Peak reduction algorithms for a smart community

As the energy consumption in residential sector keeps growing, it is essential to find a systematic way of managing the energy consumption in a smart community consisting of a number of smart houses so that the available resource is utilized properly without causing peak demand. In this paper, we propose peak demand management techniques for a smart community using different types of coordination mechanisms for coordination of multiple house agents working in the same environment. In this we propose few peak reduction algorithms which use centralized model, decentralized model and Pareto resource allocation model for resource allocation. We describe the smart energy community architecture and propose user comfort model for the appliances based on user preferences and power reduction capability that comes from the load profile of appliances. We used performance indices such as community comfort, community involvement to analyze and compare our algorithms and finally find the best performing algorithm among them.