Thesis

Multi-Objective Home Energy Management System with Multi-Class Appliances using Meta-Heuristic Techniques

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Abstract

The day to day increase in world’s population is producing a gap between the demand and supply of electricity. Traditional Grid (TG) with the aging infrastructure is unable to address the increasing electricity demand. Installation of new generation systems is not a good solution to tackle the high demand of electricity. Smart Grid (SG) enhanced the TG by adopting information and communication based technological solutions to address the increasing electricity demand. Smart Home Energy Management System (SHEMS) plays an important role in the efficacy of SG. To get the most out of the existing system, several demand response schemes have been presented by researchers. These schemes try to schedule the appliances in such a way that electricity consumption cost and peak-to-average ratio are minimized along with maximum User Comfort (UC). However, there exists a trade-off between UC and electricity consumption cost. In this thesis, a SHEMS is developed to minimize the appliances waiting time and Peak to Average Ratio (PAR). For appliances waiting time minimization a novel population based scheme namely UC Maximization (UCM) is developed. UCM schedule the home appliances in such a way that appliances waiting time is minimized economically. Furthermore, we developed an Improved Algorithm for PAR Reduction (IAPR) to enhance the reliability of power grid. To evaluate the effectiveness of UCM in terms of appliances waiting time reduction, comparison is made with two well known meta-heuristic techniques namely Flower Pollination Algorithm (FPA) and Jaya Optimization Algorithm (JOA). Experimental results show that UCM scheme outperforms FPA by 5.97% and JOA by 53.9%. Moreover, UCM scheme reduced the electricity consumption cost and PAR by 58% and 56% as compared to unscheduled scenario. To validate the effectiveness of IAPR in terms of PAR minimization, comparison is made with the renowned meta-heuristic optimization schemes namely Strawberry Algorithm (SA) and Salp Swarms Algorithm (SSA). Experimental results show that IAPR outperforms SSA by 69.4% and SA by 42.7% in terms of PAR reduction using the critical peak pricing scheme. Moreover, using the real time pricing scheme IAPR exceeds SSA and SA by 61.67% and 37.77% respectively .

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Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows that a fully automated Energy Management System (EMS) is a necessary prerequisite to DR in these areas. In this paper, we propose a novel EMS formulation for DR problems in these sectors. Specifically, we formulate a fully automated EMS's rescheduling problem as a reinforcement learning (RL) problem (referred to as the device based RL problem), and show that this RL problem decomposes over devices under reasonable assumptions. Compared with existing formulations, our new formulation (1) does not require explicitly modeling the user's dissatisfaction on job rescheduling, (2) enables the EMS to self-initiate jobs, (3) allows the user to initiate more flexible requests and (4) has a computational complexity linear in the number of devices. We also propose several new performance metrics for RL algorithms applied to the device based RL problem, and demonstrate the simulation results of applying Q-learning, one of the most popular and classical RL algorithms, to a representative example.
Article
Various forms of demand side management (DSM) programs are being deployed by utility companies for load flattening amongst the residential power users. These programs are tailored to offer monetary incentives to electricity customers so that they voluntarily consume electricity in an efficient way. Thus, DSM presents households with numerous opportunities to lower their electricity bills. However, systems that combine the various DSM strategies with a view to maximizing energy management benefits have not received sufficient attention. This study therefore proposes an intelligent energy management framework that can be used to implement both energy storage and appliance scheduling schemes. By adopting appliance scheduling, customers can realize cost savings by appropriately scheduling their power consumption during the low peak hours. More savings could further be achieved through smart electricity storage. Power storage allows electricity consumers to purchase power during off-peak hours when electricity prices are low and satisfy their demands when prices are high by discharging the batteries. For optimal cost savings, the customers must constantly monitor the price fluctuations in order to determine when to switch between the utility grid and the electricity storage devices. However, with a high penetration of consumer owned storage devices, the charging of the batteries must be properly coordinated and appropriately scheduled to avoid creating new peaks. This paper therefore proposes an autonomous smart charging framework that ensures both the stability of the power grid and customer savings.
Article
Exciting yet challenging times lie ahead. The electrical power industry is undergoing rapid change. The rising cost of energy, the mass electrification of everyday life, and climate change are the major drivers that will determine the speed at which such transformations will occur. Regardless of how quickly various utilities embrace smart grid concepts, technologies, and systems, they all agree onthe inevitability of this massive transformation. It is a move that will not only affect their business processes but also their organization and technologies.
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Recent estimates and forecasts of the oil, gas, coal resources and their reserve/production ratio, nuclear and renewable energy potential, and energy uses are surveyed. A brief discussion of the status, sustainability (economic, environmental and social impact), and prospects of fossil, nuclear and renewable energy use, and of power generation (including hydrogen, fuel cells, micropower systems, and the futuristic concept of generating power in space for terrestrial use), is presented. Comments about energy use in general, with more detailed focus on insufficiently considered areas of transportation and buildings are brought up. Ways to resolve the problem of the availability, cost, and sustainability of energy resources alongside the rapidly rising demand are discussed. The author’s view of the promising energy R&D areas, their potential, foreseen improvements and their time scale, and last year’s trends in government funding are presented.
Demand side response in the domestic sector-a literature review of major trials
  • Frontier Economics
  • Sustainability First
Economics, Frontier, and Sustainability First. "Demand side response in the domestic sector-a literature review of major trials." Final Report, London, August (2012).
On Maximizing User Comfort using a Novel Meta-Heuristic Technique in Smart Home
  • Sajjad Khan
  • Ali Zahoor
  • Nadeem Khan
  • Waleed Javaid
  • Ahmad
  • Abid Raza
  • Hafiz Abbasi
  • Muhammad Faisal
Sajjad Khan, Zahoor Ali Khan, Nadeem Javaid, Waleed Ahmad, Raza Abid Abbasi and Hafiz Muhammad Faisal, "On Maximizing User Comfort using a Novel Meta-Heuristic Technique in Smart Home", in 33rd International Conference on Advanced Information Networking and Applications (AINA).
Energy Efficient Scheduling of Smart Homes
  • Sajjad Khan
  • Nadeem Javaid
  • Zahoor Ali Khan
  • Sahibzada Muhammad Shuja
  • Muhammad Abdullah
  • Annas Chand
Sajjad Khan, Nadeem Javaid, Zahoor Ali khan, Sahibzada Muhammad Shuja, Muhammad Abdullah and Annas Chand, "Energy Efficient Scheduling of Smart Homes", in 33rd International Conference on Advanced Information Networking and Applications (AINA), 2019
Flower pollination algorithm for global optimization
  • Xin-She Yang
Yang, Xin-She. "Flower pollination algorithm for global optimization." In International conference on unconventional computing and natural computation, pp. 240-249. Springer, Berlin, Heidelberg, 2012.
Electricity load forecasting for each day of week using deep CNN
  • Sajjad Khan
  • Nadeem Javaid
  • Annas Chand
  • Abdul Basit Majeed Khan
  • Fahad Rashid
  • Imran Uddin Afridi
Sajjad Khan, Nadeem Javaid, Annas Chand, Abdul Basit Majeed Khan, Fahad Rashid and Imran Uddin Afridi, "Electricity load forecasting for each day of week using deep CNN", in 33rd International Conference on Advanced Information Networking and Applications (AINA), 2019.
Electricity Price Prediction by Enhanced Combination of Autoregression Moving Average and Kernal Extreme Learing Machine
  • Nadeem Sahibzada Muhammad Shuja
  • Sajjad Javaid
  • Umair Khan
  • Sarfraz
  • Hamza Syed
  • Muhammad Ali
  • Tahir Taha
  • Mehmood
Sahibzada Muhammad Shuja, Nadeem Javaid, Sajjad Khan, Umair Sarfraz, Syed Hamza ALi, Muhammad Taha and Tahir Mehmood,"Electricity Price Prediction by Enhanced Combination of Autoregression Moving Average and Kernal Extreme Learing Machine", in 33rd International Conference on Advanced Information Networking and Applications (AINA), 2019.
Efficient Scheduling of Smart Home Appliances for Energy Management by Cost and PAR Optimization Algorithm in Smart Grid
  • Nadeem Sahibzada Muhammad Shuja
  • Sajjad Javaid
  • Hina Khan
  • Murtaza Akmal
  • Qazi Hanif
  • Zain Ahmad Fazalullah
  • Khan
Sahibzada Muhammad Shuja, Nadeem Javaid, Sajjad Khan, Hina Akmal, Murtaza Hanif, Qazi Fazalullah and Zain Ahmad Khan, "Efficient Scheduling of Smart Home Appliances for Energy Management by Cost and PAR Optimization Algorithm in Smart Grid", in 33rd International Conference on Advanced Information Networking and Applications (AINA), 2019.
A New Memory Updation Heuristic Scheme for Energy Management System in Smart Grid
  • Waleed Ahmad
  • Nadeem Javaid
  • Sajjad Khan
  • Maria Zuraiz
  • Tayyab Awan
  • Muhammad Amir
  • Raza Abid Abbasi
Waleed Ahmad, Nadeem Javaid, Sajjad Khan, Maria Zuraiz, Tayyab Awan, Muhammad Amir and Raza Abid Abbasi, "A New Memory Updation Heuristic Scheme for Energy Management System in Smart Grid", in 33rd International Conference on Advanced Information Networking and Applications (AINA), 2019.
Pro Utility Pro Consumer Comfort Demand Side Management in Smart Grid
  • Waleed Ahmad
  • Nadeem Javaid
  • Basit Karim
  • Muhammad Syed Qasim Jan
  • Ali
  • Abid Raza
  • Sajjad Abbasi
  • Khan
Waleed Ahmad, Nadeem Javaid, Basit Karim, Syed Qasim Jan, Muhammad Ali, Raza Abid Abbasi and Sajjad Khan, "Pro Utility Pro Consumer Comfort Demand Side Management in Smart Grid", in 33rd International Conference on Advanced Information Networking and Applications (AINA), 2019. 93 Thesis by: Sajjad Khan CONFERENCE PROCEEDINGS
Minimizing Daily Electricity Cost using Bird Chase Scheme with Electricity Management Controller in a Smart Home
  • Nadeem Raza Abid Abbasi
  • Javaid
  • Sajjad Amanullah
  • Khan
Raza Abid Abbasi, Nadeem Javaid, Amanullah, Sajjad Khan, Hafiz Muhammad Faisal and Sajawal Ur Rehman Khan, "Minimizing Daily Electricity Cost using Bird Chase Scheme with Electricity Management Controller in a Smart Home", in 33rd International Conference on Advanced Information Networking and Applications (AINA), 2019.
Minimizing Daily Cost and Maximizing User Comfort using a New Metaheuristic Technique
  • Nadeem Raza Abid Abbasi
  • Sajjad Javaid
  • Khan
  • Shujat Ur Rehman
  • Rana Muhammad Amanullah
  • Waleed Asif
  • Ahmad
Raza Abid Abbasi, Nadeem Javaid, Sajjad Khan, Shujat ur Rehman, Amanullah, Rana Muhammad Asif and Waleed Ahmad, "Minimizing Daily Cost and Maximizing User Comfort using a New Metaheuristic Technique", in 33rd International Conference on Advanced Information Networking and Applications (AINA), 2019.