CN115619139A - Method, system, device and medium for calculating net load of source network load storage hierarchical partition - Google Patents

Method, system, device and medium for calculating net load of source network load storage hierarchical partition Download PDF

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CN115619139A
CN115619139A CN202211238444.XA CN202211238444A CN115619139A CN 115619139 A CN115619139 A CN 115619139A CN 202211238444 A CN202211238444 A CN 202211238444A CN 115619139 A CN115619139 A CN 115619139A
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power
net load
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宋毅
原凯
赵冬
胡丹蕾
姜世公
罗凤章
吴璇
葛楠
冯少亭
吴强
黄河
蔡超
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
Tianjin University
Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
Tianjin University
Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention relates to a method, a system, equipment and a medium for calculating net load storage layered partition net load, which is characterized by comprising the following steps: acquiring basic data of a power distribution system of a region to be researched; establishing a net load calculation unit model of a power distribution system in a region to be researched based on a pre-established net load superposition model by taking the net load curve peak-valley difference smaller than a preset threshold value as a target; and solving the established net load calculation unit model according to the acquired basic data by adopting an optimization algorithm to obtain a resource optimization scheduling strategy of the distribution system in the area to be researched, wherein the peak-valley difference of the net load curve is smaller than a preset threshold value, and further calculating to obtain the net load curve of the distribution system in the area to be researched under the condition that the distributed power supply conforming to the actual operation condition of the power grid is introduced according to the resource optimization scheduling strategy.

Description

Method, system, device and medium for calculating net load of source network load storage hierarchical partition
Technical Field
The invention relates to the field of optimized operation of a power distribution system in the technical field of power systems, in particular to a method, a system, equipment and a medium for calculating net loads of a source network, a load storage and a layering partition.
Background
At present, china develops high-proportion distributed new energy access, electric energy replacement acceleration and load interaction enhancement, so that tasks of a power distribution network born in a novel electric power system are obviously changed, the power distribution system gradually develops into a novel regional electric power system with the functions of electric energy collection, transmission, storage and the like, and the aspects of component elements, topological structures, operation modes and the like are further complicated. In order to adapt to new problems and new challenges faced by the development of a novel power system under the large-scale access of new energy, a source load is divided into a plurality of units, and each unit is formed into a hierarchical structure through different division modes to form a layered and partitioned power distribution system. For a distribution network load demand curve, the shape of the load curve can be obviously changed by accessing various flexible resources such as distributed power supplies, energy storage, demand side response and the like, so that in order to obtain the load demand curve which is closer to the actual operation condition of a power grid, the influence of the flexible resources on the shape of the load curve should not be ignored, the scheduling potential of various flexible resources in the power distribution network should be fully excavated, the influence effect of the flexible resources on the load curve is quantized, and a net load curve is obtained.
However, the existing net load calculation research result is mainly for the reason that the load curve presents the characteristic of a 'duck curve' after the distributed photovoltaic is accessed in a large quantity, and the obtained load demand curve still does not accord with the actual operation condition of the power grid.
Disclosure of Invention
In view of the foregoing problems, an object of the present invention is to provide a method, a system, a device, and a medium for calculating net load of a source-network load-storage hierarchical partition, which can comprehensively consider the influence of other flexible resources, such as energy storage and flexible load, on the load curve shape.
In order to realize the purpose, the invention adopts the following technical scheme: in a first aspect, a method for calculating a source-network load storage hierarchical partition payload is provided, which includes:
acquiring basic data of a power distribution system of a region to be researched;
establishing a net load calculation unit model of a power distribution system in a region to be researched based on a pre-established net load superposition model by taking the minimum peak-valley difference of the net load curve as a target;
and solving the established net load calculation unit model by adopting an optimization algorithm according to the acquired basic data to obtain a resource optimization scheduling strategy of the power distribution system of the area to be researched, wherein the peak-valley difference of the net load curve is smaller than a preset threshold value, and further calculating to obtain the net load curve of the power distribution system of the area to be researched introduced by the distributed power source according to the resource optimization scheduling strategy.
Further, the net load superposition model is established based on a gridding division mode, the net load superposition model comprises power supply units, power supply grids and power supply partitions from bottom to top, a plurality of power supply units form the power supply grids, and a plurality of power supply grids form the power supply partitions.
Further, the net load power of the power supply unit i in the net load superposition model is:
Figure BDA0003883950310000021
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003883950310000022
net load power for power supply unit i;
Figure BDA0003883950310000023
using electric power for a conventional load of a power supply unit i;
Figure BDA0003883950310000024
the output power of the distributed power supply of the power supply unit i;
Figure BDA0003883950310000025
the energy storage charging and discharging power is the energy storage charging and discharging power of the power supply unit i;
Figure BDA0003883950310000026
using electric power for the flexible load of the power supply unit i;
Figure BDA0003883950310000027
the interactive power of the power supply unit i and other power supply units is provided;
the net load power of the power supply grid j in the net load superposition model is as follows:
Figure BDA0003883950310000028
wherein, K t1 Is the coincidence rate of the load; k t2 Is the coincidence rate of demand responses; k is t3 The simultaneous rate of energy storage; n is j The number of power supply units included in the power supply grid j is set;
Figure BDA0003883950310000029
net load power for supply grid j;
Figure BDA00038839503100000210
interaction power of the power supply grid j and other power supply grids;
the net load of the power supply partition k in the net load superposition model is as follows:
Figure BDA00038839503100000211
wherein m is k The number of power supply grids in the power supply partition k;
Figure BDA00038839503100000212
the net load power for power supply partition k;
Figure BDA00038839503100000213
the interactive power of the power supply partition k with other power supply partitions.
Further, the basic data of the to-be-researched regional power distribution system comprises composition data, composition structure data and equipment parameter data, wherein the composition data comprises user load, a typical daily load curve, a photovoltaic installation and energy storage equipment, the composition structure data comprises a power supply grid level in a net load superposition model of the to-be-researched regional power distribution system, and the equipment parameter data comprises photovoltaic installation capacity, rated capacity and rated power of the energy storage equipment.
Further, the establishing of the net load calculation unit model of the distribution system in the area to be researched based on the pre-established net load superposition model with the aim that the peak-to-valley difference of the net load curve is smaller than the preset threshold value comprises the following steps:
taking the difference between the peaks and the valleys of the net load curve smaller than a preset threshold as a target, performing smooth optimization on the load curve based on a pre-established net load superposition model, and determining a target function of a net load calculation unit model;
constraints of the payload calculation unit model are determined.
Further, the objective function of the net load calculation element model is:
Figure BDA00038839503100000214
wherein N is the number of power supply units contained in the power supply grid M;
Figure BDA00038839503100000215
net load power at time t for power supply unit i, and:
Figure BDA0003883950310000031
wherein the content of the first and second substances,
Figure BDA0003883950310000032
and
Figure BDA0003883950310000033
the load power, the distributed photovoltaic output power, the energy storage charge and discharge power, the demand side response power and the interaction power with other power supply units of the power supply unit i at the moment t are respectively.
Further, the constraints of the net load calculation unit model include an energy storage device constraint, a demand response constraint, a peak net load constraint and an inter-unit interaction power constraint.
In a second aspect, there is provided a source-network load-store hierarchically partitioned payload computing system, comprising:
the data acquisition module is used for acquiring basic data of a power distribution system of a region to be researched;
the net load calculation unit model building module is used for building a net load calculation unit model of the power distribution system in the area to be researched based on a net load superposition model which is built in advance by taking the net load curve peak-valley difference smaller than a preset threshold value as a target;
and the optimization solving module is used for solving the established net load calculation unit model according to the acquired basic data by adopting an optimization algorithm to obtain a resource optimization scheduling strategy of the distribution system of the area to be researched, wherein the peak-to-valley difference of the net load curve is smaller than a preset threshold value, and then calculating to obtain the net load curve of the distribution system of the area to be researched introduced by the distributed power supply according to the resource optimization scheduling strategy.
In a third aspect, a processing device is provided, which includes computer program instructions, where the computer program instructions, when executed by the processing device, are configured to implement the steps corresponding to the source network load storage hierarchical partition payload calculation method described above.
In a fourth aspect, a computer-readable storage medium is provided, where computer program instructions are stored on the computer-readable storage medium, and when executed by a processor, the computer program instructions are used to implement the steps corresponding to the source network load storage hierarchical partition payload calculation method.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the method comprehensively considers the introduction of the distributed power supply and fully considers the influence of flexible resources such as the distributed power supply, energy storage, demand response and the like on the load curve, and obtains the net load curve which is more consistent with the actual situation.
2. The method fully excavates the scheduling potential of flexible resources such as energy storage and flexible load in the power distribution network, and quantifies the effect of the influence of the flexible resources on the net load curve.
3. The invention establishes a net load calculation unit model which takes the minimum peak-valley difference of the net load curve as a target function and takes the constraints of the energy storage device, the demand side response constraint, the peak load constraint, the inter-unit interaction power constraint and the like as constraint conditions, and obtains the net load curve which is more fit with the actual power grid operation condition by calling a CPLEX optimization solver.
In conclusion, the method can be widely applied to the field of optimized operation of the power distribution system.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Like parts are designated with like reference numerals throughout the drawings. In the drawings:
FIG. 1 is a schematic flow chart of a method provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a power supply unit payload calculation model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of grid cell partitioning according to an embodiment of the present invention;
fig. 4 is a schematic diagram of typical daily load and solar photovoltaic output curves of the first power supply unit and the second power supply unit in fig. 3 according to an embodiment of the present invention, where fig. 4 (a) is a schematic diagram of a typical daily load and solar photovoltaic output curve of the first power supply unit in fig. 3, and fig. 4 (b) is a schematic diagram of a typical daily load and solar photovoltaic output curve of the second power supply unit in fig. 3;
FIG. 5 is a schematic diagram of a net load curve under a two-way scheme of the power supply unit of FIG. 3 according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a net load curve of the second embodiment of the power supply unit in FIG. 3 according to an embodiment of the present invention;
fig. 7 is a schematic diagram of net load curves under two schemes of the power grid M according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It should also be understood that additional or alternative steps may be used.
Although the terms first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be only used to distinguish one element, component, region, layer or section from another region, layer or section. Terms such as "first," "second," and other numerical terms when used herein do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.
According to the method, the system, the equipment and the medium for calculating the net load of the source network load storage layering and partitioning, the influence of flexible resources such as distributed power sources, energy storage and demand response on a load curve is fully considered, the scheduling potential of various active resources in a power distribution network is fully excavated, the shape of the load curve is changed by means of active management, the load curve is smoothly optimized, and the load demand curve more fitting the actual power grid operation condition is obtained.
Example 1
As shown in fig. 1, this embodiment provides a method for calculating a source-network load storage hierarchical partition payload, including the following steps:
1) And establishing a net load superposition model based on a gridding division mode.
Specifically, the net load superposition model comprises power supply units, power supply grids and power supply subareas from bottom to top, wherein the power supply units form the power supply grids, and the power supply grids form the power supply subareas.
Specifically, the power supply section: the method is a basic unit for developing high-voltage distribution network planning, and is mainly used for high-voltage distribution network substation distribution and target network frame construction. Generally, the power supply system can be divided into administrative divisions of county (district), and for a city (county) with a large total power demand, the power supply system can be divided into a plurality of power supply subareas, and the load of each power supply subarea does not exceed 1000MW in principle.
Power supply grid: the method is a basic unit for developing the target grid structure planning of the medium-voltage distribution network, and in the power supply grid, the wire outlet interval of a superior power supply and gallery resources in the network are planned overall according to the coordination and global optimization principles of all levels, so that the grid structure of the medium-voltage distribution network is determined. The power supply system is suitable for being divided by combining with obvious geographical forms such as roads, railways, rivers, hills and the like, the power supply range is relatively independent, the types of power supply areas are unified, and the scale of a power grid is moderate.
A power supply unit: the method is a minimum unit for planning the power distribution network, is further subdivided on the basis of a power supply grid, plans medium-voltage network wiring, distribution facility layout, user and distributed power supply access in a power supply unit according to the conditions of land functions, development conditions, geographical conditions, load distribution, current situation power grids and the like, and formulates corresponding medium-voltage power distribution network construction projects. Typically consisting of several adjacent plots (or user blocks) of similar development and essentially uniform power supply reliability requirements. During division, the complementary characteristics of various loads in the power supply unit are comprehensively considered, the development requirement of the distributed power supply is considered, and the utilization rate of equipment is improved.
Specifically, as shown in fig. 2, the calculation formula of the payload power of the power supply unit i in the payload superposition model is as follows:
Figure BDA0003883950310000051
wherein the content of the first and second substances,
Figure BDA0003883950310000052
net load power for power supply unit i;
Figure BDA0003883950310000053
the electric power is used for the conventional load of the power supply unit i;
Figure BDA0003883950310000054
the output power of the distributed power supply of the power supply unit i;
Figure BDA0003883950310000055
the energy storage charging and discharging power is the energy storage charging and discharging power of the power supply unit i;
Figure BDA0003883950310000056
using electric power for the flexible load of the power supply unit i;
Figure BDA0003883950310000057
the power supply unit i is the interactive power with other power supply units.
The net load power calculation formula of the power supply grid j in the net load superposition model is as follows:
Figure BDA0003883950310000058
wherein, K t1 Is the coincidence rate of the load; k is t2 Is the coincidence rate of demand responses; k is t3 The synchronous rate of energy storage; n is j The number of power supply units included in the power supply grid j is set;
Figure BDA0003883950310000059
net load power for supply grid j;
Figure BDA00038839503100000510
the interaction power for power grid j with other power grids.
The net load calculation formula of the power supply partition k in the net load superposition model is as follows:
Figure BDA0003883950310000061
wherein m is k The number of power supply grids in the power supply partition k;
Figure BDA0003883950310000062
the net load power for power supply partition k;
Figure BDA0003883950310000063
the interactive power with other power supply partitions for power supply partition k.
2) And acquiring basic data of a power distribution system of the area to be researched.
Specifically, the basic data comprises composition data, composition structure data and equipment parameter data, wherein the composition data comprises user load, a typical daily load curve, a photovoltaic installation, energy storage equipment and the like, the composition structure data comprises a power supply grid level in a net load superposition model to which a regional power distribution system to be researched belongs, and the equipment parameter data comprises photovoltaic installation capacity, rated capacity and rated power of the energy storage equipment and the like.
3) The method comprises the following steps of establishing a net load calculation unit model of a power distribution system in a region to be researched based on an established net load superposition model by taking the net load curve peak-valley difference smaller than a preset threshold as a target, and specifically comprising the following steps:
3.1 Considering the influence of flexible resources such as distributed power supplies, energy storage and demand response on the load curve, optimizing the running state of the energy storage, optimizing the demand response (flexible load) based on the established net load superposition model by taking the peak-valley difference of the net load curve smaller than the preset threshold value as a target, performing smooth optimization on the load curve, and determining the target function of the net load calculation unit model.
Specifically, the preset threshold may be set according to an actual situation, which is not described herein.
Specifically, the energy storage device releases electric energy at the moment of a peak of power utilization, and absorbs the electric energy at the moment of a valley of the power utilization to smooth a load curve, and meanwhile, the introduction of a flexible load can effectively reduce the difference between the peak and the valley of the load.
Specifically, the objective function of the payload calculation unit model is:
Figure BDA0003883950310000064
wherein N is the number of power supply units contained in the power supply grid M;
Figure BDA0003883950310000065
net load power at time t for power supply unit i, and:
Figure BDA0003883950310000066
wherein the content of the first and second substances,
Figure BDA0003883950310000067
and
Figure BDA0003883950310000068
the load power, the distributed photovoltaic output power, the energy storage charge and discharge power, the demand side response power and the interaction power with other power supply units of the power supply unit i at the moment t are respectively.
3.2 Determine constraints of the payload calculation unit model.
Specifically, the constraints include an energy storage constraint, a demand response constraint, a peak payload constraint, and an inter-cell interaction power constraint.
Specifically, the energy storage device constraints are:
Figure BDA0003883950310000071
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003883950310000072
and
Figure BDA0003883950310000073
respectively the electric quantity, the charging power and the discharging power of an Energy Storage System (ESS) at the moment t; eta ch Efficiency of charging for the ESS; eta dis Is the discharge efficiency of the ESS; m is an infinite number;
Figure BDA0003883950310000074
for the state of charge of the ESS at time t,
Figure BDA0003883950310000075
for the discharging state of ESS at time t, while charging
Figure BDA0003883950310000076
Is 1 at the time of discharge
Figure BDA0003883950310000077
Figure BDA0003883950310000077
Figure BDA0003883950310000077
1, 0 when free;
Figure BDA0003883950310000078
and
Figure BDA0003883950310000079
maximum and minimum values of the state of charge of the ESS at the moment t; e ess Rated capacity for stored energy; p is ess Is the rated power of the stored energy.
Specifically, the flexible load is used as an important demand side resource, peak staggering and peak avoidance to a certain degree can be achieved by scheduling the flexible load, and pressure in the operation process of the distribution network system is relieved. The flexible load can be divided into three types of translatable load, translatable load and reducible load, and the reducible load is mainly considered in the embodiment, so the demand response constraint is:
Figure BDA00038839503100000710
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00038839503100000711
the reducible load of the power supply unit i at the time t;
Figure BDA00038839503100000712
and
Figure BDA00038839503100000713
respectively providing a reducible load minimum value and a reducible load minimum value of the power supply unit i at the moment t;
Figure BDA00038839503100000714
respectively an upper limit and a lower limit of load reduction capacity of the reducible load in one scheduling period;
Figure BDA00038839503100000715
in the reduction state of the power supply unit i at the time t, the load can be reduced, namely the power supply unit i is reduced to 1 at the time t and is not reduced to 0; t is the scheduling time.
Specifically, the peak payload constraint is:
Figure BDA00038839503100000716
wherein the content of the first and second substances,
Figure BDA00038839503100000717
the annual peak load limit.
Specifically, the inter-cell interaction power constraint is:
Figure BDA00038839503100000718
wherein the content of the first and second substances,
Figure BDA00038839503100000719
the maximum value of the inter-cell link interaction power is defined as the maximum value of the inter-cell link interaction power, and the inter-cell interaction power cannot exceed the maximum value.
4) And solving the established net load calculation unit model according to the acquired basic data of the distribution system of the area to be researched by adopting an optimization algorithm to obtain a resource optimization scheduling strategy of the distribution system of the area to be researched, wherein the peak-to-valley difference of the net load curve is smaller than a preset threshold value, and further calculating to obtain the net load curve of the distribution system of the area to be researched introduced by the distributed power supply according to the resource optimization scheduling strategy.
Specifically, the optimization algorithm may employ a CPLEX optimization solver. More specifically, simulation and optimization analysis are carried out based on an MATLAB platform, and according to the obtained basic data of the regional power distribution system to be researched, an MATLAB modeling tool YALMIP is adopted and a CPLEX optimization solver is called to carry out optimization solving, so that the resource optimization scheduling strategy of the regional power distribution system to be researched, which enables the net load curve peak-valley difference to be smaller than a preset threshold value, can be solved. The CPLEX optimization solver is specially used for solving four basic problems of large-scale Linear Programming (LP), quadratic Programming (QP), constrained quadratic programming (QCQP), second-order cone programming (SOCP) and the like and corresponding Mixed Integer Programming (MIP) problems.
Specifically, the resource optimization scheduling strategy comprises an energy storage scheduling strategy and a flexible load scheduling strategy.
Specifically, the net load power = load power + distributed photovoltaic output power + energy storage charge-discharge power + demand side response power + interactive power between the load power + distributed photovoltaic output power + energy storage charge-discharge power + demand side response power + and other power supply units, and therefore, a net load curve can be calculated through the energy storage scheduling strategy and the flexible load scheduling strategy obtained through solving.
The method for calculating the payload of the source network load storage hierarchical partition of the invention is explained in detail by the specific embodiment as follows:
1) And establishing a net load superposition model based on a gridding division mode.
2) Basic data of the distribution system of the area to be researched as shown in fig. 3 are obtained, and as shown in table 1 below, typical daily load and photovoltaic output curves of two power supply units are shown in fig. 4.
Table 1: area grid situation
Figure BDA0003883950310000081
3) And establishing a net load calculation unit model of the power distribution system of the region to be researched based on the established net load superposition model by taking the net load curve peak-valley difference smaller than a preset threshold as a target, wherein the net load calculation unit model comprises a target function and constraint conditions, and the constraint conditions comprise energy storage device constraint, demand response constraint, peak net load constraint and unit interactive power constraint.
Specifically, the objective function of the established payload calculation unit model is:
Figure BDA0003883950310000082
4) And solving the established net load calculation unit model by adopting a CPLEX optimization solver according to the acquired basic data of the to-be-researched regional power distribution system to obtain a resource optimization scheduling strategy of the to-be-researched regional power distribution system, wherein the peak-to-valley difference of the net load curve is smaller than a preset threshold value, and further calculating to obtain the net load curve of the to-be-researched regional power distribution system introduced by the distributed power supply according to the resource optimization scheduling strategy.
Specifically, in the embodiment, two schemes are selected for comparison, wherein the first scheme is a net load curve obtained by subtracting a photovoltaic from a traditional load in the prior art, and optimization scheduling flexibility resources are not considered; and the second scheme is a net load curve obtained by adopting the method disclosed by the invention. According to the scheme, the two power supply units only consider the influence of a large number of distributed photovoltaic accesses on a load curve; under the second scheme, the two power supply units comprehensively consider the massive introduction of the distributed photovoltaic and the optimal scheduling of the energy storage and the flexible load.
As shown in fig. 5 to 7, the comparison of the net load curves shows that the net load curve of the second scheme of the method of the present invention is smoother, the peak-valley difference is smaller, the peak clipping and valley filling of the power distribution system can be realized, the grid pressure is greatly relieved, and the adjustment effect on the load curve is significant. The method of the invention fully considers the influence of flexible resources such as distributed power supply, energy storage, demand response and the like on the load curve, fully excavates the scheduling potential of various active resources in the power distribution network, changes the shape of the load curve by means of active management, and carries out smooth optimization of the load curve. Due to the large-scale access of distributed photovoltaic in the novel power distribution network, the load curve has the characteristic of a duck curve, the fluctuation of the load demand curve is greatly increased, and the stable operation and control of the power grid in practice are not facilitated. The method can realize the effect of 'peak clipping and valley filling', the smoothness degree is increased to a certain extent, and the specific indexes are shown in the following tables 2 to 4:
table 2: degree of regulation of load curve of power supply unit
Figure BDA0003883950310000091
Table 3: degree of adjustment of two-load curve of power supply unit
Figure BDA0003883950310000092
Table 4: m load curve adjustment degree of power supply grid
Figure BDA0003883950310000093
Figure BDA0003883950310000101
Example 2
The present embodiment provides a source-network load-storage hierarchical partition payload computing system, including:
and the data acquisition module is used for acquiring basic data of the power distribution system of the area to be researched.
And the net load calculation unit model establishing module is used for establishing a net load calculation unit model of the power distribution system in the area to be researched based on a pre-established net load superposition model by taking the net load curve peak-valley difference smaller than a preset threshold value as a target.
And the optimization solving module is used for solving the established net load calculation unit model according to the acquired basic data by adopting an optimization algorithm to obtain a resource optimization scheduling strategy of the power distribution system of the area to be researched, wherein the peak-valley difference of the net load curve is smaller than a preset threshold value, and then calculating to obtain the net load curve of the power distribution system of the area to be researched introduced by the distributed power supply according to the resource optimization scheduling strategy.
The system provided in this embodiment is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.
Example 3
This embodiment provides a processing device corresponding to the method for calculating a payload of a source network load storage hierarchical partition provided in embodiment 1, where the processing device may be applied to a processing device of a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, and the like, to execute the method of embodiment 1.
The processing equipment comprises a processor, a memory, a communication interface and a bus, wherein the processor, the memory and the communication interface are connected through the bus so as to complete mutual communication. The memory stores a computer program that can be executed on the processing device, and the processing device executes the source network load storage hierarchical partition payload calculation method provided by embodiment 1 when executing the computer program.
In some implementations, the Memory may be a high-speed Random Access Memory (RAM), and may also include non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
In other implementations, the processor may be various general-purpose processors such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), and the like, and is not limited herein.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. 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 and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that the above-described configurations of computing devices are merely some of the configurations associated with the present application and do not constitute limitations on the computing devices to which the present application may be applied, as a particular computing device may include more or fewer components, or some components in combination, or have a different arrangement of components.
Example 4
This embodiment provides a computer program product corresponding to the source network storage and partitioning payload calculation method provided in this embodiment 1, and the computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the source network storage and partitioning payload calculation method described in this embodiment 1 are loaded.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any combination of the foregoing.
The implementation principle and technical effect of the computer-readable storage medium provided by the above embodiments are similar to those of the above method embodiments, and are not described herein again.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above embodiments are only used for illustrating the present invention, and the structure, connection manner, manufacturing process and the like of each component can be changed, and equivalent changes and improvements made on the basis of the technical scheme of the present invention should not be excluded from the protection scope of the present invention.

Claims (10)

1. A method for calculating payload of a source-network load storage hierarchical partition is characterized by comprising the following steps:
acquiring basic data of a power distribution system of a region to be researched;
establishing a net load calculation unit model of a power distribution system in a region to be researched based on a pre-established net load superposition model by taking the net load curve peak-valley difference smaller than a preset threshold value as a target;
and solving the established net load calculation unit model according to the acquired basic data to obtain a resource optimization scheduling strategy of the distribution system of the area to be researched, which enables the peak-to-valley difference of the net load curve to be smaller than a preset threshold value, and further calculating to obtain the net load curve of the distribution system of the area to be researched when the distributed power supply is introduced according to the resource optimization scheduling strategy.
2. The method of claim 1, wherein the model of payload superposition is created based on a grid-based partitioning, the model of payload superposition comprises, from bottom to top, power supply units, power supply grids, and power supply partitions, a number of power supply units forming a power supply grid, and a number of power supply grids forming a power supply partition.
3. The method of claim 2, wherein the net load power of the power supply unit i in the net load superposition model is:
Figure FDA0003883950300000011
wherein the content of the first and second substances,
Figure FDA0003883950300000012
net load power for power supply unit i;
Figure FDA0003883950300000013
using electric power for a conventional load of a power supply unit i;
Figure FDA0003883950300000014
the output power of the distributed power supply of the power supply unit i;
Figure FDA0003883950300000015
the energy storage charging and discharging power is the energy storage charging and discharging power of the power supply unit i;
Figure FDA0003883950300000016
using electric power for the flexible load of the power supply unit i;
Figure FDA0003883950300000017
the interactive power of the power supply unit i and other power supply units is provided;
the net load power of the power supply grid j in the net load superposition model is as follows:
Figure FDA0003883950300000018
wherein, K t1 Is the coincidence rate of the load; k t2 Is the coincidence rate of demand responses; k t3 The simultaneous rate of energy storage; n is a radical of an alkyl radical j The number of power supply units included in the power supply grid j is set;
Figure FDA0003883950300000019
net load power for supply grid j;
Figure FDA00038839503000000110
the interactive power of the power supply grid j and other power supply grids;
the net load of the power supply partition k in the net load superposition model is as follows:
Figure FDA00038839503000000111
wherein m is k The number of power supply grids in the power supply partition k;
Figure FDA00038839503000000112
the net load power for power supply partition k;
Figure FDA00038839503000000113
the interactive power of the power supply partition k with other power supply partitions.
4. The method of claim 1, wherein the basic data of the regional power distribution system to be researched comprises composition data, composition structure data and equipment parameter data, wherein the composition data comprises user load, typical daily load curve, photovoltaic installed equipment and energy storage equipment, the composition structure data comprises power supply grid level in a net load superposition model of the regional power distribution system to be researched, and the equipment parameter data comprises photovoltaic installed capacity, rated capacity and rated power of the energy storage equipment.
5. The method of claim 1, wherein the establishing a model of a payload calculation unit of a regional power distribution system to be studied based on a pre-established payload superposition model with the aim that a difference between peaks and valleys of a payload curve is smaller than a preset threshold comprises:
taking the difference between the peak and the valley of the net load curve smaller than a preset threshold as a target, carrying out smooth optimization on the load curve based on a pre-established net load superposition model, and determining a target function of a net load calculation unit model;
constraints of the payload calculation unit model are determined.
6. The method of claim 5, wherein the objective function of the model of the payload calculation unit is:
Figure FDA0003883950300000021
wherein N is the number of power supply units contained in the power supply grid M;
Figure FDA0003883950300000022
a net load power at time t for power supply unit i, and:
Figure FDA0003883950300000023
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003883950300000024
and
Figure FDA0003883950300000025
the load power, the distributed photovoltaic output power, the energy storage charge and discharge power, the demand side response power and the interaction power with other power supply units of the power supply unit i at the moment t are respectively.
7. The method of claim 6, wherein the constraints of the net-load-storage hierarchical-partition payload calculation model include energy storage constraints, demand-response constraints, peak payload constraints, and inter-cell interaction power constraints.
8. A source-network, load-storage, hierarchically partitioned payload computing system, comprising:
the data acquisition module is used for acquiring basic data of a power distribution system of a region to be researched;
the net load calculation unit model building module is used for building a net load calculation unit model of the power distribution system in the area to be researched based on a net load superposition model which is built in advance by taking the net load curve peak-valley difference smaller than a preset threshold value as a target;
and the optimization solving module is used for solving the established net load calculation unit model according to the acquired basic data to obtain a resource optimization scheduling strategy of the distribution system in the area to be researched, wherein the peak-valley difference of the net load curve is smaller than a preset threshold value, and further calculating to obtain the net load curve of the distribution system in the area to be researched introduced by the distributed power supply according to the resource optimization scheduling strategy.
9. A processing device comprising computer program instructions, wherein the computer program instructions, when executed by the processing device, are adapted to implement the steps corresponding to the source network load storage hierarchical partition payload calculation method of any of claims 1-7.
10. A computer readable storage medium, having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, are configured to implement the corresponding steps of the source network load storage hierarchical partition payload calculation method according to any one of claims 1 to 7.
CN202211238444.XA 2022-10-11 2022-10-11 Method, system, device and medium for calculating net load of source network load storage hierarchical partition Pending CN115619139A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117852927A (en) * 2024-03-06 2024-04-09 中能智新科技产业发展有限公司 Source network charge storage integrated planning production simulation method, system and storage medium
CN117852927B (en) * 2024-03-06 2024-06-04 中能智新科技产业发展有限公司 Source network charge storage integrated planning production simulation method, system and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117852927A (en) * 2024-03-06 2024-04-09 中能智新科技产业发展有限公司 Source network charge storage integrated planning production simulation method, system and storage medium
CN117852927B (en) * 2024-03-06 2024-06-04 中能智新科技产业发展有限公司 Source network charge storage integrated planning production simulation method, system and storage medium

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