Instead of planting new electricity generation units, there is a need to design an efficient energy management system to achieve a normalized trend of power consumption. Smart grid has been evolved as a solution, where Demand Response (DR) strategy is used to modify the consumer's nature of demand. In return, utilities pay incentives to the consumer. This concept is equally applicable on residential and commercial areas; however, the increasing load demand and irregular electricity load profile in residential area have encouraged us to propose an efficient home energy management system for optimal scheduling of home appliances. Whereas, electricity consumers have stochastic nature, for which nature-inspired optimization techniques provide optimal solutions. However, these optimization techniques behave stochastically according to the situation. For this reason, we have proposed different optimization techniques for different scenarios. The objectives of this thesis include: reduction in electricity bill and peak to average ratio, minimization of waiting time to start appliances (comfort maximization) and minimization of wastage of surplus energy by exploiting the coordination among appliances and homes. In order to meet the electricity demand of the consumers, the energy consumption patterns of a consumer are maintained through scheduling the appliances in day-ahead and realtime bases. It is applicable by the defined fitness criterion for the proposed hybrid bacterial foraging genetic algorithm and hybrid elephant adaptive cuckoo search optimization techniques, which helps in balancing the load during On-peak and Off-peak hours. Moreover, the concept of coordination and coalition among home appliances is presented for real-time scheduling. The fitness criterion helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of the appliance. A multi-objective optimization based solution is proposed to resolve the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. Two optimization techniques: binary multiobjective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms are proposed to obtain the Pareto front.
The main objective of DR is to encourage the consumer to shift the peak load and gets
incentives in terms of cost reduction. However, prices remain the same for all the users even if they shift the peak load or not. In this thesis, Game Theory (GT) based Time of Use
pricing model is presented to define the pricing strategy for On-peak and Off-peak hours.
The price is defined for each user according to the utilized load using coalitional GT. Further, the proposed pricing model is analyzed for scheduled and unscheduled load. In this regards, Salp swarm and rainfall algorithms are used for scheduling of appliances and an aggregated fitness criterion is defined for load shifting to avoid the peak rebound effect.
We also proposed the coordination and coalition based Energy Management System-as-a-
Service on Fog (EMSaaS_Fog). With the increase in number of electricity consumers, the
computational complexity of energy management system is becoming a threat for efficiency of a system in real-time environment. To deal with this dilemma, the utility shifts computational and storage units on cloud and fog. The proposed EMSaaS_Fog effectively handles the coalition among the apartments within a building to maintain balance between the demand and supply. Moreover, we consider a small community, which consists of multiple smart homes. Microgrid is installed at each residence for electricity generation. It is connected with the fog server to share and store information. Smart energy consumers are able to share detail of excess energy with each other through the fog server.