A Time-Series Reduction Acceleration Method for Electricity–Hydrogen–Carbon Integrated Energy System

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel clustering-based acceleration method for integrated energy system optimization that can be extended to other multi-energy models.

🏢実務担当者:Useful for energy companies and grid operators seeking faster and more accurate operational planning for decarbonized multi-energy systems.

🏛政策担当者:Demonstrates computational feasibility for large-scale E-H-C system planning, supporting policy design for integrated energy markets and infrastructure.

With the rapid development of novel power systems, the multi-energy coupling of Electricity-Hydrogen-Carbon (E-H-C) has become a crucial pathway to achieving deep decarbonization. However, the full time-series operational planning model, which includes large-scale renewable energy integration and complex chemical synthesis processes, faces a severe dimensionality curse, making it extremely difficult to solve. To address this pain point, this paper first constructs a simplified nodal integrated E-H-C model. Subsequently, an improved time-series dimensionality reduction acceleration algorithm is proposed. Targeting the fundamental flaws of traditional K-means clustering, this algorithm designs an adaptive strategy for selecting the optimal number of clusters based on a preliminary screening and a multi-dimensional evaluation metric weighting framework. Furthermore, it introduces a probability distribution method to optimize the initial cluster centers. Case study results demonstrate that the proposed acceleration algorithm significantly reduces the model solving time. Compared with traditional clustering methods, it achieves higher accuracy and stability, successfully minimizing the total profit error to an exceptional degree.

• semanticscholar https://doi.org/10.1109/epsic70071.2026.11590161first seen 2026-07-13 07:01:51