arXiv subjects

Qinglai Guo

Publications and source records attributed to Qinglai Guo.

At least 19 recordsLinked to original sources

arXiv2026

Detecting Cyber Attacks in Power System AGC Using a Drifted Ornstein-Uhlenbeck Process

The Automatic Generation Control (AGC) system, reliant on real-time measurements over communication networks, is susceptible to stealthy false data injection attacks (FDIAs), risking equipment damage and economic losses. We propose a robust FDIA detection method using maximum likelihood estimation (MLE) of a drifted multivariate Ornstein-Uhlenbeck (OU) process. Independent of load observability, in various cyberattack scenarios, the proposed FDIA detection method delivers accurate and rapid detection of sophisticated FDIAs, outperforming traditional unknown input observer (UIO) methods, which miss detections, and Long Short-Term Memory Autoencoder (LSTM-AE) approaches, which suffer from prolonged detection times.

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arXiv2026

Risk Assessment and Vulnerability Identification of Energy-Transportation Infrastructure Systems to Extreme Weather

The interaction between extreme weather events and interdependent critical infrastructure systems involves complex spatiotemporal dynamics. Multi-type emergency decisions within energy-transportation infrastructures significantly influence system performance throughout the extreme weather process. A comprehensive assessment of these factors faces challenges in model complexity, heterogeneous differences between energy and transportation systems, and cross-sector privacy. This paper proposes a risk assessment framework that integrates the heterogeneous energy and transportation systems in the form of a unified network flow model, which enables full accommodation of multiple types of energy-transportation emergency decisions while capturing the compound spatiotemporal impacts of extreme weather on both systems simultaneously. Based on this framework, a targeted method for identifying system vulnerabilities is further developed. This method employs neural network surrogates to achieve privacy protection and accelerated identification while maintaining consideration of system interdependencies. Numerical experiments demonstrate that the proposed framework and method can reveal the risk levels faced by urban infrastructure systems, identify vulnerabilities that should be prioritized for reinforcement, and strike a balance between accuracy and speed.

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arXiv2025

Grid-Aware Real-Time Dispatch of Microgrid with Generalized Energy Storage: A Prediction-Free Online Optimization Approach

This paper proposes a novel prediction-free two-stage coordinated dispatch framework for the real-time dispatch of grid-connected microgrid with generalized energy storages (GES). The proposed framework explicitly addresses grid awareness, non-anticipativity constraints, and the time-coupling characteristics of GES, providing microgrid operators with a near-optimal, reliable, and adaptable dispatch tool. In the offline stage, we generate the hindsight state-of-charge (SoC) trajectories of GES by solving the multi-period economic dispatch with historical scenarios. Subsequently, leveraging this historical information (SoC trajectories, net loads, and electricity prices), we synthesize and dynamically update online references for both SoC and opportunity cost through kernel regression. We propose an adaptive Lagrange multiplier-based online convex optimization algorithm, which innovatively incorporates reference tracking for global vision and expert-tracking for step-size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of both dynamic regret and time-varying hard constraint violation. Numerical studies using ground-truth data from the Australian Energy Market Operator demonstrate that the proposed method outperforms state-of-the-art methods, reducing operational costs by 5.0-6.2% and voltage violations by 0.8-9.1%. These improvements mainly result from mitigating myopia by reference tracking and the adaptive capability provided by dynamically updated references and adaptive Lagrange multipliers. Sensitivity analysis demonstrates the robustness, computational efficiency, and scalability of the proposed method.

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arXiv2024

GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent

Ensuring that the outputs of neural networks satisfy specific constraints is crucial for applying neural networks to real-life decision-making problems. In this paper, we consider making a batch of neural network outputs satisfy bounded and general linear constraints. We first reformulate the neural network output projection problem as an entropy-regularized linear programming problem. We show that such a problem can be equivalently transformed into an unconstrained convex optimization problem with Lipschitz continuous gradient according to the duality theorem. Then, based on an accelerated gradient descent algorithm with numerical performance enhancement, we present our architecture, GLinSAT, to solve the problem. To the best of our knowledge, this is the first general linear satisfiability layer in which all the operations are differentiable and matrix-factorization-free. Despite the fact that we can explicitly perform backpropagation based on automatic differentiation mechanism, we also provide an alternative approach in GLinSAT to calculate the derivatives based on implicit differentiation of the optimality condition. Experimental results on constrained traveling salesman problems, partial graph matching with outliers, predictive portfolio allocation and power system unit commitment demonstrate the advantages of GLinSAT over existing satisfiability layers. Our implementation is available at \url{https://github.com/HunterTracer/GLinSAT}.

cs.AI↗

arXiv2024

Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach

Transient stability-constrained preventive redispatch plays a crucial role in ensuring power system security and stability. Since redispatch strategies need to simultaneously satisfy complex transient constraints and the economic need, model-based formulation and optimization become extremely challenging. In addition, the increasing uncertainty and variability introduced by renewable sources start to drive the system stability consideration from deterministic to probabilistic, which further exaggerates the complexity. In this paper, a Graph neural network guided Distributional Deep Reinforcement Learning (GD2RL) method is proposed, for the first time, to solve the uncertainty-aware transient stability-constrained preventive redispatch problem. First, a graph neural network-based transient simulator is trained by supervised learning to efficiently generate post-contingency rotor angle curves with the steady-state and contingency as inputs, which serves as a feature extractor for operating states and a surrogate time-domain simulator during the environment interaction for reinforcement learning. Distributional deep reinforcement learning with explicit uncertainty distribution of system operational conditions is then applied to generate the redispatch strategy to balance the user-specified probabilistic stability performance and economy preferences. The full distribution of the post-redispatch transient stability index is directly provided as the output. Case studies on the modified New England 39-bus system validate the proposed method.

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arXiv2024

Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map

Transmission interface power flow adjustment is a critical measure to ensure the security and economy operation of power systems. However, conventional model-based adjustment schemes are limited by the increasing variations and uncertainties occur in power systems, where the adjustment problems of different transmission interfaces are often treated as several independent tasks, ignoring their coupling relationship and even leading to conflict decisions. In this paper, we introduce a novel data-driven deep reinforcement learning (DRL) approach, to handle multiple power flow adjustment tasks jointly instead of learning each task from scratch. At the heart of the proposed method is a multi-task attribution map (MAM), which enables the DRL agent to explicitly attribute each transmission interface task to different power system nodes with task-adaptive attention weights. Based on this MAM, the agent can further provide effective strategies to solve the multi-task adjustment problem with a near-optimal operation cost. Simulation results on the IEEE 118-bus system, a realistic 300-bus system in China, and a very large European system with 9241 buses demonstrate that the proposed method significantly improves the performance compared with several baseline methods, and exhibits high interpretability with the learnable MAM.

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arXiv2024

Preventive Audits for Data Applications Before Data Sharing in the Power IoT

With the increase in data volume, more types of data are being used and shared, especially in the power Internet of Things (IoT). However, the processes of data sharing may lead to unexpected information leakage because of the ubiquitous relevance among the different data, thus it is necessary for data owners to conduct preventive audits for data applications before data sharing to avoid the risk of key information leakage. Considering that the same data may play completely different roles in different application scenarios, data owners should know the expected data applications of the data buyers in advance and provide modified data that are less relevant to the private information of the data owners and more relevant to the nonprivate information that the data buyers need. In this paper, data sharing in the power IoT is regarded as the background, and the mutual information of the data and their implicit information is selected as the data feature parameter to indicate the relevance between the data and their implicit information or the ability to infer the implicit information from the data. Therefore, preventive audits should be conducted based on changes in the data feature parameters before and after data sharing. The probability exchange adjustment method is proposed as the theoretical basis of preventive audits under simplified consumption, and the corresponding optimization models are constructed and extended to more practical scenarios with multivariate characteristics. Finally, case studies are used to validate the effectiveness of the proposed preventive audits.

cs.CR↗

arXiv2024

Active Support of Inverters for Improving Short-Term Voltage Security in 100% IBRsPenetrated Power Systems

Due to the energy crisis and environmental pollution, the installed capacity of inverter-based resources (IBRs) in power grids is rapidly increasing, and grid-following control (GFL) is the most prevalent at present. Meanwhile, grid-forming control-based (GFM) devices have been installed in the grid to provide active support for frequency and voltage. In the future GFL devices combined with GFM will be promising, especially in power systems with high penetration or 100% IBRs. When a short-circuit fault occurs in the grid, the controlled current source characteristic of the GFL devices leads to insufficient dynamic voltage support (DVS), while the GFM devices usually reduce the internal voltage to limit the current. Thus, deep voltage sags and undesired disconnections of IBRs may occur. Moreover, due to the dispersed locations and the control strategies' diversity of IBRs, the voltage support of different devices may not be fully coordinated, which is not conducive to short-term voltage security (STVS). To address this issue, a control scheme based on the simulation of transient characteristics of synchronous machines (SMs) is proposed. Then, a new fault ride-through strategy (FRT) is proposed based on the characteristic differences between GFL and GFM devices, and an optimization model of multi-device control parameters is formulated to meet the short-term voltage security constraints (SVSCs) and device capacity constraints. Finally, a fast solution method based on analytical modeling is proposed for the model. Test results based on the doublegenerator-one-load system, the IEEE 14-bus system, and other systems of different sizes show that the proposed method can effectively enhance the active support capability of GFL and GFM to the grid voltage, and avoid the large-scale disconnection of IBRs

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arXiv2023

Short-Term Voltage Security Constrained UC to Prevent Trip Faults in High Wind Power Penetrated Power Systems

For high wind power-penetrated power systems, the multiple renewable energy station short-circuit ratio (MRSCR) is often insufficient due to weak grid structures. Additionally, transient voltage sag/overvoltage issues may cause trip faults of wind turbines (WTs). Due to the time delay in WTs' controllers, it is difficult for WTs alone to meet the reactive power demands in different stages of the transient process. Some synchronous machines (SMs) must be retained through unit commitment (UC) scheduling to improve MRSCR and prevent trip faults of WTs. The MRSCR and short-term voltage security constrained-UC model is a mixed integer nonlinear programming (MINLP) problem with differential algebraic equations (DAEs) and symbolic matrix inversion, which is intractable to solve. Based on the dynamic characteristics of different devices, the original model is simplified as a general MINLP model without DAEs. Then, generalized Benders decomposition is applied to improve the solution efficiency. The relaxed MRSCR constraints are formulated in the master problem to improve the convergence, and the precise MRSCR constraints are formulated in the subproblems to consider the impact of voltage profiles. Case studies based on several benchmark systems and a provincial power grid verify the validity and efficiency of the proposed method

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arXiv2023

Cooperative IoT Data Sharing with Heterogeneity of Participants Based on Electricity Retail

With the development of Internet of Things (IoT) and big data technology, the data value is increasingly explored in multiple practical scenarios, including electricity transactions. However, the isolation of IoT data among several entities makes it difficult to achieve optimal allocation of data resources and convert data resources into real economic value, thus it is necessary to introduce the IoT data sharing mode to drive data circulation. To enhance the accuracy and fairness of IoT data sharing, the heterogeneity of participants is sufficiently considered, and data valuation and profit allocation in IoT data sharing are improved based on the background of electricity retail. Data valuation is supposed to be relevant to attributes of IoT data buyers, thus risk preferences of electricity retailers are applied as characteristic attributes and data premium rates are proposed to modify data value rates. Profit allocation should measure the marginal contribution shares of electricity retailers and data brokers fairly, thus asymmetric Nash bargaining model is used to guarantee that they could receive reasonable profits based on their specific contribution to the coalition of IoT data sharing. Considering the heterogeneity of participants comprehensively, the proposed IoT data sharing fits for a large coalition of IoT data sharing with multiple electricity retailers and data brokers. Finally, to demonstrate the applications of IoT data sharing in smart grids, case studies are utilized to validate the results of data value for electricity retailers with different risk preferences and the efficiency of profit allocation using asymmetric Nash bargaining model.

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arXiv2022

Energy Circuit-based Integrated Energy Management System: Theory, Implementation, and Application

Integrated energy systems (IESs), in which various energy flows are interconnected and coordinated to release potential flexibility for more efficient and secure operation, have drawn increasing attention in recent years. In this article, an integrated energy management system (IEMS) that performs online analysis and optimization on coupling energy flows in an IES is comprehensively introduced. From the theory perspective, an energy circuit method (ECM) that models natural gas networks and heating networks in the frequency domain is discussed. This method extends the electric circuit modeling of power systems to IESs and enables the IEMS to manage large-scale IESs. From the implementation perspective, the architecture design and function development of the IEMS are presented. Tutorial examples with illustrative case studies are provided to demonstrate its functions of dynamic state estimation, energy flow analysis, security assessment and control, and optimal energy flow. From the application perspective, real-world engineering demonstrations that apply IEMSs in managing building-scale, park-scale, and city-scale IESs are reported. The economic and environmental benefits obtained in these demonstration projects indicate that the IEMS has broad application prospects for a low/zero-carbon future energy system.

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arXiv2022

Flux Linkage Based Evaluation Method for Voltage Inertia and Voltage Recovery Capability Under Large Disturbances

High-voltage direct current (HVDC) transmission applications and the growth of the dynamic load in large-scale receiving-end grids lead to a higher risk of short-term voltage instability. An effective way to address this problem is to improve the system's dynamic voltage support capability by changing the operation status of dynamic var devices, and the dynamic var reserve (DVR) is commonly used. Due to the time delay in synchronous machine excitation systems, the dynamic var reserved at steady state cannot be exploited immediately under large disturbances. In addition, some reactive power is produced immediately through electromagnetic induction. The voltage support effect of the two capabilities is analyzed based on the flux linkage and an approximate simulation of the fault impact. Then two novel indexes for evaluating the voltage inertia and voltage recovery capability are proposed, which are related to the voltage nadir and voltage recovery speed. The indexes' physical meanings are revealed by comparison with the frequency response, and the potential applications in planning and optimal reactive power dispatch (ORPD) are introduced. Numerical simulations based on the IEEE 39-bus system verify that the indexes can quantify the voltage support capabilities, and the minimum voltage support requirements are obtained to maintain systems' security.

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arXiv2022

An Efficient Optimal Energy Flow Model for Integrated Energy Systems Based on Energy Circuit Modeling in the Frequency Domain

With more energy networks being interconnected to form integrated energy systems (IESs), the optimal energy flow (OEF) problem has drawn increasing attention. Extant studies on OEF models mostly utilize the finite difference method (FDM) to address partial-differential-equation (PDE) constraints related to the dynamics in natural gas networks (NGNs) and district heating networks (DHNs). However, this time-domain approach suffers from a heavy computational burden with regard to achieving high finite-difference accuracy. In this paper, a novel OEF model that formulates NGN and DHN constraints in the frequency domain and corresponding model compaction techniques for efficient solving are contributed. First, an energy circuit method (ECM) that algebraizes the PDEs of NGNs and DHNs in the frequency domain is introduced. Then, an ECM-based OEF model is formulated, which contains fewer variables and constraints than an FDM-based OEF model and thereby yields better solving efficiency. Finally, variable space projection is employed to remove implicit variables, by which another constraint generation algorithm is enabled to remove redundant constraints. These two techniques further compact the OEF model and bring about a second improvement in solving efficiency. Numerical tests on actual systems indicate the final OEF model reduces variables and constraints by more than 95% and improves the solving efficiency by more than 10 times. In conclusion, the proposed OEF model and solving techniques well meet the optimization needs of large-scale IESs.

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arXiv2022

A Stochastic Planning Method for Low-carbon Building-level Integrated Energy System Considering Electric-Heat-V2G Coupling

The concept of low-carbon building is proposed to ameliorate the climate change caused by environmental problems and realize carbon neutrality at the building level in urban areas. In addition, renewable energy curtailment in the power distribution system, as well as low efficiency due to independent operation of traditional energy systems, has been addressed by the application of integrated energy system (IES) to some extent. In this paper, we propose a planning method for low-carbon building-level IES, in which electric vehicles (EV) and the mode of Vehicle to Grid (V2G) are considered and further increase the flexibility of low-carbon buildings. The proposed planning model optimize the investment, operation costs and CO2 emission for building-level IES, so as to achieve the maximum benefit of the construction of the low-carbon building and help the realization of carbon neutrality. Moreover, we consider the uncertainty of distributed renewable energy, multi-energy load fluctuation and the random behavior of EV users, then formulating a two-stage stochastic programming model with chance constraints, in which heuristic moment matching scenario generation (HMMSG) and sample average approximation (SAA) method are applied. In case study, a real IES commercial building in Shanghai, where photovoltaic (PV), energy storage system (ESS), fuel cell (FC), EV, etc. are included as planning options, is used as numerical example to verify the effectiveness of the proposed planning method, with functions of ESS and EV in IES are analyzed in detail in different operation scenarios.

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arXiv2021

Accuracy and Application Scope Analysis for Linearized Branch Flow Model in Radial Distribution Systems

An in-depth analysis of linearized branch flow (LBF) model considering current injection and absolute value of impedance is proposed in this paper. The form of LBF model is based on two equations: the current injection to meet KCL and the voltage drop to meet KVL. By representing the absolute value of complex load power with the current injection, LBF model is much simpler than alternating current power flow (ACPF) model. The results on theoretical analysis and numerical studies show that LBF exhibits the high accuracy in bus voltage magnitude but a poor performance in branch flow. Moreover, LBF is also compared with fast decoupled linearized power flow (FDLPF) model to verify its efficiency, thus proving its superiority for fast evaluation of large-scale distribution systems with high accuracy in voltage magnitude. Finally, this paper analyzes three factors to lower LBF's errors of branch flow, as well as LBF's possible application scope.

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arXiv2021

Two-stage Planning for Electricity-Gas Coupled Integrated Energy System with CCUS Considering Carbon Tax and Price Uncertainty

In this article, we propose two-stage planning models for Electricity-Gas Coupled Integrated Energy System (EGC-IES), in which traditional thermal power plants (TTPPs) are considered to be retrofitted into carbon capture power plants (CCPPs), with power to gas (PtG) coupling CCPPs to gas system. The sizing and siting of carbon capture, utilisation and storage (CCUS)/PtG facilities, as well as the operation cost of TTPPs/CCPPs/gas sources/PtG, are all considered in the proposed model, including penalty on carbon emissions and revenue of CCUS. With changing policy on climate change and carbon emission regulation, the uncertainties of carbon price and carbon tax are also analysed and considered in the proposed planning model. The stochastic planning, and robust planning methods are introduced to verify mutually through economic and carbon indices. The proposed methods' effectiveness in reducing carbon emissions, increasing profit of CCUS from EGC-IES are demonstrated through various cases and discussions.

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arXiv2021

A Conditional Value-at-Risk Based Planning Model for Integrated Energy System with Energy Storage and Renewables

Owing to the potential higher energy supply efficiency and operation flexibility, integrated energy system (IES), which usually includes electric power, gas and heating/cooling systems, is considered as one of the primary forms of energy carrier in the future. However, with the increasing complexity of multiple energy devices and systems integration, IES planning is facing a significant challenge in terms of risk assessment. To this end, an energy hub (EH) planning model considering renewable energy sources (RES) and energy storage system (ESS) integration is proposed in this paper, in which the risk is measured by Conditional Value-at-Risk (CVaR). The proposed IES planning model includes two stages: 1) investment planning on equipment types and capacity (e.g., energy converters, distributed RES and ESS) and 2) optimizing the potential risk loss in operation scenarios along with confidence level and risk preference. The problem solving is accelerated by Benders Decomposition and Improved Backward Scenario Reduction Method. The numerical results illustrate the effectiveness of proposed method in balancing the potential operation risk and investment cost. Moreover, the effectiveness of reducing potential operation risk by introducing ESS and RES are also verified.

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arXiv2020

A Data-Driven Warm Start Approach for Convex Relaxation in Optimal Gas Flow

In this letter, we propose a data-driven warm start approach, empowered by artificial neural networks, to boost the efficiency of convex relaxations in optimal gas flow. Case studies show that this approach significantly decreases the number of iterations for the convex-concave procedure algorithm, and optimality and feasibility of the solution can still be guaranteed.

math.OC↗