Flexible Coordinated Control Method for New Energy, Thermal Power, and Electrolytic Aluminum Across Multiple Time Scales

Abstract

This invention relates to a flexible coordinated control method for multiple time scales of new energy, thermal power, and electrolytic aluminum. The method includes: in the first prediction period, obtaining the electrolytic aluminum load power corresponding to the first prediction period; predicting the first new energy power based on historical new energy data; obtaining the start-up and shutdown status of the thermal power units based on the electrolytic aluminum load power, the first new energy power, and the cost of the thermal power units; in the second prediction period, obtaining the second new energy power within the second prediction period based on the new energy data updated in the first period; obtaining the output of each unit in the thermal power units based on the second new energy power, the start-up and shutdown status of the thermal power units, and the electrolytic aluminum load power; in the third prediction period, obtaining the third new energy power based on the new energy data updated in the second period; and obtaining a control strategy for the energy required for electrolytic aluminum production based on the output of each unit in the thermal power units and the third new energy power.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, specifically providing a flexible coordinated control method for new energy-thermal power-electrolytic aluminum multi-timescale. Background Technology

[0002] In existing new power system control and optimization technologies that primarily utilize new energy sources, especially when it comes to the utilization of new energy sources such as wind and solar power, a major problem is the volatility and uncertainty of new energy power generation.

[0003] Traditional optimization methods often employ a one-time optimization approach, first predicting the power generation of renewable energy sources, and then optimizing power system dispatch using a large time scale, such as monthly, weekly, or daily. While this method can balance power system supply and demand to some extent, it struggles to overcome the short-term fluctuations in renewable energy generation, making precise dispatch difficult. Ultimately, this leads to a significant mismatch between the optimization results and actual operation, failing to maximize the utilization of renewable energy.

[0004] Accordingly, there is a need in this field for a new power system control optimization method to solve the above problems. Summary of the Invention

[0005] To overcome the above-mentioned shortcomings, this invention is proposed to provide a solution, or at least a partial solution, to the serious mismatch between the optimization results and actual operation in the prior art, which fails to maximize the utilization of new energy sources.

[0006] In a first aspect, the present invention provides a flexible coordinated control method for multiple time scales of new energy, thermal power, and electrolytic aluminum. The method includes: in a first prediction period, obtaining the electrolytic aluminum load power corresponding to the first prediction period; predicting based on historical new energy data to obtain the first new energy power within the first prediction period; obtaining the start-up and shutdown 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 period, obtaining the second new energy power within the second prediction period based on the new energy data updated for the first time; obtaining the output of each unit in the thermal power unit based on the second new energy power, the start-up and shutdown status of the thermal power unit, and the electrolytic aluminum load power; in a third prediction period, obtaining the third new energy power within the third prediction period based on the new energy data updated for the second time; and obtaining a control strategy for the energy required for electrolytic aluminum production based on the output of each unit in the thermal power unit and the third new energy power; wherein the time length of the prediction period decreases sequentially according to the order of the first prediction period, the second prediction period, and the third prediction period; and selecting energy storage to improve power quality based on the strength of the main power grid connected to the electrolytic aluminum system.

[0007] In a second aspect, a control device is provided, comprising a processor and a storage device, the storage device being adapted to store multiple computer programs, the computer programs being adapted to be loaded and run by the processor to execute the new energy-thermal power-electrolytic aluminum multi-timescale flexible coordinated control method described in any of the above-mentioned technical solutions of the new energy-thermal power-electrolytic aluminum multi-timescale flexible coordinated control method.

[0008] In a third aspect, a computer-readable storage medium is provided, wherein a plurality of computer programs are stored therein, the computer programs being adapted to be loaded and run by a processor to perform the new energy-thermal power-electrolytic aluminum multi-timescale flexible coordinated control method described in any of the above-described technical solutions of the new energy-thermal power-electrolytic aluminum multi-timescale flexible coordinated control method.

[0009] The present invention comprises one or more of the following technical solutions:

[0010] Beneficial effects:

[0011] In implementing the technical solution of this invention, by adopting a multi-timescale flexible coordinated control method for new energy, thermal power, and electrolytic aluminum, this technology effectively improves the power system's adaptability to the volatility and uncertainty of new energy power generation. By employing a phased forecasting and scheduling strategy, it not only accurately matches power supply and demand but also significantly improves the utilization rate of new energy. Especially for power systems represented by electrolytic aluminum production, this technology, through a control strategy that finely adjusts the required energy, ensures the stability of the production process and contributes to the economic operation and sustainable development of the power system. Attached Figure Description

[0012] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. Furthermore, similar numbers in the drawings are used to denote similar components, wherein:

[0013] Figure 1 This is a schematic diagram of a power system scenario for a flexible coordinated control method for new energy-thermal power-electrolytic aluminum multi-timescale according to an embodiment of the present invention.

[0014] Figure 2 This is a schematic diagram of the main steps of a flexible coordinated control method for new energy-thermal power-electrolytic aluminum multi-timescale according to an embodiment of the present invention;

[0015] Figure 3 This is a schematic diagram of the secondary steps of a flexible coordinated control method for new energy-thermal power-electrolytic aluminum multi-timescale according to an embodiment of the present invention;

[0016] Figure 4 This is a schematic diagram of the secondary steps of a flexible coordinated control method for new energy-thermal power-electrolytic aluminum multi-timescale according to an embodiment of the present invention;

[0017] Figure 5 This is a schematic diagram of the secondary steps of a flexible coordinated control method for new energy-thermal power-electrolytic aluminum multi-timescale according to an embodiment of the present invention;

[0018] Figure 6 This is a flowchart illustrating the first prediction stage of a multi-timescale flexible coordinated control method for new energy-thermal power-electrolytic aluminum according to an embodiment of the present invention.

[0019] Figure 7 This is a flowchart illustrating the second prediction stage of a flexible coordinated control method for new energy-thermal power-electrolytic aluminum multi-timescale according to an embodiment of the present invention.

[0020] Figure 8 This is a flowchart illustrating the third prediction stage of a multi-timescale flexible coordinated control method for new energy-thermal power-electrolytic aluminum according to an embodiment of the present invention.

[0021] Figure 9 This is an equivalent circuit diagram of aluminum production in an electrolytic aluminum plant using a new energy-thermal power-electrolytic aluminum multi-timescale flexible coordinated control method according to an embodiment of the present invention. Detailed Implementation

[0022] Some embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] In the description of this invention, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and may also include software components, such as computer programs, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing 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 terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0024] In one application scenario of the present invention, such as Figure 1 As shown, the power system includes an aluminum electrolysis plant, new energy generator sets, energy storage power stations, a control system, and thermal power generator sets. The thermal power generator sets prioritize supplying electricity to the aluminum electrolysis plant because, in this scenario, they are supporting facilities for the plant.

[0025] like Figure 1 As shown, preferably, the electrolytic aluminum plant may also have an aluminum waste heat recovery system.

[0026] In this scenario, under certain circumstances, when the electricity supplied by thermal power generating units and new energy generating units is insufficient for the use of the aluminum electrolytic plant—for example, when the power output of new energy generating units is drastically reduced due to weather conditions—the aluminum electrolytic plant can purchase electricity from the grid. However, since purchasing electricity from the grid is relatively expensive, this power system aims to minimize the need for such purchases.

[0027] like Figure 1 As shown, preferably, an energy storage power station can be selected to be installed in the electrolytic aluminum system.

[0028] In this scenario, when the main power grid connected to the electrolytic aluminum system is a weak grid, the high proportion of new energy fluctuations affects the stable operation of the electrolytic aluminum system. Therefore, in this power system, a hybrid energy storage system of energy type and power type is equipped to shift wind and solar fluctuations.

[0029] In this scenario, when the main power grid connected to the electrolytic aluminum system is a strong grid, the main grid has strong support capabilities. Therefore, in this power system, energy storage is selected according to the power quality requirements.

[0030] See appendix Figure 2 , Figure 2 This is a schematic flowchart illustrating the main steps of a flexible coordinated control method for multiple time scales of new energy, thermal power, and electrolytic aluminum according to an embodiment of the present invention. Figure 2 As shown, the flexible coordinated control method for new energy-thermal power-electrolytic aluminum multi-timescale in this embodiment of the invention mainly includes the following steps S10-S70.

[0031] Step S10: In the first prediction cycle, obtain the electrolytic aluminum load power corresponding to the first prediction cycle.

[0032] In this embodiment, the prediction period refers to a time phase for predicting certain parameters in the power system, which corresponds to a period of time in reality. This prediction is typically performed in multiple periods, with nested relationships between them. Specifically, in this application, the nested relationship means that the prediction result of the previous prediction period is usually used when making predictions in the next prediction period, and the time period corresponding to the next prediction period is within the time period corresponding to the previous prediction period. The design of multiple prediction periods aims to improve the accuracy of predictions and the adaptability of scheduling schemes by refining the time scale. Furthermore, the values ​​considered for solving within each prediction period also take into account the characteristics of the corresponding power devices, thereby enabling the final strategy to adapt to the instability of new energy generation and reduce the absorption capacity of the power system.

[0033] At each stage, the power system reassesses the impact of updated data on the power system based on real-time data and updated information, ensuring that dispatch decisions can adapt to changes in actual operating conditions, thereby achieving efficient management and optimization of power system resources.

[0034] In one implementation, the first prediction period corresponds to Figure 6 As shown, the obtained electrolytic aluminum load power affects the start-up and shutdown status of subsequent thermal power units and the utilization strategy of new energy power generation. Methods for obtaining electrolytic aluminum load power typically include manual forecasting or machine forecasting. Manual forecasting is based on production plans or experience, while machine forecasting is often based on historical data or uses forecasting algorithms for real-time prediction.

[0035] In one implementation, the historical data-based forecasting method relies on past operating records and load data. By analyzing historical load variation trends, seasonal factors, and the impact of related industrial activities, a forecasting model for electrolytic aluminum load power is constructed. This allows for the prediction of the electrolytic aluminum load power for the corresponding first forecast period based on historical data.

[0036] In one implementation, an example of a first forecast period is given here. For instance, the length of the first forecast period is 24 hours, corresponding to 0:00-24:00 on January 1, 2024. Obtaining the electrolytic aluminum load power corresponding to the first forecast period means obtaining the predicted value of the electrolytic aluminum load power for each hour during that day.

[0037] In summary, by setting a first prediction period and obtaining the electrolytic aluminum load power within that first prediction period, this technical application aims to establish a flexible and efficient power system optimization framework. This step lays the foundation for the operation of subsequent methods.

[0038] Step S20: Based on historical renewable energy data, make predictions to obtain the first renewable energy power within the first prediction period.

[0039] In this embodiment, new energy sources include wind and / or solar energy, as well as other new energy sources used for power generation in power systems. New energy sources in this embodiment do not include hydrogen energy; they refer to energy sources significantly affected by the natural environment, such as wind and solar energy.

[0040] In one implementation, the new energy source includes wind and solar power. The first new energy power output within the first forecast period refers to the predicted value of new energy power generation forecast before the start of that period.

[0041] In one implementation, the power output of the primary renewable energy source is predicted by constructing a forecasting model. Historical renewable energy data includes, but is not limited to, past weather conditions, seasonal variations, and the actual power generation of renewable energy power generation facilities. By analyzing this data, key factors affecting renewable energy power generation can be identified, and a forecasting model can then be constructed. For example, a forecasting model for solar power generation might consider factors such as solar irradiance, sunshine duration, and seasonal variations; while a forecasting model for wind power generation might consider parameters such as wind speed, wind direction, and temperature.

[0042] In one implementation, the predictive model is constructed using statistical methods or machine learning algorithms. Statistical methods may include time series analysis, such as the autoregressive moving average (ARMA) model, used to analyze and predict data sequences with time correlations. Machine learning algorithms, such as random forests, support vector machines, or deep learning networks, are capable of handling more complex nonlinear relationships and can learn and extract useful features from large amounts of historical data.

[0043] Taking the forecasting of new energy sources, including wind and electricity, as an example, assuming the forecast period is from 0:00 to 24:00 on January 1, 2024, the forecasting model first analyzes historical new energy data from the days surrounding that day and the days preceding it, combined with recent weather forecasts, such as the predicted weather conditions and wind speed changes for that day. Subsequently, the model integrates this information and outputs predicted values ​​for wind power generation and photovoltaic power generation for each hour on January 1, 2024. These predicted values ​​are considered the primary new energy power output within the first forecast period, providing preliminary reference data for the operation and scheduling of the power system.

[0044] By using forecasts based on historical renewable energy data, the power system can better understand the potential output of renewable energy generation in the future, thereby more effectively planning and adjusting the power system's operating strategies. This not only helps improve the utilization efficiency of renewable energy but also helps reduce operating costs and ensure the stability and reliability of the power system.

[0045] Step S30: Based on the electrolytic aluminum load power, the first new energy power, and the cost of the thermal power unit, obtain the start-up and shutdown status of the thermal power unit.

[0046] In this embodiment, thermal power generating units, as a major component of the power system, typically have a predetermined number of units and technical parameters, such as maximum generating capacity and operating costs. These data provide basic physical and economic constraints for the operation of thermal power generating units.

[0047] In this embodiment, the start-up and shutdown status of the thermal power units within the first prediction period refers to the operating status of each unit at different times within that period. The first prediction period typically refers to a specific time range, and the start-up and shutdown decisions of the thermal power units need to be optimized for each time period within the first prediction period.

[0048] In one implementation, the start-up and shutdown status of thermal power units is determined during the power system optimization scheduling process based on a comprehensive consideration of the electrolytic aluminum load power, the primary new energy power, and the operating cost of the thermal power units.

[0049] In this embodiment, the start-up and shutdown status of the thermal power unit is calculated by optimizing the model.

[0050] In one embodiment, the cost of a thermal power unit includes: the start-up and shutdown cost of the thermal power unit and the operating cost of the thermal power unit. In this embodiment, the start-up and shutdown status of the thermal power unit is obtained through steps S301-S303, such as... Figure 3 As shown, the details are as follows:

[0051] Step S301: Set a first objective function based on the start-up and shutdown costs and the operating costs of the thermal power unit, wherein the first objective function aims to minimize the operating costs of the thermal power unit.

[0052] In one implementation, economic efficiency is maximized by setting a reasonable objective function. In this implementation, the proposed first objective function aims to minimize the operating cost of thermal power units. This objective is achieved by accurately calculating the operating cost, start-up and shutdown cost, and other conditions of each thermal power unit within each time period. The setting of the first objective function considers both operating cost and start-up and shutdown cost, thus ensuring the economic efficiency of power system operation.

[0053] In one implementation, a mathematical expression for the objective function is given, as follows:

[0054]

[0055]

[0056]

[0057] Specifically, the objective function The combined output power of thermal power units in each predicted time period t and start / stop status here, This represents the operating cost of a thermal power unit at a given output power, while This represents the start-up and shutdown costs.

[0058] In a preferred embodiment, operating costs are typically proportional to power generation, while start-up and shutdown costs are proportional to the number of times the unit is started and stopped.

[0059] In this embodiment, operating costs The fitting was performed using a quadratic function, where a i b i and c i These represent the coefficients of the quadratic term, the coefficients of the linear term, and the 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 a function of output power. In this embodiment, start-up and shutdown costs... This includes startup costs and shutdown costs, both of which are closely related to the operation of switching the unit from a shutdown state to an operating state (or vice versa).

[0060] In this embodiment, the advantage of setting the first objective function lies in its ability to comprehensively reflect the economic efficiency and operational flexibility of thermal power units. By subsequently optimizing this first objective function in conjunction with other constraints, a cost-effectiveness-maximizing operating strategy can be obtained, that is, minimizing the overall operating cost while meeting the load demand of the power system. Furthermore, considering 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.

[0061] Step S302: Construct the first constraint condition based on the predicted electrolytic aluminum load power, the first new energy power, and the power of the thermal power unit.

[0062] In one implementation, a first constraint ensures a balance between power supply and demand, thereby satisfying the maximum absorption requirement.

[0063] In one implementation, the mathematical expression of the first constraint is given as follows:

[0064]

[0065] In this embodiment, the first constraint states that within any given time period t, the total power generation of all generating resources in the power system equals the load demand. Specifically, this includes thermal power units (composed of N... g Power generation (composed of individual generating units), photovoltaic power generation Wind power generation and electrolytic aluminum load power In this embodiment, the first new energy power = photovoltaic power generation + wind power generation.

[0066] In this embodiment, in the first constraint condition, This represents the total power generation of all operating thermal power units during time period t, where This represents the power generation of the i-th thermal power unit during time period t. This represents the start / stop status of the unit during time period t (1 for start-up, 0 for shutdown). and These represent the photovoltaic and wind power generation during time period t, respectively. This refers to the power demand of the electrolytic aluminum load during that period.

[0067] 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 sources.

[0068] For example, if solar and wind power generation is projected to be high during 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 over-generation. Conversely, if renewable energy generation is projected to be low during a certain period, while the electrolytic aluminum load is high, the number of operating thermal power units should be increased to meet the increased electricity demand.

[0069] 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 a stable power supply for key industrial loads such as electrolytic aluminum production.

[0070] Step S303: Based at least on the first objective function and the first constraint, obtain the start-up and shutdown status of the thermal power unit.

[0071] In one implementation, by utilizing a pre-defined first objective function and first constraints, and comprehensively considering the cost-effectiveness of thermal power units and the power system's supply-demand balance, the optimal start-up and shutdown strategy for each thermal power unit within the first forecast period is derived. In this implementation, the above method aims to ensure that the power system achieves economical operation and stability while meeting load demand.

[0072] First, the first objective function By quantifying the operating and start-up / shutdown costs of thermal power units, a cost minimization objective is provided for the optimization model. This objective function reflects the total cost incurred by the thermal power units under different power generation levels and start-up / shutdown conditions, aiming to find the lowest-cost operating scheme.

[0073] Next, the first constraint condition This ensures that the power system can meet load demands at any time, including those from electrolytic aluminum production and other electricity needs. This condition, by ensuring supply and demand balance, provides a fundamental constraint on the start-up and shutdown decisions of thermal power units.

[0074] Within 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 deals with a multi-variable, multi-time-period decision problem, involving decision variables including the power generation and start-up / shutdown status of each thermal power unit in each time period. During the optimization process, the algorithm searches for the lowest-cost start-up / shutdown combination of thermal power units, while satisfying the first constraint—power supply and demand balance.

[0075] For example, considering the uncertainty of renewable energy generation and the continuity requirements of electrolytic aluminum production, the optimization model may recommend starting more thermal power units during periods when renewable energy generation is expected to be low in order to ensure the stability of power supply; conversely, during periods when renewable energy generation is expected to be high, the model may suggest reducing the operation of thermal power units to reduce the operating costs of the power system.

[0076] This method yields a detailed start-up and shutdown plan for thermal power units, specifying whether each unit should operate at different times during the first forecast period, and the corresponding operating power. This not only helps maximize the economic benefits of the power system but also ensures its stability and reliability, guaranteeing the smooth operation of critical loads such as electrolytic aluminum production. Furthermore, a rational start-up and shutdown strategy for thermal power units can promote the efficient utilization of new energy sources and reduce carbon emissions across the entire power system.

[0077] In another embodiment, the start-up and shutdown status of the thermal power unit is obtained through steps S301-S302 and S304-S305, such as... Figure 4 As shown, the details are as follows:

[0078] Step S304: Construct a second constraint condition based on the output power ramp-up limit of each unit in the thermal power unit.

[0079] In one implementation, a second constraint is established to ensure that thermal power units adhere to ramp limits on their output power during operation. Ramp limits refer to the rate at which the power output of a thermal power unit increases or decreases, which helps maintain the stability and reliability of the power system. Specifically, the second constraint specifies the maximum rate of increase U of the power change for each thermal power unit. i and the maximum rate of decline D i This ensures that adjustments to thermal power units are carried out within technical and safety limits.

[0080] In this embodiment, the consideration of ramp limiting is based on the physical characteristics and safe operation standards of thermal power units. Since the start-up, shutdown, and power regulation of thermal power units all require a certain amount of time, excessively rapid power changes may damage equipment or affect grid stability. Therefore, ramp limiting is introduced as a constraint for dispatch optimization to avoid overly rapid power adjustments and ensure the smooth operation of thermal power units.

[0081] In one implementation, the second constraint takes the form of: This formula ensures that the power change of a power generation unit (a single generator set) between two adjacent moments will not exceed the preset rise and fall limits.

[0082] In one implementation, introducing a ramp limit as a second constraint into the optimization model can yield the following effects. First, it helps ensure the stability of the power system by limiting the rate of power change, reducing fluctuations in grid frequency and voltage, and thus maintaining the safe operation of the power system. Second, the second constraint helps improve the economics of the power system by optimizing generation plans and balancing the needs of generation costs and power system stability. Finally, considering the ramp limit also helps improve the operating efficiency of thermal power units and extend equipment life because it avoids frequent operation of equipment under extreme conditions, reducing maintenance costs and failure rates.

[0083] In summary, by considering the ramp-up limitations of thermal power units in step S304, the second constraint condition is constructed, ensuring the stable and safe operation of the power system.

[0084] Step S305: Based on the first objective function, the first constraint condition, and the second constraint condition, obtain the start-up and shutdown status of the thermal power unit.

[0085] In one implementation, the main optimization method is similar to step S303, and will not be described again here.

[0086] In one implementation, by combining a first objective function, a first constraint, and a newly added second constraint, the start-up and shutdown status of thermal power units is optimized, which significantly improves the precision and practicality of power system dispatching.

[0087] The main difference between this step and step S303 is the introduction of a power ramp-up limit for thermal power units as a new constraint. Step S303 determines the start-up and shutdown status of thermal power units solely based on the first objective function and the first constraint, namely, the cost minimization objective and the power supply and demand balance constraint. While this process can guarantee the basic requirements of the power system in terms of economy and supply and demand balance, this step considers more aspects related to the safety of thermal power unit operation.

[0088] In this embodiment, by adding a second constraint, namely the ramp-up limit of the thermal power unit, in step S305, the dispatching decision can more accurately reflect the actual operating capacity and safety boundary of the thermal power unit. This means that when optimizing the start-up and shutdown plan of the thermal power unit, not only cost and supply-demand balance are considered, but also the rate limit of the power adjustment of the thermal power unit is taken into account, thereby avoiding equipment damage or grid stability problems that may be caused by excessively rapid power changes.

[0089] In summary, step S305, by introducing ramp limits into the optimization model, not only maintains the economic operation and supply-demand balance of the power system, but also enhances the stability and security of the power system, reflecting a deep consideration of actual operational constraints and an accurate grasp of details in power system dispatch optimization.

[0090] Step S40: In the second prediction period, based on the new energy data updated in the first prediction period, the second new energy power in the second prediction period is obtained.

[0091] In one implementation, the second prediction period is as follows: Figure 7 As shown, the preliminary forecast of renewable energy power can be corrected in the second forecast period based on the latest renewable energy data (the renewable energy data after the first update) to adapt to real-time changes in the natural environment.

[0092] In this embodiment, the second forecast period is typically set within the first forecast period, and its length is shorter than the first forecast period, in order to enable more precise and real-time scheduling decisions. For example, if the first forecast period is 24 hours, then the second forecast period may be set to 4 hours. This setting aims to capture short-term fluctuations in renewable energy generation caused by factors such as changes in weather conditions throughout the day.

[0093] It should be noted that if the period corresponding to the second forecast period is only partially within one of the first forecast periods, then there will be another first forecast period immediately following the first forecast period. In this case, the second forecast period will span two consecutive first forecast periods. However, in normal circumstances, the second forecast period is usually set within one of the first forecast periods.

[0094] In this embodiment, during the second forecast period, the dispatching power system will correct the predicted value of the first renewable energy power based on the latest acquired renewable energy data, such as real-time wind speed and solar radiation intensity. In one embodiment, a forecasting algorithm is used to adapt to the latest data; specifically, in another embodiment, a fuzzy algorithm is used to infer the current power of the second renewable energy source. This real-time or near-real-time forecast correction ensures that power generation forecasts are more closely aligned with actual conditions, reducing improper resource allocation due to forecast errors.

[0095] The benefits of this step include improved power system adaptability to fluctuations in renewable energy generation, optimized allocation of power generation resources, and, particularly, the ability to promptly adjust operating parameters in the subsequent power system when renewable energy generation changes significantly, ensuring the continuity and reliability of power supply. Furthermore, precise control of operating parameters in the subsequent power system can effectively reduce operating costs and minimize unnecessary fuel consumption and emissions.

[0096] For example, if the latest renewable energy data indicates an upcoming period of high wind speeds, predicting a significant increase in wind power generation, the power system can accordingly reduce the output of some 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 operating strategies of thermal power units in advance to ensure a stable power supply.

[0097] In summary, step S40, by dynamically predicting and correcting the power generation of new energy sources over a shorter time scale, not only enhances the power system's responsiveness to fluctuations in new energy sources but also supports the economic efficiency and environmental friendliness of the power system.

[0098] Step S50: Based on the second new energy power, the start-stop status of the thermal power unit and the electrolytic aluminum load power, obtain the output of each unit in the thermal power unit.

[0099] In one implementation, the specific output of each thermal power unit in the second forecast period is determined by integrating three key factors: the power of the second new energy source, the current start-up and shutdown status of the thermal power units, and the power of the electrolytic aluminum load.

[0100] In this embodiment, the main advantage of selecting the output of each unit in the thermal power plant during the second forecast period is that it allows the power system to react quickly based on the latest second renewable energy power. Since the second forecast period is typically short, this near-real-time adjustment strategy can more accurately match power supply and demand, especially when dealing with sudden changes in renewable energy generation, effectively reducing economic losses and power system risks caused by forecast errors.

[0101] In this embodiment, by integrating the latest second renewable energy power forecast, this step makes power system dispatch more flexible and can adjust the output of thermal power units in a timely manner to adapt to the increase or decrease of renewable energy power generation.

[0102] In one implementation, through steps S501-S504, such as Figure 5 As shown, the details are as follows:

[0103] Step S501: Set a second objective function based on the second new energy power and the real-time electricity price, wherein the second objective function aims to minimize the operating cost of the thermal power unit.

[0104] In one implementation, the economy and flexibility of power system optimization are improved by introducing real-time electricity prices as a key physical quantity. Specifically, the second objective function considers the electricity generated from renewable energy facilities such as photovoltaics and wind power, and the time-of-use price R of this electricity in the grid. Grid This led to optimization of the operating costs of thermal power units.

[0105] In one implementation, the mathematical expression of the second objective function is given as follows:

[0106]

[0107] In the second objective function (P) PV +P WT )·R Grid This represents the revenue that the modified renewable energy generation can bring to the power grid, that is, the revenue brought by the second renewable energy power. Where P... PV and P WT R refers to the power generation of photovoltaic and wind power respectively during a certain time period t. Grid This is the time-of-use electricity price for that period. This reflects the operating costs of all thermal power units over the same period, where C i (p i Let be the operating cost function of the i-th thermal power unit. In this embodiment, the second objective function aims to minimize the operating cost of the thermal power unit.

[0108] In this embodiment, by setting a second objective function, the power system dispatch optimization not only considers the operating cost of thermal power units, but also incorporates the revised renewable energy generation and the real-time electricity price of the grid. This enables the power system to respond more flexibly to fluctuations in market electricity prices and adjust the operating status of thermal power units according to the price level and the changes in the newly revised renewable energy generation, thereby maximizing the economic benefits of the entire power system.

[0109] 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.

[0110] In one implementation, not only is the power system able to meet the demand of electrolytic aluminum load while optimizing the operation of thermal power units, but the overall absorption rate is also minimized.

[0111] In one implementation, a mathematical expression for the third constraint is given as follows:

[0112]

[0113] This constraint ensures that at any given moment, the power supply of the power system (including the output from thermal power units and the power from secondary new energy sources) matches the power demand of the electrolytic aluminum load.

[0114] The second constraint emphasizes the balance between the utilization of new 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 new energy power generation, ensuring stable power supply to key loads such as electrolytic aluminum production, while reducing the absorption rate of the power system.

[0115] In another implementation, the third constraint condition is constructed through steps S502-1 to S502-2, as follows:

[0116] In this embodiment, the waste heat recovery mechanism is first introduced. Waste heat recovery from the electrolytic cell is achieved by adjusting the flow rate of the heat exchange medium. Adjusting the flow rate leads to changes in the furnace walls (composed of solidified dielectric material) inside the electrolytic cell, and these changes cannot be stabilized within a short period. To achieve the adjustment of the heat exchange medium and overcome the instability of waste heat recovery within a short time, this step is performed in the second prediction cycle, making the scheduling of waste heat recovery more reasonable. Generally speaking, the duration of the second prediction cycle is appropriate, shorter than the first but longer than the third. If this step were performed in the third prediction cycle, the time requirement for stable waste heat recovery might not be met. The energy balance equation for the electrolytic temperature of aluminum electrolysis is:

[0117] A electricity +A remnant =A reaction +A heating +A loss

[0118] In the formula, A electricity Energy provided for the operating voltage; A remnant A represents the energy in the space before the anode gas enters the flue gas thermal power system; reaction The energy required for the electrolytic aluminum chemical reaction; A heating The energy required to heat the electrolytic material to the electrolysis temperature; A loss This refers to the energy lost due to thermal radiation from the electrolytic cell.

[0119] Step S502-1: Obtain data on the electrical energy saved by waste heat recovery.

[0120] In this embodiment, the energy saved by waste heat recovery is achieved by using the waste heat recovery technology of the aluminum electrolysis plant to recover part of the heat energy that would otherwise be wasted during the aluminum electrolysis process and reuse it for the energy needed in the aluminum electrolysis process.

[0121] In one implementation, expressing the parameter as the electrical energy saved through waste heat recovery helps in redesigning the third constraint. In this implementation, the electrical energy saved through waste heat recovery is a solution to be found.

[0122] In one embodiment, the method for obtaining data on the electrical energy saved by waste heat recovery includes steps S502-1-A to S502-1-B, as follows:

[0123] Step S502-1-A: Establish a relational equation based on the recovered heat and the flue gas heat recovery coefficient in the aluminum electrolysis plant.

[0124] In this embodiment, the flue gas heat recovery coefficient is a variable to be solved, and the value of the flue gas heat recovery coefficient can be adjusted by adjusting the equipment corresponding to the flue gas heat recovery coefficient.

[0125] In one implementation, a relational equation is established to quantitatively analyze the waste heat recovery from flue gas in an aluminum electrolysis plant. The flue gas heat recovery coefficient, as a key parameter, reflects the efficiency of waste heat recovery. The magnitude of this coefficient directly affects the amount of heat recovered, and consequently, the electrical energy saved by the power system.

[0126] In this embodiment, a mathematical expression is given for the relationship between the recovered heat and the heat recovery coefficient of flue gas in an aluminum electrolysis plant, as follows:

[0127] A loss_recycle =V g ×ρ g ×C pg ×ΔT.

[0128] In the relational equation, the flue gas flow rate V can be used as a basis. g Smoke density ρ g Specific heat of flue gas C pg Based on the temperature difference ΔT between the flue gas and the exhaust gas temperature, the heat energy loss A of the flue gas emitted from the electrolytic cell is calculated. 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.

[0129] In this embodiment, by finely adjusting the equipment parameters corresponding to the flue gas heat recovery coefficient, the flue gas heat recovery efficiency can be effectively controlled, allowing for the selection of equipment that maximizes heat energy recovery. This not only improves the energy efficiency of the aluminum electrolysis plant but also helps reduce operating costs and carbon emissions caused by energy waste.

[0130] Step S502-1-B: Based on the recovered heat, obtain the data on the electrical energy saved by waste heat recovery.

[0131] In this embodiment, the recovered heat is directly proportional to the electrical energy saved by the waste heat recovery.

[0132] One implementation demonstrates the direct economic benefits of waste heat recovery during aluminum electrolysis production, namely, reducing additional electricity demand by recovering and utilizing the heat energy that would otherwise be wasted.

[0133] Converting recovered heat energy into saved electricity is a highly 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 their load on the grid, thereby achieving a dual optimization of cost savings and environmental benefits throughout the power system.

[0134] In one implementation, the relationship between the recovered heat and the electrical energy saved by waste heat recovery is given as follows:

[0135]

[0136] Where P al_recycle Data on the electrical energy saved through waste heat recovery.

[0137] Step S502-2: Construct a third constraint condition based on the electrolytic aluminum load power, the second new energy power, the electrical energy saved by the waste heat recovery, and the power of the thermal power unit.

[0138] In one implementation, the energy saved through waste heat recovery calculated in the above process is incorporated into the overall optimal scheduling of the power system, constructing a third constraint that includes the power of electrolytic aluminum load, the power of the second new energy source, and the energy saved through waste heat recovery. This constraint comprehensively considers the supply and demand balance of the power system and introduces the new dynamic factor of energy saved through waste heat recovery, further optimizing the power allocation of thermal power units.

[0139] By optimizing the solution for energy savings through waste heat recovery in the electrolytic aluminum production process, the power system can more flexibly adjust the output of thermal power units to adapt to constantly changing load demands and renewable energy generation. This method not only improves the economic efficiency and environmental friendliness of the power system operation but also enhances its adaptability to fluctuations in renewable energy and load changes, ensuring the stability and reliability of power supply.

[0140] Step S503: Construct a fourth constraint condition based on the maximum power constraint and minimum power constraint of each unit in the thermal power unit and the new energy power generation unit.

[0141] In one implementation, a fourth constraint is introduced to constrain the maximum and minimum power output of thermal power units and new energy power generating units, ensuring that the operating power of all power generation units is within the technically and safety-permissible range. The fourth constraint ensures that the generating units do not exceed the operating range allowed by their design and operation parameters, thereby avoiding the risk of equipment overload or insufficient power output.

[0142] In one implementation, the mathematical expression of the fourth constraint that the generator set needs to satisfy is given as follows:

[0143] P i，min ≤P i ≤P i，max ,

[0144] In this formula, Pi represents each unit in a thermal power unit or each unit in a new 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 It is not lower than its minimum stable output value P. i,min This is fundamental to achieving stable and efficient operation of the power system. For thermal power units, the maximum power limit reflects the unit's maximum generating capacity, while the minimum power limit is related to the minimum output power required to maintain stable operation. Although new energy power generating units, such as wind and solar power, are greatly affected by weather and environmental conditions, a reasonable operating power range is also set in this implementation to optimize their power generation efficiency and ability to participate in grid dispatch.

[0145] Step S504: Based on the second objective function, the third constraint, and the fourth constraint, at least the predicted power of each unit in the thermal power unit is obtained.

[0146] In one implementation, the core of step S504 lies in combining the second objective function, the third constraint, and the fourth constraint to comprehensively optimize the specific output of each unit in the thermal power unit during the second prediction cycle. This step takes into account the characteristics of the thermal power unit, such as its start-up and shutdown states and minimum stable load, ensuring the practicality of the optimization results and the feasibility of its execution.

[0147] In this embodiment, by optimizing the output of thermal power units in the second forecast period, this step not only considers the operating costs of thermal power units and changes in renewable energy generation, but also meticulously takes into account the demand of electrolytic aluminum load and the physical operating limitations of thermal power units. This method enables the power system to adapt more flexibly to short-term fluctuations in power supply and demand, especially when facing significant changes in renewable energy generation, allowing for timely adjustments to the output of thermal power units to ensure the stability and economy of power supply.

[0148] In summary, by comprehensively considering the operating characteristics of thermal power units and the real-time demand of the power system in the second forecast period, an economical and reliable power generation optimization scheme was achieved. This scheme not only improves the economic efficiency of the power system but also enhances its adaptability to fluctuations in renewable energy sources, ensuring stable power supply to critical loads such as electrolytic aluminum production, and demonstrating the effectiveness of optimized power system dispatch.

[0149] Furthermore, in another embodiment, step S504-1 is set corresponding to steps S502-1 and S502-2.

[0150] Step S504-1: Based on the second objective function, the third constraint, and the fourth constraint, the predicted power of each unit in the thermal power unit and the waste heat recovery strategy of electrolytic aluminum are obtained.

[0151] In one implementation, compared to step S504, this implementation introduces a new dimension into the optimized dispatch of the power system—a waste heat recovery strategy for electrolytic aluminum production. The introduction of this strategy not only enriches the content of optimized power system dispatch 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 process in electrolytic aluminum production, this step effectively compensates for the lack of precision in relying solely on the power regulation of thermal power units, while maximizing energy utilization and further reducing the operating costs of the power system.

[0152] Specifically, in the optimization model, by combining the second objective function, the third constraint, and the fourth constraint, the model not only calculates the optimal power output of each unit in the thermal power plant, but also comprehensively considers the contribution of waste heat recovery from electrolytic aluminum to the overall energy efficiency of the power system, providing a more comprehensive decision-making basis for power system dispatch. This integrated regulation mechanism enables the power system to achieve higher energy utilization efficiency and lower operating costs while meeting load demand.

[0153] Step S60: In the third prediction period, based on the new energy data after the second update, the third new energy power in the third prediction period is obtained.

[0154] In this embodiment, the prediction period length decreases sequentially in the order of the first prediction period, the second prediction period, and the third prediction period.

[0155] In one implementation, during the third prediction period, such as Figure 8As shown, based on the latest updated renewable energy data, the renewable energy power forecast is further refined to obtain the third renewable energy power forecast. Compared to the first two forecast periods, the third forecast period is shorter, for example, 15 minutes. This design aims to improve the power system's response to short-term fluctuations in renewable energy power, further reduce the power curtailment rate, and ensure a high degree of matching between power supply and demand.

[0156] In one implementation, since the third prediction period is shorter, the transient renewable energy power collected before the start of the third prediction period can be used as the renewable energy power for the third prediction period. Alternatively, the calculation method for the second renewable energy power can be used, which will not be elaborated here.

[0157] In this embodiment, the setting of a third forecasting cycle allows the power system to more accurately predict and adjust power supply, especially in response to rapid changes in renewable energy generation. By updating and optimizing renewable energy generation again, the power system can adjust the control strategy for the energy required for electrolytic aluminum production in real time, thereby effectively responding to fluctuations in renewable energy generation and reducing energy waste caused by surplus or shortage.

[0158] In one implementation, a control strategy for the energy required for electrolytic aluminum production is obtained in the third prediction cycle, wherein, based on the inventors' research, it is believed that the aluminum production of the electrolytic aluminum plant can be achieved through... Figure 9 express,

[0159] VAH is the high-voltage side voltage of the electrolytic aluminum load bus; VAL is the low-voltage side voltage of the electrolytic aluminum load bus; k is the turns ratio of the step-down transformer; LSR is the inductance value 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 generally be considered constant for the same electrolytic cell. Therefore, the active power PA1 consumed in electrolytic aluminum is:

[0160]

[0161] As can be seen from the expression for the active power consumption of electrolytic aluminum loads, the active power of high-energy-consuming electrolytic aluminum loads is related to their DC-side voltage. Based on this characteristic, the active power regulation of electrolytic aluminum loads is divided into three types: AC-side voltage regulation, on-load tap-changing transformer regulation, and saturated reactor regulation. AC-side voltage regulation allows for rapid response to regulation signals. Experimental verification shows that the active power response speed of electrolytic aluminum loads is on the order of seconds, and power adjustment can be completed within 2-3 seconds, meeting the control requirements in the third prediction cycle.

[0162] Step S70: Based on the output of each unit in the thermal power unit and the power of the third new energy source, obtain the energy control strategy required for electrolytic aluminum production.

[0163] In this embodiment, the optimization is essentially based on the output of each unit in the thermal power plant and the power of the third new energy source. The result of the optimization is a control strategy for the energy required for electrolytic aluminum production. The specific optimization method has been explained above and will not be repeated here.

[0164] In one implementation, steps S701-S706 are used to finally obtain the energy control strategy required for electrolytic aluminum production.

[0165] 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 production is obtained.

[0166] In one implementation, the calculated predicted aluminum production, by considering the latest power forecasts for the third renewable energy source and the power forecasts for each thermal power unit, is an accurate estimate of the energy consumption requirements during the electrolytic aluminum production process. This step demonstrates a proactive response to the volatility of renewable energy generation in power system optimization scheduling, while ensuring that the energy requirements for electrolytic aluminum production are met. By meticulously considering the latest energy supply situation, step S701 not only suppresses energy fluctuations in the power system but also provides a foundation for subsequent economic benefit analysis.

[0167] In one implementation, producing one ton of electrolytic aluminum requires approximately 13,500 kWh of electricity. By comparing available power resources (including renewable energy generation and thermal power plant supply) with the energy requirements for electrolytic aluminum production, the possible aluminum production N over a specific period can be estimated. Al This process provides a basis for optimizing electrolytic aluminum production plans and adjusting power supply strategies.

[0168] In one implementation, the mathematical relationship between aluminum production, the third new energy power, and the predicted power of each unit in the thermal power plant is as follows:

[0169]

[0170] Step S702: Based on the aluminum production, the revenue and cost of electrolytic aluminum, calculate the net revenue of electrolytic aluminum.

[0171] In one implementation, the impact of power supply strategies on the economic benefits of electrolytic aluminum production is quantified, providing a basis for decision-making in developing more economical power dispatch and electrolytic aluminum production strategies.

[0172] In this embodiment, the net profit of electrolytic aluminum is calculated by combining the predicted aluminum production with the revenue and costs of electrolytic aluminum production. This net profit reflects the economic benefits of electrolytic aluminum production under the current power system and market conditions. In one embodiment, the mathematical expression of the net profit of electrolytic aluminum is as follows:

[0173] Eal =N al ·(E al,per -C al,per )

[0174] Step S703: Set a third objective function based on the net profit of the electrolytic aluminum and the electricity purchased from the grid, wherein the third objective function pursues the minimum electricity purchased from the grid.

[0175] In one implementation, the third objective function combines the considerations of steps S701 and S702, with the goal of maximizing the revenue from electrolytic aluminum production while minimizing the power system's absorption rate.

[0176] In one implementation, the mathematical expression of the third objective function is as follows:

[0177] minf3 = -E al +F dev ,

[0178] The third objective function not only pursues the economic benefits of electrolytic aluminum production, but also focuses on the overall operating costs and efficiency of the power system. This aims to not only improve the economic efficiency of electrolytic aluminum production, but also effectively utilize new energy power generation to reduce the overall energy consumption and operating costs of the power system.

[0179] In this embodiment, the third objective function aims to minimize the amount of electricity purchased from the grid, while taking into account the net revenue from electrolytic aluminum and a penalty term F proportional to the amount of electricity purchased from the grid. dev The design of the third objective function reflects the pursuit of optimizing power system operating costs while ensuring the economic viability of electrolytic aluminum production. Penalty term F dev The introduction of electricity, especially against the backdrop of large fluctuations in electricity market prices, helps to incentivize electrolytic aluminum producers to reduce their dependence on the power grid by adjusting their production plans, thereby reducing the amount of electricity purchased from the grid and improving the energy efficiency of the power system.

[0180] Step S704: Construct the fifth constraint condition based on the control and regulation constraints of electrolytic aluminum power.

[0181] In one implementation, the fifth constraint is mathematically expressed as follows:

[0182] P al,min ≤P al ≤P al,max ,

[0183] In one implementation, the introduced fifth constraint focuses on regulating electricity use during aluminum electrolysis production to ensure that the power demand of the aluminum electrolysis plant is maintained within a safe and economical range. By setting minimum and maximum limits on the power used in aluminum electrolysis, P... almin and P almaxThis constraint directly reflects the basic demand and upper limit tolerance of the power supply in the electrolytic aluminum production process, and is more in line with the actual electrolytic aluminum production process.

[0184] Step S705: Construct the sixth constraint based on the maximum allowable operating time within the minimum power range of the electrolytic aluminum power.

[0185] In one implementation, the sixth constraint is mathematically expressed as follows:

[0186]

[0187] In one implementation, a sixth constraint is established to limit the maximum permissible duration T of the electrolytic aluminum power within the minimum operating range. max This ensures that electrolytic aluminum production will not suffer from reduced efficiency or equipment damage due to prolonged low-power operation.

[0188] Step S706: Based on the third objective function, the fifth constraint, and the sixth constraint, obtain the energy control strategy required for electrolytic aluminum production.

[0189] In one implementation, the optimization process comprehensively considers the latest third renewable energy power, the net profit of electrolytic aluminum, and the electricity usage constraints in the electrolytic aluminum production process to output a control strategy for the energy required for electrolytic aluminum production. Through this step, the resulting energy control strategy for electrolytic aluminum production not only improves the renewable energy absorption rate and reduces wind and solar curtailment, but also optimizes the production efficiency and cost control of electrolytic aluminum, achieving the dual goals of economic efficiency and environmental friendliness in power system operation.

[0190] In this embodiment, the power system is controlled based on the start-up and shutdown status of the thermal power unit, the output of each unit in the thermal power unit, and the control strategy for the energy required for electrolytic aluminum production.

[0191] The implementation of the above strategies not only addresses the uncertainties and volatility of new energy power generation and enhances the power system's capacity to absorb new energy, but also allows for adjustments to the operating mode of electrolytic aluminum plants based on market electricity prices and production costs, optimizing production costs and improving the economic efficiency of the entire power system and electrolytic aluminum production. Furthermore, the gradient calculations, with each output taking into account the characteristics of different power installations, better balance the goals of power system absorption rate and the net profit of electrolytic aluminum plants.

[0192] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effects of the present invention, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of the present invention.

[0193] Those skilled in the art will understand that all or part of the processes in the method of the above embodiment of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes a computer program, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0194] Furthermore, the present invention also provides a control device. In one embodiment of the 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 new energy-thermal power-electrolytic aluminum multi-timescale flexible coordinated control method of the above-described method embodiments. The processor can be configured to execute the program in the storage device, which includes, but is not limited to, a program for executing the new energy-thermal power-electrolytic aluminum multi-timescale flexible coordinated control method of the above-described method embodiments. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. This control device can be a control device device device comprising various electronic devices.

[0195] Furthermore, the present invention also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium 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-timescale flexible coordinated control method of the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described new energy-thermal power-electrolytic aluminum multi-timescale flexible coordinated control method. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a storage device device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0196] Furthermore, it should be understood that since the various modules are only provided to illustrate 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 processor's software, hardware, or a combination of software and hardware. Therefore, the number of modules shown in the figures is merely illustrative.

[0197] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principles of the present invention; therefore, the technical solutions after splitting or combining will fall within the protection scope of the present invention.

[0198] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles 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 such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A new energy-thermal power-electrolytic aluminum multi-time scale flexible coordination control method, characterized in that, The method comprises the following steps: In a first prediction period, an electrolytic aluminum load power corresponding to the first prediction period is obtained; Based on historical new energy data, a first new energy power in the first prediction period is obtained through prediction; Based on the electrolytic aluminum load power, the first new energy power, and the cost of the thermal power generating unit, a start-stop state of the thermal power generating unit is obtained; In a second prediction period, based on the first updated new energy data, a second new energy power in the second prediction period is obtained; 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, based on the second updated new energy data, a third new energy power in the third prediction period is obtained; Based on the output of each unit in the thermal power generating unit and the third new energy power, a control strategy for the 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; A first objective function is set based on the start-stop cost of the thermal power generating unit and the operation cost of the thermal power generating unit, wherein the first objective function pursues the minimum operation cost of the thermal power generating unit, and the start-stop state of the thermal power generating unit is obtained based on at least the first objective function; A second objective function is set based on the second new energy power and the real-time electricity price, wherein the second objective function pursues the minimum operation cost of the thermal power generating unit, and the power of each unit in the predicted thermal power generating unit is obtained based on the second objective function; Based on the predicted third new energy power and the power of each unit in the predicted thermal power generating unit, a predicted aluminum output is obtained; based on the aluminum output, the revenue and cost calculation of electrolytic aluminum, a pure revenue of electrolytic aluminum is obtained; A third objective function is set based on the pure revenue of electrolytic aluminum and the grid electricity purchase quantity, wherein the third objective function pursues the minimum grid electricity purchase quantity, and the control strategy for the energy required for electrolytic aluminum production is obtained based on the third objective function.

2. The new energy-thermal power-electrolytic aluminum multi-time scale flexible coordination control method according to claim 1, characterized in that, The cost of the thermal power generating unit includes the start-stop cost of the thermal power generating unit and the operation cost of the thermal power generating unit; the start-stop state of the thermal power generating unit is obtained based on the electrolytic aluminum load power, the first new energy power, and the cost of the thermal power generating unit, which comprises: A first objective function is set based on the start-stop cost of the thermal power generating unit and the operation cost of the thermal power generating unit, wherein the first objective function pursues the minimum operation cost of the thermal power generating unit; A first constraint condition is constructed based on the predicted electrolytic aluminum load power, the first new energy power, and the power of the thermal power generating unit; The start-stop state of the thermal power generating unit is obtained based on at least the first objective function and the first constraint condition.

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

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

5. The new energy-thermal power-electrolytic aluminum multi-time scale flexible coordination control method according to claim 4, characterized in that, The third constraint condition is constructed based on at least the power of the aluminum electrolysis load, the second new energy power, and the thermal power generating unit, and includes: Obtaining data of saved electric energy by waste heat recovery; The third constraint condition is constructed based on the power of the aluminum electrolysis load, the second new energy power, the data of saved electric energy by waste heat recovery, and the thermal power generating unit; The power of each unit in the predicted thermal power generating unit is obtained based on the second objective function, the third constraint condition, and the fourth constraint condition, and includes: The power of each unit in the predicted thermal power generating unit and the waste heat recovery strategy for aluminum electrolysis are obtained based on the second objective function, the third constraint condition, and the fourth constraint condition.

6. The new energy-thermal power-electrolytic aluminum multi-time scale flexible coordination control method according to claim 5, characterized in that, The method for obtaining the data of saved electric energy by waste heat recovery includes: A relational equation is established based on the recovered heat and the flue gas heat recovery coefficient set for the corresponding aluminum electrolysis plant; The data of saved electric energy by waste heat recovery is obtained based on the recovered heat, wherein the recovered heat and the data of saved electric energy by waste heat recovery satisfy a proportional relationship, wherein the flue gas heat recovery coefficient is a to-be-solved quantity, and the size of the flue gas heat recovery coefficient can be adjusted by adjusting the equipment corresponding to the flue gas heat recovery coefficient.

7. The new energy-thermal power-electrolytic aluminum multi-time scale flexible coordination control method according to claim 1, characterized in that, The control strategy for the energy required for aluminum electrolysis production is obtained based on the output of each unit in the thermal power generating unit and the third new energy power, and includes: The predicted aluminum production is obtained based on the predicted third new energy power and the predicted power of each unit in the thermal power generating unit; The net income of aluminum electrolysis is obtained based on the aluminum production, the income and cost calculation of aluminum electrolysis; A third objective function is set based on the net income of aluminum electrolysis and the grid electricity purchase quantity, wherein the third objective function pursues the minimum grid electricity purchase quantity; A fifth constraint condition is constructed based on the control regulation constraint of aluminum electrolysis power; A sixth constraint condition is constructed based on the maximum time allowed for the operation of aluminum electrolysis power within the minimum power range; The control strategy for the energy required for aluminum electrolysis production is obtained based on the third objective function, the fifth constraint condition, and the sixth constraint condition.

8. The new energy-thermal power-electrolytic aluminum multi-time scale flexible coordination control method according to claim 1, characterized in that, The first prediction period and the second prediction period each consist of time periods, the length of the time period of the first prediction period is consistent with the length of the time period of the second prediction period, and / or Control the power system based on the start-stop state 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 new energy-thermal power-electricity aluminum electrolysis multi-time scale flexible coordination control method according to claim 1, comprising: When the large power grid connected to the electrolytic aluminum system is a weak power grid, energy type and power type hybrid energy storage are equipped.

10. The new energy-thermal power-electricity aluminum electrolysis multi-time scale flexible coordination control method according to claim 1, comprising: When the large power grid connected to the electrolytic aluminum system is a strong power grid, energy storage is selected according to the need for power quality.

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