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AMI Data Quality and Collection Method Considerations for Improving the Accuracy of Distribution Models
Abstract and Figures
Spectral clustering is applied to the problem of phase identification of electric customers to investigate the data needs (resolution and accuracy) of advanced metering infrastructure (AMI). More accurate models are required to accurately interconnect high penetrations of PV/DER and for optimal electric grid operations. This paper demonstrates the effects of different data collection implementations and common errors in AMI datasets on the phase identification task. This includes measurement intervals, data resolution, collection periods, time synchronization issues, noisy measurements, biased meters, and mislabeled phases. High quality AMI data is a critical consideration to model correction and accurate hosting capacity analyses.
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... For commercial meters, the data shows 22% of meters collect data at the 60-minute interval, 5% at the 30-minute interval, 72% at the 15-minute interval, and 1% at the one-minute interval. For industrial meters, 7% collect data at the 60-minute interval, 3% at the 30-minute interval and 90% at the 15-minute interval [13]. ...
The expansion of Advanced Metering Infrastructure (AMI) has provided building operators and researchers detailed information on building energy consumption. The majority of AMI systems, however, record data at relatively low resolutions of 15, 30, or 60 minutes, due to cost, storage and bandwidth limitations. Emerging applications in power flow analysis, Quasi-Static Time-Series Simulation (QSTS), smart grid integration and load matching, however, require data at higher resolutions. Short-term energy demand can deviate significantly from long-term averages, with an unknown magnitude and frequency when only low-resolution load profile data is available. This paper presents a novel data-driven approach to predict characteristics of the missing high-resolution information in a low-resolution signal, applicable to both measured and modeled building load profile data, utilizing machine learning regression algorithms. In the proposed framework, the relationship between characteristics of high-resolution and low-resolution signals is learned from the decomposition and characterization of a subset of high-resolution building data. This paper validates the underlying hypotheses and methodology of this approach through a single-building case study, training a variety of machine learning models on one year of data, and using the resulting model to predict high-resolution characteristics in a different year. An Ensemble Tree regression model demonstrates a high predictive accuracy (R
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of 0.79-0.92) for several statistical metrics of the high-resolution load profile. These results support the broader potential for leveraging low-resolution information to accurately constrain predictions of missing high-resolution information in building load profiles, which may greatly increase the utility of both measured and modeled data in many practical and research applications.
... Most of these are purely modelling shortcuts (item 1). Blakely et al. [6] discuss considerations that utilities need to make when implementing data collection policies (item 4), and propose some techniques to address them. Conversely, our work focuses on network data errors (item 2). ...
Existing digital distribution network models, like those in the databases of network utilities, are known to contain erroneous or
untrustworthy information. This can compromise the effectiveness of physics-based engineering simulations and technologies, in
particular those that are needed to deliver the energy transition. The large-scale rollout of smart meters presents new opportunities
for data-driven system identification in distribution networks, enabling the improvement of existing data sets. Despite the
increasing academic attention to system identification for distribution networks, researchers often make troublesome assumptions
on what data is available and/or trustworthy. In this paper, we highlight some differences between academic efforts and
first-hand industrial experiences, in order to steer the former towards more applicable research solutions.
... OpenDSS was used to produce the AMI data. The dataset is based on work in [14]. Tests to the online algorithm were made using the synthetic dataset with event simulations. ...
With the growing integration of smart grid technology, including the installation of advanced meter infrastructure (AMI), combined with grid modernization initiatives and pushes for renewable energy, accurate and current utility models of the distribution system become increasingly important. During a given year, the distribution system may experience a variety of changes and event occurrences; this poses a challenge in maintaining accurate and up-to-date models. This work proposes a method for the detection of phase change events in an online fashion and with small data requirements. The proposed algorithm uses spectral clustering to obtain predicted voltage phases as new segments of data are obtained so that the predicted phases can be observed over time. Additionally, this work proposes a set of metrics used to evaluate the confidence of the clustering of an individual window and over time. Many phase identification algorithms require months of data, but this method demonstrates that many customers can be determined with significantly less data. 40% of customers were correctly identified within 8 days and 99% within 20 days. The proposed algorithm was tested on a synthetic dataset with simulated phase change events. The tests using the synthetic data successfully detected 100% of phase change events while incurring zero false positive events.
... The slow-response mechanical VVC equipments, OLTC transformer and CBs, are controlled in the upper stage with a longer control period of 1 h to regulate the overall voltage profile whereas the reactive power outputs of fast-response DG inverters are dispatched in the lower stage with a shorter control period of 15-min in response to fast voltage variations. The control period of upper stage and lower stage are chosen based on the time period of SCADA system and advanced metering infrastructure (AMI) in some real practices [22,23], as well as recommendations from relevant guideline and literature [8,24]. In the whole control period, DG inverters keep operating at maximum power point tracking (MPPT) mode to capture maximum active power [25,26]. ...
The integration of increasing share of renewable based distributed generation in distribution networks brings great challenges to voltage control. To address this issue, this paper presents a two-stage distributionally robust chance-constrained receding horizon control algorithm. In the proposed method, the distributionally robust chance-constrained reformulation of chance-constrained voltage control is derived, which is not only accurate, but also computationally efficient. Rather than perfect knowledge about the uncertainty associated with renewable generation, the proposed method only requires partial information of the underlying probability distribution. In addition, the mechanical voltage regulation devices and the DG inverters are controlled in two stages, considering their different characteristics in voltage control. By taking into account both the current and forecasted renewable generation, the proposed method utilizes receding horizon control to determine the control actions of voltage regulation devices. The effectiveness of the proposed method is demonstrated by case studies on unbalanced IEEE-123 bus system.
... The synthetic dataset was originally based on work in [22] and modified to include synthetic events. There are 1379 residential customers and one year of 15-minute AMI data. ...
The installation of digital sensors, such as advanced meter infrastructure (AMI) meters, has provided the means to implement a wide variety of techniques to increase visibility into the distribution system, including the ability to calibrate the utility models using data-driven algorithms. One challenge in maintaining accurate and up-to-date distribution system models is identifying changes and event occurrences that happen during the year, such as customers who have changed phases due to maintenance or other events. This work proposes a method for the detection of phase change events that utilizes techniques from an existing phase identification algorithm. This work utilizes an ensemble step to obtain predicted phases for windows of data, therefore allowing the predicted phase of customers to be observed over time. The proposed algorithm was tested on four utility datasets as well as a synthetic dataset. The synthetic tests showed the algorithm was capable of accurately detecting true phase change events while limiting the number of false-positive events flagged. In addition, the algorithm was able to identify possible phase change events on two real datasets.
This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.
There is a paradigm shift from traditional power distribution systems to smart grids (SGs) due to advances in information and communication technology. An advanced metering infrastructure (AMI) is one of the main components in an SG. Its relevance comes from its ability to collect, process, and transfer data through the internet. Although the advances in AMI and SG techniques have brought new operational benefits, they introduce new security and privacy challenges. Security has emerged as an imperative requirement to protect an AMI from attack. Currently, ensuring security is a major challenge in the design and deployment of an AMI. This study provides a systematic survey of the security of AMI systems from diverse perspectives. It focuses on attacks, mitigation approaches, and future visions. The contributions of this article are fourfold: First, the vulnerabilities that may exist in all components of an AMI are described and analyzed. Second, it considers attacks that exploit these vulnerabilities and the impact they can have on the performance of individual components and the overall AMI system. Third, it discusses various countermeasures that can protect an AMI system. Fourth, it presents the open challenges relating to AMI security as well as future research directions. The uniqueness of this review is its comprehensive coverage of AMI components with respect to their security vulnerabilities, attacks, and countermeasures. The future vision is described at the end.
This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.
This paper discusses common types of errors that are frequently present in utility distribution system models and which can significantly influence distribution planning and operational assessments that rely on the model accuracy. Based on Google Earth imagery and analysis of correlation coefficients, this paper also illustrates some common error types and demonstrates methods to correct the errors. Error types include mislabeled interconnections between customers and service transformers , three-phase customers labeled as single-phase, unmarked transformers, and customers lacking coordinates. Identifying and correcting for these errors is critical for accurate distribution planning and operational assessments, such as load flow and hosting capacity analysis.
Smart grid technologies and wide-spread installation of advanced metering infrastructure (AMI) equipment present new opportunities for the use of machine learning algorithms paired with big data to improve distribution system models. Accurate models are critical in the continuing integration of distributed energy resources (DER) into the power grid, however the low-voltage models often contain significant errors. This paper proposes a novel spectral clustering approach for validating and correcting customer electrical phase labels in existing utility models using the voltage timeseries produced by AMI equipment. Spectral clustering is used in conjunction with a sliding window ensemble to improve the accuracy and scalability of the algorithm for large datasets. The proposed algorithm is tested using real data to validate or correct over 99% of customer phase labels within the primary feeder under consideration. This is over a 94% reduction in error given the 9% of customers predicted to have incorrect phase labels.
Quasi-Static-Time-Series (QSTS) simulation is a valuable tool for evaluating the behavior of power systems through time. By performing daily, yearly and other time-based simulations, it is possible to characterize time-varying power conversion devices such as photovoltaic panels, storage, loads, and capacitors, among others within the power system. However, depending on the time-step resolution and simulation duration, the sequential simulation may require a considerable amount of computing time to complete. This paper describes the OpenDSS-PM program which is the new Parallel Machine version of EPRI's open-source distribution system simulator program, OpenDSS, to accelerate QSTS simulations using multi-core computers. OpenDSS-PM is used to implement temporal parallelization and circuit solutions with Diakoptics based on actors as techniques to reduce the time required in QSTS. The results reveal that these techniques enable a significant reduction in time using common computer architectures.
Operating distribution systems with a growing number of distributed energy resources requires accurate feeder models down to the point of interconnection. Many of the new resources are located in the secondary low-voltage distribution circuits that typically are not modeled or modeled with low level of detail. This paper presents a practical and computational efficient approach for estimating the secondary circuit topologies from historical voltage and power measurement data provided by smart meters and distributed energy resource sensors. The accuracy of the algorithm is demonstrated on a 66-node test circuit utilizing real AMI data. The algorithm is also utilized to estimate the secondary circuit topologies of the Georgia Tech distribution system. Challenges and practical implementation approaches of the algorithm are discussed. The paper demonstrates the computational infeasibility of exhaustive secondary circuit topology estimation approaches and presents an efficient algorithm for verifying whether two radial secondary circuits have identical topologies.
The number of photovoltaic (PV) systems in the electric grid is growing at an unprecedented speed. This is rapidly transforming the ways in which the traditional distribution grid is being planned and operated. A problem faced by utilities is that, in many cases, the PV system installed does not correspond to the size or type filed with the installation permit, or simply the installation took place without a permit. In order to maintain grid reliability and safety, utilities must be able to detect and monitor all PV installations in their network. This paper proposes a data-driven approach for the detection, verification, and estimation of residential PV system installations. We use a change-point detection algorithm to screen out abnormal energy consumption behaviors including unauthorized PV installations. Then the existence of the unauthorized PV installation is further verified through a statistical inference known as permutation test with Spearman's rank coefficient. The proposed hypothesis test takes the customer's load profiles before and after the detected change-point as inputs, which are estimated through Gaussian kernel density method. Finally, the local cloud cover index is integrated with smart meter measurements to estimate the size of the PV system. The proposed method has been tested and validated with actual smart meter measurements under several scenarios.
The connectivity model of a power distribution network can easily become outdated due to system changes occurring in the field. Maintaining and sustaining an accurate connectivity model is a key challenge for distribution utilities worldwide. This work shows that voltage time series measurements collected from customer smart meters exhibit correlations that are consistent with the hierarchical structure of the distribution network. These correlations may be leveraged to cluster customers based on common ancestry and help verify and correct an existing connectivity model. Additionally, customers may be clustered in combination with voltage data from circuit metering points, spatial data from the geographical information system, and any existing but partially accurate connectivity model to infer customer to transformer and phase connectivity relationships with high accuracy.
We report analysis and validation results based on data collected from multiple feeders of a large electric distribution network in North America. To the best of our knowledge, this is the first large scale measurement study of customer voltage data and its use in inferring network connectivity information.
Climate change, awareness of energy efficiency, new trends in electricity markets, the obsolescence of the actual electricity model, and the gradual conversion of consumers to prosumer profiles are the main agents of progressive change in electricity systems towards the Smart Grid paradigm. The introduction of multiple distributed generation and storage resources, with a strong involvement of renewable energies, exposes the necessity of advanced metering or Smart Metering systems, able to manage and control those distributed resources. Due to the heterogeneity of the Smart Metering systems and the specific features of each grid, it is easy to find in the related literature a wide range of solutions with different features. This work describes the key elements in a Smart Metering system and compiles the most employed technologies and standards as well as their main features. Since Smart Metering systems can perform jointly with other activities, these growing initiatives are also addressed. Finally, a revision of the main trends in Smart Metering uses and deployments worldwide is included.
