Please use this identifier to cite or link to this item: http://prr.hec.gov.pk/jspui/handle/123456789/15549
Title: Towards Smart Cities: A Move for Efficient Energy Management From a Home to Cities Exploiting Clouds
Authors: Bukhsh, Rasool
Keywords: Physical Sciences
Computer Science
Issue Date: 2020
Publisher: COMSATS University, Islamabad.
Abstract: Towards Smart Cities: A Move for Efficient Energy Management From a Home to Cities Exploiting Clouds The reliable, efficient, sustainable and optimal management of city resources to facilitate the inhabitants defines the Smart City (SC). The resources of every sector of a SC are managed for their efficient utilization. The power sector is the backbone of a SC, which should be well planned in its design and structure to optimize power utilization. The integration of Information and Communication Technology (ICT) with conventional power grid allows two-way communication between supply and demand sides, which is defined as Smart Grid (SG). The intelligent monitoring and control systems for SG optimize the power generation and power consumption on supply and demand sides, respectively. On supply-side, fossil fuel is used to run the power generators to fulfill power demand, which is expensive and also emits Carbon-dioxide (CO2) in the environment. High power demand requires more generation as a result more CO2 is released in the environment, which causes the greenhouse effect. Optimized energy demand (power management on demand-side) ensures the optimized power production, which reduces the energy cost and emission of CO2. The demand-side is divided into industrial, commercial and residential sectors. The energy management programs optimize the power demand for these sectors. The industrial and commercial sectors are rigid for their energy demand due to their business portfolio; however, the residential sector is flexible. A energy management program of a home optimizes the energy demand by shifting its load demand of from on-peak to off-time time-slots. This optimization reduces energy cost of the home and power production on supply-side. Moreover, the integration of Renewable Energy Sources (RESs) on demand-side mitigates power demand from the utility (supply-side). The residential sector is further classified into islanded Smart Homes (SHs) and smart community for energy management. In an islanded SH, the load is shifted from on-peak to off-peak time to reduce power consumption cost while avoiding the peak demand for the supply-side. However, when multiple SHs in a community shift load to avoid peak demand, it may generate a rebound peak and the problem of inefficient power demand persists. So, a global solution is required to be proposed for a smart community by considering power sources and power demand. x In an islanded SH, (shiftable) appliances are scheduled with an intelligent algorithm to optimize power consumption cost. The high pricing from the supply-side advocates the on-peak time and the algorithm shifts load to off-peak time to reduce power consumption cost. The algorithm is installed as a programs on Energy Management Controller (EMC) of a SH, which controls the operations of SH’s appliance. In this research, divide and conquer behavior of Elephant Herding Optimization (EHO) is hybridized with the Genetic Algorithm (GA), Firefly Algorithm (FA), Bacteria Foraging Algorithm (BFA) and Binary Particle Swarm Optimization (BPSO). These four hybridized algorithms efficiently schedule the appliances to reduce the energy cost for a SH as compared to existing algorithms and unscheduled power demand. For a smart community, energy optimization program(s) is(are) installed on a centralized computing platform near or within the community. The program acts as a service that considers various factors simultaneously to optimize power utilization. In this research, fog based energy management services are proposed for smart communities. The efficient utilization of computing resources of fogs provides near-real-time energy management services. Service broker policies and load balancing algorithms are proposed to reduce the Processing Time (PT), computing cost and energy demand to run the services on the fogs with reduce Response Time (RT). Microgrids (MGs) are integrated to reduce energy consumption cost for communities on demand-side, which also reduce the power demand from the utility on supply-side. The MGs generate cheap and environment-friendly power from the RESs. The SHs of communities also have Energy Storage Systems (ESSs). In this research, energy policies are proposed to encourage the integration of RESs and ESSs with the existing system. These policies reduce energy consumption cost for communities by providing opportunities of energy trading, integrating cheap power of RESs and shared ESSs. The policies are programmed to run on the fogs as services. In this research, Linear Program (LP) models are proposed for the policies, which take less PT, computing cost of fogs and respond in near-real-time. Simulations are performed for various scenarios with proposed resource utilization techniques, e.g.; service broker policies and load balancing algorithms. Simulations are also performed for proposed power policies to integrate RESs, ESSs and opportunities for energy trading. Results show that proposed energy management services run efficiently on fogs with reduced PT, computing cost, reduced energy required by computing resources and reduced RT for end-users.
Gov't Doc #: 20679
URI: http://prr.hec.gov.pk/jspui/handle/123456789/15549
Appears in Collections:PhD Thesis of All Public / Private Sector Universities / DAIs.

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