| With the rapid growth of the economy,urbanization and motorization are also speeding,progressively developing a fossil-based energy consumption mode,presenting numerous issues to human society in sustainable development,such as energy scarcity,environmental pollution,and climate change.The quick advancement of energy reform and widespread usage of renewable energy are critical to tackling the problem.As an essential place of energy consumption,the city consumes 75%of the energy and emits more than 80%of the greenhouse gases,among which,residential areas as a basic part of the city,its energy consumption is very worthy of attention,so improving the renewable energy utilization rate of residential areas and reducing carbon dioxide emissions have become an urgent problem to be solved in order to achieve the "double carbon" goal.However,the uncertainty and volatility of photovoltaic and other renewable energy sources and the diversity and uncertainty of residential energy usage provide significant problems to the scheduling of the residential area multi-energy microgrid(RAMEM).As a result,taking multi-source uncertainties into account,this thesis investigates the optimal scheduling model of the RAMEM in order to improve the optimal scheduling method of the RAMEM and improve the utilization efficiency of renewable energy,which is conducive to the green and low-carbon transformation and upgrading of the energy field.and ultimately improve the overall development benefits of economy,energy and environment.This thesis’s primary research contents are as follows:(1)In light of the different load needs of resident users and the problem that multi-objective solution approaches are prone to local optimization and optimum strategy selection,a RAMEM multi-objective optimal scheduling model considering user categorization on the load side is built.Firstly,the social network is utilized to categorize the resident users based on social aggregation and demand response degree among the users in the residential region.The load is classified into four groups based on the operation characteristics of the user load,and the matching mathematical model is constructed.Secondly,the multi-objective optimal scheduling model of service provider profit,user cost,carbon emissions,and utilization rate of renewable energy is built from the standpoint of economy-energy-environment.Then,to address the issue of multi-objective solutions easily falling into a local optimum,the Levy flight is utilized to improve the chicken swarm multi-objective optimization approach.To identify the best approach.we employ the Muirhead average operator to examine it from various perspectives,providing a variety of decision bases for practical implementation.Finally,the model is validated using a multi-scenario example.The results demonstrate that using the multi-energy microgrid system can improve the overall benefits of the economy-energy-environment.User classification allows for more accurate energy scheduling plans.Moreover,the improved chicken swarm intelligence method can jump out of the local optimal and provide a foundation for finding the multi-objective optimal solution.The selection of the best approach offers the foundation for actual deployment.The findings of the further study have provided the groundwork for future research on source-load uncertainty.(2)A RAMEM multi-objective robust optimal scheduling model considering source-load uncertainties,is developed to address the uncertainty and volatility of renewable energy and the uncertainty of user load demand.Firstly,to address the uncertainty of renewable energy,the parameter range of a Gaussian mixture model is supplied using probability box theory,and light intensity is fitted to make the fitting result more realistic.The Normal distribution is used to mimic the load difference of different types of customers in order to address the uncertainty of load demand.Secondly,the uncertainty model improves the confidence gap decision theory.Furthermore,a RAMEM multi-objective robust scheduling model based on probability box theory improved the multi-scenario confidence gap decision theory is built.Finally,the model is validated using a multi-scenario example.The results reveal that source-load uncertainty will impact the system’s scheduling approach,improving the overall advantages of economy-energy-environment at the price of inhabitants’ satisfaction with energy consumption.Hence,this chapter’s research results provide a basis for the follow-up research of the vehicle to grid flexibly alleviates source-load uncertainties.(3)A two-layer multi-objective optimal scheduling model of RAMEM considering source-load-vehicle uncertainties is constructed to address the scheduling problem caused by the uncertainties of source-load in residential areas,considering that vehicle to grid can alleviate the impact of the uncertainty of source-load.Firstly,a descriptive model is built for the uncertain aspects of electric vehicle entry,stay,exit.and charging conditions.To avoid the problem of dimensional disaster in multi-scene sampling,the simultaneous backward reduction technique based on the improvement of quantity-contour distance is used to reduce the sampling scene,and the division rule of electric vehicles cluster is constructed based on the characteristics of the charging and discharging period of electric vehicles.Secondly,based on the improved S-shaped utility function,an electric vehicle optimal scheduling model considering electric vehicle owners’ risk attitudes is built to represent the influence of electric vehicle owners with varying risk attitudes on the multi-energy microgrid system scheduling.Then,a two-layer multi-objective optimal scheduling model that considers source-load-vehicle uncertainties is built.Finally,the model is validated using a multi-scenario example.The results show that the carbon emission reduction benefits of including electric vehicles in the multi-energy microgrid system scheduling are significant when multiple uncertainties are considered.The charge-discharge characteristics of electric vehicles improve residents’ satisfaction with energy use.However,in the scarcity of a renewable energy season,the involvement of electric vehicles in dispatch would not provide significant environmental advantages.It will increase the multi-energy microgrid system’s carbon emissions.As a consequence,the findings of this chapter give a research foundation and proposals for a future study on the decrease of source-load-vehicle uncertainties through group collaboration in residential areas.(4)A three-layer multi-objective cooperative optimal scheduling model for residential area groups considering source-load-vehicle-storage uncertainties is constructed in light of the different allocation of renewable energy in different residential areas,to improve the utilization rate of renewable energy further reduce the use of traditional energy and weaken the impact of source-load-vehicle-storage uncertainties.Firstly,the prospect of collaboration between residential area groups,the shared energy storage power station and the electric vehicle charging station is examined using the service,operation,and profit models.Secondly,profit models for the residential area groups,shared energy storage power station,and electric vehicle charging station are built.The carbon emission model and the renewable energy utilization model under the cooperation of residential area groups are constructed.Finally,the model is validated using a multi-scenario example.The results reveal that residential area groups collaboration enhances the total economy-energy-environment benefit,significantly raises the utilization rate of renewable energy in residential area groups,and decreases carbon emissions.Furthermore,the use of shared energy storage stations and electric vehicle charging stations dramatically improves the use of renewable energy,thereby reducing carbon emissions.Meanwhile,to achieve the goal of long-term cooperation,the capacity of shared energy storage stations must be configured according to the actual application scenario.All of the models discussed above give a theoretical foundation for practical implementation. |
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