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

Abstract

The application relates to a resource optimization method and device of a photovoltaic storage and charging station and a computer device. The method comprises the following steps: acquiring photovoltaic data, storage energy data, charging pile data, a billing table, response invitation information and electricity price information, inputting the data into a resource optimization model, generating a first constraint by a first constraint construction module in the model according to the billing table data, generating a second constraint by a second constraint construction module in the model according to the storage energy data and the charging pile data, obtaining target data under the condition that the first constraint is met and a target function reaches a maximum value by an efficiency evaluation module in the model according to the photovoltaic data, the billing table data, the response invitation information, the electricity price information and the second constraint, and updating corresponding data in the photovoltaic storage and charging station by using the target data. The method realizes cost minimization and benefit maximization of the photovoltaic storage and charging station, and further improves the accuracy of resource optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optical storage and charging micro-grid, and in particular to a resource optimization method and device for an optical storage and charging station, a computer device, a computer readable storage medium and a computer program product. BACKGROUND

[0002] Under the background of the reform of the power industry, the optimization of the power market, the improvement of the electricity price management mode, and the perfection of the distribution asset management are gradually promoted. The development of these technical fields will help to break the monopoly, introduce competition, improve efficiency, reduce cost, and realize the optimal allocation of resources. For the energy resource management of the optical storage and charging station, various energy control resources such as energy storage, charging piles, and photovoltaic can participate in the optimal allocation.

[0003] At present, the optical storage and charging station operation cost optimization management technology covers micro-grid collaborative management technology, energy storage peak clipping and valley filling control technology, response invitation control strategy, basic electricity fee optimization technology, user electricity price fluctuation, etc. The technology is not uniformly integrated, and only part of the technology has taken correlation analysis. There are two aspects of superposition and conflict between the technologies.

[0004] However, the current optical storage and charging station operation cost optimization management technology has the problem of low accuracy of resource optimization. SUMMARY

[0005] Therefore, it is necessary to provide a resource optimization method, device, computer equipment, computer readable storage medium and computer program product for an optical storage and charging station, which can improve the accuracy of resource optimization.

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

[0007] obtaining photovoltaic data of a photovoltaic system, energy storage data of an energy storage system, charging pile data of a charging pile operation system, billing table data of a billing table, response invitation information of a resource demander, and electricity price information;

[0008] inputting the photovoltaic data, the energy storage data, the charging pile data, the billing table data, the response invitation information, and the electricity price information into a pre-constructed resource optimization model, generating a total electric energy load constraint by a total electric energy load constraint construction module in the resource optimization model according to the billing table data, and generating a load downward total amount constraint by a load downward total amount constraint construction module in the resource optimization model according to the energy storage data and the charging pile data;

[0009] The target energy storage data and the target charging pile data are obtained by the operating cost comprehensive benefit evaluation module in the resource optimization model according to photovoltaic data, billing table data, response invitation information, electricity price information and load total quantity constraint, and under the condition that the total electrical energy load constraint is met and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value.

[0010] The energy storage data in the energy storage system is updated by using the target energy storage data, and the charging pile data in the charging pile operation system is updated by using the target charging pile data.

[0011] In one of the embodiments, the energy storage data includes forward active electrical energy, reverse active electrical energy and maximum dischargeable quantity of energy storage; and the charging pile data includes charging pile charging power, charging pile charging duration, V2G pile charging power, V2G pile charging duration, V2G pile discharging power, V2G pile charging duration and maximum dischargeable quantity of V2G pile.

[0012] The load total quantity constraint construction module further includes an energy storage load construction submodule, a charging pile load construction submodule and a V2G pile load construction submodule.

[0013] The load total quantity constraint is generated by the load total quantity constraint construction module in the resource optimization model according to the energy storage data and the charging pile data, and includes:

[0014] The forward active electrical energy and the reverse active electrical energy are input into the energy storage load construction submodule, and the energy storage load is obtained by the energy storage load construction submodule.

[0015] The charging pile charging power and the charging pile charging duration are input into the charging pile load construction submodule, and the charging pile load is obtained by the charging pile load construction submodule.

[0016] The V2G pile charging power, the V2G pile charging duration, the V2G pile discharging power and the V2G pile charging duration are input into the V2G pile load construction submodule, and the V2G pile load is obtained by the V2G pile load construction submodule.

[0017] The energy storage load, the charging pile load, the V2G pile load, the maximum dischargeable quantity of energy storage and the maximum dischargeable quantity of V2G pile are input into the load total quantity constraint construction submodule, and the load total quantity constraint is obtained.

[0018] In one of the embodiments, the billing table data includes demand, the response invitation information includes a response time period, and the photovoltaic data includes power generation power and daily power generation trend data.

[0019] The target energy storage data and the target charging pile data are obtained by 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 total load downward quantity constraint, and the target function in the operating cost comprehensive benefit evaluation module reaches the maximum value, including:

[0020] The target energy storage load, the target charging pile load and the target V2G load are obtained by inputting the demand, the electricity price information, the response time period, the power generation power, the daily power generation trend data and the total load downward quantity constraint into the operating cost comprehensive benefit evaluation module, and the target function in the operating cost comprehensive benefit evaluation module reaches the maximum value under the condition that the total electric energy load constraint is met.

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

[0022] In one embodiment, the electricity price information includes a peak period electricity price, a peak period electricity price, a flat period electricity price and a valley period electricity price, a basic electricity price, a peak period, a peak period, a flat period and a valley period.

[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 target energy storage load, the target charging pile load and the target V2G load are obtained by inputting the demand, the electricity price information, the response time period, the power generation power, the daily power generation trend data and the total load downward quantity constraint into the operating cost comprehensive benefit evaluation module, and the target function in the operating cost comprehensive benefit evaluation module reaches the maximum value under the condition that the total electric energy load constraint is met.

[0025] According to any one of the current load downward total quantity in the peak period, the peak period, the flat period, the valley period and the total load downward quantity constraint, the peak period electricity, the peak period electricity, the flat period electricity and the valley period electricity are obtained.

[0026] Based on the pre-set time length interval, the power generation power, the daily power generation trend data, the response time period and the current load downward total quantity, the response time period electricity is obtained.

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

[0028] The flat period electricity quantity, the flat period electricity price, the valley period electricity quantity, the valley period electricity price, the demand, the 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;

[0029] 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 target energy storage load, the target charging pile load and the target V2G load under the condition that the total electrical energy load constraint is met and the objective function in the operating cost upward and downward comprehensive benefit evaluation submodule reaches the maximum value are obtained.

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

[0031] The energy storage load construction submodule determines the candidate forward active electrical energy and the candidate reverse active electrical energy from the forward active electrical energy and the reverse active electrical energy according to the pre-set target time point, and generates the energy storage load according to the candidate forward active electrical energy and the candidate reverse active electrical energy.

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

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

[0034] In one of the embodiments, the V2G pile load is obtained through the V2G pile load construction submodule, including:

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

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

[0037] In an exemplary embodiment, the billing table data includes forward active total electric energy;

[0038] 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, including:

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

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

[0041] The data acquisition module is configured to acquire photovoltaic data of a photovoltaic system, storage data of a storage system, charging pile data of a charging pile operation system, billing table data of a billing table, response invitation information of a resource demander, and electricity price information.

[0042] The constraint construction module is configured to input the photovoltaic data, the storage data, the charging pile data, the billing table data, the response invitation information, and the electricity price information into a pre-constructed resource optimization model, generate a total electric energy load constraint by a total electric energy load constraint construction module in the resource optimization model according to the billing table data, and generate a load downward total amount constraint by a load downward total amount constraint construction module in the resource optimization model according to the storage data and the charging pile data.

[0043] The target data acquisition module is configured to acquire target storage data and target charging pile data by a comprehensive benefit evaluation module of operating cost 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 downward total amount constraint, in a case that the total electric energy load constraint is met and a target function in the comprehensive benefit evaluation module of operating cost reaches a maximum value.

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

[0045] In a third aspect, the present application also provides a computer device including a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0046] acquire photovoltaic data of a photovoltaic system, energy storage data of an energy storage system, charging pile data of a charging pile operation system, metering table data of a metering table, response invitation information of a resource demander, and electricity price information;

[0047] input the photovoltaic data, the energy storage data, the charging pile data, the metering table data, the response invitation information, and the electricity price information into a pre-constructed resource optimization model, generate, by a total electric energy load constraint construction module in the resource optimization model, a total electric energy load constraint according to the metering table data, and generate, by a load downward total quantity constraint construction module in the resource optimization model, a load downward total quantity constraint according to the energy storage data and the charging pile data;

[0048] acquire, by an operating cost comprehensive benefit evaluation module in the resource optimization model, target energy storage data and target charging pile data under a condition that a total electric energy load constraint is met and a target function in the operating cost comprehensive benefit evaluation module reaches a maximum value according to the photovoltaic data, the metering table data, the response invitation information, the electricity price information, and the load downward total quantity constraint;

[0049] update the energy storage data in the energy storage system by using the target energy storage data, and update the charging pile data in the charging pile operation system by using the target charging pile data.

[0050] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the following steps:

[0051] acquire photovoltaic data of a photovoltaic system, energy storage data of an energy storage system, charging pile data of a charging pile operation system, metering table data of a metering table, response invitation information of a resource demander, and electricity price information;

[0052] input the photovoltaic data, the energy storage data, the charging pile data, the metering table data, the response invitation information, and the electricity price information into a pre-constructed resource optimization model, generate, by a total electric energy load constraint construction module in the resource optimization model, a total electric energy load constraint according to the metering table data, and generate, by a load downward total quantity constraint construction module in the resource optimization model, a load downward total quantity constraint according to the energy storage data and the charging pile data;

[0053] acquire, by an operating cost comprehensive benefit evaluation module in the resource optimization model, target energy storage data and target charging pile data under a condition that a total electric energy load constraint is met and a target function in the operating cost comprehensive benefit evaluation module reaches a maximum value according to the photovoltaic data, the metering table data, the response invitation information, the electricity price information, and the load downward total quantity constraint;

[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 comprising a computer program which, when executed by a processor, implements the following steps:

[0056] acquiring photovoltaic data of a photovoltaic system, energy storage data of an energy storage system, charging pile data of a charging pile operation system, tariff data of a tariff, response invitation information and electricity price information of a resource demander;

[0057] inputting the photovoltaic data, the energy storage data, the charging pile data, the tariff data, the response invitation information and the electricity price information into a pre-constructed resource optimization model, generating a total electric energy load constraint according to the tariff data by a total electric energy load constraint construction module in the resource optimization model, and generating a load downward total quantity constraint according to the energy storage data and the charging pile data by a load downward total quantity constraint construction module in the resource optimization model;

[0058] acquiring, by an operating cost comprehensive benefit evaluation module in the resource optimization model, target energy storage data and target charging pile data under the condition that the total electric energy load constraint is met and a target function in the operating cost comprehensive benefit evaluation module reaches a maximum value according to the photovoltaic data, the tariff data, the response invitation information, the electricity price information and the load downward total quantity constraint;

[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 photovoltaic storage and charging station optimize the photovoltaic data, storage data, charging pile data, billing table data, response invitation information and electricity price information of the photovoltaic system, storage system, charging pile operation system and billing table, and then input the photovoltaic data, storage data, charging pile data, billing table data, response invitation information and electricity price information into the pre-constructed resource optimization model. 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. The load downward total quantity constraint construction module in the resource optimization model generates the load downward total quantity constraint according to the storage data and charging pile data. The operating cost comprehensive benefit evaluation module in the resource optimization model acquires the target storage data and target charging pile data according to the photovoltaic data, billing table data, response invitation information, electricity price information and load downward total quantity constraint, in the case that the total electric energy load constraint is met and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value. The target storage data is used to update the storage data in the storage system, and the target charging pile data is used to update the charging pile data in the charging pile operation system. Through comprehensive analysis of the response invitation information, electricity price information and load information, and through comprehensive analysis of the resource optimization model, the cost minimization and benefit maximization of the photovoltaic storage and charging station are realized, and the accuracy of resource optimization is improved. BRIEF DESCRIPTION OF DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creative labor.

[0062] Figure 1 An application environment diagram of the resource optimization method of the photovoltaic storage and charging station in an embodiment;

[0063] Figure 2 A flowchart of the resource optimization method of the photovoltaic storage and charging station in an embodiment;

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

[0065] Figure 4 An algorithm flowchart of the resource optimization model in another embodiment;

[0066] Figure 5 A structural block diagram of the resource optimization device of the photovoltaic storage and charging station in an embodiment;

[0067] Figure 6 Figure 1 is a diagram of an internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

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

[0069] The resource optimization method of the light storage and charging station provided by the embodiments of the present application can be applied to the application environment as shown in Figure 1 The light storage and charging station 102 includes a photovoltaic system, an energy storage system, a micro-grid control system, a power grid, a system load, a security monitoring, etc. The micro-grid control system includes a billing table, a resource demander (a virtual power plant, etc.), and macroscopic electricity price information, etc. The security monitoring is used to monitor the light storage and charging station, so as to facilitate timely discovery and maintenance when some components fail. The light storage and charging station 102 communicates with the server 104 through a network. The data storage system can store data required to be processed by the server 104. The data storage system can be integrated on the server 104, or placed on a cloud or other network server. The server 104 acquires 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, inputs the photovoltaic data, the energy storage data, the charging pile data, the billing table data, the response invitation information and the electricity price information into a pre-constructed resource optimization model, generates total electrical energy load constraints according to the billing table data by a total energy load constraint construction module in the resource optimization model, generates load downward total quantity constraints according to the energy storage data and the charging pile data by a load downward total quantity constraint construction module in the resource optimization model, and then acquires target energy storage data and target charging pile data under the condition that the total electrical energy load constraints are met and a target function in an operating cost comprehensive benefit evaluation module reaches a maximum value according to the photovoltaic data, the billing table data, the response invitation data, the electricity price information and the load downward total quantity constraints by the operating cost comprehensive benefit evaluation module in the resource optimization model. 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 can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0070] In one exemplary embodiment, as shown in Figure 2 a resource optimization method of a light storage and charging station is provided. The method is applied to Figure 1The server 104 in the photovoltaic storage charging station 102 is taken as an example for illustration, including the following steps S201 to S204. Among them:

[0071] In step S201, 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 demand side and electricity price information are obtained.

[0072] The photovoltaic data can be understood as data related to photovoltaic power generation, such as power generation power, etc. The energy storage data can be understood as stored energy data, and also includes the charging and discharging strategy configured for the entire system. The charging pile data can be understood as data associated with the charging pile, including type, charging and discharging power, charging and discharging time, etc. The billing table data can be understood as data related to the total energy consumption of the entire system. The electricity price information includes electricity prices at different times, electricity prices at different times, and basic electricity prices.

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

[0074] In step S202, the photovoltaic data, energy storage data, charging pile data, billing table data, response invitation information and electricity price information are input into the pre-constructed resource optimization model. The total energy load constraint module in the resource optimization model generates the total energy load constraint according to the billing table data, and the load downward total constraint module in the resource optimization model generates the load downward total constraint according to the energy storage data and the charging pile data.

[0075] The total energy load constraint can be understood as the maximum load value that the entire photovoltaic storage charging station can load at the current time. The load downward total constraint can be understood as the value range of the load downward total amount of the entire photovoltaic storage charging station. Here, the maximum value of the load downward total amount is obtained, and the constraint is that the value within the range from 0 to the load downward total amount can be used as the load downward total amount. The range of the latter is smaller than that of the former, and the latter must be within the constraint of the former.

[0076] Optionally, the server 104 inputs the photovoltaic data, the energy storage data, the charging pile data, the tariff data, the response invitation information and the electricity price information into a pre-constructed resource optimization model, selects the tariff data at the set time by a total electric energy load constraint construction module in the resource optimization model to generate the minute-level total electric energy load constraint, and selects the energy storage data and the charging pile data at the set time by a load downward total amount constraint construction module in the resource optimization model to generate the load downward total amount constraint. By constructing the constraint condition, the selection for subsequent obtaining the maximum comprehensive benefit of the operating cost is set, and unrealistic target data that does not conform to the actual situation is avoided to achieve the target of the model, so that re-input training is required, and the cost is increased.

[0077] In step S203, the target energy storage data and the target charging pile data are obtained by an operating cost comprehensive benefit evaluation module in the resource optimization model according to the photovoltaic data, the tariff data, the response invitation information, the electricity price information and the load downward total amount constraint, in a case that the total electric energy load constraint is met and a target function in the operating cost comprehensive benefit evaluation module reaches a maximum value.

[0078] The target function can be understood as a comprehensive benefit obtained by subtracting the operating cost from the operating benefit, the operating benefit is an operating cost downward benefit, and the operating cost is an operating cost upward loss.

[0079] For example, the server 104 obtains the current load downward total amount, the current load downward total amount benefit and the actual load upward value loss by the operating cost comprehensive benefit evaluation module in the resource optimization model according to the photovoltaic data, the tariff data, the response invitation information, the electricity price information and any one of the current load downward total amount in the load downward total amount constraint, in a case that the total electric energy load constraint is met and the current load downward total amount benefit minus the actual load upward value loss reaches a maximum value. The target energy storage data and the target charging pile data are determined according to the current load downward total amount, the response invitation benefit, the peak clipping and valley filling benefit and the basic electricity fee benefit in the photovoltaic energy storage charging station operating benefit are effectively coupled, so that the maximum benefit of the photovoltaic energy storage charging station is realized, and the target energy storage data and the target charging pile data in the maximum benefit scenario are determined, which lays a foundation for issuing an update instruction.

[0080] In step S204, 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.

[0081] Optionally, the server 104 issues an instruction to the optical storage and charging site 102 to update the energy storage data in the energy storage system in the optical storage and charging site 102 to target energy storage data, and to update the charging pile data in the charging pile operation system therein to target charging pile data, it can be known that here does not simply refer to a single data, and can include specific data such as charging and discharging rate, charging and discharging time length, etc. Through the optimized allocation of resources, the maximum benefit of the model can be realized in the optical storage and charging site, and the operating cost of the optical storage and charging site can also be reduced.

[0082] In the above resource optimization method of the optical storage and charging site, 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 tariff data of the tariff table, the response invitation information and the electricity price information of the resource demand side are obtained, and then the photovoltaic data, the energy storage data, the charging pile data, the tariff data, the response invitation information and the electricity price information are input into the pre-constructed resource optimization model. The total electrical energy load constraint is generated by the total electrical energy load constraint construction module in the resource optimization model according to the tariff data, the load downward total quantity constraint is generated by the load downward total quantity constraint construction module in the resource optimization model according to the energy storage data and the charging pile data, and the target energy storage data and the target charging pile data are obtained by the operating cost comprehensive benefit evaluation module in the resource optimization model according to the photovoltaic data, the tariff data, the response invitation information, the electricity price information and the load downward total quantity constraint, under the condition that the total electrical energy load constraint is satisfied and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value. The energy storage data in the energy storage system is updated by using the target energy storage data, and the charging pile data in the charging pile operation system is updated by using the target charging pile data. Through comprehensive analysis of the response invitation information, the electricity price information and the load information, and through comprehensive analysis of the resource optimization model, the cost minimization and the benefit maximization of the optical storage and charging site are realized, and the accuracy of resource optimization is improved.

[0083] In one embodiment, the energy storage data includes forward active electrical energy, reverse active electrical energy and maximum dischargeable electrical energy of the energy storage; the charging pile data includes charging pile charging power, charging pile charging time length, V2G pile charging power, V2G pile charging time length, V2G pile discharging power, V2G pile charging time length and V2G pile maximum dischargeable electrical energy; and the load downward total quantity constraint construction module further includes an energy storage load construction submodule, a charging pile load construction submodule and a V2G pile load construction submodule.

[0084] The load downward total quantity constraint is generated by the load downward total quantity constraint construction module in the resource optimization model 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, obtaining the energy storage load through the energy storage load construction submodule; inputting the charging pile charging power and the charging pile charging time into the charging pile load construction submodule, 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 discharging power and the V2G pile charging time into the V2G pile load construction submodule, 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 discharging capacity of the energy storage and the maximum discharging capacity of the V2G pile into the load downward total quantity constraint construction submodule, obtaining the load downward total quantity constraint.

[0085] The energy storage data includes the forward active electric energy, the reverse active electric energy and the maximum discharging capacity of the energy storage; the charging pile data includes the charging pile charging power, the charging pile charging time, the V2G pile charging power, the V2G pile charging time, the V2G pile discharging power, the V2G pile charging time and the maximum discharging capacity of the V2G pile.

[0086] The V2G pile (Vehicle to Grid charging pile) can be understood as a vehicle networking charging pile, and the V2G technology allows the electric vehicle to communicate with the power grid in both directions. Not only can the electric vehicle be charged, but also the electric energy of the electric vehicle can be fed back to the power grid. This means that the electric vehicle can become a temporary power supply when not in use, supporting the stability of the power grid; charging can be understood as belonging to forward active or reverse active, and discharging can be understood as belonging to reverse active or forward active.

[0087] For example, the server 104 inputs the forward active electric energy and the reverse active electric energy into the energy storage load construction submodule to obtain the energy storage load, inputs the charging pile charging power and the charging pile charging time into the charging pile load construction submodule to obtain the charging pile load, inputs the V2G pile charging power, the V2G pile charging time, the V2G pile discharging 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 discharging capacity of the energy storage and the maximum discharging capacity of the V2G pile into the load downward total quantity constraint construction submodule to obtain the load downward total quantity constraint. The modules for constructing the load downward total quantity constraint are independent of each other and are associated with each other, so that the data can be easily queried and modified, ensuring the accuracy of the constructed load downward total quantity constraint and improving the adaptability to the environment.

[0088] In one of the embodiments, the billing table data includes the demand, the response invitation information includes the response time period, and the photovoltaic data includes the power generation power and the daily power generation trend data;

[0089] The operating cost comprehensive benefit evaluation module in the resource optimization model, according to the photovoltaic data, billing table data, response invitation information, electricity price information and load total quantity constraint, obtains the target energy storage data and the target charging pile data under the condition that the total electric energy load constraint is met and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value, including: inputting the demand, electricity price information, response time period, power generation power, daily power generation trend data and load total quantity constraint into the operating cost comprehensive benefit evaluation module, and obtaining the target energy storage load, the target charging pile load and the target V2G load under the condition that the total electric energy load constraint is met and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value; according to the target energy storage load, the target charging pile load and the target V2G load, the target positive active electric energy, the target charging pile charging power, the target charging pile charging time, the target V2G pile charging power, the target V2G pile charging time, the target V2G pile discharging power and the target V2G pile charging time are determined.

[0090] Wherein, the demand can be understood as the maximum electric energy demand of the light storage and charging station in a time period, and the daily power generation trend data can be understood as the predicted daily photovoltaic power generation capacity.

[0091] Optionally, the server 104 inputs the demand, electricity price information, response time period, power generation power, daily power generation trend data and load total quantity constraint into the operating cost comprehensive benefit evaluation module, obtains the corresponding current load total quantity benefit and actual load up value loss according to any one of the demand, electricity price information, response time period, power generation power, daily power generation trend data and load total quantity constraint, and determines the target energy storage load, the target charging pile load and the target V2G load according to the current load total quantity under the condition that the total electric energy load constraint is met and the current load total quantity benefit minus the actual load up value loss reaches the maximum value. According to the target energy storage load, the target charging pile load and the target V2G load, the target positive active electric energy, the target charging pile charging power, the target charging pile charging time, the target V2G pile charging power, the target V2G pile charging time, the target V2G pile discharging power and the target V2G pile charging time are determined. Through the comprehensive evaluation of the operating cost, the system can effectively optimize the configuration of electric power resources, ensure that the resource utilization rate is maximized in the case of electric power demand fluctuation, and in addition, by reasonably scheduling the energy storage load and the V2G load, the system can better integrate renewable energy, reduce the dependence on traditional energy, and support sustainable development.

[0092] In an exemplary embodiment, the electricity price information includes peak electricity price, peak electricity price, flat electricity price and valley electricity price, peak, peak, flat and valley, basic electricity price; the operating cost comprehensive benefit evaluation module includes operating cost downward trend coupling analysis submodule, operating cost upward trend coupling analysis submodule and 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 load downward total quantity constraints are input into the operating cost comprehensive benefit evaluation module, and the target energy storage load, target charging pile load and target V2G load under the condition that the total electrical energy load constraint is met and the objective function in the operating cost comprehensive benefit evaluation module reaches the maximum value are obtained through the operating cost comprehensive benefit evaluation module, including:

[0094] According to any one of the peak, peak, flat, valley and load downward total quantity constraints, the peak electricity, peak electricity, flat electricity and valley electricity are obtained; based on the pre-set time length interval, power generation, daily power generation trend data, response time period and current load downward total quantity, the response time period electricity is obtained.

[0095] The peak electricity, peak electricity price, peak electricity, peak electricity price, demand, basic electricity price and response time period electricity are input into the operating cost downward trend coupling analysis submodule, and the current load downward total quantity benefit is obtained through the operating cost downward trend coupling analysis submodule; the flat electricity, flat electricity price, valley electricity, valley 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 target energy storage load, target charging pile load and target V2G load under the condition that the total electrical energy load constraint is met and the objective function in the operating cost upward and downward comprehensive benefit evaluation submodule reaches the maximum value are obtained through the operating cost upward and downward comprehensive benefit evaluation submodule.

[0096] The peak period can be understood as the period of time when the power demand reaches the highest peak in a day or a period of time, the peak period can be understood as the period of time when the power demand or load reaches the maximum value, which should be slightly lower than the peak period, and belongs to the second highest period, the flat period can be understood as the period of time when the power demand is relatively stable, and the valley period can be understood as the period of time when the power demand is low, wherein the peak electricity price> the peak electricity price> the flat electricity price> the valley electricity price. Due to the change of electricity demand, the change of power generation cost is caused, so the electricity price pricing of different periods is different.

[0097] The peak period power, the peak period price, the demand, the basic price, and the response time period power are input into the operating cost downward trend coupling analysis submodule to obtain the current load downward total amount benefit; and the flat period power, the flat period price, the valley period power, the valley period price, the demand, and the basic price are input into the operating cost upward trend coupling analysis submodule to obtain the actual load upward value loss. The current load downward total amount and the actual load upward value loss are input into the operating cost up-down comprehensive benefit evaluation submodule. The current load downward total amount and the actual load upward value loss are used to adjust the range of the load downward total amount constraint to change the current load downward total amount. The maximum value of the current load downward total amount benefit minus the actual load upward value loss is obtained. The target energy storage load, the target charging pile load, and the target V2G pile load are obtained according to the current load downward total amount. Through coupling analysis of data from multiple sources, in-depth analysis of data is realized, higher economic benefits can be achieved, the cost of the light storage and charging station is minimized, and the benefits are maximized. At the same time, the optimization effect of resource optimization is better.

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

[0099] Optionally, the energy storage load construction submodule generates the time length level energy storage load according to the candidate forward active power energy and the candidate reverse active power energy from the forward active power energy and the reverse active power energy at multiple time points based on a pre-set target time point, such as T time on the current day. The corresponding previous day is then pushed back by a certain time length based on T, and so on. A certain number of candidate forward active power energy and candidate reverse active power energy are selected and set, and the time length level energy storage load is generated according to the candidate forward active power energy and the candidate reverse active power energy. By generating a specific level of energy storage load, the change of the energy storage load in a period of time can be easily displayed, and a certain number of data are selected for load construction, which also avoids the excessive computing power cost caused by processing a large amount of data, thereby speeding up the optimization speed of resource optimization.

[0100] In one of the embodiments, the charging pile load is obtained by a charging pile load construction submodule, including: obtaining, by the charging pile load construction submodule, charging pile forward active power energy based on charging pile charging power and charging pile charging duration, and determining candidate charging pile forward active power energy from the charging pile forward active power energy according to a pre-set target time point, and generating the charging pile load based on the candidate charging pile forward active power energy.

[0101] For example, the charging pile load construction submodule obtains the charging pile forward active power energy based on the charging pile charging power and the charging pile charging duration. Here, the charging duration is not the duration from the start of charging to the end of full charging, but the time length from the start of charging to the current sampling time point. According to the pre-set target time point, for example, if the current time is T, then the corresponding time of the previous day is T minus a certain time length, and so on. From the charging pile forward active power energy at multiple time points, a certain number of candidate charging pile forward active power energy is selected, and the charging pile load is generated according to the time length of the candidate charging pile forward active power energy. By generating a charging pile load of a certain level, the change of the charging pile load in a period of time can be displayed, and a certain number of data is selected for load construction, which avoids the excessive computing cost caused by processing a large amount of data, and thus accelerates the optimization speed of resource optimization.

[0102] In one of the embodiments, the charging pile load is obtained by a charging pile load construction submodule, including: obtaining, by the charging pile load construction submodule, charging pile forward active power energy based on charging pile charging power and charging pile charging duration, and determining candidate charging pile forward active power energy from the charging pile forward active power energy according to a pre-set target time point, and generating the charging pile load based on the candidate charging pile forward active power 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 duration, and obtains the reverse active electric energy of the charging pile based on the V2G pile discharging power and the V2G pile discharging duration. Here, the charging duration is not the duration from the start of charging to the end of full charging, but the time length from the start of charging to the current sampling time point. The discharging duration is the same as the charging duration, which will not be described here. According to the pre-set target time point, for example, if the current day is T time, then the corresponding previous day is T time, and so on. From the forward active electric energy of the V2G pile and the reverse active electric energy of the V2G pile at multiple time points, a certain number of candidate forward active electric energy of the V2G pile and candidate reverse active electric energy of the V2G pile are selected, and a time length level V2G pile load is generated according to the candidate forward active electric energy of the V2G pile and the candidate reverse active electric energy of the V2G pile. By generating a V2G pile load of a certain level, the change of the V2G pile load in a period of time can be displayed, and a certain number of data are selected for load construction, which avoids the excessive computing cost caused by processing a large amount of data, and thus the optimization speed of resource optimization is accelerated.

[0104] In one embodiment, the billing table data includes forward active total electric energy; 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, including: the total electric energy load constraint construction module determines target forward active total electric energy that meets a set condition from the forward active total electric energy according to a pre-set target time point, and generates a total electric energy load constraint based on the target forward active total electric energy.

[0105] For example, the total electric energy load constraint construction module selects a certain number of candidate forward active total electric energy from the forward active total electric energy at multiple time points according to a pre-set target time point, for example, if the current day is T time, then the corresponding previous day is T time, and so on. A time length level total electric energy load is generated according to the candidate forward active total electric energy. By generating a total electric energy load constraint of a certain level, the change of the total electric energy load in a period of time can be displayed, and a certain number of data are selected for load construction, which avoids the excessive computing cost caused by processing a large amount of data, and thus the optimization speed of resource optimization is accelerated.

[0106] In one exemplary embodiment, as shown in Figure 3 FIG. 1 shows a working principle diagram of a resource optimization method of a light storage and charging station. Wherein:

[0107] I. Model input and output parameters:

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

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

[0110] (2) Energy storage system: based on the standard data interface document provided by the energy storage equipment manufacturer, collect the chargeable amount (kWh), the dischargeable amount (kWh), the charge-discharge strategy, the forward / reverse active energy, and the data issued by the instruction is the charge-discharge strategy.

[0111] (3) Charging pile operation system: based on the standard data interface document provided by the charging pile operation system manufacturer, collect the type of charging pile, charging power, V2G pile charge-discharge power, V2G pile dischargeable amount (kWh), and the data issued by the instruction is charging pile charging power adjustment (percentage%, range 0% to 100%), V2G pile charge-discharge power adjustment (percentage%, range 0% to 100%), V2G pile discharge power (percentage%, range 0% to 100%), charging pile active energy, V2G forward / reverse active energy.

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

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

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

[0115] (2) Electricity price information: by querying the electricity price notification of the official website of the municipal power supply bureau, obtain the electricity price charging rule information, including electricity classification, time-of-use (peak, peak, flat, valley period) electricity price, time-of-use (peak, peak, flat, valley period) time period, basic electricity price, etc.

[0116] Figure 3 The algorithm flowchart in the energy optimization model based on the operation cost analysis of the light storage and charging station in the above resource optimization model is shown in Figure 4 The following data are all for an example, and the technical solution of the present application is not limited to only this condition.

[0117] Step 1: Model input data collection:

[0118] (1) Collect the associated data of photovoltaic system, energy storage system, charging pile operation system and billing meter through the data transceiver module. The specific data range is shown in

I, Model input and output parameters / 1, Data transceiver module, realize data acquisition and instruction issuing channel

[0119] (2) Obtain the response invitation information and electricity price information. The specific data range is shown in

I, Model input and output parameters / 2, Response invitation information and electricity price information

[0120] Step two: benchmark key factor curve modeling:

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

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

[0123] Among them, the previous five days are divided into two cases, one is when the day is a weekday (Monday to Friday), the previous five days are five days before the weekend; two is when the day is a rest day (Saturday, Sunday), the previous five days are five days before. The time interval of the curve is 5 minutes / time.

[0124] Taking T time point as an example, the calculation formula of 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] Among them, P t1 is the positive active total energy at T time point of the day (data from billing meter).

[0127] P t11 is the positive active total energy at T time point of the previous five minutes of the day (data from billing meter).

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

[0129] P t21 is the positive active total energy at T time point of the previous five minutes of the previous day (data from billing meter).

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

[0131] P t51 The positive active total power at the T-5 time point of the previous fifth day (data from the billing table) is pushed forward by 5 minutes.

[0132] (2) The reference 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 previous five days are divided into two cases: one is when the day is a weekday (Monday to Friday), the previous five days are five days before the weekend; two is when the day is a rest day (Saturday and Sunday), the previous five days are five days before. The time interval of the curve is 5 minutes / time.

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

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

[0136] Among them, P tcn1 is the positive active power at the T time point of the day (data from the energy storage system).

[0137] P tcn11 is the positive active power at the T-5 time point of the day (data from the energy storage system).

[0138] P tzcn1 is the negative active power at the T time point of the day (data from the energy storage system).

[0139] P tzcn11 is the negative active power at the T-5 time point of the day (data from the energy storage system).

[0140] P tcn5 is the active power at the T time point of the previous fifth day (data from the energy storage system).

[0141] P tcn51 is the active power at the T-5 time point of the previous fifth day (data from the energy storage system).

[0142] P tzcn5 is the negative active power at the T time point of the previous fifth day (data from the energy storage system).

[0143] Ptzcn51 The reverse active power energy at the time point of T-5 minutes ago of the previous fifth day (data from the energy storage system).

[0144] (3) The reference 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 previous five days are divided into two cases, one is when the day is a weekday (Monday to Friday), the previous five days are five days ago excluding weekends; two is when the day is a rest day (Saturday and Sunday), the previous five days are five days ago. The time interval of the curve is 5 minutes / time.

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

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

[0148] Among them, P tcd1 is the active power energy at the T time point of the day (data from the charging pile system).

[0149] P tcd11 is the active power energy at the time point of T-5 minutes ago of the day (data from the charging pile system).

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

[0151] P tcd21 is the active power energy at the time point of T-5 minutes ago of the previous day (data from the charging pile system).

[0152] P tcd5 is the active power energy at the T time point of the previous fifth day (data from the charging pile system).

[0153] P tcd51 is the active power energy at the time point of T-5 minutes ago of the previous fifth day (data from the charging pile system).

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

[0155] Wherein, the first five days are divided into two cases, one is when the day is a working day (Monday to Friday), the first five days are removed from the weekend to five days ago; Two is when the day is a rest day (Saturday, Sunday), the first five days are five days ago. The time interval of the curve is 5 minutes / time.

[0156] The calculation formula of the average minute-level V2G load △P pv is:

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

[0158] Wherein P tc1 is the positive active energy at T time point of the day (data from V2G system).

[0159] P tc11 is the positive active energy at T time point of the day (data from V2G system).

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

[0161] P tcz11 is the negative active energy at T time point of the day (data from V2G system).

[0162] P tc5 is the positive active energy at T time point of the fifth day ago (data from V2G system).

[0163] P tc51 is the positive active energy at T time point of the fifth day ago (data from V2G system).

[0164] P tcz5 is the negative active energy at T time point of the fifth day ago (data from V2G system).

[0165] P tcz51 is the negative active energy at T time point of the fifth day ago (data from V2G system).

[0166] Step three: load up and down total analysis (i.e. load down total constraint construction submodule, (2), (3), (4) in step two and step three constitute the above load down total constraint construction module):

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

[0168] The specific formula is as follows:

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

[0170] Where P zcn is the maximum dischargeable capacity 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 dischargeable capacity of V2G;△P pv is the average minute-level V2G load.

[0171] Step four: Coupling analysis of downward trend of operating cost (i.e. operating cost downward region coupling analysis sub-module):

[0172] The operating cost downward trend benefit S d curve, in each peak period time period, according to the actual load P ps downward value, the actual load downward value benefit S dps is calculated, and the maximum value is found.

[0173] The actual load downward value benefit S dps = (sharpening power × sharpening price) + (peak power × peak price) + (response period power × response subsidy) - demand exceeding value × demand unit price

[0174] Where response period power = actual load downward value distribution in response time period - (front five days photovoltaic power generation - photovoltaic predicted power generation),

[0175] If the demand is not exceeded, the demand exceeding value × demand unit price is zero.

[0176] According to the above calculation process, the range of actual load downward value P ps is from 0 to P zd (0, P zd ), and the maximum benefit S dpsmax of actual load downward value is calculated in turn.

[0177] Step five: Loss coupling analysis of upward trend of operating cost (i.e. operating cost upward region coupling analysis sub-module):

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

[0179] Actual load uplink value loss S ups = (flat period electricity × flat period electricity price) + (valley period electricity × valley period electricity price) +) - demand exceeding value × demand unit price

[0180] Wherein, if the demand is not exceeded, the demand exceeding value × demand unit price is zero.

[0181] According to the above calculation process, the range of actual load uplink value Pus is from 0 to P zd (0, P zd ), the actual load uplink value loss S upsmix is calculated in turn.

[0182] Step six: operating cost up and down comprehensive benefit evaluation (i.e. operating cost up and down comprehensive benefit evaluation submodule):

[0183] According to the actual load downlink value P ps in step four, the actual load downlink value maximum benefit S dpsmax is calculated, and the actual load uplink value loss S upsmix of the actual load uplink value Pus in step five is combined to obtain the maximum value S sx of the operating cost up and down comprehensive benefit S sxmax .

[0184] S sx =S dpsmax -S upsmix

[0185] Step seven: analysis strategy execution management (i.e. operating cost up and down comprehensive benefit evaluation submodule, steps four, five, six, seven constitute the above operating cost comprehensive benefit evaluation module):

[0186] According to the maximum value S sxmax of the operating cost up and down comprehensive benefit, the corresponding step four operating cost downlink trend strategy + step five operating cost uplink trend strategy is output as the execution strategy. The execution strategy is sent to the equipment through the data receiving and sending module.

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

[0188] 1. The application effectively couples the response invitation benefit, peak load shifting benefit and basic electricity benefit in the operating benefit of the light storage and charging station, avoids the problem that the benefit of the light storage and charging station cannot be maximized due to only considering a single benefit or only coupling part of the benefits that are easy to couple.

[0189] 2. The target energy storage data and target charging pile data under the condition of benefit maximization are input to the light storage and charging station at the same time, an adjustment instruction is generated to adjust the corresponding part of the resources, and the operating cost of the light storage and charging station is reduced.

[0190] 3. By focusing on the response invitation information, electricity price information and basic information of the light storage and charging station, and considering the benefit and operating cost, the adaptation and accuracy of resource optimization to the current environment are improved.

[0191] It should be understood that although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

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

[0193] In an exemplary embodiment, as shown in Figure 5 a resource optimization device of a light storage and charging station is provided, comprising: 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 configured to acquire photovoltaic data of a photovoltaic system, energy storage data of an energy storage system, charging pile data of a charging pile operation system, billing table data of a billing table, response invitation information of a resource demander and electricity price information.

[0195] The constraint construction module 502 is configured to input the photovoltaic data, the energy storage data, the charging pile data, the billing table data, the response invitation information and the electricity price information into a pre-constructed resource optimization model, generate a total electric energy load constraint according to the billing table data by a total electric energy load constraint construction module in the resource optimization model, and generate a load downward total quantity constraint according to the energy storage data and the charging pile data by a load downward total quantity constraint construction module in the resource optimization model;

[0196] The target data acquisition module 503 is configured to acquire target energy storage data and target charging pile data under the condition that the total electric energy load constraint is met and a target function in the operating cost comprehensive benefit evaluation module reaches a maximum value according to the photovoltaic data, the billing table data, the response invitation information, the electricity price information and the load downward total quantity constraint by an operating cost comprehensive benefit evaluation module in the resource optimization model.

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

[0198] In one embodiment, the energy storage data includes forward active electric energy, reverse active electric energy and maximum dischargeable electric energy of the energy storage; the charging pile data includes charging power of the charging pile, charging duration of the charging pile, charging power of the V2G pile, charging duration of the V2G pile, discharging power of the V2G pile, charging duration of the V2G pile and maximum dischargeable electric energy of the V2G pile; the load downward total quantity constraint construction module further 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 the energy storage load construction submodule, the charging pile load construction submodule, the V2G pile load construction submodule and a constraint construction submodule, wherein:

[0199] The energy storage load construction submodule is configured to input the forward active electric energy and the reverse active electric energy into the energy storage load construction submodule, and obtain the energy storage load by the energy storage load construction submodule;

[0200] The charging pile load construction submodule is configured to input the charging power of the charging pile and the charging duration of the charging pile into the charging pile load construction submodule, and obtain the charging pile load by the charging pile load construction submodule;

[0201] The V2G pile load construction submodule is configured to input the charging power of the V2G pile, the charging duration of the V2G pile, the discharging power of the V2G pile and the charging duration of the V2G pile into the V2G pile load construction submodule, and obtain the V2G pile load by the V2G pile load construction submodule;

[0202] The constraint construction submodule is configured for constructing a total load downward constraint of the energy storage load, the charging pile load, the V2G pile load, a maximum dischargeable amount of the energy storage, and a maximum dischargeable amount of the V2G pile, and obtaining the total load downward constraint.

[0203] In one of the embodiments, the billing table data includes a demand, the response invitation information includes a response time period, the photovoltaic data includes a power generation and a daily power generation trend data, and the target data acquisition module 504 is further configured to input the demand, the electricity price information, the response time period, the power generation, the daily power generation trend data, and the total load downward constraint into the operating cost comprehensive benefit evaluation module, and acquire, through the operating cost comprehensive benefit evaluation module, the target energy storage load, the target charging pile load, and the target V2G load under the condition that a total electric energy load constraint is met and a target function in the operating cost comprehensive benefit evaluation module reaches a maximum value; and determine, according to the target energy storage load, the target charging pile load, and the target V2G load, a target forward active electric energy, a target charging pile charging power, a target charging pile charging time length, a target V2G pile charging power, a target V2G pile charging time length, a target V2G pile discharging power, and a target V2G pile charging time length.

[0204] In one of the embodiments, the electricity price information includes a peak period electricity price, a peak period electricity price, a flat period electricity price, and a valley period electricity price, the peak period, the peak period, the flat period, and the valley period, and a 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 configured to acquire, according to any one of the peak period, the peak period, the flat period, the valley period, and the total load downward constraint, a peak period electricity, a peak period electricity, a flat period electricity, and a valley period electricity; acquire, based on a pre-set time length interval, the power generation, the daily power generation trend data, the response time period, and the current total load downward constraint, a response time period electricity; input the peak period electricity, the peak period electricity price, the peak period electricity, the peak period electricity price, the demand, the basic electricity price, and the response time period electricity into the operating cost downward trend coupling analysis submodule, and acquire, through the operating cost downward trend coupling analysis submodule, a current total load downward constraint benefit; input the flat period electricity, the flat period electricity price, the valley period electricity, the valley period electricity price, the demand, and the basic electricity price into the operating cost upward trend coupling analysis submodule, and acquire, through the operating cost upward trend coupling analysis submodule, an actual load upward value loss; input the current total load downward constraint and the actual load upward value loss into the operating cost upward and downward comprehensive benefit evaluation submodule, and acquire, through the operating cost upward and downward comprehensive benefit evaluation submodule, the target energy storage load, the target charging pile load, and the target V2G load under the condition that a total electric energy load constraint is met and a target function in the operating cost upward and downward comprehensive benefit evaluation submodule reaches a maximum value.

[0205] In one embodiment, the energy storage load building submodule is further configured to determine, by the energy storage load building submodule, 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 the pre-set target time point, and generate the energy storage load according to the candidate forward active electric energy and the candidate reverse active electric energy.

[0206] In one embodiment, the charging pile load building submodule is further configured to obtain, by the charging pile load building submodule, charging pile forward active electric energy based on charging pile charging power and charging pile charging duration, determine candidate charging pile forward active electric energy from the charging pile forward active electric energy according to the pre-set target time point, and generate the charging pile load based on the candidate charging pile forward active electric energy.

[0207] In one exemplary embodiment, the V2G pile load building submodule is further configured to obtain, by the V2G pile load building submodule, V2G pile forward active electric energy according to V2G pile charging power and V2G pile charging duration, and obtain V2G pile reverse active electric energy based on V2G pile discharging power and V2G pile discharging duration; determine, by the V2G pile load building submodule, candidate V2G pile forward active electric energy and candidate V2G pile reverse active electric energy from the V2G pile forward active electric energy and the V2G pile reverse active electric energy according to the pre-set target time point, and generate the V2G pile load according to the candidate V2G pile forward active electric energy and the candidate V2G pile reverse active electric energy.

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

[0209] The above-mentioned various modules in the resource optimization apparatus of the optical energy storage and charging station can be realized by software, hardware and combinations thereof in whole or in part. The above-mentioned various modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations of the above-mentioned various modules.

[0210] In one exemplary embodiment, a computer device is provided, which can be a server, and the internal structure diagram thereof can be as shown in Figure 6As shown in the figure. 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 the 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 capability. 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, billing table 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 external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to realize a resource optimization method of a photovoltaic energy storage and charging station.

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

[0212] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the resource optimization method of the photovoltaic energy storage and charging station in the above-mentioned embodiments.

[0213] In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, and the computer program is executed by the processor to realize the resource optimization method of the photovoltaic energy storage and charging station in the above-mentioned embodiments.

[0214] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by the processor to realize the resource optimization method of the photovoltaic energy storage and charging station in the above-mentioned embodiments.

[0215] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0216] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to 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. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0217] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0218] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A resource optimization method for a light storage and charging station, characterized in that, The method comprises: acquiring photovoltaic data of a photovoltaic system, energy storage data of an energy storage system, charging pile data of a charging pile operation system, tariff data of a tariff, response invitation information of a resource demander, and electricity price information; the energy storage data comprises forward active electric energy, reverse active electric energy, and maximum dischargeable energy storage capacity; the charging pile data comprises charging pile charging power, charging pile charging duration, V2G pile charging power, V2G pile charging duration, V2G pile discharging power, V2G pile charging duration, and V2G pile maximum dischargeable capacity; inputting the photovoltaic data, energy storage data, charging pile data, tariff data, response invitation information, and electricity price information into a pre-constructed resource optimization model, generating total electric energy load constraints according to the tariff data by a total electric energy load constraint construction module in the resource optimization model, inputting the forward active electric energy and reverse active electric energy into an energy storage load construction submodule in the resource optimization model, obtaining energy storage load by the energy storage load construction submodule, inputting the charging pile charging power and the charging pile charging duration into a charging pile load construction submodule in the resource optimization model, obtaining charging pile load by the charging pile load construction submodule, inputting the V2G pile charging power, V2G pile charging duration, V2G pile discharging power, and V2G pile charging duration into a V2G pile load construction submodule in the resource optimization model, obtaining V2G pile load by the V2G pile load construction submodule, and inputting the energy storage load, charging pile load, V2G pile load, maximum dischargeable energy storage capacity, and V2G pile maximum dischargeable capacity into a load downward total quantity constraint construction submodule in the resource optimization model to obtain load downward total quantity constraints; obtaining target energy storage data and target charging pile data under the condition that the total electric energy load constraints are met and a target function in the operating cost comprehensive benefit evaluation module reaches a maximum value by an operating cost comprehensive benefit evaluation module in the resource optimization model; updating the energy storage data in the energy storage system by using the target energy storage data, and updating the charging pile data in the charging pile operation system by using the target charging pile data.

2. The method of claim 1, wherein, The tariff data comprises demand, the response invitation information comprises a response time period, the photovoltaic data comprises power generation power and daily power generation trend data; the target energy storage data and the target charging pile data obtained by the operating cost comprehensive benefit evaluation module in the resource optimization model according to the photovoltaic data, tariff data, response invitation information, electricity price information, and load downward total quantity constraints under the condition that the total electric energy load constraints are met and a target function in the operating cost comprehensive benefit evaluation module reaches a maximum value comprises: The required amount, electricity price information, response time period, power generation, daily power generation trend data and load downward total quantity constraint are input into the operation cost comprehensive benefit evaluation module, and the target energy storage load, target charging pile load and target V2G load are obtained under the condition that the total electric energy load constraint is met and the objective function in the operation 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 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 discharging power and target V2G pile charging time are determined.

3. The method of claim 2, wherein, 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 operation cost comprehensive benefit evaluation module includes operation cost downward trend coupling analysis submodule, operation cost upward trend coupling analysis submodule and operation cost upward and downward comprehensive benefit evaluation submodule; The required amount, electricity price information, response time period, power generation, daily power generation trend data and load downward total quantity constraint are input into the operation cost comprehensive benefit evaluation module, and the target energy storage load, target charging pile load and target V2G load are obtained under the condition that the total electric energy load constraint is met and the objective function in the operation cost comprehensive benefit evaluation module reaches the maximum value; According to any one of the peak period, peak period, flat period, valley period and the current load downward total quantity constraint in the load downward total quantity constraint, the peak period electric quantity, peak period electric quantity, flat period electric quantity and valley period electric quantity are obtained; Based on the pre-set time length interval, the power generation, the daily power generation trend data, the response time period and the current load downward total quantity, the response time period electric quantity is obtained; The peak period electric quantity, peak period electricity price, peak period electric quantity, peak period electricity price, required amount, basic electricity price and response time period electric quantity are input into the operation cost downward trend coupling analysis submodule, and the current load downward total quantity benefit is obtained through the operation cost downward trend coupling analysis submodule; The flat period electric quantity, flat period electricity price, valley period electric quantity, valley period electricity price, required amount and basic electricity price are input into the operation cost upward trend coupling analysis submodule, and the actual load upward value loss is obtained through the operation cost upward trend coupling analysis submodule; The current load downward total quantity and actual load upward value loss are input into the operation cost upward and downward comprehensive benefit evaluation submodule, and the target energy storage load, target charging pile load and target V2G load are obtained under the condition that the total electric energy load constraint is met and the objective function in the operation cost upward and downward comprehensive benefit evaluation submodule reaches the maximum value.

4. The method of claim 1, wherein, The energy storage load obtained 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.

5. The method of claim 1, wherein, The charging pile load construction submodule obtains a charging pile load, including: The charging pile load construction submodule obtains charging pile forward active electric energy based on the charging pile charging power and the charging pile charging duration, determines candidate charging pile forward active electric energy from the charging pile forward active electric energy according to a preset target time point, and generates a charging pile load based on the candidate charging pile forward active electric energy.

6. The method of claim 1, wherein, The V2G pile load construction submodule obtains a V2G pile load, including: The V2G pile load construction submodule obtains V2G pile forward active electric energy according to the V2G pile charging power and the V2G pile charging duration, and obtains V2G pile reverse active electric energy based on the V2G pile discharging power and the V2G pile discharging duration; The V2G pile load construction submodule determines candidate V2G pile forward active electric energy and candidate V2G pile reverse active electric energy from the V2G pile 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 pile forward active electric energy and the candidate V2G pile reverse active electric energy.

7. The method of claim 1, wherein, The billing table data includes forward active total electric energy; 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, including: The total electric energy load constraint construction module determines target forward active total electric energy that meets a set condition from the forward active total electric energy according to a preset target time point, and generates the total electric energy load constraint based on the target forward active total electric energy.

8. A resource optimization apparatus for a light storage and charging station, characterized by, The device includes: The data acquisition module is configured to acquire photovoltaic data of a photovoltaic system, energy storage data of an energy storage system, charging pile data of a charging pile operation system, billing table data of a billing table, response invitation information of a resource demander, and electricity price information; the energy storage data includes forward active electric energy, reverse active electric energy, and maximum dischargeable energy of the energy storage system; the charging pile data includes charging pile charging power, charging pile charging duration, V2G pile charging power, V2G pile charging duration, V2G pile discharging power, V2G pile charging duration, and maximum dischargeable energy of the V2G pile; The constraint construction module is configured to input the photovoltaic data, the energy storage data, the charging pile data, the billing table data, the response invitation information and the electricity price information into a pre-constructed resource optimization model, generate a total electric energy load constraint according to the billing table data by a total electric energy load constraint construction module in the resource optimization model, input the forward active electric energy and the reverse active electric energy into an energy storage load construction submodule in the resource optimization model, obtain an energy storage load by the energy storage load construction submodule, input the charging pile charging power and the charging pile charging time length into a charging pile load construction submodule in the resource optimization model, obtain a charging pile load by the charging pile load construction submodule, input the V2G pile charging power, the V2G pile charging time length, the V2G pile discharging power and the V2G pile charging time length into a V2G pile load construction submodule in the resource optimization model, obtain a V2G pile load by the V2G pile load construction submodule, and input 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 a load downward total amount constraint construction submodule in the resource optimization model to obtain a load downward total amount constraint. The target data acquisition module is configured to acquire, by an operating cost comprehensive benefit evaluation module in the resource optimization model, target energy storage data and target charging pile data under the condition that the total electric energy load constraint is met and a target function in the operating cost comprehensive benefit evaluation module reaches a maximum value according to the photovoltaic data, the billing table data, the response invitation information, the electricity price information and the load downward total amount constraint. The data updating module is configured to update the energy storage data in the energy storage system by using the target energy storage data and update the charging pile data in the charging pile operation system by using the target charging pile data. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 7.

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