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Transactive energy systems for distributed blackstart and service recovery

Abstract Current transactive controls use marginal benefits and marginal costs to achieve an economic market efficiency during normal grid operations. However, the transactive mechanisms designed for normal economic operations cannot be applied directly for the contingencies because the grid operations during contingencies are often dictated by technical needs rather than purely economic criteria. For instance, one of the key technical requirements for the blackstart is to have at least one blackstart capable resource cleared which cannot be ensured by the transactive mechanism designed for normal economic operations because they work primarily based on the marginal benefit and marginal cost of the participants. This article presents one of the first attempts to develop a transactive mechanism to be used during grid contingencies. A distributed blackstart and service recovery is used as an example contingency to evaluate the performance of the proposed transactive mechanism. The performance of the proposed transactive mechanism is demonstrated for various use cases using a modified IEEE‐123 node test system. The simulation results demonstrated the proof of concept of applying a transactive mechanism to enable distributed blackstart and service recovery by engaging the mix of blackstart capable and non‐capable distributed energy resources.

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Deep Reinforcement Learning for Microgrid Cost Optimization Considering Load Flexibility

This paper proposes a novel Soft-Actor-Critic (SAC) based Deep Reinforcement Learning (DRL) method for optimizing the cost of microgrid operation by leveraging load flexibility. The proposed SAC-DRL method is designed to coordinate the control of distributed energy resources (DERs) and flexible load, addressing practical energy billing formation by power distribution utilities. Key contributions include an innovative reward function to mitigate sparse reward challenges and a mixed control strategy for discrete and continuous variables, ensuring radial network topology and minimizing power loss. We evaluate the proposed method on the model of a real microgrid located in Southern California, U.S.. The SAC-DRL model is tested to demonstrate its efficacy in reducing grid dependence, optimizing resource use, and minimizing costs. The results highlight the potential of DRL in modern energy systems, offering a sustainable and economically efficient solution for energy management in microgrids.

deep reinforcement learning↗

The Value of Distributed Wind: A Valuation Framework

While there has been an increase in distributed energy resource (DER) adoption, particularly for solar photovoltaic (PV) DERs, distributed wind deployment is still relatively low. However, distributed wind has significant growth potential for behind-the-meter applications and can provide improved resource diversity and resilience in high-DER grid systems. In order to make optimized decisions about distributed wind deployment, it is important to identify and characterize the value streams of distributed wind in a way that will help policy makers, microgrid developers, utilities, project developers, and others understand the potential value of distributed wind. In our paper, we develop a framework to provide a consistent and comprehensive approach to finding the net benefits of distributed wind to various stakeholders in different scenarios. We do this by considering the main use cases for distributed wind, and then developing a framework which is flexible for these various use cases and the relevant stakeholders while maintaining the same overall structure for analysis. Ongoing work includes applying this framework to valuing two distributed wind power systems, one in a remote village in Alaska and one in a front-of-the-meter deployment in Iowa. Our analysis aims to understand the full value that distributed wind is providing in these systems, including the resilience benefits it is contributing. As a better understanding of distributed wind valuation is gained, it will be easier for stakeholders to make informed decisions in order to have resilient renewable energy systems.

17 WIND ENERGY↗

Power Sector Transformation Pathways: Exploring Objectives, Factors, and Technology Innovations to Inform Power Sector Pathway Decisions

The power and heating sectors are responsible for around 30% of global CO2 emissions and this share is likely to grow as countries move towards electrification of the transportation and building sectors. Therefore, enabling forward-thinking, long term strategies in the power sector is a crucial area for international collaboration and support. The rapid transition of power systems toward modern, low-carbon pathways and technologies will be necessary to meet key climate goals while also enabling other critical objectives such as economic development, energy access, and energy system resiliency, among others. Many developed and developing countries are well-poised to champion power sector transformation (PST), however, further support is needed to identify pathways and implement critical near and long-term actions. To address this need, the Low Emission Development Strategies Global Partnership (LEDS GP) together with an expert team including Agora Energiewende, Energy Innovation, European Climate Foundation, Fraunhofer Institute for Systems and Innovation Research , Prayas Energy Group and LEDS Latin America and Caribbean Platform, developed a framework identifying four key pathways that can be integrated and combined to support low-carbon PST. These four pathways are: (1) Distributed Energy Resource Revolution, (2) Bulk Power Transformation, (3) Transmission and Distribution Interactivity, and (4) Distributed Transactional Future. To support exploration of pathways, this study examines PST objectives and factors that may lead countries or jurisdictions to place a greater relative emphasis on certain pathways as well as a framework and complementary tool for considering the emphasis in a hands on manner. The report provides case studies on application of the approach to bring life to the topic. The case studies include Haiti, Kenya, Mexico, Southeast Asia, and the European Union, among others. The report also presents actions to-be-taken in the near-, medium- and long-term to support the realization of pathways and long-term transformation of the power sector. The report, together with a complementary spreadsheet tool developed under the effort, can be used to enable stakeholder engagement and visioning for the power sector over the long-term, taking into account key factors and potential innovations. It can also be used to understand and inform relevant analysis tools and models based on which pathways are emphasized, and to identify key actions in the near- and medium-term to avoid lock-in of carbon-intensive technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Aggregate data‐driven dynamic modeling of active distribution networks with DERs for voltage stability studies

Abstract Electric distribution networks increasingly host distributed energy resources based on power electronic converter (PEC) toward active distribution networks (ADN). Despite advances in computational capabilities, electromagnetic transient models are limited in scalability because of their reliance on exact data about the distribution system and each of its components. Similarly, the use of the DER_A model, which is intended to examine the combined dynamic behavior of many DERs, is limited by the difficulty in parameterization. There is a need for improved dynamic models of DERs for use in large power system simulations for stability analysis. This paper proposes an aggregate model‐free, data‐driven approach for deriving a dynamic partitioned model (DPM) of ADNs. Detailed residential distribution feeders were first developed, including PEC‐based DERs and composite load models (CMLDs), from which the aggregated DPM was derived. The performance was evaluated through various case studies and validated against the detailed ADN model and state‐of‐the‐art DER_A model with CMLD. The data‐driven DPM achieved a of over 90%, accurately representing the aggregated dynamic behavior of ADNs. Furthermore, the DPM significantly accelerated the simulation process with a computational speedup of 68 times compared to the detailed ADN and a 3.5 times speedup compared to the DER_A CMLD model.

42 ENGINEERING↗

Solid State Power Substation DC Node Optimization and Controller Hardware-In-The-Loop Demonstration

A solid state power substation (SSPS) node is a microgrid that integrates distributed energy resources and loads and injects/absorbs power to/from the SSPS distribution network. It is an essential building block of a futuristic distribution grid network. This paper presents the development and demonstration of optimization use cases of a SSPS DC node. By adopting multi-layer hierarchical control architecture and developing automatic device identification and dynamic optimization formulation algorithms, the SSPS DC node can perform plug-and-play resource integration and seamless transition of the optimized node operation under on and off grid condition without sophisticated algorithms, control mode changes, and user interactions. Four optimization use cases including economic dispatches with price signal changes, a sudden PV power drop, and a single directional meter and its associated costs with sending power back to the grid, and resiliency under a grid inverter trip condition were demonstrated through the real-time controller hardware-in-the-loop simulation.

Kim, Namwon↗

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.

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AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Report for Task 10: Controllability and Observability Analysis (Deliverable D7))

It is common to have less than full observability for distribution systems, as the number of available measurements are not sufficient. Considering increasing penetration of distributed energy resources (DERs), it is imperative to understand and control distribution systems better. Developed for the transmission system, the original numerical observability analysis methods were based on the canonical DC Power Flow (DC-PF) model. However, the DC-PF model is ill-suited for the distribution system in which line resistance to reactance (R/X) ratios can be near unity. In this report, we apply the non-iterative Observability Test (OT) and Observable Island Identification Procedure (OIIP) in to the gain matrix from the distribution system state estimator. Because sensors are few in distribution systems, pseudo-measurements based on historical load profiles and zero-injection virtual measurements are often employed to ensure observability. However, both the observability and the accuracy of the state estimates are uncertain when historical data is limited/unavailable.

42 ENGINEERING↗

Integrated Multi-Fidelity Model and Co-Simulation Platform for Distribution System Transient and Dynamic Analysis—DistribuDyn

As inverter-based, distributed energy resources (DERs) and variable loads continue to proliferate across distribution systems, it is increasingly important to understand their impacts on system stability and reliability. However, most commercially available simulation tools for distribution planning primarily focus on steady-state and quasi-steady state analysis, offering limited capabilities to study transient and dynamic behaviors.

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Synchrophasors-based Master State Awareness Estimator for Cybersecurity in Power Grid: Testbed Implementation & Field Demonstration

The integration of distributed energy resources(DERs) and expansion of complex network in the distribution grid requires an advanced distributed state estimator to monitor the grid health at micro-level. The distribution state estimator will improve the situational awareness and resiliency of distributed power system. This paper proposes a synchrophasors-based master state awareness (MSA) estimator to enhance the cybersecurity in distribution grid by providing a real-time estimation of system operating states to control center operators. In this paper, the proposed MSA estimator utilizes only phasor measurements, bus magnitudes and angles, from phasor measurement units (PMUs),deployed in local substations, to estimate the system states and also detects data integrity attacks, such as load tripping attack that disconnects the load. To validate the proof of concept, we implement the proposed methodology in cyber-physical testbed environment at the Idaho National Laboratory (INL) Electric Grid Security Testbed. Further, to address the “valley of death” and support technology commercialization, field demonstration is also performed at the Critical Infrastructure Test Range Complex(CITRC) at the INL. Our experimental results reveal a promising performance in detecting load tripping attack and providing an accurate situational awareness through an alert visualization dashboard in real-time

42 ENGINEERING↗

US 2023/0182605 A1 Network constraint energy management system for electric vehicle depot charging and scheduling

Network constraint energy management system for electric vehicle (EV) depot charging and scheduling. In an embodiment, a power schedule is received from an economic dispatch application for a charging depot comprising EV charging station(s) and distributed energy resource(s). The power schedule may be simulated on a distribution network model of the charging depot, according to load flow analysis, to determine whether any grid-code violations occur. In response to the detection of violation(s), a constraint may be generated for each violating node, and the economic dispatch application may be re-executed with the constraint(s) to produce a new power schedule, until no violations are detected. When not all load demand can be satisfied by the power schedule, a charging schedule may be adjusted to ensure that critical energy requirements are satisfied. The final power and charging schedules may be used to schedule and control power generation and charging in the charging depot.

Hafiz, Faeza↗

2021 Prototype Manufacture and Installation Awardee: Pecos Wind Power, Inc.

Through the 2021 Competitiveness Improvement Project (CIP), Pecos Wind Power will manufacture a prototype of its 85-kilowatt (kW) horizontal-axis distributed wind turbine, the PW85, a new wind turbine that began development in 2017 when the company was founded. The PW85 wind turbine includes an industry-leading rotor diameter (30 meters) and full-span variable pitch blades to target a levelized cost of energy (LCOE) of $0.103/kilowatt-hour in low annual wind speeds (6 meters per second). This is 55% lower than the average small wind turbine project installed in 2018. The goal of this project is to spur the development of increasingly lower-cost, high-capacity-factor distributed wind turbines. As a result, Pecos Wind Power will manufacture and install wind turbines that increase the geographic area in which distributed wind power is cost competitive with retail-priced electricity and other distributed energy resources - primarily solar energy.

CIP↗

Resilience in Infrastructure Systems [Slides]

Threats and Resilience of the US Energy Infrastructure are described, including a summary of the CleanStart Distributed Energy Resource Management System (DERMS) project.

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High-Frequency Signature-Based Fault Detection for Future MV Distribution Grids: Preprint

Increased deployment of Distributed Energy Resources (DERs) and microgrids are significantly changing the operation of distribution grid. Increasing penetration of power electronics based DERs also raise several operational problems such as protection coordination issues, voltage raise, recloser synchronization, stability, and frequency control. Legacy systems are designed for unidirectional power flow. Bidirectional power flows, and varying short circuit levels are some of the major challenges in implementing these legacy protection schemes for future power grid with high penetration of power electronics based DERs. Inverter based DER’s can contribute to a maximum of 2 p.u. fault current. Interconnection standards might require even lower fault current contribution. If these protection related challenges are not addressed, it will result in limiting the penetration of DERs in distribution feeders. In this paper, the feasibility of high frequency signature generated by transients is evaluated to identify and locate faults in a distribution system. The paper will discuss the need for an accurate line modeling approach to accurately capture the high frequency signatures. These high frequency signatures are naturally occurring electromagnetic transients for events such as switching events in the network, faults events, etc. This is different than that implemented in transmission systems as the proposed approach utilizes advanced signal processing techniques to retrieve valuable information from the high frequency signatures generated during faults and does not just rely on time delay measurements between reflections of the high frequency signals. In the full paper, information of a modified IEEE distribution feeder test system to accurately model the high frequency signatures will be provided. The distribution lines will be modeled using universal line modeling (ULM) technique in an electromagnetic transients program (EMTP) modeling platform. Different test cases will be simulated and these test cases will include different fault types, multiple fault locations, capacitor bank switching, etc. The feeder system modeled for these simulations will also include power electronics based DERs. Additionally, experimental results demonstrating the high frequency signatures generated due to a transient event in a medium voltage (13.8 kV) distribution line will also be presented in the full paper. Furthermore, the analysis combined with simulation and experimental results in the full paper will help demonstrate the feasibility and efficacy of the proposed protection scheme. The developed protection scheme will not just help solve problems generated by bidirectional current flow, varying short-circuit current capacity, but also help in proliferation of DERs in distribution systems.

41 EE - Solar Energy Technologies Office (EE-4S)↗

Robust Restoration From Cyber-Physical Attacks in Active Distribution Grids With Grid-Edge IBRs

The inverter-based resources (IBRs) have enabled the integration of renewable energy at the grid edge with enhanced control capabilities to support the reliable operation of power grids. Different control frameworks, such as hierarchical or distributed architecture, have been proposed with the expansion of cyber networks for real-time monitoring and control. This evolution of critical infrastructure into cyber-physical systems also brings more vulnerabilities for the broadened attack surfaces, and significantly increases the possibility of physical system failures or outages caused by cyberattacks. Among tremendous efforts in the defense-in-depth approach, it remains challenging to provide prompt detection and accurate location of attack entry points or paths. Therefore, the prevailing restoration framework may struggle to fully consider the cyber-physical interdependence, successfully isolate the compromised cyber and physical components, and safely recover the systems without the potential risks leading to secondary outages. This paper is motivated to develop a cyber-physical restoration framework for distribution grids to recover from cyber attacks by harnessing grid-edge IBRs. The framework is first built on the operational guidelines of IBRs considering the compromised cyber layer. Then, an ambiguity set is established to represent the uncertainty of attack scenarios and their possibility levels. Next, a distributionally robust optimization model is developed to provide the optimal load restoration strategy across all scenarios. The effectiveness of the proposed model is demonstrated through various use cases on the modified IEEE 13-node and 123-node test systems. Finally, simulation results demonstrate the effectiveness and advancement of developed post-attack restoration strategies.

Cybersecurity↗

Co-Simulation of PSS/E, OpenDSS, and PSCAD for Power Systems Stability Analysis With Inverter-Based Resources: Preprint

The increasing penetration of inverter-based resources (IBRs) is reshaping the dynamic behavior of power systems. IEEE standard 1547-2018 suggests that distributed energy resources (DERs) should provide grid services such as voltage and frequency supports. On the other hand, dynamic events caused by IBRs such as sub-synchronous oscillation have been reported. These developments necessitate improvement in the current modeling capabilities to better understand the interdependencies within power systems. These include interactions between transmission and distribution systems, among IBRs themselves, and between IBRs and conventional resources. In this paper, we present a co-simulation model integrating PSS/E, OpenDSS, and PSCAD to analyze IBR impacts on the stability of transmission and distribution systems. A key challenge in developing a co-simulation model is ensuring interoperability among different simulators (interfacing and data flow) while maintaining accurate results. Using the developed model, we simulate the impact of IBRs on power systems in two test cases: 1) fault ride through (FRT) capability during a generation trip contingency; 2) IBR-induced sub-synchronous oscillation. The results show the effectiveness of the co-simulation model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Mining Smart Meter Data to Enhance Distribution Grid Observability for Behind-the-Meter Load Control: Significantly improving system situational awareness and providing valuable insights

Distributed Energy Resources (DERs) are playing an increasingly important role in power systems. In 2023, five categories of DERs-distributed solar, electric vehicles (EVs), energy storage, residential smart thermostats, and small-scale combined heat and power-are expected to contribute about 104 GW to the U.S. summer peak (see GTM, 2018). With the increasing integration of DERs in power distribution systems, distributed load control is imperative to smooth the fluctuations that they introduce. However, a main challenge is that distribution systems lack systematic situational awareness because of their limited sensors. Furthermore, most customer-level behind-the-meter (BTM) DERs, such as rooftop photovoltaics (PVs), are being integrated into distribution systems, which complicates the system monitoring and control. Furthermore, enhanced electric grid monitoring is needed to promote renewable integration while ensuring reliability, but current approaches rely on expensive sensors.

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A Resilient Integrated Resource Planning Framework for Transmission Systems: Analysis and Optimization

This article presents a resilient Integrated Resource Planning (IRP) framework designed for transmission systems, with a specific focus on analyzing and optimizing responses to High-Impact Low-Probability (HILP) events. The framework aims to improve the resilience of transmission networks in the face of extreme events by prioritizing the assessment of events with significant consequences. Unlike traditional reliability-based planning methods that average the impact of various outage durations, this work adopts a metric based on the proximity of outage lines to generators to select HILP events. The system’s baseline resilience is evaluated by calculating load curtailment in different parts of the network resulting from HILP outage events. The transmission network is represented as an undirected graph. Graph-theoretic techniques are used to identify islands with or without generators, potentially forming segmented grids or microgrids. This article introduces Expected Load Curtailment (ELC) as a metric to quantify the system’s resilience. The framework allows for the re-evaluation of system resilience by integrating additional generating resources to achieve desired resilience levels. Optimization is performed in the re-evaluation stage to determine the optimal placement of distributed energy resources (DERs) for enhancing resilience, i.e., minimizing ELC. Case studies on the IEEE 24-bus system illustrate the effectiveness of the proposed framework. In the broader context, this resilient IRP framework aligns with energy sustainability goals by promoting robust and resilient transmission networks, as the optimal placement of DERs for resilience enhancement not only strengthens the system’s ability to withstand and recover from disruptions but also contributes to efficient resource utilization, advancing the overarching goal of energy sustainability.

24 POWER TRANSMISSION AND DISTRIBUTION↗