Two-Level Decentralized-Centralized Control of Distributed Energy Resources in Grid-Interactive Efficient Buildings
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This paper investigates the impact of the delay resulting from a blockchain, a promising security measure, for a hierarchical control system of inverters connected to the grid. The blockchain communication network is designed at the secondary control layer for resilience against cyberattacks. To represent the latency in the communication channel, a model is developed based on the complexity of the blockchain framework. Taking this model into account, this work evaluates the plant’s performance subject to communication delays, introduced by the blockchain, among the hierarchical control agents. In addition, this article considers an optimal model-based control strategy that performs the system’s internal control loop. The work shows that the blockchain’s delay size influences the convergence of the power supplied by the inverter to the reference at the point of common coupling. In the results section, real-time simulations on OPAL-RT are performed to test the resilience of two parallel inverters with increasing blockchain complexity.
Load unbalance in electric distribution systems is unavoidable. Unbalanced load currents will lead to negative sequence (NS) voltages that may damage electric equipment. Unbalanced power flow analysis is a common tool to detect and mitigate NS voltage issues and requires accurate models of grid components. While traditional source models are available, grid-forming (GFM) inverter models are not well developed. GFM inverters implement various control strategies, which affect their power flow models. Here, in this paper, a novel GFM inverter model considering control effects is proposed. We show that for some control methods, unbalanced system loading will lead to unbalanced terminal voltages of the GFM inverters, which are modeled through an equivalent negative sequence impedance. The proposed models are initially validated using a simple test circuit. Then, they are applied in the power flow analysis on the IEEE 13-bus and 34-bus systems to demonstrate the accuracy improvement over the state-of-the-art. Using time-domain simulations as benchmarks, we show that the proposed models reduce the calculation error of negative sequence voltages by at least 25% in unbalanced distribution systems.
Under existing grid operations, large synchronous generators provide sufficient rotational inertia to form a rigid backbone for the bulk power system. With photovoltaics (PV) forecasted to provide more than 600 GW of generation by 2050 under the U.S. Department of Energy’s SunShot Initiative objectives, however, it is clear that power electronic inverters will play a dominant role in future systems, and low-inertia stability must be ensured to maintain system reliability. Today, the risks to system stability can be observed on geographically small islands, such as Hawaii, which contain a relatively large amount of installed PV. These risks stem from a fundamental shortcoming of contemporary control strategies—existing inverter controllers cannot guarantee grid stability. Given that future power systems driven by sustainable resources will be characterized by low inertia, locations such as Hawaii provide a glimpse into the obstacles facing future power systems. Considering these challenges, the aim of this project was to develop and demonstrate distributed inverter controllers that enable the reliable control of low-inertia power systems with hundreds of gigawatts of integrated PV.
The purpose of this project is to understand dynamic phenomena by designing dynamic models of utility-sale solar photovoltaic (PV) power plants and their interactions with grids; and construct effective coordination strategies for stability enhancement for solar PVs to respond to rapidly changing grid conditions.
Forced oscillation events have become a challenging problem with the increasing penetration of renewable and other inverter-based resources (IBRs), especially when the forced oscillation frequency coincides with the dominant natural oscillation frequency. A severe forced oscillation event can deteriorate power system dynamic stability, damage equipment, and limit power transfer capability. This paper proposes a two-dimension scanning forced oscillation grid vulnerability analysis method to identify areas/zones in the system that are critical to forced oscillation. These critical areas/zones can be further considered as effective actuator locations for the deployment of forced oscillation damping controllers. Additionally, active power modulation control through IBRs is also proposed to reduce the forced oscillation impact on the entire grid. The proposed methods are demonstrated through a case study on a synthetic Texas power system model. The simulation results demonstrate that the critical areas/zones of forced oscillation are related to the areas that highly participate in the natural oscillations and the proposed oscillation damping controller through IBRs can effectively reduce the forced oscillation impact in the entire system.
This is a more urgent problem than it might sound. The westernmost Hawaiian island of significant size, Kauai is home to around 70,000 residents and 30,000 tourists at any given time. Renewable energy accounts for 70 percent of the energy produced in a typical year - a proportion that's among the highest in the world and that can be hard to sustain for such a small and isolated grid. During the day, the local system operator, the Kauai Island Utility Cooperative, sometimes reaches levels of 90 percent from solar alone. But on 2 April, the 26-MW generator was running near its peak output, to compensate for the drop in solar output as the sun set. At the moment when it failed, that single generator had been supplying 60 percent of the load for the entire island, with the rest being met by a mix of smaller generators and several utility-scale solar-and-battery systems.
State redistribution is an algorithm that stabilizes cut cells for embedded boundary grid methods. This work extends the earlier algorithm in several important ways. First, state redistribution is extended to three spatial dimensions. Second, we discuss several algorithmic changes and improvements motivated by the more complicated cut cell geometries that can occur in higher dimensions. In particular, we introduce a weighted version with less dissipation in an easily generalizable framework. Third, we demonstrate that state redistribution can also stabilize a solution update that includes both advective and diffusive contributions. Notably, the stabilization algorithm is shown to be effective for incompressible as well as compressible reacting flows. Finally, we discuss the implementation of the algorithm for several exascale-ready simulation codes based on AMReX, demonstrating ease of use in combination with domain decomposition, hybrid parallelism and complex physics.
Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness. The measurements from these sensors can be leveraged for real-time monitoring, control, and protection. However, these measurements are typically irregularly sampled. These measure-ments may also be intermittent due to communication bandwidth limitations. To tackle this problem, this paper proposes a novel latent neural ordinary differential equations (LODE) approach to aggregate the unevenly sampled multivariate time-series measurements. The proposed approach is flexible in performing both imputations and predictions while being computationally efficient. Simulation results on IEEE 37 bus test systems illustrate the efficiency of the proposed approach.
The proposed work focuses on the possibility of achieving the reduction in the size of the interfacing filter for the grid connection for a back-to-back modular multilevel converter system with the use of third order LCL filters. To achieve the same attenuation, it is possible to reduce the size of the interfacing filters by the usage of third order filters. However, this kind of filtering comes with the limitation of having sustained oscillations due to lack of damping especially during transient changes. Therefore, in this work, a methodology has been proposed based on the principle of internal model to cater for this unwanted oscillations. The third order system is modeled inside the microcontroller with the damping enabled. The error between the output from the model and the actual are compared and the error is utilized to accomplish the active damping strategy. The proposed architecture is verified via computer simulations based on MATLAB/Simulink domain and various case study results along with their discussion is presented in this paper.
As the integration of renewable energy resources such as wind and solar generation continues to grow, the use of hybrid power plants with both synchronous machines and power electronic inverters operating in parallel becomes more common. This paper studies the impact on small-signal stability for these pairs of devices, first as an isolated two-device system and then as part of a 9-bus test system. Each operational scenario was studied with both a grid-following inverter and a grid-forming inverter. In each scenario, the state variables associated with the synchronous generator's excitation system were found to be responsible for the inception of instability. These results underscore the importance of accurate modeling of excitation systems when modeling power systems with hybrid power plants.
Power system restoration is an important part of system planning. Power utilities are required to maintain black start capable generators that can energize the transmission system and provide cranking power to non-blackstart capable generators. Traditionally, hydro and diesel units are used as black start capable generators. With the increased penetration of bulk size solar farms, inverter based generation can play an important role in faster and parallel black start thus ensuring system can be brought back into service without the conventional delays that can be expected with limited black start generators. Inverter-based photovoltaic (PV) power plants have advantages that are suitable for black start. This paper proposes the modeling, control, and simulation of a grid-forming inverter-based PV-battery power plant that can be used as a black start unit. The inverter control includes both primary and secondary control loops to imitate the control of a conventional synchronous machine. The proposed approach is verified using a test system modified from the IEEE 9-bus system in the time-domain electromagnetic transient simulation tool PSCAD. The simulation results shows voltage and frequency stability during a multi-step black-start and network energization process.
Blockchain technology is a relatively novel technology that can be used to develop more decentralized, autonomous and tamper-evident solutions. A feature that can aid Transactive Energy Systems to reach their goals by enabling individual actors to communicate and reach consensus with other participants in a more decentralized fashion. However, technical barriers to evaluate and adopt this type of technology within the electrical domain still exist. To facilitate this task, BLOSEM Unified Testing Platform (UTP), a DOE-sponsored, multi-lab effort intends to accelerate the development of solutions by offering a common set of reusable services that can be used to interconnect existent grid tools with blockchain services. UTP is intended to serve as development platform that can provide application engineers with the technical means to evaluate potential blockchain solutions, by enabling them to concentrate on the actual application functionalities while at the same time abstracting the connectivity and performance measurement tasks. The use of BLOSEM UTP is further demonstrated by implementing two potential use cases that are intended to validate both the feasibility of implementing these applications as blockchain-based solutions while also demonstrating the features provided by UTP.
Smart buildings, especially Grid-interactive Efficient Buildings (GEBs), suffer from cyber-attacks and physical faults due to the integration of a large number of sensors and controls, connected devices, and associated communication networks. This study demonstrated a real-time advanced building resilient platform, called CYber Defense and REsilient System (CYDRES), which is deployable for existing and emerging Building Automation Systems (BASs). CYDRES aims to empower GEBs with cyber-attack-immune capabilities through multi-layer prevention and adaptation mechanisms to monitor, detect, and respond to cyber-attacks and physical operational faults. CYDRES is demonstrated through real-time experiments in a Hardware-in-the-Loop (HIL) testbed.
To support high spatial and temporal resolution land surface modeling, this dataset provides hourly time step historic weather forcing at 1-km spatial resolution for the entire North America. The latest Daymet V4 data provides gridded historic daily weather observations at 1-km spatial resolution from 1980 to 2024. Using sub-daily temporal information from the ECMWF ERA5 reanalysis, Daymet was further temporally downscaled to hourly time steps and provided in the format required for land surface model simulations. The process of temporal downscaling preserves the relative magnitude in each hourly time step from ERA5 while maintaining the total and average values from Daymet for each day. This results in a blended 1980-2024 Daymet-ERA5 dataset. Available variables include surface air temperature, precipitation, specific humidity, shortwave and longwave radiation, wind speed, and pressure. These data can be used as a high-resolution meteorological forcing dataset to support high-resolution land surface modeling where accurate meteorological forcing datasets built from historic observations and/or reanalysis datasets are desirable.
This dataset supports research on graph foundation models for optimal power flow (OPF) on electric grids using HydraGNN. It contains heterogeneous graph representations of PGLib-OPF cases spanning systems from 14 to 13,659 buses, together with packed HDF5 datasets for pretraining, feasibility classification, and N-1 contingency analysis. The release includes OPF solution data, downstream fine-tuning datasets, pretrained HeteroSAGE and HeteroHEAT model checkpoints, hyperparameter-optimization summaries across multiple heterogeneous GNN architectures, and aggregated fine-tuning results for sample-efficiency studies. The dataset is designed to enable scalable training, evaluation, and transfer-learning studies for OPF surrogate modeling, including node-level AC-OPF solution prediction, graph-level prediction, feasibility classification, operating-condition generalization, and contingency-response tasks.
This project aims to develop and enhance a low-cost, highly scalable control solution for Small and Medium-Sized Commercial Buildings (SMCB), assess the business potential at multiple sites, and perform commercialization efforts. The technology can be applied to any buildings served by multiple units, with the benefits being greatest for open-spaced buildings, such as banks, retail stores, restaurants, and factories. This project aims to develop an affordable control solution for: 1) SMCB grid responsiveness, 2) reduction of GHG by changing unit operations, 3) greater reduction in utility costs, and 4) rapid adoption in the marketplace. The proposed technology will be built on a previously developed and demonstrated MPC solution. The minimal sensor requirement and less need of control expertise are the unique feature of the algorithm that leads to low capital and maintenance costs, and short installation and implementation time. These attributes contribute to low capital and maintenance costs, as well as a short installation and implementation time. However, these advantages come with a trade-off: increased difficulties and unreliability when applying traditional modeling and MPC control approaches due to limited information. This final report describes the modeling approaches developed and tested to overcome these challenges. It begins by outlining the modeling challenge posed by minimal sensor requirements, then delves into the proposed modeling approaches, which primarily involve system identification. Finally, preliminary test results for a simulation case study are presented.