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A privacy-preserving average consensus algorithm is designed based on the Beaver triple technique against passive adversaries. The Beaver triple technique is integrated into a restructure of the discrete-time average consensus algorithm to preserve the privacy of initial values of agents in a multiagent system. The performance of the algorithm is theoretically analyzed.
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Here we establish finite time termination algorithms for consensus algorithms based on geometric properties that yield finite-time guarantees, suited for use in high dimension and in the absence of a central authority. These pursuits motivate a new peer to peer convex hull algorithm which is utilized for one stopping algorithm. Further an alternative lightweight norm based stopping criteria is also developed. The practical utility of the algorithm is illustrated through MATLAB simulations.
This paper considers a leader-following consensus in the presence of unknown but bounded cyber-attacks. Specifically, we consider the following cyber-attack scenarios: (i) an attacker aims to destabilize the consensus dynamics by injecting exogenous signals to both the actuators of the followers and/or the communication network, (ii) an eavesdropper adversary aims to obtain information on the physical state of the agents. To this end, a novel resilient leader-following consensus algorithm based on a competitive interaction method is proposed. In addition, it is demonstrated that by appropriately choosing the information exchanged between the agents, the proposed control framework also enables the cooperative system to either distributively identify the compromised communication links in real-time or to protect the privacy of the physical state of the agents from the eavesdropper. Here, a numerical example is provided to illustrate the proposed resilient control algorithms.
This paper presents a novel approach to enable multiple Unmanned Aerial Systems approaching a common intersection to independently schedule their arrival time while maintaining a safe separation. Aircraft merging at a common intersection are grouped into a network and each aircraft broadcasts its arrival time interval to the network. A distributed consensus algorithm elects a leader among the aircraft approaching the intersection and helps synchronize the information received by each aircraft. The consensus algorithm ensures that each aircraft computes a schedule with the same input information. The elected leader also dictates when a schedule must be computed, which may be triggered when a new aircraft joins the network. Preliminary results illustrating the collaborative behavior of the vehicles are presented.
This is the final project report for the Grid Modernization Laboratory Consortium (GMLC) Resilient Distribution System (RDS) Citadels project. The primary goal of this GMLC project was to increase the operational flexibly of power systems by engaging microgrids distributedly, coordinated using consensus algorithms. The primary goal was successfully achieved. The primary goal was divided into three areas: Implement peer-to-peer control between microgrid controllers using the Open Field Message Bus (OpenFMB) approach; Develop and implement consensus algorithms on commercially available hardware that allows a group of microgrids to distributedly implement operational controls; Develop the architectures and controls to enable groups of microgrids to coordinate their operations to support the bulk power system during abnormal events, and end-use loads in the event the bulk power systems fail.
A privacy-preserving average consensus algorithm is proposed that synergizes the Beaver triple in secret sharing theory and noise obfuscation. The algorithm safeguards the initial values of agents against passive adversaries in a multiagent system. It is proved that the proposed algorithm can concurrently ensure average consensus and privacy, while also reducing the online computation and communication overhead compared to encryption-based ones. In addition, it imposes a less stringent condition for privacy preservation compared to certain noise-obfuscation techniques.
The increasing deployment of distributed energy resources (DERs) and microgrids (MGs) in power distribution systems has enabled the adjustment of reactive power consumption as seen at the substation, which can be used to provide voltage support for the bulk power system (BPS). Leveraging this new capability will provide greater resiliency to the power system as a whole. Here, the goal of this paper is to develop and compare three different algorithms, namely distributed optimal power flow, distributed consensus algorithm, and fully decentralized collaborative autonomy for unbalanced distribution systems for microgrid coordination. These algorithms use networked MGs to support the BPS voltage when a contingency at the bulk grid results in abnormally low voltages, which may be a precursor to voltage collapse. Our comparative analysis includes both qualitative and quantitative assessments of the three algorithms and a discussion of the trade-offs between the decentralized and distributed methods in normal and disrupted conditions. Each algorithm was evaluated on the modified IEEE 13-bus system and a real power distribution system at Chattanooga, Tennessee, that encompasses more than 4500 buses. Each algorithms excels differently and may be suited for different scenarios depending on the condition, operations, and priorities of the power and communication systems.
This report includes two main accomplishments of the peer-to-peer communication control for resilient operation of networked microgrids project in FY24, which include a scheme for cyberattack-aware coordination of networked microgrids for supporting voltages of bulk power systems and a scheme for price signal-based operations of EV-rich networked microgrids with mixed ownership. First, the cyberattack-aware scheme enables networked microgrids to distributedly determine the amount of reactive power injection to support the voltage of bulk power system (BPS) in a fair manner. In this scheme, a risk-informed algorithm is presented to generate the peer-to- peer (P2P) communication graph with minimal risk of attack on communication links. To deal with cyberattacks on MG controllers, the resilient consensus algorithm (CA) is utilized for MG controllers to robustly estimate the total reactive power headroom, from which the MGs can accurately provide the needed amount of reactive power injection for supporting the voltage of BPS. The CA implementation and performance within the P2P communication framework are demonstrated on the IEEE 39-bus system with 6 microgrids contained in the distribution feeder under different cyberattack scenarios. Second, the price-based scheme enables the usage of the real-time price signal for the operations of electric vehicle (EV)-rich networked-microgrids with mixed ownership, in which not all the microgrids can communicate with the distribution system operator (DSO). In this scheme, a max consensus is introduced to enable the real-time price signal to be propagated from the DSO to all the microgrids, from which each microgrid controller will manage the DERs to balance the load demand and the power injection from the EV charging stations within its microgrid. Numerical results over one day with 288 slots of 5-minute intervals on the modified 123-node test feeder including 3 microgrids with high penetration of EV are presented to evaluate how the price signal affects the operations of networked microgrids under different charging strategies of the EV charging stations. The result indicates that our proposed EVCS (dis)charging strategy, which leverages the flexibility of EVs to support the grid through discharging during peak demand, proves to be a cost-effective solution that reduces operational costs while improving the social welfare of EV charging.
Inverter-based storages are poised to play a prominent role in future power grid with massive renewable generation. Grid-forming inverters (GFMs) are emerging as a dominant technology with synchronous generators (SG)-like characteristics through primary control loops. Advanced secondary-layer control schemes, e.g., consensus algorithms, allow GFM-interfaced storage units to participate in frequency regulations and restore nominal frequency following grid disturbances. However, it is imperative to ensure critical frequency safety limits are not violated while the grid transitions from pre- to post-disturbance operating point. This paper presents a novel safety-enforced consensus method, having three distinct objectives: safe transient frequency evolution, minimizing frequency deviation, and coordinated power sharing. The proposed technique is illustrated using a GFM-interfaced grid-wide storage network on the IEEE 68-bus system under multiple grid transient scenarios.
Networking of microgrids can provide the operational flexibility needed for the increasing number of DERs deployed at the distribution level and supporting end-use demand when there is loss of the bulk power system. But, networked microgrids are vulnerable to cyber-physical attacks and faults due to the complex interconnections. As such, it is necessary To design resilient control systems to support the operations of networked microgrids in responses to cyber-physical attacks and faults. This paper introduces a resilient communication scheme for interconnecting multiple microgrids to support critical demand, in which the interconnection decision can be made distributedly by each microgrid controller even in the presence of cyberattacks to some communication links or microgrid controllers. This scheme blends a randomized peer-to-peer communication network for exchanging information among controllers and resilient consensus algorithms for achieving reliable interconnection agreement. The network of 6 microgrids divided from a modified 123-node test distribution feeder is used to demonstrate the effectiveness of the proposed resilient communication scheme.
This paper presents a framework for distributed inertia management based on consensus algorithm. We propose a methodology to achieve optimal operation of inertia sources such as distributed energy resources (DERs) and synchronous generators in real time. Additionally, we analyze the algorithm under communication constraints and evaluate its robustness under scenarios involving communication time delays and packet losses. The proposed approach is validated via simulations on a 4-node system test feeder, demonstrating its effectiveness and resilience.
Active distribution system resources have the potential to support the bulk power system (BPS) during abnormal conditions, increasing reliability and resilience of power systems and end-use customers. This paper investigates for the first time the BPS voltage support provided by networked microgrids at the feeder level. A peer-to-peer (P2P) communication control framework is introduced to enable the coordination of multiple microgrids in a mixed ownership environment. This allows the microgrids to run a consensus algorithm (CA) to distributedly determine their own reactive power injections. Thus, the reactive power injection contribution is realized in a fair manner by having the same ratio of reactive power injection to the reactive power headroom for all microgrids. The CA implementation and performance within the P2P network framework are demonstrated on the IEEE 39-bus testcase system with 10 microgrids part of the distribution feeder assumed to be connected to one of the BPS buses.
The NOAA/NASA Pathfinder program was created by the Earth Observing System (EOS) Program Office to determine how satellite-based data sets can be processed and used to study global change. The data sets are designed to be long time-sedes data processed with stable calibration and community consensus algorithms to better assist the research community. The Ocean Altimeter Pathfinder Project involves the reprocessing of all altimeter observations with a consistent set of improved algorithms, based on the results from TOPEX/POSEIDON (T/P), into easy-to-use data sets for the oceanographic community for climate research. This report describes the processing schemes used to produce a consistent data set and two of the products derived f rom these data. Other reports have been produced that: a) describe the validation of these data sets against tide gauge measurements and b) evaluate the statistical properties of the data that are relevant to climate change. The use of satellite altimetry for earth observations was proposed in the early 1960s. The first successful space based radar altimeter experiment was flown on SkyLab in 1974. The first successful satellite radar altimeter was flown aboard the Geos-3 spacecraft between 1975 and 1978. While a useful data set was collected from this mission for geophysical studies, the noise in the radar measured and incomplete global coverage precluded ft from inclusion in the Ocean Altimeter Pathfinder program. This program initiated its analysis with the Seasat mission, which was the first satellite radar altimeter flown for oceanography.
The NOAA/NASA Pathfinder program was created by the Earth Observing System (EOS) Program Office to determine how existing satellite-based data sets can be processed and used to study global change. The data sets are designed to be long time-series data processed with stable calibration and community consensus algorithms to better assist the research community. The Ocean Altimeter Pathfinder Project involves the reprocessing of all altimeter observations with a consistent set of improved algorithms, based on the results from TOPEX/POSEIDON (T/P), into easy-to-use data sets for the oceanographic community for climate research. Details are currently presented in two technical reports: Report# 1: Data Processing Handbook Report #2: Data Set Validation This report describes the validation of the data sets against a global network of high quality tide gauge measurements and provides an estimate of the error budget. The first report describes the processing schemes used to produce the geodetic consistent data set comprised of SEASAT, GEOSAT, ERS-1, TOPEX/ POSEIDON, and ERS-2 satellite observations.
In this presentation we will explore several distributed consensus algorithms developed to maintain coordination among a fleet of heterogeneous UAS subject to external disturbances. An overview of the network assumptions and autopilot performance bounds required to meet the coordination objective will be provided. In particular, we will leverage passivity-based stability conditions for bidirectional networks that require the communication graph to be connected in an integral sense, but may fail to connect pointwise in time during the entire mission. Non-ideal autopilots that can only track a desired speed profile with a prescribed precision induce an error in vehicle coordination. The autopilot performance bounds will be propagated through the coordination dynamics to understand the limits introduced by autopilots.
High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)- enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.
High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)-enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.