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.
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, Matthew S.; Azzolini, Joseph A.; Yusuf, Jubair Y.; Jones, Christian B.; Furlani Bastos, Alvaro F.; 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.
Reno, Matthew J.; Blakely, Logan; Trevizan, Rodrigo D.; Pena, Bethany D.; Lave, Matthew S.; Azzolini, Joseph A.; Yusuf, Jubair; Jones, Christian B.; Furlani Bastos, Alvaro F.; 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.
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.
The state of charge (SoC) estimated by Battery Management Systems (BMSs) could be vulnerable to False Data Injection Attacks (FDIAs), which aim to disturb state estimation. Inaccurate SoC estimation, due to attacks or suboptimal estimators, could lead to thermal runaway, accelerated degradation of batteries, and other undesirable events. In this paper, an ambient temperature-dependent model is adopted to represent the physics of a stack of three series-connected battery cells, and an Unscented Kalman Filter (UKF) is utilized to estimate the SoC for each cell. A Cumulative Sum (CUSUM) algorithm is used to detect FDIAs targeting the voltage sensors in the battery stack. The UKF was more accurate in state and measurement estimation than the Extended Kalman Filter (EKF) for Maximum Absolute Error (MAE) and Root Mean Squared Error (RMSE). The CUSUM algorithm described in this paper was able to detect attacks as low as ±1 mV when one or more voltage sensor was attacked under various ambient temperatures and attack injection times.
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.
Variables estimated by Battery Management Systems (BMSs) such as the State of Charge (SoC) may be vulnerable to False Data Injection Attacks (FDIAs). Bad actors could use FDIAs to manipulate sensor readings, which could degrade Battery Energy Storage Systems (BESSs) or result in poor system performance. In this paper we propose a method for accurate SoC estimation for series-connected stacks of batteries and detection of FDIA in cell and stack voltage sensors using physics-based models, an Extended Kalman Filter (EKF), and a Cumulative Sum (CUSUM) algorithm. Utilizing additional sensors in the battery stack allowed the system to remain observable in the event of a single sensor failure, allowing the system to continue to accurately estimate states when one sensor at a time was offline. A priori residual data for each voltage sensor was used in the CUSUM algorithm to find the minimum detectable attack (500 μV) with no false positives.
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.