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Smart Grid Initiative started after realizing the urge for changes in conventional electric power grids. These changes should be made in response to a number of emerging issues in the electricity industry. The increasing involvement of renewable energy technologies, either as large- scale generators or as small-rated distributed generators (DGs), poses a challenge for the grid. The renewable energy generators being intermittent and uncontrollable brings worrying uncertainty at the supply side of the grid. This uncertainty makes the grid’s operators anxious about balancing generation with load, which is a necessary condition for the security of the power system. Demand side management (DSM) offers a promising solution for the uncontrollability of renewable energy. Residential customers, through new entities called demand response (DR) aggregators, can bring DR services for addressing the aforementioned intermittency in supply. A cost-minimization framework is set for power supply-demand adjustment with the involvement of variable resources (i.e., renewable energy generators). The resources in the power supply-demand adjustment problem are demand reduction through aggregators, power flow exchange between areas, and balancing generators’ services. The method is simulated in the IEEJ East 30-machine test system after dividing it into 4 areas. The results of the followed method show a lower cost than the traditional method of using only balancing generators’ services. This work builds on a previous work of researchers from Keio Univ. in Japan. DR aggregators also use the Smart Grid Resource Allocation (SGRA) approach, which is a load shifting technique done by a DR aggregator. The DR aggregator performs a heuristic optimization in order to move part of residential appliances from peak to off-peak times. The effects of integrating multiple aggregators into the transmission level power grid are studied and simulated in the Roy Billinton test system (RBTS) after dividing it into 2 areas. The results show peak demand reductions, electricity prices reduction, and lower peak-to-average ratio (PAR) for the system under consideration. In line with integrating DR aggregators, a carbon tax function from the work of Prof. W. Nordhaus, a Nobel Memorial Prize winner in economics sciences, is adopted to design a carbon emission-based tax function and apply it to the fossil fueled generators in the system. The adopted carbon tax leads to less dispatch of coal and natural gas-based generators. As a result, CO2 emissions reduction is achieved and calculated using the set math models. The DR applications prove to represent a complementary element to the imposition of carbon taxation in achieving emissions-reduction. That is, imposing carbon taxation drives increases in electricity prices, while applying DR reduces the mean electricity price by lowering the PAR of the system load profile. In addition, a test bed is designed to find a relationship between the aggregator’s performance and utility pricing mechanisms. The experiment aims to find how the utility pricing mechanisms affect profitability of the aggregators and peak load shifting. These pricing mechanisms include fixed tariff, time-of-use (TOU) pricing, and real-time pricing (RTP). The simulation-based study shows that aggregators make the highest profits when run in parallel with utilities applying fixed tariffs, while they make the highest shifted peak load when run in parallel with utilities applying RTPs. Furthermore, survey-based data about the use patterns of three smart home appliances are incorporated in the SGRA approach. These three appliances include dishwasher, washing machine, and dryer. Beside using data about these appliances, additional rescheduling constraints are proposed to improve the comfort of participating customers. The results show profitabili