New dual-layer optimization strategy boosts revenue and cuts carbon in integrated energy systems

As China accelerates its "Carbon Peaking and Carbon Neutrality" goals, a novel optimization strategy published in ENGINEERING Energy (formerly Frontiers in Energy) offers a breakthrough solution for managing complex park-level integrated energy systems. The research introduces a dual-layer collaborative framework that simultaneously increases energy station profitability by 5.09% and enhances consumer benefits by 2.46% while significantly cutting carbon emissions.

Park-level integrated energy systems (PIES) that combine solar, wind, and combined heat and power (CHP) generation are essential for decarbonizing industrial parks and urban districts. However, these systems face a critical challenge: the inherent variability of renewable energy sources and the rigid, top-down structure of traditional energy markets prevent real-time coordination between suppliers and consumers.

"The conventional vertical trading structure treats energy stations and users as separate entities with no meaningful feedback," explains Dr. Jun Shen from the Key Laboratory of Cryogenics, Chinese Academy of Sciences. "Our dual-layer approach transforms this into a collaborative dialogue where stations optimize pricing while users actively shape their demand—a true win-win scenario."

The research team developed a sophisticated mathematical model where:

• Upper Layer (Energy Stations): Strategically sets electricity and heat prices, optimizes equipment output, and manages biogas production from organic waste
• Lower Layer (Users): Responds to price signals by adjusting flexible loads, shifting consumption patterns, and reducing peak demand

A key innovation is the integration of anaerobic digestion reactors (ADRs) that convert organic waste—such as agricultural residues, sludge, and food waste—into biogas. This renewable methane fuel reduces dependence on volatile natural gas pipelines while simultaneously solving waste management challenges. The model shows how biogas production naturally stabilizes as digestion temperature reaches optimal levels around 40°C, creating a reliable, sustainable fuel source.

The framework incorporates a stepped carbon trading mechanism that dynamically adjusts carbon pricing based on actual emissions. Unlike traditional linear carbon pricing, this tiered approach provides stronger incentives for emissions reduction, cutting carbon trading costs by 5.23% and total emissions by 2.54% compared to scenarios without carbon trading.

To solve this complex optimization problem, researchers employed a hybrid algorithm combining Differential Evolution (DE) for global search with Quadratic Programming (QP) for precise local optimization. This DE-QP approach converged in just 38 iterations—42% faster than genetic algorithm alternatives—demonstrating superior computational efficiency for real-time energy management.

Key Results:

• 5.09% increase in energy station revenue through optimized pricing and demand response
• 2.46% improvement in consumer surplus by empowering users to adjust consumption
• 5.23% reduction in carbon trading costs via stepped pricing mechanism
• 2.54% decrease in total carbon emissions compared to non-carbon-trading scenarios
• 16.7% faster computation time than competing optimization methods

The simulation also revealed that strategic battery energy storage management allows stations to sell surplus renewable electricity to the grid during low-demand periods while discharging stored energy during peak hours, maximizing both economic and environmental benefits.

"This research provides a practical blueprint for the next generation of smart energy districts," says Dr. Haochen Hua from Hohai University. "By treating energy systems as interactive ecosystems rather than top-down hierarchies, we can accelerate the low-carbon transition while maintaining economic viability."

The strategy is particularly relevant for industrial parks, university campuses, and urban developments seeking to integrate high renewable energy penetration while ensuring grid stability and user comfort.

Publication Details: The research article, "Low-carbon collaborative dual-layer optimization for energy station considering joint electricity and heat demand response," was published in ENGINEERING Energy (formerly Frontiers in Energy), 2025, Volume 19, Issue 1, Pages 100–113. The work was supported by the National Natural Science Foundation of China.

DOI: 10.1007/s11708-024-0958-0
Article Link: https://doi.org/10.1007/s11708-024-0958-0

Journal Citation:
Xu, S., Wu, X., Shen, J., & Hua, H. (2025). Low-carbon collaborative dual-layer optimization for energy station considering joint electricity and heat demand response. Frontiers in Energy, 19(1), 100–113. https://doi.org/10.1007/s11708-024-0958-0