Optimized allocation control method for access of new energy green electricity to electrolytic aluminum system

Abstract

The invention relates to an optimal allocation control method for access of new energy green electricity to an electrolytic aluminum system. The method comprises the following steps: acquiring aluminum-making electrical load predicted power and a historical green electricity value set; obtaining a predicted green electricity value based on the historical green electricity value set, wherein the aluminum-making electrical load predicted power, the historical green electricity value set and the predicted green electricity value are all values based on time periods or time points; performing optimization based on the predicted green power value, the aluminum-making electrical load predicted power and a first model to obtain the actual power of the aluminum-making electrical load and the total output of the thermal power generating unit; and optimization is carried out based on the actual power of the aluminum-making electrical load and the total output of the thermal power generating units to obtain the output of each thermal power generating unit. By means of the technology, efficient optimization of energy distribution among all parts in the electrolytic aluminum system is achieved. The optimal scheduling promotes wider application of green energy, also reduces the operation cost of an electrolytic aluminum system, and makes a positive contribution to reduction of carbon emission.

Description

Technical Field

[0001] The present invention relates to the field of regional power optimization, and specifically provides a method for optimizing the deployment and control of connecting new energy green electricity to an electrolytic aluminum system. Background Art

[0002] If there is an aluminum industry in an electric power system, it is easy to face the problem of high carbon emissions and high operating costs. Especially when green electricity (such as wind power, solar energy) and aluminum industry exist in a region at the same time, how to effectively adjust and optimize the electricity consumption of aluminum enterprises, the reasonable allocation of energy between green electricity and thermal power determines the amount of carbon emissions and the level of operating costs.

[0003] Existing technologies mostly rely on experience and judgment, and fail to fully achieve effective coordination and utilization of energy sources.

[0004] Accordingly, the art needs a new optimization solution for the electrolytic aluminum system to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects, the present invention is proposed to provide a solution or at least partially solve the technical problems in the prior art of relying on experience to determine the energy distribution in the electrolytic aluminum system, thereby causing high carbon emissions and high costs.

[0006] In a first aspect, the present invention provides a method for optimizing the deployment and control of accessing new energy green electricity to an electrolytic aluminum system, the method comprising: obtaining a predicted power of an aluminum production electricity load 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 predicted power of the aluminum production electricity load, the set of historical green electricity values, and the predicted green electricity value are all values ​​based on a time period or a time point; optimizing based on the predicted green electricity value, the predicted power of the aluminum production electricity load, and a first model to obtain the actual power of the aluminum production electricity load and the total output of thermal power units; optimizing based on the actual power of the aluminum production electricity load and the total output of the thermal power units to obtain the output of each thermal power unit.

[0007] As an alternative or supplement to the above scheme, in a method according to an embodiment of the present invention, the first model includes an aluminum production adjustment module, "optimization is performed based on the predicted green electricity value, the predicted power of the aluminum production electricity load and the first model to obtain the actual power of the aluminum production electricity load and the total output of the thermal power unit", including: optimization is performed 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 to obtain the actual power of the aluminum production electricity load and the total output of the thermal power unit.

[0008] As an alternative or supplement to the above scheme, in a method according to an embodiment of the present invention, "obtaining a predicted green electricity value based on the historical green electricity value set" includes: obtaining a first series based on the historical green electricity value set; accumulating the first series to obtain a second series; and obtaining a predicted green electricity value based on the second series and a grayscale model.

[0009] As an alternative or supplement to the above scheme, in a method according to an embodiment of the present invention, "optimization is performed based on the actual power of the aluminum production electricity load and the total output of the thermal power units to obtain the output of each thermal power unit" includes: obtaining the output of each thermal power unit based on the second objective function, the second constraint, the actual power of the aluminum production electricity load and the total output of the thermal power units.

[0010] As an alternative or supplement to the above solution, in a method according to an embodiment of the present invention, the mathematical expression of the aluminum adjustment module includes: 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 is the adjusted electrolytic aluminum power, n1 is the upward adjustment position, n2 is the downward adjustment position, P u is the power increase per gear, P d is the power adjustment for each gear, Pe is the current electrolytic aluminum power, b1 is the number of gears to be adjusted up, and b2 is the number of gears to be adjusted down.

[0011] As an alternative or supplement to the above solution, in a method according to an embodiment of the present invention, the mathematical expression of the aluminum production adjustment module further includes: n1+n2≤M, where M is the maximum number of adjustments; in are the adjustment time differences for upward and downward adjustment, They are the adjustment time difference limits for upward and downward adjustment respectively.

[0012] As an alternative or supplement to the above solution, in a method according to an embodiment of the present invention, the first objective function includes: N W is the number of wind farms; is the output of the i-th wind farm in period t.

[0013] As an alternative or supplement to the above solution, in a method according to an embodiment of the present invention, the second objective function includes:

[0014] Where F is the total cost of system operation, C G is the thermal power operation cost, C P is the penalty cost for wind curtailment; Indicates the start and stop status of the thermal power unit; a j ,b j ,c j is the operating cost coefficient of thermal power unit j; ρ i is the wind abandonment penalty coefficient of the i-th wind turbine; ΔT is the system unit time period.

[0015] In a second aspect, a control device is provided, which includes a processor and a storage device, wherein the storage device is suitable for storing multiple computer programs, and the computer programs are suitable for being loaded and run by the processor to execute the new energy green electricity access to the electrolytic aluminum system optimization and deployment control method described in any one of the technical solutions of the above-mentioned new energy green electricity access to the electrolytic aluminum system optimization and deployment control method.

[0016] In a third aspect, a computer-readable storage medium is provided, which stores a plurality of computer programs, wherein the computer programs are suitable for being loaded and run by a processor to execute the method for optimizing the deployment and control of a new energy and green electricity system connected to an electrolytic aluminum system as described in any one of the technical solutions of the method for optimizing the deployment and control of a new energy and green electricity system connected to an electrolytic aluminum system.

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

[0018] In the technical solution of the present invention, the technology is used to achieve efficient optimization of the energy distribution between the aluminum production power load, green electricity and thermal power in the electrolytic aluminum system. This optimized scheduling not only significantly improves the absorption rate of renewable energy in the power grid and promotes the wider application of green energy, but also reduces the operating 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 THE DRAWINGS

[0019] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In addition, similar numbers in the figures are used to represent similar components, among which:

[0020] Figure 1It is a flow chart of the main steps of a method for optimizing the deployment of new energy and green electricity access to an electrolytic aluminum system according to an embodiment of the present invention;

[0021] Figure 2 It is a flow chart of the secondary steps of a method for optimizing the deployment and control of connecting new energy and green electricity to an electrolytic aluminum system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] Some embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0023] Here we first explain some terms involved in the present invention.

[0024] Absorption rate: measures the ability of the power system to effectively utilize renewable energy, reflecting the amount of renewable energy electricity received and utilized by the power grid during a specific period of time. A high absorption rate means less reliance on fossil fuels and lower carbon emissions, while a low absorption rate may lead to reliance on traditional energy, which is not conducive to environmental protection. Improving the absorption rate is a key goal for achieving green energy transformation and sustainable development.

[0025] See attached Figure 1 , Figure 1 The following is a flow chart of the main steps of a method for optimizing the deployment of new energy green electricity into an electrolytic aluminum system according to an embodiment of the present invention. Figure 1 and Figure 2 As shown, the optimization and allocation control method for connecting new energy green electricity to the electrolytic aluminum system in the embodiment of the present invention mainly includes the following steps S10 to S40.

[0026] Step S10: Obtain the predicted power of the aluminum production electricity load and a set of historical green electricity values.

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

[0028] In one embodiment, the historical green power value set reflects the green power values ​​in each period or time point. It should be noted that in this embodiment, the time point and the period are expressed in the same way. For example, a set of historical green power values ​​in the historical green power value set is: time 00:00 00:15 00:30 00:45 Wind power generation 40kW 45kW 50kW 40kW

[0029] Obviously, this is a set of historical wind power generation within a cycle. Assuming that the start time of this cycle is 00:00, the above four data correspond to three time periods, namely 00:00-00:15, 00:15-00:30, and 00:30-00:45. The power generation in each time period is 40kW, 45kW, and 50kW respectively. In summary, time points and time periods can be converted to each other under preset rules, that is, time points and time periods are the same expression in this embodiment, and both have the same meaning.

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

[0031] In this embodiment, the predicted power of the aluminum production load is based on empirical data. In this embodiment, the predicted power of the aluminum production load is also the predicted power of the corresponding time period. In the prior art, the progress of aluminum production can be directly controlled according to the predicted power of the aluminum production load. In a scenario provided by this embodiment, aluminum is produced using the electrolytic aluminum process of the electrolytic cell. The prior art can directly control the progress of the electrolytic aluminum production according to the predicted power of the aluminum production load. However, if the predicted power of the aluminum production load is directly used to directly control the progress of aluminum production, there will be a great waste of green electricity in the same area, resulting in a low absorption rate in the entire area, resulting in irrational use of energy, thereby increasing economic costs and causing energy waste.

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

[0033] In this embodiment, the predicted green power value is obtained by using the grey model and the historical green power value set. The predicted green power value is the predicted future corresponding period or generated electric energy.

[0034] In one implementation, a sequence is generated starting from the collected historical data, which is called 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, directly predicting it may result in large errors. To solve this problem, the first sequence is then accumulated to generate a second sequence. The accumulation process helps to eliminate random fluctuations in the data, so that the sequence shows a smoother and continuous trend, thereby laying the foundation for the application of the grayscale model.

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

[0036] After the parameters are estimated, the model uses these parameters to generate a forecast series. This forecast series predicts the future performance of green electricity based on the trends and patterns of historical data. However, due to the possible accumulation of errors in the forecast process, the forecast results need to be further optimized. This is done by discarding the original data part of the forecast series, keeping only the forecast part, and re-accumulating and building the model based on it until the set number of forecasts is reached.

[0037] Repeating this process helps to gradually reduce the prediction error and enhance the model's ability to capture future green power output trends. This method can effectively predict the power generation of green energy such as wind power and solar power in the future, that is, predict the green power value, thus providing an important basis for the scheduling and management of the electrolytic aluminum system.

[0038] In general, this process combines multiple steps such as data collection, series accumulation, grayscale model building and parameter estimation to form a comprehensive forecasting framework. This framework is particularly suitable for dealing with situations where the amount of data is small and there is uncertainty, such as the forecast of renewable energy power generation. This method can effectively improve the accuracy of the forecast and provide support for the optimal scheduling and integration of green electricity in the electrolytic aluminum system.

[0039] Step S30: Optimize based on the predicted green electricity value, the predicted power of the aluminum production power load and the first model to obtain the actual power of the aluminum production power load and the total output of the thermal power unit.

[0040] In this embodiment, optimization means that after the model is fed with data, the result obtained satisfies the limitations of the model itself, the objective function and the constraints.

[0041] In one embodiment, the first model includes an aluminum production regulating module. Based on the predicted green electricity value, the predicted power of the aluminum production electricity load and the first model, optimization is performed to obtain the actual power of the aluminum production electricity load and the total output of the thermal power unit, including optimizing based on the predicted green electricity value, the predicted power of the aluminum production electricity load, the aluminum production regulating module, the first objective function and the first constraint condition to obtain the actual power of the aluminum production electricity load and the total output of the thermal power unit.

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

[0043] P L (t) = P e +n1P u +n2P d ,

[0044] 0≤n1≤b1,0≤n2≤b2,

[0045] P u =0.1×P e / b1,P d =0.4×P e / b2,

[0046] Where P L is the adjusted electrolytic aluminum power, n1 is the upward adjustment position, n2 is the downward adjustment position, P u is the power increase per gear, P d is the power reduction per gear, P e is the current electrolytic aluminum power, b1 is the number of upward gears, and b2 is the number of downward gears.

[0047] In this embodiment, when modeling the aluminum production regulating equipment, it is considered that the power of the aluminum production regulating equipment based on the electrolytic aluminum process cannot be adjusted frequently, otherwise the equipment will be damaged, so the gear is limited. Preferably, in one embodiment, when the electrolytic aluminum participates in the demand response, the upward adjustment amount is 10% of the current electrolytic aluminum power, and the downward adjustment amount is 40% of the current electrolytic aluminum power. Preferably, in one embodiment, the number of upward adjustment gears is set to 5, and the number of downward adjustment gears is set to 8.

[0048] The mathematical expression of the aluminum adjustment module also includes:

[0049] n1+n2≤M,

[0050] Wherein M is the maximum number of adjustments. In this embodiment, the above formula is to extend the service life of the aluminum adjustment device and limit the number of power adjustments thereof.

[0051] The mathematical expression of the aluminum adjustment module also includes:

[0052]

[0053]

[0054] in are the adjustment time differences for upward and downward adjustment, are the adjustment time difference limits for upward adjustment and downward adjustment respectively. In this embodiment, the above formula is used to extend the service life of the aluminum adjustment equipment and to limit the time interval of its power adjustment.

[0055] When optimizing, the first objective function and the first constraint condition must also be satisfied. When completing the optimization in this step, it is based on satisfying the first constraint condition and finding the situation that best satisfies the first objective function as the final optimization result. The first objective function makes the actual power of the aluminum production load and the corresponding consumption rate of the total output of the thermal power unit the highest.

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

[0057]

[0058] N W is the number of wind farms; is the output of the i-th wind farm in period t.

[0059] Based on the first objective function, it can be seen that the goal is to maximize the consumption of green electricity, and the power loads of thermal power, wind power and aluminum production are optimized while meeting the first constraint.

[0060] The first constraint is as follows:

[0061]

[0062] is the total active output of thermal power units; is the predicted power of the aluminum production electricity load; N Al is the number of aluminum electrolytic cells; is the power of an aluminum electrolytic cell; It is an aluminum electrolytic cell for regulating power.

[0063] The first constraint also includes:

[0064]

[0065]

[0066] Among them, P Gmax , P Gmin They are the upper and lower limits of thermal power unit output; They are the upper and lower limits of load spinning reserve respectively; In this embodiment, the above formula takes into account the uncertainty of green power output and the error between the predicted power and the predicted green power value of aluminum production load and the actual value, so the spinning reserve is increased.

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

[0068]

[0069] is the predicted power of the i-th wind turbine.

[0070] The first constraint also includes the constraints of thermal power units as follows:

[0071] Output power constraints:

[0072]

[0073] Climbing speed constraint:

[0074]

[0075]

[0076] in is the output of the thermal power unit in the period t-1, P Gup and P Gdown It is the up and down ramp rate limit of the thermal power unit.

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

[0078] The power constraints are as follows:

[0079]

[0080] P Hmax , P Hmin It is the upper and lower limits of the regulating power of the electrolytic aluminum load.

[0081] The adjustment times are constrained as follows:

[0082]

[0083] Indicates the regulation status of period t and period t-1, Indicates that electrolytic aluminum participates in regulation during period t. It means that electrolytic aluminum does not participate in the regulation during period t.

[0084] In this embodiment, optimization is performed 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 to obtain the actual power of the aluminum production electricity load and the total output of the thermal power unit.

[0085] Step S40: Optimize based on the actual power of the aluminum production electricity load and the total output of the thermal power units to obtain the output of each thermal power unit.

[0086] In this embodiment, the output of each thermal power unit is obtained through 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 units.

[0087] In one implementation, the second objective function is as follows:

[0088] minF=C G +C P ,

[0089]

[0090]

[0091] Where F is the total cost of system operation, C G is the thermal power operation cost, C P is the penalty cost for wind curtailment; Indicates the start and stop status of the thermal power unit; a j ,b j ,c j is the operating cost coefficient of thermal power unit j; ρ i is the wind curtailment penalty coefficient of the i-th wind turbine; ΔT1 is the number of wind curtailment periods in the system.

[0092] In this embodiment, the second objective function takes the minimum system operating cost as the optimization goal, optimizes the output of each thermal power unit, and takes into account the thermal power fuel cost and the wind power curtailment cost.

[0093] The second constraint condition includes thermal power power constraint, thermal power ramp constraint, and thermal power output constraint, among which the thermal power power constraint is as follows:

[0094]

[0095] N G is the number of thermal power units; It is the output of thermal power units.

[0096] Thermal power ramp constraints are as follows:

[0097]

[0098]

[0099] in is the output of thermal power unit; P Gj,u , P Gj,d It is the up and down climbing output limit of thermal power units.

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

[0101]

[0102] in, It is the upper and lower limits of the output power of thermal power units.

[0103] In this embodiment, thermal power is further optimized to obtain the output of each thermal power unit. In this way, each thermal power unit can be adjusted to the optimal output level according to actual needs 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 thermal power units can also maintain the stability of the power grid, adapt to the power needs of the aluminum industry and other industrial users, and ensure the continuity and reliability of power supply.

[0104] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0105] It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of each of the above method embodiments when executed by the processor. Among them, the computer program includes a computer program, and the computer program can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0106] Furthermore, the present invention also provides a control device. In an embodiment of a control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the above-mentioned method embodiment of the green electricity access to the electrolytic aluminum system optimization allocation control method, and the processor can be configured to execute the program in the storage device, which includes but is not limited to the program for executing the above-mentioned method embodiment of the green electricity access to the electrolytic aluminum system optimization allocation control method. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The control device can be a control device device formed by various electronic devices.

[0107] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the above-mentioned method embodiment of the new energy green electricity access to the electrolytic aluminum system optimization and allocation control method, and the program can be loaded and run by the processor to implement the above-mentioned new energy green electricity access to the electrolytic aluminum system optimization and allocation control method. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary 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 invention, the physical devices corresponding to these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only schematic.

[0109] Those skilled in the art will appreciate that the modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

[0110] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for optimizing and allocating control of new energy green electricity access to an electrolytic aluminum system, characterized in that: include: Obtain the predicted power of aluminum production electricity load and the historical green power value set; Based on the historical green electricity value set, a predicted green electricity value is obtained, wherein the predicted power of the aluminum production power load, the historical green electricity value set, and the predicted green electricity value are all values ​​based on a time period or a time point; Based on the predicted green electricity value, the predicted power of the aluminum production power load and the first model, optimization is performed to obtain the actual power of the aluminum production power load and the total output of the thermal power unit; Based on the actual power of the aluminum production electricity load and the total output of the thermal power units, optimization is performed to obtain the output of each thermal power unit.

2. The method for optimizing and allocating new energy and green electricity to an electrolytic aluminum system according to claim 1 is characterized in that: The first model includes an aluminum production adjustment module, "optimizing based on the predicted green power value, the predicted power of the aluminum production power load and the first model to obtain the actual power of the aluminum production power 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 electricity 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 electricity load and the total output of the thermal power unit, wherein the first objective function makes the absorption rate corresponding to the actual power of the aluminum production electricity load and the total output of the thermal power unit the highest.

3. The method for optimizing and allocating new energy and green electricity to an electrolytic aluminum system according to claim 1 or 2, characterized in that: “Obtaining a predicted green power value based on the historical green power value set” includes: Based on the historical green power value set, a first number series is obtained; Accumulate the first sequence to obtain the second sequence; Based on the second number series and the grayscale model, a predicted green power value is obtained.

4. The method for optimizing and allocating new energy and green electricity to an electrolytic aluminum system according to claim 1 or 2, characterized in that: "Based on the actual power of aluminum production electricity load and the total output of thermal power units, the output of each thermal power unit is optimized", including: Based on the second objective function, the second constraint, the actual power of the aluminum production electricity load and the total output of the thermal power units, optimization is performed to obtain the output of each thermal power unit.

5. The method for optimizing and allocating new energy and green electricity to an electrolytic aluminum system according to claim 2 is characterized in that: The mathematical expression of the aluminum adjustment 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 is the adjusted electrolytic aluminum power, n1 is the upward adjustment position, n2 is the downward adjustment position, P u is the power increase per gear, P d is the power reduction per gear, P e is the current electrolytic aluminum power, b1 is the number of upward gears, and b2 is the number of downward gears.

6. The method for optimizing and allocating new energy and green electricity to an electrolytic aluminum system according to claim 5 is characterized in that: The mathematical expression of the aluminum adjustment module also includes: n1+n2≤M, Where M is the maximum number of adjustments; in are the adjustment time differences of up-regulation and down-regulation, T1 max 、T2 max They are the adjustment time difference limits for upward and downward adjustment respectively.

7. The method for optimizing and allocating new energy and green electricity to an electrolytic aluminum system according to any one of claims 2, 5 and 6, characterized in that: The first objective function includes: N W is the number of wind farms; is the output of the i-th wind farm in period t.

8. The method for optimizing and allocating new energy and green electricity to an electrolytic aluminum system according to claim 4 is characterized in that: The second objective function includes: minF=C G +C P , Where F is the total cost of system operation, C G is the thermal power operation cost, C P is the penalty cost for wind curtailment; Indicates the start and stop status of the thermal power unit; a j ,b j ,c j is the operating cost coefficient of thermal power unit j; ρ i is the wind curtailment penalty coefficient of the i-th wind turbine; ΔT1 is the number of wind curtailment periods in the system.

9. A control device, comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of computer programs, characterized in that: The computer program is suitable for being loaded and run by the processor to execute the optimization and deployment control method for accessing the new energy green electricity to the electrolytic aluminum system as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a plurality of computer programs, characterized in that: The computer program is suitable for being loaded and run by a processor to execute the optimization and deployment control method for accessing the new energy green electricity to the electrolytic aluminum system as described in any one of claims 1 to 8.

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