This paper proposes a general and practical approach to estimate the economic benefits of optimally controlling deferrable loads in a Distribution System Operator's (DSO) grid, without relying on historical observations. We achieve this by learning the simulated response of flexible loads to random control signals, using a non-parametric global forecasting model. An optimal control policy is found by including the latter in an optimization problem. We apply this method to electric water heaters and heat pumps operated through ripple control and show how flexibility, including rebound effects, can be characterized and controlled. Finally, we show that the forecaster's accuracy is sufficient to completely bypass the simulations and directly use the forecaster to estimate the economic benefit of flexibility control.
Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia
School of Computing and Information Technology（计算与信息科技学院）
Optimal Two-Tier Forecasting Power Generation Model in Smart Grids
School of Computing and Information Sciences, Florida International University（佛罗里达国际大学计算与信息科学学院）；Department of Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电气与计算机工程系）
External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting
Department of Electrical, Computer and Energy Engineering, Arizona State University（电气、计算机与能源工程系，亚利桑那州立大学）；Laboratory for Information and Decision Systems, Massachusetts Institute of Technology（信息与决策系统实验室，麻省理工学院）
