Cost and demand power optimization in energy management

The demand for electrical energy is increasing day by day. New production facilities are being commissioned to satisfy energy demand. However, electricity generation with traditional methods and renewable energy sources has negative aspects in terms of economic, environmental and grid integration. Thanks to the increasing transformation of the classical electricity grid into a smart grid structure, load management strategies in demand side management become a feasible structure. With smart metering devices and communication infrastructure, consumers have become able to monitor and control their consumption. Within the scope of this thesis study, a daily consumption optimization of a residential type consumer was carried out. In the optimization process, plans were made through Genetic Algorithm, Binary Particle Swarm Optimization and Lightning Search Algorithm. Planning was carried out separately using time-of-use pricing and real-time pricing for 1 hour, 30 minutes and 15 minutes, and their success performances were compared. As a result of the simulation studies, all of the methods achieved cost and PAR minimization.