4.7 Article

Stochastic Reactive Power Management in Microgrids With Renewables

期刊

IEEE TRANSACTIONS ON POWER SYSTEMS
卷 30, 期 6, 页码 3386-3395

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2014.2369452

关键词

Convex relaxation; loss minimization; optimal power flow; photovoltaic inverters; reactive power compensation; stochastic approximation; voltage regulation

资金

  1. Institute of Renewable Energy and the Environment (IREE) [RL-0010-13]
  2. University of Minnesota
  3. NSF [CCF-1423316, CCF-1442686]
  4. China Scholarship Council
  5. Directorate For Engineering
  6. Div Of Electrical, Commun & Cyber Sys [1548015] Funding Source: National Science Foundation
  7. Division of Computing and Communication Foundations
  8. Direct For Computer & Info Scie & Enginr [1423316] Funding Source: National Science Foundation
  9. Division of Computing and Communication Foundations
  10. Direct For Computer & Info Scie & Enginr [1442686] Funding Source: National Science Foundation

向作者/读者索取更多资源

Distribution microgrids are being challenged by reverse power flows and voltage fluctuations due to renewable generation, demand response, and electric vehicles. Advances in photovoltaic (PV) inverters offer new opportunities for reactive power management provided PV owners have the right investment incentives. In this context, reactive power compensation is considered here as an ancillary service. Accounting for the increasing time-variability of distributed generation and demand, a stochastic reactive power compensation scheme is developed. Given uncertain active power injections, an online reactive control scheme is devised. This scheme is distribution-free and relies solely on power injection data. Reactive injections are updated using the Lagrange multipliers of a second-order cone program. Numerical tests on an industrial 47-bus microgrid and the residential IEEE 123-bus feeder corroborate the reactive power management efficiency of the novel stochastic scheme over its deterministic alternative, as well as its capability to track variations in solar generation and household demand.

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