Controlling Smart Inverters Using Proxies: A Chance-Constrained DNN-Based Approach

@article{Gupta2022ControllingSI,
  title={Controlling Smart Inverters Using Proxies: A Chance-Constrained DNN-Based Approach},
  author={Sarthak Gupta and Vassilis Kekatos and Ming Jin},
  journal={IEEE Transactions on Smart Grid},
  year={2022},
  volume={13},
  pages={1310-1321}
}
Coordinating inverters at scale under uncertainty is the desideratum for integrating renewables in distribution grids. Unless load demands and solar generation are telemetered frequently, controlling inverters given approximate grid conditions or proxies thereof becomes a key specification. Although deep neural networks (DNNs) can learn optimal inverter schedules, guaranteeing feasibility is largely elusive. Rather than training DNNs to imitate already computed optimal power flow (OPF… 
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References

SHOWING 1-10 OF 32 REFERENCES
Data-Driven Optimal Power Flow: A Physics-Informed Machine Learning Approach
TLDR
A data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework is developed that decomposes the OPF model features into three stages to reduce the learning complexity and correct the learning bias.
Machine learning for communicationcognizant smart inverter control
  • Proc. IEEE Intl. Conf. on Smart Grid Commun., Tempe, AZ, Nov. 2020, pp. 1–6.
  • 2020
Safe Off-Policy Deep Reinforcement Learning Algorithm for Volt-VAR Control in Power Distribution Systems
TLDR
This work proposes a safe off-policy deep reinforcement learning algorithm to solve Volt-VAR control problems in a model-free manner, and outperforms the existing reinforcement learning algorithms and conventional optimization-based approaches on a large feeder.
Learning Optimal Resource Allocations in Wireless Systems
TLDR
DNNs are trained here with a model-free primal-dual method that simultaneously learns a DNN parameterization of the resource allocation policy and optimizes the primal and dual variables.
Combining Deep Learning and Optimization for Preventive Security-Constrained DC Optimal Power Flow
TLDR
A novel approach that combines deep learning and robust optimization techniques is proposed that predicts directly the SCOPF implementable solution and results in significant time reduction for obtaining feasible solutions with an optimality gap below 0.1%.
Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks
TLDR
Numerical tests on three benchmark power systems corroborate the advanced generalization and constraint satisfaction capabilities for the OPF solutions predicted by an SI-DNN over a conventionally trained DNN, especially in low-data setups.
Learning to Optimize Power Distribution Grids using Sensitivity-Informed Deep Neural Networks
TLDR
Numerical tests showcase that sensitivity-informed deep learning can enhance prediction accuracy in terms of mean square error (MSE) by 2-3 orders of magnitude at minimal computational overhead.
Fast Probabilistic Hosting Capacity Analysis for Active Distribution Systems
TLDR
PHCA is expedited here by leveraging the powerful tool of multiparametric programming (MPP) to find the exact minimizers for 518,400 OPF instances on the IEEE 123-bus feeder by solving only 6,905 of them, and 86,400 instances on a 1,160-busFeeder by solved only 2,111 instances.
Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids
TLDR
A distributed consensus-based optimization approach is developed to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries, and the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch.
Optimal Power Flow Using Graph Neural Networks
Optimal power flow (OPF) is one of the most important optimization problems in the energy industry. In its simplest form, OPF attempts to find the optimal power that the generators within the grid
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