An optimized configuration method and system for energy storage intelligent soft switching that balances reliability and flexibility

Abstract

This invention discloses a method and system for optimizing the configuration of intelligent soft switches for energy storage that balances reliability and resilience. The method involves inputting distribution network parameters, cost parameters, relevant technical parameters of the E-SOP (Energy-Supported System Program), and planning-related parameters to initialize the planning system. It then generates reference probability distributions of renewable energy output and load power, obtaining Wasserstein fuzzy sets of renewable energy output and load power, and generating first-moment fuzzy sets of the distribution network line fault probability distribution. Based on these first-moment fuzzy sets, a two-stage E-SOP sub-Browser bar programming model that balances reliability and resilience is established. The two-stage E-SOP sub-Browser bar programming model is solved, and the optimal configuration and operation scheme of the E-SOP are output. This invention overcomes the shortcomings of existing planning methods, such as insufficient robustness or excessive conservatism; it balances the function of E-SOP in improving the reliability and resilience of the distribution system, further enhancing the efficiency of the distribution system; and it achieves efficient and rapid model solving.

Description

Technical Field

[0001] This invention belongs to the field of power system planning technology, specifically relating to an optimized configuration method and system for energy storage intelligent soft switches that balances reliability and flexibility. Background Art

[0002] With the increasing penetration rate of distributed generation (DG) units such as wind and solar power in distribution networks, the power flow regulation capabilities of open-loop distribution networks are relatively weak, making it difficult to meet the requirements for flexible and reliable operation. Furthermore, in recent years, natural disasters such as typhoons and rainstorms have become increasingly frequent, causing severe damage to the power system, resulting in a series of power outages and huge economic losses. The distribution network structure is weak, and control methods are insufficient, making it inflexible in responding to low-probability, high-loss extreme events.

[0003] Introducing soft open points integrated with energy storage systems (E-SOPs) into distribution networks can effectively improve their reliability and resilience, thereby addressing the challenges of large-scale renewable energy penetration and frequent extreme events. An E-SOP consists of two parts: an energy storage system (ESS) and a soft open point (SOP). The ESS enables rapid bidirectional power regulation, alleviating the time mismatch between renewable energy output and load peaks, and providing power support to critical loads in emergencies. The SOP enables flexible interconnection between different feeders in the distribution network, improving the power flow control capability. Currently, E-SOP configuration costs are high, limiting large-scale deployment and requiring optimized configuration. However, existing E-SOP planning methods have the following problems:

[0004] 1) In terms of equipment planning, existing planning methods mostly only consider the planning of ESS or only consider the configuration method of SOP, and few technologies consider the configuration method of E-SOP.

[0005] 2) Most current planning methods only focus on either reliability improvement or resilience improvement. However, E-SOP has the dual function of improving both the reliability and resilience of the distribution network. It is necessary to consider both aspects in the planning process.

[0006] 3) Regarding the handling of stochasticity, the planning of E-SOP needs to consider the stochasticity of new energy output, load size and distribution network disconnection faults. Existing stochastic planning methods mainly include stochastic planning and robust planning.

[0007] Among them, stochastic programming is optimized based on a set of typical scenarios, but the selection of typical scenarios depends too much on the quality of the original data, making it difficult to guarantee the robustness of the planning results; robust programming does not consider the probability distribution information of random quantities such as new energy output, and only seeks the worst scenario within the fluctuation range for planning, resulting in overly conservative planning results; therefore, E-SOP planning needs to overcome the shortcomings of insufficient robustness of existing stochastic programming and overly conservative robust programming. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide an energy storage intelligent soft switch optimization configuration method and system that takes into account both reliability and flexibility, in order to address the shortcomings of the prior art. This method and system solves the problems of failure to achieve E-SOP configuration, failure to comprehensively consider the normal operation scenarios and extreme events of the distribution network, and insufficient robustness or overly conservative planning results in the prior art.

[0009] The present invention adopts the following technical solution:

[0010] An optimized configuration method for intelligent soft-switching energy storage that balances reliability and resilience includes the following steps:

[0011] S1. Input the distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning-related parameters to initialize the planning system;

[0012] S2. Generate the reference probability distribution of new energy output and load power, obtain the Wasserstein fuzzy set of new energy output and load power, and generate the first-order moment fuzzy set of the probability distribution of distribution network line faults.

[0013] S3. Based on the first-order moment fuzzy set of the fault probability distribution of the distribution network line obtained in step S2, establish a two-stage E-SOP sub-Browse bar programming model that takes into account both reliability and resilience.

[0014] S4. Solve the two-stage E-SOP sub-bar programming model obtained in step S3; output the optimal configuration scheme and operation scheme of E-SOP.

[0015] Preferably, step S2 specifically includes:

[0016] S201, Reference probability distribution of generated new energy output and load power

[0017] S202. Construct the support set of the Wasserstein fuzzy set and define the true probability distribution. Compared with the reference probability distribution Statistical distance between them; construct Wasserstein fuzzy sets Characterizing the true probability distribution of new energy output and load power The fluctuation range is used to generate Wasserstein fuzzy sets of new energy output and load power;

[0018] S203. Construct the support set of the first-order moment fuzzy set to generate the first-order moment fuzzy set of the probability distribution of faults in the power distribution network.

[0019] Preferably, the first-order moment fuzzy set of the line fault probability distribution:

[0020]

[0021] in, Representatives supporting the group M,s All possible probability distributions; Represents the probability distribution Calculation expectations.

[0022] Preferably, step S3 specifically includes:

[0023] S301, The goal is to minimize the total cost throughout the entire lifecycle of the E-SOP, including the investment cost C of the E-SOP. INV In addition to the worst-case expectation of operating costs, a first-phase planning model is established to determine the optimal installation location and capacity of the E-SOP;

[0024] S302. Establish the second-stage operation model and determine the optimal operation mode of E-SOP under the worst probability distribution of DG output, load power and line fault.

[0025] Preferably, the first-stage planning model satisfies the following constraints:

[0026]

[0027] C INV ≤C INV,max

[0028] in, The minimum power allowed to be configured for the ESS; P ESS Power configured for ESS; The maximum power that the ESS is allowed to configure; The minimum capacity allowed to be configured for ESS; E ESS The capacity configured for ESS; The maximum capacity that ESS can be configured with; To determine whether node j has the SOP installed using the 0-1 variable, if This means the SOP will be installed; otherwise, it will not be installed. The minimum capacity that can be configured for a single SOP; The SOP capacity installed for node j; N represents the maximum capacity that can be configured for a single SOP. bus N represents the total number of nodes in the system. SOP The number of ports for the E-SOP; C INV For investment costs; C INV,max This is the upper limit for investment costs.

[0029] Preferably, the second-stage planning model satisfies the following constraints:

[0030] E-SOP operating constraints include ESS constraints and SOP constraints. The ESS constraints are as follows:

[0031]

[0032] in, and These represent the charging and discharging power of ESS at time t in scenario s, respectively. P represents the remaining energy state of the ESS at time t in scenario s; T represents the total number of scheduling periods in a day; P ESS Power configured for ESS; Let s be the remaining energy state of ESS at time t-1 in scenario s; For ESS charging efficiency; The discharge efficiency of the ESS; E is the lower limit of the allowable state of charge (SOC) of the ESS. ESS The capacity configured for ESS This represents the maximum allowable SOC for the ESS.

[0033] The constraints in the SOP section of the E-SOP are as follows:

[0034]

[0035] Among them, Ω bus It is the set of all nodes in the system; The active power injected into the distribution network by the SOP installed at node j at time t in scenario s; The active power loss of the SOP installed on node j at time t in scenario s; Let s be the discharge power of ESS at time t in scenario s; The charging power of ESS at time t in scenario s; η SOP For the efficiency of SOP; μ represents the reactive power injected into the distribution network by the SOP installed at node j at time t in scenario s; SOP This is the upper limit coefficient for reactive power at SOP; The SOP capacity installed for node j.

[0036] Distribution network power balance constraints

[0037]

[0038] in, Inject active power into node k of the distribution network transformer substation; Inject active power from node k into distributed renewable energy units; This refers to the curtailment of wind and solar power generated by distributed renewable energy units installed at node k. The discharge power of the ESS; The charging power of ESS; Let be the active power of the load at node k; Let K be the active power of the load shedding at node k; Ω represents the power loss on line mn. line It is the set consisting of all lines in the system;

[0039] distflow current constraint

[0040]

[0041] Among them, P s,n,t With Q s,n,t These represent the active and reactive power injected into the distribution network by node n at time t in scenario s, respectively. and These represent the active and reactive power flowing from node m to node n on line mn, respectively; r mn With x mn These represent the resistance and reactance of line mn, respectively; V s,m,t The square of the voltage at node m; V is the square of the current in line mn. s,n,t The square of the voltage at node n;

[0042] P s,n,t With Q s,n,t The specific calculation formula is as follows:

[0043]

[0044] in, Inject the active power of the distribution network into node n at time t in scenario s. Let be the active power of the load at node n. Let n be the active power of the load shedding at node n. Inject reactive power from the distribution network into node n at time t in scenario s. Let n be the reactive power of the load shedding at node n. Let be the reactive power of the load at node n.

[0045] Disconnection constraint:

[0046]

[0047] Where M is a number; This represents the maximum current allowed to flow through line mn; To determine whether line mn is disconnected at time t, a 0-1 variable is used. If the result is positive, it means there was no disconnection; otherwise, a disconnection occurred.

[0048] Distribution network safety constraints:

[0049]

[0050] Among them, U k,max with U k,min These are the maximum and minimum allowable voltages for node k, respectively; The maximum active power that the transformer station can provide.

[0051] Preferably, step S4 specifically includes:

[0052] S401. Determine the second-order cone relaxation of SOP operation constraints and power flow constraints;

[0053] S402. Let y be a vector composed of all variables related to the operation of E-SOP in the second stage, forming the vector form of the two-stage E-SOP sub-Browse bar programming model;

[0054] S403, Form an equivalent model of the two-stage E-SOP sub-Browse bar programming model;

[0055] S404. The equivalent model of the two-stage E-SOP sub-bar model is solved using the column and constraint generation algorithm.

[0056] Preferably, the two-stage E-SOP distributed bar programming model is written as the following equivalent model:

[0057]

[0058] stAx≤b

[0059] ||ε W || ∞ ≤γ W

[0060] Ey≤f-Gξ-Hz-Jx

[0061] ||By||2≤r T y

[0062] Where z represents all The column vector formed; Ξ M Let ξ be the support set of z, that is, the set of all possible values ​​of z; ξ is the support set of all z. and The set that constitutes; Ξ W Let ξ be the support set, i.e., the set of all possible values ​​of ξ; c is the first-stage model value vector; the superscript T in all symbols represents matrix transpose; x is the first-stage decision variable; d is the second-stage model value vector; y is the second-stage decision variable; β M ρ is a column vector of auxiliary variables in the first-stage model; ρ represents all... The column vector formed; γ W This is a column vector of auxiliary variables for the first-stage model; ε is a column vector formed by the thresholds of the Wasserstein fuzzy sets. W ε is an auxiliary variable column vector of the first-stage model; A is the coefficient matrix of the first-stage model; b is the right-hand vector of the first-stage model; ε W denoted as an auxiliary variable column vector in the first-stage model; E is the coefficient matrix of y in the second-stage model; f is the right-hand vector of the second-stage model; G is the coefficient matrix of ξ in the second-stage model; H is the coefficient matrix of z in the second-stage model; J is the coefficient matrix of x in the second-stage model; B is the second-order cone coefficient matrix of y in the second-stage model; and r is the second-order cone coefficient vector of ξ in the second-stage model.

[0063] Preferably, the equivalent model for solving the two-stage E-SOP sub-bar model using the column and constraint generation algorithm is as follows:

[0064] Step 1: Set the lower bound LB = -∞, the upper bound UB = +∞, the number of iterations k = 0, and give the maximum allowable error ∈;

[0065] Step 2: Solve the main problem MP to obtain the optimal value of MP, ObjMP, and the optimal solution to the main problem. Update the lower bound LB = ObjMP;

[0066] Step 3: Solve the subproblem to obtain its optimal value ObjSP, and update the upper bound.

[0067] Step 4: If (UB-LB) / LB≤∈, then stop the loop and output x. * And terminate; otherwise, update k = k + 1 and execute Step 5;

[0068] Step 5: Add a new variable (y) to MP k ,z k ,ξ k And add constraint Ey to MP.k ≤f-Gξ k -Hz k -Jx、z l ∈Ξ M ξ l ∈Ξ W Return to Step 2;

[0069] S406: Outputs the optimal configuration and operation scheme of E-SOP.

[0070] Secondly, embodiments of the present invention provide an energy storage intelligent soft-switching optimized configuration system that balances reliability and resilience, comprising:

[0071] The data module takes into account power distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning-related parameters, and initializes the planning system.

[0072] The set module generates a reference probability distribution of new energy output and load power, obtains the Wasserstein fuzzy set of new energy output and load power, and generates a first-order moment fuzzy set of the probability distribution of distribution network line faults.

[0073] The module is constructed based on the first-order moment fuzzy set of the obtained distribution network line fault probability distribution to establish a two-stage E-SOP sub-Browsing planning model that takes into account both reliability and resilience.

[0074] The output module solves the two-stage E-SOP sub-bar programming model and outputs the optimal configuration and operation scheme of E-SOP.

[0075] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described energy storage smart soft-switching optimized configuration method that balances reliability and resilience.

[0076] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described energy storage smart soft-switching optimized configuration method that balances reliability and flexibility.

[0077] Compared with the prior art, the present invention has at least the following beneficial effects:

[0078] An optimized configuration method for intelligent soft switches in energy storage that balances reliability and resilience is proposed. This method considers the optimal planning scheme of E-SOP in the distribution network, rather than planning ESS or SOP separately. By leveraging the synergistic effect of ESS and SOP in E-SOP, the reliability and resilience of the distribution network are further improved. A planning method that balances the improvement of distribution network reliability and resilience is proposed, thereby fully leveraging the function of E-SOP and improving the overall operating efficiency of the distribution network. The method adopts a distributed robust planning approach, which combines the advantages of traditional stochastic optimization and robust optimization, and has the significant advantage of balancing conservatism and robustness. It can overcome the shortcomings of insufficient robustness or excessive conservatism in existing planning methods.

[0079] Furthermore, step S2 presents a mixed fuzzy set for renewable energy output, load power, and line faults, thus laying the foundation for establishing the E-SOP sub-Browser bar programming model in step S3. The two random variables, renewable energy output and load demand, have abundant historical data; therefore, Wasserstein fuzzy sets can be established to better characterize their random fluctuation range. The line random variable lacks corresponding historical data, with only the fault rate of each line available; therefore, a first-order moment fuzzy set is established to characterize line fault conditions during the planning process. The fuzzy set establishment method proposed in step S2 fully considers the richness of historical data for different random variables, making the fuzzy set establishment more accurate and feasible.

[0080] Furthermore, step S3 establishes a two-stage E-SOP planning-operation model. In the first stage, the E-SOP planning scheme is determined, and in the second stage, the E-SOP operation scheme under normal scenarios and extreme events is determined. The established E-SOP sub-robust planning model overcomes the shortcomings of overly sensitive stochastic programming models and overly conservative robust programming models, exhibiting a clear advantage in balancing conservatism and robustness. In addition, the established E-SOP sub-robust planning model simultaneously considers the functions of improving the reliability of distribution network operation under normal scenarios and enhancing resilience under extreme events, thus realizing full-scenario planning for E-SOP.

[0081] Furthermore, step S4 provides the equivalent model transformation and solution method for the E-SOP planning model. Since the E-SOP established in step S2 is a two-stage non-convex model, it is difficult to solve using existing algorithms. Therefore, step S4 uses the second-order cone relaxation technique to transform it into an equivalent model, and uses the column and constraint generation algorithm to solve it, obtaining the optimal planning scheme and operation scheme of E-SOP.

[0082] Furthermore, employing a column and constraint generation algorithm to solve the equivalent model of the two-stage E-SOP sub-Brussels bar model can effectively improve the model solution speed. For the equivalent model of the two-stage E-SOP sub-Brussels bar model, there are two algorithms for solving it: the Benders decomposition algorithm and the column and constraint generation algorithm. The Benders decomposition algorithm has poor convergence properties, resulting in a slow convergence speed when solving the large-scale programming E-SOP sub-Brussels bar model. The column and constraint generation algorithm, developed based on the Benders decomposition algorithm, improves convergence properties and facilitates faster model solution.

[0083] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0084] In summary, this invention establishes an E-SOP (Electronic-Supported Programming) sub-robust programming method that balances reliability and resilience, and proposes a corresponding solution method. This model fully considers the richness of historical data for various random variables such as renewable energy output, load power, and line faults, thus making the characterization of random variables more accurate. The model exhibits significant advantages in both conservatism and robustness, overcoming the shortcomings of insufficient robustness or excessive conservatism in existing planning methods. Furthermore, the model incorporates the E-SOP's function of improving the reliability and resilience of the distribution system, further enhancing the efficiency of the distribution system. The proposed solution method enables efficient and rapid solution of the model.

[0085] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0086] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the following description of the relative embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0087] Figure 1 A flowchart of the method provided by the present invention;

[0088] Figure 2 This is a schematic diagram of the E-SOP structure;

[0089] Figure 3 Topology diagram of the IEEE 33-node test system incorporating distributed photovoltaic units;

[0090] Figure 4 The test system topology diagram after configuring E-SOP;

[0091] Figure 5This is a power curve of ESS and SOP at various times under normal scenarios;

[0092] Figure 6 The graphs show the network loss power curves for different configuration schemes in a typical scenario.

[0093] Figure 7 Voltage curves for each node under different configuration schemes in a typical scenario;

[0094] Figure 8 The reactive power curves for each SOP under different configuration schemes in a typical scenario are shown.

[0095] Figure 9 Load supply power curves for different configuration schemes under extreme events;

[0096] Figure 10 ESS charge and discharge power curves for different configuration schemes under extreme events;

[0097] Figure 11 A schematic diagram of a computer device provided in an embodiment of the present invention;

[0098] Figure 12 This is a block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0099] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0100] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0101] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0102] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0103] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0104] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0105] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0106] This invention provides an optimized configuration method for energy storage smart soft switches that balances reliability and resilience. First, it inputs parameters such as historical data of the distribution network to initialize the planning system. Then, it generates a hybrid fuzzy set of renewable energy output, load power, and line faults: a Wasserstein fuzzy set is established to characterize the probability distribution function of renewable energy output and load power; a first-order moment fuzzy set is established to characterize the probability distribution function of line faults during distribution network operation. Furthermore, it establishes a distribution network E-SOP configuration model that comprehensively considers both reliability and resilience improvement. This model is a two-stage distributed bar optimization problem: the first stage plans the E-SOP configuration scheme, and the second stage optimizes the E-SOP operation scheme. The first-stage model aims to minimize the worst-case expected sum of investment cost and operating cost, considering constraints such as E-SOP installation capacity and location limitations to determine the E-SOP installation location and capacity. The second-stage model aims to determine the worst-case expected operating cost, including wind and solar curtailment penalties, network loss penalties, and load shedding penalties, considering E-SOP operation constraints and distribution network operation constraints to determine the optimal E-SOP operation scheme. Finally, the model is solved using the proposed hybrid fuzzy set-based Bruker model algorithm.

[0107] This invention provides an optimized configuration method for intelligent soft-switching energy storage that balances reliability and resilience, comprising the following steps:

[0108] S1. Planning system initialization;

[0109] S101. Input distribution network parameters, including: the number of each node, rated voltage, and operating voltage range; the number of each line, the node it is connected to, the type and length of the conductor, the maximum current flowing through it, and historical fault rate data (including historical data under normal scenarios and historical data under extreme events such as typhoons, the same below); the node where each user is located and the historical power operation data for each hour every day; the type of each DG, the node it is located at, and the historical power output data for each hour (or every 15 minutes) every day.

[0110] Specifically, it is required that the number of days for recording user power history data in normal scenarios is the same as the number of days for recording DG output history data in normal scenarios, and also the same under extreme events; let the normal scenario be s=1 and the extreme event be s=2, then N are collected for each scenario respectively. s Data for the day. Specifically, let the lower and upper limits of the allowable voltage amplitude at node k be U, respectively. k,min with U k,max Let the maximum allowable current to flow through line mn be . Let Ω be the set of all nodes and wires in the system. bus With Ω line Let N be the total number of nodes and wires in the system. bus With Nline Record the maximum transmission power of the upstream substation interconnection line as: Let the failure rates of lines m and n be respectively Let the records of the DG installed on node k on day n be as follows: Let the records of the user installed on node k on day n be as follows:

[0111] S102. Input cost parameters, including: ESS investment cost per unit capacity. Unit power ESS investment cost Unit capacity SOP investment cost c SOP Installation cost of a single ESS power station Installation cost of a single SOP converter Unit energy load shedding penalty cost The cost of curtailing wind and solar power per unit of energy and the cost of network loss per unit of energy Among them, the unit energy load shedding penalty cost The cost of curtailing wind and solar power per unit of energy and the cost of network loss per unit of energy For preset cost parameters, they can generally be set as follows: 45 yuan / kWh 4 yuan / kWh and 0.8 yuan / kWh.

[0112] S103. Input the relevant technical parameters of E-SOP, including: ESS charging efficiency in E-SOP. ESS discharge efficiency The upper limit of the allowed state of charge (SOC) during ESS operation SOC lower limit SOP converter port number N SOP The active power loss rate η of SOP SOP SOP reactive power upper limit coefficient μ SOP ESS and SOP service life y r (Unit: year)

[0113] S104. Input planning-related parameters, including: minimum power of ESS in E-SOP. Maximum power minimum capacity Maximum capacity Minimum power of a single-seat SOP converter Maximum power SOP Installation Quantity Limit Maximum permissible investment cost C INV,maxDiscount rate r.

[0114] S2. Generate a mixed fuzzy set of new energy output, load power and line faults;

[0115] S201. Generate a reference probability distribution for renewable energy output and load power. Let the normal scenario be s=1 and the extreme event be s=2. The historical data for renewable energy output and load power on day n are as follows: So, what is the reference probability distribution of new energy output and load power? Each component for:

[0116]

[0117] S202. Generate Wasserstein fuzzy sets for new energy power output and load power. The reference distribution shown in Equation (1) is not equal to the true probability distribution. The principle of sub-Bruker optimization is to assume that the statistical distance between the true probability distribution and the reference probability distribution in the probability space does not exceed a certain threshold, thereby constructing a fuzzy set. The steps for constructing the fuzzy set for new energy power output and load power are as follows:

[0118] 1) The support set for the Wasserstein fuzzy set is constructed as follows:

[0119]

[0120] in, and These represent the maximum possible power of the DG and load of node k in scenario s, respectively. Generally, in a normal scenario s=1, the installed capacity can be taken, while in an extreme event s=2, it can be taken as 0.1 to 0.5 times the installed capacity.

[0121] 2) Define the true probability distribution Compared with the reference probability distribution The statistical distance between them. Fuzzy sets defined by the Wasserstein distance possess superior mathematical properties, facilitating subsequent solutions; therefore, the Wasserstein distance is used in this invention, defined as follows:

[0122]

[0123] Among them, Π is ξ s With ξ s The joint probability distribution.

[0124] 3) Construct the following Wasserstein fuzzy set To depict the true probability distribution of new energy output and load power Fluctuation range:

[0125]

[0126] in, Representatives supporting the group W,s All possible probability distributions. In equation (4) This is a preset value, and its specific calculation method is as follows:

[0127]

[0128] Where β is the confidence level, typically set to 95%; and γ is the mean of the historical data sample.

[0129] S203. Generate the first-order moment fuzzy set of the probability distribution of faults in the power distribution network.

[0130] Since data on power distribution network line faults is limited, and only average fault probability information can be collected in practice, a first-order moment fuzzy set can be established to characterize the fluctuation range of its probability distribution. The specific steps are as follows:

[0131] 1) Construct the support set of the first-order moment fuzzy set:

[0132]

[0133] in, This is a 0-1 variable representing whether line mn has failed at time t in scenario s. A value of 0 indicates that a failure has occurred. This represents the maximum number of lines that can simultaneously experience faults in a distribution network. Generally speaking, under normal circumstances... The value can be 0 or 1, under extreme events. The value ranges from 4 to 6.

[0134] 2) Construct the first-order moment fuzzy set of the line fault probability distribution:

[0135]

[0136] in, Representatives supporting the group M,s All possible probability distributions; Represents the probability distribution Calculation expectations.

[0137] S3. Establish a two-stage E-SOP sub-bar programming model that takes into account both reliability and flexibility;

[0138] S301. Establish the first-stage planning model to determine the optimal installation location and capacity of E-SOP.

[0139] The first-phase planning model aims to minimize the total cost over the entire lifecycle of the E-SOP, specifically including the investment cost C of the E-SOP. INV And the worst-case expectation of operating costs, expressed as follows:

[0140]

[0141] Among them, C INV Let x be the investment cost of the E-SOP; x is a vector consisting of all E-SOP planning variables, specifically including the continuous variable P that determines the ESS power in the E-SOP. ESS Determine the continuous variable E of the ESS capacity. ESS 0-1 variables to determine whether the SOP converter is installed at node j. Continuous variables determining the capacity of the j-node SOP converter Right now Q(x,ξ,z) represents the optimal operating cost of the distribution network given the E-SOP installation method x, the output of new energy load ξ, and the line fault z. Represents the joint probability Calculate the expectation of Q; Representative with and As a variable, determine The upper limit is the optimal operating cost for calculating the worst probability distribution.

[0142] E-SOP planning costs include the capacity cost, power cost, and installation cost of the ESS, as well as the capacity cost and installation cost of the SOP, and their specific expressions are as follows:

[0143]

[0144] The first-stage planning model satisfies the following constraints:

[0145]

[0146] C INV ≤C INV,max (15)

[0147] Equations (11) and (12) constrain the power and capacity of the ESS in the E-SOP to be within a reasonable range; Equation (13) constrains the capacity of the SOP converter in the E-SOP to be within a reasonable range; Equation (14) constrains the total number of ports in the E-SOP to be a given value; and Equation (15) constrains the total investment cost to not exceed the budget limit.

[0148] S302. Establish the second-stage operation model and determine the optimal operation mode of E-SOP under the worst probability distribution of DG output, load power and line fault.

[0149] Given the E-SOP installation method x, the output of renewable energy load ξ, and the line fault z, the expression for the optimal operating cost Q of the distribution network is as follows:

[0150]

[0151] Where, p s The probability of a typical scenario or an extreme event occurring; The cost of wind and solar power curtailment penalties in scenario S. Let K be the amount of wind and solar power curtailment at node k in scenario s at time t. To incur the cost of penalties for network damage, Let mn be the power loss of line mn. The power loss of the SOP converter installed at node k; To reduce the cost of load shedding, For load shedding power; D y The total number of days in a year, i.e., D y =365; ΔT is the time interval between two scheduling periods. If historical data in S101 is collected at an hourly interval, then ΔT is 1 hour here; otherwise, ΔT is 15 minutes.

[0152] The second-stage operational model includes the following constraints:

[0153] 1) E-SOP operational constraints

[0154] E-SOP operating constraints include ESS constraints and SOP constraints. The ESS constraints are as follows:

[0155]

[0156] in, and These represent the charging and discharging power of ESS at time t in scenario s, respectively. The State of Energy (SOE) of the ESS is given by equation (20). T is the total number of scheduling periods in a day. If ΔT is 1 hour, then T = 24; otherwise, T = 96. Equations (20) and (21) constrain the charging and discharging power of the ESS to not exceed the installed power. Equation (22) gives the formula for the SOE of the ESS to transfer between two adjacent time periods. Equation (23) constrains the SOE of the ESS to be within a reasonable range. Equation (24) constrains the SOE of the ESS to return to its initial value at the end of a scheduling day in a normal scenario, thereby ensuring the sustainability of the ESS operation. However, in extreme events, the ESS takes load supply as its main objective and does not need to consider the sustainability of operation.

[0157] The constraints in the SOP section of the E-SOP are as follows:

[0158]

[0159] Equation (25) constrains the net power in the E-SOP to be 0; Equation (26) gives the calculation formula for the power loss of the SOP converter; Equation (27) constrains the reactive power range of the SOP; Equation (28) constrains the apparent power of the SOP to not exceed the installed capacity.

[0160] 2) Power balance constraints in distribution networks

[0161]

[0162] in, Inject active power into node k of the distribution network transformer substation.

[0163] 3) Distflow power flow constraints

[0164]

[0165] Among them, P s,n,t With Q s,n,t These represent the active and reactive power injected into the distribution network by node n at time t in scenario s, respectively. and These represent the active and reactive power flowing from node m to node n on line mn, respectively; r mn With x mn These represent the resistance and reactance of line mn, respectively; V s,m,t The square of the voltage at node m; P is the square of the current in line mn. s,n,t With Q s,n,t The specific calculation formula is as follows:

[0166]

[0167] 4) Disconnection constraint:

[0168]

[0169] Where M is a large number, taken as 100; This represents the maximum current allowed to flow through line mn.

[0170] 5) Distribution network security constraints

[0171]

[0172] Among them, U k,max with U k,min These are the maximum and minimum allowable voltages for node k, respectively; The maximum active power that the substation can provide. Equation (39) constrains the node voltage to be maintained within a reasonable range under normal scenarios; Equations (40) to (42) respectively constrain the active power of the substation, the power of wind and solar curtailment, and the positive number of load shedding power, and they cannot exceed the upper limit.

[0173] S4. Solve the two-stage E-SOP sub-bar programming model; output the optimal configuration and operation scheme of E-SOP.

[0174] Second-order cone relaxation of S401, SOP operation constraints and power flow constraints;

[0175] For the radical terms in equations (26) and (28) and the product term of decision variables in equation (33), second-order cone relaxation is performed to obtain the second-order cone constraints as follows:

[0176]

[0177] S402. Form the vector form of the two-stage E-SOP sub-Browser programming model; let y be the vector composed of all variables related to the operation of E-SOP in the second stage, then the two-stage E-SOP sub-Browser programming model is written in the following vector form:

[0178]

[0179] in:

[0180]

[0181] Where A, E, G, H, J, and B are correlation coefficient matrices; b, d, f, and r are correlation coefficient vectors. Similarly, fuzzy sets and their support sets can be written in the following vector form:

[0182]

[0183] Ξ M ={z∣R M z≤g M} (50)

[0184]

[0185] Ξ W ={ξ∣R W ξ≤g W} (52)

[0186] Among them, R M With R M Let ρ and g be the correlation coefficient matrix. M , g W This is the correlation coefficient vector.

[0187] S403. Form an equivalent model of the two-stage E-SOP sub-Browsing programming model. For the two-stage E-SOP sub-Browsing programming model shown in equations (46) to (48), there are two sup operations in the objective function, which cannot be solved directly. Therefore, the outer sup operation is first converted into the following equivalent form:

[0188]

[0189] Where, α M With β M Represents the dual variable.

[0190] Furthermore, using strong duality theory, equation (53) is transformed into the following equivalent form:

[0191]

[0192] Similarly, for the inner sup, i.e., R(x,z), it is equivalent to the following form:

[0193]

[0194] Where, α W,n and γ W As the dual variable, ε W These are auxiliary variables introduced.

[0195] Finally, the two-stage E-SOP distributed bar programming model can be written as the following equivalent model:

[0196]

[0197] S404. The equivalent model of the two-stage E-SOP sub-bar model is solved by using the column-and-constraint generation algorithm (C&CG).

[0198] The steps of the C&CG algorithm are as follows:

[0199] Step 1: Set the lower bound LB = -∞, the upper bound UB = +∞, the number of iterations k = 0, and give the maximum allowable error ∈.

[0200] Step 2: Solve the master problem (MP) as shown in equation (57):

[0201]

[0202] ObjMP, the optimal value of MP, and the optimal solution to the main problem are obtained. Update the lower bound LB = ObjMP.

[0203] Step 3: Solve the sub-problem (SP):

[0204]

[0205] Where, α SP β SP ν SP With μ SP All are dual variables; considering that SP as shown in equation (58) is a max-min bilevel problem, it cannot be solved directly, but its dual problem can be solved as follows:

[0206]

[0207] Solve equation (59) to obtain its optimal value ObjSP, and update the upper bound.

[0208] Step 4: If (UB-LB) / LB≤∈, then stop the loop and output x. * And terminate; otherwise, update k = k + 1 and execute Step 5;

[0209] Step 5: Add a new variable (y) to MP k ,z k ,ξ k And add constraint Ey to MP. k ≤f-Gξ k -Hz k -Jx、z l ∈Ξ M ξ l ∈Ξ W Return to Step 2.

[0210] S406: Outputs the optimal configuration and operation scheme of E-SOP.

[0211] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "platform."

[0212] In another embodiment of the present invention, an energy storage smart soft switch optimization configuration system that balances reliability and flexibility is provided. This system can be used to implement the above-mentioned energy storage smart soft switch optimization configuration method that balances reliability and flexibility. Specifically, the energy storage smart soft switch optimization configuration system that balances reliability and flexibility includes a data module, an aggregation module, a construction module, and an output module.

[0213] The data module takes into account power distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning-related parameters, and initializes the planning system.

[0214] The set module generates a reference probability distribution of new energy output and load power, obtains the Wasserstein fuzzy set of new energy output and load power, and generates a first-order moment fuzzy set of the probability distribution of distribution network line faults.

[0215] The module is constructed based on the first-order moment fuzzy set of the obtained distribution network line fault probability distribution to establish a two-stage E-SOP sub-Browsing planning model that takes into account both reliability and resilience.

[0216] The output module solves the two-stage E-SOP sub-bar programming model and outputs the optimal configuration and operation scheme of E-SOP.

[0217] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment can be used in the operation of an energy storage intelligent soft-switching optimization configuration method that balances reliability and flexibility, including:

[0218] Input distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning-related parameters to initialize the planning system; generate reference probability distributions of renewable energy output and load power, obtain Wasserstein fuzzy sets of renewable energy output and load power, and generate first-moment fuzzy sets of distribution network line fault probability distributions; establish a two-stage E-SOP sub-Browser bar programming model that balances reliability and resilience based on the obtained first-moment fuzzy sets of distribution network line fault probability distributions; solve the obtained two-stage E-SOP sub-Browser bar programming model; and output the optimal configuration and operation scheme of E-SOP.

[0219] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that more specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable 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 devices, magnetic storage devices, or any suitable combination of the foregoing.

[0220] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying 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 readable storage medium can also be any readable medium other than a readable storage medium that 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 readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0221] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0222] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the energy storage smart soft switch optimization configuration method that balances reliability and resilience in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps:

[0223] Input distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning-related parameters to initialize the planning system; generate reference probability distributions of renewable energy output and load power, obtain Wasserstein fuzzy sets of renewable energy output and load power, and generate first-moment fuzzy sets of distribution network line fault probability distributions; establish a two-stage E-SOP sub-Browser bar programming model that balances reliability and resilience based on the obtained first-moment fuzzy sets of distribution network line fault probability distributions; solve the obtained two-stage E-SOP sub-Browser bar programming model; and output the optimal configuration and operation scheme of E-SOP.

[0224] Please see Figure 11 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the energy storage intelligent soft-switching optimization configuration method that balances reliability and flexibility as described in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the energy storage intelligent soft-switching optimization configuration system that balances reliability and flexibility as described in this embodiment. To avoid repetition, these details are not elaborated here.

[0225] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 11 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0226] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, CPUs, graphics processing units (GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, quantum computing-based data processing logic units, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0227] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0228] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0229] Any references to memory, databases, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0230] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0231] Please see Figure 12 The terminal device 600 is an electronic device, which takes the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0232] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0233] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0234] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0235] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0236] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0237] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0238] The case study of this invention selected the IEEE 33-node system with distributed photovoltaics, and the verification was carried out based on publicly available historical data on photovoltaic output, load fluctuation and line failure rate.

[0239] Example setup introduction

[0240] The topology of the IEEE 33-node system with distributed photovoltaics is as follows: Figure 3 As shown in the diagram, distributed photovoltaic (PV) units of 0.5MW, 0.8MW, 0.5MW, 0.5MW, and 1MW are connected at nodes 13, 20, 22, 23, and 27, respectively. Nodes 10, 11, 16, 19, 24, and 31 are critical load nodes. The transformer station at node 1 can provide 10MW of active power, and backfeeding is not permitted. For ease of subsequent analysis, the feeder consisting of nodes 6–18 is designated as feeder 1, nodes 19–22 as feeder 2, and nodes 26–33 as feeder 3.

[0241] The relevant parameters involved in step S1 are shown in Table 1.

[0242] Table 1. Parameter settings for the case study

[0243]

[0244] Comparison of different configuration options

[0245] 1. To illustrate the superiority of E-SOP in improving reliability / resilience, and the necessity of balancing reliability and resilience improvement, the following six configuration schemes are compared and analyzed:

[0246] Option 1: Adopt the solution proposed in this paper, and configure E-SOP while taking into account the improvement of reliability and elasticity, that is, the configuration solution proposed in this paper;

[0247] Option 2: Configure E-SOP only under the premise of elasticity improvement, that is, only consider the case where s=1;

[0248] Option 3: Configure E-SOP only under the premise of elasticity improvement, that is, only consider the case of s=2;

[0249] Option 4: Configure ESS and SOP while taking into account both reliability and resilience improvement, but do not constitute E-SOP;

[0250] Option 5: Configure only ESS while ensuring both reliability and resilience improvements;

[0251] Option 6: Use the original system without any configuration.

[0252] 2. The optimal configuration results and operating costs under each configuration scheme are shown in Table 2. In particular, the configuration results of Scheme 1 proposed in this paper are as follows: Figure 4 As shown.

[0253] Table 2 Comparison of configuration results and operation results for different schemes

[0254]

[0255] 3. Compared with the original system, Scheme 1 reduces various operating costs by introducing E-SOP in the distribution network.

[0256] Under normal circumstances, the power curtailment and network loss decreased by 43.10% and 56.15%, respectively; under extreme circumstances, the load shedding decreased by 52.24%. This demonstrates that introducing E-SOP into the distribution network can effectively improve its reliability and resilience.

[0257] 4. Compare Scheme 1, Scheme 4 and Scheme 5.

[0258] Schemes 1 and 4 outperform Scheme 5. This is because the SOP enables flexible interconnection between feeders, expanding the ESS's regional adjustment capability. Furthermore, the E-SOP in Scheme 1 allows for flexible adjustment of the ESS's charging and discharging power between feeders, overcoming the limitation of fixed ESS access capacity in Scheme 4. Therefore, the E-SOP proposed in this paper performs better.

[0259] 5. Compare Scheme 1, Scheme 2 and Scheme 3.

[0260] Scheme 2, which focuses solely on reliability improvements, offers slight improvements in reducing light curtailment and network losses, but significantly increases load shedding under extreme events. Furthermore, while Scheme 3 shows slight improvements in various operational metrics, its investment costs rise dramatically. Therefore, considering only resilience enhancements may lead to overly conservative allocation results. The configuration model proposed in this paper achieves a balance between reliability, resilience, and investment costs.

[0261] Analysis of E-SOP's Operational Mode for Enhancing Reliability and Resilience

[0262] 1. E-SOP improves reliability in normal scenarios

[0263] In conventional scenarios, E-SOP improves the reliability of the distribution network by promoting the consumption of new energy sources and reducing network losses.

[0264] In promoting the consumption of new energy sources, the charge-discharge curve of E-SOP conforms to the spatial distribution of the load and the temporal characteristics of photovoltaic output, such as... Figure 5As shown. Spatially, the E-SOP absorbs energy from feeder 2, which has a smaller load and more distributed photovoltaics, and injects it into feeders 1 and 3, which have a heavier load, thus achieving power mutual assistance between feeders. Temporally, the E-SOP charges at noon and discharges at night to supplement peak shaving and valley filling, promoting the consumption of photovoltaic power.

[0265] Regarding reducing network losses, the line losses of different allocation schemes were compared, such as... Figure 6 As shown. Compared to Schemes 6 and 5, the systems employing E-SOP and ESS+SOP further introduce SOP to optimize power flow distribution, thereby reducing current flow on high-impedance lines. Furthermore, as... Figure 7 As shown, the E-SOP and ESS+SOP systems utilize the reactive power regulation capability of the SOP to effectively increase the voltage of the line-end bus through reactive power compensation, thereby further reducing network losses. However, compared with ESS+SOP, the ESS in E-SOP only requires one SOP converter to supply power to other feeders (ESS+SOP requires two), thus reducing the capacity occupation of the SOP by the ESS. Therefore, when the SOP capacity of the two schemes is similar, the SOP in E-SOP can provide more reactive power to the system, such as... Figure 8 As shown, this allows for a better increase in voltage levels.

[0266] 2. E-SOP enhances resilience under extreme conditions

[0267] In extreme events, the most severe faults are line breaks on lines 4-5, 21-22, and 24-25. The load demand and supply situations at various times under extreme events for scenarios 1, 2, 3, and 6 are as follows: Figure 9 As shown, the charging and discharging power of ESS under different schemes is as follows: Figure 10 As shown.

[0268] In configurations without a Standard Operating Plan (SOP), due to the lack of power flow paths, the Energy Storage Element (ESS) can only provide power to the bus near its installation location, failing to alleviate load reduction on other feeders. Therefore, systems relying solely on the ESS, despite having a large installed capacity, suffer from low utilization. Conversely, the ESS+SOP allocation scheme lacks the ability to flexibly allocate energy storage resources among feeders, resulting in lower energy storage utilization efficiency compared to the E-SOP proposed in this paper. Therefore, the E-SOP configuration proposed in this invention offers greater advantages in improving resilience.

[0269] In summary, the present invention provides an optimized configuration method and system for energy storage smart soft switches that balances reliability and resilience. It can provide an optimal planning scheme for energy storage smart soft switches that combines robustness and conservatism, thereby improving the reliability of the distribution network under normal scenarios and its resilience under extreme events, and enhancing the overall benefits of the distribution network. The planning results provide a reference for distribution network planners.

[0270] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0271] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0272] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0273] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0274] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0275] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0276] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0277] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0278] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0279] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0280] The above content is only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the configuration of intelligent soft-switching energy storage systems, balancing reliability and resilience, characterized in that: The following steps are involved: S1. Input the distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning-related parameters to initialize the planning system; S2. Generate the reference probability distribution of new energy output and load power, obtain the Wasserstein fuzzy set of new energy output and load power, and generate the first-order moment fuzzy set of the probability distribution of distribution network line faults. S3. Based on the first-order moment fuzzy set of the fault probability distribution of the distribution network line obtained in step S2, establish a two-stage E-SOP sub-Browse bar programming model that takes into account both reliability and resilience. S4. Solve the two-stage E-SOP sub-bar programming model obtained in step S3; output the optimal configuration and operation scheme of E-SOP, specifically: S401. Determine the second-order cone relaxation of SOP operation constraints and power flow constraints; S402. Let y be a vector composed of all variables related to the operation of E-SOP in the second stage, forming the vector form of the two-stage E-SOP sub-Browse bar programming model; S403, Form an equivalent model of the two-stage E-SOP sub-Browse bar programming model; The two-stage E-SOP distributed bar programming model can be written as the following equivalent model: Where z represents all The column vector formed; Ξ M Let ξ be the support set of z, that is, the set of all possible values ​​of z; ξ is the support set of all z. and The set that constitutes; Let Ξ be the active power of the load at node k. W Let ξ be the support set, i.e., the set of all possible values ​​of ξ; c is the first-stage model value vector; the superscript T in all symbols represents matrix transpose; x is the first-stage decision variable; d is the second-stage model value vector; y is the second-stage decision variable; β M ρ is a column vector of auxiliary variables in the first-stage model; ρ represents all... The column vector formed; γ W This is a column vector of auxiliary variables for the first-stage model; ε is a column vector formed by the thresholds of the Wasserstein fuzzy sets. W ε is an auxiliary variable column vector of the first-stage model; A is the coefficient matrix of the first-stage model; b is the right-hand vector of the first-stage model; ε W denoted as an auxiliary variable column vector in the first-stage model; E is the coefficient matrix of y in the second-stage model; f is the right-hand vector of the second-stage model; G is the coefficient matrix of ξ in the second-stage model; H is the coefficient matrix of z in the second-stage model; J is the coefficient matrix of x in the second-stage model; B is the second-order cone coefficient matrix of y in the second-stage model; and r is the second-order cone coefficient vector of ξ in the second-stage model. S404. Solve the equivalent model of the two-stage E-SOP sub-Brow bar model using the column and constraint generation algorithm; S405: Outputs the optimal configuration and operation scheme of E-SOP.

2. The energy storage intelligent soft-switching optimization configuration method that balances reliability and flexibility according to claim 1, characterized in that, Step S2 is as follows: S201, Reference probability distribution of generated new energy output and load power S202. Construct the support set of the Wasserstein fuzzy set and define the true probability distribution. Compared with the reference probability distribution Statistical distance between them; construct Wasserstein fuzzy sets Characterizing the true probability distribution of new energy output and load power The fluctuation range is used to generate Wasserstein fuzzy sets of new energy output and load power; S203. Construct the support set of the first-order moment fuzzy set to generate the first-order moment fuzzy set of the probability distribution of faults in the power distribution network.

3. The energy storage intelligent soft-switching optimization configuration method that balances reliability and flexibility according to claim 2, characterized in that, The first-order moment fuzzy set of the line fault probability distribution: in, Representatives supporting the group M,s All possible probability distributions; Represents the probability distribution Calculation expectations, This represents a 0-1 variable indicating whether line mn has failed at time t in scenario s.

4. The energy storage smart soft-switching optimization configuration method that balances reliability and flexibility according to claim 1, characterized in that, Step S3 is as follows: S301, The goal is to minimize the total cost throughout the entire lifecycle of the E-SOP, including the investment cost C of the E-SOP. INV In addition to the worst-case expectation of operating costs, a first-phase planning model is established to determine the optimal installation location and capacity of the E-SOP; S302. Establish the second-stage operation model and determine the optimal operation mode of E-SOP under the worst probability distribution of DG output, load power and line fault.

5. The energy storage intelligent soft-switching optimization configuration method that balances reliability and flexibility according to claim 4, characterized in that, The first-stage planning model satisfies the following constraints: C INV ≤C INV,max in, The minimum power allowed to be configured for the ESS; P ESS Power configured for ESS; The maximum power that the ESS is allowed to configure; The minimum capacity allowed to be configured for ESS; E ESS The capacity configured for ESS; The maximum capacity that ESS can be configured with; To determine whether node j has the SOP installed using the 0-1 variable, if This means the SOP will be installed; otherwise, it will not be installed. The minimum capacity that can be configured for a single SOP; The SOP capacity installed for node j; N represents the maximum capacity that can be configured for a single SOP. bus N represents the total number of nodes in the system. SOP The number of ports for the E-SOP; C INV For investment costs; C INV,max This is the upper limit for investment costs.

6. The energy storage smart soft-switching optimization configuration method that balances reliability and flexibility according to claim 4, characterized in that, The second-stage planning model satisfies the following constraints: E-SOP operating constraints include ESS constraints and SOP constraints. The ESS constraints are as follows: in, and These represent the charging and discharging power of ESS at time t in scenario s, respectively. Let P be the remaining energy state of the ESS at time t in scenario s; T is the total number of scheduling periods in a day; ΔT is the time interval between two scheduling periods; ESS Power configured for ESS; Let s be the remaining energy state of ESS at time t-1 in scenario s; For ESS charging efficiency; The discharge efficiency of the ESS; E is the lower limit of the allowable state of charge (SOC) of the ESS. ESS The capacity configured for ESS This represents the maximum allowable SOC for the ESS. The constraints in the SOP section of the E-SOP are as follows: Among them, Ω bus It is the set of all nodes in the system; The active power injected into the distribution network by the SOP installed at node j at time t in scenario s; The active power loss of the SOP installed on node j at time t in scenario s; Let s be the discharge power of ESS at time t in scenario s; The charging power of ESS at time t in scenario s; η SOP For the efficiency of SOP; μ represents the reactive power injected into the distribution network by the SOP installed at node j at time t in scenario s; SOP This is the upper limit coefficient for reactive power at SOP; The SOP capacity installed for node j; Distribution network power balance constraints in, Inject active power into node k of the distribution network transformer substation; Inject active power from node k into distributed renewable energy units; This refers to the curtailment of wind and solar power generated by distributed renewable energy units installed at node k. The discharge power of the ESS; The charging power of ESS; Let be the active power of the load at node k; Let K be the active power of the load shedding at node k; Ω represents the power loss on line mn. line It is the set consisting of all lines in the system; distflow current constraint Among them, P s,n,t With Q s,n,t These represent the active and reactive power injected into the distribution network by node n at time t in scenario s, respectively. and These represent the active and reactive power flowing from node m to node n on line mn, respectively; r mn With x mn These represent the resistance and reactance of line mn, respectively; V s,m,t The square of the voltage at node m; V is the square of the current in line mn. s,n,t The square of the voltage at node n; P s,n,t With Q s,n,t The specific calculation formula is as follows: in, Inject the active power of the distribution network into node n at time t in scenario s. Let be the active power of the load at node n. Let n be the active power of the load shedding at node n. Inject reactive power from the distribution network into node n at time t in scenario s. Let n be the reactive power of the load shedding at node n. Let n be the reactive power of the load at node n; Disconnection constraint: Where M is a number; This represents the maximum current allowed to flow through line mn; To determine whether line mn is disconnected at time t, a 0-1 variable is used. This indicates that no disconnection has occurred; otherwise, a disconnection has occurred. Distribution network safety constraints: Among them, U k,max with U k,min These are the maximum and minimum allowable voltages for node k, respectively; The maximum active power that the transformer station can provide.

7. An optimized configuration system for intelligent soft-switching energy storage that balances reliability and flexibility, characterized in that, include: The data module takes into account power distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning-related parameters, and initializes the planning system. The set module generates a reference probability distribution of new energy output and load power, obtains the Wasserstein fuzzy set of new energy output and load power, and generates a first-order moment fuzzy set of the probability distribution of distribution network line faults. The module is constructed based on the first-order moment fuzzy set of the obtained distribution network line fault probability distribution to establish a two-stage E-SOP sub-Browsing planning model that takes into account both reliability and resilience. The output module solves for the two-stage E-SOP distributed bar programming model; it outputs the optimal configuration and operation scheme of E-SOP; specifically: S401. Determine the second-order cone relaxation of SOP operation constraints and power flow constraints; S402. Let y be a vector composed of all variables related to the operation of E-SOP in the second stage, forming the vector form of the two-stage E-SOP sub-Browse bar programming model; S403, Form an equivalent model of the two-stage E-SOP sub-Browse bar programming model; The two-stage E-SOP distributed bar programming model can be written as the following equivalent model: Where z represents all The column vector formed; Ξ M Let ξ be the support set of z, that is, the set of all possible values ​​of z; ξ is the support set of all z. and The set constituted Ξ represents the active power of the load at node k; W Let ξ be the support set, i.e., the set of all possible values ​​of ξ; c is the first-stage model value vector; the superscript T in all symbols represents matrix transpose; x is the first-stage decision variable; d is the second-stage model value vector; y is the second-stage decision variable; β M ρ is a column vector of auxiliary variables in the first-stage model; ρ represents all... The column vector formed; γ W This is a column vector of auxiliary variables for the first-stage model; ε is a column vector formed by the thresholds of the Wasserstein fuzzy sets. W ε is an auxiliary variable column vector of the first-stage model; A is the coefficient matrix of the first-stage model; b is the right-hand vector of the first-stage model; ε W denoted as an auxiliary variable column vector in the first-stage model; E is the coefficient matrix of y in the second-stage model; f is the right-hand vector of the second-stage model; G is the coefficient matrix of ξ in the second-stage model; H is the coefficient matrix of z in the second-stage model; J is the coefficient matrix of x in the second-stage model; B is the second-order cone coefficient matrix of y in the second-stage model; and r is the second-order cone coefficient vector of ξ in the second-stage model. S404. Solve the equivalent model of the two-stage E-SOP sub-Brow bar model using the column and constraint generation algorithm; S405: Outputs the optimal configuration and operation scheme of E-SOP.

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