Active power distribution network optimization operation method considering source and load and related device

Abstract

The invention discloses an active power distribution network optimization operation method considering a source and a load and a related device, and belongs to the technical field of novel micro-grid optimization. The method comprises the following steps: respectively establishing a soft open point model, a static reactive power compensator model and a capacitor bank model of the active power distribution network; establishing a robust optimization operation model of the active power distribution network through network reconstruction according to the soft open point model, the static reactive power compensator model and the capacitor bank model; setting an objective function for the robust optimization operation model of the active power distribution network, and determining constraint conditions of the robust optimization operation model of the active power distribution network; and performing linearization processing on the robust optimization operation model of the active power distribution network, and solving an optimal value of a target function according to a constraint condition to obtain an optimal environment variable of operation of the power distribution network. The optimization method provided by the invention has excellent intelligence and flexibility, can adapt to optimization requirements of various complex power distribution networks, and is higher in precision, fewer in iteration times and shorter in calculation time.

Description

Technical Field

[0001] The present invention belongs to the technical field of novel microgrid optimization, and relates to an active distribution network optimization operation method and related devices taking sources and loads into consideration. Background Art

[0002] With the growing global demand for clean energy and the continuous breakthroughs in technology, renewable energy sources (RESs) such as solar energy and wind energy are gradually becoming an important part of the power supply system. However, the inherent characteristics of these renewable energy sources, namely that their power generation is highly dependent on natural conditions (such as light intensity, wind speed, etc.), make the power supply present significant intermittency and uncertainty. At the same time, with the rapid development of social economy and the diversification of people's lifestyles, the load of active power distribution network has become more and more complex and unpredictable. The periodic fluctuations of industrial production, the differences in residents' electricity consumption habits, and the impact of emergencies have jointly aggravated the randomness and volatility of power load.

[0003] With the rise of renewable energy sources (RESs) and the increase in uncertainty in active power distribution network load, the volatility of distribution networks has become more significant, and the randomness of power loads has also increased, resulting in reverse flow, voltage fluctuations, and frequent extreme operations in distribution network operations. In this case, uncertainties from both the source side and the load side may cause the distribution network to operate in complex and extreme situations, making reactive power and voltage regulation extremely difficult and potentially threatening the safety and stability of the power grid. To avoid this situation, there is an urgent need to optimize the operation of the distribution network.

[0004] Because the uncertainty of energy supply and demand makes the power flow pattern of the distribution network more complex and unpredictable, the traditional deterministic optimization model is not very accurate, difficult to adapt to the optimization needs of various complex distribution networks, and cannot fully solve these safety and economic problems. Summary of the invention

[0005] The purpose of the present invention is to provide an active distribution network optimization operation method and related devices taking into account sources and loads, so as to solve the technical problems in the prior art that due to the uncertainty of sources and loads, traditional deterministic optimization models are difficult to adapt to the optimization requirements of various complex distribution networks, have long calculation time and low accuracy.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides an active distribution network optimization operation method considering sources and loads, comprising the following steps:

[0008] The soft-opening point model, static VAR compensator model and capacitor bank model of active distribution network are established respectively;

[0009] According to the soft opening point model, the static VAR compensator model and the capacitor bank model, a robust optimization operation model of the active distribution network is established through network reconstruction;

[0010] The objective function is set for the robust optimization operation model of the active distribution network.

[0011] Determine the constraints of the robust optimization operation model of the active distribution network;

[0012] The robust optimization operation model of the active distribution network is linearized, and the optimal value of the objective function is solved according to the constraint conditions to obtain the optimal environmental variables for the operation of the distribution network, and the operation parameters of the distribution network are adjusted and optimized through the optimal environmental variables.

[0013] Furthermore, the expression of the soft opening point model is:

[0014]

[0015] S min ≤S i,t ≤S max (2)

[0016] S i,1 =S i,T (3)

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] In the formula, S i,t+1 is the energy storage state at time t+1; S i,t is the energy storage state at time t; is the charging power at time t; is the discharge power at time t; η cha is the charging efficiency; η dis is the discharge efficiency; S min is the minimum capacity of the energy storage system; S max is the maximum capacity of the energy storage system; S i,1 Indicates its initial energy storage state; S i,TIndicates the energy storage state at the end of the cycle; For charging instructions; is the maximum charging power; For discharge instruction; is the maximum discharge power; is the total power flow at time t; Q SOPmin is the minimum reactive power; Q SOPmax is the maximum reactive power; is the reactive power at time t; S SOP is the apparent power;

[0023] The expression of the static VAR compensator model is:

[0024]

[0025] in, is the reactive power output at time t; Q SVCmin is the minimum value of reactive power; Q SVCmax is the maximum value of reactive power;

[0026] The expression of the capacitor bank model is:

[0027]

[0028]

[0029]

[0030] In the formula, is the reactive power output of capacitor bank i at time t; is the discrete control variable at time t; Q CB,step is the reactive power of the capacitor at each step.

[0031] Furthermore, the step of establishing a robust optimization operation model of the active distribution network through network reconstruction according to the soft opening point model, the static VAR compensator model and the capacitor bank model specifically includes:

[0032] The network is reconstructed according to the soft opening point model, the static VAR compensator model and the capacitor bank model, so that the system performance is optimized during the operation of the distribution network, and finally the robust optimization operation model of the active distribution network is reconstructed; the specific expression of the robust optimization operation model of the active distribution network is:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039] In the formula, F jk,t is the current from node j to node k at time t; v is the power generation; F 1k,t is the power demand; F ij,t is the flow through line ij at time t; and All are power flow constraints; is the switch state of line ij at time t; n is the number of network nodes; Z Lmax is the maximum number of switching times allowed; Z sLmax is the maximum total number of swap operations allowed.

[0040] Furthermore, the step of setting the objective function for the robust optimization operation model of the active distribution network specifically includes:

[0041] Considering the explicit variable x in the decision variable domain and the explicit variable y in the uncertain variable domain, setting the objective function, forming a maximum-minimum problem, the main goal is to minimize the highest possible value of the function. The specific formula is:

[0042]

[0043]

[0044] Where f is the overall objective function; f1 is the first sub-objective; f2 is the second sub-objective; α is the weight coefficient of the first sub-objective; β is the weight coefficient of the second sub-objective; μ is the scaling factor; r ij is the resistance of line ij; I ij is the current flowing through line ij; Δt is the step length; U i,t is the voltage value of node i at time t; U N is the nominal voltage value of the node;

[0045] Describe the uncertainty from the source and load, and determine the set of uncertain variables Y in the objective function; the specific formula is:

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] Among them, Y is a set of uncertain variables; is the predicted value; is the maximum value of the variable; is the minimum value of the variable; and are binary variables; ω is the allowed percentage change; Λ j is the maximum number of adjustments allowed for variable y at node j.

[0052] Furthermore, the step of determining the constraint conditions of the robust optimization operation model of the active distribution network specifically includes:

[0053] Considering the power flow constraints of network reconstruction, the constraints are set as follows:

[0054]

[0055]

[0056]

[0057] Among them, P ij,t is the active power between nodes ij at time t; Q ij,t is the reactive power between nodes ij at time t; P j,t is the active power of node j at time t; Q j,t is the reactive power of node j at time t; P jk,t is the active power flow between node j and node k at time t; Q jk,t is the reactive power flow between node j and node k at time t; U i,t is the voltage of node i at time t; U j,t is the voltage of node j at time t; is the switch state between node i and node j; x ij is the reactance between node i and node j; r ij is the resistance between node i and node j; P j.t is the active power of node j at time t; Q j.t is the reactive power of node j at time t; is the active load of node j at time t; is the distributed generation active power of node j at time t; is the active power of the distribution network of node j at time t; is the energy storage active power of node j at time t; is the reactive load of node j at time t; is the reactive distributed generation power of node j at time t; is the reactive power of the distribution network at node j at time t; is the reactive capacitor power of node j at time t; is the reactive controllable compensator power of node j at time t; is the reactive power of the capacitor bank at node j at time t;

[0058] Considering the substation operation constraints; while providing active power, the substation also needs to adjust the exchange of reactive power to ensure that the total power throughput remains within the specified range; the specific formula is:

[0059]

[0060] Among them, P Gridmin The minimum active power limit of the distribution network; P Grid,max The maximum active power limit of the distribution network; Q Grid,min It is the minimum reactive power limit of the distribution network; Q Grid,max It is the maximum value limit of reactive power of distribution network; is the active power of the distribution network at time t; is the reactive power of the distribution network at time t;

[0061] Considering the operating constraints of distributed generation DG, i The active output limit and reactive output limit at time t are defined, and the specific formula is:

[0062]

[0063] in, is the minimum active power of distributed energy; is the maximum active power of distributed energy; P i, D t G is the active power of distributed energy at time t; is the reactive power of distributed energy at time t; θ is the phase angle;

[0064] Set system safety constraints to keep all parameters within safe operating boundaries; the specific formula is:

[0065]

[0066]

[0067] in, is the minimum voltage limit of node i; is the maximum voltage limit of node i; is the switch state between node i and node j; is the maximum current between node i and node j; is the minimum active power between node i and node j; is the maximum active power between node i and node j; is the minimum reactive power between node i and node j; is the maximum reactive power between node i and node j.

[0068] Furthermore, the step of linearizing the robust optimization operation model of the active distribution network and solving the optimal value of the objective function according to the constraint conditions to obtain the optimal environmental variables for the operation of the distribution network specifically includes:

[0069] New variables are introduced to replace the squared voltage and squared current terms in the constraints. The specific formula is:

[0070]

[0071] Among them, l ij,t represents the square term of the current between nodes i and j at time t; V i,t represents the square term of the voltage between node i and node j at time t;

[0072] The absolute value terms in the robust optimization operation model of the active distribution network are linearized using the following formula without losing generality:

[0073]

[0074] Among them, Z t+1 -Z t is the increment of variable Z between time step t and time step t+1; Z max is the maximum range in which Z appears; Z min is the minimum range in which Z appears; O t is a binary variable indicating whether an increment occurs at time step t; σ max The maximum number of times allowed for the total number of increases and decreases;

[0075] A solution method based on relaxed cooperative co-evolutionary algorithm is introduced. The optimal value of the objective function is solved through the linearized robust optimization operation model of the active distribution network, and the optimal environmental variables for the operation of the distribution network are obtained.

[0076] Furthermore, the step of introducing a solution method based on a relaxed cooperative co-evolutionary algorithm, solving the optimal value of the objective function through the robust optimization operation model of the linearized active distribution network, and obtaining the optimal environmental variables for the operation of the distribution network specifically includes:

[0077] A solution method based on a relaxed cooperative co-evolutionary algorithm is introduced, and the solution is iteratively solved between the first-level minimization and the second-level maximization to transform the infinite continuous constraints into manageable finite constraints; the decision variables and the first objective function values ​​are obtained by iteratively solving the first level, and the lower bound is updated with the first objective function value, and then the decision variables are fixed to solve the second level and obtain the environmental variables and the second objective function values, and the upper bound is updated with the second objective function value; if the difference between the first objective function value and the second objective function value is greater than the preset value, the iteration is continued; if the difference between the first objective function value and the second objective function value is less than the preset value, the iteration is terminated, the second objective function value is output, and the environmental variables are output as the optimal environmental variables.

[0078] In a second aspect, the present invention provides an active distribution network optimization operation system considering sources and loads, comprising:

[0079] A model building module is used to respectively build a soft-opening point model, a static VAR compensator model and a capacitor bank model of an active distribution network;

[0080] A network reconstruction module, used to establish a robust optimization operation model of the active distribution network through network reconstruction according to the soft opening point model, the static VAR compensator model and the capacitor bank model;

[0081] The objective function setting module is used to set the objective function for the robust optimization operation model of the active distribution network.

[0082] A constraint condition determination module, used to determine the constraint conditions of the robust optimization operation model of the active distribution network;

[0083] The optimal solution solving module is used to linearize the robust optimization operation model of the active distribution network, and solve the optimal value of the objective function according to the constraint conditions to obtain the optimal environmental variables for the operation of the distribution network, and adjust the operating parameters of the optimized distribution network through the optimal environmental variables.

[0084] In a third aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0085] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] The present invention discloses an active distribution network optimization operation method and related devices considering source and load. The network is reconstructed by establishing a soft opening point model, a static VAR compensator model and a capacitor group model, and a robust optimization operation model of the active distribution network is established; then the objective function and constraint conditions are set, and the robust optimization operation model of the active distribution network is linearized and solved, and finally the optimal environmental variables are obtained. The present invention can effectively improve the reactive power distribution of the system, reduce line losses, and improve voltage quality by accurately modeling the static VAR compensator (SVC) and the capacitor group, and optimizing their configuration and operation. By introducing the soft opening point model, the method can flexibly adjust the network structure of the distribution network and enhance the flexibility and controllability of the system. By constructing an operation model based on robust optimization theory, uncertainty and disturbance factors (such as load fluctuations, changes in renewable energy output, etc.) can be fully considered, and an operation strategy that can maintain good performance in various possible scenarios can be designed. This greatly improves the robustness and stability of the system and reduces the risk of power outages or power quality degradation caused by uncertainty. Compared with traditional optimization methods, the optimization method proposed in this invention has superior intelligence and flexibility, can adapt to the optimization needs of various complex distribution networks, and has higher accuracy, fewer iterations, and shorter calculation time. This innovative method is expected to improve the reliability of distribution network operation and is expected to be widely used in future power systems.

[0088] Furthermore, the present invention uses the second-order cone relaxation technology to linearize the robust optimization operation model of the active distribution network, thereby converting it into a convex optimization problem to achieve efficient solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0090] Figure 1 is a flow chart of the method of the present invention;

[0091] Figure 2 is a schematic diagram of the system of the present invention;

[0092] Figure 3 This is a flow chart of the method strategy of an embodiment of the present invention;

[0093] Figure 4 Schematic diagram of an improved IEEE 33 bus test system according to an embodiment of the present invention;

[0094] Figure 5 1 is a diagram showing the operation results of CB, SVC and ESS according to an embodiment of the present invention;

[0095] Figure 6 A diagram showing the fluctuation of voltages of various nodes in the system according to an embodiment of the present invention in different time periods;

[0096] Figure 7 Schematic diagram of network loss and voltage offset in different scenarios of an embodiment of the present invention;

[0097] Figure 8 It is a schematic diagram of the computer device structure of the present invention. DETAILED DESCRIPTION

[0098] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0099] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0100] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0101] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0102] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0103] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0104] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0105] See also Figure 1 The embodiment of the present invention discloses an active distribution network optimization operation method considering sources and loads, comprising the following steps:

[0106] S1, establish the soft opening point model, static VAR compensator model and capacitor bank model of the active distribution network respectively;

[0107] S101, Soft Open Point (SOP) modeling:

[0108] As a controllable switch for bidirectional power flow, SOP can change the power route, balance the load, and solve problems such as congestion and voltage drop by precisely controlling the phase angle and voltage amplitude, thereby improving the flexibility and power quality of the distribution network.

[0109] The SOP and the Energy Storage System (ESS) are integrated, specifically: using two VSCs connected by a DC-DC converter and attached batteries for energy storage and retrieval. By forming a SOP-ESS system, the SOP is used to control the power flow and the ESS is used for energy transfer, thereby achieving effective voltage regulation, as shown in Equations (1) to (8).

[0110]

[0111] S min ≤S i,t ≤S max (2)

[0112] S i,1 =S i,T (3)

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] Among them, formula (1) updates the energy storage state at time t+1 based on the energy generated during charging and the energy consumed during discharging. Formula (2) ensures that the storage level remains within the predefined minimum and maximum capacity range. Formula (3) means that the initial SOC at the beginning of the cycle should be equal to the final state at the end of the cycle. Formulas (4) and (5) ensure that the charge / discharge power of battery i at time t is between 0 and its maximum charge / discharge power. Binary variable Indicates whether the battery is charging (1) or not charging (0). Equation (6) ensures that the total power flow of the ESS at time t (to the set (Ω p ) is equal to the difference between the discharge power and the charge power. Equation (7) sets the reactive power of SOPS Formula (8) requires that the apparent power of SOPS at time t is Do not exceed its rated capacity.

[0119] S102, Static Var Compensator (SVC) Modeling:

[0120] Static Var Compensator (SVC) is an advanced component used to provide dynamic reactive power support in modern power systems. In the present invention, it will provide continuous reactive power compensation, help effectively regulate voltage and maintain grid stability, as shown in formula (9).

[0121]

[0122] S103, Capacitor Banks (CBs) model:

[0123] Capacitor banks (CBs) are different from static var compensators (SVCs). They are discrete reactive compensation devices and belong to the category of low-speed regulation resources. In the present invention, they are mainly used to compensate reactive power and stabilize voltage in the power system. They operate in a step-by-step manner and require more time to adjust, as shown in formulas (10) to (12).

[0124]

[0125]

[0126]

[0127] The reactive power output of the capacitor is defined in equation (10): is a discrete control variable representing the number of steps (or units) of capacitor bank i connected to the system at time t. Equation (11) constrains the total number of switching operations (up or down) that capacitor bank i can perform over the entire time horizon t. Equation (12) ensures that the number of steps of capacitor bank i connected to the system at time t remains within its physical limits.

[0128] S2, establishing a robust optimization operation model of the active distribution network through network reconstruction according to the soft opening point model, the static VAR compensator model and the capacitor bank model;

[0129] Through network reconstruction, the system performance can be optimized during the operation of the distribution network (DN). Specifically, the network topology is changed by controlling the state of the contact switches (such as circuit breakers, reclosers, and sectionalizers) connecting different parts of the network. By selectively opening or closing these switches, the direction and distribution of the power flow can be changed to achieve various goals, such as minimizing losses, maximizing reliability, enhancing voltage distribution, or isolating faulty parts, as shown in equations (13) to (18).

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136] The network reconfiguration is subject to certain constraints to ensure that the system remains functional and safe. Specifically, Equation (13) applies Kirchhoff's current law to each node j except the reference node (node ​​1). The negative sign indicates that if there is power injection at that node, its effect is to reduce the net flow into that node. Equation (14) establishes the relationship between the total power output of the reference node (node ​​1) to its connected child nodes k and the total power demand or generation λ of that particular node. v Equivalence relationship between them. Formula (15) defines the power flow F flowing through line ij at time t ij,t Equation (16) ensures that at time t there are exactly n-1 lines closed, where n is the number of network nodes. This is a requirement for the radial configuration of the network. Equation (17) limits the number of times a particular line ij can switch between open and closed states at optimization level T-1. Equation (18) imposes a limit on the total number of switching operations that can occur in the entire network at optimization horizon T-1.

[0137] S3, setting the objective function for the robust optimization operation model of the active distribution network,

[0138] The objective function of the robust optimization operation model of active distribution networks (ADNs) established by S2 is set to form a maximum-minimum problem. The main goal is to minimize the highest possible value of the function f. Two different sets of variables need to be considered: "explicit variables from x, the decision variable domain, and explicit variables from y, the uncertain variable domain". Specifically, as shown in Equation (19) and Equation (20):

[0139]

[0140]

[0141] Where f is the overall objective function, defined as the weighted sum of two sub-objectives f1 and f2. The weights α and β are used to balance the importance of each sub-objective. μ acts as a scaling factor, which is used to transform the measurements from one dimension to another in a uniform manner. α+β=1 ensures that the total weights sum to 1, indicating a standardized combination of objectives. f1 represents the goal of minimizing the total network losses. The goal of f2 is to minimize the deviation of the node voltage from its nominal value. The optimization problem seeks to find the best operating strategy that minimizes the total network losses while keeping the voltage deviation as low as possible under the worst-case assumption of certain parameters (which may be captured by the variable y). The trade-off between these two objectives is controlled by the parameters α and β, allowing flexible prioritization based on specific operating requirements or constraints of the power system.

[0142] The uncertainty from the source and load is described by equations (21) to (25). Specifically, the set of uncertain variables Y can be expressed as The maximum value is expressed as The minimum value is expressed as As shown in formula (21). Binary variable and t represents the decision of whether to increase or decrease the variable y at node j and time t. Equations (22) to (23) define how to calculate the maximum and minimum adjustment values ​​of y at node j and time t. They convert the previous value are scaled by factors (1+ω) and (1-ω), respectively, where ω represents the allowed percentage change. Equation (24) ensures that for any given j and t, and There is at most one non-zero value in , which means that the variable y cannot increase and decrease at the same time. Equation (25) limits the total number of times y can be adjusted (increased or decreased) within the entire time range T of a given node j. j Indicates the maximum number of adjustments allowed for variable y at node j.

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] S4, determining the constraints of the robust optimization operation model of the active distribution network;

[0149] S401, considering the power flow constraints of network reconstruction, when considering network reconstruction, the traditional DistFlow model in the active distribution network ADNs should be adjusted. The specific constraints are shown in equations (26) to (28).

[0150]

[0151]

[0152]

[0153] Equation (26) indicates that at node j, time t, active and reactive power are conserved, indicating that the output power minus the loss is equal to the input power plus the node injection power. Equation (27) relates the square of the node and node voltage to the active and reactive power of the line flowing between them, using the large M method with a binary variable t to indicate the line Is it closed or open. Equation (28) defines the power balance of the node at the moment.

[0154] S402, due to the limited capacity of distribution networks (DNs), the transmission from the main grid to these networks through substations needs to be carefully managed. Specifically, while providing active power, the substation must also regulate the exchange of reactive power to ensure that the total power throughput remains within the specified range. This precaution is crucial to mitigate the impact of power variations within DNs on the stability of the main grid, as shown in Equation (29).

[0155]

[0156] S403, considering the DG operation constraints, DG i The active / reactive output limit at time t is defined as shown in equation (30).

[0157]

[0158] S404, set system safety constraints. In DN operation, keep the bus voltage within a limited range to ensure equipment function and power quality. At the same time, manage branch current and transmission power to prevent overload, equipment failure and service interruption, and further ensure network reliability and efficiency. This requires relevant constraints on system safety to keep all parameters within safe operating boundaries and obtain optimal performance, as shown in Equations (31) and (32).

[0159]

[0160]

[0161] Wherein, equation (31) defines the acceptable range of voltage amplitude and current. Equation (32) specifies the active / reactive power flow P through the connecting busbars i and j at time t. ij,t of the border.

[0162] S5, linearizing the robust optimization operation model of the active distribution network, solving the optimal value of the objective function according to the constraint conditions, obtaining the optimal environmental variables for the operation of the distribution network, and adjusting and optimizing the operation parameters of the distribution network through the optimal environmental variables.

[0163] Because the formulated model contains many nonlinear and non-convex components, traditional optimization solvers cannot directly solve the problem. To circumvent this problem, the present invention starts with the linearization of nonlinear terms to facilitate the approximation of linear counterparts and enhance the traceability of the model. Then, the model is converted into MISOCP format using a second-order cone relaxation method, making it easier to solve with a specialized optimization algorithm.

[0164] S501, model linearization. In order to solve the square terms of voltage and current in the constraints, the present invention introduces new variables to replace these terms, as shown in formula (33).

[0165]

[0166] Then, equations (20), (26), (27), and (31) can be replaced by equations (34), (35), (36), and (37), respectively.

[0167]

[0168]

[0169]

[0170]

[0171] The SOCP method is used for the last equation in equation (36), and the relaxed constraint is expressed as equation (38):

[0172]

[0173] In addition, the established model contains terms in the form of absolute values, such as Equations (11), (17) and (18). Without loss of generality, the absolute value terms in these constraints can be linearized using Equation (39).

[0174]

[0175] Among them, Z t+1 -Z t is the increment of variable Z between time step t and time step t+1; Z max is the maximum range in which Z appears; Z min is the minimum range in which Z appears; O t is a binary variable indicating whether an increment occurs at time step t; σ max The maximum number of times allowed for the total number of increases and decreases;

[0176] The first inequality in (39) restricts the increase or decrease of the value of Z between time steps t and t+1. t will be used to allow increases or decreases. The last inequality places an upper limit on the total number of operations (increases or decreases) that can occur over the entire time horizon T-1. max is the maximum number of operations allowed, which can represent the budget constraint on how many times Z can be adjusted during the operation.

[0177] S502, introduces a solution method based on a relaxed cooperative co-evolutionary algorithm, which iterates between the first-level minimization and the second-level maximization to transform infinite continuous constraints into manageable finite constraints. As a time-of-day algorithm, it produces increasingly accurate solutions over time and calculates the decision variable x in each iteration. * (simulation operation variable) and environment variable y * (load fluctuations and renewable energy output), thus facilitating detailed data analysis. The relaxed evolutionary algorithm gradually improves the solution accuracy by transforming the complex continuous minimum-maximum problem. Its two-level iterative structure allows concurrent calculation of the optimal x * ,y * and robustness index S, making it particularly suitable for handling uncertainties in operating environments while providing rich data for each step of analysis. The purpose of the first layer of optimization is to minimize a variable ρ, which is introduced to limit the maximum value of the first objective function, as shown in (40)-(41); finding the minimum value ρ that makes the objective function below its threshold is the solution to this layer. Due to the continuity of the problem, infinite constraints are proposed, which are solved by relaxation methods to transform it into a series of tractable subproblems. Each subproblem is associated with a discrete value of the uncertainty variable yk in a finite set SY, which can approximate the solution to the original problem. The optimal value of the first objective function, denoted as fm * , and the resulting decision variable x * Together, they are then passed to the optimized second layer for further processing, as shown in equations (40) and (41).

[0178]

[0179]

[0180] The second-level optimization expression is shown in Equation (42), where the decision variable remains at the optimal value x obtained from the first level. * Unlike the first layer, the environment variables are now unrestricted and span the entire continuous domain Y, rather than being confined to a finite set SY. The purpose of this layer is to calculate the second objective function value fM * And determine the optimal environment variable y in the worst case * , reflecting the full range of potential uncertainties.

[0181]

[0182] In the formula, is the target reactive power of the static VAR compensator at node i; is the target reactive power of the capacitor bank at node i; is the target state of the line between node i and node j; is the target active power of the generator at node i; is the target reactive power of the generator at node i; is the target active power of the static synchronous compensator at node i; is the target reactive power of the static synchronous compensator at node i;

[0183] The relaxed evolutionary algorithm is executed as an iterative process. The iteration is performed by solving the first layer optimization (x * ,fm * ), using the first objective function value fm * Update the lower bound, then fix x * To solve the second layer and find (y * , fM * ), using the second objective function value fM * Update the upper bound. If the difference between fM and fm is less than 0.001, the iteration terminates and returns (x * ,y * , fm); otherwise, y * Add to SY and repeat the process.

[0184] S6, conduct example verification analysis:

[0185] Using the MATLAB platform and the improved IEEE 33 bus test system, a comprehensive model of the distribution network is established, and an example verification analysis is carried out to evaluate the feasibility of the method of the present invention.

[0186] The present invention further emphasizes system safety, economic efficiency and operational flexibility by introducing a robust operation model for active distribution networks. The model fully considers the uncertainty of load patterns and renewable energy output. It integrates a variety of regulatory resources, including distributed generation, energy storage systems, reactive compensation devices and flexible network reconstruction strategies. Finally, the model is linearized using second-order cone relaxation technology, thereby converting it into a convex optimization problem for efficient solution. Compared with traditional optimization operation methods, the method proposed in the present invention has superior intelligence and flexibility, can adapt to the optimization needs of various complex distribution networks, and has higher accuracy, fewer iterations and shorter calculation time. This innovative method is expected to improve the reliability of distribution network operation.

[0187] Example:

[0188] See also Figure 3, this embodiment provides a method for robust optimization operation of an active distribution network with multiple regulating resources considering source and load uncertainties. The specific implementation method is the same as above and will not be repeated here. In order to verify the effectiveness and accuracy of the method in this article, this embodiment is compared with the traditional robust optimization algorithm on an improved IEEE 33 bus test system. The improved IEEE 33 bus test system evaluates voltage stability within a safe operating range of 0.95 to 1.05 per unit to ensure the reliability of power service. This enhanced system design is characterized by contact switches installed on lines 9-15, 8-21, and 25-29, respectively, and each contact switch is initially set to an open state. Controllable switches are installed on lines 12-13, 20-21, and 6-26 and are started in a closed state. In addition, a two-terminal SOPS device is connected to buses 18 and 33. The detailed layout of the system architecture is shown in Figure 4 shown.

[0189] The energy capacity of ESS in SOPS is 1mwh. Circuit Breakers (CBs) are allowed to perform up to three operations in the entire dispatch range. In order to balance economic considerations and safety, the coefficients α and β are determined to be 0.8 and 0.2, respectively. A representative day is divided into 24-hour intervals, and each dispatch cycle spans 1 hour. RE and load fluctuation parameter Λ Load Set it to 8 and define the weight factor ω as 10%.

[0190] The comparison results are shown in Table 1.

[0191] Table 1

[0192] method Obj(pu) Iter No Resolution time(s) M1 0.06765 4 205.42 M2 0.06761 7 439.75

[0193] The method of the present invention is referred to herein as Method 1 (M1), while the conventional method is designated as Method 2 (M2). It is noteworthy that M1 shows a reduced iteration count relative to M2. In addition, the computation time consumed by M1 is only 42.47% of that of the conventional algorithm, thus confirming the superior efficiency of the proposed algorithm.

[0194] From the optimization results, switches TS1 and D3 are continuously engaged, while D1 and TS3 are always in a non-engaged state. A unique state change occurs on switch D2, which changes from a closed state to an open state at exactly 23:00, and this maneuver is reciprocated by TS2. Figure 5 As shown, CB activation occurs at the specified times: 15:00, 17:00, and 18:00. From 10:00 to 20:00, excess renewable energy enters the ESS through the standard operating system, making the ESS show an upward trend. On the contrary, the ESS is conducive to emission during the time period without surplus renewable energy.

[0195] like Figure 6 As shown, the voltage on each bus in the experiment is included in the safety margin of the operating range. The voltage of buses 30 to 33 is significantly higher during the period from 9:00 to 17:00 at noon. The voltage of buses 2 to 12 remains consistent, while the voltage of buses 23 to 33 is basically limited to between 0.97 and 1.00 during the night and early morning hours (extending from 1:00 to 8:00) and late at night (18:00 to 24:00). The voltage of the remaining buses is generally in the range of 1.00 to 1.02pu. The peak of the terminal voltage is mainly due to the large output of renewable energy devices and the coordinated implementation of multiple active management measures.

[0196] The performance of the proposed method is calculated, and the iteration order of the proposed algorithm is shown in Table 2. Here, f m and f M The unit (pu) represents the optimal result of the objective function in the initial and subsequent optimization stages. M is regarded as the upper bound (UB), f m is regarded as the lower bound (LB). The gap of each iteration can be calculated as |UB-LB| / UB. As shown in Table 2, after 4 rounds of iterative calculations, it reaches less than 5×10 -4 The convergence accuracy.

[0197] Table 2

[0198] #Iter <![CDATA[f M (UB)]]> <![CDATA[f m (LB)]]> (Gap|UB-LB| / UB) 1 0.06765 0.01229 81.84％ 2 0.06765 0.05886 12.99％ 3 0.06765 0.06286 7.08％ 4 0.06765 0.06763 0.03％

[0199] The scenario comparison analysis is performed to verify the impact of uncertainty in renewable energy supply and demand on the system and the ability to maintain voltage quality standards under extreme conditions in the second stage of the model. The present invention uses a series of carefully designed scenarios (as shown in Table 3) to reflect a series of real-world conditions, such as high renewable energy penetration, sudden load peaks, and long periods of low demand, which are then used to rigorously test and verify the robustness of the model.

[0200] Table 3

[0201]

[0202]

[0203] Note: DT stands for deterministic model.

[0204] In Table 3, Λ RE and Λ LoadRepresent the expected total deviation level of renewable energy generation and power load, respectively. Using a consistent bus system and the same equipment specifications, the results of the experiment, including indicators such as network loss (Ploss), voltage offset deltV and the occurrence rate of voltage exceeding the allowed limit (Nv_over_limit), are shown in the following table (see Figure 7 and Table 4).

[0205] Table 4

[0206] Scenario S1 S2 S3 S4 S5 S6 S7 S8 Nv_over_limit 48 36 112 0 0 0 0 0

[0207] It can be seen that in the deterministic model (i.e., S1), when the renewable energy penetration rate is high, obvious voltage deviations occur, resulting in a large number of voltage overlimit instances. Compared with S1, S2 shows higher network losses and lower economic efficiency, although it has a smaller voltage deviation; however, this does not mean higher safety due to the serious threat of voltage overlimit events. S3 represents the largest voltage deviation and over-lower limit occurrence, and the network loss is similar to S2, indicating comparable economic results under different voltage distributions. The RO model proposed in scenarios 4, 5, and 6 greatly reduces the voltage deviation and eliminates the voltage overlimit, although at the cost of a slight decrease in economic performance and a more conservative result. Increasing the fluctuation deviation coefficient ω of scenarios 4 to 6 will lead to increased network losses, increased voltage deviations, and decreased economy and safety. Similarly, in scenarios 5, 7, and 8, as Λ RE and Λ Load As increases, network losses and voltage deviations also increase, which indicates that the economy and safety of the system will decrease due to the more conservative treatment of uncertainties.

[0208] In summary, the present invention proposes a robust optimization distribution network operation model that includes multiple regulation resources, which solves the operation challenges of the distribution network under the condition of source and load uncertainty. The model takes into account a variety of regulation resources and builds a complete framework to mitigate the uncertainty from the source and load ends. Compared with the traditional robust method, this method has considerable accuracy, fewer iterations, and shorter calculation time. It is very effective in reducing power loss and maintaining voltage stability.

[0209] See also Figure 2 The embodiment of the present invention discloses a robust optimization distribution network operation model including multiple regulation resources, including a model construction module, a network reconstruction module, an objective function setting module, a constraint condition determination module and an optimal solution solving module.

[0210] The model building module is used to respectively establish a soft opening point model, a static VAR compensator model and a capacitor bank model of the active distribution network;

[0211] The expression of the soft opening point model is:

[0212]

[0213] S min ≤S i,t ≤S max (2)

[0214] S i,1 =S i,T (3)

[0215]

[0216]

[0217]

[0218]

[0219]

[0220] The expression of the static VAR compensator model is:

[0221]

[0222] The expression of the capacitor bank model is:

[0223]

[0224]

[0225]

[0226] The network reconstruction module is used to establish a robust optimization operation model of the active distribution network through network reconstruction according to the soft opening point model, the static VAR compensator model and the capacitor bank model;

[0227] The specific expression of the established robust optimization operation model of the active distribution network is:

[0228]

[0229]

[0230]

[0231]

[0232]

[0233]

[0234] The objective function setting module is used to set the objective function for the robust optimization operation model of the active distribution network;

[0235] Considering the explicit variable x in the decision variable domain and the explicit variable y in the uncertain variable domain, setting the objective function, forming a maximum-minimum problem, the main goal is to minimize the highest possible value of the function. The specific formula is:

[0236]

[0237]

[0238] Describe the uncertainty from the source and load, and determine the set of uncertain variables Y in the objective function; the specific formula is:

[0239]

[0240]

[0241]

[0242]

[0243]

[0244] The constraint condition determination module is used to determine the constraint conditions of the robust optimization operation model of the active distribution network;

[0245] Considering the power flow constraints of network reconstruction, the constraints are set as follows:

[0246]

[0247]

[0248]

[0249] Considering the substation operation constraints; while providing active power, the substation also needs to adjust the exchange of reactive power to ensure that the total power throughput remains within the specified range; the specific formula is:

[0250]

[0251] Considering the operating constraints of distributed generation DG, i The active output limit and reactive output limit at time t are defined, and the specific formula is:

[0252]

[0253] Set system safety constraints to keep all parameters within safe operating boundaries; the specific formula is:

[0254]

[0255]

[0256] The optimal solution solving module is used to linearize the robust optimization operation model of the active distribution network, and solve the optimal value of the objective function according to the constraint conditions to obtain the optimal environmental variables for the operation of the distribution network, and adjust the operating parameters of the optimized distribution network through the optimal environmental variables.

[0257] New variables are introduced to replace the squared voltage and squared current terms in the constraints. The specific formula is:

[0258]

[0259] The absolute value terms in the robust optimization operation model of the active distribution network are linearized using the following formula without losing generality:

[0260]

[0261] A solution method based on relaxed cooperative co-evolutionary algorithm is introduced. The optimal value of the objective function is solved through the linearized robust optimization operation model of the active distribution network, and the optimal environmental variables for the operation of the distribution network are obtained.

[0262] In one embodiment of the present invention, a computer device is provided, the computer device comprising a processor and a memory, the memory being used to store a computer program, the computer program comprising program instructions, and the processor being used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of an active distribution network optimization operation method considering source and load.

[0263] The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for optimizing the operation of an active distribution network considering sources and loads in the above-mentioned embodiment.

[0264] 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. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0265] 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.

[0266] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0267] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0268] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the operation of an active distribution network considering sources and loads, characterized in that: The following steps are involved: The soft-opening point model, static VAR compensator model and capacitor bank model of active distribution network are established respectively; According to the soft opening point model, the static VAR compensator model and the capacitor bank model, a robust optimization operation model of the active distribution network is established through network reconstruction; The objective function is set for the robust optimization operation model of the active distribution network. Determine the constraints of the robust optimization operation model of the active distribution network; The robust optimization operation model of the active distribution network is linearized, and the optimal value of the objective function is solved according to the constraint conditions to obtain the optimal environmental variables for the operation of the distribution network, and the operation parameters of the distribution network are adjusted and optimized through the optimal environmental variables.

2. The method for optimizing the operation of an active distribution network considering sources and loads according to claim 1, characterized in that: The expression of the soft opening point model is: S min ≤S i,t ≤S max (2) S i,1 =S i,T (3) In the formula, S i,t+1 is the energy storage state at time t+1; S i,t is the energy storage state at time t; is the charging power at time t; is the discharge power at time t; η cha is the charging efficiency; η dis is the discharge efficiency; S min is the minimum capacity of the energy storage system; S max is the maximum capacity of the energy storage system; S i,1 Indicates its initial energy storage state; S i,T Indicates the energy storage state at the end of the cycle; For charging instructions; is the maximum charging power; For discharge instruction; is the maximum discharge power; is the total power flow at time t; Q SOPmin is the minimum reactive power; Q SOPmax is the maximum reactive power; is the reactive power at time t; S SOP is the apparent power; The expression of the static VAR compensator model is: in, is the reactive power output at time t; Q SVCmin is the minimum value of reactive power; Q SVCmax is the maximum value of reactive power; The expression of the capacitor bank model is: In the formula, is the reactive power output of capacitor bank i at time t; is the discrete control variable at time t; Q CB ,step is the reactive power of the capacitor at each step.

3. The method for optimizing the operation of an active distribution network considering sources and loads according to claim 1, characterized in that: The step of establishing a robust optimization operation model of the active distribution network through network reconstruction according to the soft opening point model, the static VAR compensator model and the capacitor bank model specifically includes: The network is reconstructed according to the soft opening point model, the static VAR compensator model and the capacitor bank model, so that the system performance is optimized during the operation of the distribution network, and finally the robust optimization operation model of the active distribution network is reconstructed; the specific expression of the robust optimization operation model of the active distribution network is: In the formula, F jk,t is the current from node j to node k at time t; v is the power generation; F 1k,t is the power demand; F ij,t is the flow through line ij at time t; and All are power flow constraints; is the switch state of line ij at time t; n is the number of network nodes; Z Lmax is the maximum number of switching times allowed; Z sLmax is the maximum total number of swap operations allowed.

4. The method for optimizing the operation of an active distribution network considering sources and loads according to claim 1, characterized in that: The step of setting the objective function for the robust optimization operation model of the active distribution network specifically includes: Considering the explicit variable x in the decision variable domain and the explicit variable y in the uncertain variable domain, setting the objective function, forming a maximum-minimum problem, the main goal is to minimize the highest possible value of the function. The specific formula is: Where f is the overall objective function; f1 is the first sub-objective; f2 is the second sub-objective; α is the weight coefficient of the first sub-objective; β is the weight coefficient of the second sub-objective; μ is the scaling factor; r ij is the resistance of line ij; I ij is the current flowing through line ij; Δt is the step length; U i,t is the voltage value of node i at time t; U N is the nominal voltage value of the node; Describe the uncertainty from the source and load, and determine the set of uncertain variables Y in the objective function; the specific formula is: Among them, Y is a set of uncertain variables; is the predicted value; is the maximum value of the variable; is the minimum value of the variable; and are binary variables; ω is the allowed percentage change; Λ j is the maximum number of adjustments allowed for variable y at node j.

5. The method for optimizing the operation of an active distribution network considering sources and loads according to claim 1, characterized in that: The step of determining the constraint conditions of the robust optimization operation model of the active distribution network specifically includes: Considering the power flow constraints of network reconstruction, the constraints are set as follows: Among them, P ij,t is the active power between nodes ij at time t; Q ij,t is the reactive power between nodes ij at time t; P j,t is the active power of node j at time t; Q j,t is the reactive power of node j at time t; P jk,t is the active power flow between node j and node k at time t; Q jk,t is the reactive power flow between node j and node k at time t; U i,t is the voltage of node i at time t; U j,t is the voltage of node j at time t; is the switch state between node i and node j; x ij is the reactance between node i and node j; r ij is the resistance between node i and node j; P j.t is the active power of node j at time t; Q j.t is the reactive power of node j at time t; is the active load of node j at time t; is the distributed generation active power of node j at time t; is the active power of the distribution network of node j at time t; is the energy storage active power of node j at time t; is the reactive load of node j at time t; is the reactive distributed generation power of node j at time t; is the reactive power of the distribution network at node j at time t; is the reactive capacitor power of node j at time t; is the reactive controllable compensator power of node j at time t; is the reactive power of the capacitor bank at node j at time t; Considering the substation operation constraints; while providing active power, the substation also needs to adjust the exchange of reactive power to ensure that the total power throughput remains within the specified range; the specific formula is: Among them, P Gridmin The minimum active power limit of the distribution network; P Grid,max The maximum active power limit of the distribution network; Q Grid,min It is the minimum reactive power limit of the distribution network; Q Grid,max It is the maximum value limit of reactive power of distribution network; is the active power of the distribution network at time t; is the reactive power of the distribution network at time t; Considering the operating constraints of distributed generation DG, i The active output limit and reactive output limit at time t are defined, and the specific formula is: in, is the minimum active power of distributed energy; is the maximum value of distributed energy active power; is the active power of distributed energy at time t; is the reactive power of distributed energy at time t; θ is the phase angle; Set system safety constraints to keep all parameters within safe operating boundaries; the specific formula is: in, is the minimum voltage limit of node i; is the maximum voltage limit of node i; is the switch state between node i and node j; is the maximum current between node i and node j; is the minimum active power between node i and node j; is the maximum active power between node i and node j; is the minimum reactive power between node i and node j; is the maximum reactive power between node i and node j.

6. The method for optimizing the operation of an active distribution network considering sources and loads according to claim 1, characterized in that: The step of linearizing the robust optimization operation model of the active distribution network and solving the optimal value of the objective function according to the constraint conditions to obtain the optimal environmental variables for the operation of the distribution network specifically includes: New variables are introduced to replace the squared voltage and squared current terms in the constraints. The specific formula is: Among them, l ij,t represents the square term of the current between nodes i and j at time t; V i,t represents the square term of the voltage between node i and node j at time t; The absolute value terms in the robust optimization operation model of the active distribution network are linearized using the following formula without losing generality: Among them, Z t+1 -Z t is the increment of variable Z between time step t and time step t+1; Z max is the maximum range in which Z appears; Z min is the minimum range in which Z appears; O t is a binary variable indicating whether an increment occurs at time step t; σ max The maximum number of times allowed for the total number of increases and decreases; A solution method based on relaxed cooperative co-evolutionary algorithm is introduced. The optimal value of the objective function is solved through the linearized robust optimization operation model of the active distribution network, and the optimal environmental variables for the operation of the distribution network are obtained.

7. The method for optimizing the operation of an active distribution network considering sources and loads according to claim 6, characterized in that: The solution method based on the relaxed cooperative co-evolution algorithm is introduced, and the optimal value of the objective function is solved through the robust optimization operation model of the linearized active distribution network to obtain the optimal environmental variables for the operation of the distribution network. Specifically, the steps include: A solution method based on a relaxed cooperative co-evolutionary algorithm is introduced, and the solution is iteratively solved between the first-level minimization and the second-level maximization to transform the infinite continuous constraints into manageable finite constraints; the decision variables and the first objective function value are obtained by iteratively solving the first level, and the lower bound is updated with the first objective function value, and then the decision variables are fixed to solve the second level and obtain the environmental variables and the second objective function value, and the upper bound is updated with the second objective function value; if the difference between the first objective function value and the second objective function value is greater than the preset value, the iteration is continued; if the difference between the first objective function value and the second objective function value is less than the preset value, the iteration is terminated, the second objective function value is output, and the environmental variables are output as the optimal environmental variables.

8. An active distribution network optimization operation system considering sources and loads, characterized in that: include: A model building module, used to respectively build a soft-opening point model, a static VAR compensator model and a capacitor bank model of an active distribution network; A network reconstruction module, used to establish a robust optimization operation model of the active distribution network through network reconstruction according to the soft opening point model, the static VAR compensator model and the capacitor bank model; The objective function setting module is used to set the objective function for the robust optimization operation model of the active distribution network. A constraint condition determination module, used to determine the constraint conditions of the robust optimization operation model of the active distribution network; The optimal solution solving module is used to linearize the robust optimization operation model of the active distribution network, and solve the optimal value of the objective function according to the constraint conditions to obtain the optimal environmental variables for the operation of the distribution network, and adjust the operating parameters of the optimized distribution network through the optimal environmental variables.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images