Reno, Matthew J.; Blakely, Logan; Trevizan, Rodrigo D.; Pena, Bethany D.; Lave, Matthew S.; Azzolini, Joseph A.; Yusuf, Jubair Y.; Jones, Christian B.; Furlani Bastos, Alvaro F.; Chalamala, Rohit C.; Korkali, Mert K.; Sun, Chih-Che S.; Donadee, Jonathan D.; Stewart, Emma M.; Donde, Vaibhav D.; Peppanen, Jouni P.; Hernandez, Miguel H.; Deboever, Jeremiah D.; Rocha, Celso R.; Rylander, Matthew R.; Siratarnsophon, Piyapath S.; Grijalva, Santiago G.; Talkington, Samuel T.; Mason, Karl M.; Vejdan, Sadegh V.; Khan, Ahmad U.; Mbeleg, Jordan S.; Ashok, Kavya A.; Divan, Deepak D.; Li, Feng L.; Therrien, Francis T.; Jacques, Patrick J.; Rao, Vittal R.; Francis, Cody F.; Zaragoza, Nicholas Z.; Nordy, David N.; Glass, Jim G.; Holman, Derek H.; Mannon, Tim M.; Pinney, David P.
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.
Reno, Matthew J.; Blakely, Logan; Trevizan, Rodrigo D.; Pena, Bethany D.; Lave, Matthew S.; Azzolini, Joseph A.; Yusuf, Jubair Y.; Jones, Christian B.; Furlani Bastos, Alvaro F.; Chalamala, Rohit C.; Korkali, Mert K.; Sun, Chih-Che S.; Donadee, Jonathan D.; Stewart, Emma M.; Donde, Vaibhav D.; Peppanen, Jouni P.; Hernandez, Miguel H.; Deboever, Jeremiah D.; Rocha, Celso R.; Rylander, Matthew R.; Siratarnsophon, Piyapath S.; Grijalva, Santiago G.; Talkington, Samuel T.; Gomez-Peces, Cristian G.; Mason, Karl M.; Vejdan, Sadegh V.; Khan, Ahmad U.; Mbeleg, Jordan S.; Ashok, Kavya A.; Divan, Deepak D.; Li, Feng L.; Therrien, Francis T.; Jacques, Patrick J.; Rao, Vittal S.; Francis, Cody F.; Zaragoza, Nicholas Z.; Nordy, David N.; Glass, Jim G.
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.
Many remote communities are subject to poor electric service, which low power quality and reliability being common concerns. To compensate, many isolated communities employ diesel generation units to bolster utility inputs or to fully support key loads in the event of an outage. While this is effective, it can be a very expensive mode of operation requiring oversized units to ensure reliable power. Declining prices of both renewable generation and energy storage systems have the potential to improve this situation, though careful planning is needed to make these hybrid energy systems financially attractive. This paper presents analytical methods to enable informed decision making with respect to future planning incorporating renewables and energy storage systems to enhance system reliability and reduce operating costs. These methods are demonstrated in a case study for the San Carlos Apache Tribe, which is located in a sparsely populated region next to Coolidge, Arizona that has limited power generation and transmission resources. Currently, the energy tariffs are high and the system suffers from frequent power interruptions, adding up to an average of around 100 power interruptions per year. To reduce electricity costs and improve power quality, the tribe is currently installing solar photovoltaic arrays in several sites inside of the reservation. We have analyzed the potential benefits and optimal of energy storage systems associated with solar power generation to reduce the tribe's costs with electricity and contribute to improve reliability of critical loads. Results show that energy storage has the potential to reduce electricity costs significantly and provide backup power for critical loads during several hours.
We consider the problem of decentralized control of reactive power provisioned by distributed energy resources for voltage support in the distribution grid. We assume that the reactance matrix of the grid is unknown and potentially time-varying. We present conditions for stability of the system when the reactive power at each inverter is set using a potentially heterogeneous droop curve. These conditions utilize energy dissipation requirements and can be naturally satisfied even when the reactance matrix is unknown by using an adaptive controller and when the reactance matrix is time-varying.
This paper presents a literature review on current practices and trends on cyberphysical security of grid-connected battery energy storage systems (BESSs). Energy storage is critical to the operation of Smart Grids powered by intermittent renewable energy resources. To achieve this goal, utility-scale and consumer-scale BESS will have to be fully integrated into power systems operations, providing ancillary services and performing functions to improve grid reliability, balance power and demand, among others. This vision of the future power grid will only become a reality if BESS are able to operate in a coordinated way with other grid entities, thus requiring significant communication capabilities. The pervasive networking infrastructure necessary to fully leverage the potential of storage increases the attack surface for cyberthreats, and the unique characteristics of battery systems pose challenges for cyberphysical security. This paper discusses a number of such threats, their associated attack vectors, detection methods, protective measures, research gaps in the literature and future research trends.
Highlights: Battery energy storage may improve energy efficiency and reliability of hybrid energy systems composed by diesel and solar photovoltaic power generators serving isolated communities.In projects aiming update of power plants serving electrically isolated communities with redundant diesel generation, battery energy storage can improve overall economic performance of power supply system by reducing fuel usage, decreasing capital costs by replacing redundant diesel generation units, and increasing generator system life by shortening yearly runtime.Fast-acting battery energy storage systems with grid-forming inverters might have potential for improving drastically the reliability indices of isolated communities currently supplied by diesel generation. Abstract: This paper will highlight unique challenges and opportunities with regard to energy storage utilization in remote, self-sustaining communities. The energy management of such areas has unique concerns. Diesel generation is often the go-to power source in these scenarios, but these systems are not devoid of issues. Without dedicated maintenance crews as in large, interconnected network areas, minor interruptions can be frequent and invasive not only for those who lose power, but also for those in the community that must then correct any faults. Although the immediate financial benefits are perhaps not readily apparent, energy storage could be used to address concerns related to reliability, automation, fuel supply concerns, generator degradation, solar utilization, and, yes, fuel costs to name a few. These ideas are shown through a case study of the Levelock Village of Alaska. Currently, the community is faced with high diesel prices and a difficult supply chain, which makes temporary loss of power very common and reductions in fuel consumption very impactful. This study will investigate the benefits that an energy storage system could bring to the overall system life, fuel costs, and reliability of the power supply. The variable efficiency of the generators, impact of startup/shutdown process, and low-load operation concerns are considered. The technological benefits of the combined system will be explored for various scenarios of future diesel prices and technology maintenance/replacement costs as well as for the avoidance of power interruptions that are so common in the community currently. Graphic abstract: [Figure not available: see fulltext.] Discussion: In several cases, energy storage can provide a means to promote energy equity by improving remote communities’ power supply reliability to levels closer to what the average urban consumer experiences at a reduced cost compared to transmission buildout. Furthermore, energy equity represents a hard-to-quantify benefit achieved by the integration of energy storage to isolated power systems of under-served communities, which suggests that the financial aspects of such projects should be questioned as the main performance criterion. To improve battery energy storage system valuation for diesel-based power systems, integration analysis must be holistic and go beyond fuel savings to capture every value stream possible.
High penetration of distributed energy resources presents challenges for monitoring and control of power distribution systems. Some of these problems might be solved through accurate monitoring of distribution systems, such as what can be achieved with distribution system state estimation (DSSE). With the recent large-scale deployment of advanced metering infrastructure associated with existing SCADA measurements, DSSE may become a reality in many utilities. In this paper, we present a sensitivity analysis of DSSE with respect to phase mislabeling of single-phase service transformers, another class of errors distribution system operators are faced with regularly. The results show DSSE is more robust to phase label errors than a power flow-based technique, which would allow distribution engineers to more accurately capture the impacts and benefits of distributed PV.
Increasing installation of renewable energy resources makes the power system inertia a time-varying quantity. Furthermore, converter-dominated grids have different dynamics compared to conventional grids and therefore estimates of the inertia constant using existing dynamic power system models are unsuitable. In this paper, a novel inertia estimation technique based on convolutional neural networks that use local frequency measurements is proposed. The model uses a non-intrusive excitation signal to perturb the system and measure frequency using a phase-locked loop. The estimated inertia constants, within 10% of actual values, have an accuracy of 97.35% and root mean square error of 0.2309. Furthermore, the model evaluated on unknown frequency measurements during the testing phase estimated the inertia constant with a root mean square error of 0.1763. The proposed model-free approach can estimate the inertia constant with just local frequency measurements and can be applied over traditional inertia estimation methods that do not incorporate the dynamic impact of renewable energy sources.
Topology identification in transmission systems has historically been accomplished using SCADA measurements. In distribution systems, however, SCADA measurements are insufficient to determine system topology. An accurate system topology is essential for distribution system monitoring and operation. Recently there has been a proliferation of Advanced Metering Infrastructure (AMI) by the electrical utilities, which improved the visibility into distribution systems. These measurements offer a unique capability for Distribution System Topology Identification (DSTI). A novel approach to DSTI is presented in this paper which utilizes the voltage magnitudes collected by distribution grid sensors to facilitate identification of the topology of the distribution network in real-time using Linear Discriminant Analysis (LDA) and Regularized Diagonal Quadratic Discriminant Analysis (RDQDA). The results show that this method can leverage noisy voltage magnitude readings from load buses to accurately identify distribution system reconfiguration between radial topologies during operation under changing loads.
Estimated parameters in Battery Energy Storage Systems (BESSs) may be vulnerable to cyber-attacks such as False Data Injection Attacks (FDIAs). FDIAs, which typically evade bad data detectors, could damage or degrade Battery Energy Storage Systems (BESSs). This paper will investigate methods to detect small magnitude FDIA using battery equivalent circuit models, an Extended Kalman Filter (EKF), and a Cumulative Sum (CUSUM) algorithm. A priori error residual data estimated by the EKF was used in the CUSUM algorithm to find the lowest detectable FDIA for this battery equivalent model. The algorithm described in this paper was able to detect attacks as low as 1 mV, with no false positives. The CUSUM algorithm was compared to a chi-squared based FDIA detector. In this study the CUSUM was found to detect attacks of smaller magnitudes than the conventional chi-squared detector.
Distribution system topology identification has historically been accomplished by unencrypting the information that is received from the smart meters and then running a topology identification algorithm. Unencrypted smart meter data introduces privacy and security issues for utility companies and their customers. This paper introduces security aware machine learning algorithms to alleviate the privacy and security issues raised with un-encrypted smart meter data. The security aware machine learning algorithms use the information received from the Advanced Metering Infrastructure (AMI) and identifies the distribution systems topology without unencrypting the AMI data by using fully homomorphic NTRU and CKKS encryption. The encrypted smart meter data is then used by Linear Discriminant Analysis, Convolution Neural Network, and Support Vector Machine algorithms to predict the distribution systems real time topology. This method can leverage noisy voltage magnitude readings from smart meters to accurately identify distribution system reconfiguration between radial topologies during operation under changing loads.
In a power distribution network with energy storage systems (ESS) and advanced controls, traditional monitoring and protection schemes are not well suited for detecting anomalies such as malfunction of controllable devices. In this work, we propose a data-driven technique for the detection of incidents relevant to the operation of ESS in distribution grids. This approach leverages the causal relationship observed among sensor data streams, and does not require prior knowledge of the system model or parameters. Our methodology includes a data augmentation step which allows for the detection of incidents even when sensing is scarce. The effectiveness of our technique is illustrated through case studies which consider active power dispatch and reactive power control of ESS.