Towards a local learning (innovation) model of solar photovoltaic deployment
Towards a local learning (innovation) model of solar photovoltaic deployment
复制标题
DOI：
10.1016/j.enpol.2007.09.015
复制
发表时间：
2008-02
期刊：
影响因子：
9
通讯作者：
K. Shum;C. Watanabe
中科院分区：
文献类型：
--
作者：
K. Shum;C. Watanabe
众所周知，在太阳能光伏（PV）系统的部署中，主要系统组件（如太阳能电池/组件）的成本动态受到由供应商侧的生产学习和研发驱动的经验曲线效应的影响。然而，不太清楚的是，在下游太阳能光伏价值链中，在用户附近进行的系统集成或系统部署的经济性，涉及其他市场参与者。专家们一致认为，太阳能光电系统的供应商必须根据当地用户和应用的独特要求，定制其灵活的特点，并在价格/性能的基础上进行竞争。因此，缺乏对系统定制经济学驱动因素的理解，是我们对这一可再生能源技术选择的总体经济学理解的一个缺陷。利用经验曲线框架研究了小型光伏并网发电系统的非组件BOS成本。国际能源机构对美国光伏统计数据的初步分析似乎表明，在所有类型的并网小型光伏项目的累积安装方面，正在发生一种应用类型的学习。随着时间的推移，这种学习的有效性也在提高。一个新颖的方面是对这种经验曲线效应或学习模式的解释。我们借鉴了工业管理文献中产品平台的概念，并考虑不同类型的本地小规模并网光伏定制项目，以适应不同的特质和本地应用需求的标准平台。面向用户的系统定制的经济学涉及项目间学习的精炼概念，而不是量驱动的边做边学。我们将这种项目间学习正式化为动态范围经济，这可能被用来管理光伏部署的本地和下游方面。这种动态经济可作为能源政策的重点，对安装设计和培训的标准化产生影响，促进不同集成项目之间的知识重用，并使项目间的学习成为可能。
It is by now familiar that in the deployment of solar photovoltaic (PV) systems, the cost dynamics of major system component like solar cell/module is subjected to experience curve effects driven by production learning and research and development at the supplier side. What is less clear, however, is the economics of system integration or system deployment that takes place locally close to the user, involving other market players, in the downstream solar PV value chain. Experts have agreed that suppliers of solar PV system must customize their flexible characteristics to address local unique users’ and applications requirements and compete on price/performance basis. A lack of understanding of the drivers of the economics of system customization therefore is a deficiency in our understanding of the overall economics of this renewable energy technology option. We studied the non-module BOS cost for grid-connected small PV system using the experience curve framework. Preliminary analysis of PV statistics of the US from IEA seems to suggest that learning in one application type is taking place with respect to the cumulative installation among all types of grid-connected small PV projects. The effectiveness of this learning is also improving over time. A novel aspect is the interpretation of such experience curve effect or learning pattern. We draw upon the notion of product platform in the industrial management literature and consider different types of local small-scale grid-tied PV customization projects as adapting a standard platform to different idiosyncratic and local application requirements. Economics of system customization, which is user-oriented, involves then a refined notion of inter-projects learning, rather than volume-driven learning by doing. We formalized such inter-projects learning as a dynamic economy of scope, which can potentially be leveraged to manage the local and downstream aspect of PV deployment. This dynamic economy may serve as a focus of energy policy having implications on standardization of design and training for installation, facilitating knowledge reuse among different integration projects and enabling inter-projects learning.