Research On Energy Optimization And Load Response Control Of Household Microgrid

Household microgrid is an intellectualized network control system which integrates distributed energy generation,user load power consumption and some energy storage equipment such as batteries among residents to optimize energy and control load response.Household microgrid will become a necessary part of smart house and smart grid in the future.Under the influence of the Internet of Things,household microgrid energy management system can interconnect residential loads,achieve overall management and optimization,and has important significance in realizing renewable energy reuse,stabilizing the operation of large power grid,realizing peak load reduction and valley filling of power grid,and rational power consumption of residential users.Firstly,the basic structure and related technologies of household microgrid are summarized.The core part of the household microgrid,i.e.the components of the household energy optimization system and its related functions,is mainly introduced.In the related technologies used,the related concepts,classifications and characteristics of intelligent instruments,demand response and energy optimization algorithms are introduced.Secondly,the household microgrid is modeled.The first is the modeling of photovoltaic power generation.According to the characteristics of photovoltaic power generation,the power model of photovoltaic power generation is established,and BP neural network is used to predict photovoltaic power generation.The second is the user load modeling.First,the characteristics of different types of loads are analyzed and the load operation state model is established based on these characteristics.The third is the modeling of energy storage devices.Different energy storage technologies are introduced and their advantages and disadvantages are analyzed with examples.Batteries are selected as energy storage devices of household microgrid to establish the working model of batteries.Then several energy optimization control models are established,such as the minimum electricity consumption cost model,the minimum CO2 emission model and the comfort model.The improved discrete binary particle swarm optimization(DBPSO)algorithm is used as the method to solve the optimization model.Finally,the optimization results of several models are obtained and analyzed by simulation algorithm,which proves that the algorithm has good effect in realizing the minimum user electricity cost model,the minimum CO2 emission model and the user electricity comfort.On the basis of load optimization,the global optimization of battery charging and discharging in energy storage device is carried out to further improve the effect of minimizing electricity cost and CO2 emission.