# Improving Sequential Market Coordination via Value-oriented Renewable Energy Forecasting

arXiv: https://arxiv.org/abs/2405.09004 · HTML: https://mlsanity.com/paper/2405.09004/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 15 May 2024.

## Builds on (most cited first, 8 of 8)

- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [From Predictive to Prescriptive Analytics](https://mlsanity.com/paper/1402.5481/) (2014)
- [MIPaaL: Mixed Integer Program as a Layer](https://mlsanity.com/paper/1907.05912/) (2019)
- [Toward Value-oriented Renewable Energy Forecasting: An Iterative Learning Approach](https://mlsanity.com/paper/2309.00803/) (2023)
- [Setting Reserve Requirements to Approximate the Efficiency of the Stochastic Dispatch](https://mlsanity.com/paper/1805.04712/) (2018)
- [Prescribing net demand for two-stage electricity generation scheduling](https://mlsanity.com/paper/2108.01003/) (2021)
- [A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties](https://mlsanity.com/paper/1510.08335/) (2015)
- [Uncertainty-Informed Renewable Energy Scheduling: A Scalable Bilevel Framework](https://mlsanity.com/paper/2312.03868/) (2023)

## Abstract (arXiv)

Large penetration of renewable energy sources (RESs) brings huge uncertainty into the electricity markets. The current deterministic clearing approach in the day-ahead (DA) market, where RESs participate based on expected production, has been criticized for causing a lack of coordination between the DA and real-time (RT) markets, leading to high overall operating costs. Previous works indicate that improving day-ahead RES entering quantities can significantly mitigate the drawbacks of deterministic clearing. In this work, we propose using a trained forecasting model, referred to as value-oriented forecasting, to determine RES Improved Entering Quantities (RIEQ) more efficiently during the operational phase. Unlike traditional models that minimize statistical forecasting errors, our approach trains model parameters to minimize the expected overall operating costs across both DA and RT markets. We derive the exact form of the loss function used for training, which becomes piecewise linear when market clearing is modeled by linear programs. Additionally, we provide the analytical gradient of the loss function with respect to the forecast, enabling an efficient training strategy. Numerical studies demonstrate that our forecasts significantly reduce overall operating costs for deterministic market clearing compared to conventional forecasts based on expected RES production.