Publications

Accurate distribution system models are becoming increasingly critical for grid modernization tasks, and inaccurate phase labels are one type of modeling error that can have broad impacts on analyses using the distribution system models. This work demonstrates a phase identification methodology that leverages advanced metering infrastructure (AMI) data and additional data streams from sensors (relays in this case) placed throughout the medium-voltage sector of distribution system feeders. Intuitive confidence metrics are employed to increase the credibility of the algorithm predictions and reduce the incidence of false-positive predictions. The method is first demonstrated on a synthetic dataset under known conditions for robustness testing with measurement noise, meter bias, and missing data. Then, four utility feeders are tested, and the algorithm’s predictions are proven to be accurate through field validation by the utility. Lastly, the ability of the method to increase the accuracy of simulated voltages using the corrected model compared to actual measured voltages is demonstrated through quasi-static time-series (QSTS) simulations. The proposed methodology is a good candidate for widespread implementation because it is accurate on both the synthetic and utility test cases and is robust to measurement noise and other issues.

Electric Vehicles (EV) present a unique challenge to electric power system (EPS) operations because of the potential magnitude and timing of load increases due to EV charging. Time-of-Use (TOU) electricity pricing is an established way to reduce peak system loads. It is effective at shifting the timing of some customer-activated residential loads – such as dishwashers, washing machines, or HVAC systems – to off-peak periods. EV charging, though, can be larger than typical residential loads (up to 19.2 kW) and may have on-board controls that automatically begin charging according to a pre-set schedule, such as when off-peak periods begin. To understand and quantify the potential impact of EV charging's response to TOU pricing, this paper simulates 10 distribution feeders with predicted 2030 EV adoption levels. The simulation results show that distribution EPS experience an increase in peak demand as high as 20% when a majority of the charging begins immediately after on-peak times end, as might occur if EV charging is automatically scheduled. However, if charging start times are randomized within the off-peak period, EV charging is spread out and the simulations showed a decrease in the peak load to be 5% lower than results from simulations that did not implement TOU rates.

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.

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Over the past 50 years, the Renewable Energy Program at Sandia has advanced research in the field with a focus on three key goals; 1) reduce the cost, 2) improve resilience and reliability and, 3) decrease the regulatory burden of renewable energy. Sandia’s expertise, coupled with the Village of Questa’s expanding renewable energy portfolio, presents the opportunity to deploy the Labs’ deep science and engineering capabilities towards the energy goals of KCEC and the Village of Questa. Preliminary research efforts by Sandia technical staff has broadly identified early opportunities for further research, development, and demonstration in the emerging renewable energy segment of agrivoltaics. Agrivoltaics is an emerging and promising area of photovoltaics which entails land use considerations as well as concerns regarding landscape transformation, biodiversity, and ecosystem well-being. In recent years, agrivoltaics systems have been the subject of numerous studies due to their potential in the food-energy (and water) nexus. This document is a preliminary evaluation of the projects performance opportunities of agrivoltaics as a renewable energy technology strategy in the region of Questa, NM.

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Reducing the duration and frequency of blackouts in remote communities poses an engineering challenge for grid operators. Outage effects can also be mitigated locally through microgrids. This paper develops a systematic procedure to account for these challenges by creating microgrids prioritizing high value assets within vulnerable communities. Nighttime satellite imagery is used to identify vulnerable communities. Using an asset classification and rating system, multi-Asset clusters within these communities are prioritized. Infrastructure data, geographic information systems, satellite imagery, and spectral clustering are used to form and rank microgrid candidates. A microgrid sizing algorithm is included to guide through the microgrid design process. An application of the methodology is presented using real event, location, and asset data.

The electric grid has undergone rapid, revolutionary changes in recent years; from the addition of advanced smart technologies to the growing penetration of distributed energy resources (DERs) to increased interconnectivity and communications. However, these added communications, access interfaces, and third-party software to enable autonomous control schemes and interconnectivity also expand the attack surface of the grid. To address the gap of DER cybersecurity and secure the grid-edge to motivate a holistic, defense-in-depth approach, a proactive intrusion detection and mitigation system (PIDMS) device was developed to secure PV smart inverter communications. The PIDMS was developed as a distributed, flexible bump-in-the-wire (BITW) solution for protecting PV smart inverter communications. Both cyber (network traffic) and physical (power system measurements) are processed using network intrusion monitoring tools and custom machinelearning algorithms for deep packet analysis and cyber-physical event correlation. The PIDMS not only detects abnormal events but also deploys mitigations to limit or eliminate system impact; the PIDMS communicates with peer PIDMSs at different locations using the MQTT protocol for increased situational awareness and alerting. The details of the PIDMS methodology and prototype development are detailed in this report as well as the evaluation results within a cyber-physical emulation environment and subsequent industry feedback.

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Many Electric Power Systems (EPS) already include geographically dispersed photovoltaic (PV) systems. These PV systems may not be co-located with highest-priority loads and, thus, easily integrated into a microgrid; rather PV systems and priority loads may be far away from one another. Furthermore, because of the existing EPS configuration, non-critical loads between the distant PV and critical load(s) cannot be selectively disconnected. To achieve this, the proposed approach finds ideal switch locations by first defining the path between the critical load and a large PV system, then identifies all potential new switch locations along this path, and finally discovers switch locations for a particular budget by finding the ones the produce the lowest Loss of Load Probability (LOLP), which is when load exceed generation. Discovery of the switches with the lowest LOLP involves a Particle Swarm Optimization (PSO) implementation. The objective of the PSO is to minimize the microgird's LOLP. The approach assumes dynamic microgrid operations, where both the critical and non-critical loads are powered during the day and only the critical load at night. To evaluate the approach, this paper includes a case study that uses the topology and Advanced Metering Infrastructure (AMI) data from an actual EPS. For this example, the assessment found new switch locations that reduced the LOLP by up to 50% for two distant PV location scenarios.

Limited access to transmission lines after a major contingency event can inhibit restoration efforts. After Hurricane Maria, for example, flooding and landslides damaged roads and thus limited travel. Transmission lines are also often situated far from maintained roadways, further limiting the ability to access and repair them. Therefore, this paper proposes a methodology for assessing Puerto Rico's infrastructure (i.e., roads and transmission lines) to identify potentially hard to reach areas due to natural risks or distance to roads. The approach uses geographic information system (GIS) data to define vulnerable areas, that may experience excessive restoration times. The methodology also uses graph theory analysis to find transmission lines with high centrality (or importance). Comparison of these important transmission lines with the vulnerability results found that many reside near roads that are at risk for landslides or floods.

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.

There are now over 2.5 million Distributed Energy Resource (DER) installations connected to the U.S. power system. These installations represent a major portion of American electricity critical infrastructure and a cyberattack on these assets in aggregate would significantly affect grid operations. Virtualized Operational Technology (OT) equipment has been shown to provide practitioners with situational awareness and better understanding of adversary tactics, techniques, and procedures (TTPs). Deploying synthetic DER devices as honeypots and canaries would open new avenues of operational defense, threat intelligence gathering, and empower DER owners and operators with new cyber-defense mechanisms against the growing intensity and sophistication of cyberattacks on OT systems. Well-designed DER canary field deployments would deceive adversaries and provide early-warning notifications of adversary presence and malicious activities on OT networks. In this report, we present progress to design a high-fidelity DER honeypot/canary prototype in a late-start Laboratory Directed Research and Development (LDRD) project.

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The adoption of distributed photovoltaic (PV) systems grew significantly in recent years. Market projections anticipate future growth for both residential and commercial installations. To understand grid impacts associated with distributed PV, useful hosting capacity studies require accurate representations of the spatial distribution of PV adoptions. Prediction of PV locations and numbers depends on median income data, building use zoning maps, and permit records to understand existing trends and predict future adoption rates and locations throughout an entire city. Using the PV adoption data, advanced and realistic simulations were performed to capture the distributed PV impacts on the grid. Also, using graph theory community detection hundreds of neighborhood microgrids can be discovered for the entire city by identifying densely connected loads that are sparsely connected to other communities. Then, based on the PV adoption predictions, this work identified the contribution of PV within each of the newly discovered graph theory defined microgrid communities.

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Dynamic operations of electric power switches in microgrid mode allows for distributed photovoltaic (PV) systems to support a critical load and enable the transfer of electrical power to non-critical loads. Instead of relying on an expensive system that includes a constant generation source (e.g. fossil fuel based generators), this work assess the potential balance of load and PV generation to properly charge a critical load battery while also supporting non-critical loads during the day. This work assumes that the battery is sized to only support the critical load and that the PV at the critical load is undersized. To compensate for the limited power capacity, a battery charging algorithm predicts and defines battery demand throughout the day; a particle swarm optimization (PSO) scheme connects and disconnects switch sections inside a distribution system with the objective of minimizing the difference between load and generation. The PSO reconfiguration scheme allows for continuous operations of a critical load as well as inclusion of non-critical loads.

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The electric grid is rapidly being modernized with novel technologies, adaptive and automated grid-support functions, and added connectivity with internet-based communications and remote interfaces. These advancements render the grid increasingly 'smart' and cyber-physical, but also broaden the vulnerability landscape and potential for malicious, cascading disturbances. The grid must be properly defended with security mechanisms such as intrusion detection systems (IDSs), but these tools must account for power system behavior as well as network traffic to be effective. In this paper, we present a cyber-physical IDS, the proactive intrusion detection and mitigation system (PIDMS), that analyzes both cyber and physical data streams in parallel, detects intrusion, and deploys proactive response. We demonstrate the PIDMS with an exemplar case study exploring a packet replay attack scenario focused on photovoltaic inverter communications; the scenario is tested with an emulated, cyber-physical grid environment with hardware-in-the-loop inverters.

The use of grid-edge sensing in distribution model calibration is a significant aid in reducing the time and cost associated with finding and correcting errors in the models. This work proposes a novel method for the phase identification task employing correlation coefficients on residential advanced metering infrastructure (AMI) combined with additional sensors on the medium-voltage distribution system to enable utilities to effectively calibrate the phase classification in distribution system models algorithmically. The proposed method was tested on a real utility feeder of ∼800 customers that includes 15-min voltage measurements on each phase from IntelliRupters® and 15-min AMI voltage measurements from all customers. The proposed method is compared with a standard phase identification method using voltage correlations with the substation and shows significantly improved results. The final phase predictions were verified to be correct in the field by the utility company.

An increase in Electric Vehicles (EV) will result in higher demands on the distribution electric power systems (EPS) which may result in thermal line overloading and low voltage violations. To understand the impact, this work simulates two EV charging scenarios (home-and work-dominant) under potential 2030 EV adoption levels on 10 actual distribution feeders that support residential, commercial, and industrial loads. The simulations include actual driving patterns of existing (non-EV) vehicles taken from global positioning system (GPS) data. The GPS driving behaviors, which explain the spatial and temporal EV charging demands, provide information on each vehicles travel distance, dwell locations, and dwell durations. Then, the EPS simulations incorporate the EV charging demands to calculate the power flow across the feeder. Simulation results show that voltage impacts are modest (less than 0.01 p.u.), likely due to robust feeder designs and the models only represent the high-voltage (“primary”) system components. Line loading impacts are more noticeable, with a maximum increase of about 15%. Additionally, the feeder peak load times experience a slight shift for residential and mixed feeders (≈1 h), not at all for the industrial, and 8 h for the commercial feeder.

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Recent trends in the growth of distributed energy resources (DER) in the electric grid and newfound malware frameworks that target internet of things (IoT) devices is driving an urgent need for more reliable and effective methods for intrusion detection and prevention. Cybersecurity intrusion detection systems (IDSs) are responsible for detecting threats by monitoring and analyzing network data, which can originate either from networking equipment or end-devices. Creating intrusion detection systems for PV/DER networks is a challenging undertaking because of the diversity of the attack types and intermittency and variability in the data. Distinguishing malicious events from other sources of anomalies or system faults is particularly difficult. New approaches are needed that not only sense anomalies in the power system but also determine causational factors for the detected events. In this report, a range of IDS approaches were summarized along with their pros and cons. Using the review of IDS approaches and subsequent gap analysis for application to DER systems, a preliminary hybrid IDS approach to protect PV/DER communications is formed in the conclusion of this report to inform ongoing and future research regarding the cybersecurity and resilience enhancement of DER systems.

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The benefits and risks associated with Volt-Var Curve (VVC) control for management of voltages in electric feeders with distributed, roof-top photovoltaic (PV) can be defined using a stochastic hosting capacity analysis methodology. Although past work showed that a PV inverter's reactive power can improve grid voltages for large PV installations, this study adds to the past research by evaluating the control method's impact (both good and bad) when deployed throughout the feeder within small, distributed PV systems. The stochastic hosting capacity simulation effort iterated through hundreds of load and PV generation scenarios and various control types. The simulations also tested the impact of VVCs with tampered settings to understand the potential risks associated with a cyber-attack on all of the PV inverters scattered throughout a feeder. The simulation effort found that the VVC can have an insignificant role in managing the voltage when deployed in distributed roof-top PV inverters. This type of integration strategy will result in little to no harm when subjected to a successful cyber-attack that alters the VVC settings.

An overall capacity assessment and an analysis of the system's X/R ratios for six actual distribution feeders was conducted to characterize the voltage response to various levels of distributed Electric Vehicle Supply Equipment (EVSE). The evaluation identified the capacity of the system at which a voltage violation occurred. This included a review of the uncontrolled and controlled cases to quantify the value of injecting reactive power as the grid voltage decreases. The evaluation found that the implementation of a Volt-Var curve with a global voltage reference provided a notable increase in capacity. A local reference voltage, measured at the point of common coupling, did not increase the capacity of every feeder in the experiment. The review of the X/R line properties using a Principal Component Analysis (PCA) identified groups within the six feeders that corresponded with each system's voltage response rate. This suggests the X/R ratios provide a direct prediction of the feeder's ability to avoid voltage violations while charging EVs.

Although common practice for estimating photovoltaic (PV) degradation rate (RD) assumes a linear behavior, field data have shown that degradation rates are frequently nonlinear. This article presents a new methodology to detect and calculate nonlinear RD based on PV performance time-series from nine different systems over an eight-year period. Prior to performing the analysis and in order to adjust model parameters to reflect actual PV operation, synthetic datasets were utilized for calibration purposes. A change-point analysis is then applied to detect changes in the slopes of PV trends, which are extracted from constructed performance ratio (PR) time-series. Once the number and location of change points is found, the ordinary least squares method is applied to the different segments to compute the corresponding rates. The obtained results verified that the extracted trends from the PR time-series may not always be linear and therefore, 'nonconventional' models need to be applied. All thin-film technologies demonstrated nonlinear behavior whereas nonlinearity detected in the crystalline silicon systems is thought to be due to a maintenance event. A comparative analysis between the new methodology and other conventional methods demonstrated levelized cost of energy differences of up to 6.14%, highlighting the importance of considering nonlinear degradation behavior.

Optimum and reliable photovoltaic (PV) plant performance requires accurate diagnostics of system losses and failures. Data-driven approaches can classify such losses however, the appropriate PV data features required for accurate classification remains unclear. To avoid misclassification, this study reviews the potential issues associated with inabilities to separate fault conditions that overlap using certain data features. Feature selection techniques that define each feature's importance and identify the set of features necessary for producing the most accurate results are also explored. The experiment quantified the amount of overlap using both maximum power point (MPP) and current and voltage (I-V) curve data sets. The I -V data provided an overall increase in classification accuracy of 8% points above the case where only MPP was available.

It is a common approach to assume a constant performance drop during the photovoltaic (PV) lifetime. However, operational data demonstrated that PV degradation rate (R_{D}) may exhibit nonlinear behavior, which neglecting it may increase financial risks. This study presents and compares three approaches, based on open-source libraries, which are able to detect and calculate nonlinear R_{D}. Two of these approaches include trend extraction and change-point detection methods, which are frequently used statistical tools. Initially, the processed monthly PV performance ratio (PR) time-series are decomposed in order to extract the trend and change-point analysis techniques are applied to detect changes in the slopes. Once the number of change-points is optimized by each model, the ordinary least squares (OLS) method is applied on the different segments to compute the corresponding rates. The third methodology is a regression analysis method based on simultaneous segmentation and slope extraction. Since the 'real' R_{D} value is an unknown parameter, this investigation was based on synthetic datasets with emulated two-step degradation rates. As such, the performance of the three approaches was compared exhibiting mean absolute errors ranging from 0 to 0.46%/year whereas the change-point position detection differed from 0 to 10 months.

Optimum and reliable photovoltaic (PV) plant performance requires accurate diagnostics of system losses and failures. Data-driven approaches can classify such losses however, the appropriate PV data features required for accurate classification remains unclear. To avoid misclassification, this study reviews the potential issues associated with inabilities to separate fault conditions that overlap using certain data features. Feature selection techniques that define each feature's importance and identify the set of features necessary for producing the most accurate results are also explored. The experiment quantified the amount of overlap using both maximum power point (MPP) and current and voltage (I-V) curve data sets. The I -V data provided an overall increase in classification accuracy of 8% points above the case where only MPP was available.

Reducing the risk of cyber-attacks that affect the confidentiality, integrity, and availability of distributed Photovoltaic (PV) inverters requires the implementation of an Intrusion Detection System (IDS) at the grid-edge. Often, IDSs use signature or behavior-based analytics to identify potentially harmful anomalies. In this work, the two approaches are deployed and tested on a small, single-board computer; the computer is setup to monitor and detect malevolent traffic in-between an aggregator and a single PV inverter. The Snort, signature-based, analysis tool detected three of the five attack scenarios. The behavior-based analysis, which used an Adaptive Resonance Theory Artificial Neural Network, successfully identified four out of the five attacks. Each of the approaches ran on the single-board computer and decreased the chances of an undetected breach in the PV inverters control system.

The penetration of Internet-of-Things (IoT) devices in the electric grid is growing at a rapid pace; from smart meters at residential homes to distributed energy resource (DER) system technologies such as smart inverters, various devices are being integrated into the grid with added connectivity and communications. Furthermore, with these increased capabilities, automated grid-support functions, demand response, and advanced communication-assisted control schemes are being implemented to improve the operation of the grid. These advancements render our power systems increasingly cyber-physical. It is no longer sufficient to only focus on the physical interactions, especially when implementing cybersecurity mechanisms such as intrusion detection systems (IDSs) and mitigation schemes that need to access both cyber and physical data. This new landscape necessitates novel methods and technologies to successfully interact and understand the overall cyber-physical system. Specifically, this paper will investigate the need and definition of cyber-physical observability for the grid.

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Included in the 1-page brief are sections on project purpose, technical approach, 2019 plans and expectations, and references.

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Environmental stress can degrade photovoltiac (PV) modules. We perform a literature search to identify models that estimate the damage caused by exposure to various environmental stressors, including temperature, radiation, and humidity. The weather-related variables, including ambient temperature, irradiance and humidity are calculated using the Global Land Data Assimilation System (GLDAS). The analysis also calculated degradation-model stressors, module temperature, plane of array irradiance, and relative humidity and compared these PV-specific variables to identify correlations and the translation required to represent the stressor accurately. The results show that global horizontal (GHI) irradiance can be used instead of plane-of-array irradiance to represent radiation dose. However, module temperature can be significantly different from ambient temperatures and specific humidity is significantly different from relative humidity.

In this paper, we present a new, light-weight approach for extracting the five single diode parameters (IL, Io, RS, RSH, and nNsVt) for advanced, in-field monitoring of in situ current and voltage (I-V) tracing devices. The proposed procedure uses individual I-V curves, and does not require the irradiance or module temperature measurement to calculate the parameters. It is suitable for operation on a small, single board computer at the point of I-V curve measurement. This allows for analysis to occur in the field, and eliminates the need to transfer large amounts of data to centralized databases. Observers can receive alerts directly from the in-field devices based on the extraction, and analysis of the commonly used single diode equivalent model parameters. This paper defines the approach and evaluates its accuracy by subjecting it to I-V curves with known parameters. Its performance is defined using actual I-V curves generated from an in situ scanning devices installed within an actual photovoltaic production field. The algorithm is able to operate at a high accuracy for multiple module types and performed well on actual curves extracted in the field.