Open Access

ARTICLE

Two-Stage Optimization Scheduling Model for Flexible Resource Aggregation in High-Share Renewable Energy Power Systems Considering Multi-Type Demand Response

Yongzhi Liu, Qiang Li, Qianpeng Hao^*, Yaowen Liu, Wenze Li, Zijun Zhao, Zhenyu Chen, Yuxiang Liu, Jiaxing Ren, Wei Han
Inner Mongolia Electric Power (Group) Co., LTD., Hohhot 010010, China
* Corresponding Author: Qianpeng Hao. Email:

Energy Engineering https://doi.org/10.32604/ee.2026.074322

Received 08 October 2025; Accepted 09 May 2026; Published online 18 August 2026

Abstract

Aiming at the prominent problems of insufficient coordination of multi-type flexible resources and insufficient utilization of demand response potential in high-proportion renewable energy power systems, this paper proposes a two-stage optimal scheduling model for flexible resource aggregation that integrates price-based and segmented incentive-based demand response. In the first stage, the price-based demand response is adopted to optimize the time distribution of elastic loads with the goal of minimizing the net load fluctuation, so as to smooth the net load curve. In the second stage, based on the optimized net load, the segmented incentive-based demand response market mechanism is combined with electrochemical energy storage, pumped storage and flexibility-transformed thermal power units to construct a scheduling model with the minimum system operation cost as the goal. The quadratic terms in the model are linearized by SOS2 constraints, and the Gurobi solver is used for efficient solution. The results show that the proposed model can reduce the net load variance by 46.5%, reduce the system operation cost by 2.33%, avoid the frequent start-stop of thermal power units, significantly enhance the system flexibility and renewable energy consumption capacity, and provide an effective scheduling solution for the safe and economic operation of new power systems.

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Keywords

Flexible resource aggregation; two-stage optimization; price-based demand response; incentive-based demand response; high-share renewable energy

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