The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy- efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities.

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Efficient Energy Management for the Internet of Things in Smart Cities

The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies

Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.

https://www.mdpi.com/1996-1073/10/4/549/pdf

An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources

The concept of smart home is widely favored, as it enhances the lifestyle of the residents involving multiple disciplines, ie, lighting, security, and much more As the smart home networks continue to grow in size and complexity, it is essential to address a handful among the myriads of challenges related to data loss due to the interference and efficient energy management In this paper, we propose a smart home control system using a coordinator-based ZigBee networking The working of the proposed system is three fold: 1) smart interference control system controls the interference caused due to the co-existence of IEEE 80211x-based wireless local area networks and wireless sensor networks; 2) smart energy control system is developed to integrate sunlight with light source and optimizes the energy consumption of the household appliances by controlling the unnecessary energy demands; and 3) smart management control system to efficiently control the operating time of the electronic appliances The performance of the proposed smart home is testified through computer simulation Simulation results show that the proposed smart home system is less affected by the interference and efficient in reducing the energy consumption of the appliances used in a smart home

Internet of Things Based Energy Aware Smart Home Control System

Optimal operation of hybrid AC/DC microgrids under uncertainty of renewable energy resources: A comprehensive review

In this paper, we propose a solution to the problem of scheduling of a smart home appliance operation in a given time range. In addition to power-consuming appliances, we adopt a photovoltaic (PV) panel as a power-producing appliance that acts as a micro-grid. An appliance operation is modeled in terms of uninterruptible sequence phases, given in a load demand profile with a goal of minimizing electricity cost fulfilling duration, energy requirement, and user preference constraints. An optimization algorithm, which can provide a schedule for smart home appliance usage, is proposed based on the mixed-integer programming technique. Simulation results demonstrate the utility of our proposed solution for appliance scheduling. We further show that adding a PV system in the home results in the reduction of electricity bills and the export of energy to the national grid in times when solar energy production is more than the demand of the home.

Appliance Scheduling Optimization in Smart Home Networks

In this paper, we propose a solution to the problem of scheduling of smart appliances operations in a given time range. We adopt a photovoltaic (PV) panel as a power producing appliance that acts as micro-grid. Appliance operation is modeled in terms of un-interruptible sequence phases, given in load demand profile. An optimization algorithm, which can provide a schedule for smart home appliances usage is proposed. Simulation results demonstrate the utility of our proposed solution for appliance scheduling. We further show that adding a PV panel in the home results in reduction of electricity bill and export of energy to the national grid.

http://ieeexplore.ieee.org/iel7/7368726/7379911/07379994.pdf

F. A. Qayyum

Papers

Appliance Scheduling Optimization in Smart Home Networks

Appliance scheduling optimization in smart home networks comprising of smart appliances and a photovoltaic panel