A source-network-load-storage integrated power market collaborative optimization method and system

Abstract

The application provides a power market collaborative optimization method and system facing source network load storage integration, relates to the technical field of Internet, and comprises the following steps: determining a charging compensation value based on the regulation and control willingness degree of a vehicle owner, modeling a plurality of electric vehicle sub-clusters respectively by using the type of the charging compensation value, and obtaining a compensation model of each electric vehicle sub-cluster; the electric vehicle sub-cluster comprises a translatable charging cluster, a reducible charging cluster and a controllable charging and discharging cluster; taking the minimum charging cost as the target, solving each compensation model, determining the optimal charging and discharging power of each electric vehicle in the electric vehicle cluster under the current electricity price and the charging compensation value, obtaining the up-regulation standby capacity and the down-regulation standby capacity of the electric vehicle cluster in a to-be-regulated period based on the optimal charging and discharging power, and determining the charging and discharging strategy of the electric vehicle cluster in the to-be-regulated period by taking the minimization of the total energy cost of the system as the safety constraint of the target function and combining the up-regulation standby capacity and the down-regulation standby capacity.

Description

Technical Field

[0001] This invention relates to the field of Internet technology, and in particular to a method and system for collaborative optimization of the power market oriented towards the integration of power generation, grid, load and storage. Background Technology

[0002] In recent years, the demand for flexibility resources in the power system has increased dramatically. Electric vehicles, with their considerable energy storage capacity and bidirectional charging and discharging capabilities, have become a key flexibility resource supporting power system regulation. Electric vehicle aggregators, by aggregating distributed electric vehicles to form large-scale dispatchable resource pools, are gradually becoming important participants in the electricity market.

[0003] However, China's domestic electricity spot market is still in its early stages of development, with a particularly prominent issue of insufficient coordination between market rules and technical models. Although existing research has explored models for electric vehicle aggregators to participate in the market, most methods have not fully considered the constraints of actual market entry barriers and nodal pricing mechanisms, resulting in insufficient practicality. Furthermore, with the increasing penetration rate of distributed energy resources, the demand for flexibility resources in distribution networks is becoming increasingly urgent, necessitating market-technology synergy optimization to improve the profitability of electric vehicle aggregators and the grid's regulation capabilities. Summary of the Invention

[0004] In view of this, the present invention proposes a collaborative optimization method and system for the integrated power market of power generation, grid, load and storage.

[0005] The technical solution of this invention is implemented as follows: The first aspect of this invention provides a method for coordinated optimization of the power market oriented towards the integration of power generation, grid, load, and storage, comprising:

[0006] The charging compensation value is determined based on the owner's willingness to adjust the charging system, and the electric vehicle clusters are modeled using the type of the charging compensation value to obtain the compensation models for multiple electric vehicle sub-clusters. The electric vehicle sub-clusters include movable charging clusters, reduceable charging clusters, and controllable charging and discharging clusters.

[0007] With the goal of minimizing charging costs, each of the aforementioned compensation models is solved to determine the optimal charging and discharging power of each electric vehicle in the electric vehicle cluster under the current electricity price and the charging compensation value; based on the optimal charging and discharging power, the upward and downward reserve capacity of the electric vehicle cluster during the adjustment period is obtained.

[0008] The objective function is constrained by minimizing the total energy cost of the system. The charging and discharging strategy of the electric vehicle cluster during the control period is determined by combining the increase and decrease of the reserve capacity. The safety constraints include system power balance constraints, network power flow security constraints, and reserve capacity constraints.

[0009] Based on the above technical solutions, preferably, the step of determining the charging compensation value based on the vehicle owner's degree of control willingness includes:

[0010] The degree of the owner's willingness to regulate is determined based on the electric vehicle's battery level when it enters the network, battery level when it leaves the network, maximum battery level, network entry time, and network exit time.

[0011] Based on the above technical solutions, preferably, for the movable charging cluster, the type of the charging compensation value includes time-shift compensation; the step of modeling the electric vehicle cluster using the type of the charging compensation value to obtain compensation models for multiple electric vehicle sub-clusters includes:

[0012] The charging time window is determined based on the electric vehicle's grid access time and grid disconnection time;

[0013] Based on the electric vehicle's charge amount, charging efficiency, and maximum charging power, and in conjunction with the charging time window, the translation time is determined.

[0014] Based on the translation time and unit translation compensation, the time translation compensation is determined, and the time translation compensation is used to model the movable charging cluster to obtain the compensation model corresponding to the movable charging cluster.

[0015] Based on the above technical solutions, preferably, for the reducible charging cluster, the type of the charging compensation value includes power reduction compensation; the step of modeling the electric vehicle clusters using the type of the charging compensation value to obtain compensation models for multiple electric vehicle sub-clusters includes:

[0016] The power reduction range is determined based on the maximum charging power of the electric vehicle;

[0017] The power reduction amount within the charging time window is obtained based on the power reduction range, the amount of electricity to be charged by the electric vehicle, and the charging efficiency.

[0018] Based on the power reduction amount and the unit reduction compensation, the power reduction compensation is determined, and the power reduction compensation is used to model the reduceable charging cluster to obtain the compensation model corresponding to the reduceable charging cluster.

[0019] Based on the above technical solutions, preferably, for the controllable charge-discharge cluster, the type of the charging compensation value includes charging excitation compensation and discharging excitation compensation; the step of modeling the electric vehicle cluster using the type of the charging compensation value to obtain compensation models for multiple electric vehicle sub-clusters includes:

[0020] Based on the upper limit of charging and the lower limit of discharging of the electric vehicle, the charging incentive compensation and discharging incentive compensation of the electric vehicle within the charging time window are obtained.

[0021] The controllable charge-discharge cluster is modeled based on the charging excitation compensation and the discharging excitation compensation to obtain the compensation model corresponding to the controllable charge-discharge cluster.

[0022] Based on the above technical solutions, preferably, the step of obtaining the upward and downward reserve capacity of the electric vehicle cluster during the adjustment period based on the optimal charging and discharging power includes:

[0023] When the reserve capacity is increased, the remaining energy of the electric vehicle's battery shall not be less than the minimum allowable energy of the battery;

[0024] When a reduced reserve capacity is provided, the sum of the battery energy of the electric vehicle and the reduced reserve capacity shall not exceed the maximum battery capacity.

[0025] Based on the above technical solutions, preferably, before applying a safety constraint to the objective function by minimizing the total energy cost of the system, the method further includes:

[0026] An objective function is created based on the generation cost, electricity utilization, reserve bonus, penalty cost for not using the power, and the charging compensation value.

[0027] Furthermore, a second aspect of the present invention provides a power market collaborative optimization system for integrated power generation, grid, load, and storage, comprising: a compensation creation module, a capacity determination module, and a strategy determination module; wherein,

[0028] The compensation creation module is configured to determine the charging compensation value based on the degree of the vehicle owner's willingness to adjust, and to model the electric vehicle clusters separately using the type of the charging compensation value to obtain the compensation models of multiple electric vehicle sub-clusters; the electric vehicle sub-clusters include movable charging clusters, reduceable charging clusters, and controllable charging and discharging clusters.

[0029] The capacity determination module is configured to solve each of the compensation models with the goal of minimizing charging cost, and determine the optimal charging and discharging power of each electric vehicle in the electric vehicle cluster under the current electricity price and the charging compensation value; based on the optimal charging and discharging power, the module obtains the upward and downward reserve capacity of the electric vehicle cluster during the adjustment period.

[0030] The strategy determination module is configured to apply a security constraint to the objective function by minimizing the total energy cost of the system, and, in conjunction with the increase and decrease of reserve capacity, determine the charging and discharging strategy of the electric vehicle cluster within the control period; the security constraints include system power balance constraints, network power flow security constraints, and reserve capacity constraints.

[0031] More preferably, a third aspect of the present invention provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the power market collaborative optimization method for source-grid-load-storage integration described in the first aspect.

[0032] More preferably, in a fourth aspect of the present invention, a computer storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the power market collaborative optimization method for source-grid-load-storage integration described in the first aspect.

[0033] The power market collaborative optimization method and system of the present invention, which is oriented towards the integration of power generation, grid, load and storage, has the following advantages over the prior art:

[0034] 1. By classifying electric vehicle clusters into three categories—shiftable, power-reducible, and controllable charging / discharging—corresponding to time shifting, power reduction, and bidirectional regulation respectively, the compensation standard is directly linked to market rules, improving the compatibility of the solution with the electricity market. Using the minimization of the total system energy cost as the objective function, and considering the electricity price differences at different nodes, electric vehicles are guided to charge at low-price nodes and discharge at high-price nodes, reducing overall costs. Simultaneously, network power flow constraints are incorporated into the optimization model to avoid local network overload or voltage exceedances, ensuring the strategy is physically executable and complies with grid safety rules.

[0035] 2. By solving the compensation model, the upward and downward adjustment of reserve capacity are obtained, clarifying the scale of reserve services that the electric vehicle cluster can provide, and providing data support for market pricing. Reserve capacity constraints are directly incorporated into the optimization of charging and discharging strategies to ensure that the strategies meet market access requirements for reserve service response speed and duration.

[0036] 3. Based on determining the optimal charging and discharging power for individuals with the minimum charging cost, the system coordinates the overall strategy by minimizing the total system cost, taking into account both individual rationality and collective optimality, and integrating constraints such as power balance, power flow security, and reserve capacity. This ensures that the strategy is feasible in terms of market rules, grid security, and user interests, and achieves efficient resource allocation and price transmission across markets. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1A flowchart illustrating a collaborative optimization method for the integrated power market based on the source-grid-load-storage model, provided in an embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram of the IEEE 33-node topology provided in an embodiment of the present invention;

[0040] Figure 3 A schematic diagram of the structure of a power market collaborative optimization system for the integration of power generation, grid, load and storage, provided in an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0043] In some embodiments, such as Figure 1 As shown, Figure 1 This is a flowchart illustrating a power market collaborative optimization method for integrated power generation, grid, load, and storage, provided by an embodiment of the present invention. The power market collaborative optimization method for integrated power generation, grid, load, and storage provided by the present invention includes:

[0044] S110 determines the charging compensation value based on the owner's willingness to adjust, and models the electric vehicle clusters separately using the type of charging compensation value to obtain the compensation models for multiple electric vehicle sub-clusters; the electric vehicle sub-clusters include movable charging clusters, reduceable charging clusters, and controllable charging and discharging clusters.

[0045] In some embodiments, determining the charging compensation value based on the vehicle owner's willingness to adjust the settings includes:

[0046] The degree of owner's willingness to regulate is determined based on the electric vehicle's battery level when it is registered with the grid, battery level when it is deregistered, maximum battery level, registration time, and deregistration time.

[0047] The battery level of an electric vehicle upon grid connection reflects the urgency of current charging demand. The battery level upon grid disconnection determines the minimum necessary charging requirement. The maximum battery level, i.e., the upper limit of battery capacity, affects charging flexibility and buffer space. The time of grid connection and disconnection corresponds to the charging time window, determining the time flexibility of regulation.

[0048] In some embodiments, for a movable charging cluster, the type of charging compensation value includes time-shift compensation; the electric vehicle cluster is modeled separately using the type of charging compensation value to obtain compensation models for multiple electric vehicle sub-clusters, including:

[0049] The charging time window is determined based on the electric vehicle's grid access time and grid disconnection time;

[0050] Based on the electric vehicle's chargeable quantity, charging efficiency, and maximum charging power, and combined with the charging time window, the translation time is determined.

[0051] The time translation compensation is determined based on the translation time and unit translation compensation, and the time translation compensation is used to model the movable charging cluster to obtain the compensation model corresponding to the movable charging cluster.

[0052] In this embodiment, only the charging time can be adjusted, without changing the charging power. The compensation model for the movable charging cluster is shown in equation (1-5). During the scheduling process, the constraints of the movable power and movable time also need to be considered (equations 6-7). For this type of user, the system provides movable compensation based on the amount of their charging time movable, according to the unit compensation price (equation 5).

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] in, This indicates the EVn charging power that can be shifted within the charging cluster during time period t; This indicates the charging status of EVn within the shiftable charging cluster during time period t, where 0 indicates not charging and 1 indicates charging. This indicates the amount of EVn charged within the movable charging cluster; , These represent the upper and lower limits of EVn charging capacity within the movable charging cluster, respectively. , These are the arrival and departure times of EVn, respectively. This represents the maximum charging power of EVn. For EV charging efficiency, This indicates the translation compensation of EVn within the movable charging cluster; This represents the charging translation time of EVn within the movable charging cluster; This indicates compensation for unit relocation.

[0061] In some embodiments, for a charge reduction cluster, the type of charging compensation value includes power reduction compensation; the electric vehicle clusters are modeled separately using the type of charging compensation value to obtain compensation models for multiple electric vehicle sub-clusters, including:

[0062] Determine the power reduction range based on the maximum charging power of the electric vehicle;

[0063] The power reduction amount within the charging time window is obtained based on the power reduction range, the amount of electric vehicle to be charged, and the charging efficiency.

[0064] The power reduction compensation is determined based on the power reduction amount and the unit reduction compensation, and the power reduction compensation is used to model the reducible charging cluster to obtain the compensation model corresponding to the reducible charging cluster.

[0065] In this embodiment, the compensation model for the charge cluster that can be reduced is shown in equation (8-14). This type of cluster allows for reduction of charging power during peak hours. Its model must satisfy the power reduction and reduction time constraints (equations 13-14), and adjust the power through a load reduction coefficient. The system provides reduction compensation based on the power reduction and reduction time, according to the unit compensation price (equation 12).

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] In the formula, This indicates that the charging power of EVn within the charging cluster can be reduced during time period t; Indicates the load reduction factor; This indicates the EVn reduction status within the charging cluster during time period t, where 0 indicates no reduction and 1 indicates reduction in progress. This indicates that the amount of EVn charging within the charging cluster can be reduced; , These are the upper and lower limits that can reduce the amount of EVn charging within the charging cluster, respectively. This indicates that the reduction compensation for EVn within the charging cluster can be reduced. This indicates that the employer has reduced compensation. This indicates that the charging reduction time of EVn within the charging cluster can be reduced.

[0074] In some embodiments, for a controllable charge-discharge cluster, the type of charging compensation value includes charging excitation compensation and discharging excitation compensation; the electric vehicle cluster is modeled using the type of charging compensation value to obtain compensation models for multiple electric vehicle sub-clusters, including:

[0075] Based on the upper limit of charging and the lower limit of discharging of electric vehicles, the charging incentive compensation and discharging incentive compensation of electric vehicles within the charging time window are obtained.

[0076] Based on charging excitation compensation and discharging excitation compensation, a controllable charge-discharge cluster is modeled to obtain the corresponding compensation model for the controllable charge-discharge cluster.

[0077] In this embodiment, precise control of charging and discharging power is allowed, and the compensation model for the controllable charging and discharging cluster is shown in equations (15-22). This model needs to consider both the power control constraint and the charging / discharging state constraint (equations 20-22). Based on the flexibility it provides, the system provides charging excitation compensation and discharging excitation compensation according to the principle of "the greater the flexibility, the higher the compensation" (equation 19).

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] In the formula, This represents the EVn charging power within the controllable charge / discharge cluster during time period t; , These represent the load control coefficients under charging and discharging conditions, respectively. , This indicates the charging and discharging status of EVn within the controllable charging and discharging cluster during time period t. 0 indicates that no charging or discharging behavior has been performed, and 1 indicates that charging or discharging behavior is in progress. This indicates the amount of EVn charged within the controllable charge / discharge cluster; , These represent the upper and lower limits of EVn charge / discharge within the controllable charge / discharge cluster, respectively. , These represent the unit control excitation compensation under charging and discharging states, respectively.

[0085] S120 aims to minimize charging costs by solving various compensation models to determine the optimal charging and discharging power of each electric vehicle in the electric vehicle cluster under the current electricity price and charging compensation value; based on the optimal charging and discharging power, it obtains the upward and downward reserve capacity of the electric vehicle cluster during the regulation period.

[0086] In some embodiments, obtaining the upward and downward reserve capacity of the electric vehicle cluster during the adjustment period based on the optimal charging and discharging power includes:

[0087] With increased reserve capacity provided, the remaining energy of the electric vehicle's battery shall not be lower than the minimum allowable energy of the battery;

[0088] When a reduced reserve capacity is provided, the sum of the electric vehicle's battery energy and the reduced reserve capacity shall not exceed the battery's maximum capacity.

[0089] In this embodiment, after completing the cluster classification model, the optimal charging and discharging power of each EV under the current electricity price and incentive price is solved with the goal of minimizing the user's charging cost. ( =-1, 0, 1, representing movable charging clusters, scalable charging clusters, and controllable charging and discharging clusters, respectively.

[0090]

[0091] Based on this optimal power, the equivalent increased and decreased reserve capacity that the cluster can provide in each time period t is calculated. The increased reserve capacity can be achieved by increasing the discharge power or switching from charging to discharging. The decreased reserve capacity can be achieved by reducing the charging power or switching from discharging to charging (Equations 24-25).

[0092]

[0093] In the formula, , These represent the equivalent upward and downward adjustment of reserve funds during time period t, respectively.

[0094] To ensure the reliability of the backup capacity, it must be constrained to meet the energy delivery capability under extreme demand conditions for 2 consecutive hours (Equations 26-27), and the reserved energy space must be ensured to be within the battery capacity limit (Equations 28-29). The output of this step is the reliable upper and lower boundaries of the backup capacity for EVA to participate in the backup market bidding.

[0095]

[0096]

[0097]

[0098]

[0099] In the formula, , The upper and lower limits of the electric vehicle battery energy space reserved are to ensure that the backup capacity is feasible when it is called upon.

[0100] S130 uses the minimization of total system energy cost as a safety constraint on the objective function. Combining the increase and decrease of reserve capacity, the charging and discharging strategy of the electric vehicle cluster during the control period is determined. The safety constraints include system power balance constraints, network power flow safety constraints, and reserve capacity constraints.

[0101] In some embodiments, before applying a security constraint to the objective function to minimize the total energy cost of the system, the method further includes:

[0102] An objective function is created based on generation cost, electricity utility, reserve bonus, penalty cost for not using electricity, and charging compensation value.

[0103] In this embodiment, the distribution system operator (DSO) performs joint clearing calculations for safety-constrained unit combination and safety-constrained economic dispatch with the objective function of minimizing the total energy cost of the system (Equation 30).

[0104]

[0105] In the formula, , The market price of electrical energy; , The two measures are to raise the standby price and lower the standby price, respectively. , These are the rewards for providing backup and the penalty price for the backup not being available; , These represent the possibilities of system calls for both up and down backup; To provide cluster Compensation for EVn users.

[0106] The power generation cost of a power generator can be expressed as (Equation 31).

[0107]

[0108] The electricity consumption efficiency on the user side can be reflected by the demand function (Equation 32):

[0109]

[0110] The system power balance constraint (Equations 33-34) is that the sum of the electricity won in the power market and the electricity won in the reserve market at each node equals the load demand of that node.

[0111]

[0112] In the formula, , Indicates the network node number; , Indicates the node in time period t Electricity volume won by market members in the electricity market and reserve market; For time period t node The load.

[0113] Network power flow safety constraints (Equations 35-38) are used to ensure that line power does not exceed the limit and to guarantee the safe operation of the system.

[0114]

[0115] in, , They represent the lines during time period t. Active and reactive power; , They represent the lines during time period t. Active and reactive power; , Line for time period t Upper limits for active and reactive power; , The lines for time period t are respectively Lower limits for active and reactive power; Represents a set of network topologies.

[0116] The reserve capacity constraint can be directly adopted using the EVA reserve capacity boundary of each electric vehicle aggregator (Equation 26-29). By solving this optimization model, the cleared electricity volume, cleared electricity price (node ​​LMP), and the winning bid status of each market participant can be determined simultaneously in the electricity market and the reserve market.

[0117] In an alternative embodiment, please refer to Figure 2 , Figure 2This invention provides a schematic diagram of the IEEE 33-node topology; it reads distribution network information and confirms market participants. The distribution network is an IEEE 33-node standard system, and the electricity market contains 2 power generators (G1, G2) and 5 electric vehicle aggregators (EVA1, EVA2, EVA3, EVA4, EVA5). A market collaborative optimization model is established with the goal of minimizing market energy costs, comprehensively considering node power balance constraints, power flow constraints, and reserve constraints. The node price for calculating opportunity costs during ancillary service market clearing is determined through safety-constrained unit combination and safety-constrained economic dispatch procedures. The market clearing result is obtained by using CPLEX, including solving the market collaborative optimization model using the CPLEX solver and outputting the profits of market participants. The profits of each market participant are shown in Table 1 under the scenarios where electric vehicle aggregators only participate in the electricity market and not in the reserve market (Scenario 1) and under the scenario where electric vehicle aggregators participate in the electricity-reserve market for collaborative optimization (Scenario 2).

[0118]

[0119] As can be seen, based on the power market collaborative optimization method for the integration of power generation, grid, load and storage proposed in this application, i.e., under scenario 2, the profits of each market participant have been significantly improved.

[0120] In some embodiments, please refer to Figure 3 , Figure 3 This is a schematic diagram of a power market collaborative optimization system for integrated power generation, grid, load, and storage, provided as an embodiment of the present invention. The present invention provides a power market collaborative optimization system 300 for integrated power generation, grid, load, and storage, comprising: a compensation creation module 310, a capacity determination module 320, and a strategy determination module 330; wherein,

[0121] The compensation creation module 310 is configured to determine the charging compensation value based on the degree of the car owner's willingness to control the charging, and to model the electric vehicle clusters separately using the type of charging compensation value to obtain the compensation models of multiple electric vehicle sub-clusters; the electric vehicle sub-clusters include movable charging clusters, reduceable charging clusters, and controllable charging and discharging clusters.

[0122] The capacity determination module 320 is configured to solve various compensation models with the goal of minimizing charging costs, and determine the optimal charging and discharging power of each electric vehicle in the electric vehicle cluster under the current electricity price and charging compensation value; based on the optimal charging and discharging power, the module obtains the upward and downward reserve capacity of the electric vehicle cluster during the regulation period.

[0123] The strategy determination module 330 is configured to apply a safety constraint to the objective function by minimizing the total energy cost of the system, and combine the increase and decrease of the reserve capacity to determine the charging and discharging strategy of the electric vehicle cluster during the control period; the safety constraints include system power balance constraints, network power flow safety constraints, and reserve capacity constraints.

[0124] In some embodiments, the compensation creation module 310 is specifically configured as follows:

[0125] The degree of owner's willingness to regulate is determined based on the electric vehicle's battery level when it is registered with the grid, battery level when it is deregistered, maximum battery level, registration time, and deregistration time.

[0126] In some embodiments, for a movable charging cluster, the type of charging compensation value includes time-shift compensation; the compensation creation module 310 is specifically configured as follows:

[0127] The charging time window is determined based on the electric vehicle's grid access time and grid disconnection time;

[0128] Based on the electric vehicle's chargeable quantity, charging efficiency, and maximum charging power, and combined with the charging time window, the translation time is determined.

[0129] The time translation compensation is determined based on the translation time and unit translation compensation, and the time translation compensation is used to model the movable charging cluster to obtain the compensation model corresponding to the movable charging cluster.

[0130] In some embodiments, for a slashable charging cluster, the type of charging compensation value includes power reduction compensation; the compensation creation module 310 is specifically configured as follows:

[0131] Determine the power reduction range based on the maximum charging power of the electric vehicle;

[0132] The power reduction amount within the charging time window is obtained based on the power reduction range, the amount of electric vehicle to be charged, and the charging efficiency.

[0133] The power reduction compensation is determined based on the power reduction amount and the unit reduction compensation, and the power reduction compensation is used to model the reducible charging cluster to obtain the compensation model corresponding to the reducible charging cluster.

[0134] In some embodiments, for a controllable charge-discharge cluster, the type of charging compensation value includes charging excitation compensation and discharging excitation compensation; the compensation creation module 310 is specifically configured as follows:

[0135] Based on the upper limit of charging and the lower limit of discharging of electric vehicles, the charging incentive compensation and discharging incentive compensation of electric vehicles within the charging time window are obtained.

[0136] Based on charging excitation compensation and discharging excitation compensation, a controllable charge-discharge cluster is modeled to obtain the corresponding compensation model for the controllable charge-discharge cluster.

[0137] In some embodiments, the capacity determination module 320 is specifically configured as follows:

[0138] With increased reserve capacity provided, the remaining energy of the electric vehicle's battery shall not be lower than the minimum allowable energy of the battery;

[0139] When a reduced reserve capacity is provided, the sum of the electric vehicle's battery energy and the reduced reserve capacity shall not exceed the battery's maximum capacity.

[0140] In some embodiments, the power market collaborative optimization system 300 for integrated power generation, grid, load, and storage further includes a function creation module; the function creation module is specifically configured as follows:

[0141] An objective function is created based on generation cost, electricity utility, reserve bonus, penalty cost for not using electricity, and charging compensation value.

[0142] It should be noted that the power market collaborative optimization system for source-grid-load-storage integration provided in this application embodiment and the power market collaborative optimization method for source-grid-load-storage integration provided in this application embodiment are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned power market collaborative optimization method for source-grid-load-storage integration, and the repeated parts will not be described again.

[0143] In some embodiments, please refer to Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 400 provided in this application includes a processor 410 and a memory 420; the memory 420 stores a computer program, wherein the computer program, when executed by the processor, implements the aforementioned power market collaborative optimization method oriented towards integrated power generation, grid, load, and storage.

[0144] Specifically, processor 410 may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. Processor 410 may also include onboard memory for caching purposes. Processor 410 may be a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of this application.

[0145] Memory 420 may be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, memory 420 may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, instruments, or propagation media. Specific examples of memory 420 include: magnetic storage devices such as magnetic tape or hard disk drives (HDDs); optical storage devices such as optical discs (CD-ROMs); and may also be random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0146] This application also provides a computer-readable medium storing a computer program that, when executed by a processor, implements the aforementioned method for coordinated optimization of the power market oriented towards integrated source-grid-load-storage systems. This computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into that device / apparatus / system. The aforementioned computer-readable medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0147] According to embodiments of this application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, optical fiber, radio frequency signals, etc., or any suitable combination thereof.

[0148] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application. Therefore, the scope of this application should not be limited to the above embodiments, but should be defined not only by the appended claims, but also by their equivalents. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the protection scope of this invention.

Claims

1. A collaborative optimization method for the integrated power market of power generation, grid, load, and storage, characterized in that, include: The charging compensation value is determined based on the owner's willingness to adjust the charging system, and the electric vehicle clusters are modeled using the type of the charging compensation value to obtain the compensation models for multiple electric vehicle sub-clusters. The electric vehicle sub-clusters include movable charging clusters, reduceable charging clusters, and controllable charging and discharging clusters. With the goal of minimizing charging costs, each of the aforementioned compensation models is solved to determine the optimal charging and discharging power of each electric vehicle in the electric vehicle cluster under the current electricity price and the charging compensation value; based on the optimal charging and discharging power, the upward and downward reserve capacity of the electric vehicle cluster during the adjustment period is obtained. An objective function is created based on generation cost, electricity utility, reserve bonus, penalty cost for non-use, and the charging compensation value. The objective function is constrained to minimize the total energy cost of the system. The charging and discharging strategy of the electric vehicle cluster is determined by combining the increase and decrease of reserve capacity. The safety constraints include system power balance constraints, network power flow safety constraints, and reserve capacity constraints.

2. The power market collaborative optimization method for integrated source-grid-load-storage as described in claim 1, characterized in that, The determination of the charging compensation value based on the vehicle owner's willingness to adjust the settings includes: The degree of the owner's willingness to regulate is determined based on the electric vehicle's battery level when it enters the network, battery level when it leaves the network, maximum battery level, network entry time, and network exit time.

3. The power market collaborative optimization method for integrated source-grid-load-storage as described in claim 2, characterized in that, For the movable charging cluster, the type of the charging compensation value includes time-shift compensation; the modeling of the electric vehicle cluster using the type of the charging compensation value to obtain compensation models for multiple electric vehicle sub-clusters includes: The charging time window is determined based on the electric vehicle's grid access time and grid disconnection time; Based on the electric vehicle's charge amount, charging efficiency, and maximum charging power, and in conjunction with the charging time window, the translation time is determined. Based on the translation time and unit translation compensation, the time translation compensation is determined, and the time translation compensation is used to model the movable charging cluster to obtain the compensation model corresponding to the movable charging cluster.

4. The power market collaborative optimization method for integrated source-grid-load-storage as described in claim 2, characterized in that, For the reducible charging cluster, the type of charging compensation value includes power reduction compensation; the process of modeling the electric vehicle clusters using the type of charging compensation value to obtain compensation models for multiple electric vehicle sub-clusters includes: The power reduction range is determined based on the maximum charging power of the electric vehicle; The power reduction amount within the charging time window is obtained based on the power reduction range, the amount of electricity to be charged by the electric vehicle, and the charging efficiency. Based on the power reduction amount and the unit reduction compensation, the power reduction compensation is determined, and the power reduction compensation is used to model the reduceable charging cluster to obtain the compensation model corresponding to the reduceable charging cluster.

5. The power market collaborative optimization method for integrated source-grid-load-storage as described in claim 2, characterized in that, For the controllable charge-discharge cluster, the type of the charging compensation value includes charging excitation compensation and discharging excitation compensation; the modeling of the electric vehicle cluster using the type of the charging compensation value yields compensation models for multiple electric vehicle sub-clusters, including: Based on the upper limit of charging and the lower limit of discharging of the electric vehicle, the charging incentive compensation and discharging incentive compensation of the electric vehicle within the charging time window are obtained. The controllable charge-discharge cluster is modeled based on the charging excitation compensation and the discharging excitation compensation to obtain the compensation model corresponding to the controllable charge-discharge cluster.

6. The power market collaborative optimization method for integrated source-grid-load-storage as described in claim 1, characterized in that, The step of obtaining the upward and downward reserve capacity of the electric vehicle cluster during the adjustment period based on the optimal charging and discharging power includes: When the reserve capacity is increased, the remaining energy of the electric vehicle's battery shall not be less than the minimum allowable energy of the battery; When a reduced reserve capacity is provided, the sum of the battery energy of the electric vehicle and the reduced reserve capacity shall not exceed the maximum battery capacity.

7. A collaborative optimization system for the integrated power market of power generation, grid, load, and storage, characterized in that, include: The system includes a compensation creation module, a capacity determination module, and a strategy determination module; among them, The compensation creation module is configured to determine the charging compensation value based on the degree of the vehicle owner's willingness to adjust, and to model the electric vehicle clusters separately using the type of the charging compensation value to obtain the compensation models of multiple electric vehicle sub-clusters; the electric vehicle sub-clusters include movable charging clusters, reduceable charging clusters, and controllable charging and discharging clusters. The capacity determination module is configured to solve each of the compensation models with the goal of minimizing charging cost, and determine the optimal charging and discharging power of each electric vehicle in the electric vehicle cluster under the current electricity price and the charging compensation value; based on the optimal charging and discharging power, the module obtains the upward and downward reserve capacity of the electric vehicle cluster during the adjustment period. The strategy determination module is configured to create an objective function based on power generation cost, power consumption utility, reserve bonus and penalty cost for not calling, and the charging compensation value, so as to minimize the total energy cost of the system and impose a security constraint on the objective function. Combined with the increase and decrease of reserve capacity, the module determines the charging and discharging strategy of the electric vehicle cluster within the control period. The security constraints include system power balance constraints, network power flow security constraints, and reserve capacity constraints.

8. An electronic device comprising a processor and a memory; said memory having a storage for a computer program, wherein, When the computer program is executed by the processor, it implements the power market collaborative optimization method for source-grid-load-storage integration as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that, It stores a computer program, wherein when the computer program is executed by a processor, it implements the power market collaborative optimization method for source-grid-load-storage integration as described in any one of claims 1 to 6.

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