Fuzzy chance constraint optimal power flow calculation method based on credibility theory

Abstract

The invention discloses a fuzzy opportunity constraint optimal power flow calculation method based on a credibility theory, and belongs to the technical field of renewable energy consumption of a power distribution network. The method is characterized by comprising the following steps: constructing a credibility distribution function of a net load prediction error; establishing a fuzzy opportunity constraint optimization model considering the net load prediction uncertainty; deriving deterministic equivalence of voltage, active power and reactive power fuzzy opportunity constraints based on a credibility theory; further deriving a deterministic equivalent linear expression by introducing an auxiliary variable; establishing a power distribution network certainty optimization model considering prediction uncertainty; and solving the power distribution network optimization model by selecting different confidence levels to obtain an optimal controllable generator scheduling scheme meeting different safety margins. According to the method, the optimal power flow model based on the credibility theory is constructed, the voltage violation risk under the influence of uncertainty can be effectively reduced, and the problem of safe and economical operation of the power distribution network containing renewable energy sources is solved.

Description

technical field [0001] A fuzzy chance-constrained optimal power flow calculation method based on credibility theory belongs to the technical field of renewable energy consumption in distribution networks. Background technique [0002] Several core tasks of power system operation, such as unit commitment, reserve procurement, market clearing, and safety assessment, rely on Occasional Proportional Feedback (OPF) solutions. In recent years, with the accelerated development of the global economy, energy shortage and environmental pollution have become serious problems currently facing, which also promotes the research and development of renewable energy power generation. However, uncertainty is introduced into the OPF problem due to inaccurate forecasts of electricity generation from renewable energy sources (RESs). And as the share of RESs generation in the distribution system increases, forecast uncertainty increases by orders of magnitude. This leads to the large prediction...

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Problems solved by technology

However, empirical measurements of solar and wind power generation are often asymmetric

Thus, Gaussian assumptions of uncertainty in RESs, as symmetry assumptions, may misestimate the impact of forecast errors from nodes with significant asymmetries, leading to ineffective and inefficient operational decisions

In addition, uncertainty includes ambiguity in addition to randomness. In existing chance-constrained OPF models, studies on uncertain RESs mainly focus on the stochastic nature of RESs, while credibility theory is used to deal with the consideration of There are few studies on the uncertainty of RESs with fuzzy properties

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