Grid-interactive Buildings: Modeling, Operations, and Security

Smart grids and smart buildings are two highly interdependent energy infrastructure systems. Buildings rely on the grid to provide reliable power while their flexibility can also be utilized to enhance the reliability and efficiency of power system operations. The quantification of heating, ventilation, and air condition (HVAC) system flexibility is critical to the operations of both the grid and buildings in demand response (DR) programs. However, the flexibility quantification is challenging due to the non-linearity and non-convexity of thermal dynamics associated with HVAC components. This dissertation proposes a novel HVAC flexibility quantification method based on a semidefinite programming (SDP) formulation. The SDP is reformulated from the non-convex problem of HVAC power optimization, and can be solved efficiently in real-time. The physics-based HVAC model is incorporated to ensure the reliability and accuracy of solutions. The quantification results are organized into an HVAC flexibility table that can provide response strategies on adjusting HVAC setpoints in response to the grid signals received. The developed response strategies minimize occupant discomfort while satisfying grid requirements. A case study of a test building model is carried out to illustrate the flexibility quantification framework and compares the performance of two DR strategies. Buildings that are involved in the energy market need to follow certain power profiles. The robustness of power tracking is critical to the evaluation of their quality of service. Due to the easy accessibility of building automation systems, building sensor attacks can be launched to affect the power tracking accuracy. A robust HVAC control algorithm that can handle the uncertainty of sensor attack signal distribution is proposed to enhance the building power tracking. A Wasserstein distance-based ambiguity set is defined to bound the uncertain distortion between the predicted attack signal distribution and the true distribution. The worst-case distribution within the ambiguity set that has the largest expected power tracking error is solved. Then the robust control decision is made upon this worst-case distribution. In this way, the power tracking error can be reduced by 20%. The reliability of temperature maintenance is also enhanced by the proposed distributionally robust optimization. Besides sensor attack, the control signal of building automation system can also be overwritten if the proxy aggregator is attacked. This type of attack can impact the frequency stability of the entire system by manipulating load power across the system. To study the vulnerability of the system under control signal attack, an optimization-based attack model that incorporates the grid transient model and physics-based building model is proposed. The proposed attack model solves for the time series executable control signals that coordinate the system states and building limits at the minimum cost of building temperature deviation. This attack model is used for the vulnerability assessment of the IEEE 68-bus 16-machine system from two perspectives. The vulnerability of buses and aggregators can be obtained from the trajectories of the coordinated attack signals

Sensor Attacks and Resilient Defense on HVAC Systems for Energy Market Signal Tracking

The power flexibility from smart buildings makes them suitable candidates for providing grid services. The building automation system (BAS) that employs model predictive control (MPC) for grid services relies heavily on sensor data gathered from IoT-based HVAC systems through communication networks. However, cyber-attacks that tamper sensor values can compromise the accuracy and flexibility of HVAC system power adjustment. Existing studies on grid-interactive buildings mainly focus on the efficiency and flexibility of buildings' participation in grid operations, while the security aspect is lacking. In this paper, we investigate the effects of cyber-attacks on HVAC systems in grid-interactive buildings, specifically their power-tracking performance. We design a stochastic optimization-based stealthy sensor attack and a corresponding defense strategy using a resilient control framework. The attack and its defense are tested in a physical model of a test building with a single-chiller HVAC system. Simulation results demonstrate that minor falsifications caused by a stealthy sensor attack can significantly alter the power profile, leading to large power tracking errors. However, the resilient control framework can reduce the power tracking error by over 70% under such attacks without filtering out compromised data

Design and implementation of an automatic nursing assessment system based on CDSS technology

BACKGROUND: Various quantitative and quality assessment tools are currently used in nursing to evaluate a patient's physiological, psychological, and socioeconomic status. The results play important roles in evaluating the efficiency of healthcare, improving the treatment plans, and lowing relevant clinical risks. However, the manual process of the assessment imposes a substantial burden and can lead to errors in digitalization. To fill these gaps, we proposed an automatic nursing assessment system based on clinical decision support system (CDSS). The framework underlying the CDSS included experts, evaluation criteria, and voting roles for selecting electronic assessment sheets over paper ones.METHODS: We developed the framework based on an expert voting flow to choose electronic assessment sheets. The CDSS was constructed based on a nursing process workflow model. A multilayer architecture with independent modules was used. The performance of the proposed system was evaluated by comparing the adverse events' incidence and the average time for regular daily assessment before and after the implementation.RESULTS: After implementation of the system, the adverse nursing events' incidence decreased significantly from 0.43 % to 0.37 % in the first year and further to 0.27 % in the second year (p-value: 0.04). Meanwhile, the median time for regular daily assessments further decreased from 63 s to 51 s.CONCLUSIONS: The automatic assessment system helps to reduce nurses' workload and the incidence of adverse nursing events.</p

Case report: Page kidney with multiple serosal effusions caused by bilateral spontaneous renal subcapsular hemorrhage

Page kidney is caused by the perirenal or subcapsular accumulation of blood or fluid pressing on the renal parenchyma and is a rare cause of secondary hypertension. In this study, we report a case of Page caused by bilateral spontaneous subcapsular renal hematoma, the main manifestations of which were secondary hypertension, multiple serous effusions, and renal insufficiency. After admission, drug blood pressure control was ineffective. After bilateral perirenal effusion puncture and drainage were performed to relieve bilateral perirenal compression, blood pressure gradually dropped to normal, multi-serous cavity effusion (pericardial, thoracic, and abdominal effusion) gradually disappeared, and kidney function returned to normal. Secondary hypertension caused by Page kidney can be treated. When Page kidney is complicated with multiple serous effusions, the effect of antihypertensive drugs alone is poor, and early perineal puncture drainage can achieve better clinical efficacy

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

Communication intrusion detection in Advanced Metering Infrastructure (AMI) is an eminent security technology to ensure the stable operation of the Smart Grid. However, methods based on traditional machine learning are not appropriate for learning high-dimensional features and dealing with the data imbalance of communication traffic in AMI. To solve the above problems, we propose an intrusion detection scheme by combining feature dimensionality reduction and improved Long Short-Term Memory (LSTM). The Stacked Autoencoder (SAE) has shown excellent performance in feature dimensionality reduction. We compress high-dimensional feature input into low-dimensional feature output through SAE, narrowing the complexity of the model. Methods based on LSTM have a superior ability to detect abnormal traffic but cannot extract bidirectional structural features. We designed a Bi-directional Long Short-Term Memory (BiLSTM) model that added an Attention Mechanism. It can determine the criticality of the dimensionality and improve the accuracy of the classification model. Finally, we conduct experiments on the UNSW-NB15 dataset and the NSL-KDD dataset. The proposed scheme has obvious advantages in performance metrics such as accuracy and False Alarm Rate (FAR). The experimental results demonstrate that it can effectively identify the intrusion attack of communication in AMI