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.
Distribution system models play a critical role in the modern grid, driving distributed energy resource integration through hosting capacity analysis and providing insight into critical areas of interest such as grid resilience and stability. Thus, the ability to validate and improve existing distribution system models is also critical. This work presents a method for identifying service transformers which contain errors in specifying the customers connected to the low-voltage side of that transformer. Pairwise correlation coefficients of the smart meter voltage time series are used to detect when a customer is not in the transformer grouping that is specified in the model. The proposed method is demonstrated both on synthetic data as well as a real utility feeder, and it successfully identifies errors in the transformer labeling in both datasets.
Distributed photovoltaic (PV) systems equipped with advanced inverters can control real and reactive power output based on grid and atmospheric conditions. The Volt-Var control method allows inverters to regulate local grid voltages by producing or consuming reactive power. Based on their power ratings, the inverters may need to curtail real power to meet the reactive power requirements, which decreases their total energy production. To evaluate the expected curtailment associated with Volt-Var control, yearlong quasi-static time-series (QSTS) simulations were conducted on a realistic distribution feeder under a variety of PV system design considerations. Overall, this paper found that the amount of curtailed energy is low (< 0.55%) compared to the total PV energy production in a year but is affected by several PV system design considerations.
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.
This paper proposes an optimal relay placement approach for microgrids. The proposed approach considers both grid-connected and islanded microgrid modes. The algorithm separately calculates the System Average Interruption Frequency Index (SAIFI) of a microgrid in each operating mode. Then, two weighting factors corresponding to different operating modes are used to calculate the overall SAIFI of the microgrid. The objective is to find the optimal relay locations such that the microgrid overall SAIFI is minimized. The power electronics interfaces associated with distributed energy resources may be classified as grid following or grid forming. As opposed to grid-following distributed energy resources (DERs) such as typical solar inverters, grid-forming inverters are able to control the microgrid voltage and frequency at the point of their interconnection. Therefore, these DERs can facilitate the formation of sub-islands in the microgrid when the protective relays isolate a portion of the microgrid. If there is at least one grid-forming DER available in a sub-island, that sub-island can continue supplying its local load. The exchange market algorithm (EMA) is used for optimizing functions. The effectiveness of the proposed optimal relay placement approach is verified using an 18-bus microgrid and IEEE 123-bus test system.
As more renewable generation connects to distribution systems, it is imminent that existing distribution feeders will be converted to microgrids-systems that offer resilience by providing the flexibility of supporting the grid in normal operation and operating as self-sustained islands when the grid is disconnected. However, inverter control and feeder protection will need to be tuned to the operating modes of the microgrid. This paper offers an insight into the issues involved by taking a case study of a real-world feeder located in the southwestern US that was converted to a microgrid with three solar PV units connecting to the feeder. Different inverter control configurations and adaptive protection using different settings for different operating conditions are proposed for safe operation of this microgrid. The solution also helps to create a framework for protection and coordination of other similar microgrids.
During the last decade, utility companies around the world have experienced a significant increase in the occurrences of either planned or unplanned blackouts, and microgrids have emerged as a viable solution to improve grid resiliency and robustness. Recently, power converters with grid-forming capabilities have attracted interest from researchers and utilities as keystone devices enabling modern microgrid architectures. Therefore, proper and thorough testing of Grid-Forming Inverters (GFMIs) is crucial to understand their dynamics and limitations before they are deployed. The use of closed-loop real-time Power Hardware-in-the-Loop (PHIL) simulations will facilitate the testing of GFMIs using a digital twin of the power system under various contingency scenarios within a controlled environment. So far, lower to medium scale commercially available GFMIs are difficult to interface into PHIL simulations because of their lack of a synchronization mechanism that allows a smooth and stable interconnection with a voltage source such as a power amplifier. Under this scenario, the use of the well-known Ideal Transformer Method to create a PHIL setup can lead to catastrophic damages of the GFMI. This paper addresses a simple but novel method to interface commercially available GFMIs into a PHIL testbed. Experimental results showed that the proposed method is stable and accurate under standalone operation with abrupt (step) load-changing dynamics, followed by the corresponding steady state behavior. Such results were validated against the dynamics of the GFMI connected to a linear load bank.
During the last decade, utility companies around the world have experienced a significant increase in the occurrences of either planned or unplanned blackouts, and microgrids have emerged as a viable solution to improve grid resiliency and robustness. Recently, power converters with grid-forming capabilities have attracted interest from researchers and utilities as keystone devices enabling modern microgrid architectures. Therefore, proper and thorough testing of Grid-Forming Inverters (GFMIs) is crucial to understand their dynamics and limitations before they are deployed. The use of closed-loop real-time Power Hardware-in-the-Loop (PHIL) simulations will facilitate the testing of GFMIs using a digital twin of the power system under various contingency scenarios within a controlled environment. So far, lower to medium scale commercially available GFMIs are difficult to interface into PHIL simulations because of their lack of a synchronization mechanism that allows a smooth and stable interconnection with a voltage source such as a power amplifier. Under this scenario, the use of the well-known Ideal Transformer Method to create a PHIL setup can lead to catastrophic damages of the GFMI. This paper addresses a simple but novel method to interface commercially available GFMIs into a PHIL testbed. Experimental results showed that the proposed method is stable and accurate under standalone operation with abrupt (step) load-changing dynamics, followed by the corresponding steady state behavior. Such results were validated against the dynamics of the GFMI connected to a linear load bank.
IEEE Journal of Emerging and Selected Topics in Power Electronics
Augustine, Sijo; Reno, Matthew J.; Brahma, Sukumar M.; Lavrova, Olga
This report presents a novel fault detection, characterization, and fault current control algorithm for a standalone solar-photovoltaic (PV) based dc microgrids. The protection scheme is based on the current derivative algorithm. The overcurrent and current directional/differential comparison based protection schemes are incorporated for the dc microgrid fault characterization. For a low impedance fault, the fault current is controlled based on the current/voltage thresholds and current direction. Generally, the droop method is used to control the power-sharing between the converters by controlling the reference voltage. In this article, an adaptive droop scheme is also proposed to control the fault current by calculating a virtual resistance R droop , and to control the converter output reference voltage. For a high impedance fault, differential comparison method is used to characterize the fault. These algorithms effectively control the converter pulsewidth and reduce the flow of source current from a particular converter, which helps to increase the fault clearing time. Additionally, a trip signal is sent to the corresponding dc circuit breaker (DCCB), to isolate the faulted converter, feeder or a dc bus. The dc microgrid protection design procedure is detailed, and the performance of the proposed method is verified by simulation analysis.
Siratarnsophon, Piyapath; Hernandez, Miguel; Peppanen, Jouni; Deboever, Jeremiah; Rylander, Matthew; Reno, Matthew J.
Distribution system modelling and analysis with growing penetration of distributed energy resources (DERs) requires more detailed and accurate distribution load modelling. Smart meters, DER monitors, and other distribution system sensors provide a new level of visibility to distribution system loads and DERs. However, there is a limited understanding of how to efficiently leverage the new information in distribution system load modelling. This study presents the assessment of 11 methods to leverage the emerging information for improved distribution system active and reactive power load modelling. The accuracy of these load modelling methods is assessed both at the primary and the secondary distribution levels by analysing over 2.7 billion data points of results of feeder node voltages and element phase currents obtained by performing annual quasi-static time series simulations on EPRI's Ckt5 feeder model.
Sikeridis, Dimitrios; Bidram, Ali; Devetsikiotis, Michael; Reno, Matthew J.
Distribution and transmission protection systems are considered vital parts of modern smart grid ecosystems due to their ability to isolate faulted segments and preserve the operation of critical loads. Current protection schemes increasingly utilize cognitive methods to proactively modify their actions according to extreme power system changes. However, the effectiveness and robustness of these information-driven solutions rely entirely on the integrity, authenticity, and confidentiality of the data and control signals exchanged on the underlying relay communication networks. In this paper, we outline a scalable adaptive protection platform for distribution systems, and introduce a novel blockchain-based distributed network architecture to enhance data exchange security among the smart grid protection relays. The proposed mechanism utilizes a tiered blockchain architecture to counter the current technology limitations providing low latency with better scalability. The decentralized nature removes singular points of failure or contamination, enabling direct secure communication between smart grid relays. We also present a security analysis that demonstrates how the proposed framework prohibits any alterations on the blockchain ledger providing integrity and authenticity of the exchanged data (e.g., realtime measurements/relay settings). Finally, the performance of the proposed approach is evaluated through simulation on a blockchain benchmarking framework with the results demonstrating a promising solution for secure smart grid protection system communication.
The energy grid becomes more complex with increasing penetration of renewable resources, distributed energy storage, distributed generators, and more diverse loads such as electric vehicle charging stations. The presence of distributed energy resources (DERs) requires directional protection due to the added potential for energy to flow in both directions down the line. Additionally, contingency requirements for critical loads within a microgrid may result in looped or meshed systems. Computation speeds of iterative methods required to coordinate loops are improved by starting with a minimum breakpoint set (MBPS) of relays. A breakpoint set (BPS) is a set of breakers such that, when opened, breaks all loops in a mesh grid creating a radial system. A MBPS is a BPS that consists of the minimum possible number of relays required to accomplish this goal. In this paper, a method is proposed in which a minimum spanning tree is computed to indirectly break all loops in the system, and a set difference is used to identify the MBPS. The proposed method is found to minimize the cardinality of the BPS to achieve a MBPS.
Timeseries power and voltage data recorded by electricity smart meters in the US have been shown to provide immense value to utilities when coupled with advanced analytics. However, Advanced Metering Infrastructure (AMI) has diverse characteristics depending on the utility implementing the meters. Currently, there are no specific guidelines for the parameters of data collection, such as measurement interval, that are considered optimal, and this continues to be an active area of research. This paper aims to review different grid edge, delay tolerant algorithms using AMI data and to identify the minimum granularity and type of data required to apply these algorithms to improve distribution system models. The primary focus of this report is on distribution system secondary circuit topology and parameter estimation (DSPE).
Accurate distribution secondary low-voltage circuit models are needed to enhance overall distribution system operations and planning, including effective monitoring and coordination of distributed energy resources located in the secondary circuits. We present a full-scale demonstration across three real feeders of a computationally efficient approach for estimating the secondary circuit topologies and parameters using historical voltage and power measurements provided by smart meters. The method is validated against several secondary configurations, and compares favorably to satellite imagery and the utility secondary model. Feeder-wide results show how much parameters can vary from simple assumptions. Model sensitivities are tested, demonstrating only modest amounts of data and resolutions of data measurements are needed for accurate parameter and topology results.
Distribution system analysis requires yearlong quasi-static time-series (QSTS) simulations to accurately capture the variability introduced by high penetrations of distributed energy resources (DER) such as residential and commercial-scale photovoltaic (PV) installations. Numerous methods are available that significantly reduce the computational time needed for QSTS simulations while maintaining accuracy. However, analyzing the results remains a challenge; a typical QSTS simulation generates millions of data points that contain critical information about the circuit and its components. This paper provides examples of visualization methods to facilitate the analysis of QSTS results and to highlight various characteristics of circuits with high variability.