Open Access

ARTICLE

An Electricity-Carbon Synergy-Driven Optimization Method for Low-Carbon Operation of Multi-Energy Parks

Jiangyang Yuan¹, Jiaowen Wu¹, Yi Gao¹, Yuhao Fu², Yuntao Bu², Tianyu Chen², Hao Yu^2,*

1 State Grid Tianjin Economic Research Institute, Tianjin, 300171, China
2 State Key Laboratory of Smart Power Distribution Equipment and System, Tianjin University, Tianjin, 300072, China

* Corresponding Author: Hao Yu. Email:

Energy Engineering 2026, 123(2), 2 https://doi.org/10.32604/ee.2025.070715

Received 22 July 2025; Accepted 17 September 2025; Issue published 27 January 2026

Abstract

In the pursuit of carbon peaking and neutrality goals, multi-energy parks, as major energy consumers and carbon emitters, urgently require low-carbon operational strategies. This paper proposes an electricity-carbon synergy-driven optimization method for the low-carbon operation of multi-energy parks. The method integrates multi-energy complementary scheduling with a tiered carbon trading mechanism to balance operational security, economic efficiency, and environmental objectives. A mixed-integer linear programming model is developed to characterize the coupling relationships and dynamic behaviors of key equipment, including photovoltaic systems, ground-source heat pumps, thermal storage electric boilers, combined heat and power units, and electrical energy storage systems. Furthermore, a tiered carbon trading model is established that incorporates carbon quota allocation and tiered carbon pricing to internalize carbon costs and discourage high-emission practices. Multi-scenario comparative analyses demonstrate that the electricity-carbon synergy scenario achieves a 42.64% reduction in carbon emissions compared to economy-oriented operation, while limiting the increase in operational costs to 20.85%. The carbon-prioritized scenario further reduces emissions by 9.7%, underscoring the inhibitory effect of the tiered carbon pricing mechanism on high-carbon activities. Sensitivity analyses confirm the model’s robustness against fluctuations in energy load, uncertainty in renewable generation, and variations in carbon price. This optimization method provides theoretical support for multi-energy coordinated scheduling and carbon responsibility allocation in industrial parks, offering valuable insights for promoting green transformation initiatives.

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Keywords

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