Microgrid time domain rolling optimization scheduling method based on DDR-MPC

Abstract

The invention relates to the technical field of microgrid scheduling, in particular to a microgrid time domain rolling optimization scheduling method based on DDR-MPC, and the method comprises the steps: building a day-ahead optimization scheduling model taking the lowest daily comprehensive operation cost as a target at a day-ahead scheduling stage based on differential price type demand response; in an intra-day scheduling stage, combining the differentiated demand response with a model prediction control method, and establishing an intra-day time domain rolling optimization scheduling modeltaking minimum rolling time domain comprehensive operation cost as a target based on DDR-MPC; and solving a microgrid time domain rolling optimization scheduling model by adopting a time domain rolling composite differential evolution algorithm. According to the method, DDR and MPC methods are combined, so that the comprehensive operation cost of the microgrid can be remarkably reduced, and the reliability of microgrid optimal scheduling is improved; the time domain rolling composite differential evolution algorithm provided by the invention can efficiently solve the model, can significantlyimprove the optimization convergence speed, and has good dynamic environment adaptability.

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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