Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

Rasheed, Muhammad Babar; Javaid, Nadeem; Ahmad, Ashfaq; Jamil, Mohsin; Khan, Zahoor Ali; Qasim, Umar; Alrajeh, Nabil

doi:10.3390/en9080593

Cited by 48 publications

(32 citation statements)

References 33 publications

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“…For the sake of simplicity we consider only 4 appliances, but more can be included for larger systems. Furthermore we leverage on the works of [23] for a guide on consumption and priority aspects while deliberately ignoring the pricing regimes.…”

Section: Performance Evaluationmentioning

confidence: 99%

An Algorithm for using Internet of Things (IoTs) to Improve Load Management in Electric Power Grid

Mahlangu¹,

Musungwini²,

Nleya³

2018

IJCA

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Several new technologies, for example, the Internet of Things (IoTs) continue to surface in the frontage of ubiquitous and pervasive computing. Internet of things is slowly being embraced in different domains to support control and monitoring systems. The advent of microprocessors embedded with communication facilities has enabled the possibility of integrating ICTs within people and their environments. With the power utilities industry grappling with serious issues like load management which has a potential of damaging their equipment, first movers are turning to advanced technologies such as the IoTs to achieve demandside management. The purpose of this study was to conduct a theoretical and empirical review on the approaches used by Power utilities to manage load in order to determine how IoTs can be used particularly by Zimbabwe's Power utility to solve the supply-demand problem. Currently, the load management approaches used by the Power utility in Zimbabwe are load shifting and load shedding. A conceptual model was developed using C++ guided by the algorithm for load management. The model development was motivated by the research gap establish from the literature review and the problem faced by the Power utility in Zimbabwe in balancing demand and supply during peak periods. The idea is modelled around continuous monitoring of the feeder or substation, activating the automatic control and carrying out load allocation procedure based on priority settings, connection, and re-connection timings. The proposed model is also based on the dependence of demand and consumer priority perception. The model will ensure that electricity is available for basic and essential use in times of increased demand. It requires that load demand is constantly monitored to trigger automatic control and appliances should connect to the electric power grid using smart plugs.

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Section: Performance Evaluationmentioning

confidence: 99%

An Algorithm for using Internet of Things (IoTs) to Improve Load Management in Electric Power Grid

Mahlangu¹,

Musungwini²,

Nleya³

2018

IJCA

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“…For instance, minimization of charging costs, is the key objective for EV owner. To address those concerns, authors in [14] proposed EV charging algorithm to maximize the comfort, and in [15][16][17] presented techniques to manage flexibility considering customer preferences.…”

Section: Recent Related Workmentioning

confidence: 99%

Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

2017

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This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator's perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by enabling EV participation in day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to five times the cost they were paying without control.

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“…All the statistical analysis and model regressions were processed statistically by STATA12.0 software. In addition to income, residential electricity consumption can be influenced by a variety of factors, such as the electricity price, the alternative energy price, the urbanization rate, geographic characteristics, and the weather [9][10][11][12][13][14][15]. Similar to Schmalensee et al [8], we include only per capita income in the reduced function, leaving the other explanatory variables uncontrolled for the following reasons.…”

Section: Methodsmentioning

confidence: 99%

“…Yoo and Lee detected an inverted U-shaped relationship between per capita electricity consumption and per capita income in OECD (Organization for Economic Co-operation and Development) and developed countries [14]. McNeil and Letschert described an S-shaped relationship between household income level and appliance ownership [15]. Therefore, it is difficult to specify a fixed model that can capture the non-deterministic relationship in advance.…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Residential Electricity Consumption and Income: A Piecewise Linear Model with Panel Data

et al. 2016

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There are many uncertainties and risks in residential electricity consumption associated with economic development. Knowledge of the relationship between residential electricity consumption and its key determinant-income-is important to the sustainable development of the electric power industry. Using panel data from 30 provinces for the 1995-2012 period, this study investigates how residential electricity consumption changes as incomes increase in China. Previous studies typically used linear or quadratic double-logarithmic models imposing ex ante restrictions on the indistinct relationship between residential electricity consumption and income. Contrary to those models, we employed a reduced piecewise linear model that is self-adaptive and highly flexible and circumvents the problem of "prior restrictions". Robust tests of different segment specifications and regression methods are performed to ensure the validity of the research. The results provide strong evidence that the income elasticity was approximately one, and it remained stable throughout the estimation period. The income threshold at which residential electricity consumption automatically remains stable or slows has not been reached. To ensure the sustainable development of the electric power industry, introducing higher energy efficiency standards for electrical appliances and improving income levels are vital. Government should also emphasize electricity conservation in the industrial sector rather than in residential sector.

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Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

Cited by 48 publications

References 33 publications

An Algorithm for using Internet of Things (IoTs) to Improve Load Management in Electric Power Grid

An Algorithm for using Internet of Things (IoTs) to Improve Load Management in Electric Power Grid

Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

The Relationship between Residential Electricity Consumption and Income: A Piecewise Linear Model with Panel Data

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