Building Flexibility Estimation and Control for Grid Ancillary Services

The increased adoption of intermittent renewable energy, such as wind and solar, onto the electrical grid is increasing the need for greater demand flexibility and the development of more advanced demand management solutions. For example, in March 2017 solar and wind set record highs in California, contributing over 49% of its power supply. Furthermore, Hawaii has committed to meeting 100% of its electrical demand from renewables by 2045. This transformation requires solutions to robustly and cost-effectively manage dynamic changes on the grid while ensuring quality of service. Advanced demand response approaches are a key way of enabling this required grid flexibility. Advances in direct digital control of building systems, combined with the increased connectivity of end devices now enable greater participation. To achieve this, end devices will need to estimate the amount of grid services (flexibility) they can offer, and then automatically fulfil that commitment when called upon without noticeable loss in quality of service (e.g. indoor comfort). This paper presents data-driven methods for estimating the demand flexibility of commercial buildings and the control architecture to enable the execution of committed reserves while ensuring quality of service. In particular, we describe the methodology for 1) qualifying the HVAC system to provide three power grid ancillary services (frequency response, frequency regulation and ramping services) based on defined metrics for response and ramp time, 2) quantifying the magnitude and frequency bandwidth of the service it can provide, and 3) controlling the building’s cooling and heating demand within the specified flexibility limits to provide grid service. UTRC’s high performance building test-bed, a medium-sized commercial office building was used for the experimental study. The building testing was focused on the air-side electricity consumer - the supply air fans in the AHU. The resulting data verifies that air-side HVAC loads (ventilation fans) are sufficiently responsive to meet the requirements of frequency regulation (\u3c5 seconds response time) and ramping services (\u3c10 minutes response time) with ON/OFF control command, direct fan speed control, and indirect control through static pressure set-point adjustment. The proposed frequency regulation control changes the command to the AHU fan motor speed (and hence power consumption) by indirectly modifying the duct static pressure set-point to track a given regulation reference signal. This architecture was selected for equipment reliability and ease of implementation. The experimental frequency response data from static pressure set-point to AHU fan power consumption shows that each ventilation fan can provide up to 1.5 kW for frequency regulation (16.7% of its rated power) during operational hours without impacting the indoor climate or baseline controls, and the acceptable frequency range was identified as 0.0055 - 0.022 Hz based on the grid response metrics and controls requirement. The accuracy of the flexibility estimation and the performance of the frequency regulation controller were verified through closed-loop active response experiment. Moreover, we describe how a population of commercial buildings with different flexibilities can be engaged and coordinated to provide adequate and reliable frequency regulation service to the grid

Model Predictive Control and Fault Detection and Diagnostics of a Building Heating, Ventilation, and Air Conditioning System

The paper presents Model Predictive Control (MPC) and Fault Detection and Diagnostics (FDD) technologies, their on-line implementation, and results from several demonstrations conducted for a large-size HVAC system. The two technologies are executed at the supervisory level in a hierarchical control architecture as extensions of a baseline Building Management System (BMS). The MPC algorithm generates optimal set points for the HVAC actuator loops which minimize energy consumption while meeting equipment operational constraints and occupant comfort constraints. The MPC algorithm is implemented using a new tool, the Berkeley Library for Optimization Modeling (BLOM), which generates automatically an efficient optimization formulation directly from a simulation model. The FDD algorithm detects and classifies in real-time potential faults of the HVAC actuators based on data from multiple sensors. The performance and limitations of FDD and MPC algorithms are illustrated and discussed based on measurement data recorded from multiple tests

A critical review of cyber-physical security for building automation systems

Modern Building Automation Systems (BASs), as the brain that enables the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as optimized automation via outsourced cloud analytics and increased building-grid integrations. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber-secure resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. The impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber-secure resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed.Comment: 38 pages, 7 figures, 6 tables, submitted to Annual Reviews in Contro

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Data-driven modeling of power generation for a coal power plant under cycling

Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants. The power plants operating on the base load are forced to cycle, to adjust to the fluctuating power demands. This results in an inefficient operation of the coal power plants, which leads up to higher operating losses. To overcome such operational challenge associated with cycling and to develop an optimal process control, this work analyzes a set of models for predicting power generation. Moreover, the power generation is intrinsically affected by the state of the power plant components, and therefore our model development also incorporates additional power plant process variables while forecasting the power generation. We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model. We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting. The trained deep neural network (DNN) LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting. The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short, medium and long range predictions. The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting, and also allows to interpret the significance of internal power plant components on the power generation. This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods