Optimal dispatching control method for electrolytic aluminum system with new energy green electricity access

Abstract

The present application relates to a kind of new energy green electricity access electrolytic aluminium system optimization deployment control method, the method includes: obtaining the predicted power of aluminium production electricity load and historical green electricity value set;Predicted green electricity value is obtained based on the historical green electricity value set, wherein the predicted power of aluminium production electricity load, the historical green electricity value set, the predicted green electricity value are all based on time period or time point value;Optimization is carried out based on the predicted green electricity value, the predicted power of aluminium production electricity load and first model, and the actual power of aluminium production electricity load and total output of thermal power unit are obtained;Based on the actual power of aluminium production electricity load and total output of thermal power unit, the output of each thermal power unit is obtained by optimization.The present application realizes the efficient optimization of energy distribution between each part in electrolytic aluminium system.Through this optimization scheduling, the wider application of green energy is promoted, the operation cost of electrolytic aluminium system is also reduced, and positive contribution is made to reduce carbon emissions.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of regional power optimization, and particularly provides a new energy green electricity access electrolytic aluminum system optimization deployment control method. BACKGROUND

[0002] In an electric power system, if there is an aluminum production industry, it is easy to face the problems of high carbon emission and high operation cost. Especially in a region where green electricity (such as wind energy and solar energy) and aluminum production industry exist at the same time, how to effectively adjust and optimize the reasonable distribution of energy among aluminum production enterprise electricity, green electricity and thermal power determines the amount of carbon emission and the level of operation cost.

[0003] In the prior art, it is mostly dependent on experience, and the effective coordination and utilization of energy cannot be fully achieved.

[0004] Correspondingly, there is a need in the art for a new electrolytic aluminum system deployment optimization scheme to solve the above problems. SUMMARY

[0005] In order to overcome the above defects, the present application is proposed to provide a solution or at least partially solve the technical problems of high carbon emission and high cost caused by relying on experience to determine the energy distribution in the electrolytic aluminum system in the prior art.

[0006] In a first aspect, the present application provides a new energy green electricity access electrolytic aluminum system optimization deployment control method, the method comprising: obtaining aluminum production electricity load prediction power and a set of historical green electricity values; obtaining a predicted green electricity value based on the set of historical green electricity values, wherein the aluminum production electricity load prediction power, the set of historical green electricity values and the predicted green electricity value are values based on a time period or a time point; performing optimization based on the predicted green electricity value, the aluminum production electricity load prediction power and a first model to obtain aluminum production electricity load actual power and total thermal power unit output; and performing optimization based on the aluminum production electricity load actual power and the total thermal power unit output to obtain the output of each thermal power unit.

[0007] As an alternative or supplement to the above scheme, in the method according to an embodiment of the present application, the first model comprises an aluminum regulation module, and "performing optimization based on the predicted green electricity value, the aluminum production electricity load prediction power and the first model to obtain aluminum production electricity load actual power and total thermal power unit output" comprises: performing optimization based on the predicted green electricity value, the aluminum production electricity load prediction power, the aluminum regulation module, a first objective function and a first constraint condition to obtain aluminum production electricity load actual power and total thermal power unit output.

[0008] As an alternative or supplement to the above solution, in the method according to an embodiment of the application, the "obtaining the predicted green electricity value based on the set of historical green electricity values" comprises: obtaining a first sequence based on the set of historical green electricity values; accumulating the first sequence to obtain a second sequence; and obtaining the predicted green electricity value based on the second sequence and a grey model.

[0009] As an alternative or supplement to the above solution, in the method according to an embodiment of the application, the "obtaining the output of each thermal power generating unit based on the actual power of the aluminum production electricity load and the total output of the thermal power generating units" comprises: obtaining the output of each thermal power generating unit based on the second objective function, the second constraint condition, the actual power of the aluminum production electricity load and the total output of the thermal power generating units.

[0010] As an alternative or supplement to the above solution, in the method according to an embodiment of the application, the mathematical expression of the aluminum production adjustment module comprises: , , wherein P is the adjusted electrolytic aluminum power, is the up-regulated gear, is the down-regulated gear, is the up-regulated power per gear, is the down-regulated power per gear, is the current electrolytic aluminum power, is the number of up-regulated gears, is the number of down-regulated gears.

[0011] As an alternative or supplement to the above solution, in the method according to an embodiment of the application, the mathematical expression of the aluminum production adjustment module further comprises: wherein M is the maximum number of adjustments; , wherein , are the adjustment time differences of the up-regulation and the down-regulation respectively, , are the adjustment time difference limits of the up-regulation and the down-regulation respectively.

[0012] As an alternative or supplement to the above solution, in the method according to an embodiment of the application, the first objective function comprises: , is the number of wind farms; is the output of the i th wind farm at the t th time period.

[0013] As an alternative or supplement to the above solution, in the method according to an embodiment of the application, the second objective function comprises: , ,

[0014] ​, wherein is the total cost of system operation, is the cost of thermal power operation, is the cost of wind power penalty; represents the start-stop state of the thermal power unit; is the operation cost coefficient of the thermal power unit j; is the wind power penalty coefficient of the i-th wind power unit; ΔT is the unit period of the system.

[0015] In a second aspect, a control device is provided, comprising a processor and a storage device, the storage device being adapted to store a plurality of computer programs, the computer programs being adapted to be loaded and run by the processor to execute the new energy green electricity access electrolytic aluminum system optimization deployment control method of any one of the technical solutions of the above-mentioned new energy green electricity access electrolytic aluminum system optimization deployment control method.

[0016] In a third aspect, a computer readable storage medium is provided, which has a plurality of computer programs stored therein, the computer programs being adapted to be loaded and run by a processor to execute the new energy green electricity access electrolytic aluminum system optimization deployment control method of any one of the technical solutions of the above-mentioned new energy green electricity access electrolytic aluminum system optimization deployment control method.

[0017] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0018] In the implementation of the technical solutions of the present application, through the present technology, efficient optimization of energy distribution between aluminum production electricity load, green electricity and thermal power in the electrolytic aluminum system is realized. This optimized scheduling not only significantly improves the consumption rate of renewable energy in the power grid, promotes the wider application of green energy, but also reduces the operation cost of the electrolytic aluminum system, and makes a positive contribution to reducing carbon emissions. In addition, the application of this technology also enhances the overall stability and reliability of the electrolytic aluminum system, ensuring the continuity of power supply and the efficient operation of aluminum production. BRIEF DESCRIPTION OF DRAWINGS

[0019] The disclosure of the present application will become more readily understood by referring to the accompanying drawings. It will be readily understood to those skilled in the art that these drawings are merely intended to illustrate the present application and are not intended to limit the scope of protection of the present application. In addition, similar numbers in the figures are used to represent similar components, wherein:

[0020] Figure 1 is the main step flowchart of the new energy green electricity access electrolytic aluminum system optimization deployment control method according to an embodiment of the present application;

[0021] Figure 2is a secondary step flow diagram of a new energy green electricity access electrolytic aluminum system optimal deployment control method according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] Some embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art will understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0023] Some terms related to the present application will be explained first.

[0024] Consumption rate: measures the ability of a power system to effectively utilize renewable energy, reflecting the amount of renewable energy received and utilized by the power grid within a specific period. High consumption rate means reducing dependence on fossil fuels and reducing carbon emissions, while low consumption rate may lead to dependence on traditional energy, which is not conducive to environmental protection. Improving the consumption rate is a key goal of green energy transformation and sustainable development.

[0025] Referring to the accompanying Figure 1 , Figure 1 is a main step flow diagram of a new energy green electricity access electrolytic aluminum system optimal deployment control method according to an embodiment of the present application. As shown in Figure 1 and Figure 2 , the new energy green electricity access electrolytic aluminum system optimal deployment control method in the embodiment of the present application mainly includes the following steps S10-S40.

[0026] Step S10: Obtain the aluminum production electricity load forecast power and the historical green electricity value set.

[0027] In this embodiment, the aluminum production electricity load forecast power is a forecast power obtained based on historical experience, and the historical green electricity value set is historical data for multiple time periods or time points in the past, which also contains multiple sets of historical green electricity values in each time period or time point.

[0028] In one embodiment, the historical green electricity value set reflects the green electricity value in each time period or time point. It should be noted that in this embodiment, the time point and the time period are the same expression. For example, one set of historical green electricity values in the historical green electricity value set is,

[0029] Obviously, it is a set of historical wind power generation in a period, assuming that the starting time of the current period is 00:00, then the above four data correspond to three time periods, respectively 00:00~00:15, 00:15~00:30, 00:30~00:45. The power generation of each time period is 40kW, 45kW, 50kW respectively. In summary, the time point and the time period can be converted to each other under the preset rule, that is, the time point and the time period are the same expression in this embodiment, and both have the same meaning.

[0030] In this embodiment, green electricity at least includes one of wind energy or light energy. In this embodiment, the green electricity value can be a direct representation of green electricity or an indirect representation of green electricity. The direct representation of green electricity is the light intensity at a certain time point or the wind speed at a certain time point, and the indirect representation of green electricity is the light energy power generation at a certain time point or the wind energy power generation at a certain time point. In a certain model, the direct representation of green electricity and the indirect representation of green electricity satisfy a certain specific function relationship, that is, when the direct representation of green electricity is known, the indirect representation of green electricity can be obtained through the function relationship, and vice versa.

[0031] In this embodiment, the aluminum-making electricity load prediction power is based on empirical data. In this embodiment, the aluminum-making electricity load prediction power is also the prediction power corresponding to the time period. In the prior art, the progress of aluminum making can be directly controlled according to the aluminum-making electricity load prediction power. In a scenario provided in this embodiment, the electrolytic aluminum process using electrolytic cells is used to make aluminum. The prior art can directly control the working progress of electrolytic aluminum according to the aluminum-making electricity load prediction power. However, if the aluminum-making electricity load prediction power is directly used to directly control the progress of aluminum making, there is a great waste of green electricity in the same region, which makes the consumption rate of the whole region low, causes unreasonable use of energy, and thus increases the economic cost and causes waste of energy.

[0032] Step S20: obtaining a predicted green electricity value based on the set of historical green electricity values.

[0033] In this embodiment, the predicted green electricity value is obtained by the gray model and the set of historical green electricity values. The predicted green electricity value is the predicted future corresponding period or generated power.

[0034] In one embodiment, starting with the collected historical data, a sequence is generated, referred to as the first sequence. This sequence directly reflects the historical performance of green power. However, since the original data may contain random fluctuations and nonlinear characteristics, direct prediction may result in large errors. To solve this problem, the first sequence is then subjected to an accumulation operation to generate a second sequence. The accumulation process helps to eliminate random fluctuations in the data, making the sequence exhibit a more smooth and continuous trend, thereby laying the foundation for the application of the grey model.

[0035] The next step in building the grey model is parameter estimation. This usually involves calculating the parameter vector matrix of the grey model. The parameter vector matrix is the core of the model prediction, which contains key information about the development of the sequence. In this embodiment, the grey model uses the GM(1,1) model, which is a grey prediction model used for single-variable time series data prediction. The estimation of model parameters is done through mathematical and statistical methods, with the goal of finding parameters that best represent the trend of historical data.

[0036] After parameter estimation, the model generates a predicted sequence using these parameters. This predicted sequence is based on the trends and patterns of historical data to predict future green power performance. However, due to the accumulation of errors in the prediction process, it is necessary to further optimize the prediction results. This is done by discarding the original data part of the predicted sequence and only keeping the predicted part, and then re-accumulating and building the model based on this, until the set number of predictions is reached.

[0037] Repeating this process helps to gradually reduce prediction errors and enhance the model's ability to capture future green power output trends. Through this method, the power generation of green energy such as wind and light can be effectively predicted in the future, i.e. the predicted green power value, thereby providing an important basis for the scheduling and management of the electrolytic aluminum system.

[0038] Overall, this process combines data collection, sequence accumulation, grey model establishment, and parameter estimation, forming a comprehensive prediction framework. This framework is particularly suitable for handling small amounts of data and uncertainty, such as renewable energy power generation prediction. Through this method, the accuracy of the prediction can be effectively improved, providing support for the optimal scheduling and integration of green power in the electrolytic aluminum system.

[0039] Step S30: Based on the predicted green power value, the aluminum-making electricity load prediction power, and the first model, the aluminum-making electricity load actual power and the total output of the thermal power unit are obtained.

[0040] In this embodiment, the meaning of optimization is that after the model is brought into the data, the results obtained at the same time meet the restrictions of the model itself, the objective function, and the restrictions of the constraint conditions.

[0041] In one embodiment, the first model comprises an aluminum production adjustment module. The actual power of the aluminum production electricity load and the total output of the thermal power unit are obtained based on the predicted green electricity value, the predicted power of the aluminum production electricity load, the first model, and optimization. The actual power of the aluminum production electricity load and the total output of the thermal power unit are obtained based on the predicted green electricity value, the predicted power of the aluminum production electricity load, the aluminum production adjustment module, the first objective function, and the first constraint condition.

[0042] In the present embodiment, the aluminum production adjustment module is a simulation model of the aluminum production adjustment device. In the present embodiment, the aluminum production adjustment uses an electrolytic aluminum process based on an electrolytic aluminum cell. The aluminum production adjustment module is also constructed based on this. In the present embodiment, the mathematical expression of the aluminum production adjustment module is as follows:

[0043]

[0044]

[0045]

[0046] wherein P is the adjusted electrolytic aluminum power, n1 is the up-regulation gear, n2 is the down-regulation gear, is the up-regulation power of each gear, is the down-regulation power of each gear, is the current electrolytic aluminum power, is the number of up-regulation gears, is the number of down-regulation gears. In the present embodiment, when modeling the aluminum production adjustment device, it is considered that the power of the aluminum production adjustment device based on the electrolytic aluminum process cannot be frequently adjusted, otherwise the device will be damaged, so the gears are limited. Preferably, in one embodiment, the up-regulation amount of the electrolytic aluminum when participating in demand response is 10% of the current electrolytic aluminum power, and the down-regulation amount is 40% of the current electrolytic aluminum power. Preferably, in one embodiment, the number of up-regulation gears is set to 5, and the number of down-regulation gears is set to 8.

[0047] The mathematical expression of the aluminum production adjustment module also includes:

[0048]

[0049] wherein n is the maximum number of adjustments. In the present embodiment, the above formula is to limit the number of power adjustments of the aluminum production adjustment device in order to prolong the service life of the aluminum production adjustment device.

[0050] M The mathematical expression of the aluminum production adjustment module also includes:

[0051]

[0052] ​​​​​​,

[0053] ,

[0054] wherein , are respectively the up-regulation and down-regulation time difference, 、 are respectively the up-regulation and down-regulation time difference limits. In the embodiment, the above formula is to limit the time interval of power regulation of the aluminum production regulation device in order to prolong the service life of the aluminum production regulation device.

[0055] When optimization is performed, the first objective function and the first constraint condition are also met. When the optimization in this step is completed, the case that best meets the first objective function is found on the basis of meeting the first constraint condition as the final optimization result. The first objective function makes the actual power of the aluminum production electricity load and the consumption rate corresponding to the total output of the thermal power unit highest

[0056] In the embodiment, taking green electricity as an example of wind energy, the first objective function is as follows:

[0057] ,

[0058] is the number of wind farms; is the output of the i th wind farm at the t time period.

[0059] Based on the first objective function, the maximum consumption of green electricity is taken as the target, and the thermal power, wind power and aluminum production electricity load are optimized under the condition of meeting the first constraint condition.

[0060] The first constraint condition is as follows:

[0061] ,

[0062] is the total active power output of the thermal power unit; is the predicted power of the aluminum production electricity load; is the number of electrolytic aluminum cells; is the power of an electrolytic aluminum cell; is the regulated power of an electrolytic aluminum cell.

[0063] The first constraint condition also includes:

[0064] ,

[0065] ,

[0066] wherein, 、 are respectively the upper and lower limits of the thermal power unit output , are the upper and lower limits of the load spinning reserve, respectively; , are the upper and lower limits of the wind power spinning reserve, respectively. In the embodiment, the above formula is to consider the uncertainty of green power output, considering that there is an error between the predicted power of aluminum production load and the predicted green power value and the actual value, so the spinning reserve is increased.

[0067] The first constraint condition also includes the wind power output constraint as follows:

[0068] ,

[0069] is the predicted power of the ith wind turbine.

[0070] The first constraint condition also includes the thermal power unit constraint as follows:

[0071] Output power constraint:

[0072] ,

[0073] Ramp rate constraint:

[0074] ,

[0075] ,

[0076] wherein is the output of the thermal power unit at the t-1 period, and are the upper and lower ramp rate limits of the thermal power unit.

[0077] The first constraint condition also includes the power constraint of the aluminum production regulation module as follows:

[0078] wherein the regulation power constraint is as follows:

[0079] ,

[0080] , are the upper and lower limits of the regulation power of the electrolytic aluminum load.

[0081] The regulation frequency constraint is as follows:

[0082] ,

[0083] , denotes the regulation state of the t period and the t-1 period, denotes the electrolytic aluminum participating in regulation at the t period, t period of electrolytic aluminum does not participate in the adjustment.

[0084] In the embodiment, the actual power of the aluminum power load and the total output of the thermal power unit are obtained based on the predicted green electricity value, the aluminum power load predicted power, the aluminum power adjustment module, the first target function and the first constraint condition.

[0085] Step S40: based on the actual power of the aluminum power load and the total output of the thermal power unit, the output of each thermal power unit is obtained.

[0086] In the embodiment, the output of each thermal power unit is obtained based on the second target function, the second constraint condition, the actual power of the aluminum power load and the total output of the thermal power unit.

[0087] In one embodiment, the second target function is as follows:

[0088] ,

[0089] ,

[0090] ,

[0091] Wherein is the total cost of system operation, is the operation cost of thermal power, is the cost of wind power penalty; represents the start-stop state of the thermal power unit; is the operation cost coefficient of the thermal power unit j; is the wind power penalty coefficient of the i th wind turbine; and ΔT1 is the number of system wind power abandonment periods.

[0092] In the embodiment, the second target function takes the minimum system operation cost as the optimization target, optimizes the output of each thermal power unit, and considers the fuel cost of thermal power and the wind power abandonment cost.

[0093] The second constraint condition includes thermal power constraint, thermal power climbing constraint and thermal power output constraint, wherein the thermal power constraint is as follows:

[0094] ,

[0095] is the number of thermal power units; is the output of the thermal power unit.

[0096] The thermal power climbing constraint is as follows:

[0097] ,

[0098] ,

[0099] wherein is the thermal power output of the thermal power unit; , is the upper and lower ramping power limit of the thermal power unit.

[0100] The thermal power output constraints are as follows:

[0101]

[0102] wherein, is the upper and lower limit of the thermal power output of the thermal power unit.

[0103] In this embodiment, the thermal power is further optimized to obtain the output of each thermal power unit. Through this method, each thermal power unit can be adjusted to the optimal output level according to the actual demand and cost efficiency. This not only helps to reduce the operating cost of the entire electrolytic aluminum system, but also improves the utilization rate of renewable energy, thereby reducing carbon emissions and environmental impact. At the same time, optimizing the output of the thermal power unit can also maintain the stability of the power grid, adapt to the power demand of the aluminum industry and other industrial users, and ensure the continuity and reliability of power supply.

[0104] It should be noted that although the above embodiments describe the steps in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, the different steps do not necessarily have to be executed in such an order, they can be executed simultaneously (in parallel) or in other orders, and these changes are within the scope of protection of the present application.

[0105] Those skilled in the art can understand that all or part of the processes in the method of the above embodiment can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of each method embodiment described above can be implemented. The computer program includes computer programs, which can be in source code form, object code form, executable files or some intermediate forms, etc. The computer readable storage medium can include any entity or device, medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program. It should be noted that the computer readable storage medium does not include electrical carrier signals and telecommunication signals.

[0106] Further, the present application also provides a control device. In an embodiment of the control device according to the present application, the control device comprises a processor and a storage device, the storage device can be configured to store a program of the new energy green electricity access electrolytic aluminum system optimal deployment control method of the above-mentioned method embodiments, and the processor can be configured to execute the program in the storage device, which includes but is not limited to the program of the new energy green electricity access electrolytic aluminum system optimal deployment control method of the above-mentioned method embodiments. For the convenience of description, only the parts related to the embodiments of the present application are shown, and the specific technical details not disclosed are referred to the method part of the embodiments of the present application. The control device can be a control device equipment formed by various electronic devices.

[0107] Further, the present application also provides a computer readable storage medium. In an embodiment of the computer readable storage medium according to the present application, the computer readable storage medium can be configured to store a program of the new energy green electricity access electrolytic aluminum system optimal deployment control method of the above-mentioned method embodiments, which can be loaded and run by a processor to realize the above-mentioned new energy green electricity access electrolytic aluminum system optimal deployment control method. For the convenience of description, only the parts related to the embodiments of the present application are shown, and the specific technical details not disclosed are referred to the method part of the embodiments of the present application. The computer readable storage medium can be a storage device equipment formed by various electronic devices, and optionally, the computer readable storage medium in the embodiments of the present application is a non-transitory computer readable storage medium.

[0108] Further, it should be understood that, since the setting of each module is only for illustrating the functional units of the device of the present application, the corresponding physical device of the module can be the processor itself, or a part of software, hardware or the combination of software and hardware in the processor. Therefore, the number of each module in the figure is only illustrative.

[0109] Those skilled in the art can understand that each module in the device can be adaptively split or combined. Such splitting or combining of specific modules does not cause the technical solution to deviate from the principles of the present application, and therefore, the technical solution after splitting or combining will fall within the protection scope of the present application.

[0110] So far, the technical solution of the present application has been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without deviating from the principles of the present application, and the technical solution after the changes or replacements will fall within the protection scope of the present application.

Claims

1. A method for optimizing and controlling the integration of new energy green electricity into an electrolytic aluminum system, characterized in that, include: Obtain the predicted power load for aluminum production and a set of historical green electricity values; The predicted green electricity value is obtained based on the historical green electricity value set, wherein the predicted power of the aluminum production electricity load, the historical green electricity value set, and the predicted green electricity value are all values ​​based on time periods or points in time; Based on the predicted green electricity value, the predicted power of the aluminum production electricity load, and the first model, the actual power of the aluminum production electricity load and the total output of the thermal power unit are obtained through optimization. Based on the actual power of the electricity load for aluminum production and the total output of thermal power units, the output of each thermal power unit is obtained through optimization. The first model includes an aluminum production regulation module, which "optimizes the predicted green electricity value, the predicted power of the aluminum production load, and the first model to obtain the actual power of the aluminum production load and the total output of the thermal power unit," including: Based on the predicted green electricity value, the predicted power of the aluminum production load, the aluminum production adjustment module, the first objective function, and the first constraint condition, optimization is performed to obtain the actual power of the aluminum production load and the total output of the thermal power unit. The first objective function makes the absorption rate corresponding to the obtained actual power of the aluminum production load and the total output of the thermal power unit the highest. The mathematical expression of the aluminum production control module includes: P L (t)=P e +n1P u +n2P d , 0≤n1≤b1, 0≤n2≤b2, P u =0.1×P e / b1,P d =0.4×P e / b2, Where P L The adjusted electrolytic aluminum power, n1 is the upward adjustment level, n2 is the downward adjustment level, P u It increases the power for each gear, P d It is to reduce the power in each gear, P e This represents the current electrolytic aluminum power output. b1 indicates the number of gears to be adjusted upwards, and b2 indicates the number of gears to be adjusted downwards.

2. The optimized allocation and control method for integrating new energy green electricity into an electrolytic aluminum system according to claim 1, characterized in that, "Obtaining predicted green electricity values ​​based on the historical green electricity value set" includes: Based on the aforementioned set of historical green electricity values, the first sequence is obtained; By summing the first sequence, we obtain the second sequence; Based on the second data series and the grayscale model, the predicted green electricity value is obtained.

3. The optimized allocation and control method for integrating new energy green electricity into an electrolytic aluminum system according to claim 1, characterized in that, "Optimization based on the actual power load of aluminum production and the total output of thermal power units yields the output of each thermal power unit," including: The output of each thermal power unit is obtained by optimizing based on the second objective function, the second constraint condition, the actual power of the aluminum production load, and the total output of the thermal power units.

4. The optimized allocation and control method for integrating new energy green electricity into an electrolytic aluminum system according to claim 1, characterized in that, The mathematical expression for the aluminum production control module also includes: n1+n2≤M, Where M is the maximum number of adjustments; in These are the adjustment time differences for upward and downward adjustments, respectively. These are the adjustment time difference limits for upward and downward adjustments, respectively.

5. The optimized allocation and control method for integrating new energy green electricity into an electrolytic aluminum system according to claim 1 or 4, characterized in that, The first objective function includes: N W It refers to the number of wind farms; ΔT represents the power output of the i-th wind farm during time period t; ΔT represents the system's unit time period.

6. The optimized allocation and control method for integrating new energy green electricity into an electrolytic aluminum system according to claim 3, characterized in that, The second objective function includes: min F=C G +C P , Where F is the total operating cost of the system, and C G It is the operating cost of thermal power, C P It is the cost of penalties for wind curtailment; Indicates the start-up and shutdown status of the thermal power unit; a j b j c j ρ is the operating cost coefficient of thermal power unit j; i ΔT1 is the wind curtailment penalty coefficient for the i-th wind farm; ΔT1 is the number of wind curtailment periods in the system; N G N is the number of thermal power units; W It refers to the number of wind farms.

7. A control device comprising a processor and a storage device, said storage device being adapted to store a plurality of computer programs, characterized in that, The computer program is adapted to be loaded and run by the processor to perform the optimized allocation and control method for accessing new energy green electricity to the electrolytic aluminum system as described in any one of claims 1 to 6.

8. A computer-readable storage medium storing a plurality of computer programs, characterized in that, The computer program is adapted to be loaded and run by a processor to perform the optimized allocation and control method for accessing new energy green electricity into the electrolytic aluminum system as described in any one of claims 1 to 6.

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