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Voltage Calculations in Secondary Distribution Networks via Physics-Inspired Neural Network Using Smart Meter Data
The increasing penetration of distributed energy resources (DERs) leads to voltage issues across distribution networks, necessitating voltage calculations by utilities. Electric model-free voltage calculation offers an enticing solution. However, most researches mainly focus on primary distribution networks ignoring secondary distribution networks and commonly overlook extreme voltage case calculations, which require the model’s extrapolation abilities. Here, in addressing the gaps, this paper presents a customized physics-inspired neural network (PINN) model, the structure of which is inspired by the derived coupled power flow model of primary-secondary distribution networks. To ensure precision and rapid convergence, a crafted training framework for the PINN model is proposed. The PINN’s “structure-mimetic” design enables superior extrapolation for unseen scenarios and enhances physical information awareness. We demonstrate this through two applications: hosting capacity analysis and customer-transformer connectivity. The effectiveness and advantages of the proposed PINN model are validated on two public testing systems and one utility distribution feeder model.
Data-Driven Modeling of High-Resolution Residential Load Profiles Using Low-Resolution Smart Meter Measurements
Accurate and high-resolution residential load profiles are essential for power system modeling, demand response planning, and effective grid operation. As the energy sector moves towards a more actively managed distribution system, the ability to understand residential energy consumption at a minute-by-minute scale becomes increasingly critical. High-resolution load profiles provide key insights into demand patterns and user behavior, enabling grid operators to design more effective energy solutions; however, residential load measurements in the field are typically recorded at low resolutions, such as 15-60 minutes, which makes it hard to study the characteristics of different residential customers. This paper addresses these challenges by introducing a data-driven approach to generate realistic, high-resolution residential load profiles based on lowre-solution measurements and weather information. The proposed method retains the key features of the actual residential load measurements while offering appliance-level energy consumption details for each residential building. The results demonstrate the effectiveness of the proposed load profile generator, proving its capability to support utilities in optimizing residential energy management and ensuring a more reliable and resilient grid.
A Model-free Approach for Estimating Service Transformer Capacity Using Residential Smart Meter Data
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Calculating PV Hosting Capacity in Low-Voltage Secondary Networks using Only Smart Meter Data
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A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data
Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.
Smart Meter Data: A Gateway for Reducing Solar Soft Costs with Model-Free Hosting Capacity Maps
Public-facing solar hosting capacity (HC) maps, which show the maximum amount of solar energy that can be installed at a location without adverse effects, have proven to be a key driver of solar soft cost reductions through a variety of pathways (e.g., streamlining interconnection, siting, and customer acquisition processes). However, current methods for generating HC maps require detailed grid models and time-consuming simulations that limit both their accuracy and scalability—today, only a handful out of almost 2,000 utilities provide these maps. This project developed and validated data-driven algorithms for calculating solar HC using data from AMI without the need of detailed grid models or simulations. The algorithms were validated on utility datasets and incorporated as an application into NRECA’s Open Modeling Framework (OMF.coop) for the over 260 coops and vendors throughout the US to use. The OMF is free and open-source for everyone.
A multi-agent approach to distribution system fault section estimation in smart grid environment
We report that Multi-Agent Systems (MAS) are seen from different areas as one of the paramount trends for the next generation of power systems. Numerous published studies about MAS discuss its utilization in power distribution networks but none focuses on the prior step to restoration and self-healing that is fault section estimation. This paper aims to show how MAS can improve the utilities’ reliability indexes and consumer satisfaction by overcoming the multiple fault section estimation problem. In order to do this, the authors considered using MAS as a means of communication between smart meters. The purpose of smart meters usage is to employ devices that are already present in smart grids, mainly because of their reading and saving data capacity. The proposed method was tested on a radial feeder generated by the authors. The network was built on HYPERSIM, a software platform of OPAL-RT Technologies. The simulation results show that this MAS provides speed, efficiency, and automation for the process of fault section estimation.
Communication-efficient certificate revocation management for Advanced Metering Infrastructure and IoT Integration
Advanced Metering Infrastructure forms a communication network for the collection of power data from smart meters in Smart Grid. As the communication between smart meters could be secured utilizing public-key cryptography, however, public-key cryptography still has certain challenges in terms of certificate revocation and management particularly related distribution and storage overhead of revoked certificates. To address this challenge, in this work, we propose a novel revocation management approach by utilizing cryptographic accumulators which reduces the space requirements for revocation information significantly and thus enables efficient distribution of such information to all smart meters. We implemented the proposed approach on both ns-3 network simulator and a testbed. We demonstrated its superior performance with respect to traditional methods for revocation management.
Advanced Metering Infrastructure for Distribution Planning and Operation: Closing the Loop on Grid-Edge Visibility
In the recent history of electric utilities, the potential to have visibility of the grid edge is gaining significance for the reliable planning and operation of a clean energy smart grid. Before the recent large-scale customer adoption of distributed energy resources (DERs), distribution networks were planned with a simpler fit-and-forget philosophy. The importance of customer-sited DERs to achieve climate change goals marks a major shift for utility operations. Changes to traditional fit-and-forget planning paradigms require better availability of grid-edge data. Smart metering is an enterprise-wide tool that is enabling visibility when and where it has been most needed. As of 2020, the rollout of smart metering, or advanced metering infrastructure (AMI), had reached more than 100 million meters in the United States, and nearly half of all electricity customers are now equipped with a smart meter (Figure 1). Smart meters are quickly becoming a ubiquitous data capture feature of smart grids. AMI enables utilities to record and measure electricity usage and power-flow metrics at a minimum of hourly intervals and at least once a day. At a minimum, AMI enables interval metering, automatic meter reading enabling accurate and time-interval billing, and the ability to provide feedback on customer energy consumption.
A Novel Data Segmentation Method for Data-driven Phase Identification
This paper presents a smart meter phase identification algorithm for two cases: meter-phase-label-known and meter-phase-label-unknown. To improve the identification accuracy, a data segmentation method is proposed to exclude data segments that are collected when the voltage correlation between smart meters on the same phase is weakened. Then, using the selected data segments, a hierarchical clustering method is used to calculate the correlation distances and cluster the smart meters. If the phase labels are unknown, a Connected-Triple-based Similarity (CTS) method is adapted to further improve the phase identification accuracy of the ensemble clustering method. The methods are developed and tested on both synthetic and real feeder data sets. Here, simulation results show that the proposed phase identification algorithm outperforms the state-of-the-art methods in both accuracy and robustness.
Practical Implementation of GPU-based Computing at the Grid Edge for Resilience Scenarios
This paper presents a practical implementation of GPU-accelerated computing at the grid edge to enhance power system resilience through next-generation smart meters. Advanced Metering Infrastructure (AMI) systems rely predominantly on centralized processing architectures, which limit real-time response capabilities during grid disturbances. This work proposes the integration of GPU-enabled computational platforms directly within smart meter to enable local execution support for power system analytics, fault detection algorithms, and optimization routines. The proposed framework uses the Julia programming language to leverage highperformance parallel computing capabilities while maintaining code portability and development efficiency. We use two experimental scenarios to benchmark the computational feasibility of this approach: sparse linear system solutions representative of power flow analyses, and multi-stage production cost simulations incorporating unit commitment and economic dispatch operations. Results demonstrate that computationally intensive power system algorithms, such as those supporting resilience scenario calculations, can be effectively executed at the distribution edge using commercially available embedded GPU hardware. Keywords—GPU acceleration, edge computing, smart meters, grid resilience, AMI, resilience.
Phase identification using co‐association matrix ensemble clustering
Calibrating distribution system models to aid in the accuracy of simulations such as hosting capacity analysis is increasingly important in the pursuit of the goal of integrating more distributed energy resources. The recent availability of smart meter data is enabling the use of machine learning tools to automatically achieve model calibration tasks. This research focuses on applying machine learning to the phase identification task, using a co‐association matrix‐based, ensemble spectral clustering approach. The proposed method leverages voltage time series from smart meters and does not require existing or accurate phase labels. This work demonstrates the success of the proposed method on both synthetic and real data, surpassing the accuracy of other phase identification research.
A Novel Authentication Management for the Data Security of Smart Grid
Bidirectional wireless communication is employed in various smart grid components such as smart meters and control and monitoring applications where security is vital. The Trusted Third Party (TTP) and wireless connectivity between the smart meter and the third party in the key management-based encryption techniques for the smart grid are expected to be totally trustworthy and dependable. In a wired/wireless medium, however, a man-in-the-middle may seek to disrupt, monitor and manipulate the network, or simply execute a replay attack, revealing its vulnerability. Recognizing this, this study presents a novel authentication management (model) comprised of two layer security schema. The first layer implements an efficient novel encryption method for secure data exchange between meters and control center with the help of two partially trusted simple servers (constitutes the TTP). In this setting, one server handles the data encryption between the meter and control center/central database, and the other server administers the random sequence of data transmission. The second layer monitors and verifies exchanged data packets among smart meters. It detects abnormal packets from suspicious sources. To implement this node-to-node authentication, One class support vector machine algorithm is proposed which takes advantages of the location information as well as the data transmission history (node identification, packet size, and data transmission frequency). This schema secures data communication, and imposes a comprehensive privacy throughout the system without considerably extending the complexity of the conventional key management scheme.
AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Final Report)
The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.
Preparing Distribution Utilities for the Future - Unlocking Demand-Side Management Potential: A Novel Analytical Framework
The balance of supply and demand in the power systems has traditionally been served solely through generation and network capacity planning and operations. However, with increased requirements for flexibility due to the uptake in variable renewable generation sources such as wind and solar there is a need to increased demand-side flexibility. In addition, there are increased communications and flexibility capabilities emerging on the demand-side from the adoption of advanced metering infrastructures and smart meter deployment and intelligent loads such as smart thermostats and schedulable white goods (e.g. dishwashers and washing machines). Unlocking demand-side flexibility can bring system benefits from peak load reduction bringing about generation capacity and network upgrade deferral, to reducing demand and more efficient utilization of generation and network capacity. Unlocking demand-side flexibility is an evolving process for utilities and solutions must be tailored to each specific customer group. Demand-side management (DSM) is a broad set of tools that can include demand response (both dispatchable and non-dispatchable), energy efficiency and distributed energy resources and demand-side technologies. The National Renewable Energy Laboratory (NREL), in collaboration with BSES Rajdhani Power Ltd. (BRPL) and Deloitte, examined the potential of DSM in BRPL’s service territory, developing detailed information on customer classes and willingness to participate in DSM. The study developed modeling frameworks for load analysis and the analysis tools to assess the potential of time-of-use tariffs in motivating customers to reduce their peak period energy consumption. The study shows that BRPL customers, specifically their domestic customers, are willing to participate in DSM programs and that time-of-use pricing can help BRPL reduce their peak demand and help unlock demand-side flexibility.
Differentially Private K -Means Clustering Applied to Meter Data Analysis and Synthesis
The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. Here, in this paper, we address the problem of allowing third parties to apply K-means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.
Robust Medium-Voltage Distribution System State Estimation using Multi-Source Data
Due to the lack of sufficient online measurements for distribution system observability, pseudo-measurements from short-term load or distributed renewable energy resources (DERs) forecasting are used. However, the accuracy of them is low and thus significantly limits the performance of distribution system state estimation (DSSE). In this paper, a robust DSSE that integrates multi-source measurement data is proposed. Specifically, the historical low-voltage (LV) side smart meters are used to forecast load and DERs injections via the support vector machine (SVM) with optimally tuned parameters. By contrast, the online smart meters at LV side are utilized to derive equivalent power injections at the MV/LV transformers, yielding more accurate pseudo-measurements compared to the forecasted injections. Furthermore, to deal with bad data caused by communication loss, instrumental errors and cyber attacks, robust DSSE that relies on generalized maximum-likelihood (GM)-estimation criterion is developed. The projection statistics are developed to adjust the weights of each measurement, leading to better balance between pseudo- and real-time measurements. Numerical results conducted on modified IEEE 33-bus system with DG integration demonstrate the effectiveness and robustness of the proposed method.