Charging station interworking alliance collaborative optimization operation method and device, storage medium and computing equipment

Abstract

The invention discloses a charging station interworking alliance collaborative optimization operation method and device, a storage medium and computing equipment, and the method comprises two layers of frameworks, upper-layer collaborative optimization is divided into day-ahead optimization and intra-day rolling optimization, and the day-ahead optimization is carried out to obtain an electric vehicle charging plan by taking the minimum operation cost of a charging station interworking alliance as a target; performing intra-day rolling optimization as an input parameter, updating energy storage charging and discharging power and resource intercommunication power, and operating the alliance according to the result; the lower-layer cost allocation is divided into day-ahead settlement and intra-day settlement, and the day-ahead settlement performs Shapley value calculation according to a day-ahead optimization result to obtain a day-ahead plan allocation operation cost; and in-day settlement, according to the day-ahead plan distribution operation cost and the in-day physical operation cost, calculating the in-day actual distribution operation cost, and according to the distribution operation cost, carrying out settlement on the alliance. According to the method, the total operation cost of the alliance can be effectively reduced, the cost distribution of excitation compatibility is realized, and the operation risk is effectively shared by a two-stage settlement mechanism.

Description

Technical Field

[0001] This invention belongs to the field of power system and automation technology, and specifically relates to a method, device, storage medium and computing equipment for collaborative optimization of charging station interconnection alliance. Background Technology

[0002] Distributed charging stations are gradually becoming important load aggregation units and flexible resource sources in new power systems. However, most charging stations are currently in a state of "resource silos," participating in the electricity market independently and facing severe challenges: disorderly charging behavior exacerbates grid imbalances and is inefficient; charging stations equipped with photovoltaics and energy storage struggle to achieve optimal matching of internal resources and loads; and the limited scale of individual charging stations prevents them from reducing overall electricity purchase costs through resource sharing and complementary advantages. Aggregating and optimizing the operation of distributed charging stations is the only way to overcome these difficulties and achieve cost reduction and efficiency improvement. Furthermore, allowing direct energy and value exchange between distributed energy sources and loads presents significant application prospects.

[0003] Existing research focuses on optimizing scheduling or market mechanism design, often neglecting the key institutional constraint of network access costs. Furthermore, existing cost allocation methods are mostly based on perfectly predicted day-ahead plans, lacking a dynamic and fair adjustment mechanism for intraday operational deviations, and failing to organically integrate the three aspects of collaborative scheduling, financial settlement, and cost allocation. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a collaborative optimization operation method for charging station interconnection alliances. This method solves the optimal scheduling strategy for each station and employs the Shapley value method of cooperative game theory and two-stage settlement to resolve the cost allocation problem. It also provides a quantitative analysis tool that can assess the economic feasibility of physical interconnection projects, and its core mechanism can provide a reference for future P2P rule design.

[0005] To achieve the above-mentioned technical objectives and effects, the present invention is implemented through the following technical solution:

[0006] This invention provides a method for collaborative optimization of operation of a charging station interconnection alliance, wherein at least one charging station in the alliance includes photovoltaic and energy storage resources, and the method includes:

[0007] Based on short-term photovoltaic output forecasts for charging stations, short-term forecasts of conventional loads, and charging demand, and considering grid connection costs, an optimization model is established with the goal of minimizing the operating cost of the charging station interconnection alliance. Day-ahead optimization is performed to calculate the independent operating cost of the charging station, the alliance operating cost, the sub-alliance operating cost, and the energy storage charging and discharging power, resource interconnection power, resource interconnection cost, and electric vehicle charging plan obtained from the alliance optimization. The charging demand includes: electric vehicle charging start time and charging amount; the electric vehicle charging plan includes: charging start time, charging end time, and charging power.

[0008] Based on the Shapley value method of cooperative game theory, the allocated operating costs of each charging station are calculated using the day-ahead planning and settlement model;

[0009] Based on the recently optimized electric vehicle charging plan, photovoltaic ultra-short-term forecast, and conventional load ultra-short-term forecast, with the goal of minimizing the operating cost of the charging station interconnection alliance, daily rolling optimization is carried out to calculate the optimized energy storage charging and discharging power, resource interconnection power, resource interconnection cost, and grid access cost.

[0010] A daily deviation settlement model is built, and the final actual allocated operating cost of each charging station is obtained by combining the daily optimized operating allocation cost. The settlement is then carried out in conjunction with the resource interconnection cost.

[0011] The charging station interconnection alliance operates based on the optimized energy storage charging and discharging power and resource interconnection power of the day, and notifies users to execute the electric vehicle charging plan optimized by the day.

[0012] Preferably, the optimization model is expressed as:

[0013] ,

[0014] in, For the alliance's operating costs; , , The operating costs are for stations S1, S2, and S3, respectively. Station S1 includes photovoltaic, energy storage, conventional load, and charging load, while stations S2 and S3 only include conventional load and charging load.

[0015] The operating cost of station S1 is expressed as follows:

[0016] ,

[0017] in, The cost of purchasing electricity from the grid for station S1; Revenue from selling electricity to the grid; Revenue generated from providing resources to S2 and S3 stations; The unit price for purchasing electricity from the power grid; for The amount of electricity purchased from the grid by station S1 at any given time; The unit price of electricity sold to the power grid; for The power output of station S1 sold to the grid at any given time; Provide resource unit prices from station S1 to stations S2 and S3; This is the discount factor; and These represent the resource interconnection power provided by station S1 to stations S2 and S3, respectively. For time intervals; For time points;

[0018] The operating costs of stations S2 and S3 are as follows:

[0019] ,

[0020] ,

[0021] in, for The power purchased from the grid by station S2 at any given time. for The amount of electricity purchased from the grid by station S3 at any given time. The unit price for resource sharing via the internet.

[0022] Preferably, the following constraints must also be satisfied during the solution process of the optimization model:

[0023] The power balance constraint is:

[0024] ,

[0025] in, for Predicted photovoltaic power output at station S1 at time; for Energy storage discharge power at station S1 at any given time; for Energy storage charging power of Station S1 at any time; , and These are the regular loads of stations S1, S2, and S3, respectively. , and The number of electric vehicles charging at stations S1, S2, and S3 are respectively. , and Electric vehicles for stations S1, S2 and S3 respectively The charging load;

[0026] Energy storage power constraints are:

[0027] ,

[0028] in, and These represent the energy storage discharge and charging status of station S1, with values ​​of 0 or 1. and These are the maximum discharge and maximum charging power of the S1 station's energy storage, respectively.

[0029] The energy storage SOC constraint is:

[0030] ,

[0031] in, Energy storage for S1 station SOC value at time t, For the energy storage capacity of station S1, and The upper and lower limits of SOC are set for energy storage respectively;

[0032] The constraints of the power grid connection points are:

[0033] ,

[0034] in, and These represent the electricity purchase status and electricity sales status of station S1 from the power grid, with values ​​of 0 or 1. , and These represent the maximum switching power at the grid connection points of stations S1, S2, and S3, respectively.

[0035] Resource interoperability constraints are:

[0036] ,

[0037] in, This represents the maximum interconnection power of station S1;

[0038] The charging demand constraint for electric vehicles is:

[0039] ,

[0040] In the formula, For electric vehicles exist The charging power at any given moment; and For electric vehicles Arrival and departure times; For electric vehicles The charging capacity required;

[0041] The charging power constraint for electric vehicles is:

[0042] ,

[0043] in, and Electric vehicles Maximum and minimum charging power.

[0044] Preferably, the day-ahead planning and settlement model is expressed as follows:

[0045] ,

[0046] in, Indicates the optimized charging station Shapley value for cost savings It represents a collection of charging stations. This indicates the number of elements in the charging station set. yes A subset is a sub-alliance. yes The number of subsets, Sub-alliance Based on the total cost of cooperation, It is a charging station Join the sub-alliance The marginal contribution that follows It is a weighting factor, representing the sub-coalition. The probability of occurrence; the sub-alliance includes empty sets, sub-alliance of charging stations operating independently, and sub-alliance of two or more charging stations operating in cooperation.

[0047] Preferably, the intraday deviation settlement model is expressed as follows:

[0048] ,

[0049] in, For charging stations The actual allocated operating costs, For charging stations The current cost of operating independently, For charging stations The operating costs allocated in the current plan, This represents the total actual operating cost for the day. This represents the total operating cost optimized recently.

[0050] Preferably, the method further includes:

[0051] If calculations cannot be performed based on intraday rolling optimization, the charging station interconnection alliance will use the day-ahead optimization results as the standard, operate based on the energy storage charging and discharging power and resource interconnection power obtained from the day-ahead optimization, and settle the day-ahead planned allocation operating costs calculated based on the day-ahead Shapley value.

[0052] The present invention also provides a charging station interoperability alliance collaborative optimization operation device for implementing the above-mentioned charging station interoperability alliance collaborative optimization operation method, the device comprising:

[0053] The day-ahead optimization module is used to establish an optimization model based on short-term photovoltaic output forecasts of charging stations, short-term forecasts of conventional loads, and charging demand, while considering grid connection costs. The model aims to minimize the operating cost of the charging station interconnection alliance. Day-ahead optimization is performed to calculate the independent operating cost of the charging station, the alliance operating cost, the sub-alliance operating cost, and the energy storage charging and discharging power, resource interconnection power, resource interconnection cost, and electric vehicle charging plan obtained from the alliance optimization. The charging demand includes: electric vehicle charging start time and charging amount; the electric vehicle charging plan includes: charging start time, ending time, and charging power; and, based on the cooperative game Shapley value method, the day-ahead planning settlement model is used to calculate the allocated operating cost of each charging station.

[0054] The intraday optimization module is used to perform intraday rolling optimization based on the previously optimized electric vehicle charging plan, photovoltaic ultra-short-term forecast, and conventional load ultra-short-term forecast, with the goal of minimizing the operating cost of the charging station interconnection alliance. It calculates the energy storage charging and discharging power, resource interconnection power, resource interconnection cost, and grid access cost. It also builds an intraday deviation settlement model, which, combined with the previously optimized operating allocation cost, obtains the final actual allocated operating cost of each charging station and settles the cost in conjunction with the resource interconnection cost.

[0055] The operation scheduling module is used to issue scheduling instructions to charging stations for execution based on the day-ahead optimized electric vehicle charging plan, the day-ahead optimized energy storage charging and discharging power, and resource interconnection power.

[0056] The financial settlement module is used to settle accounts based on the actual allocation of operating costs and resource interconnection costs optimized within the day.

[0057] Preferably, the device further includes:

[0058] The data storage module is used to store photovoltaic output forecast data, load data, charging demand data, electricity purchase price, electricity sales price, and resource sharing price of the charging station.

[0059] The present invention also provides a computer-readable storage medium for storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform any of the methods in the above-described charging station interoperability alliance collaborative optimization operation method.

[0060] The present invention also provides a computing device, comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods in the above-described charging station interoperability alliance collaborative optimization operation method.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0062] This invention can solve the optimal scheduling strategy for each station and uses the Shapley value method of cooperative game theory and two-stage settlement to solve the cost allocation problem. It provides a quantitative analysis tool that can not only evaluate the economic feasibility of physical interconnection projects, but its core mechanism can also provide a reference for the design of future P2P rules. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of a two-layer framework for a charging station interoperability alliance collaborative optimization operation method provided by the present invention;

[0064] Figure 2 This is a flowchart illustrating a collaborative optimization operation method in one embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0066] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0067] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0068] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0069] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0070] It should be emphasized here that the step markers mentioned below are not a limitation on the order of the steps, but should be understood as meaning that the steps can be executed in the order mentioned in the embodiments, or in a different order than in the embodiments, or several steps can be executed simultaneously.

[0071] A charging station interconnection alliance must include at least one charging station with resources such as photovoltaics and energy storage, thus possessing the capability to supply resources externally. This invention provides a collaborative optimization operation method for charging station interconnection alliances, employing a two-layer framework. The upper-layer collaborative optimization is divided into day-ahead optimization and intraday rolling optimization. With the goal of minimizing the operating cost of the charging station interconnection alliance, day-ahead optimization yields an electric vehicle charging plan, which is used as input parameters for intraday rolling optimization, updating the energy storage charging and discharging power and resource interconnection power. The alliance operates according to this result. The lower-layer cost allocation is divided into day-ahead settlement and intraday settlement. Day-ahead settlement calculates the planned operating cost based on the day-ahead optimization results using Shapley values. Intraday settlement calculates the actual intraday operating cost based on the planned operating cost and the intraday physical operating cost, and the alliance settles its operating cost accordingly. The implementation process is described in [link to implementation details]. Figure 1 Specifically, it includes:

[0072] Based on the short-term photovoltaic output forecast of charging stations, the short-term forecast of conventional load, and charging demand, and taking into account grid connection costs, the day-ahead plan is optimized with the goal of minimizing the operating cost of the charging station interconnection alliance. The electric vehicle charging plan is calculated, and the Shapley value is calculated to obtain the day-ahead plan allocation operating cost for each charging station.

[0073] Based on the electric vehicle charging plan, photovoltaic ultra-short-term forecast, and conventional load ultra-short-term forecast optimized by the alliance, daily rolling optimization is carried out. With the goal of minimizing the operating cost of the charging station interconnection alliance, the energy storage charging and discharging power and resource interconnection power are calculated. Combined with the daily planned operating allocation cost, the actual deviation is settled intraday to obtain the final actual allocated operating cost of each charging station.

[0074] The charging station alliance operates based on the daily optimized energy storage charging and discharging power and resource interconnection power, and notifies users to implement the electric vehicle charging plan optimized above.

[0075] The following section uses charging stations S1, S2, and S3 as examples to further explain the above-mentioned charging station interconnection alliance collaborative optimization operation method. S1 includes photovoltaic, energy storage, conventional load, and charging load, while S2 and S3 only include conventional load and charging load. (See [link to relevant documentation]). Figure 2The specific implementation process is as follows:

[0076] Step 1: Build the day-ahead optimization model;

[0077] Step 2: Based on the short-term photovoltaic and conventional load forecasts for station S1, the short-term conventional load forecasts for stations S2 and S3, and the charging demand of each station, and considering grid connection costs, with the goal of minimizing the operating cost of the charging station interconnection alliance, CPLEX is invoked for day-ahead optimization. This calculates the independent operating costs of stations S1, S2, and S3, the alliance operating cost, the sub-alliance operating cost, and the optimized energy storage charging and discharging power, resource interconnection power, electric vehicle charging plan, and resource interconnection cost. The charging demand includes: electric vehicle charging start time and charging amount; the optimized electric vehicle charging plan includes: charging start time, end time, and charging power.

[0078] Step 3: Based on the Shapley value method of cooperative game theory, the allocated operating costs of each charging station are calculated using the day-ahead planning and settlement model;

[0079] Step 4: Build an intraday rolling optimization model;

[0080] Step 5: Based on the ultra-short-term photovoltaic forecast and ultra-short-term conventional load forecast of S1 station, the ultra-short-term load forecast of S2 and S3 stations, and the electric vehicle charging plan optimized by the alliance, with the goal of minimizing the operating cost of the charging station interconnection alliance, perform intraday rolling optimization, calculate the intraday optimized energy storage charging and discharging power of S1 station, the resource interconnection power, interconnection cost and grid access cost of S1 and S2 stations, the resource interconnection power, interconnection cost and grid access cost of S1 and S3 stations, and the alliance operating cost;

[0081] Step 6: Using the intraday deviation settlement model, calculate the actual allocated operating cost of each charging station, and settle the cost by combining the interconnection cost between stations S1 and S2 and the interconnection cost between stations S1 and S3.

[0082] Step 7: If calculation cannot be performed based on intraday rolling optimization, the charging station interconnection alliance shall use the day-ahead optimization results as the standard, operate according to the energy storage charging and discharging power and resource interconnection power obtained from the day-ahead optimization, and settle the day-ahead planned allocation of operating costs and resource interconnection costs based on the day-ahead Shapley value.

[0083] It should be noted that the cost of resource sharing is higher than the direct surplus electricity revenue of the resource supplier, but lower than the direct grid purchase cost of the resource receiver.

[0084] Network interconnection costs refer to the costs incurred in recovering the construction and maintenance of resource interconnection. These costs are collected for each instance of resource interconnection, and the party responsible for these costs is designated as the resource recipient.

[0085] In this embodiment of the invention, day-ahead optimization includes alliance optimization, sub-alliance optimization, and independent operation optimization.

[0086] The sub-alliances include the S1 and S2 sub-alliances, the S1 and S3 sub-alliances, and the S2 and S3 sub-alliances. In the recent optimization, we can obtain the independent operating costs of S1, S2, and S3 stations, the operating costs of the S1 and S2 sub-alliances, the S2 and S3 sub-alliances, the S1 and S3 sub-alliances, and the operating costs of the S1, S2, and S3 station alliance.

[0087] In this embodiment of the invention, the optimization model aimed at minimizing the operating cost of the charging station interoperability alliance is specifically represented as follows:

[0088] ,

[0089] In the formula, For the alliance's operating costs; , , These are the operating costs for stations S1, S2, and S3, respectively.

[0090] The cost model for charging station S1 is as follows:

[0091] ,

[0092] In the formula, The operating cost of station S1; The cost of purchasing electricity from the grid for station S1; Revenue from selling electricity to the grid; Revenue generated from providing resources to S2 and S3 stations; The unit price for purchasing electricity from the power grid; for The amount of electricity purchased from the grid by station S1 at any given time; The unit price of electricity sold to the power grid; for The power output of station S1 sold to the grid at any given time; Provide resource unit prices from station S1 to stations S2 and S3; It is the discount factor, and ; and These represent the resource interconnection power provided by station S1 to stations S2 and S3, respectively. For time intervals; For time points.

[0093] The cost models for charging stations S2 and S3 are as follows:

[0094] ,

[0095] ,

[0096] In the formula, and The operating costs for stations S2 and S3 are respectively. for The power purchased from the grid by station S2 at any given time. for The amount of electricity purchased from the grid by station S3 at any given time. The unit price for resource sharing via the internet.

[0097] Based on the above optimization model, the resource interconnection cost and network access cost are expressed as follows:

[0098] Taking the interconnection between S1 and S2 as an example: the cost of resource interconnection is ,

[0099] The cost of crossing the network is: .

[0100] The above optimization model also needs to satisfy the following constraints during the solution process: power balance constraint, energy storage power constraint, energy storage SOC constraint, grid connection point constraint, resource interconnection constraint, electric vehicle charging demand constraint, and electric vehicle charging power constraint, as detailed below:

[0101] The power balance constraint is:

[0102] ,

[0103] In the formula, for Predicted photovoltaic power output at station S1 at time; for Energy storage discharge power at station S1 at any given time; for Energy storage charging power of Station S1 at any time; , and These are the regular loads of stations S1, S2, and S3, respectively. , and These represent the number of vehicles charging at stations S1, S2, and S3, respectively. , and Electric vehicles at stations S1, S2 and S3 respectively The charging load.

[0104] Energy storage power constraints are:

[0105] ,

[0106] In the formula, and These represent the energy storage discharge and charging status of station S1, with values ​​of 0 or 1. and These represent the maximum discharge and maximum charging power of the S1 station's energy storage, respectively.

[0107] The energy storage SOC constraint is:

[0108] ,

[0109] In the formula, Energy storage for S1 station SOC value at time t, For the energy storage capacity of station S1, and These are the upper and lower limits of the State of Charge (SOC) set for energy storage.

[0110] The constraints of the power grid connection points are:

[0111] ,

[0112] In the formula, and These represent the electricity purchase status and electricity sales status of station S1 from the power grid, with values ​​of 0 or 1. , and These represent the maximum switching power at the grid connection points of stations S1, S2, and S3, respectively.

[0113] Resource interoperability constraints are:

[0114] ,

[0115] In the formula, This represents the maximum interconnection power of station S1.

[0116] The charging demand constraint for electric vehicles is:

[0117] ,

[0118] In the formula, For electric vehicles exist The charging power at any given moment; and For electric vehicles Arrival and departure times; For electric vehicles The charging capacity required.

[0119] The charging power constraint for electric vehicles is:

[0120] ,

[0121] In the formula, and Electric vehicles Maximum and minimum charging power.

[0122] In this embodiment of the invention, the day-ahead planning and settlement model based on the Shapley value method of cooperative game theory is expressed as follows:

[0123] ,

[0124] In the formula, Indicates the optimized charging station Cost-saving Shapley Value, a collection of charging stations These represent charging stations S1, S2, and S3, respectively. This indicates the number of elements in the charging station set. yes a subset of yes The number of subsets, Sub-alliance The total cost of cooperation (here, sub-alliances include the costs of empty sets, independent operation, and sub-alliance operation). It is a charging station Join the sub-alliance The marginal contribution that follows It is a weighting factor, representing the sub-coalition. The probability of occurrence.

[0125] It should be noted that the sub-alliance The scenarios include: S1, S2, and S3 operating independently; S1 and S2 operating jointly; S1 and S3 operating jointly; and S2 and S3 operating jointly. The sub-alliance optimization and constraint models are reduced from the aforementioned optimization and constraint models. The current calculation of Shaley value cost allocation requires calculating the operating costs under each of these scenarios separately.

[0126] To explain the day-ahead planning and settlement model, we assume that the costs of stations S1, S2, and S3 operating independently, S1 and S2 operating jointly, S1 and S3 operating jointly, S2 and S3 operating jointly, and S1, S2, and S3 operating jointly are M1, M2, M3, M12, M13, M23, and M123, respectively. The Shapley value calculation considers the marginal contribution of a station joining the alliance. Taking the Shapley value calculation for station S1 as an example, there are six possible joining orders: S1, S2, S3; S1, S3, S2; S2, S1, S3; S2, S3, S1; S3, S2, S1; and S3, S1, S2. For the first order S1, S2, S3, Given an empty set, the marginal contribution after adding S1 is: Similarly, for the second order S1, S3, S2, the marginal contribution after adding S1 is also... The third sequence is S2, S1, S3. When S1 is added, The set is Then the marginal contribution is Similarly, the Shapley value for station S1 is calculated as follows:

[0127] .

[0128] The intraday deviation calculation model is as follows:

[0129] ,

[0130] In the formula, For charging stations The actual allocated operating costs, For charging stations The current cost of operating independently, For charging stations The operating costs allocated in the current plan, This represents the total actual operating cost for the day. This represents the total operating cost optimized recently.

[0131] Based on the above inventive concept, the present invention also provides a charging station interoperability alliance collaborative optimization operation device, comprising:

[0132] The data storage module is used to store photovoltaic output forecast data, load data, charging demand data, electricity purchase price, electricity sales price and resource sharing price of the charging station;

[0133] The day-ahead optimization module is used to establish an optimization model based on short-term photovoltaic output forecasts of charging stations, short-term forecasts of conventional loads, and charging demand, while considering grid connection costs. The model aims to minimize the operating cost of the charging station interconnection alliance. Day-ahead optimization is performed to calculate the independent operating cost of the charging station, the alliance operating cost, the sub-alliance operating cost, and the energy storage charging and discharging power, resource interconnection power, resource interconnection cost, and electric vehicle charging plan obtained from the alliance optimization. The charging demand includes: electric vehicle charging start time and charging amount; the electric vehicle charging plan includes: charging start time, ending time, and charging power; and, based on the cooperative game Shapley value method, the day-ahead planning settlement model is used to calculate the allocated operating cost of each charging station.

[0134] The intraday optimization module is used to perform intraday rolling optimization based on the previously optimized electric vehicle charging plan, photovoltaic ultra-short-term forecast, and conventional load ultra-short-term forecast, with the goal of minimizing the operating cost of the charging station interconnection alliance. It calculates the energy storage charging and discharging power, resource interconnection power, resource interconnection cost, and grid access cost. It also builds an intraday deviation settlement model, which, combined with the previously optimized operating allocation cost, obtains the final actual allocated operating cost of each charging station and settles the cost in conjunction with the resource interconnection cost.

[0135] The operation scheduling module is used to issue scheduling instructions to charging stations for execution based on the day-ahead optimized electric vehicle charging plan, the day-ahead optimized energy storage charging and discharging power, and resource interconnection power.

[0136] The financial settlement module is used to settle accounts based on the actual allocation of operating costs and resource interconnection costs optimized within the day.

[0137] Based on the above inventive concept, the present invention also provides a computer-readable storage medium for storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform any of the methods in the above-described charging station interoperability alliance collaborative optimization operation method.

[0138] Based on the above-described inventive concept, the present invention also provides a computing device, comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods in the above-described charging station interoperability alliance collaborative optimization operation method.

[0139] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0141] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0142] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0143] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

[0144] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for collaborative optimization operation of a charging station interconnection alliance, wherein at least one charging station in the charging station interconnection alliance includes photovoltaic and energy storage resources, characterized in that, The method includes: Based on short-term photovoltaic output forecasts for charging stations, short-term forecasts of conventional loads, and charging demand, and considering grid connection costs, an optimization model is established with the goal of minimizing the operating cost of the charging station interconnection alliance. Day-ahead optimization is performed to calculate the independent operating cost of the charging station, the alliance operating cost, the sub-alliance operating cost, and the energy storage charging and discharging power, resource interconnection power, resource interconnection cost, and electric vehicle charging plan obtained from the alliance optimization. The charging demand includes: electric vehicle charging start time and charging amount; the electric vehicle charging plan includes: charging start time, charging end time, and charging power. Based on the Shapley value method of cooperative game theory, the allocated operating costs of each charging station are calculated using the day-ahead planning and settlement model; Based on the recently optimized electric vehicle charging plan, photovoltaic ultra-short-term forecast, and conventional load ultra-short-term forecast, with the goal of minimizing the operating cost of the charging station interconnection alliance, daily rolling optimization is carried out to calculate the optimized energy storage charging and discharging power, resource interconnection power, resource interconnection cost, and grid access cost. A daily deviation settlement model is built, and the final actual allocated operating cost of each charging station is obtained by combining the daily optimized operating allocation cost. The settlement is then carried out in conjunction with the resource interconnection cost. The charging station interconnection alliance operates based on the optimized energy storage charging and discharging power and resource interconnection power of the day, and notifies users to execute the electric vehicle charging plan optimized by the day.

2. The charging station interoperability alliance collaborative optimization operation method according to claim 1, characterized in that, The optimization model is expressed as follows: ， in, For the alliance's operating costs; , , The operating costs are for stations S1, S2, and S3, respectively. Station S1 includes photovoltaic, energy storage, conventional load, and charging load, while stations S2 and S3 only include conventional load and charging load. The operating cost of station S1 is expressed as follows: ， in, The cost of purchasing electricity from the grid for station S1; Revenue from selling electricity to the grid; Revenue generated from providing resources to S2 and S3 stations; The unit price for purchasing electricity from the power grid; for The amount of electricity purchased from the grid by station S1 at any given time; The unit price of electricity sold to the power grid; for The power output of station S1 sold to the grid at any given time; Provide resource unit prices from station S1 to stations S2 and S3; This is the discount factor; and These represent the resource interconnection power provided by station S1 to stations S2 and S3, respectively. For time intervals; For time points; The operating costs of stations S2 and S3 are as follows: ， ， in, for The power purchased from the grid by station S2 at any given time. for The amount of electricity purchased from the grid by station S3 at any given time. The unit price for resource sharing via the internet.

3. The charging station interoperability alliance collaborative optimization operation method according to claim 2, characterized in that, The following constraints must also be satisfied during the solution process of the optimization model: The power balance constraint is: ， in, for Predicted photovoltaic power output at station S1 at time; for Energy storage discharge power at station S1 at any given time; for Energy storage charging power of Station S1 at any time; , and These are the regular loads of stations S1, S2, and S3, respectively. , and The number of electric vehicles charging at stations S1, S2, and S3 are respectively. , and Electric vehicles for stations S1, S2 and S3 respectively The charging load; Energy storage power constraints are: ， in, and These represent the energy storage discharge and charging status of station S1, with values ​​of 0 or 1. and These are the maximum discharge and maximum charging power of the S1 station's energy storage, respectively. The energy storage SOC constraint is: ， in, Energy storage for S1 station SOC value at time t, For the energy storage capacity of station S1, and These are the upper and lower limits of the State of Charge (SOC) set for energy storage. The constraints of the power grid connection points are: ， in, and These represent the electricity purchase status and electricity sales status of station S1 from the power grid, with values ​​of 0 or 1. , and These represent the maximum switching power at the grid connection points of stations S1, S2, and S3, respectively. Resource interoperability constraints are: ， in, This represents the maximum interconnection power of station S1; The charging demand constraint for electric vehicles is: ， In the formula, For electric vehicles exist The charging power at any given moment; and For electric vehicles Arrival and departure times; For electric vehicles The charging capacity required; The charging power constraint for electric vehicles is: ， in, and Electric vehicles Maximum and minimum charging power.

4. The charging station interoperability alliance collaborative optimization operation method according to claim 1, characterized in that, The day-ahead settlement model is expressed as follows: ， in, Indicates the optimized charging station Shapley value for cost savings It represents a collection of charging stations. This indicates the number of elements in the charging station set. yes A subset is a sub-alliance. yes The number of subsets, Sub-alliance Based on the total cost of cooperation, It is a charging station Join the sub-alliance The marginal contribution that follows It is a weighting factor, representing the sub-coalition. The probability of occurrence; the sub-alliance includes empty sets, sub-alliance where charging stations operate independently, and sub-alliance where two or more charging stations operate in cooperation.

5. The charging station interoperability alliance collaborative optimization operation method according to claim 1, characterized in that, The intraday deviation calculation model is expressed as follows: ， in, For charging stations The actual allocated operating costs, For charging stations The current cost of operating independently, For charging stations The operating costs allocated in the current plan, This represents the total actual operating cost for the day. This represents the total operating cost optimized recently.

6. The charging station interoperability alliance collaborative optimization operation method according to claim 1, characterized in that, The method further includes: If calculations cannot be performed based on intraday rolling optimization, the charging station interconnection alliance will use the day-ahead optimization results as the standard, operate based on the energy storage charging and discharging power and resource interconnection power obtained from the day-ahead optimization, and settle the day-ahead planned allocation operating costs calculated based on the day-ahead Shapley value.

7. A charging station interoperability alliance collaborative optimization operation device, characterized in that, The apparatus for implementing the collaborative optimization operation method of the charging station interoperability alliance as described in claim 1 includes: The day-ahead optimization module is used to establish an optimization model based on short-term photovoltaic output forecasts of charging stations, short-term forecasts of conventional loads, and charging demand, while considering grid connection costs. The model aims to minimize the operating cost of the charging station interconnection alliance. Day-ahead optimization is performed to calculate the independent operating cost of the charging station, the alliance operating cost, the sub-alliance operating cost, and the energy storage charging and discharging power, resource interconnection power, resource interconnection cost, and electric vehicle charging plan obtained from the alliance optimization. The charging demand includes: electric vehicle charging start time and charging amount; the electric vehicle charging plan includes: charging start time, ending time, and charging power; and, based on the cooperative game Shapley value method, the day-ahead planning settlement model is used to calculate the allocated operating cost of each charging station. The intraday optimization module is used to perform intraday rolling optimization based on the previously optimized electric vehicle charging plan, photovoltaic ultra-short-term forecast, and conventional load ultra-short-term forecast, with the goal of minimizing the operating cost of the charging station interconnection alliance. It calculates the energy storage charging and discharging power, resource interconnection power, resource interconnection cost, and grid access cost. It also builds an intraday deviation settlement model, which, combined with the previously optimized operating allocation cost, obtains the final actual allocated operating cost of each charging station and settles the cost in conjunction with the resource interconnection cost. The operation scheduling module is used to issue scheduling instructions to charging stations for execution based on the day-ahead optimized electric vehicle charging plan, the day-ahead optimized energy storage charging and discharging power, and resource interconnection power. The financial settlement module is used to settle accounts based on the actual allocation of operating costs and resource interconnection costs optimized within the day.

8. The charging station interoperability alliance collaborative optimization operation device according to claim 7, characterized in that, The device further includes: The data storage module is used to store photovoltaic output forecast data, load data, charging demand data, electricity purchase price, electricity sales price, and resource sharing price of the charging station.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods in the charging station interoperability alliance collaborative optimization operation method according to claims 1 to 6.

10. A computing device, characterized in that, It includes one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods in the charging station interoperability alliance collaborative optimization operation method according to claims 1 to 6.

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