Particle-Swarm-Optimization-Based Operation of Secondary Heat Supply Networks

Guo Tang, Kaiyuan Chen, Liteng Wang, Ning Zhang, Junwei Zhang, Xiaojie Lin and Yanling Wu ()
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Guo Tang: College of Energy Engineering, Zhejiang University, Hangzhou 310058, China
Kaiyuan Chen: College of Energy Engineering, Zhejiang University, Hangzhou 310058, China
Liteng Wang: College of Energy Engineering, Zhejiang University, Hangzhou 310058, China
Ning Zhang: College of Energy Engineering, Zhejiang University, Hangzhou 310058, China
Junwei Zhang: College of Energy Engineering, Zhejiang University, Hangzhou 310058, China
Xiaojie Lin: College of Energy Engineering, Zhejiang University, Hangzhou 310058, China
Yanling Wu: College of Energy Engineering, Zhejiang University, Hangzhou 310058, China

Sustainability, 2025, vol. 17, issue 8, 1-34

Abstract: Urban centralized heating systems, as a crucial component of the energy transition, face new challenges in terms of reliable and balanced operation, energy-saving performance, and optimized control. Based on the accurate quantification of user heat load, an operational optimization method for secondary heating networks is proposed. By accurately analyzing the actual heating demands of different users according to building characteristics and climatic conditions and integrating the hydraulic and thermal modeling of a pipeline network, a Particle Swarm Optimization (PSO) algorithm is employed to optimize the valve opening degrees of users and the secondary side, achieving the optimal operating state of the secondary network that matches user load and obtaining the optimal valve regulation strategy. The results of a case analysis show that, after optimization, the overall variance of return water temperature for heat users decreased by 12.16%, and the electricity consumption of the secondary network circulation pump was reduced by 16.46%, demonstrating the effectiveness and practicality of the proposed optimization method. On the basis of ensuring hydraulic balance in the heating system, the method meets the individual heating demands of users, effectively improves user thermal comfort, and reduces energy consumption, addressing the issues of excessive and uneven heat supply.

Keywords: district heating system; load quantification analysis; mechanistic modeling; operational optimization; demand-based heat supply; secondary heat supply networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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