Matching of air conditioning power and PV panel output power based on dynamic controlling of air conditioning air supply volume

ABSTRACT

This paper presents a dynamic control strategy of air-conditioning air supply volume based on statistical data of the spatial and temporal distribution of occupants in the building, aiming at reducing overheating in buildings under local heat island effect. Nowadays, energy consumption in air-conditioning system is relatively high when considering the limited number of occupants and chiller plants operating inefficiently because the traditional central air-conditioning system was not designed to operate at different cooling load demands. Therefore, variable air volume (VAV) air-conditioning system is applied in this paper with corresponding wind volume control method. To address the above issue, in this paper: (1) A spatiotemporal distribution of occupants in the building is analyzed, and the relationship between occupant distribution and characteristics of the thermal dissipating load is established; (2) A strategy for dynamic controlling of the air supply volume in time and space is proposed; (3) A distributed photovoltaic system supplies power to cooling load of the building, and a strategy is proposed for optimizing the match between cooling power and photovoltaic (PV) output power using competitive swarm optimization algorithm; and (4) Air blower total amount of wind control method is adopted in VAV air-conditioning system for dynamic controlling of the air supply volume. This paper uses a simulation to verify the feasibility and optimality of the above strategies. The simulation results show that the building energy consumption is greatly reduced by 39.52%, the PV accommodation is improved as 77.14%, and four control indicators are well satisfied due to the air blower total amount of wind control method.

KEYWORDS:

• Dynamic control
• heat-dissipating load
• cooling load
• photovoltaic accommodation

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Additional information

Funding

This work was supported by the National Natural Science Foundation of China (61433004), the Fundamental Research Funds for the Central Universities (N160402003), and State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (LAPS17013).

Notes on contributors

Qi Tang

Qi Tang received the B.S. degree in new energy science and engineering from Northeastern University, Shenyang, China, in 2014, where she is pursing the M.D. degree in electrical engineering. Her research interests include power system optimization scheduling operation.

Fengnan Zhang

Fengnan Zhang received the B.S. degree in electrical engineering from North China Electric Power University, and now she is pursing M.D. degree in Northeastern University.

Boyu Liu

Boyu Liu is pursing the M.D. degree in electrical engineering from Northeastern University.