A method and device for coordinated control of medium and low voltage distribution networks based on reliability assessment

Abstract

The present invention discloses a method and device for coordinated control of medium and low voltage distribution networks based on reliability assessment, and belongs to the technical field of power system optimization. The method projects the substations in the low voltage distribution network into the feasible space to obtain the feasible domain, and projects and aggregates the flexible resources such as distributed photovoltaic, electric heating loads and distributed energy storage in the low voltage distribution network substations into the constraint conditions of the virtual load nodes in the medium voltage distribution network. Based on this, a medium voltage distribution network model and the corresponding objective function are constructed, and the corresponding switch action sequence of the medium voltage distribution network is obtained after solving the objective function. The method proposes a coordinated reliability improvement strategy for medium and low voltage distribution networks considering virtual load nodes, and represents all the flexible resources of the low voltage distribution network substations through virtual load nodes, which reduces the variable dimension and calculation complexity of the reliability evaluation of medium and low voltage distribution networks, realizes the linkage between the flexible resources of the substations and the switches of the medium voltage distribution network, and effectively improves the reliability of medium and low voltage distribution networks.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization, and specifically relates to a method and device for coordinated control of medium and low voltage distribution networks based on reliability assessment. Background Art

[0002] The increase in the proportion of new energy has affected the operation and development mode of the existing power system. With the large-scale access of distributed power sources and energy storage to residential users, the flexibility resources of low-voltage distribution network stations have become more diverse, including energy storage, solar photovoltaics, electric vehicles, etc. These resources have enhanced the power supply reliability and flexibility of low-voltage distribution network stations, and provided more options for distribution network regulation. However, the randomness and volatility of flexibility resources have also brought unprecedented challenges to the power system. In addition, the different types of resources, large parameter differences, small capacity and large number of resources make it difficult to achieve precise regulation, making the operating environment of the distribution network increasingly complex.

[0003] Traditional methods for improving the reliability of distribution networks mainly rely on increasing investment in grid infrastructure, strengthening grid structure, and switching, but their investment costs are high and the construction period is long. However, as the terminal link of the distribution network, the low-voltage distribution network has huge potential for regulating and controlling its flexibility resources, and its role in improving the reliability of the distribution network has not yet been effectively explored and utilized. How to efficiently aggregate the flexibility resources of the low-voltage distribution network and realize the coordinated regulation of the medium and low voltage distribution networks is the key to improving the reliability of the distribution network.

[0004] At present, the regulation between medium-voltage distribution network and low-voltage distribution network is often relatively independent, lacking an effective coordination mechanism, which makes it difficult for the flexibility resources of low-voltage distribution network to fully realize their potential and match the dispatching needs of medium-voltage distribution network, thus limiting the improvement of the overall reliability of distribution network. At the same time, most of the existing reliability assessment methods only consider the switching action transfer to improve reliability, or consider the regulation of flexibility resources to improve reliability, but a large number of flexibility resources are often distributed on the low-voltage distribution network side, and existing research rarely considers the linkage of medium and low voltage distribution networks to improve the reliability of distribution network. At the same time, traditional reliability research methods rely heavily on iterative distributed optimization algorithms or complex reinforcement learning methods. Due to the complexity of the algorithm itself and the large amount of computing requirements in the iterative process, the computational efficiency is low, and it is difficult to meet the needs of fast response and efficient decision-making in the distribution network. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method and device for coordinated control of medium and low voltage distribution networks based on reliability assessment, so as to solve the problem of lack of coordinated control methods for medium and low voltage distribution networks in the prior art.

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

[0007] The first aspect of the present invention discloses a method for coordinated control of medium and low voltage distribution networks based on reliability assessment, comprising the following steps:

[0008] Obtaining a regulation range of flexibility resources in a low-voltage distribution network, wherein the flexibility resources include electric heating loads, distributed energy storage, and distributed photovoltaics;

[0009] Project the substations in the low-voltage distribution network into the feasible space, and obtain the feasible domain of the substations in the feasible space through the adjustment range of the flexibility resources;

[0010] The substation is introduced as a virtual load node of the medium-voltage distribution network, and a medium-voltage distribution network model and an objective function of the medium-voltage distribution network model are constructed. The objective function of the medium-voltage distribution network model is related to the reliability index, and the reliability index includes the user interruption duration, the power shortage of the power system, and the expected time of power shortage. The reliability index is adjusted by adjusting the switch action sequence in the medium-voltage distribution network, and then the objective function of the medium-voltage distribution network model is solved and optimized to obtain the optimization result. The constraint conditions of the objective function of the medium-voltage distribution network model include node voltage safety constraint, node current safety constraint, virtual load node power constraint, radial constraint and flow constraint. The virtual load node power constraint is the feasible domain of the substation.

[0011] According to the optimization results of the objective function of the medium-voltage distribution network model, the switching action sequence corresponding to the medium-voltage distribution network is obtained.

[0012] A further improvement of the present invention is:

[0013] Preferably, in the step of projecting the substation in the low-voltage distribution network into the feasible space and obtaining the feasible domain of the substation in the feasible space through the adjustment range of the flexibility resource, the process of projecting the substation in the low-voltage distribution network into the feasible space is:

[0014] Initialize the feasible space and construct a mathematical model of the low-voltage distribution network in the feasible space;

[0015] Introducing the adjustment range of flexibility resources as a constraint condition in the mathematical model of low-voltage distribution network;

[0016] Obtain the feasible region based on the constraints.

[0017] Preferably, in the step of obtaining the feasible region based on the constraint conditions, the feasible region of the station area is obtained by an outer cutting method based on the constraint conditions.

[0018] Preferably, in the step of obtaining the feasible region based on the constraint conditions, the specific process of obtaining the feasible region of the station area by the outer cutting method is:

[0019] S231, identifying umbrella constraints from constraint conditions, eliminating non-umbrella constraints, obtaining umbrella constraint conditions, and forming a simplified model and a simplified model objective function;

[0020] S232, in the feasible space, according to the dual problem, solve to obtain a first dual vertex, and obtain a first boundary based on the first dual vertex; according to the dual problem, solve to obtain a second dual vertex, and obtain a second boundary based on the second dual vertex; according to the dual problem, solve to obtain a third dual vertex, and obtain a third boundary based on the third dual vertex;

[0021] S233, according to the dual problem, introduce the umbrella constraint condition to solve the simplified model objective function, update the first dual vertex, the second dual vertex and the third dual vertex in sequence, and correspondingly update the first boundary, the second boundary and the third boundary in sequence;

[0022] S234, repeat S233 until the objective function of the simplified model is 0, and the feasible region of the station area is obtained;

[0023] S235, output the feasible region of the station area.

[0024] Preferably, the process of constructing the medium voltage distribution network model is:

[0025] Obtaining the network topology of the medium voltage distribution network;

[0026] Modeling self-load in network topology;

[0027] Set equipment parameters in the medium voltage distribution network;

[0028] Access the virtual load node to obtain the medium voltage distribution network model.

[0029] Preferably, the objective function of the medium voltage distribution network model is:

[0030]

[0031] Among them, min means minimization, for t The duration of user interruption at the moment, for t The power shortage of the power system at the moment, for t The expected time of low battery at the moment, represent t The binary scalar of each switch state at the moment, represent t The adjustment range of the flexible resource at the moment, or is or, which refers to any one of the three reliability indicators, F () is the reliability function used to optimize the medium voltage distribution network.

[0032] Preferably, the user interruption duration at time t The calculation formula is:

[0033]

[0034] Where: n is the number of nodes, t For the moment, is the set of time intervals of the failover process, is the feeder node set; For Node n The number of users, is a binary variable, Represents a time period.

[0035] Preferably, the power shortage of the power system at time t The calculation formula is:

[0036]

[0037] Where: n is the number of nodes, t For the moment, Representative Node n exist t The sum of the power of all users connected to the feeder at any given moment, is the set of time intervals of the failover process, Represents the time period, is a binary variable, is the feeder node set.

[0038] Preferably, the expected time of insufficient power at time t is The calculation formula is:

[0039]

[0040] Where: t For the moment, is the expected time of insufficient power at time t; p For the time period, q For the number of days, For the p Time period q sky Hourly outage capacity is greater than or equal to The probability of For the p Time period q sky hourly load, X is the probability value, l is the number of time periods in a year, For the p The number of days in the time period, K is the number of hours in the time dimension, for p The load power reduced during the time period.

[0041] The second aspect of the present invention discloses a medium and low voltage distribution network coordinated control device based on reliability assessment, comprising:

[0042] A low voltage distribution network module, which obtains the adjustment range of the flexibility resources in the low voltage distribution network, wherein the flexibility resources include electric heating load, distributed energy storage and distributed photovoltaic;

[0043] The feasible region module projects the substations in the low-voltage distribution network into the feasible space and obtains the feasible region of the substations in the feasible space through the adjustment range of the flexibility resources;

[0044] The medium-voltage distribution network module introduces the substation as a virtual load node of the medium-voltage distribution network, constructs a medium-voltage distribution network model and a medium-voltage distribution network model objective function, wherein the medium-voltage distribution network model objective function is related to a reliability index, and the reliability index includes user interruption duration, power system power shortage, and power shortage time expectation; by adjusting the switch action sequence in the medium-voltage distribution network, the reliability index is adjusted, and then the medium-voltage distribution network model objective function is solved and optimized to obtain an optimization result; the constraints of the medium-voltage distribution network model objective function include node voltage safety constraints, node current safety constraints, virtual load node power constraints, radial constraints, and flow constraints, and the virtual load node power constraints are the feasible domain of the substation;

[0045] The optimization module obtains the switch action sequence corresponding to the medium voltage distribution network according to the optimization result of the objective function of the medium voltage distribution network model.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention discloses a method for coordinated control of medium and low voltage distribution networks based on reliability assessment. The method projects the substations in the low voltage distribution network into the feasible space to obtain the feasible domain, and projects and aggregates the flexible resources such as distributed photovoltaic, electric heating load and distributed energy storage in the low voltage distribution network substations into the medium voltage distribution network as the constraint conditions of the virtual load nodes in the medium voltage distribution network. The method effectively integrates various resources in the low voltage distribution network and improves the observability and controllability of the low voltage distribution network substations. Furthermore, the method constructs a medium voltage distribution network model considering virtual load nodes, and equates the low voltage distribution network substations to the virtual load nodes of the medium voltage distribution network, which greatly reduces the difficulty of dispatching and managing the medium and low voltage distribution networks, and the dispatchers can more easily grasp the overall operating status of the low voltage distribution network to formulate more effective control strategies. This method proposes a coordinated reliability improvement strategy for medium and low voltage distribution networks considering virtual load nodes. Virtual load nodes are used to represent all flexibility resources in the low voltage distribution network substation, which reduces the variable dimension and computational complexity of reliability assessment of medium and low voltage distribution networks, realizes the linkage between substation flexibility resources and medium voltage distribution network switches, and effectively improves the reliability of medium and low voltage distribution networks.

[0048] The present invention also discloses a medium- and low-voltage distribution network coordinated control device based on reliability assessment, which incorporates virtual load nodes in the medium-voltage distribution network, and the virtual load nodes are substations in the low-voltage distribution network. The device not only takes into account the complex dynamic characteristics of the virtual load nodes, such as the law of load fluctuations over time, the flexibility of demand response, and potential distributed energy access, but also comprehensively considers the impact of the switching actions of the medium-voltage distribution network. The power of the virtual load node is introduced as a constraint condition in the objective function optimization process of the medium-voltage distribution network, which can accurately reflect the behavior mode of the substation in the low-voltage distribution network under different working conditions when it is used as a virtual load node. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of a method for coordinated control of medium and low voltage distribution networks based on reliability assessment of the present invention;

[0050] Figure 2 A flowchart for projecting the substations in the low voltage distribution network into the feasible space;

[0051] Figure 3 It is a schematic diagram of the process structure of the feasible domain;

[0052] Among them, (a) is a schematic diagram of the feasible space; (b) is a schematic diagram of adding dual vertices; (c) is a schematic diagram of obtaining the boundary; (d) is a schematic diagram of the final feasible domain;

[0053] Figure 4 The flowchart of the method for obtaining the feasible region of the station area by the outer cutting method;

[0054] Figure 5 Flowchart for constructing a medium voltage distribution network model;

[0055] Figure 6 This is a diagram of a medium and low voltage distribution network coordinated control device based on reliability assessment of the present invention;

[0056] Figure 7 This is a flow chart of a method for coordinated control of medium and low voltage distribution networks based on reliability assessment in Example 1;

[0057] Among them, 1. The first boundary; 2. The second boundary; 3. The third boundary; 4. The feasible domain; 5. The infeasible space. DETAILED DESCRIPTION

[0058] In the following, the terms "first", "second", "third", and "fourth" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first", "second", "third", and "fourth" may explicitly or implicitly include one or more of the features.

[0059] The co-shooting method provided in the embodiment of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiment of the present application does not impose any restrictions on the specific type of the terminal device.

[0060] It should be noted that the terms "first", "second", etc. in the specification and drawings of the present invention are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0061] In the power system, electric energy starts from power plants or large substations, and is first transmitted to substations or distribution stations in various regions through high-voltage transmission networks. At substations or distribution stations, electric energy is reduced to medium-voltage electric energy through transformers and enters the medium-voltage distribution network. The medium-voltage distribution network transmits electric energy to each user's power consumption area or distribution substation, and then is reduced to low-voltage electric energy through step-down transformers. Finally, the low-voltage distribution network transmits low-voltage electric energy to each user terminal to meet the user's electricity demand. The low-voltage distribution network consists of multiple substations. Each substation is an independent power supply unit responsible for providing electricity to users in a specific area. The substation transmits electric energy from the distribution transformer to the end user through a low-voltage distribution line to achieve the distribution and supply of electric energy. Therefore, the medium-voltage distribution network, the low-voltage distribution network and the substation are interconnected and influence each other. However, in the existing technology, the regulation and control between the medium-voltage distribution network and the low-voltage distribution network are often relatively independent, and lack an effective coordination mechanism, which makes it impossible for the substation's flexible resources to fully realize their potential and difficult to match with the dispatching needs of the medium-voltage distribution network, thereby limiting the improvement of the overall reliability of the distribution network.

[0062] In order to solve the above problems, the first aspect of the present invention discloses a method for coordinated control of medium and low voltage distribution networks based on reliability evaluation, the method comprising the following steps:

[0063] S1, obtaining the adjustment range of the flexibility resources in the low-voltage distribution network, wherein the flexibility resources include electric heating load, distributed energy storage and distributed photovoltaic;

[0064] S2, project the substations in the low-voltage distribution network into the feasible space, and obtain the feasible region of the substations in the feasible space through the adjustment range of the flexibility resources;

[0065] S3, introduce the substation as a virtual load node of the medium-voltage distribution network, construct a medium-voltage distribution network model and a medium-voltage distribution network model objective function, wherein the medium-voltage distribution network model objective function is related to a reliability index, wherein the reliability index includes the user interruption duration, the power shortage of the power system, and the expected power shortage time; adjust the reliability index by adjusting the switch action sequence in the medium-voltage distribution network, and then solve and optimize the medium-voltage distribution network model objective function; the constraints of the medium-voltage distribution network model objective function include node voltage safety constraints, node current safety constraints, virtual load node power constraints, radial constraints, and flow constraints, and the virtual load node power constraints are the feasible domain of the substation;

[0066] S4, obtaining a switch action sequence corresponding to the medium voltage distribution network according to the optimization result of the objective function of the medium voltage distribution network model.

[0067] This method introduces the substation in the low-voltage distribution network as a virtual load node into the medium-voltage distribution network, considers the dynamic characteristics of the virtual load node and the topological structure of the medium-voltage distribution network, and designs a medium-voltage distribution network model containing virtual load nodes to reflect its input-output relationship. The input is the upper and lower limits of the virtual load node power as the constraint condition, and the output is the output result of the virtual load node and the switch performing load transfer. A method for optimizing switch action based on reliability indicators is proposed, considering rapid recovery and load transfer under fault conditions, and an optimization algorithm is used to solve the optimal switch action sequence. Finally, the sequential Monte Carlo simulation method is used to evaluate the reliability of the medium-voltage distribution network.

[0068] It should be understood that the flexibility resources in the present invention refer to resources that can increase the flexibility, elasticity and flexibility of the energy supply and demand power system and serve the dynamic supply and demand balance of the energy-consuming power system. Such as coal-fired power, hydropower, nuclear power, gas power, wind power, photovoltaics, industrial loads, commercial loads and public facility loads. Furthermore, the flexibility resources in the present invention include electric heating loads, distributed energy storage and distributed photovoltaics.

[0069] Furthermore, it is necessary to determine the adjustment range of each flexibility resource. The specific flexibility resources and the corresponding adjustable ranges are as follows:

[0070] (1) Electric heating load

[0071] When the electric heating load is large in volume and scale but relatively dispersed in geographical location, the dispersed electric heating load is divided according to geographical location, and the electric heating loads in different substations in the low-voltage distribution network are aggregated into an organic whole to achieve cluster aggregation and regulation of the electric heating load, and modeling is performed based on the characteristics of the electric heating load after clustering.

[0072] The electric heating load has the ability to quickly respond to dispatch instructions and can increase or decrease power consumption in a short period of time while ensuring user experience. The constructed models include a single electric heating load model and an electric heating load aggregation model.

[0073] The calculation formula of a single electric heating load model is shown in the following formula (1):

[0074] (1)

[0075] In the formula, j is the number of electric heating loads, T is the temperature, for t Moment j Indoor temperature of electric heating load, is the first j Indoor temperature of electric heating load, For the j The change value of outdoor temperature under normal operation of electric heating load is For outdoor, For the j The equivalent thermal resistance of an electric heating load, For the j The rated cooling power or heating power of each electric heating load, c is the electric heating load, for t The outdoor temperature at the time, For the j The energy efficiency ratio of an electric heating load is for t Moment j The operating status of an electric heating load.

[0076] in, t Moment j The operating status of the electric heating load is shown in the following formula (2): when the indoor temperature exceeds the set temperature upper limit, the electric heating load is turned off, otherwise the electric heating load remains in operation.

[0077] (2)

[0078] Among them, 0 means the electric heating load is turned off, 1 means the electric heating load is in operation, and otherwise means other situations. In other situations, the status remains unchanged. is the first j Indoor temperature of electric heating load, For the j The upper limit of indoor temperature under normal operating conditions of an electric heating load.

[0079] No. j The upper limit constraint of the indoor temperature under normal operation of an electric heating load is as follows (3):

[0080] (3)

[0081] in, For the j The dead zone width of the electric heating load.

[0082] No. j The change value of indoor temperature under normal operation of electric heating load The calculation formula is:

[0083] (4)

[0084] Where exp( ) represents the natural exponential function, t For the moment, For the j The equivalent thermal resistance of an electric heating load, is the equivalent heat capacity of the jth electric heating load.

[0085] t Moment j Indoor temperature of electric heating load The constraint is as follows:

[0086] (5)

[0087] Where: To meet the minimum temperature for user comfort, The maximum temperature that satisfies user comfort.

[0088] The electric heating load aggregation model is shown in the following equations (6) to (10):

[0089] (6)

[0090] (7)

[0091] (8)

[0092] (9)

[0093] (10)

[0094] In the formula, j is the number of electric heating units, for t The aggregate power of electric heating load at all times; For the j Operating power of electric heating load; for t Moment j The operating status of each electric heating load; For the j The equivalent heat capacity of an electric heating load; The total duration of the electric heating load being turned on. The total duration of the electric heating load being turned off, on means on, off means off. For the j The rated cooling power or heating power of each electric heating load, c represents the heating electricity load; For the j The equivalent thermal resistance of an electric heating load; For the j Output power of each electric heating load when it is turned on; for t Moment jIndoor temperature of each electric heating load; for t The outdoor temperature at the time, a Represents the outdoors; For the j The dead zone width of an electric heating load, ln( ) is the natural logarithmic function.

[0095] (2) Distributed energy storage

[0096] Distributed energy storage users earn profits through low storage and high discharge operation strategies, so their adjustment actions are very sensitive to the price signals or incentive signals released by the power grid company, and there is almost no uncertainty. The power range of distributed energy storage during charging is shown in formula (11), and the power range during discharge is shown in formula (12).

[0097] (11)

[0098] (12)

[0099] Where: for t Moment i The actual power of a distributed energy storage device, BES represents distributed energy storage, is the maximum charging power of distributed energy storage, ch represents charging; is the maximum discharge power of distributed energy storage, and di represents discharge.

[0100] (3) Distributed photovoltaic

[0101] In actual applications, the adjustable power lower limit and adjustable power upper limit of distributed photovoltaics may be dynamically adjusted according to grid requirements, the charging and discharging status of the energy storage system, and other factors. t Actual power generation of distributed photovoltaic at the moment It will also be affected by weather conditions, so it is necessary to estimate future power generation capacity through a prediction model. Based on this, the adjustable range constraint of distributed photovoltaic power generation is shown in formula (13), and the power generation constraint is shown in formula (14).

[0102] (13)

[0103] (14)

[0104] In the formula, for t The maximum power generation of distributed photovoltaic at the moment, PV Indicates distributed photovoltaics. The maximum power generation is determined by weather, light and other conditions. fort Adjustable power limit at all times, for t Adjustable power limit at all times, for t The set power generation at the time, set Indicates settings.

[0105] In some implementation schemes of the present invention, a substation in the low-voltage distribution network is projected into the feasible space in S2, and the feasible domain of the substation is obtained in the feasible space through the adjustment range of the flexibility resources, specifically through the outer cutting method.

[0106] It should be noted that, according to the polyhedral form of the feasible domain, it is necessary to find all the dual vertices of the dual space in order to characterize the feasible domain. However, the redundant constraints in the power system make the number of variables in the dual problem too large. Even a regional distribution network with a simple structure is difficult to retrieve all the dual vertices in the high-dimensional space by traversing. In order to avoid the problem of repeated boundary generation and missing dual vertices in the traditional feasible domain characterization method of searching from the inside out, the present invention adopts the following method: Figure 2 The feasible domain characterization method based on the outer cutting method shown in Figure 2 and Figure 3 , the specific method includes the following steps:

[0107] S21, such as Figure 3 As shown in Figure (a), initialize a sufficiently large feasible space ,like Figure 3 The rectangle shown by the dotted line in the middle constructs the mathematical model of the low-voltage distribution network in the feasible space. is the current feasible space, H is the index of the feasible space; and is the current feasible space The boundary parameters contain the boundary information of the feasible domain 4 convex polytopes. x is a variable.

[0108] S22, the adjustment range of the flexibility resources is incorporated into the mathematical model of the low-voltage distribution network as a constraint condition, and the constraint condition is input into the subsequent feasible domain 4 characterization process as a part of the mathematical model of the medium-voltage distribution network.

[0109] S23, obtaining a feasible domain 4 based on the constraint conditions.

[0110] As a preferred solution, see Figure 3 and Figure 4 In S23, the specific process of obtaining the feasible region 4 based on the constraint conditions is:

[0111] S231, identifying umbrella constraints in the mathematical model of the low-voltage distribution network from the constraint conditions, eliminating non-umbrella constraints, and forming a simplified model and a simplified model objective function.

[0112] S232, such as Figure 3 As shown in Figures (b) and (c), in the feasible space, according to the dual problem, the first dual vertex E is solved, and the first boundary 1 is obtained based on the first dual vertex E; according to the dual problem, the second dual vertex F is solved, and the second boundary 2 is obtained based on the second dual vertex F; according to the dual problem, the third dual vertex G is solved, and the third boundary 3 is obtained based on the third dual vertex G.

[0113] S233, according to the dual problem, introduce the umbrella constraint condition to solve the simplified model objective function, update the first dual vertex E, the second dual vertex F and the third dual vertex G in sequence, and correspondingly update the first boundary 1, the second boundary 2 and the third boundary 3 in sequence.

[0114] S234, such as Figure 3 As shown in Figure (d), S233 is repeated until the objective function of the simplified model is 0, and the feasible domain 4 of the final station area is obtained, and the shaded part outside the feasible domain is the infeasible space 5.

[0115] S235, outputting the feasible domain 4 of the substation, the specific process is to output the current feasible space as the feasible domain 4, and the power space obtained after the projection of the feasible domain 4 is the power range of the virtual load node.

[0116] In the above process, according to the polyhedral form definition of feasible domain 4, each dual vertex generates a boundary of feasible domain 4, that is, the dual vertex of the dual space is found in the feasible space. , a boundary of feasible region 4 is generated through the dual vertex ,in, for The transposed matrix of A and are two coefficient matrices, x is a variable; according to the simplified model and the current feasible space, the umbrella constraint identification algorithm is adopted, and according to the dual problem, a mixed integer linear programming is obtained, and a dual vertex is obtained after introducing the umbrella constraint solution. During the updating process, if the simplified model objective function value is greater than the allowable deviation, the boundary corresponding to the feasible cut is generated according to the dual vertex, and the boundary is updated to the current feasible space, and step S233 is repeated until the simplified model objective function is satisfied to be 0.

[0117] During the updating process, each dual vertex is the farthest point in the current feasible space.

[0118] In some embodiments of the present invention, see Figure 5,In S3, the medium-voltage distribution network collaborative reliability improvement control strategy of ,the virtual load node is considered, and the medium-voltage distribution network model ,is constructed, which specifically includes the following processes:

[0119] S31, obtaining a network topology structure of a medium voltage distribution network.

[0120] S32, self-load modeling is performed in the network topology structure, and the self-load is used as an input parameter or variable in the medium voltage distribution network model.

[0121] S33, setting equipment parameters in the medium voltage distribution network, and specific equipment may be transformers, switches, lines, etc.

[0122] S34, access the virtual load node to complete the establishment of the medium voltage distribution network model.

[0123] In S3, the medium-voltage distribution network is used as the background, the reliability index is used as the objective function of the medium-voltage distribution network model, and the corresponding constraints are set for the medium-voltage distribution network model. In this process, a medium-voltage distribution network reliability evaluation method considering the flexibility resources of the substation of the low-voltage distribution network is established based on the sequential Monte Carlo simulation method. Specifically, the power range of the virtual load node is considered in the constraints of the medium-voltage distribution network model. Taking the minimum of any of the three reliability indicators of the objective function of the medium-voltage distribution network model as the optimization target, the objective function of the medium-voltage distribution network model is established, and the OpenDSS (Open Distributed System Simulator, an open source power system distribution network) analysis software is used to establish the objective function and solve it. Therefore, the objective function of the medium-voltage distribution network model can be expressed as:

[0124] (15)

[0125] In the formula, min means minimization, is the user interruption duration at time t, is the power shortage of the power system at time t, for t The expected time of low battery at the moment, and are two decision variables, represent t The binary scalar of each switch state at the moment, represent t The adjustment range of the flexibility resource at the moment, switch is a switch, RE As a flexible resource, by utilizing the flexible resources in the low-voltage distribution network, the operating reliability of the medium-voltage distribution network can be improved, and the power outage time of users in the fault area can be minimized. F() is a reliability function used to optimize the medium voltage distribution network. Its input parameters include the switch status and the adjustment range of flexibility resources, and the output is to minimize the selected reliability index.

[0126] Among the above reliability indicators, one of the reliability indicators can be selected to be minimized. The specific reliability indicator selected depends on the actual application scenario and requirements. The solution algorithm is an optimization algorithm or a heuristic algorithm.

[0127] After the medium-voltage distribution network model and the objective function of the medium-voltage distribution network model are established, fault simulation is carried out, and the optimization algorithm is used to solve the optimal switch action sequence considering the rapid recovery and load transfer under fault conditions. During the solution process, the objective function of the medium-voltage distribution network model is minimized by adjusting the switch action sequence in the medium-voltage distribution network and changing the value of the reliability index. When the objective function of the medium-voltage distribution network model is minimized, the optimal switch action sequence of the medium-voltage distribution network can be obtained.

[0128] Specifically, the calculation formulas for the three reliability indicators are as follows:

[0129] (1) , t The user interruption duration indicator at a certain moment is defined as: time period The average power recovery time of feeder users due to extreme faults is expressed as follows:

[0130] (16)

[0131] Where: n is the number of nodes, A collection of time intervals representing the failover process, is the set of feeder nodes, For Node n The number of users, is a binary variable describing a specific moment t node n Load interruption situation, when When the node n exist t Always in a power-off state, otherwise, the node n In load recovery state, Represents the time period, which is usually a constant. In the present invention, the time period is set to 1 hour.

[0132] (2) , t The power shortage of the power system at the moment. The power shortage index of the power system is used to evaluate the total power loss of the power system under extreme fault scenarios. Its mathematical expression is as follows:

[0133] (17)

[0134] Where: Representative Node n exist t The sum of the power of all users connected to the feeder at any given moment, is the set of time intervals of the failover process, Represents the time period, usually a constant; is the feeder node set.

[0135] (3) , t The expected power shortage time at the moment is the expected value of the power system reducing power supply to users due to the forced shutdown of the unit, and it comprehensively expresses the number of power outages, average duration and average power outage power. The calculation formula is:

[0136] (18)

[0137] Where: is the expected time of insufficient power at time t, p is the time period, q is the number of days, For the p Time period q sky Hourly outage capacity is greater than or equal to The probability of For the p Time period q sky hourly load, X is the probability value, l is the number of time periods in a year, For the p The number of days in the time period, K is the number of hours in the time dimension, for p The load power reduced during the time period.

[0138] Specifically, the constraints of the objective function include node voltage safety constraints, node current safety constraints, virtual load node power constraints, radial constraints and power flow constraints; in the public distribution network, the calculation formula of the corresponding constraints is:

[0139] (1) The calculation formula for node voltage safety constraint is:

[0140] (19)

[0141] Where: For Node n The lower limit of the voltage allowed, For Node n The upper limit of the voltage allowed, For Node n Voltage value.

[0142] (2) The calculation formula for node current safety constraint is:

[0143] (20)

[0144] Where: for k The current value of the line; for k The upper limit of current allowed by the line.

[0145] (3) The calculation formula for the virtual load node power constraint is:

[0146] (twenty one)

[0147] Where: is a universal quantifier, is the set of all time points; For flexibility resource projection nodes t The upper limit of power regulation at the moment, For flexibility resource projection nodes t The power regulation lower limit at the moment, is the power of the flexibility resource projection node at time t, is the upper limit of power per unit time, is the lower limit of power per unit time, The power of flexible resources projected at all times, is the power prediction of the node under regulation conditions, For flexibility resource projection nodes t The power of the previous moment.

[0148] (4) Radial constraints

[0149] During the distribution network reconstruction process, it is necessary to ensure that the distribution network is in a radial topology state. The calculation formula is:

[0150] (twenty two)

[0151] Where: Represents the set of section switches in the feeder; sw is the abbreviation of switch. For a specific time t Next, from the node n To Node m The connection status, is the set of all time points, Represents a static state, which is the ideal state of the topology.

[0152] (5) Power flow constraints

[0153] Establish a sequential reconstruction model in a rectangular coordinate system, assuming Feeder segment uv status, where u and v are the two end nodes of the feeder segment, both of which belong to n Nodes A collection of For Node u The plural form of voltage, Indicates the plural form, where and Separate nodes u The real and imaginary parts of the complex form of the voltage, r is an imaginary unit. So, at every moment Down, is a set consisting of all time points, nodes , the power balance equation can be expressed as follows:

[0154] (twenty three)

[0155] (twenty four)

[0156] Where: Feeder segment The conductivity, Feeder segment The electrical susceptance, Feeder segment The parallel susceptance, sh Indicates parallel connection, For Node Active power transmitted by the upstream feeder, up For upstream, node Reactive power transmitted by the upstream feeder, is the active power of distributed generation, Active power of the power system for energy storage, is the reactive power of distributed generation, Reactive power of power systems for energy storage, is the node at time t With Node The connectivity status, for t Time feeder segment uv The active power, for tTime feeder segment uv The reactive power, for t Time Node The reactive power, and Node u exist t The real and imaginary parts of the complex form of the voltage at the moment, and Respectively in t Time Node v The real and imaginary parts of the complex form of the voltage, for t Time Node The power, for t Time Node The voltage, Represents the node at time t Connected nodes.

[0157] See also Figure 6 The second aspect of the present invention discloses a medium and low voltage distribution network coordinated control device based on reliability assessment, comprising:

[0158] A low voltage distribution network module, which obtains the adjustment range of the flexibility resources in the low voltage distribution network, wherein the flexibility resources include electric heating load, distributed energy storage and distributed photovoltaic;

[0159] The feasible region module projects the substations in the low-voltage distribution network into the feasible space and obtains the feasible region of the substations in the feasible space through the adjustment range of the flexibility resources;

[0160] The medium-voltage distribution network module introduces the substation as a virtual load node of the medium-voltage distribution network, constructs a medium-voltage distribution network model and a medium-voltage distribution network model objective function, wherein the medium-voltage distribution network model objective function is related to a reliability index, and the reliability index includes the user interruption duration, the power shortage of the power system, and the expected time of power shortage; by adjusting the switch action sequence in the medium-voltage distribution network, the reliability index is adjusted, and then the medium-voltage distribution network model objective function is solved and optimized; the constraints of the medium-voltage distribution network model objective function include node voltage safety constraints, node current safety constraints, virtual load node power constraints, radial constraints, and flow constraints, and the virtual load node power constraints are the feasible domain of the substation;

[0161] The optimization module obtains the switch action sequence corresponding to the medium voltage distribution network according to the optimization result of the objective function of the medium voltage distribution network model.

[0162] The following is further described in conjunction with specific embodiments.

[0163] Example 1

[0164] See also Figure 7 , is a specific embodiment of a medium and low voltage distribution network coordinated control method based on reliability evaluation of the present invention, comprising the following steps:

[0165] S1, obtaining the adjustment range of the flexibility resources in the low-voltage distribution network, wherein the flexibility resources include electric heating load, distributed energy storage and distributed photovoltaic;

[0166] S2, project the substation in the low-voltage distribution network into the feasible space, and obtain the feasible region of the substation in the feasible space through the adjustment range of the flexibility resources 4;

[0167] S3, introduce the substation as a virtual load node of the medium-voltage distribution network, build a medium-voltage distribution network model and the objective function of the medium-voltage distribution network model. The specific process of building the medium-voltage distribution network model includes: confirming the network topology of the medium-voltage distribution network, modeling the load in the medium-voltage distribution network, setting the equipment parameters in the medium-voltage distribution network, introducing virtual load nodes in the medium-voltage distribution network, and then performing normal scenario simulation, fault simulation and recovery process simulation; by adjusting the switch action sequence in the medium-voltage distribution network, adjusting the reliability index, and then solving the optimization of the medium-voltage distribution network model objective function, the optimization result is obtained, which is the optimized switch action sequence;

[0168] S4, obtain the switch action sequence corresponding to the medium voltage distribution network according to the optimization result of the objective function, and end the whole process.

[0169] In order to further improve the operating efficiency and reliability of the entire medium-voltage distribution network, the present invention proposes a switch action strategy based on reliability optimization. The core of this strategy is to dynamically adjust the switch state in the medium-voltage distribution network through an intelligent algorithm to achieve the purpose of optimizing power flow distribution, reducing the risk of line overload, and improving power supply quality. The formulation of the strategy fully considers multi-dimensional information such as the predicted changes in load, the changes in the topological structure caused by the switching action of the medium-voltage distribution network, and the uncertainty of the output of flexible resources, ensuring that in actual operation, it can not only respond quickly to emergencies, but also continuously optimize the steady-state performance of the power system.

[0170] In order to verify the effectiveness of the proposed model, strategy and algorithm, the present invention adopts the sequential Monte Carlo simulation method to conduct a comprehensive and in-depth evaluation of the reliability of the medium-voltage distribution network, and optimizes the switch action sequence based on the evaluation results. This method can accurately quantify the reliability indicators of the power system under various conditions, such as power supply availability, failure rate, average power outage time, etc., by simulating a large number of possible power system operation scenarios, including normal operation, fault occurrence and recovery process. Through comparative analysis, the specific contribution of optimization measures to improving the overall performance of the medium-voltage distribution network can be intuitively demonstrated, providing strong data support for subsequent power grid planning, operation and maintenance.

[0171] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A coordinated control method for medium and low voltage distribution networks based on reliability assessment, characterized in that: The following steps are involved: Obtaining a regulation range of flexibility resources in a low-voltage distribution network, wherein the flexibility resources include electric heating loads, distributed energy storage, and distributed photovoltaics; Project the substations in the low-voltage distribution network into the feasible space, and obtain the feasible domain of the substations in the feasible space through the adjustment range of the flexibility resources; The substation is introduced as a virtual load node of the medium-voltage distribution network, and a medium-voltage distribution network model and an objective function of the medium-voltage distribution network model are constructed. The objective function of the medium-voltage distribution network model is related to the reliability index, and the reliability index includes the user interruption duration, the power shortage of the power system, and the expected time of power shortage. The reliability index is adjusted by adjusting the switch action sequence in the medium-voltage distribution network, and then the objective function of the medium-voltage distribution network model is solved and optimized to obtain the optimization result. The constraint conditions of the objective function of the medium-voltage distribution network model include node voltage safety constraint, node current safety constraint, virtual load node power constraint, radial constraint and flow constraint. The virtual load node power constraint is the feasible domain of the substation. The objective function of the medium voltage distribution network model is: Among them, min means minimization, for t The duration of user interruption at the moment, for t The power shortage of the power system at the moment, for t The expected time of low battery at the moment, represent t The binary scalar of each switch state at the moment, represent t The adjustment range of the flexible resource at the moment, or is or, which refers to any one of the three reliability indicators, F () is the reliability function used to optimize the medium voltage distribution network; According to the optimization results of the objective function of the medium-voltage distribution network model, the switching action sequence corresponding to the medium-voltage distribution network is obtained.

2. According to a method for coordinated control of medium and low voltage distribution networks based on reliability assessment according to claim 1, it is characterized in that: In the step of projecting the substation in the low-voltage distribution network into the feasible space and obtaining the feasible domain of the substation in the feasible space through the adjustment range of the flexibility resource, the process of projecting the substation in the low-voltage distribution network into the feasible space is: Initialize the feasible space and construct a mathematical model of the low-voltage distribution network in the feasible space; Introducing the adjustment range of flexibility resources as a constraint condition in the mathematical model of low-voltage distribution network; Obtain the feasible region based on the constraints.

3. A method for coordinated control of medium and low voltage distribution networks based on reliability assessment according to claim 2, characterized in that: In the step of obtaining the feasible region based on the constraint conditions, the feasible region of the station area is obtained by an outer cutting method based on the constraint conditions.

4. A method for coordinated control of medium and low voltage distribution networks based on reliability assessment according to claim 3, characterized in that: In the step of obtaining the feasible region based on the constraint conditions, the specific process of obtaining the feasible region of the station area by the outer cutting method is: S231, identifying umbrella constraints from constraint conditions, eliminating non-umbrella constraints, obtaining umbrella constraint conditions, and forming a simplified model and a simplified model objective function; S232, in the feasible space, according to the dual problem, solve to obtain a first dual vertex, and obtain a first boundary based on the first dual vertex; according to the dual problem, solve to obtain a second dual vertex, and obtain a second boundary based on the second dual vertex; according to the dual problem, solve to obtain a third dual vertex, and obtain a third boundary based on the third dual vertex; S233, according to the dual problem, introduce the umbrella constraint condition to solve the simplified model objective function, update the first dual vertex, the second dual vertex and the third dual vertex in sequence, and correspondingly update the first boundary, the second boundary and the third boundary in sequence; S234, repeat S233 until the objective function of the simplified model is 0, and the feasible region of the station area is obtained; S235, output the feasible region of the station area.

5. The method for coordinated control of medium and low voltage distribution networks based on reliability assessment according to claim 1 is characterized in that: The process of constructing the medium voltage distribution network model is as follows: Obtaining the network topology of the medium voltage distribution network; Modeling self-load in network topology; Set equipment parameters in the medium voltage distribution network; Access the virtual load node to obtain the medium voltage distribution network model.

6. A method for coordinated control of medium and low voltage distribution networks based on reliability assessment according to claim 1, characterized in that: User interruption duration at time t The calculation formula is: （16） Where: n is the number of nodes, t For the moment, is the set of time intervals of the failover process, is the feeder node set; For Node n The number of users, is a binary variable, Represents a time period.

7. A method for coordinated control of medium and low voltage distribution networks based on reliability assessment according to claim 1, characterized in that: The power system power shortage at time t The calculation formula is: Where: n is the number of nodes, t For the moment, Representative Node n exist t The sum of the power of all users connected to the feeder at any given moment, is the set of time intervals of the failover process, Represents the time period, is a binary variable, is the feeder node set.

8. The method for coordinated control of medium and low voltage distribution networks based on reliability assessment according to claim 1 is characterized in that: Expected low battery time at time t The calculation formula is: Where: t For the moment, is the expected time of insufficient power at time t; p For the time period, q For the number of days, For the p Time period q sky Hourly outage capacity is greater than or equal to The probability of For the p Time period q sky hourly load, X is the probability value, l is the number of time periods in a year, For the p The number of days in the time period, K is the number of hours in the time dimension, for p The load power reduced during the time period.

9. A medium and low voltage distribution network coordinated control device based on reliability assessment, characterized in that: include: A low voltage distribution network module, which obtains the adjustment range of the flexibility resources in the low voltage distribution network, wherein the flexibility resources include electric heating load, distributed energy storage and distributed photovoltaic; The feasible region module projects the substations in the low-voltage distribution network into the feasible space and obtains the feasible region of the substations in the feasible space through the adjustment range of the flexibility resources; The medium-voltage distribution network module introduces the substation as a virtual load node of the medium-voltage distribution network, constructs a medium-voltage distribution network model and a medium-voltage distribution network model objective function, wherein the medium-voltage distribution network model objective function is related to a reliability index, and the reliability index includes user interruption duration, power system power shortage, and power shortage time expectation; by adjusting the switch action sequence in the medium-voltage distribution network, the reliability index is adjusted, and then the medium-voltage distribution network model objective function is solved and optimized to obtain an optimization result; the constraints of the medium-voltage distribution network model objective function include node voltage safety constraints, node current safety constraints, virtual load node power constraints, radial constraints, and flow constraints, and the virtual load node power constraints are the feasible domain of the substation; The objective function of the medium voltage distribution network model is: Among them, min means minimization, for t The duration of user interruption at the moment, for t The power shortage of the power system at the moment, for t The expected time of low battery at the moment, represent t The binary scalar of each switch state at the moment, represent t The adjustment range of the flexible resource at the moment, or is or, which refers to any one of the three reliability indicators, F () is the reliability function used to optimize the medium voltage distribution network; The optimization module obtains the switch action sequence corresponding to the medium voltage distribution network according to the optimization result of the objective function of the medium voltage distribution network model.

Images