期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 33, 期 3, 页码 3255-3264出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2017.2762599
关键词
Probabilistic load forecasting; photovoltaic generation; behind-the-meter PV; net load; copula; discrete dependent convolution; maximal information coefficient (MIC)
资金
- National Key RAMP
- D Program of China [2016YFB0900100]
- National Science Foundation of China [51325702, 51677096]
Distributed renewable energy, particularly photo-voltaics (PV), has expanded rapidly over the past decade. Distributed PV is located behind the meter and is, thus, invisible to the retailers and the distribution system operator. This invisible generation, thus, injects additional uncertainty in the net load and makes it harder to forecast. This paper proposes a data-driven probabilistic net load forecasting method specifically designed to handle a high penetration of behind-the-meter (BtM) PV. The capacity of BtM PV is first estimated using a maximal information coefficient based correlation analysis and a grid search. The net load profile is then decomposed into three parts (PV output, actual load, and residual) which are forecast individually. Correlation analysis based on copula theory is conducted on the distributions and dependencies of the forecasting errors to generate a probabilistic net load forecast. Case studies based on ISO New England data demonstrate that the proposed method outperforms other approaches, particularly when the penetration of BtM PV is high.
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