A collaborative artificial intelligence optimization method based on firefly swarm algorithm for source network load storage

Abstract

The invention discloses an artificial intelligence optimization method of source network charge-storage coordination based on firefly swarm algorithm, which comprises the following steps: S100, establishing an optimized scheduling model based on source-network-load-storage coordination; S200, based on the optimal scheduling model in step S100, a collaborative optimization method based on the firefly swarm algorithm is established, and the coordination and interaction characteristics among the power sources, the source networks and the network load storage are comprehensively considered, and the source- Network- Load--storage Coordination and interaction, so as to realize the operation mode and technology of maximizing the utilization of energy resources, the problem of cooperative optimalscheduling of load and storage in source network is solved efficiently by firefly algorithm, By designing that each firefly only interacts with its neighbors in the moving process, the distribution ofthe firefly swarm is dispersed, but the relevant relationship is made, the diversity of feasible solutions is improved, the local optimal solution is avoided prematurely, and the maximum utilizationof renewable resources is realized.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of power transmission scheduling, and in particular to an artificial intelligence optimization method for source-network-load-storage collaboration based on the firefly swarm algorithm. Background technique [0002] With the depletion of fossil energy and the continuous deterioration of the human living environment, people pay more and more attention to the low-carbon, high-efficiency, and economical aspects of energy development. Large-scale renewable energy and distributed energy are connected to the power system, making the power system The original vertically integrated management has been gradually transformed into a multi-interactive structure with coordinated source, grid, load, and storage, which has greatly enhanced the flexibility and reliability of system operation. However, due to the increasing number of participants in the power system, how to meet the The different in...

AI Technical Summary

Problems solved by technology

[0003] Current research mainly focuses on the operation modes of source-network coordination, source-network-load coordination, and network-load-storage coordination. Among them, source-grid coordination refers to the use of advanced technologies to improve the ability of the grid to accommodate multiple types of power sources; -Network-load coordination refers to the use of flexible loads to track power output and participate in grid demand management; network-load-storage coordination refers to incorporating energy storage and distributed energy into demand-side response, participating in system operation regulation, and enhancing the system The ability to accept new energy, however, has not yet fully studied the subject of source-network-load-storage interaction. The coordination and interaction of source-network-load-storage requires the use of energy conversion technology and information technology to realize the development of energy resources Utilization, effective management of the transmission network, generalized demand response (load and energy storage), so as to promote the consumption of renewable energy and ensure the safe and stable operation of the system

[0004] As an effective method to obtain the global optimal solution, the heuristic algorithm is widely used in academia. The heuristic algorithm is easy to implement and has a wide range of applications. It can effectively solve the dimensionality problem brought about by the increase in the scale of the generator. , the commonly used heuristic algorithms include ant colony algorithm, particle swarm algorithm, simulated annealing method, firefly swarm algorithm, neural network, etc. Among them, the firefly swarm algorithm designs each firefly to only interact with its neighbors during the moving process, so that the firefly swarm The distribution of the firefly swarm algorithm is relatively scattered but related, which improves the diversity of feasible solutions and avoids prematurely falling into the local optimal solution. However, the firefly swarm algorithm is rarely used in the coordinated scheduling of source network load storage.

Images