CN115619005A - Intelligent power utilization network resource optimal configuration method and system - Google Patents
Intelligent power utilization network resource optimal configuration method and system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The invention relates to the technical field of power utilization network resource optimization, and discloses an intelligent power utilization network resource optimization configuration method and system, wherein the method comprises the following steps: acquiring power grid trajectory data, and calculating to obtain a load prediction error sequence of the power grid trajectory data; calculating to obtain an uncertain set of the load prediction error sequence based on the load prediction error sequence of the power grid trajectory data; constructing a double-layer optimization model objective function based on uncertain set constraints; and converting the target function of the constructed double-layer optimization model into a mixed integer programming function and solving to obtain the micro-grid load scheduling strategy. The method provided by the invention considers the uncertainty of the electricity utilization behaviors of users in different electricity utilization areas, constructs the uncertain sets of the load power of the microgrid in different areas, takes the uncertain sets as the constraint conditions of the optimization objective function, obtains the load scheduling strategy of the microgrid with stronger robustness under the constraint of the uncertain sets, and optimizes the power distribution power error and the power distribution cost.
Description
Technical Field
The invention relates to the technical field of network resource optimization, in particular to an intelligent power utilization network resource optimization configuration method and system.
Background
With the rapid development of new energy technologies, more and more new energy power generation resources are integrated into a smart power grid, a user serves as a main regulation and control body of the power grid, uncertainty of behaviors of the user can have a great influence on the regulation and control effect of the power grid, particularly, a price-insensitive user can reduce the estimated adjustable elastic space of the power grid to a great extent on the basis of remaining control right, and the deviation of power grid operation scheduling is caused due to insufficient accuracy of response power grid requirements. In order to reduce the fluctuation influence of the uncertainty of the user behavior on the power grid supply and demand balance, accurately respond to the power grid demand and further improve the robustness of the power grid, the patent provides an intelligent power utilization network resource optimization configuration method and system, the influence of the load prediction error and the uncertainty behavior of the user is comprehensively considered, and the adjustable elastic space of the power utilization network is improved.
Disclosure of Invention
In view of the above, the invention provides an intelligent power utilization network resource optimization configuration method, and aims to 1) consider the uncertainty of power utilization behaviors of users in different power utilization areas, construct an uncertain set of microgrid load power in different areas, use the uncertain set as a constraint condition of an optimization objective function, and obtain a microgrid load scheduling strategy under the constraint of the uncertain set, wherein the microgrid load scheduling strategy can realize lower power distribution error and power distribution cost under the condition that the power utilization behaviors of the users in different power utilization areas are uncertain; 2) The method comprises the steps of constructing a double-layer optimization model objective function, wherein the optimization objective of a lower-layer optimization objective function is that the error between the distribution power of a distribution network and the actual use power of a microgrid is minimum under an uncertain condition, the optimization objective of an upper-layer optimization objective function is that the distribution cost is minimum, and the upper-layer optimization objective function is constrained based on the lower-layer optimization objective function, so that the actual load aggregate power of the microgrid based on distribution uncertainty is greater than the loss load aggregate power when the upper-layer objective function optimizes the regulation and control cost, and further the distribution power error and the distribution cost are optimized overall, and an optimal microgrid load scheduling strategy is obtained.
The invention provides an intelligent power utilization network resource optimal configuration method, which comprises the following steps:
s1: acquiring power grid track data, and calculating to obtain a load prediction error sequence of the power grid track data, wherein the power grid track data comprises a historical time sequence track sequence of a load aggregated power actual value and a predicted value of a micro-grid;
s2: calculating to obtain an uncertain set of the load prediction error sequence based on the load prediction error sequence of the power grid trajectory data;
s3: constructing a double-layer optimization model objective function based on uncertain set constraints, wherein the lower layer of the double-layer optimization model considers robustness caused by load prediction errors, and the upper layer considers the economy of load scheduling;
s4: converting the constructed double-layer optimization model objective function into a mixed integer programming function;
s5: and carrying out optimization solution on the converted mixed integer programming function to obtain a micro-grid load scheduling strategy.
As a further improvement of the method of the invention:
optionally, the collecting power grid trajectory data in the step S1 includes:
collecting power grid trajectory data, wherein the power grid comprises a power distribution network and a microgrid, the power distribution network is used for transmitting current to the microgrid, the microgrid represents a circuit set of a power utilization area, and all power utilization circuits of the power utilization area form the microgrid of the area;
the power grid trajectory data comprise historical time sequence trajectory sequences of actual values and predicted values of load aggregation power of the micro-grid, and the power grid trajectory data are expressed as follows:
wherein:
P i (h) Representing the actual value of load aggregation power of the microgrid i at the historical moment h, wherein the load aggregation is the aggregation of all controllable load resources in the microgrid, and n represents the number of the microgrids connected with the power distribution network;
h 0 representing the initial historical time, h, of acquisition of grid trajectory data r And representing the collection ending historical moment of the power grid track data collection.
Optionally, the step S1 of calculating a load prediction error sequence of the grid trajectory data includes:
the load prediction error sequence of the power grid track data is as follows:
(u i (h 0 ),u i (h 1 ),...,u i (h j ),...,u i (h r )),i∈[1,n]
wherein:
u i (h j ) Indicating the micro-grid i at historical time h j The load prediction error value.
Optionally, the calculating in S2 step obtains an uncertainty set of the load prediction error sequence, including:
the uncertain set calculation process of the load prediction error sequence comprises the following steps:
s21: extracting non-repetitive load prediction error values of a load prediction error sequence of any microgrid i:
u i ＝[u i,1 ,u i,2 ,...,u i,s ,...u i,S ]
wherein:
u i representing a sequence of extracted load prediction error values, any two load prediction error values in the sequence being different;
u i,s representing the s-th nonrepeating load prediction error value, u, extracted from the load prediction error sequence of the microgrid i i,s ≤u i,s+1 ；
S represents the total number of the non-repetitive load prediction error values extracted from the load prediction error sequence of the microgrid i;
s22: determining the level of significance as
The level of significance
Corresponding Z value is
Z represents a standard score obeying a normal distribution;
s23: determining a sequence of load prediction error values u separately i Mean arbitrary load prediction error value u i,s Lower boundary of (1)
And upper bound
u i,k ,u i,c ∈u i
Wherein:
μ i Mean value of load prediction error sequence, sigma, representing microgrid i i Representing the standard deviation of the load prediction error sequence of the microgrid i;
r +1 represents the number of times of the collected historical data;
s24: constructing load prediction error sequence uncertainty set E of any microgrid i i ：
Wherein:
Optionally, the constructing a double-layer optimization model objective function based on the uncertainty set constraint in step S3 includes:
constructing a double-layer optimization model objective function based on uncertain set constraint, wherein the uncertain set is a constraint condition of a double-layer optimization objective function, and the form of the double-layer optimization objective function is as follows:
wherein:
F 1 expressing a lower-layer optimization objective function, wherein the optimization objective of the lower-layer optimization objective function is that the error between the distribution power of the power distribution network and the actual use power of the microgrid is minimum under an uncertain condition;
F 2 representing an upper-layer optimization objective function, wherein the optimization objective of the upper-layer optimization objective function is the minimum power distribution cost;
E i representing the uncertain set of load prediction error sequences, τ, of the microgrid i i Indicating the load prediction error therein, (1-tau) i )P i (t) represents the actual power used by the microgrid i at time t, P i (t) represents the power distributed by the distribution network to the microgrid i at time t, and N represents the total number of times;
cost 1 cost, representing the distribution cost of the distribution network to the load aggregated power of the microgrid 2 Represents the loss cost of unused allocated power;
the constraint conditions of the double-layer optimization objective function comprise load prediction error sequence uncertainty sets of different micro-grids and constraint conditions for constraining the upper-layer optimization objective function based on the lower-layer optimization objective function:
and the constraint of the lower-layer optimization objective function on the upper-layer optimization objective function represents that the actual load aggregate power of the microgrid based on the distribution uncertainty is greater than the loss load aggregate power when the upper-layer objective function optimizes the regulation and control cost.
Optionally, the converting the objective function of the double-layer optimization model into the mixed integer programming function in the step S4 includes:
converting a double-layer optimization model objective function into a mixed integer programming function based on Lagrange multipliers, wherein the converted mixed integer programming function L of the upper-layer optimization objective function 2 Comprises the following steps:
mixed integer programming function L of post-conversion lower-layer optimization objective function 1 Comprises the following steps:
τ i ∈E i
wherein:
λ 1 ,λ 2 are lagrange multipliers.
Optionally, the performing an optimized solution on the mixed integer programming function in the step S5 includes:
optimizing and solving the mixed positive number planning function to obtain a micro-grid load scheduling strategy, wherein the micro-grid load scheduling strategy represents the power distributed to different micro-grids by the power distribution network at different moments; the optimization solving process of the mixed positive number programming function comprises the following steps:
s51: constructing a mixed integer planning total function L:
L＝L 1 +L 2
s52: separately computing functions L vs. λ 1 ,λ 2 Has a partial derivative of 0;
s53: converting the partial derivative calculation result into determinant, and making the determinant equal to 0 to obtain a plurality of groups of lambada 1 ,λ 2 Selecting a set of lambda with the largest product 1 ,λ 2 As a result of the solution
S54: will be provided with
Substituting function L to λ 1 ,λ 2 The partial derivatives of the power distribution network are equal to 0, and the power P distributed to different micro-grids i by the power distribution network at different moments t i (t)。
In order to solve the above problems, the present invention provides an intelligent power utilization network resource optimal configuration system, including:
the data acquisition device is used for acquiring power grid track data, calculating to obtain a probability distribution uncertain set of the power grid track data, and calculating to obtain an uncertain set of a load prediction error sequence based on a load prediction error sequence of the power grid track data;
the model construction device is used for constructing a double-layer optimization model objective function based on uncertain set constraints;
and the network optimization module is used for converting the established double-layer optimization model objective function into a mixed integer programming function, and performing optimization solution on the converted mixed integer programming function to obtain a micro-grid load scheduling strategy.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one instruction; and
and the processor executes the instructions stored in the memory to realize the intelligent power utilization network resource optimization configuration method.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, where at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the foregoing method for optimally configuring resources of an intelligent power consumption network.
Compared with the prior art, the invention provides an intelligent power utilization network resource optimal configuration method, which has the following advantages:
firstly, the scheme provides a method for quantifying uncertainty of user electricity utilization behaviors, and as the user electricity utilization behaviors in different electricity utilization areas are different, non-repeated load prediction error value extraction is carried out on a load prediction error sequence of a micro-grid i formed by any electricity utilization area:
u i ＝[u i,1 ,u i,2 ,...,u i,s ,...u i,S ]
wherein: u. u i Representing a sequence of extracted load prediction error values, any two load prediction error values in the sequence being different; u. u i,s Represents the s th non-repetitive load prediction error value, u, extracted from the load prediction error sequence of the microgrid i i,s ≤u i,s+1 (ii) a S represents the total number of the non-repetitive load prediction error values extracted from the load prediction error sequence of the microgrid i; determining the level of significance as
The level of significance
Corresponding Z value is
Z represents a standard score following a normal distribution; determining a sequence of load prediction error values u separately i Mean arbitrary load prediction error value u i,s Lower boundary of (1)
And upper bound
u i,k ,u i,c ∈u i
Wherein:
represents satisfaction
U corresponding to the maximum k value of i,k ；
Wherein:
represents u i,s Probability of falling between the upper and lower bounds. According to the scheme, uncertainty of power utilization behaviors of users in different power utilization areas is considered, uncertain sets of load power of the micro-grid in different areas are constructed, the uncertain sets are used as constraint conditions of an optimization objective function, and a micro-grid load scheduling strategy is obtained under the constraint of the uncertain sets.
Meanwhile, the scheme provides a double-layer optimization model objective function, and the form of the double-layer optimization objective function is as follows:
wherein: f 1 Expressing a lower-layer optimization objective function, wherein the optimization objective of the lower-layer optimization objective function is that the error between the distribution power of the power distribution network and the actual use power of the microgrid is minimum under an uncertain condition; f 2 Representing an upper-layer optimization objective function, wherein the optimization objective of the upper-layer optimization objective function is that the power distribution cost is minimum; e i Representing the uncertain set of load prediction error sequences, τ, of the microgrid i i Represents the load prediction error therein (1-tau) i )P i (t) represents the actual power used by the microgrid i at time t, P i (t) represents the power distributed by the distribution network to the microgrid i at time t, and N represents the total number of times; cost 1 Representing the cost, of distribution of the distribution network to the load aggregated power of the microgrid 2 Representing the cost of the loss of unused allocated power. According to the scheme, a double-layer optimization model objective function is constructed, the optimization objective of the lower-layer optimization objective function is that the error between the distribution power of the distribution network and the actual use power of the microgrid is minimum under an uncertain condition, the optimization objective of the upper-layer optimization objective function is the minimum distribution cost, the upper-layer optimization objective function is constrained based on the lower-layer optimization objective function, and the fact that the actual load aggregated power of the microgrid based on the distribution uncertainty is greater than the loss load aggregated power when the upper-layer objective function optimizes the regulation and control cost is shown, so that the distribution power error and the distribution cost are further optimized overall, and the optimal microgrid load scheduling strategy is obtained.
Drawings
Fig. 1 is a schematic flowchart of a method for optimally configuring resources of an intelligent power consumption network according to an embodiment of the present invention;
fig. 2 is a functional block diagram of an intelligent power consumption network resource optimization configuration system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing an intelligent power consumption network resource optimal configuration method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides an intelligent power utilization network resource optimal configuration method. The executing subject of the intelligent power utilization network resource optimization configuration method includes, but is not limited to, at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiment of the present application. In other words, the intelligent electricity consumption network resource optimization configuration method may be executed by software or hardware installed in a terminal device or a server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1:
s1: and collecting power grid track data, and calculating to obtain a load prediction error sequence of the power grid track data, wherein the power grid track data comprises a historical time sequence track sequence of a load aggregated power actual value and a predicted value of the micro-grid.
The step S1 of collecting power grid track data comprises the following steps:
collecting power grid trajectory data, wherein the power grid comprises a power distribution network and a microgrid, the power distribution network is used for transmitting current to the microgrid, the microgrid represents a circuit set of a power utilization area, and all power utilization circuits of the power utilization area form the microgrid of the area;
the power grid trajectory data comprise historical time sequence trajectory sequences of actual values and predicted values of load aggregated power of the micro-grid, and are expressed as follows:
wherein:
P i (h) Representing the actual value of load aggregation power of the microgrid i at the historical moment h, wherein the load aggregation is the aggregation of all controllable load resources in the microgrid, and n represents the number of the microgrids connected with the power distribution network;
h 0 representing the initial historical time, h, of acquisition of grid trajectory data r And representing the collection ending historical moment of the power grid track data collection.
The step S1 of calculating a load prediction error sequence of the grid trajectory data includes:
the load prediction error sequence of the power grid track data is as follows:
(u i (h 0 ),u i (h 1 ),...,u i (h j ),...,u i (h r )),i∈[1,n]
wherein:
u i (h j ) Indicating the micro-grid i at historical time h j The load prediction error value.
S2: and calculating to obtain an uncertain set of the load prediction error sequence based on the load prediction error sequence of the power grid trajectory data.
The step S2 of calculating an uncertainty set of the load prediction error sequence includes:
the uncertain set calculation process of the load prediction error sequence comprises the following steps:
s21: extracting non-repetitive load prediction error values of a load prediction error sequence of any microgrid i:
u i ＝[u i,1 ,u i,2 ,...,u i,s ,...u i,S ]
wherein:
u i representing a sequence of extracted load prediction error values, any two load prediction error values in the sequence being different;
u i,s representing the s-th nonrepeating load prediction error value, u, extracted from the load prediction error sequence of the microgrid i i,s ≤u i,s+1 ；
S represents the total number of the non-repetitive load prediction error values extracted from the load prediction error sequence of the microgrid i;
s22: determining the level of significance as
The level of significance
Corresponding Z value is
Z represents a standard score obeying a normal distribution;
s23: determining a sequence of load prediction error values u separately i Mean arbitrary load prediction error value u i,s Lower boundary of (1)
And upper bound
u i,k ,u i,c ∈u i
Wherein:
μ i Representing the mean value, σ, of the load prediction error sequence of the microgrid i i Representing the standard deviation of the load prediction error sequence of the microgrid i;
r +1 represents the number of times of the collected historical data;
s24: load prediction error sequence uncertainty set E for constructing any microgrid i i ：
Wherein:
S3: and constructing a double-layer optimization model objective function based on uncertain set constraints, wherein the lower layer of the double-layer optimization model considers robustness caused by load prediction errors, and the upper layer considers the economy of load scheduling.
And in the step S3, constructing a double-layer optimization model objective function based on uncertain set constraints, which comprises the following steps:
constructing a double-layer optimization model objective function based on uncertain set constraint, wherein the uncertain set is a constraint condition of a double-layer optimization objective function, and the form of the double-layer optimization objective function is as follows:
wherein:
F 1 representing a lower-layer optimization objective function, wherein the optimization objective of the lower-layer optimization objective function is that the error between the distribution power of the power distribution network and the actual use power of the microgrid is minimum under an uncertain condition;
F 2 representing an upper-layer optimization objective function, wherein the optimization objective of the upper-layer optimization objective function is that the power distribution cost is minimum;
E i representing the uncertain set of load prediction error sequences, τ, of the microgrid i i Indicating the load prediction error therein, (1-tau) i )P i (t) represents the actual power used by the microgrid i at time t, P i (t) represents the power distributed by the distribution network to the microgrid i at time t, and N represents the total number of times;
cost 1 representing the cost, of distribution of the distribution network to the load aggregated power of the microgrid 2 Represents the loss cost of unused allocated power;
the constraint conditions of the double-layer optimization objective function comprise load prediction error sequence uncertainty sets of different micro-grids and constraint conditions for constraining the upper-layer optimization objective function based on the lower-layer optimization objective function:
and the constraint of the lower-layer optimization objective function on the upper-layer optimization objective function represents that the actual load aggregate power of the microgrid based on the distribution uncertainty is greater than the loss load aggregate power when the upper-layer objective function optimizes the regulation and control cost.
S4: and converting the constructed target function of the double-layer optimization model into a mixed integer programming function.
In the step S4, converting the double-layer optimization model objective function into a mixed integer programming function, including:
converting a double-layer optimization model objective function into a mixed integer programming function based on Lagrange multipliers, wherein the converted mixed integer programming function L of the upper-layer optimization objective function 2 Comprises the following steps:
mixed integer programming function L of transformed lower-layer optimized objective function 1 Comprises the following steps:
τ i ∈E i
wherein:
λ 1 ,λ 2 are lagrange multipliers.
S5: and carrying out optimization solution on the converted mixed integer programming function to obtain a micro-grid load scheduling strategy.
In the step S5, performing optimization solution on the mixed integer programming function, including:
optimizing and solving the mixed positive number planning function to obtain a micro-grid load scheduling strategy, wherein the micro-grid load scheduling strategy represents the power distributed to different micro-grids by the power distribution network at different moments; the optimization solving process of the mixed positive number programming function comprises the following steps:
s51: constructing a mixed integer planning total function L:
L＝L 1 +L 2
s52: separately computing functions L vs. λ 1 ,λ 2 Has a partial derivative of 0;
s53: converting the partial derivative calculation result into determinant, and making determinant equal to 0 to obtain several groups of lambda 1 ,λ 2 Selecting a set of lambda with the largest product 1 ,λ 2 As a result of the solution
S54: will be provided with
Substituting function L to λ 1 ,λ 2 The partial derivative result of (a) is that the partial derivative is equal to 0, and the power P distributed by the power distribution network to different micro power grids i at different moments t i (t)。
Example 2:
fig. 2 is a functional block diagram of an intelligent power consumption network resource optimal configuration system according to an embodiment of the present invention, which is capable of implementing the intelligent power consumption network resource optimal configuration method in embodiment 1.
The system 100 for optimizing and configuring the resources of the intelligent power utilization network can be installed in electronic equipment. According to the realized functions, the intelligent power utilization network resource optimization configuration system can comprise a data acquisition device 101, a model construction device 102 and a network optimization module 103. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform a fixed function, and are stored in a memory of the electronic device.
The data acquisition device 101 is used for acquiring power grid track data, calculating to obtain a probability distribution uncertain set of the power grid track data, and calculating to obtain an uncertain set of a load prediction error sequence based on a load prediction error sequence of the power grid track data;
the model construction device 102 is used for constructing a double-layer optimization model objective function based on uncertain set constraints;
and the network optimization module 103 is configured to convert the established double-layer optimization model objective function into a mixed integer programming function, and perform optimization solution on the converted mixed integer programming function to obtain a microgrid load scheduling policy.
In detail, when the modules in the intelligent power consumption network resource optimal configuration system 100 in the embodiment of the present invention are used, the same technical means as the intelligent power consumption network resource optimal configuration method described in fig. 1 above are adopted, and the same technical effect can be produced, which is not described herein again.
Example 3:
fig. 3 is a schematic structural diagram of an electronic device for implementing an intelligent power utilization network resource optimal configuration method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as a program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, e.g. a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of the program 12, but also to temporarily store data that has been output or will be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes or executes programs or modules (programs 12 for realizing optimal configuration of resources of the intelligent power utilization network, and the like) stored in the memory 11 and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a communication interface 13, and optionally, the communication interface 13 may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device 1 and for displaying a visualized user interface.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The program 12 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, enable:
acquiring power grid track data, and calculating to obtain a load prediction error sequence of the power grid track data, wherein the power grid track data comprises a historical time sequence track sequence of a load aggregation power actual value and a predicted value of a micro-grid;
calculating to obtain an uncertain set of the load prediction error sequence based on the load prediction error sequence of the power grid trajectory data;
constructing a double-layer optimization model objective function based on uncertain set constraints;
converting the constructed double-layer optimization model objective function into a mixed integer programming function;
and carrying out optimization solution on the converted mixed integer programming function to obtain a micro-grid load scheduling strategy.
Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiments corresponding to fig. 1 to fig. 3, which is not repeated herein.
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, herein are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one of 8230, and" comprising 8230does not exclude the presence of additional like elements in a process, apparatus, article, or method comprising the element.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (7)
1. An intelligent power utilization network resource optimal configuration method is characterized by comprising the following steps:
s1: acquiring power grid track data, and calculating to obtain a load prediction error sequence of the power grid track data, wherein the power grid track data comprises a historical time sequence track sequence of a load aggregated power actual value and a predicted value of a micro-grid;
s2: calculating an uncertain set of the load prediction error sequence based on the load prediction error sequence of the power grid trajectory data, wherein the uncertain set comprises the following steps:
the uncertain set calculation process of the load prediction error sequence comprises the following steps:
s21: extracting non-repetitive load prediction error values of a load prediction error sequence of any microgrid i:
u i ＝[u i,1 ,u i,2 ,...,u i,s ,...u i,s ]
wherein:
u i representing a sequence of extracted load prediction error values, any two load prediction error values in the sequence being different;
u i,s representing the s-th nonrepeating load prediction error value, u, extracted from the load prediction error sequence of the microgrid i i,s ≤u i,s+1 ；
S represents the total number of the non-repetitive load prediction error values extracted from the load prediction error sequence of the microgrid i;
s22: determining the level of significance as
The level of significance
Corresponding Z value is
Z represents a standard score following a normal distribution;
s23: respectively determine negativitySeries of load prediction error values u i Mean arbitrary load prediction error value u i,s Lower boundary of (1)
And upper bound
u i,k ,u i,c ∈u i
Wherein:
μ i Representing the mean value, σ, of the load prediction error sequence of the microgrid i i Representing micro-grids iLoad prediction error sequence standard deviation;
r +1 represents the number of times of the collected historical data;
s24: load prediction error sequence uncertainty set E for constructing any microgrid i i ：
Wherein:
s3: constructing a double-layer optimization model objective function based on uncertain set constraints;
s4: converting the constructed double-layer optimization model objective function into a mixed integer programming function;
s5: and carrying out optimization solution on the converted mixed integer programming function to obtain a micro-grid load scheduling strategy.
2. The method for optimizing and configuring resources of an intelligent power utilization network according to claim 1, wherein the collecting of the power grid trajectory data in the step S1 includes:
collecting power grid track data, wherein the power grid track data comprise historical time sequence track sequences of actual values and predicted values of load aggregated power of a micro-grid, and the power grid track data are expressed as follows:
wherein:
P i (h) Representing the actual value of the load aggregation power of the microgrid i at the historical moment h, wherein n represents the number of the microgrids connected with the power distribution network;
h 0 representing the initial historical time, h, of acquisition of grid trajectory data r And representing the collection ending historical moment of the power grid track data collection.
3. The method according to claim 2, wherein the step S1 of calculating the load prediction error sequence of the grid trajectory data includes:
the load prediction error sequence of the power grid track data is as follows:
(u i (h 0 ),u i (h 1 ),...,u i (h j ),...,u i (h r )),i∈[1,n]
wherein:
u i (h j ) Representing the micro-grid i at historical time h j The load prediction error value.
4. The method according to claim 1, wherein the step S3 of constructing a double-layer optimization model objective function based on an uncertainty set constraint includes:
constructing a double-layer optimization model objective function based on uncertain set constraint, wherein the uncertain set is a constraint condition of a double-layer optimization objective function, and the form of the double-layer optimization objective function is as follows:
wherein:
F 1 representing a lower-layer optimization objective function, wherein the optimization objective of the lower-layer optimization objective function is that the error between the distribution power of the power distribution network and the actual use power of the microgrid is minimum under an uncertain condition;
F 2 representing an upper-layer optimization objective function, wherein the optimization objective of the upper-layer optimization objective function is that the power distribution cost is minimum;
E i load prediction error sequence uncertainty set, τ, representing microgrid i i Represents the load prediction error therein (1-tau) i )P i (t) represents the actual power used by the microgrid i at time t, P i (t) represents the power distributed by the distribution network to the microgrid i at time t, and N represents the total number of times;
cost 1 cost, representing the distribution cost of the distribution network to the load aggregated power of the microgrid 2 Represents the cost of loss of unused allocated power;
the constraint conditions of the double-layer optimization objective function comprise load prediction error sequence uncertainty sets of different micro-grids and constraint conditions for constraining an upper-layer optimization objective function based on a lower-layer optimization objective function:
and the constraint of the lower-layer optimization objective function on the upper-layer optimization objective function represents that the actual load aggregate power of the microgrid based on the distribution uncertainty is greater than the loss load aggregate power when the upper-layer objective function optimizes the regulation and control cost.
5. The method according to claim 4, wherein the step S4 of converting the objective function of the double-layer optimization model into the mixed integer programming function includes:
will be double based on lagrange multiplierConverting the target function of the layer optimization model into a mixed integer programming function, wherein the mixed integer programming function L of the target function is optimized at the upper layer after conversion 2 Comprises the following steps:
mixed integer programming function L of transformed lower-layer optimized objective function 1 Comprises the following steps:
wherein:
λ 1 ,λ 2 are lagrange multipliers.
6. The method according to claim 5, wherein the step S5 of performing optimal solution on the mixed integer programming function includes:
optimizing and solving the mixed positive number planning function to obtain a micro-grid load scheduling strategy, wherein the micro-grid load scheduling strategy represents the power distributed to different micro-grids by the power distribution network at different moments; the optimization solving process of the mixed positive number programming function comprises the following steps:
s51: constructing a mixed integer planning total function L:
L＝L 1 +L 2
s52: separately computing functions L vs. λ 1 ,λ 2 Has a partial derivative of 0;
s53: converting the partial derivative calculation result into determinant, and making determinant equal to 0 to obtain several groups of lambda 1 ,λ 2 Selecting a set of lambda with the largest product 1 ,λ 2 As a result of the solution
S54: will be provided with
Substituting function L to λ 1 ,λ 2 The partial derivatives of the power distribution network are equal to 0, and the power P distributed to different micro-grids i by the power distribution network at different moments t i (t)。
7. An intelligent power utilization network resource optimal configuration system, characterized in that the system comprises:
the data acquisition device is used for acquiring power grid track data, calculating to obtain a probability distribution uncertain set of the power grid track data, and calculating to obtain an uncertain set of a load prediction error sequence based on a load prediction error sequence of the power grid track data;
the model construction device is used for constructing a double-layer optimization model objective function based on uncertain set constraints;
the network optimization module is used for converting the established double-layer optimization model objective function into a mixed integer programming function, and performing optimization solution on the converted mixed integer programming function to obtain a micro-grid load scheduling strategy so as to implement the intelligent power utilization network resource optimization configuration method as claimed in claims 1 to 6.
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| CN116523277A (en) * | 2023-07-05 | 2023-08-01 | 北京观天执行科技股份有限公司 | Intelligent energy management method and system based on demand response |
| CN116683450A (en) * | 2023-07-21 | 2023-09-01 | 连云港海连送变电工程有限公司 | Intelligent power monitoring network directional phase sequence checking method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116523277A (en) * | 2023-07-05 | 2023-08-01 | 北京观天执行科技股份有限公司 | Intelligent energy management method and system based on demand response |
| CN116523277B (en) * | 2023-07-05 | 2023-10-20 | 北京观天执行科技股份有限公司 | Intelligent energy management method and system based on demand response |
| CN116683450A (en) * | 2023-07-21 | 2023-09-01 | 连云港海连送变电工程有限公司 | Intelligent power monitoring network directional phase sequence checking method and device |
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