Open Access

ARTICLE

Electricity and Carbon Coordinated Scheduling of Low-Carbon Parks: A Double-Layer Distributionally Robust Optimization Approach

Weichang Hang¹, Yu Jiang¹, Yizhou Zhou^2,*, Youlin Xuan², Haiquan Huang¹, Jiawei Chang¹, Ping Chen¹
1 Yancheng Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Yancheng, China
2 School of Electrical and Power Engineering, Hohai University, Nanjing, China
* Corresponding Author: Yizhou Zhou. Email:

Energy Engineering https://doi.org/10.32604/ee.2026.081648

Received 06 March 2026; Accepted 17 April 2026; Published online 18 May 2026

Abstract

The transition toward low-carbon energy systems requires scheduling strategies that coordinate power dispatch with explicit carbon accountability in integrated parks. To address the limited coupling among carbon tracing, differentiated demand response, and uncertainty-aware scheduling in existing studies, this paper proposes an electricity-carbon coordinated scheduling method for low-carbon parks based on carbon emission flow (CEF) and double-layer distributionally robust optimization (DRO). First, a park-oriented CEF model is established to quantify nodal carbon potential and to trace carbon responsibility from generation to load, including the carbon transfer effect of energy storage. Second, a priority-aware demand response mechanism combining hierarchical time-of-use pricing and stepped incentive compensation is constructed so that different user categories respond according to scheduling priority and carbon-reduction value. Third, a double-layer DRO framework is formulated, where the upper layer optimizes grid purchase, gas-turbine output, and carbon trading with carbon-potential feedback, and the lower layer coordinates load adjustment and energy storage under photovoltaic uncertainty. Case studies on an improved IEEE 33-node system show that the proposed method improves robustness under renewable uncertainty, significantly enhances the transfer capability of high-priority industrial loads, and yields lower expected and worst-case testing costs than benchmark uncertainty-handling strategies. These results verify the method’s advantage in simultaneously improving economic performance, carbon management transparency, and operational reliability.

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Keywords

Low-carbon park; carbon emission flow; demand response; distributionally robust optimization

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