Virtual power plant multi-cycle aggregation and collaborative optimization method and device considering load resource heterogeneity
By constructing a virtual power plant aggregation and collaborative optimization model, the problem of virtual power plant execution difficulties caused by load resource heterogeneity was solved, achieving improved flexibility and reduced costs, and increasing ancillary service revenue.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-14
- Publication Date
- 2026-03-31
AI Technical Summary
Existing technologies are unable to effectively address the issues of resource segmentation, time scale disconnect, and computation-implementation disconnect caused by load resource heterogeneity, leading to difficulties in the execution of virtual power plants in energy trading and ancillary services.
A method and apparatus for multi-cycle aggregation and collaborative optimization of virtual power plants considering the heterogeneity of load resources are provided. By constructing a virtual power plant aggregation and collaborative optimization model, aggregation and collaborative optimization schemes for participating in grid dispatch are generated, including the up/down adjustment of various load resources, discharge and charging power, and load adjustment amount to the baseline.
It enhances the flexibility of virtual power plants, reduces expected electricity purchase costs and risks, increases ancillary service revenue, and is suitable for engineering implementation in different regional markets.
Smart Images
Figure CN121769890A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of demand response scheduling technology, specifically to a method and apparatus for multi-cycle aggregation and collaborative optimization of virtual power plants that considers the heterogeneity of load resources. Background Technology With the large-scale grid connection of renewable energy and the acceleration of electrification, the flexibility and regulation capabilities of the power system have become key factors restricting the safe and economical operation of the system. Virtual power plants (VPPs) can participate in the energy market and ancillary services market by aggregating and coordinating distributed controllable loads, energy storage, and distributed power sources, providing services such as peak shaving, frequency regulation, and emergency support.
[0002] However, the following problems exist. First, there is strong resource heterogeneity. Electric vehicle charging stations, battery swapping stations, industrial loads, and commercial / temperature-controlled loads differ significantly in response speed, energy capacity, service quality, and comfort constraints. Traditional methods often model and bid separately for each resource and market, making it difficult to form a unified feasible domain and price response mechanism. Second, there is a disconnect in time scales. Energy trading is often granular at 15-minute or hourly levels, while frequency regulation is executed at the second level. Without a mapping between equivalent energy and mileage, the problem of "feasible on the energy side, but not feasible on the ancillary service side" often arises. Third, there is a disconnect between calculation and implementation. When multiple types of loads and multiple time scales are superimposed, if the model is nonlinear and difficult to expand, it can easily lead to time-consuming solutions and difficulties in engineering implementation. Summary of the Invention
[0003] To overcome the above-mentioned shortcomings, this invention proposes a method and apparatus for multi-cycle aggregation and collaborative optimization of virtual power plants that considers the heterogeneity of load resources.
[0004] Firstly, a method for multi-cycle aggregation and collaborative optimization of virtual power plants considering load resource heterogeneity is provided, wherein the method includes: Solve the pre-constructed virtual power plant aggregation and collaborative optimization model to obtain the optimization results; Based on the optimization results, an aggregation and collaborative optimization scheme for participating in power grid dispatch is generated; The optimization results include at least one of the following: the up / down reserve provided by various load resources, the discharge and charging power, and the load adjustment amount to the baseline.
[0005] Preferably, the load resource category includes at least one of the following: electric vehicle load, battery swapping station load, industrial load, and commercial load.
[0006] Preferably, the pre-constructed virtual power plant aggregation and collaborative optimization model includes: an objective function that considers the heterogeneity of load resources and its corresponding constraints.
[0007] Furthermore, the objective function is as follows:
[0008]
[0009] In the above formula, T represents the total time period; , Time periods The energy market's buy and sell electricity prices; For time period Electricity sales capacity, For time period The power consumption of electricity purchased; , Time periods Adjusting capacity fees upwards and downwards; For time period Mileage fee; For time period The lower bound of the mileage; For load resources The unit cycle life cost coefficient; For load resources During the period The equivalent cyclic energy; The duration of the time period; , Time periods The aggregation of upper and lower backups; wherein, the time period The above and below aggregates are available for use as follows:
[0010] In the above formula, , These represent the up and down reserves that resource j can provide during time period t.
[0011] Furthermore, the constraints are as follows:
[0012]
[0013]
[0014]
[0015]
[0016] In the above formula, This is the upper limit of the absolute power at the point of grid connection (PCC). For virtual power plants in time periods The polymer reference power; , respectively load resources The rated upper limit of charging and discharging; , respectively load resources During the period Discharge and charge power; This is a preset constant; These are mutually exclusive binary variables, where 1 represents charging and 0 represents discharging. For load resources During the period Frequency modulation equivalent energy bias; , These are the upper and lower coefficients of the equivalent energy, respectively; For time period The lower bound of the mileage; , The upper and lower mileage coefficients are respectively.
[0017] Furthermore, during the process of solving the pre-constructed virtual power plant aggregation and collaborative optimization model, electric vehicle load resources satisfy the following constraints: ,
[0018] , ,
[0019]
[0020] ,
[0021] In the above formula, For time period Electric vehicle charging power For time period The discharge power of electric vehicles; For time period The adjustable reserve capacity of electric vehicles; For time period The adjustable reserve capacity of electric vehicles; The rated upper limit for charging stations for electric vehicles; The upper limit of the station-end discharge rating for electric vehicles; For time period The state variables of the electric vehicle, where 1 indicates that discharge is allowed and 0 indicates that discharge is not allowed; This is a preset constant; For time period The charge and discharge states are mutually exclusive binary variables, where 1 represents charging and 0 represents discharging. For time period The equivalent state of charge of a polymer battery; For time period The equivalent state of charge of a polymer battery; , These are the charge / discharge efficiencies, respectively. Equivalent aggregation capacity; The duration of the time period; , These are the upper and lower bounds of the operation, respectively. Minimum SOC requirement for off-site operations; This is the set of arrival / departure time periods.
[0022] Furthermore, during the process of solving the pre-constructed virtual power plant aggregation and collaborative optimization model, the load resources of the battery swapping station type satisfy the following constraints: ,
[0023] ,
[0024] , ,
[0025] In the above formula, For time period Equivalent energy inventory; For time period Equivalent energy inventory; , These are the charge / discharge efficiencies, respectively. , Time periods The charging and discharging power within the station; The duration of the time period; This refers to the energy requirement for a single battery swap. For time period Number of battery swaps; , These represent the lower and upper limits of inventory, respectively. , For time period The backup capacity of the battery swapping station can be adjusted downwards or upwards. , These are the upper limits of the rated charging and discharging power of the battery swapping station, respectively. For time period The load mutual exclusion binary for battery swapping stations is represented by 1 for charging and 0 for discharging. This is a preset constant.
[0026] Furthermore, during the process of solving the pre-constructed virtual power plant aggregation and collaborative optimization model, industrial load resources satisfy the following constraints:
[0027]
[0028]
[0029]
[0030]
[0031]
[0032]
[0033]
[0034]
[0035] In the above formula, For time period process section The actual power; For time period process section The binary representation is 1 for running and 0 for stopping. , They are respectively process sections Lower / upper power limits; The duration of the time period; For process section The equivalent electrical energy required to complete the task; For process section The runtime required to complete the task; For process section The maximum number of interruptible events that can be interrupted; For process section Maximum continuous interruptible duration; This is an operator for finding the length of the longest consecutive string of zeros. For time period The load adjustment relative to the baseline; For time period process section Baseline power; , Time periods Controllable industrial load adjustment for backup; , They are respectively process sections Controllable industrial load ramp-up / ramp-down limits; For time period process section The actual power.
[0036] Furthermore, during the process of solving the pre-built virtual power plant aggregation and collaborative optimization model, commercial load resources satisfy the following constraints:
[0037]
[0038]
[0039]
[0040]
[0041] In the above formula, For time period The indoor temperature; For time period The indoor temperature; For time period The outdoor temperature; The heat exchange dispersion coefficient; This is the power-temperature effect coefficient; For time period The power consumption of the air conditioner; , These are the upper and lower limits of the comfortable temperature; , These are the upper and lower limits of air conditioner power; , These are reserves for adjusting the load of commercial buildings upwards and downwards.
[0042] Secondly, a virtual power plant multi-cycle aggregation and collaborative optimization device considering load resource heterogeneity is provided, the virtual power plant multi-cycle aggregation and collaborative optimization device considering load resource heterogeneity includes: The analysis module is used to solve the pre-built virtual power plant aggregation and collaborative optimization model to obtain the optimization results; The generation module is used to generate, based on the optimization results, an aggregated and collaborative optimization scheme for participating in power grid dispatch; The optimization results include at least one of the following: the up / down reserve provided by various load resources, the discharge and charging power, and the load adjustment amount to the baseline.
[0043] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects: This invention provides a method and apparatus for multi-cycle aggregation and collaborative optimization of virtual power plants considering the heterogeneity of load resources. The method includes: solving a pre-constructed virtual power plant aggregation and collaborative optimization model to obtain optimization results; and generating aggregation and collaborative optimization schemes for participation in grid dispatch based on the optimization results. The optimization results include at least one of the following: up / down reserve, discharge and charging power provided by various load resources, and load adjustment amounts to the baseline. The technical solution provided by this invention can enhance the flexibility of heterogeneous loads, reduce expected power purchase costs and risks, improve ancillary service revenue and deviation compliance, and is suitable for engineering implementation in different regional markets. Attached Figure Description
[0044] Figure 1 This is a schematic diagram of the main steps of the virtual power plant multi-cycle aggregation and collaborative optimization method considering load resource heterogeneity in an embodiment of the present invention. Detailed Implementation
[0045] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.
[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0047] Example 1 See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a virtual power plant multi-cycle aggregation and collaborative optimization method considering load resource heterogeneity, according to an embodiment of the present invention. Figure 1 As shown, the virtual power plant multi-cycle aggregation and collaborative optimization method considering load resource heterogeneity in this embodiment of the invention mainly includes the following steps: Step S101: Solve the pre-built virtual power plant aggregation and collaborative optimization model to obtain the optimization results; Step S102: Based on the optimization results, generate an aggregation and collaborative optimization scheme for participating in power grid dispatch; The optimization results include at least one of the following: the up / down reserve provided by various load resources, the discharge and charging power, and the load adjustment amount to the baseline.
[0048] In this embodiment, the load resource category includes at least one of the following: electric vehicle load, battery swapping station load, industrial load, and commercial load.
[0049] In this embodiment, the pre-constructed virtual power plant aggregation and collaborative optimization model includes: an objective function that considers the heterogeneity of load resources and its corresponding constraints.
[0050] In one implementation, to simultaneously consider energy billing and ancillary service billing, this implementation adopts the following linearly solvable comprehensive objective: electricity purchase cost is priced using the purchase price, electricity sales revenue is priced using the selling price, ancillary service revenue consists of capacity adjustment fees and mileage fees, and the cyclic life costs of four types of resources are also included. The objective function is as follows:
[0051]
[0052] In the above formula, T represents the total time period; , Time periods The energy market's buy and sell electricity prices; For time period Electricity sales capacity, For time period The power consumption of electricity purchased; , Time periods Adjusting capacity fees upwards and downwards; For time period Mileage fee; For time period The lower bound of the mileage; For load resources The unit cycle life cost coefficient; For load resources During the period The equivalent cyclic energy; The duration of the time period; , Time periods The aggregation of upper and lower backups; wherein, the time period The above and below aggregates are available for use as follows:
[0053] In the above formula, , These represent the up and down reserves that resource j can provide during time period t.
[0054] In one implementation, the constraints are as follows:
[0055]
[0056]
[0057]
[0058]
[0059] In the above formula, This is the upper limit of the absolute power at the point of grid connection (PCC). For virtual power plants in time periods The polymer reference power; , respectively load resources The rated upper limit of charging and discharging; , respectively load resources During the period Discharge and charge power; This is a preset constant; These are mutually exclusive binary variables, where 1 represents charging and 0 represents discharging. For load resources During the period Frequency modulation equivalent energy bias; , These are the upper and lower coefficients of the equivalent energy, respectively; For time period The lower bound of the mileage; , The upper and lower mileage coefficients are respectively.
[0060] In one implementation, to avoid "repeatedly occupying" the same (or aggregated equivalent) charging / discharging power for the reserve, the energy-side power and the reserved power used for adjustments should be checked together to the rated power limit to ensure physical accessibility and performance feasibility. During the process of solving the pre-built virtual power plant aggregation and collaborative optimization model, electric vehicle load resources satisfy the following constraints: ,
[0061] , ,
[0062]
[0063] ,
[0064] In the above formula, For time period Electric vehicle charging power For time period The discharge power of electric vehicles; For time period The adjustable reserve capacity of electric vehicles; For time period The adjustable reserve capacity of electric vehicles; The rated upper limit for charging stations for electric vehicles; The upper limit of the station-end discharge rating for electric vehicles; For time period The state variables of the electric vehicle, where 1 indicates that discharge is allowed and 0 indicates that discharge is not allowed; This is a preset constant; For time period The charge and discharge states are mutually exclusive binary variables, where 1 represents charging and 0 represents discharging. For time period The equivalent state of charge of a polymer battery; For time period The equivalent state of charge of a polymer battery; , These are the charge / discharge efficiencies, respectively. Equivalent aggregation capacity; The duration of the time period; , These are the upper and lower bounds of the operation, respectively. Minimum SOC requirement for off-site operations; This is the set of arrival / departure time periods.
[0065] In one implementation, the service capacity of a battery swapping station depends on "fully charged battery inventory / equivalent energy inventory". This inventory is driven by both charging / discharging and battery swapping demand, and forms the basis for ensuring service levels and deploying reserve capacity. During the process of solving the pre-built virtual power plant aggregation and collaborative optimization model, the battery swapping station-type load resources satisfy the following constraints: ,
[0066] ,
[0067] , ,
[0068] In the above formula, For time period Equivalent energy inventory; For time period Equivalent energy inventory; , These are the charge / discharge efficiencies, respectively. , Time periods The charging and discharging power within the station; The duration of the time period; This refers to the energy requirement for a single battery swap. For time period Number of battery swaps; , These represent the lower and upper limits of inventory, respectively. , For time period The backup capacity of the battery swapping station can be adjusted downwards or upwards. , These are the upper limits of the rated charging and discharging power of the battery swapping station, respectively. For time period The load mutual exclusion binary for battery swapping stations is represented by 1 for charging and 0 for discharging. This is a preset constant.
[0069] In one implementation, during the process of solving the pre-built virtual power plant aggregation and collaborative optimization model, industrial load resources satisfy the following constraints:
[0070] ,
[0071] ,
[0072]
[0073] ,
[0074]
[0075] In the above formula, For time period process section The actual power; For time period process section The binary representation is 1 for running and 0 for stopping. , They are respectively process sections Lower / upper power limits; The duration of the time period; For process section The equivalent electrical energy required to complete the task; For process section The runtime required to complete the task; For process section The maximum number of interruptible events that can be interrupted; For process section Maximum continuous interruptible duration; This is an operator for finding the length of the longest consecutive string of zeros. For time period The load adjustment relative to the baseline; For time period process section Baseline power; , Time periods Controllable industrial load adjustment for backup; , They are respectively process sections Controllable industrial load ramp-up / ramp-down limits; For time period process section The actual power.
[0076] In one implementation, for commercial loads, ensuring that the room temperature is within the permissible comfort range while constraining equipment power to not exceed the rated upper / lower limits, guarantees operational safety and user experience. During the process of solving the pre-built virtual power plant aggregation and collaborative optimization model, commercial load resources satisfy the following constraints: ,
[0077]
[0078] ,
[0079] In the above formula, For time period The indoor temperature; For time period The indoor temperature; For time period The outdoor temperature; The heat exchange dispersion coefficient; This is the power-temperature effect coefficient; For time period The power consumption of the air conditioner; , These are the upper and lower limits of the comfortable temperature; , These are the upper and lower limits of air conditioner power; , These are reserves for adjusting the load of commercial buildings upwards and downwards.
[0080] Example 2 Based on the same inventive concept, this invention also provides a virtual power plant multi-cycle aggregation and collaborative optimization device considering load resource heterogeneity, the virtual power plant multi-cycle aggregation and collaborative optimization device considering load resource heterogeneity includes: The analysis module is used to solve the pre-built virtual power plant aggregation and collaborative optimization model to obtain the optimization results; The generation module is used to generate, based on the optimization results, an aggregated and collaborative optimization scheme for participating in power grid dispatch; The optimization results include at least one of the following: the up / down reserve provided by various load resources, the discharge and charging power, and the load adjustment amount to the baseline.
[0081] Preferably, the load resource category includes at least one of the following: electric vehicle load, battery swapping station load, industrial load, and commercial load.
[0082] Preferably, the pre-constructed virtual power plant aggregation and collaborative optimization model includes: an objective function that considers the heterogeneity of load resources and its corresponding constraints.
[0083] Furthermore, the objective function is as follows:
[0084]
[0085] In the above formula, T represents the total time period; , Time periods The energy market's buy and sell electricity prices; For time period Electricity sales capacity, For time period The power consumption of electricity purchased; , Time periods Adjusting capacity fees upwards and downwards; For time period Mileage fee; For time period The lower bound of the mileage; For load resources The unit cycle life cost coefficient; For load resources During the period The equivalent cyclic energy; The duration of the time period; , Time periods The aggregation of upper and lower backups; wherein, the time period The above and below aggregates are available for use as follows:
[0086] In the above formula, , These represent the up and down reserves that resource j can provide during time period t.
[0087] Furthermore, the constraints are as follows:
[0088]
[0089]
[0090]
[0091]
[0092] In the above formula, This is the upper limit of the absolute power at the point of grid connection (PCC). For virtual power plants in time periods The polymer reference power; , respectively load resources The rated upper limit of charging and discharging; , respectively load resources During the period Discharge and charge power; This is a preset constant; These are mutually exclusive binary variables, where 1 represents charging and 0 represents discharging. For load resources During the period Frequency modulation equivalent energy bias; , These are the upper and lower coefficients of the equivalent energy, respectively; For time period The lower bound of the mileage; , The upper and lower mileage coefficients are respectively.
[0093] Furthermore, during the process of solving the pre-constructed virtual power plant aggregation and collaborative optimization model, electric vehicle load resources satisfy the following constraints: ,
[0094] , ,
[0095]
[0096] ,
[0097] In the above formula, For time period Electric vehicle charging power For time period The discharge power of electric vehicles; For time period The adjustable reserve capacity of electric vehicles; For time period The adjustable reserve capacity of electric vehicles; The rated upper limit for charging stations for electric vehicles; The upper limit of the station-end discharge rating for electric vehicles; For time period The state variables of the electric vehicle, where 1 indicates that discharge is allowed and 0 indicates that discharge is not allowed; This is a preset constant; For time period The charge and discharge states are mutually exclusive binary variables, where 1 represents charging and 0 represents discharging. For time period The equivalent state of charge of a polymer battery; For time period The equivalent state of charge of a polymer battery; , These are the charge / discharge efficiencies, respectively. Equivalent aggregation capacity; The duration of the time period; , These are the upper and lower bounds of the operation, respectively. Minimum SOC requirement for off-site operations; This is the set of arrival / departure time periods.
[0098] Furthermore, during the process of solving the pre-constructed virtual power plant aggregation and collaborative optimization model, the load resources of the battery swapping station type satisfy the following constraints: ,
[0099] ,
[0100] , ,
[0101] In the above formula, For time period Equivalent energy inventory; For time period Equivalent energy inventory; , These are the charge / discharge efficiencies, respectively. , Time periods The charging and discharging power within the station; The duration of the time period; This refers to the energy requirement for a single battery swap. For time period Number of battery swaps; , These represent the lower and upper limits of inventory, respectively. , For time period The backup capacity of the battery swapping station can be adjusted downwards or upwards. , These are the upper limits of the rated charging and discharging power of the battery swapping station, respectively. For time period The load mutual exclusion binary for battery swapping stations is represented by 1 for charging and 0 for discharging. This is a preset constant.
[0102] Furthermore, during the process of solving the pre-constructed virtual power plant aggregation and collaborative optimization model, industrial load resources satisfy the following constraints:
[0103] ,
[0104] ,
[0105]
[0106] ,
[0107]
[0108] In the above formula, For time period process section The actual power; For time period process section The binary representation is 1 for running and 0 for stopping. , They are respectively process sections Lower / upper power limits; The duration of the time period; For process section The equivalent electrical energy required to complete the task; For process section The runtime required to complete the task; For process section The maximum number of interruptible events that can be interrupted; For process section Maximum continuous interruptible duration; This is an operator for finding the length of the longest consecutive string of zeros. For time period The load adjustment relative to the baseline; For time period process section Baseline power; , Time periods Controllable industrial load adjustment for backup; , They are respectively process sections Controllable industrial load ramp-up / ramp-down limits; For time period process section The actual power.
[0109] Furthermore, during the process of solving the pre-built virtual power plant aggregation and collaborative optimization model, commercial load resources satisfy the following constraints: ,
[0110]
[0111] ,
[0112] In the above formula, For time period The indoor temperature; For time period The indoor temperature; For time period The outdoor temperature; The heat exchange dispersion coefficient; This is the power-temperature effect coefficient; For time period The power consumption of the air conditioner; , These are the upper and lower limits of the comfortable temperature; , These are the upper and lower limits of air conditioner power; , These are reserves for adjusting the load of commercial buildings upwards and downwards.
[0113] Example 3 Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby realizing the steps of the virtual power plant multi-cycle aggregation and collaborative optimization method considering load resource heterogeneity in the above embodiments.
[0114] Example 4 Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the virtual power plant multi-cycle aggregation and collaborative optimization method considering load resource heterogeneity in the above embodiments.
[0115] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0116] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A method for multi-period aggregation and collaborative optimization of a virtual power plant considering heterogeneity of load resources, characterized in that, The method comprises: solving a pre-constructed virtual power plant aggregation and collaborative optimization model to obtain an optimization result; generating an aggregation and collaborative optimization scheme for participating in grid dispatch based on the optimization result; The optimization result includes at least one of the following: up / down reserve, discharge and charge power provided by each type of load resource, and load adjustment amount to the baseline.
2. The method of claim 1, wherein, The category of the load resource includes at least one of the following: electric vehicle load, battery swap station load, industrial load, and commercial load.
3. The method of claim 1, wherein, The pre-constructed virtual power plant aggregation and collaborative optimization model includes a target function considering the heterogeneity of load resources and corresponding constraint conditions.
4. The method of claim 3, wherein, The target function is as follows: In the above formula, T is the total time period; , are the energy market buy and sell prices for time period ; is the sold power for time period , is the purchased power for time period ; , are the up and down capacity fees for time period ; is the mileage fee for time period ; is the mileage lower bound for time period ; is the unit cycle life cost coefficient of the load resource ; is the equivalent cycle energy of the load resource for time period ; is the time period length; , are the aggregated up and down reserves for time period ; wherein the aggregated up and down reserves for time period are as follows: In the above formula, , These represent the up and down reserves that resource j can provide during time period t.
5. The method of claim 4, wherein, The constraint conditions are as follows: In the above formula, is the upper limit of the absolute value of the grid-connected point (PCC) power; is the aggregated reference power of the virtual power plant in the time period ; , are the rated charging and discharging upper limits of the load resource ; , are the discharging and charging powers of the load resource in the time period ; is a preset constant; is a mutually exclusive binary variable, 1 indicating charging and 0 indicating discharging; is the frequency modulation equivalent energy bias of the load resource in the time period ; , are the equivalent energy upper and lower coefficients; is the lower bound of the mileage in the time period ; , are the upper and lower coefficients of the mileage.
6. The method of claim 2, wherein, In the process of solving the pre-constructed virtual power plant aggregation and collaborative optimization model, the electric vehicle load resource satisfies the following constraints: ， ， ， ， In the above formula, charging power of the electric vehicle for the time period charging power of the electric vehicle for the time period discharging power of the electric vehicle for the time period discharging power of the electric vehicle for the time period downward adjustable reserve capacity of the electric vehicle for the time period downward adjustable reserve capacity of the electric vehicle for the time period upward adjustable reserve capacity of the electric vehicle for the time period upward adjustable reserve capacity of the electric vehicle for the time period station-side charging upper limit of the electric vehicle a station end discharge rating upper limit for an electric vehicle; is a state variable of the electric vehicle for the time period 1 indicates that discharging is allowed, and 0 indicates that discharging is not allowed; is a preset constant; is a state variable of the electric vehicle for the time period 1 indicates charging, and 0 indicates discharging; is an equivalent aggregate battery state of charge for the time period ; is an equivalent aggregate battery state of charge for the time period ; , are charging / discharging efficiencies, respectively; is an equivalent aggregate capacity; is a time period length; , are running upper and lower bounds, respectively; is a minimum SOC requirement off-station; is a set of to / from station constraint time periods.
7. The method of claim 2, wherein, In the process of solving the pre-constructed virtual power plant aggregation and collaborative optimization model, the battery swap station load resource satisfies the following constraints: ， ， ， ， In the above formula, is the equivalent energy inventory of the time period ; is the equivalent energy inventory of the time period ; , are the charging / discharging efficiencies, respectively; , are the in-station charging and discharging power of the time period , respectively; is the time length of the time period; is the single swap energy demand; is the number of swaps of the time period ; , are the lower and upper limits of the inventory, respectively; , is the lower / upper reserve of the swap station of the time period ; , are the rated charging and discharging power upper limits of the swap station, respectively; is the swap station type load mutual exclusion binary of the time period , 1 represents charging and 0 represents discharging; is a preset constant.
8. The method of claim 2, wherein, In the process of solving the pre-constructed virtual power plant aggregation and collaborative optimization model, the industrial load resource satisfies the following constraints: In the above formula, is the process segment is the actual power of the process segment ; is the process segment is the operation of the binary, 1 represents operation, 0 represents stop; , , is the power lower / upper limit of the process segment ; is the time length of the time segment ; is the equivalent electric energy required for the process segment to complete the work; is the operation time length required for the process segment to complete the work; is the cumulative interruptable upper limit of the process segment ; is the maximum continuous interruptable time length of the process segment ; is the operator taking the longest continuous 0 string length; is the load adjustment amount of the time segment relative to the baseline; is the baseline power of the process segment of the time segment , are respectively the up / down regulation reserve of the controllable industrial load of the time segment ; , are respectively the controllable industrial load up / down ramping limit of the process segment ; is the actual power of the process segment of the time segment .
9. The method of claim 2, wherein, In the process of solving the pre-constructed virtual power plant aggregation and collaborative optimization model, the commercial load resource satisfies the following constraints: in the above formula, is the indoor temperature for the time period ; is the indoor temperature for the time period ; is the outdoor temperature for the time period ; is the heat exchange discrete coefficient; is the power-temperature influence coefficient; is the air conditioning electric power for the time period ; , are the upper and lower limits of the comfort temperature, respectively; , are the upper and lower limits of the air conditioning power, respectively; , are the upper / lower reserve of the commercial building load, respectively.
10. A device for multi-period aggregation and collaborative optimization of a virtual power plant considering the heterogeneity of load resources based on the method of any one of claims 1-9, characterized in that, The device comprises: an analysis module for solving a pre-constructed virtual power plant aggregation and collaborative optimization model to obtain an optimization result; a generation module for generating an aggregation and collaborative optimization scheme for participating in grid dispatch based on the optimization result; The optimization result includes at least one of the following: up / down reserve, discharge and charge power provided by each type of load resource, and load adjustment amount to the baseline.