Resource optimization method and device for optical storage and charging station and computer equipment

Abstract

The invention relates to a resource optimization method and device for an optical storage and charging station and computer equipment. The method comprises the steps that photovoltaic data, energy storage data of an energy storage system, charging pile data, billing meter data, response invitation information and electricity price information are acquired and then input into a resource optimization model, a first constraint construction module in the resource optimization model generates a first constraint according to the billing meter data, and the first constraint is used for generating a second constraint according to the billing meter data; a second constraint construction module generates a second constraint according to the energy storage data and the charging pile data, and a benefit evaluation module obtains target data meeting the first constraint and under the condition that the target function reaches the maximum value according to the photovoltaic data, the billing meter data, the response invitation information, the electricity price information and the second constraint; and updating the corresponding data in the optical storage and charging station by using the target data. According to the method, the cost minimization and the benefit maximization of the optical storage and charging station are realized, and the accuracy of resource optimization is further improved.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic storage and charging microgrids, and in particular to a resource optimization method, device, computer equipment, computer-readable storage medium and computer program product for a photovoltaic storage and charging station. Background Art

[0002] Under the background of power industry reform, the optimization of power market, improvement of electricity price management mode, and improvement of distribution asset management are gradually advancing. The development of these technical fields will help break the monopoly, introduce competition, improve efficiency, reduce costs, and achieve optimal allocation of resources. For the energy resource management of photovoltaic storage and charging stations, there are multiple energy control resources such as energy storage, charging piles, photovoltaics, etc. that can participate in the optimization configuration.

[0003] At present, the operating cost optimization management technology of photovoltaic storage and charging stations covers microgrid collaborative management technology, peak shaving and valley filling control technology of energy storage, response invitation control strategy, basic electricity fee optimization technology, user electricity price fluctuations, etc. The technologies have not been unified and integrated, and only some of them have adopted correlation analysis. There are two aspects of overlap and conflict in the relationship between the technologies.

[0004] However, the current operating cost optimization management technology for photovoltaic storage and charging stations has the problem of low accuracy in resource optimization. Summary of the invention

[0005] Based on this, it is necessary to provide a resource optimization method, device, computer equipment, computer-readable storage medium and computer program product for a photovoltaic storage and charging station that can improve the accuracy of resource optimization in response to the above technical problems.

[0006] In a first aspect, the present application provides a resource optimization method for a photovoltaic storage and charging station, comprising:

[0007] Obtain photovoltaic data of the photovoltaic system, energy storage data of the energy storage system, charging pile data of the charging pile operation system, billing meter data of the billing meter, response invitation information and electricity price information of the resource demander;

[0008] The photovoltaic data, energy storage data, charging pile data, billing meter data, response invitation information and electricity price information are input into the pre-built resource optimization model. The total electric energy load constraint construction module in the resource optimization model generates the total electric energy load constraint based on the billing meter data. The load downlink total amount constraint construction module in the resource optimization model generates the load downlink total amount constraint based on the energy storage data and charging pile data.

[0009] Through the comprehensive benefit evaluation module of operating cost in the resource optimization model, based on photovoltaic data, meter data, response invitation information, electricity price information and total load downlink constraint, the target energy storage data and target charging pile data are obtained when the total electric energy load constraint is satisfied and the objective function in the comprehensive benefit evaluation module of operating cost reaches the maximum value;

[0010] The target energy storage data is used to update the energy storage data in the energy storage system, and the target charging pile data is used to update the charging pile data in the charging pile operation system.

[0011] In one embodiment, the energy storage data includes forward active electric energy, reverse active electric energy and the maximum dischargeable amount of energy storage; the charging pile data includes charging pile charging power, charging pile charging time, V2G pile charging power, V2G pile charging time, V2G pile discharge power, V2G pile charging time and the maximum dischargeable amount of V2G pile;

[0012] The load downlink total amount constraint construction module also includes an energy storage load construction submodule, a charging pile load construction submodule, and a V2G pile load construction submodule;

[0013] The load downlink total amount constraint construction module in the resource optimization model generates the load downlink total amount constraint based on the energy storage data and charging pile data, including:

[0014] Inputting the positive active electric energy and the reverse active electric energy into the energy storage load construction submodule, and obtaining the energy storage load through the energy storage load construction submodule;

[0015] Input the charging power and charging time of the charging pile into the charging pile load construction submodule, and obtain the charging pile load through the charging pile load construction submodule;

[0016] Input the V2G pile charging power, V2G pile charging time, V2G pile discharge power and V2G pile charging time into the V2G pile load construction submodule, and obtain the V2G pile load through the V2G pile load construction submodule;

[0017] The energy storage load, charging pile load, V2G pile load, maximum dischargeable amount of energy storage and maximum dischargeable amount of V2G pile are input into the load downlink total amount constraint construction submodule to obtain the load downlink total amount constraint.

[0018] In one embodiment, the meter data includes demand, the response invitation information includes a response time period, and the photovoltaic data includes power generation and daily power generation trend data;

[0019] Through the comprehensive benefit evaluation module of operating cost in the resource optimization model, according to the photovoltaic data, meter data, response invitation information, electricity price information and load downlink total amount constraint, the target energy storage data and target charging pile data are obtained when the total electric energy load constraint is satisfied and the objective function in the comprehensive benefit evaluation module of operating cost reaches the maximum value, including:

[0020] Input the demand, electricity price information, response time period, power generation, daily power generation trend data and total load downlink constraint into the operating cost comprehensive benefit evaluation module, and obtain the target energy storage load, target charging pile load and target V2G load through the operating cost comprehensive benefit evaluation module when the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value;

[0021] According to the target energy storage load, target charging pile load and target V2G load, the target forward active electrical energy, target charging pile charging power, target charging pile charging time, target V2G pile charging power, target V2G pile charging time, target V2G pile discharge power and target V2G pile charging time are determined.

[0022] In one embodiment, the electricity price information includes peak electricity price, peak electricity price, normal electricity price and valley electricity price, peak electricity price, peak electricity price, normal electricity price and valley electricity price, and basic electricity price;

[0023] The operating cost comprehensive benefit evaluation module includes an operating cost downward trend coupling analysis submodule, an operating cost upward trend coupling analysis submodule, and an operating cost upward and downward comprehensive benefit evaluation submodule;

[0024] The demand, electricity price information, response time period, power generation, daily power generation trend data and total load downlink constraint are input into the operating cost comprehensive benefit evaluation module. The operating cost comprehensive benefit evaluation module is used to obtain the target energy storage load, target charging pile load and target V2G load when the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value, including:

[0025] According to any current total amount of load downlink among the total amount of load downlink, peak period, flat period, valley period and peak period, the peak amount of electricity, flat period and valley period are obtained respectively;

[0026] Based on the preset time interval, power generation, daily power generation trend data, response time period and the current load downlink total amount, the power consumption in the response time period is obtained;

[0027] Input the peak electricity quantity, peak electricity price, peak electricity quantity, peak electricity price, demand, basic electricity price and response time period electricity quantity into the operating cost downward trend coupling analysis submodule, and obtain the current load downward total benefit through the operating cost downward trend coupling analysis submodule;

[0028] Input the normal-period electricity quantity, normal-period electricity price, off-peak electricity quantity, off-peak electricity price, demand quantity, and basic electricity price into the operating cost upward trend coupling analysis submodule, and obtain the actual load upward value loss through the operating cost upward trend coupling analysis submodule;

[0029] The current total amount of downlink load and the actual uplink load value loss are input into the operating cost uplink and downlink comprehensive benefit evaluation submodule, and the target energy storage load, target charging pile load and target V2G load are obtained through the operating cost uplink and downlink comprehensive benefit evaluation submodule when the total electric energy load constraint is satisfied and the objective function in the operating cost uplink and downlink comprehensive benefit evaluation submodule reaches the maximum value.

[0030] In an exemplary embodiment, the energy storage load is obtained by the energy storage load construction submodule, including:

[0031] The energy storage load construction submodule determines candidate forward active electric energy and candidate reverse active electric energy from the forward active electric energy and the reverse active electric energy according to a preset target time point, and generates an energy storage load according to the candidate forward active electric energy and the candidate reverse active electric energy.

[0032] In one embodiment, the charging pile load is obtained by the charging pile load construction submodule, including:

[0033] The charging pile load construction submodule obtains the positive active electric energy of the charging pile based on the charging power and charging time of the charging pile, and determines the positive active electric energy of the candidate charging pile from the positive active electric energy of the charging pile according to the pre-set target time point, and generates the charging pile load based on the positive active electric energy of the candidate charging pile.

[0034] In one embodiment, obtaining the V2G pile load through the V2G pile load construction submodule includes:

[0035] The V2G pile load construction submodule obtains the forward active electric energy of the V2G pile according to the V2G pile charging power and the V2G pile charging time, and obtains the reverse active electric energy of the V2G pile based on the V2G pile discharge power and the V2G pile discharge time;

[0036] The V2G pile load construction submodule determines the candidate V2G forward active electric energy and the candidate V2G pile reverse active electric energy from the V2G forward active electric energy and the V2G pile reverse active electric energy according to the preset target time point, and generates the V2G pile load according to the candidate V2G forward active electric energy and the candidate V2G pile reverse active electric energy.

[0037] In an exemplary embodiment, the meter data includes total forward active electrical energy;

[0038] The total electric energy load constraint building module in the resource optimization model generates the total electric energy load constraint according to the billing table data, including:

[0039] The total electric energy load constraint construction module determines the target total forward active electric energy that meets the set conditions from the total forward active electric energy according to the preset target time point, and generates the total electric energy load constraint based on the target total forward active electric energy.

[0040] In a second aspect, the present application also provides a resource optimization device for a photovoltaic storage and charging station, including:

[0041] A data acquisition module is used to acquire photovoltaic data of the photovoltaic system, energy storage data of the energy storage system, charging pile data of the charging pile operation system, billing meter data of the billing meter, response invitation information of the resource demander, and electricity price information;

[0042] A constraint construction module is used to input photovoltaic data, energy storage data, charging pile data, billing meter data, response invitation information and electricity price information into a pre-built resource optimization model, and the total electric energy load constraint construction module in the resource optimization model generates the total electric energy load constraint based on the billing meter data, and the load downlink total amount constraint construction module in the resource optimization model generates the load downlink total amount constraint based on the energy storage data and charging pile data;

[0043] The target data acquisition module is used to obtain the target energy storage data and target charging pile data when the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value through the operating cost comprehensive benefit evaluation module in the resource optimization model according to the photovoltaic data, billing meter data, response invitation information, electricity price information and load downlink total amount constraint;

[0044] The data update module is used to update the energy storage data in the energy storage system using the target energy storage data, and to update the charging pile data in the charging pile operation system using the target charging pile data.

[0045] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0046] Obtain photovoltaic data of the photovoltaic system, energy storage data of the energy storage system, charging pile data of the charging pile operation system, billing meter data of the billing meter, response invitation information and electricity price information of the resource demander;

[0047] The photovoltaic data, energy storage data, charging pile data, billing meter data, response invitation information and electricity price information are input into the pre-built resource optimization model. The total electric energy load constraint construction module in the resource optimization model generates the total electric energy load constraint based on the billing meter data. The load downlink total amount constraint construction module in the resource optimization model generates the load downlink total amount constraint based on the energy storage data and charging pile data.

[0048] Through the comprehensive benefit evaluation module of operating cost in the resource optimization model, based on photovoltaic data, meter data, response invitation information, electricity price information and total load downlink constraint, the target energy storage data and target charging pile data are obtained when the total electric energy load constraint is satisfied and the objective function in the comprehensive benefit evaluation module of operating cost reaches the maximum value;

[0049] The target energy storage data is used to update the energy storage data in the energy storage system, and the target charging pile data is used to update the charging pile data in the charging pile operation system.

[0050] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0051] Obtain photovoltaic data of the photovoltaic system, energy storage data of the energy storage system, charging pile data of the charging pile operation system, billing meter data of the billing meter, response invitation information and electricity price information of the resource demander;

[0052] The photovoltaic data, energy storage data, charging pile data, billing meter data, response invitation information and electricity price information are input into the pre-built resource optimization model. The total electric energy load constraint construction module in the resource optimization model generates the total electric energy load constraint based on the billing meter data. The load downlink total amount constraint construction module in the resource optimization model generates the load downlink total amount constraint based on the energy storage data and charging pile data.

[0053] Through the comprehensive benefit evaluation module of operating cost in the resource optimization model, based on photovoltaic data, meter data, response invitation information, electricity price information and total load downlink constraint, the target energy storage data and target charging pile data are obtained when the total electric energy load constraint is satisfied and the objective function in the comprehensive benefit evaluation module of operating cost reaches the maximum value;

[0054] The target energy storage data is used to update the energy storage data in the energy storage system, and the target charging pile data is used to update the charging pile data in the charging pile operation system.

[0055] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0056] Obtain photovoltaic data of the photovoltaic system, energy storage data of the energy storage system, charging pile data of the charging pile operation system, billing meter data of the billing meter, response invitation information and electricity price information of the resource demander;

[0057] The photovoltaic data, energy storage data, charging pile data, billing meter data, response invitation information and electricity price information are input into the pre-built resource optimization model. The total electric energy load constraint construction module in the resource optimization model generates the total electric energy load constraint based on the billing meter data. The load downlink total amount constraint construction module in the resource optimization model generates the load downlink total amount constraint based on the energy storage data and charging pile data.

[0058] Through the comprehensive benefit evaluation module of operating cost in the resource optimization model, based on photovoltaic data, meter data, response invitation information, electricity price information and total load downlink constraint, the target energy storage data and target charging pile data are obtained when the total electric energy load constraint is satisfied and the objective function in the comprehensive benefit evaluation module of operating cost reaches the maximum value;

[0059] The target energy storage data is used to update the energy storage data in the energy storage system, and the target charging pile data is used to update the charging pile data in the charging pile operation system.

[0060] The resource optimization method, device, computer equipment, computer-readable storage medium and computer program product of the above-mentioned photovoltaic storage and charging station obtain the photovoltaic data of the photovoltaic system, the energy storage data of the energy storage system, the charging pile data of the charging pile operation system, the billing table data of the billing meter, the response invitation information and electricity price information of the resource demander, and then input the photovoltaic data, energy storage data, charging pile data, billing table data, response invitation information and electricity price information into a pre-constructed resource optimization model, and the total electric energy load constraint construction module in the resource optimization model generates a total electric energy load constraint according to the billing table data, and the resource optimization The load downlink total constraint construction module in the model generates the load downlink total constraint based on the energy storage data and charging pile data. Through the comprehensive benefit evaluation module of the operating cost in the resource optimization model, the target energy storage data and target charging pile data are obtained based on the photovoltaic data, meter data, response invitation information, electricity price information and load downlink total constraint, which satisfies the total electric energy load constraint and the objective function in the comprehensive benefit evaluation module of the operating cost reaches the maximum value. The target energy storage data is used to update the energy storage data in the energy storage system, and the target charging pile data is used to update the charging pile data in the charging pile operation system. By comprehensively analyzing the response invitation information, electricity price information and load information, and through comprehensive analysis by the resource optimization model, the cost minimization and benefit maximization of the photovoltaic storage and charging station are achieved, thereby improving the accuracy of resource optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 This is an application environment diagram of a resource optimization method for a photovoltaic storage and charging station in one embodiment;

[0063] Figure 2 It is a schematic flow chart of a resource optimization method of a photovoltaic storage and charging station in one embodiment;

[0064] Figure 3 A working principle diagram of a resource optimization method for a photovoltaic storage and charging station in one embodiment;

[0065] Figure 4 A schematic diagram of an algorithm flow of a resource optimization model in another embodiment;

[0066] Figure 5 It is a structural block diagram of a resource optimization device of a photovoltaic storage and charging station in one embodiment;

[0067] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0069] The resource optimization method of the photovoltaic storage and charging station provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the photovoltaic storage and charging station 102 includes components such as photovoltaic system, energy storage system, microgrid control system, power grid, system load, security monitoring, etc. The microgrid control system includes billing meters, resource demanders (virtual power plants, etc.) and macro electricity price information, etc. Security monitoring is used to monitor the photovoltaic storage and charging station, so that when some components fail, they can be discovered and repaired in time. The photovoltaic storage and charging station 102 communicates with the server 104 through the network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains photovoltaic data of the photovoltaic system, energy storage data of the energy storage system, charging pile data of the charging pile operation system, billing table data of the billing table, response invitation information and electricity price information of the resource demander, and inputs the photovoltaic data, energy storage data, charging pile data, billing table data, response invitation information and electricity price information into a pre-built resource optimization model. The total energy load constraint construction module in the resource optimization model generates a total electric energy load constraint based on the billing table data. The load downlink total amount constraint construction module in the resource optimization model generates a load downlink total amount constraint based on the energy storage data and the charging pile data. Then, through the operating cost comprehensive benefit evaluation module in the resource optimization model, according to the photovoltaic data, billing table data, response invitation data, electricity price information and load downlink total amount constraint, the target energy storage data and target charging pile data are obtained under the condition that the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value. Finally, the target energy storage data is used to update the energy storage data in the energy storage system, and the target charging pile data is used to update the charging pile data in the charging pile operation system. The server 104 may be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0070] In an exemplary embodiment, Figure 2 As shown, a resource optimization method for a photovoltaic storage and charging station is provided, and the method is applied to Figure 1The server 104 in the example is used as an example to illustrate the method, which includes the following steps S201 to S204. Among them:

[0071] Step S201, obtaining photovoltaic data of the photovoltaic system, energy storage data of the energy storage system, charging pile data of the charging pile operation system, billing table data of the billing table, response invitation information of the resource demander and electricity price information.

[0072] Among them, photovoltaic data can be understood as data related to photovoltaic power generation, such as power generation, etc. Energy storage data can be understood as stored electric energy data, and also includes the charging and discharging strategy configured by the entire system. Charging pile data can be understood as data associated with charging piles, including type, charging and discharging power, charging and discharging time and other information. Meter data can be understood as data related to the total electric energy consumed by the entire system. Electricity price information includes electricity prices for different periods, electricity prices for different periods, and basic electricity prices.

[0073] For example, the server 104 obtains photovoltaic data from the photovoltaic storage and charging station 102 through the network based on the standard data interface document provided by the photovoltaic manufacturer, obtains energy storage data based on the standard data interface document provided by the energy storage equipment manufacturer, obtains charging pile data based on the standard data interface document provided by the charging pile operation system manufacturer, collects billing table data based on the infrared collector + DLT645 dedicated standard protocol docking billing table data, obtains response invitation information based on the API standard protocol of the virtual power plant / demand response platform, and the response invitation business process, and obtains electricity price information by querying the electricity price notification on the official website of the power supply bureau. By obtaining data from multiple sources, the foundation is laid for subsequent data coupling analysis.

[0074] Step S202, input photovoltaic data, energy storage data, charging pile data, billing meter data, response invitation information and electricity price information into a pre-built resource optimization model, and the total electric energy load constraint construction module in the resource optimization model generates a total electric energy load constraint based on the billing meter data, and the load downlink total amount constraint construction module in the resource optimization model generates a load downlink total amount constraint based on the energy storage data and charging pile data.

[0075] Among them, the total electric energy load constraint can be understood as the maximum load value that the entire photovoltaic storage and charging station can bear at the current moment, and the total load downstream constraint can be understood as the value range of the total load downstream of the energy storage and charging piles of the entire photovoltaic storage and charging station. What is obtained here is the maximum value of the total load downstream, and the constraint is that any value within the range from 0 to this total load downstream can be used as the total load downstream. The latter range is smaller than the former, and the latter must be under the constraint of the former.

[0076] Optionally, the server 104 inputs the photovoltaic data, energy storage data, charging pile data, billing table data, response invitation information and electricity price information into a pre-built resource optimization model, and the total electric energy load constraint construction module in the resource optimization model selects the billing table data at the set time to generate the minute-level total electric energy load constraint, and the load downlink total amount constraint construction module in the resource optimization model selects the energy storage data and charging pile data at the set time to generate the load downlink total amount constraint. By constructing the constraint conditions, the selection is set for the subsequent maximization of the comprehensive benefits of the operating cost, avoiding the occurrence of unrealistic target data in order to blindly approach the goal to be achieved by the model, thereby requiring re-input training and increasing costs.

[0077] Step S203, through the operating cost comprehensive benefit evaluation module in the resource optimization model, according to photovoltaic data, meter data, response invitation information, electricity price information and load downlink total amount constraint, obtain the target energy storage data and target charging pile data when the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value.

[0078] Among them, the objective function can be understood as the comprehensive benefit obtained by deducting operating costs from operating benefits. Operating benefits refer to the downward benefits of operating costs, and operating costs refer to the upward losses of operating costs.

[0079] For example, server 104 uses the comprehensive benefit evaluation module of operating costs in the resource optimization model to obtain the corresponding current load downlink total benefit and actual load uplink value loss based on photovoltaic data, meter data, response invitation information and electricity price information, as well as any current load downlink total in the load downlink total constraint. When the total electric energy load constraint is met and the current load downlink total benefit minus the actual load uplink value loss reaches the maximum value, the target energy storage data and target charging pile data are determined based on the current load downlink total. This effectively couples the response invitation benefit, peak shaving and valley filling benefit, and basic electricity fee benefit in the operating benefits of the photovoltaic storage and charging station, thereby maximizing the benefits of the photovoltaic storage and charging station. At the same time, it also determines the target energy storage data and target charging pile data under the maximum benefit scenario, laying the foundation for issuing update instructions.

[0080] Step S204, using the target energy storage data to update the energy storage data in the energy storage system, and using the target charging pile data to update the charging pile data in the charging pile operation system.

[0081] Optionally, the server 104 sends instructions to the photovoltaic storage and charging station 102 to update the energy storage data in the energy storage system of the photovoltaic storage and charging station 102 to the target energy storage data, and to update the charging pile data in the charging pile operation system to the target charging pile data. It can be known that this does not simply refer to a single data, but may include concrete data such as charging and discharging rate and charging and discharging duration. Through the optimal allocation of resources, the maximum benefit of the model can be achieved in this photovoltaic storage and charging station, and at the same time, the operating cost of the photovoltaic storage and charging station can also be reduced.

[0082] In the resource optimization method of the above-mentioned photovoltaic storage and charging station, the photovoltaic data of the photovoltaic system, the energy storage data of the energy storage system, the charging pile data of the charging pile operation system, the billing table data of the billing table, the response invitation information and electricity price information of the resource demander are obtained, and then the photovoltaic data, energy storage data, charging pile data, billing table data, response invitation information and electricity price information are input into a pre-constructed resource optimization model, and the total electric energy load constraint construction module in the resource optimization model generates the total electric energy load constraint according to the billing table data, and the load downlink total amount constraint construction module in the resource optimization model generates the load downlink total amount constraint according to the energy storage data and the charging pile data. Through the operating cost comprehensive benefit evaluation module in the resource optimization model, according to the photovoltaic data, the billing table data, the response invitation information, the electricity price information and the load downlink total amount constraint, the target energy storage data and the target charging pile data are obtained when the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value, and the target energy storage data is used to update the energy storage data in the energy storage system, and the target charging pile data is used to update the charging pile data in the charging pile operation system. By comprehensively analyzing the response invitation information, electricity price information and load information, and through comprehensive analysis by the resource optimization model, the cost minimization and benefit maximization of the photovoltaic storage and charging station are achieved, thereby improving the accuracy of resource optimization.

[0083] In one embodiment, the energy storage data includes forward active electric energy, reverse active electric energy and the maximum dischargeable amount of energy storage; the charging pile data includes charging pile charging power, charging pile charging time, V2G pile charging power, V2G pile charging time, V2G pile discharge power, V2G pile charging time and V2G pile maximum dischargeable amount; the load downlink total amount constraint construction module also includes an energy storage load construction submodule, a charging pile load construction submodule, and a V2G pile load construction submodule;

[0084] The load downlink total amount constraint construction module in the resource optimization model generates a load downlink total amount constraint according to the energy storage data and the charging pile data, including: inputting the forward active electric energy and the reverse active electric energy into the energy storage load construction submodule, and obtaining the energy storage load through the energy storage load construction submodule; inputting the charging power of the charging pile and the charging time of the charging pile into the charging pile load construction submodule, and obtaining the charging pile load through the charging pile load construction submodule; inputting the V2G pile charging power, the V2G pile charging time, the V2G pile discharge power and the V2G pile charging time into the V2G pile load construction submodule, and obtaining the V2G pile load through the V2G pile load construction submodule; inputting the energy storage load, the charging pile load, the V2G pile load, the maximum dischargeable amount of the energy storage and the maximum dischargeable amount of the V2G pile into the load downlink total amount constraint construction submodule, and obtaining the load downlink total amount constraint.

[0085] Among them, the energy storage data includes forward active electrical energy, reverse active electrical energy and the maximum dischargeable amount of energy storage; the charging pile data includes charging pile charging power, charging pile charging time, V2G pile charging power, V2G pile charging time, V2G pile discharge power, V2G pile charging time and the maximum dischargeable amount of V2G pile.

[0086] V2G piles (Vehicle to Grid charging piles) can be understood as vehicle networking charging piles. V2G technology allows electric vehicles to communicate with the power grid in both directions, which can not only charge electric vehicles, but also feed the electric energy of electric vehicles back to the power grid. This means that when electric vehicles are not in use, they can become a temporary power source to support the stability of the power grid; charging can be understood as positive active power or reverse active power, and discharging can be understood as reverse active power or positive active power.

[0087] For example, the server 104 inputs the forward electric energy and the reverse active electric energy into the pure load construction submodule to obtain the energy storage load, and inputs the charging power and the charging time of the charging pile into the charging pile load construction submodule to obtain the charging pile load, and inputs the V2G pile charging power, the V2G pile charging time, the V2G pile discharge power and the V2G pile charging time into the V2G pile load construction submodule to obtain the V2G pile load, and finally inputs the energy storage load, the charging pile load, the V2G pile load, the maximum dischargeable amount of the energy storage and the maximum dischargeable amount of the V2G pile into the load downlink total amount constraint construction submodule to obtain the load downlink total amount constraint. The modules for constructing the load downlink total amount constraint are independent of each other and related to each other, and it is easy to query and modify the data, which ensures the accuracy of the constructed load downlink total amount constraint and improves the adaptability to the environment.

[0088] In one embodiment, the meter data includes demand, the response invitation information includes a response time period, and the photovoltaic data includes power generation and daily power generation trend data;

[0089] Through the comprehensive benefit evaluation module of operating cost in the resource optimization model, according to the photovoltaic data, meter data, response invitation information, electricity price information and load downlink total amount constraint, the target energy storage data and target charging pile data are obtained when the total electric energy load constraint is satisfied and the objective function in the comprehensive benefit evaluation module of operating cost reaches the maximum value, including: the demand, electricity price information, response time period, power generation power, daily power generation trend data and load downlink total amount constraint are input into the comprehensive benefit evaluation module of operating cost, and the target energy storage load, target charging pile load and target V2G load are obtained through the comprehensive benefit evaluation module of operating cost when the total electric energy load constraint is satisfied and the objective function in the comprehensive benefit evaluation module of operating cost reaches the maximum value; according to the target energy storage load, target charging pile load and target V2G load, the target forward active electric energy, target charging pile charging power, target charging pile charging time, target V2G pile charging power, target V2G pile charging time, target V2G pile discharge power and target V2G pile charging time are determined.

[0090] Among them, demand can be understood as the maximum power demand of the photovoltaic storage and charging station within a period of time, and the daily power generation trend data can be understood as a predicted daily photovoltaic power generation.

[0091] Optionally, the server 104 inputs the demand, electricity price information, response time period, power generation, daily power generation trend data and load downlink total amount constraint into the operating cost comprehensive benefit evaluation module, and obtains the corresponding current load downlink total amount benefit and actual load uplink value loss according to any current load downlink total amount among the demand, electricity price information, response time period, power generation, daily power generation trend data and load downlink total amount constraint, and determines the target energy storage load, target charging pile load and target V2G load according to the current load downlink total amount when the total electric energy load constraint is met and the current load downlink total amount benefit minus the actual load uplink value loss reaches the maximum value, and determines the target forward active electric energy, target charging pile charging power, target charging pile charging time, target V2G pile charging power, target V2G pile charging time, target V2G pile discharge power and target V2G pile charging time according to the target energy storage load, target charging pile load and target V2G load. Through a comprehensive assessment of operating costs, the system can effectively optimize the allocation of power resources to ensure maximum resource utilization when power demand fluctuates. In addition, through the reasonable scheduling of energy storage loads and V2G loads, the system can better integrate renewable energy, reduce dependence on traditional energy, and support sustainable development.

[0092] In an exemplary embodiment, the electricity price information includes peak period electricity price, peak period electricity price, flat period electricity price and valley period electricity price, peak period, peak period, flat period and valley period, basic electricity price; the operating cost comprehensive benefit evaluation module includes an operating cost downward trend coupling analysis submodule, an operating cost upward trend coupling analysis submodule and an operating cost upward and downward comprehensive benefit evaluation submodule;

[0093] The demand, electricity price information, response time period, power generation, daily power generation trend data and total load downlink constraint are input into the operating cost comprehensive benefit evaluation module. The operating cost comprehensive benefit evaluation module is used to obtain the target energy storage load, target charging pile load and target V2G load when the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value, including:

[0094] According to any current load downlink amount among the peak period, peak period, flat period, valley period and load downlink total amount constraints, obtain the peak period electricity, peak period electricity, flat period electricity and valley period electricity; based on the pre-set time length interval, power generation, daily power generation trend data, response time period and the current load downlink total amount, obtain the response time period electricity;

[0095] The peak electricity volume, peak electricity price, peak electricity volume, peak electricity price, demand, basic electricity price and electricity volume in the response time period are input into the operating cost downward trend coupling analysis submodule, and the current load downward total benefit is obtained through the operating cost downward trend coupling analysis submodule; the normal period electricity volume, normal period electricity price, valley period electricity volume, valley period electricity price, demand and basic electricity price are input into the operating cost upward trend coupling analysis submodule, and the actual load upward value loss is obtained through the operating cost upward trend coupling analysis submodule; the current load downward total volume and the actual load upward value loss are input into the operating cost upward and downward comprehensive benefit evaluation submodule, and the operating cost upward and downward comprehensive benefit evaluation submodule is used to obtain the target energy storage load, target charging pile load and target V2G load when the total electric energy load constraint is met and the objective function in the operating cost upward and downward comprehensive benefit evaluation submodule reaches the maximum value.

[0096] Among them, the peak period can be understood as the period when electricity demand reaches the highest peak in a day or a period of time. The peak period can be understood as the period usually used to describe the time when electricity demand or load reaches the maximum value in a certain period of time. It should be slightly lower than the peak period and belong to the second highest period. The flat period can be understood as the period when electricity demand is relatively stable. The valley period can be understood as the time when electricity demand is lower. Among them, the peak period electricity price > peak period electricity price > flat period electricity price > valley period electricity price. Since changes in electricity demand bring changes in power generation costs, the electricity prices in different periods are different.

[0097] For example, the peak electricity quantity of the current load downlink in the peak period, the peak electricity quantity in the peak period, the flat electricity quantity in the flat period and the valley electricity quantity in the valley period are obtained respectively, and the actual photovoltaic power generation is obtained based on the preset time length interval and the power generation power, and the predicted photovoltaic power generation is obtained according to the time length interval and the daily power generation trend data, the current load downlink total amount in the response time period, and the response time period electricity quantity is obtained based on the response to the current load downlink total amount, the actual photovoltaic power generation and the predicted photovoltaic power generation, and the peak electricity quantity, the peak electricity price, the peak electricity quantity, the peak electricity price, the demand, the basic electricity price and the response time period electricity quantity are input into the operating cost downlink trend coupling analysis submodule to obtain the current load The total benefit of downlink and load; and the input of normal power, normal power price, valley power, valley power price, demand, and basic power price into the coupling analysis submodule of the upward trend of operating cost, and the actual load uplink value loss is obtained. The current total downlink load and the actual load uplink value loss are input into the comprehensive benefit evaluation submodule of the uplink and downlink of operating cost, and the current total downlink load and the actual load uplink value loss are used to adjust the range of the total downlink load constraint to change the current total downlink load, and the maximum value of the current total downlink load benefit minus the actual load uplink value loss is obtained. The target energy storage load, target charging pile load, and target V2G pile load are obtained according to the current total downlink load. Through the coupling analysis of data from multiple sources, the in-depth analysis of the data is realized, which can achieve higher economic benefits, minimize the cost and maximize the benefits of the photovoltaic storage and charging station, and at the same time, the optimization effect of resource optimization is better.

[0098] In one embodiment, the energy storage load is obtained by the energy storage load construction submodule, including: the energy storage load construction submodule determines the candidate forward active electric energy and the candidate reverse active electric energy from the forward active electric energy and the reverse active electric energy according to a preset target time point, and generates the energy storage load according to the candidate forward active electric energy and the candidate reverse active electric energy.

[0099] Optionally, the energy storage load construction submodule is based on a preset target time point. For example, if the current day is time T, then the corresponding previous day is pushed forward a certain length of time based on T, and so on, from the forward active electric energy and reverse active electric energy at multiple time points, a set number of candidate forward active electric energy and candidate reverse active electric energy are selected, and a time-length-level energy storage load is generated based on the candidate forward active electric energy and candidate reverse active electric energy. By generating a specific level of energy storage load, it is convenient to show the changes in the energy storage load over a period of time, and a certain amount of data is selected for load construction, which also avoids the redundant computing power cost caused by processing a large amount of data, thereby accelerating the optimization speed of resource optimization.

[0100] In one of the embodiments, the charging pile load is obtained through a charging pile load construction submodule, including: the charging pile load construction submodule obtains the forward active electrical energy of the charging pile based on the charging power of the charging pile and the charging time of the charging pile, and determines the forward active electrical energy of the candidate charging pile from the forward active electrical energy of the charging pile according to a pre-set target time point, and generates the charging pile load based on the forward active electrical energy of the candidate charging pile.

[0101] For example, the charging pile load construction submodule obtains the positive active electric energy of the charging pile based on the charging power and charging time of the charging pile. The charging time here is not the time from the start of charging to the end of full charging, but the time from the start of charging to the current sampling time point. According to the pre-set target time point, for example, if the current day is time T, then the corresponding previous day is pushed forward by a certain length of time on the basis of T, and so on. From the positive active electric energy of the charging pile at multiple time points, a set number of candidate charging pile positive active electric energy is selected, and the time length level charging pile load is generated according to the candidate charging pile positive active electric energy. By generating a specific level of charging pile load, it is convenient to show the change of the charging pile load over a period of time, and a certain amount of data is selected for load construction, which also avoids the redundant computing power cost caused by processing a large amount of data, thereby accelerating the optimization speed of resource optimization.

[0102] In an exemplary embodiment, the V2G pile load is obtained by a V2G pile load construction submodule, including: the V2G pile load construction submodule obtains the V2G pile forward active electric energy according to the V2G pile charging power and the V2G pile charging time, and obtains the V2G pile reverse active electric energy based on the V2G pile discharge power and the V2G pile discharge time; the V2G pile load construction submodule determines the candidate V2G forward active electric energy and the candidate V2G pile reverse active electric energy from the V2G forward active electric energy and the V2G pile reverse active electric energy according to a preset target time point, and generates the V2G pile load according to the candidate V2G forward active electric energy and the candidate V2G pile reverse active electric energy.

[0103] Optionally, the V2G pile load construction submodule obtains the forward active electric energy of the charging pile based on the V2G pile charging power and the V2G pile charging time, and obtains the reverse active electric energy of the charging pile based on the V2G pile discharge power and the V2G pile discharge time. The charging time here is not the time from the start of charging to the end of full charging, but the time from the start of charging to the current sampling time point. The discharge time is the same as the charging time, which will not be repeated here. According to the pre-set target time point, for example, if the current day is time T, then the corresponding previous day is pushed forward by a certain time length based on T, and so on, from the V2G pile forward active electric energy and the V2G pile reverse active electric energy at multiple time points, select a set number of candidate V2G pile forward active electric energy and candidate V2G pile reverse active electric energy, and generate a time length level V2G pile load according to the candidate V2G pile forward active electric energy and the V2G pile reverse active electric energy. By generating a specific level of V2G pile load, it is easy to show the changes in V2G pile load over a period of time, and selecting a certain amount of data for load construction, it also avoids the unnecessary computing power costs caused by processing a large amount of data, thereby speeding up the optimization speed of resource optimization.

[0104] In one embodiment, the billing table data includes total forward active electrical energy; the total electrical energy load constraint construction module in the resource optimization model generates a total electrical energy load constraint based on the billing table data, including: the total electrical energy load constraint construction module determines the target total forward active electrical energy that meets the set conditions from the total forward active electrical energy based on a pre-set target time point, and generates the total electrical energy load constraint based on the target total forward active electrical energy.

[0105] For example, the total electric energy load constraint construction module is based on a preset target time point. For example, if the current day is time T, then the corresponding previous day is pushed forward a certain length of time based on T, and so on, from the total positive active electric energy at multiple time points, a set number of candidate positive active total electric energy is selected, and the time length level total electric energy load is generated based on the candidate positive active total electric energy. By generating a specific level of total electric energy point load constraints, it is easy to show the changes in the total electric energy load over a period of time, and a certain amount of data is selected for load construction, which also avoids the excess computing power cost caused by processing a large amount of data, thereby speeding up the optimization speed of resource optimization.

[0106] In an exemplary embodiment, Figure 3 As shown, a working principle diagram of a resource optimization method for a photovoltaic storage and charging station is provided.

[0107] 1. Model input and output parameters:

[0108] 1. Data transceiver module, to realize data collection and command issuing channel, to collect power generation (frequency 5 minutes / time):

[0109] (1) Photovoltaic system: Based on the standard data interface document provided by the photovoltaic manufacturer, power generation (kW), and daily power generation trend data.

[0110] (2) Energy storage system: Based on the standard data interface document provided by the energy storage equipment manufacturer, the system collects the chargeable capacity (kWh), dischargeable capacity (kWh), charge and discharge strategy, and forward / reverse active electric energy. At the same time, the data sent by the command is the charge and discharge strategy.

[0111] (3) Charging pile operation system: Based on the standard data interface document provided by the charging pile operation system manufacturer, the type of charging pile, charging power, V2G pile charging and discharging power, and V2G pile dischargeable capacity (kWh) are collected. At the same time, the data issued by the command include charging pile charging power adjustment (percentage%, ranging from 0% to 100%), V2G pile charging power adjustment (percentage%, ranging from 0% to 100%), V2G pile discharge power (percentage%, ranging from 0% to 100%), charging pile active power, and V2G forward / reverse active power.

[0112] (4) Billing meter: The data of the billing meter is connected through the infrared collector + DLT645 special standard protocol to collect the total forward active power, active power, demand, etc.

[0113] 2. Response to invitation information and electricity price information:

[0114] (1) Response invitation information: Based on the API standard protocol of the virtual power plant / demand response platform and the response invitation business process, the response invitation information is obtained, including the invitation response time and power consumption.

[0115] (2) Electricity price information: By querying the electricity price notification on the official website of the Municipal Power Supply Bureau, you can obtain information on electricity price billing rules, including electricity consumption classification, time-of-use (peak, flat, and off-peak) electricity prices, time periods (peak, flat, and off-peak) and basic electricity prices.

[0116] Figure 3 The algorithm flow chart of the energy optimization model based on the operation cost analysis of the photovoltaic storage and charging station (i.e. the resource optimization model mentioned above) is as follows: Figure 4 As shown (the following data are examples, and the technical solution of this application is not limited to this condition):

[0117] Step 1: Model input data collection:

[0118] (1) Collect the relevant data of the photovoltaic system, energy storage system, charging pile operation system, and billing meter through the data transceiver module. For the specific data range, see [I. Model input and output parameters / 1. Data transceiver module, realizing data collection and command issuance channel].

[0119] (2) Obtaining response invitation information and electricity price information. For specific data ranges, see [I. Model input and output parameters / 2. Response invitation information and electricity price information].

[0120] Step 2: Modeling the benchmark key factor curve:

[0121] The benchmark key factors include total load, energy storage load, charging pile load, and V2G load.

[0122] (1) The total load curve of the benchmark key factor is the average minute-level total load curve of the previous five days (i.e., the total electric energy load constraint building block):

[0123] Among them, the first five days are divided into two cases. One is that when the current day is a working day (Monday to Friday), the first five days are counted five days ago excluding the weekend; the other is that when the current day is a rest day (Saturday or Sunday), the first five days are counted five days ago. The time interval of the curve is 5 minutes / time.

[0124] Taking time point T as an example, the calculation formula for the average minute-level total energy load △Pp is:

[0125] △P p =[(P t1 -P t11 )+(P t2 -P t21 )+...+(P t5 -P t51 )]÷5

[0126] Where P t1 It is the total positive active power at time T of the day (data comes from the billing meter).

[0127] P t11 It is the total positive active power at 5 minutes before T of the day (data comes from the billing meter).

[0128] P t2 It is the total positive active energy at time T of the previous day (data comes from the billing meter).

[0129] P t21 It is the total positive active energy at 5 minutes before T of the previous day (data comes from the billing meter).

[0130] P t5 It is the total positive active energy at time point T on the fifth day before (data comes from the billing meter).

[0131] P t51 It is the total positive active energy at the time point 5 minutes before T on the fifth day (data comes from the billing meter).

[0132] (2) The benchmark key factor energy storage curve is the average minute-level energy storage load curve of the previous five days (i.e., the energy storage load construction submodule):

[0133] Among them, the first five days are divided into two cases. One is that when the current day is a working day (Monday to Friday), the first five days are counted five days ago excluding the weekend; the other is that when the current day is a rest day (Saturday or Sunday), the first five days are counted five days ago. The time interval of the curve is 5 minutes / time.

[0134] Taking time point T as an example, the average minute-level energy storage load △P pcn The calculation formula is:

[0135] △P pcn =[(P tcn1 -P tcn11 -P tzcn1 +P tzcn11 )+...+(P tcn5 -P tcn51 -P tzcn5 +P tzcn51 )]÷5

[0136] Where P tcn1 It is the positive active electric energy at time T of the day (data comes from the energy storage system).

[0137] P tcn11 It is the positive active electric energy at the time 5 minutes before T of the day (data comes from the energy storage system).

[0138] P tzcn1 It is the reverse active electric energy at time T of the day (data comes from the energy storage system).

[0139] P tzcn11 It is the reverse active electric energy at the time point 5 minutes before T of the day (data comes from the energy storage system).

[0140] P tcn5 It is the active electric energy at time point T on the fifth day before (data comes from the energy storage system).

[0141] P tcn51 It is the active electric energy at the time point 5 minutes forward of T on the fifth day (data comes from the energy storage system).

[0142] P tzcn5 It is the reverse active electric energy at time point T on the fifth day before (data comes from the energy storage system).

[0143] Ptzcn51 It is the reverse active electric energy at the time point 5 minutes forward of T on the fifth day (data comes from the energy storage system).

[0144] (3) The benchmark key factor charging pile curve is the average minute-level charging pile load curve of the previous five days (i.e., the charging pile load construction submodule):

[0145] Among them, the first five days are divided into two cases. One is that when the current day is a working day (Monday to Friday), the first five days are counted five days ago excluding the weekend; the other is that when the current day is a rest day (Saturday or Sunday), the first five days are counted five days ago. The time interval of the curve is 5 minutes / time.

[0146] Taking time point T as an example, the average minute-level charging pile load △P pcd The calculation formula is:

[0147] △P pcd =[(P tcd1 -P tcd11 )+(P tcd2 -P tcd21 )+...+(P tcd5 -P tcd51 )]÷5

[0148] Where P tcd1 is the active electric energy at time T of the day (data comes from the charging pile system).

[0149] P tcd11 The active electric energy at 5 minutes before T of the day (data comes from the charging pile system).

[0150] P tcd2 is the active electrical energy at time T of the previous day (data comes from the charging pile system).

[0151] P tcd21 It is the active electric energy at 5 minutes before T of the previous day (data comes from the charging pile system).

[0152] P tcd5 It is the active electric energy at time point T on the fifth day before (data comes from the charging pile system).

[0153] P tcd51 It is the active electric energy at the time point 5 minutes before T on the fifth day (data comes from the charging pile system).

[0154] (4) The benchmark key factor V2G curve is the average minute-level V2G load curve of the previous five days (V2G pile load construction submodule):

[0155] Among them, the first five days are divided into two cases. One is that when the current day is a working day (Monday to Friday), the first five days are counted five days ago excluding the weekend; the other is that when the current day is a rest day (Saturday or Sunday), the first five days are counted five days ago. The time interval of the curve is 5 minutes / time.

[0156] Taking time point T as an example, the average minute-level V2G load △P pv The calculation formula is:

[0157] △P pv =[(P tc1 -P tc11 -P tzc1 +P tzc11 )+...+(P tc5 -P tc51 -P tzc5 +P tzc51 )]÷5

[0158] Where P tc1 It is the positive active electric energy at time T of the day (data comes from the V2G system).

[0159] P tc11 It is the positive active electric energy at the time point 5 minutes before T of the day (data comes from the V2G system).

[0160] P tcz1 is the reverse active electric energy at time T of the day (data comes from the V2G system).

[0161] P tcz11 It is the reverse active electric energy at the time point 5 minutes before T of the day (data comes from the V2G system).

[0162] P tc5 It is the positive active electric energy at time point T on the fifth day before (data comes from the V2G system).

[0163] P tc51 It is the positive active electric energy at the time point 5 minutes forward of T on the fifth day (data comes from the V2G system).

[0164] P tcz5 It is the reverse active electric energy at time point T on the fifth day before (data comes from the V2G system).

[0165] P tcz51 It is the reverse active electric energy at the time point 5 minutes forward of T on the fifth day (data comes from the V2G system).

[0166] Step 3: Load uplink and downlink total amount analysis (i.e., load downlink total amount constraint construction submodule, (2), (3), (4) in step 2 and step 3 constitute the above load downlink total amount constraint construction module):

[0167] The total load downward Pzd curve is analyzed for energy storage, charging piles, and V2G, and the time interval of the curve is 5 minutes / time.

[0168] The specific formula is as follows:

[0169] Total downstream load P zd =(P zcn -△P pcn )+△P pcd + (P zv -△P pv )

[0170] Where P zcn is the maximum dischargeable amount of energy storage; △P pcn is the average minute-level energy storage load; △P pcd is the average minute-level charging pile load; P zv is the maximum discharge capacity of V2G; △P pv is the average minute-level V2G load.

[0171] Step 4: Coupling analysis of the operating cost downward trend model (i.e., the operating cost downward regional coupling analysis submodule):

[0172] Operating cost downward trend benefit S d Curve, in each peak period, according to the actual load down value P ps Calculate the actual load downside value benefit S under different distributions during the peak period and peak period dps , find the maximum value.

[0173] Actual load downside value benefit S dps = (peak period electricity quantity × peak period electricity price) + (peak period electricity quantity × peak period electricity price) + (response period electricity quantity × response subsidy) - demand excess value × demand unit price

[0174] The power consumption in the response time period = the actual load downlink value distributed in the response time period - (the power generation of photovoltaic power generation in the previous five days - the predicted power generation of photovoltaic power generation).

[0175] If the demand does not exceed the limit, the excess demand value × the unit price of demand is zero.

[0176] In summary, according to the calculation process, the actual load downlink value P ps The range is from 0 to P zd (0, P zd ), calculate the maximum benefit S of the actual load downlink value in turn dpsmax .

[0177] Step 5: Loss coupling analysis of the upward trend of operating costs (i.e. the operating cost upward regional coupling analysis submodule):

[0178] The loss coupling of the upward trend of operating costs, the time dimension is the flat and valley period before each peak time period, according to the different distribution of the actual load upward value Pus in the valley period and the flat period, calculate the actual load upward value loss S ups , find the minimum value.

[0179] Actual load uplink value loss S ups = (normal electricity quantity × normal electricity price) + (off-peak electricity quantity × off-peak electricity price) +) - excess demand value × demand unit price

[0180] If the demand does not exceed the limit, the excess value of demand × the unit price of demand is zero.

[0181] In summary, the calculation process is as follows: according to the actual load, the upstream value Pus ranges from 0 to P zd (0, P zd ), calculate the actual load uplink loss S upsmix .

[0182] Step 6: Comprehensive benefit evaluation of operating costs (i.e., comprehensive benefit evaluation sub-module of operating costs):

[0183] According to the actual load downlink value P in step 4 ps The maximum benefit S corresponding to the actual load downlink value dpsmax , and the actual load uplink value loss S of the actual load uplink value Pus in step 5 upsmix Combined calculations to obtain the comprehensive benefits S of operating costs sx The maximum value S sxmax .

[0184] S sx =S dpsmax -S upsmix

[0185] Step 7: Analysis strategy execution management (i.e., operating cost up and down comprehensive benefit evaluation submodule, steps 4, 5, 6, and 7 constitute the above operating cost comprehensive benefit evaluation module):

[0186] The maximum value of comprehensive benefits according to the operating cost S sxmax , the corresponding operating cost downward trend strategy in step 4 + the operating cost upward trend strategy in step 5, output the execution strategy. The execution strategy is sent to the device through the data transceiver module.

[0187] Compared with the prior art, this application has the following advantages:

[0188] 1. This application effectively couples the response invitation benefit, peak shaving and valley filling benefit and basic electricity fee benefit in the operating benefits of photovoltaic storage and charging stations, avoiding the problem of only considering a single benefit or only coupling the easily coupled parts, which leads to the failure to maximize the benefits of photovoltaic storage and charging stations.

[0189] 2. At the same time, the target energy storage data and target charging pile data that maximize the benefits are input to the photovoltaic storage and charging station, and adjustment instructions are generated to adjust the corresponding resources, thereby reducing the operating costs of the photovoltaic storage and charging station.

[0190] 3. By paying attention to the response invitation information, electricity price information and basic information of photovoltaic storage and charging stations, while considering benefits and operating costs, the adaptability and accuracy of resource optimization to the current environment are improved.

[0191] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0192] Based on the same inventive concept, the embodiment of the present application also provides a resource optimization device for a photovoltaic storage and charging station for implementing the resource optimization method for the photovoltaic storage and charging station involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the resource optimization device for one or more photovoltaic storage and charging stations provided below can refer to the limitations of the resource optimization method for photovoltaic storage and charging stations above, and will not be repeated here.

[0193] In an exemplary embodiment, Figure 5 As shown, a resource optimization device for a photovoltaic storage and charging station is provided, including: a data acquisition module 501, a constraint construction module 502, a target data acquisition module 503 and a data update module 504, wherein:

[0194] The data acquisition module 501 is used to acquire photovoltaic data of the photovoltaic system, energy storage data of the energy storage system, charging pile data of the charging pile operation system, billing table data of the billing table, response invitation information of the resource demander, and electricity price information;

[0195] The constraint construction module 502 is used to input the photovoltaic data, energy storage data, charging pile data, billing table data, response invitation information and electricity price information into a pre-built resource optimization model, and the total electric energy load constraint construction module in the resource optimization model generates the total electric energy load constraint according to the billing table data, and the load downlink total amount constraint construction module in the resource optimization model generates the load downlink total amount constraint according to the energy storage data and the charging pile data;

[0196] The target data acquisition module 503 is used to obtain the target energy storage data and the target charging pile data when the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value through the operating cost comprehensive benefit evaluation module in the resource optimization model according to the photovoltaic data, the billing meter data, the response invitation information, the electricity price information and the load downlink total amount constraint;

[0197] The data updating module 504 is used to update the energy storage data in the energy storage system using the target energy storage data, and to update the charging pile data in the charging pile operation system using the target charging pile data.

[0198] In one embodiment, the energy storage data includes forward active electric energy, reverse active electric energy and the maximum dischargeable amount of energy storage; the charging pile data includes charging pile charging power, charging pile charging time, V2G pile charging power, V2G pile charging time, V2G pile discharge power, V2G pile charging time and V2G pile maximum dischargeable amount; the load downlink total amount constraint construction module also includes an energy storage load construction submodule, a charging pile load construction submodule, and a V2G pile load construction submodule; the constraint construction module 502 includes: an energy storage load construction submodule, a charging pile load construction submodule, a V2G pile load construction submodule and a constraint construction submodule, wherein:

[0199] An energy storage load construction submodule is used to input the positive active electric energy and the reverse active electric energy into the energy storage load construction submodule, and obtain the energy storage load through the energy storage load construction submodule;

[0200] A charging pile load construction submodule is used to input the charging power and charging time of the charging pile into the charging pile load construction submodule, and obtain the charging pile load through the charging pile load construction submodule;

[0201] A V2G pile load construction submodule is used to input the V2G pile charging power, V2G pile charging time, V2G pile discharge power and V2G pile charging time into the V2G pile load construction submodule, and obtain the V2G pile load through the V2G pile load construction submodule;

[0202] The constraint construction submodule is used to input the energy storage load, charging pile load, V2G pile load, maximum dischargeable amount of energy storage and maximum dischargeable amount of V2G pile into the load downlink total amount constraint construction submodule to obtain the load downlink total amount constraint.

[0203] In one embodiment, the billing table data includes demand, the response invitation information includes the response time period, the photovoltaic data includes the power generation and the daily power generation trend data, and the target data acquisition module 504 is also used to input the demand, electricity price information, response time period, power generation, daily power generation trend data and load downlink total amount constraint into the operating cost comprehensive benefit evaluation module, and obtain the target energy storage load, target charging pile load and target V2G load through the operating cost comprehensive benefit evaluation module when the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value; according to the target energy storage load, target charging pile load and target V2G load, determine the target forward active electric energy, target charging pile charging power, target charging pile charging time, target V2G pile charging power, target V2G pile charging time, target V2G pile discharge power and target V2G pile charging time.

[0204] In an exemplary embodiment, the electricity price information includes peak electricity price, peak electricity price, flat electricity price and valley electricity price, peak period, peak period, flat period and valley period, basic electricity price; the operating cost comprehensive benefit evaluation module includes an operating cost downward trend coupling analysis submodule, an operating cost upward trend coupling analysis submodule and an operating cost upward and downward comprehensive benefit evaluation submodule; the target data acquisition module 503 is further used to obtain the peak electricity quantity, peak electricity quantity, flat electricity quantity and valley electricity quantity according to any current load downward total amount among the peak period, peak period, flat period, valley period and load downward total amount constraints; based on the preset time length interval, power generation, daily power generation trend data, response time period and current load downward total amount, obtain the response time period electricity quantity; , demand, basic electricity price and response time period electricity quantity are input into the operating cost downward trend coupling analysis submodule, and the current load downward total benefit is obtained through the operating cost downward trend coupling analysis submodule; the normal period electricity quantity, normal period electricity price, valley period electricity quantity, valley period electricity price, demand and basic electricity price are input into the operating cost upward trend coupling analysis submodule, and the actual load upward value loss is obtained through the operating cost upward trend coupling analysis submodule; the current load downward total quantity and the actual load upward value loss are input into the operating cost upward and downward comprehensive benefit evaluation submodule, and the operating cost upward and downward comprehensive benefit evaluation submodule is used to obtain the target energy storage load, target charging pile load and target V2G load when the total electric energy load constraint is met and the objective function in the operating cost upward and downward comprehensive benefit evaluation submodule reaches the maximum value.

[0205] In one embodiment, the energy storage load construction submodule is also used to determine the candidate forward active electric energy and the candidate reverse active electric energy from the forward active electric energy and the reverse active electric energy according to a preset target time point by the energy storage load construction submodule, and generate the energy storage load according to the candidate forward active electric energy and the candidate reverse active electric energy.

[0206] In one of the embodiments, the charging pile load construction submodule is also used to obtain the forward active electrical energy of the charging pile based on the charging power and the charging time of the charging pile by the charging pile load construction submodule, and determine the forward active electrical energy of the candidate charging pile from the forward active electrical energy of the charging pile according to a pre-set target time point, and generate the charging pile load based on the forward active electrical energy of the candidate charging pile.

[0207] In an exemplary embodiment, the V2G pile load construction submodule is also used to obtain the V2G pile forward active electric energy based on the V2G pile charging power and the V2G pile charging time, and to obtain the V2G pile reverse active electric energy based on the V2G pile discharge power and the V2G pile discharge time; the V2G pile load construction submodule determines the candidate V2G forward active electric energy and the candidate V2G pile reverse active electric energy from the V2G forward active electric energy and the V2G pile reverse active electric energy according to a pre-set target time point, and generates the V2G pile load according to the candidate V2G forward active electric energy and the candidate V2G pile reverse active electric energy.

[0208] In one embodiment, the billing table data includes the total forward active electric energy; the constraint construction module 502 is also used to determine the target total forward active electric energy that meets the set conditions from the total forward active electric energy according to a pre-set target time point by the total electric energy load constraint construction module, and generate the total electric energy load constraint based on the target total forward active electric energy.

[0209] Each module in the resource optimization device of the above-mentioned solar storage and charging station can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations of each of the above modules.

[0210] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store photovoltaic data, energy storage data, charging pile data, meter data, response invitation information and electricity price information. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a resource optimization method for a photovoltaic storage and charging station is implemented.

[0211] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0212] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the resource optimization method of the photovoltaic storage and charging station in the above embodiment is implemented.

[0213] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the resource optimization method of the photovoltaic storage and charging station in the above embodiment is implemented.

[0214] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the resource optimization method of the photovoltaic storage and charging station in the above embodiment.

[0215] 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, stored data, displayed data, 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 relevant data must comply with relevant regulations.

[0216] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0217] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.

[0218] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A resource optimization method for a photovoltaic storage and charging station, characterized in that: The method comprises: Obtain photovoltaic data of the photovoltaic system, energy storage data of the energy storage system, charging pile data of the charging pile operation system, billing meter data of the billing meter, response invitation information and electricity price information of the resource demander; The photovoltaic data, energy storage data, charging pile data, billing table data, response invitation information and electricity price information are input into a pre-built resource optimization model, and a total electric energy load constraint construction module in the resource optimization model generates a total electric energy load constraint according to the billing table data, and a load downlink total amount constraint construction module in the resource optimization model generates a load downlink total amount constraint according to the energy storage data and charging pile data; Through the operation cost comprehensive benefit evaluation module in the resource optimization model, according to the photovoltaic data, meter data, response invitation information, electricity price information and load downlink total amount constraint, the target energy storage data and target charging pile data are obtained when the total electric energy load constraint is satisfied and the objective function in the operation cost comprehensive benefit evaluation module reaches the maximum value; The target energy storage data is used to update the energy storage data in the energy storage system, and the target charging pile data is used to update the charging pile data in the charging pile operation system.

2. The method according to claim 1, characterized in that The energy storage data includes forward active electric energy, reverse active electric energy and the maximum dischargeable amount of energy storage; the charging pile data includes charging pile charging power, charging pile charging time, V2G pile charging power, V2G pile charging time, V2G pile discharge power, V2G pile charging time and the maximum dischargeable amount of V2G pile; The load downlink total amount constraint construction module also includes an energy storage load construction submodule, a charging pile load construction submodule, and a V2G pile load construction submodule; The load downlink total amount constraint construction module in the resource optimization model generates a load downlink total amount constraint according to the energy storage data and the charging pile data, including: Inputting the forward active electric energy and the reverse active electric energy into the energy storage load construction submodule, and obtaining the energy storage load through the energy storage load construction submodule; Input the charging power of the charging pile and the charging time of the charging pile into the charging pile load construction submodule, and obtain the charging pile load through the charging pile load construction submodule; Input the V2G pile charging power, V2G pile charging time, V2G pile discharge power and V2G pile charging time into the V2G pile load construction submodule, and obtain the V2G pile load through the V2G pile load construction submodule; The energy storage load, charging pile load, V2G pile load, maximum dischargeable amount of energy storage and maximum dischargeable amount of V2G pile are input into the load downlink total amount constraint construction submodule to obtain the load downlink total amount constraint.

3. The method according to claim 2, characterized in that The billing table data includes demand, the response invitation information includes a response time period, and the photovoltaic data includes power generation and daily power generation trend data; The target energy storage data and target charging pile data are obtained by the operation cost comprehensive benefit evaluation module in the resource optimization model according to the photovoltaic data, meter data, response invitation information, electricity price information and load downlink total amount constraint, when the total electric energy load constraint is satisfied and the objective function in the operation cost comprehensive benefit evaluation module reaches the maximum value, including: The demand, electricity price information, response time period, power generation, daily power generation trend data and total load downlink constraint are input into the operating cost comprehensive benefit evaluation module, and the target energy storage load, target charging pile load and target V2G load are obtained through the operating cost comprehensive benefit evaluation module when the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value; According to the target energy storage load, target charging pile load and target V2G load, the target forward active electrical energy, target charging pile charging power, target charging pile charging time, target V2G pile charging power, target V2G pile charging time, target V2G pile discharge power and target V2G pile charging time are determined.

4. The method according to claim 3, characterized in that The electricity price information includes peak period electricity price, peak period electricity price, normal period electricity price and valley period electricity price, peak period, peak period, normal period and valley period, and basic electricity price; The operating cost comprehensive benefit evaluation module includes an operating cost downward trend coupling analysis submodule, an operating cost upward trend coupling analysis submodule and an operating cost upward and downward comprehensive benefit evaluation submodule; The demand, electricity price information, response time period, power generation, daily power generation trend data and total load downlink constraint are input into the operating cost comprehensive benefit evaluation module, and the target energy storage load, target charging pile load and target V2G load are obtained through the operating cost comprehensive benefit evaluation module when the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value, including: According to the peak period, peak period, flat period, valley period and any one of the current load downlink total amounts in the load downlink total amount constraint, the peak period, flat period, valley period and peak period, the peak period, flat period and valley period are respectively obtained; Based on the preset time length interval, the generated power, the daily generation trend data, the response time period and the current load downlink total amount, the response time period power is obtained; Input the peak electricity quantity, peak electricity price, peak electricity quantity, peak electricity price, demand, basic electricity price and response time period electricity quantity into the operating cost downward trend coupling analysis submodule, and obtain the current load downward total benefit through the operating cost downward trend coupling analysis submodule; Input the normal-period electricity quantity, normal-period electricity price, off-peak electricity quantity, off-peak electricity price, demand quantity and basic electricity price into the operating cost upward trend coupling analysis submodule, and obtain the actual load upward value loss through the operating cost upward trend coupling analysis submodule; The current total downlink load and the actual uplink load value loss are input into the operating cost uplink and downlink comprehensive benefit evaluation submodule, and the target energy storage load, target charging pile load and target V2G load are obtained through the operating cost uplink and downlink comprehensive benefit evaluation submodule when the total electric energy load constraint is satisfied and the objective function in the operating cost uplink and downlink comprehensive benefit evaluation submodule reaches the maximum value.

5. The method according to claim 2, characterized in that: The step of obtaining the energy storage load through the energy storage load construction submodule includes: The energy storage load construction submodule determines candidate forward active electric energy and candidate reverse active electric energy from the forward active electric energy and the reverse active electric energy according to a preset target time point, and generates an energy storage load according to the candidate forward active electric energy and the candidate reverse active electric energy.

6. The method according to claim 2, characterized in that The step of obtaining the charging pile load through the charging pile load construction submodule includes: The charging pile load construction submodule obtains the positive active electric energy of the charging pile based on the charging power and charging time of the charging pile, and determines the positive active electric energy of the candidate charging pile from the positive active electric energy of the charging pile according to a preset target time point, and generates the charging pile load based on the positive active electric energy of the candidate charging pile.

7. The method according to claim 2, characterized in that The step of obtaining the V2G pile load through the V2G pile load construction submodule includes: The V2G pile load construction submodule obtains the forward active electric energy of the V2G pile according to the V2G pile charging power and the V2G pile charging time, and obtains the reverse active electric energy of the V2G pile based on the V2G pile discharge power and the V2G pile discharge time; The V2G pile load construction submodule determines candidate V2G forward active electric energy and candidate V2G pile reverse active electric energy from the V2G forward active electric energy and the V2G pile reverse active electric energy according to a preset target time point, and generates a V2G pile load according to the candidate V2G forward active electric energy and the candidate V2G pile reverse active electric energy.

8. The method according to claim 1, characterized in that The billing table data includes the total forward active electric energy; The total electric energy load constraint construction module in the resource optimization model generates the total electric energy load constraint according to the billing table data, including: The total electric energy load constraint construction module determines the target total forward active electric energy that meets the set conditions from the total forward active electric energy according to a preset target time point, and generates the total electric energy load constraint based on the target total forward active electric energy.

9. A resource optimization device for a photovoltaic storage and charging station, characterized in that: The device comprises: A data acquisition module is used to acquire photovoltaic data of the photovoltaic system, energy storage data of the energy storage system, charging pile data of the charging pile operation system, billing meter data of the billing meter, response invitation information of the resource demander, and electricity price information; A constraint construction module, used to input the photovoltaic data, energy storage data, charging pile data, billing table data, response invitation information and electricity price information into a pre-constructed resource optimization model, and the total electric energy load constraint construction module in the resource optimization model generates a total electric energy load constraint according to the billing table data, and the load downlink total amount constraint construction module in the resource optimization model generates a load downlink total amount constraint according to the energy storage data and charging pile data; A target data acquisition module is used to obtain target energy storage data and target charging pile data when the total electric energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value through the operating cost comprehensive benefit evaluation module in the resource optimization model according to the photovoltaic data, meter data, response invitation information, electricity price information and load downlink total amount constraint; A data updating module is used to update the energy storage data in the energy storage system using the target energy storage data, and to update the charging pile data in the charging pile operation system using the target charging pile data.

10. 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, the steps of the method according to any one of claims 1 to 8 are implemented.

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