Towards Improving Unit Commitment Economics: An Add-On Tailor for Renewable Energy and Reserve Predictions

Generally, day-ahead unit commitment (UC) is conducted in a predict-then-optimize process: it starts by predicting the renewable energy source (RES) availability and system reserve requirements; given the predictions, the UC model is then optimized to determine the economic operation plans. In fact,...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on sustainable energy 2024-10, Vol.15 (4), p.2547-2566
Main Authors: Chen, Xianbang, Liu, Yikui, Wu, Lei
Format: Article
Language:English
Subjects:
Bi-level mixed-integer programming
Costs
Economics
Integer programming
Mathematical models
Mixed integer
Mixed integer linear programming
Neural networks
Operating costs
Optimization
Power system economics
Predictions
Predictive models
prescriptive analytics
renewable energy
Renewable energy sources
Renewable resources
Reserve requirements
Training
Unit commitment
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Generally, day-ahead unit commitment (UC) is conducted in a predict-then-optimize process: it starts by predicting the renewable energy source (RES) availability and system reserve requirements; given the predictions, the UC model is then optimized to determine the economic operation plans. In fact, predictions within the process are raw . In other words, if the predictions are further tailored to assist UC in making the economic operation plans against realizations of the RES and reserve requirements, UC economics will benefit significantly. To this end, this paper presents a cost-oriented tailor of RES-and-reserve predictions for UC, deployed as an add-on to the predict-then-optimize process. The RES-and-reserve tailor is trained by solving a bi-level mixed-integer programming model: the upper level trains the tailor based on its induced operating cost; the lower level, given tailored predictions, mimics the system operation process and feeds the induced operating cost back to the upper level; finally, the upper level evaluates the training quality according to the fed-back cost. Through this training, the tailor learns to customize the raw predictions into cost-oriented predictions. Moreover, the tailor can be embedded into the existing predict-then-optimize process as an add-on, improving the UC economics. Lastly, the presented method is compared to traditional, binary-relaxing, neural network-based, stochastic, and robust methods.
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2024.3426337