skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: A Study of Virtual Energy Storage From Thermostatically Controlled Loads Under Time-Varying Weather Conditions
We propose a control architecture for distributed coordination of a collection of on/off TCLs (thermostatically con- trolled loads), such as residential air conditioners, to provide the same service to the power grid as a large battery. A key constraint is to ensure that consumers’ quality of service (QoS) is maintained. Our proposal involves replac- ing the thermostats at the loads by a randomized controller, following recent proposals in this direction. The new local controller has a tunable parameter that serves as the control command from the balancing authority (BA). Com- pared to prior work in this area, our proposed architecture can handle large disturbances from the outside temperature. Weather-induced disturbance also imposes an algorithm-independent limit on the capacity of the virtual energy storage the loads can provide. This key limitation, which was ignored in prior work, is incorporated in our formulation in a principled manner.  more » « less
Award ID(s):
1646229
PAR ID:
10076821
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
5th International High Performance Buildings Conference
Volume:
5
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, American Control Conference)
    null (Ed.)
    With increase in the frequency of natural disasters such as hurricanes that disrupt the supply from the grid, there is a greater need for resiliency in electric supply. Rooftop solar photovoltaic (PV) panels along with batteries can provide resiliency to a house in a blackout due to a natural disaster. Our previous work showed that intelligence can reduce the size of a PV+battery system for the same level of post-blackout service compared to a conventional system that does not employ intelligent control. The intelligent controller proposed is based on model predictive control (MPC), which has two main challenges. One, it requires simple yet accurate models as it involves real-time optimization. Two, the discrete actuation for residential loads (on/off) makes the underlying optimization problem a mixed-integer program (MIP) which is challenging to solve. An attractive alternative to MPC is reinforcement learning (RL) as the real-time control computation is both model-free and simple. These points of interest accompany certain trade-offs; RL requires computationally expensive offline learning, and its performance is sensitive to various design choices. In this work, we propose an RL-based controller. We compare its performance with the MPC controller proposed in our prior work and a non-intelligent baseline controller. The RL controller is found to provide a resiliency performance — by commanding critical loads and batteries—similar to MPC with a significant reduction in computational effort. 
    more » « less
  2. ; ; (, IEEE Open Journal of the Communications Society)
    SATCOM is crucial for tactical networks, particularly submarines with sporadic communi- cation requirements. Emerging SATCOM technologies, such as low-earth-orbit (LEO) satellite networks, provide lower latency, greater data reliability, and higher throughput than long-distance geostationary (GEO) satellites. Software-defined networking (SDN) has been introduced to SATCOM networks due to its ability to enhance management while strengthening network control and security. In our previous work, we proposed a SD-LEO constellation for naval submarine communication networks, as well as an extreme gradient boosting (XGBoost) machine-learning (ML) approach for classifying denial-of-service attacks against the constellation. Nevertheless, zero-day attacks have the potential to cause major damage to the SATCOM network, particularly the controller architecture, due to the scarcity of data for training and testing ML models due to their novelty. This study tackles this challenge by employing a predictive queuing analysis of the SD-SATCOM controller design to rapidly generate ML training data for zero- day attack detection. In addition, we redesign our singular controller architecture to a decentralized controller architecture to eliminate singular points of failure. To our knowledge, no prior research has investigated using queuing analysis to predict SD-SATCOM controller architecture network performance for ML training to prevent zero-day attacks. Our queuing analysis accelerates the training of ML models and enhances data adaptability, enabling network operators to defend against zero-day attacks without precollected data. We utilized the CatBoost algorithm to train a multi-output regression model to predict network performance statistics. Our method successfully identified and classified normal, non-attack samples and zero-day cyberattacks with over 94% accuracy, precision, recall, and f1-scores. 
    more » « less
  3. ; (, ASME Journal of Engineering for Sustainable Buildings and Cities)
    null (Ed.)
    Abstract An autonomous adaptive model predictive control (MPC) architecture is presented for control of heating, ventilation, and air condition (HVAC) systems to maintain indoor temperature while reducing energy use. Although equipment use and occupant changes with time, existing MPC methods are not capable of automatically relearning models and computing control decisions reliably for extended periods without intervention from a human expert. We seek to address this weakness. Two major features are embedded in the proposed architecture to enable autonomy: (i) a system identification algorithm from our prior work that periodically re-learns building dynamics and unmeasured internal heat loads from data without requiring re-tuning by experts. The estimated model is guaranteed to be stable and has desirable physical properties irrespective of the data; (ii) an MPC planner with a convex approximation of the original nonconvex problem. The planner uses a descent and convergent method, with the underlying optimization problem being feasible and convex. A yearlong simulation with a realistic plant shows that both of the features of the proposed architecture—periodic model and disturbance update and convexification of the planning problem—are essential to get performance improvement over a commonly used baseline controller. Without these features, long-term energy savings from MPC can be small while with them, the savings from MPC become substantial. 
    more » « less
  4. ; (, CoNEXT ’19 Companion)
    Software-Defined Networking (SDN) is a dynamic, and manageable network architecture which is more cost-effective than existing network architectures. The idea behind this architecture is to centralize intelligence from the network hardware and funnel this intelligence to the management system (controller) [2]-[4]. Since the centralized SDN controller controls the entire network and manages policies and the flow of the traffic throughout the network, it can be considered as the single point of failure [1]. It is important to find some ways to identify different types of attacks on the SDN controller [8]. Distributed Denial of Service (DDoS) attack is one of the most dangerous attacks on SDN controller. In this work, we implement DDoS attack on the Ryu controller in a tree network topology using Mininet emulator. Also, we use a machine learning method, Vector Machines (SVM) to detect DDoS attack. We propose to install flows in switches, and we consider time attack pattern of the DDoS attack for detection. Simulation results show the effects of DDoS attacks on the Ryu controller is reduced by 36% using our detection method. 
    more » « less
  5. ; ; (, 2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS))
    Network service mesh architectures, by interconnecting cloud clusters, provide access to services across distributed infrastructures. Typically, services are replicated across clusters to ensure resilience. However, end-to-end service performance varies mainly depending on the service loads experienced by individual clusters. Therefore, a key challenge is to optimize end-to-end service performance by routing service requests to clusters with the least service processing/response times. We present a two-phase approach that combines an optimized multi-layer optical routing system with service mesh performance costs to improve end-to-end service performance. Our experimental strategy shows that leveraging a multi-layer architecture in combination with service performance information improves end-to-end performance. We evaluate our approach by testing our strategy on a service mesh layer overlay on a modified continental united states (CONUS) network topology. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email