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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 559 records · Page 31
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DOE OSTI2019

Dynamic Microgrids With Self-Organized Grid-Forming Inverters in Unbalanced Distribution Feeders

In contrast to conventional static microgrids (MGs), MGs with dynamic and adjustable territories (i.e., dynamic MGs) are proposed and implemented in this paper. Dynamic MGs are commonly dominated by grid-forming inverters and nested in unbalanced distribution feeders. Unlike balanced systems where only positive-sequence components exist, proper operation of unbalanced dynamic MGs presents additional challenges. A distributed secondary control strategy is developed in this study for distributed generators (DGs) interfaced with gridforming inverters in unbalanced dynamic MGs by providing coordinated regulations on both positive- and negative-sequence system models. System frequency and voltage are under constant regulation, along with voltage unbalance (VU) management for multiple critical load buses (CLB). The proposed control strategy enables seamless system transition during unbalanced dynamic MGs reconfiguration, and guarantees proportional positive- and negative-sequence power sharing among connected DGs with respect to system topology variation. Detailed controller designs are provided and stability analyses are derived. The proposed control strategy is fully implemented in hardware controllers and validated on a Hardware-in-the-Loop (HIL) MG testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Dynamic Microgrids in Resilient Distribution Systems With Reconfigurable Cyber-Physical Networks

Modern distribution systems energized by inverter-interfaced distributed generators (DGs) operate as coupled cyber-physical networks (C/P-networks), where the controllable components in the physical network (P-network) are coordinated through the cyber network (C-network). The concept of dynamic microgrids (MGs) operation has been adopted to enable distribution system autonomous operation with varying electric boundaries. To further enhance system operation resiliency and flexibility, dynamic MGs operation with reconfigurable C/P-networks is discussed in this work. An evaluation framework is proposed to assess the operational feasibility of distribution feeders with multiple inverter-based dynamic MGs and come out with possible restoration solutions in the context of cross-layer C/P-network reconfiguration. Furthermore, distributed controllers are developed for components with different operating characteristics to realize seamless system topology variations and provide coordinated secondary regulation in various operation modes. Furthermore, the proposed evaluation framework along with the developed distributed controller has been validated using a Hardware-in-the-Loop (HIL) real-time CPS testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Distributed Secondary Control of Grid-Forming Inverters and AC Microgrids: Impacts of Voltage Feedback Choices

In this paper, the operational choices in distributed secondary control are examined, and their impacts on grid-forming (GFM) inverters and grid operation are investigated. In particular, the effects of feedback voltage choices, either average voltage feedback (AVF) or terminal voltage feedback (TVF), are studied with respect to reactive power sharing, voltage regulation, and grid-forming operation during black start. The secondary control used is a subgradient-based distributed cooperative control, and it provides the voltage, angle, and frequency references to the GFM inverters and their primary tracking controls. The overall control architecture employs multi-rate sampling such that the secondary control is slower than the primary control. The operational choices and their control performance are illustrated using a four-GFM-inverter microgrid in MATLAB/SIMULINK. Furthermore, the results show the effectiveness of the secondary controller, and several conclusions are drawn on the voltage feedback signals as the design choices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Peer-to-Peer Energy Trading in a Prosumer-Based Community Microgrid: A Game-Theoretic Model

This paper proposes a novel game-theoretic model for peer-to-peer (P2P) energy trading among the prosumers in a community. The buyers can adjust the energy consumption behavior based on the price and quantity of the energy offered by the sellers. There exist two separate competitions during the trading process: 1) price competition among the sellers; and 2) seller selection competition among the buyers. The price competition among the sellers is modeled as a noncooperative game. The evolutionary game theory is used to model the dynamics of the buyers for selecting sellers. Moreover, an M-leader and N-follower Stackelberg game approach is used to model the interaction between buyers and sellers. Two iterative algorithms are proposed for the implementation of the games such that an equilibrium state exists in each of the games. The proposed method is applied to a small community microgrid with photo-voltaic and energy storage systems. Simulation results show the convergence of the algorithms and the effectiveness of the proposed model to handle P2P energy trading. The results also show that P2P energy trading provides significant financial and technical benefits to the community, and it is emerging as an alternative to cost-intensive energy storage systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Privacy-Preserving Robust Consensus for Distributed Microgrid Control Applications

Consensus-based distributed control has been proposed for coordinating distributed energy resources (DERs) in microgrids (MGs). As one key component, distributed average observers are used to estimate the average of a group of reference signals (e.g., voltage, current, or power). State-of-the-art distributed average observers could lead to loss of privacy due to information exchange on the communication channels. The DERs' reference signals, which contain private information, could be inferred by an eavesdropper. In this article, a privacy-preserving distributed average observer is proposed that is based on robust consensus and uses the state decomposition method to preserve privacy. Compared to the existing methods, the proposed observer does not require the knowledge of the reference signal's derivative and gives accurate and smooth estimation, and is thus applicable for MG distributed control applications. A detailed analysis regarding the convergence and privacy properties of the proposed observer is presented. Here, the proposed observer is implemented on hardware controllers and validated in the context of distributed MG control applications through hardware-in-the-loop (HIL) tests.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Topology-Agnostic, Scalable, Self-Healing, and Cost-Aware Protection of Microgrids

Most microgrid protection schemes found in published literature suffer from a lack of generality in that they work well for the assumed topology, including type and placement of sources. Other generic protection schemes tend to be too complicated, too expensive, or both. To overcome these draw- backs, a topology-agnostic, scalable, and cost-aware protection based on fundamental principles that work in the presence of high penetration of inverter-based resources (IBRs) is developed and tested in this paper. Here, the protection system also implements stable automatic reconfiguration of the healthy sections of the system after clearance of fault, thus increasing resilience by self- healing. To achieve this ambitious goal, stable inverter models are developed that operate in unbalanced networks in grid-connected and islanded modes, even with 100% IBRs, share power without conflicting controls, and can ride through faults while limiting fault currents. The scheme is tested for primary and backup protection and reconfiguration on the IEEE 123-node feeder in grid-connected and islanded modes with 15 IBRs connected to the system.

42 ENGINEERING↗
DOE OSTI2018

A Novel Transactive Energy Control Mechanism for Collaborative Networked Microgrids

Collaboration of networked microgrids (NMGs) with diverse generation sources is a promising solution to smooth volatile generation output and enhance the utilization efficiency of renewable energies. In addition to centralized and decentralized collaboration mechanisms, transactive energy control (TEC) is an emerging and effective market-based control to enable energy transactions among distributed entities such as NMGs. However, existing studies on TEC suffer from several major weaknesses such as unconstrained/simplified model formulations and slow convergence rates. This paper proposes here a novel TEC mechanism to tackle these weaknesses. First, the centralized mechanism, de-centralized mechanism, and subgradient-based TEC mechanism to coordinate the operation of NMGs are briefly reviewed and modeled by a scenario-based stochastic optimization method. A new TEC mechanism is then proposed, consisting of a TEC framework, mathematical model, pricing rule, and algorithm. The optimality of the proposed TEC mathematical model and pricing rule is demonstrated. The effectiveness of the proposed TEC mechanism is verified in case studies where the NMGs operate in grid-connected, islanded, and congested modes. The advantages of the proposed TEC mechanism are also illustrated through comparisons with the centralized mechanism, decentralized mechanism, and subgradient-based TEC mechanism.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Approximating Trajectory Constraints With Machine Learning – Microgrid Islanding With Frequency Constraints

Here, we introduce deep earning aided constraint encoding to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Hybrid Stochastic-Interval Operation Strategy for Multi-Energy Microgrids

Increasing deployment of distributed energy resources intensifies interactions among electric, gas, heating, and cooling systems. Inherent uncertainties ranging from internal parameters to external inputs also impose significant challenges on system operation. A coordinated scheduling method is, therefore, desired for facilitating efficient operation of multi-energy microgrids under multiple uncertainties. In this paper, a deterministic coordinated scheduling model is developed first with detailed modeling of system dynamics and device parameters to fully characterize complex interactions among multi-energy carriers. Then, a hybrid stochastic-interval method is proposed to account for heterogeneous uncertainties in decisions of operational strategies. Specifically, performance and efficiency uncertainties of distributed energy resources, and injection uncertainty of renewable sources and multienergy demands are characterized by interval-based uncertain operation regions and probability distributions, respectively. A scenario-based two-stage algorithm is developed to solve the problem so that multiple uncertainties are preserved in the entire decision-making process. Moreover, adjustable operational strategies are determined to hedge uncertainty by considering operators’ risk preferences. The validity of the proposed method is verified by comprehensive case studies on a test system.

hybrid stochastic-interval approach↗
DOE OSTI2020

Multiperiod Distribution System Restoration With Routing Repair Crews, Mobile Electric Vehicles, and Soft-Open-Point Networked Microgrids

This paper proposes a distribution system restoration model which is in response to multiple outages caused by natural disasters. The proposed restoration model includes the coordination of routing repair crews (RRCs), mobile batterycarried vehicles (MBCVs), and networked microgrids (NMGs) formed by soft open points (SOPs). The travel and repair time constraints are modeled for each RRC; travel path and charging strategy are modeled for each MBCV; and the network reconfiguration is developed considering the optimal operation of SOP-based NMGs. Furthermore, the proposed model is presented as a mixedinteger linear program which is solved by an auxiliary induce function based algorithm to reduce the computational complexity. The modified IEEE 33-bus and 69-bus distribution systems are tested with multiple outages. The presented results demonstrate the effectiveness of the proposed model

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Nonlinear Multiple Models Adaptive Secondary Voltage Control of Microgrids

This article proposes a model-free secondary voltage control (SVC) for microgrids (MG) using nonlinear multiple models adaptive control. Firstly, a linear robust adaptive controller is designed to guarantee the voltage stability in the bounded-input- bounded-output (BIBO) manner so as to meet the operation requirements of MGs. Secondly, a nonlinear adaptive controller is developed to improve the voltage tracking performance with the help of artificial neural networks (ANNs). A switching mechanism for coordinating such two controllers is designed to guarantee the closed-loop stability while achieving accurate voltage tracking. Here, by an online identification based on the input and output data of MGs, the proposed method does not resort to any apriori information of system model and primary control, thus exhibiting good robustness, ease of deployment and disturbance rejection.

17 WIND ENERGY↗
DOE OSTI2020

Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids

This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Lastly, numerical experiments have been devised to verify the performance of the proposed method.

, policy gradient↗
DOE OSTI2021

Pricing and Energy Trading in Peer-to-Peer Zero Marginal-Cost Microgrids

Efforts to utilize 100% renewable energy in community microgrids require new approaches to energy markets and transactions to efficiently address periods of scarce energy supply. In this paper we contribute to the promising approach of peer-to-peer (P2P) energy trading in two main ways: analysis of a centralized, welfare-maximizing economic dispatch that characterizes optimal price and allocations, and a novel P2P system for negotiating energy trades that yields physically feasible and at least weakly Pareto-optimal outcomes. Our main results are 1) that optimal pricing is insufficient to induce agents with batteries to take optimal actions, 2) a novel P2P algorithm to addresses this while keeping private information, 3) a formal proof that this algorithm converges to the centralized solution in the case of two agents negotiating for a single period, and 4) numerical simulations of the P2P algorithm performance with up to 10 agents and 24 periods that show it converges on average to total welfare within 0.1% of the social optimum in on the order of 10s to 100s of iterations, increasing with the number of agents, time periods, and total storage capacity.

batteries↗
DOE OSTI2023

Bi-Level Adaptive Storage Expansion Strategy for Microgrids Using Deep Reinforcement Learning

Battery energy storage (BES) is a versatile resource for the secure and economic operation of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES expansion planning for MGs are computationally complicated. This work proposes a data-driven bi-level multi-period BES expansion planning framework to determine the siting, sizing, and timing of BES installations. The proposed planning framework unifies deep reinforcement learning (DRL) and linear programming, thereby decoupling the determinations for the integer and continuous decision variables in two time scales, respectively. In the upper level, a rainbow DRL agent with quantile regression is trained to provide dynamic planning policies to accommodate stochastic renewable energy resources (RESs), load, and battery price changes efficiently. Further, the lower level computes the optimal operation of MGs with frequency constraints to hedge the islanding contingency. The two levels communicate with one another by exchanging storage configuration and operating expenses in order to accomplish the shared goal of minimizing investment and operation costs. Comparative case studies on an MG are carried out to demonstrate the superiority of the proposed DRL-based solution to the mixed-integer linear programming counterpart on efficiency, scalability, and adaptability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Singular Perturbation-Based Large-Signal Order Reduction of Microgrids for Stability and Accuracy Synthesis With Control

The increasing penetration of distributed energy resources (DERs) highlights the growing importance of microgrids (MGs) in enhancing power system reliability. Employing electromagnetic transient (EMT) analysis in MGs becomes crucial for controlling the rapid transients. However, this requires an accurate but high-order model of power electronics and their underlying control loops, complexifying the stability analysis from the viewpoint of a higher control level. To overcome these challenges, this paper proposes a large-signal order reduction (LSOR) method for MGs with considerations of external control inputs and the detailed dynamics of underlying control levels based on singular perturbation theory (SPT). Specially, we innovatively proposed and strictly proved a general stability and accuracy assessment theorem that allows us to analyze the dynamic stability of a full-order nonlinear system by only leveraging our derived reduced-order model (ROM) and boundary layer model (BLM). Furthermore, this theorem furnishes a set of conditions that determine the accuracy of the developed ROM. Lastly, by embedding such a theorem into the SPT, we propose a novel LSOR approach with guaranteed accuracy and stability analysis equivalence. Case studies are conducted on MG systems to show the effectiveness of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Distribution System Blackstart and Restoration Using DERs and Dynamically Formed Microgrids

Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids (MGs), sequentially energize non-blackstart units, and restore loads. However, these approaches often result in isolated MGs. In DERs-aided blackstart, the continuous operation of these MGs is limited by the finite energy capacity of commonly used blackstart units like battery energy storage (BES)-based gridforming inverters (GFMIs). To address this issue, this article proposes a holistic blackstart and restoration framework that incorporates synchronization between dynamic MGs and the entire DS with the transmission grid (TG). To support synchronization, we leveraged virtual synchronous generator-based control for GFMIs to estimate their frequency response to load pick-up events using only initial/final quasi-steady-state points. Subsequently, a synchronization switching condition is developed to model synchronizing switches, aligning them seamlessly with a linearized branch flow problem. Finally, we designed a bottomup blackstart and restoration framework that considers the switching structure of the DS, energizing/synchronizing switches, DERs with grid-following inverters, and BES-based GFMIs with frequency security constraints. In conclusion, the proposed framework is validated in IEEE-123-bus system, considering cases with two and four GFMIs under various TG recovery instants.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Secure and Adaptive Hierarchical Multi-Timescale Framework for Resilient Load Restoration Using a Community Microgrid

Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMGs are challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. Here, this paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using new obtained forecasts, followed by the RT dispatch stage. To make the decisions more secure and robust against forecast errors, a novel concept called delayed recourse is designed. The approach is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS and hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure distribution network operation under different operating scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Fuel Cell Backup Power System for Grid Service and Microgrid in Telecommunication Applications

This paper presents the feasibility and economics of using fuel cell backup power systems in telecommunication cell towers to provide grid services (e.g., ancillary services, demand response (DR)) as well as power for cell towers during emergency conditions. This study evaluates the strategic integration of clean, efficient, and reliable fuel cell systems with the grid for improved economic benefits. The backup systems can potentially enhance capabilities through information exchange with the power grid, which adds value for grid services that depend on location and time. The economic analysis focused on the potential revenue for distributed telecommunications fuel cell backup units to provide value-added power supply. This paper includes case studies on current fuel cell backup power locations and regional grid service programs. The grid service benefits and system configurations for different operation modes provide opportunities for expanding backup fuel cell applications responsive to grid needs. The objective of this work is primarily on how fuel cells can become a significant part of the telecom backup power fleet to reduce system costs, environmental impact, and dependence on fossil fuels, while ensuring continuity of indispensable service for mobile users. The study identifies different fuel cell applications and nano/microgrid approaches for an extensive network of fuel cells as distributed energy resources. The possibilities of various application scenarios extend to fuel cell technologies and microgrids for reliable power supply.

30 DIRECT ENERGY CONVERSION↗