Research on Packet Control Strategy of Constant-Frequency Air-Conditioning Demand Response Based on Improved Particle Swarm Optimization Algorithm

Qian Liu (), Guangnu Fu, Gang Ma, Jun He and Weikang Li
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Qian Liu: School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing 210023, China
Guangnu Fu: State Grid Zhejiang Longgang Electric Power Supply Corporation, Longgang 325802, China
Gang Ma: School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing 210023, China
Jun He: College of Electrical Engineering of Shanghai University of Electric Power, Yangpu District, Shanghai 200090, China
Weikang Li: School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing 210023, China

Energies, 2022, vol. 15, issue 23, 1-12

Abstract: To better utilize air-conditioning load in terms of demand side response potential and improve precision and speed, a control strategy of traditional temperature-control air conditioning, determining frequency load as the research object, and air conditioning determined with a frequency theory model and the Monte Carlo method, were used to construct a power aggregation model. This was combined with user feedback to study thermal comfort as a lateral load demand response resource to determine the potential demand response of power systems. Based on the state-queuing model, an air-conditioning load grouping control strategy using an improved particle swarm optimization algorithm is proposed which can accurately control the air-conditioning load following the reference load.

Keywords: air-conditioning load; demand response; the aggregation model; improved particle swarm optimization; the control strategy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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