Journal
IET GENERATION TRANSMISSION & DISTRIBUTION
Volume 11, Issue 14, Pages 3620-3627Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2017.0427
Keywords
distributed power generation; photovoltaic power systems; load flow; correlation methods; Monte Carlo methods; power generation scheduling; probability; correlated photovoltaic power uncertains; load uncertainties; grid-connected microgrid day-ahead scheduling; distributed generators; PV-DG power; PV-DG loads; probabilistic optimal power flow model; P-OPF model; modified Harr's two-point estimation method; MH-2PEM; computation-efficient estimation; Monte Carlo simulation; equivalent distribution feeder system; confidence levels; day-ahead operation; voltage 44 kV
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Funding
- Fundamental Research Funds for the Central Universities
- Beijing Natural Science Foundation [4172048]
- Jiangxi Provincial Department of Science and Technology [20121BBE50023, 20133BCB22002, 612049302056]
- State Key Laboratory of Power Transmission Equipment and System Security and New Technology (Chongqing University) [2007DA10512716405]
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Due to the increasing integration of photovoltaic-based distributed generators (PV-DGs), uncertainties resulted from both PV-DG power and loads have posed a serious challenge in microgrid day-ahead scheduling and operation. In this study, the effect of uncertainties in both PV-DG power and loads on the microgrid day-ahead scheduling is assessed. Specifically, the correlation between the PV-DG power and load uncertainties is taken into account as this is closer to the reality. The probabilistic optimal power flow (P-OPF) model is formulated to analyse the impact of the correlated PV-DG power and load uncertainties. A modified Harr's two-point estimation method (MH-2PEM) is introduced to provide computation-efficient estimation of the P-OPF solution. Results obtained by using the MH-2PEM and Monte Carlo simulation are compared in an equivalent 44kV distribution feeder system and the accuracy and efficiency of the MH-2PEM are verified. The variation ranges of the microgrid day-ahead scheduling solution resulted from uncertainties in PV power and load are obtained with various confidence levels.
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