Energy storage intelligent soft switch optimal configuration method and system giving consideration to reliability and elasticity

Abstract

The invention discloses an energy storage intelligent soft switch optimal configuration method and system giving consideration to reliability and elasticity, and the method comprises the steps: inputting a power distribution network parameter, a cost parameter, related technical parameters of an E-SOP, and planning related parameters, and initializing a planning system; generating reference probability distribution of new energy output and load power, obtaining a Wasserstein fuzzy set of the new energy output and load power, and generating a first moment fuzzy set of power distribution network line fault probability distribution; establishing a two-stage E-SOP distribution robust planning model considering reliability and elasticity based on the obtained first-order moment fuzzy set of the power distribution network line fault probability distribution; solving the obtained two-stage E-SOP distribution robust planning model; and outputting the optimal configuration scheme and the operation scheme of the E-SOP. According to the method, the defect that an existing planning method is insufficient in robustness or is too conservative can be overcome; the function of improving the reliability and elasticity of the power distribution system by the E-SOP is considered, and the benefit of the power distribution system is further improved; and efficient and rapid solving of the model is realized.

Description

Background Art

[0002] As the penetration rate of distributed renewable energy units (DG) such as wind power and photovoltaic power in distribution networks continues to increase, the open-loop distribution network has a weak power flow control capability and can no longer meet the needs of flexible and reliable operation of distribution networks. In addition, in recent years, natural disasters such as typhoons and rainstorms have become increasingly frequent, causing serious damage to the power system, resulting in a series of power outages and huge economic losses. The distribution network has a weak structure and a lack of control means, and lacks flexibility in dealing with extreme events with low probability and high losses.

[0003] Introducing soft open point integrated with energy storage system (E-SOP) in the distribution network can effectively improve the reliability and flexibility of the distribution network, thereby coping with the challenges of large-scale penetration of new energy and frequent extreme events. E-SOP consists of two parts: energy storage system (ESS) and soft open point (SOP). Among them, ESS can realize fast bidirectional power regulation, alleviate the time mismatch between new energy output and load peak, and provide power support for critical loads in emergency situations; SOP realizes flexible interconnection between different feeders in the distribution network and improves the power flow control capability of the distribution network.

[0004] Currently, the configuration cost of E-SOP is high and cannot be accessed on a large scale, so it needs to be optimized. However, the existing E-SOP planning method has the following problems:

[0005] 1) In terms of equipment planning, most existing planning methods only consider the planning of ESS, or only consider the configuration method of SOP, and few technologies consider the configuration method of E-SOP.

[0006] 2) Most current planning methods only focus on improving either reliability or resilience, but E-SOP has the dual function of improving both reliability and resilience of the distribution network. It is necessary to consider both in a coordinated manner during the planning process.

[0007] 3) In terms of randomness processing methods, the planning of E-SOP needs to consider the randomness of renewable energy output, load size, and distribution network disconnection failures. The existing random planning methods mainly include random planning and robust planning.

[0008] Among them, the stochastic planning method is optimized based on a set of typical scenarios, but the selection of typical scenarios is overly dependent on the quality of the original data, making it difficult to ensure the robustness of the planning results; robust planning does not consider the probability distribution information of random quantities such as new energy output, and only looks for the worst scenario within the fluctuation range for planning, resulting in overly conservative planning results; therefore, it is necessary for E-SOP planning to overcome the shortcomings of insufficient robustness of existing stochastic planning and overly conservative robust planning. Summary of the invention

[0009] The technical problem to be solved by the present invention is to provide a method and system for optimizing the configuration of energy storage intelligent soft switches that takes into account both reliability and flexibility in response to the above-mentioned deficiencies in the prior art, so as to solve the technical problems in the prior art that E-SOP configuration cannot be achieved, the conventional operation scenarios and extreme events of the distribution network cannot be comprehensively considered, and the planning results are not robust enough or are too conservative.

[0010] The present invention adopts the following technical solutions:

[0011] A method for optimizing configuration of energy storage intelligent soft switches taking into account both reliability and flexibility comprises the following steps:

[0012] S1. Input distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning related parameters, and initialize the planning system;

[0013] S2, generate reference probability distribution of renewable energy output and load power, obtain Wasserstein fuzzy sets of renewable energy output and load power, and generate first-order moment fuzzy sets of distribution network line fault probability distribution;

[0014] S3, based on the first-order moment fuzzy set of the distribution network line failure probability distribution obtained in step S2, a two-stage E-SOP distributed robust programming model that takes into account both reliability and flexibility is established;

[0015] S4, solving the two-stage E-SOP distributed robust programming model obtained in step S3; outputting the optimal configuration plan and operation plan of the E-SOP.

[0016] Preferably, step S2 specifically includes:

[0017] S201. Generate reference probability distribution of new energy output and load power

[0018] S202. Construct the support set of Wasserstein fuzzy sets and define the true probability distribution With reference probability distribution Statistical distance between; Construct Wasserstein fuzzy sets Characterize the true probability distribution of renewable energy output and load power The fluctuation range of new energy output and load power is generated by Wasserstein fuzzy sets;

[0019] S203, constructing the support set of the first-order moment fuzzy set, and generating the first-order moment fuzzy set of the probability distribution of distribution network line faults.

[0020] Preferably, the first-order moment fuzzy set of line fault probability distribution is:

[0021]

[0022] in, Representatives in the support group M,s All possible probability distributions above; Represents the probability distribution Calculate expectations.

[0023] Preferably, step S3 specifically includes:

[0024] S301. The goal is to minimize the total cost of the E-SOP throughout its life cycle, including the investment cost of the E-SOP C INV As well as the worst expectation of operating costs, a first-stage planning model is established to determine the best installation location and installation capacity of E-SOP;

[0025] S302: Establish a second-stage operation model to determine the optimal operation mode of the E-SOP under the worst probability distribution of DG output, load power and line faults.

[0026] Preferably, the first stage planning model satisfies the following constraints:

[0027]

[0028] C INV ≤C INV,max

[0029] in, P is the minimum power allowed by ESS; ESS The power configured for the ESS; The maximum power allowed for ESS; The minimum capacity allowed for ESS; E ESS The capacity configured for the ESS; The maximum capacity allowed for ESS; To determine whether the node numbered j is installed with the 0-1 variable of SOP, if It means to install SOP, otherwise it will not be installed; The minimum capacity allowed for a single SOP; The installed SOP capacity for node number j; The maximum capacity allowed for a single SOP; N bus is the total number of nodes in the system; N SOP is the number of ports of E-SOP; C INV is the investment cost; C INV,max The upper limit of investment cost.

[0030] Preferably, the second stage planning model satisfies the following constraints:

[0031] E-SOP operation constraints include ESS constraints and SOP constraints. The ESS constraints are as follows:

[0032]

[0033]

[0034] in, and are the charging and discharging power of ESS at time t in scenario s, respectively; is the remaining energy state of ESS at time t in scenario s; T is the total number of scheduling periods in a day; P ESS The power configured for the ESS; is the residual energy state of ESS at time t-1 in scenario s; is the charging efficiency of ESS; is the discharge efficiency of ESS; is the lower limit of ESS state of charge (SOC), E ESS The capacity configured for the ESS, The upper limit of the SOC allowed by the ESS;

[0035] The constraints of the SOP part of E-SOP are as follows:

[0036]

[0037]

[0038]

[0039]

[0040] Among them, Ω bus It is the set of all nodes in the system; is the active power injected into the distribution network by the SOP installed at node j at time t in scenario s; is the active power loss of the SOP installed at node j at time t in scenario s; is the discharge power of ESS at time t in scenario s; is the charging power of ESS at time t in scenario s; η SOP For the efficiency of SOP; is the reactive power injected into the distribution network by the SOP installed at node j at time t in scenario s; μ SOP is the SOP reactive power upper limit coefficient; The installed SOP capacity for node number j.

[0041] Power balance constraints in distribution networks

[0042]

[0043] in, Inject active power of k nodes into the distribution network transformer station; Inject active power of k nodes into distributed renewable energy units; is the abandoned wind and solar power of the distributed renewable energy units installed at the k node; is the discharge power of ESS; is the charging power of ESS; is the active power of the k-node load; is the load shedding active power of node k; is the network loss power on line mn; Ω line is the set of all lines in the system;

[0044] distflow power flow constraints

[0045]

[0046]

[0047]

[0048]

[0049] Among them, P s,n,t With Q s,n,t are the active power and reactive power injected into the distribution network by n nodes at time t in scenario s, respectively; and are respectively the active and reactive power flowing from node m to node n on line mn; r mn With x mn are the resistance and reactance of line mn respectively; V s,m,t is the square of the voltage at node m; is the square of the current on line mn, V s,n,t is the square of the voltage at node n;

[0050] Ps,n,t With Q s,n,t The specific calculation formula is as follows:

[0051]

[0052]

[0053] in, The active power injected into the distribution network by n nodes at time t in scenario s, is the active power of the n-node load, is the load shedding active power of n nodes, The reactive power injected into the distribution network by n nodes at time t in scenario s, is the load shedding reactive power of n nodes, is the reactive power of the n-node load.

[0054] Breakline constraints:

[0055]

[0056]

[0057]

[0058] Where M is a number; is the maximum current allowed to flow through line mn; is a 0-1 variable to determine whether line mn is disconnected at time t. If yes, it means that the line is not disconnected, otherwise, the line is disconnected.

[0059] Distribution network security constraints:

[0060]

[0061]

[0062]

[0063]

[0064] Among them, U k,max with U k,min are the maximum and minimum voltages allowed at node k respectively; It is the maximum active power that the transformer station can provide.

[0065] Preferably, step S4 is specifically:

[0066] S401, determining the second-order cone relaxation of the SOP operation constraint and the power flow constraint;

[0067] S402, let y be a vector composed of all variables related to the E-SOP operation in the second stage, forming a vector form of the two-stage E-SOP distributed robust programming model;

[0068] S403, forming an equivalent model of the two-stage E-SOP distributed robust programming model;

[0069] S404, using the column and constraint generation algorithm to solve the equivalent model of the two-stage E-SOP distribution robust model.

[0070] Preferably, the two-stage E-SOP distributed robust programming model is written as the following equivalent model:

[0071]

[0072] stAx≤b

[0073] ||ε W || ∞ ≤γ W

[0074] Ey≤f-Gξ-Hz-Jx

[0075] ||By||2≤r T y

[0076] Among them, z is all The column vector formed by M is the support set of z, that is, the set of all possible values ​​of z; ξ is the set of all possible values ​​of z. and The set composed of W is the support set of ξ, that is, the set of all possible values ​​of ξ; c is the model value vector of the first stage; the superscript T in all symbols represents matrix transposition; x is the decision variable of the first stage; d is the model value vector of the second stage; y is the decision variable of the second stage; β M is a column vector of auxiliary variables in the first stage model; ρ is all The column vector formed by W is a column vector of auxiliary variables for the first-stage model; is the column vector of Wasserstein fuzzy set thresholds; W 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 side vector of the first-stage model; ε Wis an auxiliary variable column vector of the first-stage model; E is the coefficient matrix of y in the second-stage model; f is the right-hand side 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.

[0077] Preferably, the equivalent model of solving the two-stage E-SOP distribution robust model using the column and constraint generation algorithm is specifically:

[0078] Step 1: Set the lower bound LB = -∞, the upper bound UB = +∞, the number of iterations k = 0, and set the maximum allowable error ∈; Step 2: Solve the main problem MP, obtain the optimal value ObjMP of MP, the optimal solution of the main problem Update lower bound LB = ObjMP;

[0079] Step 3: Solve the subproblem, obtain its optimal value ObjSP, and update the upper bound

[0080] Step 4: If (UB-LB) / LB≤ ∈ , then stop the loop and output x * And terminate; otherwise update k=k+1 and execute Step 5;

[0081] Step 5: Add a variable (y k ,z k ,ξ k ), and add a new constraint Ey to MP k ≤f-Gξ k -Hz k -Jx,z l ∈Ξ M , l ∈Ξ W ; Return to Step2;

[0082] S406: Output the optimal configuration plan and operation plan of E-SOP.

[0083] In a second aspect, an embodiment of the present invention provides an energy storage intelligent soft switch optimization configuration system that takes both reliability and flexibility into consideration, including:

[0084] Data module, input distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning related parameters, and initialize the planning system;

[0085] The collection module generates the reference probability distribution of renewable energy output and load power, obtains the Wasserstein fuzzy set of renewable energy output and load power, and generates the first-order moment fuzzy set of the probability distribution of distribution network line failure;

[0086] The construction module establishes a two-stage E-SOP distributed robust programming model that takes into account both reliability and flexibility based on the first-order moment fuzzy set of the distribution network line failure probability distribution;

[0087] The output module solves the two-stage E-SOP distributed robust programming model and outputs the optimal configuration plan and operation plan of the E-SOP.

[0088] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for optimizing configuration of energy storage intelligent soft switches that takes into account both reliability and flexibility when executing the computer program.

[0089] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for optimizing configuration of energy storage intelligent soft switches that takes into account both reliability and flexibility.

[0090] Compared with the prior art, the present invention has at least the following beneficial effects:

[0091] An energy storage intelligent soft switch optimization configuration method that takes into account both reliability and flexibility considers the optimal planning scheme of E-SOP in the distribution network, rather than the separate planning of ESS or SOP; through the synergy of ESS and SOP in E-SOP, the reliability and flexibility of the distribution network are further improved; a planning method that takes into account both the improvement of distribution network reliability and the improvement of flexibility is proposed, so as to give full play to the function of E-SOP and realize the improvement of the overall operation efficiency of the distribution network; the method of distributed robust planning is adopted, which combines the advantages of traditional stochastic optimization and robust optimization, has the obvious advantages of taking into account both conservatism and robustness, and can overcome the shortcomings of insufficient robustness or over-conservatism of existing planning methods.

[0092] Furthermore, step S2 gives a mixed fuzzy set of renewable energy output, load power and line faults, thus laying the foundation for establishing the distributed robust planning model of E-SOP in step S3. The two random variables of renewable energy output and load demand have a large amount of historical data, so a Wasserstein fuzzy set can be established to better characterize their random fluctuation range; the line random variable lacks corresponding historical data, and only the failure rate of each line can be obtained, so a first-order moment fuzzy set is established to characterize the line failure situation during the planning process. The fuzzy set establishment method proposed in step S2 fully considers the richness of historical data of different random variables, making the fuzzy set establishment more accurate and feasible.

[0093] Further, step S3 establishes E-SOP planning-operation two-stage model, determines the planning scheme of E-SOP in the first stage, and determines the operation scheme of E-SOP under conventional scenes and extreme events in the second stage. The established E-SOP sub-robust planning model overcomes the shortcomings that the sensitivity of the random programming model result is too strong and the robust planning model result is too conservative, and has the obvious advantage of taking into account conservatism and robustness. In addition, the established E-SOP sub-robust planning model simultaneously considers the function of E-SOP improving the reliability of distribution network operation in conventional scenes and improving elasticity under extreme events, thereby realizing the full scene planning of E-SOP.

[0094] Further, step S4 provides an equivalent model conversion 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 it using existing algorithms. Therefore, step S4 converts it into an equivalent model through a second-order cone relaxation technique, and uses a column and constraint generation algorithm to solve it, thereby obtaining the optimal planning scheme and operation scheme of the E-SOP.

[0095] Furthermore, the column and constraint generation algorithm is used to solve the equivalent model of the two-stage E-SOP distributed robust model, which can effectively improve the speed of model solution. There are two algorithms for solving the equivalent model of the two-stage E-SOP distributed robust model, namely the Benders decomposition algorithm and the column and constraint generation algorithm. The Benders decomposition algorithm has poor convergence properties and converges too slowly when solving the large-scale E-SOP distributed robust model. The column and constraint generation algorithm is developed on the basis of the Benders decomposition algorithm, which improves the convergence properties and is conducive to the rapid solution of the model.

[0096] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0097] In summary, the present invention establishes an E-SOP distributed robust planning method that takes into account both reliability and flexibility improvement, and proposes a corresponding solution method. The model fully considers the richness of historical data of different random variables such as new energy output, load power and line faults, so that the characterization of random variables is more accurate; the model takes into account the obvious advantages of conservatism and robustness, and can overcome the shortcomings of existing planning methods that are not robust enough or too conservative; the model takes into account the function of E-SOP to improve the reliability and flexibility of the distribution system, and can further improve the efficiency of the distribution system; the proposed solution method can achieve efficient and rapid solution of the model.

[0098] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings to be used in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0100] Figure 1 A flow chart of the method provided by the present invention;

[0101] Figure 2 It is the structural diagram of E-SOP;

[0102] Figure 3 This is the topology diagram of the IEEE33 node test system with distributed photovoltaic units added;

[0103] Figure 4 This is the topology diagram of the test system after configuring E-SOP;

[0104] Figure 5 It is the power curve of ESS and SOP at each moment in the conventional scenario;

[0105] Figure 6 It is the power curve diagram of network loss of each line in different configuration schemes of conventional scenarios;

[0106] Figure 7 Voltage curves of nodes in different configuration schemes for conventional scenarios;

[0107] Figure 8 Reactive power curves of various SOPs for different configurations in conventional scenarios;

[0108] Fig. 9 Provide power curves for loads with different configurations under extreme events;

[0109] Fig.10The charging and discharging power curves of ESS under different configuration schemes under extreme events;

[0110] Fig.11 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0111] Fig.12 The present invention is a block diagram of an electronic device provided according to an embodiment. DETAILED DESCRIPTION

[0112] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0113] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0114] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0115] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0116] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. 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.

[0117] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0118] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0119] The present invention provides an energy storage intelligent soft switch optimization configuration method that takes into account both reliability and elasticity. First, parameters such as distribution network historical data are input to initialize the planning system. Then, a mixed fuzzy set of new energy output, load power and line fault is generated: a Wasserstein fuzzy set is established to characterize the probability distribution function of the output of new energy and the size of load power; a first-order moment fuzzy set is established to characterize the probability distribution function of line faults during the operation of the distribution network. Furthermore, a distribution network E-SOP configuration model that comprehensively takes into account reliability and elasticity improvement is established. The model is a two-stage distributed robust optimization problem of planning E-SOP configuration schemes in the first stage and optimizing E-SOP operation schemes in the second stage. The first stage model takes the minimum worst expected sum of investment cost and operating cost as the goal, considers constraints such as E-SOP installation capacity restrictions and installation location restrictions, and determines the E-SOP installation location and installation capacity; the second stage model takes the worst expected operating cost as the goal, and the operating cost includes the penalty cost of wind abandonment and light abandonment, the penalty cost of network loss, and the penalty cost of load shedding. Considering the constraints such as E-SOP operation constraints and distribution network operation constraints, the optimal operation plan of E-SOP is determined. Finally, the model is solved by the proposed hybrid fuzzy set robust model algorithm.

[0120] The present invention provides an energy storage intelligent soft switch optimization configuration method taking into account both reliability and flexibility, comprising the following steps:

[0121] S1, planning system initialization;

[0122] S101. Input distribution network parameters, including: the number, rated voltage, and operating voltage range of each node; the number, connected nodes, model and length of the wire, maximum current flowing through the line, and historical data of failure rate (including historical data under conventional scenarios and historical data under extreme events such as typhoons, the same below); the node where each user is located and the power operation history data every hour of each day; the type of each DG, the node where it is located, and the output size operation history data every hour (or every 15 minutes) every day.

[0123] In particular, the number of days for recording user power history data in normal scenarios is required to be the same as the number of days for recording DG output history data in normal scenarios, and the same in extreme events; let s = 1 for normal scenarios and s = 2 for extreme events, then N records are collected in each scenario. s In particular, let the lower and upper limits of the voltage amplitude allowed at node k be U k,min with U k,max , the maximum current allowed to flow through line mn is Let the set of all nodes and wires in the system be Ω bus With Ω line , let the total number of nodes and wires in the system be N bus With N line , the maximum transmission power of the upper substation tie line is The failure rates of lines mn are The records of DG installed on node k on day n are The records of users installed on node k on day n are

[0124] S102. Input cost parameters, including: ESS investment cost per unit capacity ESS investment cost per unit power Unit capacity SOP investment cost c SOP , Single ESS power station installation cost Single SOP inverter installation cost Unit energy load shedding penalty cost Penalty cost per unit of energy for wind and solar curtailment And the penalty cost per unit energy loss Among them, the unit energy load shedding penalty cost is Penalty cost per unit of energy for wind and solar curtailment And the penalty cost per unit energy loss To preset the cost parameters, it can generally be taken as: 45 yuan / kWh, 4 yuan / kWh and 0.8 yuan / kWh.

[0125] S103. Input relevant technical parameters of E-SOP, including: ESS charging efficiency in E-SOP ESS discharge efficiency The upper limit of the state of charge (SOC) allowed during ESS operation SOC lower limit SOP converter port number N SOP , SOP power loss rate η SOP 、SOP reactive power upper limit coefficient μ SOP ; ESS and SOP service life r (Unit is year).

[0126] S104. Input planning related parameters, including: minimum power of ESS in E-SOP Maximum Power minimum capacity Maximum capacity Minimum power of single SOP inverter Maximum Power SOP installation quantity limit Maximum allowable investment cost C INV,max , discount rate r.

[0127] S2, generate mixed fuzzy sets of renewable energy output, load power and line faults;

[0128] S201. Generate reference probability distribution of new energy output and load power. Let the conventional scenario be s=1, the extreme event be s=2, and the historical data of new energy output and load power on the nth day be Then the reference probability distribution of new energy output and load power is Each component for:

[0129]

[0130] S202. Generate Wasserstein fuzzy sets of new energy output and load power. The reference distribution shown in formula (1) is not equal to the true probability distribution. The principle of distributed robust 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 of new energy output and load power are as follows:

[0131] 1) The support set of Wasserstein fuzzy set is constructed as follows:

[0132]

[0133] in, and They represent the possible maximum power of DG and load of node k in scenario s respectively. Generally speaking, in the conventional scenario s=1, the installed capacity can be taken, while in the extreme event s=2, 0.1 to 0.5 times of the installed capacity can be taken.

[0134] 2) Define the true probability distribution With reference probability distribution The statistical distance between them. The fuzzy set defined by the Wasserstein distance has better mathematical properties and is convenient for subsequent solutions. Therefore, the Wasserstein distance is used in the present invention, and its definition is as follows:

[0135]

[0136] Where Π is ξ s With ξ s The joint probability distribution of .

[0137] 3) Construct the following Wasserstein fuzzy set To describe the true probability distribution of renewable energy output and load power Fluctuation range:

[0138]

[0139] in, Representatives in the support group W,s All possible probability distributions. is a preset value, and its specific calculation method is as follows:

[0140]

[0141]

[0142] Among them, β is the confidence level, which is generally taken as 95%; γ is the mean of the historical data samples.

[0143] S203: Generate a first-order moment fuzzy set of distribution network line fault probability distribution.

[0144] Since there is little data on distribution network line faults, in reality only the average fault probability information can be collected, so a first-order moment fuzzy set can be established to characterize the fluctuation range of its probability distribution. The specific steps are as follows:

[0145] 1) Construct the support set of the first-order moment fuzzy set:

[0146]

[0147] in, A 0-1 variable representing whether line mn has a fault at time t in scenario s. If it is 0, it means a fault has occurred. Represents the maximum number of lines that fail simultaneously in the distribution network. Generally speaking, in a normal scenario The value is 0 or 1. The value range is 4 to 6.

[0148] 2) Construct the first-order moment fuzzy set of line fault probability distribution:

[0149]

[0150] in, Representatives in the support group M,s All possible probability distributions above; Represents the probability distribution Calculate expectations.

[0151] S3, establish a two-stage E-SOP distributed robust planning model that takes into account both reliability and flexibility;

[0152] S301. Establish a first-stage planning model to determine the optimal installation location and installation capacity of the E-SOP.

[0153] The first stage planning model aims to minimize the total cost of the E-SOP throughout its life cycle, including the investment cost C of the E-SOP. INV And the worst expected running cost is expressed as follows:

[0154]

[0155] Among them, C INV is the investment cost of the E-SOP; x is the vector of all E-SOP planning variables, including the continuous variable P that determines the ESS power in the E-SOP ESS , continuous variable E that determines ESS capacity ESS , a 0-1 variable that determines whether the SOP converter is installed at node j Determine the continuous variable of the SOP converter capacity at node j Right now Q(x,ξ,z) represents the optimal operating cost of the distribution network under the given E-SOP installation mode x, renewable energy load output ξ, and line fault z; Represents the joint probability Calculate the expectation of Q below; Representative and For variables, determine The upper limit of , that is, calculating the optimal operating cost under the worst probability distribution.

[0156] The E-SOP planning cost includes the capacity cost, power cost, installation cost of ESS and the capacity cost and installation cost of SOP. The specific expressions are as follows:

[0157]

[0158] The first stage planning model meets the following constraints:

[0159]

[0160]

[0161]

[0162]

[0163] C INV ≤C INV,max (15)

[0164] Among them, 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 of the E-SOP to be a given value; equation (15) constrains the total investment cost to not exceed the budget upper limit.

[0165] S302: Establish a second-stage operation model to determine the optimal operation mode of the E-SOP under the worst probability distribution of DG output, load power and line faults.

[0166] Given the E-SOP installation mode x, the new energy load output ξ and the line fault z, the expression of the optimal operation cost Q of the distribution network is as follows:

[0167]

[0168]

[0169]

[0170]

[0171] Among them, p s The probability of occurrence of common scenarios or extreme events; is the penalty cost of wind and solar power abandonment in scenario s, is the amount of wind and solar power curtailment at node k at time t in scenario s; Penalty costs for network loss, is the power loss of line mn, is the power loss of the SOP converter installed at node k; Penalty cost for load shedding, is the load shedding power; D y is the total number of days in a year, that is, D y =365; ΔT is the time interval between two scheduling periods. If the historical data in S101 is collected at intervals of one hour, ΔT here is 1 hour, otherwise ΔT is 15 minutes.

[0172] The second stage of running the model includes the following constraints:

[0173] 1) E-SOP operation constraints

[0174] E-SOP operation constraints include ESS constraints and SOP constraints, where the ESS constraints are as follows:

[0175]

[0176]

[0177]

[0178]

[0179]

[0180] in, and are the charging and discharging power of ESS at time t in scenario s, respectively; is the remaining energy state (SOE) of ESS; 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 ESS not to exceed the installed power; Equation (22) gives the transfer formula of ESS SOE in two adjacent periods; Equation (23) constrains ESS SOE to be within a reasonable range; Equation (24) constrains ESS SOE to return to the initial value at the end of a scheduling day in a normal scenario, thereby ensuring the sustainability of ESS operation. In extreme events, ESS takes load supply as its main goal and no longer needs to consider the sustainability of operation.

[0181] The constraints of the SOP part of E-SOP are as follows:

[0182]

[0183]

[0184]

[0185]

[0186] Among them, formula (25) constrains the net power in E-SOP to be 0; formula (26) gives the calculation formula for the power loss of the SOP converter; formula (27) constrains the reactive power range of the SOP; formula (28) constrains the apparent power of the SOP not to exceed the installed capacity.

[0187] 2) Power balance constraints of distribution network

[0188]

[0189] in, Inject active power of k nodes into the distribution network transformer station.

[0190] 3) Distflow Constraints

[0191]

[0192]

[0193]

[0194]

[0195] Among them, P s,n,t With Q s,n,t are the active power and reactive power injected into the distribution network by n nodes at time t in scenario s, respectively; and are respectively the active and reactive power flowing from node m to node n on line mn; r mn With x mn are the resistance and reactance of line mn respectively; V s,m,t is the square of the voltage at node m; is the square of the current on line mn. s,n,t With Q s,n,t The specific calculation formula is as follows:

[0196]

[0197]

[0198] 4) Disconnection constraint:

[0199]

[0200]

[0201]

[0202] Among them, M is a relatively large number, which is 100; It is the maximum current allowed to flow through the line mn.

[0203] 5) Distribution network security constraints

[0204]

[0205]

[0206]

[0207]

[0208] Among them, U k,max with U k,min are the maximum and minimum voltages allowed at node k respectively; is the maximum active power that the transformer station can provide. Equation (39) constrains the node voltage to be maintained within a reasonable range in the normal scenario; Equations (40) to (42) respectively constrain the transformer station active power, wind and solar power abandonment, and load shedding power to be positive numbers, and they cannot exceed the upper limit.

[0209] S4. Solve the two-stage E-SOP distributed robust programming model; output the optimal configuration plan and operation plan of E-SOP.

[0210] S401, second-order cone relaxation of SOP operation constraints and power flow constraints;

[0211] For the square root terms in equations (26) and (28) and the product terms of decision variables in equation (33), second-order cone relaxation is performed to obtain the second-order cone constraints as follows:

[0212]

[0213]

[0214]

[0215] S402, forming a vector form of a two-stage E-SOP distributed robust programming model; y is denoted as a vector consisting of all variables related to the E-SOP operation in the second stage, and the two-stage E-SOP distributed robust programming model is written in the following vector form:

[0216]

[0217] stAx≤b

[0218] in:

[0219]

[0220]

[0221] Among them, A, E, G, H, J, B are correlation coefficient matrices; b, d, f, r are correlation coefficient vectors. Similarly, the fuzzy set and its support set are written in the following vector form:

[0222]

[0223] Ξ M = {z|R M z≤g M} (50)

[0224]

[0225] Ξ W ={ξ|R W ξ≤g W} (52)

[0226] Among them, R M With R M is the correlation coefficient matrix, ρ, g M , g W is the correlation coefficient vector.

[0227] S403, forming an equivalent model of the two-stage E-SOP distributed robust programming model. For the two-stage E-SOP distributed robust 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 is first converted into the following equivalent form:

[0228]

[0229] Among them, α M With β M represents the dual variable.

[0230] Furthermore, using the strong duality theory, equation (53) is transformed into the following equivalent form:

[0231]

[0232] Similarly, the inner layer sup, i.e. R(x,z), is equivalently converted into the following form:

[0233]

[0234] Among them, α W,n and γ W is the dual variable, ε W is the introduced auxiliary variable.

[0235] Finally, the two-stage E-SOP distributed robust programming model can be written as the following equivalent model:

[0236]

[0237] S404. Use the column-and-constraint generation algorithm (C&CG) to solve the equivalent model of the two-stage E-SOP distribution robust model.

[0238] The steps of the C&CG algorithm are as follows:

[0239] Step 1: Set the lower bound LB = -∞, the upper bound UB = +∞, the number of iterations k = 0, and give the maximum allowable error ∈.

[0240] Step 2: Solve the master problem (MP) as shown in equation (57):

[0241]

[0242] Obtain the optimal value of MP ObjMP and the optimal solution to the main problem Update the lower bound LB = ObjMP.

[0243] Step 3: Solve the sub-problem (SP):

[0244]

[0245] Among them, α SP , β SP , ν SP With μ SP are dual variables. Considering that SP is a double-layer problem of max-min form as shown in formula (58), it cannot be solved directly. However, its dual problem can be solved as follows:

[0246]

[0247] Solve equation (59) to obtain its optimal value ObjSP and update the upper bound

[0248] Step 4: If (UB-LB) / LB≤∈, stop the loop and output x * And terminate; otherwise update k=k+1 and execute Step 5;

[0249] Step 5: Add a variable (y k ,z k ,ξ k), and add a new constraint Ey to MP k ≤f-Gξ k -Hz k -Jx,z l ∈Ξ M , l ∈Ξ W ; Return to Step 2.

[0250] S406: Output the optimal configuration plan and operation plan of E-SOP.

[0251] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0252] In another embodiment of the present invention, a system for optimizing and configuring an energy storage intelligent soft switch taking into account both reliability and flexibility is provided. The system can be used to implement the above-mentioned method for optimizing and configuring an energy storage intelligent soft switch taking into account both reliability and flexibility. Specifically, the system for optimizing and configuring an energy storage intelligent soft switch taking into account both reliability and flexibility includes a data module, a collection module, a construction module, and an output module.

[0253] Among them, the data module inputs distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning-related parameters to initialize the planning system;

[0254] The collection module generates the reference probability distribution of renewable energy output and load power, obtains the Wasserstein fuzzy set of renewable energy output and load power, and generates the first-order moment fuzzy set of the probability distribution of distribution network line failure;

[0255] The construction module establishes a two-stage E-SOP distributed robust programming model that takes into account both reliability and flexibility based on the first-order moment fuzzy set of the distribution network line failure probability distribution;

[0256] The output module solves the two-stage E-SOP distributed robust programming model and outputs the optimal configuration plan and operation plan of the E-SOP.

[0257] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute 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 (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the energy storage intelligent soft switch optimization configuration method that takes into account both reliability and flexibility, including:

[0258] Input distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning-related parameters to initialize the planning system; generate reference probability distribution of renewable energy output and load power, obtain Wasserstein fuzzy sets of renewable energy output and load power, and generate first-order moment fuzzy sets of distribution network line failure probability distribution; establish a two-stage E-SOP distributed robust planning model that takes into account reliability and flexibility based on the obtained first-order moment fuzzy sets of distribution network line failure probability distribution; solve the obtained two-stage E-SOP distributed robust planning model; output the optimal configuration plan and operation plan of E-SOP.

[0259] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0260] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0261] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0262] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the energy storage intelligent soft switch optimization configuration method taking into account both reliability and flexibility in the above embodiment; the processor may load and execute the following steps of one or more instructions in the computer-readable storage medium:

[0263] Input distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning-related parameters to initialize the planning system; generate reference probability distribution of renewable energy output and load power, obtain Wasserstein fuzzy sets of renewable energy output and load power, and generate first-order moment fuzzy sets of distribution network line failure probability distribution; establish a two-stage E-SOP distributed robust planning model that takes into account reliability and flexibility based on the obtained first-order moment fuzzy sets of distribution network line failure probability distribution; solve the obtained two-stage E-SOP distributed robust planning model; output the optimal configuration plan and operation plan of E-SOP.

[0264] See also Fig.11 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the energy storage intelligent soft switch optimization configuration method taking into account both reliability and flexibility in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the energy storage intelligent soft switch optimization configuration system taking into account both reliability and flexibility in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0265] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Fig.11 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0266] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0267] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0268] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0269] Any reference to memory, database or other media used in the embodiments provided in the present 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. As an illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0270] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0271] See also Fig.12 The terminal device 600 is an electronic device, and the electronic device is in the form of a general 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 the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0272] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0273] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0274] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0275] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0276] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the 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, etc.

[0277] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0278] The example analysis of the present invention selects an IEEE33 node system with distributed photovoltaics, and verifies it based on the public historical data of photovoltaic output, load fluctuation and line failure rate.

[0279] Example Setup Introduction

[0280] The topology diagram of the IEEE33 node system with distributed photovoltaics is as follows: Figure 3 As shown. Among them, 0.5MW, 0.8MW, 0.5MW, 0.5MW and 1MW distributed photovoltaic units are connected to nodes 13, 20, 22, 23 and 27 respectively. Nodes 10, 11, 16, 19, 24 and 31 are important load nodes. The transformer station of node 1 can provide 10MW of active power, and power reverse transmission is not allowed. In order to facilitate subsequent analysis, the feeder composed of nodes 6 to 18 is specified as feeder 1, nodes 19 to 22 are feeder 2, and nodes 26 to 33 are feeder 3.

[0281] The relevant parameters involved in step S1 are shown in Table 1.

[0282] Table 1. Parameter settings for the example

[0283]

[0284] The historical data of photovoltaic unit output and load power involved in step S1 are from public data sets: https: / / ieeexplore.ieee.org / document / 8931650 .

[0285] Comparison of different configuration solutions

[0286] 1. In order to illustrate the superiority of E-SOP in improving reliability and elasticity, as well as the necessity of taking both reliability and elasticity into consideration, the following six configuration schemes are set up for comparative analysis:

[0287] Solution 1: Adopt the solution proposed in this article and configure E-SOP under the premise of taking into account reliability and elasticity improvement, that is, the configuration solution proposed in this article;

[0288] Solution 2: Configure E-SOP only under the premise of considering elasticity improvement, that is, only consider the case of s=1;

[0289] Solution 3: Configure E-SOP only under the premise of considering elasticity improvement, that is, only consider the case of s=2;

[0290] Solution 4: Configure ESS and SOP under the premise of taking into account reliability and flexibility improvement, but it does not constitute E-SOP;

[0291] Solution 5: Only configure ESS while taking into account reliability and elasticity improvement;

[0292] Solution 6: Use the original system without any configuration.

[0293] 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 shown.

[0294] Table 2 Comparison of configuration and operation results of different solutions

[0295]

[0296] 3. Compared with the original system, Scheme 1 reduces various operating costs by introducing E-SOP in the distribution network.

[0297] Among them, under normal circumstances, abandoned optical power and network loss decreased by 43.10% and 56.15% respectively; under extreme circumstances, load shedding decreased by 52.24%. It can be seen that the introduction of E-SOP in the distribution network can effectively improve the reliability and flexibility of the distribution network.

[0298] 4. Compare Plan 1, Plan 4 and Plan 5.

[0299] The operation effects of Scheme 1 and Scheme 4 are better than Scheme 5. This is because SOP realizes flexible interconnection between feeders and expands the regional regulation capability of ESS. In addition, the E-SOP in Scheme 1 can realize flexible regulation of the charging and discharging power of ESS between feeders, overcoming the disadvantage of fixed access capacity of ESS in Scheme 4. Therefore, the E-SOP proposed in this paper has better performance.

[0300] 5. Compare Plan 1, Plan 2 and Plan 3.

[0301] Solution 2, which only focuses on improving reliability, has a slight improvement in reducing abandoned light and network losses, but significantly increases load shedding in extreme events. In addition, in Solution 3, although various operating indicators have improved slightly, the investment cost has increased significantly. Therefore, only considering the improvement of elasticity may lead to overly conservative allocation results. The configuration model proposed in this paper strikes a balance between reliability, elasticity and investment cost.

[0302] Analysis of E-SOP's operation mode to improve reliability and flexibility

[0303] 1. E-SOP improves reliability in conventional scenarios

[0304] E-SOP improves the reliability of the distribution network in conventional scenarios by promoting the consumption of new energy and reducing network losses.

[0305] In terms of promoting the consumption of new energy, the charge and discharge curve of E-SOP is consistent with the spatial distribution of load and the time characteristics of photovoltaic output, such as Figure 5As shown in the figure. From a spatial perspective, E-SOP absorbs energy from feeder 2, which has a smaller load and more photovoltaic distribution, and injects it into feeders 1 and 3, which have heavier loads, thus achieving power mutual assistance between feeders. From a temporal perspective, E-SOP charges at noon and discharges at night, which supplements peak shaving and valley filling, and promotes the consumption of photovoltaics.

[0306] In terms of reducing network losses, the line losses of different allocation schemes are compared, such as Figure 6 Compared with Scheme 6 and Scheme 5, the systems using E-SOP and ESS+SOP further introduce SOP to optimize power flow distribution, thereby reducing the current flow on high-impedance lines. In addition, Figure 7 As shown in Figure 1, 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 busbar through reactive power compensation, thereby further reducing network losses. However, compared with ESS+SOP, the ESS in the E-SOP only needs one SOP converter to supply power to other feeders (ESS+SOP requires two), thereby reducing the capacity occupation of the ESS on the SOP. Therefore, when the SOP capacities of the two schemes are similar, the SOP in the E-SOP can provide more reactive power to the system, as shown in Figure 1. Figure 8 As shown, the voltage level can be better improved.

[0307] 2. E-SOP improves resilience under extreme conditions

[0308] In the extreme event, the most serious faults are the disconnection of lines 4-5, 21-22 and 24-25. The load demand supply of schemes 1, 2, 3 and 6 at each time under the extreme event is shown in the figure below: Fig. 9 As shown in Figure 2, the charging and discharging power of ESS under different schemes is as follows: Fig.10 shown.

[0309] In the configuration scheme without SOP, due to the lack of power flow path, ESS can only provide power support to the bus near its installation, and cannot alleviate the load reduction of other feeders. Therefore, the system that relies only on ESS has a low utilization rate despite a large amount of installed capacity. In contrast, the ESS+SOP allocation scheme lacks the ability to flexibly allocate energy storage resources between feeders, and the energy storage utilization efficiency is lower than that of the E-SOP proposed in this article. Therefore, the E-SOP configuration proposed in this invention is more advantageous for improving flexibility.

[0310] In summary, the present invention provides an energy storage intelligent soft switch optimization configuration method and system that takes into account both reliability and flexibility, which can provide an optimal planning scheme for energy storage intelligent soft switches that takes into account both robustness and conservatism, thereby improving the reliability of the distribution network in conventional scenarios and the flexibility in extreme events, and improving the comprehensive benefits of the distribution network; the planning results provide a reference for distribution network planners.

[0311] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by 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 embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0312] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0313] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0314] In the embodiments provided by the present 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 only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0315] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0316] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0317] If the integrated module / unit is implemented in the form of 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, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased 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 electric carrier signals and telecommunication signals.

[0318] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0319] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0320] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0321] The above contents are 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 energy storage intelligent soft switches taking into account both reliability and flexibility, characterized in that: The following steps are involved: S1. Input distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning related parameters, and initialize the planning system; S2, generate reference probability distribution of renewable energy output and load power, obtain Wasserstein fuzzy sets of renewable energy output and load power, and generate first-order moment fuzzy sets of distribution network line fault probability distribution; S3, based on the first-order moment fuzzy set of the distribution network line failure probability distribution obtained in step S2, a two-stage E-SOP distributed robust programming model that takes into account both reliability and flexibility is established; S4, solving the two-stage E-SOP distributed robust programming model obtained in step S3; outputting the optimal configuration plan and operation plan of the E-SOP.

2. The energy storage intelligent soft switch optimization configuration method taking into account both reliability and flexibility according to claim 1 is characterized in that: Step S2 is specifically as follows: S201. Generate reference probability distribution of new energy output and load power S202. Construct the support set of Wasserstein fuzzy sets and define the true probability distribution With reference probability distribution Statistical distance between; Construct Wasserstein fuzzy sets Characterize the true probability distribution of renewable energy output and load power The fluctuation range of new energy output and load power is generated by Wasserstein fuzzy sets; S203, constructing the support set of the first-order moment fuzzy set, and generating the first-order moment fuzzy set of the probability distribution of distribution network line faults.

3. The energy storage intelligent soft switch optimization configuration method taking into account both reliability and flexibility according to claim 2 is characterized in that: The first-order moment fuzzy set of line fault probability distribution: in, Representatives in the support group M,s All possible probability distributions above; Represents the probability distribution Calculate expectations.

4. The energy storage intelligent soft switch optimization configuration method taking into account both reliability and flexibility according to claim 1 is characterized in that: Step S3 is specifically as follows: S301. The goal is to minimize the total cost of the E-SOP throughout its life cycle, including the investment cost of the E-SOP C INV As well as the worst expectation of operating costs, a first-stage planning model is established to determine the best installation location and installation capacity of E-SOP; S302: Establish a second-stage operation model to determine the optimal operation mode of the E-SOP under the worst probability distribution of DG output, load power and line faults.

5. The energy storage intelligent soft switch optimization configuration method taking into account both reliability and flexibility according to claim 4 is characterized in that: The first stage planning model meets the following constraints: C INV ≤C INV,max in, P is the minimum power allowed by ESS; ESS The power configured for the ESS; The maximum power allowed for ESS; The minimum capacity allowed for ESS; E ESS The capacity configured for the ESS; The maximum capacity allowed for ESS; To determine whether the node numbered j is installed with the 0-1 variable of SOP, if If it means to install SOP, otherwise it will not be installed; The minimum capacity allowed for a single SOP; The installed SOP capacity for node number j; The maximum capacity allowed for a single SOP; N bus is the total number of nodes in the system; N SOP is the number of ports of E-SOP; C INV is the investment cost; C INV,max The upper limit of investment cost.

6. The energy storage intelligent soft switch optimization configuration method taking into account both reliability and flexibility according to claim 4 is characterized in that: The second stage planning model meets the following constraints: E-SOP operation constraints include ESS constraints and SOP constraints. The ESS constraints are as follows: s＝1 in, and are the charging and discharging power of ESS at time t in scenario s, respectively; is the remaining energy state of ESS at time t in scenario s; T is the total number of scheduling periods in a day; P ESS The power configured for the ESS; is the residual energy state of ESS at time t-1 in scenario s; is the charging efficiency of ESS; is the discharge efficiency of ESS; is the lower limit of ESS state of charge (SOC), E ESS The capacity configured for the ESS, The upper limit of the SOC allowed by the ESS; The constraints of the SOP part of E-SOP are as follows: Among them, Ω bus It is the set of all nodes in the system; is the active power injected into the distribution network by the SOP installed at node j at time t in scenario s; is the active power loss of the SOP installed at node j at time t in scenario s; is the discharge power of ESS at time t in scenario s; is the charging power of ESS at time t in scenario s; η SOP For the efficiency of SOP; is the reactive power injected into the distribution network by the SOP installed at node j at time t in scenario s; μ SOP is the upper limit coefficient of reactive power of SOP; The installed SOP capacity for node number j; Power balance constraints in distribution networks in, Inject active power of k nodes into the distribution network transformer station; Inject active power of k nodes into distributed renewable energy units; is the abandoned wind and solar power of the distributed renewable energy units installed at the k node; is the discharge power of ESS; is the charging power of ESS; is the active power of the k-node load; is the load shedding active power of node k; is the network loss power on line mn; Ω line is the set of all lines in the system; distflow power flow constraints Among them, P s,n,t With Q s,n,t are the active power and reactive power injected into the distribution network by n nodes at time t in scenario s, respectively; and are respectively the active and reactive power flowing from node m to node n on line mn; r mn With x mn are the resistance and reactance of line mn respectively; V s,m,t is the square of the voltage at node m; is the square of the current on line mn, V s,n,t is 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, The active power injected into the distribution network by n nodes at time t in scenario s, is the active power of the n-node load, is the load shedding active power of n nodes, The reactive power injected into the distribution network by n nodes at time t in scenario s, is the load shedding reactive power of n nodes, is the reactive power of n node load; Breakline constraints: Where M is a number; is the maximum current allowed to flow through line mn; is a 0-1 variable to determine whether line mn is disconnected at time t. If yes, it means that no disconnection occurs, otherwise, disconnection occurs; Distribution network security constraints: s＝1 Among them, U k,max with U k,min are the maximum and minimum voltages allowed at node k respectively; It is the maximum active power that the transformer station can provide.

7. The energy storage intelligent soft switch optimization configuration method taking into account both reliability and flexibility according to claim 1 is characterized in that: Step S4 is specifically as follows: S401, determining the second-order cone relaxation of the SOP operation constraint and the power flow constraint; S402, let y be a vector composed of all variables related to the E-SOP operation in the second stage, forming a vector form of the two-stage E-SOP distributed robust programming model; S403, forming an equivalent model of the two-stage E-SOP distributed robust programming model; S404, using the column and constraint generation algorithm to solve the equivalent model of the two-stage E-SOP distribution robust model.

8. The energy storage intelligent soft switch optimization configuration method taking into account both reliability and flexibility according to claim 7 is characterized in that: The two-stage E-SOP distributed robust programming model can be written as the following equivalent model: stAx≤b ||e W || ∞ ≤γ W Ey≤f-Gξ-Hz-Jx ||By||2≤r T y Among them, z is all The column vector formed by M is the support set of z, that is, the set of all possible values ​​of z; ξ is the set of all possible values ​​of z. and The set composed of W is the support set of ξ, that is, the set of all possible values ​​of ξ; c is the model value vector of the first stage; the superscript T in all symbols represents matrix transposition; x is the decision variable of the first stage; d is the model value vector of the second stage; y is the decision variable of the second stage; β M is a column vector of auxiliary variables in the first stage model; ρ is all The column vector formed by W is a column vector of auxiliary variables for the first-stage model; is the column vector of Wasserstein fuzzy set thresholds; W 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 side vector of the first-stage model; ε W is an auxiliary variable column vector of the first-stage model; E is the coefficient matrix of y in the second-stage model; f is the right-hand side 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.

9. The energy storage intelligent soft switch optimization configuration method taking into account both reliability and flexibility according to claim 7 is characterized in that: The equivalent model of solving the two-stage E-SOP distribution robust model using the column and constraint generation algorithm is as follows: Step 1: Set the lower bound LB = -∞, the upper bound UB = +∞, the number of iterations k = 0, and the maximum allowable error ∈ of the solution; Step 2: Solve the main problem MP and obtain the optimal value ObjMP of MP and the optimal solution of the main problem Update lower bound LB = ObjMP; Step 3: Solve the subproblem, obtain its optimal value ObjSP, and update the upper bound Step 4: If (UB-LB) / LB≤∈, stop the loop and output x * and terminate; otherwise update k=k+1 and execute Step 5; Step 5: Add a variable (y k ,z k ,ξ k ), and add a new constraint Ey to MP k ≤f-Gξ k -Hz k -Jx,z l ∈Ξ M , l ∈Ξ W ; Return to Step2; S406: Output the optimal configuration plan and operation plan of E-SOP.

10. An energy storage intelligent soft switch optimization configuration system that takes into account both reliability and flexibility, characterized in that: include: Data module, input distribution network parameters, cost parameters, relevant technical parameters of E-SOP, and planning related parameters, and initialize the planning system; The collection module generates the reference probability distribution of renewable energy output and load power, obtains the Wasserstein fuzzy set of renewable energy output and load power, and generates the first-order moment fuzzy set of the probability distribution of distribution network line failure; The construction module establishes a two-stage E-SOP distributed robust programming model that takes into account both reliability and flexibility based on the first-order moment fuzzy set of the distribution network line failure probability distribution; The output module solves the two-stage E-SOP distributed robust programming model and outputs the optimal configuration plan and operation plan of the E-SOP.

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