Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation

Zhengwei Qu, Chenglin Xu, Kai Ma and Zongxu Jiao
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Zhengwei Qu: School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
Chenglin Xu: School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
Kai Ma: School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
Zongxu Jiao: School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China

Energies, 2019, vol. 12, issue 13, 1-15

Abstract: In this paper, a fuzzy neural network controller for regulating demand-side thermostatically controlled loads (TCLs) is designed with the aim of stabilizing the frequency of the smart grid. Specifically, the balance between power supply and demand is achieved by tracking the automatic generation control (AGC) signal in an electric power system. The particle swarm optimization (PSO) and error back propagation (BP) algorithms are used to optimize the control parameters and consequently reduce the tracking errors. The fuzzy neural network can be applied to solve load control problems in power systems, since its self-learning and associative storage functions can deal with the highly nonlinear relationship between input and output. Simulation results show the advantage of the fuzzy neural network control scheme in terms of frequency regulation error and consumer comfort.

Keywords: automatic generation control; fuzzy neural network control; thermostatically controlled loads; back propagation algorithm; particle swarm optimization (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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