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Towards a local learning (innovation) model of solar photovoltaic deployment

机译：走向太阳能光伏部署的本地学习（创新）模型

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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.
机译：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）的成本动态会受到供应商方面的生产学习和研发驱动的经验曲线影响。然而，尚不清楚的是，系统集成或系统部署的经济性在下游太阳能光伏价值链中靠近用户的地方进行，涉及其他市场参与者。专家们一致认为，太阳能光伏系统的供应商必须定制其灵活的特性，以满足当地独特的用户和应用需求，并在价格/性能基础上进行竞争。因此，缺乏对系统定制经济的驱动力的理解，这不足以使我们对这种可再生能源技术选择的整体经济缺乏理解。我们使用经验曲线框架研究了并网小型光伏系统的非模块BOS成本。对来自IEA的美国光伏统计数据的初步分析似乎表明，正在针对所有类型的并网小型光伏项目中的累积安装进行一种应用类型的学习。随着时间的流逝，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的产品平台概念，并考虑将不同类型的本地小型并网光伏定制项目作为适应不同个性和本地应用需求的标准平台。系统定制的经济学是面向用户的，然后涉及到项目间学习的精炼概念，而不是边做边做的数量驱动学习。我们将项目间的学习形式化为动态范围经济，可以潜在地利用它来管理PV部署的本地和下游方面。这种动态的经济可能会成为能源政策的重点，这将影响安装设计和培训的标准化，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。

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