Microgrid capacity allocation optimization method considering risk losses

Abstract

The invention provides a microgrid capacity allocation optimization method considering risk losses. The method includes obtaining parameters, and fitting out the probability distribution function of wind, photovoltaic and load power; constructing wind-photovoltaic-load uncertainty sets of multiple sections, and calculating risk power; constructing a two-stage robust optimization model of power capacity configuration, and performing decoupling into a main problem module and a subproblem model; and utilizing a column and constraint generation algorithm to obtain the optimal configuration of thecapacity of a microgrid. The method can solve the capacity allocation scheme of the microgrid, so that the risk losses of the capacity allocation scheme of the microgrid can be reduced, and the balance problem of economy and robustness can be solved; and the configured microgrid within planning years has good robustness and economy and gives the risk loss range of the scheme, so that the method has reference and guidance values for the future operation mode of the microgrid, and necessary technical support can be provided for the planning work of the microgrid.

Description

technical field [0001] The present invention relates to the technical field of micro-grid planning, and more specifically, to a method for optimizing micro-grid capacity allocation in consideration of risk loss. Background technique [0002] With the rapid development of the economy, the demand for electricity continues to increase, fossil energy is gradually exhausted, and environmental pollution is becoming more and more serious. Making full use of renewable clean energy and promoting low-carbon electricity is an important development trend of the power industry. Due to the flexible access to distributed power sources such as wind turbines and photovoltaics, microgrids have received extensive attention and applications. The optimal configuration of power capacity is the basis of microgrid planning, which affects the economy and reliability of microgrid operation. Due to the uncertainty and randomness of renewable energy (wind speed, light intensity, etc.) Load shedding r...

AI Technical Summary

Problems solved by technology

However, both scenario analysis and stochastic programming rely on probability curves, so the fitting of the probability model may be inaccurate and cannot reflect the actual application scenario

In engineering practice, it is difficult to obtain a large number of sample points to accurately describe the probability model

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