One day ahead cost optimisation for a multi energy source building using a genetic algorithm
This paper proposes strategies for operating cost optimisation of a multi energy source building. The optimisation is based on a day ahead forecast of building energy usage. The building in question is powered by multiple energy sources including a wind turbine, a photovoltaic system, a lead acid battery system, and the national power grid. The optimisation method presented in this paper is a genetic algorithm. This algorithm uses the energy demand of the building, energy supplied from the wind turbine and photovoltaic system, and real time electricity pricing to optimise the operating timetables for the batteries. Simulation results demonstrated that daily operating costs can be reduced by up to 32 using the genetic algorithm with a fixed charge/discharge rate, and by as much as 56 when variable charge/discharge rates are employed, in comparison to a standard decision based strategy.
2016 UKACC 11th International Conference on Control (CONTROL)