Day-ahead economic dispatch method for virtual power plant

Abstract

The invention provides a day-ahead economic dispatch method for a virtual power plant. The method includes the following steps that first, a day-ahead economic dispatch mixed integer linear programming mathematical model making benefits of the virtual power plant maximum is built, and in an objective function, a stochastic programming method is adopted for handling the uncertainty of electricity price; second, constraint conditions are built to make an electric power system meet power constraint, unit operation constraint and contract electric quantity constraint, and in the constraint conditions, a robust optimization method is adopted for handling the uncertainty of generated output of renewable energy; third, a prediction error coefficient and a robust coefficient are set; fourth, calculation software is used for obtaining an optimum day-ahead economic dispatch result. Compared with the prior part, the method has the advantages of being good in calculation result, high in calculation efficiency and the like.

Description

technical field [0001] The invention relates to a day-ahead economic scheduling method for a virtual power plant, belonging to the field of power system scheduling. Background technique [0002] In recent years, fossil fuels have become increasingly scarce and environmental pollution has intensified. In order to solve the above problems, renewable energy power generation, especially wind power and photovoltaic power generation, has developed rapidly. Although the power generation reserves of renewable energy are huge and clean, they often have strong randomness, and grid connection alone will have a great impact on the grid, which is not conducive to the stable operation of the grid. In the electricity market environment, the market activities of wind farms are very risky, and their actual power generation often deviates from the bidding power, thus suffering from imbalance penalties, so they are at a disadvantage in the competition with traditional power plants. However, t...

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Problems solved by technology

However, the prediction accuracy of wind turbine output is generally low, and the fluctuation between the actual output and the predicted output is relatively large. This kind of uncertainty problem is more suitable for the robust optimization method to deal with. If the stochastic programming method is used to deal with it, a large number of factors need to be considered Scheme samples, the calculation burden is heavy, and the decision-making scheme cannot guarantee the security of the system well

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