A Novel Iteration Based Two-Stage Optimization in Energy Management Systems Application For Cost Minimization

The use of distributed energy resources based on renewable energies such as photovoltaic panels, wind turbines, biomass etc. is increasing at an exponential rate. New types of electrical loads including electric vehicles and heat pumps are also creating increased peak demand, grid uncertainty and instability. However, the consumption of electricity is increasing and so are the associated costs in a rising energy market. As a result, energy management systems are becoming more and more essential to tackle these issues. This paper deals with an economical optimization of energy storage systems and electric vehicles in a residential microgrid. The study proposes an energy management system for a residential microgrid that includes household loads, photovoltaic generation, energy storage systems and electric vehicles. The economic objective of reducing electricity bill is achieved by designing and testing a novel two-stage optimization consisting of genetic algorithm and gradient based method with suitable parameters to define new set-points of operation for the energy storage systems and the charging/discharging of electric vehicles that can significantly reduce the simulation time as well as solve the problem of not complying with the constraints of genetic algorithm.