Energy Optimization Management Of Office Buildings Considering Demand Response

With the construction of energy technology and smart grid,demand response has gradually become an important means for users to participate in the operation regulation of power system.As an important demand response resource,how to improve the response potential and resource utilization rate of office buildings has gradually become a focus of people’s attention.The thesis puts forward a strategy for load optimizing and energy management with demand response based on an office building in order to increase the economy;mean while it considers the weather’s type to study energy dispatching and load optimizing.The specific works are as follows:(1)According to the energy use characteristics of the building to classes the load.According to the characteristics of loads,air conditioning and lighting loads with response potential are selected to regulate and control.On this basis,considering the constraints of equipment,flexible load model,curtailable load model,photovoltaic power generation and energy storage system models are established;among them,the flexible load model takes into account the thermal storage characteristics of the building itself and the external real time ambient radiation to correct the disturbance of the external environment,and introduces the temperature characteristic quantity to release the strong coupling between the indoor temperature and the air conditioning switching state at each moment;daylight dynamic illuminance model is used to predict indoor natural illuminance,and it is also used to reduce lighting load to establish a regulation model of lighting load.(2)An improved quantum genetic algorithm is proposed while consider the nonlinear and time-varying problem of building energy scheduling system;the performance of the quantum genetic algorithm is improved by dynamically adjusting the quantum rotation gate,changing the probability of quantum states,and performing the variational operation of Hadamrad gate;finally,four test functions are used to verify the improvement of the improved algorithm in terms of iteration speed and solution accuracy.(3)Established an optimization objective function based on time-of-use tariff considered the factors of electricity cost,energy storage discount,electricity comfort and fluctuation of peak-to-valley difference on the grid side,and it is solved by using the improved quantum genetic algorithm meanwhile operates the strategy based on the weather’s type.The simulation results show the applicability and economy of the method in the proposed problem,which can effectively reduce the cost of electricity consumption and energy storage depreciation cost of buildings while ensuring the comfort of electricity consumption.In particular,the optimal power-economic balance point can be found better in cloudy weather.