# Operating Envelopes under Probabilistic Electricity Demand and Solar Generation Forecasts

arXiv: https://arxiv.org/abs/2207.09818 · HTML: https://mlsanity.com/paper/2207.09818/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [An Unsupervised Deep Learning Approach for Scenario Forecasts](https://mlsanity.com/paper/1711.02247/) (2017)
- [Chance Constraints for Improving the Security of AC Optimal Power Flow](https://mlsanity.com/paper/1803.08754/) (2018)

## Abstract (arXiv)

The increasing penetration of distributed energy resources in low-voltage networks is turning end-users from consumers to prosumers. However, the incomplete smart meter rollout and paucity of smart meter data due to the regulatory separation between retail and network service provision make active distribution network management difficult. Furthermore, distribution network operators oftentimes do not have access to real-time smart meter data, which creates an additional challenge. For the lack of better solutions, they use blanket rooftop solar export limits, leading to suboptimal outcomes. To address this, we designed a conditional generative adversarial network (CGAN)-based model to forecast household solar generation and electricity demand, which serves as an input to chance-constrained optimal power flow used to compute fair operating envelopes under uncertainty.