Simulation Study for Optimized Demand Side Management in Smart Grid Smart grid is envisioned to meet the 21st century energy requirements in a sophisticated manner with real time approach by integrating the latest digital communications and advanced control technologies to the existing power grid. It will dynamically connect all the stake holders of smart grid through enhanced energy efficiency awareness corridor. Smart Homes (SHs), Home Energy Management Systems (HEMS) and effect of home appliances scheduling in smart grid are now familiar research topics in electrical engineering. Peak load management and reduction of Peak to Average Ratio (PAR) and associated methods are under focus of researchers since decades. These topics have got new dimensions in smart grid environment. This dissertation aims at simulation study for effective Demand SideManagement (DSM) in smart grid environment. This work is mainly focused on optimal load scheduling for energy cost minimization and peak load reduction. This work comprehensively reviews the smart grid applications, communication technologies, load management techniques, pricing schemes and related topics in order to provide an insight to the environment required for dynamic DSM. Various network attributes such as Internet Protocol (IP) support, power usage, data rate etc. are considered to compare the communications technologies in smart grid context. Techniques suitable for Home Area Networks (HANs) such as ZigBee, Bluetooth, Wi-Fi, 6LoWPAN and Z-wave are discussed and compared in context of consumer concerns and network attributes. A similar approach in context of utilities’ concerns is adopted for wireless communications techniques for Neighborhood Area Networks (NANs), which include WiMAX and GSM based cellular standards. Issues and challenges regarding dynamic DSM in smart grid have been discussed briefly. DSM is supposed to have a vital role in future energy management systems and is one of the hot research areas. This study presents detailed review and analytical comparison of DSM techniques along with related technologies and implementation challenges in smart grid. It also covers consumers and utilities concerns in context of DSM to enhance the readers’ intuition about the topic. Two major types of DSM schemes, incentive based and dynamic pricing based, have been discussed and compared analytically. Dynamic pricing based HEMS are emphasized as important tools for peak load reduction and consumers’ energy cost minimization. Dynamic pricing based HEMS and their associated optimization techniques along with analytical comparison of the latest schemes have been described. Comparison of DSM techniques and study of latest HEMS scheme provided the base for new ideas of partial baseline load and reserved interrupting load to formulate two unique energy cost minimization problems. These models resulted the following two solutions in which scheduling has been carried out through many different algorithms to reduce peak load and consequently the PAR. This work includes novel appliance scheduling solution named; Comprehensive Home Energy Management Architecture (CHEMA), with multiple integrated scheduling options in smart grid environment. Multiple layers of enhanced architecture are modeled in Simulink with embedded MATLAB code. Single Knapsack is used for scheduling and four different cases for cost reduction are modeled. Fault identification and electricity theft control have also been added along with the carbon foot prints reduction for environmental concerns. Simulation results have shown the peak load reduction of 22.9% for unscheduled load with Persons Presence Controller (PPC), 23.15% for scheduled load with PPC and 25.56% for flexible load scheduling. Similarly total cost reduction of 23.11%, 24% and 25.7% has been observed, respectively. Smart grid interface layer and load forecasting layers are not implemented in current work and will be focused in future work. Another novel comparative approach has also been proposed in this research, which investigates the effect of multiple pricing schemes and optimization techniques for cost minimization and peak load reduction. The proposed model uses multiple pricing schemes including Time of Use (ToU), Real Time Pricing (RTP) day ahead case and Critical Peak Pricing (CPP). Proposed optimization problem has been solved with multiple optimization techniques including Knapsack, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Knapsack is used with two options of limited slots scheduling and whole day scheduling. Comparative results of the multiple pricing and optimization schemes have been discussed. Results show that the best combination achieved with GA and CPP with 39.9223% cost reduction. PSO showed the 43.73% cost reduction with all the pricing schemes. The proposed schemes have many applications for peak load reduction and energy cost minimization to benefit consumers and utilities. A user can schedule his load using one of the options provided in CHEMA according to his preferences. Similarly, maintenance activities can be accommodated without disturbing the pre-defined schedule by using reserved interrupting slots. In large buildings, reserved slots can be used to schedule heavy loads without generating a peak.