Research On Optimization Scheduling Model And Method Of Large-scale Energy Storage And Multi-energy Coupling Syste

In order to achieve the goals of carbon neutrality and carbon emissions peak,it is an important task for China’s energy industry to promote low-carbon and clean transformation of the power system by vigorously developing renewable energy generation technologies.However,as high-proportion renewable energy is integrated into the grid,the volatility of renewable energy output widens the net load peak-valley difference,challenging the flexible regulation capacity of the power system.Although traditional thermal power units can adjust their output to cope with the volatility of renewable energy,but their adjustment capacity is limited,and they produce high CO₂emissions,contradicting the current trend of low-carbon economic transformation.With the scale development of energy storage,coordinated operation of energy storage and various energy sources can greatly improve the stability of the power system operation,compensate for the gap in flexible regulation capacity,and reduce environmental pollution.In addition,the carbon emission trading mechanism is an important mechanism for China to achieve carbon emission reduction goals and sustainable development,which can effectively balance low-carbon and economic considerations.In this context,this paper conducts research on the optimization scheduling of large-scale energy storage and multi-energy coupling systems from the perspectives of policy and technology,and the main research contents are summarized as follows.Firstly,to address the problem of multi-energy system generation modeling,this paper analyzes the working principles and generation characteristics of various energy systems and establishes relevant mathematical models.The analysis and modeling of various energy systems mainly include photovoltaic,wind power,hydro power,and energy storage systems.In particular,the paper focuses on the flexible regulation characteristics of energy storage systems and introduces a new meta-heuristic algorithm,the Moth Search Algorithm（MSA）,and elucidates the principle of the algorithm,laying the foundation for improving the efficiency of solving large-scale energy storage and multi-energy coupling systemsSecondly,to address the optimization scheduling problem of large-scale energy storage and multi-energy coupling systems,this paper starts from the perspective of deep peak-shaving of thermal power units,considers the deep peak-shaving cost of thermal power units and the operating cost of energy storage,describes the uncertainty of wind power output using scenario analysis methods,and uses the Matpower Optimal Scheduling Tools（MOST）toolbox and CPLEX solver for solving.Thus,a joint scheduling model for wind,photovoltaic,thermal,and energy storage with consideration of deep peak-shaving of thermal power is established.Simulation experiments are carried out in a modified IEEE 6-machine 30-bus system to verify the necessity of considering deep peak-shaving of thermal power and the access of large-scale energy storage to the system.Next,in response to the environmental sustainability of the power system,this paper starts from the perspective of motivating the low-carbon output of power units.Using the market’s free carbon emission quotas as the benchmark,the paper constructs a carbon trading cost model based on the carbon emission intensity of different units.Then introducing clean and low-carbon pumped storage units to smooth out load fluctuations,and using a two-stage optimization strategy to improve the efficiency of solving the multi-energy complementary system.Thus,a joint optimization scheduling model for thermal,wind,photovoltaic,and energy storage considering carbon trading is established.Case studies are conducted in a modified IEEE 7-machine and 57-bus system to verify the effectiveness of the proposed model.In addition,the relevant discussions and conclusions can provide some reference value for the government to formulate carbon emission trading schemes.Finally,in order to address the issues of renewable energy consumption and carbon reduction,this paper constructs a hydro-thermal-wind-photovoltaic-storage coupled dispatching system（HTWPS）,one that integrates cascade hydro-wind-photovoltaic-pumped storage（CHWPPS）.In addition,a Greedy strategy,adaptive Crossover operator,and adaptive T-distribution mutation based Moth Search Algorithm（GCTMSA）is proposed to solve the dynamic economic scheduling decision variables of the power system.Matlab software is used to carry out simulation calculations in a modified 6-machine and 30-bus system and a simplified provincial system.The results show that compared with the basic MSA,Artificial Bee Colony,Differential Evolution,Particle Swarm Optimization,Genetic Algorithm,and Biogeography-Based Optimization,GCTMSA has stronger search capability and stability.Furthermore,the impact of CHWPPS and lithium-ion battery energy storage on the system is further analyzed.The related findings and conclusions can provide some reference value for the research and practice of power generation plans in renewable energy-rich regions.