Distribution robustness optimization method suitable for power distribution network unit combination

Abstract

The invention discloses a distribution robustness optimization method suitable for a power distribution network unit combination, and the method comprises the steps: constructing a fuzzy set based onstatistical data based on the statistical data of renewable energy output; introducing an auxiliary variable, and rewriting a fuzzy set; based on the fuzzy set, establishing a power distribution network unit combination model based on distribution robustness optimization; converting the model into a mixed integer second-order cone programming model by applying an affine strategy, a strong dual theory and a second-order cone dual theory; and solving the mixed integer second-order cone programming model. According to the method, the problems that stochastic programming depends too much on accurate probability distribution and the calculated amount is too large are solved, and the problem that traditional robust optimization is too conservative is effectively avoided. According to the method,the power distribution network unit commitment model is finally converted into the mixed integer second-order cone programming model which is easy to solve, the calculation efficiency is greatly improved, the model can be directly solved on modeling software such as GAMS, and the solving difficulty is reduced.

Description

technical field [0001] The invention belongs to the field of power system scheduling and optimization, and in particular relates to a distribution optimization method for a distribution network unit combination. Background technique [0002] With the increasingly serious problems of environmental pollution and energy shortage, it is urgent to develop clean energy to replace traditional fossil energy. In recent years, a large number of grid-connected new energy sources such as gas turbines and renewable energy generators (small wind turbines, photovoltaics) have become the main development trend of the current distribution network. [0003] Unit combination is a fundamental problem in gas turbine scheduling. However, the randomness and intermittency of renewable energy output bring great challenges to the scheduling of gas turbine units. Most of the current research uses stochastic programming and robust optimization to deal with uncertain problems. However, these two metho...

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Problems solved by technology

However, the randomness and intermittency of renewable energy output bring great challenges to the scheduling of gas turbine units

Most of the current research uses stochastic programming and robust optimization to deal with uncertain problems. However, these two methods have their inevitable shortcomings: stochastic programming requires accurate probability distributions of uncertain parameters, which are difficult to obtain in practice. Moreover, stochastic programming needs to generate a large number of sample points through sampling, and the calculation scale is large; robust optimization ignores the probability distribution information of uncertain parameters, and the obtained optimization results are often too conservative

However, the solution of the current distributional robust optimization model mostly transforms it into a semidefinite programming problem, resulting in difficult solution and low computational efficiency

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