Optimizing Risk in a Gauss-Markov Process - Energy Storage Strategies for Renewable Integration

优化高斯-马尔可夫过程中的风险 - 可再生能源并网的储能策略

基本信息

批准号：
1933243
负责人：
Matthew Peet
金额：
$ 30.46万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933243&HistoricalAwards=false
关键词：
Optimizing Risk Gauss Markov Process

项目摘要

All power derived from renewable energy resources such as wind and solar depends on the weather. Changes in power from renewables can be predictable, e.g. due to the rising and setting of the sun - or can be hard to predict, e.g. due to clouds blocking out the sun. Consumer demand for power, likewise, has always been partly predictable and partly random. The amount of power a utility must generate is the consumer demand minus the renewable power generated. As power from renewables increases, the unpredictable part of this required generation also increases. Utilities have begun to respond to this increased risk by investing in battery storage and quick-start generators. However, there is currently no way to quantify the risk caused by renewable energy resources meaning utilities don't know how many batteries to buy or how to use them efficiently. For example, a utility might buy 1 GWh of battery storage. However, the 1 GWh is useless if it is discharged too early in the day and is unavailable when a storm blows in and reduces solar production unexpectedly. The goal of this project, then, is to develop accurate weather-based models to forecast the probability that renewable generators will experience large drops in production. The project then proposes algorithms to use these risk models to determine optimal battery charge-discharge programs for both consumers and utilities. In addition, the project uses these models to determine optimal electricity pricing structures including demand charges for a regulated utility. The project includes several educational and outreach activities. An established program at Arizona State University called Science is Fun will be utilized for outreach to K-12 students.This project has three areas of focus. The first focus is to develop useful Gauss-Markov (G-M) models of solar generation. These models are based on datasets provided by Wunderground and Arizona utility SRP and condition on pressure changes, humidity and temperature. Machine Learning is then used to map daily forecast data to the model which is most effective at reducing cost. The second focus is to solve stochastic Dynamic Programming (DP) problems with non-separable objective functions. Specifically, minimizing the expected maximum of a G-M process and computing the probability distribution of the maximum of a G-M process over a finite time-interval. Such stochastic DPs are reformulated using the recently proposed Naturally Forward Separable (NFS) framework which allows them to be solved recursively using the Bellman equation with minimal computation time. The third focus is to apply the NFS DP framework to newly developed models of solar generation and produce algorithms for optimal battery programming and associated spinning reserve. These algorithms are then used to propose a model for optimal utility pricing of consumption and demand charges based on principles of feedback and not based on marginal pricing. The algorithms are also used to evaluate the impact on risk and cost of utility-owned solar vs rooftop solar.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

从风和太阳能等可再生能源资源中得出的所有功率都取决于天气。可再生能源的功率变化可以预测，例如由于太阳的上升和落下 - 或很难预测，例如由于云阻止了太阳。同样，消费者对权力的需求一直是可以预测的，部分是随机的。公用事业必须产生的功率是消费者的需求减去可再生能源产生的功率。随着可再生能源的力量的增加，这一必需一代的不可预测的部分也会增加。公用事业已经开始通过投资电池存储和快速启动发电机来应对这种增加的风险。但是，目前无法量化可再生能源资源引起的风险，这意味着公用事业不知道要购买多少电池或如何有效使用电池。例如，公用事业公司可能会购买1 GWH的电池存储。但是，如果1 GWH在一天早些时候放电，那么当暴风雨爆炸并意外地减少太阳能生产时，它是没有用的。因此，该项目的目的是开发基于天气的准确模型，以预测可再生发电机将经历大量生产下降的可能性。然后，该项目建议使用这些风险模型来确定用于消费者和公用事业的最佳电池充电计划。此外，该项目使用这些模型来确定最佳的电力定价结构，包括受监管公用事业的需求费用。该项目包括几项教育和外展活动。亚利桑那州立大学的一项既定计划，称为科学娱乐，将用于向K-12学生推广。该项目具有三个重点。第一个重点是开发有用的高斯 - 马尔科夫（G-M）太阳能生成模型。这些模型基于Wunderground和Arizona Utility SRP提供的数据集以及压力变化，湿度和温度的条件。然后，机器学习用于将每日预测数据映射到最有效的降低成本的模型。第二个重点是解决具有不可分割的目标函数的随机动态编程（DP）问题。具体而言，将G-M过程的预期最大值最小化，并计算有限的时间间隔内G-M过程的最大值的概率分布。使用最近提出的自然可分离（NFS）框架对这种随机DP进行重新重新制定，该框架可以使用最小的计算时间使用Bellman方程来递归解决。第三个重点是将NFS DP框架应用于新开发的太阳能生成模型，并生产用于最佳电池编程和相关旋转储备的算法。然后，这些算法用于提出一个模型，以根据反馈原理而不是基于边际定价的原理对消费的最佳定价和需求费用。该算法还用于评估对公用事业拥有的太阳能与屋顶太阳能的风险和成本的影响。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估标准来评估值得支持的。