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Research On Capacity Configuration Optimization Of Wind Power Heat Pump Hybrid Energy Storage System Based On Particle Swarm Optimization

Posted on:2022-10-25Degree:MasterType:Thesis
Country:ChinaCandidate:L A ZhuFull Text:PDF
GTID:2492306575977889Subject:Civil engineering
Abstract/Summary:PDF Full Text Request
With the rapid development of wind power,wind power accounts for an increasing proportion of the power grid.However,due to the volatility of wind power resources and the geographic mismatch between grid construction and wind energy resources,wind energy resources are simply absorbed through the grid,and wind power is difficult to connect to the grid and the power quality of the grid is poor,which severely restricts the development of wind power and the promotion of renewable energy.To curb the phenomenon of wind abandonment in the northern region,and expand the way to consume wind power resources.This paper takes a residential villa community in a certain area as an example,a heat pump hybrid energy storage system is designed for independent heating by wind power.In this paper,Reserrch results are conduct as followed:(1)Based on the De ST software technology,the building energy consumption model of the villa community in Shenyang was established,and the hourly building load curve for the whole year was obtained;According to the wind speed distribution in Shenyang area,the wind power generation model of wind turbines was established,and the wind power curve was obtained day by day and month by month;On this basis,the shortcomings of the electric energy storage and heat storage system are analyzed,a wind power heat pump heating scheme with hybrid energy storage is designed,and the non-energy storage equipment such as wind turbines,water source heat pumps,inverters and other non-energy storage equipment are initially selected.(2)Aiming at the established wind-power heat pump hybrid energy storage heating system,three different heating operation modes are analyzed in this paper.Numerical models of major equipment such as water source heat pumps,lithium batteries,and solid heat storage units have been established respectively.Finally,a capacity configuration cost and operating cost model for electric energy storage capacity configuration and hybrid energy storage capacity configuration is established.(3)This paper takes the minimum energy storage capacity allocation investment cost and operating cost as the objective function to establish a hybrid energy storage capacity allocation optimization model.Based on the particle swarm algorithm with capacity configuration,operating cost and power system reliability as constraints,the optimal capacity ratio of power storage capacity and heat storage capacity configuration is 0.85 when the system is configured with a hybrid energy storage system.According to the heat pump heating coefficient influence curve,the capacity configuration of the system can be optimized when the coefficient of performance(COP)of the water source heat pump heating performance is above 5:4.The comparison with the electric energy storage configuration system verifies the advantages of the hybrid energy storage system in wind power heat pump heating.(4)The system operation model is established based on the TRNSYS platform,and the simulation model of the wind power heat pump hybrid energy storage system is established.Through simulation calculation and analysis,the overall operation effect of the system is obtained.According to the wind speed curve and heat supply data of a typical day,the hourly operation curves of lithium batteries,heat storage units,and heat pump units are respectively calculated,and the operation effect of the wind power heat pump hybrid energy storage system is verified through the water tank temperature curve and the indoor temperature curve.The results show that even in periods of low wind power and high heating load,the energy storage configuration can still meet the heating demand.
Keywords/Search Tags:Wind power heat pump, Hybrid energy storage, Capacity configuration optimization, Particle swarm optimization, Simulation analysis
PDF Full Text Request
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