Cooperative configuration method and device for distributed energy storage system and electric vehicle charging station

Abstract

The embodiment of the invention provides a cooperative configuration method and device for a distributed energy storage system and an electric vehicle charging station, relates to the technical field of electric power, and can reduce the fluctuation risk of a power distribution network. The collaborative configuration method for the distributed energy storage system and the electric vehicle charging station comprises the following steps: determining a first-stage cost based on an obtained equipment investment cost parameter, wherein the first-stage cost is used for representing an investment cost before a planning stage; determining second-stage cost based on predetermined operation and maintenance cost, network loss cost and load loss cost; third-stage cost is determined based on the operation and maintenance cost, the network loss cost, the load loss cost and predetermined new energy power abandoning punishment cost; constructing a collaborative configuration model based on the first-stage cost, the second-stage cost, the third-stage cost and predetermined model constraints; and configuring the energy storage system and the electric vehicle charging station based on the collaborative configuration model.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method and device for collaboratively configuring a distributed energy storage system and an electric vehicle charging station. Background Art

[0002] As global climate change and energy crises become increasingly severe, various regions are actively promoting the transformation of energy structures, and the penetration rate of distributed renewable energy, such as photovoltaic and wind power, in distribution networks continues to rise. Compared with conventional centralized power generation models, these distributed renewable energy sources are inherently volatile and unstable, and are greatly affected by natural conditions such as weather, seasons, and time, resulting in unpredictable fluctuations in their output power. This volatility has brought a series of challenges to the planning, operation, and dispatching of distribution networks, seriously threatening the safety, economy, and absorption capacity of the power system.

[0003] In order to improve the absorption rate of new energy, distributed energy storage systems (DESS) and electric vehicle charging stations (EVCS) have been gradually introduced into distribution networks to balance the volatility of new energy and enhance the adaptability of the power grid. Distributed energy storage systems effectively reduce the volatility problem caused by the connection of new energy to the grid by releasing energy during peak electricity demand and storing excess new energy energy during periods of underestimated demand. At the same time, with the widespread popularity of electric vehicles, charging stations have become an important load unit in the distribution network, and the amount of electricity consumed during the charging process of electric vehicles has a significant impact on the load of the power grid. Therefore, how to effectively coordinate distributed energy storage systems and electric vehicle charging stations to improve the absorption rate of new energy is a key issue that needs to be solved urgently. Summary of the invention

[0004] To solve the above problems, the present application provides a method and device for collaborative configuration of a distributed energy storage system and an electric vehicle charging station.

[0005] In a first aspect, a method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station is provided, comprising:

[0006] Determine the first-stage cost based on the acquired equipment investment cost parameters. The first-stage cost is used to characterize the investment cost before the planning stage. The equipment investment cost parameters include the investment cost parameters of the line, substation, transformer, energy storage system and electric vehicle charging station;

[0007] The second-stage cost is determined based on the predetermined operation and maintenance cost, network loss cost and load loss cost. The second-stage cost represents the operation cost in the planning stage.

[0008] The third-stage cost is determined based on the operation and maintenance cost, network loss cost, load loss cost and the predetermined penalty cost for renewable energy power abandonment. The third-stage cost is used to characterize the operating cost after the planning stage.

[0009] Building a collaborative configuration model based on the first-stage cost, the second-stage cost, the third-stage cost and pre-determined model constraints;

[0010] Configure energy storage systems and electric vehicle charging stations based on a collaborative configuration model.

[0011] Furthermore, the steps for determining the operation and maintenance costs, network loss costs and load loss costs include:

[0012] Determine the operation and maintenance costs based on the acquired operation and maintenance parameters of the lines, transformers, energy storage systems and electric vehicle charging stations;

[0013] Determine the network loss cost based on the obtained transformer impedance amplitude, injection current, line impedance amplitude and current;

[0014] The load loss cost is determined based on the acquired load loss size of the load loss node.

[0015] Furthermore, the steps to determine the penalty cost of renewable energy curtailment include:

[0016] The penalty cost for abandoning renewable energy power is determined based on the predicted output and actual output of wind turbines and the predicted output and actual output of photovoltaic units.

[0017] Furthermore, the model constraints include: node voltage constraints, line capacity constraints, harmonic voltage distortion rate constraints, short-circuit current constraints, wind turbine injection power constraints, and photovoltaic injection power constraints;

[0018] The steps to determine model constraints include:

[0019] Determine the node voltage constraint based on the obtained voltage lower limit, voltage upper limit and voltage at the moment of the node in the distribution line;

[0020] Determine the line capacity constraint based on the acquired active power, reactive power and line capacity of the distribution line;

[0021] Determine the harmonic voltage distortion rate constraint based on the obtained maximum harmonic voltage distortion rate, the harmonic voltage amplitude of the typical spectrum, the harmonic voltage amplitude of the harmonic source and the fundamental voltage of the grid connection point;

[0022] Determine the short-circuit current constraint based on the acquired short-circuit current contributed by the source power source, the short-circuit current contributed by the DG, and the maximum short-circuit current;

[0023] Determine the wind turbine injection power constraint based on the acquired active power upper limit and reactive power upper limit of the single wind turbine;

[0024] The photovoltaic injection power constraint is determined based on the acquired active power upper limit of a single photovoltaic.

[0025] Furthermore, the model constraint also includes a power flow constraint, and the power flow constraint includes: an active power condition of a branch, a reactive power condition of a branch, and a voltage condition of the first node;

[0026] The steps to determine model constraints also include:

[0027] Determine the active power condition of the branch based on the acquired active power flowing from the first node to the second node, the load active power of the first node, the DG power of the first node, the energy storage charging power of the first node, and the energy storage discharging power of the first node, where the first node and the second node are nodes at both ends of the branch respectively;

[0028] Determine a reactive power condition of the branch based on the acquired reactive power flowing from the first node to the second node and the load reactive power of the first node;

[0029] The voltage condition of the first node is determined based on the acquired node voltage, resistance, reactance, active power and reactive power of the branch.

[0030] In a second aspect, the present application provides a coordinated configuration device for a distributed energy storage system and an electric vehicle charging station, comprising:

[0031] A first determination module is used to determine the first-stage cost based on the acquired equipment investment cost parameters, where the first-stage cost is used to characterize the investment cost before the planning stage, and the equipment investment cost parameters include investment cost parameters of lines, substations, transformers, energy storage systems, and electric vehicle charging stations;

[0032] A second determination module is used to determine the second stage cost based on the predetermined operation and maintenance cost, network loss cost and load loss cost, the second stage cost represents the operation cost in the planning stage;

[0033] The third determination module is used to determine the third-stage cost based on the operation and maintenance cost, network loss cost, load loss cost and the predetermined penalty cost of new energy power abandonment. The third-stage cost is used to characterize the operation cost after the planning stage;

[0034] A model building module, used for building a collaborative configuration model based on the first stage cost, the second stage cost, the third stage cost and the predetermined model constraints;

[0035] The configuration module configures the energy storage system and electric vehicle charging station based on the collaborative configuration model.

[0036] Furthermore, the steps for determining the operation and maintenance costs, network loss costs and load loss costs include:

[0037] Determine the operation and maintenance costs based on the acquired operation and maintenance parameters of the lines, transformers, energy storage systems and electric vehicle charging stations;

[0038] Determine the network loss cost based on the obtained transformer impedance amplitude, injection current, line impedance amplitude and current;

[0039] The load loss cost is determined based on the acquired load loss size of the load loss node.

[0040] Furthermore, the steps to determine the penalty cost of renewable energy curtailment include:

[0041] The penalty cost for abandoning renewable energy power is determined based on the predicted output and actual output of wind turbines and the predicted output and actual output of photovoltaic units.

[0042] Furthermore, the model constraints include: node voltage constraints, line capacity constraints, harmonic voltage distortion rate constraints, short-circuit current constraints, wind turbine injection power constraints, and photovoltaic injection power constraints;

[0043] The steps to determine model constraints include:

[0044] Determine the node voltage constraint based on the obtained voltage lower limit, voltage upper limit and voltage at the moment of the node in the distribution line;

[0045] Determine the line capacity constraint based on the acquired active power, reactive power and line capacity of the distribution line;

[0046] Determine the harmonic voltage distortion rate constraint based on the obtained maximum harmonic voltage distortion rate, the harmonic voltage amplitude of the typical spectrum, the harmonic voltage amplitude of the harmonic source and the fundamental voltage of the grid connection point;

[0047] Determine the short-circuit current constraint based on the acquired short-circuit current contributed by the source power source, the short-circuit current contributed by the DG, and the maximum short-circuit current;

[0048] Determine the wind turbine injection power constraint based on the acquired active power upper limit and reactive power upper limit of the single wind turbine;

[0049] The photovoltaic injection power constraint is determined based on the acquired active power upper limit of a single photovoltaic.

[0050] Furthermore, the model constraint also includes a power flow constraint, and the power flow constraint includes: an active power condition of a branch, a reactive power condition of a branch, and a voltage condition of the first node;

[0051] The steps to determine model constraints also include:

[0052] Determine the active power condition of the branch based on the acquired active power flowing from the first node to the second node, the load active power of the first node, the DG power of the first node, the energy storage charging power of the first node, and the energy storage discharging power of the first node, where the first node and the second node are nodes at both ends of the branch respectively;

[0053] Determine a reactive power condition of the branch based on the acquired reactive power flowing from the first node to the second node and the load reactive power of the first node;

[0054] The voltage condition of the first node is determined based on the acquired node voltage, resistance, reactance, active power and reactive power of the branch.

[0055] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for collaboratively configuring the distributed energy storage system and the electric vehicle charging station are implemented.

[0056] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station.

[0057] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned method for collaborative configuration of a distributed energy storage system and an electric vehicle charging station.

[0058] The technical solution provided in the embodiment of the present application constructs a collaborative configuration model through the costs of different stages and pre-determined model constraints, and configures the energy storage system and the electric vehicle charging station based on the collaborative configuration model, so that when configuring the energy storage system and the electric vehicle charging station, the overall cost can be reduced, the utilization efficiency of resources can be optimized, and the volatility risk of the distribution network can be reduced. And through the reasonable configuration and coordinated regulation of the energy storage system and the electric vehicle charging station, the fluctuating new energy power generation can be effectively absorbed, the utilization efficiency of new energy can be improved, and the phenomenon of wind and solar power abandonment can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate some embodiments of this specification or technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0060] Figure 1A schematic diagram of a flow chart of a method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station provided in an embodiment of the present application;

[0061] Figure 2 A schematic diagram of a flow chart of another method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station provided in an embodiment of the present application;

[0062] Figure 3 A schematic diagram of a flow chart of another method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station provided in an embodiment of the present application;

[0063] Figure 4 A schematic diagram of a flow chart of another method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station provided in an embodiment of the present application;

[0064] Figure 5 A schematic diagram of the structure of a coordinated configuration device of a distributed energy storage system and an electric vehicle charging station provided in an embodiment of the present application;

[0065] Figure 6 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in some embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on some embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0067] It should be noted that the terms "first", "second", etc. in the specification and claims of this document and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of this document described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment. It should be noted that the acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of relevant laws and regulations.

[0068] As described in the background technology, how to effectively coordinate distributed energy storage systems and electric vehicle charging stations is a technical problem that needs to be solved urgently. As a flexible regulating device, the distributed energy storage system can discharge during peak electricity demand and charge during low electricity demand, thereby balancing the volatility of new energy. However, in the relevant technology, the regulation capacity of the distribution network is usually improved by independently deploying energy storage systems. Although independent energy storage systems can achieve a certain degree of peak shaving and valley filling to reduce the impact of new energy fluctuations, due to the lack of coordinated scheduling with other load units (such as electric vehicle charging stations), the utilization efficiency of the energy storage system is often not high, and it is difficult to take into account both economy and absorption rate.

[0069] In addition, electric vehicle charging stations have developed rapidly in the process of electric vehicle popularization, and have gradually become an important load of the distribution network. In related technologies, charging stations usually perform load management separately, such as limiting charging power during peak load periods or implementing time-of-use electricity prices to reduce the impact on the power grid. However, this method cannot actively respond to fluctuations in the output of new energy. Therefore, when the output of new energy fluctuates greatly, it is impossible to effectively absorb excess new energy into electric vehicles.

[0070] In the related art, there are also configuration schemes that attempt to disperse the energy storage equipment and charging piles in different distribution network areas to cover a wider load area and electricity demand. Through this decentralized configuration method, the capacity and location of the energy storage and charging piles are allocated according to the load and charging demand of their respective areas. For example, energy storage systems are configured in areas with heavy loads to meet the peak-shaving and valley-filling needs of local loads, while charging stations are added in areas with high demand for electric vehicle charging. However, this configuration scheme lacks a global design and cannot effectively coordinate the configuration of energy storage systems and charging piles. Since it is difficult for dispersed energy storage systems and charging stations to form a interconnection effect, when there is an excess of new energy generation, the energy storage system and charging stations cannot fully absorb the excess new energy, affecting the utilization rate of distributed new energy.

[0071] From the above content, it can be seen that the related technology has at least the following disadvantages:

[0072] Insufficient coordination: Related technologies usually configure and manage energy storage systems and charging stations independently, lacking systematic coordination and scheduling methods, resulting in low resource utilization efficiency. For example, the charging demand of electric vehicles is not linked to the output of new energy in real time, resulting in the inability to absorb excess electricity when more new energy is generated.

[0073] Slow response speed: Existing independent scheduling or demand response control methods are unable to track the rapid fluctuations in renewable energy output in real time, resulting in wind and solar power abandonment when renewable energy fluctuates greatly, making it difficult to achieve efficient utilization of distributed renewable energy.

[0074] Poor economic efficiency: The independent configuration of distributed energy storage systems and charging stations increases the investment cost of the system, and also brings additional operation and maintenance costs, making it difficult to find a balance between economy and new energy absorption capacity.

[0075] Based on this, the present application provides a method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station, such as Figure 1 As shown, it is a flow chart of a method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station provided in an embodiment of the present application, comprising the following steps:

[0076] S101. Determine the first phase cost based on the acquired equipment investment cost parameters.

[0077] Among them, the first-stage cost is used to characterize the investment cost before the planning stage, and the equipment investment cost parameters include the investment cost parameters of lines, substations, transformers, energy storage systems and electric vehicle charging stations.

[0078] S102: Determine the second phase cost based on the predetermined operation and maintenance cost, network loss cost and load loss cost.

[0079] Among them, the second stage cost represents the operating cost in the planning stage.

[0080] S103. Determine the cost of the third phase based on the operation and maintenance cost, network loss cost, load loss cost and the predetermined penalty cost for abandoning power from new energy sources.

[0081] Among them, the third stage cost is used to characterize the operating cost after the planning stage.

[0082] S104: Construct a collaborative configuration model based on the first-stage cost, the second-stage cost, the third-stage cost and pre-determined model constraints.

[0083] S105. Configure the energy storage system and the electric vehicle charging station based on the collaborative configuration model.

[0084] The technical solution provided in the embodiment of the present application constructs a collaborative configuration model through the costs of different stages and pre-determined model constraints, and configures the energy storage system and the electric vehicle charging station based on the collaborative configuration model, so that when configuring the energy storage system and the electric vehicle charging station, the overall cost can be reduced, the utilization efficiency of resources can be optimized, and the volatility risk of the distribution network can be reduced. And through the reasonable configuration and coordinated regulation of the energy storage system and the electric vehicle charging station, the fluctuating new energy power generation can be effectively absorbed, the utilization efficiency of new energy can be improved, and the phenomenon of wind and solar power abandonment can be reduced.

[0085] The following is an explanation of each of the above steps:

[0086] S101. Determine the first phase cost based on the acquired equipment investment cost parameters.

[0087] Among them, the first-stage cost is used to characterize the investment cost before the planning stage, and the equipment investment cost parameters include the investment cost parameters of lines, substations, transformers, energy storage systems and electric vehicle charging stations.

[0088] Specifically, the investment cost parameters of the line include: the optional type set of the line, the line node set, the capital recovery rate of the line, the investment cost coefficient of the line, the line length, and the line investment variable. The investment parameters of the substation include: the capital recovery rate of the substation, the substation node set, the investment cost coefficient of the substation, and the investment variable of the substation. The investment cost parameters of the transformer include: the capital recovery rate of the transformer, the optional type set of the transformer, the investment cost coefficient of the transformer, and the investment variable of the transformer. The investment cost parameters of the energy storage system include: the capital recovery rate of the energy storage system, the fixed cost of the energy storage system, the investment cost coefficient of the energy storage system, the fixed power of the energy storage system, and the investment variable of the energy storage system. The investment cost parameters of the electric vehicle charging station include: the capital recovery rate of the electric vehicle charging station, the fixed cost of the electric vehicle charging station, the investment cost coefficient of the electric vehicle charging station, the maximum load of the electric vehicle charging station, and the investment variable of the electric vehicle charging station.

[0089] It should be noted that the investment variables of the above-mentioned equipment (lines, substations, transformers, energy storage systems and electric vehicle charging stations) are 0-1 variables. For example, if the investment variable of the line is 0, it means that the corresponding line will not be built, and if the investment variable of the line is 1, it means that the corresponding line will be built.

[0090] In some embodiments, the first phase cost is determined according to the following formula:

[0091]

[0092] In the above formula, represents the first-stage cost, l represents the line classification, {NRF,NAF} represents the line classification set, (s,r) represents the system node, k represents the optional type, K l Indicates the optional type set of the line, Y l Represents the set of line nodes, RR l represents the capital recovery rate of the line, represents the investment cost coefficient of the line, l sr Indicates the line length, represents the line investment variable, RR SS represents the capital recovery rate of the substation, Ω SS represents the substation node set, represents the investment cost coefficient of the substation, represents the investment variable of the substation, RR NT Indicates the capital recovery rate of the transformer, K NT Indicates the optional type of transformer, represents the investment cost coefficient of the transformer, represents the transformer investment variable, RR ES represents the capital recovery rate of the energy storage system, represents the fixed cost of the energy storage system, represents the investment cost coefficient of the energy storage system, pf represents the power factor of the system operation, represents the fixed power of the energy storage system, Represents the investment variable of the energy storage system, RR EV represents the capital recovery rate of the electric vehicle charging station, represents the fixed cost of the electric vehicle charging station, represents the investment cost coefficient of electric vehicle charging stations, represents the maximum load of the electric vehicle charging station, Variable representing the investment in electric vehicle charging stations.

[0093] S102: Determine the second phase cost based on the predetermined operation and maintenance cost, network loss cost and load loss cost.

[0094] Among them, the second stage cost represents the operating cost in the planning stage.

[0095] In some embodiments, the second phase cost is the sum of operation and maintenance cost, network loss cost and load loss cost.

[0096] Specifically, the second stage cost can be characterized according to the following formula:

[0097]

[0098] in, represents the operation and maintenance cost of the tth stage, The network loss cost in stage t, represents the load loss cost in stage t.

[0099] S103. Determine the cost of the third phase based on the operation and maintenance cost, network loss cost, load loss cost and the predetermined penalty cost for abandoning power from new energy sources.

[0100] Among them, the third stage cost is used to characterize the operating cost after the planning stage.

[0101] In some embodiments, the third stage cost is the sum of operation and maintenance cost, network loss cost, load loss cost and renewable energy power abandonment penalty cost.

[0102] Specifically, the third stage cost is characterized by the following formula:

[0103]

[0104] in, Indicates n T The operation and maintenance cost of the stage, n T It belongs to the time stage in the third stage, which is the stage after the planning stage. Indicates n T The network loss cost at this stage, Indicates n T The load loss cost of the stage, Indicates n T The penalty cost for abandoning new energy power in this stage.

[0105] It should be noted that although both the second-stage cost and the third-stage cost include operation and maintenance cost, operating cost, and network loss cost, the second-stage cost and the third-stage cost belong to different stages of costs. For example, the operation and maintenance cost in the second-stage cost is the operation and maintenance cost in the planning stage, while the operation and maintenance cost in the third stage is the operation and maintenance cost generated by the continued operation of the system after the planning stage. The calculation methods of the two are the same, and only the stages involved are different. Therefore, after obtaining the operation and maintenance cost in the second-stage cost, by modifying the corresponding stage rest, the operation and maintenance cost in the third-stage cost can also be directly generated based on the operation and maintenance cost in the second-stage cost. Network loss cost and load loss cost can also be determined by similar methods.

[0106] S104: Construct a collaborative configuration model based on the first-stage cost, the second-stage cost, the third-stage cost and pre-determined model constraints.

[0107] In some embodiments, the collaborative configuration model is constructed based on the first stage cost, the cost coefficient of the first stage, the second stage cost, the cost coefficient of the second stage, the third stage cost, and the cost coefficient of the third stage.

[0108] Specifically, the collaborative configuration model is characterized by the following formula:

[0109]

[0110] Among them, c TPV represents the present value of the total cost (the output of the collaborative configuration model), The cost coefficient representing the first stage cost, represents the first stage cost, (1+i) -t The cost coefficient representing the cost of the second stage, represents the operation and maintenance cost of the tth stage, The network loss cost in stage t, represents the load loss cost in stage t, The cost coefficient representing the cost of the third stage, Indicates n T The operation and maintenance costs of each stage, Indicates n T The network loss cost at this stage, Indicates n T The load loss cost of the stage, Indicates n T The penalty cost for abandoning new energy power in this stage.

[0111] It should be noted that the collaborative configuration model is constructed for the purpose of minimizing the present value of the total cost. The collaborative configuration model belongs to the MILP model and can be solved using a solver on the solving platform by calling the corresponding function.

[0112] S105. Configure the energy storage system and the electric vehicle charging station based on the collaborative configuration model.

[0113] In some embodiments, the first-stage cost, the cost coefficient of the first-stage cost, the second-stage cost, the cost coefficient of the second-stage cost, the third-stage cost, and the cost coefficient of the third-stage cost related to the energy storage system to be configured and the electric vehicle charging station are input into the collaborative configuration model to obtain the minimum total cost of configuring the energy storage system and the electric vehicle charging station.

[0114] Furthermore, after obtaining the minimum total cost, a specific configuration scheme of the energy storage system and the electric vehicle charging station is determined according to the minimum total cost. The configuration scheme includes the capacity, location, and number of the energy storage system and the number and location of the electric vehicle charging stations.

[0115] In some embodiments, Figure 2 As shown in Figure 1, the steps to determine the operation and maintenance costs, network loss costs, and load loss costs include:

[0116] S201. Determine the operation and maintenance cost based on the acquired operation and maintenance parameters of the lines, transformers, energy storage systems, and electric vehicle charging stations.

[0117] Among them, the operation and maintenance parameters of the line include: the classification of the line, the optional type set of the line, the line node set, the operation and maintenance cost parameters of the line, and the line variables between nodes. The operation and maintenance parameters of the transformer include: the classification of the transformer, the classification set of the transformer, the operation and maintenance cost coefficient of the transformer, and the usage variable of the transformer. The operation and maintenance parameters of the energy storage system include: the operation and maintenance cost coefficient of the energy storage system, and the usage variable of the energy storage system. The operation and maintenance parameters of the electric vehicle charging station include: the optional type set of the electric vehicle charging station, the set of nodes to be built for the electric vehicle charging station, the operation and maintenance cost of the electric vehicle charging station, and the usage variable of the electric vehicle charging station. The line variables between nodes are also called line usage variables, including the usage variables of the line between nodes r and s and the line usage variables between nodes s and r, which are 0-1 variables. For example, when the usage variable of the line between nodes r and s is 0, it indicates that the corresponding line is used, and when it is 1, it indicates that the corresponding line is not used.

[0118] Specifically, the operation and maintenance cost is determined according to the following formula:

[0119]

[0120] In the above formula, represents the operation and maintenance cost, t represents the planning stage, l represents the line classification, L represents the line classification set, (s, r) represents the system node, k represents the optional type, K l Indicates the optional type set of the line, Y l represents the set of line nodes, represents the operation and maintenance cost coefficient of the line, represents the line variable between nodes s and r, represents the line variable between nodes r and s, tr represents the classification of transformers, TR represents the classification set of transformers, Transformer operation and maintenance cost coefficient, Indicates the variables used by the transformer, The operation and maintenance cost coefficient of the energy storage system, Represents the usage variable of the energy storage system, K EV An optional set of types representing electric vehicle charging stations, Ω EV represents the set of nodes to be built for electric vehicle charging stations, The operation and maintenance costs of electric vehicle charging stations, A variable representing the usage of electric vehicle charging stations.

[0121] S202: Determine the network loss cost based on the acquired impedance amplitude of the transformer, the injected current, the impedance amplitude of the line, and the current.

[0122] Specifically, the network loss fee is determined according to the following formula:

[0123]

[0124] In the above formula, represents the network loss cost, b represents the load level, B represents the set of load levels, pf represents the power factor of the system operation, represents the cost coefficient of power supply from the substation, TR represents the classification set of transformers, TR={ET,NT}, K tr An optional set of types representing transformers, Ω SS represents the substation node set, represents the impedance amplitude of the transformer, represents the injected current of the transformer, It represents the impedance amplitude per unit line length (also called the impedance amplitude of the line). represents the line current from node s to r (also called line current), represents the line current from node r to s, and All are continuous variables greater than zero.

[0125] S203: Determine the load loss cost based on the acquired load loss size of the load loss node.

[0126] Specifically, the load loss cost is determined according to the following formula:

[0127]

[0128] represents the load loss cost, Ω LN represents the load node set, C U represents the cost coefficient of system load loss, Indicates the load loss size of the node.

[0129] In some embodiments, the step of determining the penalty cost for abandoning renewable energy power includes: determining the penalty cost for abandoning renewable energy power based on the acquired predicted output and actual output of the wind turbine and the predicted output and actual output of the photovoltaic unit.

[0130] Specifically, the penalty cost for abandoning new energy power is determined according to the following formula:

[0131]

[0132] in, represents the penalty cost of renewable energy power abandonment, P WTG,pe represents the predicted wind power output, P WTG Indicates the actual output of wind power, P PVG,pre Represents the predicted photovoltaic output, P PVG Indicates the actual photovoltaic output.

[0133] It should be noted that in order to ensure the safe operation of the distribution network, the collaborative configuration model needs to meet certain constraints. In some embodiments, the model constraints include: node voltage constraints, line capacity constraints, harmonic voltage distortion rate constraints, short-circuit current constraints, wind turbine injection power constraints, and photovoltaic injection power constraints; Figure 3 As shown, the steps to determine the model constraints include:

[0134] S301. Determine a node voltage constraint based on the acquired voltage lower limit, voltage upper limit and moment voltage of a node in a distribution line.

[0135] Specifically, the node voltage constraint is characterized by the following formula:

[0136] V i,min ≤V s,i,t ≤V i,max

[0137] Among them, V i,min Represents the lower voltage limit of node i in the distribution line for safe operation, V i,max Represents the upper voltage limit for safe operation of node i in the distribution line, V s,i,t Represents the voltage of distribution line node i at time t in scenario s.

[0138] S302: Determine a line capacity constraint based on the acquired active power, reactive power and line capacity of the distribution line.

[0139] Specifically, the line capacity constraint is characterized by the following formula:

[0140]

[0141] Among them, P s,ij,t represents the active power flowing through the ij branch of the distribution line at time t under scenario s, Q s,ij,t represents the reactive power flowing through the ij branch of the distribution line at time t under scenario s, S ij Represents the line capacity of the ij branch of the distribution line.

[0142] S303: Determine a harmonic voltage distortion rate constraint based on the acquired maximum harmonic voltage distortion rate, the harmonic voltage amplitude of the typical spectrum, the harmonic voltage amplitude of the harmonic source, and the fundamental voltage of the grid connection point.

[0143] Specifically, the harmonic voltage distortion rate constraint is characterized by the following formula:

[0144] THD V ≤THD MAX

[0145]

[0146] Among them, THD V Indicates harmonic voltage distortion rate, THD MAX Indicates the maximum harmonic voltage distortion rate, which represents the maximum harmonic voltage distortion rate allowed to ensure network safety operation, U h Indicates the harmonic voltage amplitude of the hth harmonic source, U 1 Indicates the fundamental voltage at the grid connection point, U h-specturm Indicates the harmonic voltage amplitude of the typical spectrum, U 1-spectrum Represents the frequency spectrum of the fundamental voltage.

[0147] S304: Determine a short-circuit current constraint based on the acquired short-circuit current contributed by the source power source, the short-circuit current contributed by the DG, and the maximum short-circuit current.

[0148] Specifically, the short-circuit current constraint is characterized by the following formula:

[0149]

[0150] in, represents the short-circuit current contributed by the source power supply, Represents the short-circuit current contributed by distributed generation (DG), I sc,max Indicates the maximum short-circuit current, which represents the maximum short-circuit current allowed to ensure safe operation of the network.

[0151] S305 . Determine a wind turbine injection power constraint based on the acquired active power upper limit and reactive power upper limit of the single wind turbine.

[0152] Specifically, the wind turbine injection power constraint is determined according to the following formula:

[0153]

[0154] in, Indicates the upper limit of active power of a single wind turbine. Indicates the upper limit of reactive power of a single wind turbine. Indicates the number of fans in the system. Represents the active power injected into the system by the fan, Ω MTG Represents a collection of wind turbine nodes.

[0155] S306: Determine a photovoltaic injection power constraint based on the acquired active power upper limit of a single photovoltaic.

[0156] Specifically, the photovoltaic injection power constraint is characterized by the following formula:

[0157]

[0158] in, Indicates the upper limit of active power of a single PV. Represents the active power of the photovoltaic injection system, Ω PVG Represents a collection of photovoltaic nodes.

[0159] In some embodiments, the model constraint also includes a power flow constraint, and the power flow constraint includes: an active power condition of a branch, a reactive power condition of a branch, and a voltage condition of the first node; Figure 4 As shown, the step of determining the model constraints also includes:

[0160] S401. Determine the active power condition of the branch based on the acquired active power flowing from the first node to the second node, the load active power of the first node, the DG power of the first node, the energy storage charging power of the first node, and the energy storage discharging power of the first node.

[0161] The first node and the second node are nodes at both ends of the branch, respectively. Exemplarily, the power distribution circuit includes multiple branches, and taking branch jk as an example, the nodes at both ends of the branch are the first node j and the second node k.

[0162] Specifically, the active power condition of the branch is characterized by the following formula:

[0163]

[0164] In the above formula, P s,ij,t represents the active power flowing through the branch ij of the distribution line at time t under scenario s, P s,jk,t represents the active power flowing from node j to node k on the distribution line at time t in scenario s (also called the active power flowing from the first node j to the second node k), represents the load active power of node j at time t in the distribution line under scenario s (also called the load active power of the first node j), represents the DG power of node j at time t in the distribution line under scenario s (also called the DG power of the first node j), represents the energy storage charging power of node j at time t in the distribution line under scenario s (also called the energy storage charging power of the first node j), represents the energy storage discharge power of node j at time t in the distribution line under scenario s (also called the energy storage discharge power of the first node j).

[0165] S402: Determine a reactive power condition of a branch based on the acquired reactive power flowing from the first node to the second node and the load reactive power of the first node.

[0166] Specifically, the reactive power condition of the branch is characterized by the following formula:

[0167]

[0168] Among them, Q s,ij,t represents the reactive power flowing from node j to node k on the distribution line at time t in scenario s (also known as the reactive power flowing from the first node j to the second node k), Q s,jk,t represents the load reactive power of node j at time t in the distribution line under scenario s (also called the load reactive power of the first node j).

[0169] S403: Determine a voltage condition of the first node based on the acquired node voltage, resistance, reactance, active power, and reactive power of the branch.

[0170] Specifically, the voltage condition of the first node is characterized by the following formula:

[0171] V s,j,t =V s,i,t -(r ij P s,ij,t +x ij Q s,ij,t )

[0172] Among them, V s,j,t represents the voltage of the first node j, V s,i,t represents the voltage at node i in the distribution line, r ij represents the resistance of branch ij, P s,ij,t represents the active power of branch ij, x ij Represents the reactance of the branch, Q s,ij,t Represents the reactive power of branch ij.

[0173] The technical solution provided in the embodiment of the present application constructs a collaborative configuration model through the costs of different stages and pre-determined model constraints, and configures the energy storage system and the electric vehicle charging station based on the collaborative configuration model, so that when configuring the energy storage system and the electric vehicle charging station, the overall cost can be reduced, the utilization efficiency of resources can be optimized, and the volatility risk of the distribution network can be reduced. And through the reasonable configuration and coordinated regulation of the energy storage system and the electric vehicle charging station, the fluctuating new energy power generation can be effectively absorbed, the utilization efficiency of new energy can be improved, and the phenomenon of wind and solar power abandonment can be reduced.

[0174] It should be noted that the information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0175] It should be noted that the technical solution provided in this application provides users with corresponding operation entrances for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0176] Figure 5 A distributed energy storage system and an electric vehicle charging station collaborative configuration device provided in an embodiment of the present application is used to execute the above-mentioned distributed energy storage system and electric vehicle charging station collaborative configuration method, such as Figure 5 As shown, the coordinated configuration device of the distributed energy storage system and the electric vehicle charging station includes: a first determination module 501, a second determination module 502, a third determination module 503, a model building module 504 and a configuration module 505.

[0177] The first determination module 501 determines the first stage cost based on the acquired equipment investment cost parameters, where the first stage cost is used to characterize the investment cost before the planning stage, and the equipment investment cost parameters include investment cost parameters of lines, substations, transformers, energy storage systems, and electric vehicle charging stations;

[0178] A second determination module 502 is used to determine the second stage cost based on the predetermined operation and maintenance cost, network loss cost and load loss cost, the second stage cost represents the operation cost in the planning stage;

[0179] The third determination module 503 is used to determine the third stage cost based on the operation and maintenance cost, the network loss cost, the load loss cost and the predetermined penalty cost of the new energy power abandonment, and the third stage cost is used to represent the operation cost after the planning stage;

[0180] A model building module 504, for building a collaborative configuration model based on the first stage cost, the second stage cost, the third stage cost and the predetermined model constraints;

[0181] The configuration module 505 configures the energy storage system and the electric vehicle charging station based on the collaborative configuration model.

[0182] Furthermore, the steps for determining the operation and maintenance costs, network loss costs and load loss costs include:

[0183] Determine the operation and maintenance costs based on the acquired operation and maintenance parameters of the lines, transformers, energy storage systems and electric vehicle charging stations;

[0184] Determine the network loss cost based on the obtained transformer impedance amplitude, injection current, line impedance amplitude and current;

[0185] The load loss cost is determined based on the acquired load loss size of the load loss node.

[0186] Furthermore, the steps to determine the penalty cost of renewable energy curtailment include:

[0187] The penalty cost for abandoning renewable energy power is determined based on the predicted output and actual output of wind turbines and the predicted output and actual output of photovoltaic units.

[0188] Furthermore, the model constraints include: node voltage constraints, line capacity constraints, harmonic voltage distortion rate constraints, short-circuit current constraints, wind turbine injection power constraints, and photovoltaic injection power constraints;

[0189] The steps to determine model constraints include:

[0190] Determine the node voltage constraint based on the obtained voltage lower limit, voltage upper limit and voltage at the moment of the node in the distribution line;

[0191] Determine the line capacity constraint based on the acquired active power, reactive power and line capacity of the distribution line;

[0192] Determine the harmonic voltage distortion rate constraint based on the obtained maximum harmonic voltage distortion rate, the harmonic voltage amplitude of the typical spectrum, the harmonic voltage amplitude of the harmonic source and the fundamental voltage of the grid connection point;

[0193] Determine the short-circuit current constraint based on the acquired short-circuit current contributed by the source power source, the short-circuit current contributed by the DG, and the maximum short-circuit current;

[0194] Determine the wind turbine injection power constraint based on the acquired active power upper limit and reactive power upper limit of the single wind turbine;

[0195] The photovoltaic injection power constraint is determined based on the acquired active power upper limit of a single photovoltaic.

[0196] Furthermore, the model constraint also includes a power flow constraint, and the power flow constraint includes: an active power condition of a branch, a reactive power condition of a branch, and a voltage condition of the first node;

[0197] The steps to determine model constraints also include:

[0198] Determine the active power condition of the branch based on the acquired active power flowing from the first node to the second node, the load active power of the first node, the DG power of the first node, the energy storage charging power of the first node, and the energy storage discharging power of the first node, where the first node and the second node are nodes at both ends of the branch respectively;

[0199] Determine a reactive power condition of the branch based on the acquired reactive power flowing from the first node to the second node and the load reactive power of the first node;

[0200] The voltage condition of the first node is determined based on the acquired node voltage, resistance, reactance, active power and reactive power of the branch.

[0201] The technical solution provided in the embodiment of the present application constructs a collaborative configuration model through the costs of different stages and pre-determined model constraints, and configures the energy storage system and the electric vehicle charging station based on the collaborative configuration model, so that when configuring the energy storage system and the electric vehicle charging station, the overall cost can be reduced, the utilization efficiency of resources can be optimized, and the volatility risk of the distribution network can be reduced. And through the reasonable configuration and coordinated regulation of the energy storage system and the electric vehicle charging station, the fluctuating new energy power generation can be effectively absorbed, the utilization efficiency of new energy can be improved, and the phenomenon of wind and solar power abandonment can be reduced.

[0202] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer device, and specifically, the computer device may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0203] An embodiment of the present invention provides a computer device, including a memory and a processor, the memory is used to store information including program instructions, the processor is used to control the execution of the program instructions, and when the program instructions are loaded and executed by the processor, the steps of the embodiment of the above-mentioned distributed energy storage system and electric vehicle charging station collaborative configuration method are implemented. For a specific description, please refer to the embodiment of the above-mentioned distributed energy storage system and electric vehicle charging station collaborative configuration method.

[0204] Reference below Figure 6 , which shows a schematic diagram of the structure of a computer device 600 suitable for implementing an embodiment of the present application.

[0205] like Figure 6 As shown, the computer device 600 includes a central processing unit (CPU) 601, which can perform various appropriate operations and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the computer device 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0206] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal feedback device (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed as needed as the storage section 608.

[0207] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program tangibly contained on a machine-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 609, and / or installed from the removable medium 611.

[0208] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0209] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0210] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0211] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0212] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0213] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0214] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0215] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0216] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0217] The present application may 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 may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0218] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0219] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station, characterized in that: include: Determine the first stage cost based on the acquired equipment investment cost parameters, where the first stage cost is used to characterize the investment cost before the planning stage, and the equipment investment cost parameters include investment cost parameters for lines, substations, transformers, energy storage systems, and electric vehicle charging stations; Determine the second stage cost based on the predetermined operation and maintenance cost, network loss cost and load loss cost, the second stage cost represents the operation cost in the planning stage; Determine the third-stage cost based on the operation and maintenance cost, the network loss cost, the load loss cost and the predetermined penalty cost for abandoning power from new energy sources, wherein the third-stage cost is used to characterize the operation cost after the planning stage; Building a collaborative configuration model based on the first-stage cost, the second-stage cost, the third-stage cost and predetermined model constraints; The energy storage system and the electric vehicle charging station are configured based on the collaborative configuration model.

2. The method according to claim 1, characterized in that: The steps of determining the operation and maintenance cost, the network loss cost and the load loss cost include: Determining the operation and maintenance cost based on the acquired operation and maintenance parameters of the line, the transformer, the energy storage system, and the electric vehicle charging station; Determining the network loss fee based on the acquired impedance amplitude of the transformer, the injected current, the impedance amplitude and the current of the line; The load loss cost is determined based on the acquired load loss size of the load loss node.

3. The method according to claim 1, characterized in that The steps to determine the penalty cost of renewable energy curtailment include: The penalty cost for abandoning electricity of renewable energy is determined based on the predicted output and actual output of the wind turbine generator set and the predicted output and actual output of the photovoltaic unit.

4. The method according to claim 1, characterized in that: The model constraints include: node voltage constraints, line capacity constraints, harmonic voltage distortion rate constraints, short-circuit current constraints, wind turbine injection power constraints and photovoltaic injection power constraints; The step of determining the model constraints comprises: Determine the node voltage constraint based on the obtained voltage lower limit, voltage upper limit and moment voltage of the node in the distribution line; Determining a line capacity constraint based on the acquired active power, reactive power and line capacity of the distribution line; Determining the harmonic voltage distortion rate constraint based on the acquired maximum harmonic voltage distortion rate, the harmonic voltage amplitude of the typical spectrum, the harmonic voltage amplitude of the harmonic source and the fundamental voltage of the grid connection point; Determine the short-circuit current constraint based on the acquired short-circuit current contributed by the source power source, the short-circuit current contributed by the DG, and the maximum short-circuit current; Determine the wind turbine injection power constraint based on the acquired active power upper limit and reactive power upper limit of the single wind turbine; The photovoltaic injection power constraint is determined based on the acquired active power upper limit of a single photovoltaic.

5. The method according to claim 4, characterized in that The model constraint also includes a power flow constraint, and the power flow constraint includes: an active power condition of a branch, a reactive power condition of a branch, and a voltage condition of a first node; The step of determining the model constraints further comprises: Determine the active power condition of the branch based on the acquired active power flowing from the first node to the second node, the load active power of the first node, the DG power of the first node, the energy storage charging power of the first node, and the energy storage discharging power of the first node, wherein the first node and the second node are nodes at both ends of the branch respectively; Determine the reactive power condition of the branch based on the acquired reactive power flowing from the first node to the second node and the load reactive power of the first node; The voltage condition of the first node is determined based on the acquired node voltage, resistance, reactance, active power and reactive power of the branch.

6. A coordinated configuration device for a distributed energy storage system and an electric vehicle charging station, characterized in that: include: A first determination module is used to determine the first stage cost based on the acquired equipment investment cost parameters, where the first stage cost is used to characterize the investment cost before the planning stage, and the equipment investment cost parameters include investment cost parameters of lines, substations, transformers, energy storage systems, and electric vehicle charging stations; A second determination module is used to determine the second stage cost based on the predetermined operation and maintenance cost, network loss cost and load loss cost, wherein the second stage cost represents the operation cost in the planning stage; A third determination module is used to determine the third stage cost based on the operation and maintenance cost, the network loss cost, the load loss cost and the predetermined penalty cost for power abandonment of new energy, wherein the third stage cost is used to characterize the operation cost after the planning stage; A model building module, used to build a collaborative configuration model based on the first-stage cost, the second-stage cost, the third-stage cost and a predetermined model constraint; A configuration module configures the energy storage system and the electric vehicle charging station based on the collaborative configuration model.

7. The device according to claim 6, characterized in that The steps of determining the operation and maintenance cost, the network loss cost and the load loss cost include: Determining the operation and maintenance cost based on the acquired operation and maintenance parameters of the line, the transformer, the energy storage system, and the electric vehicle charging station; Determining the network loss fee based on the acquired impedance amplitude of the transformer, the injected current, the impedance amplitude and the current of the line; The load loss cost is determined based on the acquired load loss size of the load loss node.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station as described in any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station as described in any one of claims 1 to 5 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the method for collaboratively configuring a distributed energy storage system and an electric vehicle charging station as described in any one of claims 1 to 5 are implemented.

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