Virtual power plant-microgrid collaborative optimization scheduling method and system for peak regulation scenario

Abstract

The embodiment of the application provides a kind of virtual power plant-microgrid collaborative optimization scheduling method and system for peak regulation scene, its method includes: virtual power plant sends different time period peak regulation demand to each microgrid, receives the adjustable capacity and the regulation cost of each microgrid in different time period according to the different time period peak regulation demand;The virtual power plant constructs the objective function of minimizing virtual power plant total peak regulation cost according to the adjustable capacity and the regulation cost of each microgrid in different time period;The virtual power plant establishes the constraint condition of virtual power plant total peak regulation cost, including regulation ability constraint, power balance constraint and variable boundary constraint;The virtual power plant solves the objective function under the constraint condition including regulation ability constraint, power balance constraint and variable boundary constraint, obtains the power regulation amount of each microgrid, so that each microgrid carries out collaborative optimization scheduling according to its corresponding power regulation amount.

Description

Technical Field

[0001] This application relates to the field of virtual power plant technology, and in particular to a method and system for collaborative optimization scheduling of virtual power plants and microgrids for peak shaving scenarios. Background Technology

[0002] A virtual power plant is an energy management system that integrates and manages distributed energy resources, energy storage, and controllable loads to optimize the operation of the power system. As the proportion of distributed energy resources such as wind and solar power in the distribution network increases, the intermittent and random nature of their output leads to increased load fluctuations in the distribution network. For example, solar power output fluctuates significantly during the day and is zero at night, forming a "duck curve." Traditional peak-shaving power sources (such as coal-fired units) need frequent start-ups and shutdowns or deep peak shaving, resulting in decreased efficiency, increased costs, and pressure for low-carbon transformation. Flexible, low-carbon peak-shaving resources are needed to participate in the operation of the distribution network. Microgrids, as independent units containing energy storage, adjustable loads, and distributed power sources, have the potential to quickly respond to peak-shaving demands.

[0003] Traditional peak-shaving methods have the following bottlenecks: 1) Coal-fired units: slow peak-shaving speed, high start-up and shutdown costs, and large carbon emissions, which do not meet the "dual carbon" target; 2) Gas-fired units: high cost and uneven resource distribution; 3) Large-scale energy storage: high investment cost and geographically limited layout. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method for collaborative optimization scheduling of virtual power plants and microgrids for peak-shaving scenarios. One or more embodiments of this application also relate to a collaborative optimization scheduling system for virtual power plants and microgrids for peak-shaving scenarios, a computing device, a computer-readable storage medium, and a computer program, to address the technical deficiencies in the prior art.

[0005] According to a first aspect of the embodiments of this application, a method for collaborative optimization scheduling of virtual power plants and microgrids in peak-shaving scenarios is provided, including:

[0006] The virtual power plant sends peak-shaving demands for different time periods to each microgrid and receives the adjustable capacity and adjustment cost determined by each microgrid for different time periods based on the peak-shaving demands for those different time periods.

[0007] The virtual power plant constructs an objective function that minimizes the total peak-shaving cost of the virtual power plant based on the adjustable capacity and regulation cost of each microgrid at different time periods.

[0008] The virtual power plant establishes constraints on the total peak-shaving cost of the virtual power plant, including constraints on regulation capacity, power balance, and variable boundary constraints.

[0009] The virtual power plant solves the objective function under the constraints including regulation capacity constraints, power balance constraints, and variable boundary constraints to obtain the power regulation amount of each microgrid, so that each microgrid can perform collaborative optimization scheduling based on its corresponding power regulation amount.

[0010] Preferably, the objective function for minimizing the total peak-shaving cost of the virtual power plant includes:

[0011]

[0012] Where: nt represents the number of time periods per day; This indicates the number of microgrids contained in the virtual power plant; This represents the regulation cost of microgrid m during time period t; Indicates the conversion factor for time period length; This represents the increase in load on microgrid m during time period t; This represents the reduction in load of microgrid m during time period t.

[0013] Preferably, the adjustment capability constraint includes:

[0014] The increase in load of microgrid m in time period t is not greater than the product of the absolute value of the adjustable capacity matrix of microgrid m in time period t and the load increase indicator of microgrid m in time period t.

[0015] The load reduction of microgrid m in time period t is no greater than the product of the absolute value of the adjustable capacity matrix of microgrid m in time period t and the load reduction indicator of microgrid m in time period t.

[0016] The sum of the load increase and load decrease indicators of microgrid m in time period t is no greater than 1.

[0017] Preferably, the power balance constraint includes:

[0018] The sum of the power regulation of all microgrids equals the total regulation power demand of the virtual power plant.

[0019] Preferably, the variable boundary constraints include:

[0020] The increase in load of microgrid m in time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of microgrid m in time period t;

[0021] The load reduction of microgrid m in time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of microgrid m in time period t;

[0022] The load increase flag for microgrid m during time period t takes the value of 0 or 1;

[0023] The load reduction flag for microgrid m during time period t is either 0 or 1.

[0024] According to a second aspect of the embodiments of this application, a virtual power plant-microgrid collaborative optimization scheduling system for peak-shaving scenarios is provided, including a virtual power plant and multiple microgrids; wherein, the virtual power plant includes:

[0025] The receiving module is configured to receive the adjustable capacity and adjustment cost of each microgrid in different time periods based on the peak shaving demand in different time periods by sending peak shaving demand to each microgrid in different time periods.

[0026] The objective function construction module is configured to construct an objective function that minimizes the total peak-shaving cost of the virtual power plant based on the adjustable capacity and regulation cost of each microgrid at different time periods.

[0027] The constraint module is configured to establish constraints for the total peak-shaving cost of the virtual power plant, including regulation capacity constraints, power balance constraints, and variable boundary constraints.

[0028] The collaborative optimization scheduling module is configured to solve the objective function under the constraints including regulation capacity constraints, power balance constraints and variable boundary constraints to obtain the power regulation amount of each microgrid, so that each microgrid can perform collaborative optimization scheduling according to its corresponding power regulation amount.

[0029] Preferably, the module for constructing the objective function includes:

[0030]

[0031] Where: nt represents the number of time periods per day; This indicates the number of microgrids contained in the virtual power plant; This represents the regulation cost of microgrid m during time period t; Indicates the conversion factor for time period length; This represents the increase in load on microgrid m during time period t; This represents the reduction in load of microgrid m during time period t.

[0032] Preferably, the constraint establishment module includes:

[0033] The regulation capacity constraints include: the increase in load of microgrid m in time period t is not greater than the product of the absolute value of the adjustable capacity matrix of microgrid m in time period t and the load increase indicator of microgrid m in time period t; the decrease in load of microgrid m in time period t is not greater than the product of the absolute value of the adjustable capacity matrix of microgrid m in time period t and the load decrease indicator of microgrid m in time period t; and the sum of the load increase indicator and the load decrease indicator of microgrid m in time period t is not greater than 1.

[0034] The power balance constraint includes: the sum of the power regulation of all microgrids equals the total regulation power demand of the virtual power plant;

[0035] The variable boundary constraints include: the increase in load of microgrid m in time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of microgrid m in time period t; the decrease in load of microgrid m in time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of microgrid m in time period t; the value of the load increase flag of microgrid m in time period t is 0 or 1; the value of the load decrease flag of microgrid m in time period t is 0 or 1.

[0036] According to a third aspect of the embodiments of this application, a computing device is provided, comprising:

[0037] Memory and processor;

[0038] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement any of the steps of the virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios.

[0039] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, which stores computer-executable instructions that, when executed by a processor, implement the steps of any one of the methods for collaborative optimization scheduling of virtual power plants and microgrids for peak-shaving scenarios.

[0040] According to a fifth aspect of the present application, a computer program is provided, wherein when the computer program is executed in a computer, the computer is instructed to perform the steps of the above-described method for collaborative optimization scheduling of virtual power plants and microgrids for peak-shaving scenarios.

[0041] The virtual power plant-microgrid collaborative optimization scheduling scheme for peak-shaving scenarios provided in this application embodiment involves the virtual power plant sending peak-shaving demands for different time periods to each microgrid and receiving the adjustable capacity and regulation costs determined by each microgrid for different time periods based on these demands. The virtual power plant then constructs an objective function to minimize the total peak-shaving cost of the virtual power plant based on the adjustable capacity and regulation costs of each microgrid in different time periods. The virtual power plant establishes constraints on the total peak-shaving cost, including regulation capacity constraints, power balance constraints, and variable boundary constraints. Under these constraints, the virtual power plant solves the objective function to obtain the power regulation amount for each microgrid, enabling each microgrid to perform collaborative optimization scheduling based on its corresponding power regulation amount. By rationally allocating the power regulation amounts of each microgrid, the overall regulation cost is minimized while meeting peak-shaving demands, thereby improving the economy and flexibility of the virtual power plant operation. Attached Figure Description

[0042] Figure 1 This is a flowchart of a virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios provided in one embodiment of this application;

[0043] Figure 2 This is an overall flowchart of a virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios provided in one embodiment of this application;

[0044] Figure 3 This is a schematic diagram of a virtual power plant-microgrid collaborative optimization scheduling system for peak-shaving scenarios provided in one embodiment of this application;

[0045] Figure 4 This is a structural block diagram of a computing device provided in one embodiment of this application. Detailed Implementation

[0046] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0047] The terminology used in one or more embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this application. The singular forms “a,” “the,” and “the” used in one or more embodiments of this application and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” used in one or more embodiments of this application refers to and includes any or all possible combinations of one or more associated listed items.

[0048] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this application, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0049] This application provides a virtual power plant-microgrid collaborative optimization scheduling method for peak shaving scenarios. This application also relates to a virtual power plant-microgrid collaborative optimization scheduling system for peak shaving scenarios, a computing device, a computer-readable storage medium, and a computer program, which will be described in detail in the following embodiments.

[0050] Individual microgrids have limited regulation capacity, and different microgrids exhibit differences in adjustable capacity (MGcap) and regulation cost (MGcost). Utilizing a widely distributed microgrid cluster for coordinated peak shaving, and optimizing the regulation strategies of multiple microgrids through algorithms, can fully tap the overall peak shaving potential of the microgrid cluster, reduce the system's dependence on large-scale peak-shaving power sources, and achieve optimal resource allocation. Therefore, this application proposes a virtual power plant-microgrid coordinated optimization scheduling model for peak shaving scenarios, realizing coordinated peak shaving optimization between virtual power plants and multiple microgrids, and minimizing peak shaving costs through mixed integer linear programming (MILP). The core idea is to decompose the peak shaving demand of the virtual power plant into multiple microgrids for execution, considering the regulation capacity and cost differences of each microgrid at different time periods, to achieve optimal coordination among multiple microgrids. The overall interaction process between the virtual power plant and multiple microgrids includes:

[0051] Step 1: The virtual power plant receives peak-shaving requests from the upstream power grid;

[0052] Step 2: The virtual power plant issues demand response invitation information to the microgrid based on peak shaving needs, such as increasing the load by 1MW in 10-12 hours, or reducing the load by 2MW in 19-20 hours;

[0053] Step 3: After receiving the invitation information, the microgrid calculates the adjustable capacity and cost for the corresponding time period according to the invitation response period, and reports it to the virtual power plant;

[0054] Step 4: The virtual power plant optimizes the response power of each microgrid based on the adjustable capacity and cost reported by each microgrid, and sends the results to each microgrid.

[0055] Step 5: The microgrid performs internal optimization operation based on the response power issued by the virtual power plant;

[0056] Furthermore, the present invention provides further explanation of the specific implementation process of steps 3-4, such as... Figure 1 As shown.

[0057] Figure 1 A flowchart of a virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios is shown according to an embodiment of this application, which specifically includes the following steps.

[0058] Step S101: The virtual power plant sends peak-shaving demands for different time periods to each microgrid and receives the adjustable capacity and adjustment cost determined by each microgrid for different time periods based on the peak-shaving demands for different time periods.

[0059] Step S102: The virtual power plant constructs an objective function that minimizes the total peak-shaving cost of the virtual power plant based on the adjustable capacity and regulation cost of each microgrid at different time periods;

[0060] In one embodiment of this application, the objective function for minimizing the total peak-shaving cost of the virtual power plant includes:

[0061]

[0062] Where: nt represents the number of time periods per day; This indicates the number of microgrids contained in the virtual power plant; This represents the regulation cost of microgrid m during time period t; Indicates the conversion factor for time period length; This represents the increase in load on microgrid m during time period t; This represents the reduction in load of microgrid m during time period t.

[0063] Step S103: The virtual power plant establishes constraints on the total peak-shaving cost of the virtual power plant, including constraints on regulation capacity, power balance, and variable boundary constraints.

[0064] In one embodiment of this application, the adjustment capability constraint includes:

[0065] The increase in load of microgrid m in time period t is not greater than the product of the absolute value of the adjustable capacity matrix of microgrid m in time period t and the load increase indicator of microgrid m in time period t.

[0066] The load reduction of microgrid m in time period t is no greater than the product of the absolute value of the adjustable capacity matrix of microgrid m in time period t and the load reduction indicator of microgrid m in time period t.

[0067] The sum of the load increase and load decrease indicators of microgrid m in time period t is no greater than 1.

[0068] In one embodiment of this application, the power balance constraint includes:

[0069] The sum of the power regulation of all microgrids equals the total regulation power demand of the virtual power plant.

[0070] In one embodiment of this application, the variable boundary constraints include:

[0071] The increase in load of microgrid m in time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of microgrid m in time period t;

[0072] The load reduction of microgrid m in time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of microgrid m in time period t;

[0073] The load increase flag for microgrid m during time period t takes the value of 0 or 1;

[0074] The load reduction flag for microgrid m during time period t is either 0 or 1.

[0075] Step S104: The virtual power plant solves the objective function under the constraints including regulation capacity constraints, power balance constraints and variable boundary constraints to obtain the power regulation amount of each microgrid, so that each microgrid can perform collaborative optimization scheduling according to its corresponding power regulation amount.

[0076] This application embodiment, after obtaining the power regulation amount of each microgrid from the virtual power plant, also provides a distributed energy optimization scheduling method based on the virtual power plant. Specifically, it includes: constructing a corresponding objective function with the goal of minimizing the resource consumption of the virtual power plant, combining the resource consumption of the equipment included in the virtual power plant within a target period and the amount of electricity purchased and sold by the virtual power plant within the target period; constructing constraints corresponding to the target equipment based on the power and status of the target equipment at different times within the target period, wherein the target equipment is each of the equipment included in the virtual power plant; and determining the distributed energy optimization scheduling result of the virtual power plant in the target time period according to the objective function and the constraints.

[0077] The virtual power plant includes equipment such as micro gas turbines. Accordingly, the step of constructing a corresponding objective function by combining the resource consumption of the equipment in the virtual power plant within a target period and the amount of electricity purchased and sold by the virtual power plant within the target period includes: determining the power generation resource consumption and start-up resource consumption of the micro gas turbine within the target period; determining the electricity purchase resource consumption and electricity sales resource acquisition of the virtual power plant within the target period; and constructing a corresponding objective function based on the power generation resource consumption, the start-up resource consumption, the electricity purchase resource consumption, and the electricity sales resource acquisition.

[0078] In one embodiment of this application, the equipment included in the virtual power plant further includes charging piles; correspondingly, the method further includes: determining the accumulated resources of charging opportunities, accumulated resources of discharging opportunities, accumulated resources of interruptible loads, and accumulated resources of transferable loads corresponding to the charging piles; and constructing a corresponding objective function based on the accumulated resources of charging opportunities, the accumulated resources of discharging opportunities, the accumulated resources of interruptible loads, the accumulated resources of transferable loads, the amount of electricity purchased by the virtual power plant in the target period, and the amount of electricity acquired.

[0079] In one embodiment of this application, the method further includes: determining the charging opportunity accumulation resources corresponding to the charging pile based on the charging power of the charging pile, the index value of the target index corresponding to the charging pile in each time period, and the minimum value of the index value of the target index corresponding to each time period, wherein the target index is each of the multiple indexes of the charging pile.

[0080] In one embodiment of this application, the virtual power plant includes equipment including a micro gas turbine device; correspondingly, the step of constructing the constraint conditions corresponding to the target device based on the power and status of the target device at different times within the target period includes: determining upper and lower power limits based on the startup variables and rated power of the micro gas turbine device; constructing a first inequality constraint condition corresponding to the micro gas turbine device based on the upper and lower power limits and the power of the micro gas turbine device at different times within the target period; and constructing a second inequality constraint condition corresponding to the micro gas turbine device based on the running time of the micro gas turbine device and the sum of the operating states of the micro gas turbine device within the running time.

[0081] In one embodiment of this application, the virtual power plant includes an energy storage device; correspondingly, the step of constructing the constraint conditions corresponding to the target device based on the power and status of the target device at different times within the target period includes: determining a first power upper limit value of the energy storage device based on the charging status flag and rated power of the energy storage device; constructing inequality constraint conditions corresponding to the charging power of the energy storage device at different times within the target period based on the first power upper limit value; determining a second power upper limit value of the energy storage device based on the discharging status flag and rated power of the energy storage device; and constructing inequality constraint conditions corresponding to the discharging power of the energy storage device at different times within the target period based on the second power upper limit value.

[0082] In one embodiment of this application, the virtual power plant includes charging piles; correspondingly, the step of constructing the constraints corresponding to the target device based on the power and status of the target device at different times within the target period includes: determining the upper limit of the transfer power of the charging pile based on the resource transfer flag of the charging pile at different times within the target period and the transferable power limit of the charging pile; and constructing inequality constraints corresponding to the transfer power of the charging pile at different times within the target period based on the upper limit.

[0083] In one embodiment of this application, the step of constructing the constraints corresponding to the target device based on the power and status of the target device at different times within the target period includes: determining the power of each target device included in the virtual power plant at different times within the target period; and constructing a corresponding power balance equation constraint based on the sum of the power and the total system load of the virtual power plant.

[0084] In one embodiment of this application, the virtual power plant includes an energy storage device; correspondingly, the step of constructing the constraint conditions corresponding to the target device based on the power and state of the target device at different times within the target period includes: constructing the equation constraint conditions corresponding to the state of charge transition of the energy storage device based on the state of charge of the energy storage device at time t, the state of charge of the energy storage device at time t-1, the charging power and discharging power of the energy storage device at different times within the target period, and the rated capacity of the energy storage device.

[0085] In summary, the virtual power plant-microgrid collaborative optimization scheduling scheme for peak-shaving scenarios provided in this application utilizes a widely distributed microgrid group for collaborative peak shaving. By coordinating and optimizing the regulation strategies of multiple microgrids through algorithms, the overall peak-shaving potential of the microgrid group can be fully explored, reducing the system's dependence on large-scale peak-shaving power sources and achieving optimal resource allocation. At the same time, it also comprehensively considers various distributed energy sources, energy storage systems, flexible loads, and interactions with the main power grid, minimizing the total daily operating cost of the virtual power plant while meeting system operation constraints.

[0086] The following is in conjunction with the appendix Figure 2 Taking the application of the virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios provided in this application in the field of power system management as an example, the method will be further explained. Figure 2 The diagram illustrates a process flow of a virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios, provided by an embodiment of this application, which specifically includes the following steps.

[0087] Step 201: Input parameters;

[0088] The input parameters include the virtual power plant's peak-shaving demand, the microgrid's adjustable capacity matrix, and the microgrid's adjustment cost matrix. Specific input parameters are shown in Table 1.

[0089] Table 1: Input Parameter Table

[0090]

[0091] Step 202: Initialize the optimization model;

[0092] Step 203: Construct the objective function;

[0093] Decision variables: For each microgrid m at each time period t:

[0094] The microgrid increases its load (non-negative). Microgrids reduce load (non-negative); : Load increase indicator (0-1); : Load reduction indicator (0-1).

[0095] Objective function: Minimize total peak shaving cost:

[0096]

[0097] Where: nt represents the number of time periods per day; This indicates the number of microgrids contained in the virtual power plant; This represents the regulation cost of microgrid m during time period t; Indicates the conversion factor for time period length; This represents the increase in load on microgrid m during time period t; This represents the reduction in load of microgrid m during time period t.

[0098] Step 204: Set variable boundaries;

[0099]

[0100] This represents the absolute value of the adjustable capacity matrix of microgrid m during time period t.

[0101] The variable boundaries include: the increase in load of microgrid m in time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of microgrid m in time period t; the decrease in load of microgrid m in time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of microgrid m in time period t; the value of the load increase flag of microgrid m in time period t is 0 or 1; the value of the load decrease flag of microgrid m in time period t is 0 or 1.

[0102] Step 205: Add constraints on adjustment capability;

[0103]

[0104] The regulation capacity constraints include: the increase in load of microgrid m in time period t is not greater than the product of the absolute value of the adjustable capacity matrix of microgrid m in time period t and the load increase indicator of microgrid m in time period t; the decrease in load of microgrid m in time period t is not greater than the product of the absolute value of the adjustable capacity matrix of microgrid m in time period t and the load decrease indicator of microgrid m in time period t; and the sum of the load increase indicator and the load decrease indicator of microgrid m in time period t is not greater than 1.

[0105] Step 206: Add power balance constraints; the sum of power regulation of all microgrids equals the total regulation power demand of the virtual power plant. Its formula is:

[0106]

[0107] This represents the sum of power regulation across all microgrids.

[0108] It should be noted that this application also includes a two-way adjustment mechanism, mutual exclusion constraints, and an economic orientation;

[0109] The bidirectional regulation mechanism includes:

[0110] Load increase mode: ;

[0111] Load reduction mode: ;

[0112] Idle mode: .

[0113] Mutual exclusion constraints include:

[0114] The same microgrid cannot simultaneously increase and decrease its load at the same time.

[0115] pass accomplish.

[0116] Economic orientation includes:

[0117] Automatically select the microgrid with the lowest cost to perform peak shaving;

[0118] Consider the spatiotemporal differences in the regulation capability of microgrids.

[0119] Step 207: Solve the MILP;

[0120] Step 208: Result Extraction;

[0121] Step 209: Output the optimization solution.

[0122] The input parameters include the actual regulation of the microgrid, the microgrid regulation status, the total peak-shaving cost, and the solution status code. The specific output parameters are shown in Table 2.

[0123] Table 2: Output Parameter Table

[0124]

[0125] This application's embodiments are based on a microgrid regulation cost (MGcost) optimization scheduling strategy. It prioritizes low-cost microgrids for peak shaving and minimizes the overall regulation cost while meeting peak shaving requirements by rationally allocating the power regulation of each microgrid. This avoids the overuse of high-cost regulation resources and improves the economy and flexibility of virtual power plant operation. Furthermore, the microgrid can dynamically respond to peak shaving requirements based on its own capacity (MGcap), enhancing the virtual power plant's ability to cope with load fluctuations. It also supports multi-microgrid collaborative scheduling to adapt to complex virtual power plant topologies.

[0126] This application also provides an embodiment of a virtual power plant-microgrid collaborative optimization scheduling system for peak-shaving scenarios. Figure 3 This application provides a schematic diagram of the structure of a virtual power plant-microgrid collaborative optimization scheduling system for peak-shaving scenarios, as shown in one embodiment. Figure 3 As shown, it includes a virtual power plant and multiple microgrids; wherein, the virtual power plant includes:

[0127] The receiving module is configured to receive the adjustable capacity and adjustment cost of each microgrid in different time periods based on the peak shaving demand in different time periods by sending peak shaving demand to each microgrid in different time periods.

[0128] The objective function construction module is configured to construct an objective function that minimizes the total peak-shaving cost of the virtual power plant based on the adjustable capacity and regulation cost of each microgrid at different time periods.

[0129] The constraint module is configured to establish constraints for the total peak-shaving cost of the virtual power plant, including regulation capacity constraints, power balance constraints, and variable boundary constraints.

[0130] The collaborative optimization scheduling module is configured to solve the objective function under the constraints including regulation capacity constraints, power balance constraints and variable boundary constraints to obtain the power regulation amount of each microgrid, so that each microgrid can perform collaborative optimization scheduling according to its corresponding power regulation amount.

[0131] In one embodiment of this application, the module for constructing the objective function includes:

[0132]

[0133] Where: nt represents the number of time periods per day; This indicates the number of microgrids contained in the virtual power plant; This represents the regulation cost of microgrid m during time period t; Indicates the conversion factor for time period length; This represents the increase in load on microgrid m during time period t; This represents the reduction in load of microgrid m during time period t.

[0134] In one embodiment of this application, the constraint establishment module includes:

[0135] The regulation capacity constraints include: the increase in load of microgrid m in time period t is not greater than the product of the absolute value of the adjustable capacity matrix of microgrid m in time period t and the load increase indicator of microgrid m in time period t; the decrease in load of microgrid m in time period t is not greater than the product of the absolute value of the adjustable capacity matrix of microgrid m in time period t and the load decrease indicator of microgrid m in time period t; and the sum of the load increase indicator and the load decrease indicator of microgrid m in time period t is not greater than 1.

[0136] The power balance constraint includes: the sum of the power regulation of all microgrids equals the total regulation power demand of the virtual power plant;

[0137] The variable boundary constraints include: the increase in load of microgrid m in time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of microgrid m in time period t; the decrease in load of microgrid m in time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of microgrid m in time period t; the value of the load increase flag of microgrid m in time period t is 0 or 1; the value of the load decrease flag of microgrid m in time period t is 0 or 1.

[0138] The above is an illustrative scheme of a virtual power plant-microgrid collaborative optimization scheduling system for peak-shaving scenarios according to this embodiment. It should be noted that the technical solution of this virtual power plant-microgrid collaborative optimization scheduling system for peak-shaving scenarios belongs to the same concept as the technical solution of the aforementioned virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios. Details not described in detail in the technical solution of the virtual power plant-microgrid collaborative optimization scheduling system for peak-shaving scenarios can be found in the description of the technical solution of the aforementioned virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios.

[0139] Figure 4 A structural block diagram of a computing device 400 according to an embodiment of this application is shown. The components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. The processor 420 is connected to the memory 410 via a bus 430, and a database 450 is used to store data.

[0140] The computing device 400 also includes an access device 440, which enables the computing device 400 to communicate via one or more networks 460. Examples of these networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 440 may include one or more of any type of wired or wireless network interface (e.g., a Network Interface Card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) interface, a Wi-MAX interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and so on.

[0141] In one embodiment of this application, the aforementioned components of the computing device 400 and Figure 4 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 4 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this application. Those skilled in the art can add or replace other components as needed.

[0142] The computing device 400 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or PCs. The computing device 400 can also be a mobile or stationary server.

[0143] The processor 420 is used to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the above-mentioned virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios.

[0144] The above is a schematic scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device belongs to the same concept as the technical solution of the above-described virtual power plant-microgrid collaborative optimization scheduling method for peak shaving scenarios. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the above-described virtual power plant-microgrid collaborative optimization scheduling method for peak shaving scenarios.

[0145] An embodiment of this application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-described virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios.

[0146] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solution of the above-described virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the above-described virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios.

[0147] An embodiment of this application also provides a computer program, wherein when the computer program is executed in a computer, the computer is instructed to perform the steps of the above-described virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios.

[0148] The above is an illustrative scheme of a computer program according to this embodiment. It should be noted that the technical solution of this computer program belongs to the same concept as the above-described technical solution of the virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios. For details not described in detail in the technical solution of the computer program, please refer to the description of the above-described technical solution of the virtual power plant-microgrid collaborative optimization scheduling method for peak-shaving scenarios.

[0149] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0150] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0151] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments of this application.

[0152] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0153] The preferred embodiments disclosed above are merely illustrative of this application. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments of this application. These embodiments are selected and specifically described in this application to better explain the principles and practical applications of the embodiments of this application, thereby enabling those skilled in the art to better understand and utilize this application. This application is limited only by the claims and their full scope and equivalents.

Claims

1. A method for coordinated optimization scheduling of virtual power plant-microgrid for peak regulation scenario, characterized in that, The virtual power plant receives, by sending different time period peak shaving demands to each micro-grid, adjustable capacity and adjustment cost of each micro-grid in different time periods determined according to the different time period peak shaving demands; The virtual power plant constructs a target function of minimizing total peak shaving cost of the virtual power plant according to the adjustable capacity and adjustment cost of each micro-grid in different time periods; The virtual power plant establishes constraint conditions of the total peak shaving cost of the virtual power plant containing adjustment capacity constraints, power balance constraints and variable boundary constraints; The virtual power plant solves the target function under the constraint conditions containing the adjustment capacity constraints, the power balance constraints and the variable boundary constraints to obtain power adjustment amount of each micro-grid so that each micro-grid is cooperatively and optimally scheduled according to the corresponding power adjustment amount; The adjustment capacity constraints include: the increase load amount of the micro-grid m in the time period t is not greater than the product of the absolute value of the adjustable capacity matrix of the micro-grid m in the time period t and the load increase flag of the micro-grid m in the time period t; The objective function of minimizing the total peak shaving cost of the virtual power plant comprises: ; Wherein: nt represents the number of time periods per day; represents the number of microgrids contained in the virtual power plant; represents the adjustment cost of the microgrid m at the time period t; represents the time period length conversion coefficient; represents the increased load amount of the microgrid m at the time period t; represents the decreased load amount of the microgrid m at the time period t; The decrease load amount of the micro-grid m in the time period t is not greater than the product of the absolute value of the adjustable capacity matrix of the micro-grid m in the time period t and the load decrease flag of the micro-grid m in the time period t; The sum of the load increase flag and the load decrease flag of the micro-grid m in the time period t is not greater than 1; The power balance constraints include: the sum of power adjustment of all micro-grids is equal to total adjustment power demand of the virtual power plant; The variable boundary constraints include: the increase load amount of the micro-grid m in the time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of the micro-grid m in the time period t; The decrease load amount of the micro-grid m in the time period t is not less than 0 and not greater than the absolute value of the adjustable capacity matrix of the micro-grid m in the time period t; The load increase flag of the micro-grid m in the time period t is 0 or 1; The load decrease flag of the micro-grid m in the time period t is 0 or 1. The virtual power plant and a plurality of micro-grids are included, wherein the virtual power plant includes a processor configured to implement the method for cooperatively and optimally scheduling the virtual power plant and the micro-grids in a peak shaving scenario according to claim 1, and the virtual power plant includes:

2. A virtual power plant-microgrid collaborative optimization scheduling system for peak regulation scenarios, characterized in that, A receiving module configured to receive, by sending different time period peak shaving demands to each micro-grid, adjustable capacity and adjustment cost of each micro-grid in different time periods determined according to the different time period peak shaving demands; A target function constructing module configured to construct a target function of minimizing total peak shaving cost of the virtual power plant according to the adjustable capacity and adjustment cost of each micro-grid in different time periods; A constraint condition establishing module configured to establish constraint conditions of the total peak shaving cost of the virtual power plant containing adjustment capacity constraints, power balance constraints and variable boundary constraints; A cooperatively and optimally scheduling module configured to solve the target function under the constraint conditions containing the adjustment capacity constraints, the power balance constraints and the variable boundary constraints to obtain power adjustment amount of each micro-grid so that each micro-grid is cooperatively and optimally scheduled according to the corresponding power adjustment amount; The target function constructing module includes: The constraint condition establishing module includes: ； Wherein: nt represents the number of time periods per day; represents the number of microgrids contained in the virtual power plant; represents the adjustment cost of the microgrid m at the time period t; represents the time period length conversion coefficient; represents the increased load amount of the microgrid m at the time period t; represents the decreased load amount of the microgrid m at the time period t; ​ The regulation capability constraint includes: an increase load amount of the micro-grid m at the time period t is not greater than a product of an absolute value of the adjustable capacity matrix of the micro-grid m at the time period t and the load increase flag of the micro-grid m at the time period t; a decrease load amount of the micro-grid m at the time period t is not greater than a product of an absolute value of the adjustable capacity matrix of the micro-grid m at the time period t and the load decrease flag of the micro-grid m at the time period t; and a sum of the load increase flag and the load decrease flag of the micro-grid m at the time period t is not greater than 1; The power balance constraint includes: a total sum of power regulation of all micro-grids is equal to a total regulation power demand of the virtual power plant; The variable boundary constraint includes: the increase load amount of the micro-grid m at the time period t is not less than 0 and not greater than an absolute value of the adjustable capacity matrix of the micro-grid m at the time period t; the decrease load amount of the micro-grid m at the time period t is not less than 0 and not greater than an absolute value of the adjustable capacity matrix of the micro-grid m at the time period t; the load increase flag of the micro-grid m at the time period t is 0 or 1; and the load decrease flag of the micro-grid m at the time period t is 0 or 1.

3. A computing device, comprising: a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the method for coordinated optimization scheduling of a virtual power plant-micro-grid in a peak regulation scenario according to claim 1.

4. A computer readable storage medium storing computer executable instructions, and the computer executable instructions, when executed by a processor, implement the steps of the method for coordinated optimization scheduling of a virtual power plant-micro-grid in a peak regulation scenario according to claim 1.

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