A time-domain rolling optimization scheduling method for microgrid based on ddr-mpc

Abstract

The present invention relates to the technical field of micro-grid scheduling, in particular to a DDR‑MPC-based micro-grid time-domain rolling optimal scheduling method, including: in the day-ahead scheduling stage, based on differentiated price-based demand response, to establish the lowest daily comprehensive operating cost The day-ahead optimization scheduling model for the goal; in the intraday scheduling stage, the differentiated demand response is combined with the model predictive control method, and based on DDR‑MPC, an intraday time-domain rolling optimization scheduling model with the goal of minimizing the rolling time-domain comprehensive operating cost is established; The time-domain rolling compound differential evolution algorithm is used to solve the micro-grid time-domain rolling optimal scheduling model. The combination of DDR and MPC methods in the present invention can significantly reduce the comprehensive operating cost of the microgrid and improve the reliability of microgrid optimal scheduling; the time-domain rolling compound differential evolution algorithm proposed by the present invention can efficiently solve the above models, which can significantly improve Excellent convergence speed and good adaptability to dynamic environment.

Description

technical field [0001] The invention relates to the technical field of micro-grid scheduling, in particular to a DDR-MPC-based micro-grid time-domain rolling optimization scheduling method. Background technique [0002] Currently, microgrids are developing rapidly because of their flexible adjustment capabilities. However, as the penetration rate of renewable energy represented by wind power and photovoltaics increases, it becomes more difficult to coordinate sources and loads in the microgrid; the uncertainty of renewable energy output often causes deviations in microgrid optimization scheduling. Model predictive control (model predictive control, MPC) is a model-based finite time-domain closed-loop optimal control algorithm. By using the measured value and prediction model of the current period, and introducing feedback correction, the prediction error can be corrected in time, thereby improving the optimal control. precision. Using the MPC method in microgrid optimal sc...

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Problems solved by technology

However, the existing related research does not take into account the timeliness of demand response and the impact of different types of DR strategies on MPC-based microgrid optimal scheduling, nor does it involve specific DR scheduling strategies, which will cause demand response resources to not be fully utilized , making it difficult for microgrid scheduling to achieve the expected optimization effect; at the same time, these studies did not take into account the difference in the response elasticity coefficient between different users, did not classify the load, and considered the price-based demand response. There is a linear relationship between the quantity and the electricity price change, which does not conform to the actual situation and cannot reflect the characteristics of the actual demand response

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