GEP considering unit commitment constraints and data-driven robust optimization under uncertainties

What is it about?

This article presents a Generation Expansion Planning (GEP) methodology considering the impact of unit commitment constraints under uncertainties of both Renewable Energy Sources (RES) and forecasted load. Spatial and temporal data-driven robust optimization under the correlation of RES uncertainty is analyzed. As the intermittency nature of RES complicates dynamic characteristics of the net load profile and increases the need for operational flexibility, a robust GEP model is proposed considering the unit commitment constraints and data-driven robust optimization in addition to the correlation among different RES uncertainties. Long- and short-term uncertainty is represented and incorporated into the proposed GEP model. The GEP is solved through three stages. In the first stage, the GEP model focuses on the RES generation planning considering the long-term uncertainties. The impact of unit commitment constraints under short-term uncertainty is considered in the second stage. An appropriate Energy Storage System (ESS) is studied in the third stage. The results have demonstrated that: (a) considering the data-driven robust optimization under correlation of RES uncertainty reduces the conservativeness and (b) neglecting the impact of unit commitment constraints under uncertainties within the GEP models leads to untrustworthy results. A battery storage system is used within the proposed model to enhance system flexibility.

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Photo by Alex Eckermann on Unsplash

Photo by Alex Eckermann on Unsplash

Why is it important?

The paper presents a novel generation expansion planning methodology taking into consideration the impact of unit commitment constraints under uncertainties of both renewable energy sources and forecasted load.

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