WO2024088327A1 - 控制分布式储能设备充放电的方法、系统及设备 - Google Patents

控制分布式储能设备充放电的方法、系统及设备 Download PDF

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WO2024088327A1
WO2024088327A1 PCT/CN2023/126637 CN2023126637W WO2024088327A1 WO 2024088327 A1 WO2024088327 A1 WO 2024088327A1 CN 2023126637 W CN2023126637 W CN 2023126637W WO 2024088327 A1 WO2024088327 A1 WO 2024088327A1
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energy storage
distributed
user
resources
demand
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PCT/CN2023/126637
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English (en)
French (fr)
Inventor
胥威汀
乔云池
李旻
王子峣
刘阳
潘一凡
李奥
王苗苗
元博
徐波
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国网四川省电力公司经济技术研究院
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Priority to EP23825600.2A priority Critical patent/EP4383502A1/en
Publication of WO2024088327A1 publication Critical patent/WO2024088327A1/zh

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/35—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering with light sensitive cells
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin

Definitions

  • the present application relates to the technical field of power system operation analysis, for example, to a method, system and device for controlling the charging and discharging of distributed energy storage equipment.
  • the present application provides a method for controlling the charging and discharging of distributed energy storage equipment, including: quantifying the supply and demand resources of distributed energy storage equipment, the supply and demand resources of distributed photovoltaic equipment, the supply and demand resources of distributed cogeneration equipment, and the supply and demand resources of distributed boilers, respectively, to obtain the quantification results of distributed energy storage supply and demand resources; obtaining the amount of idle energy storage resources submitted by users with surplus energy storage resources, the amount of idle energy storage resources is calculated by the users with surplus energy storage resources with the goal of minimizing the comprehensive daily energy cost on the actual operation day and based on the quantification results of the distributed energy storage supply and demand resources; pushing the cloud energy storage resource price of the actual operation day to the user, and obtaining the cloud energy storage resource demand submitted by the user; the cloud energy storage resource demand is calculated by the user with the goal of minimizing the comprehensive daily energy cost on the actual operation day and based on the cloud energy storage resource price, the quantification results of the distributed energy storage supply and demand resources and the user's own needs; matching the amount of
  • the present application provides a system for controlling the charging and discharging of distributed energy storage equipment, including: a supply and demand resource quantification module, which is configured to quantify the supply and demand resources of distributed energy storage equipment, the supply and demand resources of distributed photovoltaic equipment, the supply and demand resources of distributed cogeneration equipment and the supply and demand resources of distributed boilers to obtain the quantification results of distributed energy storage supply and demand resources; an idle energy storage resource quantity acquisition module, which is configured to obtain the idle energy storage resource quantity submitted by the energy storage resource surplus user, and the idle energy storage resource quantity is calculated by the energy storage resource surplus user with the goal of minimizing the comprehensive daily energy cost on the actual operation day, and is obtained according to the distributed energy storage supply and demand resource quantification results; an information push module, which is configured to push the cloud energy storage resource price on the actual operation day to the user.
  • a supply and demand resource quantification module which is configured to quantify the supply and demand resources of distributed energy storage equipment, the supply and demand resources of distributed photovoltaic equipment, the supply and demand resources of distributed cogeneration equipment and the supply
  • a cloud energy storage resource demand acquisition module configured to acquire the cloud energy storage resource demand submitted by the user; the cloud energy storage resource demand is calculated by the user with the goal of minimizing the comprehensive daily energy cost on the actual operation day, and is obtained based on the cloud energy storage resource price, the quantified results of the distributed energy storage supply and demand resources, and the user's own needs; a matching module, configured to match the idle energy storage resource amount with the cloud energy storage resource demand to obtain the actual charging and discharging power of the distributed energy storage device.
  • the present application provides an electronic device, comprising:
  • a memory configured to store at least one program
  • the at least one processor implements the method for controlling the charging and discharging of the distributed energy storage device as described above.
  • the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for controlling the charging and discharging of a distributed energy storage device as described above is implemented.
  • FIG1 is a schematic diagram of the structure of a cloud energy storage park provided in Example 2 of the present application.
  • FIG2 is a schematic diagram of a 24-hour time-of-use electricity price for general industrial and commercial users provided in Example 2 of the present application;
  • FIG3 is a schematic diagram of an expected charging/discharging strategy before the application of the cloud energy storage mode provided in Example 2 of the present application;
  • FIG4 is a schematic diagram of an expected charging/discharging strategy after the cloud energy storage mode is applied according to Example 2 of the present application;
  • FIG5 is a schematic flow chart of a method for controlling charging and discharging of a distributed energy storage device provided in an embodiment of the present application
  • FIG6 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
  • the embodiments of the present application disclose a method, system, device and storage medium for controlling the charging and discharging of a distributed energy storage device.
  • this embodiment provides a method for controlling the charging and discharging of a distributed energy storage device, comprising the following steps:
  • S1 Quantify the supply and demand resources of distributed energy storage equipment, distributed photovoltaic equipment, distributed cogeneration equipment and distributed boilers respectively to obtain the quantification results of distributed energy storage supply and demand resources.
  • S1 includes:
  • S11 Establish physical models of distributed energy storage equipment, distributed photovoltaic equipment, distributed cogeneration equipment and distributed boilers.
  • S12 Quantify the supply and demand resources of the distributed energy storage equipment using the physical model of the distributed energy storage equipment, quantify the supply and demand resources of the distributed photovoltaic equipment using the physical model of the distributed photovoltaic equipment, quantify the supply and demand resources of the distributed cogeneration equipment using the physical model of the distributed heat and power equipment, and quantify the supply and demand resources of the distributed boiler using the physical model of the distributed boiler.
  • Distributed energy storage devices can store/release electric energy during operation, allowing users to achieve operations such as clean energy consumption, shifting electricity consumption curves, and optimizing power generation plans for multi-energy coupling devices.
  • the physical model of its distributed energy storage device can be expressed as:
  • ⁇ ST is the charge/discharge efficiency of the distributed energy storage device; is a 0-1 variable used to indicate the charging and discharging state of energy storage. When it is 1, it means charging, and when it is 0, it means discharging; is the maximum storage capacity of the distributed energy storage device of user d.
  • the output power of distributed photovoltaic equipment is related to factors such as light radiation intensity and light incident angle.
  • the physical model of its photovoltaic device can usually be expressed as:
  • Distributed electric boilers can convert electrical energy into thermal energy.
  • the physical model of its distributed electric boiler can be expressed as:
  • S2 Obtain the amount of idle energy storage resources submitted by the energy storage resource surplus user.
  • the idle energy storage resource amount is calculated by the energy storage resource surplus user with the goal of minimizing the comprehensive daily energy cost on the actual operation day and based on the quantification results of the distributed energy storage supply and demand resources.
  • users with surplus energy storage resources can submit their idle energy storage resources on the operation day to the cloud energy storage platform, and the cloud energy storage platform is responsible for matching them with the demand for cloud energy storage resources in the market.
  • the objective function can be expressed as: minC i.DR +C iG +C iM -C i.IL ,
  • C i.DR is the electricity purchase cost of energy storage resource surplus user i
  • C iG is the gas purchase cost of energy storage resource surplus user i
  • C iM is the equipment maintenance cost of energy storage resource surplus user i
  • C i.IL is the income that energy storage resource surplus user i may obtain after submitting idle energy storage resources. The equipment maintenance cost is ignored in this paper.
  • the idle energy storage resources P i.YIL* and E i.YIL* submitted by energy storage resource surplus user i to the platform can be obtained.
  • the idle energy storage resources P i.IL and E i.IL submitted to the cloud energy storage platform can be expressed as:
  • users control the output of each distributed device according to the above optimal energy utilization strategy.
  • the charging and discharging requests submitted by users to the cloud energy storage platform and but for:
  • S3 Push the cloud energy storage resource price of the actual operation day to the user, and obtain the cloud energy storage resource demand submitted by the user.
  • the cloud energy storage resource demand is calculated by the user with the goal of minimizing the comprehensive daily energy cost of the actual operation day, based on the cloud energy storage resource price, the quantified results of the distributed energy storage supply and demand resources, and the user's own needs.
  • the cloud energy storage platform needs to first push the price of cloud energy storage resources to users. Users in need of energy storage resources can submit their demand for cloud energy storage resources to the cloud energy storage platform based on the price of cloud energy storage resources and their own needs (such as completing clean energy consumption, shifting electricity consumption curves, etc.).
  • Energy storage resource demand users will determine the demand for cloud energy storage resources based on the electricity and heat loads of the next day and the price of cloud energy storage resources one day before the actual operation date.
  • the demand decision of energy storage resource demand users is based on the goal of minimizing the daily comprehensive energy cost.
  • the objective function can be expressed as: minC j.DR +C jG +C jM +C j.ST ,
  • Cj.DR is the electricity purchase cost of energy storage resource demand user j
  • CjG is the gas purchase cost of energy storage resource demand user j
  • CjM is the equipment maintenance cost of energy storage resource demand user j
  • Cj.IL is the income that energy storage resource demand user j may obtain after submitting idle energy storage resources. The equipment maintenance cost is ignored in this paper.
  • C j.ST k*Price E E j.YDM + k*Price P P j.YDM .
  • constraints include:
  • P t j.DR is the power purchased by user j in time period t, which must be greater than or equal to 0; is the natural gas price in time period t; k is the acquisition price coefficient of cloud energy storage resources; Price jE and Price jP are the estimated values of the cloud energy storage resource price by user j; E j.YIL and P j.YIL are the idle amount of energy storage resources that user j is going to submit to the cloud energy storage platform;
  • the optimal cloud energy storage resource demand decision E j.YDM* and P j.YDM* of energy storage resource demand user j can be obtained.
  • the cloud energy storage resource demand E j.DM and P j.DM submitted by energy storage resource demand user j to the cloud energy storage platform can be expressed as follows:
  • S4 Match the idle energy storage resource amount with the cloud energy storage resource demand amount to obtain the actual charging and discharging power of the distributed energy storage device.
  • the cloud energy storage platform On the day of operation, users control the output of each distributed device according to the above optimal energy consumption strategy.
  • the cloud energy storage platform has the power to control these idle energy storage resources on the actual operation day.
  • the cloud energy storage platform sends a control command to this part of the energy storage resources, users with surplus energy storage resources need to execute it. Therefore, for a certain period t on the actual operation day, the actual charging/discharging power of the energy storage equipment of users with surplus energy storage resources can be expressed as:
  • the method for controlling the charging and discharging of distributed energy storage equipment provided in this embodiment first analyzes the behaviors of surplus energy storage resource users and demand users in the park in the cloud energy storage business model based on the distributed energy equipment operation model. Then, in the real-time control stage, a user surplus/demand decision model and an energy use strategy model are established to optimize the charging and discharging of energy storage equipment, thereby improving the energy use efficiency of the park.
  • this embodiment selects six enterprise users (I 1 -I 6 ) in the comprehensive energy park A, and sets appropriate installed capacities of distributed photovoltaic equipment, distributed CHP units, distributed electric boilers and distributed energy storage equipment according to their local photovoltaic installed area and electricity and heat demand. Then, the cloud energy storage operation model is simulated with these six enterprises as users of the cloud energy storage platform to verify the effectiveness of the above method. The rationality and feasibility of the cloud energy storage business model and its operation model are verified.
  • the structure of the integrated energy park A is shown in Figure 1, and the parameter information of the equipment in the integrated energy park A is shown in Table 1.
  • User I1 is an office building group, which belongs to general industrial and commercial users
  • user I2 is a processing and manufacturing industry, which belongs to general industrial and commercial users
  • user I3 is a hotel, which belongs to general industrial and commercial users
  • user I4 is a textile and chemical fiber industry, which belongs to a large industrial user
  • user I5 is a cement manufacturing industry, which belongs to a large industrial user
  • user I6 is a steel manufacturing industry, which belongs to a large industrial user.
  • the electricity price of users I1 - I6 (the load of users I3 - I6 refers to their non-residential load and life load) is calculated according to the general industrial and commercial user electricity price, and the 24-hour time-of-use electricity price is shown in Figure 2.
  • the cloud energy storage operation model users need to decide the amount of idle energy storage resources to be submitted to the cloud energy storage platform based on the electricity and heat load of the next day during the idle energy storage submission phase.
  • There is no restriction on the electricity/heat load of users I 1 -I 6 , and the power ratio of photovoltaic equipment Without any restriction, let the estimated value of the cloud energy storage resource price by surplus user i be the minimum value of the cloud energy storage resource price, and the purchase price coefficient k 0.8.
  • the amount of idle energy storage resources submitted by each user to the cloud energy storage platform can be obtained, as shown in Table 2 below.
  • users I 1 , I 3 , and I 5 have submitted surplus energy storage resources to the cloud energy storage platform, and are users with surplus energy storage resources.
  • the idle energy storage resource submission amount of users I 2 , I 4 , and I 6 is zero.
  • This embodiment takes user I 1 as an example to analyze its idle energy storage resource submission strategy in detail.
  • Table 3 compares the expected energy cost of user I 1 before and after the cloud energy storage mode is applied. From the total amount, the expected energy cost of user I 1 is reduced by 129.81 yuan after the cloud energy storage mode is applied.
  • This part of the cost reduction is composed of the changes in the expected electricity purchase cost, the expected natural gas purchase cost, and the expected energy storage resource sales income: after the cloud energy storage mode is applied, the expected natural gas purchase cost of user I 1 increases by 64 yuan, but the expected electricity purchase cost decreases by about 53 yuan, and the expected energy storage resource sales income increases by about 140 yuan. That is to say, after applying the cloud energy storage model, although the change in user I 1's energy use strategy has increased the sum of the expected electricity purchase cost and gas purchase cost, due to the surplus of energy storage resources, the corresponding income of this surplus will offset the increase in energy purchase cost, and ultimately reduce the expected total energy cost under the cloud energy storage model.
  • Table 3 The comparison of user I 1's expected energy cost before and after the application of the cloud energy storage model is shown in Table 3.
  • Figure 3 shows the expected charging/discharging strategy of energy storage of user I 1 before the application of cloud energy storage mode.
  • the user's charging period is period 2, 3, 4, 5, 13, 14, 15, 16, among which the user's charging power in period 2 reaches a maximum value of 500kW
  • the user's discharging period is period 7, 8, 9, 10, 11, 12, 17, 18, 19, 21, 22, 23, 24, among which the energy storage device's discharge power in period 17 reaches a maximum value of 376kW.
  • Figure 4 shows the expected charging/discharging strategy of the user's energy storage device after the application of cloud energy storage mode.
  • the charging period is 3, 4, 5, 13, 14, 15, 23, among which the energy storage device's charging power in periods 3 and 4 reaches a maximum value of 376kW
  • the discharging period is period 8, 9, 10, 11, 17, 18, 19, 21, 22, 24, among which the energy storage device's discharge power reaches a maximum value of 376kW.
  • this embodiment provides a system for controlling charging and discharging of a distributed energy storage device, including:
  • a supply and demand resource quantification module is configured to quantify the supply and demand resources of distributed energy storage equipment, the supply and demand resources of distributed photovoltaic equipment, the supply and demand resources of distributed cogeneration equipment, and the supply and demand resources of distributed boilers to obtain the quantification results of distributed energy storage supply and demand resources;
  • An idle energy storage resource quantity acquisition module is configured to acquire the idle energy storage resource quantity submitted by the energy storage resource surplus user, wherein the idle energy storage resource quantity is calculated by the energy storage resource surplus user with the goal of minimizing the comprehensive daily energy cost on the actual operation day and based on the quantification result of the distributed energy storage supply and demand resources;
  • An information push module is configured to push the cloud energy storage resource price of the actual operation day to the user
  • a cloud energy storage resource demand acquisition module is configured to acquire the cloud energy storage resource demand submitted by the user; the cloud energy storage resource demand is calculated by the user with the goal of minimizing the comprehensive daily energy cost on the actual operation day, and based on the cloud energy storage resource price, the quantified results of the distributed energy storage supply and demand resources, and the user's own needs;
  • the matching module is configured to match the idle energy storage resource amount with the cloud energy storage resource demand amount to obtain the actual charging and discharging power of the distributed energy storage device.
  • the method and system for controlling the charging and discharging of distributed energy storage devices analyze the behaviors of users with surplus energy storage resources and users with demand for energy storage resources in combination with the utilization of distributed energy storage devices by users and the demand for energy storage resources. By matching the amount of idle energy storage resources with the demand for cloud energy storage resources, the actual charging and discharging power of distributed energy storage devices is controlled, thereby optimizing the charging and discharging of energy storage devices and improving the comprehensive utilization rate of energy storage and the absorption capacity of new energy.
  • FIG6 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
  • the electronic device includes: one or more processors 110 and a memory 120.
  • processors 110 For example.
  • memory 120 For example.
  • the electronic device may further include: an input device 130 and an output device 140 .
  • the electronic device may not include the input device 130 and the output device 140 .
  • the processor 110, the memory 120, the input device 130 and the output device 140 in the electronic device may be connected via a bus or other means, and FIG6 takes the connection via a bus as an example.
  • the memory 120 is a computer-readable storage medium that can be configured to store software programs, computer executable programs, and modules.
  • the processor 110 executes various functional applications and data processing by running the software programs, instructions, and modules stored in the memory 120 to implement any of the methods in the above embodiments.
  • the memory 120 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc.
  • the memory may include a volatile memory such as a random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device.
  • RAM random access memory
  • the memory 120 may be a non-transitory computer storage medium or a transient computer storage medium.
  • the non-transitory computer storage medium may be, for example, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.
  • the memory 120 may optionally include a memory remotely disposed relative to the processor 110, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network may include the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
  • the input device 130 may be configured to receive input digital or character information and generate key signal input related to user settings and function control of the electronic device.
  • the output device 140 may include a display device such as a display screen.
  • This embodiment also provides a computer-readable storage medium storing a computer program, wherein the computer program is used to execute the above method.
  • the storage medium may be a non-transitory storage medium.
  • All or part of the processes in the above-mentioned embodiment method can be completed by executing related hardware through a computer program.
  • the program can be stored in a non-transitory computer-readable storage medium.
  • the program When executed, it can include the processes of the embodiment of the above-mentioned method, wherein the non-transitory computer-readable storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a RAM, etc.
  • the embodiment of the present application has the following characteristics: combining the user's distributed energy storage equipment utilization and energy storage resource demand, a distributed energy storage supply and demand resource quantification model is established, which can efficiently and accurately mine high-value information such as different users' electricity consumption behaviors, and effectively support distributed energy storage cloud services; Analyzing the utilization of energy storage can effectively measure the supply and demand of distributed energy storage resources in the park.
  • a charging and discharging control method for the park's energy storage equipment is developed. According to the charging and discharging control method, the actual charging and discharging power of the distributed energy storage equipment is configured to improve the energy efficiency of the park and enable the coordinated operation of multiple energy sources in the park.

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Abstract

一种控制分布式储能设备充放电的方法、系统及设备，所述方法包括：分别对分布式储能设备的供需资源、分布式光伏设备的供需资源、分布式热-电联产设备的供需资源和分布式锅炉的供需资源进行量化，得到分布式储能供需资源量化结果(S1)；获取储能资源盈余用户提交的闲置储能资源量(S2)；向用户推送实际运行日的云储能资源价格，获取用户提交的云储能资源需求量(S3)；将闲置储能资源量与所述云储能资源需求量进行匹配，得到分布式储能设备的实际充放电功率(S4)。

Description

控制分布式储能设备充放电的方法、系统及设备
本申请要求在2022年10月28日提交中国专利局、申请号为202211333948.X的中国专利申请的优先权，以上申请的全部内容通过引用结合在本申请中。
技术领域
本申请涉及电力系统运行分析技术领域，例如涉及一种控制分布式储能设备充放电的方法、系统及设备。
背景技术
在“碳达峰、碳中和”目标下，面对构建新型电力系统的迫切需求，储能被视作重要抓手和关键环节。基于此，将现存的大量分布式储能以资源共享模式聚合到云端，形成一定的虚拟储能容量来替代传统用户侧的实体储能，具有“一储多场”应用形式的云储能概念应运而生。
发明内容
一方面，本申请提供一种控制分布式储能设备充放电的方法，包括：分别对分布式储能设备的供需资源、分布式光伏设备的供需资源、分布式热-电联产设备的供需资源和分布式锅炉的供需资源进行量化，得到分布式储能供需资源量化结果；获取储能资源盈余用户提交的闲置储能资源量，所述闲置储能资源量由储能资源盈余用户以实际运行日的综合日用能成本最小为目标，并根据所述分布式储能供需资源量化结果计算得到；向用户推送实际运行日的云储能资源价格，获取用户提交的云储能资源需求量；所述云储能资源需求量由用户以实际运行日的综合日用能成本最小为目标，并根据所述云储能资源价格、所述分布式储能供需资源量化结果和自身需求计算得到；将所述闲置储能资源量与所述云储能资源需求量进行匹配，得到分布式储能设备的实际充放电功率。
另一方面，本申请提供一种控制分布式储能设备充放电的系统，包括：供需资源量化模块，设置为对分布式储能设备的供需资源、分布式光伏设备的供需资源、分布式热-电联产设备的供需资源和分布式锅炉的供需资源进行量化，得到分布式储能供需资源量化结果；闲置储能资源量获取模块，设置为获取储能资源盈余用户提交的闲置储能资源量，所述闲置储能资源量由储能资源盈余用户以实际运行日的综合日用能成本最小为目标，并根据所述分布式储能供需资源量化结果计算得到；信息推送模块，设置为向用户推送实际运行日的云储能资源价 格；云储能资源需求量获取模块，设置为获取用户提交的云储能资源需求量；所述云储能资源需求量由用户以实际运行日的综合日用能成本最小为目标，并根据所述云储能资源价格、所述分布式储能供需资源量化结果和自身需求计算得到；匹配模块，设置为将所述闲置储能资源量与所述云储能资源需求量进行匹配，得到分布式储能设备的实际充放电功率。
另一方面，本申请提供一种电子设备，包括：
至少一个处理器；
存储器，设置为存储至少一个程序，
当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如上所述的控制分布式储能设备充放电的方法。
另一方面，本申请提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上所述的控制分布式储能设备充放电的方法。
附图说明
下面将对实施例中所需要使用的附图作简单地介绍，应当理解，以下附图仅示出了本申请的某些实施例，因此不应被看作是对范围的限定，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1为本申请实施例2提供的云储能园区结构示意图；
图2为本申请实施例2提供的一般工商业用户24小时分时电价示意图；
图3为本申请实施例2提供的云储能模式应用前的预期充/放电策略示意图；
图4为本申请实施例2提供的云储能模式应用后的预期充/放电策略示意图；
图5为本申请实施例提供的一种控制分布式储能设备充放电的方法的流程示意图；
图6为本申请实施例提供的一种电子设备的结构示意图。
具体实施方式
分布式储能技术尚且成熟，但是具体的运营模式与分散的单一储能装置间协同运行方案的缺乏制约了储能市场化的发展，分别在体现在：用户侧使用分布式储能经济性不高，云储能服务方案不完善以及分布式储能在电网运行优化控制模式单一。
针对上述情况，本申请实施例公开了一种控制分布式储能设备充放电的方法、系统、设备及存储介质。
下面结合实施例和附图，对本申请作进一步的详细说明，本申请的示意性实施方式及其说明仅用于解释本申请，并不作为对本申请的限定。
实施例1
由于常规的分布式储能技术不利于提高储能的综合利用率和新能源的消纳能力，参考图5，本实施例提供一种控制分布式储能设备充放电的方法，包括以下步骤：
S1：分别对分布式储能设备的供需资源、分布式光伏设备的供需资源、分布式热-电联产设备的供需资源和分布式锅炉的供需资源进行量化，得到分布式储能供需资源量化结果。
例如，其中，S1包括：
S11：建立分布式储能设备的物理模型、分布式光伏设备的物理模型、分布式热-电联产设备的物理模型和分布式锅炉的物理模型。
S12：利用所述分布式储能设备的物理模型对分布式储能设备的供需资源进行量化，利用所述分布式光伏设备的物理模型对分布式光伏设备的供需资源进行量化，利用所述分布式热-电联产设备的物理模型对分布式热-电联产设备的供需资源进行量化，利用所述分布式锅炉的物理模型对分布式锅炉的供需资源进行量化。
(1)分布式储能设备在运行过程中可以存储/释放电能，使得用户可以实现清洁能源消纳、平移用电曲线、优化多能耦合设备发电计划等操作。对于平台所有用户组成的集合D中的第d(1≤d≤n+m)个用户，其分布式储能设备的物理模型可以表示为：



其中，为用户d的分布式储能设备的最大充/放电功率；和分别为用户d的分布式储能设备在t时段的充/放电功率；ηST为分布式储能设备的充/放电效率；为0-1变量，用以表示储能的充放电状态，为1时表示充电，为0时表示放电；为用户d的分布式储能设备的最大存储容量。
(2)分布式光伏设备的输出功率与光照辐射强度、光照入射角度等因素有 关。对于集合D中的第d(1≤d≤n+m)个用户，其光伏设备的物理模型通常可以表示为：

其中，表示用户d的分布式光伏设备在t时段的最大出力；为光伏设备在t时段的功率比，与光照辐射强度、光照入射角度、太阳能板的效率等因素有关；为用户d的光伏设备装机容量；表示用户d的光伏设备在t时段的出力。
(3)分布式热-电联产(combined heat and power，CHP)设备在输出电功率的同时可以满足用户的热需求。对于集合D中的第d(1≤d≤n+m)个用户，其分布式热-电联产设备的物理模型可用下式表示：



其中，表示用户d的分布式CHP设备在t时段的电输出功率；Ft d.CHP为分布式CHP设备在t时段消耗天然气的功率；ηCHP.e为分布式CHP设备的发电效率；q为天然气热值；表示分布式CHP设备在t时段的最大热输出功率；ηCHP.l为分布式CHP设备的散热损失系数；δ为分布式CHP设备的制热系数；当近似认为ηCHP.e和ηCHP.l保持不变时，则分布式CHP机组的电/热输出功率之比为定值，用kCHP表示。
(4)分布式电锅炉可以将电能转化为热能。对于集合D中的第d(1≤d≤n+m)个用户，其分布式电锅炉的物理模型可以表示为：

其中，为用户d的分布式电锅炉输出的热功率；Pt d.EB为电锅炉输入的电功率；ηEB为电锅炉的转换系数。
S2：获取储能资源盈余用户提交的闲置储能资源量。所述闲置储能资源量由储能资源盈余用户以实际运行日的综合日用能成本最小为目标，并根据所述分布式储能供需资源量化结果计算得到。
在闲置储能提交阶段，储能资源盈余用户可以向云储能平台提交运行日当天的闲置储能资源，而云储能平台则负责将其与市场上的云储能资源需求量进行匹配。
其中，储能资源盈余用户的最小综合日用能成本的模型表达式如下：
(1)目标函数
储能资源盈余用户需要在实际运行日前一天根据第二日的电、热负荷等情况，考虑储能与其他分布式设备间的相互配合，以综合日用能成本最小为目标决定向平台提交的闲置储能量，对于所有储能资源盈余用户组成的集合I中的第i个用户，其目标函数可以表示为：
minCi.DR+Ci.G+Ci.M-Ci.IL，
式中，Ci.DR为储能资源盈余用户i的购电成本；Ci.G为储能资源盈余用户i的购气成本；Ci.M为储能资源盈余用户i的设备维护成本；Ci.IL为储能资源盈余用户i提交闲置储能资源后可能获得的收入。设备维护成本本文忽略不计。
目标函数表达式中，

Ci.IL＝k*Pricei.EEi.YIL+k*Pricei.PPi.YIL，
式中，为用户i在时段t的购电功率，需大于等于0；为时段t的天然气价格；k为云储能资源的收购价格系数；Pricei.E和Pricei.P为用户i对云储能资源价格的估计值；Ei.YIL和Pi.YIL为用户i准备向云储能平台提交的储能资源闲置量。
(2)约束条件：





(3)闲置储能资源量的模型
根据上述优化模型可以得到储能资源盈余用户i向平台提交的闲置储能资源量Pi.YIL*和Ei.YIL*，其向云储能平台提交的闲置储能资源Pi.IL和Ei.IL可以表示为：
用户在实际运行日中就按照上述最优用能策略控制各分布式设备的出力和而用户提交给云储能平台的充放电请求和则 为：
S3：向用户推送实际运行日的云储能资源价格，获取用户提交的云储能资源需求量。所述云储能资源需求量由用户以实际运行日的综合日用能成本最小为目标，并根据所述云储能资源价格、所述分布式储能供需资源量化结果和自身需求计算得到。
在云储能需求提交阶段，云储能平台需要首先向用户推送云储能资源的价格，储能资源需求用户则可根据云储能资源的价格以及自身需求(例如完成清洁能源消纳、平移用电曲线等)向云储能平台提交云储能资源的需求量。
其中，储能资源需求用户的最小综合日用能成本的模型表达式为：
(1)目标函数
储能资源需求用户会在实际运行日前一天根据第二日的电、热负荷等情况以及云储能资源的价格决定云储能资源的需求量。储能资源需求用户的需求量决策以日综合用能成本最小为目标，对于平台所有储能资源需求用户组成的集合J中的第j个用户，其目标函数可以表示为：
minCj.DR+Cj.G+Cj.M+Cj.ST，
式中，Cj.DR为储能资源需求用户j的购电成本；Cj.G为储能资源需求用户j的购气成本；Cj.M为储能资源需求用户j的设备维护成本；Cj.IL为储能资源需求用户j提交闲置储能资源后可能获得的收入。设备维护成本本文忽略不计。
目标函数表达式中，


Cj.ST＝k*PriceEEj.YDM+k*PricePPj.YDM。
(2)约束条件

式中，令则：

此外，约束条件还有：



其中，Pt j.DR为用户j在时段t的购电功率，需大于等于0；为时段t的天然气价格；k为云储能资源的收购价格系数；Pricej.E和Pricej.P为用户j对云储能资源价格的估计值；Ej.YIL和Pj.YIL为用户j准备向云储能平台提交的储能资源闲置量；
根据上述优化模型可以得到储能资源需求用户j的最优云储能资源需求量决策Ej.YDM*和Pj.YDM*，则储能资源需求用户j向云储能平台提交的云储能资源需求量Ej.DM和Pj.DM可以用下式表示为：
S4：将所述闲置储能资源量与所述云储能资源需求量进行匹配，得到分布式储能设备的实际充放电功率。
用户在运行日当天就按照上述最优用能策略控制各分布式设备的出力，但需要注意的是，储能资源盈余用户的闲置储能资源被云储能平台收购后，云储能平台就拥有了在实际运行日对这部分闲置储能资源进行控制的权力。当云储能平台对这部分储能资源发送控制命令时，储能资源盈余用户则需要执行。因此，对于实际运行日的某一时段t，储能资源盈余用户的储能设备实际充/放电功率可以表示为：

其中，和分别为储能资源盈余用户i的最优充/放电策略，运算(·)+和(·)-表示为：
综上，本实施例提供的一种控制分布式储能设备充放电的方法，首先针对园区中的储能资源盈余用户以及需求用户，在分布式能源设备运行模型的基础上对其在云储能商业模式中的行为进行了详细分析。然后在实时控制阶段建立了用户的盈余/需求量决策模型以及用能策略模型，优化储能设备的充放电，使得园区的用能效率得以提升。
实施例2
为说明上述方法的有效性，本实施例选取综合能源园区A的6家企业用户(I1-I6)，结合其实地的光伏可装机面积以及电、热需求，分别设置适当的分布式光伏设备、分布式CHP机组、分布式电锅炉以及分布式储能设备装机容量，然后以这6家企业为云储能平台的用户，对云储能运行模型进行仿真，以此验 证云储能商业模式及其运行模型的合理性和可行性。综合能源园区A的结构如图1所示，综合能源园区A中设备的参数信息见表1。
表1
用户I1为写字楼群，属于一般工商业用户，用户I2为加工制造行业，属于一般工商业用户，用户I3为酒店，属于一般工商业用户，用户I4为纺织化纤行业，属于大工业用户，用户I5为水泥制造行业，属于大工业用户，用户I6为钢铁制造行业，属于大工业用户。对于用户I3-I6的非居民用户用电以及生活用电来说，由于其从事生产行业，属于金融性，故这部分用电按照一般的工商业用电计算。所以本实施例对用户I1-I6(用户I3-I6的负荷指其非居民负荷以及生活负荷)的电价按照一般工商业用户电价计算，24小时分时电价如图2所示。
此外，园区附近还拥有其他居民用电负荷，该区域的其他用电负荷曲线不做限制。煤电厂上网电价λnp＝0.4012元，输变电设备平均单位容量成本λinv＝4500元/kw，均摊年限Y′＝10年。
根据云储能运行模型，用户需要在闲置储能提交阶段根据第二日的电、热负荷等情况决定向云储能平台的闲置储能资源量。用户I1-I6的电/热负荷情况不做限制，光伏设备的功率比不做限制，令盈余用户i对云储能资源价格的估计值为云储能资源价格的最小值，收购价格系数k＝0.8，根据闲置储能量决策模型可以得到各用户向云储能平台提交的闲置储能资源量，如下表2所示。

表2
从表2中可以看出，用户I1、I3、I5向云储能平台提交了盈余的储能资源量，为储能资源盈余用户，用户I2、I4、I6的闲置储能资源提交量则为零。本实施例以用户I1为例，对其闲置储能资源的提交策略进行详细分析。表3对比了应用云储能模式前后用户I1的预期用能成本，从总量来看，应用云储能模式后用户I1的预期用能成本下降了129.81元，这部分下降的成本由预期电量购买成本、预期天然气购买成本以及预期储能资源出售收益的变动构成：应用云储能模式后，用户I1的预期天然气购买成本上升了64元，但预期电能购买成本下降了约53元，同时增加了额外约140元的预期储能资源出售收入。也就是说，在应用云储能模式后，用户I1用能策略的改变虽然使得预期购电成本以及购气成本之和有所上升，但由于储能资源出现了盈余量，而这部分盈余量对应的收入会抵消购能成本的增加，最终使得其在云储能模式下的预期总用能成本得到减少。应用云储能模式前后用户I1预期用能成本对比见表3。
表3
图3为云储能模式应用前用户I1的储能预期充/放电策略，功率大于0时表示储能设备处于充电状态，功率小于0时表示储能设备处于放电状态。用户的充电时段为时段2、3、4、5、13、14、15、16，其中用户在2时段的充电功率达到最大值500kW，用户的放电时段为时段7、8、9、10、11、12、17、18、19、21、22、23、24，其中储能设备在时段17的放电功率达到最大值376kW。图4为云储能模式应用后用户储能设备的预期充/放电策略，充电时段为3、4、5、13、14、15、23，其中储能设备在时段3、4的充电功率达到最大值376kW，放电时段为时段8、9、10、11、17、18、19、21、22、24，其中储能设备的放电功率达到最大值376kW。
对比图3和图4可以看出，在引入云储能模式后，储能设备充电峰值的出现时间发生了转移，从原本的时段2变为了时段3、4，并且充电峰值也从500kW降低至376kW，由于放电峰值未发生改变，因此用户I1就拥有了123kW的闲置功率容量。此外，引入云储能模式后，用户I1的储能设备存储的最大电量也有所减小，从而提供了417kW/h的闲置存储容量。
综上所述，引入云储能模式后，用户出于用能成本的考虑会改变预期用能策略来降低储能设备的充/放电功率峰值、电量存储峰值，并最终向云储能平台提交闲置储能资源来降低自身预期用能成本。
实施例3
与实施例1对应的，本实施例提供一种控制分布式储能设备充放电的系统，包括：
供需资源量化模块，设置为对分布式储能设备的供需资源、分布式光伏设备的供需资源、分布式热-电联产设备的供需资源和分布式锅炉的供需资源进行量化，得到分布式储能供需资源量化结果；
闲置储能资源量获取模块，设置为获取储能资源盈余用户提交的闲置储能资源量，所述闲置储能资源量由储能资源盈余用户以实际运行日的综合日用能成本最小为目标，并根据所述分布式储能供需资源量化结果计算得到；
信息推送模块，设置为向用户推送实际运行日的云储能资源价格；
云储能资源需求量获取模块，设置为获取用户提交的云储能资源需求量；所述云储能资源需求量由用户以实际运行日的综合日用能成本最小为目标，并根据所述云储能资源价格、所述分布式储能供需资源量化结果和自身需求计算得到；
匹配模块，设置为将所述闲置储能资源量与所述云储能资源需求量进行匹配，得到分布式储能设备的实际充放电功率。
本申请实施例提供的控制分布式储能设备充放电的方法及系统，结合用户的分布式储能设备利用情况与储能资源需求情况，对储能资源盈余用户和需求用户的行为进行分析，通过匹配闲置储能资源量和云储能资源需求量，实现对分布式储能设备的实际充放电功率进行控制，从而优化储能设备的充放电，提高储能的综合利用率和新能源的消纳能力。
实施例4
图6是本申请实施例提供的一种电子设备的结构示意图，如图6所示，该电子设备包括：一个或多个处理器110和存储器120。图6中以一个处理器110 为例。
所述电子设备还可以包括：输入装置130和输出装置140。
可以理解地，所述电子设备也可以不包括，输入装置130和输出装置140。
所述电子设备中的处理器110、存储器120、输入装置130和输出装置140可以通过总线或者其他方式连接，图6中以通过总线连接为例。
存储器120作为一种计算机可读存储介质，可设置为存储软件程序、计算机可执行程序以及模块。处理器110通过运行存储在存储器120中的软件程序、指令以及模块，从而执行多种功能应用以及数据处理，以实现上述实施例中的任意一种方法。
存储器120可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储根据电子设备的使用所创建的数据等。此外，存储器可以包括随机存取存储器(Random Access Memory，RAM)等易失性存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件或者其他非暂态固态存储器件。
存储器120可以是非暂态计算机存储介质或暂态计算机存储介质。该非暂态计算机存储介质，例如至少一个磁盘存储器件、闪存器件或其他非易失性固态存储器件。在一些实施例中，存储器120可选包括相对于处理器110远程设置的存储器，这些远程存储器可以通过网络连接至电子设备。上述网络的实例可以包括互联网、企业内部网、局域网、移动通信网及其组合。
输入装置130可设置为接收输入的数字或字符信息，以及产生与电子设备的用户设置以及功能控制有关的键信号输入。输出装置140可包括显示屏等显示设备。
本实施例还提供一种计算机可读存储介质，存储有计算机程序，所述计算机程序用于执行上述方法。
存储介质可以是非暂态(non-transitory)存储介质。
上述实施例方法中的全部或部分流程可以通过计算机程序来执行相关的硬件来完成的，该程序可存储于一个非暂态计算机可读存储介质中，该程序在执行时，可包括如上述方法的实施例的流程，其中，该非暂态计算机可读存储介质可以为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)或RAM等。
本申请实施例具有如下的特性：结合用户的分布式储能设备利用情况与储能资源需求情况，建立了分布式储能供需资源量化模型，可高效、准确地挖掘出不同用户用电行为等高价值信息，有效支撑分布式储能云服务；对供需侧用户的 储能利用情况进行分析，可有效衡量园区分布式储能资源供需情况；同时制定了园区储能设备的充放电控制方法，根据充放电控制方法对分布式储能设备的实际充放电功率进行配置，实现提高园区的用能效率，使得园区多种能源能够协调运行。
以上所述的具体实施方式，对本申请的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上所述仅为本申请的具体实施方式而已，并不用于限定本申请的保护范围，凡在本申请的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本申请的保护范围之内。

Claims (12)

  1. 一种控制分布式储能设备充放电的方法，包括：
    分别对分布式储能设备的供需资源、分布式光伏设备的供需资源、分布式热-电联产设备的供需资源和分布式锅炉的供需资源进行量化，得到分布式储能供需资源量化结果；
    获取储能资源盈余用户提交的闲置储能资源量，所述闲置储能资源量由储能资源盈余用户以实际运行日的综合日用能成本最小为目标，并根据所述分布式储能供需资源量化结果计算得到；
    向用户推送实际运行日的云储能资源价格，获取用户提交的云储能资源需求量；所述云储能资源需求量由用户以实际运行日的综合日用能成本最小为目标，并根据所述云储能资源价格、所述分布式储能供需资源量化结果和自身需求计算得到；
    将所述闲置储能资源量与所述云储能资源需求量进行匹配，得到分布式储能设备的实际充放电功率。
  2. 根据权利要求1所述的一种控制分布式储能设备充放电的方法，其中，所述分别对分布式储能设备的供需资源、分布式光伏设备的供需资源、分布式热-电联产设备的供需资源和分布式锅炉的供需资源进行量化，得到分布式储能供需资源量化结果包括：
    建立分布式储能设备的物理模型、分布式光伏设备的物理模型、分布式热-电联产设备的物理模型和分布式锅炉的物理模型；
    利用所述分布式储能设备的物理模型对分布式储能设备的供需资源进行量化，利用所述分布式光伏设备的物理模型对分布式光伏设备的供需资源进行量化，利用所述分布式热-电联产设备的物理模型对分布式热-电联产设备的供需资源进行量化，利用所述分布式锅炉的物理模型对分布式锅炉的供需资源进行量化。
  3. 根据权利要求2所述的一种控制分布式储能设备充放电的方法，其中，所述分布式储能设备的物理模型表示为：



    其中，为用户d的分布式储能设备的最大充/放电功率；和分别为用户d的分布式储能设备在t时段的充电功率和放电功率；ηST为分布式储能设备的充/放电效率；为0-1变量，用以表示储能的充放电状态，为1时表示充电，为0时表示放电；为用户d的分布式储能设备的最大存储容量。
  4. 根据权利要求2所述的一种控制分布式储能设备充放电的方法，其中，所述分布式光伏设备的物理模型的表达式为：

    其中，表示用户d的分布式光伏设备在t时段的最大出力；为光伏设备在t时段的功率比，与光照辐射强度、光照入射角度、太阳能板的效率有关；为用户d的光伏设备装机容量；表示用户d的光伏设备在t时段的出力。
  5. 根据权利要求2所述的一种控制分布式储能设备充放电的方法，其中，所述分布式热-电联产设备的物理模型表示为：



    其中，表示用户d的分布式热-电联产CHP设备在t时段的电输出功率；为分布式CHP设备在t时段消耗天然气的功率；ηCHP.e为分布式CHP设备的发电效率；q为天然气热值；表示分布式CHP设备在t时段的最大热输出功率；ηCHP.l为分布式CHP设备的散热损失系数；δ为分布式CHP设备的制热系数；当近似认为ηCHP.e和ηCHP.l保持不变时，则分布式CHP机组的电输出功率和热输出功率之比为定值，用kCHP表示。
  6. 根据权利要求2所述的一种控制分布式储能设备充放电的方法，其中，所述分布式锅炉的物理模型表示为：

    其中，为用户d的分布式电锅炉输出的热功率；为电锅炉输入的电功率；ηEB为电锅炉的转换系数。
  7. 根据权利要求1所述的一种控制分布式储能设备充放电的方法，其中，
    储能资源盈余用户的最小综合日用能成本的模型表达式为：
    min Ci.DR+Ci.G+Ci.M-Ci.IL，


    Ci.IL＝k*Pricei.EEi.YIL+k*Pricei.PPi.YIL，
    其中，Ci.DR为储能资源盈余用户i的购电成本；Ci.G为储能资源盈余用户i的购气成本；Ci.M为储能资源盈余用户i的设备维护成本；Ci.IL为储能资源盈余用户i提交闲置储能资源后可能获得的收入；
    约束条件：





    其中，为用户i在时段t的购电功率，需大于等于0；为时段t的天然气价格；k为云储能资源的收购价格系数；Pricei.E和Pricei.P为用户i对云储能资源价格的估计值；Ei.YIL和Pi.YIL为用户i准备向云储能平台提交的储能资源闲置量；
    闲置储能资源量的模型表达式为
  8. 根据权利要求1所述的一种控制分布式储能设备充放电的方法，其中，
    储能资源需求用户的最小综合日用能成本的模型表达式为：
    min Cj.DR+Cj.G+Cj.M+Cj.ST，


    Cj.ST＝k*PriceEEj.YDM+k*PricePPj.YDM，
    其中，Cj.DR为储能资源需求用户j的购电成本；Cj.G为储能资源需求用户j的购气成本；Cj.M为储能资源需求用户j的设备维护成本；Cj.IL为储能资源需求用户j提交闲置储能资源后获得的收入；
    约束条件：





    其中，为用户j在时段t的购电功率，大于或等于0；为时段t的天然气价格；k为云储能资源的收购价格系数；Pricej.E和Pricej.P为用户j对云储能资源价格的估计值；Ej.YIL和Pj.YIL为用户j准备向云储能平台提交的储能资源闲置量；
    云储能资源需求量的模型表达式为：
  9. 根据权利要求1所述的一种控制分布式储能设备充放电的方法，其中，所述将所述闲置储能资源量与所述云储能资源需求量进行匹配，得到分布式储能设备的实际充放电功率包括：
    建立储能资源盈余用户的分布式储能设备的实际充放电功率模型；
    求解所述分布式储能设备的实际充放电功率模型得到分布式储能设备的实际充放电功率；
    所述分布式储能设备的实际充放电功率模型的表达式为：

    其中，和分别为储能资源盈余用户i的最优充电策略和最优放电策略，运算(·)+和(·)-表示为：
  10. 一种控制分布式储能设备充放电的系统，包括：
    供需资源量化模块，设置为对分布式储能设备的供需资源、分布式光伏设备的供需资源、分布式热-电联产设备的供需资源和分布式锅炉的供需资源进行量化，得到分布式储能供需资源量化结果；
    闲置储能资源量获取模块，设置为获取储能资源盈余用户提交的闲置储能资源量，所述闲置储能资源量由储能资源盈余用户以实际运行日的综合日用能成本最小为目标，并根据所述分布式储能供需资源量化结果计算得到；
    信息推送模块，设置为向用户推送实际运行日的云储能资源价格；
    云储能资源需求量获取模块，设置为获取用户提交的云储能资源需求量；所述云储能资源需求量由用户以实际运行日的综合日用能成本最小为目标，并根据所述云储能资源价格、所述分布式储能供需资源量化结果和自身需求计算得到；
    匹配模块，设置为将所述闲置储能资源量与所述云储能资源需求量进行匹配，得到分布式储能设备的实际充放电功率。
  11. 一种电子设备，包括：
    至少一个处理器；
    存储器，设置为存储至少一个程序，
    当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如权利要求1-9中任一项所述的控制分布式储能设备充放电的方法。
  12. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1-9中任一项所述的控制分布式储能设备充放电的方法。
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