Output power prediction method and device, storage medium and electronic equipment

Abstract

The invention relates to an output power prediction method and device, a storage medium and electronic equipment, and the method comprises the steps: iteratively executing the following operations: obtaining first prediction data, inputting the first prediction data into a scheduling optimization model, obtaining a prediction load curve of a consumption unit and a prediction consumption electric quantity of a new energy power generation unit, and inputting the predicted load curve and the predicted consumed electric quantity into the target function to obtain a performance value corresponding to the coupling system, and when the performance value does not reach a preset condition, obtaining new first prediction data until the obtained performance value reaches the preset condition, the objective function is used for representing a corresponding relation between a performance value corresponding to the coupling system and the following coefficients: a carbonization coefficient, an electricity price coefficient and a consumption coefficient; and determining the first predicted output power of the consumption unit according to the performance value obtained by the last iteration. In the operation process of the coupling system, the absorption efficiency and the environmental protection property of the coupling system can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of electric power technology, and in particular to an output power prediction method, device, storage medium and electronic device. Background Art

[0002] The coal chemical industry and new energy coupling system is mainly a system that combines traditional coal chemical technology with new energy technology. In the operation process of the coupling system, energy management strategies are added to achieve efficient utilization of energy in the coupling system.

[0003] In the related art, usually in the energy management strategy, the energy in the coupling system is allocated by optimizing the economic efficiency of the power grid or by reducing the operating cost of the coupling system. However, when using this energy management strategy to allocate the energy in the operation process of the coupling system, the absorption efficiency and environmental protection of the coupling system may be reduced. Summary of the invention

[0004] The purpose of the present disclosure is to provide an output power prediction method, device, storage medium and electronic device to solve the problems in the related art.

[0005] In order to achieve the above object, the present disclosure provides an output power prediction method, which is applied to a coupling system of coal chemical industry and new energy, and the method comprises:

[0006] Iteratively perform the following operations: obtain first prediction data, and input the first prediction data into the scheduling optimization model to obtain the predicted load curve of the consumption unit and the predicted consumption power of the new energy power generation unit, input the predicted load curve and the predicted consumption power into the objective function, and obtain a performance value corresponding to the coupling system, when the performance value does not meet the preset condition, obtain new first prediction data until the obtained performance value meets the preset condition, wherein the first prediction data includes the output power prediction data of the new energy power generation unit of the coupling system within a preset time period and the electricity price prediction data of the coupling system, and the objective function is used to characterize the corresponding relationship between the performance value corresponding to the coupling system and the following coefficients: carbonization coefficient, electricity price coefficient and consumption coefficient;

[0007] According to the performance value obtained in the last iteration, the first predicted output power of the absorption unit is determined.

[0008] Optionally, the method further comprises:

[0009] Determine a rated output power range of the consumption unit within a target preset time period, wherein the target preset time period is greater than a preset time period corresponding to the first prediction data in each iteration process;

[0010] Calculate the sum of the first predicted output power output in each iteration process and the first predicted output power obtained in the previous iteration process to obtain the second predicted output power;

[0011] When the second predicted output power is not within the rated output power range, the rated output power range is adjusted so that the coupling system operates according to a target performance value required by a user.

[0012] Optionally, determining the rated output power range of the consumption unit in a target preset time period includes:

[0013] Determine second prediction data and third prediction data, wherein the second prediction data is used to represent the output power prediction data of the new energy power generation unit of the coupling system within the target preset time period, and the third prediction data is used to represent the predicted power data corresponding to the conventional load demand in the coupling system;

[0014] Determining predicted power difference data according to the second predicted data and the third predicted data;

[0015] The predicted power difference data is input into the scheduling optimization model, and the result output by the scheduling optimization model is adjusted according to the performance preset conditions to obtain the rated output power range.

[0016] Optionally, the constraints of the scheduling optimization model are:

[0017] The output power of the new energy power generation unit is greater than or equal to zero or less than or equal to a preset power threshold, and the preset power threshold is the sum of the output powers of the adjustable load unit and the energy storage unit in the coupling system.

[0018] Optionally, the consumption unit includes an energy storage unit, and the constraint condition of the scheduling optimization model is:

[0019] The output power of the energy storage unit is less than or equal to the rated power value;

[0020] The battery capacity of the energy storage unit is greater than or equal to a first preset capacity threshold and less than or equal to a second preset capacity threshold, the first preset capacity threshold is the minimum rated battery capacity of the energy storage unit, and the second preset capacity threshold is the maximum rated battery capacity of the energy storage unit.

[0021] Optionally, the consumption unit includes a heat storage unit and a water electrolysis hydrogen production unit, and the constraint conditions of the scheduling optimization model are:

[0022] The output power of the heat storage unit is less than or equal to the sum of the maximum output powers of the heat storage unit and the water electrolysis hydrogen production unit;

[0023] The output power of the water electrolysis hydrogen production unit is greater than or equal to a first power threshold and less than or equal to a second power threshold, the first power threshold is the minimum rated power output during the operation of the water electrolysis hydrogen production unit, and the second power threshold is the maximum capacity value of the hydrogen storage container.

[0024] Optionally, the objective function is calculated as follows:

[0025] Max(C)=k1*δ carbon +k2*δ price +k3*δ consump

[0026] Wherein, Max(C) is the performance value of the objective function; δ carbon The carbonization coefficient, δ, used to characterize the coupled system price The electricity price coefficient used to characterize the coupled system, δ consump It is used to characterize the absorption coefficient of the coupling system, k1 is the carbonization weight coefficient of the coupling system, k2 is the electricity price weight coefficient of the coupling system, and k3 is the absorption weight coefficient of the coupling system.

[0027] In a second aspect, an output power prediction device is applied to a coupling system of coal chemical industry and new energy, the system comprising an iteration module and a determination module;

[0028] The iteration module is used to perform the following operations: obtain first prediction data, and input the first prediction data into the scheduling optimization model to obtain the predicted load curve of the consumption unit and the predicted consumption power of the new energy power generation unit, input the predicted load curve and the predicted consumption power into the objective function, and obtain a performance value corresponding to the coupling system. When the performance value does not meet the preset condition, obtain new first prediction data until the obtained performance value meets the preset condition, wherein the first prediction data includes the output power prediction data of the new energy power generation unit of the coupling system within a preset time period and the electricity price prediction data of the coupling system, and the objective function is used to characterize the corresponding relationship between the performance value corresponding to the coupling system and the following coefficients: carbonization coefficient, electricity price coefficient and consumption coefficient;

[0029] The determination module is used to determine the first predicted output power of the absorption unit according to the performance value obtained in the last iteration.

[0030] Optionally, the output power prediction device further includes:

[0031] A first determination submodule, used to determine a rated output power range of the consumption unit within a target preset time period, wherein the target preset time period is greater than a preset time period corresponding to the first prediction data in each iteration process;

[0032] A calculation module, used for calculating the sum of the first predicted output power outputted in each iteration process and the first predicted output power obtained in the previous iteration process, to obtain a second predicted output power;

[0033] An adjustment module is used to adjust the rated output power range when the second predicted output power is not within the rated output power range, so that the coupling system operates according to a target performance value required by a user.

[0034] Optionally, the first determining submodule includes:

[0035] A second determination submodule is used to determine second prediction data and third prediction data, wherein the second prediction data is used to represent the output power prediction data of the new energy power generation unit of the coupling system within the target preset time period, and the third prediction data is used to represent the predicted power data corresponding to the conventional load demand in the coupling system;

[0036] A third determining submodule, configured to determine predicted power difference data according to the second predicted data and the third predicted data;

[0037] The adjustment submodule is used to input the predicted power difference data into the scheduling optimization model, and adjust the result output by the scheduling optimization model according to the performance preset conditions to obtain the rated output power range.

[0038] Optionally, the constraints of the scheduling optimization model in the iterative module are:

[0039] The output power of the new energy power generation unit is greater than or equal to zero or less than or equal to a preset power threshold, and the preset power threshold is the sum of the output powers of the adjustable load unit and the energy storage unit in the coupling system.

[0040] Optionally, the consumption unit includes an energy storage unit, and the constraint condition of the scheduling optimization model in the iteration module is:

[0041] The output power of the energy storage unit is less than or equal to the rated power value;

[0042] The battery capacity of the energy storage unit is greater than or equal to a first preset capacity threshold and less than or equal to a second preset capacity threshold, the first preset capacity threshold is the minimum rated battery capacity of the energy storage unit, and the second preset capacity threshold is the maximum rated battery capacity of the energy storage unit.

[0043] Optionally, the consumption unit includes a heat storage unit and a water electrolysis hydrogen production unit, and the constraint conditions of the scheduling optimization model in the iteration module are:

[0044] The output power of the heat storage unit is less than or equal to the sum of the maximum output powers of the heat storage unit and the water electrolysis hydrogen production unit;

[0045] The output power of the water electrolysis hydrogen production unit is greater than or equal to a first power threshold and less than or equal to a second power threshold, the first power threshold is the minimum rated power output during the operation of the water electrolysis hydrogen production unit, and the second power threshold is the maximum capacity value of the hydrogen storage container.

[0046] Optionally, the calculation formula of the objective function in the iteration module is:

[0047] Max(C)=k1*δ carbon +k2*δ price +k3*δ consump

[0048] Wherein, Max(C) is the performance value of the objective function; δ carbon The carbonization coefficient, δ, used to characterize the coupled system price The electricity price coefficient used to characterize the coupled system, δ consump It is used to characterize the absorption coefficient of the coupling system, k1 is the carbonization weight coefficient of the coupling system, k2 is the electricity price weight coefficient of the coupling system, and k3 is the absorption weight coefficient of the coupling system.

[0049] In a third aspect, the present disclosure further provides a non-temporary computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods provided in the first aspect of the present disclosure.

[0050] In a fourth aspect, the present disclosure further provides an electronic device, including:

[0051] a memory having a computer program stored thereon;

[0052] A processor is used to execute the computer program in the memory to implement the steps of any one of the methods provided in the first aspect of the present disclosure.

[0053] Through the above technical solution, in the iterative process, the following operations are performed: first, the first prediction data is obtained, and the first prediction data is input into the scheduling optimization model, and the predicted load curve and the predicted power consumption output by the scheduling optimization model are input into the objective function to obtain a performance value corresponding to the coupling system. After that, when the performance value does not meet the preset conditions, new first prediction data is obtained until the obtained performance value meets the preset conditions. Among them, the objective function is used to characterize the corresponding relationship between the performance value corresponding to the coupling system and the following coefficients: carbonization coefficient, electricity price coefficient and consumption coefficient; according to the performance value obtained in the last iteration, the first predicted output power of the consumption unit is determined. During the operation of the coupling system, the consumption efficiency and environmental protection of the coupling system can be improved.

[0054] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0056] Figure 1 is a schematic diagram showing an output power prediction method according to an exemplary embodiment of the present disclosure.

[0057] Figure 2 is a schematic diagram showing a coupling system according to an exemplary embodiment of the present disclosure.

[0058] Figure 3 It is a schematic diagram showing an iterative process of a scheduling optimization model according to an exemplary embodiment of the present disclosure.

[0059] Figure 4 is a schematic diagram showing determination of a rated output power range according to an exemplary embodiment of the present disclosure.

[0060] Figure 5 is a schematic diagram showing an output power prediction device according to an exemplary embodiment of the present disclosure.

[0061] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0062] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0063] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0064] The coal chemical industry and new energy coupling system is mainly a system that combines traditional coal chemical technology with new energy technology. In the operation process of the coupling system, energy management strategies are added to achieve efficient utilization of energy in the coupling system.

[0065] In the related art, usually in the energy management strategy, the energy in the coupling system is allocated by optimizing the economic efficiency of the power grid or by reducing the operating cost of the coupling system. However, when using this energy management strategy method to allocate the energy of the coupling system during operation, the absorption efficiency and environmental protection of the coupling system may be reduced.

[0066] In view of this, the present disclosure provides an output power prediction method, device, storage medium and electronic device to solve the problem in the related art that when using this energy management strategy method to allocate the energy of the coupling system during operation, the absorption efficiency and environmental protection of the coupling system may be reduced.

[0067] like Figure 1 As shown, Figure 1 is a schematic diagram showing an output power prediction method according to an exemplary embodiment of the present disclosure, referring to Figure 1 , the output power prediction method is used to process the coal chemical industry and new energy coupling system, including:

[0068] S101: Iteratively perform the following operations: obtain first prediction data, and input the first prediction data into the scheduling optimization model to obtain the predicted load curve of the consumption unit and the predicted consumption power of the new energy power generation unit, input the predicted load curve and the predicted consumption power into the objective function, and obtain a performance value corresponding to the coupling system. When the performance value does not meet the preset condition, obtain new first prediction data until the obtained performance value meets the preset condition, wherein the first prediction data includes the output power prediction data of the new energy power generation unit of the coupling system within a preset time period and the electricity price prediction data of the coupling system, and the objective function is used to characterize the corresponding relationship between the performance value corresponding to the coupling system and the following coefficients: carbonization coefficient, electricity price coefficient and consumption coefficient;

[0069] S102: Determine a first predicted output power of the absorption unit according to the performance value obtained in the last iteration.

[0070] Through the above technical solution, in the iterative process, the following operations are performed: first, the first prediction data is obtained, and the first prediction data is input into the scheduling optimization model, and the predicted load curve and the predicted power consumption output by the scheduling optimization model are input into the objective function to obtain a performance value corresponding to the coupling system. After that, when the performance value does not meet the preset conditions, new first prediction data is obtained until the obtained performance value meets the preset conditions. Among them, the objective function is used to characterize the corresponding relationship between the performance value corresponding to the coupling system and the following coefficients: carbonization coefficient, electricity price coefficient and consumption coefficient; according to the performance value obtained in the last iteration, the first predicted output power of the consumption unit is determined. During the operation of the coupling system, the consumption efficiency and environmental protection of the coupling system can be improved.

[0071] In order to enable those skilled in the art to better understand the output power prediction method provided by the present disclosure, the above steps are described in detail with examples below.

[0072] For example, the scheduling optimization model can be used to optimize the energy in the coupled system to achieve an algorithm model for optimal scheduling and utilization of the energy in the coupled system. The scheduling optimization model can be a particle swarm algorithm optimization model, a neural network algorithm model, or a genetic algorithm model, which is not specifically limited in the embodiments of the present disclosure. Iteration can be a process in which the scheduling optimization model is updated by continuously collecting data input into the scheduling optimization model and processing the data by repeating the same steps during the optimization process of the scheduling optimization model.

[0073] In the disclosed embodiment, in each iteration process, the following steps are repeatedly performed: by collecting first prediction data, the first prediction data can be the output power prediction data of the new energy power generation unit within a preset time period and the electricity price prediction data of the coupled system. Then, the collected first prediction data is input into the scheduling optimization model to obtain the predicted load curve of the consumption unit and the predicted consumption amount of the new energy power generation unit. Among them, the predicted load curve can be the predicted curve between the demand load and time of the consumption unit in the coupled system within the preset time period. The predicted consumption amount can be the amount of electricity consumed in the coupled system within the preset time period. Figure 2 As shown, the consumption unit can be an energy storage unit for storing electricity, or an adjustable load unit for consuming electricity. The energy storage unit can be an electrochemical energy storage unit, the new energy power generation unit can be a photovoltaic power generation unit, a wind power generation unit, and the adjustable load unit can be a heat storage unit or a water electrolysis hydrogen production unit, which is not detailed in the embodiments of the present disclosure.

[0074] After that, the predicted load curve and the predicted power consumption are input into the objective function to obtain the performance value. The objective function can be used to characterize the corresponding relationship between the performance value corresponding to the coupling system and the following coefficients: carbonization coefficient, electricity price coefficient and consumption coefficient. Among them, the carbonization coefficient can be the ratio between the carbon emissions saved by the production of the new energy power generation unit in the coupling system and the carbon emissions actually generated by the non-new energy power generation unit. The electricity price coefficient can be the ratio between the electricity price generated by the new energy power generation unit in the coupling system and the electricity price that the entire coupling system should theoretically generate. The consumption coefficient can be the ratio between the power consumed by the new energy power generation unit and the actual predicted output power generated by the new energy power generation unit.

[0075] By adding the carbonization coefficient, electricity price coefficient and absorption coefficient to the objective function, the lowest economic cost can be achieved during the operation of the coupled system, while the absorption efficiency of the electricity generated by the new energy power generation units in the coupled system can be improved, and the carbonization performance of the coupled system can be improved, thereby increasing the environmental friendliness of the coupled system during operation.

[0076] After each iteration process is completed, it can be determined whether the performance value reaches the preset condition. If the performance value reaches the preset condition, the iteration process is stopped. And according to the performance value obtained in the last iteration process, the first predicted output power of the consumption unit is determined. Among them, the preset condition can be to determine the size between the performance value obtained in the last iteration process and all the performance values ​​obtained in the previous iteration process. If the performance value obtained in the last iteration process reaches the maximum, the first predicted output power of the consumption unit can be determined according to the performance value obtained in the last iteration.

[0077] By comparing the performance values ​​obtained in the iterative process and determining the first predicted output power of the consumption unit according to the objective function corresponding to the maximum performance value, low economic cost can be achieved during the operation of the coupled system, while improving the power consumption and environmental friendliness of the coupled system.

[0078] In a possible embodiment, the method further comprises:

[0079] Determine the rated output power range of the consumption unit within a target preset time period, where the target preset time period is greater than the preset time period corresponding to the first prediction data in each iteration process;

[0080] Calculate the sum of the first predicted output power output in each iteration process and the first predicted output power obtained in the previous iteration process to obtain the second predicted output power;

[0081] When the second predicted output power is not within the rated output power range, the rated output power range is adjusted so that the coupling system operates according to a target performance value required by the user.

[0082] It should be understood that if Figure 3 As shown, during the operation of the coupling system, the first predicted output power of the consumption unit will be obtained in each iteration process. After each iteration, all the first predicted output powers obtained in the previous iteration process can be summed up to obtain the second predicted output power. After each iteration, it is determined whether the second predicted output power is within the rated output power range. If it is not within the rated output power range, the rated output power range can be adjusted according to the first predicted output power and the second predicted output power obtained by actual processing, so that the coupling system can be operated according to the target performance value required by the user. Among them, the target performance value required by the user can be the weight coefficient value corresponding to the consumption coefficient, carbonization coefficient and economic coefficient set by the user according to the demand, and the performance value calculated according to the weight coefficient value. It can make the coupling system improve the power consumption efficiency and the environmental protection of the coupling system during the operation of low economic cost. Among them, the target preset time period can be the sum of multiple preset time periods, and this is not specifically limited in the embodiment of the present disclosure.

[0083] In a possible manner, determining the rated output power range of the consumption unit in the target preset time period includes:

[0084] Determine second prediction data and third prediction data, the second prediction data is used to represent the output power prediction data of the new energy power generation unit of the coupling system within the target preset time period, and the third prediction data is used to represent the prediction power data corresponding to the conventional load demand in the coupling system;

[0085] Determining predicted power difference data according to the second predicted data and the third predicted data;

[0086] The predicted power difference data is input into the scheduling optimization model, and the results output by the scheduling optimization model are adjusted according to the performance preset conditions to obtain the rated output power range.

[0087] It should be understood that the storage energy of each unit in the consumption unit has an upper and lower limit range. When the storage energy of a unit reaches its upper limit, the storage energy of each unit in the consumption unit cannot be adjusted according to the electricity price coefficient, carbonization coefficient and consumption coefficient required by the user. Therefore, the rated power output range can be determined by the predicted power difference data between the second prediction data and the third prediction data, as well as the performance preset conditions, and the rated output power range can be adjusted in real time. The coupling system can adjust the output power of the consumption unit according to the different electricity price coefficients, carbonization coefficients and consumption coefficients required by the user. Low economic cost can be achieved, while improving the efficiency of electricity consumption and the environmental protection of the coupling system.

[0088] Among them, Figure 4 As shown, the performance preset condition can be that when the user demand tends to be low economic cost, the actual power can be allocated to the energy storage unit first at low electricity prices, and then to the heat storage and water electrolysis hydrogen production units according to the peak-shaving and valley-filling characteristics of the energy storage unit. At high electricity prices, the actual power is allocated to the heat storage unit and the water electrolysis hydrogen production unit first. At this time, the energy storage unit does not absorb new energy and needs to perform discharge operations. When the user demand tends to be power consumption efficiency, the actual power can be allocated to the heat storage unit and the water electrolysis hydrogen production unit first, and then to the energy storage unit. When the user demand tends to be low-carbon and environmentally friendly, it is necessary to calculate the carbon emissions per unit of electricity of the water electrolysis hydrogen production unit and the heat storage unit, and compare which one has a higher carbon emission, and allocate the actual power according to the level of carbon emissions. Then, according to the different preset conditions in the performance preset conditions, the upper and lower limits of the output power of each unit in the consumption unit are obtained, and the upper and lower limits of each unit output are merged to obtain the rated output power.

[0089] In a possible way, the constraints of the scheduling optimization model are:

[0090] The output power of the new energy power generation unit is greater than or equal to zero or less than or equal to a preset power threshold, and the preset power threshold is the output power of the consumption unit in the coupling system.

[0091] It should be understood that during the operation of the coupled system, the constraint condition can be expressed by the following calculation formula:

[0092]

[0093] Among them, P PV is the output power of the new energy power generation unit, P MPPT is the maximum output power of the new energy power generation unit, P H2 +P Heat +P BESS is the output power of the absorption unit in the coupling system, where P BESS is the output power of the energy storage unit in the consumption unit, P Heat is the output power of the heat storage unit in the consumption unit, P H2 It is the output power of the water electrolysis hydrogen production unit in the consumption unit.

[0094] In this calculation formula, when the output power of the new energy power generation unit is the maximum output power output by the new energy power generation unit, the maximum output power is less than or equal to the output power of the consumption unit. When the output power of the new energy power generation unit is equal to the output power of the consumption unit, the output power of the new energy power generation unit is less than or equal to the maximum output power of the new energy power generation unit.

[0095] In a possible way, the consumption unit includes an energy storage unit, and the constraints of the dispatch optimization model are:

[0096] The output power of the energy storage unit is less than or equal to the rated power value;

[0097] The battery capacity of the energy storage unit is greater than or equal to a first preset capacity threshold and less than or equal to a second preset capacity threshold, the first preset capacity threshold is the minimum rated battery capacity of the energy storage unit, and the second preset capacity threshold is the maximum rated battery capacity of the energy storage unit.

[0098] It should be understood that the constraints of the scheduling optimization model in the energy storage unit can be expressed by the following calculation formula, which can be:

[0099] 0≤P BESS ≤P BESS.nom

[0100] SOC min *E BESS.nom ≤E BESS ≤SOC max *E BESS.nom

[0101] Among them, P BESS is the output power of the energy storage unit, P BESS.nom is the rated power value of the energy storage unit, E BESS is the battery capacity of the energy storage unit, SOC min is the minimum rated battery capacity of the energy storage unit, SOC max is the maximum rated battery capacity of the energy storage unit, E BESS.nom is the rated capacity of the energy storage unit.

[0102] In a possible way, the consumption unit includes a heat storage unit and a water electrolysis hydrogen production unit, and the constraints of the scheduling optimization model are:

[0103] The output power of the heat storage unit is less than or equal to the sum of the maximum output powers of the heat storage unit and the water electrolysis hydrogen production unit;

[0104] The output power of the water electrolysis hydrogen production unit is greater than or equal to a first power threshold and less than or equal to a second power threshold. The first power threshold is the minimum rated power output during the operation of the water electrolysis hydrogen production unit, and the second power threshold is the maximum capacity value of the hydrogen storage container.

[0105] It should be understood that in the heat storage unit, the constraint condition can be expressed by the following calculation formula:

[0106] 0≤P heat ≤P heat.nom

[0107] P heat.nomIt can be the sum of the maximum output powers of the heat storage unit and the water electrolysis hydrogen production unit.

[0108] In the water electrolysis hydrogen production unit, the prepared hydrogen is affected by the storage area. The upper limit is the maximum capacity of the storage area, and the lower limit can be the minimum rated power output during operation. The constraint condition can be expressed by the following calculation:

[0109]

[0110] β*P H2.nom It is the minimum rated power output during the operation of the water electrolysis hydrogen production unit.

[0111] In a possible way, the objective function is calculated as:

[0112] Max(C)=k1*δ carbon +k2*δ price +k3*δ consump

[0113] Wherein, Max(C) is the performance value of the objective function; δ carbon The carbonization coefficient, δ, used to characterize the coupled system price The electricity price coefficient used to characterize the coupled system, δ consump It is used to characterize the absorption coefficient of the coupling system, k1 is the carbonization weight coefficient of the coupling system, k2 is the electricity price weight coefficient of the coupling system, and k3 is the absorption weight coefficient of the coupling system.

[0114] It should be understood that k1, k2, and k3 are three different weight coefficients, the sum of the three weight coefficients is 1, and the three weight coefficients can be determined according to user needs. For example, if the user's demand is to make the coupling system achieve low economic cost, the value of the electricity price weight coefficient k2 can be controlled to reach the highest among the three weight coefficients. For example, the three values ​​can be determined as: k1 is 0.3, k2 is 0.4, and k3 is 0.3. If the user's demand is to make the coupling system achieve high absorptivity, the value of the absorptivity weight coefficient k3 can be controlled to reach the highest among the three weight coefficients. For example, the three values ​​can be determined as: k1 is 0.3, k2 is 0.3, and k3 is 0.4. If the user's demand is to make the coupling system achieve high environmental protection, the value of the electricity price weight coefficient k1 can be controlled to reach the highest among the three weight coefficients. For example, the three values ​​can be determined as: k1 is 0.4, k2 is 0.3, and k3 is 0.3.

[0115] Among them, the carbonization system can be expressed by the following calculation formula:

[0116]

[0117] η H2 (i) is the output efficiency of the water electrolysis hydrogen production unit within a preset time period, η Heat (i) is the output efficiency of the heat storage unit within the preset time period, δ H2 is the hydrogen conversion factor, δ Heat is the heat and electricity conversion coefficient, T is the target preset time period, ΔT is the time granularity, and i is used to represent the i-th preset time period. AD(i) represents the amount of production or consumption activities that lead to greenhouse gas emissions during the preset time period, such as the consumption of each fossil fuel, the consumption of limestone raw materials, the net purchased electricity, the net purchased steam, etc. EF(i) is the coefficient corresponding to the activity level data.

[0118] The electricity price coefficient can be expressed by the following calculation formula:

[0119]

[0120] P pv (i) is the power output value of the new energy power generation unit within the preset time period, P bess.ch (i) can be the charging power of the energy storage unit within a preset time period, P bess.dis (i) can be the discharge power of the energy storage unit within a preset time period, P grid (i) is the grid power in the preset time period, C grid (i) is the time-of-use electricity price within the preset time period.

[0121] The absorption coefficient can be expressed by the following calculation formula:

[0122]

[0123] In order to absorb the electricity generated by the new energy power generation units, It is the actual amount of electricity generated by the renewable energy power generation unit. The higher the value of the absorption coefficient, the higher the proportion of electricity absorbed in the coupled system.

[0124] Therefore, by inputting the output results of the scheduling optimization model into the objective function for processing, during the operation of the coupled system, low economic costs can be guaranteed while improving the power consumption and environmental protection.

[0125] Based on the same concept, the present disclosure also provides an output power prediction device 500, such as Figure 5 As shown, Figure 5 is a schematic diagram showing an output power prediction device 500 according to an exemplary embodiment of the present disclosure, referring to Figure 5, applied to the coupling system of coal chemical industry and new energy, the output power prediction device 500 includes an iteration module 501 and a determination module 502;

[0126] Iteration module 501 is used to perform the following operations: obtain first prediction data, and input the first prediction data into the scheduling optimization model to obtain the predicted load curve of the consumption unit and the predicted consumption power of the new energy power generation unit, input the predicted load curve and the predicted consumption power into the objective function, and obtain a performance value corresponding to the coupling system. When the performance value does not meet the preset condition, obtain new first prediction data until the obtained performance value meets the preset condition, wherein the first prediction data includes the output power prediction data of the new energy power generation unit of the coupling system within a preset time period and the electricity price prediction data of the coupling system, and the objective function is used to characterize the corresponding relationship between the performance value corresponding to the coupling system and the following coefficients: carbonization coefficient, electricity price coefficient and consumption coefficient;

[0127] The determination module 502 is used to determine the first predicted output power of the absorption unit according to the performance value obtained in the last iteration.

[0128] Optionally, the output power prediction device 500 further includes:

[0129] A first determination submodule, used to determine a rated output power range of the consumption unit within a target preset time period, wherein the target preset time period is greater than a preset time period corresponding to the first prediction data in each iteration process;

[0130] A calculation module, used for calculating the sum of the first predicted output power outputted in each iteration process and the first predicted output power obtained in the previous iteration process, to obtain a second predicted output power;

[0131] An adjustment module is used to adjust the rated output power range when the second predicted output power is not within the rated output power range, so that the coupling system operates according to a target performance value required by a user.

[0132] Optionally, the first determining submodule includes:

[0133] A second determination submodule is used to determine second prediction data and third prediction data, wherein the second prediction data is used to represent the output power prediction data of the new energy power generation unit of the coupling system within the target preset time period, and the third prediction data is used to represent the predicted power data corresponding to the conventional load demand in the coupling system;

[0134] A third determining submodule, configured to determine predicted power difference data according to the second predicted data and the third predicted data;

[0135] The adjustment submodule is used to input the predicted power difference data into the scheduling optimization model, and adjust the result output by the scheduling optimization model according to the performance preset conditions to obtain the rated output power range.

[0136] Optionally, the constraints of the scheduling optimization model in the iteration module 501 are:

[0137] The output power of the new energy power generation unit is greater than or equal to zero or less than or equal to a preset power threshold, and the preset power threshold is the sum of the output powers of the adjustable load unit and the energy storage unit in the coupling system.

[0138] Optionally, the consumption unit includes an energy storage unit, and the constraint condition of the scheduling optimization model in the iteration module 501 is:

[0139] The output power of the energy storage unit is less than or equal to the rated power value;

[0140] The battery capacity of the energy storage unit is greater than or equal to a first preset capacity threshold and less than or equal to a second preset capacity threshold, the first preset capacity threshold is the minimum rated battery capacity of the energy storage unit, and the second preset capacity threshold is the maximum rated battery capacity of the energy storage unit.

[0141] Optionally, the consumption unit includes a heat storage unit and a water electrolysis hydrogen production unit, and the constraint conditions of the scheduling optimization model in the iteration module 501 are:

[0142] The output power of the heat storage unit is less than or equal to the sum of the maximum output powers of the heat storage unit and the water electrolysis hydrogen production unit;

[0143] The output power of the water electrolysis hydrogen production unit is greater than or equal to a first power threshold and less than or equal to a second power threshold, the first power threshold is the minimum rated power output during the operation of the water electrolysis hydrogen production unit, and the second power threshold is the maximum capacity value of the hydrogen storage container.

[0144] Optionally, the calculation formula of the objective function in the iteration module 501 is:

[0145] Max(C)=k1*δ carbon +k2*δ price +k3*δ consump

[0146] Wherein, Max(C) is the performance value of the objective function; δ carbon The carbonization coefficient, δ, used to characterize the coupled system price The electricity price coefficient used to characterize the coupled system, δ consumpIt is used to characterize the absorption coefficient of the coupling system, k1 is the carbonization weight coefficient of the coupling system, k2 is the electricity price weight coefficient of the coupling system, and k3 is the absorption weight coefficient of the coupling system.

[0147] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0148] Based on the same concept, the present disclosure also provides a non-temporary computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of an output power prediction method provided in an embodiment of the present disclosure are implemented.

[0149] Based on the same concept, the present disclosure also provides an electronic device, including:

[0150] a memory having a computer program stored thereon;

[0151] A processor is used to execute the computer program in the memory to implement the steps of an output power prediction method provided in an embodiment of the present disclosure.

[0152] Figure 6 FIG. 6 is a block diagram of an electronic device 600 according to an exemplary embodiment. Figure 6 As shown, the electronic device 600 may include: a processor 601 and a memory 602. The electronic device 600 may also include one or more of a multimedia component 603, an input / output (I / O) interface 604, and a communication component 605.

[0153] The processor 601 is used to control the overall operation of the electronic device 600 to complete all or part of the steps in the above-mentioned output power prediction method. The memory 602 is used to store various types of data to support the operation of the electronic device 600, and these data may include instructions for any application or method used to operate on the electronic device 600, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 602 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 603 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 602 or sent through the communication component 605. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 604 provides an interface between the processor 601 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 605 is used for wired or wireless communication between the electronic device 600 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 605 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0154] In an exemplary embodiment, the electronic device 600 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned output power prediction method.

[0155] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned output power prediction method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 602 including program instructions, and the above-mentioned program instructions can be executed by the processor 601 of the electronic device 600 to complete the above-mentioned output power prediction method.

[0156] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0157] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0158] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A method for predicting output power, characterized in that: Applied to the coupling system of coal chemical industry and new energy, the method comprises: Iteratively perform the following operations: obtain first prediction data, and input the first prediction data into the scheduling optimization model to obtain the predicted load curve of the consumption unit and the predicted consumption power of the new energy power generation unit, input the predicted load curve and the predicted consumption power into the objective function, and obtain a performance value corresponding to the coupling system, when the performance value does not meet the preset condition, obtain new first prediction data until the obtained performance value meets the preset condition, wherein the first prediction data includes the output power prediction data of the new energy power generation unit of the coupling system within a preset time period and the electricity price prediction data of the coupling system, and the objective function is used to characterize the corresponding relationship between the performance value corresponding to the coupling system and the following coefficients: carbonization coefficient, electricity price coefficient and consumption coefficient; According to the performance value obtained in the last iteration, the first predicted output power of the absorption unit is determined.

2. The method according to claim 1, characterized in that The method further comprises: Determine a rated output power range of the consumption unit within a target preset time period, wherein the target preset time period is greater than a preset time period corresponding to the first prediction data in each iteration process; Calculate the sum of the first predicted output power output in each iteration process and the first predicted output power obtained in the previous iteration process to obtain the second predicted output power; When the second predicted output power is not within the rated output power range, the rated output power range is adjusted so that the coupling system operates according to a target performance value required by a user.

3. The method according to claim 2, characterized in that The step of determining the rated output power range of the consumption unit in a target preset time period includes: Determine second prediction data and third prediction data, wherein the second prediction data is used to represent the output power prediction data of the new energy power generation unit of the coupling system within the target preset time period, and the third prediction data is used to represent the predicted power data corresponding to the conventional load demand in the coupling system; Determining predicted power difference data according to the second predicted data and the third predicted data; The predicted power difference data is input into the scheduling optimization model, and the result output by the scheduling optimization model is adjusted according to the performance preset conditions to obtain the rated output power range.

4. The method according to any one of claims 1 to 3, characterized in that: The constraints of the scheduling optimization model are: The output power of the new energy power generation unit is greater than or equal to zero or less than or equal to a preset power threshold, and the preset power threshold is the sum of the output powers of the adjustable load unit and the energy storage unit in the coupling system.

5. The method according to any one of claims 1 to 3, characterized in that: The consumption unit includes an energy storage unit, and the constraint conditions of the scheduling optimization model are: The output power of the energy storage unit is less than or equal to the rated power value; The battery capacity of the energy storage unit is greater than or equal to a first preset capacity threshold and less than or equal to a second preset capacity threshold, the first preset capacity threshold is the minimum rated battery capacity of the energy storage unit, and the second preset capacity threshold is the maximum rated battery capacity of the energy storage unit.

6. The method according to any one of claims 1 to 3, characterized in that: The consumption unit includes a heat storage unit and a water electrolysis hydrogen production unit. The constraints of the scheduling optimization model are: The output power of the heat storage unit is less than or equal to the sum of the maximum output powers of the heat storage unit and the water electrolysis hydrogen production unit; The output power of the water electrolysis hydrogen production unit is greater than or equal to a first power threshold and less than or equal to a second power threshold, the first power threshold is the minimum rated power output during the operation of the water electrolysis hydrogen production unit, and the second power threshold is the maximum capacity value of the hydrogen storage container.

7. The method according to any one of claims 1 to 3, characterized in that: The calculation formula of the objective function is: Max(C)=k1*δ carbon +k2*d price +k3*d consump Wherein, Max(C) is the performance value of the objective function; δ carbon The carbonization coefficient, δ, used to characterize the coupled system price The electricity price coefficient used to characterize the coupled system, δ consump It is used to characterize the absorption coefficient of the coupling system, k1 is the carbonization weight coefficient of the coupling system, k2 is the electricity price weight coefficient of the coupling system, and k3 is the absorption weight coefficient of the coupling system.

8. An output power prediction device, characterized in that: A coupling system applied to coal chemical industry and new energy, the system comprising an iteration module and a determination module; The iteration module is used to perform the following operations: obtain first prediction data, and input the first prediction data into the scheduling optimization model to obtain the predicted load curve of the consumption unit and the predicted consumption power of the new energy power generation unit, input the predicted load curve and the predicted consumption power into the objective function, and obtain a performance value corresponding to the coupling system. When the performance value does not meet the preset condition, obtain new first prediction data until the obtained performance value meets the preset condition, wherein the first prediction data includes the output power prediction data of the new energy power generation unit of the coupling system within a preset time period and the electricity price prediction data of the coupling system, and the objective function is used to characterize the corresponding relationship between the performance value corresponding to the coupling system and the following coefficients: carbonization coefficient, electricity price coefficient and consumption coefficient; The determination module is used to determine the first predicted output power of the absorption unit according to the performance value obtained in the last iteration.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

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