Characterization of a New Botrytis Species and Fungicide Resistance in Botrytis Cinerea from Blackberry

Gray mold caused by the fungus Botrytis cinerea is the economically most important pre- and postharvest disease of blackberry. As part of a South Carolina fungicide resistance monitoring program, blackberry fruit were collected to survey for pathogenic fungi. Phylogenetic as well as morphological analysis indicated a new species, described as Botrytis caroliniana. A rapid method using polymerase chain reaction was developed to differentiate B. cinerea and B. caroliniana. A distribution and prevalence study indicated these two species co-existed in four out of six locations investigated. The control of gray mold in commercial fields largely relies on fungicide application. Therefore, survey was conducted to determine the occurrence and prevalence of fungicide resistance. The fungicide resistance profile was described in 198 B. cinerea isolates from blackberry. Of these isolates, 72% were resistant to thiophanate-methyl, 59% were resistant to pyraclostrobin, 56% were resistant to boscalid, 11% were resistant to fenhexamid, 10% were resistant to cyprodinil, 8.6% were resistant to iprodione, and 1% were resistant to fludioxonil. A statistical model revealed that multifungicide resistance patterns did not evolve randomly in populations. Resistance to thiopanate-methyl, pyraclostrobin, boscalid, and fenhexamid was based on target gene mutations, including E198A and E198V in β-tubulin, G143A in cytochrome b, H272Y and H272R in SdhB, and F412I in Erg27, respectively. In addition, a new genotype associated with fenhexamid resistance was found in one strain (i.e., Y408H and deletion of P298). Two levels of resistance, low resistance (LR) and moderate resistance (MR), to fludioxonil were found in three field isolates, and MR was caused by a previously described mutation (R632I) in transcription factor Mrr1. The LR and MR isolates were able to cause lesions and sporulated on detached fruit. The results obtained in this study contribute to our understanding of fungal biology and fungicide resistance development in gray mold fungi and are useful for improving resistance management practices

Annual Benefit Analysis of Integrating the Seasonal Hydrogen Storage into the Renewable Power Grids

There have been growing interests in integrating hydrogen storage into the power grids with high renewable penetration levels. The economic benefits and power grid reliability are both essential for the hydrogen storage integration. In this paper, an annual scheduling model (ASM) for energy hubs (EH) coupled power grids is proposed to investigate the annual benefits of the seasonal hydrogen storage (SHS). Each energy hub consists of the hydrogen storage, electrolyzers and fuel cells. The electrical and hydrogen energy can be exchanged on the bus with energy hub. The physical constraints for both grids and EHs are enforced in ASM. The proposed ASM considers the intra-season daily operation of the EH coupled grids. Four typical daily profiles are used in ASM to represent the grid conditions in four seasons, which reduces the computational burden. Besides, both the intra-season and cross-season hydrogen exchange and storage are modeled in the ASM. Hence, the utilization of hydrogen storage is optimized on a year-round level. Numerical simulations are conducted on the IEEE 24-bus system. The simulation results indicate that the seasonal hydrogen storage can effectively save the annual operation cost and reduce the renewable curtailments.Comment: 5 pages, 4 figure

Active Linearized Sparse Neural Network-based Frequency-Constrained Unit Commitment

Conventional synchronous generators are gradually being re-placed by low-inertia inverter-based resources. Such transition introduces more complicated operation conditions, frequency deviation stability and rate-of-change-of-frequency (RoCoF) security are becoming great challenges. This paper presents an active linearized sparse neural network (ALSNN) based frequency-constrained unit commitment (ALSNN-FCUC) model to guarantee frequency stability following the worst generator outage case while ensuring operational efficiency. A generic data-driven predictor is first trained to predict maximal frequency deviation and the highest locational RoCoF simultaneously based on a high-fidelity simulation dataset, and then incorporated into ALSNN-FCUC model. Sparse computation is introduced to avoid dense matrix multiplications. An active data sampling method is proposed to maintain the bindingness of the frequency related constraints. Besides, an active ReLU linearization method is implemented to further improve the algorithm efficiency while retaining solution quality. The effectiveness of proposed ALSNN-FCUC model is demonstrated on the IEEE 24-bus system by conducting time domain simulations using PSS/E

Transmission Planning for Climate-impacted Renewable Energy Grid: Data Preparation, Model Improvement, and Evaluation

As renewable energy is becoming the major resource in future grids, the weather and climate can have higher impact on the grid reliability. Transmission expansion planning (TEP) has the potential to reinforce a transmission network that is suitable for climate-impacted grids. In this paper, we propose a systematic TEP procedure for climate-impacted renewable energy-enriched grids. Particularly, this work developed an improved model for TEP considering climate impact (TEP-CI) and evaluated the system reliability with the obtained transmission investment plan. Firstly, we created climate-impacted spatio temporal future grid data to facilitate the TEP-CI study, which includes the future climate-dependent renewable production as well as the dynamic rating profiles of the Texas 123-bus backbone transmission system (TX-123BT). Secondly, we proposed the TEP-CI which considers the variation in renewable production and dynamic line rating, and obtained the investment plan for future TX-123BT. Thirdly, we presented a customized security-constrained unit commitment (SCUC) specifically for climate-impacted grids. The future grid reliability under various investment scenarios is analyzed, based on the daily operation conditions from SCUC simulations. The whole procedure presented in this paper enables numerical studies on grid planning considering climate-impacts. It can also serve as a benchmark for other TEP-CI research and performance evaluation.Comment: 9 pages, 8 figure

Computational Enhancement for Day-Ahead Energy Scheduling with Sparse Neural Network-based Battery Degradation Model

Battery energy storage systems (BESS) play a pivotal role in future power systems as they contribute to achiev-ing the net-zero carbon emission objectives. The BESS systems, predominantly employing lithium-ion batteries, have been exten-sively deployed. The degradation of these batteries significantly affects system efficiency. Deep neural networks can accurately quantify the battery degradation, however, the model complexity hinders their applications in energy scheduling for various power systems at different scales. To address this issue, this paper pre-sents a novel approach, introducing a linearized sparse neural network-based battery degradation model (SNNBD), specifically tailored to quantify battery degradation based on the scheduled BESS daily operational profiles. By leveraging sparse neural networks, this approach achieves accurate degradation predic-tion while substantially reducing the complexity associated with a dense neural network model. The computational burden of inte-grating battery degradation into day-ahead energy scheduling is thus substantially alleviated. Case studies, conducted on both microgrids and bulk power grids, demonstrated the efficiency and suitability of the proposed SNNBD-integrated scheduling model that can effectively address battery degradation concerns while optimizing day-ahead energy scheduling operations

Neural Network-based Power Flow Model

Power flow analysis is used to evaluate the flow of electricity in the power system network. Power flow calculation is used to determine the steady-state variables of the system, such as the voltage magnitude/phase angle of each bus and the active/reactive power flow on each branch. The DC power flow model is a popular linear power flow model that is widely used in the power industry. Although it is fast and robust, it may lead to inaccurate line flow results for some transmission lines. Since renewable energy sources such as solar farms or offshore wind farms are usually located far away from the main grid, accurate line flow results on these critical lines are essential for power flow analysis due to the unpredictable nature of renewable energy. Data-driven methods can be used to partially address these inaccuracies by taking advantage of historical grid profiles. In this paper, a neural network (NN) model is trained to predict power flow results using historical power system data. Although the training process may take time, once trained, it is very fast to estimate line flows. A comprehensive performance analysis between the proposed NN-based power flow model and the traditional DC power flow model is conducted. It can be concluded that the proposed NN-based power flow model can find solutions quickly and more accurately than DC power flow model

Convolutional Neural Network-based RoCoF-Constrained Unit Commitment

The fast growth of inverter-based resources such as wind plants and solar farms will largely replace and reduce conventional synchronous generators in the future renewable energy-dominated power grid. Such transition will make the system operation and control much more complicated; and one key challenge is the low inertia issue that has been widely recognized. However, locational post-contingency rate of change of frequency (RoCoF) requirements to accommodate significant inertia reduction has not been fully investigated in the literature. This paper presents a convolutional neural network (CNN) based RoCoF-constrained unit commitment (CNN-RCUC) model to guarantee RoCoF stability following the worst generator outage event while ensuring operational efficiency. A generic CNN based predictor is first trained to track the highest locational RoCoF based on a high-fidelity simulation dataset. The RoCoF predictor is then formulated as MILP constraints into the unit commitment model. Case studies are carried out on the IEEE 24-bus system, and simulation results obtained with PSS/E indicate that the proposed method can ensure locational post-contingency RoCoF stability without conservativeness