Micro-grids’ operations offer local reliability; in the event of faults or low voltage/frequency events on the utility side, micro-grids can disconnect from the main grid and operate autonomously while providing a continued supply of power to local customers. With the ever-increasing penetration of renewable generation, however, operations of micro-grids become increasingly complicated because of the associated fluctuations of voltages. As a result, transformer taps are adjusted frequently, thereby leading to fast degradation of expensive tap-changer transformers. In the islanding mode, the difficulties also come from the drop in voltage and frequency upon disconnecting from the main grid. To appropriately model the above, non-linear AC power flow constraints are necessary. Computationally, the discrete nature of tap-changer operations and the stochasticity caused by renewables add two layers of difficulty on top of a complicated AC-OPF problem. To resolve the above computational difficulties, the main principles of the recently developed “l1-proximal” Surrogate Lagrangian Relaxation are extended. Testing results based on the nine-bus system demonstrate the efficiency of the method to obtain the exact feasible solutions for micro-grid operations, thereby avoiding approximations inherent to existing methods; in particular, fast convergence of the method to feasible solutions is demonstrated. It is also demonstrated that through the optimization, the number of tap changes is drastically reduced, and the method is capable of efficiently handling networks with meshed topologies.
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Ensemble Learning based Linear Power Flow
This paper develops an ensemble learning-based linearization approach for power flow with reactive power modeled, where the polynomial regression (PR) is first used as a basic learner to capture the linear relationships between the bus voltages as the independent variables and the active or reactive power as the dependent variable in rectangular coordinates. Then, gradient boosting (GB) and bagging as ensemble learning methods are introduced to combine all basic learners to boost the model performance. The inferred linear power flow model is applied to solve the well-known optimal power flow (OPF) problem. The simulation results on IEEE standard power systems indicate that (1) ensemble learning methods can significantly improve the efficiency of PR, and GB works better than bagging; (2) as for solving OPF, the data-driven model outperforms the DC model and the SDP relaxation in both accuracy, and computational efficiency.
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- Award ID(s):
- 1808988
- NSF-PAR ID:
- 10299582
- Date Published:
- Journal Name:
- 2020 IEEE Power & Energy Society General Meeting (PESGM)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PESGM41954.2020.9281793
