Distributed energy storage capacity optimal configuration method, system and equipment

Abstract

The invention discloses a distributed energy storage capacity optimization configuration method, system and device. The method comprises the steps of obtaining photovoltaic data corresponding to each producer at each time point and first power data of a community; the photovoltaic data are input into a preset first capacity optimization model, energy storage configuration capacity optimization is carried out with the lowest first cost corresponding to a community as the target, the first energy storage capacity corresponding to the community is obtained, and the community comprises a plurality of producers; determining a power transaction value in the community based on the first power data, inputting the first energy storage capacity and the power transaction value into a preset second capacity optimization model, and performing energy storage configuration capacity optimization by taking the lowest second cost corresponding to each producer as a target to obtain a second energy storage capacity corresponding to each producer, and configuring the energy storage capacity of each producer according to the second energy storage capacity. According to the invention, optimal configuration of community distributed shared energy storage can be realized.

Description

Technical Field

[0001] The present application relates to the field of energy storage configuration, and particularly to a method, system and device for optimizing the configuration of distributed energy storage capacity. Background Art

[0002] More and more users are no longer just passive consumers of energy, but have become "prosumers" who can produce and consume energy. This transformation benefits from the rapid development of distributed energy technologies, especially the wide application of renewable energy such as solar photovoltaic and wind energy, enabling households, enterprises and even communities to generate electricity independently, achieve energy self-sufficiency and even feed the surplus electricity back into the grid. Although combining distributed prosumers and energy storage systems can effectively suppress the volatility of new energy output and promote the wide consumption of new energy, with the increase in the amount of distributed energy fed into the grid, new challenges have been posed to the configuration of energy storage capacity.

[0003] Current energy storage capacity configurations mostly target independent energy storage systems and do not consider the issues of local consumption of distributed energy and energy storage utilization rate brought about by the access of distributed prosumers. Therefore, the cost is high and the energy utilization rate is low. Thus, how to achieve the sharing of distributed energy and energy storage and realize the optimal configuration of community distributed shared energy storage is an urgent problem to be solved.

[0004] Application Content

[0005] The present application provides a method, system and device for optimizing the configuration of distributed energy storage capacity of photovoltaic prosumers to achieve the optimal configuration of community distributed shared energy storage and improve the utilization rate of energy storage resources.

[0006] In a first aspect, the present application provides a method for optimizing the configuration of distributed energy storage capacity, including:

[0007] Obtaining the photovoltaic data corresponding to each prosumer at each time point and the first power data of the community, where the photovoltaic data includes output data and load data;

[0008] Inputting the photovoltaic data into a preset first capacity optimization model to optimize the energy storage configuration capacity with the lowest first cost corresponding to the community as the goal, and obtaining the first energy storage capacity corresponding to the community, where the community includes several prosumers;

[0009] Determining the value of electricity trading within the community based on the first power data, inputting the first energy storage capacity and the value of electricity trading into a preset second capacity optimization model to optimize the energy storage configuration capacity with the lowest second cost corresponding to each prosumer as the goal, obtaining the second energy storage capacity corresponding to each prosumer, and configuring the energy storage capacity of each prosumer according to the second energy storage capacity.

[0010] In the embodiments of the present application, by obtaining the photovoltaic data corresponding to each producer and consumer at each time point and the first power data of the community, the power generation capacity and power demand of each producer and consumer at different time points can be accurately grasped, providing data support for subsequent energy storage capacity configuration; by inputting the photovoltaic data into a preset first capacity optimization model, the first energy storage capacity corresponding to the lowest first cost of the community can be obtained, ensuring the reasonable distribution of the energy storage capacity at the community level, thereby improving the utilization rate of the energy storage system; by inputting the first energy storage capacity and the power trading value into a preset second capacity optimization model, the second energy storage capacity corresponding to the lowest second cost of each producer and consumer can be obtained, which can ensure the reasonable distribution of the energy storage capacity among the producers and consumers, further improving the utilization rate of the energy storage resources. Compared with the prior art, the present application can achieve the optimal configuration of community distributed shared energy storage and improve the utilization rate of energy storage resources.

[0011] Further, before inputting the photovoltaic data into the preset first capacity optimization model, it further includes:

[0012] Obtain the electricity cost value of the community;

[0013] According to the photovoltaic data, the first power data, and the electricity cost value, establish a first objective function and first constraint conditions with the lowest first cost, where the first constraint conditions include community power balance constraint, operation constraint, energy state constraint, and electricity self-sufficiency rate constraint.

[0014] In this way, according to the photovoltaic data, the first power data, and the electricity cost value, establishing a first objective function and first constraint conditions with the lowest first cost can accurately obtain a first capacity optimization model considering the photovoltaic output situation, overall power demand, and electricity price, facilitating the subsequent obtaining of the first energy storage capacity of the community, ensuring the reasonable distribution of the energy storage capacity at the community level, and thereby improving the utilization rate of the energy storage system.

[0015] Further, the establishment of the first objective function with the lowest first cost according to the photovoltaic data, the first power data, and the electricity cost value is specifically:

[0016] Determine the net load of each producer and consumer according to the output data and the load data, and based on the first power data and the net load, determine the power purchase and sale power of the community from the power grid. Based on the electricity cost value and the power purchase and sale power, determine the power purchase cost of the community;

[0017] Determine the distributed energy storage cost based on the cost information of the energy storage system, where the distributed energy storage cost includes investment cost and operation cost;

[0018] Determine the first power loss cost during the sharing of distributed energy storage based on the first power data;

[0019] Determine a first objective function based on the electricity purchase cost, the distributed energy storage cost, and the first power loss cost.

[0020] In this way, according to the photovoltaic data, the first power data, and the electricity price value, a first objective function with the lowest first cost is established, and a first capacity optimization model considering the photovoltaic output, the overall electricity demand, and the electricity price can be accurately obtained, which is convenient for obtaining the first energy storage capacity of the community subsequently, ensuring the reasonable allocation of the energy storage capacity at the community level, and thus improving the utilization rate of the energy storage system.

[0021] Furthermore, the expression of the first capacity optimization model is specifically:

[0022] The first objective function is:

[0023] C com = C con + C bess + C om + C trans ;

[0024]

[0025] The community power balance constraint is:

[0026]

[0027] The operation constraint is:

[0028] - P bess ≤ P bat (t) ≤ P bess ;

[0029]

[0030] The energy state constraint is:

[0031] p bess_min ≤ p b (t) ≤ p bess ;

[0032] The electricity self-sufficiency rate constraint is:

[0033]

[0034] In the formula, C com is the first cost of the community; C con is the electricity purchase cost of the community from the power grid; C bess is the investment cost of the distributed energy storage; C om is the operation cost of the distributed energy storage; C transis the first power loss cost during distributed energy storage sharing; p grid (t) is the electricity purchase and sale power of the community from the power grid; λ grid is the electricity cost value, λ buy (t) and λ sell (t) are the purchase electricity price and the sale electricity price from the power grid respectively; is the net load of each prosumer; p bat (t) is the first power data of the community; is the output data of each prosumer; is the load data of each prosumer; K P is the unit power cost of the energy storage; K inv is the unit power cost of the energy storage auxiliary equipment energy storage converter; K om is the annual maintenance cost coefficient of the energy storage; r is the discount rate; L is the investment life of the energy storage system; P bess is the first energy storage capacity; η trans is the line transmission loss coefficient; K trans is the cost of unit loss power; p b (t) is the energy state of the energy storage; P bat is the first power data of the energy storage. When the energy storage is charging, P bat (t)>0. When the energy storage is discharging, P bat (t)<0; η ch and η dis are the charging efficiency and the discharging efficiency of the energy storage respectively; p bess_min is the minimum energy state of the energy storage system; ss th is the self-sufficiency rate of community electricity consumption.

[0035] Furthermore, the power trading value within the community is determined based on the first power data, specifically:

[0036] Obtain the number information of the prosumers in the community, and determine the revenue sharing value of the community based on the number information of the prosumers and the first power data;

[0037] Determine the price compensation factor based on the revenue sharing value;

[0038] Obtain the second power data of each prosumer, and determine the internal supply-demand ratio of the community based on the output data, the load data and the second power data;

[0039] If the internal supply-demand ratio of the community meets the preset conditions, then determine the power trading value based on the internal supply-demand ratio of the community and the price compensation factor.

[0040] Determining the value of electricity transactions within the community based on the first power data helps identify the imbalance between electricity supply and demand within the community and quantify the economic value brought by this imbalance. Through electricity transactions, prosumers can be incentivized to sell electricity to the community when there is an excess and purchase electricity from the community when there is a shortage, thus achieving an optimal allocation of electricity resources.

[0041] Furthermore, the calculation formula for determining the value of electricity transactions based on the supply-demand ratio within the community and the price compensation factor is specifically as follows:

[0042]

[0043] 0 ≤ β(t) ≤ λ buy (t) - λ sell (t);

[0044] In the formula, P r sell (t) and P r buy (t) are both the value of electricity transactions within the community. Among them, P r sell (t) is the selling price of electricity within the community, and P r buy (t) is the purchase price of electricity within the community; λ sell (t) and are respectively the selling electricity price and the purchasing electricity price from the power grid; β(t) is the price compensation factor; SDR(t) is the supply-demand ratio within the community.

[0045] Furthermore, before inputting the first energy storage capacity and the value of electricity transactions into the preset second capacity optimization model, it also includes:

[0046] Obtain the transferred electricity quantities of each prosumer;

[0047] Based on the first energy storage capacity, the value of electricity transactions, and the transferred electricity quantities, establish a second objective function and second constraint conditions with the lowest second cost, where the second constraint conditions include energy storage constraints, capacity constraints, and prosumer power balance constraints.

[0048] In this way, based on the first energy storage capacity, the value of electricity transactions, and the transferred electricity quantities, establishing a second objective function and second constraint conditions with the lowest second cost can accurately consider the second capacity optimization model of the community energy storage capacity, the value of electricity transactions, and the transferred electricity quantities, facilitating the subsequent obtaining of the second energy storage capacity of each prosumer, ensuring the reasonable allocation of the first energy storage capacity among each prosumer, and thus improving the utilization rate of the energy storage system.

[0049] Furthermore, the expression of the second capacity optimization model is specifically as follows:

[0050] The second objective function is as follows:

[0051]

[0052] The energy storage constraint is as follows:

[0053]

[0054] The capacity constraint is as follows:

[0055]

[0056] The producer - consumer power balance constraint is as follows:

[0057]

[0058] In the formula, F total is the second cost after each producer - consumer configures the energy storage system; is the electricity consumption cost of producer - consumer i before configuring the energy storage system; is the investment cost of the distributed energy storage system; is the operating cost of the distributed energy storage system; is the electricity purchase cost of producer - consumer i; is the second power loss cost during the charging and discharging process of producer - consumer i; is the electricity sales revenue of producer - consumer i; p i sel (t) is the electricity quantity sold by producer - consumer i to other producer - consumers; is the electricity quantity deposited by producer - consumer i into other energy storage systems; and are both the electricity trading values within the community. Among them, is the electricity selling price within the community, is the electricity purchase price within the community; is the electricity quantity sold by producer - consumer i to the power grid; λ buy and λ sell are the electricity purchase price and selling price from / to the power grid respectively; p i buy (t) is the electricity quantity purchased by producer - consumer i from other producer - consumers; is the electricity quantity purchased by producer - consumer i from other energy storage systems; is the electricity quantity purchased by producer - consumer i from the power grid; K p is the unit power cost of the energy storage; K inv is the unit power cost of the energy storage auxiliary equipment, the energy storage converter; K om is the annual maintenance cost coefficient of the energy storage; r is the discount rate; L is the investment period of the energy storage system; is the second energy storage capacity of producer i; K trans is the cost per unit loss power; η trans is the line transmission loss coefficient; p i b is the energy state of producer i; η ch and η dis are the charging efficiency and discharging efficiency of the energy storage system respectively; p i bess_min is the minimum energy state of producer i; is the charging and discharging power of the self-owned energy storage of photovoltaic producer i, and the charging and discharging states are distinguished by taking positive / negative values; p i bat,ch (t) is the discharging power of the self-owned energy storage of photovoltaic producer i; p bat,dis (t) is the charging power of the self-owned energy storage of photovoltaic producer i; is the net load of producer i.

[0059] In a second aspect, the present application provides a distributed energy storage capacity optimization configuration system, including: an acquisition module, a first configuration module, and a second configuration module;

[0060] The acquisition module is configured to acquire the corresponding photovoltaic data of each producer at each time point and the first power data of the community, wherein the photovoltaic data includes output data and load data;

[0061] The first configuration module is configured to input the photovoltaic data into a preset first capacity optimization model, optimize the energy storage configuration capacity with the lowest first cost corresponding to the community as the goal, and obtain the first energy storage capacity corresponding to the community, wherein the community includes several producers;

[0062] The second configuration module is configured to determine the power trading value within the community based on the first power data, input the first energy storage capacity and the power trading value into a preset second capacity optimization model, optimize the energy storage configuration capacity with the lowest second cost corresponding to each producer as the goal, obtain the second energy storage capacity corresponding to each producer, and configure the energy storage capacity of each producer according to the second energy storage capacity.

[0063] In the embodiments of the present application, by obtaining the photovoltaic data corresponding to each producer and consumer at each time point and the first power data of the community, the power generation capacity and power demand of each producer and consumer at different time points can be accurately grasped, providing data support for subsequent energy storage capacity configuration; by inputting the photovoltaic data into a preset first capacity optimization model, the first energy storage capacity corresponding to the lowest first cost of the community can be obtained, ensuring the reasonable distribution of the energy storage capacity at the community level, thereby improving the utilization rate of the energy storage system; by inputting the first energy storage capacity and the power trading value into a preset second capacity optimization model, the second energy storage capacity corresponding to the lowest second cost of each producer and consumer can be obtained, which can ensure the reasonable distribution of the energy storage capacity among the producers and consumers, and further improve the utilization rate of the energy storage resources. Compared with the prior art, the present application can achieve the optimal configuration of community distributed shared energy storage and improve the utilization rate of energy storage resources.

[0064] In a third aspect, the present application provides a terminal device, including: one or more processors; a memory coupled to the processor for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the distributed energy storage capacity optimization configuration method as described in the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a schematic diagram of community distributed shared energy storage provided by the present application;

[0066] Figure 2 is a schematic flow diagram of energy sharing and energy storage sharing provided by the present application;

[0067] Figure 3 is a schematic flow diagram of an embodiment of the distributed energy storage capacity optimization configuration method provided by the present application;

[0068] Figure 4 is a schematic structural diagram of an embodiment of the distributed energy storage capacity optimization configuration system provided by the present application;

[0069] Figure 5 is a schematic structural diagram of an embodiment of the terminal device provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Apparently, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present application belong to the scope of protection of the present application.

[0071] It should be understood that the step numbers used in the text are only for convenience of description and do not limit the order of execution of the steps.

[0072] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0073] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0074] The term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0075] Thanks to the popularization of renewable energy, users are transforming from passive energy consumers to "prosumers". Currently, households, enterprises and even communities can generate electricity independently, achieving energy self-sufficiency and even selling surplus electricity back to the grid. The combination of distributed prosumers and energy storage systems can suppress the volatility of new energy output and promote the wide consumption of new energy, but the increase in the amount of electricity fed into the grid has led to new challenges in energy storage configuration. The current energy storage capacity configuration does not consider the local consumption of distributed energy and the utilization rate of energy storage, resulting in high costs and low efficiency. Therefore, it is urgent to solve the problems of distributed energy and energy storage sharing and the optimal configuration of community shared energy storage.

[0076] Next, the nouns involved in this application are analyzed:

[0077] The Shapley value revenue allocation strategy is a method for fairly allocating benefits in cooperative games, aiming to solve the contradictions arising from benefit allocation among multiple players during the cooperation process. In cooperative games, each member contributes to the overall revenue of the coalition, but how to fairly and reasonably allocate these revenues is a complex issue. The Shapley value method realizes the fair allocation of benefits by calculating the marginal contribution rate of each member to the coalition.

[0078] Based on this, the embodiments of this application provide a method, system and device for optimizing the configuration of distributed energy storage capacity, which can achieve the optimal configuration of community distributed shared energy storage and improve the utilization rate of energy storage resources.

[0079] A method, system and device for optimizing the configuration of distributed energy storage capacity provided by the embodiments of this application are specifically described through the following embodiments. First, the method for optimizing the configuration of distributed energy storage capacity in the embodiments of this application is described.

[0080] The distributed energy storage capacity optimization configuration method provided by the embodiments of the present application relates to the field of energy storage configuration. The distributed energy storage capacity optimization configuration method provided by the embodiments of the present application can be applied to a terminal, or to a server side, or can be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that realizes a distributed energy storage capacity optimization configuration, etc., but is not limited to the above forms.

[0081] The present application can also be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0082] Next, the distributed shared energy storage of the present application will be analyzed:

[0083] Please refer to Figure 1 , Figure 1It is a schematic diagram of community distributed shared energy storage provided by this application, including an energy management system, a community, and prosumers. Among them, the energy management system is responsible for the operation status of energy storage and photovoltaic power generation of each user within the community, achieving the purpose of internal power trading and distributed energy storage sharing within the community, thereby reducing the energy storage investment cost of users and promoting the local consumption of photovoltaic power. The community contains multiple photovoltaic prosumers. Prosumers can achieve power trading and energy storage sharing within the community. Each user has rooftop photovoltaic power generation, an energy storage system, and corresponding loads. Each user has electricity consumption and power generation behaviors, and can either purchase electricity from the power grid or generate electricity to the power grid, that is, a user is a prosumer. Within the community, each photovoltaic prosumer is equipped with a smart meter to record their respective electricity trading situations. The power trading between prosumers and between prosumers and the distribution network is uniformly managed and recorded by the community energy management system. When a prosumer has an excess of its own electricity, the surplus electricity can be traded among other prosumers and corresponding fees can be charged. At the same time, the excess electricity can be stored in its own energy storage or the energy storage of other users. When a photovoltaic prosumer has a power deficit, the power deficit can be provided by other users, its own energy storage, and the energy storage of other users. When buying and selling electricity to the distribution network, it is uniformly managed by the community energy management center. Finally, the income distribution or expense expenditure is realized through the electricity trading situations recorded by the smart meters.

[0084] The electricity trading rule of this application is realized based on the surplus-deficit complementarity between different photovoltaic prosumers within the community. In order to improve the consumption capacity of photovoltaic power generation within the community and reduce the demand for distributed energy storage capacity, based on the source-load differences of different photovoltaic prosumers, the surplus-deficit complementarity between different photovoltaic prosumers is first realized, and the remaining electricity is then stored by the energy storage. For the distributed energy storage sharing rule, it is based on the principle of time-sharing reuse of energy storage resources among users. That is, in order to improve the utilization rate of energy storage, reduce the investment cost of energy storage, and avoid redundant investment of ordinary distributed energy storage users, the remaining energy storage resources will be shared and utilized according to the energy storage utilization situation in different time periods. The realization of distributed energy storage sharing here is also reflected in the form of energy sharing.

[0085] Please refer to Figure 2 , Figure 2It is a schematic flow diagram of energy sharing and energy storage sharing provided by this application. Among them, when a prosumer has surplus power, first use the surplus power to meet the power deficit of other prosumers. If there is still surplus power, then charge the surplus power into its own independent energy storage. When its own independent energy storage is full and there is still surplus power, then charge it into the energy storage of other prosumers (shared energy storage). After all the independent energy storages in the shared community are full, the remaining power is sold to the power grid; when a prosumer has a power deficit, first purchase the excess power from other prosumers. If there is still a power deficit, then obtain it from its own independent energy storage. If there is still a power deficit after the own independent energy storage is discharged, then obtain power from the energy storage of other prosumers (shared energy storage). When the power in the energy storage in the shared community is completely discharged, power will be purchased from the power grid.

[0086] Embodiment 1

[0087] Please refer to Figure 3 , Figure 3 is a schematic flow diagram of an embodiment of the method for optimizing the configuration of distributed energy storage capacity provided by this application, including steps S101 to S103;

[0088] Step S101, obtain the corresponding photovoltaic data of each prosumer at each time point and the first power data of the community, where the photovoltaic data includes output data and load data;

[0089] In some embodiments, sensors and intelligent devices are installed to record the power generation situation of the prosumer's photovoltaic, that is, the photovoltaic output data

[0090] In some embodiments, the power consumption situation of the prosumer, that is, the load data, is obtained through an intelligent electricity meter or an energy management system Based on the load data the power consumption demand of the prosumer can be understood.

[0091] In some embodiments, the charging and discharging power of the community, that is, the first power data p bat (t), is obtained through an energy management system or a monitoring center.

[0092] Step S102, input the photovoltaic data into a preset first capacity optimization model, and optimize the energy storage configuration capacity with the lowest first cost corresponding to the community as the goal to obtain the first energy storage capacity corresponding to the community, where the community includes several prosumers;

[0093] In some embodiments, before inputting the photovoltaic data into a preset first capacity optimization model, the method further includes: obtaining the electricity cost value of the community; establishing a first objective function and first constraint conditions with the lowest first cost according to the photovoltaic data, the first power data, and the electricity cost value, where the first constraint conditions include community power balance constraint, operation constraint, energy state constraint, and electricity self-sufficiency rate constraint.

[0094] In some embodiments, obtaining the electricity cost value of the community specifically means obtaining the electricity cost value λ of the community from the power grid grid , where the electricity cost value λ grid includes the purchase electricity price λ buy (t) and the selling electricity price λ sell (t).

[0095] In some embodiments, establishing a first objective function with the lowest first cost according to the photovoltaic data, the first power data, and the electricity cost value includes: determining the net load of each prosumer according to the output data and the load data , and determining the power purchase and sale power p of the community from the power grid based on the first power data p bat (t) and the net load . Determining the power purchase cost C grid of the community based on the electricity cost value λ grid and the power purchase and sale power p grid (t); determining the distributed energy storage cost based on the cost information of the energy storage system, where the distributed energy storage cost C con + C bess includes the investment cost C om and the operation cost C bess ; determining the first power loss cost C om when the distributed energy storage is shared based on the first power data p bat (t); determining the first objective function based on the power purchase cost C trans , the distributed energy storage cost C con + C bess and the first power loss cost C om . trans It should be noted that the cost information of the energy storage system includes the unit power cost K

[0096] of the energy storage, the unit power cost K P of the energy storage auxiliary equipment (energy storage converter), the annual maintenance cost coefficient K inv of the energy storage, the discount rate r, the investment life L of the energy storage system, and the energy storage capacity. om

[0097] According to the photovoltaic data, the first power data, and the electricity cost value, a first objective function and first constraint conditions with the lowest first cost are established, enabling the accurate acquisition of a first capacity optimization model that takes into account photovoltaic output, overall electricity demand, and electricity price. This facilitates the subsequent determination of the first energy storage capacity of the community, ensuring the reasonable allocation of the energy storage capacity at the community level and thereby improving the utilization rate of the energy storage system.

[0098] In some embodiments, the expression of the first capacity optimization model is specifically:

[0099] The first objective function is:

[0100] C com = C con + C bess + C om + C trans ;

[0101]

[0102] The community power balance constraint is:

[0103]

[0104] The operation constraint is:

[0105] -P bess ≤ P bat (t) ≤ P bess ;

[0106]

[0107] The energy state constraint is:

[0108] p bess_min ≤ p b (t) ≤ p bess ;

[0109] The electricity self-sufficiency rate constraint is:

[0110]

[0111] In the formula, C com is the first cost of the community; C con is the electricity purchase cost of the community from the power grid; C bess is the investment cost of the distributed energy storage; C om is the operation cost of the distributed energy storage; C trans is the first power loss cost during the sharing of the distributed energy storage; p grid (t) is the power purchase and sale power of the community from the power grid; λ gridis the electricity cost value, λ buy (t) and λ sell (t) are the purchase electricity price and the selling electricity price from the power grid respectively; is the net load of each producer and consumer; p bat (t) is the first power data of the community; is the output data of each producer and consumer; is the load data of each producer and consumer; K P is the unit power cost of the energy storage; K inv is the unit power cost of the energy storage converter, an auxiliary device of the energy storage; K om is the annual maintenance cost coefficient of the energy storage; r is the discount rate; L is the investment life of the energy storage system; P bess is the first energy storage capacity; η trans is the line transmission loss coefficient; K trans is the cost of unit loss power; p b (t) is the energy state of the energy storage; P bat is the first power data of the energy storage. When the energy storage is charging, P bat (t) > 0, and when the energy storage is discharging, P bat (t) < 0; η ch and η dis are the charging efficiency and discharging efficiency of the energy storage respectively; p bess_min is the minimum energy state of the energy storage system; ss th is the self-sufficiency rate of community electricity consumption.

[0112] In some embodiments, after determining the first capacity optimization model, the energy storage configuration capacity is optimized with the goal of minimizing the annual comprehensive electricity cost of the distributed photovoltaic producer and consumer community, that is, considering the first cost C com The first energy storage capacity P corresponding to the community is calculated based on the minimum first cost C com bess .

[0113] It should be noted that for users in the same community, the connection lines between shared energy storages are interconnected. Therefore, the same line transmission loss rate is used to characterize the first power loss cost C trans when the distributed energy storage is shared. The community power balance constraint is that the balance of real-time power of user interaction must be maintained.

[0114] It should be noted that the first capacity optimization model mainly considers the annual comprehensive electricity cost problem of the distributed photovoltaic producer and consumer community, that is, considering the first cost. Regarding the community as a whole, according to the total output data of photovoltaic producers and consumers within the community and the total load demand, that is, the load data, under the constraint of meeting the self-sufficiency rate of community electricity consumption, the total distributed energy storage capacity is optimized to obtain the first energy storage capacity. ​

[0115] In step S103, based on the first power data, determine the electricity trading value within the community, input the first energy storage capacity and the electricity trading value into a preset second capacity optimization model, optimize the configured energy storage capacity with the goal of minimizing the second cost corresponding to each prosumer, obtain the second energy storage capacity corresponding to each prosumer, and configure the energy storage capacity of each prosumer according to the second energy storage capacity.

[0116] In some embodiments, the determining the electricity trading value within the community based on the first power data includes: obtaining the number information of prosumers in the community, and determining the revenue sharing value of the community based on the number information of prosumers and the first power data; determining a price compensation factor based on the revenue sharing value; obtaining the second power data of each prosumer, and determining the internal supply-demand ratio within the community based on the output data, the load data, and the second power data; if the internal supply-demand ratio within the community meets a preset condition, determine the electricity trading value based on the internal supply-demand ratio within the community and the price compensation factor.

[0117] In some embodiments, obtaining the number information of prosumers in the community and determining the revenue sharing value of the community based on the number information of prosumers and the first power data is specifically as follows. First, it is necessary to first obtain the number of prosumers in the community to determine the revenue distribution value. Second, determine the traditional Shapley value revenue distribution value based on the number information of prosumers. The calculation formula of the traditional Shapley value revenue distribution strategy is: In the formula, represents the revenue sharing value under the traditional Shapley value, |S| represents the number of prosumers participating in the community S, n represents the number of all participating prosumers, i represents the prosumer participating in the community S, V(S) represents the characteristic function of the community S, and V(S\{i}) represents the characteristic function of the community after removing the participating prosumer i. However, the traditional Shapley value revenue distribution strategy is the revenue distribution in an ideal situation, ignoring the differences of each prosumer within the community, and unable to meet the actual needs of each participating prosumer within the community. That is, each photovoltaic prosumer within the community has a different degree of utilization of the shared energy storage. During the charging and discharging process of the shared energy storage, power loss is bound to occur, causing economic losses to the users of the shared energy storage. In order to encourage prosumers to actively participate in the cooperation alliance, in the sharing of savings in revenue, the impact of power loss during the sharing of energy storage should be considered. Therefore, for prosumers with relatively large power loss during the use of the shared energy storage, appropriately increase the sharing of the annual electricity savings revenue, and then determine the revenue sharing value of the community. Let the new weight of prosumer i be f i : where f i is the sharing weight of the i-th prosumer, is the second power loss cost of the i-th producer and seller, where Therefore, the weight difference between the new weight and the old weight of the producer and seller i is: where Therefore, the adjustment of the annual electricity savings income sharing is made using the weight difference: In the formula, represents the sharing of the electricity savings income by the producer and seller considering the improved Shapley value method, that is, the income sharing value of the community, represents the income sharing value under the traditional Shapley value, Δf i is the weight difference of the producer and seller i, and V(Φ) represents the marginal contribution of the community Φ.

[0118] It should be noted that the game model affects the cooperation mode of each producer and seller within the community. There are a total of 5 possible game modes for the capacity optimization configuration of distributed energy storage under the cooperative game mode, namely, the non-cooperative mode of perfect competition, the perfect cooperation mode, and the partial cooperation mode of two-party cooperation and competition with the other party. Each photovoltaic producer and seller, as an individual with independent will, can choose a suitable game mode according to its own utility maximization. Different weight values can be taken under each cooperation mode, affecting the income amount of each producer and seller, that is, the income sharing value.

[0119] In some embodiments, based on the income sharing value the price compensation factor β(t) is determined. Specifically, when the income sharing value of the community is obtained after that, the price compensation factor β(t) can be adaptively set based on the income sharing value It should be noted that the price compensation factor can not only be set according to the income sharing value but also be a custom constant, which is not limited in this application.

[0120] In some embodiments, the second power data of each producer and seller is obtained, and the internal supply-demand ratio of the community is determined based on the output data, the load data, and the second power data. Specifically, the second power data of each producer and seller is obtained, where the second power data includes the charging power of the self-owned energy storage and the discharging power and based on the output data the load data and the second power data and the internal supply-demand ratio SDR(t) of the community is determined. Among them, the calculation formula of the internal supply-demand ratio SDR(t) of the community is:

[0121] In the formula, SDR(t) is the internal supply-demand ratio of the community, Output data of each producer and seller Load data of each producer and seller Charging power of the self-owned energy storage of PV producer i Discharging power of the self-owned energy storage of PV producer i

[0122] In some embodiments, when the internal supply-demand ratio SDR(t) of the community is obtained, it is necessary to determine whether SDR(t) meets the preset conditions. If it meets the preset conditions, the electricity trading value is determined based on the internal supply-demand ratio SDR(t) of the community and the price compensation factor β(t). The electricity trading value includes P r sell (t) and P r buy (t), where when SDR(t) = 0, it means there is no electricity trading within the community, that is, each producer and seller has no surplus output, and all power shortages need to be purchased from the power grid. At this time, the purchase and sale electricity price, that is, the electricity trading value, is equal to the purchase and sale electricity price of the power grid; when SDR(t) = 1, it means that the supply and demand are balanced within the energy sharing community, and the purchase and sale electricity prices within the community, that is, the electricity trading values, are equal and are both equal to the price sold to the power grid plus the price compensation factor β(t); when 0 < SDR(t) < 1 or 1 < SDR(t), it means that the electricity trading price within the community changes with the change of the supply-demand ratio SDR(t). Among them, the calculation formula for determining the electricity trading value based on the internal supply-demand ratio of the community and the price compensation factor is specifically:

[0123]

[0124] 0 ≤ β(t) ≤ λ buy (t) - λ sell (t);

[0125] In the formula, P r sell (t) and P r buy (t) are both the electricity trading values within the community. Among them, P r sell (t) is the electricity selling price within the community, and P r buy (t) is the electricity purchase price within the community; λ sell (t) and are the selling electricity price and purchase electricity price from the power grid respectively; β(t) is the price compensation factor; SDR(t) is the internal supply-demand ratio of the community

[0126] Determining the value of electricity transactions within the community based on the first power data helps identify the imbalance between electricity supply and demand within the community and quantify the economic value brought by this imbalance. Through electricity transactions, prosumers can be incentivized to sell electricity to the community when there is an excess and purchase electricity from the community when there is a shortage, thereby achieving an optimal allocation of electricity resources.

[0127] In some embodiments, before inputting the first energy storage capacity and the electricity transaction value into a preset second capacity optimization model, it further includes: obtaining the transferred electricity of each prosumer; establishing a second objective function and second constraint conditions with the lowest second cost according to the first energy storage capacity, the electricity transaction value, and the transferred electricity, where the second constraint conditions include energy storage constraints, capacity constraints, and prosumer power balance constraints.

[0128] In some embodiments, obtaining the transferred electricity of each prosumer specifically means obtaining the electricity transferred between each prosumer, between each prosumer and the power grid, and between each prosumer and the energy storage. The transferred electricity includes, but is not limited to, the electricity p i sel (t) sold by prosumer i to other prosumers, the electricity deposited by prosumer i into other energy storages the electricity sold by prosumer i to the power grid the electricity p i buy (t) purchased by prosumer i from other prosumers, the electricity purchased by prosumer i from other energy storages the electricity purchased by prosumer i from the power grid

[0129] In some embodiments, according to the first energy storage capacity P bess 、the electricity transaction value P r sell (t) and P r buy (t) and the transferred electricity, establish a second objective function and second constraint conditions with the lowest second cost. The expression of the second capacity optimization model is specifically:

[0130] The second objective function is:

[0131]

[0132] The energy storage constraint is:

[0133]

[0134] The capacity constraint is:

[0135]

[0136] The producer - consumer power balance constraint is as follows:

[0137]

[0138] In the formula, F total is the second cost after each producer - consumer configures an energy storage system; is the electricity consumption cost of producer - consumer i before configuring the energy storage system; is the investment cost of the distributed energy storage system; is the operating cost of the distributed energy storage system; is the electricity purchase cost of producer - consumer i; is the second power loss cost during the charge - discharge process of producer - consumer i; is the electricity sales revenue of producer - consumer i; p i sel (t) is the electricity quantity sold by producer - consumer i to other producer - consumers; is the electricity quantity deposited by producer - consumer i into other energy storage systems; and are both the electricity trading values within the community. Among them, is the electricity selling price within the community, is the electricity purchase price within the community; is the electricity quantity sold by producer - consumer i to the power grid; λ buy and λ sell are the purchase electricity price and selling electricity price from the power grid respectively; p i buy(t) is the electricity quantity purchased by producer - consumer i from other producer - consumers; is the electricity quantity purchased by producer - consumer i from other energy storage systems; is the electricity quantity purchased by producer - consumer i from the power grid; K p is the unit power cost of the energy storage; K inv is the unit power cost of the energy storage auxiliary equipment, the energy storage converter; K om is the annual maintenance cost coefficient of the energy storage; r is the discount rate; L is the investment period of the energy storage system; is the second energy storage capacity of producer - consumer i; K trans is the cost of unit loss power; η trans is the line transmission loss coefficient; p i b is the energy state of producer - consumer i; η ch and η dis are the charging efficiency and discharging efficiency of the energy storage system respectively; p i bess_min is the minimum energy state of producer - consumer i; is the charge - discharge power of the self - owned energy storage of photovoltaic producer - consumer i, and the charge - discharge state is distinguished by taking positive / negative values; p i bat,ch(t) is the discharge power of the self-owned energy storage of PV producer i; p bat,dis (t) is the charging power of the self-owned energy storage of PV producer i; the net load of producer i.

[0139] In some embodiments, after determining the second capacity optimization model, the annual electricity cost savings of each PV producer in the community after configuring energy storage, that is, considering the second cost F total is used as the goal to optimize the configured capacity of energy storage. Then, based on the lowest second cost F total the second energy storage capacity of producer i in the optimal scenario

[0140] It should be noted that the second capacity optimization model considers the annual electricity cost savings of each PV producer in the community after configuring energy storage. Since power trading and energy storage sharing can be carried out within the community, that is, due to sharing, the electricity cost of each producer is not only related to its own PV output and energy storage capacity, but also related to the PV output and energy storage capacity of other producers. At the same time, each producer is an independent decision-making entity. According to its own power consumption situation, it can independently decide the capacity of its distributed energy storage or decide the capacity of its distributed energy storage in the form of an alliance. Therefore, the second capacity optimization model aims to maximize the annual electricity cost savings of producers, that is, the second cost F total is established as the objective function.

[0141] In this way, according to the first energy storage capacity, the power trading value, and the transferred electricity quantity, a second objective function and second constraint conditions with the lowest second cost are established, and a second capacity optimization model that can accurately consider the community energy storage capacity, power trading value, and transferred electricity quantity can be obtained, which is convenient for obtaining the second energy storage capacity of each producer later, ensuring the reasonable allocation of the first energy storage capacity among producers, and thus improving the utilization rate of the energy storage system.

[0142] By obtaining the PV data corresponding to each producer at each time point and the first power data of the community in the embodiments of the present application, the power generation capacity and power demand of each producer at different time points can be accurately grasped, providing data support for subsequent energy storage capacity configuration; by inputting the PV data into a preset first capacity optimization model, the first energy storage capacity corresponding to the lowest first cost in the community can be obtained, ensuring the reasonable allocation of the energy storage capacity at the community level, and thus improving the utilization rate of the energy storage system; by inputting the first energy storage capacity and the power trading value into a preset second capacity optimization model, the second energy storage capacity corresponding to the lowest second cost of each producer can be obtained, which can ensure the reasonable allocation of the energy storage capacity among producers and further improve the utilization rate of energy storage resources. Compared with the prior art, the present application can achieve the optimal configuration of community distributed shared energy storage and improve the utilization rate of energy storage resources.

[0143] It should be noted that the distributed energy storage capacity optimization configuration method provided in this application includes two models. Among them, the first capacity optimization model realizes the optimal configuration of distributed energy storage through capacity constraints at the community level and the individual level on the basis of considering the power loss during the energy storage sharing process, and then obtains the first energy storage capacity. The second capacity optimization model realizes the maximum cost savings of electricity consumption for each participant within the community through the method of cooperative game under the condition of minimizing the annual electricity cost of the community, that is, the second cost, to realize the capacity configuration of distributed shared energy storage, and then obtains the second energy storage capacity.

[0144] Embodiment 2

[0145] Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of an embodiment of the distributed energy storage capacity optimization configuration system provided in this application, including: an acquisition module 100, a first configuration module 200, and a second configuration module 300;

[0146] The acquisition module 100 is used to acquire the photovoltaic data corresponding to each producer and consumer at each time point and the first power data of the community. Among them, the photovoltaic data includes output data and load data;

[0147] The first configuration module 200 is used to input the photovoltaic data into a preset first capacity optimization model, optimize the energy storage configuration capacity with the lowest first cost corresponding to the community as the goal, and obtain the first energy storage capacity corresponding to the community, where the community includes several producers and consumers;

[0148] The second configuration module 300 is used to determine the electricity trading value within the community based on the first power data, input the first energy storage capacity and the electricity trading value into a preset second capacity optimization model, optimize the energy storage configuration capacity with the lowest second cost corresponding to each producer and consumer as the goal, obtain the second energy storage capacity corresponding to each producer and consumer, and configure the energy storage capacity of each producer and consumer according to the second energy storage capacity.

[0149] Regarding the information interaction, execution process, etc. among the modules in the above-mentioned distributed energy storage capacity optimization configuration system, since they are based on the same concept as the embodiment of the distributed energy storage capacity optimization configuration method in the first aspect of the present invention, the achieved technical effects are basically the same. For specific content, reference can be made to the description in Embodiment 1 of the method of the present invention, and details will not be repeated here.

[0150] The device embodiments described above are only illustrative. The modules described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0151] Please refer to Figure 5 , Figure 5 which schematically shows the hardware structure of a terminal device in an embodiment. The terminal device includes:

[0152] A processor 501, which can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0153] A memory 502, which can be implemented in forms such as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 502 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 502 and are called by the processor 501 to execute the large model-based dialogue risk assessment method of the embodiments of the present application;

[0154] An input / output interface 503, which is used to implement information input and output;

[0155] A communication interface 504, which is used to implement communication and interaction between this device and other devices, and can implement communication through a wired method (such as USB, network cable, etc.) or through a wireless method (such as a mobile network, WIFI, Bluetooth, etc.);

[0156] A bus 505, which transmits information between various components of the device (such as the processor 501, the memory 502, the input / output interface 503, and the communication interface 504);

[0157] Among them, the processor 501, the memory 502, the input / output interface 503, and the communication interface 504 are communicatively connected to each other inside the device through the bus 505.

[0158] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0159] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present application. It should be understood that the above are only specific embodiments of the present application and are not used to limit the protection scope of the present application.

[0160] It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A distributed energy storage capacity optimization configuration method, characterized in that: include: Acquire the photovoltaic data corresponding to each producer and seller at each time point and the first power data of the community, wherein the photovoltaic data includes output data and load data; Inputting the photovoltaic data into a preset first capacity optimization model, optimizing the energy storage configuration capacity with the goal of minimizing the first cost corresponding to the community, and obtaining a first energy storage capacity corresponding to the community, wherein the community includes a number of producers and sellers; The electricity transaction value within the community is determined based on the first power data, the first energy storage capacity and the electricity transaction value are input into a preset second capacity optimization model, the energy storage configuration capacity is optimized with the goal of minimizing the second cost corresponding to each producer and seller, the second energy storage capacity corresponding to each producer and seller is obtained, and the energy storage capacity of each producer and seller is configured according to the second energy storage capacity.

2. The distributed energy storage capacity optimization configuration method according to claim 1 is characterized in that: Before inputting the photovoltaic data into a preset first capacity optimization model, the method further includes: Obtain the value of electricity to the community; According to the photovoltaic data, the first power data and the electricity cost value, a first objective function and a first constraint condition with the lowest first cost are established, wherein the first constraint condition includes a community power balance constraint, an operation constraint, an energy state constraint and an electricity self-sufficiency rate constraint.

3. The distributed energy storage capacity optimization configuration method according to claim 2 is characterized in that: The establishing of a first objective function with the lowest first cost according to the photovoltaic data, the first power data and the power cost value is specifically: Determine the net load of each producer and seller according to the output data and the load data, determine the power purchased and sold by the community from the power grid based on the first power data and the net load, and determine the power purchase cost of the community based on the power cost value and the power purchased and sold; Determining the distributed energy storage cost based on the cost information of the energy storage system, wherein the distributed energy storage cost includes investment cost and operation cost; Determine a first power loss cost when distributed energy storage is shared based on the first power data; A first objective function is determined based on the electricity purchasing cost, the distributed energy storage cost, and the first power loss cost.

4. The distributed energy storage capacity optimization configuration method according to claim 3 is characterized in that: The expression of the first capacity optimization model is specifically: The first objective function is: C com =C con +C bess +C om +C trans ; The community power balance constraint is: The operating constraints are: -P bess ≤P bat (t)≤P bess ; The energy state constraint is: p bess_min ≤p b (t)≤p bess ; The electricity self-sufficiency rate constraint is: In the formula, C com The first cost of the community; C con is the cost of electricity purchased by the community from the grid; C bess is the investment cost of distributed energy storage; C om is the operating cost of distributed energy storage; C trans is the first power loss cost when distributed energy storage is shared; p grid (t) is the power purchased and sold by the community from the power grid; grid is the power cost, λ buy (t) and λ sell (t) are the purchase price and sales price of electricity from the power grid, respectively; is the net load of each producer and marketer; p bat (t) is the first power data of the community; Output data for each producer and marketer; is the load data of each producer and marketer; K P is the unit power cost of energy storage; K inv K is the unit power cost of the energy storage converter of the energy storage auxiliary equipment; om is the annual maintenance cost coefficient of energy storage; r is the discount rate; L is the investment period of the energy storage system; P bess is the first energy storage capacity; η trans is the line transmission loss coefficient; K trans is the cost per unit power loss; p b (t) is the energy state of energy storage; P bat is the first power data of the energy storage, when the energy storage is charged, P bat (t)>0, when the energy storage is discharged, P bat (t)<0; η ch and η dis are the charging efficiency and discharging efficiency of energy storage respectively; p bess_min is the minimum energy state of the energy storage system; ss th It is the electricity self-sufficiency rate of the community.

5. The distributed energy storage capacity optimization configuration method according to claim 1 is characterized in that: The determining of the power transaction value within the community based on the first power data is specifically: Acquire information on the number of producers and sellers in the community, and determine a profit sharing value of the community based on the information on the number of producers and sellers and the first power data; determining a price compensation factor based on the revenue sharing value; Acquire the second power data of each producer and seller, and determine the supply-demand ratio within the community based on the output data, the load data and the second power data; If the supply-demand ratio within the community meets a preset condition, the power transaction value is determined based on the supply-demand ratio within the community and the price compensation factor.

6. The distributed energy storage capacity optimization configuration method according to claim 5 is characterized in that: The calculation formula for determining the power transaction value based on the supply-demand ratio within the community and the price compensation factor is specifically: 0≤β(t)≤λ buy (t)-λ sell (t); Where P r sell (t) and P r buy (t) are the electricity transaction values ​​within the community, among which, P r sell (t) is the electricity price within the community, P r buy (t) is the electricity purchase price within the community; sell (t) and are the electricity sales price and electricity purchase price from the power grid respectively; β(t) is the price compensation factor; SDR(t) is the supply-demand ratio within the community.

7. The distributed energy storage capacity optimization configuration method according to any one of claims 1 to 6, characterized in that: Before inputting the first energy storage capacity and the power transaction value into a preset second capacity optimization model, the method further includes: Obtain the amount of electricity transferred by each producer and seller; According to the first energy storage capacity, the electricity trading value and the transferred amount, a second objective function and a second constraint condition with the second lowest cost are established, wherein the second constraint condition includes energy storage constraint, capacity constraint and producer-seller power balance constraint.

8. The distributed energy storage capacity optimization configuration method according to claim 7 is characterized in that: The expression of the second capacity optimization model is specifically: The second objective function is: The energy storage constraint is: The capacity constraint is: The prosumer power balance constraint is: In the formula, F total The second cost after configuring energy storage systems for each producer and marketer; is the electricity cost of producer and seller i before configuring the energy storage system; The investment cost of the distributed energy storage system; The operating cost of the distributed energy storage system; is the electricity purchase cost of producer and seller i; is the second power loss cost of producer and seller i during the charging and discharging process; is the electricity sales revenue of producer and seller i; The amount of electricity sold by producer and seller i to other producers and sellers; The amount of electricity stored in other energy storage systems for producer and seller i; and are the electricity transaction values ​​within the community, among which, is the electricity selling price within the community, The price of electricity purchased within the community; is the amount of electricity sold to the grid by producer and seller i; buy and λ sell are the purchase price and sales price of electricity from the power grid respectively; p i buy (t) is the amount of electricity purchased by producer and seller i from other producers and sellers; The amount of electricity purchased by producer and seller i from other energy storage systems; K is the amount of electricity purchased by producer and seller i from the power grid; p is the unit power cost of energy storage; K inv K is the unit power cost of the energy storage converter of the energy storage auxiliary equipment; om is the annual maintenance cost coefficient of energy storage; r is the discount rate; L is the investment period of the energy storage system; K is the second energy storage capacity of producer and seller i; trans is the cost per unit power loss; η trans is the line transmission loss coefficient; p i b is the energy state of producer and seller i; η ch and η dis are the charging efficiency and discharging efficiency of the energy storage system respectively; p i bess_min is the minimum energy state of producer and seller i; is the charging and discharging power of the self-owned energy storage of the photovoltaic producer and seller i, and the charging and discharging state is distinguished by taking positive / negative values; p i bat,ch (t) is the discharge power of the self-owned energy storage of photovoltaic producer and seller i; p bat,dis (t) is the charging power of the photovoltaic producer and seller i’s own energy storage; pinet is the net load of producer and seller i.

9. A distributed energy storage capacity optimization configuration system, characterized in that: include: an acquisition module, a first configuration module, and a second configuration module; The acquisition module is used to acquire the photovoltaic data corresponding to each producer and seller at each time point and the first power data of the community, wherein the photovoltaic data includes output data and load data; The first configuration module is used to input the photovoltaic data into a preset first capacity optimization model, optimize the energy storage configuration capacity with the goal of minimizing the first cost corresponding to the community, and obtain the first energy storage capacity corresponding to the community, wherein the community includes a plurality of producers and sellers; The second configuration module is used to determine the electricity trading value within the community based on the first power data, input the first energy storage capacity and the electricity trading value into a preset second capacity optimization model, optimize the energy storage configuration capacity with the goal of minimizing the second cost corresponding to each producer and seller, obtain the second energy storage capacity corresponding to each producer and seller, and configure the energy storage capacity of each producer and seller according to the second energy storage capacity.

10. A terminal device, characterized in that: include: one or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the distributed energy storage capacity optimization configuration method as described in any one of claims 1-8.

Images