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An Innovative Heuristic Algorithm for IoT-enabled Smart Homes for Developing Countries
This dissertation explores and identifies that home energy management systems (HEMSs) are used to implement demand side management in homes. Based on integration of renewable energy sources (RESs) and energy storage systems (ESSs), HEMS operation (HEMO) is classified into demand response (DR) and DR synergized with RESs and ESSs optimal dispatch (DRSREOD). DR-based HEMO depends on shifting of the consumer load towards off-peak times. DRSREOD-based HEMS benefits the consumer and the utility by reducing the cost of generation, reducing energy bills, minimizing green house gas (GHG) emissions, achieving overall energy savings and increasing energy sustainability. The contributions in this dissertation are three fold. First, this dissertation reviews the most recent literature on various models for DRSREOD-based HEMO. The reviewed models for HEMO are classified into dichotomous approaches as DR versus DRSREOD-based individual versus coordinated, deterministic versus stochastic, single-objective versus multi-objective and conventional techniques versus advanced heuristics-based. In addition, the tradeoffs among the dichotomous approaches, challenges pertinent to coordination and eminent issues related to standardization requirements for modeling home appliances (HAs) are investigated. Second, an improved algorithm for a DRSREOD-based HEMS is then proposed in this dissertation. This heuristic-based algorithm considers DR, photovoltaic (PV) availability, the state of charge and charge/discharge rates of the storage battery and the sharing-based parallel operation of more than one power sources to supply the required load. The HEMS problem has been solved to minimize the cost of energy (CE) and time-based discomfort (T BD) with conflicting tradeoffs. The mixed scheduling of appliances (delayed scheduling for some appliances and advanced scheduling for others) is introduced to improve the CE and T BD performance parameters using an inclining block rate (IBR) pricing scheme. A set of optimized tradeoffs between CE and T BD has been computed to address multiobjectivity using a multi-objective genetic algorithm with pareto optimization to perform the tradeoff analysis and to enable consumers to select the most feasible solution. Third, a drastically rising demand of electricity has forced a number of utilities in developing countries to impose large-scale load shedding (LS). A HEMS based on DRSREOD integrated with an LS-compensating dispatchable generator (LDG) (DRSREODLDG) ensures an uninterrupted supply of power for the consumers subjected to LS. The LDG operation to compensate the interrupted supply of power during the LS hours; however, accompanies the release of GHGs emissions as well that need to be minimized to conserve the environment. A 3-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of DRSREODLDG-based HEMS. The method provides the tradeoffs between the net cost of energy (CEnet) to be paid by the consumer, the T BD due to shifting of HAs to participate in the HEMS operation and minimal emissions (T EM iss) from the local LDG. At step-1, primary tradeoffs for CEnet, T BD and T EM iss are generated through a heuristic that takes into account PVs availability, the state of charge and the related rates for the storage system, mixed shifting of HAs, IBR, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. At step-2, a constraint filter based on the average value of T EM iss is used to filter out the tradeoffs with extremely high values of T EM iss. At step-3, a constraint filter made up of an average surface fit for T EM iss is applied to screen out the tradeoffs with marginally high values of T EM iss. The selected solutions are classified for critical tradeoff analysis to enable the consumer by choosing the best option from a diverse set of eco-efficient tradeoffs between CEnet, T BD and T EM iss. Finally, this thesis focuses on decomposed-weighted-sum particle swarm optimization (DWS-PSO) approach which is proposed for optimal operations of price-driven DR (PDDR) and PDDR- synergized with the renewable and energy storage dispatch (PDDR-RED) based HEMSs. Simulation results show the effectiveness of all the proposed schemes in comparison to the previous schemes.