Multi-energy complementation microgrid scheduling method based on multi-time scales

Abstract

The invention relates to a multi-energy complementation microgrid scheduling method based on multi-time scales. According to the technical points, the method comprises the following steps of: 1, setting a microgrid system scene, and respectively modeling a combined cooling heating and power device, an ice-storage air conditioner and a storage battery device in the microgrid system scene; 2, based on photovoltaic and wind power output scene generation and elimination technologies, analyzing the uncertainties of photovoltaic output and wind power output, and eliminating the fluctuation influences of the output of photovoltaic and wind power renewable energy sources; 3, establishing day-ahead combined optimization scheduling model; 4, establishing a microgrid real-time optimization scheduling model; 5, based on an improved particle swarm optimization algorithm, solving the microgrid day-ahead combined optimization scheduling model and the microgrid real-time optimization scheduling model, and obtaining a multi-energy complementation microgrid operation strategy under multi-time scales. According to the invention, the operation cost of the microgrid is lowered, the utilization efficiency of energy is fully improved, and the method has an important effect on the aspect of peak shaving and load shifting of a power grid.

Description

technical field [0001] The invention relates to the technical field of microgrid scheduling, in particular to a multi-time scale-based multi-energy complementary microgrid scheduling method. Background technique [0002] At present, as an important part of the smart grid, the microgrid has great potential in reducing energy consumption and improving the reliability and flexibility of the power system. one of the key research directions. Existing micro-grid scheduling strategies do not fully consider the volatility of renewable energy such as wind power and photovoltaics, or only consider the impact of the volatility of renewable energy in day-ahead scheduling, but insufficient research on its volatility in real-time scheduling; in addition , the operation mode of ice storage air conditioner is complex, and there is almost no microgrid model including ice storage air conditioner in the current research; in view of the situation that there are many devices and complex constra...

AI Technical Summary

Problems solved by technology

Existing micro-grid scheduling strategies do not fully consider the volatility of renewable energy such as wind power and photovoltaics, or only consider the impact of the volatility of renewable energy in day-ahead scheduling, but insufficient research on its volatility in real-time scheduling; in addition , the operation mode of ice storage air conditioner is complex, and there is almost no microgrid model including ice storage air conditioner in the current research; in view of the situation that there are many devices and complex constraints in the microgrid system, find a microgrid optimal scheduling algorithm suitable for multi-objective and multi-time scales , is a prominent problem facing microgrid builders

Images