Optimal Energy Scheduling in Microgrids with Photovoltaic (PV) Generation and Energy Storage Systems

该奖项支持基础研究，以解决对公共健康和福利的两个主要威胁，即全球变暖和空气污染，使太阳能光伏能源的最佳利用。该项目旨在创建集成在微电网中的储能系统的最佳调度，这是太阳能解决方案的关键部分，并通过解决储能系统的退化过程以及太阳能输出和负载电力需求的不确定性来克服太阳能的两个关键限制，即不稳定性和不确定性。研究结果有助于充分利用储能系统，并最大限度地降低其寿命成本，使太阳能成为可再生能源的重要来源。此外，该项目将通过为不同青年开发跨学科课程和各种科学活动，吸引、激励和培训美国学生，为他们进入能源、电力和控制等跨学科领域的劳动力做好准备。该项目解决了电网应用中储能系统的基本权衡问题，其中涉及需要更频繁的充电/放电循环和相应退化过程的光伏发电的集成。该研究将为以下未解决的问题找到答案，这些问题对于微电网的最佳能量调度至关重要：（1）储能系统的多种退化机制如何耦合，以及电池容量衰减最终如何受到负载和环境条件的影响;（2）太阳辐射、市场价格和负载的不确定性对电池负载曲线的影响，以及如何解决微电网优化调度中的不确定性。具体而言，该项目有以下四个目标：（1）获得对电池退化机制及其耦合方式的基本理解，并将这些知识整合到高保真电池模型中;（2）创建一个低阶电池模型，可以快速生成预测结果而不会失去准确性;（3）了解微电网中的不确定性现象，并创建一个模型来整合所有现象;（4）创建一个模型来创建一个模型。以及（4）通过考虑电池退化和微电网的不确定性来开发优化调度的算法。拟议的研究将发现微电网的不确定性如何影响单个组件，包括储能退化;预计结果将构成电力工程界的关键知识。此外，本项目将开发一种用于近实时优化调度的随机优化技术。预计这里开发的一般框架将适用于电力工程以外的应用。

This award supports fundamental research to address two major threats to public health and welfare, namely global warming and air pollution, by enabling the optimal use of solar photovoltaic energy. This project seeks to create optimal scheduling of energy storage systems incorporated in microgrids, which is a key part of a solar energy solution, and to overcome two critical limits of solar energy, intermittency and uncertainty, by addressing the degradation process of energy storage systems and the uncertainty in solar output and power demand at loads. The research results can help to fully utilize energy storage systems and to minimize their lifetime cost, enabling solar energy to become a vital source of renewable energy. Additionally, the project will engage, motivate, and train US students preparing them for the workforce in the interdisciplinary area of energy, power, and control through the development of interdisciplinary curricula and various science activities for diverse youth. This project addresses a fundamental trade-off in energy storage systems for power grid applications, which involves the integration of PV generation requiring more frequent charge/discharge cycles and the corresponding degradation process. The research will find answers to the following unsolved questions that are essential for optimal energy scheduling of a microgrid: (1) how multiple degradation mechanisms of energy storage systems are coupled, and how battery capacity fade is eventually affected by loading and environmental conditions; (2) what is the impact of uncertainty of solar radiation, market prices, and load on battery loading profiles, and how can uncertainty be addressed in optimal scheduling of microgrids. Specifically, the project has the following four objectives: (1) gain a fundamental understanding of battery degradation mechanisms and how they couple, and integrate that knowledge into a high-fidelity battery model; (2) create a low-order battery model that can generate the predicted results quickly without losing accuracy; (3) understand uncertainty phenomena in microgrids, and create a model to incorporate all of the phenomena; and (4) develop algorithms for optimal scheduling by considering battery degradation and microgrid uncertainty. The proposed research will discover how the uncertain nature of a microgrid can affect individual components, including energy storage degradation; the results are expected to constitute critical knowledge for the power engineering community. In addition, this project will develop a stochastic optimization technique for near-real-time optimal scheduling. It is expected that the general framework developed here will be adaptable for applications beyond power engineering.