Demand Side Management in Smart Grid 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 optimal operation (HEMO)isclassifiedintopricedrivendemandresponse(DR)andDRsynergizedwithRESs and ESSs optimal dispatch (DR-RED). DR-based HEMO depends on shifting of the consumer load towards off-peak times. DR-RED-based HEMS benefits the consumer and the utility by reducing the cost of generation, reducing energy bills, minimizing green-housegas emissions, achieving overall energy savings and increasing energy sustainability. The methodology to compute an optimal scheme for the aforementioned operations is called HEMS optimization. The terms optimal HEMS operation and HEMS optimization (both abbreviated as HEMO) has been used interchangeably in this dissertation. The contributions in this dissertation are three fold. First, this dissertation reviews the most recent literature on various models for DR-RED-based HEMO. The reviewed models for HEMO are classified into dichotomous approaches as DRversus DR-RED-based,individual versus coordinated, deterministic versus stochastic, single-objective versus multi-objective and conventional techniques versus advanced heuristics-based. The tradeoffs among the dichotomous approaches are analyzed and the challenges pertinent to coordination, standardization in modeling of home appliances, testing of HEMS algorithms, and handling of multi-objectivity are investigated. Second, animprovedalgorithmforaDR-RED-basedHEMSisthenproposedinthisdissertation. This heuristic algorithm considers DR, photovoltaic 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 scheduled load. The HEMS problem has been solved to minimize the cost of energy (CE) and time-based discomfort (TBD) with conflicting tradeoffs. An innovative scheme for mixed scheduling of shiftable home appliances (delayed scheduling for some appliances and advanced scheduling for others) is introduced to improve the CE and TBD performance parameters. The peak load has also been minimized using an inclining block rate pricing scheme. Multi-objective genetic algorithm with pareto optimization (MOGA/PO) has been used to handle multi-objectivity. Pareto optimal setfortradeoffsbetweenCE andTBDhasbeencomputedtoprovidetheconsumerwiththe selection of choice for a feasible solution. Algorithm for DR-RED-based HEMS for mixed scheduling outperformed the ones based on delayed scheduling (presently in use) for CE and TBD. x Third, a drastically rising demand of electricity has forced a number of utilities in developing countries to impose large-scale load shedding. An algorithm for HEMS optimal operation for DR-RED integrated with a load-shedding-compensating dispatchable generator (LDG) (DR-RED-LDG) has been presented. An inventive method for optimal sizing of an LDG that ensures an uninterrupted supply of power to the consumers exposed to scheduled load-sheddingisalsoproposed. TheLDGoperationtocompensatetheinterruptedsupplyof powerduringtheload-sheddinghours;however,accompaniesthereleaseofgreen-house-gas emissions as well that need to be minimized to conserve the environment. A 3-step simulations based posteriori method is proposed to develop a scheme for eco-efficient operation of DR-RED-LDG-based HEMS. The method is novel in nature and provides the tradeoffs between net cost of energy (CEnet) to be paid by the consumer, TBD due to shifted operations of home appliances to participate in the demand response, and minimal value of total emissions (TEMiss) from the local LDG. Atstep-1,primarytradeoffsforCEnet,TBDandTEMissaregeneratedthroughaMOGA/PObased heuristic that takes into account mixed scheduling of home appliances, photovoltaic availability, the state of charge and the related rates for the storage system, inclining block ratescheme,sharing-basedparalleloperationofpowersources,andsellingoftherenewable energy to the utility. At step-2, a constraint filter based on the average value of TEMiss is usedtofilteroutthetradeoffswithextremelyhighvaluesofTEMiss. Atstep-3,aconstraint filtermadeupofanaveragesurfacefitforTEMissisappliedtoscreenoutthetradeoffswith marginallyhighvaluesofTEMiss. Thesurfacefitisdevelopedusingpolynomialregression and least sum of square method. The selected solutions are classified for critical tradeoff analysistoprovideadiversesetofeco-efficienttradeoffsbetweenCEnet,TBDandTEMiss to the consumer enabling him choose the best as per his needs. Finally,thisthesisfocusesondevelopmentofanalgorithmbasedondecomposed-weightedsum particle swarm optimization (DWS-PSO) approach for posteriori which is proposed for optimal operations of DR- and DR-RED-based HEMSs. The technique makes use of particle swarm optimization as problem solver and decomposition of the solutions to the weighted sum method to handle multi-objectivity for tradeoff solutions. A diversified set of test problems along-with uniquely defined performance metrics are also proposed for a diversified performance analysis of the algorithms. A procedure for the performance analysis was presented and the proposed DWS-PSO algorithm was analyzed for a diversified performance. Simulations results show the effectiveness of all the proposed schemes/ algorithms in comparison to the previous ones.