A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty

TL;DR

This paper introduces a real-time, uncertainty-aware energy dispatch framework for virtual power plants that combines deep learning forecasting, stochastic optimization, and online data augmentation to improve robustness and adaptability under renewable energy uncertainties.

Contribution

It presents a novel hybrid forecast-and-optimize framework with online data augmentation, enabling rapid adaptation to real-time data and uncertainties in energy dispatch.

Findings

Won the CityLearn Challenge 2022

Demonstrated effectiveness in smart building energy management

Enhanced robustness against data drift and environmental perturbations

Abstract

Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation. This issue has driven the necessity of widely exploiting advanced predictive control techniques under uncertainty to ensure long-term economics and decarbonization. In this paper, we propose a real-time uncertainty-aware energy dispatch framework, which is composed of two key elements: (i) A hybrid forecast-and-optimize sequential task, integrating deep learning-based forecasting and stochastic optimization, where these two stages are connected by the uncertainty estimation at multiple temporal resolutions; (ii) An efficient online data augmentation scheme, jointly involving model pre-training and online fine-tuning stages. In this way, the proposed framework is capable to rapidly adapt to the real-time data distribution, as well as to target on uncertainties caused by data drift, model discrepancy and environment perturbations in the control process, and finally to realize an optimal and robust dispatch solution. The proposed framework won the championship in CityLearn Challenge 2022, which provided an influential opportunity to investigate the potential of AI application in the energy domain. In addition, comprehensive experiments are conducted to interpret its effectiveness in the real-life scenario of smart building energy management.