Multi-target sharing operation optimization system of distributed energy storage in power distribution network

Abstract

The invention belongs to the technical field of distributed energy storage, and particularly relates to a multi-target sharing operation optimization system for distributed energy storage in a power distribution network, which comprises a data acquisition module used for acquiring power distribution network data of the power distribution network, and the power distribution network data comprises data of each node of energy storage equipment; the data of each node of the energy storage equipment comprises the maximum capacity, the maximum power and the maximum charge-discharge state quantity of the energy storage equipment; a data processing module; the distributed energy storage equipment can be installed at a plurality of places such as a user side, a distributed power supply side and a power distribution side according to requirements, values of energy storage investment cost and operation cost are calculated according to the positions, and an appropriate position value is selected as the most appropriate position of the energy storage equipment according to the different values. Compared with a traditional centralized energy storage power station, the distributed energy storage system can effectively reduce the power transmission loss and improve the energy utilization efficiency due to the scattered layout.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed energy storage, and in particular to a multi-objective shared operation optimization system of distributed energy storage in a distribution network. Background Art

[0002] Distributed energy storage is a type of energy storage method that distributes energy storage devices in multiple locations in the power system's distribution network to provide energy storage and regulation services;

[0003] With the rapid growth of load, the peak-to-valley difference continues to increase. Urban and rural distribution networks are facing problems such as low standards, weak connections, and low voltage. It is necessary to introduce energy storage technology. Distributed energy storage has flexible installation locations, which reduces the line loss and investment pressure of centralized energy storage power stations. However, compared with the traditional operation mode of large power grids, the current distributed energy storage access and output are characterized by decentralized layout and poor controllability.

[0004] Moreover, it is more troublesome to select the location and construction of energy storage equipment for distributed energy storage systems, because the site selection and capacity configuration of distributed energy storage usually involve multi-objective optimization, which requires considering multiple comprehensive indicators such as economy, minimum network loss, smoothing fluctuations, and peak-shaving. There may be conflicts between these goals, making it complicated to find a solution that balances the interests of all parties.

[0005] At the same time, when selecting a site and configuring capacity, it is necessary to consider various constraints such as power balance, node voltage, current, installed capacity, etc.; these constraints limit the design and operation of the energy storage system and increase the difficulty of the optimization problem.

[0006] To this end, the present invention provides a multi-objective shared operation optimization system for distributed energy storage in a distribution network. Summary of the invention

[0007] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0008] The technical solution adopted by the present invention to solve the technical problem is: a multi-objective shared operation optimization system of distributed energy storage in a distribution network described in the present invention comprises:

[0009] Data acquisition module: The data acquisition module is used to obtain the distribution network data of the distribution network, and the distribution network data includes the data of each node of the energy storage device;

[0010] The data of each node of the energy storage device includes the maximum capacity, maximum power, and maximum charge and discharge state quantity of the energy storage device;

[0011] Data processing module: The data processing module establishes a distributed battery energy storage system optimization configuration model based on the data of each node of the energy storage device, and the distributed battery energy storage system optimization configuration model includes all the placement locations of the energy storage device in the distribution network;

[0012] Data analysis module: The data analysis module selects a suitable energy storage device location based on all placement locations in the distribution network;

[0013] Calculate the energy storage investment cost and operating cost values ​​in all placement locations, and select the most suitable energy storage equipment construction location among the energy storage investment cost and operating cost values ​​in all placement locations;

[0014] The distributed battery energy storage system optimization configuration model includes all the placement locations of energy storage equipment in the distribution network. Distributed energy storage equipment can be installed in multiple places such as the user side, distributed power supply side and distribution side according to demand, and the energy storage investment cost and operating cost values ​​are calculated based on these locations, and one of the suitable location values ​​is selected as the most suitable location for the energy storage device based on different values. Compared with traditional centralized energy storage power stations, distributed energy storage systems can effectively reduce transmission losses and improve energy utilization efficiency due to their decentralized layout.

[0015] Preferably, based on the values ​​of energy storage investment cost and operating cost in all placement locations, selecting the most suitable energy storage equipment construction location among the values ​​of energy storage investment cost and operating cost in all placement locations includes:

[0016] The energy storage location and capacity of the energy storage device are obtained through the data acquisition module and used as the initial population, and a population set M = {(a1, b1), (a2, b2), ..., (a n , b n )};

[0017] Energy storage locations include multiple places on the user side, distributed power supply side, and distribution side;

[0018] Where M is the initial population, a i is the i-th energy storage location of the energy storage device, b i is the energy storage capacity of the corresponding energy storage device;

[0019] Establish the upper-level planning model and lower-level simulation model of the distribution network;

[0020] The upper-level planning model of the distribution network is used to determine the location of the energy storage device connected to the energy storage system and transmit it to the lower-level simulation model of the distribution network;

[0021] Calculate the cost of the energy storage equipment and K for each energy storage location i , and based on the cost of energy storage equipment at all energy storage locations and Ki The numerical comparison is performed in which the cost and K i The minimum value is the most suitable location for the energy storage equipment construction;

[0022] The lower layer simulation model of the distribution network determines the optimal capacity of the energy storage device based on the location of the energy storage device connected to the energy storage system;

[0023] Specifically, all potential locations where energy storage systems may be installed are determined. These locations may be substations, distribution nodes, or specific user-side locations in the power grid. The possible energy storage capacity range for each location is determined. This range can be based on technical limitations, economic considerations, or grid operation requirements. For each random location and capacity, one of the energy storage locations and energy storage capacity (a i , b i ), and determine the optimal construction location and optimal energy storage capacity through the upper-level distribution network planning model and the lower-level distribution network simulation model.

[0024] Preferably, the construction location of the energy storage device based on the cost and the minimum value of K as the most suitable location includes the following steps:

[0025] S1. Obtain the total investment cost C required to build energy storage equipment at each energy storage location in (a), and the operating cost C of the energy storage equipment at each energy storage location op (b);

[0026] S2. The total investment cost C required to build the energy storage equipment at each energy storage location in (a) The operating cost of the energy storage equipment at each energy storage location C op (b) Perform addition calculation to obtain the cost K of the current position i ;

[0027] S3, repeat step S2, cost K i Calculate and convert the cost K of each energy storage location i Calculate and combine the cost set K = {K1, K2, ...K n}, where the minimum value K in the cost set i That is the optimal construction location a of the corresponding energy storage equipment i .

[0028] Preferably, determining the optimal capacity of the energy storage device based on the lower layer simulation model of the distribution network includes:

[0029] Based on the optimal construction position a i After determining, and the current best construction location a i The required cost K and calculate the current position cost function K i(b), where b is the energy storage capacity of the energy storage device at the current location;

[0030] Then calculate K i (b) The derivative ΔK with respect to b i (b);

[0031] Select an initial energy storage capacity b0 as the starting point and set the growth rate μ, which determines the step size of each iterative update;

[0032] b i+1 =b i -μ*ΔK i (b);

[0033] When ΔH(b i ) When the current derivative is close to zero, it indicates that the current b i+1 Energy storage is the optimal capacity.

[0034] Preferably, based on the current best construction location a i The required cost K i include;

[0035] The electricity cost F1 of the energy storage equipment at the current best construction location, the discharge income F2 of the energy storage equipment, and the maintenance cost of the energy storage equipment;

[0036] The current best construction location is a i The cost function K i (b) The calculation method is as follows:

[0037]

[0038] Where: γ t is the electricity price at time t, is the discharge price at time t; P1 t (b) is the charging power at time t, P2 t (b) is the discharge power at time t;

[0039] The operating cost C of the energy storage equipment at each energy storage location op (b);

[0040] C in (a) The total investment cost required to build the energy storage equipment;

[0041] K i (b) The derivative ΔK with respect to b i (b) The calculation method is as follows:

[0042]

[0043] γ tis the electricity price at time t, is the discharge price at time t; P1 t (b) is the charging power at time t, P2 t (b) is the discharge power at time t.

[0044] Preferably, the multi-objective shared operation optimization system further comprises an adjustment unit, which is used to adjust the power demand and operation conditions of multiple service objects;

[0045] The multiple service objects provided by the energy storage device are marked as different participants, and the total supply provided by all participants and the total demand of all participants are obtained through the data acquisition module. The total supply and the total demand of all participants are calculated to obtain the supply-demand ratio ST, and the supply-demand ratio is used to adjust the working mode of the current energy storage device.

[0046] Preferably, the working method of adjusting the current energy storage device based on the supply-demand ratio includes:

[0047] If the supply-demand ratio ST is less than 1, it means that the supply is less than the demand, that is, there is a situation of supply exceeding demand, and it is necessary to increase the charging capacity of the corresponding energy storage equipment, or reduce the power use of some non-critical loads to meet the power demand;

[0048] If the supply-demand ratio ST is greater than 1, it means that the supply is greater than the demand, that is, there is a situation where supply exceeds demand. The corresponding energy storage device can increase discharge to utilize excess electricity, or reduce power generation to avoid power waste;

[0049] If the supply-demand ratio ST is equal to 1, it means that the supply is equal to the demand, and the supply and demand are balanced;

[0050] By rationally applying the supply-demand ratio ST, we can reasonably smooth out the peaks and fill the valleys, reduce the power demand during peak hours, lighten the burden on the power grid, and reduce the risk of power outages due to excessive load. By reducing power consumption during peak hours when electricity prices are higher and increasing power consumption during valley hours when electricity prices are lower, users can save on electricity bills. By storing energy during valley hours and releasing it during peak hours through the energy storage system, power generation resources can be used more efficiently and energy waste can be reduced.

[0051] Preferably, the energy storage device is also used to control charging and discharging according to the state of charge St of the device;

[0052] If the current state of charge St of the energy storage device is less than or equal to 50%, and the supply-demand ratio ST is less than 1, the corresponding energy storage device discharges at a lower power to avoid over-discharge;

[0053] If the current state of charge St of the energy storage device is greater than 50%, and ST is greater than 1, the corresponding energy storage device is charged at a higher power to utilize the excess power;

[0054] The energy storage system can dynamically adjust the charging and discharging power according to the real-time state of charge and supply-demand ratio to ensure the stability of the power system and the health of the battery.

[0055] Preferably, the energy storage device is also used to control its own charging and discharging process, specifically:

[0056] Set the charging and discharging range of the energy storage device, with the lowest state of charge St being 20% ​​and the highest state of charge St being 80%;

[0057] If the current state of charge St of the energy storage device is less than or equal to 20%, discharging is stopped;

[0058] If under the current circumstances, even if the supply-demand ratio ST is greater than 1, discharging should be avoided to prevent over-discharging; if the current state of charge St of the energy storage device is greater than or equal to 80%, charging is stopped;

[0059] Even if the supply-demand ratio ST is less than 1 in the current situation, charging should be avoided to prevent overcharging;

[0060] By charging and discharging properly, the service life of energy storage equipment can be extended, and the energy storage system can adapt to changes in supply and demand. The energy storage system can effectively adjust the charge and discharge power according to the real-time state of charge and supply and demand ratio to ensure the stability of the power system and the health of the battery. This helps to improve the operating efficiency of the power system, extend the service life of energy storage equipment, and achieve dual economic and environmental benefits.

[0061] Preferably, the state of charge St of the energy storage device is calculated by a ratio between the current power of the energy storage device and the total capacity of the energy storage device.

[0062] The beneficial effects of the present invention are as follows:

[0063] 1. The multi-objective shared operation optimization system of a distributed energy storage in a distribution network described in the present invention can be installed at multiple locations such as the user side, distributed power supply side and distribution side according to demand through distributed energy storage equipment, and the values ​​of energy storage investment cost and operating cost are calculated according to these locations, and one of the appropriate location values ​​is selected as the most appropriate location of the energy storage equipment according to different values. Compared with traditional centralized energy storage power stations, distributed energy storage systems can effectively reduce transmission losses and improve energy utilization efficiency due to their decentralized layout.

[0064] 2. The multi-objective shared operation optimization system of a distributed energy storage in a distribution network described in the present invention can avoid unnecessary over-investment through precise capacity optimization, ensure that the capacity of the energy storage system can meet the demand without being too redundant, thereby improving economy, and can effectively realize the site selection and capacity determination of distributed energy storage based on the application requirements and equipment conditions of the current location, strengthen the absorption and energy utilization of distributed photovoltaics; energy storage capacity constraints ensure that the energy storage capacity is within the range permitted by technology; grid operation constraints ensure that the operation of the energy storage system does not violate the operation restrictions of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The present invention will be further described below in conjunction with the accompanying drawings.

[0066] Figure 1 It is a system diagram of the present invention. DETAILED DESCRIPTION

[0067] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0068] like Figure 1 As shown, a multi-objective shared operation optimization system of distributed energy storage in a distribution network according to an embodiment of the present invention includes a data acquisition module, a data processing module and a data analysis module;

[0069] Distributed energy storage is a type of energy storage method that distributes energy storage devices in multiple locations in the power system's distribution network to provide energy storage and regulation services;

[0070] With the rapid growth of load, the peak-to-valley difference continues to increase. Urban and rural distribution networks are facing problems such as low standards, weak connections, and low voltage. Energy storage technology needs to be introduced. Distributed energy storage has flexible installation locations, which reduces the line loss and investment pressure of centralized energy storage power stations. However, compared with the traditional operation mode of large power grids, the current distributed energy storage access and output have the characteristics of decentralized layout and poor controllability. In addition, the construction and site selection of energy storage equipment is also more troublesome due to various factors.

[0071] Data acquisition module: The data acquisition module is used to obtain the distribution network data of the distribution network, and the distribution network data includes the data of each node of the energy storage device;

[0072] The data of each node of the energy storage device includes the maximum capacity, maximum power, and maximum charge and discharge state quantity of the energy storage device;

[0073] Data processing module: The data processing module establishes a distributed battery energy storage system optimization configuration model based on the data of each node of the energy storage device, and the distributed battery energy storage system optimization configuration model includes all the placement locations of the energy storage device in the distribution network;

[0074] Data analysis module: The data analysis module selects a suitable energy storage device location based on all placement locations in the distribution network;

[0075] Calculate the energy storage investment cost and operating cost values ​​in all placement locations, and select the most suitable energy storage equipment construction location among the energy storage investment cost and operating cost values ​​in all placement locations;

[0076] The distributed battery energy storage system optimization configuration model includes all the placement locations of energy storage equipment in the distribution network. Distributed energy storage equipment can be installed at multiple locations such as the user side, distributed power supply side and distribution side according to demand, and the values ​​of energy storage investment cost and operating cost are calculated according to these locations, and one of the appropriate location values ​​is selected as the most suitable location for the energy storage equipment according to different values. Compared with traditional centralized energy storage power stations, distributed energy storage systems can effectively reduce transmission losses and improve energy utilization efficiency due to their decentralized layout;

[0077] Distributed energy storage systems can discharge during peak load periods and charge during low load periods, thereby reducing peak load demand, saving electricity costs, and improving load characteristics. Distributed energy storage needs to realize load transfer and improve equipment utilization through reasonable site selection based on regional energy supply and demand conditions;

[0078] By rationally deploying energy storage equipment at key nodes in the power grid, we can better leverage peak-shaving, frequency regulation and other regulation functions, improve the efficiency of energy storage operations, optimize the operation of the power system, reduce dependence on traditional power generation resources, improve the flexibility and reliability of the system, and reduce electricity purchase costs and operation and maintenance costs through demand response mechanisms, thereby minimizing costs.

[0079] Based on the values ​​of energy storage investment cost and operating cost in all placement locations, the most suitable energy storage equipment construction location is selected from the values ​​of energy storage investment cost and operating cost in all placement locations, including:

[0080] The energy storage location and capacity of the energy storage device are obtained through the data acquisition module and used as the initial population, and a population set M = {(a1, b1), (a2, b2), ..., (a n , b n )};

[0081] Energy storage locations include multiple places on the user side, distributed power supply side, and distribution side;

[0082] Where M is the initial population, a i is the i-th energy storage location of the energy storage device, b i is the energy storage capacity of the corresponding energy storage device;

[0083] Establish the upper-level planning model and lower-level simulation model of the distribution network;

[0084] The upper-level planning model of the distribution network is used to determine the location of the energy storage device connected to the energy storage system and transmit it to the lower-level simulation model of the distribution network;

[0085] Calculate the cost of the energy storage equipment and K for each energy storage location i , and based on the cost of energy storage equipment at all energy storage locations and K i The numerical comparison is performed in which the cost and K i The minimum value is the most suitable location for the energy storage equipment construction;

[0086] The lower layer simulation model of the distribution network determines the optimal capacity of the energy storage device based on the location of the energy storage device connected to the energy storage system;

[0087] Specifically, all potential locations where energy storage systems may be installed are determined. These locations may be substations, distribution nodes, or specific user-side locations in the power grid. The possible energy storage capacity range for each location is determined. This range can be based on technical limitations, economic considerations, or grid operation requirements. For each random location and capacity, one of the energy storage locations and energy storage capacity (a i , b i ), and determine the optimal construction location and optimal energy storage capacity through the upper-level distribution network planning model and the lower-level distribution network simulation model

[0088] By setting the initial population, it is necessary to consider the operation constraints of the power grid and the technical constraints of energy storage. For example, it is possible to exclude locations that may cause unstable operation of the power grid or exclude capacities that exceed the technical allowable range.

[0089] It should be noted that the cost of the energy storage equipment at each energy storage location and K i It is calculated by the total investment cost and operating cost required to establish the energy storage equipment at the current location.

[0090] Based on the cost and K i The minimum value is the most suitable location for the energy storage equipment construction location, which includes the following steps:

[0091] S1. Obtain the total investment cost C required to build energy storage equipment at each energy storage location in (a), and the operating cost C of the energy storage equipment at each energy storage location op (b);

[0092] S2. The total investment cost C required to build the energy storage equipment at each energy storage location in (a) The operating cost of the energy storage equipment at each energy storage location C op (b) Perform addition calculation to obtain the cost K of the current position;

[0093] S3, repeat step S2, cost K i Calculate and convert the cost K of each energy storage location i Calculate and combine the cost set K = {K1, K2, ...K n}, where the minimum value K in the cost set i That is the optimal construction location a of the corresponding energy storage equipment i ;

[0094] By obtaining the total investment cost C of the energy storage equipment in (a), and position a i The operating cost of energy storage equipment C op (b) and calculate the position a i The cost K i , where position a i The cost K i The minimum value can be determined as the optimal position; distributed energy storage can effectively adjust the voltage limit caused by changes in load and new energy output by adjusting the charging and discharging power, and has important value in maintaining the voltage level of the distribution network;

[0095] Distributed battery energy storage systems can realize load shifting, voltage management and loss reduction functions, which help to improve the stability and reliability of the distribution network. Constraints: Grid operation constraints: ensure that the installation of the energy storage system does not violate the operation restrictions of the grid; Energy storage construction location constraints: ensure that the energy storage system is installed in a feasible location.

[0096] Determining the optimal capacity of the energy storage device based on the lower layer simulation model of the distribution network includes:

[0097] Based on the optimal construction position a i After determining, and the current best construction location a i The required cost K and calculate the current position cost function K i (b), where b is the energy storage capacity of the energy storage device at the current location;

[0098] Then calculate K i (b) The derivative ΔK with respect to b i (b);

[0099] Select an initial energy storage capacity b0 as the starting point and set the growth rate μ, which determines the step size of each iterative update;

[0100] b i+1 =b i -μ*ΔK i (b);

[0101] When ΔH(b i) When the current derivative is close to zero, it indicates that the current b i+1 Energy storage is the optimal capacity;

[0102] Specifically, after the best location is selected, a reasonable capacity needs to be selected, where the optimal capacity is the same as the construction location a. i The total cost required K i In connection with this, accurate capacity optimization can avoid unnecessary over-investment, ensure that the capacity of the energy storage system can meet demand without being too redundant, thereby improving economic efficiency. In addition, based on the application requirements and equipment conditions of the current location, it can effectively realize the site selection and capacity determination of distributed energy storage, strengthen the absorption and energy utilization of distributed photovoltaics; energy storage capacity constraints, ensure that the energy storage capacity is within the range permitted by technology; grid operation constraints, ensure that the operation of the energy storage system does not violate the operation restrictions of the grid;

[0103] Specifically, the growth rate μ can be selected as μ=0.01 or μ=0.001, and the growth rate is dynamically adjusted according to a predetermined strategy during the training process. For example, the growth rate is reduced after a certain number of iterations or when the performance on the validation set does not improve.

[0104] Based on the current optimal construction position a i The required cost K i include;

[0105] The electricity cost F1 of the energy storage equipment at the current best construction location, the discharge income F2 of the energy storage equipment, and the maintenance cost of the energy storage equipment;

[0106] The current best construction location is a i The cost function K i (b) The calculation method is as follows:

[0107]

[0108] Where: γ t is the electricity price at time t, is the discharge price at time t; P1 t (b) is the charging power at time t, P2 t (b) is the discharge power at time t;

[0109] The operating cost C of the energy storage equipment at each energy storage location op (b);

[0110] C in (a) The total investment cost required to build the energy storage equipment;

[0111] K i (b) The derivative ΔK with respect to b i(b) The calculation method is as follows:

[0112]

[0113] The multi-objective shared operation optimization system further includes an adjustment unit, which is used to adjust the power demand and operation conditions of multiple service objects;

[0114] Mark multiple service objects provided by the energy storage device as different participants, and obtain the total supply provided by all participants and the total demand of all participants through the data acquisition module, calculate the ratio of the total supply and the total demand of all participants to obtain the supply-demand ratio ST, and adjust the working mode of the current energy storage device according to the supply-demand ratio;

[0115] After each participant reports their own supply and demand for shared energy storage, the total demand and total supply are obtained. Supply and demand matching can be done by defining the supply-demand ratio, which is not only related to the supply and demand, but also to the network loss rate. According to the different supply-demand ratios, the supply and demand matching of shared energy storage can be divided into supply shortage, supply excess, or equal supply and demand.

[0116] The current working methods of energy storage devices based on regulating the supply-demand ratio include:

[0117] If the supply-demand ratio ST is less than 1, it means that the supply is less than the demand, that is, there is a situation of supply exceeding demand, and it is necessary to increase the charging capacity of the corresponding energy storage equipment, or reduce the power use of some non-critical loads to meet the power demand;

[0118] If the supply-demand ratio ST is greater than 1, it means that the supply is greater than the demand, that is, there is a situation where supply exceeds demand. The corresponding energy storage device can increase discharge to utilize excess electricity, or reduce power generation to avoid power waste;

[0119] If the supply-demand ratio ST is equal to 1, it means that the supply is equal to the demand, and the supply and demand are balanced;

[0120] By rationally applying the supply-demand ratio ST, we can reasonably smooth out the peak and fill the valley, reduce the power demand during peak hours, lighten the burden on the power grid, reduce the risk of power outages due to excessive load, reduce electricity consumption during peak hours when electricity prices are higher, and increase electricity consumption during valley hours when electricity prices are lower, which can help users save electricity bills. By storing energy during valley hours through the energy storage system and releasing it during peak hours, we can more effectively utilize power generation resources and reduce energy waste.

[0121] The triggering conditions of peak shaving demand response include insufficient grid reserve capacity, local load overload or other uncertain factors causing imbalance in power supply and demand; valley filling demand response is triggered when the power load level is low, the grid survey capacity is difficult to adapt to the peak-valley difference, and the volatility and intermittent impact of renewable energy.

[0122] Peak shaving refers to reducing the peak load of the power grid by reducing or shifting part of the electricity load during peak hours of electricity demand. This can be achieved through demand response, such as encouraging users to reduce electricity consumption during peak hours through economic incentives, or remotely controlling the switching of certain non-critical loads through automatic control.

[0123] The energy storage device is also used to control charging and discharging according to the state of charge St of the device;

[0124] If the current state of charge St of the energy storage device is less than or equal to 50%, and the supply-demand ratio ST is less than 1, the corresponding energy storage device discharges at a lower power to avoid over-discharge;

[0125] If the current state of charge St of the energy storage device is greater than 50%, and ST is greater than 1, the corresponding energy storage device is charged at a higher power to utilize the excess power;

[0126] The energy storage system can dynamically adjust the charging and discharging power according to the real-time state of charge and supply-demand ratio to ensure the stability of the power system and the health of the battery.

[0127] The energy storage device is also used to control its own charging and discharging process, specifically:

[0128] Set the charging and discharging range of the energy storage device, with the lowest state of charge St being 20% ​​and the highest state of charge St being 80%;

[0129] If the current state of charge St of the energy storage device is less than or equal to 20%, discharging is stopped;

[0130] If under the current circumstances, even if the supply-demand ratio ST is greater than 1, discharging should be avoided to prevent over-discharging; if the current state of charge St of the energy storage device is greater than or equal to 80%, charging is stopped;

[0131] Even if the supply-demand ratio ST is less than 1 in the current situation, charging should be avoided to prevent overcharging;

[0132] By charging and discharging properly, the service life of energy storage equipment can be extended, and the energy storage system can adapt to changes in supply and demand. The energy storage system can effectively adjust the charge and discharge power according to the real-time state of charge and supply and demand ratio to ensure the stability of the power system and the health of the battery. This helps to improve the operating efficiency of the power system, extend the service life of energy storage equipment, and achieve both economic and environmental benefits.

[0133] The state of charge St of the energy storage device is calculated by the ratio of the current power of the energy storage device and the total capacity of the energy storage device.

[0134] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A multi-objective shared operation optimization system for distributed energy storage in a distribution network, characterized by: include: Data acquisition module: The data acquisition module is used to obtain the distribution network data of the distribution network, and the distribution network data includes the data of each node of the energy storage device; The data of each node of the energy storage device includes the maximum capacity, maximum power, and maximum charge and discharge state quantity of the energy storage device; Data processing module: The data processing module establishes a distributed battery energy storage system optimization configuration model based on the data of each node of the energy storage device. The distributed battery energy storage system optimization configuration model includes all the placement locations of the energy storage device in the distribution network; Data analysis module: The data analysis module selects a suitable energy storage device location based on all placement locations in the distribution network; The energy storage investment cost and operating cost values ​​in all placement locations are calculated, and the most suitable energy storage equipment construction location is selected from the energy storage investment cost and operating cost values ​​in all placement locations.

2. A multi-objective shared operation optimization system for distributed energy storage in a distribution network according to claim 1, characterized in that: Based on the values ​​of energy storage investment cost and operating cost in all placement locations, the most suitable energy storage equipment construction location is selected from the values ​​of energy storage investment cost and operating cost in all placement locations, including: The energy storage location and capacity of the energy storage device are obtained through the data acquisition module and used as the initial population, and a population set M = {(a1, b1), (a2, b2), ..., (a n , b n )}; The energy storage equipment's energy storage locations include multiple locations on the user side, distributed power supply side, and distribution side; Where M is the initial population, a i is the i-th energy storage location of the energy storage device, b i is the energy storage capacity of the corresponding energy storage device; Establish the upper-level planning model and lower-level simulation model of the distribution network; The upper-level planning model of the distribution network is used to determine the location of the energy storage device connected to the energy storage system and transmit it to the lower-level simulation model of the distribution network; Calculate the cost of the energy storage equipment and K for each energy storage location i , and based on the cost of energy storage equipment at all energy storage locations and K i The numerical comparison is performed in which the cost and K i The minimum value is the most suitable location for the energy storage equipment construction; The distribution network lower layer simulation model determines the optimal capacity of the energy storage device based on the location of the energy storage device connected to the energy storage system.

3. A multi-objective shared operation optimization system for distributed energy storage in a distribution network according to claim 2, characterized in that: Based on the cost and K i The minimum value is the most suitable location for the energy storage equipment construction location, which includes the following steps: S1. Obtain the total investment cost C required to build energy storage equipment at each energy storage location in (a), and the operating cost C of the energy storage equipment at each energy storage location op (b); S2. The total investment cost C required to build the energy storage equipment at each energy storage location in (a) The operating cost of the energy storage equipment at each energy storage location C op (b) Perform addition calculation to obtain the cost K of the current position i ; S3, repeat step S2, cost K i Calculate and convert the cost K of each energy storage location i Calculate and combine the cost set K = {K1, K2, ...K n }, where the minimum value K in the cost set i That is the optimal construction location a of the corresponding energy storage equipment i .

4. A multi-objective shared operation optimization system for distributed energy storage in a distribution network according to claim 3, characterized in that: Determining the optimal capacity of the energy storage device based on the lower layer simulation model of the distribution network includes: Based on the optimal construction position a i After determining, and the current best construction location a i The required cost K i , and calculate the current position cost function K i (b), where b is the energy storage capacity of the energy storage device at the current location; Then calculate K i (b) The derivative ΔK with respect to b i (b); Select an initial energy storage capacity b0 as the starting point and set the growth rate μ; b i+11 =b i -μ*ΔK i (b); When ΔH(b i ) When the current derivative is close to zero, it indicates that the current b i+1 Energy storage is the optimal capacity.

5. A multi-objective shared operation optimization system for distributed energy storage in a distribution network according to claim 4, characterized in that: Based on the current optimal construction position a i The required cost K i include; The electricity cost F1 of the energy storage equipment at the current best construction location, the discharge income F2 of the energy storage equipment, and the maintenance cost of the energy storage equipment; The current best construction location is a i The cost function K i (b) The calculation method is as follows: Where: γ t is the electricity price at time t, is the discharge price at time t; P1 t (b) is the charging power at time t, P2 t (b) is the discharge power at time t; is the operating cost C of the energy storage equipment at one of the energy storage locations op (b); C in (a) The total investment cost required to build the energy storage equipment; K i (b) The derivative ΔK with respect to b i (b) The calculation method is as follows: γ t is the electricity price at time t, is the discharge price at time t; P1 t (b) is the charging power at time t, P2 t (b) is the discharge power at time t.

6. A multi-objective shared operation optimization system for distributed energy storage in a distribution network according to claim 2, characterized in that: The multi-objective shared operation optimization system further includes an adjustment unit, which is used to adjust the power demand and operation conditions of multiple service objects; The multiple service objects provided by the energy storage device are marked as different participants, and the total supply provided by all participants and the total demand of all participants are obtained through the data acquisition module. The total supply and the total demand of all participants are calculated to obtain the supply-demand ratio ST, and the supply-demand ratio is used to adjust the working mode of the current energy storage device.

7. A multi-objective shared operation optimization system for distributed energy storage in a distribution network according to claim 6, characterized in that: The working methods of current energy storage devices based on regulating the supply-demand ratio include: If the supply-demand ratio ST is less than 1, it means that the supply is less than the demand, that is, there is a situation where supply exceeds demand, and the charging capacity of the corresponding energy storage device needs to be increased; If the supply-demand ratio ST is greater than 1, it means that the supply is greater than the demand, that is, there is a situation where supply exceeds demand, and the corresponding energy storage device can increase discharge; If the supply-demand ratio ST is equal to 1, it means that the supply is equal to the demand, reaching a supply-demand balance.

8. A multi-objective shared operation optimization system for distributed energy storage in a distribution network according to claim 7, characterized in that: The energy storage device is also used to control charging and discharging according to the state of charge St of the device; If the current state of charge St of the energy storage device is less than or equal to 50%, and the supply-demand ratio ST is less than 1, the corresponding energy storage device discharges at a lower power; If the current state of charge St of the energy storage device is greater than fifty percent, and ST is greater than 1, the corresponding energy storage device is charged at a higher power.

9. A multi-objective shared operation optimization system for distributed energy storage in a distribution network according to claim 8, characterized in that: The energy storage device is also used to control its own charging and discharging process, specifically: Set the charging and discharging range of the energy storage device, with the lowest state of charge St being 20% ​​and the highest state of charge St being 80%; If the current state of charge St of the energy storage device is less than or equal to 20%, the discharge is stopped; If the current state of charge St of the energy storage device is greater than or equal to 80%, charging is stopped.

10. A multi-objective shared operation optimization system for distributed energy storage in a distribution network according to claim 9, characterized in that: The state of charge St of the energy storage device is calculated by the ratio of the current power of the energy storage device and the total capacity of the energy storage device.

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