A Method for Determining Medium- and Long-Term Renewable Energy Accommodation Capacity Considering Multiple Uncertain Influencing Factors

Tingxiang Liu, Libin Yang, Zhengxi Li, Kai Wang, Pinkun He and Feng Xiao ()
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Tingxiang Liu: State Grid Qinghai Electric Power Company Economic and Technical Research Institute, Xining 810001, China
Libin Yang: State Grid Qinghai Electric Power Company Economic and Technical Research Institute, Xining 810001, China
Zhengxi Li: State Grid Qinghai Electric Power Company Economic and Technical Research Institute, Xining 810001, China
Kai Wang: State Grid Qinghai Electric Power Company Economic and Technical Research Institute, Xining 810001, China
Pinkun He: School of Renewable Energy, North China Electric Power University, Beijing 102206, China
Feng Xiao: School of Renewable Energy, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 19, 1-17

Abstract: Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties, integrating three innovations: (1) Response Surface Methodology (RSM) is adopted for the first time to construct a closed-form polynomial of renewable energy accommodation in terms of resource hours, load, installed capacity, and transmission limits, enabling millisecond-level evaluation; (2) LASSO-regularized RSM suppresses high-dimensional overfitting by automatically selecting key interaction terms while preserving interpretability; (3) a Bayesian kernel density extension yields full posterior distributions and confidence intervals for renewable energy accommodation in small-sample scenarios, quantifying risk. A case study on a renewable-rich grid in Northwest China validates the framework: two-factor response surface models achieve R 2 > 90% with < 0.5% mean absolute error across ten random historical cases; LASSO regression keeps errors below 1.5% in multidimensional space; Bayesian density intervals encompass all observed values. The framework flexibly switches between deterministic, sparse, or probabilistic modes according to data availability, offering efficient and reliable decision support for generation-transmission planning and market clearing under multidimensional uncertainty.

Keywords: renewable energy; response surface methodology; LASSO regression method; probability density function method; adaptive framework (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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