Collaborative Research: Increasing Solar Panel Adoption by Modeling the Interrelated Impacts of Design Decisions, Industry Incentives, Public Policies, and Market Response

太阳能光伏（PV）板将来自太阳的能量转化为电能。它们在发电过程中排放零污染物，从而在取代燃煤发电厂的电力时减少颗粒物和温室气体排放。此外，由于分散的电力生产，光伏电池板的使用提高了美国的能源独立性和可靠性，并减轻了压力过大和老化的燃煤电厂的负担。虽然光伏技术越来越高效和实惠，但目前仅占美国发电量的1.13%。该奖项使用数学模型和消费者调查研究的新颖组合，以发现将增加美国家庭采用PV的设计见解和政策措施。该研究是跨学科的，从工程设计，太阳能技术研究，市场营销，经济学，公共政策，心理学和应用数学绘图。本研究的目的是通过创建一个由制造商，政策制定者，安装商和消费者做出的动态和相互关联的决策的创新模型来调查和改善美国住宅光伏电池板的采用。该模型将包括使用博弈论方法表示的竞争提供商（安装商）和消费者（房主）的复杂决策。消费者和安装者对市场条件的反应将使用在线调查分析进行估计，从现实世界的数据提供偏好/决策模型。消费者偏好将被建模为对政策活动的响应。例如，教育和广告将影响消费者是否会购买光伏面板或他们将购买哪些面板。这是对类似建模方法的重大改进，这些方法假设消费者偏好停滞不前。将这种方法与消费者购买的创新漏斗决策模型相结合，将有助于探索教育活动等非金融政策活动的影响。现有的光伏板工程和成本模型是制造商定价和设计决策的基础。将根据现有政策措施以及基于模型模拟和假设检验结果的新备选办法建立政策模型。基于网络的教学模式，这个大系统的相互关联的利益相关者和决策将帮助学生了解他们的工程教育和未来的职业生涯作为一个更大的系统，可以造福社会的一部分，例如，通过减少污染和增加美国能源独立。

Solar Photovoltaic (PV) panels convert energy from the sun into electricity. They emit zero pollutants during electricity generation and thus decrease particulate and greenhouse gas emissions when they replace electricity from coal-fired power plants. Additionally, PV-panel use increases U.S. energy independence and reliability due to decentralized electricity production, and decreases the burden on overstressed and aging coal-fired power plants. While PV technology is increasingly efficient and affordable, it currently accounts for only 1.13 percent of U.S. electricity generation. This award uses a novel combination of mathematical models and consumer survey research to discover design insights and policy measures that will increase adoption of PVs by U.S. households. The research is interdisciplinary, drawing from engineering design, solar technology research, marketing, economics, public policy, psychology, and applied mathematics. The objective of this research is to investigate and improve U.S. residential PV panel adoption by creating an innovative model of dynamic and interrelated decisions made by manufacturers, policymakers, installers, and consumers. The model will include complex decisions for both competing providers (installers) and consumers (homeowners) represented using a game-theoretic approach. Consumer and installer responses to market conditions will be estimated using online survey analysis, providing preference/decision models from real-world data. Consumer preferences will be modeled as responsive to policy campaigns. For example, education and advertising will influence whether consumers will buy PV panels or which panels they will buy. This is a significant improvement over similar modeling approaches, which assume stagnant consumer preferences. Combining this approach with an innovative funnel decision model for consumer purchases will allow for the exploration of the impact of non-financial policy campaigns, such as educational campaigns. Existing engineering and cost models of PV panels act as the backbone for manufacturer pricing and design decisions. Policy models will be built from existing policy measures as well as new options based on results of model simulations and hypothesis testing. A web-based teaching model of this large system of interrelated stakeholders and decisions will help students to understand their engineering education and future career as part of a larger system that can benefit society, for example, by decreasing pollution and increasing U.S. energy independence.