CN-122026493-A - Collaborative optimization method for participation of electric-hydrogen-methanol coupling system in electric market

CN122026493ACN 122026493 ACN122026493 ACN 122026493ACN-122026493-A

Abstract

The application relates to a collaborative optimization method for participation of an electric-hydrogen-methanol coupling system in an electric market, which comprises the steps of collecting full-time sequence operation data, simulating extreme working conditions, constructing a typical scene system by combining automatic power generation control frequency modulation to form a fusion scene set, constructing a mathematical physical model of multi-source equipment, inputting the full-time sequence operation data, the fusion scene set and scene characteristics, outputting the full-time sequence operation data, the fusion scene set and scene characteristics to a coupling relation of system energy flow and material flow and an electric-hydrogen-methanol time sequence coupling rule, optimizing in two stages based on the mathematical physical model and the fusion scene set, constructing a total benefit maximization objective function and converting the total benefit maximization objective function into a mixed integer linear programming model. The application realizes the double-identity collaborative optimization of the electric energy consumer and the flexibility service provider of the electric-hydrogen-methanol system, integrates the electricity purchasing, frequency modulation bidding and power response strategies, explores the flexibility value of the chemical production unit in the electric market, improves the economic benefit, and solves the defects of single-market optimization or electric-chemical separation scheduling.

Inventors

CHENG LIMIN
MA LILI
CAO YANG
ZHAO ZIKUN
ZHANG PENGFEI
XU CHENGZHANG

Assignees

中广核风电有限公司
电力规划总院有限公司

Dates

Publication Date: 20260512
Application Date: 20260212

Claims (10)

1. Collecting full-time operation data containing wind-light output, power grid cost and core equipment parameters, simulating extreme working conditions, combining automatic power generation control frequency modulation to construct a typical scene system, and forming a fusion scene set through scene reduction and weight distribution, wherein scene characteristics comprise time sequence relevance, probability distribution, working condition characteristics and parameter market change; based on the scene characteristics, constructing a mathematical physical model containing wind, light, an electrolytic tank, a hydrogen storage tank and an alcoholization reactor, inputting full-time operation data, fusing scene sets and scene characteristics, and outputting a coupling relation of system energy flow and material flow and a time sequence coupling rule of electricity-hydrogen and hydrogen-methanol; optimizing in two stages based on the mathematical physical model and the fusion scene set; and constructing a total income maximization objective function, converting the total income maximization objective function into a mixed integer linear programming model, solving the total income maximization objective function by adopting a solver, and visually outputting an optimal strategy and an evaluation result.
2. The method for collaborative optimization of an electro-hydro-methanol coupling system for participation in an electrical market according to claim 1 wherein constructing a mathematical physical model includes the steps of: Establishing a system model comprising wind-solar power generation, power grid electricity purchasing, an electrolytic tank, a hydrogen storage tank and an alcoholization reactor, defining an energy flow and material flow coupling path, introducing a device dynamic response delay coefficient, and establishing an electricity-hydrogen time sequence dynamic coupling conversion equation and a hydrogen-methanol time sequence dynamic coupling synthesis equation by combining a time sequence capacity change rule of the hydrogen storage tank to realize system operation state simulation; core constraint conditions are set, wherein the constraint conditions comprise electric power balance constraint, wind power output constraint, photovoltaic output constraint and hydrogen storage capacity constraint, and the safe operation of equipment and the feasibility of system operation are ensured.
3. The method for collaborative optimization of an electro-hydro-methanol coupling system for participation in an electrical market according to claim 1 wherein constructing a total revenue maximization objective function includes the steps of: The method comprises the steps of constructing a total profit maximization objective function of a system full optimization period, wherein the total profit is green hydrogen and green methanol sales profit and frequency modulation auxiliary service compensation profit, and deducting the annual equipment fixed investment, system operation maintenance and energy consumption cost; and converting the objective function into a mixed integer linear programming model, and solving by adopting a business solver to obtain the optimal daily bidding and real-time operation strategy.
4. The method for collaborative optimization of an electro-hydro-methanol coupling system for an electric market according to claim 3 wherein the objective function model is: ; Wherein, the 、 The time-series costs for the green hydrogen and green methanol regions, 、 Time series yields of green hydrogen and green methanol, respectively; the electricity consumption of the synthetic methanol generated after the system is cooperatively optimized is calculated; For a set of frequency-modulated instructions, As a result of the probability, Is the index of the frequency modulation performance, For the frequency-modulated mileage, To compensate for the criteria, I e I, Is the total income; 、 the time-series costs for the green hydrogen and green methanol regions, 、 The time series yields of green hydrogen and green methanol, respectively, T is time, T is period, For the output of wind power, Is a photovoltaic output, For grid cost, max is the maximum function.
5. The collaborative optimization method for participation in the electric market by an electro-hydro-methanol coupling system according to claim 1, wherein simulating extreme conditions and constructing a typical scenario system in combination with automatic power generation control frequency modulation comprises the steps of: Acquiring and processing time sequence random variable data containing wind and light output, power grid cost and time sequence equipment operation parameters, correcting the data through a data fusion and error correction mechanism, and quantifying time sequence association degree; And generating a typical scene related to the automatic power generation control frequency modulation auxiliary service based on the historical data, removing redundant scenes by adopting a scene reduction algorithm, dynamically distributing time sequence probability weights, and forming a complete fusion scene set with the extreme scenes.
6. The method for collaborative optimization of an electro-hydro-methanol coupling system for participation in an electrical market according to claim 2, wherein the electro-hydro time sequence dynamic coupling transformation equation is: In the formula (I), in the formula (II), Is the hydrogen quantity produced by the electrolyzer at the moment t; Is the electric energy conversion efficiency of the electrolytic cell; Is the actual input electric power of the electrolytic cell at the time t-lambda; Is the time sequence calculation step length; is the low heating value of hydrogen; the hydrogen-methanol time sequence dynamic coupling synthesis equation is as follows: In the formula (I), in the formula (II), Is the amount of methanol produced by the alcoholization reactor at the moment t; Is the conversion efficiency of the alcoholization reactor; The time sequence capacity of the hydrogen storage tank at the time t; is an alcoholization reactor t- A reaction rate coefficient at time; is the dynamic response delay coefficient of the alcoholization reactor.
7. The collaborative optimization method for participation in an electric market of an electro-hydro-methanol coupling system according to claim 1, wherein the two-stage optimization based on a mathematical physical model and a fusion scene set comprises the steps of: The day-ahead decision stage is based on a future day new energy output and power grid cost prediction scene, aims at maximizing a system total income expected value, and synergistically optimizes a day-ahead electric energy market electricity purchasing plan, a frequency modulation capacity bidding strategy and a green hydrogen and green methanol production plan, so that the market bidding and chemical production are prevented from being disjointed; and in the real-time scheduling stage, equipment operation and power grid electricity purchasing parameters are dynamically adjusted according to actual new energy output, power grid cost and frequency modulation instruction working conditions, and a deviation early warning fine adjustment mechanism is adopted.
8. The collaborative optimization method for participation in an electric power market by an electric-hydrogen-methanol coupling system according to claim 7 is characterized in that emergency dispatch is triggered under extreme working conditions, equipment priority of an electrolytic tank, a hydrogen storage tank and an alcoholization reactor is defined, market and frequency modulation performance are guaranteed preferentially, and safe and stable operation of the system is guaranteed.
9. The collaborative optimization method for participation in an electric market for an electro-hydro-methanol coupling system of claim 1, wherein risk assessment is performed at a day-ahead decision stage to assess offending risk and revenue fluctuations for different bidding strategies based on probability weights for typical scenarios.
10. The method for collaborative optimization of an electro-hydro-methanol coupling system in an electrical market according to claim 1 wherein risk assessment includes the steps of: The method comprises the steps of determining an evaluation object and basic input, wherein the evaluation object is a market bidding strategy of electricity purchasing and frequency modulation auxiliary service of all electric energy markets to be selected, and the basic input comprises a typical scene, a time sequence probability weight, a scene parameter predicted value and a system constraint condition; Constructing a profit and risk assessment index system, weighting and calculating a profit class index and a risk class index based on typical scene probability weights, and defining a default scene definition; And setting a dynamically adjustable evaluation threshold, screening a strategy which meets the threshold and has the highest expected value of total benefits, and screening according to the benefit and risk ratio when multiple strategies meet the requirements so as to realize the balance of low risk and high benefit.

Description

Collaborative optimization method for participation of electric-hydrogen-methanol coupling system in electric market Technical Field The application relates to the technical field of energy storage systems, in particular to a collaborative optimization method for participation of an electric-hydrogen-methanol coupling system in an electric power market. Background The electric-hydrogen-methanol multi-energy coupling cooperative operation can be used as a large-scale and long-period advanced energy storage technology path, solves the problems of difficult power balance, complex system regulation, damaged electric energy quality, reduced stability margin and the like caused by new energy fluctuation, and supports the safe and stable operation of a novel electric power system. Through large-scale on-site digestion, intermittent and fluctuating wind-light resources are converted into storable and transferable green methanol chemical fuels, so that the limit of power grid digestion can be broken through, and the efficient and flexible utilization of new energy is promoted. However, the prior art has obvious defects that when the electric-hydrogen-methanol integrated system participates in a multi-stage electric power market, the electric energy market, the frequency modulation and other auxiliary service markets are involved, and the internal energy flow is tightly coupled with the hydrogen and methanol material flow, so that the dynamic response characteristic difference of each link is large, and the collaborative optimization scheduling is difficult to carry out, so that the problem that the overall benefit cannot be maximized is caused. Specifically, the traditional method is used for optimizing the power system and the chemical system separately and independently, or only considers a single market, ignores the overall flexibility value of the system and the benefit opportunity of multiple time scales, and cannot fully exert the potential of the system as large-scale and long-period advanced energy storage so as to support the safe and stable operation of the novel power system. Therefore, the invention provides a collaborative optimization method for participation of an electric-hydrogen-methanol coupling system in the electric market. Disclosure of Invention In order to overcome the defects in the prior art, the application provides a cooperative optimization method for an electric-hydrogen-methanol coupling system to participate in the electric market, which adopts the following technical scheme. An electric-hydrogen-methanol coupling system participates in a collaborative optimization method of an electric power market, which comprises the following steps. S1, full-time sequence data input and scene modeling, wherein the full-time sequence operation data of a system is acquired, the full-time sequence operation data comprise random variables such as wind and light output, power grid cost and the like and core equipment operation parameters, and the data are corrected through 5G+ edge calculation and wavelet analysis-neural network fusion technology; The method comprises the steps of simulating extreme working conditions, simulating operation scenes under extreme working conditions such as typhoons, heavy rain and extreme power grid cost fluctuation, calculating initial time sequence probability weights of various extreme scenes based on historical extreme working condition occurrence frequency, carrying out cooperative constraint on the initial time sequence probability weights of all the extreme scenes to ensure that the overall weight ratio is not more than 20%, avoiding excessively influencing a conventional optimization result, forming a complete scene system by the extreme scenes, the AGC frequency modulation typical scenes and the conventional operation scenes, carrying out unified normalization processing on the time sequence probability weights of the three scenes, ensuring that the total time sequence probability weights of the three scenes is 1, synchronously inputting the time sequence probability weights into subsequent daily decision, risk assessment and real-time scheduling links, ensuring that an optimization strategy can still keep stable under the extreme conditions, and improving the robustness of a scheme. S2, constructing an electric-hydrogen-methanol system model, namely constructing a mathematical physical model of a coupling system which covers wind, solar cells, electrolytic tanks, hydrogen storage tanks and alcoholization reactors based on S1 scene characteristics, defining an energy flow and material flow coupling path, introducing a device dynamic response delay coefficient, establishing an electric-hydrogen and hydrogen-methanol time sequence dynamic coupling equation, setting core constraints such as electric balance, wind-solar output, hydrogen storage capacity and the like, and forming an optimized dispatching core model. And S3, two-stage stochastic programming optimizatio