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Application of Two-Phase Simplex Method (TPSM) for an Efficient Home Energy Management System to Reduce Peak Demand and Consumer Consumption Cost

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TLDR
In this paper, a simple home energy management using Two-Phase Simplex Method (TPSM) is proposed with an objective to reduce peak demand, reduce consumer consumption cost, and conserve consumer comfort level.
Abstract
Superabundant utilization of electricity in the residential sector is one of the major reasons for frequent peak demand. Hence, power sector necessitates an appropriate solution to control and monitor the peak demand. In this regard, implementation of an appropriate home energy management system becomes mandatory at customer premises to have an effective control over peak demand. Thus, in this research a simple home energy management using Two-Phase Simplex Method (TPSM) is proposed with an objective to (i) reduce peak demand, (ii) reduce consumer consumption cost, and (iii) conserve consumer comfort level. Further, the research proposes detailed investigations on the smart energy-home management model monitored by IoT. For simulations, different load scenarios are considered and the results are compared with the existing benchmarks available in the literature. On validations, the proposed TPSM method is found simple, reliable and efficient. More importantly, the multipurpose objectives has certainly given better results in consumer consumption cost that can give better control to peak demand. Furthermore, the usage of lucid simplex method has almost reduced the computational complexity to fasten the response time. In this regard, consumer comfort is served here is considered as a major accomplishment with the proposed work.

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References
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Journal ArticleDOI

MARLA-SG: Multi-Agent Reinforcement Learning Algorithm for Efficient Demand Response in Smart Grid

TL;DR: This article presents the development and evaluation of a Multi-Agent Reinforcement Learning Algorithm for efficient demand response in Smart Grid (MARLA-SG) and shows a simple and flexible way of choosing state elements to reduce the possible number of states, regardless of the device type, range of operation, and maximum allowable delay.
Journal ArticleDOI

Dynamic User Preference Parameters Selection and Energy Consumption Optimization for Smart Homes Using Deep Extreme Learning Machine and Bat Algorithm

TL;DR: The Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data and the predicted user parameters have improved the system’s overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management.
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