Flexible interconnection device joint optimization planning method and system for power distribution network

Abstract

The invention provides a flexible interconnection device joint optimization planning method and system for a power distribution network, and aims to solve the problem that a configuration planning result cannot meet actual requirements due to the fact that an existing scheme lacks collaborative consideration on DESS and SOP in the aspect of configuration planning of a flexible interconnection device and neglects the influence on factors such as user satisfaction. According to the scheme, a distributed energy storage device and an intelligent soft switch are subjected to collaborative planning by providing a three-layer planning model for comprehensive operation index improvement of the power distribution network and taking space-time complementation and soft storage as ideas; meanwhile, the social welfare index for expressing the user satisfaction is expressed by introducing the node marginal electricity price, and the configuration planning of the distributed energy storage device and the intelligent soft switch is realized by maximizing the social welfare, so that the planning result can effectively meet the actual demand.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution networks, and in particular relates to a method and system for joint optimization planning of flexible interconnection devices for power distribution networks. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the gradual increase in the scale of grid-connected new energy, mainly wind and solar power, and the continuous growth of new loads, the distribution network with multiple elements connected is becoming increasingly complex. The traditional radial distribution network can no longer meet the needs of safe, flexible and high-quality power supply, and the distribution network urgently needs to be transformed and upgraded.

[0004] Traditional distribution networks are gradually transforming into new forms such as smart distribution networks and active distribution networks (ADNs), and flexible interconnected devices have become the core devices for power transmission and conversion due to their strong adjustment capabilities and fast response speeds. Typical flexible interconnected devices include node-type distributed energy storage devices (DESS) and network-type smart soft switches (SOPs). Due to network losses, the adjustment range of node-type energy storage devices is limited. On this basis, introducing smart soft switches to build a flexible interconnected structure has become an effective solution. Smart soft switches provide power flow paths, further expand the adjustment range of energy storage and achieve precise energy mutual assistance between regions. In addition, their DC ports allow DC loads to be connected, forming smart energy storage soft switches (E-SOPs), further improving the comprehensive operating indicators and intelligence of distribution networks.

[0005] The inventors found that although the flexible interconnection device can independently control the active and reactive power in real time and continuously by replacing the interconnection switch in the distribution network, the existing scheme lacks the coordinated consideration of DESS and SOP in the configuration planning of the flexible interconnection device, and ignores the influence of market factors such as user satisfaction. In addition, the planning method under a single indicator often has a lag, resulting in the configuration planning results failing to meet actual needs. Summary of the invention

[0006] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a joint optimization planning method and system for flexible interconnected devices for distribution networks, so as to solve the problem that the existing solutions lack coordinated consideration of DESS and SOP in the configuration planning of flexible interconnected devices, and ignore the impact of market factors such as user satisfaction. In addition, the planning method under a single indicator often has a lag, resulting in the problem that the configuration planning results cannot meet actual needs.

[0007] According to a first aspect of an embodiment of the present invention, a method for joint optimization planning of flexible interconnected devices for a distribution network is provided, comprising:

[0008] Based on the historical data of the distribution network system with the introduction of distributed power sources, construct typical scenarios of distributed power source output;

[0009] Based on the constructed typical scenarios of distributed power source output, by optimizing and solving the pre-constructed three-layer planning model, determine the optimal siting and sizing results of distributed energy storage devices and intelligent soft switches. Among them, the specific processing process of the three-layer planning model is as follows: Considering the uncertainty of renewable energy output, for the constructed typical scenarios of distributed power source output, calculate the system node marginal price and introduce it into the three-layer planning model for cost settlement. In the upper-layer model, with the goal of minimizing the total investment cost of distributed energy storage devices to meet the load demand of current regional nodes, conduct siting and sizing of distributed energy storage devices; Based on the siting and sizing results of distributed energy storage devices, in the middle-layer model, with the goal of minimizing the annual comprehensive operating cost of the distribution network, conduct siting and sizing of intelligent soft switches; Based on the siting and sizing results of intelligent soft switches, in the lower-layer model, with the goal of maximizing social welfare, considering the network loss and voltage deviation of system operation, determine the optimal siting and sizing results of distributed energy storage devices and intelligent soft switches; The maximization of social welfare is represented by introducing the node marginal price;

[0010] Based on the obtained optimal siting and sizing results of distributed energy storage devices and intelligent soft switches, realize the joint optimization planning of flexible interconnection devices.

[0011] Furthermore, the objective function of the upper-layer model is specifically expressed as:

[0012]

[0013] Among them, represents the annual investment cost of the distributed energy storage device; represents the operation and maintenance cost of the distributed energy storage device; λ represents the discount rate; y DESS represents the service life of the distributed energy storage device; Ω DESS represents the set of nodes where the distributed energy storage device is installed; c e and c p represent the unit capacity and unit power investment costs of the distributed energy storage device; and represent the rated capacity and rated power of the distributed energy storage device installed on node i; S is the number of scenarios; T is the simulation time period of each scenario; is the annual operation and maintenance cost of the distributed energy storage device; η DESS is the operation and maintenance cost of the distributed energy storage device; represents the charging and discharging power of the distributed energy storage device on node i at time t.

[0014] Furthermore, the objective function of the middle-layer model is specifically expressed as:

[0015]

[0016]

[0017] Among them, Ω(i) represents the set of all nodes adjacent to the node, which is used to describe the candidate positions of the intelligent soft switch; c m represents the unit capacity cost of the intelligent soft switch; represents the installed capacity of the intelligent soft switch connected between node i and node j; η SOP represents the annual operation and maintenance cost coefficient of the intelligent soft switch; represents the network loss power of branch ij under scenario s; c loss is the network loss cost coefficient; represents the transmission power of the intelligent soft switch connected to node i; A i,SOP represents the loss coefficient of the intelligent soft switch; LMP s,i,t represents the nodal marginal price under scenario s; represents the power purchased from the superior power grid at time t under scenario s.

[0018] Furthermore, the objective function of the lower-layer model is specifically expressed as:

[0019]

[0020] Among them, S represents the number of scenarios; N represents the number of nodes; NDG, NS, and ND represent the number of nodes installed with distributed power sources, intelligent soft switches, and distributed energy storage devices; T represents the number of hours of the planning scenario; and respectively represent the user load and the output power of the distributed power source at node i at time t; and respectively represent the charge and discharge power of the distributed energy storage device at node i at time t; LMP s,i,t represents the nodal marginal price under scenario s; and represent the power injected by the intelligent soft switch and the distributed power source at node i at time t in scenario s.

[0021] Furthermore, the nodal marginal price is determined according to the actual value of the active power on the transmission line, and is specifically expressed as follows:

[0022]

[0023] Among them, LMP s,i,t is the marginal price of node i at time t in scenario s; and They are respectively the marginal energy price, marginal network loss price, and marginal congestion price of node i at time t in scenario s; p Loss , and p Net They are respectively the line loss power, line power flow, and net power; λ is the Lagrange multiplier under the equality constraint, i.e., the power balance constraint; μ is the Lagrange multiplier under the inequality constraint, i.e., the power generation output constraint.

[0024] Furthermore, for the optimal solution of the three-layer planning model, the nonlinear programming problem is transformed into a second-order cone programming model for solution. Specifically, a hybrid optimization algorithm based on the intelligent heuristic algorithm simulated annealing algorithm and second-order cone programming is used for solution.

[0025] According to the second aspect of the embodiments of the present invention, a joint optimization planning system for flexible interconnection devices for a distribution network is provided, including:

[0026] A scenario construction unit, which is used to construct typical scenarios of distributed power generation output based on the historical data of the distribution network system with distributed power sources introduced, through a preset scenario generation strategy;

[0027] A flexible interconnection device location and capacity determination unit, which is used to determine the optimal location and capacity determination results of distributed energy storage devices and intelligent soft switches based on the constructed typical scenarios of distributed power generation output by optimizing and solving the pre-constructed three-layer planning model. Among them, the three-layer planning model specifically performs the following processing process: Considering the uncertainty of renewable energy output, for the constructed typical scenarios of distributed power generation output, calculate the system node marginal electricity price and introduce it into the three-layer planning model for cost settlement. In the upper-layer model, with the goal of minimizing the total investment cost of distributed energy storage devices that meet the current regional node load demand, perform location and capacity determination on distributed energy storage devices; Based on the location and capacity determination results of distributed energy storage devices, in the middle-layer model, with the goal of minimizing the annual comprehensive operating cost of the distribution network, perform location and capacity determination on intelligent soft switches; Based on the location and capacity determination results of intelligent soft switches, in the lower-layer model, with the goal of maximizing social welfare, considering the network loss and voltage deviation of system operation, determine the optimal location and capacity determination results of distributed energy storage devices and intelligent soft switches; The maximization of social welfare is represented by introducing the node marginal electricity price;

[0028] An optimization planning unit, which is used to realize the joint optimization planning of flexible interconnection devices based on the obtained optimal location and capacity determination results of distributed energy storage devices and intelligent soft switches.

[0029] According to the third aspect of the embodiments of the present invention, an electronic device is provided, including a memory, a processor, and a computer program running on the memory. When the processor executes the program, it implements the joint optimization planning method for flexible interconnection devices for a distribution network.

[0030] According to a fourth aspect of the embodiments of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned joint optimization planning method for flexible interconnection devices in a distribution network is implemented.

[0031] According to a fifth aspect of the embodiments of the present invention, a computer program product is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned joint optimization planning method for flexible interconnection devices in a distribution network is implemented.

[0032] The above one or more technical solutions have the following beneficial effects:

[0033] The present invention provides a joint optimization planning method and system for flexible interconnection devices in a distribution network. The solution provides a three-layer planning model for improving the comprehensive operation index of the distribution network, and takes the idea of space-time complementarity and flexible energy storage utilization to carry out collaborative planning for distributed energy storage devices and intelligent soft switches. At the same time, by introducing the nodal marginal price to represent the social welfare index expressing user satisfaction, the configuration planning of distributed energy storage devices and intelligent soft switches is realized by maximizing the social welfare, so that the planning result can effectively meet the actual needs.

[0034] Considering that in a large-scale nodal system, the real-time calculation of nodal electricity prices is complex and difficult to be used as a planning index, the solution in this embodiment represents the nodal marginal price by calculating the nodal marginal price distribution of the distribution network to be planned.

[0035] In order to optimize and solve the three-layer planning model, the solution of the present invention transforms the non-linear programming problem into a second-order cone programming model for solution, and specifically uses a hybrid optimization algorithm based on the intelligent heuristic algorithm simulated annealing algorithm and second-order cone programming for solution.

[0036] The advantages of the additional aspects of the present invention will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The specification drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0038] Figure 1 It is a supply-demand relationship diagram of a flexible interconnected distribution network described in the embodiments of the present invention;

[0039] Figure 2 It is a topological structure diagram of an intelligent soft switch based on B2B-VSC described in the embodiments of the present invention;

[0040] Figure 3 Schematic diagram of the structure of a distributed energy storage device described in the embodiments of the present invention;

[0041] Figure 4 Schematic diagram of the structure of a flexible interconnection device described in the embodiments of the present invention;

[0042] Figure 5 Schematic diagram of a voltage source inverter with a single - side intelligent soft switch described in the embodiments of the present invention;

[0043] Figure 6 Three - layer planning model framework described in the embodiments of the present invention;

[0044] Figure 7 Optimization method flow for large - scale mixed - integer non - linear programming problems described in the embodiments of the present invention;

[0045] Figure 8 Flow chart for solving the three - layer planning model described in the embodiments of the present invention;

[0046] Figure 9 Schematic diagram of the IEEE 33 - node distribution network improved based on the joint optimization planning method of flexible interconnection devices for distribution networks described in the embodiments of the present invention;

[0047] Figure 10 Comparison chart of social welfare under different planning schemes described in the embodiments of the present invention;

[0048] Figure 11 Comparison chart of nodal electricity prices under different planning schemes described in the embodiments of the present invention;

[0049] Figures 12(a) and 12(b) are global voltage comparison charts of the joint planning configuration scheme based on the joint optimization planning method of flexible interconnection devices for distribution networks described in the embodiments of the present invention;

[0050] Figure 13 Comparison chart of the maximum relaxation deviation of each scenario under different planning schemes described in the embodiments of the present invention. Detailed implementation manners

[0051] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0052] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention.

[0053] In the case of no conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0054] In one or more embodiments, the embodiments of the present invention provide a method for jointly optimizing and planning a flexible interconnection device for a distribution network, including:

[0055] Step 1: Based on the historical data of a distribution network system with distributed power sources introduced, construct typical scenarios of the output of distributed power sources;

[0056] In a specific implementation, construct typical scenarios of the output of distributed power sources based on a preset scenario generation algorithm, where the preset scenario generation algorithm can be:

[0057] Monte Carlo simulation: This method is based on the principle of random sampling. Through a large number of random tests, it simulates the uncertainties of variables such as the output of distributed power sources and loads. In each test, the values of variables are randomly generated according to the probability distribution function of the variables, thereby constructing a scenario. Repeat a large number of tests to generate numerous scenarios, and then perform statistical analysis and screening on these scenarios to obtain representative typical scenarios. The Monte Carlo simulation method is simple and intuitive, and can handle complex probability distributions, but the calculation amount is large, and a large number of scenarios need to be generated to ensure the accuracy of the results.

[0058] Latin hypercube sampling: This is an efficient sampling method. It performs stratified sampling within the value range of each variable to ensure that the samples are evenly distributed throughout the value range, thereby improving the representativeness of the samples. Compared with Monte Carlo simulation, Latin hypercube sampling can obtain more representative scenarios with fewer sample numbers and reduce the calculation amount. In the generation of distributed power source scenarios, sample variables such as the output of distributed power sources and loads through Latin hypercube sampling, generate a series of scenarios, and then perform screening and optimization to obtain typical scenarios.

[0059] Scenario reduction technology: After generating a large number of initial scenarios, in order to reduce the computational complexity, it is necessary to use scenario reduction technology to simplify the scenarios. Commonly used scenario reduction technologies include clustering analysis, Kullback-Leibler (KL) divergence method, etc. Clustering analysis classifies similar scenarios into one category, and then selects one or several representative scenarios from each category as typical scenarios; the KL divergence method measures the probability distribution differences between different scenarios, retains the scenarios that have a greater impact on system analysis, and deletes redundant scenarios to achieve scenario reduction.

[0060] It can be understood that the solution described in this embodiment does not limit the specific scenario generation method, and can be set according to actual needs in specific implementations.

[0061] Step 2: Based on the constructed typical scenarios of distributed power generation output, by optimizing and solving the pre-constructed three-layer planning model, determine the optimal siting and sizing results of distributed energy storage devices and intelligent soft switches. Specifically, the three-layer planning model performs the following processing procedures: Considering the uncertainty of renewable energy output, for the constructed typical scenarios of distributed power generation output, calculate the system node marginal price and introduce it into the three-layer planning model for cost settlement. In the upper-layer model, with the goal of minimizing the total investment cost of distributed energy storage devices to meet the load demand of current regional nodes, conduct siting and sizing for distributed energy storage devices; Based on the siting and sizing results of distributed energy storage devices, in the middle-layer model, with the goal of minimizing the annual comprehensive operation cost of the distribution network, conduct siting and sizing for intelligent soft switches; Based on the siting and sizing results of intelligent soft switches, in the lower-layer model, with the goal of maximizing social welfare, considering the network loss and voltage deviation during system operation, determine the optimal siting and sizing results of distributed energy storage devices and intelligent soft switches; The maximization of social welfare is represented by introducing the node marginal price.

[0062] It should be noted here that the node marginal price (LMP) is a pricing mechanism used to determine the electricity price of each node in the power system. This mechanism reflects the real-time electricity price of a specific node by considering the marginal cost of power supply, transmission losses, and congestion in the power grid. The calculation of the node marginal price is based on the duality theory and an optimization model, which aims to minimize the total cost of the power system while meeting the real-time power demand and the constraints of grid stability. In practice, the electricity price of each node may vary due to geographical location, capacity limitations of transmission lines, and local supply and demand conditions. Therefore, the node marginal price provides a price signal to guide the reasonable allocation of flexible interconnection devices and the efficient operation of the power market. It is an indicator reflecting the spatio-temporal characteristics of power supply and demand. It connects distributed power sources, various loads, and network parameters at different locations. The node marginal price, as a planning indicator, serves the goal of maximizing social welfare in the operation-layer model. Therefore, using the node electricity price as an indicator in the planning model is feasible and guiding.

[0063] In a large-scale node system, the real-time calculation of node electricity prices is complex and difficult to be used as a planning indicator. Therefore, based on an improved method, calculate the LMP distribution of the distribution network to be planned as the dataset of the planning model, serving the social welfare maximization model and electricity billing in the operation layer.

[0064] Specifically, the node marginal price depends on the active power of the transmission line and is determined according to the actual value of the active power on the transmission line, and is calculated using the Karush–Kuhn–Tucker (KKT) conditions. It is described by the following formula:

[0065]

[0066] In the formula, and are the marginal energy price, marginal network loss price, and marginal congestion price of node i at time t in scenario s, respectively; p Loss , and p Net are the line loss power, line power flow, and net power, respectively; λ is the Lagrange multiplier under the equality constraint, i.e., the power balance constraint; μ is the Lagrange multiplier under the inequality constraint, i.e., the generation output constraint.

[0067] Among them, Figure 1 shows a supply-demand relationship diagram of a flexible interconnected distribution network described in this embodiment; Figure 2 shows a topology structure diagram of an intelligent soft switch based on B2B-VSC described in this embodiment; Figure 3 shows a schematic diagram of the structure of a distributed energy storage device described in this embodiment; Figure 4 shows a schematic diagram of the structure of a flexible interconnection device described in this embodiment; Figure 5 shows a schematic diagram of a voltage source inverter of a single-sided intelligent soft switch described in this embodiment; Figure 6 shows the three-layer planning model framework described in this embodiment;

[0068] In specific implementation, the solution described in this embodiment provides a three-layer planning model, including an upper-layer model, a middle-layer model, and a lower-layer model. Among them, considering the operating characteristics of the node-type flexible device DESS (distributed energy storage device), the nodes accessing DESS are used as the basic adjustment objects for the supply-demand matching of the ADN (Active Distribution Network). The siting and sizing problem of DESS is set as the upper-layer model problem, and the planning goal is to minimize the total investment cost of DESS to meet the node load demand of this region, and the DESS configuration location and capacity scheme are passed to the middle layer and the lower layer.

[0069] The middle layer and the lower layer are optimization operation sub-problems for maximizing the social welfare of the distribution network. The middle-layer model aims at the optimal configuration of SOP (intelligent soft switch), which involves the optimization of the annual comprehensive operation cost of the distribution network, including the configuration cost of SOP and the overall power purchase cost of the distribution network. The purpose of this layer of model is to further expand the power mutual assistance channels in different regions of the distribution network through the flexible deployment of SOP based on the DESS location and capacity determined in the upper layer.

[0070] The lower-level model aims to maximize the social welfare of the distribution network. By using LMP (Locationa l Margina lPr ice) as an indicator, it reflects user satisfaction and planning strategies. The model simultaneously considers the operational uncertainty of DG (Distributed Generation), network topology constraints, flexible device planning and operation constraints, and system power flow constraints. The goal is to maximize social welfare under the given grid structure and preset scenarios of the upper and middle levels. This includes optimizing the power flow of the distribution network to reduce network losses and regulating the power output of the superior grid, DESS, and SOP.

[0071] Through the iterative optimization of the three-layer model, the ultimate goal is to discover a distribution network configuration plan that meets both economic efficiency and maximizes social welfare, making full use of the spatio-temporal regulation capabilities of DESS and SOP. Through the joint planning of DESS and SOP, the flexibility problem of the supply-demand balance of the distribution network can be maximally solved, ensuring the realization of goals such as regional decentralized resource integration and optimal power flow operation under different scenarios. Furthermore, social welfare is maximized.

[0072] In the specific implementation, the upper-level model optimizes the siting and sizing problem of DESS. The upper-level objective function C UP is to minimize the equivalent annual operating cost of the planned DESS equipment in the ADN under various scenarios, where the cost includes the annual investment cost and the annual maintenance cost The upper-level model objective function is shown in equations (2) to (4).

[0073]

[0074] In the formula, represents the annual investment cost of DESS; represents the operation and maintenance cost of DESS; λ represents the discount rate; y DESS represents the service life of DESS; Ω DESS represents the set of nodes where DESS is installed; c e and c p represent the unit capacity and unit power investment costs of DESS; and P i DESS represent the rated capacity and rated power of DESS installed on node i; S is the number of scenarios; T is the simulation period of each scenario, taking 8760 hours; is the annual operation and maintenance cost of DESS; η DESS is the operation and maintenance cost of DESS; represents the charge and discharge power of DESS on node i at time t.

[0075] In terms of constraint conditions, 0-1 variables are considered to represent the installation nodes of energy storage and the connection nodes of SOP. The DC side of SOP is also considered as the installation node of DESS. When the constraint conditions are met, the access of DESS to SOP constitutes a new intelligent energy storage soft switch E-SOP.

[0076]

[0077] In the formula, and represent the maximum and minimum power capacities of DESS allowed to be installed at node i; and represent the maximum and minimum energy capacities of DESS allowed to be installed at node i; and are non-negative integers; p DESS and e DESS are the reference power capacity and reference energy capacity of DESS planning respectively. and are the charging and discharging powers of the i-th energy storage at time t respectively; is the energy stored in the energy storage at time t.

[0078] In the specific implementation, the middle-layer model aims to minimize the annual comprehensive operation cost of the distribution network and solve the SOP location and capacity determination scheme. The objective function of the middle-layer model includes the investment cost and operation cost of SOP, as well as the network loss cost and power purchase cost

[0079]

[0080] In the formula, Ω(i) represents the set of all nodes adjacent to node i, which is used to describe the candidate locations of SOP; c m represents the unit capacity cost of SOP; represents the installed capacity of SOP connected between node i and node j; η SOP represents the annual operation and maintenance cost coefficient of SOP; represents the network loss power of branch ij under scenario s; c loss is the network loss cost coefficient; represents the transmission power of SOP connected to node i; A i,SOP represents the loss coefficient of SOP; LMP s,i,t represents the node marginal electricity price under scenario s; represents the power purchased from the superior power grid at time t under scenario s.

[0081] The decision variables of the middle-layer model at the constraint level include the connection location and transmission capacity of the SOP. The middle-layer model uniformly plans the location and capacity of the SOP through integer variables. If the planned capacity of the SOP to be installed is 0, it is considered that the SOP does not need to be installed at this location. The advantage of this method is that the installation location is determined while determining the SOP capacity. The constraint conditions of the middle-layer model are as follows:

[0082]

[0083]

[0084] In the formula, s SOP is the installation capacity of a single SOP, that is, the minimum optimizable capacity of the planning model, such as 10 kVA, 50 kVA, etc.; m k is a non-negative integer; is the maximum capacity of the SOP allowed to be installed at the selected location.

[0085] By deploying DESS within the distribution network partition, it provides a time margin for the distribution network operator to conduct voltage management and power flow control. Furthermore, by deploying SOPs between distribution network partitions, a power mutual assistance channel for the distribution network is constructed to provide a space margin and achieve "flexible energy storage and utilization". The flexible interconnected distribution network structure with DESS and SOP plays an important role in maximizing social welfare. The main purpose of the lower-layer model is to optimize the social welfare maximization model of the entire system under the planning scenario when DG, DESS, and SOP are connected. The lower-layer model incorporates voltage deviation as an influencing factor into the social welfare model, and the mathematical framework of social welfare is constructed as:

[0086]

[0087] In the formula, S represents the number of scenarios; N represents the number of nodes; NDG, NS, and ND represent the number of nodes installed with DG, SOP, and DESS; T represents the number of hours in the planning scenario, taking 8760 hours; and are the user load and DG output power of node i at time t, respectively; and are the charge and discharge power of the DESS of node i at time t, respectively; LMP s,i,t represents the nodal marginal price in scenario s; and represent the power injected by the SOP and DG of node i at time t in scenario s.

[0088] In terms of constraints, the Dist-flow model is used to apply the second-order cone programming method to the transformation of the non-linear constraint model in the distribution network optimization problem. First, the method of variable substitution is used to replace the square terms of voltage and current in the power flow direction with the two-norm current-voltage terms, linearizing the power flow equation. Secondly, the second-order cone relaxation method is used to transform the current model of the branch into a second-order cone model. Similarly, the non-linear operation constraints of DESS and SOP are linearly transformed as follows:

[0089] (1) Define the square term of the newly optimized variable node voltage amplitude in the power flow equation as v i , and the square term of the branch current as l ij

[0090]

[0091]

[0092] After linearizing the power flow constraints through variable substitution, it is necessary to supplement and correct the power flow constraints at this time. The big-M method is introduced, and the supplementary constraints are as follows.

[0093] -Mα ij,t ≤P ij,t ≤Mα ij,t (20)

[0094] -Mα ij,t ≤Q ij,t ≤Mα ij,t (21)

[0095] 0≤i ij,t ≤Mα ij,t (22)

[0096] The power flow constraint formula (18) can be rewritten in the following form.

[0097]

[0098] After variable substitution, formula (18) is still a quadratic non-linear constraint. According to the above, under the conditions that the objective function is a strictly increasing function of i ij,t and the node load has no upper bound, etc., it can be cone-relaxed into the second-order cone constraint form as shown in formula (25). Formula (19) can be rewritten as formula (25).

[0099]

[0100] Here, the accuracy evaluation of convex relaxation is introduced, as shown in formula (27). When the gap value is small enough, it can be considered that the convex cone relaxation model conversion is accurate.

[0101]

[0102] (2) SOP constraint conversion problem. Since its DC port allows DC loads to be connected, forming an intelligent energy storage soft switch (E-SOP), during the modeling process, not only the operating characteristics of the SOP need to be considered for modeling. Here, a single-sided voltage source converter connecting the AC and DC sides is modeled, as shown in equations (28) to (29).

[0103] The operating constraints are as shown in equations (28) to (29) below.

[0104]

[0105] In the formula, X VSC and R VSC are the equivalent reactance and equivalent resistance of the converter.

[0106] The phase voltage U at the input side of the VSC i,t and the DC voltage at the output side are related as shown in equation (30).

[0107]

[0108] In the formula, μ is the DC voltage utilization rate. When the pulse width modulation method is SPWM, μ is 0.866.

[0109] In the operating model, for the non-linear physical constraints of the SOP, the convex relaxation method is used. The non-linear constraints in the capacity constraints of the SOP can be converted into rotation cone constraints as shown in equations (31) to (34).

[0110]

[0111] (3) For the absolute value variables and in the middle-layer model, the methods of variable substitution and convex relaxation are adopted to convert them into a form that satisfies the second-order cone structure.

[0112] For the absolute value variables and the big-M method is used to introduce the absolute value variables and and perform linearization relaxation on them. The energy storage transmission power is also introduced with absolute value variables in the same way for relaxation. As shown in equations (35) to (36).

[0113]

[0114] After model transformation, the original non-linear programming problem can be transformed into a second-order cone programming model. A single programming problem can be solved by calling existing mature mathematical optimization toolkits such as CPLEX and Gurobi on the MATLAB platform. Since the operation constraints and power flow constraints of SOP are equality constraints with square terms, and there are a large number of integer decision variables (the connection positions between SOP and DESS, the opening and closing states of tie switches, and the planned capacities of SOP and DESS) and continuous decision variables (the transmission powers of SOP and DESS) in the planning model, a complex mixed-integer non-linear problem is formed. This problem is a complex non-convex MINLP problem, and existing commercial solvers cannot directly solve non-convex problems.

[0115] To decouple the integer variables in the upper and middle layer models from the continuous variables in the lower layer model, a hybrid optimization algorithm based on the intelligent heuristic algorithm simulated annealing (SA) algorithm and SOCP (second-order cone programming) is adopted. The implementation of the single-layer SA algorithm is relatively simple and has good convergence performance. However, its performance depends on the selected annealing scheme parameters and requires a large number of random iterations. The feature of the hybrid optimization algorithm proposed in the solution of this embodiment is that the DESS and SOP planning schemes are used as the objective functions of the double-layer SA algorithm and continuously iteratively solved with the lower-layer second-order cone programming problem to further improve the solution accuracy. At the same time, parameters affecting the algorithm performance such as the iteration number limit M, the temperature reduction coefficient α, and the initial temperature T also need to be accurately and reasonably selected according to factors such as the system scale and network distribution of the research object.

[0116] Among them, Figure 7 shows the optimization method flow for large-scale mixed-integer non-linear programming problems described in this embodiment; Figure 8 shows the solution flow chart of the three-layer planning model described in this embodiment; Figure 9 shows a schematic diagram of the IEEE 33-node distribution network improved based on the joint optimization planning method of flexible interconnection devices for distribution networks described in this embodiment; Figure 10 shows a comparison chart of social welfare under different planning schemes described in this embodiment; Figure 11 shows a comparison chart of nodal electricity prices under different planning schemes described in this embodiment;

[0117] Step 3: Based on the obtained optimal siting and sizing results of distributed energy storage devices and intelligent soft switches, realize the joint optimization planning of flexible interconnection devices.

[0118] Furthermore, to prove the effectiveness of the solution described in this embodiment, corresponding experimental verifications are carried out as follows:

[0119] The extended IEEE 33-node distribution network system is used for testing. The rated voltage is 12.66 kV, and the base capacity is 10 MVA. The allowable range of node voltage is 0.95 p.u. to 1.05 p.u., and the allowable range of branch current is 0 to 1.05 p.u. To verify the economic and social welfare improvement effects of the combined planning model of DESS and SOP under high DG penetration in the distribution network, three distributed wind turbines (WT) and two distributed photovoltaic generators (PV) are integrated into the distribution system. The installation locations of WT are nodes 7, 27, and 32, with powers of 1200 kVA, 800 kVA, and 600 kVA respectively. PV are installed at nodes 17 and 22, with powers of 400 kVA and 800 kVA respectively, and the power factor is set to 0.95.

[0120] The electric energy purchased by distribution network users from the upstream power grid is provided by upstream thermal power units. To utilize the node flexibility characteristics of DESS and the network flexibility characteristics of SOP, the distribution network is divided into four zones using the distribution network cluster division method, represented by different color blocks, so as to appropriately preselect the installation areas of DESS and SOP. This can ensure that a single SOP is installed between distribution network clusters, and there are five candidate tie switches for installing SOP. One DESS device is configured within a distribution network cluster.

[0121] Table 1 SOP and DESS parameters in the planning model

[0122]

[0123] To verify the feasibility and effectiveness of the combined planning scheme of DESS and SOP proposed in the solution of this embodiment, the following 4 schemes are set up, and analysis and comparison are carried out from the aspects of economy, social welfare, and voltage respectively.

[0124] Scheme 1: Do not change the network topology, and do not install SOP and DESS;

[0125] Scheme 2: Only consider configuring SOP in the distribution network;

[0126] Scheme 3: Only consider configuring DESS in the distribution network;

[0127] Scheme 4: Consider the combined configuration of SOP and DESS.

[0128] (1) Analysis of economic improvement

[0129] Table 2 shows the results of the combined planning configuration scheme of DESS and SOP.

[0130] Table 2 Combined planning configuration scheme of DESS and SOP

[0131]

[0132] It can be found from this that Table 2 shows the comprehensive cost comparison under different scenarios. Table 3 shows the economic comparison of different planning scenarios. Among them, the annualized cost includes the equivalent annual investment cost and annual operation and maintenance cost of DESS and SOP.

[0133] Table 3 Economic Benefit Comparison of DESS and SOP Planning Scenarios

[0134]

[0135] Table 4 Economic Index Comparison of DESS and SOP Planning Scenarios

[0136]

[0137]

[0138] The above planning indicators aim to measure the economic feasibility and operation efficiency of integrating SOP and DESS into the distribution network.

[0139] In Scenarios 2 and 4, all distribution network cluster areas and tie switches are regarded as candidate locations for configuring SOP and DESS, and the power injection of DESS and SOP into the nodes is in the positive direction.

[0140] By comparing Scenario 1 with the other three configurations in Tables 3 and 4, it is found that integrating flexible resources in the distribution network can significantly improve its economic feasibility. In Scenario 3, DESS, as a node-based basic flexible interconnection device, improves the utilization rate of DG and shows certain economic benefits. While in Scenario 2, SOP is used as a network-based flexible interconnection device to establish a transmission channel between nodes to achieve more flexible two-way power transmission. Compared with the annualized cost of DESS ($70,412), the annualized cost of Scenario 2 ($14,978) is lower, but the economic benefit is better ($288,511), which is 21.54% higher than that of Scenario 3. Scenario 4 verifies that coordinating the deployment of DESS and SOP in ADN can effectively improve economic benefits, highlighting the economic advantage of its coordinated planning ($315,757). Table 4.4 shows that although the independent installation cost of DESS is relatively high, its coordinated planning with SOP can significantly reduce the network loss cost, thus improving the overall economic feasibility.

[0141] (2) Analysis of Social Welfare Improvement

[0142] Figure 4 and Figure 5Shows the comparison of social welfare indicators under different planning schemes. The average social welfare under the combined SOP and DESS planning scheme is $1275, showing a significant improvement compared to the original distribution network structure, which is $864 (47.5%). Although a single energy storage configuration scheme can improve social welfare to a certain extent, reaching $975 (12.8%), the operating characteristics of DESS installed at fixed nodes limit its influence on other distribution network cluster areas. In contrast, SOP can establish flexible interconnections between distribution network user areas, enabling cross-regional integration of social welfare resources and significantly improving social welfare. This further proves the improvement of the combined DESS and SOP planning scheme in terms of social welfare indicators.

[0143] The operation of the distribution network during the night period is characterized by low output of DG units and peak user loads, resulting in an increase in LMP, which reaches $53.3 per MWh at 21:00. The comparison of LMP distributions under different schemes highlights the positive impact of flexible resource allocation on alleviating the difference in electricity price distribution and improving user satisfaction. In Scheme 3, the addition of DESS disperses the price distribution of the system and reduces the peak electricity price. However, due to the lack of flexible connections established between distribution network clusters, it can only affect specific user areas, so its impact on LMP is limited. Scheme 4 combines multiple flexible resources, greatly enhancing the energy management ability. The coordinated planning in this scheme has achieved good results in system management and affects the electricity prices during different demand periods. This helps to improve the overall economic efficiency of the system, maximize social welfare, and further reduce the peak-to-valley difference from $23.3 per MWh to $18.2 per MWh.

[0144] (III) Security analysis

[0145] Figures 12(a) and 12(b) compare the global voltage distributions of Scheme 1 and Scheme 4 under the same conditions.

[0146] The results in Figures 12(a) and 12(b) show that there are cases of voltage violation, and the voltage level is lower than the safety limit (0.95 p.u.) at certain times. Scheme 4 combines network-based and node-based flexible devices, showing a smoother global voltage distribution and all within the safety limits. This further shows that DESS, as a flexible node resource, has the ability to regulate voltage distribution. When paired with the flexible interconnection structure of SOP, it can independently control the real power and reactive power at both ends of the feeder in real-time, dynamically, and continuously, further balancing the line load and optimizing the voltage distribution, thereby improving the active power regulation ability of the entire distribution network.

[0147] (IV) Calculation accuracy analysis of the second-order cone relaxation method

[0148] The global maximum relaxation deviations of Scheme 2 and Scheme 4 under five planning scenarios are asFigure 13 As shown. Considering the introduction of the second-order cone programming method to relax the absolute value variables of SOP and DESS, according to the relaxation deviation calculation formula described in the method, the global maximum relaxation deviations of Scheme 2 and Scheme 4 under five planning scenarios are obtained. The deviations are all at the order of magnitude of 1.0×10-6, which proves the accuracy of the adopted method.

[0149] In one or more embodiments, corresponding to the above method, this embodiment provides a flexible interconnected device joint optimization planning system for a distribution network, including:

[0150] A scenario construction unit, which is used to construct typical scenarios of distributed power generation output based on the historical data of the distribution network system with distributed power sources introduced, through a preset scenario generation strategy;

[0151] A flexible interconnected device location and capacity determination unit, which is used to determine the optimal location and capacity determination results of distributed energy storage devices and intelligent soft switches based on the constructed typical scenarios of distributed power generation output, by optimizing and solving a pre-constructed three-layer planning model. Among them, the three-layer planning model specifically performs the following processing procedures: Considering the uncertainty of renewable energy output, for the constructed typical scenarios of distributed power generation output, calculate the system node marginal price and introduce it into the three-layer planning model for cost settlement. In the upper-layer model, with the goal of minimizing the total investment cost of distributed energy storage devices to meet the current regional node load demand, conduct location and capacity determination for distributed energy storage devices; Based on the location and capacity determination results of distributed energy storage devices, in the middle-layer model, with the goal of minimizing the annual comprehensive operation cost of the distribution network, conduct location and capacity determination for intelligent soft switches; Based on the location and capacity determination results of intelligent soft switches, in the lower-layer model, with the goal of maximizing social welfare, considering the network loss and voltage deviation of system operation, determine the optimal location and capacity determination results of distributed energy storage devices and intelligent soft switches; The maximization of social welfare is represented by introducing the node marginal price;

[0152] An optimization planning unit, which is used to realize the joint optimization planning of flexible interconnected devices based on the obtained optimal location and capacity determination results of distributed energy storage devices and intelligent soft switches.

[0153] It can be understood that the system described in this embodiment corresponds to the method described in the above embodiment, and its technical details have been described in detail in Embodiment 1, so they will not be elaborated here.

[0154] In more embodiments, there is also provided:

[0155] An electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the method described in Embodiment 1 is completed. For the sake of brevity, it will not be elaborated here.

[0156] In a specific embodiment, the electronic device further includes a calculation unit for considering the uncertainty of renewable energy processing, an input unit for system parameters, and an expansion unit for considering the expansion of the distribution network, and realizes functions such as outputting multiple planning schemes under different scales.

[0157] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0158] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0159] A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the method described in the first embodiment is completed.

[0160] A computer program product has a computer program stored thereon. When the program is executed by the processor, the combined optimization planning method for the flexible interconnection device for the distribution network is implemented.

[0161] The method in the first embodiment can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0162] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with this embodiment can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0163] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for joint optimization planning of flexible interconnected devices for distribution networks, characterized in that: include: Based on the historical data of the distribution network system that introduces distributed power sources, typical scenarios of distributed power output are constructed; Based on the typical scenario of distributed power output, the optimal site selection and sizing results of distributed energy storage devices and intelligent soft switches are determined by optimizing and solving the pre-constructed three-layer planning model, wherein the three-layer planning model specifically performs the following processing procedures: considering the output uncertainty of renewable energy, for the typical scenario of distributed power output, the marginal electricity price of the system nodes is calculated and the three-layer planning model is introduced for cost settlement. In the upper-layer model, the distributed energy storage devices are sited and sized with the goal of minimizing the total investment cost of the distributed energy storage devices that meet the current regional node load demand; based on the site selection and sizing results of the distributed energy storage devices, the intelligent soft switches are sited and sized with the goal of minimizing the annual comprehensive operating cost of the distribution network in the middle-layer model; based on the site selection and sizing results of the intelligent soft switches, in the lower-layer model, the network loss and voltage deviation of the system operation are considered with the goal of maximizing social welfare, and the optimal site selection and sizing results of the distributed energy storage devices and intelligent soft switches are determined; the maximization of social welfare is represented by introducing the marginal electricity price of the node; Based on the optimal site selection and sizing results of distributed energy storage devices and intelligent soft switches, joint optimization planning of flexible interconnected devices is achieved.

2. The method for joint optimization planning of flexible interconnection devices for distribution network according to claim 1, characterized in that: The objective function of the upper model is specifically expressed as: in, represents the annual investment cost of distributed energy storage devices; represents the operation and maintenance cost of the distributed energy storage device; λ represents the discount rate; y DESS Indicates the service life of the distributed energy storage device; Ω DESS Represents the set of nodes where distributed energy storage devices are installed; c e and c p Indicates the unit capacity and unit power investment cost of distributed energy storage devices; and represents the rated capacity and rated power of the distributed energy storage device installed on node i; S is the number of scenarios; T is the simulation period of each scenario; is the annual operation and maintenance cost of the distributed energy storage device; η DESS It is the operation and maintenance cost of distributed energy storage devices; Represents the charging and discharging power of the distributed energy storage device at node i at time t.

3. The method for joint optimization planning of flexible interconnection devices for distribution network according to claim 1, characterized in that: The objective function of the middle-level model is specifically expressed as: Where Ω(i) represents the set of all nodes adjacent to the node, which is used to describe the candidate position of the intelligent soft switch; c m It represents the unit capacity cost of the intelligent soft switch; represents the installed capacity of the intelligent soft switch connected between node i and node j; η SOP Indicates the annual operation and maintenance cost coefficient of the intelligent soft switch; represents the network loss power of branch ij under scenario s; c loss is the network loss cost coefficient; A represents the power transmission of the intelligent soft switch connected to node i; i,SOP Indicates the loss factor of the intelligent soft switch; LMP s,i,t represents the node marginal electricity price under scenario s; Indicates the power purchased from the upper grid at time t in scenario s.

4. The method for joint optimization planning of flexible interconnection devices for distribution network according to claim 1, characterized in that: ,The objective function of the lower model is specifically expressed as: Among them, S represents the number of scenarios; N represents the number of nodes; NDG, NS and ND represent the number of nodes equipped with distributed power sources, intelligent soft switches and distributed energy storage devices; T represents the number of planned scenario hours; and The user load and distributed generation output power of node i at time t respectively; and The charging and discharging power of the distributed energy storage device at node i at time t; LMP s,i,t represents the node marginal electricity price under scenario s; and It represents the power injected by the intelligent soft switch and distributed generation at node i in scenario s at time t.

5. The method for joint optimization planning of flexible interconnection devices for distribution network according to claim 1, characterized in that: The node marginal electricity price is determined according to the actual value of active power on the transmission line, and is specifically expressed as follows: Among them, LMP s,i,t is the marginal electricity price of node i at time t in scenario s; and are the marginal energy price, marginal network loss price and marginal congestion price of node i at time t in scenario s; p Loss , and p Net are line loss power, line flow power and net power respectively; λ is the Lagrange multiplier under the equality constraint, i.e., the power balance constraint; μ is the Lagrange multiplier under the inequality constraint, i.e., the power generation output constraint.

6. The method for joint optimization planning of flexible interconnection devices for distribution network according to claim 1, characterized in that: The optimization solution of the three-level programming model is solved by converting the nonlinear programming problem into a second-order cone programming model, and specifically adopting a hybrid optimization algorithm based on an intelligent heuristic algorithm, a simulated annealing algorithm and a second-order cone programming to solve it.

7. A flexible interconnection device joint optimization planning system for a distribution network, characterized in that: include: A scenario construction unit, which is used to construct a typical scenario of distributed power output based on historical data of the distribution network system that introduces distributed power sources through a preset scenario generation strategy; A flexible interconnection device site selection and sizing unit is used to determine the optimal site selection and sizing results of distributed energy storage devices and intelligent soft switches based on the typical scenario of distributed power output constructed, by optimizing and solving the pre-constructed three-layer planning model, wherein the three-layer planning model specifically performs the following processing procedures: considering the output uncertainty of renewable energy, for the typical scenario of distributed power output constructed, the marginal electricity price of the system node is calculated and the three-layer planning model is introduced for cost settlement, and the distributed energy storage device is sited and sized with the goal of minimizing the total investment cost of the distributed energy storage device that meets the current regional node load demand in the upper model; based on the site selection and sizing results of the distributed energy storage device, the intelligent soft switch is sited and sized with the goal of minimizing the annual comprehensive operating cost of the distribution network in the middle model; based on the site selection and sizing results of the intelligent soft switch, in the lower model, the network loss and voltage deviation of the system operation are considered with the goal of maximizing social welfare, and the optimal site selection and sizing results of the distributed energy storage device and the intelligent soft switch are determined; the maximization of social welfare is represented by introducing the marginal electricity price of the node; The optimization planning unit is used to realize the joint optimization planning of the flexible interconnection device based on the obtained optimal site selection and capacity determination results of the distributed energy storage device and the intelligent soft switch.

8. An electronic device comprising a memory, a processor and a computer program stored and running on the memory, characterized in that: When the processor executes the program, the method for joint optimization planning of flexible interconnected devices for a distribution network as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for joint optimization planning of flexible interconnected devices for a distribution network as described in any one of claims 1 to 6 is implemented.

10. A computer program product having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for joint optimization planning of flexible interconnected devices for a distribution network as described in any one of claims 1 to 6 is implemented.

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