A Two-Level Optimal Scheduling Strategy for Central Air-Conditioners Based on Metal Model with Comprehensive State-Queueing Control Models

Yebai Qi, Dan Wang, Yu Lan, Hongjie Jia, Chengshan Wang, Kaixin Liu, Qing’e Hu and Menghua Fan
Additional contact information
Yebai Qi: Electric Power Planning & Engineering Institute, Beijing 100120, China
Dan Wang: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Yu Lan: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Hongjie Jia: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Chengshan Wang: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Kaixin Liu: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Qing’e Hu: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Menghua Fan: State Grid Energy Research Institute, Changping District, Beijing 102249, China

Energies, 2017, vol. 10, issue 12, 1-21

Abstract: Unlike some thermostatically controlled appliances (TCAs) with small capacities, Central Air-conditioner (CAC) has huge potential for demand response because of its large capacity. This paper presents a new CAC control strategy under multiple constraints. The CAC is modeled by three main modules: CAC central unit, water pumps, and temperature simulation of terminal users. The CAC’s power consumption is mainly determined by users’ load ratio. As the information and communication system have become the central nervous system of the smart grid, big data analysis is of great significance. Assuming that reliable two-way communication systems are preset, an integrated parameter priority list (IPPL) control strategy is used to control and monitor CAC. A new intelligent algorithm, Space Exploration and Unimodal Region Elimination (SEUMRE) algorithm, is introduced for solving the optimization problem of demand response targets generation under multiple constraints with the help of big data analysis. In this paper, influences and constrain factors, such as price and users’ comfortable levels are taken into account to satisfy the need of actual situation. Simulation results show that the proposed approach, when comparing with other typical optimization algorithms, yields better performances and efficiency.

Keywords: central air-conditioner; demand response; multiple constraints; microgrids; intelligent algorithm (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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