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User side energy storage system is generally discrete distributed energy storage system, which can effectively play a role in the power storage in the traditional power systems. The distributed energy storage system can be analyzed theoretically through the classical single capacitor analysis theory, and the complete solution of constant power discharge of single capacitor can be obtained through system derivation. After calculation and simulation, the charging and discharging characteristic curve of group user side energy storage photovoltaic system can be obtained. For photovoltaic characteristic curves, this article collected typical power system characteristic load curves and found that the photovoltaic output curve presents an inverted "U" shaped distribution, but the electricity consumption curve presents an irregular distribution feature. And based on Hop-field Lagrange neural network, a single photovoltaic cell capacitor is used as a neural network node, and the daily distribution curves of photovoltaic cell are obtained through continuous iterative optimization. The optimization result is to optimize the power load curve from the traditional "M" type characteristic curve to a stable horizontal curve. At the same time, the spontaneous self use ratio of photovoltaic power generation was optimized, and the thermal diagram after installing photovoltaic energy showed the full day self use ratio of photovoltaic energy storage. The research results of this article can provide theoretical and practical basis for the economic scheduling of energy storage on the user side. At the same time, for the first time, the Hop-field Lagrange neural network is matched and optimized with the photovoltaic output power generation curve, and the photovoltaic power generation convergence load curve is effectively obtained through hundreds of iterative calculations.
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