Machine Learning Assisted Approach for Security-Constrained Unit Commitment

• URL: http://arxiv.org/abs/2111.09824v1
• Date: Wed, 17 Nov 2021 03:51:26 GMT
• Title: Machine Learning Assisted Approach for Security-Constrained Unit Commitment
• Authors: Arun Venkatesh Ramesh, Xingpeng Li
• Abstract summary: Security-constrained unit commitment (SCUC) is used in the power system day-ahead generation scheduling. A good warm-start solution or a reduced-SCUC model can bring significant time savings. A novel approach is proposed to effectively utilize machine learning (ML) to provide a good starting solution and/or reduce the problem size of SCUC.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Security-constrained unit commitment (SCUC) which is used in the power system day-ahead generation scheduling is a mixed-integer linear programming problem that is computationally intensive. A good warm-start solution or a reduced-SCUC model can bring significant time savings. In this work, a novel approach is proposed to effectively utilize machine learning (ML) to provide a good starting solution and/or reduce the problem size of SCUC. An ML model using a logistic regression algorithm is proposed and trained using historical nodal demand profiles and the respective commitment schedules. The ML outputs are processed and analyzed to assist SCUC. The proposed approach is validated on several standard test systems namely, IEEE 24-bus system, IEEE 73-bus system, IEEE 118-bus system, synthetic South Carolina 500-bus system, and Polish 2383-bus system. Simulation results demonstrate that the prediction from the proposed machine learning model can provide a good warm-start solution and/or reduce the number of variables and constraints in SCUC with minimal loss in solution quality while substantially reducing the computing time.

Related papers

• Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids [14.389086937116582]
  This work proposes an approach that integrates reinforcement learning and model predictive control (MPC) to solve optimal control problems in mixed-logical dynamical systems. The proposed method significantly reduces the online computation time of the MPC approach and that it generates policies with small optimality gaps and high feasibility rates.
  arXiv  Detail & Related papers  (2024-09-17T15:17:16Z)
• Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems [3.5897534810405403]
  This paper presents a model-free, real-time, data-efficient Q-learning-based algorithm to solve the H$_infty$ control of linear discrete-time systems. An adaptive optimal controller is designed and the parameters of the action and critic networks are learned online without the knowledge of the system dynamics.
  arXiv  Detail & Related papers  (2023-09-16T05:02:41Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment [0.5076419064097734]
  Security-constrained unit commitment (SCUC) is a computationally complex process utilized in power system day-ahead scheduling and market clearing. In this paper, an advanced machine learning (ML) model is used to study the patterns in power system historical data, which inherently considers both spatial and temporal (ST) correlations in constraints. The ST-correlated ML model is trained to understand spatial correlation by considering graph neural networks (GNN) whereas temporal sequences are studied using long short-term memory (LSTM) networks.
  arXiv  Detail & Related papers  (2023-06-02T14:31:24Z)
• Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks [103.22651843174471]
  This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which aggregates them into a quantized global model and synchronizes the devices. We show that the FL training process can be described as a Markov decision process and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations.
  arXiv  Detail & Related papers  (2022-09-21T08:52:51Z)
• Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations [0.5076419064097734]
  Day-ahead operations involves a complex and computationally intensive optimization process to determine the generator commitment schedule and dispatch. Existing patterns in historical information can be leveraged for model reduction of security-constrained unit commitment (SCUC) The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, 500-Bus system, and Polish 2383-Bus system.
  arXiv  Detail & Related papers  (2022-08-13T22:44:42Z)
• Covariance-Free Sparse Bayesian Learning [62.24008859844098]
  We introduce a new SBL inference algorithm that avoids explicit inversions of the covariance matrix. Our method can be up to thousands of times faster than existing baselines. We showcase how our new algorithm enables SBL to tractably tackle high-dimensional signal recovery problems.
  arXiv  Detail & Related papers  (2021-05-21T16:20:07Z)
• Efficient time stepping for numerical integration using reinforcement learning [0.15393457051344295]
  We propose a data-driven time stepping scheme based on machine learning and meta-learning. First, one or several (in the case of non-smooth or hybrid systems) base learners are trained using RL. Then, a meta-learner is trained which (depending on the system state) selects the base learner that appears to be optimal for the current situation.
  arXiv  Detail & Related papers  (2021-04-08T07:24:54Z)
• Edge Federated Learning Via Unit-Modulus Over-The-Air Computation (Extended Version) [64.76619508293966]
  This paper proposes a unit-modulus over-the-air computation (UM-AirComp) framework to facilitate efficient edge federated learning. It uploads simultaneously local model parameters and updates global model parameters via analog beamforming. We demonstrate the implementation of UM-AirComp in a vehicle-to-everything autonomous driving simulation platform.
  arXiv  Detail & Related papers  (2021-01-28T15:10:22Z)
• A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations [69.73803123972297]
  We propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems.
  arXiv  Detail & Related papers  (2020-12-21T17:39:51Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.