4.7 Article

Optimal Load-Side Control for Frequency Regulation in Smart Grids

Journal

IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 62, Issue 12, Pages 6294-6309

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2017.2713529

Keywords

Power system control; power system; dynamics optimization; demand-side management

Funding

  1. National Science Foundation (NSF) NetSE program [CNS 0911041]
  2. NSF CPS [CNS 1544771]
  3. Advanced Research Projects Agency-Energy [DE-AR0000226]
  4. Southern California Edison
  5. National Science Council of Taiwan [NSC 103-3113-P-008-001]
  6. Caltech Resnick Institute
  7. Johns Hopkins E2SHI Seed Grant
  8. Johns Hopkins WSE startup funds
  9. Direct For Computer & Info Scie & Enginr
  10. Division Of Computer and Network Systems [1544771] Funding Source: National Science Foundation

Ask authors/readers for more resources

Frequency control rebalances supply and demand while maintaining the network state within operational margins. It is implemented using fast ramping reserves that are expensive and wasteful, and which are expected to become increasingly necessary with the current acceleration of renewable penetration. The most promising solution to this problem is the use of demand response, i.e., load participation in frequency control. Yet it is still unclear how to efficiently integrate load participation without introducing instabilities and violating operational constraints. In this paper, we present a comprehensive load-side frequency control mechanism that can maintain the grid within operational constraints. In particular, our controllers can rebalance supply and demand after disturbances, restore the frequency to its nominal value, and preserve interarea power flows. Furthermore, our controllers are distributed (unlike the currently implemented frequency control), can allocate load updates optimally, and can maintain line flows within thermal limits. We prove that such a distributed load-side control is globally asymptotically stable and robust to unknown load parameters. We illustrate its effectiveness through simulations.

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