A source-network-load-storage collaborative regulation method, system, device and medium

Abstract

The application provides a source network load storage collaborative regulation method, system, device and medium, and belongs to the technical field of new energy. The method comprises the following steps: acquiring input data of a multi-energy system; based on the input data, a source network load storage collaborative optimization model is established, the source network load storage collaborative optimization model comprises an upper-layer investment decision model, a middle-layer operation optimization model and a lower-layer frequency control model, wherein the upper-layer model takes the minimum planning investment cost as the target, the middle-layer model takes the minimum operation cost as the target, and the lower-layer model takes the minimum frequency control cost as the target; an optimization algorithm is used to solve the source network load storage collaborative optimization model, and an optimal equipment configuration scheme is obtained; and the multi-energy system is regulated in real time according to the optimal equipment configuration scheme, including equipment switching, output adjustment and load management, so as to realize source network load storage collaborative optimization. Multi-time scale collaborative optimization is realized, new energy uncertainty is effectively handled, and system economy and operation reliability are considered.

Description

Technical Field

[0001] This invention belongs to the field of new energy technology, and in particular relates to a method, system, equipment and medium for coordinated regulation of power generation, grid, load and storage. Background Technology

[0002] With the accelerated global energy transition, power systems are rapidly developing towards a high proportion of renewable energy integration. The large-scale integration of new energy sources such as wind and solar power has significantly increased the uncertainty and volatility of system operation, placing higher demands on the grid's flexible adjustment capabilities. The integrated and coordinated operation of multi-energy systems has become a key means to improve energy utilization efficiency and ensure power supply reliability, and the coordinated optimization of multiple elements such as energy sources, grids, loads, and storage has gradually become a research hotspot.

[0003] In existing technologies, hierarchical or distributed optimization methods are typically employed to address the optimization problems of multi-energy systems. For example, system regulation capabilities are improved by independently planning power sources, expanding grid capacity, or configuring energy storage devices; deterministic optimization algorithms are used to handle unit combination and economic dispatch problems; and demand-side response measures are utilized to smooth peak and valley loads and balance supply and demand imbalances. These methods can improve the economic efficiency of system operation in specific scenarios and partially alleviate the pressure on renewable energy consumption.

[0004] However, existing technologies have significant shortcomings: First, most methods only optimize a single link or a single time scale, failing to achieve full-link coordination among power generation, grid, load, and storage; second, optimization models often adopt single-layer or double-layer structures, making it difficult to simultaneously consider the multi-time-scale coupling characteristics of long-term investment decisions, medium-term operational optimization, and short-term frequency control; third, traditional optimization algorithms have limited ability to handle uncertainties in renewable energy output and load demand, and are inefficient at solving integer variables such as equipment investment and construction; finally, there is a lack of a mechanism to effectively link planning results with real-time control strategies, resulting in insufficient adaptability of optimization schemes in practical applications. Summary of the Invention

[0005] This invention provides a method, system, equipment, and medium for coordinated regulation of power generation, grid, load, and storage, which at least solves the problems of insufficient coordination among various links of power generation, grid, load, and storage, difficulty in multi-timescale optimization coupling, weak uncertainty handling capabilities, and disconnect between planning and operation control in the prior art.

[0006] In a first aspect, embodiments of this application provide a method for coordinated regulation of source, grid, load, and storage, the method comprising: Step S1: Obtain input data for the multi-energy system; Step S2: Based on the input data, establish a source-grid-load-storage coordinated optimization model. The source-grid-load-storage coordinated optimization model includes an upper-level investment decision model, a middle-level operation optimization model, and a lower-level frequency control model. The upper-level model aims to minimize the planned investment cost, the middle-level model aims to minimize the operation cost, and the lower-level model aims to minimize the frequency control cost. Step S3: Solve the source-grid-load-storage collaborative optimization model using an optimization algorithm to obtain the optimal equipment configuration scheme; Step S4: Based on the optimal equipment configuration scheme, perform real-time control of the multi-energy system, including equipment switching, output adjustment and load management, to achieve coordinated optimization of source, grid, load and storage.

[0007] Furthermore, the objective function of the upper-level investment decision model is:

[0008] in, For investment costs, For equipment The unit investment cost For equipment Rated capacity, For equipment The investment decision variables are denoted by , where 0 represents no investment and 1 represents investment.

[0009] Furthermore, the objective function of the mid-level operation optimization model is to minimize the operating cost, and its expression is:

[0010] in, Operating costs; To optimize the total number of time periods; For time period The cost of electricity generation, For time period Environmental costs; For time period Demand response incentive costs; For time period The cost of penalties for abandoning wind and solar power.

[0011] Furthermore, the expression for the cost of electricity generation is:

[0012] in, For power generation equipment The unit cost of electricity generation For equipment During the period Those who have made meritorious contributions; The expression for environmental cost is:

[0013] in, pollutants The unit pollution equivalent charging standard, pollutants unit emissions, pollutants The pollution equivalent value, For thermal power units During the period contribution; The expression for the demand response incentive cost is:

[0014] in, To reduce load The unit incentive price, To reduce load During the period The amount of reduction, For transferable load The unit incentive price, For transferable load During the period The amount transferred; The expression for the penalty cost of wind and solar power curtailment is:

[0015] in, The penalty cost per unit for curtailing wind and solar power; For wind power during the time period The predicted output; To actually contribute to wind power; For photovoltaics during the time period The predicted output; To make a real contribution to photovoltaic power.

[0016] Furthermore, the objective function of the lower-level frequency control model is:

[0017] in, For equipment Frequency regulation unit cost For equipment FM output, This refers to the regional control error.

[0018] Furthermore, the regional control error The calculation formula is:

[0019] in, This is the frequency deviation coefficient. For system frequency deviation, This refers to the power deviation of the tie line.

[0020] Further, in step S3, an optimization algorithm is used to solve the source-grid-load-storage collaborative optimization model to obtain the optimal equipment configuration scheme, specifically including: Step S31: Linearize the nonlinear constraints in the source-grid-load-storage collaborative optimization model into a linear form, and output the linearized mixed integer linear programming model; Step S32: Set algorithm parameters, including the iteration counter. Convergence threshold Upper Realm and the lower realm And initialize the values ​​of uncertainty variables, including using the expected values ​​of wind power forecast output and photovoltaic forecast output as the initial worst-case scenario, and initialize investment decision variables; Step S33: Solve the upper-level investment decision model to minimize investment costs and auxiliary variables. With the objective as the goal, and adding feasibility cut constraints obtained from previous iterations, the master problem is solved using a mixed-integer linear programming solver to obtain the investment decision solution. and auxiliary variables and update the lower bound. ; Step S34: Given the investment decision solution to the main problem Solving the mid-level operation optimization and lower-level frequency control models aims to minimize the operation cost and frequency control cost, while considering the uncertainty set and employing strong duality theory to solve the subproblems. The problem is transformed into a single-level optimization problem, solved using a linear programming solver, yielding the worst-case scenario. Objective value of subproblem and update the upper bound. ; Step S35: Calculate the relative gap ,like If the algorithm converges, proceed to step S37; otherwise, continue to step S36. Step S36: Add constraints and iterate, add the solutions to subproblems as new constraints to the main problem, add feasibility cuts, and update the iteration counter. and return to step S33; Step S37: After the algorithm converges, output the optimal investment decision solution, including the investment location and capacity of power equipment, energy storage equipment and lines.

[0021] Secondly, embodiments of this application also provide a system applied to the source-grid-load-storage coordinated regulation method as described in the above aspects, the system comprising: The data acquisition module is used to acquire input data from the multi-energy system; The optimization model construction module is used to establish a source-grid-load-storage coordinated optimization model based on the input data. The source-grid-load-storage coordinated optimization model includes an upper-level investment decision model, a middle-level operation optimization model, and a lower-level frequency control model. The upper-level model aims to minimize the planned investment cost, the middle-level model aims to minimize the operation cost, and the lower-level model aims to minimize the frequency control cost. The model solving module is used to solve the source-grid-load-storage collaborative optimization model using optimization algorithms to obtain the optimal equipment configuration scheme; The solution application module is used to perform real-time control of the multi-energy system according to the optimal equipment configuration scheme, including equipment switching, output adjustment and load management, so as to achieve coordinated optimization of source, grid, load and storage.

[0022] Thirdly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the source-grid-load-storage coordinated control method as described in the preceding aspects.

[0023] Fourthly, a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the source-grid-load-storage coordinated control method as described in the above aspects.

[0024] As can be seen from the above technical solutions, the present invention has the following advantages: The source-grid-load-storage coordinated regulation method provided in this application establishes a three-layer coordinated optimization model that includes upper-level investment decision-making, middle-level operation optimization, and lower-level frequency control. This model achieves the organic unity of planning, operation, and control, effectively avoids suboptimal problems caused by independent optimization, and significantly improves the overall economic efficiency of the system.

[0025] By employing optimization algorithms to handle uncertainties and integer variables in the model, we can effectively address the random fluctuations of new energy sources and the discrete decision-making problems of equipment, thereby enhancing the robustness and feasibility of the solution.

[0026] Real-time control based on the optimal equipment configuration scheme realizes closed-loop management from planning to operation, ensuring the practical application effect of the optimization results and improving the system's operational flexibility.

[0027] By coordinating and optimizing resources across multiple stages including power generation, grid, load, and storage, the capacity for renewable energy absorption has been significantly improved, the probability of wind and solar power curtailment has been reduced, and the efficient utilization of clean energy has been promoted.

[0028] This method achieves minimum full-cycle cost while ensuring system frequency stability through multi-timescale coordinated optimization, thereby enhancing the system's safe and stable operation.

[0029] By coordinating the dispatch of flexible resources such as demand response and energy storage, load fluctuations can be effectively mitigated, grid operation characteristics can be improved, and asset utilization efficiency can be increased. Attached Figure Description

[0030] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart of the source-grid-load-storage coordinated regulation method described in this invention. Detailed Implementation

[0032] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0033] This application provides a method, system, device, and medium for coordinated regulation of power generation, grid, load, and storage, addressing the urgent technical problem of achieving coordinated optimization across multiple time scales, effectively handling the uncertainties of new energy sources, and balancing system economy and operational reliability.

[0034] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0035] Figure 1 This is a flowchart illustrating a source-grid-load-storage coordinated regulation method provided in an embodiment of this application. Figure 1 As shown in the embodiment of this application, a source-grid-load-storage coordinated regulation method is provided, the method comprising: S1: Acquire input data for the multi-energy system; the input data includes new energy output forecast data, load demand data, equipment parameter data, and system operation constraint data; the new energy output forecast data includes minute-level or hourly forecast values ​​for wind power and photovoltaic power, the load demand data includes forecast values ​​for electricity, heat, and gas loads, the equipment parameter data includes capacity, cost, and operating limitation parameters of power sources, power grids, loads, and energy storage equipment, and the system operation constraint data includes power balance constraints, line capacity constraints, and frequency stability constraints; S2: Based on the input data, establish a source-grid-load-storage coordinated optimization model, the source-grid-load-storage coordinated optimization model aims to minimize the total system cost; the source-grid-load-storage coordinated optimization model includes an upper-level investment decision model, a middle-level operation optimization model and a lower-level frequency control model, wherein the upper-level model aims to minimize the planned investment cost, the middle-level model aims to minimize the operation cost, and the lower-level model aims to minimize the frequency control cost; S3: The source-grid-load-storage coordinated optimization model is solved using an optimization algorithm to obtain the optimal equipment configuration scheme; S4: Based on the optimal equipment configuration scheme, perform real-time control of the multi-energy system, including equipment switching, output adjustment and load management, to achieve coordinated optimization of source, grid, load and storage.

[0036] It should be noted that the objective function of the aforementioned upper-level investment decision-making model is:

[0037] in, For investment costs, For equipment The unit investment cost For equipment Rated capacity, For equipment The investment decision variables are denoted by , where 0 represents no investment and 1 represents investment.

[0038] In an exemplary embodiment, the objective function of the mid-level operation optimization model is to minimize the operating cost, and its expression is:

[0039] in, Operating costs; To optimize the total number of time periods; For time period The cost of electricity generation, For time period Environmental costs; For time period Demand response incentive costs; For time period The cost of penalties for abandoning wind and solar power.

[0040] According to yet another embodiment of the present invention, the expression for the cost of power generation is:

[0041] in, For power generation equipment The unit cost of electricity generation For equipment During the period Those who have made meritorious contributions; The expression for environmental cost is:

[0042] in, pollutants The unit pollution equivalent charging standard, pollutants unit emissions, pollutants The pollution equivalent value, For thermal power units During the period contribution; The expression for the demand response incentive cost is:

[0043] in, To reduce load The unit incentive price, To reduce load During the period The amount of reduction, For transferable load The unit incentive price, For transferable load During the period The amount transferred; The expression for the penalty cost of wind and solar power curtailment is:

[0044] in, The penalty cost per unit for curtailing wind and solar power; For wind power during the time period The predicted output; To actually contribute to wind power; For photovoltaics during the time period The predicted output; To make a real contribution to photovoltaic power.

[0045] According to an embodiment of this application, the objective function of the lower-level frequency control model is:

[0046] in, For equipment Frequency regulation unit cost For equipment FM output, This refers to the regional control error.

[0047] In one embodiment, the area control error The calculation formula is:

[0048] in, This is the frequency deviation coefficient. For system frequency deviation, This refers to the power deviation of the tie line.

[0049] As an example, in step S3, an optimization algorithm is used to solve the source-grid-load-storage collaborative optimization model to obtain the optimal equipment configuration scheme, specifically including: Step S31: Linearize the nonlinear constraints in the source-grid-load-storage collaborative optimization model into a linear form for use with a linear programming solver. This linearization process includes introducing non-negative auxiliary variables into the absolute value term of the demand response incentive cost for linearization. Specifically, this involves linearizing the reduction amount of the reducible load k in time period t. Introducing auxiliary variables and Linearizing the demand response incentive cost as follows:

[0050] And satisfy the constraints:

[0051] Output the linearized mixed-integer linear programming model; Step S32: Set algorithm parameters, including the iteration counter. Convergence threshold Upper Realm and the lower realm And initialize the values ​​of uncertainty variables, including using the expected values ​​of wind power forecast output and photovoltaic forecast output as the initial worst-case scenario, and initialize investment decision variables; Step S33: Solve the upper-level investment decision model to minimize investment costs and auxiliary variables. For the goal, that is And add feasibility cut constraints obtained from previous iterations, including for arrive ,have ,in To find the solution to the subproblem in the l-th iteration, a mixed-integer linear programming solver is used to solve the main problem, yielding the investment decision solution. and auxiliary variables and update the lower bound. ; Step S34: Given the investment decision solution to the main problem Solve the mid-level operation optimization and lower-level frequency control models, with the objective of minimizing the operation cost and frequency control cost, i.e. ,in ,and Considering the set of uncertainties, strong duality theory is used to transform the subproblems. The problem is transformed into a single-level optimization problem, solved using a linear programming solver, yielding the worst-case scenario. Objective value of subproblem and update the upper bound. ; Step S35: Calculate the relative gap ,like If the algorithm converges, proceed to step S37; otherwise, continue to step S36. Step S36: Add constraints and iterate, adding the solutions to subproblems as new constraints to the main problem, and add feasibility cuts. ,in For sub-problems in the scenario The solution is updated, and the iteration counter is updated. and return to step S33; Step S37: After the algorithm converges, output the optimal investment decision solution, including the investment location and capacity of power equipment, energy storage equipment and lines.

[0052] An optimization algorithm is employed to solve the source-grid-load-storage coordinated optimization model to obtain the optimal equipment configuration scheme. This model is a three-level optimization problem, comprising upper-level investment decision-making, middle-level operation optimization, and lower-level frequency control. The optimization algorithm handles uncertainties and integer variables in the model to arrive at the optimal equipment configuration scheme. This embodiment will detail the specific steps of solving the source-grid-load-storage coordinated optimization model using a column constraint generation algorithm.

[0053] The column constraint generation algorithm iteratively solves the main problem (investment decision) and sub-problems (runtime optimization and frequency control) to gradually approach the global optimum. The specific steps are as follows: Step S31: Transform the nonlinear constraints in the source-grid-load-storage collaborative optimization model into a linear form so that a linear programming solver (CPLEX or Gurobi) can be used.

[0054] Linearized demand response cost: Demand response incentive cost includes an absolute value term, requiring the introduction of auxiliary variables, such as the reduction amount for reducible loads (RL). Define nonnegative auxiliary variables and Linearize the cost:

[0055]

[0056] Output the linearized mixed-integer linear programming model.

[0057] Step S32: Algorithm Initialization Set the algorithm parameters, initial solution, and convergence criteria.

[0058] Set the iteration counter .

[0059] Initialize the values ​​of uncertainty variables: Assume the expected value of the predicted wind / solar power output is the initial worst-case scenario, i.e. .

[0060] Set convergence threshold (Relative gap), upper boundary The lower realm .

[0061] Initialize investment decision variables (If all values ​​are set to 0, it means no investment has been made).

[0062] Step S33: Solving the main problem (upper-level investment decision) Content: Solve the upper-level investment decision model to obtain the equipment configuration scheme under the current iteration.

[0063] Main problem model: Objective function: Minimize investment cost and auxiliary variables (representing operating and frequency control costs):

[0064] in, It is an auxiliary variable used to approximate the cost of subproblems.

[0065] Constraints: Investment constraints: Equipment cannot be repeatedly constructed. .

[0066] Added feasibility cut (iteration from previous): For ,have:

[0067] in, It is the operating cost coefficient. It is the first Solution to the subproblem in the next iteration.

[0068] System constraints: power balance, line capacity constraints.

[0069] The main problem is solved using a mixed-integer linear programming solver, and the solution is obtained. ).

[0070] Update the Nether: .

[0071] Step S34: Sub-problem solving (mid-level operation optimization and lower-level frequency control) Given the investment decision solution of the main problem Solve the mid-level operation optimization and lower-level frequency control models to evaluate the cost under the worst-case scenario.

[0072] Sub-problem model: Objective function: Minimize operating cost and frequency control cost.

[0073] in, ,and .

[0074] Constraints: Operational constraints: Equipment output limits, ramp rate constraints.

[0075] Frequency control constraint: System backup constraint.

[0076] Uncertainty Set: The uncertainty of wind power / solar power output is represented as a box set. .

[0077] Solution method: Because the sub-problem is The problem (considering uncertainty) is transformed into a single-layer optimization using strong duality theory. This is relevant for wind power uncertainty. The subproblem is transformed into:

[0078] in, , It is a dual variable.

[0079] Solve this problem using a linear programming solver. The problem is to arrive at the worst-case scenario. Objective value of subproblem .

[0080] Update upper bound: .

[0081] Step S35: Convergence check Determine if the algorithm has converged.

[0082] Calculate the relative gap: .

[0083] if If the algorithm converges, proceed to step 7; otherwise, continue to step 6.

[0084] Step S36: Add constraints and iterate Add the solutions to the subproblems as new constraints to the main problem.

[0085] Add feasibility to the main question:

[0086] in, It is a sub-problem in the scenario The solution below.

[0087] Update iterative counter .

[0088] Return to step S33.

[0089] Step S37: Output the optimal device configuration scheme After the algorithm converges, it outputs the optimal device configuration scheme.

[0090] Optimal configuration: Equipment investment and construction decisions: Indicates equipment If it is invested in, then it is 0; otherwise, it is 0. Specifically, it includes: Power equipment: Location and capacity of wind turbines and photovoltaic systems (wind turbines installed at nodes) ,capacity MW).

[0091] Energy storage equipment: such as the location and capacity of energy storage systems (energy storage deployed at nodes) ,capacity MWh).

[0092] Line investment: the capacity and route of newly built lines.

[0093] Operational strategy: Equipment output and load management plans for each time period, but from a configuration perspective, the focus is on investment decisions.

[0094] Example: The optimal configuration includes wind turbines at node 13 (capacity 200MW) and energy storage at node 8 (capacity 100MWh), minimizing the total cost.

[0095] By using a column constraint generation algorithm, the source-grid-load-storage coordinated optimization model is iteratively solved, ultimately yielding the optimal equipment configuration scheme (i.e., investment decision variables). This ensures that the total system cost is minimized while satisfying operational and frequency constraints. After algorithm convergence, the output can be directly used for real-time control. The entire process effectively handles uncertainties and integer variables, improving the system's economy and reliability.

[0096] The source-grid-load-storage coordinated regulation method provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0097] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0098] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0099] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0100] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0101] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0102] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0103] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0104] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0105] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0106] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0107] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0108] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0109] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0110] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0111] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0113] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.

[0114] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the invention.

[0115] The aforementioned electronic equipment enables the acquisition of input data from the multi-energy system for the coordinated regulation of energy sources, grid, load, and storage as described in this application. Based on the input data, a coordinated optimization model for energy sources, grid, load, and storage is established. This model includes an upper-level investment decision model, a middle-level operation optimization model, and a lower-level frequency control model. The upper-level model aims to minimize planned investment costs, the middle-level model aims to minimize operating costs, and the lower-level model aims to minimize frequency control costs. An optimization algorithm is used to solve the coordinated optimization model to obtain the optimal equipment configuration scheme. According to the optimal equipment configuration scheme, the multi-energy system is regulated in real time, including equipment switching, output adjustment, and load management, to achieve coordinated optimization of energy sources, grid, load, and storage. This achieves coordinated optimization across multiple time scales, effectively handles the uncertainty of new energy sources, and balances system economy and operational reliability.

[0116] The storage medium provided in this application stores a program product capable of achieving coordinated regulation of power generation, grid, load, and storage.

[0117] Coordinated regulation of power generation, grid, load, and storage includes: Step S1: Obtain input data for the multi-energy system; Step S2: Based on the input data, establish a source-grid-load-storage coordinated optimization model. The source-grid-load-storage coordinated optimization model includes an upper-level investment decision model, a middle-level operation optimization model, and a lower-level frequency control model. The upper-level model aims to minimize the planned investment cost, the middle-level model aims to minimize the operation cost, and the lower-level model aims to minimize the frequency control cost. Step S3: Solve the source-grid-load-storage collaborative optimization model using an optimization algorithm to obtain the optimal equipment configuration scheme; Step S4: Based on the optimal equipment configuration scheme, perform real-time control of the multi-energy system, including equipment switching, output adjustment and load management, to achieve coordinated optimization of source, grid, load and storage.

[0118] By establishing a three-layer collaborative optimization model that includes upper-level investment decision-making, mid-level operation optimization, and lower-level frequency control, the planning, operation, and control links are organically unified, effectively avoiding suboptimal problems caused by independent optimization and significantly improving the overall economic efficiency of the system.

[0119] In some possible implementations, the source-grid-load-storage coordinated regulation of this disclosure can be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0120] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0121] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0122] For those skilled in the art, designing different forms of control circuits according to the teachings of this invention does not require creative effort. Changes, modifications, substitutions, and variations made to the embodiments without departing from the principles and spirit of this invention still fall within the scope of protection of this invention.

Claims

1. A method for coordinated regulation of power generation, grid, load, and storage, characterized in that, The method includes: Step S1: Obtain input data for the multi-energy system; Step S2: Based on the input data, establish a source-grid-load-storage coordinated optimization model. The source-grid-load-storage coordinated optimization model includes an upper-level investment decision model, a middle-level operation optimization model, and a lower-level frequency control model. The upper-level model aims to minimize the planned investment cost, the middle-level model aims to minimize the operation cost, and the lower-level model aims to minimize the frequency control cost. Step S3: Solve the source-grid-load-storage collaborative optimization model using an optimization algorithm to obtain the optimal equipment configuration scheme; Step S4: Based on the optimal equipment configuration scheme, perform real-time control of the multi-energy system, including equipment switching, output adjustment and load management, to achieve coordinated optimization of source, grid, load and storage.

2. The method as described in claim 1, characterized in that, The objective function of the upper-level investment decision model is: in, For investment costs, For equipment The unit investment cost For equipment Rated capacity, For equipment The investment decision variables are denoted by , where 0 represents no investment and 1 represents investment.

3. The method as described in claim 1, characterized in that, The objective function of the mid-level operation optimization model is to minimize the operating cost, and its expression is: in, Operating costs; To optimize the total number of time periods; For time period The cost of electricity generation, For time period Environmental costs; For time period Demand response incentive costs; For time period The cost of penalties for abandoning wind and solar power.

4. The method as described in claim 3, characterized in that, The expression for the cost of electricity generation is: in, For power generation equipment The unit cost of electricity generation For equipment During the period Those who have made meritorious contributions; The expression for environmental cost is: in, pollutants The unit pollution equivalent charging standard, pollutants unit emissions, pollutants The pollution equivalent value, For thermal power units During the period contribution; The expression for the demand response incentive cost is: in, To reduce load The unit incentive price, To reduce load During the period The amount of reduction, For transferable load The unit incentive price, For transferable load During the period The amount transferred; The expression for the penalty cost of wind and solar power curtailment is: in, The penalty cost per unit for curtailing wind and solar power; For wind power during the time period The predicted output; To actually contribute to wind power; For photovoltaics during the time period The predicted output; To make a real contribution to photovoltaic power.

5. The method as described in claim 1, characterized in that, The objective function of the lower-level frequency control model is: in, For equipment Frequency regulation unit cost For equipment FM output, This refers to the regional control error.

6. The method as described in claim 5, characterized in that, The area control error The calculation formula is: in, This is the frequency deviation coefficient. For system frequency deviation, This refers to the power deviation of the tie line.

7. The method as described in claim 6, characterized in that, In step S3, an optimization algorithm is used to solve the source-grid-load-storage collaborative optimization model to obtain the optimal equipment configuration scheme, specifically including: Step S31: Linearize the nonlinear constraints in the source-grid-load-storage collaborative optimization model into a linear form, and output the linearized mixed integer linear programming model; Step S32: Set algorithm parameters, including the iteration counter. Convergence threshold Upper Realm and the lower realm And initialize the values ​​of uncertainty variables, including using the expected values ​​of wind power forecast output and photovoltaic forecast output as the initial worst-case scenario, and initialize investment decision variables; Step S33: Solve the upper-level investment decision model to minimize investment costs and auxiliary variables. With the objective as the goal, and adding feasibility cut constraints obtained from previous iterations, the master problem is solved using a mixed-integer linear programming solver to obtain the investment decision solution. and auxiliary variables and update the lower bound. ; Step S34: Given the investment decision solution to the main problem Solving the mid-level operation optimization and lower-level frequency control models aims to minimize the operation cost and frequency control cost, while considering the uncertainty set and employing strong duality theory to solve the subproblems. The problem is transformed into a single-level optimization problem, solved using a linear programming solver, yielding the worst-case scenario. Objective value of subproblem and update the upper bound. ; Step S35: Calculate the relative gap ,like If the algorithm converges, proceed to step S37; otherwise, continue to step S36. Step S36: Add constraints and iterate, add the solutions to subproblems as new constraints to the main problem, add feasibility cuts, and update the iteration counter. and return to step S33; Step S37: After the algorithm converges, output the optimal investment decision solution, including the investment location and capacity of power equipment, energy storage equipment and lines.

8. A system applied to the source-grid-load-storage coordinated control method as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition module is used to acquire input data from the multi-energy system; The optimization model construction module is used to establish a source-grid-load-storage coordinated optimization model based on the input data. The source-grid-load-storage coordinated optimization model includes an upper-level investment decision model, a middle-level operation optimization model, and a lower-level frequency control model. The upper-level model aims to minimize the planned investment cost, the middle-level model aims to minimize the operation cost, and the lower-level model aims to minimize the frequency control cost. The model solving module is used to solve the source-grid-load-storage collaborative optimization model using optimization algorithms to obtain the optimal equipment configuration scheme; The solution application module is used to perform real-time control of the multi-energy system according to the optimal equipment configuration scheme, including equipment switching, output adjustment and load management, so as to achieve coordinated optimization of source, grid, load and storage.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the source-grid-load-storage coordinated control method as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the source-grid-load-storage coordinated control method as described in any one of claims 1-7.

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