CN116307650B - Novel power distribution network source network load coordination random optimization operation method oriented to flexibility - Google Patents

Novel power distribution network source network load coordination random optimization operation method oriented to flexibility Download PDF

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CN116307650B
CN116307650B CN202310588665.8A CN202310588665A CN116307650B CN 116307650 B CN116307650 B CN 116307650B CN 202310588665 A CN202310588665 A CN 202310588665A CN 116307650 B CN116307650 B CN 116307650B
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刘佳
唐早
汤奕
朱璇
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Southeast University
Liyang Research Institute of Southeast University
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Abstract

The invention discloses a novel power distribution network source network load coordination random optimization operation method oriented to flexibility, which comprises the following steps: based on a power distribution network safety domain theory, a novel power distribution network flexibility evaluation index system comprising flexible distance expectations, standard deviations and flexible distance variation coefficients is constructed; constructing a multi-objective coordination optimization operation model oriented to system flexibility improvement; providing constraint conditions of a multi-objective coordinated optimization operation model; and solving the multi-objective coordinated optimization operation model based on the forward boundary intersection point and the dynamic niche differential evolution algorithm until the pareto optimal scheme is output. The method provided by the invention realizes multidimensional optimization of the economical efficiency and the flexibility of the system operation, the safety margin of the obtained optimized operation method in each scene is considerable and measurable, the operation state of the system can be conveniently mastered by a dispatcher, the unsafe state can be prevented and controlled, and a theoretical basis is provided for the optimized scheduling of the novel power distribution network.

Description

Novel power distribution network source network load coordination random optimization operation method oriented to flexibility
Technical Field
The invention relates to the technical field of source network load coordination optimization, in particular to a novel power distribution network source network load coordination random optimization operation method oriented to flexibility.
Background
Economy, safety and reliability are three elements of traditional power system evaluation, but with the high-proportion large-scale access of new energy sources represented by wind power and photovoltaics to a power grid, the real-time electric power and electric quantity balance of the system is challenged. As a new element for evaluating source load response capability, flexibility is included in the evaluation category of a novel electric power system, and how to improve the system operation flexibility becomes a current hot research problem. For the power distribution network, the flexible resources are distributed, discretized, large-scale and other characteristics, including the adjustment means such as distributed power output control, flexible load response, structural form optimization and the like, and the system flexibility is improved by coordinating and optimizing the source network load resources in the novel power distribution network. On the other hand, the novel power distribution network has the characteristics of initiative, activity, feeder interconnection and the like, and when a system fails, the novel power distribution network is required to have the power supply capacity of recovering the power supply load without power supply, namely, the operation safety in the failure recovery mode is required to be ensured. Therefore, the system operation safety and flexibility can be effectively improved by excavating distributed flexible resources in the novel power distribution network, so that the multi-objective collaborative optimization of power distribution network scheduling is realized.
The power distribution network security domain theory becomes a new method for identifying the operation security of the system, and compared with the traditional point-by-point simulation method, the method has the characteristics of considerable and measurable working points, controllable safety margin and the like. By defining the outlet power vector of each feeder line, all working points which can meet safety constraints (including line capacity constraints and node voltage constraints) are searched, and then a closed convex set is formed, namely the safety domain of the power distribution network. The security domain boundary does not change along with the change of the system running state, the minimum Euclidean distance from the dynamic running working point to the security boundary can be calculated to evaluate the security margin of the system, and the security distance of the working point positioned inside the security domain is defined to be positive, and the security distance of the working point positioned outside the security domain is defined to be negative. When the working point is located outside the security domain, the orthogonal direction from the working point to the security boundary is the optimal restoration control direction. With the wide access of flexible resources such as distributed power sources and flexible loads to the power distribution network, the fluctuation of the system operation working points is more frequent, and the adoption of the power distribution network safety domain theory is helpful for rapidly researching and judging the operation safety, so that the economic optimization of the operation problem is realized by utilizing the distributed flexible source load resources on the premise of ensuring the system safety. Active management measures (such as on-load voltage regulating transformer tap adjustment, distributed power output/power factor adjustment, demand response, network reconstruction and the like) are utilized, so that the running state of the system can be effectively improved, and the elasticity and toughness of the novel power distribution network are enhanced.
In view of the above-mentioned problems and the shortcomings of the prior art, the following problems are needed to be solved:
1) Aiming at different feeder lines and different operation scenes, how to represent the average level and fluctuation condition of the distance from the working point to the safety boundary, a novel power distribution network flexibility evaluation index system based on a safety domain needs to be defined;
2) From the economical and flexible viewpoints, a novel multi-target optimizing operation model of the power distribution network is provided by using distributed flexible source network load resources, and a convex relaxation conversion method of a nonlinear mixed integer programming model is researched;
3) And researching a multi-target optimization algorithm comprising source network load running cost and flexibility evaluation indexes by combining model characteristics, and analyzing the calculation performance of a normal boundary intersection point and a meta heuristic algorithm on model solving.
Disclosure of Invention
Aiming at the problems, a novel power distribution network source network load coordination random optimization operation method oriented to flexibility is provided, and multi-objective coordination pareto optimizing of system economy and flexibility is realized by optimizing multi-element distributed flexible source network load resources in the novel power distribution network under the constraint conditions of network safety, active management and the like. In addition, the system flexibility is quantitatively evaluated based on the theory of the safety domain of the power distribution network, the flexible distance variation coefficient of each operation scene is calculated, the novel safety operation flexibility level of the power distribution network is comprehensively disclosed, so that the operation economy and flexibility of the power distribution network are comprehensively improved, the active supporting capacity of the distributed flexible source network load resource is excavated, and the system toughness and the resistance to fault risks are improved.
In order to achieve the above object, the present invention is realized by the following technical scheme:
novel power distribution network source network load coordination random optimization operation method oriented to flexibility, which comprises the following steps:
constructing a novel power distribution network flexibility evaluation index system based on a power distribution network safety domain theory, wherein the novel power distribution network flexibility evaluation index system comprises a flexible distance expectation, a standard deviation and a flexible distance variation coefficient;
constructing a multi-objective coordinated optimization operation model for improving system flexibility based on the novel power distribution network flexibility evaluation index system, wherein the multi-objective coordinated optimization operation model is double multi-objective optimization comprising a flexibility objective function and an economical objective function, the flexibility objective function is a minimum flexible distance variation coefficient, and the economical objective function is a minimum total operation cost;
providing constraint conditions of the multi-objective coordinated optimization operation model, wherein the constraint conditions comprise a node power balance equation, a power flow calculation equation, radial operation constraint, network security constraint and active management constraint; converting bilinear terms in a power flow calculation equation into a linear form by using a large M method;
and solving the multi-objective coordinated optimization operation model based on the forward boundary intersection point and the dynamic niche differential evolution algorithm, and outputting an operation simulation result.
As a preferable scheme of the invention, the distribution network security domain theory is defined as a feeder line outlet power working point set under the premise of ensuring the safe operation of a system, wherein the safe operation of the system comprises no overload of a line and no out-of-limit of node voltage;
the method for constructing the novel power distribution network flexibility evaluation index system specifically comprises the following steps: establishing a distribution network security domain model according to a distribution network security domain theory, analyzing the security boundary geometric characteristics of the distribution network security domain model, and calculating the minimum Euclidean distance from a feeder line outlet power working point to a security boundary to measure the system operation security margin; based on a scene analysis method, analyzing flexible distance expectations, standard deviations and flexible distance variation coefficients of all operation scenes, and further constructing a flexible evaluation index cluster based on the flexible distance expectations, the standard deviations and the flexible distance variation coefficients to form a novel power distribution network flexibility evaluation index system.
As a preferred scheme of the present invention, mathematical expressions corresponding to the flexible distance expectation, the standard deviation and the flexible distance variation coefficient are respectively:
；
；
in the method, in the process of the invention,and->Scene +.>Flexible distance expectations and standard deviations; />Is a flexible distance variation coefficient; />For feed line->In scene->A flexible distance below; />And->The number of feeder lines and the number of scenes are respectively; />And->Respectively a feeder set and a scene set;
；
；
in the method, in the process of the invention,for scene->Lower feeder outlet power operating point, +.>For critical operating point on safety boundary +.>For the safety boundary set, ++>Is a security domain; />For the outlet power of the feeder 1, < > for>For the outlet power of the feed line 2, < > for>For feed line->Outlet power of>For feed line->Is a power output of the engine; />And->Nodes +.>Voltage and feeder->The current flow is such that,and->Nodes +.>Voltage->Lower and upper limits of->And->Are respectively feeder lines->Current->Lower and upper limits of (2);is a set of nodes.
As a preferred solution of the present invention, the running total cost includes a switching action, a distributed power running, a distributed power active management, a transaction with a main network, a network loss and a demand response cost, and the corresponding calculation expression is as follows:；
；
；
；
；
；
in the method, in the process of the invention,、/>、/>、/>、/>and->Respectively is scene->Time below->Switching action, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost; />And->The unit switch action and demand response costs are respectively; />、/>And->Respectively at node->The unit operation cost of the micro gas turbine, wind power and photovoltaic; />、/>And->Respectively at node->The unit of micro gas turbine, wind power and photovoltaic actively manages the cost; />And->Respectively is scene->Time below->Unit transaction and loss costs of (a); />For the scene->Time below->Switch->Status (S)>For the scene->Time below->Switch->State, closed 1, open 0; />、/>And->Respectively at node->Micro gas turbine, wind power and photovoltaic in scene->Time below->Is an active force of (a); />For being located at node +.>Is in scene->Time below->Is an active response of (a);and->Respectively is scene->Time below->The interactive power and the network loss of the (a); />For the scene->Time below->Is a span of (2); />、/>And->Respectively a switch set, a distributed power supply set and a node set.
As a preferred scheme of the invention, the objective function expression of the multi-objective coordination optimization operation model is as follows:
；
wherein,,for the total cost of operation->Is a flexible distance variation coefficient;
in the method, in the process of the invention,the number of running scenes; />、/>、/>、/>、/>And->Respectively is scene->Time below->Switching action, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost; />And->A scene set and a time set, respectively.
As a preferred embodiment of the present invention, the expression of the node power balance equation is:
；
；
in the method, in the process of the invention,and->Lines are respectively->Active and reactive power of (a); />And->Lines are respectively->Resistance and reactance of (a);for line->Square of the current; />And->Respectively at node->The active and reactive power outputs are predicted by the distributed power supply;and->Respectively at node->Active and reactive power is removed from the distributed power supply; />And->Respectively at node->Active and reactive loads predicted by (a); />And->Respectively at node->Active and reactive load response amounts of (a); />And->Respectively is node set and node->A set of interconnected nodes;
the expression of the tide calculation equation is as follows:
；
；
；
in the method, in the process of the invention,for node->Square of the voltage amplitude; />For node->Square of the voltage amplitude; />Is a sufficiently large positive number, taken as 10000; />For switch->State, closed 1, open 0; />Is vector transposition;
the flow calculation adopts a Distflow form after the large M method is relaxed.
The expression of the radial operation constraint is:
；
；
in the method, in the process of the invention,and->The number of nodes and the number of nodes of the transformer substation are respectively; />For line->Virtual active power of (a); />And->Switch set and node respectively->A set of interconnected nodes; />Is a substation node set.
As a preferred scheme of the present invention, the network security constraint includes a flexible distance constraint and a node voltage opportunity constraint, and the expression of the flexible distance constraint is:
；
；
in the method, in the process of the invention,for feed line->Flexible distance of (2); />For feed line->Is a minimum flexible distance of (2); />And->Are respectively feeder lines->And (ii) of the feeder line>The outlet power is in the super plane->Coefficients of (a); />The number of the feeder lines is the number; />For feed line->Is a power output of the engine; />And->Respectively a hyperplane set and a feeder line set;
the node voltage opportunity constraint expression is:
；
in the method, in the process of the invention,representing probability->A safe confidence level for the node voltage; />For node->Voltage (V)>And->Nodes +.>Voltage->Lower and upper limits of (2); />Is a set of nodes.
As a preferred aspect of the present invention, the active management constraints include a switching action constraint, an on-load tap-changing transformer regulation constraint, a distributed power supply output constraint, a distributed power supply power factor constraint, and a demand response constraint;
the expression of the switch action constraint is as follows:
in the method, in the process of the invention,、/>and->Respectively a switch set, a scene set and a time set; />For the scene->Time below->Switch->Status (S)>For the scene->Time below->Switch->State, closed 1, open 0;for switch->An upper limit of the number of actions;
the expression of the on-load tap-changing voltage-regulating transformer tap-changing restriction is as follows:
；
；
；
in the method, in the process of the invention,、/>nodes +.>Voltage and node->A voltage; />And->Lines are respectively->The on-load regulating transformer is at moment +.>And time->Tap positions of (2); />And->Lines are respectively->The upper limit and the lower limit of the tap position of the on-load voltage regulating transformer are set; />Step length is adjusted for the voltage of the on-load regulating transformer; />Is an on-load voltage regulating transformer set;
the expression of the distributed power supply output constraint is as follows:
；
；
in the method, in the process of the invention,the maximum cutting rate is output for the distributed power supply; />For being located at node +.>Is predictive of active power, +.>For being located at node +.>An upper active power output limit is predicted by the distributed power supply; />Is a distributed power supply set;and->Respectively at node->Active and reactive power is removed from the distributed power supply; />To be located at the nodeA distributed power source output power factor angle;
the expression of the distributed power supply power factor constraint is as follows:
；
in the method, in the process of the invention,and->Respectively at node->Upper and lower limits of the distributed power supply output power factor angle;
the expression of the demand response constraint is:
；
；
in the method, in the process of the invention,is a node set; />And->Respectively at node->Active and reactive load response amounts of (a); />For being located at node +.>An upper active load response amount limit of (2); />For being located at node +.>Is a load power factor angle of (2).
As a preferred scheme of the present invention, the method for solving the multi-objective coordinated optimization operation model specifically includes: the method comprises the steps of inputting a wind-solar-load time sequence curve, a system topological structure and a dynamic niche differential evolution algorithm of a region where a power distribution network is located, randomly generating population individuals, defining fitness functions based on a flexibility objective function and an economy objective function respectively, calculating individual fitness values, retaining elite individuals, eliminating individuals after falling, and updating the population individuals through crossover, selection and mutation operations until a pareto optimal scheme is output.
Compared with the prior art, the invention has the beneficial effects that: the invention provides system flexibility by utilizing active management measures such as distributed power supply output adjustment, network reconstruction, demand response and the like, digs multi-element distributed flexible source network load resources, and improves the energy management level of the novel power distribution network in a multi-dimensional manner; the operation safety and margin of the feeder line outlet power working point are visually perceived, so that system schedulers can observe the operation situation of the power distribution network and control the power distribution network in a preventive manner, and the flexibility level of the system is quantitatively evaluated by counting the flexible distance of each operation scene and calculating the flexible distance expectation, standard deviation and flexible distance variation coefficient; the invention realizes the overall optimization of the system operation economy and flexibility, and is different from the traditional economy optimization, the invention meters the full link cost of the source network load including the distributed power supply operation, the switching action, the demand response and the like, and simultaneously explores the game equilibrium relation of the economy and flexibility optimization targets.
The method provided by the invention realizes multidimensional optimization of system operation economy and flexibility, and provides active supporting capability for system flexibility improvement by reasonably adjusting flexible source network load resources in the novel power distribution network, and improves operation economy on the premise of ensuring safe operation of the system; in addition, the safety margin of the obtained optimized operation method in each scene is considerable, so that a dispatcher can grasp the operation state of the system and prevent and control the unsafe state, and a theoretical basis is provided for the optimized dispatching of the novel power distribution network.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a diagram of a novel power distribution network multi-objective optimization operation technology;
FIG. 2 is a topology structure diagram of a simulation analysis system according to an embodiment of the present invention;
FIG. 3 is a graph of wind and light load timing for a region in which embodiments of the present invention are located;
FIG. 4 is a schematic diagram of a multi-target pareto front for both economy and flexibility of embodiments of the invention;
fig. 5 is a schematic diagram of flexible distance distribution of each feeder line in two schemes according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the invention, fall within the scope of protection of the invention.
As shown in fig. 1, the embodiment of the invention provides a novel power distribution network source network load coordination random optimization operation method oriented to flexibility, which specifically comprises the following steps:
step S1: constructing a novel power distribution network flexibility evaluation index system based on a power distribution network safety domain theory;
the distribution network safety domain theory is used in the field of system optimization, the quantitative influence of the spatial position distribution of the outlet power working points of all feeder lines on the system operation safety is analyzed, and a heuristic decision method is provided for improving the system flexibility.
The distribution network security domain theory is defined as a feeder line outlet power working point set under the premise of ensuring the safe operation of the system (the line is not overloaded and the node voltage is not out of limit), a distribution network security domain model is established according to the distribution network security domain theory, the security boundary geometric characteristics of the distribution network security domain model are analyzed, and the minimum Euclidean distance from the feeder line outlet power working point to the security boundary is calculated to measure the operation security margin of the system; based on a scene analysis method, analyzing flexible distance expectations, standard deviations and flexible distance variation coefficients of all operation scenes, and further constructing a flexible evaluation index cluster based on the flexible distance expectations, the standard deviations and the flexible distance variation coefficients to form a novel power distribution network flexibility evaluation index system.
The mathematical expressions corresponding to the flexible distance expectation, the standard deviation and the flexible distance variation coefficient are respectively as follows:
；
；
；
in the method, in the process of the invention,and->Scene +.>Flexible distance expectations and standard deviations; />Is a flexible distance variation coefficient; />For feed line->In scene->A flexible distance below; />And->The number of feeder lines and the number of scenes are respectively; />And->Respectively a feeder set and a scene set;
；
；
in the method, in the process of the invention,for scene->Lower feeder outlet power operating point, +.>For a critical operating point on the safety boundary,for the safety boundary set, ++>Is a security domain; />For the outlet power of the feeder 1, < > for>For the outlet power of the feed line 2, < > for>For feed line->Outlet power of>For feed line->Is a power output of the engine; />And->Nodes +.>Voltage and feeder->Current (I)>Andnodes +.>Voltage->Lower and upper limits of->And->Are respectively feeder lines->Current->Lower and upper limits of (2); />Is a set of nodes.
Step S2: constructing a multi-objective coordinated optimization operation model for improving system flexibility based on a novel power distribution network flexibility evaluation index system, wherein the multi-objective coordinated optimization operation model is double multi-objective optimization comprising a flexibility objective function and an economical objective function, the flexibility objective function is a minimum flexible distance variation coefficient, and the economical objective function is a minimum total operation cost;
the total running cost comprises switching action, distributed power supply running, distributed power supply active management, main network transaction, network loss and demand response cost, and the corresponding calculation expression is as follows:
；
；
；
；
；
；
in the method, in the process of the invention,、/>、/>、/>、/>and->Respectively is scene->Time below->Switching action, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost;
and->The unit switch action and demand response costs are respectively; />、/>And->Respectively at node->The unit operation cost of the micro gas turbine, wind power and photovoltaic; />、/>And
respectively at node->The unit of micro gas turbine, wind power and photovoltaic actively manages the cost; />Andrespectively is scene->Time below->Unit transaction and loss costs of (a); />For the scene->Time below->Switch->Status (S)>For the scene->Time below->Switch->State, closed 1, open 0; />、/>Andrespectively at node->Micro gas turbine, wind power and photovoltaic in scene->Time below->Is an active force of (a); />For being located at node +.>Is in scene->Time below->Is an active response of (a); />And->Respectively is scene->The next momentThe interactive power and the network loss of the (a); />For the scene->The next moment
Is a span of (2); />、/>And->Respectively a switch set, a distributed power supply set and a node set.
The objective function expression of the multi-objective coordination optimization operation model is as follows:
wherein,,for the total cost of operation->Is a flexible distance variation coefficient;
in the method, in the process of the invention,the number of running scenes; />、/>、/>、/>、/>And->Respectively is scene->Time below->Switching action, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost; />And->A scene set and a time set, respectively.
Step S3: providing constraint conditions of the multi-objective coordinated optimization operation model, wherein the constraint conditions comprise a node power balance equation, a power flow calculation equation, radial operation constraint, network security constraint and active management constraint; converting bilinear terms in a power flow calculation equation into a linear form by using a large M method;
the expression of the node power balance equation is:
；
；
in the method, in the process of the invention,and->Lines are respectively->Active and reactive power of (a); />And->Respectively are provided withFor line->Resistance and reactance of (a); />For line->Square of the current; />And->Respectively at node->The active and reactive power outputs are predicted by the distributed power supply; />And->Respectively at node->Active and reactive power is removed from the distributed power supply; />And->Respectively at node->Active and reactive loads predicted by (a); />And->Respectively at node->Active and reactive load response amounts of (a); />And->Respectively is node set and node->A set of interconnected nodes;
the expression of the flow calculation equation is:
；
；
；
in the method, in the process of the invention,for node->Square of the voltage amplitude; />For node->Square of the voltage amplitude; />Is a sufficiently large positive number, taken as 10000; />For switch->State, closed 1, open 0; />Is vector transposition;
the flow calculation adopts a Distflow form after the large M method is relaxed.
The expression of the radial run constraint is:
；
；
in the method, in the process of the invention,and->The number of nodes and the number of nodes of the transformer substation are respectively; />For line->Virtual active power of (a); />And->Switch set and node respectively->A set of interconnected nodes; />Is a substation node set.
The network security constraint comprises a flexible distance constraint and a node voltage opportunity constraint, and the expression of the flexible distance constraint is as follows:
；
；
in the method, in the process of the invention,for feed line->Flexible distance of (2); />For feed line->Is a minimum flexible distance of (2); />And->Are respectively feeder lines->And (ii) of the feeder line>The outlet power is in the super plane->Coefficients of (a); />The number of the feeder lines is the number; />For feed line->Is a power output of the engine; />And->Respectively a hyperplane set and a feeder line set;
the node voltage opportunity constraint is expressed as:
in the method, in the process of the invention,representing probability->A safe confidence level for the node voltage; />For node->Voltage (V)>And->Nodes +.>Voltage->Lower and upper limits of (2); />Is a set of nodes.
The active management constraints include a switching action constraint, an on-load tap-changing transformer regulation constraint, a distributed power supply output constraint, a distributed power supply power factor constraint and a demand response constraint;
the expression of the switch action constraint is:
in the method, in the process of the invention,、/>and->Respectively a switch set, a scene set and a time set; />For the scene->Time below->Switch->Status (S)>For the scene->Time below->Switch->State, closed 1, open 0;for switch->An upper limit of the number of actions;
the expression for the tap adjustment constraint of an on-load tap changer is:
；
；
；
in the method, in the process of the invention,、/>nodes +.>Voltage and node->A voltage; />And->Lines are respectively->The on-load regulating transformer is at moment +.>And time->Tap positions of (2); />And->Lines are respectively->The upper limit and the lower limit of the tap position of the on-load voltage regulating transformer are set; />Step length is adjusted for the voltage of the on-load regulating transformer; />Is an on-load voltage regulating transformer set;
the expression of the distributed power supply output constraint is:
；
；
in the method, in the process of the invention,the maximum cutting rate is output for the distributed power supply; />For being located at node +.>Is predictive of active power, +.>For being located at node +.>An upper active power output limit is predicted by the distributed power supply; />Is a distributed power supply set; />And->Respectively at node->Active and reactive power is removed from the distributed power supply; />For being located at node +.>A distributed power source output power factor angle;
the expression of the distributed power supply power factor constraint is:
in the method, in the process of the invention,and->Respectively at node->Upper and lower limits of the distributed power supply output power factor angle;
the expression of the demand response constraint is:
；
；
in the method, in the process of the invention,is a node set; />And->Respectively at the nodes/>Active and reactive load response amounts of (a);for being located at node +.>An upper active load response amount limit of (2); />For being located at node +.>Is a load power factor angle of (2).
Step S4: and solving the multi-objective coordinated optimization operation model based on the forward boundary intersection point and the dynamic niche differential evolution algorithm, and outputting an operation simulation result.
The method specifically comprises the following steps: the method comprises the steps of inputting a wind-solar-load time sequence curve, a system topological structure and a dynamic niche differential evolution algorithm of a region where a power distribution network is located, randomly generating population individuals, defining fitness functions based on a flexibility objective function and an economy objective function respectively, calculating individual fitness values, retaining elite individuals, eliminating individuals after falling, and updating the population individuals through crossover, selection and mutation operations until a pareto optimal scheme is output.
This example is described in further detail below in connection with specific embodiments.
In this embodiment, an improved 104-node novel power distribution network is adopted for simulation, and fig. 2 shows the topology structure of the system. The novel power distribution network is based on an IEEE RBTS Bus4 system, and the original system is expanded from 7 feeder lines to 20 feeder lines and from 38 nodes to 104 nodes on the premise of not changing the capacity and the load of a transformer. The novel power distribution network is newly provided with 7 interconnection switches, each of the nodes 7, 30, 54 and 84 is provided with a micro gas turbine, each of the nodes 7, 31, 56 and 85 is provided with a wind power, and each of the nodes 10 and 38 is provided with a photovoltaic. The maximum iteration number of the dynamic niche differential evolution algorithm is 50, the population size is 100, and the scaling factor and the crossing rate are linearly decreased from 0.9 to 0.1. The wind-solar-load time sequence curve of the region is shown in figure 3. The system transformer/line, load conditions and parameter settings are shown in tables 1-3, respectively.
Table 1 transformer/line data
TABLE 2 load data
TABLE 3 simulation parameters
In this embodiment, 4 comparison schemes are set in total, namely, the flexible distance constraint lower limit is changed, and the specific design is as follows:
scheme 1: flexible distance constraints are not considered;
scheme 2: the flexible distance constraint lower limit is set to 0;
scheme 3: a medium flexible operation scheme, namely, the flexible distance constraint lower limit is set to be 0.15 MVA;
scheme 4: the higher flexible operating scheme, i.e. the flexible distance constraint lower limit, is set to 0.3 MVA.
Fig. 4 shows the overall cost of operation and the flexible distance coefficient of variation multi-objective optimized pareto front distribution for schemes 1 and 2. As can be seen from fig. 4, the system economy and flexibility index change trends are opposite, regardless of scheme 1 or scheme 2. For scheme 1, when the minimum total running cost is 19688 $, the flexible distance variation coefficient is the maximum value of 1.88, which means that the system has the best running economy but the worst flexibility; when the maximum total running cost is 122603 $, the flexible distance variation coefficient is the minimum value of 0.04, which means that the system has the worst running economy and the best flexibility. In addition, by comparing the schemes 1 and 2 and comprehensively considering the economy and flexibility of the system, the scheme 2 is better than the scheme 1, and the scheme 2 needs to consider the flexibility of the system to improve the comprehensive benefit of the scheme when the novel power distribution network is in optimal operation.
Table 4 shows the economic and flexibility index results for schemes 1-4.
Table 4 novel distribution network optimization run economics and flexibility results
As can be seen from table 4, the overall cost of operation and the flexible distance coefficient of variation for scheme 2 were reduced by 3.75% and 19.15%, respectively, compared to scheme 1; the overall cost of operation and the flexible distance variation coefficient of scheme 3 were reduced by 1.35% and 31.91%, respectively. The solution-free scheme 4 is mainly characterized in that the lower limit of the flexible distance constraint is set to be higher, and the optimal solution is difficult to find in a feasible space by adjusting the distributed flexible source network load resource. While the distributed power supply of scheme 2 operates at a higher cost, the higher distributed power supply consumption helps to reduce system grid losses and power transactions with the main grid. In addition, power distribution network schedulers can autonomously set a flexible distance constraint lower limit according to system flexibility requirements.
The flexible distance situation for each feeder of schemes 1 and 2 is shown in fig. 5. It can be seen that the flexible distances of the feeder lines are different, mainly because the flexible distances depend on the transformer/line capacity, node load and distributed power source output condition, and the flexible distance level of the feeder lines can be influenced by the novel power distribution network source network load coordination operation scheme. For schemes 1 and 2, the flexible distance of the feed lines 2, 6 and 7 varies greatly while the feed lines 12, 14 and 15 remain substantially unchanged. In addition, the flexible distances of the feeder lines 2, 6 and 13 of the scheme 1 are negative, which means that the scheme 1 cannot guarantee the operation safety under the single fault of the system.
In summary, the invention provides system flexibility by utilizing active management measures such as distributed power supply output adjustment, network reconstruction, demand response and the like, digs multi-element distributed flexible source network load resources, and improves the energy management level of the novel power distribution network in a multi-dimensional manner; the operation safety and margin of the feeder line outlet power working point are visually perceived, so that system schedulers can observe the operation situation of the power distribution network and control the power distribution network in a preventive manner, and the flexibility level of the system is quantitatively evaluated by counting the flexible distance of each operation scene and calculating the flexible distance expectation, standard deviation and flexible distance variation coefficient; the invention realizes the overall optimization of the system operation economy and flexibility, and is different from the traditional economy optimization, the invention meters the full link cost of the source network load including the distributed power supply operation, the switching action, the demand response and the like, and simultaneously explores the game equilibrium relation of the economy and flexibility optimization targets.
The method provided by the invention realizes multidimensional optimization of system operation economy and flexibility, and provides active supporting capability for system flexibility improvement by reasonably adjusting flexible source network load resources in the novel power distribution network, and improves operation economy on the premise of ensuring safe operation of the system; in addition, the safety margin of the obtained optimized operation method in each scene is considerable, so that a dispatcher can grasp the operation state of the system and prevent and control the unsafe state, and a theoretical basis is provided for the optimized dispatching of the novel power distribution network.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of various changes or substitutions within the technical scope of the present application, and these should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (6)

1. The novel power distribution network source network load coordination random optimization operation method for flexibility is characterized by comprising the following steps of:
constructing a novel power distribution network flexibility evaluation index system based on a power distribution network safety domain theory, wherein the novel power distribution network flexibility evaluation index system comprises a flexible distance expectation, a standard deviation and a flexible distance variation coefficient;
the power distribution network security domain theory is defined as a feeder line outlet power working point set under the premise of ensuring the safe operation of a system, wherein the safe operation of the system comprises no overload of a line and no out-of-limit of node voltage;
the method for constructing the novel power distribution network flexibility evaluation index system specifically comprises the following steps: establishing a distribution network security domain model according to a distribution network security domain theory, analyzing the security boundary geometric characteristics of the distribution network security domain model, and calculating the minimum Euclidean distance from a feeder line outlet power working point to a security boundary to measure the system operation security margin; based on a scene analysis method, analyzing flexible distance expectations, standard deviations and flexible distance variation coefficients of all operation scenes, and further constructing a flexible evaluation index cluster based on the flexible distance expectations, the standard deviations and the flexible distance variation coefficients to form a novel power distribution network flexibility evaluation index system;
the mathematical expressions corresponding to the flexible distance expectation, the standard deviation and the flexible distance variation coefficient are respectively as follows:
；
；
；
in the method, in the process of the invention,and->Scene +.>Flexible distance expectations and standard deviations; />Is a flexible distance variation coefficient; />For feed line->In scene->A flexible distance below; />And->The number of feeder lines and the number of scenes are respectively; />And->Respectively a feeder set and a scene set;
；
；
in the method, in the process of the invention,for scene->Lower feeder outlet power operating point, +.>For critical operating point on safety boundary +.>For the safety boundary set, ++>Is a security domain; />For the outlet power of the feeder 1, < > for>For the outlet power of the feed line 2, < > for>For feed line->Outlet power of>For feed line->Is a power output of the engine; />And->Nodes +.>Voltage and feeder->Current (I)>And->Nodes +.>Voltage (V)/>Lower and upper limits of->And->Are respectively feeder lines->Current->Lower and upper limits of (2); />Is a node set;
constructing a multi-objective coordinated optimization operation model for improving system flexibility based on the novel power distribution network flexibility evaluation index system, wherein the multi-objective coordinated optimization operation model is double multi-objective optimization comprising a flexibility objective function and an economical objective function, the flexibility objective function is a minimum flexible distance variation coefficient, and the economical objective function is a minimum total operation cost;
the objective function expression of the multi-objective coordination optimization operation model is as follows:
；
wherein,,for the total cost of operation->Is a flexible distance variation coefficient;
；
in the method, in the process of the invention,the number of running scenes; />、/>、/>、/>、/>And->Respectively is scene->Time below->Switching action, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost; />And->Respectively a scene set and a time set;
providing constraint conditions of the multi-objective coordinated optimization operation model, wherein the constraint conditions comprise a node power balance equation, a power flow calculation equation, radial operation constraint, network security constraint and active management constraint; converting bilinear terms in a power flow calculation equation into a linear form by using a large M method;
and solving the multi-objective coordinated optimization operation model based on the forward boundary intersection point and the dynamic niche differential evolution algorithm, and outputting an operation simulation result.
2. The flexible-oriented novel power distribution network source-network load coordination random optimization operation method according to claim 1, wherein the total operation cost comprises switching actions, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost, and the corresponding calculation expression is as follows:
；
；
；
；
；
；
in the method, in the process of the invention,、/>、/>、/>、/>and->Respectively is scene->Time below->Switching action, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost; />And->The unit switch action and demand response costs are respectively; />、/>And->Respectively at node->The unit operation cost of the micro gas turbine, wind power and photovoltaic; />、/>And->Respectively at node->The unit of micro gas turbine, wind power and photovoltaic actively manages the cost; />And->Respectively is scene->Time below->Unit transaction and loss costs of (a); />For the scene->Time below->Switch->Status (S)>For the scene->Time below->Switch->State, closed 1, open 0; />、/>And->Respectively at node->Micro gas turbine, wind power and photovoltaic in scene->Time below->Is an active force of (a); />For being located at node +.>Is in scene->Time below->Is an active response of (a); />And->Respectively is scene->Time below->The interactive power and the network loss of the (a); />For the scene->Time below->Is a span of (2); />、/>And->Respectively a switch set, a distributed power supply set and a node set.
3. The flexible-oriented novel power distribution network source-network load coordination random optimization operation method according to claim 1, wherein the expression of the node power balance equation is:
；
；
in the method, in the process of the invention,and->Lines are respectively->Active and reactive power of (a); />And->Lines are respectively->Resistance and reactance of (a); />For line->Square of the current; />And->Respectively at node->The active and reactive power outputs are predicted by the distributed power supply; />Andrespectively at node->Active and reactive power is removed from the distributed power supply; />And->Respectively at node->Active and reactive loads predicted by (a); />And->Respectively at node->Active and reactive load response amounts of (a); />And->Respectively is node set and node->A set of interconnected nodes;
the expression of the tide calculation equation is as follows:
；
；
；
in the method, in the process of the invention,for node->Square of the voltage amplitude; />For node->Square of the voltage amplitude; />Is a sufficiently large positive number, taken as 10000; />For switch->State, closed 1, open 0; />Is vector transposition;
the expression of the radial operation constraint is:
；
；
in the method, in the process of the invention,and->The number of nodes and the number of nodes of the transformer substation are respectively; />For line->Virtual active power of (a); />Andswitch set and node respectively->A set of interconnected nodes; />Is a substation node set.
4. The flexible-oriented novel power distribution network source network load coordination random optimization operation method according to claim 1, wherein the network security constraint comprises a flexible distance constraint and a node voltage opportunity constraint, and the expression of the flexible distance constraint is as follows:
；
；
in the method, in the process of the invention,for feed line->Flexible distance of (2); />For feed line->Is a minimum flexible distance of (2); />And->Are respectively feeder lines->And (ii) of the feeder line>The outlet power is in the super plane->Coefficients of (a); />The number of the feeder lines is the number; />For feed line->Is a power output of the engine; />And->Respectively a hyperplane set and a feeder line set;
the node voltage opportunity constraint expression is:
；
in the method, in the process of the invention,representing probability->A safe confidence level for the node voltage; />For node->Voltage (V)>And->Respectively nodesVoltage->Lower and upper limits of (2); />Is a set of nodes.
5. The flexible-oriented novel power distribution network source-network load coordination random optimization operation method according to claim 1, wherein the active management constraints comprise a switching action constraint, an on-load tap-changing transformer adjustment constraint, a distributed power supply output constraint, a distributed power supply power factor constraint and a demand response constraint;
the expression of the switch action constraint is as follows:
；
in the method, in the process of the invention,、/>and->Respectively a switch set, a scene set and a time set; />For the scene->Time below->Switch->Status (S)>For the scene->Time below->Switch->State, closed 1, open 0; />For switch->An upper limit of the number of actions;
the expression of the on-load tap-changing voltage-regulating transformer tap-changing restriction is as follows:
；
；
；
in the method, in the process of the invention,、/>nodes +.>Voltage and node->A voltage; />And->Lines are respectively->The on-load regulating transformer is at moment +.>And time->Tap positions of (2); />And->Lines are respectively->The upper limit and the lower limit of the tap position of the on-load voltage regulating transformer are set; />Step length is adjusted for the voltage of the on-load regulating transformer; />Is an on-load voltage regulating transformer set;
the expression of the distributed power supply output constraint is as follows:
；
；
in the method, in the process of the invention,the maximum cutting rate is output for the distributed power supply; />For being located at node +.>Is predictive of active power, +.>For being located at node +.>An upper active power output limit is predicted by the distributed power supply; />Is a distributed power supply set; />Andrespectively at node->Active and reactive power is removed from the distributed power supply; />For being located at node +.>A distributed power source output power factor angle;
the expression of the distributed power supply power factor constraint is as follows:
；
in the method, in the process of the invention,and->Respectively at node->Upper and lower limits of the distributed power supply output power factor angle;
the expression of the demand response constraint is:
；
；
in the method, in the process of the invention,is a node set; />And->Respectively at node->Active and reactive load response amounts of (a); />For being located at node +.>An upper active load response amount limit of (2); />For being located at node +.>Is a load power factor angle of (2).
6. The flexible-oriented novel power distribution network source network load coordination random optimization operation method according to claim 1, wherein the method for solving the multi-objective coordination optimization operation model specifically comprises the following steps: the method comprises the steps of inputting a wind-solar-load time sequence curve, a system topological structure and a dynamic niche differential evolution algorithm of a region where a power distribution network is located, randomly generating population individuals, defining fitness functions based on a flexibility objective function and an economy objective function respectively, calculating individual fitness values, retaining elite individuals, eliminating individuals after falling, and updating the population individuals through crossover, selection and mutation operations until a pareto optimal scheme is output.
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