Active power distribution network distributed flexible low-carbon energy supply system optimal configuration method and system

Abstract

The invention provides an optimal configuration method and system for a distributed flexible low-carbon energy supply system of an active power distribution network. The method comprises the steps of obtaining power operation data of the distributed flexible low-carbon energy supply system in the active power distribution network; based on the power operation data, solving a pre-constructed optimal configuration model by using a mixed integer second-order cone programming method, and outputting the optimal installation position and capacity of the distributed flexible low-carbon energy supply system; taking the optimal installation position and capacity of the distributed flexible low-carbon energy supply system as an optimal configuration scheme of the distributed flexible low-carbon energy supply system; wherein the optimal configuration model is constructed by taking minimization of investment maintenance cost and operation cost of the distributed flexible low-carbon energy supply system as a target; according to the invention, the configuration optimization model is subjected to non-linear and non-convex conversion through the mixed integer second-order cone programming method to solve the model, so that the distributed flexible low-carbon energy supply system can be quickly and accurately distributed to the active power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed flexible low-carbon energy supply systems, and in particular to a method and system for optimizing configuration of a distributed flexible low-carbon energy supply system of an active power distribution network. Background Art

[0002] At present, Distributed Flexible Low-carbon Energy Supply System (DFLCESS) is an important solution to support the operation and control of Active Distribution Network (ADN). In recent years, the research and development related to power infrastructure is driven by the rapid development of passive distribution system to active distribution system. Active distribution network is a distribution system in which embedded generation is actively controlled by a properly defined Energy Management System (EMS) to achieve specific operation goals. However, the non-direct controllability of distributed generation (DG) supplying power to the distribution network has become a major obstacle to increasing the penetration of distributed generation, especially the penetration of renewable energy resources with non-negligible volatility.

[0003] The typical approach that allows EMS to optimally control ADN relies on direct control of DG, however, this process requires significant investments in the associated telecommunications infrastructure. Another approach is to use distributed flexible low-carbon energy supply systems, which can provide distribution network operators with a variety of services ranging from local energy balancing to ancillary services, especially network services such as peak shaving and renewable energy compensation, voltage and frequency control support. DFLCESS is also able to indirectly control line congestion and can therefore be used to minimize losses and postpone major network upgrades. In this case, the first issue associated with the use of DFLCESS is to optimally allocate them according to location and energy / power rating.

[0004] However, with regard to the optimal site selection and scale of distributed flexible low-carbon energy supply systems, current research has only focused on using genetic algorithms to find the optimal energy storage capacity and formulate an objective function to minimize the operating cost of the target microgrid, using a hybrid genetic algorithm combined with a sequential quadratic programming algorithm to determine the scale and location of distributed generation energy storage and reactive compensation systems, and an integrated method for distribution network energy storage optimization that combines dynamic programming with genetic algorithms. There is no proper treatment of the nonlinearity and non-convexity of the objective function, and either the non-convex formula of the optimal power flow problem is used, or only the economic issues are dealt with, resulting in inaccurate site selection and capacity configuration for distributed flexible low-carbon energy supply systems, and the inability to quickly and accurately allocate distributed flexible low-carbon energy supply systems to active distribution networks. Summary of the invention

[0005] In order to solve the problem of optimal site selection and scale for distributed flexible low-carbon energy supply systems in the prior art, the algorithm used to solve the model constructed based on the AC optimal power flow problem does not consider the non-convexity and nonlinearity of the model, resulting in inaccurate site selection and capacity configuration for distributed flexible low-carbon energy supply systems. The present invention proposes an optimization configuration method for distributed flexible low-carbon energy supply systems of active distribution networks, including:

[0006] Obtain power operation data of distributed flexible low-carbon energy supply systems in active distribution networks;

[0007] Based on the power operation data, a pre-constructed optimization configuration model is solved using a mixed integer second-order cone programming method to output the optimal installation location and capacity of the distributed flexible low-carbon energy supply system;

[0008] Taking the optimal installation position and capacity of the distributed flexible low-carbon energy supply system as the optimized configuration scheme of the distributed flexible low-carbon energy supply system;

[0009] Among them, the optimization configuration model is constructed with the goal of minimizing the investment, maintenance and operating costs of the distributed flexible low-carbon energy supply system.

[0010] Optionally, the optimization configuration model includes the following construction process:

[0011] Determine the investment and maintenance cost of the distributed flexible low-carbon energy supply system according to the sum of the installation cost, the rated power-related investment cost, the storage capacity-related investment cost and the maintenance cost of the distributed flexible low-carbon energy supply system;

[0012] Determine the operating cost of the distributed flexible low-carbon energy supply system according to the sum of the voltage deviation energy cost, the feeder current energy cost, the distributed power unit operation energy cost and the loss load cost of the distributed flexible low-carbon energy supply system;

[0013] Taking minimizing the investment and maintenance cost and the operating cost as objectives respectively, constructing an objective function including the investment and maintenance cost and the operating cost;

[0014] According to the objective function, respectively formulate constraint conditions corresponding to the investment and maintenance cost and the operating cost;

[0015] An optimization configuration model is constructed based on the objective function, the constraints corresponding to the investment and maintenance costs, and the constraints corresponding to the operating costs.

[0016] Optionally, the expression corresponding to the objective function is as follows:

[0017]

[0018] Among them, Obj inv represents the investment and maintenance cost of the distributed flexible low-carbon energy supply system; represents a binary variable related to the presence of energy storage on bus i; FC i represents the installation cost of the distributed flexible low-carbon energy supply system on bus i; represents the rated power of the distributed flexible low-carbon energy supply system on bus i; represents the rated power associated investment cost of the distributed flexible low-carbon energy supply system on bus i; represents the energy storage of the distributed flexible low-carbon energy supply system on bus i; represents the storage capacity-related investment cost of the distributed flexible low-carbon energy supply system on bus i; α represents the annual interest rate; y represents the historical index; Obj represents the maintenance cost of the distributed flexible low-carbon energy supply system on bus i in the yth year; op represents the operating cost of the distributed flexible low-carbon energy supply system; Prob Sc represents the probability of scenario Sc; Cost1 represents the voltage deviation energy cost; Cost2 represents the feeder current energy cost; Cost3 represents the distributed generation unit operation energy cost; Cost4 represents the loss load cost; W vol Represents the voltage weighting coefficient; V i，y，Sc,t represents the RMS voltage value of bus i at time t in year y of the distributed flexible low-carbon energy supply system under scenario Sc; Indicates the preset maximum voltage threshold; Indicates the preset minimum voltage threshold; W flow Indicates the feeder current weighting coefficient; F l，y，Sc,t The current value of feeder l in the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; F thr Indicates the preset maximum feeder current threshold; W loss represents the loss weighting coefficient; r l represents the longitudinal resistance of the feeder line l between buses i and j; W EP represents the energy weighting coefficient; represents the energy cost from the upper power grid of the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; represents the cost function of a given distributed generation on bus i; represents the active power of a given distributed generation unit connected to bus i at time t in year y under scenario Sc; C start represents the initial cost; W lossDSS represents the resistance loss weighting factor; W represents the resistance loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; Cur represents the load weighting factor; It represents the active load reduction of bus i at time t in year y under scenario Sc.

[0019] Optionally, the constraint conditions corresponding to the investment and maintenance cost include: bus quantity constraint, rated power constraint, rated energy storage capacity constraint, rated power sum constraint and rated energy storage capacity sum constraint;

[0020] The constraints corresponding to the operating cost include: energy constraints, rated power constraints, nonlinear constraints, relaxation constraints, power capacity constraints, network security constraints, current flow constraints, node voltage constraints and distributed power supply operation constraints.

[0021] Optionally, based on the power operation data, a mixed integer second-order cone programming method is used to solve a pre-built optimization configuration model to output the optimal installation location and capacity of the distributed flexible low-carbon energy supply system, including:

[0022] Construct linear constraints based on pre-set voltage auxiliary variables and current auxiliary variables;

[0023] Constructing a convexity constraint based on the relaxation constraint and the current flow constraint;

[0024] Based on the linear constraint and the convexity constraint, converting the optimization configuration model into a linear convex optimization configuration model;

[0025] According to the power operation data, the linear convex optimization configuration model is solved by the GUROBI solver to obtain the optimal installation position and capacity of the distributed flexible low-carbon energy supply system;

[0026] The linear constraints include voltage deviation linear constraints and power flow linear constraints.

[0027] Optionally, the expression corresponding to the voltage deviation linear constraint is as follows:

[0028]

[0029] in, represents the voltage auxiliary variable of bus i at time t in year y under scenario Sc; v i，y，Sc,t represents the node voltage of bus i at time t in year y under scenario Sc; Indicates the preset maximum voltage threshold;

[0030] The expression corresponding to the power flow linear constraint is as follows:

[0031]

[0032] in, represents the auxiliary variable of the current on bus i at time t in year y under scenario Sc; f i，y,Sc,t represents the current of bus i at time t in year y under scenario Sc; F thr Indicates the preset maximum feeder current threshold;

[0033] The expression corresponding to the convexity constraint is as follows:

[0034]

[0035] in, represents the reactance of the distributed flexible low-carbon energy supply system on bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the energy loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; P ij，y，Sc,t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; Q ij,y，Sc,t represents the reactive power flow of buses i and j at time t in year y under scenario Sc; f ij，y，Sc,t represents the active power transfer limit between bus i and bus j at time t in year y under scenario Sc; v ij,y,Sc,t It represents the voltage amplitude of bus i and bus j at time t in year y under scenario Sc.

[0036] Optionally, the expression corresponding to the network security constraint is as follows:

[0037]

[0038] Among them, P ij，y，Sc,t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; P jk，y，Sc,t represents the active power flow of bus j and line k at time t in year y under scenario Sc; C represents the set of lines between bus i and bus j; k: (i, j) represents line k between bus i and bus j; Q ij,y，Sc,t represents the reactive power flow of bus i and bus j at time t in year y under scenario Sc; Q jk,y,Sc,t represents the reactive power flow of bus j and line k at time t in year y under scenario Sc; r ij represents the resistance between bus i and bus j; x ijrepresents the reactance between bus i and bus j; F ij，y,Sc,t represents the current flow of bus i and bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc; represents the reactive power demand of bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the reactive power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the active load reduction of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; It represents the reactive power related to the parallel susceptance between bus i and bus j of the distributed flexible low-carbon energy supply system under scenario Sc at time t in year y.

[0039] Based on the same inventive concept, the present invention also provides an active distribution network distributed flexible low-carbon energy supply system optimization configuration system, comprising:

[0040] A data acquisition module is used to obtain power operation data of a distributed flexible low-carbon energy supply system in an active distribution network;

[0041] A model solving module, used to solve a pre-built optimization configuration model based on the power operation data using a mixed integer second-order cone programming method, and output an optimal installation location and capacity of the distributed flexible low-carbon energy supply system;

[0042] An optimization configuration module, used to use the optimal installation position and capacity of the distributed flexible low-carbon energy supply system as an optimization configuration scheme of the distributed flexible low-carbon energy supply system;

[0043] Among them, the optimization configuration model is constructed with the goal of minimizing the investment, maintenance and operating costs of the distributed flexible low-carbon energy supply system.

[0044] Optionally, the configuration system further includes: a model building module, specifically used for:

[0045] Determine the investment and maintenance cost of the distributed flexible low-carbon energy supply system according to the sum of the installation cost, the rated power-related investment cost, the storage capacity-related investment cost and the maintenance cost of the distributed flexible low-carbon energy supply system;

[0046] Determine the operating cost of the distributed flexible low-carbon energy supply system according to the sum of the voltage deviation energy cost, the feeder current energy cost, the distributed power unit operation energy cost and the loss load cost of the distributed flexible low-carbon energy supply system;

[0047] Taking minimizing the investment and maintenance cost and the operating cost as objectives respectively, constructing an objective function including the investment and maintenance cost and the operating cost;

[0048] According to the objective function, respectively formulate constraint conditions corresponding to the investment and maintenance cost and the operating cost;

[0049] An optimization configuration model is constructed based on the objective function, the constraints corresponding to the investment and maintenance costs, and the constraints corresponding to the operating costs.

[0050] Optionally, the expression corresponding to the objective function is as follows:

[0051]

[0052] Among them, Obj inv represents the investment and maintenance cost of the distributed flexible low-carbon energy supply system; represents a binary variable related to the presence of energy storage on bus i; FC i represents the installation cost of the distributed flexible low-carbon energy supply system on bus i; represents the rated power of the distributed flexible low-carbon energy supply system on bus i; represents the rated power associated investment cost of the distributed flexible low-carbon energy supply system on bus i; represents the energy storage of the distributed flexible low-carbon energy supply system on bus i; represents the storage capacity-related investment cost of the distributed flexible low-carbon energy supply system on bus i; α represents the annual interest rate; y represents the historical index; Obj represents the maintenance cost of the distributed flexible low-carbon energy supply system on bus i in the yth year; op represents the operating cost of the distributed flexible low-carbon energy supply system; Prob Sc represents the probability of scenario Sc; Cost1 represents the voltage deviation energy cost; Cost2 represents the feeder current energy cost; Cost3 represents the distributed generation unit operation energy cost; Cost4 represents the loss load cost; W vol Represents the voltage weighting coefficient; V i，y，Sc,t represents the RMS voltage value of bus i at time t in year y of the distributed flexible low-carbon energy supply system under scenario Sc; Indicates the preset maximum voltage threshold; Indicates the preset minimum voltage threshold; W flow Indicates the feeder current weighting coefficient; F l，y，Sc,t The current value of feeder l in the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; F thr Indicates the preset maximum feeder current threshold; W loss represents the loss weighting coefficient; r l represents the longitudinal resistance of the feeder line l between buses i and j; W EP represents the energy weighting coefficient; represents the energy cost from the upper power grid of the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; represents the cost function of a given distributed generation on bus i; represents the active power of a given distributed generation unit connected to bus i at time t in year y under scenario Sc; C start represents the initial cost; W lossDSS represents the resistance loss weighting factor; W represents the resistance loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; Cur represents the load weighting factor; It represents the active load reduction of bus i at time t in year y under scenario Sc.

[0053] Optionally, the constraint conditions corresponding to the investment and maintenance cost include: bus quantity constraint, rated power constraint, rated energy storage capacity constraint, rated power sum constraint and rated energy storage capacity sum constraint;

[0054] The constraints corresponding to the operating cost include: energy constraints, rated power constraints, nonlinear constraints, relaxation constraints, power capacity constraints, network security constraints, current flow constraints, node voltage constraints and distributed power supply operation constraints.

[0055] Optionally, the model solving module includes:

[0056] A linear constraint construction submodule is used to construct linear constraints according to preset voltage auxiliary variables and current auxiliary variables;

[0057] A convexity constraint construction submodule, used for constructing a convexity constraint according to the relaxation constraint and the current flow constraint;

[0058] A model conversion submodule, used for converting the optimization configuration model into a linear convex optimization configuration model based on the linear constraint and the convexity constraint;

[0059] A configuration model solving submodule, used to solve the linear convex optimization configuration model through a GUROBI solver according to the power operation data, so as to obtain the optimal installation position and capacity of the distributed flexible low-carbon energy supply system;

[0060] The linear constraints include voltage deviation linear constraints and power flow linear constraints.

[0061] Optionally, the expression corresponding to the voltage deviation linear constraint is as follows:

[0062]

[0063] in, represents the voltage auxiliary variable of bus i at time t in year y under scenario Sc; v i，y，Sc,t represents the node voltage of bus i at time t in year y under scenario Sc; Indicates the preset maximum voltage threshold;

[0064] The expression corresponding to the power flow linear constraint is as follows:

[0065]

[0066] in, represents the auxiliary variable of the current on bus i at time t in year y under scenario Sc; f i，y，Sc,t represents the current of bus i at time t in year y under scenario Sc; F thr Indicates the preset maximum feeder current threshold;

[0067] The expression corresponding to the convexity constraint is as follows:

[0068]

[0069] in, represents the reactance of the distributed flexible low-carbon energy supply system on bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the energy loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; P ij，y，Sc,t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; Q ij,y,Sc,t represents the reactive power flow of buses i and j at time t in year y under scenario Sc; f ij，y,Sc,t represents the active power transfer limit between bus i and bus j at time t in year y under scenario Sc; vij,y,Sc,t It represents the voltage amplitude of bus i and bus j at time t in year y under scenario Sc.

[0070] Optionally, the expression corresponding to the network security constraint is as follows:

[0071]

[0072] Among them, P ij，y，Sc,t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; P jk，y，Sc,t represents the active power flow of bus j and line k at time t in year y under scenario Sc; C represents the set of lines between bus i and bus j; k: (i, j) represents line k between bus i and bus j; Q ij,y，Sc,t represents the reactive power flow of bus i and bus j at time t in year y under scenario Sc; Q jk,y,Sc,t represents the reactive power flow of bus j and line k at time t in year y under scenario Sc; r ij represents the resistance between bus i and bus j; x ij represents the reactance between bus i and bus j; F ij，y,Sc,t represents the current flow of bus i and bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc; represents the reactive power demand of bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the reactive power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the active load reduction of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; It represents the reactive power related to the parallel susceptance between bus i and bus j of the distributed flexible low-carbon energy supply system under scenario Sc at time t in year y.

[0073] In another aspect, the present invention further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0074] The memory is used to store one or more programs;

[0075] When the one or more programs are executed by the at least one processor, the above-mentioned method for optimizing configuration of a distributed flexible low-carbon energy supply system of an active distribution network is implemented.

[0076] On the other hand, the present invention also provides a computer-readable storage medium having an execution program stored thereon, which, when executed, implements the above-mentioned method for optimizing configuration of a distributed flexible low-carbon energy supply system of an active distribution network.

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

[0078] The present invention provides a method and system for optimizing the configuration of a distributed flexible low-carbon energy supply system in an active distribution network, comprising: obtaining power operation data of a distributed flexible low-carbon energy supply system in an active distribution network; based on the power operation data, solving a pre-constructed optimization configuration model using a mixed integer second-order cone programming method, and outputting the optimal installation position and capacity of the distributed flexible low-carbon energy supply system; using the optimal installation position and capacity of the distributed flexible low-carbon energy supply system as the optimization configuration scheme of the distributed flexible low-carbon energy supply system; wherein the optimization configuration model is constructed with the goal of minimizing the investment, maintenance and operating costs of the distributed flexible low-carbon energy supply system; the present application uses a mixed integer second-order cone programming method to perform nonlinear and non-convex transformation on the configuration optimization model to achieve the solution of the model, which is conducive to quickly and accurately allocating the distributed flexible low-carbon energy supply system to the active distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A schematic diagram of a process for optimizing configuration of a distributed flexible low-carbon energy supply system of an active distribution network provided by the present invention;

[0080] Figure 2 A schematic diagram of a bus test technology scenario of an active distribution network distributed flexible low-carbon energy supply system optimization configuration method provided by a specific embodiment of the present invention;

[0081] Figure 3 A schematic diagram of voltage cumulative distribution functions of an allocated distributed flexible low-carbon energy supply system and an unallocated distributed flexible low-carbon energy supply system in a method for optimizing configuration of a distributed flexible low-carbon energy supply system in an active distribution network provided by a specific embodiment of the present invention;

[0082] Figure 4 A schematic diagram of energy storage distribution of a distributed flexible low-carbon energy supply system in an active distribution network distributed flexible low-carbon energy supply system optimization configuration method provided by a specific embodiment of the present invention;

[0083] Figure 5A schematic diagram of the overall framework of a method for optimizing configuration of a distributed flexible low-carbon energy supply system in an active distribution network provided by a specific embodiment of the present invention;

[0084] Figure 6 A schematic diagram of the structural composition of an active distribution network distributed flexible low-carbon energy supply system optimization configuration system provided by the present invention;

[0085] Figure 7 The present invention provides a schematic structural diagram of an electronic device. DETAILED DESCRIPTION

[0086] The present invention proposes a method and system for optimizing the configuration of a distributed flexible low-carbon energy supply system in an active distribution network. The specific implementation methods of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0087] Embodiment 1:

[0088] The present invention provides a method for optimizing the configuration of a distributed flexible low-carbon energy supply system in an active distribution network, and the flow chart is as follows: Figure 1 As shown, including:

[0089] Step 1: Obtain the power operation data of the distributed flexible low-carbon energy supply system in the active distribution network;

[0090] Step 2: Based on the power operation data, the pre-built optimization configuration model is solved using the mixed integer second-order cone programming method to output the optimal installation location and capacity of the distributed flexible low-carbon energy supply system;

[0091] Step 3: Taking the optimal installation location and capacity of the distributed flexible low-carbon energy supply system as the optimal configuration scheme of the distributed flexible low-carbon energy supply system;

[0092] Among them, the optimization configuration model is constructed with the goal of minimizing the investment, maintenance and operating costs of the distributed flexible low-carbon energy supply system.

[0093] The active distribution network involved in the present invention refers to a distribution network that has both dispatchable and non-dispatched power generation resources. The power operation data of the distributed flexible energy supply system involved has random behavior characteristics in different time ranges. For example, the time range may include: daily, weekly (including weekdays and weekends, especially for loads), seasonal and annual. The power operation data can be collected by sensors and monitoring devices installed on key nodes of the active distribution network and key equipment of the distributed flexible low-carbon energy supply system. These sensors and monitoring devices can be integrated into a smart grid or an Internet of Things platform to achieve remote monitoring and real-time transmission of data; data acquisition units can also be used to collect data from each node of the distributed flexible low-carbon energy supply system; at the same time, in order to reduce the complexity of data in the calculation process, the present invention also uses a data clustering method (such as K-means clustering or hierarchical clustering method) in advance to group the acquired power operation data to increase the rationality and analyzability of the data, and before performing data analysis, the collected power operation data can also be pre-processed, including data cleaning, denoising, normalization and other steps to ensure the consistency of the data and improve the accuracy of subsequent analysis and optimization;

[0094] By inputting the processed and analyzed power operation data into the pre-built optimization configuration model for solution, the optimal installation location and capacity of the distributed flexible low-carbon energy supply system can be obtained. Specifically:

[0095] In one implementation, the above-mentioned optimization configuration model may include the following construction process:

[0096] Determine the investment and maintenance cost of the distributed flexible low-carbon energy supply system (which can be calculated to the year of investment) based on the sum of the installation cost, rated power-related investment cost, storage capacity-related investment cost, and maintenance cost of the distributed flexible low-carbon energy supply system;

[0097] Determine the operating cost of the distributed flexible low-carbon energy supply system based on the sum of the voltage deviation energy cost, feeder current energy cost, distributed power unit operation energy cost and loss load cost of the distributed flexible low-carbon energy supply system;

[0098] Taking minimizing investment and maintenance cost and operating cost as the goal respectively, an objective function including investment and maintenance cost and operating cost is constructed;

[0099] According to the objective function, the constraints corresponding to the investment maintenance cost and the operating cost are formulated respectively;

[0100] Construct an optimization configuration model based on the objective function, constraints corresponding to investment and maintenance costs, and constraints corresponding to operating costs;

[0101] In this implementation, the modeling of the distributed flexible low-carbon energy supply system takes into account the investment and maintenance costs (for example, using Obj inv The operating cost of the distributed flexible low-carbon energy supply system during its life cycle (for example, using Obj op Represented), by minimizing investment and maintenance costs and operating costs, and taking into account investment and maintenance costs (such as installation costs, investment costs associated with rated power and storage capacity, maintenance costs) and operating costs (such as voltage deviation energy costs, feeder current energy costs, distributed power unit operating energy costs, and loss load costs), and constructing an objective function, it is possible to ensure that under given conditions, the cost calculation is more accurate, the overall economic cost of the system is minimized, and it is helpful to make more economical investment decisions. In addition, the constraints corresponding to investment and maintenance costs and operating costs are set as constraints of the model. The purpose is to consider the ability of the distributed flexible low-carbon energy supply system to support the network through active power and reactive power, adapt to different energy demands and grid conditions, and make the "flexible" characteristics of the distributed flexible low-carbon energy supply system reflected in the optimization configuration model. When formulating constraints, nonlinear constraints and relaxation constraints are also considered to increase the flexibility of the model and ensure the adaptability and robustness of the system in actual operation. This implementation method can achieve multiple technical effects such as optimization of economic costs, improvement of system performance and reliability, enhancement of flexibility and adaptability, low-carbon environmental protection and sustainable development, decision support and planning optimization when constructing the optimal configuration model of distributed flexible low-carbon energy supply system. These effects jointly promote the development and application of distributed flexible low-carbon energy supply system technology and provide strong support for the transformation and upgrading of the energy industry.

[0102] For example, the expression corresponding to the above objective function can be as follows:

[0103]

[0104] Among them, Obj inv Represents the investment and maintenance cost of the distributed flexible low-carbon energy supply system; represents a binary variable related to the presence of energy storage on bus i; FC i represents the installation cost of the distributed flexible low-carbon energy supply system on bus i; represents the rated power of the distributed flexible low-carbon energy supply system on bus i; represents the rated power-related investment cost of the distributed flexible low-carbon energy supply system on bus i; represents the energy pool of the distributed flexible low-carbon energy supply system on bus i; represents the storage capacity-related investment cost of the distributed flexible low-carbon energy supply system on bus i; α represents the annual interest rate; y represents the historical index; Obj represents the maintenance cost of the distributed flexible low-carbon energy supply system on bus i in the yth year; op represents the operating cost of the distributed flexible low-carbon energy supply system; Prob Sc represents the probability of scenario Sc; Cost1 represents the voltage deviation energy cost; Cost2 represents the feeder current energy cost; Cost3 represents the distributed generation unit operation energy cost; Cost4 represents the loss load cost; W vol Represents the voltage weighting coefficient; V i，y，Sc,t represents the RMS voltage value of bus i at time t in year y of the distributed flexible low-carbon energy supply system under scenario Sc; Indicates the preset maximum voltage threshold; Indicates the preset minimum voltage threshold; W flow Indicates the feeder current weighting coefficient; F l，y，Sc,t The current value of feeder l in the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; F thr Indicates the preset maximum feeder current threshold; W loss represents the loss weighting coefficient; r l represents the longitudinal resistance of the feeder line l between buses i and j; W EP represents the energy weighting coefficient; represents the energy cost from the upper power grid of the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; represents the cost function of a given distributed generation on bus i; represents the active power of a given distributed generation unit connected to bus i at time t in year y under scenario Sc; C start represents the initial cost; W lossDSS represents the resistance loss weighting factor; W represents the resistance loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; Cur represents the load weighting factor; represents the active load reduction of bus i under scenario Sc at time t in year y; the above objective function involves multiple weighting coefficients (such as voltage weighting coefficient, current weighting coefficient, energy weighting coefficient, etc.), each of which is determined by the analytic hierarchy process, and the determination process may include: first, a pairwise comparison is made between the objectives, and the decision maker (such as the distributed network operator) defines the importance of each factor in the comparison with all other factors. The importance depends on the needs of the decision maker and can be set differently due to different networks and operators. Then, a matrix is ​​established based on these pairwise comparisons, and the final weight coefficient is calculated based on the metric; in this example, the analytic hierarchy process is used to decompose the complex decision problem into multiple levels, each level contains different factors or objectives By analyzing each layer, the mutual influence and constraint relationship between various factors can be considered more comprehensively. The hierarchical analysis method allows flexible adjustment according to specific problems and decision-makers' needs. For example, the importance of different factors such as voltage, current, and energy can be set differently according to the characteristics of the network and operators, which enhances the adaptability and practicality of the model. By determining the weighting coefficients, the value of different resources (such as voltage, current, energy, etc.) in the overall system can be more accurately evaluated, revealing the potential connections and mutual influences between different factors, which is conducive to identifying the risks and threats that the system may face, improving the overall performance and robustness of the system, and enabling the system to remain stable and reliable when facing external interference or internal changes, and guiding the rational allocation and efficient use of resources by taking corresponding measures.

[0105] In this implementation, the constraints corresponding to the above-mentioned investment and maintenance costs may include: bus quantity constraint, rated power constraint, rated energy storage capacity constraint, rated power sum constraint and rated energy storage capacity sum constraint; the constraints corresponding to the operating cost may include: energy constraint, rated power constraint, nonlinear constraint, relaxation constraint, power capacity constraint, network security constraint, current flow constraint, node voltage constraint and distributed power supply operation constraint; when formulating constraints, the present invention has good optimization capability by fully considering network voltage deviation, feeder / line congestion, grid loss, power supply load cost, investment and maintenance cost of distributed flexible low-carbon energy supply system, load reduction, and the randomness of load and renewable energy production.

[0106] For example, the above bus quantity constraint is the maximum bus quantity constraint of the distributed flexible low-carbon energy supply system, including the bus quantity constraint, rated power constraint, rated energy storage capacity constraint, and the total rated power constraint and rated energy storage capacity constraint of the distributed flexible low-carbon energy supply system that can be installed on each specific bus (for example, represented by i). In this example, the expression corresponding to the above maximum bus quantity constraint can be as follows:

[0107]

[0108] in, represents a binary variable related to the presence of energy storage on bus i; U DSS Represents the threshold voltage of the distributed flexible low-carbon energy supply system; represents the maximum power of the distributed flexible low-carbon energy supply system on bus i; represents the minimum power of the distributed flexible low-carbon energy supply system on bus i; represents the rated power of the distributed flexible low-carbon energy supply system on bus i; represents the maximum capacity of the distributed flexible low-carbon energy supply system on bus i; represents the minimum capacity of the distributed flexible low-carbon energy supply system on bus i; represents the rated capacity of the distributed flexible low-carbon energy supply system on bus i; It represents the total maximum rated power of the distributed flexible low-carbon energy supply systems that can be installed in the entire network; It represents the maximum total energy storage of distributed flexible low-carbon energy supply systems that can be installed in the entire network. In this example, by setting the bus number constraint of the distributed flexible low-carbon energy supply system, it is helpful to avoid unstable operation or management difficulties caused by excessive complexity of the system, and at the same time it is helpful to ensure that the number of distributed flexible low-carbon energy supply systems on each bus is within a reasonable range to prevent overload or uneven resource distribution. By setting the minimum and maximum power of the distributed flexible low-carbon energy supply system on each bus, it is ensured that the system will not exceed its design capacity under any circumstances, thereby maintaining the safety and stability of the system. This example optimizes the configuration and operation of the distributed flexible low-carbon energy supply system, which is conducive to reducing unnecessary energy waste and emissions and promoting the optimization and transformation of the energy structure.

[0109] For example, the expression corresponding to the above energy constraint can be as follows:

[0110]

[0111] in, represents the active power of the distributed flexible low-carbon energy supply system on bus i under scenario Sc at time t′ in year y; t′=0...t; t represents the total number of moments; Δt represents the preset time interval; represents the loss energy of the distributed flexible low-carbon energy supply system on bus i at time t′ in year y under scenario Sc; represents the initial available energy of the distributed flexible low-carbon energy supply system on bus i; represents the initial available capacity of the distributed flexible low-carbon energy supply system on bus i; in this example, the energy constraint refers to the maximum energy constraint that can be stored or obtained by the distributed flexible low-carbon energy supply system, which represents the connection constraints for each hour of the day; for example, all the injected and obtained energy from the beginning of the day to each general time interval, (including loss ) should be less than the initial available energy and capacity of the distributed flexible low-carbon energy supply system. It is assumed that the generation / absorption of reactive power of the distributed flexible low-carbon energy supply system does not affect the energy storage level, but similar to active power, it increases the loss of the distributed flexible low-carbon energy supply system (such as the loss occurring in the power electronic converter); in this example, the energy constraint ensures that from the beginning of the day to each time interval, the sum of the energy injected and obtained by the distributed flexible low-carbon energy supply system does not exceed its initial available energy and capacity, which is conducive to maintaining the energy balance of the distributed flexible low-carbon energy supply system and preventing system instability or failure caused by energy overload. In addition, the setting of energy constraints allows the system to perform flexible energy scheduling according to actual demand and time intervals, which is conducive to the system's rapid response to changes in grid demand, thereby improving the flexibility and response speed of the system; in addition, although the generation / absorption of reactive power does not affect the energy storage level, the setting of energy constraints still takes into account the impact of reactive power on system losses, which is conducive to the coordinated management of reactive power and active power, reducing system losses and improving overall energy efficiency.

[0112] For example, the expression corresponding to the above rated power constraint can be as follows:

[0113]

[0114] in, represents the active power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the energy storage capacity of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; Represents the minimum energy storage capacity of the distributed flexible low-carbon energy supply system on bus i; the rated power constraint in this example describes the maximum and minimum capacities of the distributed flexible low-carbon energy supply system and the corresponding actual rated power, ensuring that the energy storage capacity and actual rated power of the distributed flexible low-carbon energy supply system on the bus will not be lower than the set minimum value, which is conducive to preventing system overload or unstable operation. By limiting the maximum and minimum capacity of the system, it can ensure that the system can operate stably in various scenarios and provide reliable energy supply. When the system needs to increase or decrease the energy supply, it can be achieved by adjusting the energy storage capacity and actual rated power while meeting the rated power constraint. This is conducive to improving the flexibility and response speed of the system, so that it can better adapt to changes in grid demand.

[0115] For example, the expression corresponding to the above nonlinear constraint can be as follows:

[0116]

[0117] in, It represents the reactive power of the distributed flexible low-carbon energy supply system on bus i under scenario Sc at time t in the yth year; this example models the capacity curve of the distributed low-carbon energy supply system based on the distributed flexible low-carbon energy supply system using a power electronic converter to connect to the grid. In this example, the capacity curve of the distributed flexible low-carbon energy supply system is controlled by the tolerance of the power converter. When operating under a constant AC grid voltage, it can be converted into a constraint on the apparent power provided by the distributed flexible low-carbon energy supply system. By merging the linear boundary number of the a priori defined approximate original curve, the nonlinear constraint is linearized into the model. By linearizing the nonlinear constraint, the model solution process can be simplified and the computational efficiency can be improved. This is particularly important for real-time or near real-time energy management systems because they need to respond quickly to changes in grid demand. The linearized model is easier to combine with other linear models or optimization algorithms, thereby enhancing the scalability and flexibility of the entire system, which is conducive to the better integration of the distributed flexible low-carbon energy supply system into the smart grid and achieving coordinated optimization with other energy systems.

[0118] For example, the expression corresponding to the above relaxed constraint can be as follows:

[0119]

[0120] in, represents the loss energy of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; Represents the reactance of the distributed flexible low-carbon energy supply system on bus i; this example defines the losses associated with the active / passive absorption / generation of the distributed flexible low-carbon energy supply system. This constraint is non-convex, so it can be relaxed in the problem. The losses associated with the active power and reactive power of each distributed flexible low-carbon energy supply system must be equal to or greater than the envelope given by this constraint. Since the losses of the distributed flexible low-carbon energy supply system are also minimized in the objective function, the relaxed inequality constraints are the same as the traditional inequality constraints. By relaxing these non-convex constraints, they can be converted into more tractable convex constraints or inequality constraints, thereby simplifying the problem-solving process, which is conducive to reducing computational complexity and improving solution efficiency. When considering loss minimization in the objective function, the relaxed inequality constraints can still guide the system to optimize in the direction of reducing losses, which means that although the constraints are relaxed, the system will still strive to reduce losses, thereby improving energy utilization efficiency.

[0121] For example, the expression corresponding to the above power capability constraint can be as follows:

[0122]

[0123] in, represents the active power of the distributed flexible low-carbon energy supply system on bus i at time t-1 in year y under scenario Sc; represents the rising rate of the distributed flexible low-carbon energy supply system on bus i; Represents the decrease rate of the distributed flexible low-carbon energy supply system on bus i; in this example, by defining the increase and decrease limits of the power capacity of the distributed flexible low-carbon energy supply system, the sharp change of the system power can be avoided, thereby ensuring the smooth operation of the system, which is conducive to reducing the fluctuation of the power grid and improving the stability and reliability of the power grid; although the power capacity constraint limits the sharp change of the system power, by reasonably setting the increase and decrease rates, the system is still allowed to flexibly adjust the power output within a certain range, which can make the system better adapt to the changes in power grid demand, thereby improving the flexibility and response speed of the system.

[0124] For example, the expression corresponding to the above network security constraint can be as follows:

[0125]

[0126] Among them, P ij，y，Sc,t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; P jk，y,Sc,t represents the active power flow of bus j and line k at time t in year y under scenario Sc; C represents the set of lines between bus i and bus j; k: (i, j) represents line k between bus i and bus j; Q ij,y，Sc,t represents the reactive power flow of bus i and bus j at time t in year y under scenario Sc; Q jk,y,Sc,t represents the reactive power flow of bus j and line k at time t in year y under scenario Sc; r ij represents the resistance between bus i and bus j; x ij represents the reactance between bus i and bus j; F ij，y,Sc,t represents the current flow of bus i and bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc; represents the reactive power demand of bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the reactive power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the active load reduction of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the reactive power related to the parallel susceptance between bus i and bus j of the distributed flexible low-carbon energy supply system under scenario Sc at time t in the yth year; in this example, the network security constraint explains the balance of active and reactive power flows on each feeder. By accurately calculating the power flow of each feeder, the power transmission path can be optimized, power loss can be reduced, and power transmission efficiency can be improved, which is crucial for the integration and efficient operation of distributed flexible low-carbon energy supply systems. The introduction of network security constraints makes energy management more refined and intelligent. Through real-time monitoring and analysis of power grid power flow data, problems in energy management, such as power congestion and equipment overload, can be discovered and solved in a timely manner. By early warning and taking measures, the risk of system failure can be reduced, thereby improving the safety of the entire system and the energy management efficiency.

[0127] For example, the expression corresponding to the above current flow constraint can be as follows:

[0128]

[0129] Among them, v i，y，Sc，t represents the voltage amplitude of bus i at time t in year y under scenario Sc; represents the maximum current rating between bus i and bus j;

[0130] For example, the expression corresponding to the above node voltage constraint can be as follows:

[0131]

[0132] Among them, v j，y，Sc，t represents the voltage amplitude of bus j at time t in year y under scenario Sc; v i，y，Sc，t represents the voltage amplitude of bus i at time t in year y under scenario Sc; v min Indicates the minimum node voltage; v maxIndicates the maximum value of the node voltage. If the node voltage is too high or too low, it may cause damage to the power grid equipment, such as transformers and capacitors. This example sets the upper and lower limits of the voltage amplitude to ensure that the power grid equipment operates within a safe range, extend the equipment life, reduce the failure rate, and avoid power grid instability caused by excessive voltage fluctuations, such as voltage collapse and frequency instability.

[0133] For example, the expression corresponding to the above distributed power operation constraint can be as follows:

[0134]

[0135] in, represents the active power of a given distributed generation unit connected to bus i at time t in year y under scenario Sc; represents the reactive power of a given distributed generation unit connected to bus i at time t in year y under scenario Sc; It represents the minimum active power that can be generated by the distributed generation unit connected to bus i; It represents the maximum active power that can be generated by the distributed generation unit connected to bus i; It represents the minimum reactive power that can be generated by the distributed generation unit connected to bus i; It represents the maximum reactive power that can be generated by the distributed generation unit connected to bus i; represents a binary variable that a given distributed power unit is connected to bus i under scenario Sc at time t in year y; the distributed power operation constraints in this example describe the maximum and minimum active power and reactive power that can be generated by the dispatchable distributed power unit. By setting the active power and reactive power output range of the distributed power unit, it can be ensured that it operates under safe and efficient conditions, which is conducive to optimizing the utilization of distributed power sources and avoiding energy waste or grid instability caused by over-generation or under-generation; and the operation and startup costs of the distributed power units are calculated using a linear formula and modeled by a single-step cost function, which can simplify the cost calculation process, improve calculation efficiency, reduce operating costs, and improve economic benefits.

[0136] The minimum voltage deviation and the minimum power flow involved in the optimization configuration model constructed according to the above objective function and constraint conditions are not differentiable, and considering that the distributed flexible low-carbon energy supply system has no ability to provide auxiliary services for the active distribution network, especially involving voltage control and feeder / line congestion management, this leads to the voltage level of the active distribution network being not stable enough; in view of this problem, the present invention can set auxiliary variables for voltage and line current by adopting a linear programming replacement method (i.e., a mixed integer second-order cone programming method), further constrain the obtained linear function, and then solve the optimization configuration model, specifically:

[0137] In one implementation, the process of solving the pre-built optimization configuration model based on power operation data using a mixed integer second-order cone programming method to output the optimal installation location and capacity of the distributed flexible low-carbon energy supply system may include:

[0138] Construct linear constraints based on pre-set voltage auxiliary variables and current auxiliary variables;

[0139] Convexity constraints are constructed based on relaxation constraints and current flow constraints;

[0140] Based on linear constraints and convexity constraints, the optimization configuration model is transformed into a linear convex optimization configuration model;

[0141] According to the power operation data, the linear convex optimization configuration model is solved by GUROBI solver to obtain the optimal installation location and capacity of the distributed flexible low-carbon energy supply system;

[0142] Among them, linear constraints include voltage deviation linear constraints and power flow linear constraints;

[0143] In this implementation, the convex form of the AC optimal power flow problem is used to define the mixed integer second-order cone programming problem, that is, the site selection and scale of the distributed flexible energy supply system in the network are optimized by using the mixed integer second-order cone programming method. The reason for selecting the mixed integer second-order cone programming method in the present invention is that the second-order cone programming problem (SOCP) is a convex programming problem, which is a problem of minimizing or maximizing a linear function on the intersection of an affine space and a finite-dimensional second-order cone Cartesian product. The problem can be solved by the primal-dual interior point method, and various engineering problems can be expressed by SOCP, such as filter design, truss design, etc.; the present invention adopts the mixed integer second-order cone programming method to determine the optimal site selection and scale of the distributed flexible low-carbon energy supply system in the active distribution network. The method includes a mixed integer SOCP constraint formula (i.e., the linear constraint and convexity constraint in the present invention), which realizes the optimization of the site selection and scale in the optimization configuration model. By converting the optimization configuration model into a linear convex optimization configuration model and solving it using an efficient solver such as GUROBI, the optimal installation location and capacity of the distributed flexible low-carbon energy supply system can be accurately obtained. The UROBI solver is known for its efficient solution algorithm and powerful computing power. It can quickly handle large-scale and complex linear convex optimization problems. This means that when solving the optimization configuration model of the distributed flexible low-carbon energy supply system, GUROBI can quickly find the optimal solution or a near-optimal solution, rather than an approximate solution or a local optimal solution, thereby greatly shortening the solution time and improving work efficiency. It is conducive to ensuring the efficiency and economy of the system in actual operation, reducing losses during energy transmission, and improving the overall energy efficiency of the system. It can also make the power output of the distributed flexible low-carbon energy supply system more stable, reduce the impact on the power grid caused by power fluctuations, and thus enhance the stability of the system. In addition, this implementation method is not only suitable for the current power grid environment, but can also be adjusted and optimized according to future power grid development and changes, and has strong adaptability and scalability.

[0144] For example, the expression corresponding to the above voltage deviation linear constraint can be as follows:

[0145]

[0146] in, represents the voltage auxiliary variable of bus i at time t in year y under scenario Sc; v i，y，Sc,t represents the node voltage of bus i at time t in year y under scenario Sc; represents the preset maximum voltage threshold; in this example, by considering the ability of distributed flexible low-carbon energy supply systems to provide ancillary services for active distribution networks, especially those involving voltage control and management of feeder / line congestion, the complex relationship between voltage and current is converted into linear constraints, which can greatly simplify the complexity of the optimization problem. Linear constraints are easy to handle and can be solved using efficient linear programming algorithms, which is conducive to reducing computing costs, improving model solving efficiency, and more accurately reflecting the actual relationship between system voltage and current, thereby improving the accuracy of the optimization results.

[0147] For example, the expression corresponding to the above power flow linear constraint can be as follows:

[0148]

[0149] in, represents the auxiliary variable of the current on bus i at time t in year y under scenario Sc; f i，y，Sc,t represents the current of bus i at time t in year y under scenario Sc; F thr Represents the preset maximum feeder current threshold; in this example, by setting the power flow linear constraint to limit the current on the bus to not exceed the preset maximum feeder current threshold, the safe transmission of current in the power grid is ensured, which is conducive to preventing equipment overheating, damage or safety accidents caused by excessive current; and by optimizing the installation location and capacity of the distributed flexible low-carbon energy supply system, the power loss and energy cost of the power grid can be minimized. The power flow linear constraint, as part of the optimization model, is conducive to ensuring that the system achieves economical and efficient operation while meeting the current safety limit.

[0150] For example, the expression corresponding to the above convexity constraint can be as follows:

[0151]

[0152] in, represents the reactance of the distributed flexible low-carbon energy supply system on bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the energy loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; P ij，y，Sc,t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; Q ij,y，Sc,t represents the reactive power flow of buses i and j at time t in year y under scenario Sc; f ij，y，Sc,trepresents the active power transfer limit between bus i and bus j at time t in year y under scenario Sc; v ij,y,Sc,t represents the voltage amplitude of bus i and bus j under scenario Sc at time t in year y; in this example, the relaxation constraint and current flow constraint in the above-mentioned constraint conditions are transformed into convex constraints in the form of second-order cone. The convex constraint can more accurately describe the physical phenomena in the power grid, such as power flow, voltage distribution, etc. Therefore, the introduction of convex constraints in the optimization process can improve the accuracy of the optimization results and ensure that the optimal configuration of the distributed flexible low-carbon energy supply system is more in line with the actual situation. The convex constraint has a global optimal solution and uniqueness, which makes it easier to find the optimal solution in the optimization problem during the solution process; in addition, the YALMIP-MATLAB interface can be selected in this example to implement the mixed integer second-order cone Programming problem, and use the GUROBI solver to solve the problem. YALMIP is a MATLAB toolkit designed specifically for convex optimization problems. It provides flexible and powerful modeling capabilities. By choosing to use the YALMIP-MATLAB interface, users can easily define complex optimization problems, including mixed integer second-order cone programming problems, which makes the modeling process more intuitive and efficient. The combination of the YALMIP-MATLAB interface and the GUROBI solver can significantly improve the performance of solving optimization problems, and can bring specific technical effects such as efficient modeling and solving capabilities, accurate solution results, good compatibility and scalability, and optimized performance and stability.

[0153] In summary, when the algorithm used in the prior art solves the model constructed based on the AC optimal power flow problem, the non-convexity and nonlinearity of the model are not considered, resulting in inaccurate site selection and capacity configuration for the distributed flexible low-carbon energy supply system. The present invention proposes an optimization configuration method for a distributed flexible low-carbon energy supply system in an active distribution network, aiming to find a multi-objective optimization problem with the optimal trade-off between technical objectives and economic objectives, focusing on the optimal allocation of distributed flexible low-carbon energy supply systems in active distribution networks, and using the convex form of the AC optimal power flow problem to define a mixed integer second-order cone programming problem, convexifying and linearizing the target problem, and considering the technical constraints of the network (such as voltage deviation linear constraints and power flow linear constraints) in the process of solving the model, so that it can be solved quickly / accurately to optimize the site selection and scale of the distributed flexible low-carbon energy supply system in the network, which is conducive to the optimal allocation of the distributed flexible low-carbon energy supply system to the active distribution network, thereby greatly improving the power supply quality of the active distribution network in terms of alleviating voltage deviation, eliminating line congestion and load reduction, and minimizing the total local electricity cost and the investment cost of distributed power grid installation and maintenance.

[0154] Embodiment 2:

[0155] Taking into account the existence of dispatchable and non-dispatched distributed generation, in this embodiment, the IEEE 34-node test feeder is modified and used as a test technical scenario to demonstrate the effectiveness of the proposed method for optimizing the configuration of the active distribution network distributed flexible low-carbon energy supply system. Figure 2 As shown in the figure, the modifications to the IEEE 34 nodes include the introduction of distributed generation units and the grid modeling that only considers DC sequence parameters. In this technical scenario, there are both non-dispatchable distributed power units consisting of photovoltaic panels and wind turbines, and dispatchable distributed power units. Specifically:

[0156] The input parameters of the optimization configuration model are set as: data with different random behavior characteristics within a given time range. The time range of these data can include daily, weekly (including weekdays and weekends, especially for loads), seasonal and annual. Assuming that the active distribution network is connected to an external secondary transmission network, the power operation data includes the hourly energy exchange cost of the grid known 24 hours ago. In order to handle data with changing characteristics, a reasonable number of scenarios should be considered. However, considering all possible situations will lead to large-scale and computationally expensive simulations, and due to the complexity of such calculations, the number of scenarios is usually reduced to a reasonable number with the same degree of volatility / randomness as the original scenario. In this embodiment, the input parameters are first grouped by using a data clustering method to reduce the number of data for the input scenario. For example, the clustering analysis method groups data according to inherent characteristics or similarities measured or perceived. Here, the k-means clustering algorithm (K-means) is used to cluster the data: it clusters n m-dimensional R m The data set is divided into k clusters, and each of the n sets is assigned to the corresponding k clusters. This method attempts to synthesize k clusters so that the mean square distance between each data of the original set and the synthesized cluster is minimized. Specifically, a matrix containing load, photovoltaic, wind power and price data for each day of the year constitutes the data set used (for example, n can be 365 and m can be 96).

[0157] An improved IEEE 34-bus test system is used as the technical scenario for the test. The load curve of this technical scenario is considered to be voltage-independent PQ absorption. Even for photovoltaic and wind turbines, they are considered to be voltage-independent active power injections with zero reactive power components. The price scenario related to the load distribution is generated with reference to the typical average electricity price in a certain region of the country and adjusted according to the load distribution. The load data corresponds to the real measurement data of a primary high and medium voltage substation.

[0158] The corresponding simulation parameters are shown in Table 1. It is assumed that all load buses are equipped with photovoltaic units and wind turbines are connected to buses 18 and 22.

[0159] Table 1 Simulation parameters

[0160]

[0161] In order to cover the uncertainty and changes of multiple different parameters, scenarios for load, PV and wind are considered in addition to price scenarios. The K-means method is used to cluster similar scenarios and reduce their number. Assuming that the life of the distributed flexible low-carbon energy supply system is 5 years, the life of the distributed flexible low-carbon energy supply system is simulated, in which the growth / change of parameters is taken into account, and the data for each year is divided into 30 clusters, resulting in a total of 150 scenarios. The load growth is considered to be constant during the life, and the wind power generation capacity is also considered to be constant throughout the life cycle, while the PV power generation capacity is considered to be related to the load because it is usually installed at the customer end. The growth of energy and fuel prices over the years is modeled using Geometric Brownian Motion (GBM), and Table 2 shows the pairwise comparison of the selected relative weights of the input analytic hierarchy process.

[0162] Table 2 Pairwise comparison of target items

[0163]

[0164] Using the pairwise comparison method in Table 2 to input the analytic hierarchy process, the weight coefficients of each item in the objective function are as follows: Energy weight coefficient W EP =0.0562, loss weighting coefficient W loss =0.0396, voltage weighting coefficient W vol =0.2535, feeder current weighting coefficient W flow =0.6421, resistance loss weighting factor W lossDSS =0.0086, load weighting factor W Cur It is considered to be equal to 100, which is equal to the value of the unsupplied load. It should be noted that the load reduction factor is not considered in the hierarchical analysis method, but a larger weighting coefficient is applied to prevent load reduction to the maximum extent. Based on the homogeneity required by the elements of the objective function for quantifying the investment and maintenance cost elements, the external grid energy consumption cost weighting coefficient is used to weight the installation and maintenance costs of the distributed flexible low-carbon energy supply system, and the optimal installation location and capacity of the distributed flexible low-carbon energy supply system as shown in Table 3 can be obtained. It can be seen from the results of Table 3 that two buses (22 and 34) are selected in this embodiment to carry the distributed flexible low-carbon energy supply system.

[0165] Table 3. Optimal DFLCESS location and scale

[0166] bus 22 34 DFLCESS rated power (pu) 0.32 0.38 DFLCESS library capacity (pu) 0.89 1.12

[0167] The changes of other items of the objective function during the life cycle (i.e., total loss, energy cost, feeder load, and load reduction) are shown in Table 4. The results show that all elements of the objective function have been significantly improved: not only the total loss is reduced, but also the total cost of supply energy, including energy imported from the external grid and energy supplied by local distributed power units, is greatly reduced. In addition, by using the optimization configuration method provided by the present invention, the existence of load reduction and feeder overload (corresponding to the objective function) is completely eliminated, which corresponds to the network upgrade required in the absence of a distributed flexible low-carbon energy supply system.

[0168] Table 4 Changes of each item in the objective function over five years

[0169]

[0170] In order to take into account the complexity of the calculation, this embodiment uses a data clustering method (such as the K-means method) to group and cluster the input data to reduce the number of input scenarios. The input hierarchical analysis method is used to obtain the weight coefficients of each item in the objective function, and the location and scale of the optimal distributed flexible low-carbon energy supply system are obtained, and its superiority is quantified through comparison.

[0171] In summary, in this embodiment, by using Figure 2 The simulation verification of the method provided by the present invention based on the improved IEEE 34 bus test system shown is described, specifically including:

[0172] (1) Improved IEEE 34 bus test system, including the introduction of distributed generation units and grid modeling that only considers DC sequence parameters. This test case includes both non-dispatchable distributed generation units consisting of photovoltaic panels and wind turbines, as well as dispatchable distributed generation units.

[0173] (2) In order to quantify its superiority, we compare the case 1 with the optimal distributed flexible low-carbon energy supply system and the case 2 without the distributed flexible low-carbon energy supply system. Figure 3 The voltage cumulative distribution functions of Case 1 and Case 2 related to the entire time period and all network buses are shown. The results show that the optimization scheme of the present invention improves the voltage distribution compared with the case without DFLCESS, especially the voltage below 0.94pu and greater than 1p.u. is basically reduced to zero. In addition, the voltage shows a greater corresponding probability in the interval closer to the required voltage.

[0174] (3) Figure 4The two distributed flexible low-carbon energy supply systems provided for Case 1 correspond to a 2-day daily operation cycle, showing the pu energy stored by the two distributed flexible low-carbon energy supply systems in summer and winter. It can be seen from the figure that since the available photovoltaic output in summer is much higher than that in winter, the distributed flexible low-carbon energy supply system tends to use its available storage capacity to achieve the optimality of the objective function. In addition, in summer, the distributed flexible low-carbon energy supply system is used to store electricity generated by renewable resources in the day (basically electricity generated by photovoltaics) and supply it back to the grid during peak hours. From these results, it can also be seen that the satisfaction of the daily storage system-level chip is met.

[0175] In summary, this embodiment can illustrate an optimization configuration method for a distributed flexible low-carbon energy supply system of an active distribution network based on energy balance and grid support provided by the present invention, such as Figure 5 As shown, by defining a multi-objective optimization problem aimed at finding the optimal trade-off between technical objectives and economic objectives, the problem of optimally allocating distributed flexible low-carbon energy supply systems to active distribution networks is effectively solved, the target problem is convexified so that it can be solved quickly / accurately, and in the optimization process, network voltage deviation, feeder / line congestion, grid loss, power supply load cost, investment and maintenance cost, load reduction, load and renewable energy production randomness are fully considered, and it has good optimization ability. Therefore, it is explained that the present invention can effectively optimize the allocation of distributed power grids, thereby greatly improving the power supply quality of active distribution networks in terms of alleviating voltage deviations, eliminating line congestion and load reduction, and minimizing the total local electricity cost and the investment cost of distributed power grid installation and maintenance.

[0176] Embodiment 3:

[0177] The present invention based on the same inventive concept also provides an active distribution network distributed flexible low-carbon energy supply system optimization configuration system, the structural composition diagram is as follows Figure 6 As shown, including:

[0178] A data acquisition module is used to obtain power operation data of a distributed flexible low-carbon energy supply system in an active distribution network;

[0179] The model solving module is used to solve the pre-built optimization configuration model based on the power operation data using the mixed integer second-order cone programming method to output the optimal installation location and capacity of the distributed flexible low-carbon energy supply system;

[0180] An optimization configuration module, used to use the optimal installation location and capacity of the distributed flexible low-carbon energy supply system as an optimization configuration scheme for the distributed flexible low-carbon energy supply system;

[0181] Among them, the optimization configuration model is constructed with the goal of minimizing the investment, maintenance and operating costs of the distributed flexible low-carbon energy supply system.

[0182] In one implementation, the above-mentioned model solving module may include:

[0183] A linear constraint construction submodule is used to construct linear constraints according to preset voltage auxiliary variables and current auxiliary variables;

[0184] Convexity constraint construction submodule, used to construct convexity constraints based on relaxation constraints and current flow constraints;

[0185] A model conversion submodule, used to convert the optimization configuration model into a linear convex optimization configuration model based on linear constraints and convex constraints;

[0186] The configuration model solving submodule is used to solve the linear convex optimization configuration model through the GUROBI solver according to the power operation data to obtain the optimal installation location and capacity of the distributed flexible low-carbon energy supply system;

[0187] Among them, the linear constraints include voltage deviation linear constraints and power flow linear constraints.

[0188] For example, the expression corresponding to the above voltage deviation linear constraint can be as follows:

[0189]

[0190] in, represents the voltage auxiliary variable of bus i at time t in year y under scenario Sc; v i，y，Sc，t represents the node voltage of bus i at time t in year y under scenario Sc; Indicates the preset maximum voltage threshold;

[0191] For example, the expression corresponding to the above power flow linear constraint is as follows:

[0192]

[0193] in, represents the auxiliary variable of the current on bus i at time t in year y under scenario Sc; f i，y，Sc，t represents the current of bus i at time t in year y under scenario Sc; F thr Indicates the preset maximum feeder current threshold;

[0194] For example, the expression corresponding to the above convexity constraint is as follows:

[0195]

[0196] in, represents the reactance of the distributed flexible low-carbon energy supply system on bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the energy loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; P ij，y，Sc，t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; Q ij，y，Sc，t represents the reactive power flow of buses i and j at time t in year y under scenario Sc; f ij，y，Sc，t represents the active power transfer limit between bus i and bus j at time t in year y under scenario Sc; v ij，y，Sc，t It represents the voltage amplitude of bus i and bus j at time t in year y under scenario Sc.

[0197] In one implementation, the optimization configuration system may further include a model building module, specifically used for:

[0198] Determine the investment and maintenance cost of the distributed flexible low-carbon energy supply system based on the sum of the installation cost, rated power-related investment cost, storage capacity-related investment cost, and maintenance cost of the distributed flexible low-carbon energy supply system;

[0199] Determine the operating cost of the distributed flexible low-carbon energy supply system based on the sum of the voltage deviation energy cost, feeder current energy cost, distributed power unit operation energy cost and loss load cost of the distributed flexible low-carbon energy supply system;

[0200] Taking minimizing investment and maintenance cost and operating cost as the goal respectively, an objective function including investment and maintenance cost and operating cost is constructed;

[0201] According to the objective function, the constraints corresponding to the investment maintenance cost and the operating cost are formulated respectively;

[0202] An optimization configuration model is constructed based on the objective function, constraints corresponding to investment and maintenance costs, and constraints corresponding to operating costs.

[0203] For example, the expression corresponding to the above objective function can be as follows:

[0204]

[0205] Among them, Obj inv Represents the investment and maintenance cost of the distributed flexible low-carbon energy supply system; represents a binary variable related to the presence of energy storage on bus i; FC irepresents the installation cost of the distributed flexible low-carbon energy supply system on bus i; represents the rated power of the distributed flexible low-carbon energy supply system on bus i; represents the rated power-related investment cost of the distributed flexible low-carbon energy supply system on bus i; represents the energy pool of the distributed flexible low-carbon energy supply system on bus i; represents the storage capacity-related investment cost of the distributed flexible low-carbon energy supply system on bus i; α represents the annual interest rate; y represents the historical index; Obj represents the maintenance cost of the distributed flexible low-carbon energy supply system on bus i in the yth year; op represents the operating cost of the distributed flexible low-carbon energy supply system; Prob Sc represents the probability of scenario Sc; Cost1 represents the voltage deviation energy cost; Cost2 represents the feeder current energy cost; Cost3 represents the distributed generation unit operation energy cost; Cost4 represents the loss load cost; W vol Represents the voltage weighting coefficient; V i，y，Sc,t represents the RMS voltage value of bus i at time t in year y of the distributed flexible low-carbon energy supply system under scenario Sc; Indicates the preset maximum voltage threshold; Indicates the preset minimum voltage threshold; W flow Indicates the feeder current weighting coefficient; F l，y，Sc,t The current value of feeder l in the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; F thr Indicates the preset maximum feeder current threshold; W loss represents the loss weighting coefficient; r l represents the longitudinal resistance of the feeder line l between buses i and j; W EP represents the energy weighting coefficient; represents the energy cost from the upper power grid of the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; represents the cost function of a given distributed generation on bus i; represents the active power of a given distributed generation unit connected to bus i at time t in year y under scenario Sc; C start represents the initial cost; W lossDSS represents the resistance loss weighting factor; W represents the resistance loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; Cur represents the load weighting factor; It represents the active load reduction of bus i at time t in year y under scenario Sc.

[0206] In this implementation, the constraints corresponding to the above-mentioned investment and maintenance costs may include: bus quantity constraint, rated power constraint, rated energy storage capacity constraint, total rated power constraint and total rated energy storage capacity constraint; the constraints corresponding to the operating cost may include: energy constraint, rated power constraint, nonlinear constraint, relaxation constraint, power capability constraint, network security constraint, current flow constraint, node voltage constraint and distributed power supply operation constraint.

[0207] For example, the expression corresponding to the above energy constraint can be as follows:

[0208]

[0209] in, represents the active power of the distributed flexible low-carbon energy supply system on bus i under scenario Sc at time t′ in year y; t′=0...t; t represents the total number of moments; Δt represents the preset time interval; represents the loss energy of the distributed flexible low-carbon energy supply system on bus i at time t′ in year y under scenario Sc; represents the initial available energy of the distributed flexible low-carbon energy supply system on bus i; represents the initial available capacity of the distributed flexible low-carbon energy supply system on bus i;

[0210] For example, the expression corresponding to the above rated power constraint can be as follows:

[0211]

[0212] in, represents the active power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the energy storage capacity of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the minimum energy storage capacity of the distributed flexible low-carbon energy supply system on bus i;

[0213] For example, the expression corresponding to the above nonlinear constraint can be as follows:

[0214]

[0215] in, represents the reactive power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc;

[0216] For example, the expression corresponding to the above relaxed constraint can be as follows:

[0217]

[0218] in, represents the loss energy of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the reactance of the distributed flexible low-carbon energy supply system on bus i;

[0219] For example, the expression corresponding to the above power capability constraint can be as follows:

[0220]

[0221] in, represents the active power of the distributed flexible low-carbon energy supply system on bus i at time t-1 in year y under scenario Sc; represents the rising rate of the distributed flexible low-carbon energy supply system on bus i; represents the drop rate of the distributed flexible low-carbon energy supply system on bus i;

[0222] For example, the expression corresponding to the above network security constraint can be as follows:

[0223]

[0224] Among them, P ij，y，Sc,t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; P jk，y，Sc,t represents the active power flow of bus j and line k at time t in year y under scenario Sc; C represents the set of lines between bus i and bus j; k: (i, j) represents line k between bus i and bus j; Q ij,y，Sc,t represents the reactive power flow of bus i and bus j at time t in year y under scenario Sc; Q jk,y,Sc,t represents the reactive power flow of bus j and line k at time t in year y under scenario Sc; r ij represents the resistance between bus i and bus j; x ij represents the reactance between bus i and bus j; F ij，y,Sc,t represents the current flow of bus i and bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc; represents the reactive power demand of bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the reactive power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the active load reduction of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the reactive power related to the parallel susceptance between bus i and bus j of the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc;

[0225] For example, the expression corresponding to the above current flow constraint can be as follows:

[0226]

[0227] Among them, v i，y，Sc,t represents the voltage amplitude of bus i at time t in year y under scenario Sc; represents the maximum current rating between bus i and bus j;

[0228] For example, the expression corresponding to the above node voltage constraint can be as follows:

[0229]

[0230] Among them, v j，y，Sc,t represents the voltage amplitude of bus j at time t in year y under scenario Sc; v i，y，Sc,t represents the voltage amplitude of bus i at time t in year y under scenario Sc; v min Indicates the minimum node voltage; v max Indicates the maximum value of the node voltage.

[0231] Embodiment 4:

[0232] like Figure 7 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0233] 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 gate 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, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method flows or corresponding functions, so as to implement the steps of an optimization configuration method for a distributed flexible low-carbon energy supply system of an active distribution network in the above-mentioned embodiment.

[0234] Embodiment 5:

[0235] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The 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 a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of an optimization configuration method for a distributed flexible low-carbon energy supply system of an active distribution network in the above embodiment.

[0236] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0237] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0238] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, or may 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.

Claims

1. A method for optimizing the configuration of a distributed flexible low-carbon energy supply system in an active distribution network, characterized in that: include: Obtain power operation data of distributed flexible low-carbon energy supply systems in active distribution networks; Based on the power operation data, a pre-constructed optimization configuration model is solved using a mixed integer second-order cone programming method to output the optimal installation location and capacity of the distributed flexible low-carbon energy supply system; Taking the optimal installation position and capacity of the distributed flexible low-carbon energy supply system as the optimized configuration scheme of the distributed flexible low-carbon energy supply system; Among them, the optimization configuration model is constructed with the goal of minimizing the investment, maintenance and operating costs of the distributed flexible low-carbon energy supply system.

2. The method according to claim 1, characterized in that The optimization configuration model includes the following construction process: Determine the investment and maintenance cost of the distributed flexible low-carbon energy supply system according to the sum of the installation cost, the rated power-related investment cost, the storage capacity-related investment cost and the maintenance cost of the distributed flexible low-carbon energy supply system; Determine the operating cost of the distributed flexible low-carbon energy supply system according to the sum of the voltage deviation energy cost, the feeder current energy cost, the distributed power unit operation energy cost and the loss load cost of the distributed flexible low-carbon energy supply system; Taking minimizing the investment and maintenance cost and the operating cost as objectives respectively, constructing an objective function including the investment and maintenance cost and the operating cost; According to the objective function, respectively formulate constraint conditions corresponding to the investment and maintenance cost and the operating cost; An optimization configuration model is constructed based on the objective function, the constraints corresponding to the investment and maintenance costs, and the constraints corresponding to the operating costs.

3. The method according to claim 2, characterized in that The expression corresponding to the objective function is as follows: Among them, Obj inv represents the investment and maintenance cost of the distributed flexible low-carbon energy supply system; represents a binary variable related to the presence of energy storage on bus i; FC i represents the installation cost of the distributed flexible low-carbon energy supply system on bus i; represents the rated power of the distributed flexible low-carbon energy supply system on bus i; represents the rated power associated investment cost of the distributed flexible low-carbon energy supply system on bus i; represents the energy storage of the distributed flexible low-carbon energy supply system on bus i; represents the storage capacity-related investment cost of the distributed flexible low-carbon energy supply system on bus i; α represents the annual interest rate; y represents the historical index; Obj represents the maintenance cost of the distributed flexible low-carbon energy supply system on bus i in the yth year; op represents the operating cost of the distributed flexible low-carbon energy supply system; Prob Sc represents the probability of scenario Sc; Cost1 represents the voltage deviation energy cost; Cost2 represents the feeder current energy cost; Cost3 represents the distributed generation unit operation energy cost; Cost4 represents the loss load cost; W vol Represents the voltage weighting coefficient; V i,y,Sc,t represents the RMS voltage value of bus i at time t in year y of the distributed flexible low-carbon energy supply system under scenario Sc; Indicates the preset maximum voltage threshold; Indicates the preset minimum voltage threshold; W flow Indicates the feeder current weighting coefficient; F l,y,Sc,t The current value of feeder l in the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; F thr Indicates the preset maximum feeder current threshold; W loss represents the loss weighting coefficient; r l represents the longitudinal resistance of the feeder line l between buses i and j; W EP represents the energy weighting coefficient; represents the energy cost from the upper power grid of the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; represents the cost function of a given distributed generation on bus i; represents the active power of a given distributed generation unit connected to bus i at time t in year y under scenario Sc; C start represents the initial cost; W lossDSS represents the resistance loss weighting factor; W represents the resistance loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; Cur represents the load weighting factor; It represents the active load reduction of bus i at time t in year y under scenario Sc.

4. The method according to any one of claims 1 to 3, characterized in that: The constraints corresponding to the investment and maintenance cost include: bus quantity constraint, rated power constraint, rated energy storage capacity constraint, rated power sum constraint and rated energy storage capacity sum constraint; The constraints corresponding to the operating cost include: energy constraints, rated power constraints, nonlinear constraints, relaxation constraints, power capacity constraints, network security constraints, current flow constraints, node voltage constraints and distributed power supply operation constraints.

5. The method according to claim 4, characterized in that Based on the power operation data, a pre-built optimization configuration model is solved by using a mixed integer second-order cone programming method to output the optimal installation location and capacity of the distributed flexible low-carbon energy supply system, including: Construct linear constraints based on pre-set voltage auxiliary variables and current auxiliary variables; Constructing a convexity constraint based on the relaxation constraint and the current flow constraint; Based on the linear constraint and the convexity constraint, converting the optimization configuration model into a linear convex optimization configuration model; According to the power operation data, the linear convex optimization configuration model is solved by the GUROBI solver to obtain the optimal installation position and capacity of the distributed flexible low-carbon energy supply system; The linear constraints include voltage deviation linear constraints and power flow linear constraints.

6. The method according to claim 5, characterized in that The expression corresponding to the voltage deviation linear constraint is as follows: in, represents the voltage auxiliary variable of bus i at time t in year y under scenario Sc; v i,y,Sc,t represents the node voltage of bus i at time t in year y under scenario Sc; Indicates the preset maximum voltage threshold; The expression corresponding to the power flow linear constraint is as follows: in, represents the auxiliary variable of the current on bus i at time t in year y under scenario Sc; f i,y,sc,t represents the current of bus i at time t in year y under scenario Sc; F thr Indicates the preset maximum feeder current threshold; The expression corresponding to the convexity constraint is as follows: in, represents the reactance of the distributed flexible low-carbon energy supply system on bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the energy loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; P ij,y,Sc,t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; Q ij,y,Sc,t represents the reactive power flow of buses i and j at time t in year y under scenario Sc; f ij,y,Sc,t represents the active power transfer limit between bus i and bus j at time t in year y under scenario Sc; v ij,y,Sc,t It represents the voltage amplitude of bus i and bus j at time t in year y under scenario Sc.

7. The method according to claim 4, characterized in that The expression corresponding to the network security constraint is as follows: Among them, P ij,y,Sc,t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; P jk,y,Sc,t represents the active power flow of bus j and line k at time t in year y under scenario Sc; C represents the set of lines between bus i and bus j; k:(i,j) represents line k between bus i and bus j; Q ij,y,Sc,t represents the reactive power flow of bus i and bus j at time t in year y under scenario Sc; Q jk,y,Sc,t represents the reactive power flow of bus j and line k at time t in year y under scenario Sc; r ij represents the resistance between bus i and bus j; x ij represents the reactance between bus i and bus j; F ij,y,Sc,t represents the current flow of bus i and bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc; represents the reactive power demand of bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the reactive power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the active load reduction of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; It represents the reactive power related to the parallel susceptance between bus i and bus j of the distributed flexible low-carbon energy supply system under scenario Sc at time t in year y.

8. An optimization configuration system for distributed flexible low-carbon energy supply system of active distribution network, characterized in that: include: A data acquisition module is used to obtain power operation data of a distributed flexible low-carbon energy supply system in an active distribution network; A model solving module, used to solve a pre-built optimization configuration model based on the power operation data using a mixed integer second-order cone programming method, and output an optimal installation location and capacity of the distributed flexible low-carbon energy supply system; An optimization configuration module, used to use the optimal installation position and capacity of the distributed flexible low-carbon energy supply system as an optimization configuration scheme of the distributed flexible low-carbon energy supply system; Among them, the optimization configuration model is constructed with the goal of minimizing the investment, maintenance and operating costs of the distributed flexible low-carbon energy supply system.

9. The system according to claim 8, characterized in that Also included: Model building module, specifically for: Determine the investment and maintenance cost of the distributed flexible low-carbon energy supply system according to the sum of the installation cost, the rated power-related investment cost, the storage capacity-related investment cost and the maintenance cost of the distributed flexible low-carbon energy supply system; Determine the operating cost of the distributed flexible low-carbon energy supply system according to the sum of the voltage deviation energy cost, the feeder current energy cost, the distributed power unit operation energy cost and the loss load cost of the distributed flexible low-carbon energy supply system; Taking minimizing the investment and maintenance cost and the operating cost as objectives respectively, constructing an objective function including the investment and maintenance cost and the operating cost; According to the objective function, respectively formulate constraint conditions corresponding to the investment and maintenance cost and the operating cost; An optimization configuration model is constructed based on the objective function, the constraints corresponding to the investment and maintenance costs, and the constraints corresponding to the operating costs.

10. The system according to claim 9, characterized in that The expression corresponding to the objective function is as follows: Among them, Obj inv represents the investment and maintenance cost of the distributed flexible low-carbon energy supply system; represents a binary variable related to the presence of energy storage on bus i; FC i represents the installation cost of the distributed flexible low-carbon energy supply system on bus i; represents the rated power of the distributed flexible low-carbon energy supply system on bus i; represents the rated power associated investment cost of the distributed flexible low-carbon energy supply system on bus i; represents the energy storage of the distributed flexible low-carbon energy supply system on bus i; represents the storage capacity-related investment cost of the distributed flexible low-carbon energy supply system on bus i; α represents the annual interest rate; y represents the historical index; Ob represents the maintenance cost of the distributed flexible low-carbon energy supply system on bus i in the yth year; jop represents the operating cost of the distributed flexible low-carbon energy supply system; Prob Sc represents the probability of scenario Sc; Cost1 represents the voltage deviation energy cost; Cost2 represents the feeder current energy cost; Cost3 represents the distributed generation unit operation energy cost; Cost4 represents the loss load cost; W vol Represents the voltage weighting coefficient; V i,y,Sc,t represents the RMS voltage value of bus i at time t in year y of the distributed flexible low-carbon energy supply system under scenario Sc; Indicates the preset maximum voltage threshold; Indicates the preset minimum voltage threshold; W flow Indicates the feeder current weighting coefficient; F l,y,Sc,t The current value of feeder l in the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; F thr Indicates the preset maximum feeder current threshold; W loss represents the loss weighting coefficient; r l represents the longitudinal resistance of the feeder line l between buses i and j; W EP represents the energy weighting coefficient; represents the energy cost from the upper power grid of the distributed flexible low-carbon energy supply system at time t in year y under scenario Sc; represents the cost function of a given distributed generation on bus i; represents the active power of a given distributed generation unit connected to bus i at time t in year y under scenario Sc; C start represents the initial cost; W lossDSS represents the resistance loss weighting factor; W represents the resistance loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; Cur represents the load weighting factor; It represents the active load reduction of bus i at time t in year y under scenario Sc.

11. The system according to any one of claims 8 to 10, characterized in that: The constraints corresponding to the investment and maintenance cost include: bus quantity constraint, rated power constraint, rated energy storage capacity constraint, rated power sum constraint and rated energy storage capacity sum constraint; The constraints corresponding to the operating cost include: energy constraints, rated power constraints, nonlinear constraints, relaxation constraints, power capacity constraints, network security constraints, current flow constraints, node voltage constraints and distributed power supply operation constraints.

12. The system according to claim 11, characterized in that The model solving module comprises: A linear constraint construction submodule is used to construct linear constraints according to preset voltage auxiliary variables and current auxiliary variables; A convexity constraint construction submodule, used for constructing a convexity constraint according to the relaxation constraint and the current flow constraint; A model conversion submodule, used for converting the optimization configuration model into a linear convex optimization configuration model based on the linear constraint and the convexity constraint; A configuration model solving submodule, used to solve the linear convex optimization configuration model through a GUROBI solver according to the power operation data, so as to obtain the optimal installation position and capacity of the distributed flexible low-carbon energy supply system; The linear constraints include voltage deviation linear constraints and power flow linear constraints.

13. The system of claim 12, wherein: The expression corresponding to the voltage deviation linear constraint is as follows: in, represents the voltage auxiliary variable of bus i at time t in year y under scenario Sc; v i,y,Sc,t represents the node voltage of bus i at time t in year y under scenario Sc; Indicates the preset maximum voltage threshold; The expression corresponding to the power flow linear constraint is as follows: in, represents the auxiliary variable of the current on bus i at time t in year y under scenario Sc; f i,y,Sc,t represents the current of bus i at time t in year y under scenario Sc; F thr Indicates the preset maximum feeder current threshold; The expression corresponding to the convexity constraint is as follows: in, represents the reactance of the distributed flexible low-carbon energy supply system on bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; represents the energy loss of the distributed flexible low-carbon energy supply system on bus i at time t in year y under scenario Sc; P ij,y,Sc,t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; Q ij,y,Sc,t represents the reactive power flow of buses i and j at time t in year y under scenario Sc; f ij,y,Sc,t represents the active power transfer limit between bus i and bus j at time t in year y under scenario Sc; v ij,y,Sc,t It represents the voltage amplitude of bus i and bus j at time t in year y under scenario Sc.

14. The system according to claim 11, characterized in that The expression corresponding to the network security constraint is as follows: Among them, P ij,y,Sc,t represents the active power flow of bus i and bus j at time t in year y under scenario Sc; P jk,y,Sc,t represents the active power flow of bus j and line k at time t in year y under scenario Sc; C represents the set of lines between bus i and bus j; k:(i,j) represents line k between bus i and bus j; Q ij,y,Sc,t represents the reactive power flow of bus i and bus j at time t in year y under scenario Sc; Q jk,y,Sc,t represents the reactive power flow of bus j and line k at time t in year y under scenario Sc; r ij represents the resistance between bus i and bus j; x ij represents the reactance between bus i and bus j; F ij,y,Sc,t represents the current flow of bus i and bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc; represents the reactive power demand of bus j at time t in year y under scenario Sc; represents the active power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the reactive power demand of bus j at time t in year y under scenario Sc for a given distributed generation unit connected to bus i; represents the active power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the reactive power of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; represents the active load reduction of the distributed flexible low-carbon energy supply system on bus j at time t in year y under scenario Sc; It represents the reactive power related to the parallel susceptance between bus i and bus j of the distributed flexible low-carbon energy supply system under scenario Sc at time t in year y.

15. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for optimizing and configuring a distributed flexible low-carbon energy supply system of an active distribution network as described in any one of claims 1 to 7 is implemented.

16. A computing device readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, an optimization configuration method for a distributed flexible low-carbon energy supply system of an active distribution network as described in any one of claims 1 to 7 is implemented.

Images