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.

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