Multi-scene evaluation method, device and equipment for power distribution network and storage medium

Abstract

The invention provides a multi-scene evaluation method and device for a power distribution network, equipment and a storage medium, and the method comprises the steps: obtaining a power distribution network line, the output data and load data of each node of the line, and digital configuration data; performing equivalent calculation updating of the low-voltage distribution network based on the output data and the load data to obtain photovoltaic output and low-voltage load; on the basis of the photovoltaic output and the low-voltage load, the loss amount of the power distribution network line in each scene is solved through power flow analysis models corresponding to multiple scenes, and the multiple scenes comprise a self-generation and self-use mode, a residual power networking mode and a residual power reverse transmission mode; and calculating a profit and loss amount based on the loss amount, calculating a digital cost based on the digital configuration data, and taking the profit and loss amount and the digital cost as evaluation results of each scene of the power distribution network. According to the invention, through the power flow analysis models corresponding to the multiple scenes, the loss amount of the power distribution network line in each scene is solved, and the operation condition of the power grid can be comprehensively analyzed.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a multi-scenario evaluation method, device, equipment and storage medium for distribution network. Background Art

[0002] Distribution network assessment refers to the process of comprehensively analyzing the losses and benefits generated during the operation of the distribution network. It aims to quantify the social benefits of the distribution network, assist power companies or related institutions to better understand the actual situation, and provide a basis for future planning and decision-making.

[0003] In related technologies, traditional assessment methods rely on expert teams to conduct a comprehensive assessment of the existing power grid operation status, collect and analyze historical power grid data, and have experienced experts conduct estimation and analysis. However, the analysis process relies on the experience and judgment of experts, and subjective factors have a greater impact, resulting in inaccurate assessment results. Existing objective assessment methods are usually based on static and single-scenario analysis, lack comprehensive consideration of dynamic changes such as distributed power generation and load fluctuations, and lack quantitative analysis such as environmental benefits.

[0004] Based on the above analysis of the development status of this technology field, the existing technology lacks a comprehensive evaluation solution based on power flow data analysis models in different scenarios. Summary of the invention

[0005] The object of the present invention is to provide a multi-scenario evaluation method, device, equipment and storage medium for a distribution network, aiming to solve the above-mentioned problems in the prior art.

[0006] According to a first aspect of an embodiment of the present invention, a multi-scenario evaluation method for a distribution network is provided, comprising:

[0007] Obtain output data and load data of distribution network lines and nodes, as well as digital configuration data;

[0008] Based on the output data and load data, the equivalent calculation and update of the low-voltage distribution network are performed to obtain the photovoltaic output and low-voltage load, including:

[0009] Determine whether the load data of each node of the distribution network line is less than the output data. If the load data is less than the output data, the photovoltaic output is updated to the difference between the output data and the load data, and the low-voltage load is updated to zero. Otherwise, the photovoltaic output is the original output data, and the low-voltage load is updated to the difference between the load data and the output data.

[0010] Based on the photovoltaic output and low-voltage load, the power flow analysis models corresponding to multiple scenarios are used to solve the power loss of distribution network lines in various scenarios. The multiple scenarios include self-generation and self-use mode, surplus power grid-connected mode and surplus power reverse transmission mode, specifically including:

[0011] The power flow analysis models corresponding to the multiple scenarios specifically include: low-voltage self-generation and self-use model, medium-voltage self-generation and self-use model, low-voltage surplus power grid-connected model, medium-voltage surplus power grid-connected model, low-voltage surplus power reverse transmission model and medium-voltage surplus power reverse transmission model;

[0012] Among them, the objective function of the power flow analysis model corresponding to each of the multiple scenarios is to minimize the line loss, and the constraints include branch power flow balance constraints and safe and stable operation constraints;

[0013] Through the power flow analysis models corresponding to multiple scenarios, the loss of distribution network lines in each scenario is solved separately, including:

[0014] In the self-generation and self-consumption scenario, the photovoltaic output and low-voltage load are input into the low-voltage self-generation and self-consumption model, and the low-voltage distribution network line loss, new energy consumption and active power at the medium- and low-voltage connection in the self-generation and self-consumption scenario are output. The active power at the medium- and low-voltage connection is used as the medium-voltage load, and the medium-voltage load data is input into the medium-voltage self-generation and self-consumption model, which outputs the medium-voltage distribution network line loss in the self-generation and self-consumption scenario;

[0015] In the surplus power grid-connected scenario, the photovoltaic output and low-voltage load are input into the low-voltage surplus power grid-connected model, and the low-voltage distribution network line loss, new energy consumption and active power at the medium- and low-voltage connection in the surplus power grid-connected scenario are output. The active power at the medium- and low-voltage connection is used as the medium-voltage load, and the medium-voltage load data is input into the medium-voltage surplus power grid-connected model, which outputs the medium-voltage distribution network line loss in the surplus power grid-connected scenario;

[0016] In the surplus power reverse transmission scenario, based on the photovoltaic output and low-voltage load, the equivalent calculation update of the medium-voltage distribution network is performed as the medium-voltage load and medium-voltage output of the surplus power reverse transmission scenario. The medium-voltage load and medium-voltage output are input into the medium-voltage surplus power reverse transmission model, and the medium-voltage distribution network line loss and new energy output value in the surplus power reverse transmission scenario are output. The active power at the medium- and low-voltage connection is obtained according to the medium-voltage load and new energy output value. The photovoltaic output, low-voltage load and active power at the medium- and low-voltage connection are input into the low-voltage surplus power reverse transmission model to obtain the low-voltage distribution network line loss and new energy consumption in the surplus power reverse transmission scenario;

[0017] The equivalent calculation and update of the medium voltage distribution network for photovoltaic output and low voltage load specifically includes:

[0018] Calculate the sum of the low-voltage loads of each node as the low-voltage total load, calculate the sum of the photovoltaic outputs of each node as the low-voltage total output, and obtain the pre-estimated low-voltage distribution network line loss rate; take the low-voltage total output as the medium-voltage output, consider the low-voltage distribution network line loss rate on the low-voltage total load, and calculate the medium-voltage load;

[0019] The low-voltage distribution network line loss, new energy consumption and medium-voltage distribution network line loss in the corresponding scenario are taken as the loss amount.

[0020] The profit and loss amount is calculated based on the loss amount, and the digital cost is calculated based on the digital configuration data. The profit and loss amount and digital cost are used as the evaluation results of each scenario of the distribution network, including:

[0021] The calculation of profit and loss based on the loss volume specifically includes:

[0022] Based on the line loss of the low-voltage distribution network and the line loss of the medium-voltage distribution network, the line profit and loss are calculated by formula 1:

[0023] B loadloss =pr t ×ΔP loadloss Formula 1;

[0024] Among them, B loadloss represents the gain or loss caused by line loss, pr t represents the electricity price of coal-fired power generation, ΔP loadloss Indicates the loss amount of low voltage distribution network line loss or medium voltage distribution network line loss;

[0025] Based on the amount of new energy consumption, the carbon emission gains and losses are calculated using Formula 2:

[0026] B ce =ΔP DG ×K ce × ce Formula 2;

[0027] Among them, B ce Represents the gains and losses caused by carbon emissions, ΔP DG represents the amount of new energy consumption, K ce represents the carbon dioxide emissions generated by unit thermal power generation, pr ce Represents the trading price of unit carbon dioxide emissions in the national carbon trading market.

[0028] Calculation of digitalization costs based on digitalization configuration data includes:

[0029] The digitization cost is calculated using Formula 3:

[0030]

[0031] Where cost represents the digitization cost, C md represents the cost of a single deployed measurement device, M represents the number of devices required to be deployed, and C trans represents the cost of replacing a single transformer, N represents the number of transformers to be replaced, C control represents the cost of a single distributed photovoltaic control device, S represents the number of distributed photovoltaic control devices, C cpt Represents the operating environment cost of the intelligent scheduling algorithm.

[0032] According to a second aspect of an embodiment of the present invention, a multi-scenario evaluation device for a distribution network is provided, comprising:

[0033] Data acquisition module, used to obtain output data and load data of distribution network lines and nodes of the lines, as well as digital configuration data;

[0034] The low-voltage equivalent calculation module is used to perform equivalent calculation updates of the low-voltage distribution network based on output data and load data to obtain photovoltaic output and low-voltage load. It is specifically used for:

[0035] Determine whether the load data of each node of the distribution network line is less than the output data. If the load data is less than the output data, the photovoltaic output is updated to the difference between the output data and the load data, and the low-voltage load is updated to zero. Otherwise, the photovoltaic output is the original output data, and the low-voltage load is updated to the difference between the load data and the output data.

[0036] The power flow analysis module is used to solve the power loss in each scenario of the distribution network line based on the photovoltaic output and low-voltage load through the power flow analysis models corresponding to multiple scenarios. The multiple scenarios include self-generation and self-use mode, surplus power grid-connected mode and surplus power reverse transmission mode. It is specifically used for:

[0037] The power flow analysis models corresponding to the multiple scenarios specifically include: low-voltage self-generation and self-use model, medium-voltage self-generation and self-use model, low-voltage surplus power grid-connected model, medium-voltage surplus power grid-connected model, low-voltage surplus power reverse transmission model and medium-voltage surplus power reverse transmission model;

[0038] Among them, the objective function of the power flow analysis model corresponding to each of the multiple scenarios is to minimize the line loss, and the constraints include branch power flow balance constraints and safe and stable operation constraints;

[0039] Through the power flow analysis models corresponding to multiple scenarios, the loss of distribution network lines in each scenario is solved separately, including:

[0040] In the self-generation and self-consumption scenario, the photovoltaic output and low-voltage load are input into the low-voltage self-generation and self-consumption model, and the low-voltage distribution network line loss, new energy consumption and active power at the medium- and low-voltage connection in the self-generation and self-consumption scenario are output. The active power at the medium- and low-voltage connection is used as the medium-voltage load, and the medium-voltage load data is input into the medium-voltage self-generation and self-consumption model, which outputs the medium-voltage distribution network line loss in the self-generation and self-consumption scenario;

[0041] In the surplus power grid-connected scenario, the photovoltaic output and low-voltage load are input into the low-voltage surplus power grid-connected model, and the low-voltage distribution network line loss, new energy consumption and active power at the medium- and low-voltage connection in the surplus power grid-connected scenario are output. The active power at the medium- and low-voltage connection is used as the medium-voltage load, and the medium-voltage load data is input into the medium-voltage surplus power grid-connected model, which outputs the medium-voltage distribution network line loss in the surplus power grid-connected scenario;

[0042] In the surplus power reverse transmission scenario, based on the photovoltaic output and low-voltage load, the equivalent calculation update of the medium-voltage distribution network is performed as the medium-voltage load and medium-voltage output of the surplus power reverse transmission scenario. The medium-voltage load and medium-voltage output are input into the medium-voltage surplus power reverse transmission model, and the medium-voltage distribution network line loss and new energy output value in the surplus power reverse transmission scenario are output. The active power at the medium- and low-voltage connection is obtained according to the medium-voltage load and new energy output value. The photovoltaic output, low-voltage load and active power at the medium- and low-voltage connection are input into the low-voltage surplus power reverse transmission model to obtain the low-voltage distribution network line loss and new energy consumption in the surplus power reverse transmission scenario;

[0043] The equivalent calculation and update of the medium voltage distribution network for photovoltaic output and low voltage load specifically includes:

[0044] Calculate the sum of the low-voltage loads of each node as the low-voltage total load, calculate the sum of the photovoltaic outputs of each node as the low-voltage total output, and obtain the pre-estimated low-voltage distribution network line loss rate; take the low-voltage total output as the medium-voltage output, consider the low-voltage distribution network line loss rate on the low-voltage total load, and calculate the medium-voltage load;

[0045] The low-voltage distribution network line loss, new energy consumption and medium-voltage distribution network line loss in the corresponding scenario are taken as the loss amount.

[0046] The evaluation module is used to calculate the profit and loss amount based on the loss amount, calculate the digital cost based on the digital configuration data, and use the profit and loss amount and digital cost as the evaluation results of each distribution network scenario. Specifically, it is used for:

[0047] The calculation of profit and loss based on the loss volume specifically includes:

[0048] Based on the line loss of the low-voltage distribution network and the line loss of the medium-voltage distribution network, the line profit and loss are calculated by formula 1:

[0049] B loadloss =pr t ×ΔP loadloss Formula 1;

[0050] Among them, B loadloss represents the gain or loss caused by line loss, pr t represents the electricity price of coal-fired power generation, ΔP loadloss Indicates the loss amount of low voltage distribution network line loss or medium voltage distribution network line loss;

[0051] Based on the amount of new energy consumption, the carbon emission gains and losses are calculated using Formula 2:

[0052] B ce =ΔP DG ×K ce × ce Formula 2;

[0053] Among them, B ceRepresents the gains and losses caused by carbon emissions, ΔP DG represents the amount of new energy consumption, K ce represents the carbon dioxide emissions generated by unit thermal power generation, pr ce Represents the trading price of unit carbon dioxide emissions in the national carbon trading market.

[0054] Calculation of digitalization costs based on digitalization configuration data includes:

[0055] The digitization cost is calculated using Formula 3:

[0056]

[0057] Where cost represents the digitization cost, C md represents the cost of a single deployed measurement device, M represents the number of devices required to be deployed, and C trans represents the cost of replacing a single transformer, N represents the number of transformers to be replaced, C control represents the cost of a single distributed photovoltaic control device, S represents the number of distributed photovoltaic control devices, C cpt Represents the operating environment cost of the intelligent scheduling algorithm.

[0058] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the multi-scenario evaluation method for a distribution network provided in the first aspect of the present disclosure are implemented.

[0059] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which a program for implementing information transmission is stored, and when the program is executed by a processor, the steps of the multi-scenario evaluation method for the distribution network provided in the first aspect of the present disclosure are implemented.

[0060] The technical solution provided by the embodiment of the present invention includes the following beneficial effects: by using the power flow analysis models corresponding to multiple scenarios, the power loss in each scenario of the distribution network line is solved respectively, so as to comprehensively analyze the operation of the power grid; a profit and loss analysis is performed based on the power loss to analyze the benefits brought about by the change in line loss, that is, the amount of new energy consumption, and generally a comprehensive and effective quantitative evaluation scheme is constructed.

[0061] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0063] Figure 1 is a flow chart of a multi-scenario evaluation method for a distribution network according to an embodiment of the present invention;

[0064] Figure 2 It is a schematic diagram of solving the problem in the scenario of self-generation and self-use according to an embodiment of the present invention;

[0065] Figure 3 It is a schematic diagram of solving the problem in the surplus power grid-connected scenario according to an embodiment of the present invention;

[0066] Figure 4 It is a schematic diagram of solving the problem in the surplus power reverse transmission scenario of an embodiment of the present invention;

[0067] Figure 5 is a schematic diagram of a multi-scenario evaluation framework of an embodiment of the present invention;

[0068] Figure 6 is a schematic diagram of a multi-scenario evaluation device for a distribution network according to an embodiment of the present invention;

[0069] Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0071] Method Embodiment

[0072] According to an embodiment of the present invention, a multi-scenario evaluation method for a distribution network is provided. Figure 1 is a flow chart of a multi-scenario evaluation method for a distribution network according to an embodiment of the present invention. Figure 1 As shown, the multi-scenario evaluation method for a distribution network according to an embodiment of the present invention specifically includes:

[0073] In step S110, the output data and load data of the distribution network lines and each node of the lines, and the digital configuration data are obtained, which specifically includes:

[0074] The distribution network route refers to the medium voltage distribution network grid structure and route parameters, the low voltage distribution network grid structure and route parameters, the output data of each node on the route refers to the output of new energy power generation, and the load data refers to the medium voltage distribution network load parameters and the low voltage distribution network load parameters.

[0075] In step S120, based on the output data and the load data, the equivalent calculation update of the low-voltage distribution network is performed to obtain the photovoltaic output and the low-voltage load, which specifically includes:

[0076] Determine whether the load data of each node of the distribution network line is less than the output data. If the load data is less than the output data, the photovoltaic output is updated to the difference between the output data and the load data, and the low-voltage load is updated to zero. Otherwise, the photovoltaic output is the original output data, and the low-voltage load is updated to the difference between the load data and the output data.

[0077] In step S130, based on the photovoltaic output and the low-voltage load, the power flow analysis models corresponding to the multiple scenarios are used to solve the power loss of the distribution network line in each scenario. The multiple scenarios include the self-generation and self-use mode, the surplus power grid-connected mode and the surplus power reverse transmission mode, which specifically include:

[0078] The power flow analysis models corresponding to the multiple scenarios specifically include: low-voltage self-generation and self-use model, medium-voltage self-generation and self-use model, low-voltage surplus power grid-connected model, medium-voltage surplus power grid-connected model, low-voltage surplus power reverse transmission model and medium-voltage surplus power reverse transmission model;

[0079] Among them, the objective function of the power flow analysis model corresponding to each of the multiple scenarios is to minimize the line loss, and the constraints include branch power flow balance constraints and safe and stable operation constraints. That is to say, all power flow analysis models include the above two constraints, but other constraints will be considered in some cases;

[0080] Through the power flow analysis models corresponding to multiple scenarios, the loss of distribution network lines in each scenario is solved separately, including:

[0081] Scenario 1 is the self-generation and self-use mode. At this time, distributed photovoltaics can only absorb the load of photovoltaic access points of the low-voltage distribution network. The low-voltage power grid will not have reverse power transmission across voltage. Therefore, after determining the power flow of the low-voltage distribution network, the corresponding medium-voltage distribution network node load can be determined.

[0082] In the embodiment of the present invention, the focus is not on the internal design of the power flow analysis model itself, but on the complete ideas and evaluation scheme.

[0083] In the self-generation and self-consumption scenario, the photovoltaic output and low-voltage load are input into the low-voltage self-generation and self-consumption model. The low-voltage self-generation and self-consumption model is shown in Formula 4. The power flow model outputs the low-voltage distribution network line loss, new energy consumption and active power at the medium and low voltage connection in the self-generation and self-consumption scenario:

[0084]

[0085] Among them, f obj represents the total load in the low-voltage distribution network during the T period; a and b represent the low-voltage distribution network node numbers; Γ represents the set of low-voltage node numbers; j represents the medium-voltage distribution network node number to which the low-voltage distribution network is connected, and the subscript j represents the low-voltage distribution network parameter subordinate to the medium-voltage distribution network node s; Uj,a,t and Uj,b,t are the voltage values ​​of nodes a and b during the t period; Pj,ab,t represents the active power flowing through the head end of branch ab during the t period; rj,ab and xj,ab represent the impedance of branch ab; Qj,ab,t represents The reactive power flowing through the head end of branch ab during period t; Ij,ab,t represents the current flowing through branch ab during period t; pj,b,t, qj,b,t represent the active power and reactive power required by the low-voltage load of node b during period t; Pj,bk,t, Qj,bk,t represent the active power and reactive power at the head end of branch bk at time t respectively; k:b→k represents the set of all child nodes with node b as the parent node; U'max, U'min represent the upper and lower limits of the voltage of the low-voltage distribution network node respectively; p j,b,t DG represents the actual output of the distributed power source connected to node b in the low-voltage distribution network belonging to node j during period t, that is, the value of photovoltaic output, p j,b,t,max DG It represents the maximum output allowed by the distributed generation connected to node b in the low-voltage distribution network belonging to node j during period t.

[0086] The active power at the medium- and low-voltage connection is taken as the medium-voltage load, and the medium-voltage load is input into the medium-voltage self-generation and self-consumption model, and the medium-voltage distribution network line loss in the self-generation and self-consumption scenario is output. The idea of ​​the medium-voltage self-generation and self-consumption model is consistent with the low-voltage self-generation and self-consumption model, but the parameters are different, as shown in Formula 5:

[0087]

[0088] Among them, F obj represents the sum of active power losses of the medium voltage distribution network in T time periods; i and j represent node numbers; U i,t , U j,t represents the voltage value of nodes i and j in time period t; P ij,t represents the active power flowing through the head end of branch ij during period t; r ij 、x ij Represents the impedance of branch ij; Qij,t I represents the reactive power flowing through the head end of branch ij during period t; ij,t represents the current flowing through branch ij during period t; p j,t ,q i,t They represent the active power and reactive power required by node j to inject medium voltage load during period t; P jk,t , Q jk,t They represent the active power and reactive power at the head end of branch jk at time t respectively; k:j→k represents the set of all child nodes with j node as parent node; U max , U min They represent the upper and lower limits of the node voltage respectively.

[0089] Figure 2 is a schematic diagram of the solution in the self-generation and self-use scenario of an embodiment of the present invention, such as Figure 2 As shown, the framework of the self-generation and self-use scenario is shown;

[0090] Scenario 2 is the surplus power access scenario, which is similar to the self-generation and self-use mode. However, when calculating the power flow of the low-voltage distribution network, since distributed photovoltaics can be dispatched, it is equivalent to distributed photovoltaics connected to the distribution network, so it is necessary to add the maximum distributed photovoltaic output constraint;

[0091] In the surplus power grid-connected scenario, the photovoltaic output and low-voltage load are input into the low-voltage surplus power grid-connected model, and the low-voltage distribution network line loss, new energy consumption and active power at the medium and low voltage connection in the surplus power grid-connected scenario are output. The low-voltage surplus power grid-connected model is shown in Formula 6:

[0092]

[0093] Wherein, j represents the node number of the medium voltage distribution network to which the low voltage distribution network is connected; p j,b,t DG represents the actual output of the distributed power source connected to node b in the low-voltage distribution network belonging to node j during period t, i.e., the photovoltaic output; p j,b,t,max DG represents the maximum output allowed by the distributed power source connected to node b in the low-voltage distribution network belonging to node j during time period t. The embodiment of the present invention assumes that the output of the distributed power source is continuously adjustable; When b is the head-end node of the distribution network, it indicates the active power transmitted from the medium-voltage distribution network node j to the low-voltage distribution network during period t; the meanings of the other parameters are consistent with those in other scenarios, and remain unchanged when the subscript changes.

[0094] The active power at the medium-low voltage connection is taken as the medium-voltage load, and the medium-voltage load is input into the medium-voltage surplus power grid-connected model. The model result of the medium-voltage surplus power grid-connected model is consistent with Formula 5, and the medium-voltage distribution network line loss in the surplus power grid-connected scenario is output;

[0095] Figure 3Schematic diagram of the solution in the surplus power grid-connected scenario of the embodiment of the present invention, such as Figure 3 As shown, the framework of the surplus power grid-connected scenario is shown;

[0096] Scenario 3 is the surplus power return scenario, which sets the distributed power source to be dispatchable and allows the lower-level power grid to send power in the opposite direction. Unlike the previous two scenarios, when the low-voltage distribution network can send power in the opposite direction to the upper-level power grid, the output of the distributed power source in the low-voltage distribution network should be determined by the optimized dispatch of the medium-voltage distribution network. Therefore, the surplus power return scenario should first calculate the power flow of the medium-voltage distribution network to obtain the total new energy output of the low-voltage distribution network where the distributed power source can be dispatched;

[0097] In the surplus power reverse transmission scenario, based on the PV output and low-voltage load, the equivalent calculation update of the medium-voltage distribution network is performed as the medium-voltage load and medium-voltage output of the surplus power reverse transmission scenario, specifically including:

[0098] The sum of the low-voltage loads of each node is calculated as the low-voltage total load, and the sum of the photovoltaic outputs of each node is calculated as the low-voltage total output, that is, the low-voltage total photovoltaic output, to obtain the pre-estimated low-voltage distribution network line loss rate; the low-voltage total output is used as the medium-voltage output, that is, the medium-voltage photovoltaic output, and the low-voltage distribution network line loss rate is considered on the low-voltage total load to calculate the medium-voltage load, as shown in Formula 7 and Formula 8:

[0099]

[0100] Among them, p j,t They represent the active power required by the medium voltage node j during period t, that is, the load size of the medium voltage node j; p DG j,t represents the actual total distributed power output of the distributed power connected to the medium-voltage node j in period t, that is, the medium-voltage photovoltaic output of node j in period t; represents the total load of the low-voltage area belonging to node j, δ represents the line loss rate of the low-voltage distribution network, represents the total distributed output of the low-voltage area belonging to node j;

[0101] Considering the existence of line loss on the low-voltage side, if the upper-level power grid requires the new energy output of the low-voltage area to be the maximum output, and part of the new energy output is used to absorb the line loss generated by the low-voltage distribution network, the reverse transmission point will be unable to respond to the dispatching instructions of the upper-level power grid. Therefore, an estimated line loss rate is needed to ensure that the distributed power source can respond to the dispatching of the upper-level power grid;

[0102] The medium-voltage load and medium-voltage output are input into the medium-voltage surplus power reverse transmission model, and the medium-voltage distribution network line loss and new energy output value under the surplus power reverse transmission scenario are output. The distributed power source is taken into consideration when calculating the medium-voltage surplus power reverse transmission model, and the maximum output constraint of distributed photovoltaic needs to be added. The medium-voltage surplus power reverse transmission model is shown in Formula 9:

[0103]

[0104] Wherein, j represents the number of the medium voltage node to which the low voltage distribution network is connected; p DG j,t,max p represents the total maximum output allowed by the distributed power sources of the low-voltage distribution network connected to the medium-voltage node j in the period t, and its value is the sum of the maximum power generation of the distributed power sources of the low-voltage distribution network; DG j,t It represents the actual total distributed power output of the distributed power connected to the medium voltage node j in time period t. The meanings of other parameters are consistent with other scenarios, and the meanings remain unchanged when the subscripts change.

[0105] The active power at the connection between medium and low voltage is obtained according to the medium voltage load and the output value of new energy. The active power at the connection between medium and low voltage is obtained by subtracting the output value of new energy from the medium voltage load. The photovoltaic output, low voltage load and active power at the connection between medium and low voltage are input into the low voltage surplus power reverse transmission model to obtain the low voltage distribution network line loss and new energy consumption under the surplus power reverse transmission scenario. In the low voltage surplus power reverse transmission model, the medium voltage distribution network determines the total distributed power generation required to be supplied by the low voltage distribution network to which it is connected, and then the low voltage distribution network distributes the output of each distributed power source. Therefore, when calculating the power flow of the low voltage distribution network, the output of each distributed photovoltaic unit on the low voltage side must be constrained, and in order to ensure the conservation of active power transmitted from the medium voltage distribution network to the low voltage distribution network, an active power balance constraint must be added. The low voltage surplus power reverse transmission model is shown in Formula 10:

[0106]

[0107] Among them, p j，t load represents the load of medium voltage distribution network node j in period t; p j，t DG represents the actual distributed generation output of the medium voltage node in period t;

[0108] Figure 4 Schematic diagram of the solution in the residual power reverse transmission scenario of the embodiment of the present invention, such as Figure 4 As shown, the framework of the surplus power reverse transmission scenario is shown;

[0109] The low-voltage distribution network line loss, new energy consumption and medium-voltage distribution network line loss in the corresponding scenario are taken as the loss amount.

[0110] In step S140, the profit and loss amount is calculated based on the loss amount, the digital cost is calculated based on the digital configuration data, and the profit and loss amount and the digital cost are used as the evaluation results of each scenario of the distribution network, specifically including:

[0111] The calculation of profit and loss based on the loss volume specifically includes:

[0112] Based on the line loss of the low-voltage distribution network and the line loss of the medium-voltage distribution network, the line profit and loss are calculated by formula 1:

[0113] B loadloss =pr t ×ΔP loadloss Formula 1;

[0114] Among them, B loadloss represents the gain or loss caused by line loss, pr t represents the electricity price of coal-fired power generation, ΔP loadloss Indicates the loss amount of low voltage distribution network line loss or medium voltage distribution network line loss;

[0115] Based on the amount of new energy consumption, the carbon emission gains and losses are calculated using Formula 2:

[0116] B ce =ΔP DG ×K ce × ce Formula 2;

[0117] Among them, B ce Represents the gains and losses caused by carbon emissions, ΔP DG represents the amount of new energy consumption, K ce represents the carbon dioxide emissions generated by unit thermal power generation, pr ce It represents the trading price of unit carbon dioxide emission in the national carbon trading market;

[0118] Preferably, the price at which the power grid company purchases coal-fired power generation is different from the price at which it purchases distributed power generation. The resulting cost-effectiveness of purchasing electricity is shown in Formula 11:

[0119] B pg =ΔP DG ×(pr t -pr p ) Formula 11;

[0120] Among them, B pg The profit and loss of power grid enterprises due to the cost of purchasing electricity; p is the grid-connected electricity price of distributed generation surplus power, pr t Represents the price of electricity from coal-fired power generation.

[0121] Calculation of digitalization costs based on digitalization configuration data includes:

[0122] The digitization cost is calculated using Formula 3:

[0123]

[0124] Where cost represents the digitization cost, C mdrepresents the cost of a single deployed measurement device, M represents the number of devices required to be deployed, and C trans represents the cost of replacing a single transformer, N represents the number of transformers to be replaced, C control represents the cost of a single distributed photovoltaic control device, S represents the number of distributed photovoltaic control devices, C cpt Represents the operating environment cost of the intelligent scheduling algorithm.

[0125] The embodiment of the present invention uses two methods, an annualized rate of return without considering the interest rate and an annualized rate of return with considering the interest rate, to analyze the input-output. The annualized rate of return without considering the interest rate is calculated as shown in Formula 12:

[0126]

[0127] Among them, R represents the annualized rate of return without considering the interest rate; n represents the full life cycle of the investment equipment, which is also the investment year, in years.

[0128] The annualized rate of return considering the interest rate is calculated as shown in Formula 13:

[0129]

[0130] Among them, R r represents the annualized rate of return taking into account the interest rate; a is the annual interest rate that can be obtained by using the investment cost for other financial management.

[0131] The above technical solutions of the embodiments of the present invention are illustrated with reference to the following drawings.

[0132] Figure 5 is a schematic diagram of a multi-scenario evaluation framework of an embodiment of the present invention, such as Figure 5 As shown in the figure, it demonstrates the acquisition of different types of data information and the construction of distribution network flow analysis models for three different scenarios. Through the model output, the medium-voltage distribution network line loss, low-voltage distribution network line loss and distribution network new energy consumption in different scenarios can be obtained. Finally, a profit and loss analysis is performed based on the output results, and the results of multiple scenarios are evaluated.

[0133] To sum up, in response to the existing problems, the multi-scenario evaluation method of the distribution network invented this time, through the power flow analysis models corresponding to multiple scenarios, including the self-generation and self-use mode, the surplus power grid mode and the surplus power return mode, respectively solves the power loss in each scenario of the distribution network line, and can conduct a comprehensive analysis of the power grid operation; the designed power flow analysis model aims to minimize line losses. The model under each scenario takes into account the branch power flow balance and the safe and stable operation balance, and can establish other corresponding constraints according to the characteristics of the scenario; and conducts a profit and loss analysis based on the power loss to analyze the benefits brought about by the changes in line losses, that is, the amount of new energy consumption, and generally constructs a comprehensive and effective quantitative evaluation plan.

[0134] Device Embodiment

[0135] According to an embodiment of the present invention, a multi-scenario evaluation device for a distribution network is provided. Figure 6 is a schematic diagram of a multi-scenario evaluation device for a distribution network according to an embodiment of the present invention, such as Figure 6 As shown, the multi-scenario evaluation device for a distribution network according to an embodiment of the present invention specifically includes:

[0136] The data acquisition module 60 is used to acquire the output data and load data of the distribution network lines and each node of the lines, and the digital configuration data;

[0137] The low voltage equivalent calculation module 62 is used to perform an equivalent calculation update of the low voltage distribution network based on the output data and the load data to obtain the photovoltaic output and the low voltage load, and is specifically used for:

[0138] Determine whether the load data of each node of the distribution network line is less than the output data. If the load data is less than the output data, the photovoltaic output is updated to the difference between the output data and the load data, and the low-voltage load is updated to zero. Otherwise, the photovoltaic output is the original output data, and the low-voltage load is updated to the difference between the load data and the output data.

[0139] The power flow analysis module 64 is used to solve the power loss in each scenario of the distribution network line based on the photovoltaic output and the low-voltage load through the power flow analysis models corresponding to the multiple scenarios, wherein the multiple scenarios include the self-generation and self-use mode, the surplus power grid-connected mode and the surplus power reverse transmission mode, and is specifically used for:

[0140] The power flow analysis models corresponding to the multiple scenarios specifically include: low-voltage self-generation and self-use model, medium-voltage self-generation and self-use model, low-voltage surplus power grid-connected model, medium-voltage surplus power grid-connected model, low-voltage surplus power reverse transmission model and medium-voltage surplus power reverse transmission model;

[0141] Among them, the objective function of the power flow analysis model corresponding to each of the multiple scenarios is to minimize the line loss, and the constraints include branch power flow balance constraints and safe and stable operation constraints;

[0142] Through the power flow analysis models corresponding to multiple scenarios, the loss of distribution network lines in each scenario is solved separately, including:

[0143] In the self-generation and self-consumption scenario, the photovoltaic output and low-voltage load are input into the low-voltage self-generation and self-consumption model, and the low-voltage distribution network line loss, new energy consumption and active power at the medium- and low-voltage connection in the self-generation and self-consumption scenario are output. The active power at the medium- and low-voltage connection is used as the medium-voltage load, and the medium-voltage load data is input into the medium-voltage self-generation and self-consumption model, which outputs the medium-voltage distribution network line loss in the self-generation and self-consumption scenario;

[0144] In the surplus power grid-connected scenario, the photovoltaic output and low-voltage load are input into the low-voltage surplus power grid-connected model, and the low-voltage distribution network line loss, new energy consumption and active power at the medium- and low-voltage connection in the surplus power grid-connected scenario are output. The active power at the medium- and low-voltage connection is used as the medium-voltage load, and the medium-voltage load data is input into the medium-voltage surplus power grid-connected model, which outputs the medium-voltage distribution network line loss in the surplus power grid-connected scenario;

[0145] In the surplus power reverse transmission scenario, based on the photovoltaic output and low-voltage load, the equivalent calculation update of the medium-voltage distribution network is performed as the medium-voltage load and medium-voltage output of the surplus power reverse transmission scenario. The medium-voltage load and medium-voltage output are input into the medium-voltage surplus power reverse transmission model, and the medium-voltage distribution network line loss and new energy output value in the surplus power reverse transmission scenario are output. The active power at the medium- and low-voltage connection is obtained according to the medium-voltage load and new energy output value. The photovoltaic output, low-voltage load and active power at the medium- and low-voltage connection are input into the low-voltage surplus power reverse transmission model to obtain the low-voltage distribution network line loss and new energy consumption in the surplus power reverse transmission scenario;

[0146] The equivalent calculation and update of the medium voltage distribution network for photovoltaic output and low voltage load specifically includes:

[0147] Calculate the sum of the low-voltage loads of each node as the low-voltage total load, calculate the sum of the photovoltaic outputs of each node as the low-voltage total output, and obtain the pre-estimated low-voltage distribution network line loss rate; take the low-voltage total output as the medium-voltage output, consider the low-voltage distribution network line loss rate on the low-voltage total load, and calculate the medium-voltage load;

[0148] The low-voltage distribution network line loss, new energy consumption and medium-voltage distribution network line loss in the corresponding scenario are taken as the loss amount.

[0149] The evaluation module 66 is used to calculate the profit and loss amount based on the loss amount, calculate the digital cost based on the digital configuration data, and use the profit and loss amount and the digital cost as the evaluation results of each scenario of the distribution network, specifically for:

[0150] The calculation of profit and loss based on the loss volume specifically includes:

[0151] Based on the line loss of the low-voltage distribution network and the line loss of the medium-voltage distribution network, the line profit and loss are calculated by formula 1:

[0152] B loadloss =pr t ×ΔP loadloss Formula 1;

[0153] Among them, B loadloss Indicates the gain or loss caused by line loss, pr t represents the electricity price of coal-fired power generation, ΔP loadloss Indicates the loss amount of low voltage distribution network line loss or medium voltage distribution network line loss;

[0154] Based on the amount of new energy consumption, the carbon emission gains and losses are calculated using Formula 2:

[0155] B ce =ΔP DG ×K ce × ce Formula 2;

[0156] Among them, B ce Represents the gains and losses caused by carbon emissions, ΔP DG represents the amount of new energy consumption, K ce represents the carbon dioxide emissions generated by unit thermal power generation, pr ce Represents the trading price of unit carbon dioxide emissions in the national carbon trading market.

[0157] Calculation of digitalization costs based on digitalization configuration data includes:

[0158] The digitization cost is calculated using Formula 3:

[0159]

[0160] Where cost represents the digitization cost, C md represents the cost of a single deployed measurement device, M represents the number of devices required to be deployed, and C trans represents the cost of replacing a single transformer, N represents the number of transformers to be replaced, C control represents the cost of a single distributed photovoltaic control device, S represents the number of distributed photovoltaic control devices, C cpt Represents the operating environment cost of the intelligent scheduling algorithm.

[0161] To sum up, in response to the existing problems, the multi-scenario evaluation device of the distribution network invented this time, through the power flow analysis models corresponding to multiple scenarios, including the self-generation and self-use mode, the surplus power grid mode and the surplus power return mode, respectively solves the power loss in each scenario of the distribution network line, and can conduct a comprehensive analysis of the power grid operation situation; the designed power flow analysis model aims to minimize line losses. The model under each scenario takes into account the branch power flow balance and the safe and stable operation balance, and can establish other corresponding constraints according to the characteristics of the scenario; and conducts a profit and loss analysis based on the power loss to analyze the benefits brought about by the changes in line losses, that is, the amount of new energy consumption, and generally constructs a comprehensive and effective quantitative evaluation plan.

[0162] Electronic device embodiment

[0163] Figure 77 is a schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device 700 may include at least one processor 710 and a memory 720. The processor 710 may execute instructions stored in the memory 720. The processor 710 is connected to the memory 720 through a data bus. In addition to the memory 720, the processor 710 may also be connected to an input device 730, an output device 740, and a communication device 750 through a data bus.

[0164] The processor 710 may be any conventional processor, such as a commercially available CPU. The processor may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof.

[0165] The memory 720 may be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0166] In the embodiment of the present disclosure, executable instructions are stored in the memory 720, and the processor 710 can read the executable instructions from the memory 720 and execute the instructions to implement all or part of the steps of the multi-scenario evaluation method of the distribution network in any of the above exemplary embodiments.

[0167] Computer Readable Storage Medium Embodiments

[0168] In addition to the above-mentioned methods and devices, the exemplary embodiments of the present disclosure may also be a computer program product or a computer-readable storage medium storing the computer program product, wherein the computer product includes computer program instructions, which can be executed by a processor to implement all or part of the steps described in the multi-scenario evaluation method of the distribution network in any of the above-mentioned exemplary embodiments.

[0169] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages ​​and scripting languages ​​(e.g., Python). The program code may be executed entirely on the user computing device, partially on the user computing device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0170] Computer readable storage media can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of readable storage media include: a static random access memory (SRAM) with one or more wires electrically connected, an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk, or any suitable combination of the above.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-scenario evaluation method for a distribution network, characterized in that: include: Obtain output data and load data of distribution network lines and nodes, as well as digital configuration data; Based on the output data and the load data, an equivalent calculation update of the low-voltage distribution network is performed to obtain the photovoltaic output and the low-voltage load; Based on the photovoltaic output and the low-voltage load, the power loss under each scenario of the distribution network line is solved respectively through the power flow analysis models corresponding to each of the multiple scenarios, wherein the multiple scenarios include a self-generation and self-use mode, a surplus power grid-connected mode, and a surplus power reverse transmission mode; The profit and loss amount is calculated based on the loss amount, the digital cost is calculated based on the digital configuration data, and the profit and loss amount and the digital cost are used as evaluation results of each scenario of the distribution network.

2. The method according to claim 1, characterized in that The equivalent calculation and updating of the low-voltage distribution network based on the output data and the load data to obtain the photovoltaic output and the low-voltage load specifically includes: Determine whether the load data of each node of the distribution network line is less than the output data. If the load data is less than the output data, update the photovoltaic output to the difference between the output data and the load data, and update the low-voltage load to zero; otherwise, the photovoltaic output is the original output data, and update the low-voltage load to the difference between the load data and the output data.

3. The method according to claim 1, characterized in that The power flow analysis models corresponding to the multiple scenarios specifically include: a low-voltage self-generation and self-use model, a medium-voltage self-generation and self-use model, a low-voltage surplus power grid-connected model, a medium-voltage surplus power grid-connected model, a low-voltage surplus power reverse transmission model, and a medium-voltage surplus power reverse transmission model; The objective function of the power flow analysis model corresponding to each of the multiple scenarios is to minimize the line loss, and the constraints include branch power flow balance constraints and safe and stable operation constraints; Through the power flow analysis models corresponding to multiple scenarios, the loss amount of the distribution network line in each scenario is solved respectively, including: In the self-generation and self-use scenario, the photovoltaic output and the low-voltage load are input into the low-voltage self-generation and self-use model, and the low-voltage distribution network line loss, new energy consumption and active power at the medium- and low-voltage connection in the self-generation and self-use scenario are output. The active power at the medium- and low-voltage connection is used as the medium-voltage load, and the medium-voltage load is input into the medium-voltage self-generation and self-use model, and the medium-voltage distribution network line loss in the self-generation and self-use scenario is output; In the surplus power grid-connected scenario, the photovoltaic output and the low-voltage load are input into the low-voltage surplus power grid-connected model, and the low-voltage distribution network line loss, new energy consumption and active power at the medium- and low-voltage connection in the surplus power grid-connected scenario are output; the active power at the medium- and low-voltage connection is used as the medium-voltage load, and the medium-voltage load is input into the medium-voltage surplus power grid-connected model, and the medium-voltage distribution network line loss in the surplus power grid-connected scenario is output; In the surplus power reverse transmission scenario, based on the photovoltaic output and the low-voltage load, an equivalent calculation update of the medium-voltage distribution network is performed as the medium-voltage load and medium-voltage output of the surplus power reverse transmission scenario, the medium-voltage load and the medium-voltage output are input into the medium-voltage surplus power reverse transmission model, and the medium-voltage distribution network line loss and the new energy output value in the surplus power reverse transmission scenario are output. The active power at the medium- and low-voltage connection is obtained according to the medium-voltage load and the new energy output value, and the photovoltaic output, the low-voltage load and the active power at the medium- and low-voltage connection are input into the low-voltage surplus power reverse transmission model to obtain the low-voltage distribution network line loss and the new energy consumption in the surplus power reverse transmission scenario; The low-voltage distribution network line loss, the new energy consumption and the medium-voltage distribution network line loss in the corresponding scenario are taken as the loss amount.

4. The method according to claim 3, characterized in that Based on the photovoltaic output and the low-voltage load, performing equivalent calculation update of the medium-voltage distribution network specifically includes: The sum of the low-voltage loads of each node is calculated as the total low-voltage load, the sum of the photovoltaic outputs of each node is calculated as the total low-voltage output, and the pre-estimated low-voltage distribution network line loss rate is obtained; the low-voltage total output is used as the medium-voltage output, the low-voltage distribution network line loss rate is considered on the low-voltage total load, and the medium-voltage load is calculated.

5. The method according to claim 3, characterized in that: The calculation of the profit and loss amount based on the loss amount specifically includes: Based on the low voltage distribution network line loss and the medium voltage distribution network line loss, the line profit and loss is calculated by formula 1: B loadloss =pr t ×ΔP loadloss Formula 1; Among them, B loadloss represents the gain or loss caused by line loss, pr t represents the electricity price of coal-fired power generation, ΔP loadloss Indicates the loss amount of low voltage distribution network line loss or medium voltage distribution network line loss; Based on the new energy consumption, the carbon emission profit and loss is calculated by formula 2: B ce =ΔP DG ×K ce × ce Formula 2; Among them, B ce Represents the gains and losses caused by carbon emissions, ΔP DG represents the amount of new energy consumption, K ce represents the carbon dioxide emissions generated by unit thermal power generation, pr ce Represents the trading price of unit carbon dioxide emissions in the national carbon trading market.

6. The method according to claim 1, characterized in that The calculating the digitization cost based on the digitization configuration data comprises: The digitization cost is calculated using Formula 3: Where cost represents the digitization cost, C md represents the cost of a single deployed measurement device, M represents the number of devices required to be deployed, and C trans represents the cost of replacing a single transformer, N represents the number of transformers to be replaced, C control represents the cost of a single distributed photovoltaic control device, S represents the number of distributed photovoltaic control devices, C cpt Represents the operating environment cost of the intelligent scheduling algorithm.

7. A multi-scenario evaluation device for a distribution network, characterized in that: include: Data acquisition module, used to obtain output data and load data of distribution network lines and nodes of the lines, as well as digital configuration data; A low-voltage equivalent calculation module, used to perform an equivalent calculation update of the low-voltage distribution network based on the output data and the load data to obtain the photovoltaic output and the low-voltage load; A power flow analysis module, for solving the power loss of the distribution network line in each scenario based on the photovoltaic output and the low-voltage load through power flow analysis models corresponding to multiple scenarios, wherein the multiple scenarios include a self-generation and self-use mode, a surplus power grid-connected mode, and a surplus power reverse transmission mode; An evaluation module is used to calculate a profit and loss amount based on the loss amount, calculate a digital cost based on the digital configuration data, and use the profit and loss amount and the digital cost as evaluation results of each distribution network scenario.

8. The device according to claim 7, characterized in that The power flow analysis model specifically includes: Low-voltage self-generation and self-use model, medium-voltage self-generation and self-use model, low-voltage surplus power grid-connected model, medium-voltage surplus power grid-connected model, low-voltage surplus power reverse transmission model and medium-voltage surplus power reverse transmission model; Among them, the objective function of the power flow analysis model corresponding to each of the multiple scenarios is to minimize the line loss, and the constraints include branch power flow balance constraints and safe and stable operation constraints.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the multi-scenario evaluation method for a distribution network as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the steps of the multi-scenario evaluation method for a distribution network as described in any one of claims 1 to 6 are implemented.

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