@Article{ee.2026.081648,
AUTHOR = {Weichang Hang, Yu Jiang, Yizhou Zhou, Youlin Xuan, Haiquan Huang, Jiawei Chang, Ping Chen},
TITLE = {Electricity and Carbon Coordinated Scheduling of Low-Carbon Parks: A Double-Layer Distributionally Robust Optimization Approach},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/26892},
ISSN = {1546-0118},
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.},
DOI = {10.32604/ee.2026.081648}
}