CN110768294B - Random scheduling method and device for distributed power supply - Google Patents
Random scheduling method and device for distributed power supply Download PDFInfo
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Abstract
The invention discloses a distributed power supply random scheduling method, which is suitable for intelligent buildings with distributed power supplies and comprises the following steps: establishing an original random scheduling model containing a distributed power supply for the intelligent building; acquiring an input variable and an output variable of a black box random scheduling model according to the original random scheduling model; describing an equality relation of the input variable and the output variable by adopting a tensor product to fit the black box random scheduling model; and solving the optimal solution of the black box random scheduling model based on Monte Carlo power sampling and scene random conversion scheme to obtain a random scheduling scheme. The invention also discloses a corresponding random scheduling device for the distributed power supply. By implementing the method, the random scheduling model can be effectively simplified, so that the calculation amount and the calculation difficulty can be effectively reduced in the process of obtaining the optimal solution of the random scheduling scheme, and the solution efficiency of the random scheduling is further improved.
Description
Technical Field
The invention relates to the field of optimized scheduling of power systems, in particular to a distributed power supply random scheduling method and device.
Background
On a global scale, energy problems are a bottleneck that limits human exploration into the future and black technology to a great extent. Traditional fossil energy, such as oil, natural gas and the like, is increasingly difficult to exploit and difficult to meet the increasing energy demand of all mankind. The development of the distributed new energy power supply can relieve the tension of energy shortage. However, the generated energy of the new energy power generation source has certain uncertainty along with the change of factors such as weather, and the fluctuation may be relatively large, so that certain impact is brought to the scheduling of the electric energy, and the concept of random scheduling needs to be applied. In the aspect of power distribution networks, the traditional power distribution networks gradually develop towards intellectualization along with the impact of emerging industries such as artificial intelligence and smart power grids. The intelligent building can integrate a large amount of new forms of energy power generation sources such as photovoltaic board, fan etc. not only can satisfy the electric power demand of self, can also be when necessary to the electric wire netting reverse power transmission, alleviates the pressure of electric wire netting. Therefore, by utilizing the intelligent control of the intelligent building, the uncertainty of the new energy power generation can be fully considered, and the appropriate power system random scheduling scheme can be solved.
So far, the modeling and fitting of the new energy distributed power supply mainly adopts a power sampling scheme based on Monte Carlo and a random transformation scheme based on scenes, and scene transfer constraints are added in constraint conditions to link a plurality of scenes. However, in the process of implementing the invention, the inventor finds that the prior art has at least the following problems: in the prior art scheduling scheme, the calculation scale increases in multiple steps along with the increase of the number of sampling scenes, and the large-scale calculation thereof troubles many scheduling personnel. Even for high-dimensional (multiple distributed power) scenarios, no scheduling scheme can be obtained using the above approach. Therefore, for the distributed power supply of the intelligent building, an efficient random scheduling modeling scheme needs to be established so as to reduce the difficulty of solving as much as possible.
Disclosure of Invention
The embodiment of the invention aims to provide a distributed power supply random scheduling method and device, which can effectively simplify a random scheduling model, so that the calculation amount and the calculation difficulty can be effectively reduced in the process of obtaining the optimal solution of a random scheduling scheme, and the solution efficiency of random scheduling is further improved.
In order to achieve the above object, an embodiment of the present invention provides a distributed power supply random scheduling method, which is applicable to an intelligent building including a distributed power supply, and includes:
establishing an original random scheduling model containing a distributed power supply for the intelligent building; wherein the original stochastic scheduling model comprises a constraint condition and an objective function; the constraint conditions comprise a power flow equation, a distributed power supply model and variable upper and lower limit constraints;
acquiring an input variable and an output variable of a black box random scheduling model according to the original random scheduling model;
describing an equality relation of the input variable and the output variable by adopting a tensor product to fit the black box random scheduling model;
and solving the optimal solution of the black box random scheduling model based on Monte Carlo power sampling and scene random conversion scheme to obtain a random scheduling scheme.
As an improvement of the above scheme, the power flow equation is a classical polar coordinate node type equation, and specifically satisfies the following conditions:
wherein, PSi(t) and QSi(t) active power and reactive power injected by the distribution network, respectively; pGi(t) and QGi(t) active and reactive power, P, respectively, of the distributed power supply of the intelligent buildingLi(t) and QLi(t) active and reactive requirements of the intelligent building, respectively; pi c(t) and Pi d(t) is the charging power and the discharging power of the energy storage device of the intelligent building, respectively; qCi(t) is the reactive power provided by the intelligent building reactive power compensation device; vi(t) and Vj(t) is the voltage value of the intelligent building at the node i and the node j; deltaij(t) is the voltage phase angle difference between the intelligent building at the node i and the node j; pij(t) and Qij(t) active and reactive power, G, transmitted at nodes i and j, respectively, by the transformer branchesijThe self/mutual conductance of the ith row and the jth column of the node admittance matrix without the transformer branch; b isijThe self/mutual susceptances of the ith row and the jth column of the node admittance matrix without the transformer branch; sTRepresenting a set of on-load tap changers; n is a radical ofBThe number of the building nodes of the power distribution network.
As an improvement of the scheme, the model of the distributed power supply comprises a model of a new energy power generation source; the predicted output of the distributed power supply based on new energy follows normal distribution, and specifically meets the following requirements:
wherein the content of the first and second substances,
is the predicted contribution of the distributed power source of the new energy; ξ (t) is the prediction error of the distributed power supply of the new energy source, which follows a normal distribution N (μ, σ)2)；
Is a power factor angle of the distributed power supply; rGiminAnd RGimaxRespectively, a lower limit and an upper limit of the climbing power of the distributed power supply.
As an improvement of the above scheme, the objective function is the sum of the network loss cost of the power distribution network, the power generation cost and the punished operation cost of the clean energy rejection, and specifically satisfies the following conditions:
J＝C1+C2+C3；
wherein, C1The network loss cost is the cost of the difference value of the generated energy and the electricity sales amount, and specifically meets the following requirements:
C2is the power generation cost, including the cost of main network electricity and fossil energy, specifically satisfies:
C3is the punishment of the abandonment of the clean energy, and specifically meets the following requirements:
wherein, CS(t) is the segmented real-time electricity prices for the distribution grid.
As an improvement of the above scheme, the obtaining of the input variable and the output variable of the black box stochastic scheduling model according to the original stochastic scheduling model specifically includes:
converting decision variables into uniform distribution subject to upper and lower limit constraints to obtain the input variables of the black box random scheduling model; wherein the decision variable comprises an active power output P of the distributed power supplyGi(t) reactive power QGi(t) charging power P of said energy storage devicei c(t) and discharge power P of the energy storage devicei d(t)；
Obtaining the active power P injected into the power distribution networkSi(t) to obtain the output variable of the black box stochastic scheduling model.
As an improvement of the above scheme, the fitting of the black box stochastic scheduling model by describing an equality relationship between the input variable and the output variable by using a tensor product specifically includes:
defining two interpolation point sets U on each dimension of input variable1And U2；
Calculating a tensor product of a sparse grid type according to the interpolation point set;
and transforming the tensor product of the sparse grid type by introducing adaptive dimensionality so as to fit the black box random scheduling model.
As an improvement of the above scheme, the interpolation point set U1And U2The method specifically comprises the following steps:
U1＝{0}；
wherein m is the number of interpolation points of each dimension of the input variable, parameters A, B and C … are the interpolation points, and the value of the interpolation points is determined by obtaining an orthogonal polynomial and an order corresponding to the distribution characteristic of the input variable;
and calculating a sparse grid type tensor product according to the interpolation point set, wherein the formula is satisfied:
wherein the content of the first and second substances,
a column vector that is the output variable; i | ═ i1+i2+…+in，i1,i2,...,inInterpolation levels corresponding to 1,2, …, n-dimensional input variables, respectively; n is the total dimension of the input variables; p is a dimension equal to or greater than n.
As an improvement of the above scheme, the tensor product of the sparse grid type is modified by introducing adaptive dimensionality to fit the black box random scheduling model, and a formula is satisfied:
wherein the content of the first and second substances,
the total number of interpolation points related to the black box random scheduling model specifically meets the following requirements:
N＝1+(m-1)·r＝1+(m-1)·n；
and r is the number of the distributed power supplies of the intelligent building.
The embodiment of the invention also provides a distributed power supply random scheduling device, which comprises an original model establishing module, a variable acquiring module, a black box model fitting module and a scheduling scheme solving module; wherein the content of the first and second substances,
the original model establishing module is used for establishing an original random scheduling model containing a distributed power supply for the intelligent building; wherein the original stochastic scheduling model comprises a constraint condition and an objective function; the constraint conditions comprise a power flow equation, a distributed power supply model and variable upper and lower limit constraints;
the variable acquisition module is used for acquiring an input variable and an output variable of the black box random scheduling model according to the original random scheduling model;
the black box model fitting module is used for describing an equality relation of the input variable and the output variable by adopting a tensor product so as to fit the black box random scheduling model;
and the scheduling scheme solving module is used for solving the optimal solution of the black box random scheduling model based on Monte Carlo power sampling and scene random conversion scheme so as to obtain the random scheduling scheme.
The embodiment of the present invention further provides a distributed power supply random scheduling apparatus, which is characterized by comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the distributed power supply random scheduling method according to any one of the above items when executing the computer program.
Compared with the prior art, the distributed power supply random scheduling method and device disclosed by the invention have the advantages that the original random scheduling model is pre-established to obtain the input variable and the output variable of the black box random scheduling model, the black box random scheduling model is fitted by adopting the tensor product of the sparse grid and the adaptive dimensionality, and the optimal solution of the black box random scheduling model is solved based on the power sampling of Monte Carlo and the random transformation scheme of the scene to obtain the random scheduling scheme. The method can effectively simplify the random scheduling model, so that the calculation amount and the calculation difficulty can be effectively reduced in the process of obtaining the optimal solution of the random scheduling scheme, and the solution efficiency of the random scheduling is further improved.
Drawings
Fig. 1 is a schematic flowchart illustrating steps of a distributed power supply random scheduling method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps of fitting a black box random scheduling model according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a distributed power supply random scheduling apparatus according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of another distributed power random scheduling apparatus according to a third embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Fig. 1 is a schematic flowchart illustrating a step of a distributed power supply random scheduling method according to an embodiment of the present invention. The distributed power supply random scheduling method provided by the embodiment of the invention is suitable for intelligent buildings containing distributed power supplies, and comprises the following steps of S11-S14:
s11, establishing an original random scheduling model of the intelligent building containing the distributed power supply; wherein the original stochastic scheduling model comprises a constraint condition and an objective function; the constraint conditions comprise a power flow equation, a distributed power supply model and variable upper and lower limit constraints;
specifically, the power flow equation is a classical polar coordinate node type equation, and specifically satisfies the following conditions:
wherein, PSi(t) and QSi(t) active power and reactive power injected by the distribution network, respectively; pGi(t) and QGi(t) active and reactive power, P, respectively, of the distributed power supply of the intelligent buildingLi(t) and QLi(t) active and reactive requirements of the intelligent building, respectively; pi c(t) and Pi d(t) is the charging power and the discharging power of the energy storage device of the intelligent building, respectively; qCi(t) is the reactive power provided by the intelligent building reactive power compensation device; vi(t) and Vj(t) is the intelligent buildingVoltage values at node i and node j; deltaij(t) is the voltage phase angle difference between the intelligent building at the node i and the node j; pij(t) and Qij(t) active and reactive power, G, transmitted at nodes i and j, respectively, by the transformer branchesijThe self/mutual conductance of the ith row and the jth column of the node admittance matrix without the transformer branch; b isijThe self/mutual susceptances of the ith row and the jth column of the node admittance matrix without the transformer branch; sTRepresenting a set of on-load tap changers; n is a radical ofBThe number of the building nodes of the power distribution network.
The model of the distributed power supply comprises a model of a new energy power generation source; the predicted output of the distributed power supply based on new energy follows normal distribution, and specifically meets the following requirements:
wherein the content of the first and second substances,
is the predicted contribution of the distributed power source of the new energy; ξ (t) is the prediction error of the distributed power supply of the new energy source, which follows a normal distribution N (μ, σ)2)；
Is a power factor angle of the distributed power supply; rGiminAnd RGimaxRespectively of said distributed power supplyA hill climbing power lower limit and an upper limit.
Further, the model of the distributed power supply also includes a model of a fossil energy power generation source, and in order to perfect the distributed power supply model of the intelligent building, the model of the fossil energy power generation source is further described herein, the output of the fossil energy-based distributed power supply is completely controllable within the rated power, but needs to bear the power generation cost and the climbing constraint:
wherein
Is the rated power of the fossil energy distributed power source.
Furthermore, variables contained in an original random scheduling model of the intelligent building containing the distributed power supply, including random variables or decision variables and the like, are subject to upper and lower limit constraints, and the upper and lower limit constraints contain upper limits and lower limits of all the variables.
The objective function is the sum of the network loss cost of the power distribution network, the power generation cost and the operation cost of punishment of abandoning clean energy, and specifically satisfies the following conditions:
J＝C1+C2+C3；
wherein, C1The network loss cost is the cost of the difference value of the generated energy and the electricity sales amount, and specifically meets the following requirements:
C2is the power generation cost, including the cost of main network electricity and fossil energy, specifically satisfies:
C3is the punishment of the abandonment of the clean energy, and specifically meets the following requirements:
wherein, CS(t) is the segmented real-time electricity prices for the distribution grid.
S12, acquiring an input variable and an output variable of the black box random scheduling model according to the original random scheduling model;
specifically, a decision variable is converted into uniform distribution subject to upper and lower limit constraints, so as to obtain the input variable of the black box random scheduling model; wherein the decision variable comprises an active power output P of the distributed power supplyGi(t) reactive power QGi(t) charging power P of said energy storage devicei c(t) and discharge power P of the energy storage devicei d(t)；
Obtaining the active power P injected into the power distribution networkSi(t) to obtain the output variable of the black box stochastic scheduling model. To minimize the objective function, P isSi(t) is selected as the output variable.
By way of example, the charging power P of the energy storage device of the intelligent buildingi c(t) is converted into uniform distribution subject to upper and lower limit constraints, and specifically comprises the following steps:
wherein the content of the first and second substances,
and obeys uniform distribution.
And
respectively an upper limit and a lower limit of the charging power of the energy storage device.
It will be appreciated that the remaining decision variables include the active power contribution P of the distributed power supplyGi(t) reactive power QGi(t) and discharge power P of the energy storage devicei d(t) also translates to a uniform distribution subject to upper and lower bound constraints and serves as the input variable for the black box.
And S13, describing the equation relation of the input variable and the output variable by adopting a tensor product to fit the black box random scheduling model. Referring to fig. 2, a schematic flow chart of the steps of fitting the black box random scheduling model in the first embodiment of the present invention specifically includes steps S21 to S23:
s21, defining two interpolation point sets U on each dimension of input variable1And U2；
S22, calculating a sparse grid type tensor product according to the interpolation point set;
s23, transforming the tensor product of the sparse grid type by introducing adaptive dimensionality to fit the black box random scheduling model.
In particular, the set of interpolation points U1And U2The method specifically comprises the following steps:
U1＝{0}；
wherein m is the number of interpolation points of each dimension of the input variable, parameters A, B and C … are the interpolation points, and the value of the interpolation points is determined by obtaining an orthogonal polynomial and an order corresponding to the distribution characteristic of the input variable;
specifically, referring to table 1, the distribution characteristics of the input variables correspond to the orthogonal polynomials, the corresponding orthogonal polynomials are obtained by determining the distribution characteristics of the input variables, and the corresponding orders are determined according to the number of interpolation points of each dimension of the input variables, so as to calculate the values of the interpolation points a, B, and C …. For example, when the distribution characteristic of the input variable is Normal, the corresponding orthogonal polynomial is Hermite, and assuming that the number m of interpolation points of each dimension of the input variable is 3, the corresponding order is 3, and searching for the Hermite polynomial of 3 th order can be known as H3(x)＝8x312x, calculated H3(x) Zero point of (a) is 0 and
calculating the interpolation point according to the correspondence
And increasing the value of m to calculate the values of other different interpolation points, so as to calculate the interpolation point set.
TABLE 1 orthogonal polynomials to profiles of input variables
It should be understood that the above scenario is only an example, and in practical applications, the corresponding orthogonal polynomial may be selected according to distribution characteristics of different input variables, and the interpolation point set is calculated according to a corresponding order, which is not limited herein.
Step S22 specifically satisfies the formula:
wherein the content of the first and second substances,
a column vector that is the output variable; i | ═ i1+i2+…+in，i1,i2,...,inInterpolation levels corresponding to 1,2, …, n-dimensional input variables, respectively; n is the total dimension of the input variables; p is a dimension equal to or greater than n.
Further, by introducing adaptive dimensionality, the process of reconstructing the tensor product of the sparse grid type is specifically as follows:
introducing a variable r to replace p ═ r +1 and n ═ r, wherein r is the number of distributed power supplies of the intelligent building, and r ═ n is more than or equal to 2, and | > r | ≧ n ≧ 2, and substituting to obtain an equation (1) as follows:
the summation process of the above equation contains 2 types: i.e., i | ═ r and i | ═ r + 1:
when | i | ═ r ═ n, by definition | i | ═ i | >1+i2+…+inAvailable i1＝i2＝…＝in1. The summation term of equation (1) relating to i | ═ r ═ n is:
when | i | ═ r +1 ═ n +1, by definition | i | ═ i | >1+i2+…+inCan know that i1,i2,...,inThere are certainly one term of 2 and the rest are all 1. Thus, assume ix2 and ij1(j ═ 1, 2.., r, and j ≠ x), then the summation term of equation (1) involving | i | ═ r +1 ═ n +1 is:
ix2 and ij1(j ═ 1, 2.., r and j ≠ g)x) there are actually r combinations. Therefore, the formula (1) relates to a summation of | i | ═ r +1 ═ n +1, and r combinations described above need to be superimposed in total.
Combining the two situations, the formula (1) is specifically as follows:
thereby obtaining the black box random scheduling model. The total number of interpolation points related to the black box random scheduling model specifically meets the following requirements:
N＝1+(m-1)·r＝1+(m-1)·n；
and r is the number of the distributed power supplies of the intelligent building.
S14, solving the optimal solution of the black box random scheduling model based on Monte Carlo power sampling and scene random conversion scheme to obtain a random scheduling scheme.
The Monte Carlo-based power sampling and scene-based random conversion scheme is characterized in that a large number of scenes including a plurality of output conditions of random output power are extracted, and a sampling scene transfer constraint is added in a constraint condition to link a plurality of sampling scenes, so that the optimal solution of the black box random scheduling model is solved, and the random scheduling scheme is obtained.
By way of example, an intelligent building comprises three distributed power supplies with rated power of 300kW and adjustable power factor in the range of 0.95 to 1, a diesel generator with rated power of 500kW, an energy storage device with capacity of 800kWh and charging and discharging efficiency of 0.95, and a plurality of reactive power compensation devices. The black box random scheduling model of the intelligent building is constructed through the method, in the fitting process, the random variables are normally distributed, the decision variables are uniformly distributed, and according to the distribution characteristics of the input variables and the number of interpolation points of each dimension, 11-order Hermite polynomial and 11-order Legendre polynomial are used. Referring to table 2, the black box stochastic scheduling model and the original stochastic scheduling model are compared in terms of the computation time and the operating cost of the scheduling scheme required in the case of involving 100, 1000, 5000 and 10000 sampling scenarios, using Monte Carlo-based power sampling and scenario-based conversion schemes.
TABLE 2 calculation time and operating costs of two scheduling models
As can be seen from table 2, in each sampling scenario, the scheduling scheme obtained by using the black box random scheduling model has shorter calculation time, higher efficiency, and lower operation cost compared to the scheduling scheme using the original random scheduling model. Particularly, when the sampling scene reaches 10000, the original scheduling model cannot be solved to obtain a scheduling scheme, and the black box random scheduling model still obtains a corresponding scheduling scheme within acceptable calculation time. This shows that the scheduling method of the black box random scheduling model can be more suitable for the scheduling model with a larger scale, and the solving efficiency is also higher.
The distributed power supply random scheduling method provided by the embodiment of the invention obtains the input variable and the output variable of the black box random scheduling model by pre-establishing an original random scheduling model, adopts the tensor product of a sparse grid and a self-adaptive dimension to fit the black box random scheduling model, and solves the optimal solution of the black box random scheduling model based on the power sampling of Monte Carlo and the random conversion scheme of a scene to obtain the random scheduling scheme. The method can effectively simplify the random scheduling model, so that the calculation amount and the calculation difficulty can be effectively reduced in the process of obtaining the optimal solution of the random scheduling scheme, and the solution efficiency of the random scheduling is further improved.
Example two
Referring to fig. 3, which is a schematic structural diagram of a distributed power supply random scheduling apparatus in the second embodiment of the present invention, the second embodiment of the present invention further provides a distributed power supply random scheduling apparatus 20, which includes an original model building module 21, a variable obtaining module 22, a black box model fitting module 23, and a scheduling scheme solving module 24.
The original model establishing module 21 is used for establishing an original random scheduling model containing a distributed power supply for the intelligent building; wherein the original stochastic scheduling model comprises a constraint condition and an objective function; the constraint conditions comprise a power flow equation, a distributed power supply model and variable upper and lower limit constraints;
the variable obtaining module 22 is configured to obtain an input variable and an output variable of the black box random scheduling model according to the original random scheduling model;
the black box model fitting module 23 is configured to describe an equality relationship between the input variable and the output variable by using a tensor product, so as to fit the black box random scheduling model;
and the scheduling scheme solving module 24 is configured to solve an optimal solution of the black box random scheduling model based on Monte Carlo power sampling and a scene random conversion scheme to obtain a random scheduling scheme.
It should be noted that, the distributed power supply random scheduling apparatus provided in the embodiment of the present invention is configured to execute all the process steps of the distributed power supply random scheduling method in the above embodiment, and working principles and beneficial effects of the two are in one-to-one correspondence, so that details are not described again.
The distributed power supply random scheduling device provided by the embodiment of the invention obtains the input variable and the output variable of the black box random scheduling model by pre-establishing an original random scheduling model, adopts the tensor product of the sparse grid and the self-adaptive dimensionality to fit the black box random scheduling model, and solves the optimal solution of the black box random scheduling model based on the power sampling of Monte Carlo and the random transformation scheme of the scene to obtain the random scheduling scheme. The method can effectively simplify the random scheduling model, so that the calculation amount and the calculation difficulty can be effectively reduced in the process of obtaining the optimal solution of the random scheduling scheme, and the solution efficiency of the random scheduling is further improved.
EXAMPLE III
Fig. 4 is a schematic structural diagram of another distributed power random scheduling apparatus in the third embodiment of the present invention. The third embodiment of the present invention provides a distributed power random scheduling apparatus 30, which includes a processor 31, a memory 32, and a computer program stored in the memory and configured to be executed by the processor, for example, a program fitting the black box random scheduling model method. The processor, when executing the computer program, implements the steps of the method embodiment for fitting the black box stochastic scheduling model described above, such as steps S21-S23 shown in fig. 2. Alternatively, the processor implements the functions of the modules in the embodiments of the apparatuses described above when executing the computer program, for example, the random scheduling apparatus for distributed power supplies according to the second embodiment.
Illustratively, the computer program may be divided into one or more modules, which are stored in the memory 32 and executed by the processor 31 to accomplish the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the distributed power random scheduling apparatus 30. For example, the computer program may be divided into an original model building module 21, a variable obtaining module 22, a black box model fitting module 23, and a scheduling scheme solving module 24; the specific functions of each module are as follows:
the original model establishing module 21 is used for establishing an original random scheduling model containing a distributed power supply for the intelligent building; wherein the original stochastic scheduling model comprises a constraint condition and an objective function; the constraint conditions comprise a power flow equation, a distributed power supply model and variable upper and lower limit constraints;
the variable obtaining module 22 is configured to obtain an input variable and an output variable of the black box random scheduling model according to the original random scheduling model;
the black box model fitting module 23 is configured to describe an equality relationship between the input variable and the output variable by using a tensor product, so as to fit the black box random scheduling model;
and the scheduling scheme solving module 24 is configured to solve an optimal solution of the black box random scheduling model based on Monte Carlo power sampling and a scene random conversion scheme to obtain a random scheduling scheme.
The distributed power supply random scheduling apparatus 30 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or other computing devices. The distributed power random scheduling device 30 may include, but is not limited to, a processor 31 and a memory 32. Those skilled in the art will appreciate that the schematic diagram is merely an example of the distributed power random scheduling apparatus 30, and does not constitute a limitation to the distributed power random scheduling apparatus 30, and may include more or less components than those shown, or combine some components, or different components, for example, the distributed power random scheduling apparatus 30 may further include an input-output device, a network access device, a bus, etc.
The Processor 31 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor, and the processor 31 is a control center of the distributed power supply random scheduling apparatus 30, and various interfaces and lines are used to connect various parts of the entire distributed power supply random scheduling apparatus 30.
The memory 32 may be used to store the computer programs and/or modules, and the processor may implement the various functions of the distributed power random access scheduling apparatus 30 by running or executing the computer programs and/or modules stored in the memory and calling the data stored in the memory. The memory 32 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The modules integrated by the distributed power random scheduling apparatus 30 may be stored in a computer readable storage medium if they are implemented in the form of software functional units and sold or used as independent products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
It should be noted that the above-described embodiments of the distributed power random scheduling apparatus are merely illustrative, where the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, may be located in one place, or may also be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (7)
1. A distributed power supply random scheduling method is suitable for intelligent buildings with distributed power supplies, and is characterized by comprising the following steps:
establishing an original random scheduling model containing a distributed power supply for the intelligent building; wherein the original stochastic scheduling model comprises a constraint condition and an objective function; the constraint conditions comprise a power flow equation, a distributed power supply model and variable upper and lower limit constraints;
acquiring an input variable and an output variable of a black box random scheduling model according to the original random scheduling model;
describing an equality relation of the input variable and the output variable by adopting a tensor product to fit the black box random scheduling model;
solving the optimal solution of the black box random scheduling model based on Monte Carlo power sampling and scene random conversion scheme to obtain a random scheduling scheme;
the equation relationship between the input variable and the output variable is described by using a tensor product to fit the black box random scheduling model, and the method specifically comprises the following steps:
defining two interpolation point sets U on each dimension of input variable1And U2；
Calculating a tensor product of a sparse grid type according to the interpolation point set;
transforming tensor products of the sparse grid type by introducing adaptive dimensionality to fit the black box random scheduling model;
the interpolation point set U1And U2The method specifically comprises the following steps:
U1＝{0}；
wherein m is the number of interpolation points of each dimension of the input variable, parameters A, B and C … are the interpolation points, and the value of the interpolation points is determined by obtaining an orthogonal polynomial and an order corresponding to the distribution characteristic of the input variable;
and calculating a sparse grid type tensor product according to the interpolation point set, wherein the formula is satisfied:
wherein the content of the first and second substances,
a column vector that is the output variable; i | ═ i1+i2+…+in，i1,i2,...,inInterpolation levels corresponding to 1,2, …, n-dimensional input variables, respectively; n is the total dimension of the input variables; p is a dimension greater than or equal to n;
and transforming the tensor product of the sparse grid type by introducing adaptive dimensionality to fit the black box random scheduling model, so as to satisfy the formula:
wherein the content of the first and second substances,
the total number of interpolation points related to the black box random scheduling model specifically meets the following requirements:
N＝1+(m-1)·r＝1+(m-1)·n；
and r is the number of the distributed power supplies of the intelligent building.
2. The distributed power supply random scheduling method of claim 1, wherein the power flow equation is a classical polar node type equation, and specifically satisfies:
wherein, PSi(t) and QSi(t) active power and reactive power injected by the distribution network, respectively; pGi(t) and QGi(t) active and reactive power, P, respectively, of the distributed power supply of the intelligent buildingLi(t) and QLi(t) active and reactive requirements of the intelligent building, respectively; pi c(t) and Pi d(t) is the charging power and the discharging power of the energy storage device of the intelligent building, respectively; qCi(t) is the reactive power provided by the intelligent building reactive power compensation device; vi(t) and Vj(t) is the voltage value of the intelligent building at the node i and the node j; deltaij(t) is the voltage phase angle difference between the intelligent building at the node i and the node j; pij(t) and Qij(t) active and reactive power, G, transmitted at nodes i and j, respectively, by the transformer branchesijThe self/mutual conductance of the ith row and the jth column of the node admittance matrix without the transformer branch; b isijThe self/mutual susceptances of the ith row and the jth column of the node admittance matrix without the transformer branch; sTRepresenting a set of on-load tap changers; n is a radical ofBThe number of the building nodes of the power distribution network.
3. The distributed power random scheduling method of claim 2 wherein the model of the distributed power source comprises a model of a new energy generation source; the predicted output of the distributed power supply based on new energy follows normal distribution, and specifically meets the following requirements:
wherein the content of the first and second substances,
is the predicted contribution of the distributed power source of the new energy; ξ (t) is the prediction error of the distributed power supply of the new energy source, which follows a normal distribution N (μ, σ)2)；
Is a power factor angle of the distributed power supply; rGiminAnd RGimaxRespectively, a lower limit and an upper limit of the climbing power of the distributed power supply.
4. The distributed power supply random scheduling method according to claim 3, wherein the objective function is the sum of the network loss cost, the power generation cost and the operation cost of punishment of the rejection of clean energy of the power distribution network, and specifically satisfies the following conditions:
J＝C1+C2+C3；
wherein, C1The network loss cost is the cost of the difference value of the generated energy and the electricity sales amount, and specifically meets the following requirements:
C2is the power generation cost, including the cost of main network electricity and fossil energy, specifically satisfies:
C3is the punishment of the abandonment of the clean energy, and specifically meets the following requirements:
wherein, CS(t) is the segmented real-time electricity prices for the distribution grid.
5. The distributed power random scheduling method of claim 4, wherein the obtaining of the input variable and the output variable of the black box random scheduling model according to the original random scheduling model specifically comprises:
converting decision variables into uniform distribution subject to upper and lower limit constraints to obtain the input variables of the black box random scheduling model; wherein the decision variable comprises an active power output P of the distributed power supplyGi(t) reactive power QGi(t) charging power P of said energy storage devicei c(t) and discharge power P of the energy storage devicei d(t)；
Obtaining the active power P injected into the power distribution networkSi(t) to obtain the output variable of the black box stochastic scheduling model.
6. A distributed power supply random scheduling device is characterized by comprising an original model establishing module, a variable acquiring module, a black box model fitting module and a scheduling scheme solving module; wherein the content of the first and second substances,
the original model establishing module is used for establishing an original random scheduling model containing a distributed power supply for the intelligent building; wherein the original stochastic scheduling model comprises a constraint condition and an objective function; the constraint conditions comprise a power flow equation, a distributed power supply model and variable upper and lower limit constraints;
the variable acquisition module is used for acquiring an input variable and an output variable of the black box random scheduling model according to the original random scheduling model;
the black box model fitting module is used for describing an equality relation of the input variable and the output variable by adopting a tensor product so as to fit the black box random scheduling model;
the scheduling scheme solving module is used for solving the optimal solution of the black box random scheduling model based on Monte Carlo power sampling and scene random conversion scheme to obtain a random scheduling scheme;
wherein, the black box model fitting module is specifically configured to:
defining two interpolation point sets U on each dimension of input variable1And U2；
Calculating a tensor product of a sparse grid type according to the interpolation point set;
transforming tensor products of the sparse grid type by introducing adaptive dimensionality to fit the black box random scheduling model;
the interpolation point set U1And U2The method specifically comprises the following steps:
U1＝{0}；
wherein m is the number of interpolation points of each dimension of the input variable, parameters A, B and C … are the interpolation points, and the value of the interpolation points is determined by obtaining an orthogonal polynomial and an order corresponding to the distribution characteristic of the input variable;
and calculating a sparse grid type tensor product according to the interpolation point set, wherein the formula is satisfied:
wherein the content of the first and second substances,
a column vector that is the output variable; i | ═ i1+i2+…+in，i1,i2,...,inInterpolation levels corresponding to 1,2, …, n-dimensional input variables, respectively; n is the total dimension of the input variables; p is a dimension greater than or equal to n;
and transforming the tensor product of the sparse grid type by introducing adaptive dimensionality to fit the black box random scheduling model, so as to satisfy the formula:
wherein the content of the first and second substances,
the total number of interpolation points related to the black box random scheduling model specifically meets the following requirements:
N＝1+(m-1)·r＝1+(m-1)·n；
and r is the number of the distributed power supplies of the intelligent building.
7. A distributed power random scheduling apparatus comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the distributed power random scheduling method of any one of claims 1 to 5 when executing the computer program.
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