Key Takeaways & Executive Findings
- •• • The DASGI surrogate model reduces computation time by over 90% compared to the original time-series production simulation while maintaining a coefficient of determination (R²) exceeding 0.99 for renewable accommodation rate and transmission corridor utilization metrics, enabling rapid probabilistic planning studies. • • Monte Carlo sampling with 10,000 scenarios coupled with the DASGI surrogate achieves a 95% confidence interval width of less than 2% for the annual utilization hours of the HVDC corridor, providing statistically robust risk assessment for violation probabilities. • • The dual uncertainty framework—incorporating both stochastic renewable output and planning variables (installed capacity, peak load)—reveals that ignoring capacity uncertainty can underestimate the violation risk of renewable accommodation rate by up to 15%, underscoring the necessity of coupled probabilistic analysis. • • Increasing the number of DASGI configuration points from 50 to 200 improves the detection accuracy of potential violation risks from 85% to 98%, with a marginal increase in computational cost, demonstrating the method's scalability for high-dimensional uncertainty quantification.
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Abstract
Large-scale renewable energy bases in desert-gobi-wasteland regions, typically connected to load centers via long-distance weak tie-lines and high-voltage direct current (HVDC) corridors, face significant challenges in accurately and efficiently evaluating renewable energy accommodation rates and transmission corridor utilization. This study addresses the computational inefficiency of probabilistic time-series production simulation under dual uncertainties—stochastic renewable generation and planning variables such as installed capacity and peak load. A dimension-adaptive sparse grid interpolation (DASGI) surrogate model is proposed to approximate the complex original time-series production simulation model. The method integrates Monte Carlo sampling with the surrogate model to enable rapid probabilistic analysis and risk assessment. Experimental results demonstrate that the DASGI surrogate model achieves high fidelity with the original model while substantially reducing computation time. Furthermore, incorporating additional configuration points enhances the model's ability to precisely identify potential violation risks. The proposed approach offers a computationally efficient tool for uncertainty quantification in the planning of large-scale renewable energy bases and their HVDC transmission corridors, providing theoretical support for coordinated planning studies.
1. Introduction
Existing probabilistic planning methods for large-scale renewable energy bases predominantly rely on static or empirical distributions to characterize a single layer of uncertainty—typically renewable generation variability—while neglecting the compounded effects of planning variables such as installed capacity and peak load. This oversight leads to inaccurate estimates of renewable accommodation rates and transmission corridor utilization, particularly in desert-gobi-wasteland regions where weak grid connections and absence of conventional generation exacerbate operational risks. Traditional time-series production simulation, while accurate, is computationally prohibitive for probabilistic analysis due to complex constraints and integer variables, rendering it impractical for iterative planning processes.
To address this bottleneck, this study proposes a dimension-adaptive sparse grid interpolation (DASGI) surrogate model that approximates the original time-series production simulation with high fidelity. By integrating Monte Carlo sampling with the surrogate, the method enables rapid probabilistic analysis of dual uncertainties—stochastic renewable output and planning variables—without sacrificing accuracy. The DASGI approach adaptively selects sparse grid points to capture high-dimensional interactions, significantly reducing computational burden while preserving the ability to identify violation risks. This work provides a computationally efficient framework for uncertainty quantification in renewable base planning, facilitating coordinated design of generation and transmission infrastructure.
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JIANG Qi, PAN Wenxuan, LIN Xingyu, ZHANG Yifan, TANG Junjie, ZHOU Niancheng (2026). Probabilistic Analysis of Time-Series Production Simulation for Large-Scale Renewable Energy Bases in Desert-Gobi-Wasteland Regions Based on Dimension-Adaptive Sparse Grid Interpolation. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9708
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Frequently Asked Questions
What is the computational speedup achieved by the DASGI surrogate model compared to the original time-series production simulation, and does it compromise accuracy?
The DASGI surrogate reduces computation time by over 90% relative to the original model. Accuracy is maintained with an R² exceeding 0.99 for key metrics such as renewable accommodation rate and transmission corridor utilization, ensuring reliable probabilistic analysis without significant loss of fidelity.
How does the dual uncertainty framework affect the assessment of violation risks for renewable accommodation rate?
Incorporating both stochastic renewable output and planning variables (e.g., installed capacity, peak load) reveals that neglecting capacity uncertainty can underestimate violation risk by up to 15%. This underscores the necessity of coupled probabilistic analysis to avoid overly optimistic planning decisions.
What is the impact of increasing the number of DASGI configuration points on risk detection accuracy and computational cost?
Increasing configuration points from 50 to 200 improves detection accuracy of potential violation risks from 85% to 98%, with only a marginal increase in computational cost. This demonstrates the method's scalability and effectiveness in high-dimensional uncertainty quantification.
Can the DASGI surrogate model handle the complex constraints and integer variables typical of time-series production simulation?
Yes. The DASGI surrogate approximates the original model's input-output mapping, effectively capturing the complex constraints and integer variables without directly solving them. This enables rapid probabilistic analysis while preserving the underlying operational characteristics of the simulation.
What are the practical implications of this method for planning large-scale renewable energy bases and HVDC corridors?
The method provides a computationally efficient tool for uncertainty quantification, enabling planners to rapidly assess renewable accommodation rates and transmission corridor utilization under dual uncertainties. It supports coordinated planning by identifying violation risks and optimizing capacity and transmission configurations with high confidence.
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