Accurate load forecasting can create both economic and reliability benefits for power system operators. However, the cyberattack on load forecasting may mislead operators to make unsuitable ...operational decisions for the electricity delivery. To effectively and accurately detect these cyberattacks, this paper develops a machine learning-based anomaly detection (MLAD) methodology. First, load forecasts provided by neural networks are used to reconstruct the benchmark and scaling data by using the <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-means clustering. Second, the cyberattack template is estimated by the naive Bayes classification based on the cumulative distribution function and statistical features of the scaling data. Finally, the dynamic programming is utilized to calculate both the occurrence and parameter of one cyberattack on load forecasting data. A widely used symbolic aggregation approximation method is compared with the developed MLAD method. Numerical simulations on the publicly load data show that the MLAD method can effectively detect cyberattacks for load forecasting data with relatively high accuracy. Also, the robustness of MLAD is verified by thousands of attack scenarios based on Monte Carlo simulation.
Increasing integration of renewable resources brings more flexibility and poses new challenges to modern power systems, leading to highly nonlinear and complex dynamics. This paper aims to provide a ...general solution framework to traditional control problems, such as frequency control and voltage control, which attempt to maintain the stability of either synchronous generators-governed or inverter-governed systems when subjected to a disturbance and simultaneously guarantee operational constraints, providing a complete complement to existing works on control design. Building on reinforcement learning (RL) and control barrier functions, the framework includes two subsystems, i.e., a model-free controller and a barrier-certification system, which discover RL-based control actions and sequentially filter them using a barrier certificate to satisfy operational constraints. Calculating a barrier function is generally challenging for a complex power system. This is addressed by representing the barrier function using neural networks (NNs) and data-based approaches. An adaptive method is introduced to certify the neural barrier function that perseveres barrier conditions, which is more compatible with online implementation. The proposed framework synthesizes a stabilizing controller that satisfies predefined safety regions. The effectiveness of the proposed framework is demonstrated via several comparative case studies.
In this paper, we study the difference between two major strategies of cooperative driving at nonsignalized intersections: namely the "ad hoc negotiation-based" strategy and the "planning-based" ...strategy. The fundamental divide of these two strategies lies in how to determine the passing order of vehicles at intersections. The "ad hoc negotiation-based" strategy makes vehicles roughly follow first-come-first-served order but allows some adjustments. This leads to a local optimal solution in many situations. The "planning-based" strategy aims to find a global optimal passing order of vehicles. However, constrained by the planning complexity and time requirement, we often stop at a local optimal solution, too. We carry out a series of simulations to compare the solutions found by two strategies, under different traffic scenarios. Results indicate the performance of a strategy mainly depends on the passing order of vehicles that it finds. Although there exist several trajectory planning algorithms associating with the solving process of passing orders, their differences are negligible. Moreover, if the traffic demand is very low, the performance difference between two strategies is small. When the traffic demand becomes high, the "planning-based" strategy yields significantly better performance since it finds better passing orders. These findings are important to cooperative driving study.
Online transient analysis plays an increasingly important role in dynamic power grids as the renewable generation continues growing. Traditional numerical methods for transient analysis not only are ...computationally intensive but also require precise contingency information as input, and therefore, are not suitable for online applications. Existing online transient assessment studies focus on the determination of post-contingency system stability or stability margin. This paper develops a novel graph-learning framework, Deep-learning Neural Representation or DNR, for online prediction, of the time-series trajectories of the system states using initial system responses that can be measured by phasor measurement units (PMUs). The proposed DNR framework consists of two sequential modules: a Network Constructor that captures network dependencies among generators, and a Dynamics Predictor that predicts the system trajectories. The key to improved prediction performance is the introduction of the spatio-temporal message-passing operations into graph neural networks with structural knowledge. Its effectiveness and scalability are validated through comparative studies, demonstrating the prediction performance under different contingency scenarios for systems of different sizes. This framework provides a solution to online predicting post-fault system dynamics based on real-time PMU measurements. Additionally, it can also be applied to facilitate the offline transient simulation without simulating the entire trajectories.
With the increasing integration of distributed energy resources, significant efforts have been made to develop more accurate load models to mimic the dynamic response of power system load. Despite ...the prevalence of high-resolution measurement equipment and artificial intelligence algorithms, the increasing complexity in existing load models has restricted their accuracy, effectiveness, and capability in real applications. In this paper, a symbolic regression-based method combined with sparse dictionary learning is developed to automatically construct a free-form dynamic load model by synthesizing a large number of basic physics-driven mathematical functions. Firstly, the discontinuities or jumps of the dynamic load time-series data are automatically detected using a discrete wavelet transform. Then a sparse representation of the complete mathematical function set, including all possible physics-driven mathematical functions, is selected using a sparse dictionary learning algorithm. Furthermore, the formulated free-form load model parameters are initialized based on the data itself instead of being manually defined. Various tests are performed with different categories of faults at different locations to validate the performance of the proposed model.
The electric power industry faces tough optimization problems that are challenging for classical computers, e.g., the unit commitment (UC). Quantum computing has the potential to provide a speedup ...for combinatorial optimization problems utilizing ad hoc algorithms like Grover's search algorithm. The quantum oracle is indispensable for Grover's search, yet it is difficult to realize. In this letter, we develop a way to construct efficient oracles for solving UC problems in Grover's framework. The oracle features a physics-informed circuit and a variational quantum circuit picking up good solutions. A quantum algorithm that leverages the oracle is then proposed, which could reduce the number of tractable linear programming subproblems required to solve the targeted UC problem when working in conjugation with classical computers. The oracle and algorithm are validated by results obtained on real quantum hardware and simulators.
We report the first dark matter search results using the commissioning data from PandaX-4T. Using a time projection chamber with 3.7 tonne of liquid xenon target and an exposure of 0.63 tonne·year, ...1058 candidate events are identified within an approximate nuclear recoil energy window between 5 and 100 keV. No significant excess over background is observed. Our data set a stringent limit to the dark matter-nucleon spin-independent interactions, with a lowest excluded cross section (90% C.L.) of 3.8×10^{-47} cm^{2} at a dark matter mass of 40 GeV/c^{2}.
Battery energy storage (BES) is a versatile resource for the secure and economic operation of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES expansion planning for MGs ...are computationally complicated. This work proposes a data-driven bi-level multi-period BES expansion planning framework to determine the siting, sizing, and timing of BES installations. The proposed planning framework unifies deep reinforcement learning (DRL) and linear programming, thereby decoupling the determinations for the integer and continuous decision variables in two time scales, respectively. In the upper level, a rainbow DRL agent with quantile regression is trained to provide dynamic planning policies to accommodate stochastic renewable energy resources (RESs), load, and battery price changes efficiently. The lower level computes the optimal operation of MGs with frequency constraints to hedge the islanding contingency. The two levels communicate with one another by exchanging storage configuration and operating expenses in order to accomplish the shared goal of minimizing investment and operation costs. Comparative case studies on an MG are carried out to demonstrate the superiority of the proposed DRL-based solution to the mixed-integer linear programming counterpart on efficiency, scalability, and adaptability.
As power delivery systems evolve and become increasingly reliant on accurate forecasts, they will be more and more vulnerable to cybersecurity issues. A coordinated data attack by sophisticated ...adversaries can render existing data corrupt or outlier detection methods ineffective. This would have a very negative impact on operational decisions. The focus of this paper is to develop descriptive analytics-based methods for anomaly detection to protect the load forecasting process against cyberattacks to essential data. We propose an integrated solution (IS) and a hybrid implementation of IS (HIIS) that can detect and mitigate cyberattack induced long sequence anomalies. HIIS is also capable of improving true positive rates and reducing false positive rates significantly comparing with IS. The proposed HIIS can serve as an online cybersecure load forecasting scheme.
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