Security-Constrained Unit Commitment Con-sidering Locational Frequency Stability in Low-Inertia Power Grids
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Abstract (arXiv)
With increasing installation of wind and solar generation, conventional synchronous generators in power systems are gradually displaced resulting in a significant reduction in system inertia. Maintaining system frequency within acceptable ranges becomes more critical for the stability of a power system. In this paper, we first study the impact of inter-area oscillations on the system rate-of-change-of-frequency (RoCoF) security; then, the limitations on locational RoCoFs accounting for G-1 contingency stability are derived. By enforcing these frequency related constraints, a location based RoCoF constrained security constrained unit commitment (LRC-SCUC) model is proposed. Furthermore, an effective piecewise linearization (PWL) technique is employed to formulate a RoCoF linearization problem and linearize the nonlinear function representing the location based RoCoF constraints in SCUC. Simulation results reveal that the inclusion of inertia-related constraints can substantially improve the system stability at the cost of higher operation cost. The results also show that deploying virtual inertia techniques not only reduces the total cost, but also improves the system market efficiency.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2.
4 papers.
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- 2021Machine Learning Assisted Approach for Security-Constrained Unit Commitmentcited by 5
- 2023Linearization of ReLU Activation Function for Neural Network-Embedded Optimization: Optimal Day-Ahead Energy Schedulingcited by 1
- 2023Linearization of ReLU Activation Function for Neural Network-Embedded Optimization: Optimal Day-Ahead Energy Scheduling3 Oct 2023
- 2023Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment2 Jun 2023
- 2022Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations13 Aug 2022
- 2021Machine Learning Assisted Approach for Security-Constrained Unit Commitment17 Nov 2021