CN116760112A - Data-driven-based limit capacity planning method for distributed photovoltaic power distribution network - Google Patents
Data-driven-based limit capacity planning method for distributed photovoltaic power distribution network Download PDFInfo
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Abstract
A data-driven-based limit capacity planning method for a distributed photovoltaic power distribution network. Firstly, determining a planning area to draw a power grid topology, and collecting related existing measurement data such as power distribution network system data, operation data, safety boundary data and the like; and on the premise of no distributed photovoltaic reverse power transmission, establishing a distributed robust optimization model based on the distributed photovoltaic limit capacity under the constraint condition of system operation, converting the model into a mixed integer deterministic linear programming problem through a fuzzy equivalent formula, and finally solving by using a mathematical programming solver to obtain the distributed photovoltaic power distribution network limit capacity. The application fully utilizes the existing measurement data, considers the error of the change probability of the measurement data, and considers the economy and conservation so as to facilitate the solution, is efficient and accurate, and meets the actual requirements.
Description
Technical Field
The application relates to the field of capacity allocation of a distributed photovoltaic power distribution network, in particular to a data-driven-based limit capacity planning method of the distributed photovoltaic power distribution network.
Background
The distributed photovoltaic power generation has the advantages of high flexibility, low investment, short construction period, small occupied area and the like, and meets the strategic requirements of national sustainable development. Planning and construction of distributed photovoltaic power generation projects are rapidly advanced, and a large number of distributed photovoltaic power stations are directly integrated into a power distribution network. The distributed photovoltaic power generation is different from the traditional power supply, has the characteristics of randomness and fluctuation of output, poor stability, is easily influenced by various factors such as seasons, weather and the like, and cannot be taken as a stable power supply to be completely accepted. Therefore, the distribution network needs to accurately evaluate and analyze the acceptance capacity of the distributed photovoltaic, namely the limit grid-connected capacity of the distributed photovoltaic, and has important roles in promoting the safety and economic digestion of the distributed photovoltaic.
At present, research on a distributed photovoltaic grid-connected capacity planning and evaluation method at home and abroad is mainly divided into an analog method based on repeated simulation verification and an optimization method based on mathematical programming. The simulation method based on multiple simulation verification simulates the running state of the distribution network after the distributed photovoltaic is accessed through simulation software, and verifies the safety constraint one by one, so that the method is simple to implement and takes a long time; the optimization method based on mathematical programming aims at maximizing the capacity of the distributed photovoltaic grid connection, takes various safety constraints, flow equation constraints and the like of the power distribution network as constraint conditions, and adopts different mathematical programming methods to solve. The method has the advantages that the established distributed photovoltaic grid-connected capacity optimization model is accurate, the optimization result is reliable, but different results can be generated under different constraint conditions.
According to the above, most of the established distributed photovoltaic grid-connected solution models are empirical probability models, and the existing measurement data are not statistically analyzed and utilized, so that the problems of large error, long time consumption, unreasonable economy and the like are faced, and therefore, how to form an accurate distributed photovoltaic capacity planning model is a great difficulty.
Disclosure of Invention
In order to solve the defects in the prior art, the application provides a data-driven-based limit capacity planning method for a distributed photovoltaic power distribution network. Firstly, determining a planning area to draw a power grid topology, and collecting related existing measurement data such as power distribution network system data, operation data, safety boundary data and the like; and on the premise of no distributed photovoltaic reverse power transmission, establishing a distributed robust optimization model based on the distributed photovoltaic limit capacity under the constraint condition of system operation, converting the model into a mixed integer deterministic linear programming problem through a fuzzy equivalent formula, and finally solving by using a mathematical programming solver to obtain the distributed photovoltaic power distribution network limit capacity. The application fully utilizes the existing measurement data, considers the error of the change probability of the measurement data, and considers the economy and conservation so as to facilitate the solution, is efficient and accurate, and meets the actual requirements.
The application adopts the following technical scheme.
A data-driven-based limit capacity planning method for a distributed photovoltaic power distribution network comprises the following steps:
step 1, drawing a power grid topological structure and collecting related existing measurement data;
step 2, judging whether the distributed photovoltaic is reversely powered or not through thermal stability evaluation;
step 3, establishing a distributed robust optimization model of the limit capacity of the distributed photovoltaic under the constraint condition of system operation;
step 4, converting a distributed robust optimization model of the distributed photovoltaic limit capacity into a mixed integer deterministic linear programming problem through a fuzzy equivalent formula;
and 5, solving the mixed integer deterministic programming by using a mathematical programming solver to obtain a distributed photovoltaic limit capacity programming method.
Preferably, in step 1, the existing measurement data includes index measurement data of 3 dimensions;
the 3 dimensions comprise power distribution network system data, operation data and safety boundary data;
wherein the index measurement data is the voltage level V of the data bus S Transformer capacity S trans.max Efficiency coefficient eta of inverter n Power factor regulation range of inverterTransformer load sequence c1, line load sequence c2, power supply output sequence c3, and voltage limit V i.max /V i.min Line current limit I b.max /I b.max Branch transmission capacity limit s ij.max ；
Preferably, in step 2, the thermal stability evaluation refers to a calculation formula based on a transformer load time sequence, a line load time sequence and a power output time sequence in the operation data, and based on the principle that the thermal stability of the transformer or the line is not out of limit, the calculation formula is as follows:
wherein: λ (t) is the reverse load factor at time t; p (P) D (t) is active power at distributed photovoltaic t moment in the power supply range of the transformer or the line; p (P) C (t) is the active power of other power sources except the distributed photovoltaic at the moment t; p (P) L (t) is the active power of the electric load at the moment t; s is S e Is the actual operating limit for the transformer or line.
If lambda (t) is more than or equal to 80%, reversely transmitting power to the main network by the distributed photovoltaic, and prohibiting adding the distributed photovoltaic at the moment;
if lambda (t) is less than 80%, continuing to step 3.
Preferably, in step 3, the distributed photovoltaic limit capacity robust optimization model is specifically:
wherein: t is the number of time periods; c (C) PV.i The capacity accessed at the i node for the distributed photovoltaic; v (V) i.min And V i.max The upper and lower limit values are respectively the upper and lower limit values of the voltage of the node i; s is S trans.t The transformer load capacity at the moment t; s is S trans.max Transformer capacity; i b (t) is a line current value at time t; i bmin Is I bmax Respectively the upper limit value and the lower limit value of the line current; r is R ij And X ij Correcting the real part and the imaginary part of the node impedance matrix for the branch ij respectively; p (P) ij.t And Q ij.t Respectively representing the active power and the reactive power of the branch ij flowing from the node i to the node j at the time t;the power factor angle corresponding to the minimum power factor of the inverter allowed in the distributed photovoltaic operation; p (P) Dt,i And Q Dt.i The value of the period t of the active and reactive output curves of the distributed photovoltaic connected to the power distribution network node i is respectively represented; />The output value of the photovoltaic sunrise force curve t time period is the unit capacity photovoltaic of the distributed photovoltaic; s is(s) ij,max Is the maximum leg capacity between legs ij.
Preferably, step 4 specifically includes:
step 4.1, constructing a fuzzy set equivalent formula, specifically:
wherein: f actual probability distribution Density function, f 1 Empirical probability distribution Density function, D KL For the actual probability distribution density function f and the empirical probability distribution density function f 1 The Kullback-Leibler divergence between the two is H, and H is the upper limit value of the Kullback-Leibler divergence; k is an accumulated distribution function set of the actual probability distribution function f; ζ is a random variable;
and 4.2, converting a distributed robust optimization model of the distributed photovoltaic limit capacity into the following form:
maxC PV (x)
s.t.g(x.ξ)≥0
wherein: x is a decision variable, and ζ is a random input variable of distributed photovoltaic output and load; randomly input variable ζ epsilon K;
step 4.3, converting the formula into:
wherein: w= (W) 1 ,W 2 ,...,W T ) To represent that T of the distributed photovoltaic power is a random variable, i.e. the range of values of W given x and ζ, in particular,
wherein: t is the number of time periods; c (C) PV.i The capacity accessed at the i node for the distributed photovoltaic; w (W) i.t The power is output by the distributed photovoltaic at any t moment of the i node; v (V) i.min And V i.max The upper and lower limit of the voltage of the node i are respectively defined; s is S trans.t The transformer load capacity at the moment t; s is S trans.max Transformer capacity; i b (t) is a line current value at time t; i bmin Is I bmax Respectively the linesAn upper limit value of the current; i B Is the transformer capacity; r is R ij And X ij Correcting the real part and the imaginary part of the node impedance matrix for the branch ij respectively; p (P) ij.t And Q ij.t Respectively representing the active power and the reactive power of the branch ij flowing from the node i to the node j at the time t;the power factor angle corresponding to the minimum power factor of the inverter allowed in the distributed photovoltaic operation; p (P) Dt,i And Q Dt.i The value of the period t of the active and reactive output curves of the distributed photovoltaic connected to the power distribution network node i is respectively represented; />The output value of the photovoltaic sunrise force curve t time period is the unit capacity photovoltaic of the distributed photovoltaic; s is(s) ij,max Maximum leg capacity between legs ij;
step 4.4, the non-failure constraint under the extreme constraint condition is equivalent to the traditional constraint, namely:
wherein: p (P) f (A) The probability of occurrence of the event A in the actual probability distribution density function f is given; p (P) f1 (A) For event A in the empirical probability distribution density function f 1 Probability of occurrence; alpha 1+ Is a reliability correction value;
step 4.5, the distributed photovoltaic limit capacity model is equivalent to the following mixed integer deterministic linear programming problem:
s.t.g(x,ξ)≥0
V i.min ≤R ij P ij.t,a +X ij Q ij.t,a ≤V i.max ,t＝1,2,...,T,a＝1,2,...,N
S trans.t ≤S trans.max ,t＝1,2,...,T,a＝1,2,...,N
I bmin ≤I b,a (t)≤I bmax ,t＝1,2,...,T,a＝1,2,...,N
-s ij,max ≤P ij,t,a ≤s ij,max ,t＝1,2,...,T,a＝1,2,...,N
-s ij,max ≤Q ij,t,a ≤s ij,max ,t＝1,2,...,T,a＝1,2,...,N
wherein: forming a matrix of T rows and N columns by using the historical mass data, wherein T is the number of time periods; n is the group number; s is S trans.t,a The load capacity of the transformer at the moment t in the a group; i b,a (t) is the line current value at time t in group a; p (P) ij.t,a And Q ij.t,a Respectively representing the active power and the reactive power of a branch ij in the a group flowing from a node i to a node j at the time t; p (P) Dt,i,a And Q Dt.i,a And respectively representing the values of the periods t of the active and reactive output curves of the distributed photovoltaic connected to the power distribution network node i in the a group.
Compared with the prior art, the application adopts a robust optimized mathematical method, the distances between the actual probability distribution density function and the empirical probability distribution density function are quantized through the Kullback-Leibler divergence, the defect of the empirical probability distribution density function is overcome, the existing measurement data is fully utilized for statistical analysis, the error of the measurement data change probability is fully considered, the problems of large error, long time consumption, unreasonable economy and the like are overcome, and the application is efficient and accurate and meets the actual requirements.
Drawings
FIG. 1 is a schematic flow chart of a method for planning limit capacity of a distributed photovoltaic power distribution network based on data driving;
FIG. 2 is a schematic diagram of the structure of measurement data according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. The described embodiments of the application are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art without inventive faculty, are within the scope of the application, based on the spirit of the application.
The utility model provides a distributed photovoltaic power distribution network limit capacity planning method based on data driving, as shown in figures 1 and 2, specifically comprising the following steps:
step 1, drawing a power grid topological structure and collecting related existing measurement data;
in step 1, the topology is drawn from the commissioned grid. Determining an evaluation range; determining topological relations among transformers, lines and power supplies in the area; and visually displaying the evaluation grade.
The measurement data includes:
bus voltage level: after the distributed photovoltaic is connected with the equipment by the allowable capacity of thermal stability, the voltage deviation limit value of each stage of bus is estimated.
Transformer capacity: when the access capacity of the photovoltaic power generation system exceeds 25% of the rated capacity of the transformer in the distribution area, the low-voltage side of the distribution transformer of the public power grid should be provided with a low-voltage main switch, and an anti-islanding device is arranged at a bus at the low-voltage side of the distribution transformer.
Inverter efficiency coefficient: and constructing an objective function of the limit capacity of the distributed photovoltaic access power distribution network.
Inverter power factor adjustment range: and determining the active and reactive output ranges of the distributed photovoltaic, and estimating the voltage deviation value of each stage of bus.
Transformer load timing: and evaluating the thermal stability and the distributed photovoltaic newly increased margin of the transformer according to the historical load time sequence of the transformer.
Line load timing: and evaluating the thermal stability and the distributed photovoltaic newly added margin of the circuit according to the historical load time sequence of the circuit.
Power supply output timing: based on the historical output time sequence of the centralized power supply, the relation between the power load in the area and the output force of the distributed power supply is analyzed according to the power balance principle.
Node voltage limit: for voltage deviation checking, reference is made to the limits specified by the relevant national standard.
Line current limit: and (3) evaluating the thermal stability of each level of circuit, and generally taking the minimum values of the protection limit value, the CT limit value and the thermal stability limit value of the circuit.
Branch transmission capacity limit: the distributed photovoltaic access distribution network can change the system power flow distribution, and can cause the bypass capacity to be out of limit.
Step 2, judging whether the distributed photovoltaic is reversely powered or not by a thermal stability evaluation method;
in step 2, the thermal stability evaluation refers to a calculation formula based on the transformer load time sequence, the line load time sequence and the power output time sequence in the operation data, and based on the principle that the thermal stability of the transformer or the line is not out of limit, wherein the calculation formula is as follows:
wherein: λ (t) is the reverse load factor at time t; p (P) D (t) is active power at distributed photovoltaic t moment in the power supply range of the transformer or the line; p (P) C (t) is the active power of other power sources except the distributed photovoltaic at the moment t; p (P) L (t) is the active power of the electric load at the moment t; s is S e Is the actual operating limit for the transformer or line.
And if lambda (t) is more than or equal to 80%, reversely transmitting power to the main network by the distributed photovoltaic, and prohibiting the addition of the distributed photovoltaic.
If lambda (t) is less than 80%, continuing to step 3.
Step 3, establishing a distributed robust optimization model of the limit capacity of the distributed photovoltaic under the constraint condition of system operation;
step 3.1, a general model of the distributed photovoltaic limit capacity is established, and a calculation formula is as follows:
wherein: η (eta) n Is the efficiency coefficient of the inverter; i s Is the actual radiation intensity of the sun; i n For the intensity of solar radiation under standard conditions, 1.0kW/m2 is usually taken; c (C) PV And the capacity of the distribution network is accessed for distributed photovoltaic.
Step 3.2, establishing a power distribution network system operation constraint model
(1) Voltage safety constraints: the voltage of each node in the operation of the power distribution network cannot exceed the allowable range of safe operation, and the following formula is shown:
wherein: v (V) i.min And V i.max The upper and lower limit values are respectively the upper and lower limit values of the voltage of the node i; r is R ij And X ij Correcting the real part and the imaginary part of the node impedance matrix for the branch ij respectively; p (P) ij.t And Q ij.t Respectively representing the active power and the reactive power of the branch ij flowing from the node i to the node j at the time t; p (P) Dt,i The value of the period t of the active curve of the access distributed photovoltaic on the node i of the power distribution network is represented; p (P) L,i Active power for node i load; psi phi type sub And the node is a substation bus node set in the power distribution network.
(2) Transformer capacity constraint: the distributed power supply is used as a novel load, and the scale of access to the power distribution network is limited by the capacity of the transformer:
S trans.t ≤S trans.max
wherein: s is S trans The transformer load capacity at the moment t; s is S trans.max Transformer capacity.
(3) Line safety constraints: to avoid line overload, the line current should meet the line capacity limit as shown in the following equation:
I bmin ≤I b (t)≤I bmax
wherein: i b (t) is a line current value at time t; i bmin Is I bmax Respectively the upper limit value and the lower limit value of the line current;
(4) Distributed photovoltaic output constraint: the distributed photovoltaic is connected with the grid through an inverter, and the active output of the distributed photovoltaic cannot exceed the configured active capacity of the distributed photovoltaic under the assumption that an indefinite power factor control mode is adopted in the operation of the distributed photovoltaic, the reactive output can be limited by the active output and the allowable power factor of the distributed photovoltaic, and the output constraint can be expressed as follows:
wherein:the power factor angle corresponding to the minimum power factor of the inverter allowed in the distributed photovoltaic operation; p (P) Dt,i And Q Dt.i The value of the period t of the active and reactive output curves of the distributed photovoltaic connected to the power distribution network node i is respectively represented; />The output value of the photovoltaic sunrise force curve t time period is the unit capacity photovoltaic of the distributed photovoltaic; p (P) ij.t And Q ij.t Respectively represent the active and the non-active of the branch ij flowing from the node i to the node j at the time tPower of work. Psi n Is a node set of the power distribution network.
(5) Branch transmission capacity constraint: here, the two-dimensional constraint linearization method is adopted for representation, and the following formula is shown:
wherein: s is(s) ij,max The maximum branch circuit capacity between the branches ij; psi b Is the collection of all branches in the distribution network.
S3.3, combining the constraint conditions, integrating a distributed robust optimization model of the distributed photovoltaic limit capacity, which is convenient to calculate, specifically:
wherein: t is the number of time periods; c (C) PV.i The capacity accessed at the i node for the distributed photovoltaic; v (V) i.min And V i.max The upper and lower limit values are respectively the upper and lower limit values of the voltage of the node i; s is S trans.t The transformer load capacity at the moment t; s is S trans.max Transformer capacity; i b (t) is a line current value at time t; i bmin Is I bmax Respectively the upper limit value and the lower limit value of the line current; r is R ij And X ij Correcting the real part and the imaginary part of the node impedance matrix for the branch ij respectively; p (P) ij.t And Q ij.t Respectively representing the active power and the reactive power of the branch ij flowing from the node i to the node j at the time t;the power factor angle corresponding to the minimum power factor of the inverter allowed in the distributed photovoltaic operation; p (P) Dt,i And Q Dt.i The value of the period t of the active and reactive output curves of the distributed photovoltaic connected to the power distribution network node i is respectively represented; />The output value of the photovoltaic sunrise force curve t time period is the unit capacity photovoltaic of the distributed photovoltaic; s is(s) ij,max Is the maximum leg capacity between legs ij.
Step 4, converting a distributed robust optimization model of the distributed photovoltaic limit capacity into a mixed integer deterministic linear programming problem through a fuzzy equivalent formula;
step 4.1, constructing a fuzzy set equivalent formula, specifically:
wherein: f actual probability distribution Density function, f 1 Empirical probability distribution Density function, D KL For the actual probability distribution density function f and the empirical probability distribution density function f 1 The Kullback-Leibler divergence between the two is H, and H is the upper limit value of the Kullback-Leibler divergence; k is an accumulated distribution function set of the actual probability distribution function f; ζ is a random variable.
And 4.2, converting a distributed robust optimization model of the distributed photovoltaic limit capacity into the following form:
maxC PV (x)
s.t.g(x.ξ)≥0
wherein: x is a decision variable, and ζ is a random input variable of distributed photovoltaic output and load; the random input variable ζ epsilon K, if the failure of g (x.ζ) is not satisfied and is not greater than or equal to 0, the failure probability is P r The method comprises the following steps:
P r [g(x,ξ)＞0]≤α
wherein: p (P) r (A) Is the probability of event a occurring; the alpha inequality constrains the probability of failure.
Step 4.3, from the point of view of robust optimization, i.e. in all possible probability density distribution functions, the formula non-failure constraint formula is also satisfied in extreme cases, i.e. the formula is converted into:
wherein: w= (W) 1 ,W 2 ,...,W T ) T, which is a random variable representing the distributed photovoltaic power, is the range of values of W given x and ζ. In particular, the method comprises the steps of,
wherein: t is the number of time periods; c (C) PV.i The capacity accessed at the i node for the distributed photovoltaic; w (W) i.t The power is output by the distributed photovoltaic at any t moment of the i node; v (V) i.min And V i.max The upper and lower limit values are respectively the upper and lower limit values of the voltage of the node i; s is S trans.t The transformer load capacity at the moment t; s is S trans.max Transformer capacity; i b (t) is a line current value at time t; i bmin Is I bmax Respectively the upper limit value of the line current; i B Is the transformer capacity; r is R ij And X ij Correcting the real part and the imaginary part of the node impedance matrix for the branch ij respectively; p (P) ij.t And Q ij.t Respectively representing the active power and the reactive power of the branch ij flowing from the node i to the node j at the time t;the power factor angle corresponding to the minimum power factor of the inverter allowed in the distributed photovoltaic operation; p (P) Dt,i And Q Dt.i The value of the period t of the active and reactive output curves of the distributed photovoltaic connected to the power distribution network node i is respectively represented; />The output value of the photovoltaic sunrise force curve t time period is the unit capacity photovoltaic of the distributed photovoltaic; s is(s) ij,max Is the maximum leg capacity between legs ij.
Step 4.4, non-failure constraint under extreme constraint conditionsIt can also be equivalently a conventional constraint, namely:
wherein: p (P) f (A) The probability of occurrence of the event A in the actual probability distribution density function f is given; p (P) f1 (A) For event A in the empirical probability distribution density function f 1 Probability of occurrence; alpha 1+ Is a reliability correction value;
step 4.5, the distributed photovoltaic limit capacity model is equivalent to the following mixed integer deterministic linear programming problem:
s.t.g(x,ξ)≥0
V i.min ≤R ij P ij.t,a +X ij Q ij.t,a ≤V i.max ,t＝1,2,...,T,a＝1,2,...,N
S trans.t ≤S trans.max ,t＝1,2,...,T,a＝1,2,...,N
I bmin ≤I b,a (t)≤I bmax ,t＝1,2,...,T,a＝1,2,...,N
-s ij,max ≤P ij,t,a ≤s ij,max ,t＝1,2,...,T,a＝1,2,...,N
-s ij,max ≤Q ij,t,a ≤s ij,max ,t＝1,2,...,T,a＝1,2,...,N
wherein: forming a matrix of T rows and N columns by using the historical mass data, wherein T is the number of time periods; n is the group number;
wherein: y is an auxiliary variable in solution; d (D) KL For the actual probability distribution density function f and the empirical probability distribution density function f 1 The Kullback-Leibler divergence between, the a inequality constrains the probability of failure.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (8)
1. The limit capacity planning method for the distributed photovoltaic power distribution network based on data driving is characterized by comprising the following steps of:
step 1, drawing a power grid topological structure and collecting related existing measurement data;
step 2, judging whether the distributed photovoltaic is reversely powered or not through thermal stability evaluation;
step 3, establishing a distributed robust optimization model of the limit capacity of the distributed photovoltaic under the constraint condition of system operation;
step 4, converting a distributed robust optimization model of the distributed photovoltaic limit capacity into a mixed integer deterministic linear programming problem through a fuzzy equivalent formula;
and 5, solving the mixed integer deterministic programming by using a mathematical programming solver to obtain a distributed photovoltaic limit capacity programming method.
2. The data-driven-based limit capacity planning method for a distributed photovoltaic power distribution network, as set forth in claim 1, is characterized in that:
in step 1, the existing measurement data includes index measurement data of 3 dimensions;
the 3 dimensions comprise power distribution network system data, operation data and safety boundary data;
wherein the index measurement data is the voltage level V of the data bus S Transformer capacity S trans.max Efficiency coefficient eta of inverter n Power factor regulation range of inverterTransformer load sequence c1, line load sequence c2, power supply output sequence c3, and voltage limit V i.max /V i.min Line current limit I b.max /I b.max Branch transmission capacity limit s ij.max 。
3. The data-driven-based limit capacity planning method for a distributed photovoltaic power distribution network, as set forth in claim 1, is characterized in that:
in step 2, the thermal stability evaluation refers to a calculation formula based on a transformer load time sequence, a line load time sequence and a power output time sequence in operation data, and based on the principle that the thermal stability of a transformer or a line is not out of limit, wherein the calculation formula is as follows:
wherein: λ (t) is the reverse load factor at time t; p (P) D (t) is active power at distributed photovoltaic t moment in the power supply range of the transformer or the line; p (P) C (t) is the active power of other power sources except the distributed photovoltaic at the moment t; p (P) L (t) is the active power of the electric load at the moment t; s is S e Is the actual operating limit for the transformer or line.
4. A data-driven distributed photovoltaic power distribution network limit capacity planning method according to claim 1 or 3, characterized in that:
if lambda (t) is more than or equal to 80%, reversely transmitting power to the main network by the distributed photovoltaic, and prohibiting adding the distributed photovoltaic at the moment;
if lambda (t) is less than 80%, continuing to step 3.
5. The data-driven-based limit capacity planning method for a distributed photovoltaic power distribution network, as set forth in claim 1, is characterized in that:
in the step 3, the distributed photovoltaic limit capacity distribution robust optimization model specifically comprises the following steps:
wherein: t is the number of time periods; c (C) PV.i The capacity accessed at the i node for the distributed photovoltaic; v (V) i.min And V i.max The upper and lower limit values are respectively the upper and lower limit values of the voltage of the node i; s is S trans.t The transformer load capacity at the moment t; s is S trans.max Transformer capacity; i b (t) is a line current value at time t; i bmin Is I bmax Respectively the upper limit value and the lower limit value of the line current; r is R ij And X ij Correcting the real part and the imaginary part of the node impedance matrix for the branch ij respectively; p (P) ij.t And Q ij.t Respectively representing the active power and the reactive power of the branch ij flowing from the node i to the node j at the time t;the power factor angle corresponding to the minimum power factor of the inverter allowed in the distributed photovoltaic operation; p (P) Dt,i And Q Dt.i The value of the period t of the active and reactive output curves of the distributed photovoltaic connected to the power distribution network node i is respectively represented; />Photovoltaic sunrise force of unit capacity of distributed photovoltaicA force value is given out in a curve t period; s is(s) ij,max Is the maximum leg capacity between legs ij.
6. The data-driven-based limit capacity planning method for a distributed photovoltaic power distribution network, as set forth in claim 1, is characterized in that:
the step 4 specifically comprises the following steps:
step 4.1, constructing a fuzzy set equivalent formula, specifically:
wherein: f actual probability distribution Density function, f 1 Empirical probability distribution Density function, D KL For the actual probability distribution density function f and the empirical probability distribution density function f 1 The Kullback-Leibler divergence between the two is H, and H is the upper limit value of the Kullback-Leibler divergence; k is an accumulated distribution function set of the actual probability distribution function f; ζ is a random variable;
and 4.2, converting a distributed robust optimization model of the distributed photovoltaic limit capacity into the following form:
maxC PV (x)
s.t.g(x.ξ)≥0
wherein: x is a decision variable, and ζ is a random input variable of distributed photovoltaic output and load; randomly input variable ζ epsilon K;
step 4.3, converting the formula into:
wherein: w= (W) 1 ,W 2 ,...,W T ) To represent that T of the distributed photovoltaic power is a random variable, i.e. the range of values of W given x and ζ, in particular,
wherein: t is the number of time periods; c (C) PV.i The capacity accessed at the i node for the distributed photovoltaic; w (W) i.t The power is output by the distributed photovoltaic at any t moment of the i node; v (V) i.min And V i.max The upper and lower limit of the voltage of the node i are respectively defined; s is S trans.t The transformer load capacity at the moment t; s is S trans.max Transformer capacity; i b (t) is a line current value at time t; i bmin Is I bmax Respectively the upper limit value of the line current; i B Is the transformer capacity; r is R ij And X ij Correcting the real part and the imaginary part of the node impedance matrix for the branch ij respectively; p (P) ij.t And Q ij.t Respectively representing the active power and the reactive power of the branch ij flowing from the node i to the node j at the time t;the power factor angle corresponding to the minimum power factor of the inverter allowed in the distributed photovoltaic operation; p (P) Dt,i And Q Dt.i The value of the period t of the active and reactive output curves of the distributed photovoltaic connected to the power distribution network node i is respectively represented; />The output value of the photovoltaic sunrise force curve t time period is the unit capacity photovoltaic of the distributed photovoltaic; s is(s) ij,max Maximum leg capacity between legs ij;
step 4.4, the non-failure constraint under the extreme constraint condition is equivalent to the traditional constraint, namely:
wherein: p (P) f (A) The probability of occurrence of the event A in the actual probability distribution density function f is given;for event A in the empirical probability distribution density function f 1 Probability of occurrence; alpha 1+ Is a reliability correction value;
step 4.5, the distributed photovoltaic limit capacity model is equivalent to the following mixed integer deterministic linear programming problem:
s.t.g(x,ξ)≥0
V i.min ≤R ij P ij.t,a +X ij Q ij.t,a ≤V i.max ,t＝1,2,...,T,a＝1,2,...,N
S trans.t ≤S trans.max ,t＝1,2,...,T,a＝1,2,...,N
I bmin ≤I b,a (t)≤I bmax ,t＝1,2,...,T,a＝1,2,...,N
-s ij,max ≤P ij,t,a ≤s ij,max ,t＝1,2,...,T,a＝1,2,...,N
-s ij,max ≤Q ij,t,a ≤s ij,max ,t＝1,2,...,T,a＝1,2,...,N
wherein: forming a matrix of T rows and N columns by using the historical mass data, wherein T is the number of time periods; n is the group number; s is S trans.t,a The load capacity of the transformer at the moment t in the a group; i b,a (t) is the line current value at time t in group a; p (P) ij.t,a And Q ij.t,a Respectively representing the active power and the reactive power of a branch ij in the a group flowing from a node i to a node j at the time t; p (P) Dt,i,a And Q Dt.i,a And respectively representing the values of the periods t of the active and reactive output curves of the distributed photovoltaic connected to the power distribution network node i in the a group.
7. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-6.
8. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-6.
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