Browsing by Author "Xu, Yijun"

Now showing 1 - 17 of 17
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    An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System
    Xu, Yijun; Valinejad, Jaber; Korkali, Mert; Mili, Lamine M.; Wang, Yajun; Chen, Xiao; Zheng, Zongsheng (IEEE, 2021-10-20)
    The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, this paper proposes a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.
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    Anomaly Detection in Data-Driven Coherency Identification Using Cumulant Tensor
    Sun, Bo; Xu, Yijun; Wang, Qinling; Lu, Shuai; Yu, Ruizhi; Gu, Wei; Mili, Lamine M. (IEEE, 2023-12-04)
    As a model reduction tool, coherency identification has been extensively investigated by power researchers using various model-driven and data-driven approaches. Model-driven approaches typically lose their accuracy due to linear assumptions and parameter uncertainties, while data-driven approaches inevitably suffer from bad data issues. To overcome these weaknesses, we propose a data-driven cumulant tensor-based approach that can identify coherent generators and detect anomalies simultaneously. More specifically, it converts the angular velocities of generators into a fourth-order cumulant tensor that can be decomposed to reflect the coherent generators. Also, using co-kurtosis in the cumulant tensor, anomalies can be detected as well. The simulations reveal its excellent performance.
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    A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch Considering Wind Power Penetration
    Hu, Zhixiong; Xu, Yijun; Korkali, Mert; Chen, Xiao; Mili, Lamine M.; Valinejad, Jaber (IEEE, 2020-08-10)
    The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To estimate the uncertainty in the stochastic economic dispatch (SED) problem for the purpose of forecasting, the conventional Monte-Carlo (MC) method is prohibitively time-consuming for practical applications. To overcome this problem, we propose a novel Gaussian-process-emulator (GPE)-based approach to quantify the uncertainty in SED considering wind power penetration. Facing high-dimensional real-world data representing the correlated uncertainties from wind generation, a manifold-learning-based Isomap algorithm is proposed to efficiently represent the low-dimensional hidden probabilistic structure of the data. In this low-dimensional latent space, with Latin hypercube sampling (LHS) as the computer experimental design, a GPE is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated SED model. This reduced-order representative allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus test system reveal the impressive performance of the proposed method.
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    Computational social science in smart power systems: Reliability, resilience, and restoration
    Valinejad, Jaber; Mili, Lamine M.; Yu, Xinghuo; van der Wal, C. Natalie; Xu, Yijun (Institution of Engineering and Technology (IET), 2023-06-07)
    Smart grids are typically modelled as cyber–physical power systems, with limited consideration given to the social aspects. Specifically, traditional power system studies tend to overlook the behaviour of stakeholders, such as end‐users. However, the impact of end‐users and their behaviour on power system operation and response to disturbances is significant, particularly with respect to demand response and distributed energy resources. Therefore, it is essential to plan and operate smart grids by taking into account both the technical and social aspects, given the crucial role of active and passive end‐users, as well as the intermittency of renewable energy sources. In order to optimize system efficiency, reliability, and resilience, it is important to consider the level of cooperation, flexibility, and other social features of various stakeholders, including consumers, prosumers, and microgrids. This article aims to address the gaps and challenges associated with modelling social behaviour in power systems, as well as the human‐centred approach for future development and validation of socio‐technical power system models. As the cyber–physical–social system of energy emerges as an important topic, it is imperative to adopt a human‐centred approach in this domain. Considering the significance of computational social science for power system applications, this article proposes a list of research topics that must be addressed to improve the reliability and resilience of power systems in terms of both operation and planning. Solving these problems could have far‐reaching implications for power systems, energy markets, community usage, and energy strategies.
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    A Data-Driven Koopman Approach for Power System Nonlinear Dynamic Observability Analysis
    Xu, Yijun; Wang, Qinling; Mili, Lamine M.; Zheng, Zongsheng; Gu, Wei; Lu, Shuai; Wu, Zhi (IEEE, 2023-08-15)
    A prerequisite to dynamic state estimation of a stochastic nonlinear dynamic model of a power system is its observability analysis. However, due to the model nonlinearity, the traditional methods either suffer from a poor estimation accuracy if a linear approximation is performed or yield an extremely complicated procedure if the Lie-derivative method is applied to a large-scale power system. To address these weaknesses, we propose a new data-driven Koopman-based observability method by revealing the link that exists between the Koopman operator and the Lie-derivative in the Koopman canonical coordinates. This enables the proposed data-driven method not only to be fully derivative-free, which alleviates its implementation complexity but also overcomes the model nonlinearity and inaccuracy of the system. Furthermore, as an important byproduct, the proposed observability analysis scheme provides a valuable guide for the selection of the observables of the Koopman operator, which is a major difficulty for the application of this operator. Finally, we demonstrate the excellent performance of the proposed method on several IEEE standard test systems.
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    A Derivative-Free Observability Analysis Method of Stochastic Power Systems
    Zheng, Zongsheng; Xu, Yijun; Mili, Lamine M.; Liu, Zhigang; Korkali, Mert; Wang, Yuhong (IEEE, 2021)
    The observability analysis of a time-varying nonlinear dynamic model has recently attracted the attention of power engineers due to its vital role in power system dynamic state estimation. Generally speaking, due to the nonlinearity of the power system dynamic model, the traditional derivative-based observability analysis approaches either rely on the linear approximation to simplify the problem or require a complicated derivation procedure that ignores the uncertainties of the dynamic system model and of the observations represented by stochastic noises. Facing this challenge, we propose a novel polynomial-chaos-based derivative-free observability analysis approach that not only brings a low complexity, but also enables us to quantify the degree of observability by considering the stochastic nature of the dynamic systems. The excellent performances of the proposed method is demonstrated using simulations of a decentralized dynamic state estimation performed on a power system using a synchronous generator model with IEEE-DC1A exciter and a TGOV1 turbine-governor.
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    An efficient multifidelity model for assessing risk probabilities in power systems under rare events
    Xu, Yijun; Korkali, Mert; Mili, Lamine M.; Chen, Xiao (Hawaii International Conference on System Sciences, 2020)
    Risk assessment of power system failures induced by low-frequency, high-impact rare events is of paramount importance to power system planners and operators. In this paper, we develop a cost-effective multi-surrogate method based on multifidelity model for assessing risks in probabilistic power-flow analysis under rare events. Specifically, multiple polynomial-chaos-expansion-based surrogate models are constructed to reproduce power system responses to the stochastic changes of the load and the random occurrence of component outages. These surrogates then propagate a large number of samples at negligible computation cost and thus efficiently screen out the samples associated with high-risk rare events. The results generated by the surrogates, however, may be biased for the samples located in the low-probability tail regions that are critical to power system risk assessment. To resolve this issue, the original high-fidelity power system model is adopted to fine-tune the estimation results of low-fidelity surrogates by reevaluating only a small portion of the samples. This multifidelity model approach greatly improves the computational efficiency of the traditional Monte Carlo method used in computing the risk-event probabilities under rare events without sacrificing computational accuracy.
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    Global Sensitivity Analysis for Integrated Heat and Electricity Energy System
    Li, Yibo; Xu, Yijun; Yao, Shuai; Lu, Shuai; Gu, Wei; Mili, Lamine M.; Korkali, Mert (IEEE, 2024-11-18)
    Although global sensitivity analysis (GSA) is gaining increasing popularity in power systems due to its ability to measure the importance of uncertain inputs, it has not been explored in the integrated energy system (IES) in the existing literature. Indeed, when coupled multi-energy systems (e.g., heating networks) are considered, the power system operation states are inevitably altered. Accordingly, its associated GSA, which relies on Monte Carlo simulations (MCS), becomes even more computationally prohibitive since it not only increases the model complexity but also faces large uncertainties. To address these issues, this paper proposes a double-loop generalized unscented transform (GenUT)-based strategy that, for the first time, explores the GSA in the IES while simultaneously achieving high computing efficiency and accuracy. More specifically, we first propose a GenUT method that can propagate the moment information of correlated input variables following different types of probability distributions in the IES. We further design a double-loop sampling scheme for GenUT to evaluate the GSA for correlated uncertainties in a cost-effective manner. The simulations of multiple heat- and power-coupled IESs reveal the excellent performance of the proposed method
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    An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty
    Xu, Yijun; Korkali, Mert; Mili, Lamine M.; Valinejad, Jaber; Chen, Tao; Chen, Xiao (IEEE, 2021-01-12)
    A modern power system is characterized by a stochastic variation of the loads and an increasing penetration of renewable energy generation, which results in large uncertainties in its states. These uncertainties bring formidable challenges to the power system planning and operation process. To address these challenges, we propose a cost-effective, iterative response-surface-based approach for the chance-constrained AC optimal power-flow problem that aims to ensure the secure operation of the power systems considering dependent uncertainties. Starting from a stochastic-sampling-based framework, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose, instead of using the original complicated power-system model, to rely on a polynomial-chaos-based response surface. This response surface allows us to efficiently evaluate the time-consuming power-system model at arbitrary distributed sampled values with a negligible computational cost. This further enables us to efficiently conduct an online stochastic testing for the system states that not only screens out the statistical active constraints, but also assists in a better design of the tightened bounds without using any Gaussian or symmetric assumption. Finally, an iterative procedure is executed to fine-tune the optimal solution that better satisfies a predefined probability. The simulations conducted in multiple test systems demonstrate the excellent performance of the proposed method.
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    A Low-Rank Tensor Train Approach for Electric Vehicle Load Data Reconstruction Using Real Industrial Data
    Sun, Bo; Xu, Yijun; Gu, Wei; Cai, Huihuang; Lu, Shuai; Mili, Lamine M.; Yu, Wenwu; Wu, Zhi (IEEE, 2024-09-30)
    As electric vehicles (EVs) gain popularity, their interaction with the power system cannot be overlooked. Therefore, there is a growing need for accurate EV load data to facilitate precise operation and control in power systems. However, in practice, due to the high cost of high-frequency measurement devices and limited data storage capacity, only low-resolution metered EV data are available. To address this, this paper proposed a tensor completion-based method for EV load data reconstruction. More specifically, we first reformulate the load data as high-dimensional tensors and consider unknown data to be recovered as missing entries. Subsequently, we leverage the low-rank properties of high-dimensional data to perform tensor completion. To achieve this, two optimization formulations are proposed: a nuclear norm minimization algorithm based on singular value thresholding (SVT) and a tensor rank approximation algorithm via parallel matrix factorization. Both approaches are based on the tensor train (TT) rank, thanks to its well-balanced matricization scheme. This enables us to cost-effectively reconstruct high-resolution EV data using only low-resolution measurements. Simulation results using real industrial data reveal the excellent performance of the proposed methods.
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    Observability Analysis of a Power System Stochastic Dynamic Model Using a Derivative-Free Approach
    Zheng, Zongsheng; Xu, Yijun; Mili, Lamine M.; Liu, Zhigang; Korkali, Mert; Wang, Yuhong (IEEE, 2021-05-13)
    Serving as a prerequisite to power system dynamic state estimation, the observability analysis of a power system dynamic model has recently attracted the attention of many power engineers. However, because this model is typically nonlinear and large-scale, the analysis of its observability is a challenge to the traditional derivative-based methods. Indeed, the linear-approximation-based approach may provide unreliable results while the nonlinear-technique-based approach inevitably faces extremely complicated derivations. Furthermore, because power systems are intrinsically stochastic, the traditional deterministic approaches may lead to inaccurate observability analyses. Facing these challenges, we propose a novel polynomial-chaos-based derivative-free observability analysis approach that not only is free of any linear approximations, but also accounts for the stochasticity of the dynamic model while bringing a low implementation complexity. Furthermore, this approach enables us to quantify the degree of observability of a stochastic model, what conventional deterministic methods cannot do. The excellent performance of the proposed method has been demonstrated by performing extensive simulations using a synchronous generator model with IEEE-DC1A exciter and the TGOV1 turbine governor.
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    Polynomial-Chaos-Based Decentralized Dynamic Parameter Estimation Using Langevin MCMC
    Xu, Yijun; Chen, Xiao; Mili, Lamine M.; Huang, Can; Korkali, Mert (IEEE, 2019-08-01)
    This paper develops a polynomial-chaos-expansion (PCE)-based approach for decentralized dynamic parameter estimation. Under Bayesian inference framework, the non-Gaussian posterior distributions of the parameters can be obtained through Markov Chain Monte Carlo (MCMC). However, the latter method suffers from a prohibitive computing time for large-scale systems. To overcome this problem, we develop a decentralized generator model with the PCE-based surrogate, which allows us to efficiently estimate some generator parameter values. Furthermore, the gradient of the surrogate model can be easily obtained from the PCE coefficients. This allows us to use the gradient-based Langevin MCMC in lieu of the traditional Metropolis-Hasting algorithm so that the sample size can be greatly reduced. Simulations carried out on the New England system reveal that the proposed method can achieve a speedup factor of three orders of magnitude as compared to the traditional method without losing the accuracy.
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    Probabilistic Load-Margin Assessment using Vine Copula and Gaussian Process Emulation
    Xu, Yijun; Karra, Kiran; Mili, Lamine M.; Korkali, Mert; Chen, Xiao; Hu, Zhixiong (IEEE, 2020)
    The increasing penetration of renewable energy along with the variations of the loads bring large uncertainties in the power system states that are threatening the security of power system planning and operation. Facing these challenges, this paper proposes a cost-effective, nonparametric method to quantity the impact of uncertain power injections on the load margins. First, we propose to generate system uncertain inputs via a novel vine copula due to its capability in simulating complex multivariate highly dependent model inputs. Furthermore, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. The simulations conducted on the IEEE 57-bus system, to which correlated renewable generation are attached, reveal the excellent performance of the proposed method.
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    Propagating Parameter Uncertainty in Power System Nonlinear Dynamic Simulations Using a Koopman Operator-Based Surrogate Model
    Xu, Yijun; Netto, Marcos; Mili, Lamine M. (IEEE, 2022-04-04)
    We propose a Koopman operator-based surrogate model for propagating parameter uncertainties in power system nonlinear dynamic simulations. First, we augment a priori known state-space model by reformulating parameters deemed uncertain as pseudo-state variables. Then, we apply the Koopman operator theory to the resulting state-space model and obtain a linear dynamical system model. This transformation allows us to analyze the evolution of the system dynamics through its Koopman eigenfunctions, eigenvalues, and modes. Of particular importance for this letter, the obtained linear dynamical system is a surrogate that enables the evaluation of parameter uncertainties by simply perturbing the initial conditions of the Koopman eigenfunctions associated with the pseudo-state variables. Simulations carried out on the New England test system reveal the excellent performance of the proposed method in terms of accuracy and computational efficiency.
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    A Surrogate-Enhanced Scheme in Decision Making under Uncertainty in Power Systems
    Xu, Yijun; Mili, Lamine M.; Korkali, Mert; Chen, Xiao; Valinejad, Jaber; Peng, Long (IEEE, 2021)
    Facing stochastic variations of the loads due to an increasing penetration of renewable energy generation, online decision making under uncertainty in modern power systems is capturing power researchers' attention in recent years. To address this issue while achieving a good balance between system security and economic objectives, we propose a surrogate-enhanced scheme under a joint chance-constrained (JCC) optimal power-flow (OPF) framework. Starting from a stochastic-sampling procedure, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo (MC) method, we propose to use a polynomial-chaos-based surrogate that allows us to efficiently evaluate the power-system model at non-Gaussian distributed sampled values with a negligible computing cost. Learning from the MC simulated samples, we further proposed a hybrid adaptive approach to overcome the conservativeness of the JCC-OPF by utilizing correlation of the system states, which is ignored in the traditional Boole's inequality. The simulations conducted on the modified Illinois test system demonstrate the excellent performance of the proposed method.
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    Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation
    Hu, Zhixiong; Xu, Yijun; Korkali, Mert; Chen, Xiao; Mili, Lamine M.; Tong, Charles H. (IEEE, 2020)
    The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To solve this stochastic economic dispatch, the conventional Monte Carlo method is prohibitively time consuming for medium- and large-scale power systems. To overcome this problem, we propose in this paper a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Based on the dimension-reduction results obtained by the Karhunen-Loeve expansion, a Gaussian-process emulator is constructed. This surrogate allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus system reveal that the proposed method has an excellent performance as compared to the traditional Monte Carlo method.
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    Uncertainty Quantification, State and Parameter Estimation in Power Systems Using Polynomial Chaos Based Methods
    Xu, Yijun (Virginia Tech, 2019-01-31)
    It is a well-known fact that a power system contains many sources of uncertainties. These uncertainties coming from the loads, the renewables, the model and the measurement, etc, are influencing the steady state and dynamic response of the power system. Facing this problem, traditional methods, such as the Monte Carlo method and the Perturbation method, are either too time consuming or suffering from the strong nonlinearity in the system. To solve these, this Dissertation will mainly focus on developing the polynomial chaos based method to replace the traditional ones. Using it, the uncertainties from the model and the measurement are propagated through the polynomial chaos bases at a set of collocation points. The approximated polynomial chaos coefficients contain the statistical information. The method can greatly accelerate the calculation efficiency while not losing the accuracy, even when the system is highly stressed. In this dissertation, both the forward problem and the inverse problem of uncertainty quantification will be discussed. The forward problems will include the probabilistic power flow problem and statistical power system dynamic simulations. The generalized polynomial chaos method, the adaptive polynomial chaos-ANOVA method and the multi-element polynomial chaos method will be introduced and compared. The case studies show that the proposed methods have great performances in the statistical analysis of the large-scale power systems. The inverse problems will include the state and parameter estimation problem. A novel polynomial-chaos-based Kalman filter will be proposed. The comparison studies with other traditional Kalman filter demonstrate the good performances of the proposed Kalman filter. We further explored the area dynamic parameter estimation problem under the Bayesian inference framework. The polynomial-chaos-expansions are treated as the response surface of the full dynamic solver. Combing with hybrid Markov chain Monte Carlo method, the proposed method yields very high estimation accuracy while greatly reducing the computing time. For both the forward problem and the inverse problems, the polynomial chaos based methods haven shown great advantages over the traditional methods. These computational techniques can improve the efficiency and accuracy in power system planning, guarantee the rationality and reliability in power system operations, and, finally, speed up the power system dynamic security assessment.