Browsing by Author "Chen, Xiao"

Now showing 1 - 9 of 9
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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due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications.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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    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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    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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    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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    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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    Size Optimization of Utility-Scale Solar PV System  Considering Reliability Evaluation
    Chen, Xiao (Virginia Tech, 2016-07-19)
    In this work, a size optimization approach for utility-scale solar photovoltaic (PV) systems is proposed. The purpose of the method is to determine the optimal solar energy generation capacity and optimal location by the minimizing total system cost subject to the constraint that the system reliability requirements. Due to the stochastic characteristic of the solar irradiation, the reliability performance of a power system with PV generation is quite different from the one with only conventional generation. Basically, generation adequacy level of power systems containing solar energy is evaluated by reliability assessment and the most widely used reliability index is the loss of load probability (LOLP). The value of LOLP depends on various factors such as power output of the PV system, outage rate of generating facilities and the system load profile. To obtain the LOLP, the Monte Carlo method is applied to simulate the reliability performance of the solar penetrated power system. The total system cost model consists of the system installation cost, mitigation cost, and saving fuel and operation cost. Mitigation cost is accomplished with N-1 contingency analysis. The cost function minimization process is implemented in Genetic Algorithm toolbox, which has the ability to search the global optimum with relative computational simplicity.
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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.