PV SMART BUILDING LOAD OPTIMIZATION BASED ON IMPROVED QUANTUM GENETIC ALGORITHM

A multi-objective dynamic planning load optimization model for office buildings is proposed to address the problem of building load optimization, taking into account the cost of electricity consumption, energy storage discount, comfort of electricity consumption and fluctuation of peak-to-valley difference on the grid side. Combined with the time-of-use electricity price, the objective function of minimizing the net electricity cost of the building was established, and the quantum genetic algorithm with improved dynamic adjustment of the revolving gate was used to solve the problem. By dynamically adjusting the quantum revolving gate and changing the probability of the quantum state, the accuracy of the quantum genetic algorithm was improved. Load optimization and supply-side energy scheduling were accomplished while ensuring the global optimal solution was obtained. The experimental results show that the proposed algorithm has good economic benefits, which ensures the comfort of electricity consumption, and reduces the cost of electricity and the depreciation cost of energy storage devices effectively; as well as plays a good effect in reducing the peak-to-valley difference.