Regional power grid dispatching method based on robust optimization in elastic environment

Abstract

The invention belongs to the technical field of power system scheduling optimization, and more particularly relates to a regional power grid scheduling method based on robust optimization in an elastic environment, which establishes a robust optimization scheduling model considering the scheduling potential of schedulable resources on both sides of a source and a load. The method comprises the following steps: firstly, constructing an uncertainty set for renewable energy power generation output uncertainty; then, the scheduling potential of schedulable resources on the two sides of a source load is analyzed, including deep peak regulation of a thermal power generating unit, load capable of being reduced and load capable of being translated; secondly, establishing a robust optimization scheduling model in combination with sequential decision-making characteristics of power system scheduling; and finally, solving the robust optimization model. The method can be applied to formulating a day-ahead scheduling plan of a regional power grid containing renewable energy power generation, and is beneficial to improving the operation robustness, flexibility and economical efficiency of a power system.

Description

Technical field [0001] The present invention belongs to the field of regional grid dispatch optimization, more particularly, relates to a regional grid dispatching method based on robust optimization in an elastic environment. Background [0002] In recent years, wind, photovoltaic and other renewable energy power generation has been rapidly developed in the world, by the end of 2020, China's cumulative grid-connected wind power installed capacity reached 281.53 million kilowatts, accounting for 12.8% of the country's total installed power capacity。 However, renewable energy generation has obvious randomness and volatility, which exacerbates the uncertainty of grid operation. It is difficult to meet the dispatching needs by relying solely on the regulation capacity of traditional thermal power units, so it is necessary to fully tap the dispatchable potential of both sides of the power system source and charge to further promote the consumption of renewable energy. [0003] In te...

AI Technical Summary

Problems solved by technology

The stochastic programming method reflects the probabilistic characteristics of uncertain parameters, and can obtain the optimal scheduling in the sense of mathematical expectation, but there are problems such as a large number of generated scenarios, a large amount of calculation, and difficult to solve the chance constraints

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