A smart power plant multi-energy storage mode optimization scheduling method considering SOFCs

Abstract

The application provides a kind of smart power plant multi-energy storage mode optimization scheduling method considering SOFC, belongs to the technical field of smart energy, specifically includes: determining the comprehensive energy storage efficiency of battery energy storage, constructing the comprehensive energy storage efficiency of hydrogen energy storage, constructing the comprehensive energy storage efficiency of P2G equipment, constructing the comprehensive power generation efficiency of SOFC equipment;Based on the carbon capture amount of P2G equipment, the environmental benefits and carbon capture benefits of the P2G equipment are constructed;Based on the energy storage regulation target, based on the comprehensive energy storage efficiency of hydrogen energy storage, the comprehensive energy storage efficiency of battery energy storage, the comprehensive energy storage efficiency of P2G equipment, the environmental benefits and carbon capture benefits of P2G equipment, the comprehensive efficiency, carbon capture benefits and environmental benefits are used as the basis to construct the comprehensive objective function, the maximum of the comprehensive objective function is taken as the target, based on load balancing, load regulation rate, the highest state of charge of battery energy storage as constraint condition, battery energy storage, hydrogen energy storage and P2G equipment are adjusted, so that the efficiency and environmental benefits of energy storage regulation are maximized.

Description

Technical Field

[0001] This invention belongs to the field of smart energy technology, and in particular relates to a method for optimizing the scheduling of multiple energy storage modes in smart power plants that takes SOFC into account. Background Technology

[0002] As a highly efficient and low-emission electrochemical power generation device, solid oxide fuel cells (SOFCs) are of great significance in solving the global energy crisis and environmental pollution problems. They not only possess advantages such as high power generation efficiency, high waste heat utilization value, diverse fuel types, low pollutant emissions, and easy CO2 enrichment and separation, but can also be applied to natural gas power generation and clean coal power generation technologies, showing great application prospects.

[0003] Currently, to improve the flexibility of generating units, various energy storage methods are often adopted, the most common being battery energy storage, hydrogen production energy storage, and P2G energy storage. When electricity is needed, SOFCs utilize hydrogen or methane produced by P2G energy storage to supplement the electricity. Different energy storage methods have significant differences. P2G energy storage has poor energy conversion efficiency and high losses, but it has significant environmental value by reducing carbon dioxide emissions. Battery energy storage has high energy conversion efficiency and low electricity consumption, but it does not generate additional environmental value. Therefore, if different energy storage methods are not optimized and scheduled according to their characteristics and load regulation needs, different energy storage methods cannot be fully utilized, which will also affect the flexibility of the generating units and the safety of the equipment.

[0004] To address the aforementioned technical problems, this invention provides a method for optimizing the scheduling of multiple energy storage modes in smart power plants that takes SOFC into account. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted:

[0006] According to one aspect of the present invention, a method for optimizing the scheduling of multiple energy storage modes in a smart power plant that takes into account SOFC is provided.

[0007] A method for optimizing the scheduling of multiple energy storage modes in a smart power plant, taking into account SOFC, is characterized by specifically including:

[0008] S11 determines the overall energy storage efficiency of the battery storage based on the battery type, the charging state of the battery storage, and the service life of the battery storage.

[0009] S12 determines the hydrogen production conversion efficiency based on the type and service life of the hydrogen production equipment, and constructs the comprehensive energy storage efficiency of hydrogen energy storage. Based on the service life and type of P2G equipment, it determines the energy conversion efficiency and constructs the comprehensive energy storage efficiency of P2G equipment. Based on the type and service life of SOFC equipment, it determines the energy conversion efficiency and constructs the comprehensive power generation efficiency of the SOFC equipment. Based on the carbon capture capacity of P2G equipment, it constructs the environmental benefits and carbon capture benefits of the P2G equipment.

[0010] S13 Construct an energy storage regulation target and determine whether the energy storage regulation target is greater than zero. If yes, proceed to step S14. If no, based on the comprehensive power generation efficiency of the SOFC device and the comprehensive energy storage efficiency of the battery energy storage, and based on the energy storage regulation target, with the highest comprehensive efficiency as the objective, and based on load balance, load regulation rate, and battery energy storage charging state as constraints, regulate the SOFC device and battery energy storage.

[0011] S14 is based on the energy storage regulation target, and is based on the comprehensive energy storage efficiency of hydrogen energy storage, the comprehensive energy storage efficiency of battery energy storage, the comprehensive energy storage efficiency of P2G equipment, the environmental benefits and carbon capture benefits of P2G equipment. It constructs a comprehensive objective function with comprehensive efficiency, carbon capture benefits and environmental benefits as the goal, and takes the maximization of the comprehensive objective function as the objective. It regulates the battery energy storage, hydrogen energy storage and P2G equipment based on the constraints of load balance, load regulation rate and the highest charging state of battery energy storage.

[0012] By considering the service life in the construction of comprehensive energy storage efficiency, the difference in energy storage efficiency caused by the characteristics of a single device is not only considered, but the efficiency reduction caused by the service life is also taken into account, thus making the final comprehensive energy storage efficiency assessment result more accurate.

[0013] By adopting different processing methods according to different energy storage regulation objectives, different methods can be used under different load conditions, thereby ensuring the reliability and accuracy of regulation.

[0014] By constructing a comprehensive objective function based on overall efficiency, carbon capture benefits, and environmental benefits, and aiming to maximize the comprehensive objective function, and using load balance, load regulation rate, and the highest state of charge of battery energy storage as constraints, the battery energy storage, hydrogen energy storage, and P2G equipment are regulated. This not only makes load regulation consider only efficiency or benefits, but also takes both aspects into account, achieving coordinated control of different energy storage methods and further improving the economic and environmental benefits of regulation.

[0015] A further technical solution involves the following specific steps for building the overall energy storage efficiency of battery energy storage:

[0016] S21 determines the basic energy storage efficiency of the battery based on the battery type of the battery storage;

[0017] S22 determines whether the service life of the battery energy storage is greater than the first year threshold. If yes, proceed to step S23. If no, correct the basic energy storage efficiency of the battery based on the charging state of the battery energy storage to obtain the comprehensive energy storage efficiency of the battery energy storage.

[0018] S23 corrects the basic energy storage efficiency of the battery based on its charging state and service life to obtain the comprehensive energy storage efficiency of the battery.

[0019] By constructing the first-year threshold, we can more accurately link the service life of battery energy storage with the overall energy storage efficiency, making the final calculation result of the overall energy storage efficiency of battery energy storage more accurate.

[0020] A further technical solution is that the formula for calculating the overall energy storage efficiency of the battery energy storage is:

[0021]

[0022] Among them, SOC, SOC min These represent the state of charge of battery energy storage and the minimum state of charge of battery energy storage, respectively, η. dj For the basic energy storage efficiency of battery energy storage, Y, Y min These represent the service life of the battery storage, the first-year threshold, and K1, K2, K3, and K4, respectively. K1, K2, K3, and K4 are constants, and their value ranges are determined by the battery type of the battery storage.

[0023] By constructing a formula for calculating the comprehensive energy storage efficiency of battery energy storage under different conditions, the calculation of comprehensive energy storage efficiency can more accurately reflect the actual situation, and further promotes more accurate final adjustment results.

[0024] A further technical solution is that the environmental benefits of the P2G device are calculated using the following formula:

[0025]

[0026] C1 and D1 represent carbon capture and stored electricity, respectively. C1 is measured in tons and electricity in MW. K5, K6, and K7 are constants, which are determined based on the actual carbon emissions in the local area. The higher the carbon emissions and the stricter the management, the larger the values ​​will be. All values ​​are between 0 and 1.

[0027] A further technical solution involves the following specific steps for regulating SOFC equipment and battery energy storage:

[0028] S31 determines the stability of the SOFC equipment based on its type, historical failure count, and service life; determines the load regulation rate of the SOFC equipment based on its type, capacity, and service life; and constructs the comprehensive power generation efficiency of the SOFC equipment based on its energy conversion efficiency determined by its type and service life.

[0029] S32 determines the overall energy storage efficiency of the battery storage based on the battery type, state of charge, and service life of the battery storage; determines the load regulation rate of the battery storage based on the battery type, state of charge, and service life of the battery storage; and determines the stability of the battery storage based on the battery type, historical failure count, and service life of the battery storage.

[0030] S33 constructs a stability objective function based on the stability of the battery energy storage and the stability of the SOFC device, and uses load balance, load regulation rate and battery energy storage charging state as constraints. Based on the minimum stability objective threshold, it forms an alternative regulation scheme for SOFC device and battery energy storage with a stability objective function greater than the minimum stability objective threshold.

[0031] Based on the alternative adjustment schemes, and with the goal of maximizing overall efficiency, S34 uses a GWO-based optimization algorithm to optimize the adjustment schemes for the SOFC equipment and battery energy storage, thereby obtaining the adjustment schemes for the SOFC equipment and battery energy storage.

[0032] By first constructing a stability objective function based on the stability of the battery energy storage and the SOFC device, and then constructing the model based on the stability objective function, the stability of the adjustment scheme obtained by the final prediction model is greatly improved, ensuring the reliable and stable operation of the device.

[0033] By using alternative adjustment schemes as a basis and then optimizing for the highest overall efficiency, the final adjustment scheme is not only highly efficient but also has good stability, thereby enabling the final adjustment scheme to effectively guide the real-world system.

[0034] A further technical solution is that the minimum target threshold for stability is determined based on the capacity of the smart power plant and the load fluctuation of the region. The larger the capacity of the smart power plant and the more severe the load fluctuation in the region, the larger the minimum target threshold for stability.

[0035] A further technical solution is that the stability of the SOFC device is obtained using a prediction model based on the SSA-LSTM algorithm.

[0036] A further technical solution involves the following specific steps for regulating battery energy storage, hydrogen energy storage, and P2G equipment:

[0037] S41 determines the overall energy storage efficiency of the battery storage based on the battery type, state of charge, and service life of the battery storage; determines the load regulation rate of the battery storage based on the battery type, state of charge, and service life of the battery storage; and determines the stability of the battery storage based on the battery type, historical failure count, and service life of the battery storage.

[0038] S42 determines the hydrogen conversion efficiency based on the type and service life of the hydrogen production equipment, and constructs the comprehensive energy storage efficiency of the hydrogen energy storage; determines the load regulation rate of the hydrogen energy storage based on the type and service life of the hydrogen production equipment; and determines the stability of the hydrogen energy storage based on the type, service life, and historical failure count of the hydrogen production equipment.

[0039] S43 determines the overall energy storage efficiency of the P2G equipment based on its type and service life; determines the load regulation rate of the P2G equipment based on its type and service life; determines the stability of the P2G equipment based on its type, service life, and historical failure count; and obtains the environmental benefits and carbon capture benefits of the P2G equipment based on its solidified carbon production.

[0040] S44 constructs a comprehensive objective function based on the environmental benefits, carbon capture benefits, stability, and overall energy storage efficiency. With the goal of maximizing the comprehensive objective function, and with load balance and load regulation rate as constraints, it regulates the battery energy storage, hydrogen energy storage, and P2G equipment.

[0041] A further technical solution is that the calculation formula for the comprehensive objective function is as follows:

[0042]

[0043] Among them, J1, H J H C These are the comprehensive efficiency function, environmental benefits, and carbon capture benefits, respectively; K8, K9, and K... 10 K 11 All are constants, and their values ​​are determined by local environmental pressure and carbon emission pressure. The greater the environmental pressure and carbon emission pressure, the higher the H value. J The larger the value, the better.

[0044] On the other hand, this application provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed in a computer, it causes the computer to execute the above-mentioned method for optimizing the scheduling of multiple energy storage modes in a smart power plant that takes SOFC into account.

[0045] On the other hand, this application provides a computer program product, characterized in that the computer program product stores instructions, which, when executed by a computer, cause the computer to implement the above-mentioned method for optimizing the scheduling of multiple energy storage modes in a smart power plant that takes SOFC into account. Attached Figure Description

[0046] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0047] Figure 1 This is a flowchart of a smart power plant multi-energy storage mode optimization scheduling method that takes SOFC into account, according to Embodiment 1.

[0048] Figure 2 This is a framework diagram of a smart power plant multi-energy storage mode optimization scheduling system based on embodiment 1.

[0049] Figure 3 This is a flowchart illustrating the specific steps for adjusting battery energy storage, hydrogen energy storage, and P2G equipment according to Example 1.

[0050] Figure 4 This is a framework diagram of a computer-readable storage medium according to Embodiment 2. Detailed Implementation

[0051] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many ways and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0052] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0053] When scheduling different energy storage methods in the past, it was often impossible to optimize the scheduling between different energy storage methods according to their characteristics and load regulation needs. As a result, different energy storage methods could not be fully utilized, and the flexibility of the unit and the safety of the equipment were also affected.

[0054] Example 1

[0055] To solve the above problems, according to one aspect of the present invention, such as Figure 2 The diagram shown is a framework diagram of a smart power plant multi-energy storage mode optimized scheduling system that takes SOFC into account. Figure 1 As shown, a method for optimizing the scheduling of multiple energy storage modes in a smart power plant considering SOFC is provided, characterized by specifically including:

[0056] S11 determines the overall energy storage efficiency of the battery storage based on the battery type, the charging state of the battery storage, and the service life of the battery storage.

[0057] For example, battery types for energy storage include lead-acid batteries, nickel-metal hydride batteries, and lithium-ion batteries. Different battery types have different overall energy storage efficiencies, which must be determined through literature review or experimental methods.

[0058] For example, the state of charge of a battery is called its SOC. The overall energy storage efficiency varies depending on the SOC.

[0059] For example, a regression equation can be determined through experiments, and the overall energy storage efficiency can be determined based on the regression equation. Alternatively, a prediction model based on a neural network algorithm can be used, with the battery type, state of charge, and service life of the battery as inputs, and the overall energy storage efficiency as the output, to determine the overall energy storage efficiency of the battery.

[0060] S12 determines the hydrogen production conversion efficiency based on the type and service life of the hydrogen production equipment, and constructs the comprehensive energy storage efficiency of hydrogen energy storage. Based on the service life and type of P2G equipment, it determines the energy conversion efficiency and constructs the comprehensive energy storage efficiency of P2G equipment. Based on the type and service life of SOFC equipment, it determines the energy conversion efficiency and constructs the comprehensive power generation efficiency of the SOFC equipment. Based on the carbon capture capacity of P2G equipment, it constructs the environmental benefits and carbon capture benefits of the P2G equipment.

[0061] For a specific example, P2G equipment uses hydrogen and then, through a methanation reaction process, consumes the carbon dioxide produced by the system while the natural gas produced can be directly injected into the natural gas system or stored for use by gas turbines, thereby promoting the consumption of renewable energy power generation. The methane produced can then be used to generate electricity through fuel cells to supply users.

[0062] For a specific example, the environmental benefits and carbon capture benefits of P2G devices are determined by the carbon captured by SOFC devices.

[0063] S13 Construct an energy storage regulation target and determine whether the energy storage regulation target is greater than zero. If yes, proceed to step S14. If no, based on the comprehensive power generation efficiency of the SOFC device and the comprehensive energy storage efficiency of the battery energy storage, and based on the energy storage regulation target, with the highest comprehensive efficiency as the objective, and based on load balance, load regulation rate, and battery energy storage charging state as constraints, regulate the SOFC device and battery energy storage.

[0064] For example, energy storage regulation targets are determined based on load demand and power plant generation.

[0065] S14 is based on the energy storage regulation target, and is based on the comprehensive energy storage efficiency of hydrogen energy storage, the comprehensive energy storage efficiency of battery energy storage, the comprehensive energy storage efficiency of P2G equipment, the environmental benefits and carbon capture benefits of P2G equipment. It constructs a comprehensive objective function with comprehensive efficiency, carbon capture benefits and environmental benefits as the goal, and takes the maximization of the comprehensive objective function as the objective. It regulates the battery energy storage, hydrogen energy storage and P2G equipment based on the constraints of load balance, load regulation rate and the highest charging state of battery energy storage.

[0066] By considering the service life in the construction of comprehensive energy storage efficiency, the difference in energy storage efficiency caused by the characteristics of a single device is not only considered, but the efficiency reduction caused by the service life is also taken into account, thus making the final comprehensive energy storage efficiency assessment result more accurate.

[0067] By adopting different processing methods according to different energy storage regulation objectives, different methods can be used under different load conditions, thereby ensuring the reliability and accuracy of regulation.

[0068] By constructing a comprehensive objective function based on overall efficiency, carbon capture benefits, and environmental benefits, and aiming to maximize the comprehensive objective function, and using load balance, load regulation rate, and the highest state of charge of battery energy storage as constraints, the battery energy storage, hydrogen energy storage, and P2G equipment are regulated. This not only makes load regulation consider only efficiency or benefits, but also takes both aspects into account, achieving coordinated control of different energy storage methods and further improving the economic and environmental benefits of regulation.

[0069] In another possible embodiment, the specific steps for constructing the overall energy storage efficiency of battery energy storage are as follows:

[0070] S21 determines the basic energy storage efficiency of the battery based on the battery type of the battery storage;

[0071] S22 determines whether the service life of the battery energy storage is greater than the first year threshold. If yes, proceed to step S23. If no, correct the basic energy storage efficiency of the battery based on the charging state of the battery energy storage to obtain the comprehensive energy storage efficiency of the battery energy storage.

[0072] For example, the first-year threshold varies depending on the type of battery used for energy storage. Different battery types have different degradation rates, and the degradation also varies under different adjustment frequencies and depths. Specifically, a prediction model can be built using battery type, adjustment frequency, maximum adjustment depth, and average adjustment depth as inputs to determine the first-year threshold.

[0073] S23 corrects the basic energy storage efficiency of the battery based on its charging state and service life to obtain the comprehensive energy storage efficiency of the battery.

[0074] By constructing the first-year threshold, we can more accurately link the service life of battery energy storage with the overall energy storage efficiency, making the final calculation result of the overall energy storage efficiency of battery energy storage more accurate.

[0075] In another possible embodiment, the formula for calculating the overall energy storage efficiency of the battery energy storage is:

[0076]

[0077] Among them, SOC, SOC min These represent the state of charge of battery energy storage and the minimum state of charge of battery energy storage, respectively, η. dj For the basic energy storage efficiency of battery energy storage, Y, Y minThese represent the service life of the battery storage, the first-year threshold, and K1, K2, K3, and K4, respectively. K1, K2, K3, and K4 are constants, and their value ranges are determined by the battery type of the battery storage.

[0078] By constructing a formula for calculating the comprehensive energy storage efficiency of battery energy storage under different conditions, the calculation of comprehensive energy storage efficiency can more accurately reflect the actual situation, and further promotes more accurate final adjustment results.

[0079] In another possible embodiment, the environmental benefits of the P2G device are calculated using the following formula:

[0080]

[0081] C1 and D1 represent carbon capture and stored electricity, respectively. C1 is measured in tons and electricity in MW. K5, K6, and K7 are constants, which are determined based on the actual carbon emissions in the local area. The higher the carbon emissions and the stricter the management, the larger the values ​​will be. All values ​​are between 0 and 1.

[0082] In another possible embodiment, the specific steps for regulating the SOFC device and battery energy storage are as follows:

[0083] S31 determines the stability of the SOFC equipment based on its type, historical failure count, and service life; determines the load regulation rate of the SOFC equipment based on its type, capacity, and service life; and constructs the comprehensive power generation efficiency of the SOFC equipment based on its energy conversion efficiency determined by its type and service life.

[0084] S32 determines the overall energy storage efficiency of the battery storage based on the battery type, state of charge, and service life of the battery storage; determines the load regulation rate of the battery storage based on the battery type, state of charge, and service life of the battery storage; and determines the stability of the battery storage based on the battery type, historical failure count, and service life of the battery storage.

[0085] S33 constructs a stability objective function based on the stability of the battery energy storage and the stability of the SOFC device, and uses load balance, load regulation rate and battery energy storage charging state as constraints. Based on the minimum stability objective threshold, it forms an alternative regulation scheme for SOFC device and battery energy storage with a stability objective function greater than the minimum stability objective threshold.

[0086] Based on the alternative adjustment schemes, and with the goal of maximizing overall efficiency, S34 uses a GWO-based optimization algorithm to optimize the adjustment schemes for the SOFC equipment and battery energy storage, thereby obtaining the adjustment schemes for the SOFC equipment and battery energy storage.

[0087] For a specific example, the GWO algorithm can achieve global optimization. The basic idea is to simulate the hunting behavior of a wolf pack, finding the closest possible location to prey to better determine the prey's position, and moving in the direction closest to the prey to achieve target optimization. The algorithm's advantages mainly include its simple structure, good robustness, and ease of implementation in experimental coding. It has attracted widespread attention from experts and scholars both domestically and internationally, and has been successfully applied in various fields such as industry, production, and economics, becoming a popular intelligent optimization algorithm in recent years.

[0088] For a concrete example, GWO has two points in n-dimensional space and updates the position of one of the points based on the other point. To mathematically model this, the following equation is proposed:

[0089] D = C·X p (txt)

[0090] X(t+1)=X p (t)-A·D

[0091] t represents the current iteration number, A and C are coefficient vectors, and X p X is the position vector of the prey, and X is the position vector of the gray wolf.

[0092] A = 2a·r1-a

[0093] C = 2·r²

[0094] 'a' is a linear decrease from 2 to 0 with the number of iterations, and r1 and r2 are randomly selected in the range [0,1]. The equation uses vectors, so it is applicable to any dimension.

[0095] The convergence factor *a* affects the change of *A*. When |A| is greater than 1, the gray wolf population will perform a global search; when |A| is less than 1, it will perform a local search. Therefore, *A* influences the exploration and development of the algorithm to a certain extent. Thus, the convergence factor *a* is updated using an inverse incomplete function, as shown in the following expression:

[0096]

[0097] The maximum value of the convergence factor a max =2, minimum value a min =0, where t represents the current iteration number, t max It represents the total number of iterations, where λ is a random variable greater than or equal to 0, and here it takes the value 0.01.

[0098] By first constructing a stability objective function based on the stability of the battery energy storage and the SOFC device, and then constructing the model based on the stability objective function, the stability of the adjustment scheme obtained by the final prediction model is greatly improved, ensuring the reliable and stable operation of the device.

[0099] By using alternative adjustment schemes as a basis and then optimizing for the highest overall efficiency, the final adjustment scheme is not only highly efficient but also has good stability, thereby enabling the final adjustment scheme to effectively guide the real-world system.

[0100] In another possible embodiment, the minimum stability target threshold is determined based on the capacity of the smart power plant and the load fluctuation of the region, wherein the larger the capacity of the smart power plant and the more severe the load fluctuation in the region, the larger the minimum stability target threshold will be.

[0101] In another possible embodiment, the stability of the SOFC device is obtained using a prediction model based on the SSA-LSTM algorithm.

[0102] For a concrete example, an LSTM unit includes the state of the memory unit and three gate control structures: forget gate, input gate, and output gate. The cell state is the pathway for information transmission, allowing information to be continuously passed through the sequence. Theoretically, the cell state can continuously pass relevant information during sequence processing, thus enabling earlier information to be transmitted to later units, overcoming the limitations of short-term memory. The addition and removal of information are accomplished through the gate structure, which learns during training which information should be retained or forgotten.

[0103] The formula for calculating the forget gate at time t is shown in the formula, with input gate i. t The calculation formula is shown in the formula, and the output gate O t The calculation formula is shown in the formula, which represents the candidate values ​​of memory cell state information. The calculation formula is shown in the formula, where the memory cell state information C is... t The calculation formula is shown in the formula. The hidden layer state is calculated as shown in the formula, where W and U are the weight matrices of each gate; b is the bias term of each gate.

[0104] f t =σ(W f x t +U f h t-1 +b f )

[0105] i t =σ(W i x t+U i h t-1 +b i )

[0106] o t =σ(W o x t +U o h t-1 +b o )

[0107]

[0108]

[0109] h t =o t ×tanh(C t )

[0110] For a specific example, the sparrow search algorithm is a heuristic algorithm proposed by Xue Jiankai et al. in 2020 based on the population biological characteristics of sparrows. It uses the overall characteristics of the sparrow population and the characteristics of individual sparrows to establish a mathematical model, dividing the sparrow population into discoverers and joiners. Sparrows that can find better quality food are designated as discoverers, while the rest are designated as joiners. Food quality, i.e., energy reserves, is measured by fitness. Additionally, a certain number of sparrows are selected in the population to possess detection and early warning capabilities; once danger is detected, they will immediately abandon food, generally residing on the periphery. The process of sparrows continuously searching for better food is the solution optimization process.

[0111] Suppose there are n sparrows forming an n x m dimensional sparrow population X, where X... ij This represents the position of the i-th sparrow in the j-th dimension, as shown in the formula.

[0112]

[0113] In the entire sparrow population, the discoverer is responsible for finding food and providing foraging directions to the newcomers. Its position update formula is shown in the formula.

[0114]

[0115] The behavior of the participants is influenced by the discoverer. When the discoverer finds better food, the participants will immediately compete for the food. The position update formula is shown in the formula.

[0116]

[0117] In addition, 10%-20% of sparrows in the population are randomly selected as vigilant individuals to spread the warning signal. Their position update formula is shown in the formula.

[0118]

[0119] In another possible embodiment, such as Figure 3 As shown, the specific steps for adjusting battery energy storage, hydrogen energy storage, and P2G devices are as follows:

[0120] S41 determines the overall energy storage efficiency of the battery storage based on the battery type, state of charge, and service life of the battery storage; determines the load regulation rate of the battery storage based on the battery type, state of charge, and service life of the battery storage; and determines the stability of the battery storage based on the battery type, historical failure count, and service life of the battery storage.

[0121] S42 determines the hydrogen conversion efficiency based on the type and service life of the hydrogen production equipment, and constructs the comprehensive energy storage efficiency of the hydrogen energy storage; determines the load regulation rate of the hydrogen energy storage based on the type and service life of the hydrogen production equipment; and determines the stability of the hydrogen energy storage based on the type, service life, and historical failure count of the hydrogen production equipment.

[0122] S43 determines the overall energy storage efficiency of the P2G equipment based on its type and service life; determines the load regulation rate of the P2G equipment based on its type and service life; determines the stability of the P2G equipment based on its type, service life, and historical failure count; and obtains the environmental benefits and carbon capture benefits of the P2G equipment based on its solidified carbon production.

[0123] S44 constructs a comprehensive objective function based on the environmental benefits, carbon capture benefits, stability, and overall energy storage efficiency. With the goal of maximizing the comprehensive objective function, and with load balance and load regulation rate as constraints, it regulates the battery energy storage, hydrogen energy storage, and P2G equipment.

[0124] In another possible embodiment, the formula for calculating the comprehensive objective function is:

[0125]

[0126] Among them, J1, H J H C These are the comprehensive efficiency function, environmental benefits, and carbon capture benefits, respectively; K8, K9, and K... 10 K 11 All are constants, and their values ​​are determined by local environmental pressure and carbon emission pressure. The greater the environmental pressure and carbon emission pressure, the higher the H value. J The larger the value, the better.

[0127] Example 2

[0128] like Figure 4 As shown, this application provides a computer-readable storage medium storing a computer program. When the computer program is executed in a computer, it causes the computer to execute the above-mentioned method for optimizing the scheduling of multiple energy storage modes in a smart power plant that takes SOFC into account.

[0129] Example 3

[0130] This application provides a computer program product, characterized in that the computer program product stores instructions, which, when executed by a computer, cause the computer to implement the above-mentioned method for optimizing the scheduling of multiple energy storage modes in a smart power plant considering SOFC.

[0131] In this embodiment of the invention, the term "multiple" refers to two or more, unless otherwise explicitly defined. The terms "install," "connect," and "fix" should be interpreted broadly. For example, "connect" can mean a fixed connection, a detachable connection, or an integral connection. Those skilled in the art can understand the specific meaning of the above terms in this embodiment of the invention based on the specific circumstances.

[0132] In the description of the embodiments of the present invention, it should be understood that the terms "upper" and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or unit referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of the present invention.

[0133] In the description of this specification, the terms "an embodiment," "a preferred embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0134] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. For those skilled in the art, the embodiments of the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of the present invention should be included within the protection scope of the embodiments of the present invention.

Claims

1. A method for optimal scheduling of a smart power plant with multiple energy storage modes considering SOFC, characterized in that, Specifically, it includes: S11 determines the overall energy storage efficiency of the battery storage based on the battery type, the charging state of the battery storage, and the service life of the battery storage. S12 determines the hydrogen production conversion efficiency based on the type and service life of the hydrogen production equipment, and constructs the comprehensive energy storage efficiency of hydrogen energy storage. Based on the service life and type of P2G equipment, it determines the energy conversion efficiency and constructs the comprehensive energy storage efficiency of P2G equipment. Based on the type and service life of SOFC equipment, it determines the energy conversion efficiency and constructs the comprehensive power generation efficiency of the SOFC equipment. Based on the carbon capture capacity of P2G equipment, it constructs the environmental benefits and carbon capture benefits of the P2G equipment. S13 Construct an energy storage regulation target and determine whether the energy storage regulation target is greater than zero. If yes, proceed to step S14. If no, based on the comprehensive power generation efficiency of the SOFC device and the comprehensive energy storage efficiency of the battery energy storage, and based on the energy storage regulation target, with the highest comprehensive efficiency as the objective, and based on load balance, load regulation rate, and battery energy storage charging state as constraints, regulate the SOFC device and battery energy storage. S14 is based on the energy storage regulation target, taking the comprehensive energy storage efficiency of hydrogen energy storage, the comprehensive energy storage efficiency of battery energy storage, the comprehensive energy storage efficiency of P2G equipment, the environmental benefits and carbon capture benefits of P2G equipment as the basis, and constructing a comprehensive objective function based on the comprehensive efficiency, carbon capture benefits and environmental benefits. With the goal of maximizing the comprehensive objective function, and based on the constraints of load balance, load regulation rate and the highest charging state of battery energy storage, the battery energy storage, hydrogen energy storage and P2G equipment are regulated. P2G equipment is a device that uses hydrogen to produce natural gas through a methanation reaction process; SOFC (Solid Oxide Fuel Cell) equipment is a type of fuel cell equipment where energy storage regulation targets are determined based on load demand and power plant generation.

2. The multi-energy storage mode optimal scheduling method of claim 1, wherein, The specific steps for building the overall energy storage efficiency of battery energy storage are as follows: S21 determines the basic energy storage efficiency of the battery based on the battery type of the battery storage; S22 determines whether the service life of the battery energy storage is greater than the first year threshold. If yes, proceed to step S23. If no, correct the basic energy storage efficiency of the battery based on the charging state of the battery energy storage to obtain the comprehensive energy storage efficiency of the battery energy storage. S23 corrects the basic energy storage efficiency of the battery based on its charging state and service life to obtain the comprehensive energy storage efficiency of the battery.

3. The multi-energy storage mode optimal scheduling method of claim 2, wherein, The formula for calculating the overall energy storage efficiency of the battery energy storage is as follows: ； SOC, SOC min respectively are the state of charge of the battery storage, the minimum state of charge of the battery storage, is the base storage efficiency of the battery storage, Y, Y min respectively are the service life of the battery storage, the first life threshold, K1, K2, K3, K4 are constants, the value range of which is determined by the battery type of the battery storage.

4. The multi-energy storage mode optimal scheduling method of claim 1, wherein, The formula for calculating the environmental benefits of the P2G device is as follows: ； C1 and D1 represent carbon capture and stored electricity, respectively. C1 is measured in tons and electricity in MW. K5, K6, and K7 are constants, which are determined based on the actual carbon emissions in the local area. The higher the carbon emissions and the stricter the management, the larger the values ​​will be. All values ​​are between 0 and 1.

5. The multi-energy storage mode optimal scheduling method of claim 1, wherein, The specific steps for regulating SOFC equipment and battery energy storage are as follows: S31 determines the stability of the SOFC equipment based on its type, historical failure count, and service life; determines the load regulation rate of the SOFC equipment based on its type, capacity, and service life; and constructs the comprehensive power generation efficiency of the SOFC equipment based on its energy conversion efficiency determined by its type and service life. S32 determines the overall energy storage efficiency of the battery storage based on the battery type, state of charge, and service life of the battery storage; determines the load regulation rate of the battery storage based on the battery type, state of charge, and service life of the battery storage; and determines the stability of the battery storage based on the battery type, historical failure count, and service life of the battery storage. S33 constructs a stability objective function based on the stability of the battery energy storage and the stability of the SOFC device, and uses load balance, load regulation rate and battery energy storage charging state as constraints. Based on the minimum stability objective threshold, it forms an alternative regulation scheme for SOFC device and battery energy storage with a stability objective function greater than the minimum stability objective threshold. Based on the alternative adjustment schemes, and with the goal of maximizing overall efficiency, S34 uses a GWO-based optimization algorithm to optimize the adjustment schemes for the SOFC equipment and battery energy storage, thereby obtaining the adjustment schemes for the SOFC equipment and battery energy storage.

6. The multi-energy storage mode optimal scheduling method of claim 5, wherein, The minimum target threshold for stability is determined based on the capacity of the smart power plant and the load fluctuation of the region. The larger the capacity of the smart power plant and the more severe the load fluctuation in the region, the larger the minimum target threshold for stability will be.

7. The multi-energy storage mode optimal scheduling method of claim 5, wherein, The stability of the SOFC device is obtained using a prediction model based on the SSA-LSTM algorithm.

8. The multi-energy storage mode optimal scheduling method of claim 1, wherein, The specific steps for regulating battery energy storage, hydrogen energy storage, and P2G equipment are as follows: S41 determines the overall energy storage efficiency of the battery storage based on the battery type, state of charge, and service life of the battery storage; determines the load regulation rate of the battery storage based on the battery type, state of charge, and service life of the battery storage; and determines the stability of the battery storage based on the battery type, historical failure count, and service life of the battery storage. S42 determines the hydrogen conversion efficiency based on the type and service life of the hydrogen production equipment, and constructs the comprehensive energy storage efficiency of the hydrogen energy storage; determines the load regulation rate of the hydrogen energy storage based on the type and service life of the hydrogen production equipment; and determines the stability of the hydrogen energy storage based on the type, service life, and historical failure count of the hydrogen production equipment. S43 determines the overall energy storage efficiency of the P2G equipment based on its type and service life; determines the load regulation rate of the P2G equipment based on its type and service life; and determines the stability of the P2G equipment based on its type, service life, and historical failure count. The environmental benefits and carbon capture benefits of P2G equipment are obtained from the carbon solidification output based on P2G equipment. S44 constructs a comprehensive objective function based on the environmental benefits, carbon capture benefits, stability, and overall energy storage efficiency. With the goal of maximizing the comprehensive objective function, and with load balance and load regulation rate as constraints, it regulates the battery energy storage, hydrogen energy storage, and P2G equipment.

9. The multi-energy storage mode optimal scheduling method of claim 1, wherein, The formula for calculating the comprehensive objective function is as follows: ； Among them, J1, H J H C These are the comprehensive efficiency function, environmental benefits, and carbon capture benefits, respectively; K8, K9, and K... 10 K 11 All values ​​are constants, determined by local environmental and carbon emission pressures. The greater the environmental and carbon emission pressures, the higher the H value. J The larger the value, the better.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method for optimizing the scheduling of multiple energy storage modes in a smart power plant, taking into account SOFC, as described in any one of claims 1-9.

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