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

arXiv: https://arxiv.org/abs/2309.08642 · HTML: https://mlsanity.com/paper/2309.08642/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Data-Driven Model Predictive Control with Stability and Robustness Guarantees](https://mlsanity.com/paper/1906.04679/) (2019)
- [Stochastic data-driven model predictive control using Gaussian processes](https://mlsanity.com/paper/1908.01786/) (2019)
- [Adaptive MPC for Iterative Tasks](https://mlsanity.com/paper/1804.09831/) (2018)
- [On Extended Long Short-term Memory and Dependent Bidirectional Recurrent Neural Network](https://mlsanity.com/paper/1803.01686/) (2018)
- [Receding-horizon Stochastic Model Predictive Control with Hard Input Constraints and Joint State Chance Constraints](https://mlsanity.com/paper/1506.08471/) (2015)
- [A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearn](https://mlsanity.com/paper/2009.10562/) (2020)
- [A data-driven robust optimization approach to scenario-based stochastic model predictive control](https://mlsanity.com/paper/1807.05146/) (2018)
- [Stochastic Model Predictive Control with Discounted Probabilistic Constraints](https://mlsanity.com/paper/1807.07465/) (2018)

## Abstract (arXiv)

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.