This paper addresses the energy efficiency problem of household customers by observing and responding accordingly to the condition of the upstream grid; the key condition is the market price which is passed to the end-use customers though a new market entity, namely load aggregators. | Minh Y Nguyen et al Journal of SCIENCE and TECHNOLOGY 127(13): 15 - 20 A NEW APPROACH FOR ENERY SAVING TO HOUSEHOLD CUSTOMERS BASED SMARTGRID TECHNOLOGIES Minh Y Nguyen*, Thang N. Pham and Toan H. Nguyen University of Technology – TNU ABSTRACT This paper addresses the energy efficiency problem of household customers by observing and responding accordingly to the condition of the upstream grid; the key condition is the market price which is passed to the end-use customers though a new market entity, namely load aggregators. A framework based on Smargrid technologies, ., Advanced Metering Infrastructure (AMI) for monitoring home energy consumptions is proposed. The problem is to schedule and control the home electrical appliances in response to the market price to minimize the energy cost over a day. The problem is formulated using Dynamic Programming (DP) and solved by DP backward algorithm. Using stochastic optimization techniques, the proposed framework is capable of addressing the uncertainties related to the appliance performance: outside temperature and/or users’ habits, etc. Keywords: Demand response, Home energy efficiency, Heat ventilation and air conditioning, Dynamic programming, Smartgrid. INTRODUCTION* This paper discusses a new approach to energy efficiency in the residential sector by watching the household consumption from the system perspective: it is more economical and efficient not only for household customers but the system-wide if the appliances and lighting are turned on in low price times and off in the high time. This can be referred to as Demand Response (DR) program and/or Home Energy Management System (HEMS). Herein, we propose a DR framework for a household that consists of two functions: (1) Off-line scheduling according to the prediction and (2) On-line control based on both the previous scheduling and real-time load measurements. The framework is based on advanced communication and automation technologies applied to the power .
