分布式光伏资产残值预测及融资租赁定价优化研究

Research on Residual Value Prediction and Financial Leasing Pricing Optimization of Distributed Photovoltaic Assets

摘要

伴随“双碳”目标的深入实施，分布式光伏发电项目规模逐渐增大。此类项目花费多，成本高，资金压力大，融资租赁这种方式，成了解决资金问题的核心办法。但随之而来的是光伏设备的残值不好计算这一问题。由于设备用久会自然损耗，技术还在不停更新换代，市场行情也忽高忽低，受此类诸多因素影响，精准预测设备残值变得十分困难。因此，本文针对分布式光伏设备的特点，将“灰色预测”和“BP神经网络”两种方法进行结合，设计残值预测模型。建模时考虑到设备本身的损耗、市场上的供需情况、还有国家的政策导向三个关键因素，基于此设计了一套融资租赁定价方案。经过测试验证，优化后的残值预测误差能控制在3.2%以内，而且出租方和承租方的综合收益都能提升8%到12%。

Abstract

With the deepening implementation of the “dual carbon” goals, the scale of distributed photovoltaic power generation projects has been gradually expanding. These projects involve high costs and significant financial pressure, making financial leasing a key solution for capital management. However, this approach presents challenges in accurately calculating the residual value of photovoltaic equipment. Due to natural wear and tear over time, continuous technological advancements, and volatile market conditions, precise prediction of equipment residual value becomes extremely difficult. This paper addresses the characteristics of distributed photovoltaic equipment by integrating “gray prediction” and “BP neural network” methods to develop a residual value forecasting model. The modeling process considers three critical factors: equipment wear, market supply-demand dynamics, and national policy guidance. Based on this framework, a financial leasing pricing scheme is designed. Test results demonstrate that the optimized residual value prediction error can be controlled within 3.2%, while both lessors and lessees achieve an 8% to 12% increase in comprehensive returns.

关键词

分布式光伏 / 残值预测 / 融资租赁定价 / 优化模型

Key words

distributed photovoltaic / residual value prediction / financial leasing pricing / optimization model

参考文献

[1] 薛雪, 张晋铭, 傅海军, 等. 高比例光沼新能源下乡村配电网运行评价仿真实验[J/OL]. 实验技术与管理, 1-12[2025-11-28]. https://link.cnki.net/urlid/11.2034.T.20251127.1523.002.

[2] 孙卓. 基于动态最优潮流的分布式光伏接入配电网储能优化配置研究[J]. 电气技术与经济, 2025,(11): 41-43.

[3] 马啸. 油田计量间屋顶分布式光伏电站的设计与经济效益分析[J]. 石油石化节能与计量, 2025, 15(11): 76-81.