Research On Operation Strategy And Configuration Of Energy Storage System For Smoothing Wind Power Outpu

In the environment of achieving China’s carbon peak by 2030 and carbon neutral target by 2060,the use of renewable clean energy represented by wind power to replace traditional fossil energy has become a current research hotspot in the energy field.However,wind power has a high degree of randomness and volatility,the direct integration of wind power into the grid will cause serious impact on the grid,greatly affecting the security and stability of the grid.With the rapid development of energy storage technology,the use of energy storage for wind power fluctuation smoothing has become the mainstream way.In this context,this paper focuses on the operational strategy and optimal configuration of the energy storage system to smooth out wind power output.Currently,wind power smoothing strategies using either single or Hybrid Energy Storage System(HESS)have a high lag in smoothing power,which leads to an increase in the capacity allocation requirements of the Energy Storage System(ESS).To address this problem,two wind power smoothing strategies are proposed in this paper.One is a wind power leveling strategy based on grey prediction and low-pass filtering using a single energy storage,combining grey prediction and low-pass filtering algorithms,designing a weighted fusion control rule of the two algorithms,and adding a State Of Charge control module,which effectively reduces the lag of leveling power and reduces the capacity allocation requirements of the energy storage system while ensuring that the SOC of the energy storage system is in the best working range.The second is a wind power suppression strategy based on grey prediction and adaptive SG(Savizkg-Golag,SG)algorithm for hybrid energy storage,using the adaptive SG algorithm to process the grey prediction real-time predicted power to obtain the pre-output power of the hybrid energy storage system,using the Variational Mode Decomposition(VMD)algorithm.The VMD algorithm is used to decompose the pre-discharge power so that energy storage systems with different power characteristics can smooth out wind power fluctuations in different frequency bands,which can ensure a good smoothing effect while reducing the smoothing power lag,and greatly reduce the charging and discharging depth and capacity configuration requirements of the energy storage system,increasing the service life of the energy storage system.Finally,the effectiveness of the two strategies is verified by simulation.The current cost of energy storage systems is still relatively expensive,so the capacity allocation of energy storage systems should be considered under the premise of ensuring a good wind power smoothing effect.Therefore,in this paper,based on the Life Cycle Cost(LCC),we construct a net cost-volatility multi-objective optimisation function model,and use the GA-PSO(Genetic Algorithm-Particle Swarm Optimization,GA-PSO)to solve the constructed multi-objective function model.The rationality and effectiveness of this paper are verified through the analysis of various energy storage capacity allocation options,which can provide a strong decision basis for the current capacity allocation options of energy storage systems in the market.