Optimal Dispatch of Residential Photovoltaic Inverters Under Forecasting Uncertainties

Efforts to ensure reliable operation of existing low-voltage distribution systems with high photovoltaic (PV) generation have focused on the possibility of inverters providing ancillary services such as active power curtailment and reactive power compensation. Major benefits include the possibility...

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Bibliographic Details
Published in:IEEE journal of photovoltaics 2015-01, Vol.5 (1), p.350-359
Main Authors: Dall'Anese, Emiliano, Dhople, Sairaj V., Johnson, Brian B., Giannakis, Georgios B.
Format: Article
Language:English
Subjects:
Conditional value-at-risk (CVaR)
distribution networks
Forecasting
forecasting errors
inverter control
Inverters
microgrids
optimal power flow (OPF)
photovoltaic (PV) systems
Photovoltaic systems
Random variables
Reactive power
Surges
Vectors
voltage regulation
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Summary:Efforts to ensure reliable operation of existing low-voltage distribution systems with high photovoltaic (PV) generation have focused on the possibility of inverters providing ancillary services such as active power curtailment and reactive power compensation. Major benefits include the possibility of averting overvoltages, which may otherwise be experienced when PV generation exceeds the demand. This paper deals with ancillary service procurement in the face of solar irradiance forecasting errors. In particular, assuming that forecasted PV irradiance can be described by a random variable with known (empirical) distribution, the proposed uncertainty-aware optimal inverter dispatch (OID) framework indicates which inverters should provide ancillary services with a guaranteed a priori risk level of PV generation surplus. To capture forecasting errors and strike a balance between risk of overvoltages and (re)active power reserves, the concept of conditional value-at-risk is advocated. Due to AC power balance equations and binary inverter selection variables, the formulated OID involves the solution of a nonconvex mixed-integer nonlinear program. However, a computationally affordable convex relaxation is derived by leveraging sparsity-promoting regularization approaches and semidefinite relaxation techniques.
ISSN:2156-3381
2156-3403
DOI:10.1109/JPHOTOV.2014.2364125