EECS Research Publications

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    Dynamic Charge Scheduling of Solar PV-Storage Hybrid Resources Based on Solar-Load Correlation
    (Institute of Electrical and Electronics Engineers Inc., 2023-10) Rajendran, Sarangan; Liu, Esther; Peterson, Mary; Aravinthan, Visvakumar; Tamimi, Al; Yokley, Charles R.
    Solar PV-storage hybrid resources are an attractive way of reducing grid dependency for project developers when collectively serving small communities. A well-structured charging schedule of the battery storage can improve the effectiveness of the hybrid system by reducing the grid dependency at the peak demand hours and improving the utilization of the battery. Studying the correlation between load and solar curves helps to identify periods at which short-term charging or discharging of the battery can aid to those benefits. In this paper, a dynamic charging schedule is proposed, where a nominal schedule is created based on full day forecasting of load and solar output, and further adjustments are made to the schedule based on the hourly expected correlation between load and solar curves. The proposed method is then tested on a test system based on actual data for a period of one year. The results show that the proposed approach can reduce the maximum power import from the grid, overall cost of imports and improve the battery utilization.
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    Value Assessment of Transmission Lines Using Analytical Hierarchy Process
    (Institute of Electrical and Electronics Engineers Inc., 2023-10) Sarkar, Koushik; Manoharan, Arun-Kaarthick; Aravinthan, Visvakumar
    Currently transmission line usage is varying compared to its historical usage. The shift in usage is caused by large scale renewables, and DERs. This transition, however, was not anticipated by the transmission owners before. Therefore, there is a need to develop a qualitative assessment tool to value lines based on the change in usage. This work developed factors to value line on usage based on market dynamics and operational wear and tear. A methodology, called Analytical Hierarchy Process (AHP) is used to combine these factors and provide a single value to transmission line. The value obtained through AHP will not have a reasonable meaning if economic values are not assessed. Hence, line valuation is followed by the economic assessment. This economic assessment is done by distributing the revenue to lines using AHP. This process of revenue distribution would be useful to multiple transmission owners owning lines in an area. The current practice of revenue distribution is also compared with the proposed methodology using case studies. The IEEE RTS-96 is used as a test system for this work.
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    Investigation of Transmission Line Outage Impact Under Heavy Wind Penetration on LOLE Metric
    (Institute of Electrical and Electronics Engineers Inc., 2023-10) Kulkarni, Chinmay; Aravinthan, Visvakumar
    It is expected that the number of renewable energy sources will continue to increase offering unique challenges for reliability evaluation. Increase in load growth along with heavy renewable penetration will make it critical to ensure the system can deliver power to the customers. Due to fundamental difference between conventional and renewable generation existing reliability evaluation approach may no longer be sufficient. This will make it necessary to include transmission line failure in reliability evaluation if there is a significant contribution from renewable energy. The paper highlights the importance of including transmission line outage in calculation of Loss of Load Expectation (LOLE) to evaluate reliability of a transmission network and the impact of lines when there is only conventional generation on network compared with conventional and wind generation.
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    Distributed Storage Over a Public Channel: Trade-off between Privacy and Shared Key Lengths
    (Institute of Electrical and Electronics Engineers Inc., 2023-09) Keshvari, Maryam; Chou, Rémi
    Consider a user who wants to store a file across multiple servers such that at least t servers are needed to reconstruct the file and any z colluding servers cannot learn more than a fraction α of the file. Unlike traditional secret-sharing models that assume the availability of secure channels at no cost, we assume that the user can only transmit data to the servers through a public channel and that the user and each server share an individual secret key of length n bits. For a fixed key length n, we characterize the fundamental trade-off between the privacy leakage parameter α and the file length that the user can store in the servers. Furthermore, for the optimal trade-off between α and the file length, we determine (i) the minimum amount of local randomness needed by the user, (ii) the minimum amount of public communication from the user to the servers, and (iii) the minimum storage requirement at the servers.
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    A novel nonnegative matrix factorization-based model for attributed graph clustering by incorporating complementary information
    (Elsevier Ltd, 2024-05) Jannesari, Vahid; Keshvari, Maryam; Berahmand, Kamal
    Attributed graph clustering is a prominent research area, catering to the increasing need for understanding real-world systems by uncovering exhaustive meaningful latent knowledge from heterogeneous spaces. Therefore, the critical challenge of this problem is the strategy used to extract and integrate meaningful heterogeneous information from structure and attribute sources. To this end, in this paper, we propose a novel Nonnegative Matrix Factorization (NMF)-based model for attributed graph clustering. In this method, firstly, we filter structure and attribute spaces from noise and irrelevant information for clustering by applying Symmetric NMF and NMF during the clustering task, respectively. Then, to overcome the heterogeneity of discovered partitions from spaces, we suggest a new regularization term to inject the complementary information from the attribute partition into the structure by transforming them into their pairwise similarity spaces, which are homogeneous. Simultaneously, by setting orthogonality constraints on the discovered communities, we encourage the representation of distinct and non-overlapping communities within the attributed graph. Finally, we collect all these terms in a unified framework to learn a meaningful partition containing consensus and complementary information from structure and attributes. Then a new iterative multiplicative updating strategy is proposed to solve the proposed model, and its convergence is proven theoretically. Our experiments on the nine popular real-world networks illustrate the supremacy of our methods among eleven widely recognized and stat-of-the-arts attributed graph clustering methods in terms of accurately matching the ground truth and quality-based metrics.