A park-level multi-energy complementary system optimization scheduling method and device

Abstract

The invention discloses an optimal scheduling method for a park-level multi-energy complementary system. According to the actual measurement information and prediction information, combined with typical working conditions, the operation plan curve of the system is generated through multi-time scale optimal scheduling, and the optimization is obtained with the goal of the lowest comprehensive cost. Scheduling results, and evaluate the optimal scheduling results with comprehensive energy efficiency indicators to determine the rationality of the optimization parameters. When it is difficult to meet the index requirements by adjusting the optimization parameters, further suggestions for adjusting the system configuration parameters are given. The invention aims to solve the implementation problem of optimal scheduling of the park-level multi-energy complementary system in the process of continuous capacity expansion and expansion. According to the real-time scale of the park and the foreseeable renovation project, the semi-physical model can be used to flexibly adjust the optimization object, improve the The rationality of optimization results and the accuracy of scheduling implementation.

Description

technical field [0001] The invention belongs to the technical field of comprehensive energy dispatching, and in particular relates to an optimal dispatching method for a park-level multi-energy complementary system, and also relates to an optimal dispatching device for a park-level multi-energy complementary system. Background technique [0002] The regional energy system represented by the park is an effective way to promote the local consumption of renewable energy, improve the comprehensive utilization efficiency of energy, and achieve the goal of energy conservation and emission reduction. At present, although the energy supply structure of most parks has achieved "multi-energy supply", the effect of "optimization and complementarity" between various energy subsystems has not yet been achieved. Energy monitoring is not in place, energy dispatching methods are extensive, and energy use management methods Singleness is the root cause of low energy utilization efficiency an...

AI Technical Summary

Problems solved by technology

[0004] 1) The coverage rate of the energy information collection system is low, the level of automation is poor, and the energy data is fragmented and decentralized, making it difficult to provide support for the optimal scheduling of the system

[0005] 2) Research tends to the precise modeling of complex systems and the advanced construction of optimization algorithms, ignoring the rationality and feasibility of the model itself and algorithm parameters

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