Statistical Learning for DC Optimal Power Flow

@article{Ng2018StatisticalLF,
  title={Statistical Learning for DC Optimal Power Flow},
  author={Yeesian Ng and Sidhant Misra and Line A. Roald and Scott N. Backhaus},
  journal={2018 Power Systems Computation Conference (PSCC)},
  year={2018},
  pages={1-7}
}
The optimal power flow problem plays an important role in the market clearing and operation of electric power systems. However, with increasing uncertainty from renewable energy operation, the optimal operating point of the system changes more significantly in real-time. In this paper, we aim at developing control policies that are able to track the optimal set-point with high probability. The approach is based on the observation that the OPF solution corresponding to a certain uncertainty… 

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References

SHOWING 1-10 OF 31 REFERENCES

Optimal policy-based control of generation and HVDC lines in power systems under uncertainty

Developing methodologies with a good trade-off between operational costs and reliability in the presence of uncertainty from renewable energy sources is a challenging research problem. Formulating a

Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow

This work proposes a method to extend the use of corrective control of PSTs and HVDCs to react to uncertainty, and demonstrates the scalability of the method by solving the problem for the IEEE 300 bus and the Polish system test cases.

Optimal Power Flow with Weighted chance constraints and general policies for generation control

The Weighted Chance Constrained OPF (WCC-OPF) is introduced that can handle general control policies while preserving convexity and allowing for efficient computation and it is proved that the problem remains convex for any convex weighting function, and for very general generation control policies.

Probabilistic guarantees for the N-1 security of systems with wind power generation

A novel framework for designing an N-1 secure generation day-ahead dispatch for power systems with a high penetration of fluctuating power sources, e.g., wind or PV power, is proposed and a Markov chain-based model is employed.

Small test systems for power system economic studies

This paper presents two small test systems for power system economic studies. The first system is based on the original PJM 5-bus system, which contains data related to real power only, because it

Examining the limits of the application of semidefinite programming to power flow problems

This paper investigates an SDP approach utilizing modified objective and constraints to compute all solutions of the nonlinear power flow equations, and suggests SDP's promise as an efficient algorithm for identifying large numbers of solutions to the power flow equation.

Model predictive control based on linear programming - the explicit solution

The availability of the explicit structure of the MPC controller provides an insight into the type of control action in different regions of the state space, and highlights possible conditions of degeneracies of the LP, such as multiple optima.

A data-driven approach to identifying system pattern regions in market operations

  • Xinbo GengLe Xie
  • Engineering
    2015 IEEE Power & Energy Society General Meeting
  • 2015
This paper studies the fundamental coupling between individual load levels and locational marginal prices in security constrained economic dispatch. The concepts of system pattern and system pattern

Transmission management in the deregulated environment

Three very different methods of accomplishing the same task-managing the operation of the transmission system in the deregulated power system operating environment-have been implemented as

Reduced network modeling of WECC as a market design prototype

California's administration, legislature, and energy regulators have adopted aggressive targets for renewable energy, which will result in profound changes in markets and system operations as the