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1. Frequency Regulation in Hybrid Power Dynamics with Variable and Low Inertia due to Renewable Energy

   https://doi.org/10.1109/CDC.2018.8619580  

   Hidalgo-Gonzalez, Patricia; Callaway, Duncan S.; Dobbe, Roel; Henriquez-Auba, Rodrigo; Tomlin, Claire J. (December 2018, Frequency Regulation in Hybrid Power Dynamics with Variable and Low Inertia due to Renewable Energy)

   As more non-synchronous renewable energy sources (RES) participate in power systems, the system's inertia decreases and becomes time dependent, challenging the ability of existing control schemes to maintain frequency stability. System operators, research laboratories, and academic institutes have expressed the importance to adapt to this new power system paradigm. As one of the potential solutions, virtual inertia has become an active research area. However, power dynamics have been modeled as time-invariant, by not modeling the variability in the system's inertia. To address this, we propose a new modeling framework for power system dynamics to simulate a time-varying evolution of rotational inertia coefficients in a network. We model power dynamics as a hybrid system with discrete modes representing different rotational inertia regimes of the network. We test the performance of two classical controllers from the literature in this new hybrid modeling framework: optimal closed-loop Model Predictive Control (MPC) and virtual inertia placement. Results show that the optimal closed-loop MPC controller (Linear MPC) performs the best in terms of cost; it is 82 percent less expensive than virtual inertia placement. It is also more efficient in terms of energy injected/absorbed to control frequency. To address the lower performance of virtual inertia placement, we then propose a new Dynamic Inertia Placement scheme and we find that it is more efficient in terms of cost (74 percent cheaper) and energy usage, compared to classical inertia placement schemes from the literature. 

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2. Signal processing to improve the speed and accuracy of electrical impedance tomography imaging

   https://doi.org/10.1117/12.2585224  

   Tran, Hoang; Pham, Vu; Le, Thong; Yoon, Hargsoon (March 2021, SPIE)

   Kim, Jaehwan (Ed.)

   Electrical impedance tomography (EIT) is a rising and emerging imaging technique with great potential in many areas, especially in functional brain imaging applications. An EIT system with high speed and accuracy can have many applications to medical devices supporting in diagnosis and treatment of neurological disorders and diseases. In this research, EIT algorithms and hardware are developed and improved to increase reconstructed images' accuracy and decrease the reconstruction time. Due to multiplexer design limitations, EIT measurements are subject to strong capacitive effects from charging and discharging in switching cycles around 300 to 400 samples per 1280 samples (in 10 milliseconds sampling). We developed an algorithm to choose data in steady-state condition only selectively. This method improves the signal-to-noise ratio and results in better reconstruction images. An algorithm to effectively synchronize the beginning points of data was developed to increase the system's speed. This presentation also presents the EIT system's hardware architecture based on Texas Instruments Fixed-Point Digital Signal Processor - TMS320VC5509A, which is low-cost, high potential in popularity the community in the future. For high operation speed, we propose the EIT system used Sitara™ AM57x processors of Texas Instruments. 

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3. A Deep Reinforcement Learning Based Approach for Home Energy Management System

   https://doi.org/10.1109/ISGT45199.2020.9087647  

   Li, Hepeng; Wan, Zhiqiang; He, Haibo (February 2020, IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2020))

   Home energy management system (HEMS) enables residents to actively participate in demand response (DR) programs. It can autonomously optimize the electricity usage of home appliances to reduce the electricity cost based on time-varying electricity prices. However, due to the existence of randomness in the pricing process of the utility and resident's activities, developing an efficient HEMS is challenging. To address this issue, we propose a novel home energy management method for optimal scheduling of different kinds of home appliances based on deep reinforcement learning (DRL). Specifically, we formulate the home energy management problem as an MDP considering the randomness of real-time electricity prices and resident's activities. A DRL approach based on proximal policy optimization (PPO) is developed to determine the optimal DR scheduling strategy. The proposed approach does not need any information on the appliances' models and distribution knowledge of the randomness. Simulation results verify the effectiveness of our proposed approach. 

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4. Securing Time in Untrusted Operating Systems with TimeSeal

   https://doi.org/10.1109/RTSS46320.2019.00018  

   Anwar, Fatima M.; Garcia, Luis; Han, Xi; Srivastava, Mani (December 2019, 2019 IEEE Real-Time Systems Symposium (RTSS))

   null (Ed.)

   An accurate sense of elapsed time is essential for the safe and correct operation of hardware, software, and networked systems. Unfortunately, an adversary can manipulate the system's time and violate causality, consistency, and scheduling properties of underlying applications. Although cryptographic techniques are used to secure data, they cannot ensure time security as securing a time source is much more challenging, given that the result of inquiring time must be delivered in a timely fashion. In this paper, we first describe general attack vectors that can compromise a system's sense of time. To counter these attacks, we propose a secure time architecture, TIMESEAL that leverages a Trusted Execution Environment (TEE) to secure time-based primitives. While CPU security features of TEEs secure code and data in protected memory, we show that time sources available in TEE are still prone to OS attacks. TIMESEAL puts forward a high-resolution time source that protects against the OS delay and scheduling attacks. Our TIMESEAL prototype is based on Intel SGX and provides sub-millisecond (msec) resolution as compared to 1-second resolution of SGX trusted time. It also securely bounds the relative time accuracy to msec under OS attacks. In essence, TIMESEAL provides the capability of trusted timestamping and trusted scheduling to critical applications in the presence of a strong adversary. It delivers all temporal use cases pertinent to secure sensing, computing, and actuating in networked systems. 

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5. DeepSnow: Modeling the Impact of Snow on Solar Generation

   https://doi.org/10.1145/3408308.3427620  

   Bashir, Noman; Irwin, David; Shenoy, Prashant (November 2020, Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys))

   null (Ed.)

   The falling cost of solar energy deployments has resulted in ever-increasing growth in solar capacity worldwide. The primary challenge posed by increasing grid-tied solar capacity is handling its variability due to continuously changing conditions. Thus, prior work has developed highly sophisticated models to estimate and forecast solar power output based on many characteristics, including location, elevation, time, weather, shading, module type, wiring, etc. These models are highly accurate for estimating solar power, especially over long periods, for sites at low latitudes, i.e., closer to the equator. However, models for sites at higher latitudes are less accurate due to the effect of snow on solar output, since even a small amount of snow can cover panels and reduce power to zero. Improving the accuracy of these models for annual solar output by even 2--3% is significant, as power translates directly into revenue, which compounds over the system's lifetime. Thus, if a site produces 2--3% less power on average per year due to snow than a model predicts, it can mean the difference between a positive or negative return-on-investment. To address the problem, we develop DeepSnow, a data-driven approach that models the effect of snow on solar power generation. DeepSnow integrates with existing solar modeling frameworks, and uses publicly available snow data to learn its effect on solar generation. We leverage deep learning to quantify the effect of different snow variables on solar power using 4 million hourly readings from 40 solar sites. We evaluate our approach on 10 solar sites, and show that it yields a higher accuracy than the current approach for modeling snow effects used by the U.S. Department of Energy's System Advisor Model (SAM), a popular solar modeling framework. 

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