Engineering topics

Ding, Fei

Publications and source records attributed to Ding, Fei.

At least 73 records · Page 4

DOE OSTI2022

Uncertainty-Proof Hosting Capacity with Surrogate Affine Policy

Physical constraints must be enforced when dis-tributed energy resources, such as PV, are integrated into distribution network. Hosting capacity (HC) is thus introduced to define the maximum renewable that distribution system can accommodate. When the grid is further pushed towards low-carbon, many research efforts are devoted to increasing HC. Security and cost-efficiency become even more important in determining HC. This work proposes an uncertainty-proof HC with surrogate affine policy. Flexible resources are leveraged to increase HC. We propose a novel hybrid two-stage AC model with variable uncertainty set. An iterative algorithm is designed to solve the problem. The proposed model and solution approach are validated in modified 141-node feeders, and HC performance is also analyzed.

hosting capacity↗

DOE OSTI2022

Multi-Fidelity Learning for Distribution System Voltage Probabilistic Analysis with High Penetration of PVs

This paper proposes a multi-fidelity learning approach for distribution voltage probabilistic analysis with high penetration of PVs. Unlike the existing machine learning-based approaches that require a large number of high fidelity data to achieve satisfactory results, our approach strategically leverage massive low fidelity data from inaccurate model simulations and limited high fidelity historical data. The key idea is to use low-fidelity data to establish an initial model and then the high-fidelity data to calibrate and correct the constructed low-fidelity model. This allows us to fuse low- and high-fidelity data, yielding a high fidelity prediction model. Results obtained from a realistic feeder in US with 80% penetration of PVs show that the proposed approach can achieve a similar accuracy to the one with a large number of high fidelity data. This significantly highlights the advantages of the proposed method as compared to existing data-hungry machine learning methods. Different levels of fidelity data and their impacts are also investigated.

distribution system↗

DOE OSTI2022

A Peer-to-Peer Market-Based Control Strategy for a Smart Residential Community with Behind-the-Meter Distributed Energy Resources

This paper presents a distributed peer-to-peer market control strategy to manage and to enable resource sharing of behind-the-meter distributed energy resources in a residential community. In the proposed strategy, each consumer or prosumer determines the flexibility of their point of connection to the power network such that the obtained flexibility is network-feasible. Based on the feasible flexibility, the consumers and the prosumers trade power among each other at each time instance to fulfill their preferred load requirements while maximizing their payoffs and helping to regulate node voltages inside the community. Because the problem to be solved is non-convex, a distributed particle swarm optimization algorithm is used to coordinate the consumers/prosumers in a fully autonomous manner without any centralized or hierarchical coordination. Numerical simulations performed on a community of 48 homes demonstrate the efficacy of the proposed approach.

behind-the-meter↗

DOE OSTI2022

Multi-Agent Deep Reinforcement Learning for Realistic Distribution System Voltage Control Using PV Inverters

Over the last few decades, the deployment of distributed solar photovoltaic (PV) systems has increased consistently. High PV penetration could cause adverse effects on the grid, such as voltage violations. This paper proposes a new distributed soft actor-critic based multi-agent deep reinforcement learning (SAC-MADRL) control solution to minimize the PV real power curtailment while keeping the grid voltage in an acceptable range. New reward functions have been designed to coordinate different agents during the learning process, yielding improved convergence. Comparison results with other control methods on a real feeder in western Colorado U.S. with 80% penetration of PVs demonstrate that the proposed method has better capability of effectively regulating voltage while minimizing the PV real power curtailment.

distribution system↗

DOE OSTI2022

Stochastic Strategic Participation of Active Distribution Networks With High-Penetration DERs in Wholesale Electricity Markets

With the increasing penetration of distributed energy resources (DERs), traditional distribution networks as load-serving entities in wholesale electricity markets, now evolve towards active distribution networks (ADNs) which can proactively participate in wholesale markets by optimally controlling the DERs in their networks. A stochastic bilevel optimization model is proposed in this paper for the strategic participation of ADNs and DERs to provide energy and grid services in wholesale electricity markets. The bilevel optimization model can capture the interactions between the ADN and the wholesale energy and ancillary service markets, considering the uncertainties of DERs in the ADN. In the upper-level model, the ADN makes optimal decisions on energy and reserve bidding considering the availability, uncertainties, and flexibility of DERs. The joint energy and reserve market-clearing of the independent system operator (ISO) is modeled as the lower-level problem. Using strong duality theory and Karush-Kuhn Tucker (KKT) conditions, the proposed bilevel optimization problem is reformulated as mathematical programming with equilibrium constraints (MPEC) problem and further converted into a computationally-solvable mixed-integer second-order-cone programming (MISOCP) model. The simulation results demonstrate the effectiveness of the model and the interactions between an ADN and wholesale electricity markets.

active distribution network↗

DOE OSTI2022

A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks: Preprint

As a result of climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks; thus, the level of proactive preparation of the distribution system to be able to handle severe impacts of extreme weather events represents the system's resilience. One method that distribution systems use to prepare for extreme events is to form multiple microgrids and thereby isolate themselves from the grid supply by using local generation as much as possible to supply critical loads. But partitioning an existing system into multiple feasible islands capable of supporting critical loads is still challenging for distribution systems - first, because of the size of the graph partitioning problem and, second, because of the difficulty in properly formulating the desired attributes of such islands or microgrids. Therefore, this paper presents a genetic algorithmbased approach that facilitates incorporating multiple objectives for grid partitioning by formulating two types of problems - node allocation and edge elimination - and it considers multiple topological and resilience-enhancing objectives. The performance of the proposed genetic algorithm-based approach is numerically evaluated on multiple test systems as well as on a real distribution feeder in Colorado, USA.

edge elimination↗

DOE OSTI2022

A Peer-to-Peer Market-Based Control Strategy for a Smart Residential Community with Behind-the-Meter Distributed Energy Resources: Preprint

behind-the-meter↗

DOE OSTI2022

DERMS Online: A New Voltage Sensitivity-Enabled Feedback Optimization Framework

This paper proposes a distributed energy resource management system (DERMS) solution by developing a new voltage sensitivity-enabled feedback optimization framework. The key idea is to adopt a measurement feedback scheme to reformulate the original nonlinear optimization into a linear programming (LP) problem via perturb-and-observe-based voltage sensitivity analysis. The proposed solution eliminates the dependence on load knowledge and can be implemented online thanks to an efficient open-source solver for LP problems. Comparison results with other control methods on a real distribution feeder in Southern California highlight the feasibility as well as benefits for the proposed framework.

distributed energy resource management↗

DOE OSTI2022

A Hybrid Data-Driven and Model-Based Anomaly Detection Scheme for DER Operation

This paper proposes a hybrid data and model-based anomaly detection scheme to secure the operation of distributed energy resources (DERs) in distribution grids. Data-driven autoencoders are set up at the edge device level and they use local DER operational data as inputs. The abnormal statuses are detected by analyzing reconstruction errors. In parallel, modelbased state estimation (SE) is set up at the central level and it uses system-wide models and measurements as data inputs. The anomalies are identified by analyzing measurement residuals. The hybrid scheme preserves the benefits of both data-driven and model-based analyses and thus improves the robustness and the accuracy of anomaly detection. Numerical tests based on the model of a real distribution feeder in Southern California highlight the proposed scheme's effectiveness and benefits.

anomaly detection↗

DOE OSTI2022

Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method

Abstract Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes.

08 HYDROGEN↗

DOE OSTI2022

Enhanced Control, Optimization, and Integration of Distributed Energy Applications (ECO-IDEA)

With support from the U.S. Department of Energy Solar Energy Technologies Office, the National Renewable Energy Laboratory (NREL) partnered with Xcel Energy, Schneider Electric, Varentec, and Electric Power Research Institute (EPRI) to meet the goals of the Enabling Extreme Real-Time Grid Integration of Solar Energy (ENERGISE) program. This project developed and validated an innovative data-enhanced hierarchical control architecture that enables the efficient, reliable, resilient, and secure operation of future distribution systems with a high penetration of distributed energy resources like solar energy. The architecture enables a hybrid control approach where a centralized control layer is complemented by distributed control algorithms for solar inverters and autonomous control of grid edge devices. It is fully interoperable and includes all the cybersecurity aspects necessary for reliable and secure system operation. The hybrid approach can seamlessly integrate multiple voltage-regulation technologies, both at central and grid-edge levels, which enables reliable and efficient system operation in the face of unpredictable conditions. The overarching goal of the Eco-Idea project is to develop, validate, and deploy a unique and innovative Data-Enhanced Hierarchical Control (DEHC) architecture that comprehensively addresses the formidable challenges associated with proliferation of high penetration of distributed PV such as reverse power flows, transients from variability of PV systems, feeder load balancing, and voltage stability. These issues are exposing the weaknesses of existing grid operations and controls - including, but not limited to, lack of grid situational awareness, heuristic and slow-acting control actions, latency of control for emergency situations, and points of failure in communications. The proposed architecture will comprehensively resolve the deficiencies of current operational settings - where monitoring and control solutions proposed across industry and academia may not be interoperable and may not coexist in the same system - and will enable an efficient, reliable, resilient, and secure operation of future distribution systems with penetration of solar energy well beyond current limits. The DEHC architecture was developed and validated rigorously through hardware-in-loop simulations in the laboratory environment and deployed on the field.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

DERMS Online: A New Voltage Sensitivity-Enabled Feedback Optimization Framework: Preprint

This paper proposes a distributed energy resource management system (DERMS) solution by developing a new voltage sensitivity enabled feedback optimization framework. The key idea is to adopt a measurement feedback scheme to reformulate the original nonlinear optimization into a linear programming (LP) problem via perturb and observe-based voltage sensitivity analysis. The proposed solution eliminates the dependence on load knowledge and can be implemented online thanks to an efficient open-source solver for LP problems. The proposed DERMS online platform is generalizable to deal with various types of distributed energy resources (DERs), including distributed photovoltaics (PVs), energy storage, electric vehicles, demand response, etc. Comparison results with other control methods on a realistic distribution feeder in Southern California highlight the feasibility as well as benefits for the proposed framework.

distributed energy resources management↗

DOE OSTI2022

A Hybrid Data-Driven and Model-Based Anomaly Detection Scheme for DER Operation: Preprint

This paper proposes a hybrid data and model-based anomaly detection for securing the operation of distributed energy resources (DERs) in distribution grids. Data-driven autoencoders (AE) are set up at the edge level by taking local DER data and detect anomalous operations by leveraging the reconstruction ability. In parallel, model-based state estimation (SE) is running at the system level by taking system models and measurements, the anomalies are identified by analyzing the measurements residual. The hybrid scheme preserves the benefits of both data-driven and model-based analysis and thus improves the robustness and accuracy of anomaly detection. It can be established by getting full use of the existing infrastructures in distribution grids. Numerical tests on a realistic distribution feeder in Southern California highlight the effectiveness as well as benefits of the proposed scheme.

anomaly detection↗

DOE OSTI2022

AI-Driven Smart Community Control for Accelerating PV Adoption and Enhancing Grid Resilience

Rapid deployment of residential photovoltaic (PV) systems helps decarbonize our electricity supplies, but under certain circumstances, high-penetration PV may pose challenges to the electrical distribution grid. In a project funded by the U.S. Department of Energy's Solar Energy Technologies Office and Building Technologies Office, the National Renewable Energy Laboratory and its partners studied how flexible building loads and battery storage, when coordinated at home-level and community-level scales, can be used to address those challenges and enhance grid resilience. In this webinar, we will discuss the methodology, simulation and field pilot results, insights from partners, and lessons learned from the project.

artificial intelligence↗

DOE OSTI2022

Integration of Utility Distributed Energy Resource Management System and Aggregators for Evolving Distribution System Operators

With the rapid integration of distributed energy resources (DERs), distribution utilities are faced with new and unprecedented issues. New challenges introduced by high penetration of DERs range from poor observability to overload and reverse power flow problems, under-over-voltages, maloperation of legacy protection systems, and requirements for new planning procedures. Distribution utility personnel are not adequately trained, and legacy control centers are not properly equipped to cope with these issues. Fortunately, distribution energy resource management systems (DERMSs) are emerging software technologies aimed to provide distribution system operators (DSOs) with a specialized set of tools to enable them to overcome the issues caused by DERs and to maximize the benefits of the presence of high penetration of these novel resources. However, as DERMS technology is still emerging, its definition is vague and can refer to very different levels of software hierarchies, spanning from decentralized virtual power plants to DER aggregators and fully centralized enterprise systems (called utility DERMS). Although they are all frequently simply called DERMS, these software technologies have different sets of tools and aim to provide different services to different stakeholders. This paper explores how these different software technologies can complement each other, and how they can provide significant benefits to DSOs in enabling them to successfully manage evolving distribution networks with high penetration of DERs when they are integrated together into the control centers of distribution utilities.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Data-Driven Distribution System Coordinated PV Inverter Control Using Deep Reinforcement Learning

The deployment of distributed solar photovoltaic (PV) systems has increased consistently over the past decades. High penetrations of PVs could cause a series of adverse grid impacts, such as voltage violations. The recent development of smart inverter technologies rises the incentives of developing PV control solutions that regulate the inverter output power and seeking the optimization on system operational objectives. This paper proposes a data-driven control solution based on deep reinforcement learning (DRL) to optimize PV inverters for voltage regulation. The proposed solution can minimize PV real power curtailment while maintaining network voltage at an acceptable range. Comparison results between the proposed DRL control algorithms with deep deterministic policy gradient (DDPG) and volt-var control on a real feeder in west Colorado highlight the advantage of the proposed framework in controlling the system voltage while minimizing the PV real power curtailment.

deep reinforcement learning↗

DOE OSTI2022

Modeling Capabilities to Quantify the Benefits of DERMS (Cooperative Research and Development Final Report)

The funds-in under the CRADA will fund a team of National Renewable Energy Laboratory (NREL) researchers to participate in Energy I-Corps (formerly known as Lab-Corps). Energy I-Corps pairs teams of researchers with industry mentors for an intensive two-month training where the researchers define technology value propositions, conduct customer discovery interviews, and develop viable market pathways for their technologies. FedIMPACT, LLC and its affiliate IP Group, Inc., will evaluate the work completed at Energy I-Corps to determine whether it would like to pursue further commercialization and development of related technologies and background intellectual property.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Self-Organizing Map-Based Resilience Quantification and Resilient Control of Distribution Systems Under Extreme Events

Due to climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution system operators (DSO) to ensure that there is uninterrupted power supply to critical loads in their networks. To embed resilience into DSO's decision-making, resilience needs to be first quantified and then integrated into the system-level optimization. Therefore, this paper first develops a novel self-organizing map (SOM) based method (called SomRes) to quantify the time-varying resilience index of a system that can leverage the powerful classification property of SOMs and removes some of the disadvantages of subjective weight assignment methods. Using SomRes, a resilient resource allocation and operational dispatch algorithm is further developed to enhance system resilience against extreme events by considering the SomRes resilience index directly as the feedback. Here, the proposed resilience quantification approach is benchmarked with a state-of-the-art approach and the efficacy of the proposed resilient dispatch algorithm is demonstrated through several deterministic and statistical case studies on the IEEE 123-bus distribution system. Simulation studies show that the proposed SomRes quantification method is an appropriate indicator of system resilience, and the resilient resource allocation and dispatch strategy can significantly reduce critical load shedding under varying event propagation scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗