used to generate side-by-side comparisons of sensor accuracy over a small window around the events under study.
To facilitate the service restoration following outages due to hazardous weather conditions, accurate damage information, i.e., when and where the outage-causing damage occurs, is critical. Such information relies on high-resolution (spatial and temporal) outage estimation or prediction, which can only be enabled by developing models using granular, in space and time, data. In this study, to take full advantage of the high spatial and temporal resolution of utility outage data and weather radar observations, the regression problem for developing damage forecasting models is reformulated to consider the time evolving weather condition associated with each outage event. The reformulated problem considers the impacts on outages of variations and cumulative effects of weather conditions not only across the utility's service territory but also over time through the evolution of the storm. Using this reformulated approach, historical utility outage data are used to develop a new and improved damage forecasting algorithm and validate its performance improvement and applicability for more granular outage estimation.
With the growing interest for a more advanced smart grid, there is an increasing need for more detailed and accurate distribution system models and assessments. This paper investigates the accuracy of today's distribution reactive power load modeling approaches and explores alternative load allocation processes to improve the spatial diversity of the reactive power load model. The performance of each methodology is assessed on a real utility feeder model ingested with real 15-minute resolution active and reactive power AMI data, providing a full-visibility base case for benchmarking purposes. This paper shows, among other things, that power-based load allocation or an allocation with improved power factor improves the reactive power load modeling by representing the real and reactive power at the feederhead (as compared to the current-based load allocation).
As power networks move toward increased digitalization and the smart grid is becoming more of a reality, power networks are hosting elements of smart grids to meet the growing demands and requirements of advanced digital networks. One of the components of a smart grid is the use of measuring sensors. Low-power instrument transformers are one part of these advanced systems and are increasingly being used within distribution networks. As these sensors are being widely installed, network operators must ensure that the addition of such sensors will not affect the normal operation of the network. This paper discusses the potential impact of using voltage sensors in power networks to perform VLF (very low frequency) cable testing, which is a common practice in distribution networks and is used during commissioning tests and to support maintenance and condition monitoring of underground cables. This paper also investigates and compares the impact of different sensor technologies on the performance of cable VLF diagnostic testing.
Extreme contingency is among the central concepts of power system stability analysis and conveys the idea of a severe impact on the system. This paper discusses the intermittency of wind and solar energy sources as a factor that makes it necessary to rethink the concept of extreme contingency for systems with high wind and solar penetration. On the premise that contingency definitions should account for weather conditions, the paper proposes to reclassify some contingencies presently considered extreme as normal and introduces new contingency categories. The following contingencies need to be reclassified: the loss of all units of a wind/solar power plant; the loss of multiple units; and the loss of multiple plants. Contingency categories with variation of wind speed and solar irradiance and with multiple wind/solar plants disconnected pre-contingency are introduced. To explore the relevance of these categories, further analyses with adequate representation of wind/solar power plants are needed.
Estimates of power system frequency are beginning to be used in critical, real-time applications such as synthetic inertia. The behavior of the algorithms used to estimate frequency from point-on-wave signals can be complicated, and algorithm tuning difficult for non-experts. In this paper, small-signal approximations for four common frequency estimation algorithms are presented. These approximations are demonstrated to be both theoretically and empirically sound. They are valuable for three primary reasons: 1) they reduce the level of technical expertise required for effective tuning; 2) they effectively capture the dynamics of the algorithm so can be used as a proxy for the algorithm itself in the analysis of feedback control loops that employ frequency feedback; and 3) they can easily be implemented in transient stability simulation software in order to represent realistic frequency estimation.
