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Thermal Inertial Aggregation Model for Integrated Energy Systems

Integrated energy systems (IESs) are composed of multiple heterogeneous subsystems, i.e., electrical power system, natural gas system, and district heating system (DHS), which endow the whole system with excellent performance in overall efficiency and renewable energy utilization. The paper aims to offer a concise and analytical model for the thermal dynamic characteristics (i.e., thermal inertia) of the district heating network (DHN) and buildings to facilitate the analysis, planning, and operation of IESs. Firstly, an equivalent start network is introduced for modeling the radial DHN, and a synchronous response model is proposed for buildings to approximate the optimal response of heat load. Secondly, the thermal inertia aggregation model (TIAM) is proposed, which offers an accurate DHN and buildings model for the planning and operation of IESs. Finally, some properties of the TIAM are derived to reveal its potential in general applications such as analysis and evaluation. Simulation results of different scale systems demonstrate the performance of the proposed model and reveal its advantages in the computational efficiency and sensitive information protection of DHN.
- UNSW Sydney Australia
- Southeast University China (People's Republic of)
- Southeast University China (People's Republic of)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).80 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
