A smart city is an eﬃcient, reliable, and sustainable urban center that facilitates its inhabitants with a high quality of life standards via optimal management of its resources. Energy management of smart homes (SHs) is one of the most challenging and demanding issues which needs signiﬁcant eﬀort and attention. Demand side management (DSM) in smart grid (SG) authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. In DSM, scheduling of appliances based on consumer-deﬁned priorities is an important task performed by a home energy management controller (HEMC). However, user discomfort is caused by the scheduling of home appliances based on the demand response or limiting its time of use. Further, rebound peaks that are regenerated in the oﬀ-peak hours is also a major challenge in DSM. In addition, an increase in the world population is resulting in high energy demand; thus, causing a huge consumption of fossil fuels. This ultimately results in severe environmental problems for mankind and nature. Renewable energy sources (RESs) emerge as an alternative to the fossil sources. These RESs have the advantages of environmental friendliness and sustainability, which are incorporated in SHs via two modes: grid-connected (GC) or stand-alone (SA). The reliability concerns in RESs are usually met with the usage of hybrid RESs along with the integration of energy storage systems (ESS). The eﬃcient usage of these components in the hybrid RESs requires an optimum unit sizing that achieves the objectives pertaining to cost minimization and reliability in SA mode. These are some of the main concerns of a decision maker. This thesis focuses on employing meta-heuristic techniques for eﬃcient utilization of energy and RESs in SH. At ﬁrst, an evolutionary accretive comfort algorithm (EACA) is developed based on four postulations which allows the time-varying priorities to be quantiﬁed in time and device-based features. Based on the input data, considering the appliances’ power ratings, its time of use, and absolute comfort derived from priorities, the EACA is able to generate an optimal energy consumption pattern which would give maximum satisfaction at a predetermined user budget. A cost per unit comfort index (χ) which relates the consumer expenditure to the achievable comfort is also demonstrated. To test the applicability of the proposed EACA, three budget scenarios of 1.5 $/day, 2.0 $/day, and 2.5$/day are performed. Secondly, a priority-induced DSM strategy based on the load shifting technique considering various energy cycles of an appliance is presented. The day-ahead load shifting technique is mathematically formulated and mapped with multiple knapsack problem (MKP) to mitigate the rebound peaks. The proposed autonomous HEMC embeds three meta-heuristic optimization techniques: genetic algorithm (GA), enhanced diﬀerential evolution (EDE), and binary particle swarm optimization (BPSO) along with the optimal stopping rule, which is used for solving the load shifting problem. Next, we integrate the RESs and ESS in a residential sector considering GC mode. The proposed optimized home energy management system minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of the electricity market. Here the appliances are classiﬁed into shiftable and non-shiftable categories, and a hybrid GA-BPSO (HGPO) scheme outperforms to other algorithms in terms of cost and a peak-to-average ratio (PAR). Finally, meta-heuristic schemes that do not depend on algorithmic-speciﬁc parameters are focused for RESs and ESS integration in a SA system. Preliminary, Jaya algorithm is used for ﬁnding an optimal unit sizing of RESs components, including photovoltaic (PV) panels, wind turbines (WTs), and fuel cell (FC) with an objective to reduce the consumer total annual cost. The methodology is applied to real solar irradiation and wind speed data taken for Hawksbay, Pakistan. Next, an improved Jaya and the learning phase as depicted in teaching learningbased optimization (TLBO), named JLBO algorithm for optimal unit sizing of a PV-WT-Battery hybrid system is also demonstrated for another site located in Rafsanjan, Iran. The system reliability is considered using the maximum allowable loss of power supply probability (LPSPmax) provided by the consumer. Thus, the thesis objectives achieved are to have a green, reliable, economical, and sustainable power supply in the SH.