Considering real-time electricity prices, a method for optimizing mutual aid trading of microgrid user groups' electric energy

Abstract

The invention discloses a micro-grid user group electric energy optimization mutual aid transaction method considering real-time electricity price, users can according to their own distributed power supply status, and with the help of energy storage units such as lithium batteries, within the safe cycle charge and discharge range, according to Users demand the characteristics of fast charging and discharging, and the load is optimized in the real-time electricity price environment, but the time mismatch between DG and user loads results in redundant DG or lack of electric energy that needs to be purchased from the grid in different time periods. The present invention realizes multiple Users trade excess DG power in different time periods to meet their own needs, improve DG utilization, and reduce power purchase costs. This strategy generates a rooted node tree at different time points; adopts a depth-first dynamic programming algorithm, establishes the root node and child nodes according to whether there is excess DG power in the time period, and distributes the excess DG power of the root node to the child nodes. The invention can improve the overall DG utilization rate and save users' expenses on electricity purchase.

Description

Technical field [0001] The invention relates to the field of distributed power generation and energy storage equipment of a micro-grid system, in particular to a method for optimizing electric energy trade between users based on real-time price (Real-Time Price, RTP). Background technique [0002] As the microgrid system matures, distributed power (DG) and energy storage unit (Storage) technologies have also been developed and can be applied to household-level users. Distributed power sources mainly include solar energy, wind energy and other renewable energy power generation systems; for a single family, in an RTP-based environment, the load (including elastic load and rigid load) distribution can be optimized to make it compatible with DG matches with Storage and minimizes the cost of electricity purchase. However, due to the volatility of DG and the limitation of Storage, the load cannot be completely matched with DG and Storage in different time periods. In some time periods...

AI Technical Summary

Problems solved by technology

However, due to the volatility of DG and the limitation of Storage, the load cannot fully match DG and Storage in different time periods. In some time periods, the excess DG will be wasted, and in some time periods, DG cannot meet the demand and needs to be purchased from the grid. Electricity

And when the number of such users reaches a considerable scale, a large amount of electric energy will be lost when the DG is surplus, and the cost of purchasing electricity from the grid is relatively high

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