Open Access

ARTICLE

Multi-Time-Scale Optimization of Rural Integrated Energy Systems Based on Whole-Process Dynamic Simulation under Complex Operating Conditions

Zhi Xu¹, Min Ren¹, Zhiwei Yuan², He Jiang¹, Risheng Qin¹, Xinze Xi¹, Yinfeng Ma^3,*
1 Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming, China
2 Honghe Power Supply Bureau, Yunnan Power Grid Co., Ltd., Honghe, China
3 The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China
* Corresponding Author: Yinfeng Ma. Email:
(This article belongs to the Special Issue: Next-Generation Distribution System Planning, Operation, and Control)

Energy Engineering https://doi.org/10.32604/ee.2026.079755

Received 27 January 2026; Accepted 13 March 2026; Published online 01 April 2026

Abstract

The integration of high-penetration renewable energy and multi-energy loads introduces significant volatility and complexity into the operation of Rural Integrated Energy Systems (RIES). To address the challenges of ensuring economic efficiency and operational resilience, this paper proposes a novel multi-time-scale optimization framework grounded in a whole-process dynamic simulation. Initially, a dynamic simulation model is developed to accurately capture system behavior under typical rural disturbance scenarios. This model underpins a two-stage scheduling strategy: a day-ahead steady-state plan aimed at global economic optimization, followed by an intra-day rolling optimization that leverages real-time data to correct forecast deviations. The core innovation lies in a steady-state-dynamic hybrid simulation mechanism, integrated with an adaptive convex relaxation strategy. The hybrid mechanism facilitates seamless coupling of energy conversion, storage, and transmission by explicitly modeling equipment dynamic responses. To resolve the computational intractability of the original Mixed-Integer Nonlinear Programming (MINLP) problem, it is transformed into a computationally efficient Second-Order Cone Programming (SOCP) model via a convex relaxation technique, which is then solved using the SCS solver. This approach balances engineering accuracy and computational efficiency, enabling real-time scheduling of RIES under complex operating conditions. The proposed strategy presents a viable and robust solution for achieving secure, efficient, and low-carbon operation of RIES.

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Keywords

Rural integrated energy system; multi-time-scale optimization; complex operating conditions; convex optimization; second-order cone programming

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