Method and device for determining operation scheduling data of energy storage system, medium and terminal

Abstract

The invention discloses a method and device for determining operation scheduling data of an energy storage system, a medium and a terminal, and the method can quickly and reasonably determine the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of a thermal power plant through a tabu search algorithm, and achieves the effective optimization of the operation scheduling data of the energy storage system. The fuel cost of a thermal power plant is reduced, the overall economical efficiency of an electric power system is improved, and the method has high practical value for operation planning of an energy storage system. The method comprises the following steps: acquiring a load demand, the output power of a nuclear power station, the output power of a hydropower station and the theoretical photovoltaic output power; determining common output data of the energy storage system and the thermal power plant; randomly generating operation scheduling data of the energy storage system according to the common output data of the energy storage system and the thermal power plant; and based on the randomly generated operation scheduling data of the energy storage system, determining the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant by adopting a tabu search algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage systems, and in particular to a method, device, medium and terminal for determining operation scheduling data of an energy storage system. Background Art

[0002] With the vigorous development of solar photovoltaic power generation, the penetration rate of solar photovoltaic power generation in the power grid has gradually increased. In recent years, a large number of photovoltaic generators have been installed in the power system. Solar photovoltaic power generation technology is green and pollution-free. It has made great contributions to the process of building an environmentally friendly society and plays an important role in a low-carbon society. However, the output power of photovoltaic power generation is random and intermittent in nature. Therefore, photovoltaic power generation poses many challenges to the operation of the power system. Due to uncontrollable factors such as solar radiation, photovoltaic power generation cannot provide constant power. Photovoltaic power generation can only generate electricity during the day and cannot provide electricity to users at night. Therefore, another power source must be used to replace the photovoltaic power source to supply electricity to users. When there is sufficient sunlight during the day, photovoltaic power generation outputs a large power. If the power demand is small at this time, it is impossible to maintain the balance of power supply and demand. This problem is called the "surplus power problem". Therefore, in power systems with a large photovoltaic penetration rate, appropriate countermeasures need to be considered to solve the "surplus power problem" when photovoltaic power generation does not generate electricity at night and when there is sufficient sunlight. Installing a battery energy storage system (BESS) is considered to be one of the best solutions to this problem. When a "surplus power problem" occurs, the energy storage system can store excess photovoltaic output energy and then release the energy when photovoltaic power generation stops working at night.

[0003] Therefore, the energy storage system plays a very important role in the power system. How to reasonably operate and dispatch the energy storage system has become a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0004] In view of this, the present invention provides a method, device, medium and terminal for determining operation scheduling data of an energy storage system. In this method, the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant can be quickly and reasonably determined through a taboo search algorithm, thereby achieving effective optimization of the operation scheduling of the energy storage system, reducing the fuel cost of the thermal power plant, improving the overall economy of the power system, and having high practical value for the operation planning of the energy storage system.

[0005] According to one aspect of the present invention, a method for determining operation scheduling data of an energy storage system is provided, comprising:

[0006] Obtain load demand, output power of nuclear power plants, output power of hydropower plants and theoretical output power of photovoltaics;

[0007] Determine the combined output data of the energy storage system and the thermal power plant according to the load demand, the output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic system;

[0008] Randomly generate operation dispatching data of the energy storage system according to the common output data of the energy storage system and the thermal power plant;

[0009] Based on the randomly generated operation dispatching data of the energy storage system, a taboo search algorithm is adopted to determine the operation dispatching data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant.

[0010] In some embodiments, the step of determining the combined output data of the energy storage system and the thermal power plant according to the load demand, the output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic power plant includes:

[0011] The output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic power plant are subtracted from the load demand to obtain the combined output data of the energy storage system and the thermal power plant.

[0012] In some embodiments, the step of using a taboo search algorithm based on the randomly generated operation scheduling data of the energy storage system to determine the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant includes:

[0013] Determining the randomly generated operation scheduling data of the energy storage system as an initial solution;

[0014] Setting the initial solution as the optimal solution and the intermediate solution, setting the number of runs to the initial number, and storing the intermediate solution in a taboo list;

[0015] Randomly generate a neighborhood solution around the middle solution in the taboo list, and compare the neighborhood solution with the middle solution in the taboo list;

[0016] If the neighborhood solution is different from the intermediate solution in the taboo list, the fuel cost of the thermal power plant corresponding to the neighborhood solution is calculated, and the number of times that the neighborhood solution different from the intermediate solution in the taboo list is generated is counted; if the number of times that the neighborhood solution different from the intermediate solution in the taboo list is generated reaches the target number of times, the lowest thermal power plant fuel cost is screened out from the thermal power plant fuel costs corresponding to the neighborhood solutions different from the intermediate solutions in the taboo list; the neighborhood solution corresponding to the lowest thermal power plant fuel cost is determined as a solution to be selected, and the intermediate solution is updated using the solution to be selected;

[0017] If the fuel cost of the thermal power plant corresponding to the solution to be selected is not lower than the fuel cost of the thermal power plant corresponding to the optimal solution, then adding one to the number of operations to update the number of operations;

[0018] Determine whether the current number of runs is the preset update number; if it is not the preset update number, use the current intermediate solution to update the intermediate solution in the taboo list, re-execute the steps of randomly generating a neighborhood solution around the intermediate solution in the taboo list, and compare the neighborhood solution with the intermediate solution in the taboo list until the current number of runs is the preset update number.

[0019] In some embodiments, the method further comprises:

[0020] If the neighborhood solution is the same as the intermediate solution in the taboo list, the steps of randomly generating a neighborhood solution around the intermediate solution in the taboo list and comparing the neighborhood solution with the intermediate solution in the taboo list are re-executed.

[0021] In some embodiments, the method further comprises:

[0022] If the number of neighborhood solutions generated that are different from the intermediate solution in the taboo list does not reach the target generation number, the step of randomly generating a neighborhood solution around the intermediate solution in the taboo list and comparing the neighborhood solution with the intermediate solution in the taboo list is re-executed.

[0023] In some embodiments, before determining whether the current number of operations is the preset number of updates, the method further includes:

[0024] If the fuel cost of the thermal power plant corresponding to the solution to be selected is lower than the fuel cost of the thermal power plant corresponding to the optimal solution, the optimal solution is updated using the solution to be selected, and the number of operations is set to the initial number.

[0025] In some embodiments, the fuel cost of the thermal power plant corresponding to the neighborhood solution is calculated according to the following formula:

[0026]

[0027] Where Φ is the total fuel cost of a thermal power plant in one day; N is the number of thermal power plants; P t i is the output power of the i-th thermal power plant at time t; u t i is the state variable of the ith generator at time t, where u t i Including 1 and 0, when u t i When u is 1, the state of the i-th thermal power plant is in operation; t i When S is 0, the state of the i-th thermal power plant is stopped; i is the startup cost of the i-th thermal power plant; Among them, a i , b i , c i is the fuel cost coefficient of the i-th thermal power plant.

[0028] According to another aspect of the present invention, a device for determining operation scheduling data of an energy storage system is provided, comprising:

[0029] An acquisition unit, used to acquire load demand, output power of a nuclear power plant, output power of a hydropower plant, and theoretical output power of photovoltaics;

[0030] A first determination unit is used to determine the common output data of the energy storage system and the thermal power plant according to the load demand, the output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic power plant;

[0031] A random generation unit, used to randomly generate operation dispatching data of the energy storage system according to the common output data of the energy storage system and the thermal power plant;

[0032] The second determination unit is used to determine the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant by using a taboo search algorithm based on the randomly generated operation scheduling data of the energy storage system.

[0033] According to another aspect of the present invention, a storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to execute the method for determining operation scheduling data of an energy storage system as described above.

[0034] According to another aspect of the present invention, there is provided a terminal, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0035] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the method for determining the operation scheduling data of the energy storage system as described.

[0036] The present invention provides a method, device, medium and terminal for determining operation scheduling data of an energy storage system. In this method, the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of a thermal power plant can be quickly and reasonably determined through a taboo search algorithm, thereby achieving effective optimization of the operation scheduling of the energy storage system, reducing the fuel cost of the thermal power plant, improving the overall economy of the power system, and having high practical value for the operation planning of the energy storage system.

[0037] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0039] Figure 1 A flowchart of a method for determining operation scheduling data of an energy storage system provided in accordance with some embodiments is exemplarily shown;

[0040] Figure 2 A flowchart of another method for determining operation scheduling data of an energy storage system provided in accordance with some embodiments is exemplarily shown;

[0041] Figure 3 A schematic diagram of the structure of a device for determining operation scheduling data of an energy storage system provided in accordance with some embodiments is exemplarily shown;

[0042] Figure 4 A schematic diagram of the structure of a terminal provided according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0043] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that the re-execution of a certain step in the embodiments of the present application can be understood as the re-execution of a certain step and other steps located after a certain step.

[0044] In order to solve the above technical problems, the embodiments of the present application provide a method, device, medium and terminal for determining operation scheduling data of an energy storage system. In this method, the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant can be quickly and reasonably determined through a taboo search algorithm, thereby achieving effective optimization of the operation scheduling of the energy storage system, reducing the fuel cost of the thermal power plant, improving the overall economy of the power system, and having high practical value for the operation planning of the energy storage system.

[0045] Figure 1The flowchart of a method for determining operation scheduling data of an energy storage system provided in accordance with some embodiments is exemplarily shown. The method includes S100-S400.

[0046] S100, obtain load demand, output power of the nuclear power plant, output power of the hydropower plant and theoretical output power of photovoltaics.

[0047] In an embodiment of the present application, the power system is composed of a nuclear power plant, a hydropower plant, a photovoltaic power plant, and an energy storage system. The load demand is the total amount of electricity that the power system needs to provide within a specific time period. In the embodiment of the present application, the hydropower plant and the nuclear power plant always operate at the rated output power. In addition, since it is impossible to accurately predict the photovoltaic power generation output in power generation scheduling, the photovoltaic power generation output power is pre-set to a theoretical value in the embodiment of the present application. Exemplarily, the theoretical value is 0 from 0 to 6 o'clock in a day, starts to increase from 6 o'clock, and reaches a maximum value at 12 o'clock, that is, at this time the output power reaches the maximum. From 12 o'clock, the output power value begins to decline until it drops to 0 at 18:00, at which time photovoltaic power generation stops working.

[0048] S200: Determine the combined output data of the energy storage system and the thermal power plant according to the load demand, the output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic system.

[0049] In some embodiments, the step of determining the combined output data of the energy storage system and the thermal power plant according to the load demand, the output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic power plant includes:

[0050] The output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic power plant are subtracted from the load demand to obtain the combined output data of the energy storage system and the thermal power plant.

[0051] Specifically, the following formula can be used to calculate the combined output data of the energy storage system and the thermal power plant:

[0052]

[0053] Where N is the number of thermal power plants; P t i is the output power of the ith thermal power plant at time t; L t is the load demand at time t, P N is the output power of the nuclear power plant; P H is the output power of the hydropower station; P B is the output power of the energy storage system; PV t is the theoretical output power of PV (photovoltaic) at time t. The thermal power output should also meet the following requirements: P min i ≤P ti ≤P max i .

[0054] Through the above formula, we can calculate With PV t The sum of is the combined output data of the energy storage system and the thermal power plant.

[0055] S300: randomly generate operation dispatching data of the energy storage system according to the common output data of the energy storage system and the thermal power plant.

[0056] In an embodiment of the present application, the operation scheduling data may be a daily operation plan. The daily operation plan may be understood as when the energy storage system is charged and discharged during the day. In some embodiments, the generated operation scheduling data needs to meet the capacity limit requirements of the energy storage system. The capacity limit requirements may be understood as the energy storage capacity in the energy storage system not exceeding 90% of the total energy storage capacity and not less than 10% of the total energy storage capacity.

[0057] S400. Based on the randomly generated operation dispatching data of the energy storage system, a taboo search algorithm is used to determine the operation dispatching data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant.

[0058] Since hydropower stations and nuclear power stations always operate at rated output power, and the output power of photovoltaic power generation is a pre-set theoretical value, the costs corresponding to hydropower stations, nuclear power stations and photovoltaics are all fixed values ​​and do not need to be adjusted. The embodiment of the present application mainly adjusts the fuel cost of thermal power plants, and ultimately determines the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant.

[0059] In the embodiment of the present application, the taboo search algorithm can be used to quickly and reasonably determine the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant, thereby achieving effective optimization of the operation scheduling of the energy storage system, reducing the fuel cost of the thermal power plant, and improving the overall economy of the power system, which has a high practical value for the operation planning of the energy storage system.

[0060] The taboo search algorithm is an optimization algorithm that simulates human intelligence and is an extension of the local neighborhood search. The most important idea of ​​the taboo search is to mark some objects corresponding to the local optimal solution that has been searched, and try to avoid these objects in further iterative searches (rather than absolutely prohibiting loops), thereby ensuring the exploration of different effective search paths.

[0061] In some embodiments, the step of using a taboo search algorithm based on the randomly generated operation scheduling data of the energy storage system to determine the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant includes:

[0062] The randomly generated operation scheduling data of the energy storage system is determined as the initial solution. In this embodiment, the operation scheduling data of an energy storage system can be randomly generated while meeting the capacity requirements of the energy storage system, and the operation scheduling data is recorded as the initial solution. This embodiment optimizes the operation scheduling data, so each type of operation scheduling data can be regarded as a solution of the algorithm.

[0063] The initial solution is set as the optimal solution and the intermediate solution, the number of runs is set to the initial number, and the intermediate solution is stored in a taboo list. Exemplarily, the initial number is 0.

[0064] In the embodiment of the present application, the subsequent steps need to be run multiple times while meeting the requirements, so the number of runs is set here so that statistics can be performed later to complete the method in the embodiment of the present application.

[0065] A neighborhood solution is randomly generated around the middle solution in the taboo list, and the neighborhood solution is compared with the middle solution in the taboo list.

[0066] If the neighborhood solution is the same as the intermediate solution in the taboo list, the steps of randomly generating a neighborhood solution around the intermediate solution in the taboo list and comparing the neighborhood solution with the intermediate solution in the taboo list are re-executed.

[0067] In the embodiment of the present application, when the generated neighborhood solution is the same as the intermediate solution in the taboo list, a new neighborhood solution is generated and the step of comparing with the intermediate solution in the taboo list is performed.

[0068] If the neighborhood solution is different from the intermediate solution in the taboo list, the fuel cost of the thermal power plant corresponding to the neighborhood solution is calculated, and the number of times a neighborhood solution different from the intermediate solution in the taboo list is generated is counted.

[0069] In the embodiment of the present application, the ultimate goal is to find the lowest fuel cost of the thermal power plant. Therefore, when the neighborhood solution and the intermediate solution in the taboo list are different, the fuel cost of the thermal power plant corresponding to the neighborhood solution is calculated so that the lowest fuel cost of the thermal power plant can be found later.

[0070] In some embodiments, the fuel cost of the thermal power plant corresponding to the neighborhood solution is calculated according to the following formula:

[0071]

[0072] Where Φ is the total fuel cost of a thermal power plant in one day; N is the number of thermal power plants; P t i is the output power of the i-th thermal power plant at time t; u t iis the state variable of the ith generator at time t, where u t i Including 1 and 0, when u t i When u is 1, the state of the i-th thermal power plant is in operation; when u t i When S is 0, the state of the i-th thermal power plant is stopped; i is the startup cost of the i-th thermal power plant; Among them, a i , b i , c i is the fuel cost coefficient of the i-th thermal power plant.

[0073] The above formula can calculate the total fuel cost of a thermal power plant in one day.

[0074] Determine whether the number of times a neighborhood solution different from an intermediate solution in the taboo list is generated reaches a target number of times. In this embodiment, after determining that the neighborhood solution is different from an intermediate solution in the taboo list, determine whether the number of times a neighborhood solution different from an intermediate solution in the taboo list is generated reaches a target number of times.

[0075] If the number of times a neighborhood solution different from the intermediate solution in the taboo list is generated does not reach the target generation number, the step of randomly generating a neighborhood solution around the intermediate solution in the taboo list and comparing the neighborhood solution with the intermediate solution in the taboo list is re-executed.

[0076] If the number of times a neighborhood solution different from the intermediate solution in the taboo list is generated reaches the target number of times, the lowest fuel cost of the thermal power plant is screened out from the fuel costs of the thermal power plant corresponding to the neighborhood solutions different from the intermediate solutions in the taboo list; the neighborhood solution corresponding to the lowest thermal power plant fuel cost is determined as the solution to be selected, and the intermediate solution is updated using the solution to be selected.

[0077] It can be understood that when the number of times a neighborhood solution different from the intermediate solution in the taboo list is generated reaches the target generation number, the number of neighborhood solutions different from the intermediate solution in the taboo list is the number corresponding to the target generation number, and at this time, the lowest fuel cost of the thermal power plant is screened out from the fuel costs of the thermal power plant corresponding to the number of neighborhood solutions corresponding to the target production number. Exemplarily, the target generation number is 3, and then the number of neighborhood solutions different from the intermediate solution in the taboo list is 3, and the lowest fuel cost of the thermal power plant is screened out from the fuel costs of the thermal power plant corresponding to the 3 neighborhood solutions.

[0078] It is determined whether the fuel cost of the thermal power plant corresponding to the solution to be selected is lower than the fuel cost of the thermal power plant corresponding to the optimal solution.

[0079] If the fuel cost of the thermal power plant corresponding to the solution to be selected is not lower than the fuel cost of the thermal power plant corresponding to the optimal solution, then one is added to the number of operations to update the number of operations.

[0080] In some embodiments, if the fuel cost of the thermal power plant corresponding to the solution to be selected is lower than the fuel cost of the thermal power plant corresponding to the optimal solution, the optimal solution is updated using the solution to be selected, and the number of operations is set to the initial number.

[0081] Determine whether the current number of runs is the preset update number; if it is not the preset update number, use the current intermediate solution to update the intermediate solution in the taboo list, re-execute the steps of randomly generating a neighborhood solution around the intermediate solution in the taboo list, and compare the neighborhood solution with the intermediate solution in the taboo list until the current number of runs is the preset update number.

[0082] In the embodiment of the present application, if the current number of operations is the preset number of updates, it means that the algorithm has not been modified after the preset number of updates, and the operation plan can be regarded as the optimal solution as the output of the algorithm result, that is, the operation scheduling data of the energy storage system corresponding to the lowest cost of the thermal power plant. Operating the energy storage system according to the operation scheduling data can minimize the cost of the thermal power plant.

[0083] In an embodiment of the present application, a daily operation plan (i.e., an initial solution) of the energy storage system is first randomly generated, and then different neighborhood solutions are continuously generated, and the size of the fuel cost required for the domain solution is calculated. After multiple searches, the solution that minimizes the fuel cost is found as the local optimal solution. The cost of the local optimal solution is compared with the cost of the initial solution to select a better solution. After repeating the above process multiple times, the output optimal solution is the optimal operation of the energy storage.

[0084] The method in the embodiment of the present application can be applied to various types of power systems with energy storage, and has extremely high flexibility and adaptability; it is effectively applied to the scheduling of energy storage systems in power systems with a large photovoltaic power generation penetration rate, so as to minimize the fuel cost of thermal power plants; different from traditional optimization algorithms, the taboo search algorithm is used to improve the accuracy and speed of calculations, and effective optimization of energy storage operation scheduling methods can be achieved.

[0085] Another way to describe it is as follows: Figure 2 As shown, the step of determining the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant based on the randomly generated operation scheduling data of the energy storage system by using the taboo search algorithm includes: step S1, randomly generating a daily operation plan of the energy storage system, i.e., operation scheduling data. This plan is an initial solution of the algorithm, denoted as S0, and S0 is assigned to the intermediate solution S and the optimal solution SB. SB represents the optimal daily operation of the energy storage system. Let the number of operations i = 0 to complete the initialization of the algorithm.

[0086] Step S2, the algorithm starts running, which can be divided into the following stages: 1. Record S in the taboo list; 2. Randomly generate a neighborhood solution around S0 in the taboo list, and then compare it with S0 in the taboo list. If the solution is the same as the solution in the taboo list, regenerate it until the neighborhood solution is different from the solution in the taboo list. Then calculate the fuel cost of the thermal power plant required for the neighborhood solution (i.e., the daily operation plan of the BESS); 3. Repeat stage 2 j times to obtain j neighborhood solutions that are different from the solutions in the taboo list and the fuel costs corresponding to these j solutions. 4. Find the solution with the smallest fuel cost among these j solutions, record it as SB', and assign SB' to S. Compare the cost of SB' with the cost of SB. If the cost of SB' is lower than the cost of SB, assign SB' to SB and set i = 0; otherwise, SB continues to maintain its original value and set i = i + 1. 5. Determine whether i is equal to N1. If it is not equal to N1, return to stage 1, write the new S value into the taboo list, and then run to stage 5 to determine the size of i. If it is still not equal to N1, repeat the cycle of stages 1-5 until i = N1, and the algorithm jumps out of the loop. At this time, SB, which represents the optimal solution, has not been modified after N1 cycles of the algorithm, and the operation plan can be regarded as the optimal solution as the output of the algorithm result.

[0087] Step S3, after the algorithm jumps out of the loop, it outputs the result SB, which is the optimal daily operation plan of the energy storage. If the energy storage operates according to this daily operation plan, the thermal power plant has the lowest fuel cost.

[0088] In S2, the taboo search algorithm starts running. The objective function of the algorithm is set to the fuel cost of the thermal power plant, and the objective function value must be minimized. The algorithm will randomly search for multiple different domain solutions around the initial solution, and calculate the fuel cost of the thermal power plant corresponding to each neighborhood solution, and find the one with the lowest cost, recorded as SB', and compare SB' with the cost of SB. If the cost of SB' is less than the cost of SB, SB' is assigned to SB, otherwise SB remains unchanged. Repeat the above steps. If SB has not been modified within the set number of times, the search ends.

[0089] In S3, SB is output as the optimal solution, which is the optimal operation of BESS. If BESS operates according to this plan, the fuel cost of the thermal power plant can be minimized.

[0090] The embodiment of the present application provides a method, device, medium and terminal for determining operation scheduling data of an energy storage system. In this method, the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant can be quickly and reasonably determined through a taboo search algorithm, thereby achieving effective optimization of the operation scheduling of the energy storage system, reducing the fuel cost of the thermal power plant, improving the overall economy of the power system, and having high practical value for the operation planning of the energy storage system.

[0091] Furthermore, as a response to the above Figure 1 The implementation of the method shown in the figure, the embodiment of the present invention provides a device for determining the operation scheduling data of an energy storage system, such as Figure 3 As shown, including:

[0092] An acquisition unit 301 is used to acquire load demand, output power of a nuclear power plant, output power of a hydropower plant, and theoretical output power of photovoltaic power;

[0093] A first determining unit 302 is used to determine the common output data of the energy storage system and the thermal power plant according to the load demand, the output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic power plant;

[0094] A random generation unit 303, used to randomly generate operation scheduling data of the energy storage system according to the common output data of the energy storage system and the thermal power plant;

[0095] The second determining unit 304 is used to determine the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant by using a taboo search algorithm based on the randomly generated operation scheduling data of the energy storage system.

[0096] According to an embodiment of the present invention, a storage medium is provided, wherein the storage medium stores at least one executable instruction, and the computer executable instruction can execute the method for determining the operation scheduling data of the energy storage system in any of the above method embodiments.

[0097] Figure 4 A schematic diagram of the structure of a terminal provided according to an embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the terminal.

[0098] like Figure 4 As shown, the terminal may include: a processor (processor) 402 , a communication interface (Communications Interface) 404 , a memory (memory) 406 , and a communication bus 408 .

[0099] The processor 402 , the communication interface 404 , and the memory 406 communicate with each other via a communication bus 408 .

[0100] The communication interface 404 is used to communicate with other devices such as clients or other servers.

[0101] The processor 402 is used to execute the program 410, and specifically can execute the relevant steps in the above-mentioned embodiment of the method for determining the operation scheduling data of the energy storage system.

[0102] Specifically, the program 410 may include program codes, which include computer operation instructions.

[0103] The processor 402 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention. The one or more processors included in the terminal may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0104] The memory 406 is used to store the program 410. The memory 406 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0105] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for determining operation scheduling data of an energy storage system, characterized in that: include: Obtain load demand, output power of nuclear power plants, output power of hydropower plants and theoretical output power of photovoltaics; Determine the combined output data of the energy storage system and the thermal power plant according to the load demand, the output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic system; Randomly generate operation dispatching data of the energy storage system according to the common output data of the energy storage system and the thermal power plant; Based on the randomly generated operation dispatching data of the energy storage system, a taboo search algorithm is adopted to determine the operation dispatching data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant.

2. The method according to claim 1, characterized in that The step of determining the combined output data of the energy storage system and the thermal power plant according to the load demand, the output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic power plant comprises: The output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic power plant are subtracted from the load demand to obtain the combined output data of the energy storage system and the thermal power plant.

3. The method according to claim 1, characterized in that The step of using a taboo search algorithm based on the randomly generated operation scheduling data of the energy storage system to determine the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant comprises: Determining the randomly generated operation scheduling data of the energy storage system as an initial solution; Setting the initial solution as the optimal solution and the intermediate solution, setting the number of runs to the initial number, and storing the intermediate solution in a taboo list; Randomly generate a neighborhood solution around the middle solution in the taboo list, and compare the neighborhood solution with the middle solution in the taboo list; If the neighborhood solution is different from the intermediate solution in the taboo list, the fuel cost of the thermal power plant corresponding to the neighborhood solution is calculated, and the number of times that the neighborhood solution different from the intermediate solution in the taboo list is generated is counted; if the number of times that the neighborhood solution different from the intermediate solution in the taboo list is generated reaches the target number of times, the lowest thermal power plant fuel cost is screened out from the thermal power plant fuel costs corresponding to the neighborhood solutions different from the intermediate solutions in the taboo list; the neighborhood solution corresponding to the lowest thermal power plant fuel cost is determined as a solution to be selected, and the intermediate solution is updated using the solution to be selected; If the fuel cost of the thermal power plant corresponding to the solution to be selected is not lower than the fuel cost of the thermal power plant corresponding to the optimal solution, then adding one to the number of operations to update the number of operations; Determine whether the current number of runs is the preset update number; if it is not the preset update number, use the current intermediate solution to update the intermediate solution in the taboo list, re-execute the steps of randomly generating a neighborhood solution around the intermediate solution in the taboo list, and compare the neighborhood solution with the intermediate solution in the taboo list until the current number of runs is the preset update number.

4. The method according to claim 3, characterized in that Also includes: If the neighborhood solution is the same as the intermediate solution in the taboo list, the steps of randomly generating a neighborhood solution around the intermediate solution in the taboo list and comparing the neighborhood solution with the intermediate solution in the taboo list are re-executed.

5. The method according to claim 3, characterized in that: Also includes: If the number of neighborhood solutions generated that are different from the intermediate solution in the taboo list does not reach the target generation number, the step of randomly generating a neighborhood solution around the intermediate solution in the taboo list and comparing the neighborhood solution with the intermediate solution in the taboo list is re-executed.

6. The method according to claim 3, characterized in that Before determining whether the current number of operations is the preset number of updates, the method further includes: If the fuel cost of the thermal power plant corresponding to the solution to be selected is lower than the fuel cost of the thermal power plant corresponding to the optimal solution, the optimal solution is updated using the solution to be selected, and the number of operations is set to the initial number.

7. The method according to claim 3, characterized in that The fuel cost of the thermal power plant corresponding to the neighborhood solution is calculated according to the following formula: Where Φ is the total fuel cost of a thermal power plant in one day; N is the number of thermal power plants; P t i is the output power of the i-th thermal power plant at time t; u t i is the state variable of the ith generator at time t, where u t i Including 1 and 0, when u t i When is 1, the state of the i-th thermal power plant is in operation; when When S is 0, the state of the i-th thermal power plant is stopped; i is the startup cost of the i-th thermal power plant; Among them, a i , b i , c i is the fuel cost coefficient of the i-th thermal power plant.

8. A device for determining operation scheduling data of an energy storage system, characterized in that: include: An acquisition unit, used to acquire load demand, output power of a nuclear power plant, output power of a hydropower plant, and theoretical output power of photovoltaics; A first determination unit is used to determine the common output data of the energy storage system and the thermal power plant according to the load demand, the output power of the nuclear power plant, the output power of the hydropower plant and the theoretical output power of the photovoltaic power plant; A random generation unit, used to randomly generate operation dispatching data of the energy storage system according to the common output data of the energy storage system and the thermal power plant; The second determination unit is used to determine the operation scheduling data of the energy storage system corresponding to the lowest fuel cost of the thermal power plant by using a taboo search algorithm based on the randomly generated operation scheduling data of the energy storage system.

9. A storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to execute the method for determining operation scheduling data of an energy storage system according to any one of claims 1 to 7.

10. A terminal, comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the method for determining the operation scheduling data of the energy storage system according to any one of claims 1 to 7.

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