Electricity is the basic demand of consumers. With the passage of time, the demand for electricity is increasing day by day. Smart grid (SG) is evolved to satisfy the demand of consumers. To manage electricity load from peak hours to low peak hours, consumer needs to control their appliances by home energy management system (HEMS). HEMS schedule the appliances according to customers need.
Energy management using demand-side management (DSM) techniques play an
important role in SG domain. Smart meters (SM) and energy management controllers (EMC) are the important components of the SG. Intelligent energy optimization techniques play a vital role in the reduction of the electricity bill via scheduling home appliances. Through appliance’s scheduling, the consumer gets a feasible cost for consumed electricity. DSM provides the facility for consumers to schedule their appliances for the reduction of power price and rebate in peak loads. HEMS is allowed to remotely shut down their appliances in emergency conditions through direct load control programs. Meta-heuristic algorithms have been used for the optimization of the user energy consumption in an efficient way. Electricity load forecasting plays a vital role in improving the use of energy through customers to make decisions efficiently. The accuracy of load prediction is a challenging task because of randomness and noise disturbance. In this thesis, efficient algorithms are proposed to control the load in residential units. Our proposed schemes are used to minimize the user comfort delay time. Customers waiting time is inversely proportional to the total cost and peak to average ratio (PAR). The aim of the current research is to manage the power of the residential units in an optimized way and predict the exact load. Simulation results show the minimum user waiting time, however, the total cost is compromised due to the high demand of the load and predict the exact load for users. In the end, our proposed schemes show better result through simulation results.
In this thesis, we proposed new schemes which are used to lower the electricity price, PAR and user discomfort in electricity consumption side. The proposed schemes performed better than existing benchmark schemes. The proposed schemes used real-time price (RTP) signal for calculating the electricity cost and PAR. Simulation results also show that the proposed algorithms have met the objective of DSM. For prediction, the proposed scheme is performed better than benchmark schemes and predict the exact electricity load.