Online intelligent learning decision optimization method and system for combustion gas distributed energy system

Abstract

The invention discloses an online intelligent learning decision optimization method and system for a combustion gas distributed energy system, and the method comprises the following steps: collectinginput and output parameters of individual devices of an energy center station and substations, and building an individual device dynamic model according to the input and output parameters of the individual devices; building a decision optimization control model of the whole energy station, solving and obtaining an optimal unit operation instruction model, and transmitting the instruction to a DCScontroller of the energy center station and a controller of substation individual devices. The method obtains a corresponding unit optimal operation parameter group, completes the optimal distributionof unit loads, transmits the unit loads to all energy substations, achieves the overall energy efficiency optimization of the energy center station and substations, enables the overall energy utilization efficiency and economic benefits of the system to be better, achieves the maximum operation benefits, and is relatively stable in system benefits.

Description

technical field [0001] The invention relates to an online intelligent learning decision optimization method and system for a gas distributed energy system, and belongs to the technical field of energy system optimization. Background technique [0002] The existing combined operation mode of the central station of the combined production unit plus the sub-station of the cooling and heating unit, the energy sub-station is only equivalent to a relay station, which pressurizes the heat transmitted from the central station, and does not have the function of independent energy supply; in addition , generally the central station and each sub-station are operated independently by the operating personnel. However, due to the different operating experience of the operating personnel, the operating benefits of different personnel are quite different, and the system benefits fluctuate greatly with the load change. The energy system The overall economy is poor; in addition, the existing ...

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Problems solved by technology

[0003] The purpose of the present invention is to provide an online intelligent learning decision-making optimization method and system for a gas distributed energy system, which can effectively solve the problems existing in the prior art, especially the operating personnel independently carry out the central station and each substation Operation, due to the different operating experience of the operating personnel, the operating benefits of different personnel are quite different, and the system benefits fluctuate greatly with the load change, and the overall economic efficiency of the energy system is poor.

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