Multi-mode multi-heat-supply-unit load real-time optimization scheduling method and management system

Abstract

The invention discloses a multi-mode multi-heat supply unit load real-time optimization scheduling method and a management system. The method comprises the following steps: S1, establishing a database; S2, establishing a model library comprising a plurality of heat supply unit mechanism models; S3, performing adaptation on the mechanism models in the model library, wherein the process comprises the following steps: S31, calling data in a database, and predicting operation loads of all heat supply units, S32, adapting the mechanism models in the model library according to the current heat supply unit operation parameters, and S33, carrying out secondary adaptation on the mechanism model after adaptation in the step S32 by using a prediction result in the step S31; S4, performing optimization searching on the adapted mechanism model by using an optimizer; and S5, executing load distribution of multiple furnaces and multiple heat supply units of the power plant according to the provincial dispatching instruction and the optimization result. According to the invention, a plurality of mechanism models are established according to a power plant multi-furnace multi-machine unit mechanism, and multi-objective optimization of price factors and meteorological factors is considered; offer optimization in consideration of a peak regulation bidding policy; and optimization requirements under multiple working conditions can be better met through model adaptation.

Description

technical field [0001] The invention relates to the technical field of smart energy, in particular to a multi-mode multi-heating unit load real-time optimal scheduling method and management system. Background technique [0002] Nowadays, the construction of smart power plants is an important part of the development of smart energy. Among them, smart management, real-time monitoring, real-time regulation and optimization, energy saving and consumption reduction, and improvement of energy efficiency are the core of the development of smart power plants. [0003] However, in the subdivision field of power plant optimization, due to the different demands of heat supply and power supply, the strong coupling relationship between heat and power, and the policy encouragement of peak regulation and bidding, the mechanism and process model is variable. , the model needs to be adjusted accordingly; the traditional automatic generation control (AGC, Automatic Generation Control) directl...

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Problems solved by technology

[0003] However, in the subdivision field of power plant optimization, due to the different demands of heat supply and power supply, the strong coupling relationship between heat and power, and the policy encouragement of peak regulation and bidding, the mechanism and process model is variable. , the model needs to be adjusted accordingly; the traditional automatic generation control (AGC, Automatic Generation Control) directly controls the load of each generator set and electric heat storage equipment for the provincial regulation command, and the power plant side cannot independently optimize according to the adjustment performance of the unit and external conditions Adjustment, basically no optimization space or narrow optimization space

[0004] In the name "a plant-level thermal power plant load distribution method" (CN109145449A) only by constructing the quadratic exponential relationship between the standard coal consumption of each unit and the operating load of the unit to optimize, there is no strict construction of the generator set Mechanism simulation model, so this method is only an improvement of the genetic algorithm, and the scope of application is narrow, and it cannot be applied to the power plant optimization task of load distribution with strong coupling of thermoelectricity under multiple heating modes

[0005] In the name "calculation method, system and storage medium for thermal power plant load distribution" (CN108122079A), the quadratic exponential relationship between the standard coal consumption of each unit and the operating load of the unit is constructed with the above method, and genetic algorithm is used to carry out Optimizing, it cannot be applied to the load distribution optimization task of power plants with strong thermoelectric coupling under multiple heating modes

[0006] In the title "A Method for Thermal Power Plant Load Optimization Allocation Based on Hybrid Intelligent Algorithm" (CN112183843A), the reciprocal of the objective function is used as the fitness function, but the objective function has no explicit formula, and it is also an improvement on the genetic algorithm , and there is no process modeling of the application mechanism, so it cannot be applied to the load distribution optimization task of the power plant with strong coupling of thermoelectricity under the multi-heating mode

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