A method and device for double-layer optimization scheduling of integrated energy system considering wind-fire-storage

Abstract

The application discloses a kind of wind-fire-storage integrated energy system double-layer optimization scheduling method and device considering, belong to integrated energy dispatching field.The application is in view of the technical problems that single target energy storage scheduling optimization is difficult to consider economy and wind power consumption, energy storage capacity and operation constraint matching, adopts layered progressive optimization and collaborative iteration mechanism, upper layer constructs cost minimization day-ahead scheduling model, and solves fire storage wind output plan by mixed integer linear programming;Lower layer constructs the minimum correction model of wind curtailment rate, and dynamically corrects energy storage capacity and charge-discharge parameters by linear programming, and then the correction result is fed back to upper layer iteration optimization.The application realizes the dual target of system operation cost minimization and wind curtailment rate minimization, solves the adaptability and consideration difficulty of traditional dispatching scheme, improves the practicability and reliability of dispatching scheme, effectively promotes new energy consumption, gives full play to the role of fire storage collaborative peak shaving, and guarantees the safe, economic and stable operation of power system.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy dispatching, specifically relating to a two-layer optimized dispatching method and device for an integrated energy system that considers wind, thermal, and energy storage. Background Technology

[0002] Wind power and other renewable energy sources have seen their share in the power system steadily increase due to their clean and renewable advantages. However, wind power output exhibits significant randomness, volatility, and intermittency. Large-scale grid connection can easily lead to power system supply-demand imbalances and prominent wind curtailment issues, severely restricting the absorption and utilization of renewable energy. Existing energy storage dispatch optimization technologies mostly employ single-layer optimization models or focus only on a single optimization objective. Single-objective optimization struggles to balance economic efficiency with renewable energy absorption benefits. If only the lowest cost is pursued, it can easily lead to increased wind curtailment rates. Furthermore, there is a disconnect between the determination of energy storage capacity and its operation, making it difficult for the initially configured energy storage capacity to adapt to the real-time fluctuations in wind power conditions, thus limiting the practicality and optimization effectiveness of dispatch schemes.

[0003] Therefore, there is an urgent need to develop a scheduling optimization method that takes into account both economic efficiency and new energy consumption, and achieves dynamic matching between energy storage capacity and operational constraints, in order to address the shortcomings of existing technologies. Summary of the Invention

[0004] The purpose of this invention is to address the technical problems in existing technologies where single-objective energy storage scheduling optimization is difficult to balance economic efficiency with wind power absorption, and the mismatch between energy storage capacity and operational constraints. This invention provides a two-layer optimization scheduling method and device for a comprehensive energy system that considers wind, thermal, and energy storage. Through hierarchical progressive optimization and collaborative iteration mechanisms, it achieves the dual objectives of minimizing system operating costs and minimizing wind curtailment rate, thereby improving the practicality and reliability of the scheduling scheme.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A two-tiered optimization scheduling method for an integrated energy system considering wind, thermal, and energy storage includes the following steps: Step 1: Preparation of relevant data Set basic parameters, including thermal power unit operating cost, ramp rate, and capacity; energy storage unit initial capacity, charge / discharge cost coefficient, initial SOC setting, SOC range setting, adjustable capacity setting, and charge / discharge efficiency coefficient; wind turbine unit penalty coefficient; day-ahead load forecast data; and wind power forecast data. Step 2: Construction and solution of the upper-level day-ahead cost minimization scheduling model The objective function with the lowest total cost and the relevant constraints of the units are constructed at the upper level. The mixed integer linear programming method is used to solve the day-ahead output plan of thermal power units, the day-ahead acceptance plan of wind power and the day-ahead charging and discharging plan of energy storage. Step 3: Construction and solution of the modified model for minimizing the lower-level wind curtailment rate Input the initial capacity of energy storage, combine the day-ahead output plan of thermal power units, day-ahead acceptance plan of wind power and day-ahead charging and discharging plan of energy storage obtained from the upper layer, set the capacity correction coefficient k of energy storage, the charging and discharging power correction coefficient, the maximum allowable wind curtailment power, construct the objective function of minimizing wind curtailment and the relevant constraints of the unit in the lower layer, and solve it by linear programming method to obtain the corrected energy storage capacity. Step 4: Collaborative iteration and output optimization results The corrected energy storage capacity of the lower layer is fed back to the upper layer to calculate the wind curtailment rate. If the wind curtailment rate is less than the maximum allowable wind curtailment rate, the final scheduling scheme is output; otherwise, the process is iterated again until the wind curtailment rate meets the requirements.

[0006] A further improvement of this invention is that, in step 2, the construction and solution of the upper-level day-ahead cost minimization scheduling model includes: Mathematical model of thermal power unit: 1) Output upper and lower limit constraints: (1) 2) Climbing constraint: (2) in, , They represent the first The lower and upper limits of the power output of each thermal power unit; Let be the actual active power output of the i-th thermal power unit during the dispatch period t. The time step for the scheduling period, Let i be the actual active power output of the i-th thermal power unit in the previous time period. , They represent the first Each thermal power unit The load reduction and loading rate limits within a time period, with superscript G indicating thermal power unit, subscript i indicating thermal power unit number, subscript t indicating scheduling time period number, superscript down indicating load reduction, and superscript up indicating loading; Mathematical model of energy storage unit: 1) Charge and discharge constraints: (3) 2) SOC constraints: (4) in, , These represent the charge and discharge state variables of a lithium-ion battery, respectively. , These represent the maximum charging and discharging power of the lithium-ion battery, respectively. , These represent the maximum and minimum constraints of the SOC (State of Charge) of a lithium-ion battery, respectively. , These represent the SOC values ​​of the lithium-ion battery at the initial and final times, respectively. , These represent the charging and discharging efficiencies of the lithium-ion battery, respectively. This indicates the capacity of the lithium-ion battery itself; Mathematical model of wind turbine: (5) in, Indicates wind power in Power forecast for the specified time period; Energy storage regulation event judgment criteria: (6) in, This represents the state variable of the energy storage regulation event; 0 indicates that the energy storage regulation event has not been triggered, and 1 indicates that the energy storage regulation event has been triggered. The upper limit of the allowed wind curtailment rate; Objective function: (7) (8) in, This indicates the coal consumption cost of thermal power units; Indicates the number of thermal power units; Indicates coal price; , , They represent the first Coal consumption coefficient of each thermal power unit; Indicates the operating cost of energy storage; , These represent the operating cost coefficients for energy storage charging and discharging, respectively. This indicates the penalty cost for abandoning wind and solar power. This indicates the predicted wind power value; The coefficient representing the cost of wind curtailment penalty; Power balance constraints: (9).

[0007] A further improvement of this invention is that the day-ahead power output plan of thermal power units, the day-ahead wind power reception plan, and the day-ahead charge and discharge plan of energy storage are obtained by calling the CPLEX solver through the Yalmip tool.

[0008] A further improvement of this invention is that, in step 3, the construction and solution of the modified model for minimizing the lower-level wind curtailment rate includes: Energy storage regulation model: (10) (11) in, , These are the energy storage charging and discharging plans after lower-level adjustment; , These are the energy storage charging and discharging plans transmitted from the upper layer to the lower layer, respectively. The initial value of the energy storage regulation coefficient is 0.1, and it is selected according to the degree of regulation. Objective function: (12) in, The system's wind curtailment rate, Let be the predicted active power of wind power in time period t, which is the theoretical upper limit of dispatchable wind power. Let t represent the actual active power of wind power connected to the grid during the t-th time period; Constraints: (13).

[0009] A two-tiered optimized scheduling device for an integrated energy system considering wind, thermal, and energy storage, comprising: Related data preparation unit: Set basic parameters, including thermal power unit operating cost, ramp rate, and capacity; energy storage unit initial capacity, charge / discharge cost coefficient, initial SOC setting, SOC range setting, adjustable capacity setting, and charge / discharge efficiency coefficient; wind turbine unit penalty coefficient; day-ahead load forecast data; and wind power forecast data. Upper-level day-ahead cost minimization scheduling model construction and solution unit: Construct the upper-level objective function with the lowest total cost and unit-related constraints, and use the mixed integer linear programming method to solve for the day-ahead output plan of thermal power units, the day-ahead acceptance plan of wind power and the day-ahead charging and discharging plan of energy storage; Lower-level wind curtailment rate minimization correction model construction and solution unit: Input the initial capacity of energy storage, combine the day-ahead output plan of thermal power units, day-ahead acceptance plan of wind power and day-ahead charging and discharging plan of energy storage obtained from the upper level, set the capacity correction coefficient k of energy storage, the charging and discharging power correction coefficient, the maximum allowable wind curtailment power, construct the objective function of minimizing wind curtailment in the lower level and the relevant constraints of the unit, and solve it using the linear programming method to obtain the corrected energy storage capacity; Collaborative Iteration and Optimization Result Output Unit: Feeds back the corrected energy storage capacity from the lower layer to the upper layer, calculates the wind curtailment rate, and outputs the final scheduling scheme if the wind curtailment rate is less than the maximum allowable wind curtailment rate; otherwise, iterates again until the wind curtailment rate meets the requirements.

[0010] An electronic device includes: a processor and a memory coupled to the processor, the memory storing a computer program that, when executed by the processor, implements the steps of the aforementioned two-layer optimal scheduling method for an integrated energy system considering wind-fire-storage.

[0011] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a two-layer optimized scheduling method for an integrated energy system considering wind, thermal, and energy storage.

[0012] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention provides a two-layer optimized scheduling method, system, and device for an integrated energy system considering wind, thermal, and energy storage. It employs a two-layer progressive optimization architecture, with the upper layer focusing on system economic objectives and the lower layer focusing on renewable energy consumption objectives. A collaborative iterative mechanism achieves balanced optimization of these dual objectives, solving the technical challenge of single-objective scheduling failing to simultaneously consider economic efficiency and wind power consumption. Through dynamic correction of energy storage capacity and charge / discharge limits in the lower-layer model, precise matching of energy storage configuration with real-time wind power fluctuations is achieved, enhancing the practicality and adaptability of the scheduling scheme. It fully leverages the peak-shaving and regulation capabilities of thermal power units and the rapid response advantages of lithium-ion battery energy storage, effectively reducing wind curtailment rates while controlling system operating costs, ensuring the safe, economical, and stable operation of the power system, and possessing promising engineering application prospects. Attached Figure Description

[0013] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0014] Figure 1 This is the overall flowchart of the two-layer iterative method of the present invention.

[0015] Figure 2 This is a structural block diagram of the device of the present invention. Detailed Implementation

[0016] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0017] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0018] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0019] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0020] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0021] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0022] Example 1 Reference Figure 1 This invention provides a two-layer optimization scheduling method for an integrated energy system considering wind, thermal, and energy storage. This method considers an improved multi-objective Harris Hawk wind-energy storage capacity configuration based on two detailed rules, and is implemented according to the following steps: Step 1: Preparation of relevant data The basic parameters to be set mainly include the operating cost, ramp rate, and capacity of thermal power units; the initial capacity, charge / discharge cost coefficient, initial SOC setting, SOC range setting, adjustable capacity setting, and charge / discharge efficiency coefficient of energy storage units; and the penalty coefficient for wind turbine units. Day-ahead load forecast data and wind power forecast data are also required.

[0023] Step 2: Construction and solution of the upper-level day-ahead cost minimization scheduling model The upper-level objective function with the lowest total cost and the relevant constraints of the units are constructed. The mixed-integer linear programming method is adopted and the CPLEX solver is called through the Yalmip tool to obtain the day-ahead output plan of thermal power units, the day-ahead access plan of wind power, and the day-ahead charging and discharging plan of energy storage.

[0024] 1. Mathematical model of thermal power unit: 1) Output upper and lower limit constraints: (1) 2) Climbing constraint: (2) in, , They represent the first The lower and upper limits of the power output of each thermal power unit; Let be the actual active power output of the i-th thermal power unit during the dispatch period t. The time step for the scheduling period, Let i be the actual active power output of the i-th thermal power unit in the previous time period. , They represent the first Each thermal power unit The load reduction and loading rate limits within a time period are defined by the superscript G, which indicates the thermal power unit, the subscript i, which indicates the thermal power unit number, the subscript t, which indicates the scheduling time period number, the superscript down, which indicates load reduction, and the superscript up, which indicates loading.

[0025] 2. Mathematical model of energy storage unit: 1) Charge and discharge constraints: (3) 2) SOC constraints: (4) in, , These represent the charge and discharge state variables (0-1 variables) of the lithium-ion battery, respectively. , These represent the maximum charging and discharging power of the lithium-ion battery, respectively. , These represent the maximum and minimum constraints of the SOC (State of Charge) of a lithium-ion battery, respectively. , These represent the SOC values ​​of the lithium-ion battery at the initial and final times, respectively. , These represent the charging and discharging efficiencies of the lithium-ion battery, respectively. This indicates the capacity of the lithium-ion battery itself.

[0026] 3. Mathematical model of wind turbine: (5) in, Indicates wind power in Power forecast for the specified time period.

[0027] Energy storage regulation event judgment criteria: (6) in, This represents the state variable of the energy storage regulation event; 0 indicates that the energy storage regulation event has not been triggered, and 1 indicates that the energy storage regulation event has been triggered. The upper limit of the allowed wind curtailment rate; Objective function: (7) (8) in, This indicates the coal consumption cost of thermal power units; Indicates the number of thermal power units; Indicates coal price; , , They represent the first Coal consumption coefficient of each thermal power unit. Indicates the operating cost of energy storage; , These represent the operating cost coefficients for energy storage charging and discharging, respectively. This indicates the penalty cost for abandoning wind and solar power. This indicates the predicted wind power value; The coefficient representing the cost of wind curtailment penalty.

[0028] Power balance constraints: (9) Step 3: Construction and solution of the modified model for minimizing the lower-level wind curtailment rate Input the initial capacity of energy storage, combine the day-ahead output plan of thermal power units, the day-ahead acceptance plan of wind power, and the day-ahead charging and discharging plan of energy storage obtained from the upper layer, set the capacity correction coefficient k, the charging and discharging power correction coefficient, and the maximum allowable wind curtailment power, construct the objective function of minimizing wind curtailment and the relevant constraints of the units in the lower layer, and solve it using the linear programming method to obtain the corrected energy storage capacity.

[0029] Energy storage regulation model: (10) (11) in, , These are the energy storage charging and discharging plans after lower-level adjustment; , These are the energy storage charging and discharging plans transmitted from the upper layer to the lower layer, respectively. The initial value of the energy storage regulation coefficient is 0.1, and it is selected according to the degree of regulation.

[0030] Objective function: (12) in, This refers to the system's wind curtailment rate.

[0031] Constraints: (13) Step 4: Collaborative iteration and output optimization results The corrected energy storage capacity of the lower layer is fed back to the upper layer to calculate the wind curtailment rate. If the wind curtailment rate is less than the maximum allowable wind curtailment rate, the final scheduling scheme is output; otherwise, the process is iterated again until the wind curtailment rate meets the requirements.

[0032] This invention effectively overcomes the limitations of existing integrated energy system dispatching technologies through an innovative design of hierarchical progressive optimization and collaborative iterative mechanisms. In existing technologies, traditional single-layer optimization models often focus on a single objective. If only the lowest system operating cost is pursued, it can easily lead to insufficient wind power absorption and increased wind curtailment rates. If only new energy absorption is emphasized, economic efficiency is neglected, making it difficult to achieve a balance between the two objectives. Furthermore, the initial configuration of energy storage capacity is disconnected from real-time operating conditions, failing to adapt to the randomness and volatility of wind power, resulting in insufficient practicality of the dispatching scheme. Therefore, this invention abandons the single-objective optimization approach and adopts a two-layer optimization architecture, achieving precise dispatching through the collaborative linkage of the upper and lower layer models. The upper layer constructs a day-ahead scheduling model with the goal of minimizing total cost. It incorporates key conditions such as upper and lower limits of thermal power unit output, ramp-up constraints, energy storage charging / discharging and SOC constraints, and wind turbine output constraints. A mixed-integer linear programming method is employed, using the Yalmip tool to call the CPLEX solver to output the day-ahead output plans for thermal power units, wind power, and energy storage. The lower layer aims to minimize wind curtailment. Combining the upper-layer wind power output plan, it introduces an energy storage capacity correction coefficient k and a charging / discharging power correction coefficient to construct a linear programming correction model. This model dynamically adjusts energy storage configuration parameters to obtain an energy storage capacity adapted to real-time operating conditions. Through a collaborative iterative mechanism that feeds the lower-layer correction results back to the upper layer, continuous optimization continues until the wind curtailment rate meets the limit requirements, ultimately achieving a balance between the two objectives. In summary, this invention surpasses existing technologies in terms of objective balancing capability, dynamic adaptability, and engineering practicality. It fully leverages the peak-shaving capacity of thermal power units and the rapid response advantages of lithium battery energy storage, while effectively reducing operating costs and wind curtailment rates, providing a more efficient and reliable scheduling solution for integrated energy systems.

[0033] Example 2 Reference Figure 2The present invention provides a two-layer optimized scheduling device for an integrated energy system considering wind, thermal, and energy storage, comprising: Related data preparation unit: Set basic parameters, including thermal power unit operating cost, ramp rate, and capacity; energy storage unit initial capacity, charge / discharge cost coefficient, initial SOC setting, SOC range setting, adjustable capacity setting, and charge / discharge efficiency coefficient; wind turbine unit penalty coefficient; day-ahead load forecast data; and wind power forecast data. Upper-level day-ahead cost minimization scheduling model construction and solution unit: Construct the upper-level objective function with the lowest total cost and unit-related constraints, and use the mixed integer linear programming method to solve for the day-ahead output plan of thermal power units, the day-ahead acceptance plan of wind power and the day-ahead charging and discharging plan of energy storage; Lower-level wind curtailment rate minimization correction model construction and solution unit: Input the initial capacity of energy storage, combine the day-ahead output plan of thermal power units, day-ahead acceptance plan of wind power and day-ahead charging and discharging plan of energy storage obtained from the upper level, set the capacity correction coefficient k of energy storage, the charging and discharging power correction coefficient, the maximum allowable wind curtailment power, construct the objective function of minimizing wind curtailment in the lower level and the relevant constraints of the unit, and solve it using the linear programming method to obtain the corrected energy storage capacity; Collaborative Iteration and Optimization Result Output Unit: Feeds back the corrected energy storage capacity from the lower layer to the upper layer, calculates the wind curtailment rate, and outputs the final scheduling scheme if the wind curtailment rate is less than the maximum allowable wind curtailment rate; otherwise, iterates again until the wind curtailment rate meets the requirements.

[0034] The upper-level day-ahead cost minimization scheduling model construction and solution unit in this embodiment includes: Mathematical model of thermal power unit: 1) Output upper and lower limit constraints: (1) 2) Climbing constraint: (2) in, , They represent the first The lower and upper limits of the power output of each thermal power unit; Let be the actual active power output of the i-th thermal power unit during the dispatch period t. The time step for the scheduling period, Let i be the actual active power output of the i-th thermal power unit in the previous time period. , They represent the first Each thermal power unit The load reduction and loading rate limits within a time period, with superscript G indicating thermal power unit, subscript i indicating thermal power unit number, subscript t indicating scheduling time period number, superscript down indicating load reduction, and superscript up indicating loading; Mathematical model of energy storage unit: 1) Charge and discharge constraints: (3) 2) SOC constraints: (4) in, , These represent the charge and discharge state variables of a lithium-ion battery, respectively. , These represent the maximum charging and discharging power of the lithium-ion battery, respectively. , These represent the maximum and minimum constraints of the SOC (State of Charge) of a lithium-ion battery, respectively. , These represent the SOC values ​​of the lithium-ion battery at the initial and final times, respectively. , These represent the charging and discharging efficiencies of the lithium-ion battery, respectively. This indicates the capacity of the lithium-ion battery itself; Mathematical model of wind turbine: (5) in, Indicates wind power in Power forecast for the specified time period; Energy storage regulation event judgment criteria: (6) in, This represents the state variable of the energy storage regulation event; 0 indicates that the energy storage regulation event has not been triggered, and 1 indicates that the energy storage regulation event has been triggered. The upper limit of the allowed wind curtailment rate; Objective function: (7) (8) in, This indicates the coal consumption cost of thermal power units; Indicates the number of thermal power units; Indicates coal price; , , They represent the first Coal consumption coefficient of each thermal power unit; Indicates the operating cost of energy storage; , These represent the operating cost coefficients for energy storage charging and discharging, respectively. This indicates the penalty cost for abandoning wind and solar power. This indicates the predicted wind power value; The coefficient representing the cost of wind curtailment penalty; Power balance constraints: (9).

[0035] In this embodiment, the day-ahead power output plan of thermal power units, the day-ahead wind power reception plan, and the day-ahead charge and discharge plan of energy storage are obtained by calling the CPLEX solver through the Yalmip tool.

[0036] The lower-level wind curtailment rate minimization correction model construction and solution unit in this embodiment includes: Energy storage regulation model: (10) (11) in, , These are the energy storage charging and discharging plans after lower-level adjustment; , These are the energy storage charging and discharging plans transmitted from the upper layer to the lower layer, respectively. The initial value of the energy storage regulation coefficient is 0.1, and it is selected according to the degree of regulation. Objective function: (12) in, The system's wind curtailment rate, Let be the predicted active power of wind power in time period t, which is the theoretical upper limit of dispatchable wind power. Let t represent the actual active power of wind power connected to the grid during the t-th time period; Constraints: (13).

[0037] Example 3 The present invention provides an electronic device comprising: a processor and a memory coupled to the processor, the memory storing a computer program, wherein when the computer program is executed by the processor, the steps of a two-layer optimization scheduling method for an integrated energy system considering wind-fire-storage are implemented.

[0038] The electronic device may also include one or more of a multimedia component, an input / output (I / O) interface, and a communication component.

[0039] The processor controls the overall operation of the electronic device to complete all or part of the steps in the storage medium sharing method. The memory stores various types of data to support the operation of the electronic device. This data may include, for example, instructions for any application or method operating on the electronic device, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia components may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio components are used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory or transmitted via a communication component. The audio component also includes at least one speaker for outputting audio signals. The I / O interface provides an interface between the processor and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical. The communication component is used for wired or wireless communication between the electronic device and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof; therefore, the corresponding communication component may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0040] In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing a storage medium sharing method.

[0041] Example 4 The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a two-layer optimization scheduling method for an integrated energy system considering wind, thermal, and storage.

[0042] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0043] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0044] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0045] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0046] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0047] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A two-layer optimal scheduling method for an integrated energy system considering wind, thermal, and energy storage, characterized in that, Includes the following steps: Step 1: Preparation of relevant data Set basic parameters, including thermal power unit operating cost, ramp rate, and capacity; Initial capacity of energy storage units, charge / discharge cost coefficient, initial SOC setting, SOC range setting, adjustable capacity setting, charge / discharge efficiency coefficient; wind turbine penalty coefficient; day-ahead load forecast data; and wind power forecast data; Step 2: Construction and solution of the upper-level day-ahead cost minimization scheduling model The objective function with the lowest total cost and the relevant constraints of the units are constructed at the upper level. The mixed integer linear programming method is used to solve the day-ahead output plan of thermal power units, the day-ahead acceptance plan of wind power and the day-ahead charging and discharging plan of energy storage. Step 3: Construction and solution of the modified model for minimizing the lower-level wind curtailment rate Input the initial capacity of energy storage, combine the day-ahead output plan of thermal power units, day-ahead acceptance plan of wind power and day-ahead charging and discharging plan of energy storage obtained from the upper layer, set the capacity correction coefficient k of energy storage, the charging and discharging power correction coefficient, the maximum allowable wind curtailment power, construct the objective function of minimizing wind curtailment and the relevant constraints of the unit in the lower layer, and solve it by linear programming method to obtain the corrected energy storage capacity. Step 4: Collaborative iteration and output optimization results The corrected energy storage capacity of the lower layer is fed back to the upper layer to calculate the wind curtailment rate. If the wind curtailment rate is less than the maximum allowable wind curtailment rate, the final scheduling scheme is output. Otherwise, iterate again until the wind curtailment rate meets the requirements.

2. The two-layer optimal scheduling method for an integrated energy system considering wind, thermal, and energy storage as described in claim 1, characterized in that, Step 2 involves constructing and solving the upper-level day-ahead cost minimization scheduling model, including: Mathematical model of thermal power unit: 1) Output upper and lower limit constraints: (1) 2) Climbing constraint: (2) in, , They represent the first The lower and upper limits of the power output of each thermal power unit; Let be the actual active power output of the i-th thermal power unit during the dispatch period t. The time step for the scheduling period, Let i be the actual active power output of the i-th thermal power unit in the previous time period. , They represent the first Each thermal power unit The load reduction and loading rate limits within a time period, with superscript G indicating thermal power unit, subscript i indicating thermal power unit number, subscript t indicating scheduling time period number, superscript down indicating load reduction, and superscript up indicating loading; Mathematical model of energy storage unit: 1) Charge and discharge constraints: (3) 2) SOC constraints: (4) in, , These represent the charge and discharge state variables of a lithium-ion battery, respectively. , These represent the maximum charging and discharging power of the lithium-ion battery, respectively. , These represent the maximum and minimum constraints of the SOC (State of Charge) of a lithium-ion battery, respectively. , These represent the SOC values ​​of the lithium-ion battery at the initial and final times, respectively. , These represent the charging and discharging efficiencies of the lithium-ion battery, respectively. This indicates the capacity of the lithium-ion battery itself; Mathematical model of wind turbine: (5) in, Indicates wind power in Power forecast for the specified time period; Energy storage regulation event judgment criteria: (6) in, This represents the state variable of the energy storage regulation event; 0 indicates that the energy storage regulation event has not been triggered, and 1 indicates that the energy storage regulation event has been triggered. The upper limit of the allowed wind curtailment rate; Objective function: (7) (8) in, This indicates the coal consumption cost of thermal power units; Indicates the number of thermal power units; Indicates coal price; , , They represent the first Coal consumption coefficient of each thermal power unit; Indicates the operating cost of energy storage; , These represent the operating cost coefficients for energy storage charging and discharging, respectively. This indicates the penalty cost for abandoning wind and solar power. This indicates the predicted wind power value; The coefficient representing the cost of wind curtailment penalty; Power balance constraints: (9)。 3. The two-layer optimal scheduling method for an integrated energy system considering wind, thermal, and energy storage as described in claim 2, characterized in that, The daytime output plan of thermal power units, the daytime access plan of wind power, and the daytime charge and discharge plan of energy storage are obtained by calling the CPLEX solver through the Yalmip tool.

4. The two-layer optimal scheduling method for an integrated energy system considering wind, thermal, and energy storage as described in claim 1, characterized in that, Step 3 involves constructing and solving the modified model for minimizing the lower-level wind curtailment rate, including: Energy storage regulation model: (10) (11) in, , These are the energy storage charging and discharging plans after lower-level adjustment; , These are the energy storage charging and discharging plans transmitted from the upper layer to the lower layer, respectively. The initial value of the energy storage regulation coefficient is 0.1, and it is selected according to the degree of regulation. Objective function: (12) in, The system's wind curtailment rate, Let be the predicted active power of wind power in time period t, which is the theoretical upper limit of dispatchable wind power. Let t represent the actual active power of wind power connected to the grid during the t-th time period; Constraints: (13)。 5. A two-layer optimized scheduling device for an integrated energy system considering wind, thermal, and energy storage, characterized in that, include: Relevant data preparation unit: Set basic parameters, including thermal power unit operating cost, ramp rate, and capacity; Initial capacity of energy storage units, charge / discharge cost coefficient, initial SOC setting, SOC range setting, adjustable capacity setting, charge / discharge efficiency coefficient; wind turbine penalty coefficient; day-ahead load forecast data; and wind power forecast data; Upper-level day-ahead cost minimization scheduling model construction and solution unit: Construct the upper-level objective function with the lowest total cost and unit-related constraints, and use the mixed integer linear programming method to solve for the day-ahead output plan of thermal power units, the day-ahead acceptance plan of wind power and the day-ahead charging and discharging plan of energy storage; Lower-level wind curtailment rate minimization correction model construction and solution unit: Input the initial capacity of energy storage, combine the day-ahead output plan of thermal power units, day-ahead acceptance plan of wind power and day-ahead charging and discharging plan of energy storage obtained from the upper level, set the capacity correction coefficient k of energy storage, the charging and discharging power correction coefficient, the maximum allowable wind curtailment power, construct the objective function of minimizing wind curtailment in the lower level and the relevant constraints of the unit, and solve it using the linear programming method to obtain the corrected energy storage capacity; Collaborative Iteration and Optimization Result Output Unit: Feeds back the corrected energy storage capacity from the lower layer to the upper layer, calculates the wind curtailment rate, and outputs the final scheduling scheme if the wind curtailment rate is less than the maximum allowable wind curtailment rate. Otherwise, iterate again until the wind curtailment rate meets the requirements.

6. A two-layer optimized scheduling device for an integrated energy system considering wind, thermal, and energy storage as described in claim 5, characterized in that, The upper-level day-ahead cost minimization scheduling model construction and solution unit includes: Mathematical model of thermal power unit: 1) Output upper and lower limit constraints: (1) 2) Climbing constraint: (2) in, , They represent the first The lower and upper limits of the power output of each thermal power unit; Let be the actual active power output of the i-th thermal power unit during the dispatch period t. The time step for the scheduling period, Let i be the actual active power output of the i-th thermal power unit in the previous time period. , They represent the first Each thermal power unit The load reduction and loading rate limits within a time period, with superscript G indicating thermal power unit, subscript i indicating thermal power unit number, subscript t indicating scheduling time period number, superscript down indicating load reduction, and superscript up indicating loading; Mathematical model of energy storage unit: 1) Charge and discharge constraints: (3) 2) SOC constraints: (4) in, , These represent the charge and discharge state variables of a lithium-ion battery, respectively. , These represent the maximum charging and discharging power of the lithium-ion battery, respectively. , These represent the maximum and minimum constraints of the SOC (State of Charge) of a lithium-ion battery, respectively. , These represent the SOC values ​​of the lithium-ion battery at the initial and final times, respectively. , These represent the charging and discharging efficiencies of the lithium-ion battery, respectively. This indicates the capacity of the lithium-ion battery itself; Mathematical model of wind turbine: (5) in, Indicates wind power in Power forecast for the specified time period; Energy storage regulation event judgment criteria: (6) in, This represents the state variable of the energy storage regulation event; 0 indicates that the energy storage regulation event has not been triggered, and 1 indicates that the energy storage regulation event has been triggered. The upper limit of the allowed wind curtailment rate; Objective function: (7) (8) in, This indicates the coal consumption cost of thermal power units; Indicates the number of thermal power units; Indicates coal price; , , They represent the first Coal consumption coefficient of each thermal power unit; Indicates the operating cost of energy storage; , These represent the operating cost coefficients for energy storage charging and discharging, respectively. This indicates the penalty cost for abandoning wind and solar power. This indicates the predicted wind power value; The coefficient representing the cost of wind curtailment penalty; Power balance constraints: (9)。 7. A two-layer optimized scheduling device for an integrated energy system considering wind, thermal, and energy storage as described in claim 6, characterized in that, The daytime output plan of thermal power units, the daytime access plan of wind power, and the daytime charge and discharge plan of energy storage are obtained by calling the CPLEX solver through the Yalmip tool.

8. A two-layer optimized scheduling device for an integrated energy system considering wind, thermal, and energy storage as described in claim 5, characterized in that, The lower-level wind curtailment rate minimization correction model construction and solution unit includes: Energy storage regulation model: (10) (11) in, , These are the energy storage charging and discharging plans after lower-level adjustment; , These are the energy storage charging and discharging plans transmitted from the upper layer to the lower layer, respectively. The initial value of the energy storage regulation coefficient is 0.1, and it is selected according to the degree of regulation. Objective function: (12) in, The system's wind curtailment rate, Let be the predicted active power of wind power in time period t, which is the theoretical upper limit of dispatchable wind power. Let t represent the actual active power of wind power connected to the grid during the t-th time period; Constraints: (13)。 9. An electronic device, characterized in that, include: A processor and a memory coupled to the processor, the memory storing a computer program that, when executed by the processor, implements the steps of a two-layer optimized scheduling method for an integrated energy system considering wind-fire-storage as described in any one of claims 1-4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of any one of claims 1-4 of a two-layer optimized scheduling method for an integrated energy system considering wind, thermal, and storage.

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