A power transmission and distribution network coordinated energy storage system optimization planning method and device

Abstract

The disclosure provides a power transmission and distribution network coordinated energy storage system optimization planning method and device, belonging to the technical field of power transmission and distribution network and energy storage optimization planning. The method comprises the following steps: according to a preset typical daily operation scene set, a joint optimization model of a power transmission network and a power distribution network configured with battery energy storage is established, wherein the joint optimization model comprises a power distribution network planning submodel and a power transmission network planning submodel; a distributed optimization algorithm is used to solve the joint optimization model to obtain a planning scheme of the battery energy storage of the power transmission network and the power distribution network. The disclosure considers configuring battery energy storage in a hybrid power transmission and distribution network, which can promote large-scale new energy consumption in new energy-rich areas and reduce abandonment. Through the coordinated consideration of the power transmission and distribution network, the comprehensive operation efficiency of the wide-range power transmission and distribution network can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of power transmission and distribution network and energy storage optimization planning technology, and in particular to a method and apparatus for optimizing energy storage systems in coordination with power transmission and distribution networks. Background Technology

[0002] my country's new energy sector continues its rapid development, with a high proportion of new energy gradually penetrating power grids at all levels. However, the output of new energy sources is largely dependent on unpredictable and uncontrollable external weather conditions, resulting in significant fluctuations and intermittent nature. This poses a substantial challenge to the stability and security of the power system. In recent years, significant advancements have been made in energy storage technology and cost reduction. Large-scale chemical energy storage can effectively smooth out fluctuations in new energy output, enhance the grid's peak-shaving capacity, and resolve transmission channel congestion, enabling energy transfer in time and space and improving the stability and security of the power grid.

[0003] In recent years, with the large-scale integration of distributed power sources into distribution networks, traditional passive distribution networks have gradually transformed into new active distribution networks. The need for interaction between transmission and distribution networks has become increasingly apparent, with power flow between them showing a bidirectional trend and increasing coupling. Traditional independent planning of transmission and distribution networks struggles to coordinate resources and demands at different levels, fails to fully absorb new energy sources, and easily leads to unnecessary wind and solar power curtailment and grid congestion, resulting in increased grid operating costs. Therefore, it is necessary to consider optimized planning for coordinated transmission and distribution network operation.

[0004] In the coordinated optimization of transmission and distribution networks, there are significant differences in structure, parameters, and analysis methods. Furthermore, distribution networks are characterized by their large number, wide distribution, numerous nodes, and low scale. Therefore, centralized modeling of transmission and distribution networks is difficult, resulting in excessively large models and long computation times. Thus, coordinated optimization of transmission and distribution networks should employ distributed optimization algorithms. Distributed algorithms can analyze and model the transmission and distribution networks individually, while reducing model complexity, enabling parallel computation, and improving computational speed. As a distributed optimization algorithm, the cascaded analysis objective method allows stakeholders at each level to collaboratively seek optimization during the iterative process by setting penalty terms at each level, considering their autonomous operation characteristics. This ensures convergence for convex optimization problems.

[0005] Currently, the main issues in the coordinated planning of power transmission and distribution networks include coordinating power generation resources between the networks, absorbing renewable energy generation, and reducing the impact of renewable energy output fluctuations on the grid. However, none of these approaches have considered energy storage configuration while simultaneously optimizing the coordinated operation of the power transmission and distribution networks. Furthermore, power system optimization planning and operation that considers energy storage only considers the distribution or transmission network independently, without employing distributed algorithms for hierarchical optimization and scheduling of the coordinated power transmission and distribution network problem. Summary of the Invention

[0006] The purpose of this disclosure is to overcome the shortcomings of existing technologies and propose an optimization planning method and device for energy storage systems that are coordinated with power transmission and distribution networks. This disclosure considers configuring battery energy storage in hybrid power transmission and distribution networks, which can promote the large-scale consumption of renewable energy in areas rich in renewable energy and reduce waste. By considering the coordinated operation of power transmission and distribution networks, the overall operational efficiency of large-scale power transmission and distribution networks can be improved.

[0007] The first aspect of this disclosure proposes an optimization planning method for an energy storage system coordinated with power transmission and distribution networks, including:

[0008] Based on a set of preset typical daily operating scenarios, a joint optimization model is established for the transmission network and distribution network configured with battery energy storage. The joint optimization model includes a distribution network planning sub-model and a transmission network planning sub-model.

[0009] The joint optimization model is solved using a distributed optimization algorithm to obtain the planning scheme for battery energy storage in the transmission network and distribution network.

[0010] In one specific embodiment of this disclosure, the typical daily operation scenario set includes a typical output scenario set for new energy power plants and a typical load scenario set, wherein:

[0011] Historical output data of new energy power plants in the power transmission and distribution network are sampled daily. The K-means algorithm is used to cluster the daily historical output data of each new energy power plant to generate typical daily output scenarios for each new energy power plant. The typical daily output scenarios of all new energy power plants are combined into a set of typical output scenarios for new energy power plants.

[0012] Historical load data of the power transmission and distribution network is sampled daily, wherein the sampling period of the historical load data is consistent with the sampling period of the historical output data of the new energy power plants; the K-means algorithm is used to cluster the sampled daily historical load data to generate typical daily load scenarios, and all typical daily load scenarios are combined into a typical load scenario set.

[0013] In one specific embodiment of this disclosure, the distribution network planning sub-model includes:

[0014] 1) Objective function;

[0015]

[0016] In the formula, the subscript n is the distribution network serial number, F dis,n Ω represents the total investment and operating cost of the nth distribution network. S Let s represent a typical daily operating scenario set, s∈Ω S D s This represents the number of days in a year that a typical daily operating scenario 's' occurs; C inv,nThis represents the investment cost of the nth distribution network. This represents the grid connection cost of the nth renewable energy source in scenario s. This represents the cost of abandoning the nth renewable energy source in the distribution network under scenario s. This represents the penalty cost for shared variable errors between the transmission network and the nth distribution network in scenario s. This represents the cost of the nth distribution network purchasing electricity from the transmission network in scenario s. This represents the operation and maintenance cost of the nth distribution network energy storage in scenario s;

[0017] in,

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] In the formula, Ω ess G represents the set of all potential energy storage nodes. inv C represents the coefficient used to discount investment costs from present value to equivalent annual value during the planning period; 1,n,k C represents the infrastructure cost of energy storage at the k-th energy storage configuration node in the n-th distribution network. 2,n,k C represents the unit energy capacity cost of energy storage at the k-th energy storage configuration node in the n-th distribution network. 3,n,k This represents the unit power capacity cost of energy storage at the k-th energy storage configuration node in the n-th distribution network;

[0024] Ω Re This represents the set of nodes where new energy sources are connected to the distribution network, where i is the node number, i∈Ω. Re t is the sampling time point number, T is the total number of sampling periods for a typical day, and π s,n,t Let be the on-grid electricity price of new energy at the t-th sampling point in the n-th distribution network under scenario s. Let Δt be the active power of renewable energy fed into the grid at the t-th sampling point in the nth distribution network under scenario s, where Δt is the sampling period length; and β is the penalty coefficient for renewable energy abandonment. Let v be the renewable energy generation power of node i in the nth distribution network at the tth sampling point in scenario s; s,n,t and w s,n,tThese are the coefficient and weight values ​​of the shared variable penalty function at the t-th sampling point in the nth distribution network under scenario s, respectively. Let be the active power exchanged between the transmission and distribution networks on the transmission side of the nth distribution network in scenario s at the tth sampling point. Let be the active power exchanged between the transmission and distribution networks on the distribution network side at the t sampling point of the nth distribution network in scenario s; Let π be the marginal electricity of the nth distribution network at the tth sampling point in scenario s; c and π d These are the unit power operation and maintenance costs for energy storage charging and discharging, respectively.

[0025] 2) Constraints; details are as follows:

[0026] 2-1) Upper and lower bound constraints for shared variables:

[0027]

[0028] in, and These represent the maximum and minimum allowable active power transfers between the nth distribution network and the transmission network, respectively.

[0029] 2-2) Constraints on active and reactive power output of new energy sources:

[0030]

[0031]

[0032]

[0033] In the formula, and These represent the maximum and minimum active power of renewable energy connected to the grid at the t-th sampling point of node i in scenario s, respectively. Let be the reactive power of renewable energy connected to the grid at the t-th sampling point in the nth distribution network under scenario s; and These represent the maximum and minimum reactive power of renewable energy connected to the grid at the t-th sampling point of node i in scenario s, respectively. Let i be the renewable energy capacity of node i in the nth distribution network;

[0034] 2-3) Investment and operational constraints of battery energy storage systems:

[0035] E min ≤E n,i ≤E max (12)

[0036] 0≤P n,i ≤Pmax (13)

[0037]

[0038] In the formula, E n,i and P n,i E represents the installed capacity and power of battery energy storage at node i in the nth distribution network. max and E min These represent the maximum and minimum installed capacity of battery energy storage, respectively, P max For the maximum installed capacity of battery energy storage, C max and C min These represent the maximum and minimum energy storage rates of the battery, respectively.

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] In the formula, Let be a 0-1 state flag variable representing the charging active power of the battery energy storage at the t-th sampling point in the nth distribution network node i under scenario s; Let be a 0-1 state flag variable representing the discharge active power of the battery energy storage at the t sampling point in the nth distribution network node i under scenario s; and These represent the charging active power and discharging active power of the battery energy storage at the nth distribution network node i in scenario s at the tth sampling point, respectively. and These represent the reactive power absorbed and released by the battery energy storage at the nth distribution network node i in scenario s at the tth sampling point. and These are the maximum and minimum values ​​of reactive power exchanged between the battery energy storage and the grid in the nth distribution network node i, respectively.

[0047]

[0048] E n,i SOC min ≤E s,n,i,t ≤En,i SOC max (twenty three)

[0049] Among them, E s,n,i,t Let η be the stored energy of node i at the t-th sampling point in the nth distribution network under scenario s; c and η d These represent the charging efficiency and discharging efficiency of battery energy storage, respectively, and SOC. max and SOC min These represent the upper and lower limits of the state of charge for battery energy storage operation, respectively.

[0050] E s,n,i,0 =E s,n,i,T =E n,i SOC ini (twenty four)

[0051] In the formula, E s,n,i,0 and E s,n,i,T Let SOC represent the stored energy at the initial and final sampling points of the nth distribution network each day. ini This represents the initial state of charge (SOC) value for battery energy storage operation.

[0052] 2-4) Optimal power flow constraints in distribution networks:

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061] In the formula, corridor ij represents the set of transmission lines from node i to node j; and These are the active power and reactive power of the l-th line on corridor ij in the n-th distribution network under scenario s at the t-th sampling point, respectively. Let be the square of the current amplitude of the l-th line on the corridor ij in the n-th distribution network under scenario s at the t-th sampling point; V represents the current amplitude of the l-th line on corridor ij in the n-th distribution network under scenario s at the t-th sampling point;s,n,i,t Let be the voltage amplitude of the i-th node in the n-th distribution network at the t-th sampling point under scenario s; and Let be the resistance and reactance of the l-th line on corridor ij in the n-th distribution network, respectively. and Let t represent the active and reactive loads of the j-th node in the n-th distribution network under scenario s at the t-th sampling point; Let be the maximum current value of the l-th line on corridor ij in the n-th distribution network. and This represents the maximum voltage value at the i-th node in the n-th distribution network.

[0062] In one specific embodiment of this disclosure, the formula for calculating the coefficient for discounting investment costs from present value to equivalent annual value during the planning period is as follows:

[0063]

[0064] In the formula, α represents the general discount rate, and N y The planning period is the number of years.

[0065] In one specific embodiment of this disclosure, the power transmission network planning sub-model includes:

[0066] 1) Objective function;

[0067]

[0068] In the formula, F trans C represents the total cost of investment and operation of the power transmission network; inv This indicates the investment cost of the power transmission network; This represents the power generation cost of generators in the power grid under scenario s. This represents the grid connection cost of new energy sources under scenario s. This represents the cost of abandoning new energy sources in scenario s. This represents the cost of the transmission network selling electricity to the distribution network in scenario s. This represents the penalty cost for shared variable errors between transmission and distribution networks in scenario s. This indicates the operating cost of energy storage;

[0069] in,

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] In the formula, C 1,k The infrastructure cost of configuring node energy storage for the k-th energy storage in the transmission network, C 2,k C represents the unit energy capacity cost of configuring energy storage at the k-th energy storage node in the transmission network. 3,k The unit power capacity cost of configuring node energy storage for the k-th energy storage in the transmission network, E k and P k Ω represents the installed capacity and power of battery energy storage at the k-th energy storage configuration node in the transmission network, respectively; G C is the set of nodes in all transmission networks configured with generators. G,i (·) represents the power generation cost function of the generator at node i in the power transmission network. Let π be the power output of the generator at node i in the power grid under scenario s at the t-th sampling point; s,t Let be the on-grid electricity price of new energy at the t-th sampling point in scenario s. Let be the active power of renewable energy connected to the grid at node i in the power grid at the t-th sampling point under scenario s; Ω represents the renewable energy generation power of node i in the power grid at the t-th sampling point in scenario s. D It is the set of all distribution networks; and These represent the charging active power and discharging active power of the battery energy storage at node k in the power grid under scenario s at the t-th sampling point, respectively.

[0078] 2) Constraints; details are as follows:

[0079] 2-1) Upper and lower bound constraints for shared variables:

[0080]

[0081] in, and These represent the maximum and minimum active power transmitted between the transmission network and the nth distribution network, respectively.

[0082] 2-2) Constraints on active and reactive power output of new energy sources:

[0083]

[0084] In the formula, and Let be the maximum and minimum active power of renewable energy connected to the grid at sampling point t, respectively, for grid node i in scenario s. Let be the active power of renewable energy connected to the grid at node i in the power grid at the t-th sampling point under scenario s;

[0085] 2-2-2-3) Investment and operational constraints of battery energy storage systems:

[0086] E min,trans ≤E k ≤E max,trans (43)

[0087] 0≤P k ≤P max,trans (44)

[0088]

[0089] In the formula, E max,trans and E min,trans These represent the maximum and minimum installed capacity of battery energy storage in the power transmission network, respectively, P max,trans C represents the maximum installed capacity of battery energy storage in the power transmission network. max,trans and C min,trans These represent the maximum and minimum energy storage ratios for batteries in the power transmission network, respectively.

[0090]

[0091]

[0092]

[0093] In the formula, Let be a 0-1 state flag variable representing the charging active power of the battery energy storage in node k of the power grid at the t-th sampling point under scenario s; Let be the state flag (0-1 variable) of the active power of the battery energy storage in node k of the power grid at the t-th sampling point under scenario s; and These represent the charging active power and discharging active power of the battery energy storage at node k in the power grid under scenario s at the t-th sampling point, respectively.

[0094]

[0095] E k SOC min ≤E s,k,t ≤E k SOC max (50)

[0096] Among them, E s,k,tLet k be the stored electricity of node k in the power grid at the t-th sampling point in scenario s.

[0097] E s,k,0 =E s,k,T =E k SOC ini (51)

[0098] In the formula, E s,k,0 and E s,k,T These represent the stored energy at node k in the power grid under scenario s, specifically the energy stored at the initial and final sampling points each day.

[0099] 2-4) Generator single-unit power constraint:

[0100]

[0101] In the formula, P i G,max and P i G,min These represent the maximum and minimum generating power of the generator at node i in the power transmission network, respectively.

[0102] 2-5) Optimal power flow constraints of transmission networks:

[0103]

[0104]

[0105]

[0106] In the formula, Let i be the marginal electricity price of node i in the power grid at the t-th sampling point in scenario s. Let θ be the susceptance of the l-th line between nodes i and j in the transmission network. s,i,t and θ s,j,t Let be the phase angles of nodes i and j in the power grid under scenario s at the t-th sampling point. Let be the capacity of the l-th line between nodes i and j in the power transmission network.

[0107] In a specific embodiment of this disclosure, the step of using a distributed optimization algorithm to solve the joint optimization model to obtain a battery energy storage planning scheme for the transmission network and distribution network includes:

[0108] 1) Set the initial value of iteration number j to 0, and set the penalty coefficient of the consistency constraint in the j-th iteration. Weight Shared variables with the transmission network side The initial value of , where The initial value is 0. The initial value is 1. The initial value is 0;

[0109] 2) As the current v s,n,t ,Will As the current w s,n,t ,Will As the present Solve the distribution network planning sub-model and transmission network planning sub-model in the joint optimization model, and obtain the F... dis,n The total cost of investment and operation of the nth distribution network in the jth iteration The initial value of F will be obtained by solving the problem. trans The total cost of power grid investment and operation in the j-th iteration. Initial value;

[0110] The updated v obtained from the solution s,n,t w s,n,t , They are respectively denoted as and

[0111] 3-3) Let j = j + 1, and solve the marginal electricity price of node i at the t-th sampling point in the power transmission network planning sub-model under scenario s. If the nth distribution network is connected to node i of the transmission network, then the marginal electricity price of the nth distribution network at the tth sampling point in scenario s is... This equals the marginal electricity price of node i at the t-th sampling point in the power grid under scenario s.

[0112] 4) As the current v s,n,t ,Will As the current w s,n,t ,Will As the present Solve each distribution network planning sub-model sequentially, and obtain the results from the solution. As This represents the active power exchanged between the transmission and distribution networks of the nth distribution network in scenario s at the tth sampling point during the j-th iteration.

[0113] 5) Take the result from step 4) Substitute the sub-model into the power transmission network planning model and solve the power transmission network planning model. The results obtained from the solution... As This represents the active power exchanged between the transmission network and the distribution network in scenario s at the t-th sampling point during the j-th iteration.

[0114] 6) Determine whether the iteration has converged based on equations (56) and (57):

[0115]

[0116]

[0117] Where ε1 is the optimal error, representing the relative error between the transmission and distribution network costs of the two iterations; ε2 is the shared error, representing the error in the transmission of active power between the transmission and distribution networks;

[0118] If both equations (56) and (57) are satisfied, the iteration converges. The installed capacity and power E of battery energy storage in the distribution network obtained in the j-th iteration are then calculated. n,i and P n,i And the installed capacity and power E of battery energy storage in the power transmission network. k and P k As an optimized result of the energy storage plan, the plan is now complete.

[0119] If either equation (56) or (57) is not satisfied, the iteration will not converge, and the equations will be updated according to equations (58) and (59). and Then return to step 3-3);

[0120]

[0121]

[0122] Where θ is the iteration coefficient of the penalty quadratic term.

[0123] In one specific embodiment of this disclosure, the optimal error is less than or equal to 0.01, the shared error is less than or equal to 0.01, and the iteration coefficient of the penalty quadratic term is greater than or equal to 2.

[0124] A second aspect of this disclosure provides an energy storage system optimization planning device for power transmission and distribution network coordination, comprising:

[0125] The optimization model building module is used to establish a joint optimization model for the transmission network and distribution network configured with battery energy storage based on a preset set of typical daily operating scenarios. The joint optimization model includes a distribution network planning sub-model and a transmission network planning sub-model.

[0126] The energy storage planning module is used to solve the joint optimization model using a distributed optimization algorithm to obtain the battery energy storage planning scheme for the transmission network and distribution network.

[0127] A third aspect of this disclosure provides an electronic device, comprising:

[0128] At least one processor; and a memory communicatively connected to said at least one processor;

[0129] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the above-described method for optimizing the energy storage system in a power transmission and distribution network coordination manner.

[0130] A fourth aspect of this disclosure provides a computer-readable storage medium storing computer instructions for causing the computer to execute the aforementioned method for optimizing and planning a power transmission and distribution network coordinated energy storage system.

[0131] The features and beneficial effects of this disclosure are as follows:

[0132] 1. This disclosure considers configuring battery energy storage in hybrid power transmission and distribution networks to promote large-scale energy storage in areas rich in renewable energy sources.

[0133] Improving the overall operational efficiency of large-scale power transmission and distribution networks can be achieved by integrating renewable energy sources, reducing waste, and coordinating power transmission and distribution network utilization.

[0134] 2. This disclosure considers uncertainties such as renewable energy output and load changes through a clustering method based on typical scenarios. It employs a distributed algorithm with cascaded target analysis to perform hierarchical modeling and independent solution of the transmission and distribution network. The hierarchical and independent solution algorithm solves the problem of complex and difficult-to-solve hybrid transmission and distribution networks with many variables, ensuring the solution time of the model. Furthermore, it ensures the coupling relationship between transmission and distribution networks through shared variable consistency penalty, guaranteeing the operating characteristics and network constraints of each transmission and distribution network while allowing the global transmission and distribution network to reach its optimal state during the iteration process. The consumption of renewable energy is achieved by determining the configuration of energy storage. Attached Figure Description

[0135] Figure 1 This is an overall flowchart of an energy storage system optimization planning method for power transmission and distribution network coordination in an embodiment of this disclosure.

[0136] Figure 2 This is a flowchart of the algorithm for solving the coordinated optimization of power transmission and distribution networks in a specific embodiment of this disclosure. Detailed Implementation

[0137] This disclosure proposes an optimization planning method and device for energy storage systems coordinated with power transmission and distribution networks, which will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0138] The first aspect of this disclosure proposes an optimization planning method for energy storage systems coordinated with power transmission and distribution networks. The overall process is as follows: Figure 1 As shown, it includes the following steps:

[0139] 1) A multi-scenario probabilistic approach is applied to consider uncertainties such as renewable energy output and load changes, establishing a typical daily operating scenario set. This typical daily operating scenario set includes typical renewable energy plant output scenario sets and typical load scenario sets. The specific method is as follows:

[0140] 1-1) The historical actual output data of all new energy power plants in the power transmission and distribution network are sampled daily; in this disclosure, the sampling period is at least one hour, and at least one year of historical daily operation data of new energy power plants is required. In a specific embodiment of this disclosure, historical output data with a duration of one year and a sampling period of one hour are used; the K-means algorithm is used to cluster the daily historical output data of each new energy power plant to generate a typical daily output scenario for each new energy power plant. Each new energy power plant can establish multiple typical daily scenarios through clustering, and the number of typical daily scenarios for each new energy power plant can be different.

[0141] All typical daily output scenarios of new energy power plants are compiled into a set of typical output scenarios for new energy power plants;

[0142] 1-2) The historical actual load data of the power transmission and distribution network is sampled daily, wherein the sampling period and sampling cycle are consistent with step 1-1); in this disclosure, the sampling cycle is at least one hour, and at least one year of daily load data is required. In a specific embodiment of this disclosure, historical load data with a duration of one year and a sampling cycle of one hour are used; the K-means algorithm is used to cluster the sampled daily historical load data to generate typical daily load scenarios, and all typical daily load scenarios are combined into a typical load scenario set.

[0143] 1-3) Combine the typical output scenario set and the typical load scenario set of new energy power plants into a typical daily operation scenario set, and reduce the scale of the problem while considering the uncertainty of new energy output and load.

[0144] 2) Establish a joint optimization model for the transmission and distribution networks. This joint optimization model includes a distribution network planning sub-model corresponding to each distribution network and a transmission network planning sub-model corresponding to each transmission network. The specific steps are as follows:

[0145] 2-1) Establish a corresponding distribution network planning sub-model for each distribution network included in the transmission and distribution network. The model consists of an objective function and constraints.

[0146] For the nth distribution network, where n is the distribution network number, the specific steps are as follows:

[0147] 2-1-1) Determine the objective function of the nth distribution network planning sub-model;

[0148]

[0149] The objective function is the total investment and operating cost F of the nth distribution network. dis,n Minimize;

[0150] The investment cost of the nth distribution network is the investment cost C of energy storage. inv,n Ω S This is a typical daily operation scenario set, where s is the scenario number, and s∈Ω. S D s This represents the number of days in a year for a typical daily operating scenario (s).

[0151] In scenario s, the total operating cost of the nth distribution network includes: the grid connection cost of renewable energy sources in the nth distribution network. Cost of abandoning renewable energy in the nth distribution network Penalty cost for shared variable errors between the transmission network and the nth distribution network The cost of electricity purchased by the nth distribution network from the transmission network The operation and maintenance cost of the nth distribution network energy storage

[0152] in,

[0153]

[0154]

[0155]

[0156]

[0157]

[0158] In equation (2), Ω ess Let Ω be the set of all potential energy storage nodes, and k be the node indices of the potential energy storage nodes. ess G inv The factor used to discount investment costs from present value to equivalent annual value during the planning period is used. In this embodiment, it is assumed that the discount factor is consistent for all energy storage systems in the transmission and distribution network. 1,n,k C represents the infrastructure cost of configuring node energy storage in the k-th energy storage configuration within the n-th distribution network. 2,n,k C represents the unit energy capacity cost of energy storage at the k-th energy storage node in the n-th distribution network. 3,n,k The unit power capacity cost of configuring node energy storage in the k-th energy storage configuration of the n-th distribution network; E n,i and P n,i These represent the installed capacity and power of battery energy storage at node i in the nth distribution network.

[0159] Among them, G inv The calculation expression is as follows:

[0160]

[0161] Where α is the general discount rate, and N y For the planning lifespan, this embodiment of the disclosure assumes that the planning lifespan of all energy storage in the transmission and distribution network is the same.

[0162] In equation (3), Ω Re Let i be the set of nodes that allow new energy sources to connect to this distribution network, where i is the node number, i∈Ω. Re t is the sampling time point number, T is the total number of sampling periods for a typical day, and π s,n,t Let be the on-grid electricity price of new energy at the t-th sampling point in the n-th distribution network under scenario s. Let Δt be the active power of renewable energy connected to the grid at the t-th sampling point in the nth distribution network under scenario s, where Δt is the sampling period length.

[0163] In equation (4), β is the penalty coefficient for abandoning new energy sources, with a value ranging from 1 to 3. In a specific embodiment of this disclosure, it is taken as 1.5. Let be the renewable energy generation power of node i at the t-th sampling point in the nth distribution network under scenario s.

[0164] In equation (5), v s,n,t and w s,n,t These are the coefficient and weight values ​​of the shared variable penalty function at the t-th sampling point in the nth distribution network under scenario s. Let be the active power exchanged between the transmission and distribution networks on the transmission side of the nth distribution network in scenario s at the tth sampling point. This represents the active power exchanged between the transmission and distribution networks on the distribution network side at the t-th sampling point in scenario s for the nth distribution network.

[0165] In equation (6), Let $\frac{ ...

[0166] In equation (7), π c and π d These are the unit power operation and maintenance costs for energy storage charging and discharging, respectively.

[0167] 2-1-2) Determine the constraints of the nth distribution network planning sub-model; specifically as follows:

[0168] 2-1-2-1) Upper and lower bound constraints for shared variables:

[0169]

[0170] in, and These represent the maximum and minimum allowable active power transfers between the nth distribution network and the transmission network, respectively.

[0171] 2-1-2-2) Constraints on active and reactive power output of new energy sources:

[0172]

[0173]

[0174]

[0175] In the formula, and These represent the maximum and minimum active power of renewable energy connected to the grid at the t-th sampling point of node i in scenario s, respectively. Let be the reactive power of renewable energy connected to the grid at the t-th sampling point in the nth distribution network under scenario s; and These represent the maximum and minimum reactive power of renewable energy connected to the grid at the t-th sampling point of node i in scenario s, respectively. Let be the renewable energy capacity of node i in the nth distribution network.

[0176] 2-1-2-3) Investment and operational constraints of battery energy storage systems:

[0177] E min ≤E n,i ≤E max (12)

[0178] 0≤P n,i ≤P max (13)

[0179]

[0180] In the formula, E n,i and P n,i E represents the installed capacity and power of battery energy storage at node i in the nth distribution network. max and E min These represent the maximum and minimum installed capacity of battery energy storage, respectively, P max This represents the maximum installed capacity for battery energy storage. (C) max and C min These represent the maximum and minimum energy storage rates of the battery, respectively.

[0181]

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188] In the formula, Let 't' be the state flag of the active power of battery energy storage at the t sampling point in the nth distribution network node i under scenario s. 0 indicates that battery energy storage is not allowed to charge, and 1 indicates that battery energy storage is allowed to charge. Let 'S' be a 0-1 variable representing the state of the active power of battery energy storage at the t sampling point in the nth distribution network node i under scenario s, where 0 indicates that battery energy storage is not allowed to discharge and 1 indicates that battery energy storage is allowed to discharge. and These represent the charging active power and discharging active power of the battery energy storage at the nth distribution network node i in scenario s at the tth sampling point, respectively. and These represent the reactive power absorbed and released by the battery energy storage at the nth distribution network node i in scenario s at the tth sampling point. and These are the maximum and minimum values ​​of reactive power exchanged between the battery energy storage and the power grid in the nth distribution network node i, respectively.

[0189]

[0190] E n,i SOC min ≤E s,n,i,t ≤E n,i SOC max (twenty three)

[0191] Among them, E s,n,i,t Let η be the stored energy of node i at the t-th sampling point in the nth distribution network under scenario s; c and η d These represent the charging efficiency and discharging efficiency of battery energy storage, respectively, and SOC. max and SOC min These represent the upper and lower limits of the state of charge for battery energy storage operation, respectively.

[0192] Considering the continuous operation of battery energy storage, the energy storage should return to its initial energy storage state after one day of operation, i.e.:

[0193] E s,n,i,0 =E s,n,i,T =En,i SOC ini (twenty four)

[0194] In the formula, E s,n,i,0 and E s,n,i,T Let SOC represent the stored energy at node i in the nth distribution network under scenario s, at the initial and final sampling points each day. ini This represents the initial state of charge (SOC) value for battery energy storage operation.

[0195] 2-1-2-4) Optimal power flow constraints in distribution networks:

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203]

[0204] In the formula, corridor ij represents the set of transmission lines from node i to node j; and These are the active power and reactive power of the l-th line on corridor ij in the n-th distribution network under scenario s at the t-th sampling point, respectively. Let be the square of the current amplitude of the l-th line on the corridor ij in the n-th distribution network under scenario s at the t-th sampling point; V represents the current amplitude of the l-th line on corridor ij in the n-th distribution network under scenario s at the t-th sampling point; s,n,i,t Let be the voltage amplitude of the i-th node in the n-th distribution network at the t-th sampling point under scenario s. and These are the resistance and reactance of the l-th line on corridor ij in the n-th distribution network. and Let represent the active and reactive loads of the j-th node in the n-th distribution network under scenario s at the t-th sampling point. Let be the maximum current value of the l-th line on corridor ij in the n-th distribution network. and This represents the maximum voltage value at the i-th node in the n-th distribution network.

[0205] 2-2) Establish a sub-model for the transmission network planning in the power transmission and distribution network. This model consists of an objective function and constraints. The specific steps are as follows:

[0206] 2-2-1) Determine the objective function of the power transmission network planning sub-model;

[0207]

[0208] The objective function is the total investment and operating cost F of the transmission network. trans Minimize;

[0209] In the formula, the investment cost of the power transmission network is C, which is the investment cost of energy storage. inv The total operating cost of the power transmission network in scenario s includes the following: the power generation cost of generators in the power transmission network. The cost of purchasing electricity from renewable energy sources Cost of abandoning new energy The cost of selling electricity from the transmission network to the distribution network Penalty cost for shared variable errors between transmission and distribution networks Operating costs of energy storage

[0210] in,

[0211]

[0212]

[0213]

[0214]

[0215]

[0216]

[0217]

[0218] In equation (34), C 1,k The infrastructure cost of configuring node energy storage for the k-th energy storage in the transmission network, C 2,k C represents the unit energy capacity cost of configuring energy storage at the k-th energy storage node in the transmission network. 3,k The unit power capacity cost of configuring node energy storage for the k-th energy storage in the transmission network, E k and P k These represent the installed capacity and power of battery energy storage at the k-th energy storage configuration node in the power transmission network.

[0219] In equation (35), Ω GC is the set of nodes in all transmission networks configured with generators. G,i (·) represents the power generation cost function of the generator located at node i in the transmission network. Let be the power generation of the generator in node i of the power grid at the t-th sampling point in scenario s.

[0220] In equation (36), π s,t Let be the on-grid electricity price of new energy at the t-th sampling point in scenario s. Let be the active power of renewable energy connected to the grid at node i in the power grid at the t-th sampling point under scenario s.

[0221] In equation (37), Let be the renewable energy generation power of node i in the power grid at the t-th sampling point under scenario s.

[0222] In equation (39), Ω D It is the set of all distribution networks.

[0223] In equation (40), and These represent the charging active power and discharging active power of the battery energy storage at node k in the power grid under scenario s at the t-th sampling point, respectively.

[0224] 2-2-2) Determine the constraints of the power transmission network planning sub-model; specifically as follows:

[0225] 2-2-2-1) Upper and lower bound constraints for shared variables:

[0226]

[0227] in, and These represent the maximum and minimum values ​​of active power transmitted between the transmission network and the nth distribution network, respectively.

[0228] 2-2-2-2) Constraints on active and reactive power output of new energy sources:

[0229]

[0230] In the formula, and Let be the maximum and minimum active power of renewable energy connected to the grid at sampling point t, respectively, for grid node i in scenario s. Let be the active power of renewable energy connected to the grid at node i in the power grid at the t-th sampling point under scenario s.

[0231] 2-2-2-3) Investment and operational constraints of battery energy storage systems:

[0232] E min,trans ≤Ek ≤E max,trans (43)

[0233] 0≤P k ≤P max,trans (44)

[0234]

[0235] In the formula, E max,trans and E min,trans These represent the maximum and minimum installed capacity of battery energy storage in the power transmission network, respectively, P max,trans C represents the maximum installed capacity of battery energy storage in the power transmission network. max,trans and C min,trans These represent the maximum and minimum energy storage ratios for batteries in the power transmission network, respectively.

[0236]

[0237]

[0238]

[0239] In the formula, Let 't' be the state flag 0-1 variable for the charging active power of the battery energy storage in node k of the power grid under scenario s. 0 indicates that the battery energy storage is not allowed to charge, and 1 indicates that the battery energy storage is allowed to charge. Let t be a 0-1 variable representing the state of the active power of the battery energy storage in node k of the power grid under scenario s. 0 indicates that the battery energy storage is not allowed to discharge, and 1 indicates that the battery energy storage is allowed to discharge. and These represent the charging active power and discharging active power of the battery energy storage at node k in the power grid under scenario s at the t-th sampling point.

[0240]

[0241] E k SOC min ≤E s,k,t ≤E k SOC max (50)

[0242] Among them, E s,k,t Let represent the stored energy of node k at the t-th sampling point in the power grid under scenario s.

[0243] Considering the continuous operation of battery energy storage, the energy storage should return to its initial energy storage state after one day of operation, i.e.:

[0244] E s,k,0 =Es,k,T =E k SOC ini (51)

[0245] In the formula, E s,k,0 and E s,k,T These represent the stored energy at node k in the power grid under scenario s, specifically the energy stored at the initial and final sampling points each day.

[0246] 2-2-2-4) Generator single-unit power constraint:

[0247]

[0248] In the formula, P i G,max and P i G,min These represent the maximum and minimum generating power of the generator at node i in the power transmission network, respectively.

[0249] 2-2-2-5) Optimal power flow constraints of transmission networks:

[0250]

[0251]

[0252]

[0253] In the formula, Let $\frac{i}{t}$ be the marginal electricity price of node $i$ at the $t$ sampling point in the power grid under scenario $s$, which is the dual variable of constraint equation (53). Let θ be the susceptance of the l-th line between nodes i and j in the transmission network. s,i,t and θ s,j,t Let be the phase angles of nodes i and j in the power grid under scenario s at the t-th sampling point. Let be the capacity of the l-th line between nodes i and j in the power transmission network.

[0254] 3) Solve the joint optimization model established in step 2) to obtain the optimal planning scheme for battery energy storage in the power transmission and distribution network.

[0255] In this embodiment, considering the hierarchical solution of the transmission and distribution network and ensuring the consistency of boundary conditions, a distributed optimization algorithm based on the cascaded objective analysis method is used to solve the joint optimization model. The overall process is as follows: Figure 2 As shown, the specific steps are as follows:

[0256] 3-1) Set the initial value of the iteration number j to 0, and set the penalty coefficient of the consistency constraint in the j-th iteration. Weight Shared variables with the transmission network side The initial value of , where The initial value is 0. The initial value is 1. The initial value is 0.

[0257] 3-2) As the current v s,n,t ,Will As the current w s,n,t ,Will As the present Solve the distribution network planning sub-model and transmission network planning sub-model in the joint optimization model, and obtain the F dis,n The total cost of investment and operation of the nth distribution network in the jth iteration The initial value of F will be obtained by solving the problem. trans The total cost of power grid investment and operation in the j-th iteration. The initial value of .

[0258] The updated v of each sub-model will be solved s,n,t w s,n,t , They are respectively denoted as and Then, substitute the input into the subsequent model and continue iteratively solving.

[0259] 3-3) Let j = j + 1, and solve the marginal electricity price of node i at the t-th sampling point in the power transmission network planning sub-model under scenario s. If the nth distribution network is connected to node i of the transmission network, then the marginal electricity price of the nth distribution network at the tth sampling point in scenario s is... This equals the marginal electricity price of node i at the t-th sampling point in the power grid under scenario s.

[0260] 3-4) As the current v s,n,t ,Will As the current w s,n,t ,Will As the present Solve each distribution network planning sub-model sequentially, and extract shared variables from each distribution network side in the solution results. As This represents the active power exchanged between the transmission and distribution networks of the nth distribution network in scenario s at the t-th sampling point during the j-th iteration.

[0261] Each time the distribution network planning sub-model is solved, the following is obtained: the installed capacity and power of battery energy storage at the k-th energy storage configuration node in the transmission network: E k and Pk In scenario s, the active power of renewable energy connected to the grid at node i in the power grid at the t-th sampling point is: In scenario s, the active power exchanged between the transmission and distribution networks on the transmission side at the t-th sampling point in the nth distribution network:

[0262] 3-5) The result obtained in step 3-4) Substitute these variables into the transmission network planning sub-model and solve the transmission network planning sub-model. Then, use the shared variables obtained from the solution for each distribution network on the transmission network side. As the updated This represents the active power exchanged between the transmission network and the distribution network in scenario s at the t-th sampling point during the j-th iteration.

[0263] Each time the transmission network planning sub-model is solved, the following is obtained: the installed capacity and power of battery energy storage at the k-th energy storage configuration node in the transmission network: E k and P k In scenario s, the active power of renewable energy connected to the grid at node i in the power grid at the t-th sampling point is: In scenario s, the active power exchanged between the transmission and distribution networks on the transmission side at the t-th sampling point in the nth distribution network:

[0264] 3-6) Determine whether the iteration has converged based on equations (56) and (57):

[0265]

[0266]

[0267] Wherein, ε1 is the optimal error, representing the relative error between the transmission and distribution network costs of the two iterations, and its value range is required to be less than or equal to 0.01. In a specific embodiment of this disclosure, it is taken as 0.001; ε2 is the shared error, representing the error in the transmission of active power between the transmission and distribution networks, and its value range is required to be less than or equal to 0.01. In a specific embodiment of this disclosure, it is taken as 0.001.

[0268] If both equations (56) and (57) are satisfied, then the iteration converges, and the installed capacity and power E of battery energy storage in the distribution network obtained in the j-th iteration are calculated. n,i and P n,i And the installed capacity and power E of battery energy storage in the power transmission network. k and P k As an optimized result of the energy storage plan, the plan is now complete.

[0269] If either equation (56) or (57) is not satisfied, the iteration will not converge, and the coefficients of the consistency constraint penalty function will be updated according to equations (58) and (59). and weight The value is then returned to step 3-3.

[0270]

[0271]

[0272] Wherein, θ is the iteration coefficient of the penalty quadratic term, and its value range is greater than or equal to 2. The larger the value, the faster the penalty increases. In a specific embodiment of this disclosure, it is taken as 2.

[0273] To achieve the above embodiments, a second aspect of this disclosure proposes an energy storage system optimization planning device for power transmission and distribution network coordination, comprising:

[0274] The optimization model building module is used to establish a joint optimization model for the transmission network and distribution network configured with battery energy storage based on a preset set of typical daily operating scenarios. The joint optimization model includes a distribution network planning sub-model and a transmission network planning sub-model.

[0275] The energy storage planning module is used to solve the joint optimization model using a distributed optimization algorithm to obtain the battery energy storage planning scheme for the transmission network and distribution network.

[0276] To implement the above embodiments, a third aspect of this disclosure provides an electronic device, comprising:

[0277] At least one processor; and a memory communicatively connected to said at least one processor;

[0278] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the above-described method for optimizing the energy storage system in a power transmission and distribution network coordination manner.

[0279] To implement the above embodiments, a fourth aspect of this disclosure provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described method for optimizing and planning a power transmission and distribution network coordinated energy storage system.

[0280] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0281] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a method for optimizing and planning a power transmission and distribution network coordinated energy storage system according to the above embodiments.

[0282] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0283] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0284] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0285] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0286] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0287] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0288] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0289] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0290] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for optimizing the planning of an energy storage system in coordination with power transmission and distribution networks, characterized in that, include: Based on a set of preset typical daily operating scenarios, a joint optimization model is established for the transmission network and distribution network configured with battery energy storage. The joint optimization model includes a distribution network planning sub-model and a transmission network planning sub-model. The joint optimization model is solved using a distributed optimization algorithm to obtain the battery energy storage planning scheme for the transmission network and distribution network. The distribution network planning sub-model includes: 1) Objective function; In the formula, the subscript n is the distribution network serial number, F dis,n Ω represents the total investment and operating cost of the nth distribution network. S Let s represent a typical daily operating scenario set, s∈Ω S D s This represents the number of days in a year that a typical daily operating scenario 's' occurs; C inv,n This represents the investment cost of the nth distribution network. This represents the grid connection cost of the nth renewable energy source in scenario s. This represents the cost of abandoning the nth renewable energy source in the distribution network under scenario s. This represents the penalty cost for shared variable errors between the transmission network and the nth distribution network in scenario s. This represents the cost of the nth distribution network purchasing electricity from the transmission network in scenario s. This represents the operation and maintenance cost of the nth distribution network energy storage in scenario s; in, In the formula, Ω ess G represents the set of all potential energy storage nodes. inv C represents the coefficient used to discount investment costs from present value to equivalent annual value during the planning period; 1,n,k C represents the infrastructure cost of energy storage at the k-th energy storage configuration node in the n-th distribution network. 2,n,k C represents the unit energy capacity cost of energy storage at the k-th energy storage configuration node in the n-th distribution network. 3,n,k This represents the unit power capacity cost of energy storage at the k-th energy storage configuration node in the n-th distribution network; Ω Re This represents the set of nodes where new energy sources are connected to the distribution network, where i is the node number, i∈Ω. Re t is the sampling time point number, T is the total number of sampling periods for a typical day, and π s,n,t Let be the on-grid electricity price of new energy at the t-th sampling point in the n-th distribution network under scenario s. Let Δt be the active power of renewable energy fed into the grid at the t-th sampling point in the nth distribution network under scenario s, where Δt is the sampling period length; and β is the penalty coefficient for renewable energy abandonment. Let v be the renewable energy generation power of node i in the nth distribution network at the tth sampling point in scenario s; s,n,t and w s,n,t These are the coefficient and weight values ​​of the shared variable penalty function at the t-th sampling point in the nth distribution network under scenario s, respectively. Let be the active power exchanged between the transmission and distribution networks on the transmission side of the nth distribution network in scenario s at the tth sampling point. Let be the active power exchanged between the transmission and distribution networks on the distribution network side at the t sampling point of the nth distribution network in scenario s; Let π be the marginal electricity of the nth distribution network at the tth sampling point in scenario s; c and π d These are the unit power operation and maintenance costs for energy storage charging and discharging, respectively. 2) Constraints; details are as follows: 2-1) Upper and lower bound constraints for shared variables: in, and These represent the maximum and minimum allowable active power transfers between the nth distribution network and the transmission network, respectively. 2-2) Constraints on active and reactive power output of new energy sources: In the formula, and These represent the maximum and minimum active power of renewable energy connected to the grid at the t-th sampling point of node i in scenario s, respectively. Let be the reactive power of renewable energy connected to the grid at the t-th sampling point in the nth distribution network under scenario s; and These represent the maximum and minimum reactive power of renewable energy connected to the grid at the t-th sampling point of node i in scenario s, respectively. Let i be the renewable energy capacity of node i in the nth distribution network; 2-3) Investment and operational constraints of battery energy storage systems: AND min ≤E n,i ≤E max (12)0≤P n,i ≤P max (13) In the formula, E n,i and P n,i E represents the installed capacity and power of battery energy storage at node i in the nth distribution network. max and E min These represent the maximum and minimum installed capacity of battery energy storage, respectively, P max For the maximum installed capacity of battery energy storage, C max and C min These represent the maximum and minimum energy storage rates of the battery, respectively. In the formula, Let be a 0-1 state flag variable representing the charging active power of the battery energy storage at the t-th sampling point in the nth distribution network node i under scenario s; Let be a 0-1 state flag variable representing the discharge active power of the battery energy storage at the t sampling point in the nth distribution network node i under scenario s; and These represent the charging active power and discharging active power of the battery energy storage at the nth distribution network node i in scenario s at the tth sampling point, respectively. and These represent the reactive power absorbed and released by the battery energy storage at the nth distribution network node i in scenario s at the tth sampling point. and These are the maximum and minimum values ​​of reactive power exchanged between the battery energy storage and the grid in the nth distribution network node i, respectively. IN n,i ·SOC min ≤E s,n,i,t ≤E n,i ·SOC max (23) Among them, E s,n,i,t Let η be the stored energy of node i at the t-th sampling point in the nth distribution network under scenario s; c and η d These represent the charging efficiency and discharging efficiency of battery energy storage, respectively, and SOC. max and SOC min These represent the upper and lower limits of the state of charge for battery energy storage operation, respectively. AND s,n,i,0 =And s,n,i,T =And n,i ·SOC ini (24) In the formula, E s,n,i,0 and E s,n,i,T Let SOC represent the stored energy at the initial and final sampling points of the nth distribution network each day. ini This represents the initial state of charge (SOC) value for battery energy storage operation. 2-4) Optimal power flow constraints in distribution networks: In the formula, corridor ij represents the set of transmission lines from node i to node j; and These are the active power and reactive power of the l-th line on corridor ij in the n-th distribution network under scenario s at the t-th sampling point, respectively. Let be the square of the current amplitude of the l-th line on the corridor ij in the n-th distribution network under scenario s at the t-th sampling point; V represents the current amplitude of the l-th line on corridor ij in the n-th distribution network under scenario s at the t-th sampling point; s,n,i,t Let be the voltage amplitude of the i-th node in the n-th distribution network at the t-th sampling point under scenario s; and Let be the resistance and reactance of the l-th line on corridor ij in the n-th distribution network, respectively. and Let t represent the active and reactive loads of the j-th node in the n-th distribution network under scenario s at the t-th sampling point; Let be the maximum current value of the l-th line on corridor ij in the n-th distribution network. and This represents the maximum voltage value at the i-th node in the n-th distribution network. The power transmission network planning sub-model includes: 1) Objective function; In the formula, F trans C represents the total cost of investment and operation of the power transmission network; inv This indicates the investment cost of the power transmission network; This represents the power generation cost of generators in the power grid under scenario s. This represents the grid connection cost of new energy sources under scenario s. This represents the cost of abandoning new energy sources in scenario s. This represents the cost of the transmission network selling electricity to the distribution network in scenario s. This represents the penalty cost for shared variable errors between transmission and distribution networks in scenario s. This indicates the operating cost of energy storage; in, In the formula, C 1,k The infrastructure cost of configuring node energy storage for the k-th energy storage in the transmission network, C 2,k C represents the unit energy capacity cost of configuring energy storage at the k-th energy storage node in the transmission network. 3,k The unit power capacity cost of configuring node energy storage for the k-th energy storage in the transmission network, E k and P k Ω represents the installed capacity and power of battery energy storage at the k-th energy storage configuration node in the transmission network, respectively; G C is the set of nodes in all transmission networks configured with generators. G,i (·) represents the power generation cost function of the generator at node i in the power transmission network. Let π be the power output of the generator at node i in the power grid under scenario s at the t-th sampling point; s,t Let be the on-grid electricity price of new energy at the t-th sampling point in scenario s. Let be the active power of renewable energy connected to the grid at node i in the power grid at the t-th sampling point under scenario s; Ω represents the renewable energy generation power of node i in the power grid at the t-th sampling point in scenario s. D It is the set of all distribution networks; and These represent the charging active power and discharging active power of the battery energy storage at node k in the power grid under scenario s at the t-th sampling point, respectively. 2) Constraints; details are as follows: 2-1) Upper and lower bound constraints for shared variables: in, and These represent the maximum and minimum active power transmitted between the transmission network and the nth distribution network, respectively. 2-2) Constraints on active and reactive power output of new energy sources: In the formula, and Let be the maximum and minimum active power of renewable energy connected to the grid at sampling point t, respectively, for grid node i in scenario s. Let be the active power of renewable energy connected to the grid at node i in the power grid at the t-th sampling point under scenario s; 2-2-2-3) Investment and operational constraints of battery energy storage systems: AND min,trans ≤E k ≤E max,trans (43) 0≤P k ≤P max,trans (44) In the formula, E max,trans and E min,trans These represent the maximum and minimum installed capacity of battery energy storage in the power transmission network, respectively, P max,trans C represents the maximum installed capacity of battery energy storage in the power transmission network. max,trans and C min,trans These represent the maximum and minimum energy storage ratios for batteries in the power transmission network, respectively. In the formula, Let be a 0-1 state flag variable representing the charging active power of the battery energy storage in node k of the power grid at the t-th sampling point under scenario s; Let be the state flag (0-1 variable) of the active power of the battery energy storage in node k of the power grid at the t-th sampling point under scenario s; and These represent the charging active power and discharging active power of the battery energy storage at node k in the power grid under scenario s at the t-th sampling point, respectively. IN k ·SOC min ≤E s,k,t ≤E k ·SOC max (50) Among them, E s,k,t Let k be the stored electricity of node k in the power grid at the t-th sampling point in scenario s. AND s,k,0 =And s,k,T =And k ·SOC ini (51) In the formula, E s,k,0 and E s,k,T These represent the stored energy at node k in the power grid under scenario s, specifically the energy stored at the initial and final sampling points each day. 2-4) Generator single-unit power constraint: In the formula, P i G,max and P i G,min These represent the maximum and minimum generating power of the generator at node i in the power transmission network, respectively. 2-5) Optimal power flow constraints of transmission networks: In the formula, Let i be the marginal electricity price of node i in the power grid at the t-th sampling point in scenario s. Let θ be the susceptance of the l-th line between nodes i and j in the transmission network. s,i,t and θ s,j,t Let be the phase angles of nodes i and j in the power grid under scenario s at the t-th sampling point. Let be the capacity of the l-th line between nodes i and j in the power transmission network.

2. The method according to claim 1, characterized in that, The typical daily operation scenario set includes a typical output scenario set and a typical load scenario set for new energy power plants, wherein: Historical output data of new energy power plants in the power transmission and distribution network are sampled daily. The K-means algorithm is used to cluster the daily historical output data of each new energy power plant to generate typical daily output scenarios for each new energy power plant. The typical daily output scenarios of all new energy power plants are combined into a set of typical output scenarios for new energy power plants. Historical load data of the power transmission and distribution network is sampled daily, wherein the sampling period of the historical load data is consistent with the sampling period of the historical output data of the new energy power plants; the K-means algorithm is used to cluster the sampled daily historical load data to generate typical daily load scenarios, and all typical daily load scenarios are combined into a typical load scenario set.

3. The method according to claim 1, characterized in that, The formula for calculating the factor by which the investment cost during the planning period is discounted from present value to equivalent annual value is as follows: In the formula, α represents the general discount rate, and N y The planning period is the number of years.

4. The method according to claim 1, characterized in that, The method of using a distributed optimization algorithm to solve the joint optimization model to obtain the battery energy storage planning scheme for the transmission network and distribution network includes: 1) Set the initial value of iteration number j to 0, and set the penalty coefficient of the consistency constraint in the j-th iteration. Weight Shared variables with the transmission network side The initial value of , where The initial value is 0. The initial value is 1. The initial value is 0; 2) As the current v s,n,t ,Will As the current w s,n,t ,Will As the present Solve the distribution network planning sub-model and transmission network planning sub-model in the joint optimization model, and obtain the F... dis,n The total cost of investment and operation of the nth distribution network in the jth iteration The initial value of F will be obtained by solving the problem. trans The total cost of power grid investment and operation in the j-th iteration. Initial value; The updated v obtained from the solution s,n,t w s,n,t , They are respectively denoted as and 3) Let j = j + 1, and solve for the marginal electricity price of node i at the t-th sampling point in the power transmission network planning sub-model under scenario s. If the nth distribution network is connected to node i of the transmission network, then the marginal electricity price of the nth distribution network at the tth sampling point in scenario s is... This equals the marginal electricity price of node i at the t-th sampling point in the power grid under scenario s. 4) As the current v s,n,t ,Will As the current w s,n,t ,Will As the present Solve each distribution network planning sub-model sequentially, and obtain the results from the solution. As This represents the active power exchanged between the transmission and distribution networks of the nth distribution network in scenario s at the tth sampling point during the j-th iteration. 5) Take the result from step 4) Substitute the sub-model into the power transmission network planning model and solve the power transmission network planning model. The results obtained from the solution... As This represents the active power exchanged between the transmission network and the distribution network in scenario s at the t-th sampling point during the j-th iteration. 6) Determine whether the iteration has converged based on equations (56) and (57): Where ε1 is the optimal error, representing the relative error between the transmission and distribution network costs of the two iterations; ε2 is the shared error, representing the error in the transmission of active power between the transmission and distribution networks; If both equations (56) and (57) are satisfied, the iteration converges. The installed capacity and power E of battery energy storage in the distribution network obtained in the j-th iteration are then calculated. n,i and P n,i And the installed capacity and power E of battery energy storage in the power transmission network. k and P k As an optimized result of the energy storage plan, the plan is now complete. If either equation (56) or (57) is not satisfied, the iteration will not converge, and the equations will be updated according to equations (58) and (59). and Then return to step 3); Where θ is the iteration coefficient of the penalty quadratic term.

5. The method according to claim 4, characterized in that, The optimal error is less than or equal to 0.01, the shared error is less than or equal to 0.01, and the iteration coefficient of the penalty quadratic term is greater than or equal to 2.

6. A device for optimizing and planning a power transmission and distribution network coordinated energy storage system based on the method described in claim 1, characterized in that, include: The optimization model building module is used to establish a joint optimization model for the transmission network and distribution network configured with battery energy storage based on a preset set of typical daily operating scenarios. The joint optimization model includes a distribution network planning sub-model and a transmission network planning sub-model. The energy storage planning module is used to solve the joint optimization model using a distributed optimization algorithm to obtain the battery energy storage planning scheme for the transmission network and distribution network.

7. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1-5.

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