CN115759400A - Method, device and equipment for planning and evaluating secondary equipment of power distribution network - Google Patents
Method, device and equipment for planning and evaluating secondary equipment of power distribution network Download PDFInfo
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Abstract
The application provides a method, a device and equipment for planning and evaluating secondary equipment of a power distribution network, and the method comprises the following steps: acquiring first data, wherein the first data are acquired by an acquisition terminal, and the first data comprise cost data, equipment parameters and the total number of lines to be built; constructing a refined collaborative planning model based on the power distribution network visibility and performance control indexes; and constructing a multi-dimensional layered evaluation model based on the refined collaborative planning model by combining an index system, wherein the index system comprises an economic index, a low-carbon index and a business index. The energy control and energy control sightseeing performance of the power distribution network is considered comprehensively, and the accuracy of planning and evaluating the secondary equipment of the power distribution network is improved.
Description
Technical Field
The invention relates to the technical field of carbon emission reduction coordination optimization, in particular to a method, a device and equipment for planning and evaluating secondary equipment of a power distribution network.
Background
The configuration planning and evaluation method of the secondary equipment of the power distribution network comprises the following steps of configuring the secondary equipment according to conditions such as power supply area division conditions and the like by referring to relevant guide rules; or determining economic/reliability benefits brought by the configuration of the secondary equipment to the power distribution network, and establishing a mathematical model to perform optimal configuration of the secondary equipment.
The existing planning method lacks consideration on the energy control visibility of the power distribution network, is difficult to adapt to observation and control requirements of a novel power distribution network, is only limited to the configuration of various power distribution terminals, does not consider other secondary equipment, and is not comprehensive enough in evaluation and not accurate enough in evaluation.
Disclosure of Invention
The invention aims to provide a method, a device and equipment for planning and evaluating secondary equipment of a power distribution network, which comprehensively consider the energy control and energy control visibility of the power distribution network and improve the accuracy of planning and evaluating the secondary equipment of the power distribution network.
According to one aspect of the application, a method for planning and evaluating secondary equipment of a power distribution network is provided, which comprises the following steps: acquiring first data, wherein the first data are acquired by an acquisition terminal, and the first data comprise cost data, equipment parameters and the total number of lines to be built; constructing a refined collaborative planning model based on the power distribution network visibility and performance control indexes; and constructing a multi-dimensional layered evaluation model based on the refined collaborative planning model by combining an index system, wherein the index system comprises an economic index, a low-carbon index and a business index.
According to another aspect of the application, a power distribution network secondary equipment planning and evaluating device is provided, including: the system comprises an acquisition module, a data acquisition module and a data processing module, wherein the acquisition module is used for acquiring first data, the first data is acquired by an acquisition terminal, and the first data comprises cost data, equipment parameters and the total number of lines to be built; the planning module is used for constructing a refined collaborative planning model based on the power distribution network visibility and controllability indexes; and the evaluation module is used for constructing a multi-dimensional layered evaluation model by combining an index system based on the refined collaborative planning model, wherein the index system comprises an economic index, a low-carbon index and a business index.
According to another aspect of the present application, there is provided an electronic device, including a processor, and a memory and a network interface connected to the processor; the network interface is connected with a nonvolatile memory in the server; and when the processor runs, the computer program is called from the nonvolatile memory through the network interface, and the computer program is run through the memory, so that the planning and evaluation method of the secondary equipment of the power distribution network is executed.
According to the technical scheme, the planning and evaluation method and device for the secondary equipment of the power distribution network are provided, so that the economy, the low carbon performance and the energy control and energy visibility are comprehensively considered, and the configuration scheme of the secondary equipment achieves the maximization of the comprehensive benefit; the mathematical relation between the energy control and energy control attractiveness and the low carbon performance and economy of the power distribution network is quantized, and the refinement of a planning model is improved; the non-electric quantity is brought into the energy-control observability analysis range, the types of secondary equipment which can be solved by the model are increased, and selectable weight coefficients are provided in the planning and evaluation links, so that the method is suitable for various situations in the actual planning of the power distribution network.
Drawings
Fig. 1 shows a flow chart of a method for planning and evaluating secondary equipment of a power distribution network according to the present application;
FIG. 2 is a schematic diagram illustrating a business indicator according to an embodiment of the application;
FIG. 3 shows a schematic diagram of a computing device according to an embodiment of the application.
Detailed Description
In the following, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
Exemplary method
Fig. 1 shows a flowchart of a power distribution network secondary equipment planning and evaluation method according to an embodiment of the present application.
As shown in fig. 1, the method for planning and evaluating the secondary equipment of the power distribution network according to the embodiment of the present application specifically includes the following steps: s110, acquiring first data, wherein the first data is acquired by an acquisition terminal and comprises cost data, equipment parameters and the total number of lines to be built; s120, constructing a refined collaborative planning model based on the power distribution network visibility and controllability indexes; and S130, constructing a multi-dimensional layered evaluation model by combining an index system based on the refined collaborative planning model, wherein the index system comprises an economic index, a low-carbon index and a business index.
Hereinafter, each step will be described in detail.
And S110, acquiring first data, wherein the first data is acquired by an acquisition terminal, and the first data comprises cost data, equipment parameters and the total number of lines to be built.
And S120, constructing a refined collaborative planning model based on the power distribution network visibility and performance control indexes.
Considering the defects of the existing research and the actual observation needs of the power distribution network, the visibility of the power distribution network is defined as follows: the value of the state variable can be determined by direct measurement of the state variable/indirect calculation/state estimation of the remaining variables, and its error from the actual value of the state variable is within an allowable range. The mathematical expression of this definition is shown in formula (1):
in the formula: g is a real number space; x (t) is a state vector at the time t; u (t) is a control vector at the time t; y (t) is an output vector at the time t; x is a radical of a fluorine atom m (t) is a state vector obtained by measurement at time t; y is m (t) is an output vector obtained by measurement at time t; f (-) is the mapping relation between the measured state vector and the output vector and the actual state vector; epsilon is an observable error; | | · | | is a vector modulo norm; epsilon max To enable viewing of the error tolerance. When the mapping relation f (-) can uniquely determine the actual state variable and the error is within the allowable range, the state variable is said to be observable at the moment t; and all state variables corresponding to the nodes/equipment/power distribution network can be observed at the time t, so that the nodes/equipment/power distribution network can be observed at the time t.
The energy controllability of the power distribution network is defined as: the ability to transition the distribution network from any initial state to a terminal state of safe and stable economic operation within a specified time interval. The mathematical expression is shown as formula (2):
in the formula: g (-) is the mapping relation between the control vector and the state vector, and reflects the change condition of the state vector under the influence of the specific control vector; control u (t) as time tVector quantity; t is t 0 Is the state transition time; t is t max For a specified time interval; x is the number of req The method is a safe and stable termination state of economic operation. And for any state vector, if a corresponding mapping g (-) exists, so that the power distribution network can be transferred from an initial state to a termination state of safe and stable economic operation within a specified time interval, the power distribution network is called to be controllable.
In an actual distribution network, errors exist in configured measurement equipment and pseudo measurement (according to related researches, measurement results can be considered to be in accordance with normal distribution, and the errors between different measurements have no mutual influence). Therefore, the observable error ε fluctuates due to measurement and pseudo-measurement errors, and the state variables are not necessarily observable at all times. To measure the observability of the state variable in a long time scale, an observability rate index r of the state variable is defined O The calculation formula is shown in formula (3):
in the formula: t is t O Total time for state variables to be observable; t is t total Is the total time to implement the observed behavior on the state variable.
When the visibility rate of the state variables/nodes/equipment/power distribution network is higher than a given requirement, the visibility requirement is met. Further defining the comprehensive energy observation rate r sigma of the power distribution network, wherein the calculation formula is shown as formula (4):
in the formula: r is a radical of hydrogen Ok Is the visibility rate of node k; r is a radical of hydrogen Oj Is the visibility rate of the device j; m is the total number of nodes; n is the total number of devices.
According to the needs of actual services, further constructing a mapping set of primary equipment/basic indexes-state variables-controllable energy and observation requirements-secondary equipment, wherein the mapping set is in the form of:
and refining the collaborative planning model, wherein the mathematical expression of the model is as follows:
in the formula: c inv The investment cost; c R For power shortage costs; omega is a carbon emission penalty coefficient; c env Carbon emissions; xi is an observable excitation coefficient; c obs Is an observable comprehensive index; l is the total number of lines to be built; x is the number of i If the variable is 0-1, indicating whether the ith line is newly established or not; c. C l Expanding the cost for a line unit; l i Is the length of line i; n is the total number of equipment, and if the three-phase unbalanced power grid is adopted, the number of equipment related to the traditional variable is three times that of the three-phase balanced power grid; c. C n For situational awareness equipment (measurement) costs; y is j A variable of 0-1, indicating whether a jth situation awareness device (measurement) is configured; n is j Number of situation awareness devices (measurements); n is a radical of t Is the total number of time periods; e t An undersupply expectation for a period t; delta E t An under-supply expectation for a reduced t-period after configuration of the secondary device; c t The unit power shortage loss cost at the moment t; c. C carbon Carbon emission of equipment (carbon emission reduction if the carbon emission is negative); m is the total number of nodes; r is a radical of hydrogen ∑ The comprehensive energy observation rate of the power distribution network is obtained.
In this model, x i 、l i And y j 、n j C in the objective function as a decision variable inv 、C R 、C env And C obs Are directly or indirectly influenced by decision variables. Wherein, C inv Directly calculating decision variables and unit cost; c R In the calculation formula of (1), Δ E t Influenced by node observation rate and equipment fault misjudgment rate, and secondary equipment decision variable y j 、n j Related to; c env In the formula (c) carbon And a secondary equipment decision variable y j 、n j (ii) related; c obs In the formula (1), r ∑ And a secondary equipment decision variable y j 、n j It is related.
In particular, of the dependent variables of the planning model, Δ E t And c carbon Influenced by node observation rate and equipment fault misjudgment rate. Wherein, the equipment fault misjudgment rate is delta E t And c carbon The influence of (d) can be directly calculated as:
in the formula: t is a unit of F Is the equipment failure duration; delta T F Reduced equipment failure duration for timely maintenance; μ is the carbon emission per unit power generation amount.
For node visibility rate, the node visibility rate is improved, Δ E t Increasing; meanwhile, the power shortage expectation of the system is reduced, so that the power generation capacity is reduced, the carbon emission required by power generation is also reduced, and c carbon And then decreases. But node energy rate and Δ E t 、c carbon The quantitative relationship of (A) has not been clarified yet.
Therefore, a self-healing time quantitative calculation method based on expert test data is considered:
in the formula: f is the expected self-healing time of the power distribution network; n is the total number of the test data; pi i The reliability of the ith data; x is a radical of a fluorine atom i Is the value of the ith data.
From this Δ E can be calculated t And c carbon The value of (c):
in the formula: f before Configuring the expected self-healing time before measurement for the power distribution network; f after And configuring the expected self-healing time after measurement for the power distribution network.
And S130, constructing a multi-dimensional layered evaluation model based on the refined collaborative planning model and by combining an index system, wherein the index system comprises an economic index, a low-carbon index and a business index.
And (3) multi-dimensional hierarchical evaluation, wherein a specific index system is as follows:
(1) Economic index
(1) Primary system cost reduction
The reasonable configuration of the secondary equipment can reduce the investment and maintenance cost of the primary system to a certain extent, and the calculation method is different according to the type of the secondary equipment. Primary system cost Δ C cut by secondary equipment investment for different primary equipment I Different.
For transformers and switchgear, it is assumed that the equipment failure frequency follows the following equation, according to the relevant guidelines:
p＝f(d)＝Ae Bd +C (9)
in the formula: p is the failure rate when the equipment defect index is d; A. b and C are unknown coefficients and can be obtained by reference to guiding rules or fitting statistical data.
After the secondary equipment is put into use, the average failure frequency and the maintenance cost C of the equipment M The reduction (partial major repair is changed into minor repair) of the system cost can be further calculated to obtain a primary system cost reduction Delta C I ：
In the formula: c F Cost to failure;
configuring the failure rate of secondary equipment;
the failure rate after the secondary equipment is configured; delta C M The maintenance cost is reduced; p is a radical of M The frequency of equipment maintenance is increased.
For power cables and overhead lines, the cost of a primary system after secondary equipment configuration is reduced by delta C I The following were used:
ΔC I ＝αβ(C U +C F -C P )T (11)
in the formula: alpha is the failure rate of the equipment; beta is the equipment aging fault proportion; c U The power loss is caused by single failure of the equipment; c F Cost of maintenance for a single failure of the equipment; c P Single preventive maintenance cost for equipment; and T is the service life of the equipment.
For switching equipment, the cost of a primary system after secondary equipment configuration is reduced by delta C I The following:
ΔC I ＝αλ(C U +C F )T (12)
in the formula: alpha is the failure rate of the equipment; lambda is the proportion of fault loss reduced by state monitoring; c U The power loss is caused by single failure of the equipment; c F A single maintenance charge for the equipment; and T is the life cycle of the equipment.
Particularly, for overhead lines and cables, the configuration of secondary equipment not only reduces the fault/operation and maintenance cost, but also indirectly reflects the capacity expansion cost reduction of the power distribution network due to the function of FTU release capacity. For a certain area to be planned, the released power supply capacity delta P due to the improvement of the utilization rate of the power distribution network is as follows:
ΔP＝σ 2 ×η×ε×P t (13)
in the formula: sigma 2 The proportion is increased for the utilization rate of the power distribution network, and the specific value is influenced by various factors such as a power distribution automation scheme, a grid structure, load distribution and the like; eta is the utilization rate of the original power distribution network (namely the utilization rate of the planning secondary equipment before the equipment is put into use); epsilon is FTU coverage; p t And the total power supply capacity of the distribution network in the planned area is provided.
(2) Line loss power saving benefit
Reasonable secondary equipment configuration aims at a certain planning area, and line loss electric quantity delta E saved every year is assumed to be monitored by adopting the same site monitoring mode 1 The approximation is:
ΔE 1 ＝σ 1 ×ε×(1+ρ)×E t (14)
in the formula: sigma 1 The specific value is influenced by various factors such as a power distribution automation scheme, a grid structure, load distribution and the like for reducing the line loss proportion; rho is the line loss rate before the implementation of power distribution automation; e t The annual average electricity sales in the planned area.
(2) Low carbon index
(1) Direct carbon neutralization amount
Carbon emission Delta C directly reduced after secondary equipment investment dir 。
(2) Indirect carbon neutralization amount
Grid generation is accompanied by the generation of carbon emissions. Therefore, the line loss capacity saved by the investment of secondary equipment is directly reflected on economic indexes and indirectly reflected on the cost of reduced carbon emission:
ΔC ind ＝μΔE 1 (15)
in the formula: delta C ind Is an indirect carbon neutralization amount; delta E 1 Line loss electricity is saved every year; mu is the carbon emission required per unit of power generation.
(3) Service index
The service index of the present application is set as shown in fig. 2:
the calculation method of the indexes such as coverage rate, precision, response time and the like in the service indexes is simple, and other indexes are all universal in the field or are referred to by documents, and are not described again here.
During evaluation, a plurality of relevant service indexes are selected according to services related to configured secondary equipment, and are combined with economic and low-carbon indexes, and normalization is performed by a Lagrange multiplier method:
in the formula: l (-) is a Lagrangian function; x is a power distribution network state vector; beta is Lagrange multiplier; f (-) is an economic index evaluation function, including primary system cost reduction and line loss power saving income; h (-) is a low carbon index evaluation function includingAnd indirect carbon emission neutralization; g (-) is a service index evaluation function, which is the sum of a plurality of selected service indexes and weighting coefficients, N is the total number of the selected service indexes, and alpha i Weighting coefficients for the ith service index (unifying service indexes of different dimensions), C i Is the value of the ith service index.
Therefore, index evaluations of three different dimensions, namely economy, low carbon and business, can be unified, and a comprehensive evaluation result aiming at the secondary equipment configuration scheme is formed.
Exemplary applications
The practical distribution network in a certain city is taken as an example, and how to apply the model provided by the application in practice is introduced. The planning content is secondary equipment configuration of a newly-built line, and secondary equipment to be selected comprises a two-remote/three-remote FTU.
(1) Energy control and energy observation requirement-state variable mapping construction for power distribution network
The analysis is developed by taking five services of power distribution network equipment state monitoring, electric energy quality monitoring, reliability, self-healing capability and renewable energy consumption as cores, and the related mapping is constructed as follows:
TABLE 1 device State monitoring service mapping set
Table 2 electric energy quality monitoring service mapping set
Table 3 power reliability service mapping set
Table 4 distribution network self-healing service mapping set
TABLE 5 renewable energy consumption service mapping set
The detailed collaborative planning is performed, and the application model is adopted for calculation, so that the optimal FTU configuration scheme and reliability corresponding to the overhead lines with different power supply network regional levels and different segment numbers are obtained and are shown in the table 6.
TABLE 6 optimal FTU configuration scheme for overhead line
The optimal FTU configuration scheme and reliability corresponding to cable ring networks of different power supply network zone levels/different ring network cabinet numbers are shown in table 7.
Table 7 optimal FTU configuration scheme for cable ring network
Multi-dimensional hierarchical evaluation: in the evaluation of the planning scheme, the economic index evaluation function selects the cost reduction of the primary system and the line loss electric quantity saving benefit, the low-carbon index evaluation function selects the direct carbon neutralization amount and the indirect carbon neutralization amount (the Lagrange multiplier of the low-carbon index evaluation function takes 100), and the service index evaluation function selects the state monitoring coverage rate, the state monitoring precision, the average continuous power failure time of the system, the power supply reliability and the power supply self-healing speed (the Lagrange multiplier of the low-carbon index evaluation function takes 1000).
Table 8 shows the optimal FTU configuration schemes for overhead lines of different power grid area classes/different section numbers and the evaluation results thereof.
TABLE 8 optimal FTU configuration scheme for overhead line and evaluation results thereof
Table 9 shows the optimal FTU configuration schemes corresponding to cable ring networks of different power supply network area classes/different ring network unit numbers and the evaluation results thereof.
TABLE 9 optimal FTU configuration scheme for cable ring network
Exemplary devices
According to this application embodiment's power distribution network secondary equipment planning and evaluation device includes: the system comprises a fetching module, a calculating module and a calculating module, wherein the fetching module is used for acquiring first data, the first data is acquired by an acquisition terminal, and the first data comprises cost data, equipment parameters and the total number of lines to be built; the planning module is used for constructing a refined collaborative planning model based on the power distribution network visibility and controllability indexes; and the evaluation module is used for constructing a multi-dimensional layered evaluation model by combining an index system based on the refined collaborative planning model, wherein the index system comprises an economic index, a low-carbon index and a business index.
In one example, the observability is that the value of the state variable is determined by direct measurement of the state variable or indirect calculation or state estimation of the remaining variables, and the error between the value and the actual value of the state variable is within an allowable range.
In one example, controllability is the ability to transition the distribution network from any initial state to a terminal state of safe and stable economic operation within a specified time interval.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 3. The electronic device may be an electronic device integrated with the first imaging device or a stand-alone device separate from said first imaging device, which stand-alone device may communicate with said first imaging device for receiving the acquired input signals therefrom.
FIG. 3 shows a block diagram of an electronic device according to an embodiment of the application.
As shown in fig. 3, the electronic device 10 includes one or more processors 11 and memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
For example, when the electronic device is integrated with the first imaging device, the input device 13 may be the first imaging device, such as a camera, for capturing each frame image of a moving object. When the electronic device is a stand-alone device, the input means 13 may be a communication network connector for receiving the acquired input signals from said first imaging device.
The input device 13 may also include, for example, a keyboard, a mouse, and the like.
The output device 14 may output various information including the determined deflection angle information and the like to the outside. The output devices 14 may include, for example, a display, speakers, printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 10 relevant to the present application are shown in fig. 3, omitting components such as buses, input/output interfaces, and the like. In addition, the electronic device may include any other suitable components, depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores computer program instructions; when executed by a processor, the computer program instructions implement the training method of the photovoltaic power generation power prediction model provided by the embodiment of the present invention, or implement the photovoltaic power generation power prediction method provided by the embodiment of the present invention.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic Circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor Memory devices, read-Only memories (ROMs), flash memories, erasable Read-Only memories (EROMs), floppy disks, compact disk Read-Only memories (CD-ROMs), optical disks, hard disks, optical fiber media, radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments noted in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, implement the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present invention.
Claims (10)
1. A planning and evaluation method for secondary equipment of a power distribution network is characterized by comprising the following steps:
acquiring first data, wherein the first data are acquired by an acquisition terminal, and the first data comprise cost data, equipment parameters and the total number of lines to be built;
constructing a refined collaborative planning model based on the power distribution network visibility and performance control indexes;
and constructing a multidimensional layered evaluation model based on the refined collaborative planning model by combining an index system, wherein the index system comprises an economic index, a low-carbon index and a business index.
2. The method of claim 1, wherein the visuality is that the value of the state variable is determined by direct measurement of the state variable or indirect calculation or state estimation of the remaining variables, and the error between the value and the actual value of the state variable is within an allowable range; the mathematical expression of the visibility is shown as follows:
in the formula: g is a real number space; x (t) is a state vector at the time t; u (t) is a control vector at the time t; y (t) is an output vector at the time t; x is a radical of a fluorine atom m (t) is a state vector obtained by measurement at time t; y is m (t) is an output vector obtained by measurement at time t; f (-) is a mapping relation between the measured state vector and the output vector and the actual state vector; epsilon is an observable error; i | · | | is a vector modulo norm; epsilon max The allowable range of the visual error is obtained; when the mapping relation f (-) can uniquely determine the actual state variable and the error is within the allowable range, the state variable is said to be observable at the time t.
3. The method of claim 1, wherein the controllability is an ability to cause the distribution network to transition from any initial state to a final state of safe and stable economic operation within a prescribed time interval; the mathematical expression is shown as follows:
in the formula: g (-) is the mapping relation between the control vector and the state vector, and reflects the change condition of the state vector under the influence of the specific control vector; u (t) is a control vector at the moment t; t is t 0 Is the state transition time; t is t max For a specified time interval; x is a radical of a fluorine atom req The method is a safe and stable economic operation termination state.
4. The method of claim 1, wherein the refined collaborative planning model is:
min(C inv +C R +ωC env -ξC obs )
in the formula: c inv Investment cost; c R For power shortage costs; omega is a carbon emission penalty coefficient; c env Carbon emissions; xi is an observable excitation coefficient; c obs Is an observable comprehensive index; l is the total number of lines to be built; x is the number of i If the variable is 0-1, indicating whether an ith line is newly established or not; c. C l Expanding cost for a line unit; l i Is the length of line i; n is the total number of equipment, and if the three-phase unbalanced power grid is adopted, the number of equipment related to the traditional variable is three times that of the three-phase balanced power grid; c. C n For situational awareness equipment (measurement) costs; y is j A variable of 0-1, indicating whether a jth situation awareness device (measurement) is configured; n is a radical of an alkyl radical j Number of situation awareness devices (measurements); n is a radical of hydrogen t Is the total number of time periods; e t An undersupply expectation for a period t; delta E t An under-supply expectation for a reduced t-period after configuration of the secondary device; c t The unit power shortage loss cost at the moment t; c. C carbon Carbon emission of equipment is reduced (if the carbon emission is negative, carbon emission reduction is carried out); m is the total number of nodes; r is ∑ The comprehensive energy observation rate of the power distribution network is achieved.
5. The method according to claim 1, wherein the multidimensional hierarchical assessment model is obtained by combining an economic index, a low carbon index and a service index and performing normalization by a Lagrange multiplier method, and the specific multidimensional hierarchical assessment model is as follows:
in the formula: l (-) is a Lagrangian function; x is a power distribution network state vector; beta is Lagrange multiplier; f (x) is an economic indicator evaluation function; h (x) is a low-carbon index evaluation function; g (x) is a service index evaluation function.
6. The method of claim 5, wherein the economic indicator evaluation function is:
f(x)＝ΔC I +cΔE t
in the formula: delta C I The cost of a primary system is reduced; c is unit electricity price; delta E t It is desirable for the power supply to be insufficient for a reduced t period after the configuration of the secondary device.
7. The method of claim 5, wherein the low carbon index evaluation function is:
h(x)＝ΔC dir +ΔC ind
in the formula: delta C dir Carbon emission is directly reduced after secondary equipment is put into use; delta C ind Is an indirect carbon neutralization amount.
8. The method of claim 5, wherein the service indicator evaluation function is:
in the formula: alpha is alpha i A weighting coefficient of the ith service index; c i Is the value of the ith service index.
9. The utility model provides a distribution network secondary equipment plans and evaluation device which characterized in that includes:
the system comprises an acquisition module, a data acquisition module and a data processing module, wherein the acquisition module is used for acquiring first data, the first data is acquired by an acquisition terminal, and the first data comprises cost data, equipment parameters and the total number of lines to be built;
the planning module is used for constructing a refined collaborative planning model based on the power distribution network visibility and controllability indexes;
and the evaluation module is used for constructing a multi-dimensional layered evaluation model by combining an index system based on the refined collaborative planning model, wherein the index system comprises an economic index, a low-carbon index and a business index.
10. An electronic device, comprising a processor, and a memory and a network interface connected to the processor; the network interface is connected with a nonvolatile memory in the server; the processor, when running, retrieves a computer program from the non-volatile memory via the network interface and runs the computer program via the memory to perform the method of any of claims 1-8.
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