Urban distributed energy storage scheduling method and system based on charging station

Abstract

The present invention discloses a method and system for urban distributed energy storage scheduling based on charging stations, the method comprising obtaining multiple key scheduling nodes according to the power supply area of ​​the target city, setting up distributed energy storage stations at the multiple key scheduling nodes, storing the target city's trough electricity through the distributed energy storage stations and scheduling it to the peak or supplementing it to the charging piles for use. This application adopts a distributed energy storage method, effectively utilizes the trough electricity, effectively supports the use of charging piles, and further expands the available charging electricity without increasing the transmission power of the original power grid.

Description

Technical Field

[0001] The present invention belongs to the technical field of power supply, and in particular relates to a method and system for urban distributed energy storage scheduling based on charging stations. Background Art

[0002] Most cities have obvious peaks and troughs in electricity consumption. The uneven distribution of charging piles and the short-term high-power characteristics of charging piles make their electricity consumption very uncertain. There are many uncontrollable factors such as the number of people charging at night and the concentrated charging areas, making it impossible to effectively dispatch electricity. Therefore, how to collect and utilize trough electricity and use it effectively is the main issue in dispatching residents' electricity and urban charging piles at this stage. Summary of the invention

[0003] In order to make up for the above-mentioned electricity consumption defects, the present application proposes a city distributed energy storage scheduling method and system based on charging stations, which utilize charging stations to be deployed at key nodes in the city, use the charging stations to balance peaks and troughs, centrally schedule charging piles, and improve electricity utilization.

[0004] The method and technical solution include: obtaining multiple key dispatching nodes according to the power supply area of ​​the target city, setting up distributed energy storage stations at the multiple key dispatching nodes, storing the power of the target city during the trough period through the distributed energy storage stations and dispatching it to the peak period or supplementing it to the charging piles for use;

[0005] The key scheduling node determination step comprises:

[0006] Based on the characteristics of daily electricity consumption, a database of residential electricity consumption characteristics is constructed;

[0007] Divide the target city into multiple power supply areas according to the power supply affiliation, and obtain the data on the time and amount of electricity consumption of residents in each power supply area in the previous year;

[0008] The acquired time-power consumption data is associated with the residents' regular power consumption feature database and power consumption time, and the time-power consumption-power consumption relationship of each area is further obtained;

[0009] The time-power-type relationship is used as training data, and holidays and seasons are used as control variables to train a neural network to obtain a prediction model for residential power consumption in the corresponding power supply area;

[0010] According to the electricity consumption prediction model, the peak and trough data of residential electricity consumption in each area in the future year are predicted, and the dispatchable electric energy data of each area is further predicted according to the peak and trough data of electricity consumption and power supply load data;

[0011] Acquire the city charging pile data, divide the charging piles into a plurality of to-be-scheduled areas according to the power supply affiliation, and acquire energy consumption data of each to-be-scheduled area;

[0012] The dispatchable electric energy data of each power supply area is coupled with the energy consumption data of each area to be dispatched, and a distributed energy storage station is established with the power supply area closest to the area to be dispatched and with dispatchable electric energy data at least twice the energy consumption data of the area to be dispatched as the key dispatching node, and the distributed energy storage station is electrically connected to the power grid of the area and the power grid of the area to be dispatched.

[0013] Furthermore, the distributed energy storage station reserves electrical energy of no less than one half of the average daily power consumption of the power supply area.

[0014] Furthermore, the energy consumption data includes power consumption time data and power consumption data corresponding to the time.

[0015] Furthermore, all distributed energy storage stations in the target city are electrically connected via dedicated lines.

[0016] Furthermore, the dispatchable electric energy data is daily dispatchable data, the energy consumption data is daily energy consumption data, and the coupling method is to select the dispatchable data on the winter solstice or summer solstice to couple with the energy consumption data.

[0017] Furthermore, the dispatching to the peak or supplementing the use of charging piles has the highest priority for residential electricity use.

[0018] Furthermore, all the distributed energy storage stations can perform electric energy scheduling.

[0019] The present application also proposes an urban distributed energy storage scheduling system based on charging stations, and the system is used to implement any of the above methods.

[0020] Generally speaking, the power supply for urban residents relies on the main power grid composed of several large power supply hubs as centralized energy sources and sub-grids divided into various areas, where each sub-grid is divided into multiple power supply areas. The power supply areas are generally divided according to regions, and each power supply area has the ability to dispatch electricity within a certain range in the area. However, after electric vehicles became increasingly popular, there were more and more charging piles, and the charging pile meters and related power supply networks began to be separated from the electricity consumption of community residents. Many communities began to promote special energy-saving vehicle charging pile dedicated meters and traditional property meters that are independent of each other and do not rely on community power transmission lines. Based on this independent power supply technology, this application proposes to network charging piles and distributed power stations separately, no longer relying on residential electricity consumption, reducing the original power dispatching planning and power supply lines, and ensuring the stability of residential electricity consumption. At the same time, this application adopts a distributed energy storage method to effectively utilize trough electricity and effectively support the use of charging piles, further expanding the available charging power without increasing the transmission power of the original power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 The frame structure is intended to be an implementation method of a distributed energy storage method in the method of the present invention. DETAILED DESCRIPTION

[0023] The technical solution of the present application will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0024] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0025] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0026] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0027] Combination Figure 1, an implementation method of the present application. The present application proposes a method and system for urban distributed energy storage scheduling based on charging stations, which utilizes charging stations to be deployed at key nodes in the city, utilizes charging stations to balance peaks and troughs, centrally schedules charging piles, and improves the utilization rate of electric energy. In the present application, there are many technologies for scheduling electric energy for cities through distributed energy storage stations, such as CN116722544B distributed photovoltaic short-term prediction method device electronic equipment and storage medium, CN117833303A a distribution network resource aggregation scheduling method and system, etc., which all introduce how to establish a model for urban power scheduling; CN114006390B a method and system for simulating the interaction of electric vehicle charging loads in the power grid, discloses the technology of scheduling power distribution according to the vehicle charging strategy. However, the above patents focus on how to establish a theoretical model, and fail to combine with the actual needs of the actual power grid. They all fail to disclose how to solve the waste of urban trough power through energy storage stations and how to match charging piles. At the same time, they do not provide how to determine the selection method of distributed power stations, how to predict the dispatchable power for residents' electricity consumption, etc. For the power grid, in an ideal situation, the power transmission power of the power grid should be consistent. In this case, the power grid and the generator run smoothly, and the life of both the power grid and the generator will be greatly improved. However, the power transmission power of the power grid will change with the change of load, especially after the charging piles and fast charging piles for electric vehicles are introduced, the load of the power grid begins to change, and the peaks and troughs of residential electricity consumption can be calculated, while the charging of electric vehicles is unpredictable and irregular. Therefore, the above-mentioned distributed technology and power grid interaction technology are more theoretical and cannot be promoted in practice.

[0028] Therefore, based on the technical foundation of the dispatching of electric energy by distributed power stations, this application further combines the actual situation at this stage to comprehensively utilize the large number of retired power batteries that are potentially coming, gradually promote the development of urban distributed energy storage, and use the distributed energy storage stations and charging piles to form an independent network and be interrelated with the electricity consumption of residents to maximize the use of the existing power transmission network. In this embodiment, multiple key dispatching nodes are obtained according to the power supply area of ​​the target city, and distributed energy storage stations are set up at the multiple key dispatching nodes. The distributed energy storage stations store the electricity at the trough of the target city and dispatch it to the peak or supplement it to the charging piles for use; in this embodiment, the power batteries with an objective number of retirements are reused, but the total amount of power batteries at this stage is obviously unable to meet the energy storage use of the entire city trough. Therefore, this application adopts key nodes and supplements the dispatching of charging piles that are greatly affected by the power grid and are uncontrollable, which does not affect the normal use of residents' electricity and increases the utilization rate of electricity. One of the main difficulties of this application is how to determine the key dispatching nodes. The key dispatching node determination step described in this application includes: building a residential electricity feature library based on the characteristics of conventional electricity consumption in life; in this step, for conventional electricity consumption in life, such as electric lights, televisions, and refrigerators. Electricity consumption feature libraries are established for computers, air conditioners, etc., and then the target city is divided into multiple power supply areas according to the power supply affiliation, and the time-power consumption data of residents' daily life in each power supply area in the previous year are obtained; the amount of electricity consumed in each time period can be inferred based on the time and electricity consumption data, and the obtained time-power consumption data is associated with the residents' regular electricity consumption feature library and electricity consumption time to further obtain the time-power consumption-power consumption relationship of each area; this step further infers the power consumption period of high-power appliances such as air conditioners and water heaters that have a greater impact on the power grid through the time-power consumption-power consumption relationship, and can derive the potential electricity consumption habits of the area, thereby improving the accuracy of the peak and trough prediction of the area.

[0029] Furthermore, this embodiment uses the time-power-power type relationship as training data, and uses holidays and seasons as control variables to train a neural network to obtain a corresponding power supply area resident power consumption prediction model; the time-power-power type relationship of this embodiment obtains the power consumption prediction model, and multiple similar areas can be verified with each other, and the verification process includes taking areas with similar total number of households, total residential types and total living area as similar areas, and using the time-power-power type relationship of each area in the similar areas as samples to train neural networks separately to obtain a unified feature model for similar areas, and use the unified feature model to predict the future power consumption of this type of community. According to the electricity consumption prediction model, the peak and trough data of residential electricity consumption in each area in the future year are predicted, and the dispatchable electric energy data of each area is predicted based on the electricity consumption peak and trough data and the power supply load data; the urban charging pile data is obtained, and the charging piles are divided into multiple areas to be dispatched according to the power supply affiliation and the energy consumption data of each area to be dispatched is obtained; the dispatchable electric energy data of each power supply area is coupled with the energy consumption data of each area to be dispatched, and a distributed energy storage station is established with the power supply area closest to the area to be dispatched and the dispatchable electric energy data is at least twice the energy consumption data of the area to be dispatched as the key dispatching node, and the distributed energy storage station is electrically connected to the power grid of the area and the power grid of the area to be dispatched. In the coupling process of this application, the energy consumption data of a specific date and the data to be dispatched are generally used for coupling to avoid the daily average data being unable to adapt to winter and summer weather.

[0030] On the basis of the above embodiment, the reserve power of the distributed energy storage station is not less than half of the daily average power consumption of the power supply area. In this embodiment, since the reserve power needs to meet the needs of peak scheduling and charging pile scheduling, its power needs to make up for the residents' prominent peak power consumption or the charging pile power consumption when the residents have no power surplus when there is power supply from the power grid. Therefore, this application reserves half a day's power to supply power to the charging pile when there is no external power supply.

[0031] Based on the above embodiment, the energy consumption data includes power consumption time data and power consumption data corresponding to the time.

[0032] Based on the above embodiment, all distributed energy storage stations in the target city are electrically connected via dedicated lines. In this embodiment, all distributed charging stations can be dispatched with each other to improve the ability to cope with regional charging pressure.

[0033] On the basis of the above-mentioned embodiment, the dispatchable electric energy data is the daily dispatchable data, the energy consumption data is the daily energy consumption data, and the coupling mode is to select the dispatchable data of the winter solstice or the summer solstice to couple with the energy consumption data. In this embodiment, the power requirement of new energy vehicles on the winter solstice is relatively large, and the power consumption of residents on the summer solstice is relatively large. The dispatchable data and energy consumption data on the winter solstice or the summer solstice are used to reserve a certain redundancy to improve the scope of application.

[0034] Based on the above embodiment, the scheduling to the peak or supplementing the use of charging piles has the highest priority for residential electricity use.

[0035] Based on the above embodiment, all the distributed energy storage stations can perform power scheduling.

[0036] The present application also proposes an urban distributed energy storage scheduling system based on charging stations, and the system is used to implement any of the above methods.

[0037] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0038] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0039] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0040] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0041] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0042] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0043] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0044] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0045] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0046] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for dispatching urban distributed energy storage based on charging stations, characterized in that: It includes obtaining multiple key dispatching nodes according to the power supply area of ​​the target city, setting up distributed energy storage stations at the multiple key dispatching nodes, storing the power of the target city during the trough period through the distributed energy storage stations and dispatching it to the peak period or supplementing it to the charging piles for use; The key scheduling node determination step comprises: Based on the characteristics of daily electricity consumption, a database of residential electricity consumption characteristics is constructed; Divide the target city into multiple power supply areas according to the power supply affiliation, and obtain the data on the time and amount of electricity consumption of residents in each power supply area in the previous year; The acquired time-power consumption data is associated with the residents' regular power consumption feature database and power consumption time, and the time-power consumption-power consumption relationship of each area is further obtained; The time-power-type relationship is used as training data, and holidays and seasons are used as control variables to train a neural network to obtain a prediction model for residential power consumption in the corresponding power supply area; According to the electricity consumption prediction model, the peak and trough data of residential electricity consumption in each area in the future year are predicted, and the dispatchable electric energy data of each area is further predicted according to the peak and trough data of electricity consumption and power supply load data; Acquire the city charging pile data, divide the charging piles into a plurality of to-be-scheduled areas according to the power supply affiliation, and acquire energy consumption data of each to-be-scheduled area; The dispatchable electric energy data of each power supply area is coupled with the energy consumption data of each area to be dispatched, and a distributed energy storage station is established with the power supply area closest to the area to be dispatched and with dispatchable electric energy data at least twice the energy consumption data of the area to be dispatched as the key dispatching node, and the distributed energy storage station is electrically connected to the power grid of the area and the power grid of the area to be dispatched.

2. The method according to claim 1, characterized in that The distributed energy storage station reserves no less than half of the average daily power consumption of the power supply area.

3. The method according to claim 1, characterized in that The energy consumption data includes power consumption time data and power consumption data corresponding to the time.

4. The method according to claim 1, characterized in that: All distributed energy storage stations in the target city are electrically connected via dedicated lines.

5. The method according to claim 3, characterized in that: The dispatchable electric energy data is daily dispatchable data, the energy consumption data is daily energy consumption data, and the coupling method is to select the dispatchable data on the winter solstice or the summer solstice to couple with the energy consumption data.

6. The method according to claim 1, characterized in that The dispatching to the peak or supplementing the use of charging piles has the highest priority for residential electricity use.

7. The method according to claim 4, characterized in that All the distributed energy storage stations can perform electric energy dispatching.

8. An urban distributed energy storage dispatching system based on charging stations, characterized in that: The system is used to implement any one of the methods of claims 1 to 7.

Images