Charging station resource scheduling method and device, equipment, readable storage medium and product

Abstract

The application relates to a charging station resource scheduling method and device, equipment, a readable storage medium and a product. The method comprises the following steps: taking the minimum target operation cost of a target charging station as a target, constructing a resource scheduling function corresponding to the target charging station; constructing a constraint condition of the resource scheduling function; the constraint condition is used for constraining at least one of an operation parameter of a power generation device, an operation parameter of an energy storage device and an operation parameter of a charging device of the target charging station; and the resource scheduling strategy of the target charging station is determined according to the resource scheduling function and the constraint condition. The operation economy of the charging station can be improved by using the method.

Description

Technical Field

[0001] This application relates to the field of power engineering and control automation technology, and in particular to a method, apparatus, equipment, readable storage medium and product for resource scheduling of charging stations. Background Technology

[0002] With the rapid growth of new energy vehicle ownership, charging infrastructure is experiencing a wave of large-scale construction. To reduce electricity costs, improve the utilization rate of renewable energy, and improve the operation of the power distribution network, charging stations are evolving from the traditional "single electricity load" model to an integrated "photovoltaic-storage-charging" model that combines distributed photovoltaic (PV), battery energy storage system (BESS), and vehicle-to-grid (V2G) interaction. Meanwhile, the gradual improvement of market mechanisms such as time-of-use pricing, spot pricing, and demand-side response (DR) empowers charging stations to actively participate in the electricity market, achieving peak shaving and valley filling, and providing ancillary services.

[0003] However, the coupling of multiple energy sources and multiple stakeholders has brought about significant time-series coordination and constraint conflicts, including the time window and power boundary of charging orders, V2G participation willingness and battery health considerations, uncertainty of photovoltaic output, power and state of charge (SoC) constraints of energy storage, distribution network node capacity limitations and DR baseline calculation, etc. How to achieve synergistic optimization of photovoltaic, storage, charging and DR under a unified framework has become a key research focus and engineering pain point in the industry. Summary of the Invention

[0004] Therefore, it is necessary to provide a charging station resource scheduling method, apparatus, equipment, readable storage medium, and product to address the aforementioned technical problems. The charging station resource scheduling strategy determined by this method can improve the operational economy of charging stations.

[0005] Firstly, this application provides a method for scheduling charging station resources, including:

[0006] With the goal of minimizing the target operating cost of the target charging station, construct the corresponding resource scheduling function for the target charging station;

[0007] Construct constraints for the resource scheduling function; the constraints are used to constrain at least one of the operating parameters of the power generation equipment, the energy storage equipment, and the charging equipment of the target charging station.

[0008] Based on the resource scheduling function and constraints, determine the resource scheduling strategy for the target charging station.

[0009] In one embodiment, with the goal of minimizing the target operating cost of the target charging station, a resource scheduling function corresponding to the target charging station is constructed, including:

[0010] The initial operating cost of the target charging station is determined based on its vehicle charging cost, resource acquisition cost, energy storage consumption cost, and resource transfer cost; and,

[0011] Obtain the charging response revenue of the target charging station;

[0012] With the goal of minimizing the difference between initial operating costs and charging response benefits, a resource scheduling function corresponding to the target charging station is constructed.

[0013] In one embodiment, with the goal of minimizing the target operating cost of the target charging station, a resource scheduling function corresponding to the target charging station is constructed, including:

[0014] The first scheduling function is constructed with the goal of minimizing the target operating cost of the target charging station.

[0015] A second scheduling function is constructed with the goal of maximizing the additional operating reward of the target charging station;

[0016] Based on the first scheduling function and the second scheduling function, construct the resource scheduling function corresponding to the target charging station.

[0017] In one embodiment, a second scheduling function is constructed with the objective of maximizing the additional operating reward for the target charging station, including:

[0018] Obtain the first operating reward and the second operating reward; the first operating reward is obtained when the target charging station's response efficiency to the vehicle charging task meets the preset efficiency requirements; the second operating reward is obtained when the target charging station's energy storage action timeliness meets the preset timeliness requirements.

[0019] A second scheduling function is constructed with the goal of maximizing the sum of the first and second running rewards.

[0020] In one embodiment, the vehicle charging cost includes a first charging cost and a second charging cost; the first charging cost is the reputation cost incurred when the vehicle charging amount does not reach a preset charging amount within a preset time window; the second charging cost is the resource consumption cost during the vehicle charging process.

[0021] Resource acquisition costs include primary acquisition costs and secondary acquisition costs; primary acquisition costs are the costs of acquiring electricity resources; secondary acquisition costs are the costs of curtailment of solar power during the acquisition process.

[0022] In one embodiment, the energy storage cost is the difference between the discharge cost and the charging cost;

[0023] Resource transfer cost is the difference between the cost of absorbing resources and the cost of returning resources.

[0024] Secondly, this application also provides a charging station resource scheduling device, comprising:

[0025] The function construction module is used to construct the resource scheduling function corresponding to the target charging station with the goal of minimizing the target operating cost of the target charging station;

[0026] The constraint construction module is used to construct the constraints of the resource scheduling function; the constraints are used to constrain at least one of the operating parameters of the power generation equipment, the operating parameters of the energy storage equipment, and the operating parameters of the charging equipment of the target charging station.

[0027] The strategy determination module is used to determine the resource scheduling strategy for the target charging station based on the resource scheduling function and constraints.

[0028] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0029] With the goal of minimizing the target operating cost of the target charging station, construct the corresponding resource scheduling function for the target charging station;

[0030] Construct constraints for the resource scheduling function; the constraints are used to constrain at least one of the operating parameters of the power generation equipment, the energy storage equipment, and the charging equipment of the target charging station.

[0031] Based on the resource scheduling function and constraints, determine the resource scheduling strategy for the target charging station.

[0032] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0033] With the goal of minimizing the target operating cost of the target charging station, construct the corresponding resource scheduling function for the target charging station;

[0034] Construct constraints for the resource scheduling function; the constraints are used to constrain at least one of the operating parameters of the power generation equipment, the energy storage equipment, and the charging equipment of the target charging station.

[0035] Based on the resource scheduling function and constraints, determine the resource scheduling strategy for the target charging station.

[0036] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0037] With the goal of minimizing the target operating cost of the target charging station, construct the corresponding resource scheduling function for the target charging station;

[0038] Construct constraints for the resource scheduling function; the constraints are used to constrain at least one of the operating parameters of the power generation equipment, the energy storage equipment, and the charging equipment of the target charging station.

[0039] Based on the resource scheduling function and constraints, determine the resource scheduling strategy for the target charging station.

[0040] The aforementioned charging station resource scheduling method, apparatus, equipment, readable storage medium, and product aim to minimize the target operating cost of the target charging station. They construct a resource scheduling function corresponding to the target charging station; construct constraints on the resource scheduling function; and impose constraints on at least one of the operating parameters of the power generation equipment, energy storage equipment, and charging equipment of the target charging station. Based on the resource scheduling function and constraints, they determine the resource scheduling strategy for the target charging station. In this process, by constraining at least one of the operating parameters of the power generation equipment, energy storage equipment, and charging equipment of the target charging station while constructing the resource scheduling function, the solved resource equipment operating parameters meet operating standards, and on this basis, minimize the target operating cost of the target charging station, thereby improving the operational economy of the charging station. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating a charging station resource scheduling method in one embodiment;

[0043] Figure 2 This is a flowchart illustrating the resource scheduling function construction steps in one embodiment;

[0044] Figure 3 This is a flowchart illustrating the resource scheduling function construction steps in another embodiment;

[0045] Figure 4 This is a flowchart illustrating the charging station resource scheduling method in another embodiment;

[0046] Figure 5 This is a structural block diagram of a charging station resource scheduling device in one embodiment;

[0047] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0050] Before describing the embodiments of this application, some industry terms included in this application will be introduced:

[0051] Vehicle-to-Grid (V2G) is a two-way energy interaction technology between electric vehicles and the power grid. Under the premise of meeting its own charging needs, electric vehicles can transmit electrical energy back to the grid (discharge) or absorb electrical energy from the grid (charge) according to dispatch instructions and market signals, so as to realize functions such as peak shaving and valley filling, and ancillary services.

[0052] Battery Energy Storage System (BESS): An energy storage device used in charging stations. Its core function is to achieve power balance, peak-valley arbitrage, and local photovoltaic consumption by regulating charging and discharging power and coordinating distributed photovoltaic, V2G and demand response. It is the core unit for multi-resource coordinated scheduling.

[0053] State of Charge (SoC): The percentage of remaining charge in an electrochemical energy storage system or electric vehicle battery relative to its rated capacity. The value typically ranges from 0 to 1 (or 0% to 100%) and is a core indicator for constraining charging and discharging behavior and preventing overcharging and over-discharging.

[0054] Demand Response (DR): The response behavior of charging stations to adjust charging load or release energy storage and V2G potential based on electricity market signals (such as time-of-use pricing and incentive policies) or grid instructions, in order to achieve peak shaving and valley filling or ancillary services. It is divided into two categories: peak shaving response and valley filling response.

[0055] Distributed photovoltaic (PV): Distributed photovoltaic power generation equipment deployed at charging stations is a source of clean electricity supply. Its output is uncertain and needs to be balanced with local consumption and grid connection through energy storage scheduling, load adjustment and other means.

[0056] Charging Order (CO): A charging request submitted by an electric vehicle user, containing information such as the required amount of electricity, the estimated time to leave the charging station, and the willingness to participate in V2G. It is the core carrier of user-side constraints in the scheduling model.

[0057] Public Grid (PG): An external power network that provides power to charging stations or accepts surplus power from the stations (such as photovoltaic output, energy storage discharge, V2G discharge), and settles electricity billing through the grid connection point.

[0058] Point of Common Coupling (PCC): The node connecting the charging station to the public power grid, used to realize power exchange and power settlement. Its comprehensive power reflects the energy interaction status (absorption or output of active power) between the station and the power grid.

[0059] Power Conversion System (PCS): The core component of an electrochemical energy storage system, used to convert electrical energy between the energy storage battery and the grid. Its rated power determines the upper limit of the maximum charging and discharging power of the energy storage system.

[0060] Multi-Resource Collaborative Scheduling (MRCS) integrates charging orders, distributed photovoltaics, electrochemical energy storage, V2G, demand response, and distribution node capacity into a unified optimization framework. It collaboratively optimizes the scheduling methods of resource output and power exchange, with the goal of improving system economy, capacity utilization, and operational safety.

[0061] In one exemplary embodiment, such as Figure 1 As shown, a charging station resource scheduling method is provided. Taking the application of this method to a charging station resource scheduling terminal as an example, the method includes the following steps:

[0062] S110: Construct the resource scheduling function corresponding to the target charging station with the goal of minimizing the target operating cost of the target charging station.

[0063] Among them, the target charging station is a charging station with resource scheduling needs. The target charging station integrates distributed photovoltaic (PV), electrochemical energy storage (BESS) and vehicle-to-grid (V2G) interaction. The operation mode of the target charging station is the "photovoltaic-storage-charging integrated" operation mode.

[0064] Understandably, guided by the operating model of the target charging station, its operating costs include at least one of the following: energy procurement and interaction costs (including at least one of grid interaction settlement costs, distributed photovoltaic (PV) related costs, and battery energy storage (BESS) interaction costs); charging order and user-related costs (at least one of order fulfillment costs and charging service support costs); equipment operation and maintenance and loss costs (at least one of core equipment operation and maintenance costs, energy consumption and loss costs, and equipment depreciation costs); and market and compliance-related costs (at least one of demand response (DR) participation costs and distribution capacity-related costs). Accordingly, the target operating cost is the sum of all operating costs of the target charging station.

[0065] In one alternative implementation, the sub-operation costs of the target charging station under different dimensions can be determined, and a resource scheduling function corresponding to the target charging station can be constructed with the goal of minimizing the sum of the operation costs of the target charging station under various dimensions.

[0066] For example, sub-operation cost determination formulas for different dimensions of the target charging station can be constructed, and each sub-operation cost determination formula can be input into a pre-trained resource scheduling function construction model to obtain the resource scheduling function corresponding to the target charging station.

[0067] S120, the constraints for constructing the resource scheduling function.

[0068] Among them, the constraints are used to constrain at least one of the operating parameters of the power generation equipment, the operating parameters of the energy storage equipment, and the operating parameters of the charging equipment of the target charging station.

[0069] For example, the power generation equipment of the target charging station may include distributed photovoltaic (PV), the energy storage equipment may include a battery-electrical energy storage system (BESS), and the charging equipment may be a charging pile. The operating parameters of the power generation equipment may include at least one of real-time power output, predicted power output, power output status indication, unit electricity purchase cost, unit cost of power curtailment, and scheduling time window; the operating parameters of the energy storage equipment may include at least one of charging power, discharging power, state of charge, charge / discharge status indication, PCS rated power, charging efficiency, discharging efficiency, rated capacity, and self-discharge; the operating parameters of the charging equipment may include at least one of the following: maximum charging power of the charging gun, minimum charging power of the charging gun, maximum discharging power of the charging gun, minimum discharging power of the charging gun, maximum discharging power of the vehicle, minimum discharging power of the vehicle, order demand power, plug-in time, plug-out time, cumulative charging amount, demanded amount, and V2G participation willingness.

[0070] Optionally, the constraints of the resource scheduling function can be set by the user based on operational needs. This application does not impose any limitations on this. The following constraints are merely examples of constraints provided in this embodiment.

[0071] For example, when constraints are used to constrain the operating parameters of the power generation equipment at the target charging station, the constraints may include:

[0072] ;

[0073] ;

[0074] ;

[0075] In the formula, That is, the power output of the distributed power generation unit gen at time t; Let be the predicted power generation of distributed power unit gen at time t; be the output indicator 0-1 variable of distributed power unit gen, where 1 represents the normal output of distributed power unit gen, and 0 represents the opposite.

[0076] For example, when constraints are used to constrain the operating parameters of the energy storage device at the target charging station, the constraints may include:

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] In the formula, These represent the charging and discharging power of the electrochemical energy storage system BESS, respectively. The rated power of the PCS for the electrochemical energy storage system BESS; These are the 0-1 variables indicating the charging and discharging states of the electrochemical energy storage system BESS, where 1 indicates the corresponding state and 0 indicates the opposite state. This represents the total electrical power of the electrochemical energy storage system BESS.

[0082] Additionally, it is necessary to establish a correlation between the electrochemical energy storage SoC and the charge / discharge power, and to set upper and lower limits for the electrochemical energy storage SoC from the perspective of battery life protection, resulting in the following constraints:

[0083] ;

[0084] ;

[0085] In the formula, This refers to the SoC of the electrochemical energy storage system BESS; These represent the maximum and minimum SoC of the electrochemical energy storage system BESS, respectively. These represent the charging and discharging power of the electrochemical energy storage system BESS, respectively. These represent the charging and discharging efficiencies of electrochemical energy storage, respectively. The battery rated capacity of the electrochemical energy storage system (BESS) is used. In this method, to incorporate the self-discharge phenomenon of electrochemical energy storage into the model, an N-segment piecewise function is used to fit the relationship between the self-discharge of electrochemical energy storage per unit time and the system SoC. .

[0086] ;

[0087] In the formula, All are fitting constants; This is the threshold set for piecewise fitting.

[0088] For example, when constraints are used to constrain the operating parameters of the charging equipment at the target charging station, the constraints may include:

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] In the formula, These are the maximum and minimum charging power of the Pistol charging gun corresponding to the order; , These are the maximum and minimum discharge power supported by the Pistol charging gun corresponding to the order; These represent the charging power and discharging power of the order placed at time t, respectively. These are 0-1 variables indicating the charging and discharging status of the order, respectively. 1 indicates the corresponding status, and 0 indicates the opposite. This is a constant indicating the willingness of the order to participate in vehicle-to-everything (V2X) interaction; 1 indicates willingness to participate in V2X interaction, and 0 indicates otherwise. The required power for the vehicle corresponding to the order; These are the minimum and maximum discharge power supported by the vehicle in vehicle-to-everything (V2X) mode, respectively. These are the estimated gun removal and insertion times for the order; This refers to the total power consumption of the order.

[0098] The order presets the estimated departure time and the actual charging capacity required by the vehicle battery based on the departure time. Order management should take into account the above parameters, weigh the default cost of unfulfilled charging needs against the overall system revenue, optimize the vehicle-to-grid interaction plan for orders, and also play a role in prioritizing charging order scheduling.

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] In the formula, These represent the charging power and discharging power of the order placed at time t, respectively. These are the estimated gun removal and insertion times for the order; Accumulated charging power from order to time t; These are the combined charging efficiency and combined discharging efficiency of the vehicle and charging gun corresponding to the order, respectively. The insufficient charge that has not been fully charged relative to the required charge amount by the time the charging gun is removed from the order; This represents the required charging amount up to the time the gun is removed. The variable is 0-1, representing the completion of charging for an order. 1 indicates that the order has met the charging requirement, and 0 indicates that the order has not. This is an auxiliary constant for the algorithm, representing a local minimum value; This is an auxiliary constant for the algorithm, and it represents the maximum value.

[0104] It should be noted that, in order to improve the comprehensiveness of the constraint coverage, node power balancing and capacity constraints can also be considered in some embodiments. For example, power balancing and capacity constraints can be considered for power distribution nodes inside and outside the charging station, and short-term, certain-degree capacity overruns can be allowed.

[0105] For nodes within the power station, the following power balance constraints should be satisfied:

[0106] ;

[0107] In the formula, This represents the total electrical power of the node; a positive value indicates the power absorbed by the node, while a negative value indicates the power output by the node. This refers to the total power consumption of the order. This is the set of charging orders directly connected to the node based on the power distribution wiring; That is, the power output of the distributed power generation unit gen at time t. A collection of distributed generator sets directly connected to nodes based on power distribution wiring. The total electrical power of the electrochemical energy storage system BESS. It is an electrochemical energy storage collection that is directly connected to the node based on the power distribution wiring; This represents the total power supply of node m. This refers to the set of nodes directly connected to the node according to the power distribution wiring. This refers to the power consumption of non-adjustable equipment directly connected to the node according to the power distribution wiring.

[0108] The threat posed to system safety by capacity overruns at distribution nodes can essentially be measured by integrating the overrun power with the overrun duration. Therefore, this method allows for a certain degree of capacity overruns at nodes, subject to the following constraints:

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] In the formula, That is, the over-limit power of the distribution capacity of node at time t; The power distribution capacity of the node; The allowed over-limit mileage for nodes within the scheduling window; This represents the cumulative over-limit mileage of a node within the scheduling window. This refers to the self-attenuating mileage of the over-limit mileage included in the statistics; Auxiliary constant for the algorithm, representing the minimum value; This is an auxiliary constant for the algorithm, representing the maximum value.

[0114] For the sake of algorithm robustness, and to avoid the risk of the algorithm being unsolvable, the self-decaying mileage of the over-limit mileage is included in the statistics. The following constraints must be met to achieve self-attenuation only when the power distribution capacity does not exceed the limit at time t and the cumulative over-limit mileage is greater than the expected mileage. Only then will it be a non-zero value.

[0115]

[0116] If the limit is not exceeded within the scheduling window at time t, the cumulative over-limit mileage is the self-decreasing mileage per unit time. The node is marked with a variable of 0-1 when it exceeds the limit; 1 indicates that the node's power distribution capacity has exceeded the limit, and 0 indicates otherwise. The node is marked with a 0-1 variable to activate the self-decay of over-limit mileage. If it is 1, the self-decay of over-limit mileage is activated, otherwise it is 0. Auxiliary constant for the algorithm, representing the minimum value; This is an auxiliary constant for the algorithm, representing the maximum value.

[0117] As special nodes, charging station grid connection points have additional constraints based on the existing constraints for electricity cost accounting purposes:

[0118]

[0119] This refers to the comprehensive power output of the charging station's grid connection point. A positive value indicates that the grid connection node (cnode) absorbs active power from the public grid, while a negative value indicates that the grid connection node (cnode) outputs active power back to the public grid. These represent the active power absorbed and the active power returned by the grid-connected node cnode from the public network at time t, respectively. This is a 0-1 indicator variable for the grid-connected node (cnode) in the active power absorption mode, where 1 indicates active power absorption and 0 indicates active power absorption. Auxiliary constant for the algorithm, representing the minimum value; This is an auxiliary constant for the algorithm, representing the maximum value.

[0120] To further enhance the comprehensiveness of the constraints, demand response interaction-related constraints can also be considered. For example, demand-side responses can be differentiated into two types: peak shaving response and valley filling response. The response behavior of the power station based on the boundary response of the demand response event is referenced to the following constraints:

[0121]

[0122] In the formula, The grid-connected power baseline of the charging station at time t is the demand response event. This refers to the comprehensive power output of the charging station's grid connection point. A positive value indicates that the grid connection node (cnode) absorbs active power from the public grid, while a negative value indicates that the grid connection node (cnode) outputs active power back to the public grid. This is the difference between the grid-connected node power baseline and the total electrical power; This is a 0-1 variable used to help determine the relationship between the grid connection point power and the baseline power of the power station. A value of 1 indicates that the baseline power is greater than the grid connection point power, and a value of 0 indicates that the baseline power is less than the baseline power. This represents the effective response power of the grid connection point to demand response events. For the set of demand response periods; Auxiliary constant for the algorithm, representing the minimum value; Auxiliary constants for the algorithm, representing the maximum value; This is a marker constant for the response type during the demand response period; 1 indicates peak shaving response, and 0 indicates valley filling response.

[0123] S130: Determine the resource scheduling strategy for the target charging station based on the resource scheduling function and constraints.

[0124] Specifically, the resource scheduling function can be solved based on constraints, and the resource scheduling strategy for the target charging station can be determined based on the solution. For example, the equipment operating parameters obtained from the solution can be directly used as the resource scheduling strategy for the charging station.

[0125] In the aforementioned charging station resource scheduling method, the objective is to minimize the target operating cost of the target charging station. A resource scheduling function corresponding to the target charging station is constructed. Constraints are then established for this function, imposing constraints on at least one of the operating parameters of the target charging station's power generation equipment, energy storage equipment, and charging equipment. Based on the resource scheduling function and constraints, the resource scheduling strategy for the target charging station is determined. In this process, by constructing the resource scheduling function and constraining at least one of the operating parameters of the target charging station's power generation equipment, energy storage equipment, and charging equipment, the solved resource equipment operating parameters meet operational standards. Furthermore, by minimizing the target operating cost of the target charging station, the economic efficiency of its operation can be improved.

[0126] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, the process of constructing the resource scheduling function corresponding to the target charging station with the goal of minimizing the target operating cost of the target charging station is refined.

[0127] See Figure 2 The resource scheduling function construction steps shown include:

[0128] S210, determine the initial operating cost of the target charging station based on the vehicle charging cost, resource acquisition cost, energy storage consumption cost, and resource transfer cost of the target charging station.

[0129] The costs of vehicle charging include first charging costs and second charging costs. First charging costs are the reputational costs incurred when the vehicle's charging volume fails to reach the preset amount within a preset time window. Second charging costs are the resource consumption costs during the charging process. Resource acquisition costs include first acquisition costs and second acquisition costs. First acquisition costs are the costs of acquiring electricity resources. Second acquisition costs are the costs of curtailment of solar power during the acquisition process. Energy storage costs are the difference between discharging and charging costs. Resource transfer costs are the difference between resource absorption and resource return costs.

[0130] The preset time window and preset charging amount are used to characterize the theoretical charging speed of the charging station, and can be predetermined. This application does not impose any restrictions on them.

[0131] In one alternative implementation, the cost of charging a car can be determined based on the following formula:

[0132] ;

[0133] In the formula, The insufficient charge that has not been fully charged relative to the required charge amount by the time the charging gun is removed from the order; The cost of breach of contract compensation for insufficient electricity consumption per unit; Indicates the first charging cost; These represent the charging power and discharging power of the order placed at time t, respectively. This is the unit cost of compensating the order vehicle's discharge power during vehicle-to-grid interaction discharge; Time-of-use pricing for vehicle charging; This indicates the second charging cost; To optimize the time window; Collect all charging orders.

[0134] In one alternative implementation, the resource acquisition cost can be determined based on the following formula:

[0135] ;

[0136] In the formula, That is, the power output of the distributed power generation unit gen at time t; Let gen be the predicted power generation of the distributed power generation unit at time t; The unit electricity purchase cost for the amount of electricity generated by distributed power generation units (gen); The unit cost of the power curtailment for distributed power generation units is gen; T is the optimization time window. This refers to the set of all distributed generator sets.

[0137] In one alternative implementation, the energy storage cost can be determined based on the following formula:

[0138] ;

[0139] In the formula, These represent the charging and discharging power of the electrochemical energy storage system BESS, respectively. The unit price of electricity purchased by the charging station when the electrochemical energy storage system discharges (BESS). The unit price of electricity supplied by the charging station to the electrochemical energy storage system BESS when charging it; This represents the cost of discharge at time t; Let T represent the charging cost at time t; T is the optimization time window; and B is the set of all electrochemical energy storage systems.

[0140] In one alternative implementation, the resource transfer cost can be determined based on the following formula:

[0141] ;

[0142] In the formula, These represent the active power absorbed and the active power returned by the grid-connected node cnode from the public network at time t, respectively. The unit cost of time-of-use electricity at the grid connection point (cnode); The unit compensation price for the returned electricity volume of the grid connection point (cnode).

[0143] S220: Obtain the charging response revenue of the target charging station.

[0144] For example, the charging response revenue of the target charging station can be determined based on the following formula:

[0145] ;

[0146] In the formula, This represents the effective response power of the grid connection point to demand response events. This refers to the time-sharing unit compensation price for demand response events.

[0147] S230 constructs a resource scheduling function for the target charging station with the goal of minimizing the difference between the initial operating cost and the charging response benefit.

[0148] For example, the resource scheduling function corresponding to the target charging station can be as follows:

[0149] ;

[0150] In the formula, Cost of charging vehicles within the scheduling time window; To account for the resource acquisition costs of the distributed power sources in charging stations, considering the possibility of third-party investment; Then, considering the possibility that the electrochemical energy storage in the charging station is invested by a third party, the electrochemical energy storage is regarded as an internal producer and consumer of the system, and a cost function (energy storage consumption cost) is established for the power interaction between the charging station and the electrochemical energy storage. This is the cost function (resource transfer cost) for the settlement of electricity between the grid connection point and the public grid and offline grid. This refers to the revenue generated by the charging station through interaction with demand response events (charging response revenue).

[0151] In the above embodiments, a resource scheduling function corresponding to the target charging station is provided. The resource scheduling function of the target charging station takes into account the vehicle charging cost, resource acquisition cost, energy storage consumption cost and resource transfer cost, making the resource scheduling function of the target charging station more accurate.

[0152] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, another detailed step is provided for the process of constructing the resource scheduling function corresponding to the target charging station with the objective of minimizing the target operating cost of the target charging station.

[0153] See Figure 3The resource scheduling function construction steps shown include:

[0154] S310 constructs the first scheduling function with the goal of minimizing the target operating cost of the target charging station.

[0155] The first scheduling function can be the resource scheduling function described in S210-S230, which will not be elaborated here.

[0156] S320 constructs a second scheduling function with the goal of maximizing the additional operating reward of the target charging station.

[0157] In one optional implementation, a first operating reward and a second operating reward can be obtained; the first operating reward is obtained when the response efficiency of the target charging station to the vehicle charging task meets a preset efficiency requirement; the second operating reward is obtained when the timeliness of the energy storage action of the target charging station meets a preset timeliness requirement; and a second scheduling function is constructed with the goal of maximizing the sum of the first operating reward and the second operating reward.

[0158] ;

[0159] In the formula, Let t be a 0-1 variable representing whether the order meets the charging requirement at time t, where 1 means the order meets the charging requirement and 0 means it does not. The coefficient for the charging order time-series reward; , These are the charging power and discharging power of the energy storage bess at time t, respectively. The coefficient for energy storage timing rewards; This is the set of charging orders within the scheduling window; This refers to the collection of electrochemical energy storage systems within the system.

[0160] S330, construct the resource scheduling function corresponding to the target charging station based on the first scheduling function and the second scheduling function.

[0161] ;

[0162] In the formula, F1 represents the first scheduling function; F2 represents the second scheduling function; This represents the weight coefficient of the first scheduling function; These represent the weight coefficients of the second objective function. The two weight coefficients can be set according to requirements.

[0163] In the above embodiments, when constructing the resource scheduling function corresponding to the target charging station, not only the vehicle charging cost, resource acquisition cost, energy storage consumption cost, and resource transfer cost are considered, but also the additional operating rewards of the target charging station are considered, making the resource scheduling function of the target charging station more accurate.

[0164] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment. In this optional embodiment, the charging station resource scheduling method provided by this application will be described in detail.

[0165] See Figure 4 The charging station resource scheduling method shown includes:

[0166] S410, determine the initial operating cost of the target charging station based on the vehicle charging cost, resource acquisition cost, energy storage consumption cost, and resource transfer cost of the target charging station.

[0167] S420: Obtain the charging response revenue of the target charging station.

[0168] S430 constructs a first scheduling function with the goal of minimizing the difference between initial operating cost and charging response benefit.

[0169] The first operational reward is obtained when the target charging station's response efficiency to vehicle charging tasks meets preset efficiency requirements; the second operational reward is obtained when the target charging station's energy storage operation timeliness meets preset timeliness requirements; vehicle charging costs include the first charging cost and the second charging cost; the first charging cost is the reputation cost incurred when the vehicle charging volume does not reach the preset charging volume within a preset time window; the second charging cost is the resource consumption cost during the vehicle charging process; resource acquisition costs include the first acquisition cost and the second acquisition cost; the first acquisition cost is the cost of acquiring electricity resources; the second acquisition cost is the cost of curtailment of solar power during the acquisition of electricity resources. Energy storage consumption cost is the difference between discharge consumption cost and charging consumption cost; resource transfer cost is the difference between resource absorption cost and resource return cost.

[0170] S440, obtain the first run reward and the second run reward.

[0171] S450 constructs a second scheduling function with the goal of maximizing the sum of the first and second running rewards.

[0172] S460, construct the resource scheduling function corresponding to the target charging station based on the first scheduling function and the second scheduling function.

[0173] S470, the constraints for constructing the resource scheduling function.

[0174] Among them, the constraints are used to constrain at least one of the operating parameters of the power generation equipment, the operating parameters of the energy storage equipment, and the operating parameters of the charging equipment of the target charging station.

[0175] S480 determines the resource scheduling strategy for the target charging station based on the resource scheduling function and constraints.

[0176] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0177] Based on the same inventive concept, this application also provides a charging station resource scheduling device for implementing the charging station resource scheduling method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the charging station resource scheduling device provided below can be found in the limitations of the charging station resource scheduling method described above, and will not be repeated here.

[0178] In one exemplary embodiment, such as Figure 5 As shown, a charging station resource scheduling device is provided, including: a function construction module 510, a constraint construction module 520, and a strategy determination module 530, wherein:

[0179] Function construction module 510 is used to construct the resource scheduling function corresponding to the target charging station with the goal of minimizing the target operating cost of the target charging station;

[0180] The constraint construction module 520 is used to construct the constraint conditions of the resource scheduling function; the constraint conditions are used to constrain at least one of the operating parameters of the power generation equipment, the operating parameters of the energy storage equipment, and the operating parameters of the charging equipment of the target charging station.

[0181] The strategy determination module 530 is used to determine the resource scheduling strategy of the target charging station based on the resource scheduling function and constraints.

[0182] In one embodiment, the function construction module 510 includes a cost determination unit for determining the initial operating cost of the target charging station based on the vehicle charging cost, resource acquisition cost, energy storage consumption cost, and resource transfer cost of the target charging station; a revenue acquisition unit for acquiring the charging response revenue of the target charging station; and a function construction unit for constructing a resource scheduling function corresponding to the target charging station with the objective of minimizing the difference between the initial operating cost and the charging response revenue.

[0183] In one embodiment, the function construction module 510 includes a first construction unit for constructing a first scheduling function with the objective of minimizing the target operating cost of the target charging station; a second construction unit for constructing a second scheduling function with the objective of maximizing the additional operating reward of the target charging station; and a target construction unit for constructing a resource scheduling function corresponding to the target charging station based on the first and second scheduling functions.

[0184] In one embodiment, the second construction unit includes a reward acquisition subunit for acquiring a first operating reward and a second operating reward; the first operating reward is acquired when the response efficiency of the target charging station to the vehicle charging task meets a preset efficiency requirement; the second operating reward is acquired when the timeliness of the energy storage action of the target charging station meets a preset timeliness requirement; and a function construction subunit for constructing a second scheduling function with the goal of maximizing the sum of the first and second operating rewards. The vehicle charging cost includes a first charging cost and a second charging cost; the first charging cost is the reputation cost incurred when the vehicle charging volume does not reach a preset charging volume within a preset time window; the second charging cost is the resource consumption cost during the vehicle charging process; the resource acquisition cost includes a first acquisition cost and a second acquisition cost; the first acquisition cost is the cost of acquiring electricity resources; and the second acquisition cost is the cost of curtailment of solar power during the electricity resource acquisition process. The energy storage consumption cost is the difference between the discharge consumption cost and the charging consumption cost; and the resource transfer cost is the difference between the resource absorption cost and the resource return cost.

[0185] Each module in the aforementioned charging station resource scheduling device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0186] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a charging station resource scheduling method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0187] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0188] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0189] With the goal of minimizing the target operating cost of the target charging station, construct the corresponding resource scheduling function for the target charging station;

[0190] Construct constraints for the resource scheduling function; the constraints are used to constrain at least one of the operating parameters of the power generation equipment, the energy storage equipment, and the charging equipment of the target charging station.

[0191] Based on the resource scheduling function and constraints, determine the resource scheduling strategy for the target charging station.

[0192] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0193] The initial operating cost of the target charging station is determined based on its vehicle charging cost, resource acquisition cost, energy storage consumption cost, and resource transfer cost; and,

[0194] Obtain the charging response revenue of the target charging station;

[0195] With the goal of minimizing the difference between initial operating costs and charging response benefits, a resource scheduling function corresponding to the target charging station is constructed.

[0196] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0197] The first scheduling function is constructed with the goal of minimizing the target operating cost of the target charging station.

[0198] A second scheduling function is constructed with the goal of maximizing the additional operating reward of the target charging station;

[0199] Based on the first scheduling function and the second scheduling function, construct the resource scheduling function corresponding to the target charging station.

[0200] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0201] Obtain the first operating reward and the second operating reward; the first operating reward is obtained when the target charging station's response efficiency to the vehicle charging task meets the preset efficiency requirements; the second operating reward is obtained when the target charging station's energy storage action timeliness meets the preset timeliness requirements.

[0202] A second scheduling function is constructed with the goal of maximizing the sum of the first and second running rewards.

[0203] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0204] With the goal of minimizing the target operating cost of the target charging station, construct the corresponding resource scheduling function for the target charging station;

[0205] Construct constraints for the resource scheduling function; the constraints are used to constrain at least one of the operating parameters of the power generation equipment, the energy storage equipment, and the charging equipment of the target charging station.

[0206] Based on the resource scheduling function and constraints, determine the resource scheduling strategy for the target charging station.

[0207] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0208] The initial operating cost of the target charging station is determined based on its vehicle charging cost, resource acquisition cost, energy storage consumption cost, and resource transfer cost; and,

[0209] Obtain the charging response revenue of the target charging station;

[0210] With the goal of minimizing the difference between initial operating costs and charging response benefits, a resource scheduling function corresponding to the target charging station is constructed.

[0211] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0212] The first scheduling function is constructed with the goal of minimizing the target operating cost of the target charging station.

[0213] A second scheduling function is constructed with the goal of maximizing the additional operating reward of the target charging station;

[0214] Based on the first scheduling function and the second scheduling function, construct the resource scheduling function corresponding to the target charging station.

[0215] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0216] Obtain the first operating reward and the second operating reward; the first operating reward is obtained when the target charging station's response efficiency to the vehicle charging task meets the preset efficiency requirements; the second operating reward is obtained when the target charging station's energy storage action timeliness meets the preset timeliness requirements.

[0217] A second scheduling function is constructed with the goal of maximizing the sum of the first and second running rewards.

[0218] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0219] With the goal of minimizing the target operating cost of the target charging station, construct the corresponding resource scheduling function for the target charging station;

[0220] Construct constraints for the resource scheduling function; the constraints are used to constrain at least one of the operating parameters of the power generation equipment, the energy storage equipment, and the charging equipment of the target charging station.

[0221] Based on the resource scheduling function and constraints, determine the resource scheduling strategy for the target charging station.

[0222] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0223] The initial operating cost of the target charging station is determined based on its vehicle charging cost, resource acquisition cost, energy storage consumption cost, and resource transfer cost; and,

[0224] Obtain the charging response revenue of the target charging station;

[0225] With the goal of minimizing the difference between initial operating costs and charging response benefits, a resource scheduling function corresponding to the target charging station is constructed.

[0226] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0227] The first scheduling function is constructed with the goal of minimizing the target operating cost of the target charging station.

[0228] A second scheduling function is constructed with the goal of maximizing the additional operating reward of the target charging station;

[0229] Based on the first scheduling function and the second scheduling function, construct the resource scheduling function corresponding to the target charging station.

[0230] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0231] Obtain the first operating reward and the second operating reward; the first operating reward is obtained when the target charging station's response efficiency to the vehicle charging task meets the preset efficiency requirements; the second operating reward is obtained when the target charging station's energy storage action timeliness meets the preset timeliness requirements.

[0232] A second scheduling function is constructed with the goal of maximizing the sum of the first and second running rewards.

[0233] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0234] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0235] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0236] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for scheduling charging station resources, characterized in that, The method includes: With the goal of minimizing the target operating cost of the target charging station, a resource scheduling function corresponding to the target charging station is constructed. The constraints of the resource scheduling function are constructed; the constraints are used to constrain at least one of the operating parameters of the power generation equipment, the operating parameters of the energy storage equipment, and the operating parameters of the charging equipment of the target charging station. Based on the resource scheduling function and the constraints, the resource scheduling strategy for the target charging station is determined.

2. The method according to claim 1, characterized in that, The step of constructing a resource scheduling function corresponding to the target charging station, with the objective of minimizing the target operating cost of the target charging station, includes: Based on the vehicle charging cost, resource acquisition cost, energy storage consumption cost, and resource transfer cost of the target charging station, determine the initial operating cost of the target charging station; and, Obtain the charging response revenue of the target charging station; With the objective of minimizing the difference between the initial operating cost and the charging response benefit, a resource scheduling function corresponding to the target charging station is constructed.

3. The method according to claim 1, characterized in that, The step of constructing a resource scheduling function corresponding to the target charging station, with the objective of minimizing the target operating cost of the target charging station, includes: The first scheduling function is constructed with the goal of minimizing the target operating cost of the target charging station. A second scheduling function is constructed with the objective of maximizing the additional operating reward of the target charging station; Based on the first scheduling function and the second scheduling function, construct the resource scheduling function corresponding to the target charging station.

4. The method according to claim 3, characterized in that, The second scheduling function is constructed with the objective of maximizing the additional operating reward of the target charging station, including: Obtain a first operating reward and a second operating reward; the first operating reward is obtained when the response efficiency of the target charging station to the vehicle charging task meets a preset efficiency requirement; the second operating reward is obtained when the timeliness of the energy storage action of the target charging station meets a preset timeliness requirement. A second scheduling function is constructed with the goal of maximizing the sum of the first running reward and the second running reward.

5. The method according to claim 2, characterized in that, The vehicle charging cost includes a first charging cost and a second charging cost; the first charging cost is the reputation cost incurred when the vehicle charging amount does not reach the preset charging amount within a preset time window; the second charging cost is the resource consumption cost during the vehicle charging process. The resource acquisition cost includes a first acquisition cost and a second acquisition cost; the first acquisition cost is the cost of acquiring electricity resources; the second acquisition cost is the cost of curtailment of solar power during the acquisition of electricity resources.

6. The method according to claim 2, characterized in that, The energy storage cost is the difference between the discharge cost and the charging cost; The resource transfer cost is the difference between the resource absorption cost and the resource return cost.

7. A charging station resource scheduling device, characterized in that, The device includes: The function construction module is used to construct the resource scheduling function corresponding to the target charging station with the goal of minimizing the target operating cost of the target charging station; A constraint construction module is used to construct the constraint conditions of the resource scheduling function; the constraint conditions are used to constrain at least one of the operating parameters of the power generation equipment, the operating parameters of the energy storage equipment, and the operating parameters of the charging equipment of the target charging station. The strategy determination module is used to determine the resource scheduling strategy of the target charging station based on the resource scheduling function and the constraints.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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