Research On Multi-objective Scheduling Optimization Of Residential Electricity Consumption Under Stochastic Scenarios

In recent years,with the sustained growth of China’s economy,the residential electricity consumption has rapidly increased and become an important component of the overall electricity consumption structure.The development of smart grid and Internet of Things technologies has provided technical support for the management of residential electricity consumption.In addition,the adoption of real-time electricity pricing will become an important means of electricity resource management.However,real-time electricity pricing is subject to uncertainty due to various factors such as market supply and demand changes,weather conditions,etc.Moreover,user electricity consumption behavior is also subject to randomness due to various factors such as work schedules,lifestyle habits,seasonal changes,etc.The uncertainty of real-time electricity pricing and the randomness of user electricity consumption behavior pose significant challenges to the optimization of residential electricity consumption management.Therefore,this thesis aims to optimize the management of residential electricity consumption through stochastic optimization,in order to improve the quality of residential electricity consumption and reduce the fluctuation of the power grid load.In order to reduce the cost of electricity for residents and improve their comfort level,this thesis proposes a multi-objective scheduling optimization method for household equipment operation.Firstly,a residential electricity consumption model is established,taking into account both the electricity cost and user comfort,and formulated as a multi-objective optimization problem.Then,a multi-objective optimization algorithm is employed to solve the problem and obtain the optimal Pareto front solution set.Different users can choose the optimal solution according to their own needs,to balance electricity costs and comfort level.In addition,considering the randomness of user electricity consumption behavior,this thesis proposes a bottom-up high-accuracy electricity consumption behavior modeling method.This method calculates the probability transition matrix of electricity consumption behavior by constructing a non-homogeneous Markov chain,and simulates user electricity consumption behavior using Monte Carlo method.Experimental results show that the model can accurately simulate user electricity consumption behavior at different time periods.This thesis presents a multi-objective scheduling optimization technique for residential electricity consumption in the face of uncertain real-time electricity prices.The proposed method employs stochastic electricity prices and takes into consideration the unique characteristics of equipment loads.To start,household appliances are classified and modeled,while the use of distributed renewable energy and energy storage devices is incorporated into the home energy management system.Next,a two-stage multi-scenario stochastic optimization approach is utilized to establish a multi-objective scheduling optimization model.This model optimizes residential electricity consumption under stochastic electricity prices.Solving the model using a multi-objective evolutionary algorithm results in obtaining the optimal Pareto front solution set.Finally,experimental simulations are conducted to verify the effectiveness of the proposed method.The results demonstrate that the method reduces user electricity costs while maintaining comfortable electricity consumption.Additionally,the method effectively reduces fluctuations in power grid loads and improves the efficiency of power resource utilization.