New energy-thermal power-electrolytic aluminum multi-time scale flexible coordination control method

Abstract

The invention relates to a new energy-thermal power-electrolytic aluminum multi-time scale flexible coordination control method. The method comprises the following steps: in a first prediction period, obtaining electrolytic aluminum load power corresponding to the first prediction period; performing prediction based on the historical new energy data to obtain first new energy power; based on the electrolytic aluminum load power, the first new energy power and the cost of the thermal power generating unit, the start-stop state of the thermal power generating unit is obtained; in a second prediction period, obtaining second new energy power in the second prediction period based on the new energy data updated for the first time; based on the second new energy power, the start-stop state of the thermal power generating unit and the electrolytic aluminum load power, the output of each unit in the thermal power generating unit is obtained; in a third prediction period, obtaining third new energy power based on the new energy data updated for the second time; and based on the output of each unit in the thermal power generating unit and the third new energy power, obtaining a control strategy of energy required by electrolytic aluminum production.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy power generation, and specifically provides a new energy-thermal power-aluminum electrolysis multi-time scale flexible coordinated control method. Background Art

[0002] In the existing new power system control optimization technology based on new energy, especially when it comes to the use of new energy such as wind energy and solar energy, a major problem is the volatility and uncertainty of new energy power generation.

[0003] Traditional optimization methods often use one-time optimization, that is, first predict the power generation of renewable energy, and then use a larger time scale, such as months, weeks, and days, to optimize the dispatch of the power system. Although this method can balance the supply and demand of the power system to a certain extent, it is difficult to achieve accurate dispatch because there is no way to overcome the volatility of renewable energy power generation in a short period of time. Ultimately, there is a serious mismatch between the optimization results and the actual operation, and it is impossible to maximize the use of renewable energy.

[0004] Accordingly, the art needs a new power system control optimization method 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 serious mismatch between the optimization results in the prior art and the actual operation, and the problem that new energy cannot be maximized.

[0006] In a first aspect, the present invention provides a new energy-thermal power-electrolytic aluminum multi-time scale flexible coordinated control method, the method comprising: in a first prediction cycle, obtaining the electrolytic aluminum load power corresponding to the first prediction cycle; predicting based on historical new energy data to obtain a first new energy power in the first prediction cycle; obtaining the start / stop status of the thermal power unit based on the electrolytic aluminum load power, the first new energy power and the cost of the thermal power unit; in a second prediction cycle, based on the new energy data after the first update, obtaining the second new energy power in the second prediction cycle; based on the second new energy power, the start / stop status of the thermal power unit and the electrolytic aluminum load power, obtaining the output of each unit in the thermal power unit; in a third prediction cycle, based on the new energy data after the second update, obtaining the third new energy power in the third prediction cycle; based on the output of each unit in the thermal power unit and the third new energy power, obtaining a control strategy for the energy required for electrolytic aluminum production; wherein the time length of the prediction cycle decreases in the order of the first prediction cycle, the second prediction cycle and the third prediction cycle; based on the strength of the large power grid to which the electrolytic aluminum system is connected, energy storage is selected to improve the power quality.

[0007] 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 - thermal power - electrolytic aluminum multi-time scale flexible coordination control method described in any one of the technical solutions of the above-mentioned new energy - thermal power - electrolytic aluminum multi-time scale flexible coordination control method.

[0008] 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 new energy - thermal power - electrolytic aluminum multi-time scale flexible coordinated control method described in any one of the technical solutions of the above-mentioned new energy - thermal power - electrolytic aluminum multi-time scale flexible coordinated control method.

[0009] The above one or more technical solutions of the present invention have at least one or more of the following Beneficial effects:

[0010] In the implementation of the technical solution of the present invention, by implementing the new energy-thermal power-aluminum electrolysis multi-time scale flexible coordinated control method, this technology effectively improves the adaptability of the power system to the volatility and uncertainty of new energy power generation. The adoption of a phased forecasting and scheduling strategy not only accurately matches the power supply and demand, but also significantly improves the utilization rate of new energy. Especially for the power system represented by the electrolytic aluminum production, this technology ensures the stability of the production process by finely adjusting the control strategy of the required energy, and also contributes to the economic operation and sustainable development of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art 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 drawings represent similar components, wherein:

[0012] Figure 1 It is a schematic diagram of a power system scenario of a new energy-thermal power-electrolytic aluminum multi-time scale flexible coordinated control method according to an embodiment of the present invention;

[0013] Figure 2 It is a schematic flow chart of the main steps of the multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum according to an embodiment of the present invention;

[0014] Figure 3 It is a schematic flow chart of the secondary steps of the multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum according to an embodiment of the present invention;

[0015] Figure 4 It is a schematic flow chart of the secondary steps of the multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum according to an embodiment of the present invention;

[0016] Figure 5 It is a schematic flow chart of the secondary steps of the multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum according to an embodiment of the present invention;

[0017] Figure 6 It is a flow chart of the first prediction stage of the multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum according to an embodiment of the present invention;

[0018] Figure 7 It is a flow chart of the second prediction stage of the multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum according to an embodiment of the present invention;

[0019] Figure 8 It is a flow chart of the third prediction stage of the multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum according to one embodiment of the present invention;

[0020] Fig. 9 It is an equivalent circuit diagram of aluminum production in an aluminum electrolytic plant according to a new energy-thermal power-aluminum electrolytic multi-time scale flexible coordinated control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] 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.

[0022] In the description of the present invention, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software parts, such as computer programs, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store computer programs, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B", and may include only A, only B, or A and B. The singular terms "one" and "the" may also include plural forms.

[0023] In an example of an application scenario of the present invention, Figure 1 As shown in FIG. 1 , the power system includes an aluminum smelter, a new energy generator set, an energy storage power station, a control system, and a thermal power generator set. The power of the thermal power generator set is preferentially supplied to the aluminum smelter because, in this scenario, the thermal power generator set is a supporting facility of the aluminum smelter.

[0024] like Figure 1 As shown, preferably, there may also be an aluminum waste heat recovery system in the aluminum electrolytic plant.

[0025] In this scenario, at certain special moments, when the electricity supplied by thermal power generating units and new energy generating units is insufficient for the aluminum smelter to use, for example, when the electricity generated by new energy generating units is drastically reduced due to weather, and the amount of electricity generated by thermal power generating units does not meet the needs of the aluminum smelter, the aluminum smelter can purchase electricity from the power grid. However, since the cost of purchasing electricity from the power grid is relatively high, in this power system, it is necessary to reduce the purchase of electricity from the power grid as much as possible.

[0026] like Figure 1 As shown, preferably, an energy storage power station can be optionally configured in the aluminum electrolysis system.

[0027] In this scenario, when the large power grid to which the electrolytic aluminum system is connected is a weak power grid, fluctuations in a high proportion of renewable energy will affect the stable operation of the electrolytic aluminum system. Therefore, in this power system, energy-type and power-type hybrid energy storage are equipped to shift wind and solar fluctuations.

[0028] In this scenario, when the large power grid to which the electrolytic aluminum system is connected is a strong power grid, the large power grid has strong support capabilities. Therefore, in this power system, energy storage is selected based on power quality requirements.

[0029] See attached Figure 2 , Figure 2 1 is a schematic flow chart of the main steps of the multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum according to an embodiment of the present invention. Figure 2 As shown, the new energy-thermal power-electrolytic aluminum multi-time scale flexible coordinated control method in the embodiment of the present invention mainly includes the following steps S10 to S70.

[0030] Step S10: In the first prediction cycle, obtain the electrolytic aluminum load power corresponding to the first prediction cycle.

[0031] In this embodiment, the prediction period refers to a time stage for predicting certain parameters in the power system, and this time stage corresponds to a time period in real time. This prediction is usually divided into multiple cycles, and there is a nested relationship between multiple prediction cycles. Specifically, in this application, the so-called nested relationship means that the prediction results of the previous prediction period are usually used when predicting the next prediction period, and the time period corresponding to the next prediction period will be in the time period corresponding to the previous prediction period. The design of multiple prediction periods aims to improve the accuracy of the prediction and the adaptability of the scheduling scheme by refining the time scale. At the same time, the value considered to be solved in each prediction period also considers the characteristics of the power device corresponding to the corresponding value, so that the final strategy can adapt to the instability of new energy power generation and reduce the absorption of the power system.

[0032] At each stage, the power system will re-evaluate the impact of the updated data on the power system based on real-time data and updated information to ensure that dispatch decisions can adapt to changes in actual operating conditions, thereby achieving efficient management and optimization of power system resources.

[0033] In one embodiment, the first prediction period corresponds to Figure 6 As shown in the figure, the obtained electrolytic aluminum load power affects the start-stop state of the subsequent thermal power unit and the utilization strategy of new energy power generation. The method of obtaining electrolytic aluminum load power usually includes manual prediction or machine prediction. Manual prediction is based on production plan or experience, and machine prediction is often based on historical data prediction or real-time prediction using prediction algorithm.

[0034] In one embodiment, the prediction method based on historical data relies on past operation records and load data. By analyzing the load change trend, seasonal factors, and the impact of related industrial activities in the same period of history, a prediction model for electrolytic aluminum load power is constructed. Thus, the electrolytic aluminum load power corresponding to the first prediction period is predicted based on historical data.

[0035] In one implementation, the first prediction period is illustrated here as an example. For example, the length of the first prediction period is 24 hours, corresponding to 0:00-24:00 on January 1, 2024. Obtaining the electrolytic aluminum load power corresponding to the first prediction period is to obtain the electrolytic aluminum load power forecast value every hour on this day.

[0036] To sum up, in this embodiment, by setting the first prediction cycle and obtaining the electrolytic aluminum load power in the first prediction cycle, this technical application aims to establish a flexible and efficient power system optimization framework. This step will lay the foundation for the operation of subsequent methods.

[0037] Step S20: Predicting based on historical new energy data to obtain a first new energy power within a first prediction period.

[0038] In this embodiment, new energy includes wind energy and / or solar energy and other new energy energy used for power generation in other power systems. The new energy in this embodiment does not include hydrogen energy. The new energy in this embodiment refers to energy that is greatly affected by the natural environment, such as wind energy, solar energy, etc.

[0039] In one embodiment, the new energy includes wind energy and solar energy. The first new energy power in the first prediction cycle refers to the predicted value of the new energy power generation predicted before the start of the cycle.

[0040] In one embodiment, the prediction of the first new energy power is achieved by constructing a prediction model. The historical new energy data includes but is not limited to past weather conditions, seasonal changes, and actual power generation of new energy power generation facilities. By analyzing these data, key factors affecting the power generation of new energy can be identified, and then a prediction model can be constructed. For example, the prediction model for solar power generation may consider factors such as solar irradiance, sunshine time, and seasonal changes; while the prediction model for wind power generation may consider parameters such as wind speed, wind direction, and temperature.

[0041] In one embodiment, the prediction model is constructed using statistical methods or machine learning algorithms. Statistical methods may include time series analysis, such as autoregressive moving average (ARMA) models, which are used to analyze and predict time-dependent data series. Machine learning algorithms, such as random forests, support vector machines, or deep learning networks, are able to handle more complex nonlinear relationships and can learn and extract useful features from large amounts of historical data.

[0042] Taking the prediction of new energy including wind energy and electric energy as an example, assume that the prediction period is 0:00 to 24:00 on January 1, 2024. The prediction model first analyzes the historical new energy data of the same day in the past few days and the historical new energy data of the previous few days, combined with the recent weather forecast information, such as the predicted climate conditions and wind speed changes on the day. Then, the model takes this information into consideration and outputs the predicted values ​​of wind power generation and photovoltaic power generation in each hour on January 1, 2024. These predicted values ​​are regarded as the first new energy power in the first prediction period, providing preliminary reference data for the operation and dispatch of the power system.

[0043] Through predictions based on historical renewable energy data, the power system can better understand the potential output of renewable energy generation in the future, so as to more effectively plan and adjust the operation strategy of the power system. This will not only help improve the utilization efficiency of renewable energy, but also help reduce operating costs and ensure the stability and reliability of the power system.

[0044] Step S30: Based on the electrolytic aluminum load power, the first new energy power and the cost of the thermal power unit, the start / stop status of the thermal power unit is obtained.

[0045] In this embodiment, as the main part of the power system, the number and technical parameters of the thermal power generating units are usually predetermined, such as the maximum power generation capacity of the units, the operating cost, etc. These data provide basic physical and economic constraints for the operation of the thermal power generating units.

[0046] In this embodiment, the start and stop status of the thermal power unit in the first prediction cycle refers to the operating status of each unit in different time periods in the cycle. The first prediction cycle usually refers to a specific time range, and the start and stop decision of the thermal power unit needs to be optimized for each time period in the first prediction cycle.

[0047] In one embodiment, during the process of optimizing the dispatch of the power system, the start and stop status of the thermal power unit is determined based on a comprehensive consideration of the electrolytic aluminum load power, the first new energy power, and the operating cost of the thermal power unit.

[0048] In this embodiment, the start and stop states of the thermal power generation units are calculated by optimizing the model.

[0049] In one embodiment, the cost of the thermal power unit includes: the start-up and stop costs of the thermal power unit and the operating costs of the thermal power unit. In this embodiment, the start-up and stop states of the thermal power unit are obtained through steps S301-S303, such as Figure 3 As shown, the details are as follows:

[0050] Step S301: setting a first objective function based on the start-up and shutdown costs of the thermal power unit and the operating cost of the thermal power unit, wherein the first objective function seeks to minimize the operating cost of the thermal power unit.

[0051] In one embodiment, a reasonable objective function is set to maximize economic efficiency. In this embodiment, the first objective function proposed aims to minimize the operating cost of the thermal power unit, which is achieved by accurately calculating the operating cost, start-up and shutdown cost, and other conditions of each thermal power unit in each period. The setting of the first objective function takes into account two important factors, operating cost and start-up and shutdown cost, to ensure the economic efficiency of the power system operation.

[0052] In one implementation, a mathematical expression of the objective function is given as follows:

[0053]

[0054]

[0055]

[0056] Specifically, the objective function The output power of thermal power units in each forecast period t is integrated and start / stop status here, represents the operating cost of a thermal power unit at a given output power, and It represents the start-stop cost.

[0057] In a preferred embodiment, the operating cost is generally proportional to the amount of power generated, while the start-up and shutdown cost is proportional to the number of start-up and shutdown operations of the unit.

[0058] In this embodiment, the running cost The fitting adopts the quadratic function form, where a i 、b i and c i They represent the quadratic coefficient, linear coefficient and constant term of the operating cost function respectively. This expression can better simulate the nonlinear characteristics of the operating cost of thermal power units as the output power changes. It includes startup cost and shutdown cost, which are closely related to the operation of switching the unit from shutdown state to operating state (or vice versa).

[0059] In this embodiment, the advantage of setting the first objective function is that it can comprehensively reflect the economic efficiency and operational flexibility of the thermal power unit. By subsequently optimizing this first objective function in combination with other constraints, an operation strategy that maximizes cost-effectiveness can be obtained, that is, while meeting the load demand of the power system, the overall operating cost is minimized. In addition, considering the start-up and shutdown costs can effectively avoid frequent unit start-up and shutdown operations, which is of great significance for extending the service life of thermal power units and reducing maintenance costs.

[0060] Step S302: constructing a first constraint condition based on the predicted electrolytic aluminum load power, the first new energy power and the power of the thermal power unit.

[0061] In one embodiment, the first constraint condition ensures the balance between power supply and demand, thereby satisfying the maximum consumption.

[0062] In one implementation, the mathematical expression of the first constraint condition is given as follows:

[0063]

[0064] In this embodiment, the first constraint expresses that in any given period t, the total power generation of all power generation resources in the power system is equal to the load demand. Specifically, this includes the thermal power units (N g Power generation of units) and photovoltaic power generation Wind power generation And electrolytic aluminum load power In this implementation, the first new energy power=photovoltaic power generation+wind power generation.

[0065] In this embodiment, in the first constraint condition, represents the total power generation of all operating thermal power units in time period t, where represents the power generation of the ith thermal power unit in time period t, It is the start and stop status of the unit in time period t (start is 1, stop is 0). and They represent the photovoltaic and wind power generation in time period t, respectively, and It is the power demand of the electrolytic aluminum load during this period.

[0066] The purpose of the first constraint is to ensure that the power system can balance supply and demand, avoid over or under generation, and maximize the absorption of new energy.

[0067] For example, if the predicted photovoltaic and wind power generation is high in a certain period, while the electrolytic aluminum load is relatively low, the number of operating thermal power units may need to be reduced accordingly to avoid excess power generation. On the contrary, if the predicted renewable energy generation is low and the electrolytic aluminum load is high in a certain period, the number of operating thermal power units should be increased to meet the increased power demand.

[0068] In this way, the first constraint not only ensures the stable operation of the power system, but also helps to achieve the rational utilization of various power generation resources, while ensuring the stable power supply of key industrial loads such as electrolytic aluminum production.

[0069] Step S303: obtaining the start / stop state of the thermal power unit based at least on the first objective function and the first constraint condition.

[0070] In one embodiment, the first objective function and the first constraint condition that have been set are used to comprehensively consider the cost-effectiveness of the thermal power unit and the balance of supply and demand of the power system, thereby deriving the optimal start-stop strategy for each thermal power unit in the first prediction period. In this embodiment, the above method is used to ensure that the power system achieves economic operation and stability while meeting the load demand.

[0071] First, the first objective function By quantifying the operating cost and startup and shutdown cost of thermal power units, the optimization model provides a cost minimization objective. This objective function reflects the total cost of thermal power units under different power generation levels and startup and shutdown states, aiming to find the lowest cost operation plan.

[0072] Next, the first constraint This ensures that the power system can meet the load demand at any time, including the electrolytic aluminum load and other power demand. This condition provides a basic constraint for the start-up and shutdown decisions of thermal power units by ensuring the balance between supply and demand.

[0073] In this framework, optimization algorithms such as linear programming, mixed integer linear programming or dynamic programming are used to solve the objective function and constraints. The algorithm needs to deal with a multi-variable, multi-period decision problem, and the decision variables involved include the power generation and start-stop status of each thermal power unit in each period. During the optimization process, the algorithm will search for the lowest cost start-stop combination of thermal power units under the premise of satisfying the first constraint, that is, the balance of power supply and demand.

[0074] For example, taking into account the uncertainty of renewable energy power generation and the continuity requirements of electrolytic aluminum production, the optimization model may recommend starting more thermal power units during periods when renewable energy power generation is expected to be low to ensure the stability of power supply; conversely, during periods when renewable energy power generation is expected to be high, the model may recommend reducing the operation of thermal power units to reduce the operating costs of the power system.

[0075] Through this method, a detailed start-up and shutdown plan for thermal power units can be obtained, which indicates whether each thermal power unit should be operated at different times during the first forecast period, and what the specific operating power should be. This not only helps to maximize the economic benefits of the power system, but also ensures the stability and reliability of the power system and ensures the smooth operation of important loads such as electrolytic aluminum production. In addition, a reasonable start-up and shutdown strategy for thermal power units can also promote the efficient use of new energy and reduce carbon emissions from the entire power system.

[0076] In another embodiment, the start and stop status of the thermal power unit is obtained through steps S301-S302 and steps S304-S305. Figure 4 As shown, the details are as follows:

[0077] Step S304: constructing a second constraint condition based on the output power ramp limit of each unit in the thermal power unit.

[0078] In one embodiment, a second constraint is established to ensure that the thermal power unit complies with the ramp limit of its output power during operation. The ramp limit refers to the rate at which the power output of the thermal power unit increases or decreases, which helps maintain the stability and reliability of the power system. Specifically, the second constraint specifies the maximum increase rate U of each thermal power unit power change. i and the maximum descent rate D i , ensuring that adjustments to thermal power units are carried out within technical and safety ranges.

[0079] In this embodiment, the consideration of ramp limit is based on the physical characteristics and safe operation standards of thermal power units. Since the startup, shutdown and power regulation of thermal power units all require a certain amount of time to achieve, too fast power changes may cause equipment damage or affect the stability of the power grid. Therefore, the ramp limit is introduced as a constraint condition for scheduling optimization to avoid excessively rapid power adjustment and ensure the smooth operation of thermal power units.

[0080] In one implementation, the second constraint is in the form of: This formula ensures that the power variation of a power generation unit (single generator set) at two adjacent moments does not exceed the preset rise and fall limits.

[0081] In one embodiment, by introducing the ramp limit as the second constraint in the optimization model, the following effects can be achieved. First, it helps to ensure the stability of the power system by limiting the power change rate and reducing the fluctuations in the grid frequency and voltage, thereby maintaining the safe operation of the power system. Second, the second constraint helps to improve the economy of the power system by optimizing the power generation plan and balancing the power generation cost and the need for power system stability. Finally, considering the ramp limit can also help improve the operating efficiency of thermal power units and extend the life of equipment, because it avoids frequent operation of equipment under extreme conditions, reducing maintenance costs and failure rates.

[0082] In summary, in step S304, the second constraint condition is constructed by considering the ramp limit of the thermal power unit, thereby ensuring the stable and safe operation of the power system.

[0083] Step S305: Based on the first objective function, the first constraint condition and the second constraint condition, the start / stop state of the thermal power unit is obtained.

[0084] In one implementation, the main optimization method is similar to step S303 and will not be described in detail here.

[0085] In one implementation, by combining the first objective function, the first constraint condition and the newly added second constraint condition, an optimization decision is made on the start and stop status of the thermal power unit, which significantly improves the precision and practicality of the power system dispatch.

[0086] The main difference between this step and step S303 is that the output power ramp limit of the thermal power unit is introduced as a new constraint. Step S303 determines the start and stop state of the thermal power unit only based on the first objective function and the first constraint, namely, the cost minimization objective and the power supply and demand balance constraint. Although this process can ensure the basic requirements of the power system in terms of economy and supply and demand balance, this step takes more consideration of the safety of the thermal power unit during operation.

[0087] In this embodiment, by adding the second constraint condition, i.e., the ramp limit of the thermal power unit, in step S305, the scheduling decision can more accurately reflect the actual operating capacity and safety margin of the thermal power unit. This means that when optimizing the start and stop plan of the thermal power unit, not only the cost and supply and demand balance are considered, but also the rate limit of the thermal power unit to adjust the power is considered, thereby avoiding equipment damage or grid stability problems that may be caused by excessive power changes.

[0088] In summary, step S305 not only maintains the economic operation and supply-demand balance of the power system by introducing climbing restrictions in the optimization model, but also enhances the stability and security of the power system, reflecting the in-depth consideration of actual operating restrictions and accurate grasp of details in power system dispatch optimization.

[0089] Step S40: In the second prediction cycle, based on the new energy data after the first update, obtain the second new energy power in the second prediction cycle.

[0090] In one embodiment, the second prediction cycle is as follows Figure 7 As shown, the real-time changes in the natural environment can be adapted by correcting the initially predicted new energy power based on the latest new energy data (new energy data after the first update) in the second prediction cycle.

[0091] In this embodiment, the second forecast period is usually set within the first forecast period, and its length is shorter than the first forecast period, so as to achieve more precise and timely scheduling decisions. For example, if the first forecast period is 24 hours, the second forecast period may be set to 4 hours. Such a setting is intended to capture short-term fluctuations in renewable energy generation due to factors such as changes in meteorological conditions during the day.

[0092] It should be noted that if the time period corresponding to the second prediction cycle is only partially within a first prediction cycle, there is another first prediction cycle immediately after the first prediction cycle. In this case, the second prediction cycle will span two first prediction cycles connected end to end, but usually the second prediction cycle is usually set within a first prediction cycle.

[0093] In this embodiment, in the second prediction cycle, the dispatching power system will correct the predicted value of the first renewable energy power according to the latest acquired renewable energy data, such as real-time wind speed, solar radiation intensity, etc. In one embodiment, a prediction algorithm is used to adapt to the latest data. Specifically, in one embodiment, a fuzzy algorithm is used to infer the second renewable energy power at this time. This real-time or near real-time prediction correction can ensure that the power generation prediction is more in line with the actual situation and reduce improper resource allocation caused by prediction errors.

[0094] The effect of this step is to improve the adaptability of the power system to fluctuations in renewable energy generation and optimize the allocation of power generation resources. In particular, when the renewable energy generation changes significantly, the operating parameters in the subsequent power system can be adjusted in a timely manner to ensure the continuity and reliability of power supply. In addition, by accurately controlling the operating parameters in the subsequent power system, it can also effectively reduce operating costs and reduce unnecessary fuel consumption and emissions.

[0095] For example, if the latest renewable energy data indicates that a period of high wind speed is coming and wind power generation is expected to increase significantly, the power system can reduce the output of certain thermal power units to utilize more clean energy. Conversely, if it is predicted that solar power generation will decrease due to worsening weather, the power system can adjust the operation strategy of thermal power units in advance to ensure stable power supply.

[0096] In summary, step S40 not only enhances the responsiveness of the power system to fluctuations in renewable energy, but also provides support for the economy and environmental friendliness of the power system by dynamically predicting and correcting renewable energy power generation within a shorter time scale.

[0097] Step S50: Based on the second new energy power, the start / stop status of the thermal power unit and the electrolytic aluminum load power, the output of each unit in the thermal power unit is obtained.

[0098] In one implementation, the specific output of each thermal power unit in the second prediction period is determined by integrating three key factors: the second new energy power, the current start / stop status of the thermal power unit, and the electrolytic aluminum load power.

[0099] In this embodiment, the main advantage of selecting the output in the second prediction cycle to obtain the output of each unit in the thermal power unit is that it allows the power system to respond quickly according to the latest second renewable energy power. Since the length of the second prediction cycle is usually short, this near real-time adjustment strategy can more accurately match power supply and demand, especially when dealing with sudden changes in renewable energy power generation, and can effectively reduce economic losses and power system risks caused by prediction errors.

[0100] In this embodiment, by integrating the latest second renewable energy power forecast, this step makes the power system dispatch more flexible, and the output of thermal power units can be adjusted in time to adapt to the increase or decrease of renewable energy power generation.

[0101] In one embodiment, through steps S501-S504, as Figure 5 As shown, the details are as follows:

[0102] Step S501: setting a second objective function based on the second new energy power and the real-time electricity price, wherein the second objective function seeks to minimize the operating cost of the thermal power unit.

[0103] In one embodiment, by introducing the real-time electricity price as a key physical quantity in the optimal dispatch of the power system, the economy and flexibility of the optimal dispatch are improved. Specifically, the second objective function considers the power obtained from renewable energy power generation facilities such as photovoltaic and wind power, as well as the time-of-use electricity price R of this part of power in the power grid. Grid , and then optimized the operating costs of thermal power units.

[0104] In one implementation, the mathematical expression of the second objective function is given as follows:

[0105]

[0106] The second objective function (P PV +P WT )·R Grid It represents the benefits that the revised renewable energy power generation can bring in the power grid, that is, the benefits brought by the second renewable energy power. PV and P WT Refers to the power generation of photovoltaic and wind power in a certain period of time t, R Grid It is the time-of-use electricity price of the power grid during that period. It reflects the operating costs of all thermal power units in the same period, where C i (p i ) is the operating cost function of the ith thermal power unit. In this embodiment, the second objective function seeks to minimize the operating cost of the thermal power unit.

[0107] In this embodiment, by setting the second objective function, the dispatch optimization of the power system not only takes into account the operating cost of the thermal power units, but also takes into account the revised renewable energy power generation and the real-time electricity price of the power grid, so that the power system can respond more flexibly to fluctuations in market electricity prices, and adjust the operating status of the thermal power units according to the changes in electricity prices and the newly revised renewable energy power generation, thereby maximizing the economic benefits of the entire power system.

[0108] Step S502: construct a third constraint condition based at least on the electrolytic aluminum load power, the second new energy power, and the power of the thermal power unit.

[0109] In one implementation, it is ensured that the power system can meet the demand of electrolytic aluminum load while optimizing the operation of thermal power units, and the overall absorption rate is minimized.

[0110] In one implementation, a mathematical expression of the third constraint condition is given as follows:

[0111]

[0112] This constraint ensures that at any given moment, the power supply of the power system (including the output from thermal power units and the second new energy power) matches the power demand of the electrolytic aluminum load.

[0113] The second constraint emphasizes the balance between the utilization of renewable energy power generation and the operation of thermal power units, enabling the power system to adjust the output of thermal power units according to the actual output of renewable energy power generation, ensuring stable power supply to key loads such as electrolytic aluminum production, while reducing the power system's absorption rate.

[0114] In another implementation, the third constraint condition is constructed through steps S502-1 to S502-2, which is as follows:

[0115] In this embodiment, the waste heat recovery mechanism in this embodiment is first introduced. The waste heat recovery of the electrolytic cell is achieved by adjusting the flow rate of the heat exchange medium. The adjustment of the flow rate of the heat exchange medium will cause changes in the internal furnace wall (composed of solidified dielectrics) of the electrolytic cell, and the transformation cannot be stable in a short time. In order to be able to achieve the adjustment of the heat exchange medium and overcome the characteristic that the waste heat recovery transformation cannot be stable in a short time, this step is completed in the second prediction cycle, so that the scheduling of waste heat recovery is more reasonable. Because in general, the duration of the second prediction cycle is in a more appropriate position, which is shorter than the first prediction cycle and longer than the third prediction cycle. If this step is placed in the third prediction cycle, it may not meet the time requirements for stabilization of waste heat recovery. The energy balance equation for the electrolytic temperature of electrolytic aluminum is:

[0116] A electricity +A remnant =A reaction +A heating +A loss

[0117] In the formula, A electricity The energy provided for the working voltage; A remnant A is the energy in the space before the anode gas enters the flue gas heat collection power system; reaction The energy required for the chemical reaction of electrolytic aluminum; A heating The energy required to heat the electrolytic material to the electrolysis temperature; A loss The energy lost by thermal radiation in the electrolytic cell.

[0118] Step S502-1: Obtaining data on electric energy saved by waste heat recovery.

[0119] In this embodiment, the data on electricity saved by waste heat recovery is obtained by partially recovering the heat energy that would otherwise be wasted in the aluminum electrolysis process through the waste heat recovery technology of the aluminum electrolysis plant, and reusing it as electricity for the aluminum electrolysis process.

[0120] In one implementation, obtaining the parameter expression of the electric energy data saved by waste heat recovery is helpful to redesign the third constraint condition. In this implementation, the electric energy data saved by waste heat recovery is a quantity to be solved.

[0121] In one embodiment, the method for acquiring the data of electric energy saved by waste heat recovery includes steps S502-1-A to S502-1-B, which are as follows:

[0122] Step S502-1-A: Establish a relationship equation based on the recovered heat and the flue gas heat recovery coefficient in the electrolytic aluminum plant.

[0123] In this embodiment, the flue gas heat recovery coefficient is a quantity to be solved, and the size of the flue gas heat recovery coefficient can be adjusted by adjusting and controlling the device corresponding to the flue gas heat recovery coefficient.

[0124] In one implementation, a relationship equation is established to quantitatively analyze the flue gas waste heat recovery of an aluminum smelter, wherein the flue gas heat recovery coefficient is a key parameter that reflects the efficiency of flue gas waste heat recovery. The size of this coefficient directly affects the amount of recovered heat, and thus affects the amount of electrical energy that can be saved by the power system.

[0125] In this embodiment, a mathematical expression of a specific relationship equation between the recovered heat and the flue gas heat recovery coefficient in an electrolytic aluminum plant is given, as follows:

[0126] A loss_recycle =V g ×ρ g ×C pg ×ΔT.

[0127] In the relational equation, the flue gas flow rate V g , smoke density ρ g , Flue gas specific heat C pg And the temperature difference of exhaust gas temperature ΔT, calculate the heat energy loss A of the exhaust gas from the electrolyzer loss_recycle In this embodiment, the flue gas heat recovery coefficient includes: the flue gas flow rate of the electrolytic cell, the flue gas density, the flue gas specific heat, and the temperature difference of the exhaust gas temperature.

[0128] In this embodiment, by finely adjusting and controlling the equipment parameters corresponding to the flue gas heat recovery coefficient, the flue gas heat recovery efficiency can be effectively controlled, and the maximum heat recovery amount can be selected. This not only improves the energy efficiency of the electrolytic aluminum plant, but also helps to reduce operating costs and carbon emissions caused by energy waste.

[0129] Step S502-1-B: Based on the recovered heat, obtain the power data saved by waste heat recovery

[0130] In this embodiment, there is a proportional relationship between the recovered heat and the electric energy saved by the waste heat recovery.

[0131] In one embodiment, the direct economic benefit of waste heat recovery in the electrolytic aluminum production process is reflected, that is, reducing the demand for additional electrical energy by recovering and utilizing the heat energy that was originally wasted.

[0132] Converting recovered heat into saved electricity is an efficient energy management strategy that not only reduces electricity consumption and costs, but also contributes to the sustainable development of the power system. By implementing this strategy, aluminum smelters can significantly improve their energy efficiency and reduce the load on the power grid, thereby achieving dual optimization of cost savings and environmental benefits in the entire power system.

[0133] In one implementation, the relationship between the recovered heat and the electric energy data saved by waste heat recovery is given as follows:

[0134]

[0135] Where P al_recycle Data on electrical energy saved for waste heat recovery.

[0136] Step S502-2: Construct a third constraint condition based on the electrolytic aluminum load power, the second new energy power, the electric energy data saved by the waste heat recovery, and the power of the thermal power unit.

[0137] In one implementation, the electric energy data saved by waste heat recovery calculated in the above process is incorporated into the overall optimization dispatch of the power system, and a third constraint condition is constructed, including the electrolytic aluminum load power, the second new energy power, and the electric energy data saved by waste heat recovery. This constraint condition comprehensively considers the supply and demand balance of the power system, and at the same time introduces a new dynamic factor of waste heat recovery and electric energy saving, further optimizing the power distribution of thermal power units.

[0138] By optimizing the waste heat recovery and energy saving in the electrolytic aluminum production process, the power system can more flexibly adjust the output of thermal power units to adapt to the changing load demand and renewable energy generation. This method not only improves the economy and environmental friendliness of the power system operation, but also enhances the power system's ability to adapt to renewable energy fluctuations and load changes, ensuring the stability and reliability of power supply.

[0139] Step S503: constructing a fourth constraint condition based on the maximum power constraint and the minimum power constraint of each unit in the thermal power unit and the new energy power unit.

[0140] In one implementation, the fourth constraint condition is introduced to constrain the maximum power and minimum power output of thermal power units and new energy power generation units, ensuring that the operating power of all power generation units is within the technical and safety permitted range. The setting of the fourth constraint condition ensures that the power generation unit will not exceed the operating range allowed by its design and operation and maintenance parameters, thereby avoiding the risk of equipment overload or insufficient power output.

[0141] In one implementation, the mathematical expression of the fourth constraint condition that the generator set needs to satisfy is given as follows:

[0142] P i，min ≤P i ≤P i，max ,

[0143] In this formula, Pi represents each unit in the thermal power unit or each unit in the renewable energy power generation unit. The fourth constraint ensures that the output power of each power generation unit does not exceed its maximum allowable value P i,max , nor less than its minimum stable output value P i,min , which is the basis for achieving stable and efficient operation of the power system. For thermal power units, the maximum power limit reflects the maximum power generation capacity of the unit, while the minimum power limit is related to the minimum output power required to maintain stable operation of the unit. Although new energy generators, such as wind power and solar power, are greatly affected by weather and environmental conditions, in this implementation, a reasonable operating power range is also set to optimize their power generation efficiency and ability to participate in grid dispatching.

[0144] Step S504: Based on the second objective function, the third constraint condition and the fourth constraint condition, at least the predicted power of each unit in the thermal power unit is obtained.

[0145] In one embodiment, the core of step S504 is to comprehensively optimize the specific output of each unit in the thermal power unit in the second prediction cycle by combining the second objective function, the third constraint condition and the fourth constraint condition. This step takes into account the characteristics of the thermal power unit, such as the start and stop state of the thermal power unit and the minimum stable load, etc., to ensure the practicality and feasibility of the optimization result.

[0146] In this embodiment, by optimizing the output of thermal power units in the second forecast cycle, this step not only takes into account the operating cost of thermal power units and the change in renewable energy power generation, but also carefully considers the demand for electrolytic aluminum load and the physical operating limitations of thermal power units. This method enables the power system to more flexibly adapt to fluctuations in power supply and demand in the short term, especially in the face of significant changes in renewable energy power generation, and can timely adjust the output of thermal power units to ensure the stability and economy of power supply.

[0147] In summary, by comprehensively considering the operating characteristics of thermal power units and the real-time needs of the power system in the second forecast cycle, an economical and reliable generator output optimization plan is achieved. This plan not only improves the economic efficiency of the power system, but also enhances the power system's ability to adapt to fluctuations in renewable energy, ensures stable power supply for key loads such as electrolytic aluminum production, and reflects the optimization and dispatching of the power system.

[0148] Furthermore, in another implementation manner, corresponding to step S502-1 and step S502-2, a corresponding step S504-1 is set.

[0149] Step S504-1: Based on the second objective function, the third constraint condition and the fourth constraint condition, the predicted power of each unit in the thermal power unit and the electrolytic aluminum waste heat recovery strategy are obtained.

[0150] In one embodiment, relative to step S504, this embodiment introduces a new dimension in the optimization and dispatching of the power system - the electrolytic aluminum waste heat recovery strategy. The introduction of this strategy not only enriches the content of the optimization and dispatching of the power system, but also provides a new way to further improve the energy efficiency and economy of the power system. Specifically, by adjusting the waste heat recovery in the electrolytic aluminum production process, this step effectively compensates for the lack of precision in the power regulation of thermal power units, while achieving the maximization of energy utilization and further reduction of power system operation costs.

[0151] Specifically in the optimization model, by combining the second objective function, the third constraint condition and the fourth constraint condition, the model not only calculates the optimal power output of each unit in the thermal power unit, but also comprehensively considers the contribution of the electrolytic aluminum waste heat recovery to the overall energy efficiency of the power system, providing a more comprehensive decision-making basis for the power system dispatch. This comprehensive adjustment mechanism enables the power system to achieve higher energy utilization efficiency and lower operating costs while meeting load demand.

[0152] Step S60: In the third prediction cycle, based on the new energy data after the second update, the third new energy power in the third prediction cycle is obtained.

[0153] In this embodiment, the time length of the prediction period decreases in the order of the first prediction period, the second prediction period, and the third prediction period.

[0154] In one implementation, in the third prediction cycle, if Figure 8As shown in the figure, based on the latest updated new energy data, the new energy power forecast is fine-tuned again to obtain the third new energy power. Compared with the first two forecast cycles, the third forecast cycle has a shorter duration, such as 15 minutes. This design aims to improve the power system's ability to respond to short-term fluctuations in new energy power, further reduce the power system's power abandonment rate, and ensure a high degree of matching between power supply and demand.

[0155] In one implementation, since the third prediction period is shorter, the transient new energy power collected before the start of the third prediction period can be used as the new energy power for the third prediction period, or the calculation method of the second new energy power can be imitated, which will not be repeated here.

[0156] In this embodiment, the setting of the third prediction cycle allows the power system to more accurately predict and adjust power supply, especially when dealing with rapid changes in renewable energy generation. By re-updating and optimizing the renewable energy generation, the power system can adjust the control strategy of the energy required for electrolytic aluminum production in real time, thereby effectively dealing with the fluctuation of renewable energy generation and reducing the waste of electricity caused by excess or insufficient power.

[0157] In one embodiment, a control strategy for the energy required for electrolytic aluminum production is obtained in the third prediction cycle. According to the inventor's research, the aluminum production of the electrolytic aluminum plant can be controlled by Fig. 9 express,

[0158] VAH is the high voltage side voltage of the electrolytic aluminum load busbar; VAL is the low voltage side voltage of the electrolytic aluminum load busbar; k is the ratio of the step-down transformer; LSR is the inductance of the saturated reactor; E and R are the equivalent back electromotive force and equivalent resistance of the electrolytic cell of the electrolytic aluminum load, respectively, which can be considered to remain unchanged for the same electrolytic cell under normal circumstances. Therefore, the active power PA1 consumed in electrolytic aluminum is:

[0159]

[0160] From the expression of active power consumption of electrolytic aluminum load, it can be seen that the active power of high-energy-consuming electrolytic aluminum load is related to its DC side voltage. Based on this characteristic, the active power regulation of electrolytic aluminum load is divided into AC side voltage regulation, on-load tap-changing transformer regulation and saturated reactor regulation. The AC side voltage regulation method is adopted, which can quickly respond to the regulation signal. According to experimental verification, the active power response speed of electrolytic aluminum load is in seconds, and the power adjustment can be completed within 2-3 seconds, meeting the regulation needs in the third forecast period.

[0161] Step S70: Based on the output of each unit in the thermal power unit and the power of the third new energy, a control strategy for the energy required for electrolytic aluminum production is obtained.

[0162] In this embodiment, the optimization is essentially based on the output of each unit in the thermal power unit and the power of the third new energy source, wherein the optimization result is a control strategy for the energy required for electrolytic aluminum production. The specific method of optimization has been described in the previous text and will not be repeated here.

[0163] In one embodiment, steps S701-S706 are used to ultimately obtain a control strategy for the energy required for electrolytic aluminum production.

[0164] Step S701: Based on the predicted third new energy power and the predicted power of each unit in the thermal power unit, the predicted aluminum output is obtained.

[0165] In one embodiment, by considering the latest third renewable energy power forecast and the power forecast of each thermal power unit, the calculated predicted aluminum output is an accurate estimate of the energy demand in the electrolytic aluminum production process. The implementation of this step reflects the positive response to the volatility of renewable energy power generation in the power system optimization and dispatch, while ensuring that the energy demand of electrolytic aluminum production is met. By carefully considering the latest energy supply situation, step S701 not only suppresses energy fluctuations in the power system, but also provides a basis for subsequent economic benefit analysis.

[0166] In one embodiment, the production of one ton of electrolytic aluminum requires approximately 13,500 kWh of electricity. By comparing the available power resources (including renewable energy generation and thermal power generation) with the energy consumption demand for electrolytic aluminum production, the possible aluminum production N in a specific period of time can be estimated. Al This process provides a basis for optimizing electrolytic aluminum production plans and adjusting electricity supply strategies.

[0167] In one implementation, the mathematical relationship between the aluminum output, the third new energy power, and the predicted power of each unit in the thermal power unit is as follows:

[0168]

[0169] Step S702: Based on the aluminum output, the revenue and cost of electrolytic aluminum, the net revenue of electrolytic aluminum is obtained.

[0170] In one implementation, the impact of power supply strategies on the economic benefits of aluminum smelter production is quantified, providing a decision-making basis for formulating more economical power dispatch and aluminum smelter production strategies.

[0171] In this embodiment, by calculating the predicted aluminum production and the revenue and cost of electrolytic aluminum, the net revenue of electrolytic aluminum reflects the economic benefits of electrolytic aluminum production under the current power system and market environment. In one embodiment, the mathematical expression of the net revenue of electrolytic aluminum is as follows:

[0172] Eal =N al ·(E al,per -C al,per )

[0173] Step S703: A third objective function is set based on the net income of the electrolytic aluminum and the amount of electricity purchased from the power grid, wherein the third objective function seeks to minimize the amount of electricity purchased from the power grid.

[0174] In one embodiment, the third objective function combines the considerations of steps S701 and S702, with the goal of minimizing the power system's consumption rate while maximizing the revenue from electrolytic aluminum production.

[0175] In one implementation, the mathematical expression of the third objective function is as follows:

[0176] minf 3 =-E al +F dev ,

[0177] The third objective function is not only to pursue the economic benefits of electrolytic aluminum production, but also to focus on the overall operating cost and efficiency of the power system. In order to promote the economic benefits of electrolytic aluminum production, it can also effectively utilize new energy for power generation and reduce the overall energy consumption and operating costs of the power system.

[0178] In this embodiment, the third objective function aims to minimize the amount of electricity purchased from the power grid, while taking into account the net profit of electrolytic aluminum and the penalty term F which is proportional to the amount of electricity purchased from the power grid. dev The design of the third objective function reflects the pursuit of optimization of power system operation costs while ensuring the economic efficiency of electrolytic aluminum production. dev The introduction of the policy, especially in the context of large price fluctuations in the electricity market, will help encourage electrolytic aluminum producers to reduce their dependence on the power grid by adjusting their production plans, thereby reducing their power purchases from the power grid and improving the energy efficiency of the power system.

[0179] Step S704: Construct the fifth constraint condition based on the control and regulation constraints of the electrolytic aluminum power.

[0180] In one implementation, the mathematical expression of the fifth constraint is as follows:

[0181] P al,min ≤P al ≤P al,max ,

[0182] In one embodiment, the fifth constraint introduced focuses on the regulation of electricity usage during the electrolytic aluminum production process to ensure that the power demand of the electrolytic aluminum plant is maintained within a safe and economic range. By setting the minimum and maximum limits of the electrolytic aluminum power, P almin and Palmax This constraint directly reflects the basic demand and upper tolerance limit of the electrolytic aluminum production process for electricity supply, and is more in line with the actual electrolytic aluminum production process.

[0183] Step S705: Construct the sixth constraint condition based on the longest time allowed for the electrolytic aluminum power to run within the minimum power range.

[0184] In one implementation, the mathematical expression of the sixth constraint condition is as follows:

[0185]

[0186] In one embodiment, by establishing the sixth constraint condition, the maximum allowable duration T of the electrolytic aluminum power within the minimum operating range is limited. max , ensuring that electrolytic aluminum production will not affect production efficiency or cause equipment damage due to long-term low-power operation.

[0187] Step S706: Based on the third objective function, the fifth constraint condition and the sixth constraint condition, a control strategy for the energy required for electrolytic aluminum production is obtained.

[0188] In one embodiment, during the optimization process, the latest third-generation renewable energy power, the net income of electrolytic aluminum, and the power usage constraints in the electrolytic aluminum production process are comprehensively considered to output the control strategy for the energy required for electrolytic aluminum production. Through the execution of this step, the control strategy for the energy required for electrolytic aluminum production obtained not only improves the absorption rate of renewable energy and reduces the phenomenon of wind and solar power abandonment, but also optimizes the production efficiency and cost control of electrolytic aluminum, achieving the dual goals of economic and environmentally friendly operation of the power system.

[0189] In this embodiment, the power system is controlled based on the start and stop status of the thermal power units, the output of each unit in the thermal power units, and the control strategy of the energy required for electrolytic aluminum production.

[0190] The implementation of the above strategies can not only cope with the uncertainty and volatility of renewable energy generation and enhance the power system's ability to absorb renewable energy, but also adjust the operation mode of the electrolytic aluminum plant according to the market electricity price and production cost, optimize production costs, and improve the economic benefits of the entire power system and electrolytic aluminum production. In addition, the gradient calculation and each output take into account the characteristics of different power devices, and better balance the power system absorption rate, the net benefit of the electrolytic aluminum plant and other goals.

[0191] 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.

[0192] 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 the above-mentioned various 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.

[0193] 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 multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum in the above method embodiment. 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 multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum in the above method embodiment. 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.

[0194] 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 new energy-thermal power-electrolytic aluminum multi-time scale flexible coordinated control method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned new energy-thermal power-electrolytic aluminum multi-time scale flexible coordinated 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.

[0195] 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.

[0196] It is understood by those skilled in the art 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.

[0197] 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 new energy-thermal power-electrolytic aluminum multi-time scale flexible coordinated control method, characterized in that: include: In the first forecast period, the electrolytic aluminum load power corresponding to the first forecast period is obtained; Based on the historical new energy data, a prediction is made to obtain the first new energy power in the first prediction period; Based on the electrolytic aluminum load power, the first new energy power and the cost of the thermal power unit, the start / stop state of the thermal power unit is obtained; In the second prediction cycle, based on the new energy data after the first update, the second new energy power in the second prediction cycle is obtained; Based on the second new energy power, the start / stop state of the thermal power unit and the electrolytic aluminum load power, the output of each unit in the thermal power unit is obtained; In the third prediction cycle, based on the new energy data after the second update, the third new energy power in the third prediction cycle is obtained; Based on the output of each unit in the thermal power unit and the power of the third new energy source, a control strategy for energy required for electrolytic aluminum production is obtained; The time length of the prediction period decreases in the order of the first prediction period, the second prediction period, and the third prediction period.

2. The multi-time scale flexible coordinated control method of new energy-thermal power-aluminum electrolysis according to claim 1 is characterized in that: The cost of the thermal power unit includes: the start-up and shutdown cost of the thermal power unit and the operating cost of the thermal power unit; the start-up and shutdown status of the thermal power unit is obtained based on the electrolytic aluminum load power, the first new energy power and the cost of the thermal power unit, including: A first objective function is set based on the start-up and shutdown costs of the thermal power unit and the operating cost of the thermal power unit, wherein the first objective function seeks to minimize the operating cost of the thermal power unit; Constructing a first constraint condition based on the predicted electrolytic aluminum load power, the first new energy power, and the power of the thermal power unit; Based at least on the first objective function and the first constraint condition, the start / stop state of the thermal power unit is obtained.

3. The multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum according to claim 2 is characterized in that: The method further comprises: The second constraint condition is constructed based on the output power ramp limit of each unit in the thermal power unit; The step of obtaining the predicted start / stop state of the thermal power unit based at least on the first objective function and the first constraint condition includes: The start / stop state of the thermal power unit is obtained based on the first objective function, the first constraint condition and the second constraint condition.

4. The multi-time scale flexible coordinated control method of new energy-thermal power-aluminum electrolysis according to claim 1 is characterized in that: The output of each unit in the thermal power unit is obtained based on the second new energy power, the start / stop state of the thermal power unit and the electrolytic aluminum load power, including: A second objective function is set based on the second new energy power and the real-time electricity price, wherein the second objective function seeks to minimize the operating cost of the thermal power unit; A third constraint condition is constructed based at least on the electrolytic aluminum load power, the second new energy power, and the power of the thermal power unit; The fourth constraint condition is constructed based on the maximum power constraint and the minimum power constraint of each unit in the thermal power unit and the new energy power unit; Based on the second objective function, the third constraint condition and the fourth constraint condition, at least the predicted power of each unit in the thermal power unit is obtained.

5. The multi-time scale flexible coordinated control method of new energy-thermal power-aluminum electrolysis according to claim 4 is characterized in that: The third constraint condition is constructed based at least on the electrolytic aluminum load power, the second new energy power and the power of the thermal power unit, including: Obtain data on electricity saved by waste heat recovery; Constructing a third constraint condition based on the electrolytic aluminum load power, the second new energy power, the electric energy data saved by the waste heat recovery, and the power of the thermal power unit; The step of obtaining at least the predicted power of each unit in the thermal power unit based on the second objective function, the third constraint condition and the fourth constraint condition comprises: Based on the second objective function, the third constraint condition and the fourth constraint condition, the predicted power of each unit in the thermal power unit and the electrolytic aluminum waste heat recovery strategy are obtained.

6. The multi-time scale flexible coordinated control method of new energy-thermal power-aluminum electrolysis according to claim 5 is characterized in that: Methods for obtaining data on electricity saved by waste heat recovery include: Establish a relationship equation based on the recovered heat and the flue gas heat recovery coefficient set for the corresponding aluminum smelter; Based on the recovered heat, the electric energy data saved by waste heat recovery is obtained, wherein the recovered heat and the electric energy data saved by waste heat recovery satisfy a proportional relationship, wherein the flue gas heat recovery coefficient is a quantity to be solved, and the size of the flue gas heat recovery coefficient can be adjusted by adjusting the device corresponding to the control flue gas heat recovery coefficient.

7. The multi-time scale flexible coordinated control method of new energy-thermal power-aluminum electrolysis according to claim 1 is characterized in that: The control strategy for obtaining the energy required for electrolytic aluminum production based on the output of each unit in the thermal power unit and the power of the third new energy source includes: Based on the predicted power of the third new energy source and the predicted power of each unit in the thermal power unit, a predicted aluminum output is obtained; Based on the aluminum output, the revenue and cost of electrolytic aluminum, the net revenue of electrolytic aluminum is obtained; A third objective function is set based on the net income of the electrolytic aluminum and the amount of electricity purchased from the power grid, wherein the third objective function seeks to minimize the amount of electricity purchased from the power grid; Construct the fifth constraint condition based on the control and regulation constraints of the electrolytic aluminum power; The sixth constraint condition is constructed based on the maximum time allowed for the electrolytic aluminum power to operate within the minimum power range; Based on the third objective function, the fifth constraint and the sixth constraint, a control strategy for the energy required for electrolytic aluminum production is obtained.

8. The multi-time scale flexible coordinated control method of new energy-thermal power-aluminum electrolysis according to claim 1 is characterized in that: wherein the first forecast period and the second forecast period are both composed of time periods, wherein the length of the time period of the first forecast period is the same as the length of the time period of the second forecast period; and / or; The power system is controlled based on the start and stop status of the thermal power unit, the output of each unit in the thermal power unit, and the control strategy of the energy required for the electrolytic aluminum production.

9. The multi-time scale flexible coordinated control method of new energy-thermal power-electrolytic aluminum according to claim 1 comprises: When the large power grid to which the electrolytic aluminum system is connected is a weak power grid, it is equipped with energy-type and power-type hybrid energy storage.

10. The multi-time scale flexible coordinated control method of new energy-thermal power-aluminum electrolysis according to claim 1 comprises: When the large power grid to which the electrolytic aluminum system is connected is a strong power grid, energy storage is selected based on the power quality requirements.

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