Method, system and equipment for generating day-ahead energy consumption scheduling strategy of industrial park

Abstract

The invention discloses a method, system and equipment for generating a day-ahead energy consumption scheduling strategy of an industrial park, and belongs to the field of scheduling optimization of an energy storage system, and the method comprises the following steps: obtaining related data and other energy consumption data of the energy storage system; verifying the related data and other energy consumption data of the energy storage system; after the verification is passed, inputting the related data of the energy storage system and other energy consumption data into a nonlinear programming scheduling model, and outputting to obtain an optimal scheduling strategy; the nonlinear programming scheduling model adopts a scheduling model taking economic optimization as an energy consumption target or a scheduling model taking the highest photovoltaic consumption degree as an energy consumption target. According to the invention, the optimal scheduling strategy can be automatically generated according to the energy consumption target set by the user and other input data.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage system scheduling optimization, and specifically relates to a method, system and equipment for generating a day-ahead energy scheduling strategy for an industrial park. Background Art

[0002] As the demand for green and sustainable energy in industrial parks continues to grow, the integrated application of photovoltaic power generation and energy storage systems has become an important means to promote energy transformation and energy conservation and emission reduction. However, in actual applications, these systems face the difficulties of low technical integration and lack of intelligent management methods.

[0003] Existing photovoltaic and energy storage systems often lack highly integrated solutions. Photovoltaic and energy storage equipment in industrial parks are often provided by different manufacturers, lacking unified technical standards and integration solutions, which makes it difficult to develop a unified system to coordinate and control various components. In addition, most existing photovoltaic and energy storage systems lack an intelligent energy management platform, and cannot achieve real-time monitoring and optimized scheduling of energy production, storage and use. This lack of management makes it difficult for industrial parks to accurately assess the supply and demand of various energy resources when formulating energy scheduling strategies, making it difficult to achieve the goals of economic optimization and the highest photovoltaic consumption.

[0004] In response to these problems, current technical solutions mainly focus on developing campus energy management systems, which provide basic support for energy management by comprehensively monitoring and collecting campus data. However, these solutions are still insufficient in exploring intelligent management, especially for how to meet users' differentiated energy needs and automatically generate optimal energy strategies. Although some solutions try to use heuristic methods to generate strategies, they face problems such as unstable solution efficiency and inability to obtain a global optimal solution. Summary of the invention

[0005] The object of the present invention is to provide a method, system and device for generating a day-ahead energy consumption scheduling strategy for an industrial park, which can automatically generate an optimal scheduling strategy based on energy consumption targets and other input data set by a user.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, a method for generating a day-ahead energy scheduling strategy for an industrial park comprises the following steps: Obtain energy storage system-related data and other energy consumption data; Verifying the energy storage system related data and other energy consumption data; After verification, the energy storage system related data and other energy consumption data are input into the nonlinear programming scheduling model, and the optimal scheduling strategy is output; The nonlinear programming scheduling model adopts a scheduling model with economic optimization as the energy consumption target, or a scheduling model with the highest photovoltaic consumption level as the energy consumption target.

[0007] In some embodiments, the energy storage system related data includes: initial SOC, rated charge and discharge power, SOC limit, charge and discharge efficiency, and rated capacity; The other energy consumption data include: the campus electricity load at 96 o'clock on the operation day, the photovoltaic output forecast data at 96 o'clock on the operation day, the settlement electricity price at 96 o'clock on the operation day, the average photovoltaic electricity price, the maximum power supply power, the energy consumption target and whether the demand electricity charge is maintained.

[0008] In some embodiments, the scheduling model with economic optimization as the energy utilization target includes:

[0009] in, is the economic optimal objective function, For the The settlement electricity price for each period, For the The power of the power grid during each period, For the The photovoltaic power consumption in each period is is the average price of photovoltaic electricity.

[0010] In some embodiments, the scheduling model with the highest photovoltaic consumption level as the energy consumption target includes:

[0011]

[0012] in, It is the preset price buffer value.

[0013] In some implementations, the constraints of the nonlinear programming scheduling model include the following formula:

[0014]

[0015]

[0016]

[0017]

[0018] in, For the The power load of the park in each period, For the The energy storage charging and discharging power in each period, For the The power of the power grid during each period, For the The photovoltaic power consumption in each period is is the maximum power down, is the lower limit of the rated charge and discharge power, is the upper limit of the rated charge and discharge power, is the initial SOC, is the charge and discharge efficiency, is the symbolic function, For a period of time, For the SOC of each period, is the rated capacity; The constraints also include: the energy storage operations in one day shall not exceed two charges and two discharges, and power cannot be sent back to the grid.

[0019] In some implementations, when the demand electricity charge is maintained, the constraint conditions of the scheduling model with economic optimum as the energy consumption target also include:

[0020] in, For the The demand for a period of time, This is the maximum demand in the park’s history.

[0021] In some implementations, the constraint condition is relaxed, and the relaxed constraint condition further includes the following formula:

[0022] in is the default easing value; The self-discharge rate of the constraint condition is optimized, and the constraint condition after the self-discharge rate optimization also includes the following formula:

[0023] in, is the self-discharge rate of the energy storage system; A distance constraint is added to the constraint condition, and the distance constraint includes the following formula:

[0024]

[0025] in, is the control coefficient, and They are different energy storage charging and discharging powers. is the index distance function.

[0026] In some embodiments, the following steps are also included: According to the energy storage system related data and other energy consumption data, linear programming is used to obtain an optimized scheduling strategy, and the optimized scheduling strategy is post-processed; The post-processing includes the following steps: when the charging and discharging operation directions of the energy storage system are the same within a preset time period, aggregation is performed, during which the energy storage system is set to charge during a low-price period and discharge during a high-price period; and independent small charging and discharging operations that are less than a preset threshold are ignored.

[0027] In a second aspect, a system for generating a day-ahead energy scheduling strategy for an industrial park includes: Energy storage system data acquisition module, used to obtain energy storage system related data and other energy consumption data; A data verification module, used to verify the energy storage system related data and other energy consumption data; The energy consumption scheduling strategy generation module is used to input the energy storage system related data and other energy consumption data into the nonlinear programming scheduling model after verification, and output the optimal scheduling strategy. The nonlinear programming scheduling model adopts a scheduling model with economic optimization as the energy consumption target, or a scheduling model with the highest photovoltaic consumption level as the energy consumption target.

[0028] In a third aspect, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor implements the steps of a method for generating a day-ahead energy scheduling strategy for an industrial park when executing the computer program.

[0029] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for generating a day-ahead energy consumption scheduling strategy for an industrial park. The nonlinear programming scheduling model can comprehensively consider the characteristics of the energy storage system and multi-dimensional data such as the power load of the park to formulate an optimal energy scheduling strategy. In addition, the present invention adopts an intelligent method to automatically generate the day-ahead energy consumption scheduling strategy of the park, and no manual intervention is required after the configuration is completed; the present invention can automatically adjust the scheduling strategy according to different energy consumption targets set by the user, that is, economic optimization or the highest photovoltaic consumption level, and output the optimal energy consumption scheduling strategy according to the setting of the energy consumption target and the relevant data of the energy storage system.

[0030] Furthermore, the present invention provides a scheduling model with economic optimization as the energy consumption target. By considering the settlement electricity prices and the average price of photovoltaic electricity in different time periods, a scheduling strategy with the lowest cost can be formulated, which helps the park to reduce energy costs and improve economic benefits.

[0031] Furthermore, the present invention provides a scheduling model with the highest photovoltaic absorption degree as the energy consumption target, which can maximize the use of photovoltaic output and improve the utilization rate of renewable energy by optimizing the charging and discharging strategy of the energy storage system.

[0032] Furthermore, the present invention not only considers the basic energy scheduling needs, but also improves the flexibility of the scheduling model by adding relaxed constraints, optimizing the self-discharge rate and distance constraints, so as to adapt to the actual conditions of different industrial parks; in addition, by adding distance constraints, reasonable charging and discharging strategies can be adopted to avoid ineffective shallow charging and discharging, thereby improving the service life and efficiency of the energy storage system.

[0033] Furthermore, the present invention performs post-processing on the scheduling results after linear programming, has a fast running speed, simplifies the scheduling strategy, avoids ineffective shallow charging and discharging, and improves the service life and efficiency of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 An overall flow chart of a method for generating a day-ahead energy scheduling strategy for an industrial park provided in Example 1; Figure 2 A workflow diagram of a system for generating a day-ahead energy scheduling strategy for an industrial park provided in Example 4; Figure 3 A structural diagram of a system for generating a day-ahead energy scheduling strategy for an industrial park provided in Example 4. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings, and the described content is intended to explain the present invention rather than to limit it.

[0036] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or are inherent to these processes, methods, systems, products or apparatus.

[0037] First, the terminology concepts that appear in the manual are explained in detail: Time period: refers to each consecutive 15-minute time period starting from 0:00. A day is divided into 96 time periods, referred to as "96 points"; Energy strategy: refers to the power size and direction of 96 points of each energy source in the “solar power distribution and storage” industrial park; Execution Date: refers to the date on which the Strategy Generation Algorithm is executed; Operation date: refers to the date corresponding to the strategy; Day-ahead strategy: refers to the strategy generated on the execution day and used on the operation day; Energy consumption target: refers to the target that users expect the system to achieve during operation, including: economic optimization and the highest degree of photovoltaic consumption; In the following embodiments, the energy use strategy output includes the following contents at 96 o'clock on the operation day: energy storage charging and discharging power data, photovoltaic power generation system absorption power data, and grid power.

[0038] Embodiment 1 In view of the shortcomings of the prior art, this embodiment proposes a method for generating a day-ahead energy scheduling strategy for an industrial park based on the nonlinear programming theory in operations research, such as Figure 1 As shown, the specific steps include: S1, obtaining input data, including energy storage system related data and other energy consumption data, wherein the energy storage system related data includes: initial SOC ( )、Rated charge and discharge power( )、SOC limit( and )、Charge and discharge efficiency( )、Rated capacity( ); The other energy consumption data include: the power load of the park at 96 o'clock on the operation day ( )、PV output forecast data at 96 o'clock on the operation day ( )、The settlement electricity price at 96:00 on the operation day ( )、PV power average price( )、Maximum power-down power( ), energy consumption targets and whether demand electricity charges are maintained.

[0039] The energy consumption target is the energy consumption target set by the user, including the economic optimum and the highest photovoltaic absorption level; whether to maintain the demand electricity charge refers to whether the user sets whether the power supply of the power grid on the operating day does not exceed the historical maximum demand.

[0040] S2, performs data verification to verify the data integrity, data length, data type and data value range obtained in S1. Only after the data verification passes, proceed to the next step.

[0041] S3, define the objective function. According to different energy consumption targets set by the user, define different objective functions and add constraints to build a scheduling model with economic optimization as the energy consumption target and a scheduling model with the highest photovoltaic consumption level as the energy consumption target. Then, input the energy storage system related data and other energy consumption data into the nonlinear programming scheduling model, and output the optimal scheduling strategy. S3.1, define the objective function through different energy use targets; When the energy consumption goal is economic optimization, the objective function is as follows:

[0042] in, For the The power of the power grid during each period, For the The photovoltaic power consumption in each period, both of which are variables to be optimized; For the The settlement electricity price for each period is a known quantity; is the economic optimal objective function, is the average price of photovoltaic electricity, and the optimization method is to minimize the objective function.

[0043] When the energy consumption target is the highest photovoltaic consumption level, the objective function is still the objective function when the energy consumption target is the economic optimum, and Setting it to a smaller value can ensure the priority consumption of photovoltaic power, as shown in the following formula:

[0044] in, It is the preset price buffer value, which is a smaller value. The purpose is to ensure that the average price of photovoltaic electricity is always less than the lowest settlement electricity price. The optimization method is still to minimize the objective function.

[0045] S3.2, define the constraints as follows: At any time, the power supply and demand of the park is balanced:

[0046] in, For the The power load of the park in each period, For the The energy storage charging and discharging power in each time period; it should be noted that, in this embodiment, the charging and discharging power has a direction: a positive value indicates charging, and a negative value indicates discharging.

[0047] At any time, the power outage of the power grid needs to meet the threshold requirements:

[0048] At any time, the charging and discharging power of the energy storage system needs to meet the threshold requirements:

[0049] At any time, the SOC of the energy storage system must meet the SOC limit requirements:

[0050] in, is the symbolic function, Indicates a time period.

[0051] In addition, the constraints include: the energy storage operation shall not exceed two charging and two discharging operations in one day; power cannot be sent back to the grid; If the energy consumption target is set to economic optimality and the demand is not increased, add the following constraints:

[0052] in, For the The demand for a period of time, This is the maximum demand in the park’s history.

[0053] S3.3, after the objective function and constraints are defined, the existing solvers can be used to solve the problem. Commonly used solvers include: CPLEX, GLPK, etc. In addition, this optimization problem can also be defined in some software, such as MATLAB and AMPL, and solved using the built-in toolbox of the software.

[0054] S3.4, if a feasible solution is obtained, it is only necessary to obtain the corresponding variable values ​​to output the optimal scheduling strategy.

[0055] This embodiment provides an intelligent way to automatically generate the day-ahead energy consumption scheduling strategy of the park. After the configuration is completed, no manual intervention is required. The strategy can be automatically adjusted according to different energy consumption targets set by the user, namely, economic optimization and the highest photovoltaic consumption level.

[0056] Embodiment 2 The difference from the first embodiment is that the SOC loosening constraint, the optimization of the SOC estimation formula, and the distance constraint are added to the constraint conditions. (1) Relaxation of SOC constraints In actual applications, the energy storage system does not strictly abide by the set SOC constraints when performing charging and discharging operations, and it is reasonable to exceed the limit value by a small amount. Therefore, when solving the problem, the SOC value range needs to be appropriately expanded to meet the current business situation.

[0057] Therefore, the SOC relaxation constraint is as follows:

[0058] in, It is the preset relaxation value, which is a preset smaller value, such as 0.01.

[0059] (2) Optimization of SOC estimation formula In this embodiment, SOC is a very important variable. The above SOC-related formula provides a method for estimating the energy storage SOC value after the current period of charge and discharge is completed. In actual situations, the energy storage system has a self-discharge rate, which should be considered when estimating SOC. For this purpose, the above SOC-related formula can be modified as follows:

[0060] in, The self-discharge rate of the energy storage system, usually a relatively small floating point number, such as 0.0001. In addition to its practical business significance, specifying this parameter also helps the solver converge quickly.

[0061] (3) Charge and discharge continuity and charge and discharge depth constraints In order to ensure that the obtained charging and discharging strategy is executable, it is also necessary to constrain the continuity and depth of charging and discharging. The purpose of adding this constraint is to avoid ineffective shallow charging and discharging that will cause excessive loss to the energy storage battery, so a distance constraint is added to the objective function.

[0062] Since the optimization direction of the two energy consumption targets is to minimize the objective function, penalty terms such as square error penalty term and sparsity penalty term can be added to the objective function.

[0063] The squared error penalty is as follows:

[0064] The sparsity penalty is as follows:

[0065] Among them, among them, is the control coefficient, and They are different energy storage charging and discharging powers. is the index distance function (e.g. ). The above penalty term shows that when the difference between two consecutive charge and discharge powers is large or the charge and discharge operation distance is far, the value of the objective function will increase, thus forcing the variable values ​​to be as "close" as possible.

[0066] The advantage of this embodiment is that the rationality of the solution is controlled from the modeling level, its theoretical significance is more complete, and through a reasonable charging and discharging strategy, ineffective shallow charging and discharging is avoided, thereby improving the service life and efficiency of the energy storage system; the disadvantage is that the original linear programming problem is transformed into a nonlinear programming problem, which may require a better performance solver to spend more time to solve.

[0067] Embodiment 3 In order to solve the shortcomings of the second embodiment, this embodiment proposes to post-process the results of the linear programming to ensure that the obtained charging and discharging strategy is executable and to constrain the continuity and depth of charging and discharging. Post-processing refers to processing the original solution according to business rules to make them more in line with the requirements of actual operation. Post-processing mainly includes two aspects: First, if the charging and discharging operations within a period of time are in the same direction, they should be aggregated as much as possible, and in the aggregation process, according to the differences in the charging and discharging operations, try to set charging in low-price periods and discharging in high-price periods.

[0068] Second, independent and small charging and discharging operations are ignored because they are reasonable solutions generated in the solution process but have no application significance. For example, for an energy storage system with SOC reaching the upper limit, even if it is not discharged, after a period of time, due to the existence of self-discharge, some rechargeable space will be vacated. If the price is right, the algorithm will allow it to charge, but in fact there is no need to perform this operation.

[0069] The post-processing method provided in this embodiment operates at a faster speed and avoids ineffective shallow charging and discharging through a reasonable charging and discharging strategy, thereby improving the service life and efficiency of the energy storage system.

[0070] Embodiment 4 like Figure 2 and Figure 3 As shown, this embodiment provides a system for generating a day-ahead energy scheduling strategy for an industrial park, including an energy storage system data acquisition module, a data verification module, and an energy scheduling strategy generation module; The energy storage system data acquisition module acquires energy storage system related data and other energy consumption data; The energy storage system related data include: initial SOC ( )、Rated charge and discharge power( )、SOC limit( and )、Charge and discharge efficiency( )、Rated capacity( ); The other energy consumption data include: the power load of the park at 96 o'clock on the operation day ( )、PV output forecast data at 96 o'clock on the operation day ( )、The settlement electricity price at 96:00 on the operation day ( )、PV power average price( )、Maximum power-down power( ), energy consumption targets and whether demand electricity charges are maintained.

[0071] The data verification module verifies the energy storage system related data and other energy consumption data; After verification, the energy scheduling strategy generation module inputs the energy storage system related data and other energy consumption data into the nonlinear programming scheduling model, and outputs the optimal scheduling strategy. The nonlinear programming scheduling model adopts a scheduling model with economic optimization as the energy consumption target, or a scheduling model with the highest photovoltaic consumption level as the energy consumption target.

[0072] The dispatch models with economic optimum as the energy consumption target include:

[0073] in, is the economic optimal objective function, For the The settlement electricity price for each period, For the The power of the power grid during each period, For the The photovoltaic power consumption in each period is is the average price of photovoltaic electricity.

[0074] The dispatch models with the highest photovoltaic consumption level as the energy consumption target include:

[0075]

[0076] in, It is the preset price buffer value.

[0077] The constraints of the nonlinear programming scheduling model include the following formula:

[0078]

[0079]

[0080]

[0081]

[0082] in, For the The power load of the park in each period, For the The energy storage charging and discharging power in each period, For the The power of the power grid during each period, For the The photovoltaic power consumption in each period is is the maximum power down, is the lower limit of the rated charge and discharge power, is the upper limit of the rated charge and discharge power, is the initial SOC, is the charge and discharge efficiency, is the symbolic function, For a period of time, For the SOC of each period, is the rated capacity; The constraints also include: the energy storage operations in one day shall not exceed two charges and two discharges, and power cannot be sent back to the grid.

[0083] When the demand electricity charge is maintained, the constraints of the dispatch model with economic optimum as the energy consumption target also include:

[0084] in, For the The demand for a period of time, This is the maximum demand in the park’s history.

[0085] The constraint conditions are relaxed, and the relaxed constraint conditions also include the following formula:

[0086] in is the default easing value; The self-discharge rate of the constraint condition is optimized, and the constraint condition after the self-discharge rate optimization also includes the following formula:

[0087] in, is the self-discharge rate of the energy storage system; A distance constraint is added to the constraint condition, and the distance constraint includes the following formula:

[0088]

[0089] in, is the control coefficient, and They are different energy storage charging and discharging powers. is the index distance function.

[0090] In addition to the above contents, the energy consumption scheduling strategy generation module can also use linear programming to obtain an optimized scheduling strategy based on the relevant data of the energy storage system and other energy consumption data, and post-process the optimized scheduling strategy. The post-processing includes the following steps: when the charging and discharging operation directions are the same within a preset time period of the energy storage system, aggregation is performed. During the aggregation process, the energy storage system is set to charge during low-price periods and discharge during high-price periods; independent small charging and discharging operations that are less than a preset threshold are ignored.

[0091] The division of modules in the embodiments of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into one processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0092] In this embodiment, a computer device is also provided, which includes a processor and a memory, the memory is used to store a computer program (in this embodiment, the computer program includes a computing component and an iterative component, which can perform model calculation and model update), the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a method for generating a day-ahead energy scheduling strategy for an industrial park.

[0093] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for generating a day-ahead energy scheduling strategy for an industrial park, characterized in that: The following steps are involved: Obtain energy storage system-related data and other energy consumption data; Verifying the energy storage system related data and other energy consumption data; After verification, the energy storage system related data and other energy consumption data are input into the nonlinear programming scheduling model, and the optimal scheduling strategy is output; The nonlinear programming scheduling model adopts a scheduling model with economic optimization as the energy consumption target, or a scheduling model with the highest photovoltaic consumption level as the energy consumption target.

2. The method for generating a day-ahead energy scheduling strategy for an industrial park according to claim 1, characterized in that: The energy storage system related data include: initial SOC, rated charge and discharge power, SOC limit, charge and discharge efficiency, and rated capacity; The other energy consumption data include: the campus electricity load at 96 o'clock on the operation day, the photovoltaic output forecast data at 96 o'clock on the operation day, the settlement electricity price at 96 o'clock on the operation day, the average photovoltaic electricity price, the maximum power supply power, the energy consumption target and whether the demand electricity charge is maintained.

3. The method for generating a day-ahead energy scheduling strategy for an industrial park according to claim 1, characterized in that: The dispatch model with economic optimum as the energy utilization target includes: in, is the economic optimal objective function, For the The settlement electricity price for each period, For the The power of the power grid during each period, For the The photovoltaic power consumption in each period is is the average price of photovoltaic electricity.

4. The method for generating a day-ahead energy scheduling strategy for an industrial park according to claim 3, characterized in that: The dispatch model with the highest photovoltaic consumption level as the energy consumption target includes: in, It is the preset price buffer value.

5. The method for generating a day-ahead energy scheduling strategy for an industrial park according to claim 1, characterized in that: The constraints of the nonlinear programming scheduling model include the following formula: in, For the The power load of the park in each period, For the The energy storage charging and discharging power in each period, For the The power of the power grid during each period, For the The photovoltaic power consumption in each period is is the maximum power down, is the lower limit of the rated charge and discharge power, is the upper limit of the rated charge and discharge power, is the initial SOC, is the charge and discharge efficiency, is the symbolic function, For a period of time, For the SOC of each period, is the rated capacity; The constraints also include: the energy storage operations in one day shall not exceed two charges and two discharges, and power cannot be sent back to the grid.

6. The method for generating a day-ahead energy scheduling strategy for an industrial park according to claim 5, characterized in that: When the demand electricity charge is maintained, the constraints of the dispatch model with economic optimum as the energy consumption target also include: in, For the The demand for a period of time, This is the maximum demand in the park’s history.

7. The method for generating a day-ahead energy scheduling strategy for an industrial park according to claim 5, characterized in that: The constraint conditions are relaxed, and the relaxed constraint conditions also include the following formula: in is the default easing value; The self-discharge rate of the constraint condition is optimized, and the constraint condition after the self-discharge rate optimization also includes the following formula: in, is the self-discharge rate of the energy storage system; A distance constraint is added to the constraint condition, and the distance constraint includes the following formula: in, is the control coefficient, and They are different energy storage charging and discharging powers. is the index distance function.

8. The method for generating a day-ahead energy scheduling strategy for an industrial park according to claim 1, characterized in that: The following steps are also included: According to the energy storage system related data and other energy consumption data, linear programming is used to obtain an optimized scheduling strategy, and the optimized scheduling strategy is post-processed; The post-processing includes the following steps: when the charging and discharging operation directions of the energy storage system are the same within a preset time period, aggregation is performed, during which the energy storage system is set to charge during a low-price period and discharge during a high-price period; and independent small charging and discharging operations that are less than a preset threshold are ignored.

9. A system for generating a day-ahead energy scheduling strategy for an industrial park, characterized in that: include: Energy storage system data acquisition module, used to obtain energy storage system related data and other energy consumption data; A data verification module, used to verify the energy storage system related data and other energy consumption data; The energy consumption scheduling strategy generation module is used to input the energy storage system related data and other energy consumption data into the nonlinear programming scheduling model after verification, and output the optimal scheduling strategy. The nonlinear programming scheduling model adopts a scheduling model with economic optimization as the energy consumption target, or a scheduling model with the highest photovoltaic consumption level as the energy consumption target.

10. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable in the processor, wherein the processor implements the steps of a method for generating a day-ahead energy scheduling strategy for an industrial park as described in any one of claims 1 to 8 when executing the computer program.

Images