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| Source-Load Bilateral Coordination Optimization of Park-Level Integrated Energy System Based on Off-Design Carbon Flow Model |
| Zhao Liyuan1,2, Li Hui1,2, Zhang Xian1,2, Min Chunhua3, Chen Haiwen1,2 |
1. State Key Laboratory of Intelligent Power Distribution Equipment and System Hebei University of Technology Tianjin 300401 China
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of Technology Tianjin 300401 China
3. School of Energy and Environmental Engineering Hebei University of Technology Tianjin 300401 China |
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Abstract The efficient and low-carbon operation of park-level integrated energy systems is crucial for supporting energy transition and carbon reduction objectives. Traditional scheduling methods often rely on simplified constant-efficiency models for energy conversion equipment, which fail to capture their actual performance under varying loads and ambient conditions. Furthermore, although carbon emission flow theory provides a viable approach for allocating carbon responsibility on the load side, the dynamic coupling between equipment off-design characteristics and system-level carbon flow remains insufficiently studied. This paper proposed a novel source-load collaborative optimization framework based on an off-design carbon flow model.
First, we developed detailed off-design efficiency models for key energy conversion devices. The efficiency of the absorption chiller was represented as a fifth-order polynomial function of its load rate, while the electric chiller and heat pump were modeled with efficiency curves that depend on ambient temperature. The combined heat and power unit adapted to different system conditions by adjusting its thermoelectric ratio. To balance modeling accuracy with computational efficiency, nonlinear efficiency relationships were piecewise linearized using Special Ordered Sets of type 2, transforming the overall problem into a mixed-integer linear programming model. Based on the carbon flow conservation principle, a variable-condition carbon flow model was established, incorporating equipment operational characteristics. The model also integrated energy storage cycles to track dynamic carbon footprints and achieved precise allocation of load-side carbon responsibilities.
A dynamic carbon-energy-coupled pricing strategy was introduced to enhance source-load coordination. The pricing mechanism comprises two components: energy production cost and carbon emission cost. The former reflects operational and maintenance expenses under different scheduling strategies, while the latter internalizes emission costs based on the marginal carbon flow density of consumed energy. A bi-level optimization model was constructed for source-load interaction. The upper level minimizes the total cost of the system operator, and the lower level minimizes users’ energy purchase and comfort costs. Considering the cost of energy comfort can avoid significant deviations in energy consumption behavior caused by excessive demand response from users. The model was linearized and solved using the Gurobi solver. Additionally, an energy-carbon security domain is defined to ensure operational feasibility under carbon and energy constraints.
Finally, the effectiveness of the proposed method in improving the economy and environmental friendliness of the system is verified by a case study. Under two time-of-use electricity price schemes, the approach achieves total cost reductions of 18.7% and 19.6%, respectively, compared to conventional constant-efficiency models without demand response. The off-design modeling optimizes energy use according to ambient conditions and load requirements, fully leveraging the complementary advantages of multi-energy integration. The incentive-based demand response model shows good adaptability across varying load conditions, effectively enhancing the low-carbon operation and economic efficiency of the system. This study provides a practical decision-making tool for achieving low-carbon, economical, and secure operation in integrated energy parks.
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Received: 17 April 2025
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