The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.
A method for battery state of charge (SoC) estimation that compensates input noise using an adaptive square-root unscented Kalman filter (ASRUKF) is presented in this paper. In contrast to traditional state estimation approaches that consider deterministic system inputs, this method can improve the accuracy of battery state estimator by considering that the measurements of the control input variable of the filter, the cell currents, are subject to noise. Also, this paper presents two estimators for input and output noise covariance. The proposed method consists of initialization, state correction, sigma point calculations, state prediction, and covariance estimation steps and is demonstrated using simulations. We simulate two battery cycling protocols of three series-connected batteries whose SoC is estimated by the proposed method. The results show that the improved ASRUKF can track closely the states and achieves a 20.63 % reduction in SoC estimation error when compared to a benchmark that does not consider input noise.
This paper introduces a method for detecting stealthy false data injection attacks on the sensors of state of charge estimation algorithms used in battery management systems (BMSs). This method is based on sensor encoding, which is the active modification of sensor data streams. This method implements low-cost verification of the integrity of measurement data, allowing for the detection of stealthy additive attack vectors. It is considered that these attacks are crafted by malicious actors with knowledge of system models and who are capable of tampering with any number of measurements. The solution involves encoding all vulnerable measurements. The effectiveness of the method is demonstrated by simulations, where a stealthy attack on an encoded measurement vector captured by a BMS generates large residuals that trigger a chi-squared anomaly detector. Within the context of a defense-in-depth strategy, this method can be combined with other cybersecurity controls, such as encryption of data-in-transit, to equip cyberphysical systems with an additional line of defense against cyberattacks.
Battery energy storage systems (BESSs) are crucial for modernizing the power grid but are monitored by sensors that are susceptible to anomalies like failures, faults, or cyberattacks that could affect BESS functionality. Much work has been done to detect sensor anomalies, but a research gap persists in responding to anomalies. An approach is proposed to mitigate the damage caused by additive bias anomalies by employing one-of-three estimators based on the anomalies present. A tuned cumulative sum (CUSUM) algorithm is used to identify anomalies, and a set of rules are proposed to select an estimator that will isolate the effect of the anomaly. The proposed approach is evaluated using two simulated studies, one in which an anomaly impacts the input and one where an anomaly impacts an output sensor.
Battery systems are typically equipped with state of charge (SoC) estimation algorithms. Sensor measurements used to estimate SoC are susceptible to false data injection attacks (FDIAs) that aim to disturb state estimation and, consequently, damage the system. In this paper, SoC estimation methods are re-purposed to detect FDIAs targeting the current and voltage sensors of a battery stack using a combination of an improved input noise aware unscented Kalman filter (INAUKF) and a cumulative sum detector. The root mean squared error of the states estimated by the INAUKF was at least 85% lower than the traditional unscented Kalman filter for all noise levels tested. The proposed method was able to detect FDIA in the current and voltage sensors of a series-connected battery stack in 99.55% of the simulations.
For an Energy System to be truly equitable, it should provide affordable and reliable energy services to disadvantaged and underserved populations. Disadvantaged communities often face a combination of economic, social, health, and environmental burdens and may be geographically isolated (e.g., rural communities), which systematically limits their opportunity to fully participate in aspects of economic, social, and civic life.
We consider the problem of decentralized control of reactive power provided 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 a decentralized adaptive controller in which the reactive power at each inverter is set using a potentially heterogeneous droop curve and analyze the stability and the steady-state error of the resulting system. The effectiveness of the controller is validated in simulations using a modified version of the IEEE 13-bus and a 8500-node test system.
Grid-scale battery energy storage systems (BESSs) are vulnerable to false data injection attacks (FDIAs), which could be used to disrupt state of charge (SoC) estimation. Inaccurate SoC estimation has negative impacts on system availability, reliability, safety, and the cost of operation. In this article a combination of a Cumulative Sum (CUSUM) algorithm and an improved input noise-aware extended Kalman filter (INAEKF) is proposed for the detection and identification of FDIAs in the voltage and current sensors of a battery stack. The series-connected stack is represented by equivalent circuit models, the SoC is modeled with a charge reservoir model and the states are estimated using the INAEKF. Further, the root mean squared error of the states’ estimation by the modified INAEKF was found to be superior to the traditional EKF. By employing the INAEKF, this article addresses the research gap that many state estimators make asymmetrical assumptions about the noise corrupting the system. Additionally, the INAEKF estimates the input allowing for the identification of FDIA, which many alternative methods are unable to achieve. The proposed algorithm was able to detect attacks in the voltage and current sensors in 99.16% of test cases, with no false positives. Utilizing the INAEKF compared to the standard EKF allowed for the identification of FDIA in the input of the system in 98.43% of test cases.
The knowledge of long-term health and reliability of energy storage systems is still unknown, yet these systems are proliferating and are expected increasingly to assist in the maintenance of grid reliability. Understanding long-term reliability and performance characteristics to the degree of knowledge similar to that of traditional utility assets requires operational data. This guideline is intended to inform numerous stakeholders on what data are needed for given functions, how to prescribe access to those data and the considerations impacting data architecture design, as well as provide these stakeholders insight into the data and data systems necessary to ensure storage can meet growing expectations in a safe and cost-efficient manner. Understanding data needs, the systems required, relevant standards, and user needs early in a project conception aids greatly in ensuring that a project ultimately performs to expectations.
Most recently, stochastic control methods such as deep reinforcement learning (DRL) have proven to be efficient and quick converging methods in providing localized grid voltage control. Because of the random dynamical characteristics of grid reactive loads and bus voltages, such stochastic control methods are particularly useful in accurately predicting future voltage levels and in minimizing associated cost functions. Although DRL is capable of quickly inferring future voltage levels given specific voltage control actions, it is prone to high variance when the learning rate or discount factors are set for rapid convergence in the presence of bus noise. Evolutionary learning is also capable of minimizing cost function and can be leveraged for localized grid control, but it does not infer future voltage levels given specific control inputs and instead simply selects those control actions that result in the best voltage control. For this reason, evolutionary learning is better suited than DRL for voltage control in noisy grid environments. To illustrate this, using a cyber adversary to inject random noise, we compare the use of evolutionary learning and DRL in autonomous voltage control (AVC) under noisy control conditions and show that it is possible to achieve a high mean voltage control using a genetic algorithm (GA). We show that the GA additionally can provide superior AVC to DRL with comparable computational efficiency. We illustrate that the superior noise immunity properties of evolutionary learning make it a good choice for implementing AVC in noisy environments or in the presence of random cyber-attacks.
Reno, Matthew J.; Blakely, Logan; Trevizan, Rodrigo D.; Pena, Bethany; Lave, Matt; Azzolini, Joseph A.; Yusuf, Jubair; Jones, Christian B.; Furlani Bastos, Alvaro; Chalamala, Rohit; Korkali, Mert; Sun, Chih-Che; Donadee, Jonathan; Stewart, Emma M.; Donde, Vaibhav; Peppanen, Jouni; Hernandez, Miguel; Deboever, Jeremiah; Rocha, Celso; Rylander, Matthew; Siratarnsophon, Piyapath; Grijalva, Santiago; Talkington, Samuel; Mason, Karl; Vejdan, Sadegh; Khan, Ahmad U.; Mbeleg, Jordan S.; Ashok, Kavya; Divan, Deepak; Li, Feng; Therrien, Francis; Jacques, Patrick; Rao, Vittal; Francis, Cody; Zaragoza, Nicholas; Nordy, David; Glass, Jim; Holman, Derek; Mannon, Tim; Pinney, David
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.
To meet the challenges oflow-carbon power generation, distributed energy resources (DERs) such as solar and wind power generators are now widely integrated into the power grid. Because of the autonomous nature of DERs, ensuring properly regulated output voltages of the individual sources to the grid distribution system poses a technical challenge to grid operators. Stochastic, model-free voltage regulations methods such as deep reinforcement learning (DRL) have proven effective in the regulation of DER output voltages; however, deriving an optimal voltage control policy using DRL over a large state space has a large computational time complexity. In this paper we illustrate a computationally efficient method for deriving an optimal voltage control policy using a parallelized DRL ensemble. Additionally, we illustrate the resiliency of the control ensemble when random noise is introduced by a cyber adversary.
In this paper, we present a sensor encoding technique for the detection of stealthy false data injection attacks in static power system state estimation. This method implements low-cost verification of the integrity of measurement data, allowing for the detection of stealthy additive attack vectors. It is considered that these attacks are crafted by malicious actors with knowledge of the system models and capable of tampering with any number of measurements. The solution involves encoding all vulnerable measurements. The effectiveness of the method was demonstrated through a simulation where a stealthy attack on an encoded measurement vector generates large residuals that trigger a chi-squared anomaly detector (e.g. χ2). Following a defense in-depth approach, this method could be used with other security features such as communications encryption to provide an additional line of defense against cyberattacks.
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.
Reno, Matthew J.; Blakely, Logan; Trevizan, Rodrigo D.; Pena, Bethany D.; Lave, Matt; Azzolini, Joseph A.; Yusuf, Jubair; Jones, Christian B.; Furlani Bastos, Alvaro; Chalamala, Rohit; Korkali, Mert; Sun, Chih-Che; Donadee, Jonathan; Stewart, Emma M.; Donde, Vaibhav; Peppanen, Jouni; Hernandez, Miguel; Deboever, Jeremiah; Rocha, Celso; Rylander, Matthew; Siratarnsophon, Piyapath; Grijalva, Santiago; Talkington, Samuel; Gomez-Peces, Cristian; Mason, Karl; Vejdan, Sadegh; Khan, Ahmad U.; Mbeleg, Jordan S.; Ashok, Kavya; Divan, Deepak; Li, Feng; Therrien, Francis; Jacques, Patrick; Rao, Vittal; Francis, Cody; Zaragoza, Nicholas; Nordy, David; Glass, Jim
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.
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.
In recent years, the pervasive use of lithium ion (Li-ion) batteries in applications such as cell phones, laptop computers, electric vehicles, and grid energy storage systems has prompted the development of specialized battery management systems (BMS). The primary goal of a BMS is to maintain a reliable and safe battery power source while maximizing the calendar life and performance of the cells. To maintain safe operation, a BMS should be programmed to minimize degradation and prevent damage to a Li-ion cell, which can lead to thermal runaway. Cell damage can occur over time if a BMS is not properly configured to avoid overcharging and discharging. To prevent cell damage, efficient and accurate cell charging cycle characteristics algorithms must be employed. In this paper, computationally efficient and accurate ensemble learning algorithms capable of detecting Li-ion cell charging irregularities are described. Additionally, it is shown using machine and deep learning that it is possible to accurately and efficiently detect when a cell has experienced thermal and electrical stress due to cell overcharging by measuring charging cycle divergence.
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.
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.
Shah, Chinmay; Campo-Ossa, Daniel D.; Patarroyo-Montenegro, Juan F.; Guruwacharya, Nischal; Bhujel, Niranjan; Trevizan, Rodrigo D.; Andrade, Fabio; Shirazi, Mariko; Tonkoski, Reinaldo; Wies, Richard; Hansen, Timothy M.; Cicilio, Phylicia
In response to national and international carbon reduction goals, renewable energy resources like photovoltaics (PV) and wind, and energy storage technologies like fuel-cells are being extensively integrated in electric grids. All these energy resources require power electronic converters (PECs) to interconnect to the electric grid. These PECs have different response characteristics to dynamic stability issues compared to conventional synchronous generators. As a result, the demand for validated models to study and control these stability issues of PECs has increased drastically. This paper provides a review of the existing PEC model types and their applicable uses. The paper provides a description of the suitable model types based on the relevant dynamic stability issues. Challenges and benefits of using the appropriate PEC model type for studying each type of stability issue are also presented.
The state of California is leading the nation with respect to solar energy and storage. The California Energy Commission has mandated that starting in 2020 all new homes must be solar powered. In 2010 the California state legislature adopted an energy storage mandate AB 2514. This required California's three largest utilities to contract for an additiona11.3 GW of energy storage by 2020, coming online by 2024. Therefore, there is keen interest in the potential advantages of deploying solar combined with energy storage. This paper formulates the optimization problem to identify the maximum potential revenue from pairing storage with solar and participating in the California Independent System Operator (CAISO) day ahead market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes were analyzed to identify trends and opportunities for the deployment of solar plus storage.
This paper presents a techno-economic analysis of behind-the-meter (BTM) solar photovoltaic (PV) and battery energy storage systems (BESS) applied to an Electric Vehicle (EV) fast-charging station. The goal is to estimate the maximum return on investment (ROI) that can be obtained for optimum BESS and PV size and their operation. Fast charging is a technology that will speed up mass adoption of EVs, which currently requires several hours to achieve full recharge in level 1 or 2 chargers. Fast chargers demand from tens to hundreds of kilowatts from the distribution grid, potentially leading to system congestion and overload. The problem is formulated as a linear program that obtains the size of PV, power and energy ratings of BESS as well as charging and discharging scheduling of the storage system to maximize ROI under operational constraints of BESS and PV. The revenue are cost-savings of demand and time-of-use charges, with a penalty for BESS degradation. We have considered Los Angeles Department of Water and Power tariff A-2 and fast charger data derived from the EV Project. The results show that a 46.5 kW/28.3 kWh BESS can obtain a ROI of about $22.4k over 10 years for a small 4-port fast-charging station.
Increased deployment of rooftop solar in California has resulted in the "duck curve", where there is a decrease in the midday net load followed by a very fast increase in late afternoon caused by declining solar output and a simultaneous increase in load. The large observed and even larger predicted ramp rates present a reliability concern. Therefore, there is keen interest in the potential advantages of deploying solar combined with energy storage. This paper formulates the optimization problem to identify the maximum potential revenue from pairing storage with solar and participating in the California Independent System Operator (CAISO) hour-ahead scheduling process (HASP) real-time market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes was analyzed to identify trends and opportunities for the deployment of solar plus storage. In addition, a comparison of the opportunities in the day ahead and HASP real-time energy markets is presented.
This paper presents a techno-economic analysis of behind-the-meter (BTM) solar photovoltaic (PV) and battery energy storage systems (BESS) applied to an Electric Vehicle (EV) fast-charging station. The goal is to estimate the maximum return on investment (ROI) that can be obtained for optimum BESS and PV size and their operation. Fast charging is a technology that will speed up mass adoption of EVs, which currently requires several hours to achieve full recharge in level 1 or 2 chargers. Fast chargers demand from tens to hundreds of kilowatts from the distribution grid, potentially leading to system congestion and overload. The problem is formulated as a linear program that obtains the size of PV, power and energy ratings of BESS as well as charging and discharging scheduling of the storage system to maximize ROI under operational constraints of BESS and PV. The revenue are cost-savings of demand and time-of-use charges, with a penalty for BESS degradation. We have considered Los Angeles Department of Water and Power tariff A-2 and fast charger data derived from the EV Project. The results show that a 46.5 kW/28.3 kWh BESS can obtain a ROI of about $22.4k over 10 years for a small 4-port fast-charging station.
