首页|Analytic Neural Network Gaussian Process Enabled Chance-Constrained Voltage Regulation for Active Distribution Systems With PVs, Batteries and EVs
Analytic Neural Network Gaussian Process Enabled Chance-Constrained Voltage Regulation for Active Distribution Systems With PVs, Batteries and EVs
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NETL
NSTL
IEEE
This paper proposes an analytic neural network Gaussian process (NNGP)-based chance-constrained real-time voltage regulation method for active distribution systems with photovoltaics (PVs), batteries, and electric vehicles (EVs). NNGP can utilize historical measurement data to achieve real-time probabilistic node voltage estimation through Bayesian inference. Then, NNGP is fully analytically embedded into the optimal power flow model to perform voltage regulation and adapt to various topological changes. The uncertainties of voltage estimations are easily considered via the chance constraint, and it has been shown that the adoption of this chance constraint can significantly improve the reliability of voltage regulation under various scenarios. The comparison results with other methods, carried out on a real 759-node distribution system located in western Colorado, U.S., show that the proposed method can achieve accurate voltage estimation across different topologies and reliably perform voltage regulation considering PVs, batteries, and EVs.
Voltage controlVoltage measurementEstimationArtificial neural networksUncertaintyNetwork topologyBatteriesTopologyReal-time systemsRegulation
Tong Su、Junbo Zhao、Yansong Pei、Yiyun Yao、Fei Ding
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Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA
National Renewable Energy Laboratory, Golden, CO, USA
NETL
NSTL
IEEE
