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Critical peak pricing based opportunistic home energy management for demand response
Abstract and Figures
Home energy management controller (HEMC) is an important component for electricity consumer to actively participate in demand response program. It helps the consumer to manage electricity load in an effective way to reduce electricity bill. In this thesis, we design a HEMC based on two heuristic techniques: teaching learning based optimization (TLBO) and enhanced differential evolution (EDE). We also proposed a hybrid technique by combining the feature of TLBO and EDE to optimize the HEMC. The major objective of designing this controller is to minimize consumer electricity bill while preserving consumer satisfaction. For this purpose, we perform simulations for a single as well as multiple homes by utilizing day-ahead real time price (DA-RTP) and critical peak price (CPP) signals. Results show that our proposed hybrid technique achieves maximum user satisfaction at minimum electricity cost and peak to average ratio (PAR). A tradeoff analysis between user satisfaction and energy consumption cost is demonstrated in simulations.
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