A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch considering Wind Power Penetration

Journal Article · Mon Aug 10 00:00:00 EDT 2020 · IEEE Transactions on Sustainable Energy

DOI:https://doi.org/10.1109/tste.2020.3015353· OSTI ID:1727273

Hu, Zhixiong ^[1]; Xu, Yijun ^[2]; Korkali, Mert ^[3]; Chen, Xiao ^[3]; Mili, Lamine ^[2]; Valinejad, Jaber ^[2]

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1. Univ. of California, Santa Cruz, CA (United States)
2. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
3. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

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 this stochastic economic dispatch problem for forecasting purposes, the conventional Monte-Carlo method is prohibitively time-consuming for practical applications. To overcome this problem, here we propose a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch 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 as the computer experimental design, a Gaussian-process emulator is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated stochastic economic dispatch 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.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Electricity (OE)
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1727273
• Report Number(s): LLNL-JRNL--791972; 989655
• Journal Information: IEEE Transactions on Sustainable Energy, Journal Name: IEEE Transactions on Sustainable Energy Journal Issue: 1 Vol. 12; ISSN 1949-3029
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

17 WIND ENERGY
manifold learning
reduced-order modeling
renewable energy
stochastic economic dispatch
uncertainty estimation