Energy and load management of smart homes with renewable energy generation in smart grids

Energy management systems and demand side load management applications are among the most important issues of smart grid studies. In particular, demand-side load management applications in smart homes provide flexibility to the grid and support the use of renewable energy sources with intermittent generation and integration into the grid. First of all, in this thesis study, a local load shedding method has been proposed to realize load management under distributed generation conditions and different energy pricing tariffs of the grid. When the total energy demand exceeds the dynamic maximum power consumption limit determined by online energy prices and local generation, the proposed method limits the local energy consumption by disabling electrical loads according to user defined device usage priorities and power requirements of electrical household appliances. Moreover, in this thesis, the energy management system based on a local direct current distribution system with smart socket was presented for future smart homes. The modeling and use of smart sockets, which an important part of building energy management automation, are described. In addition in the thesis, an energy mixing component is presented with a time-rate multiple pulse width modulation method for the energy management system in grid connected smart homes including multi local power generation units. By using this method, a random search algorithm based on discrete stochastic optimization techniques is proposed to find low cost energy mixing rates for energy mixers. The simulation results have shown that the proposed load shedding algorithm reduce peak demand at the local level and work in accordance with different electricity tariffs. The optimized energy mixing rates managed by optimization and artificial intelligence methods have been achieved using the proposed energy mixer component for smart grid applications. Results have shown that the proposed random search algorithm for energy management can provide low cost energy mixing under changing energy price conditions for smart homes including multi-source.