Rolling optimization scheduling method based on self-adaptive change of energy scheduling time

Abstract

The invention discloses a rolling optimization scheduling method based on self-adaptive change of energy scheduling time. The method comprises the following steps: S1, establishing a power load model and an energy output model; s2, determining a multi-objective function of the low-carbon power generation dispatching model by taking the lowest power generation energy consumption and the lowest carbon emission of the power system as optimization objectives; s3, the low-carbon power generation dispatching optimization model with the double optimization targets can be converted into a single objective function with the goal of minimizing the total electric energy production cost represented by the power generation energy consumption and the total carbon emission cost of the power system; s4, introducing feedback control, and correcting a current scheduling strategy by using a real-time measurement value to reduce scheduling deviation; and performing rolling optimization on the target function. According to the scheme, on the premise of ensuring the safety and stability of the power system, the operation cost of the novel power system can be reduced, and the economy and safety of system operation are improved.

Description

technical field [0001] The invention relates to the field of power system optimization dispatching, in particular to a rolling optimization dispatching method based on self-adaptive change of energy dispatching time. Background technique [0002] As an important part of renewable energy, wind energy and photovoltaics, after a large number of new energy units are connected to the distribution network, their volatility, intermittent, low controllability and other issues not only bring great challenges to the safe and reliable operation of the distribution network, At the same time, it also brings complexity and uncertainty to distribution network planning. At the same time, flexible loads are also uncertain. For example, user demand response behavior is uncertain due to psychological behavior, weather factors, and emergencies; electric vehicles also have time-varying and random nature when accessing the Internet. On the basis of load fluctuations, renewable energy power gener...

AI Technical Summary

Problems solved by technology

In the MPC-based decentralized rolling optimization scheduling process, the scheduling time interval is a fixed value, but in the actual operation process, the power load of the system is not very large, if a fixed value is still used as the scheduling time interval, it will cause unnecessary scheduling cost

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