CN111784409A - Model construction method, device, equipment and medium for configuring peak clipping measures - Google Patents

Model construction method, device, equipment and medium for configuring peak clipping measures Download PDF

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CN111784409A
CN111784409A CN202010671724.4A CN202010671724A CN111784409A CN 111784409 A CN111784409 A CN 111784409A CN 202010671724 A CN202010671724 A CN 202010671724A CN 111784409 A CN111784409 A CN 111784409A
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刘平
邵冲
黄豫
袁康龙
雷成
聂金峰
潘旭东
覃芸
陈泽兴
李岩
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Energy Development Research Institute of China Southern Power Grid Co Ltd
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Abstract

The invention discloses a model construction method, a device, equipment and a medium for configuring peak clipping measures, wherein the method comprises the steps of obtaining time-of-use electricity price data, and constructing an electricity price elastic matrix according to time-of-use electricity price and time period information in the time-of-use electricity price data; setting the peak-to-valley electricity price ratio, the load response ratio and the response time length as variables to be optimized; constructing an objective function between peak clipping cost and peak clipping benefit, and setting a constraint condition and a cost benefit relational expression of the variable to be optimized according to the objective function to establish a linear optimization model; and solving the linear optimization model through a linear programming solver. The method constructs a model for determining the optimal configuration scheme of two peak clipping measures of peak electricity price and demand response, and can assist the planning and analysis work of the peak clipping measure of the large power grid.

Description

Model construction method, device, equipment and medium for configuring peak clipping measures
Technical Field
The invention relates to the technical field of power grids, in particular to a model construction method, a device, equipment and a medium for configuring peak clipping measures.
Background
At present, due to the fact that time difference exists in the power utilization characteristics of different types of user loads, the overall load characteristics of a power grid have certain peak-valley difference. When the peak-valley difference is too large, sufficient power supplies need to be configured and strong net racks need to be built to ensure safe and stable operation of the power grid, but a large amount of equipment is in an idle state. For this reason, it is necessary to take a certain amount of peak reduction measures to flatten the load characteristic as much as possible and to reduce the peak-to-valley difference.
In the prior art, researches on peak clipping measures mainly take a power distribution network and a micro-grid as objects, and how to construct peak clipping means and establish a peak clipping mechanism by using energy storage, load control and other modes are mainly discussed. However, for large-scale power networks such as provincial power networks and regional power networks, the current peak clipping measures mainly include two measures of time-of-use electricity price and demand response based on controllable loads, the calculation research on the peak clipping measures is limited to the analysis and evaluation after implementation, and the research on how to optimize and determine which peak clipping measures are adopted or how to combine the peak clipping measures is rarely carried out, so that an effective, convenient and reasonable method is urgently needed to assist in planning and analyzing the peak clipping measures of the large power networks.
Disclosure of Invention
The invention provides a model construction method for configuring peak clipping measures, which is used for constructing a model for determining an optimal configuration scheme of two peak clipping measures of peak electricity price and demand response and can assist in planning and analyzing the peak clipping measures of a large power grid.
In order to solve the above technical problem, an embodiment of the present invention provides a model building method for configuring a peak clipping measure, including:
acquiring time-of-use electricity price data, and constructing an electricity price elastic matrix according to time-of-use electricity price and time period information in the time-of-use electricity price data;
setting the peak-to-valley electricity price ratio, the load response ratio and the response time length as variables to be optimized;
constructing an objective function between peak clipping cost and peak clipping benefit, and setting a constraint condition and a cost benefit relational expression of the variable to be optimized according to the objective function to establish a linear optimization model;
and solving the linear optimization model through a linear programming solver.
In one embodiment of the present invention, the electricity price elastic matrix is:
Figure BDA0002581920600000021
wherein the content of the first and second substances,iiin order to have a self-elastic coefficient,ijis a coefficient of mutual elasticity, QiFor the original quantity of electricity at time i, Δ QiFor i moment varying electric quantity, piFor the original electricity price at time i, Δ ρiThe electricity prices are changed for i moment.
In one embodiment of the present invention, the objective function is:
Figure BDA0002581920600000022
wherein C represents cost and B represents benefit;
Figure BDA0002581920600000023
ΔQdafter the time-of-use electricity price is adopted, the electricity quantity change value of each time period on the d day is adopted; qdThe original electric quantity of each time interval on the day d; is an elastic coefficient matrix; rho0Dividing into rho according to time-of-use electricity price period for original electricity price vectoron、ρoff、ρoff；|Toff| represents the total number of periods of the valley period.
In one embodiment of the present invention, the cost-benefit relationship includes a power-side cost and benefit relationship, a power-grid-side cost and benefit relationship, a user-side cost and benefit relationship, and a social cost and benefit relationship.
The embodiment of the present invention further provides a model building apparatus for configuring peak clipping measures, including:
the data input module is used for acquiring time-of-use electricity price data and constructing an electricity price elastic matrix according to the time-of-use electricity price and time period information in the time-of-use electricity price data;
the optimization control system comprises a to-be-optimized variable determination module, a load response module and a control module, wherein the to-be-optimized variable determination module is used for setting a peak-to-valley electricity price ratio, a load response ratio and response duration as to-be-optimized variables;
the linear optimization model establishing module is used for constructing an objective function between peak clipping cost and peak clipping benefit, and setting a constraint condition and a cost benefit relational expression of the variable to be optimized according to the objective function so as to establish a linear optimization model;
and the optimization model calculation module is used for solving the linear optimization model through a linear programming solver.
In one embodiment of the present invention, the electricity price elastic matrix is:
Figure BDA0002581920600000031
wherein the content of the first and second substances,iiin order to have a self-elastic coefficient,ijis a coefficient of mutual elasticity, QiFor the original quantity of electricity at time i, Δ QiFor i moment varying electric quantity, piFor the original electricity price at time i, Δ ρiThe electricity prices are changed for i moment.
In one embodiment of the present invention, the objective function is:
Figure BDA0002581920600000032
wherein C represents cost and B represents benefit;
Figure BDA0002581920600000033
ΔQdafter the time-of-use electricity price is adopted, the electricity quantity change value of each time period on the d day is adopted; qdThe original electric quantity of each time interval on the day d; is an elastic coefficient matrix; rho0Dividing into rho according to time-of-use electricity price period for original electricity price vectoron、ρoff、ρoff；|Toff| represents the total number of periods of the valley period.
In one embodiment of the present invention, the cost-benefit relationship includes a power-side cost and benefit relationship, a power-grid-side cost and benefit relationship, a user-side cost and benefit relationship, and a social cost and benefit relationship.
The embodiment of the present invention further provides a model building terminal device for configuring peak clipping measures, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the processor implements the model building method for configuring peak clipping measures as described above.
The embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the above model building method for configuring peak clipping measures.
Compared with the prior art, the model construction method for configuring the peak clipping measures has the advantages that the model construction method comprises the steps of obtaining time-of-use electricity price data, and constructing an electricity price elastic matrix according to the time-of-use electricity price and time period information in the time-of-use electricity price data; setting the peak-to-valley electricity price ratio, the load response ratio and the response time length as variables to be optimized; constructing an objective function between peak clipping cost and peak clipping benefit, and setting a constraint condition and a cost benefit relational expression of the variable to be optimized according to the objective function to establish a linear optimization model; and solving the linear optimization model through a linear programming solver. The linear optimization model considering two key parameters of the peak-to-valley electricity price ratio of the peak electricity price and the user response proportion of the demand response is established, and the linear programming solver is used for solving the linear optimization model to obtain a model result, so that the linear optimization model has important significance for guiding provincial-level and even regional-level large-scale power grids to carry out research and implementation of peak clipping measures.
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FIG. 1 is a diagram of steps of a model building method for configuring peak clipping measures in an embodiment of the invention;
fig. 2 is a flowchart of a model construction method for configuring a peak clipping measure in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 and fig. 2, an embodiment of the present invention provides a model building method for configuring peak clipping measures, including the steps of:
s1, acquiring time-of-use electricity price data, and constructing an electricity price elastic matrix according to the time-of-use electricity price and time period information in the time-of-use electricity price data;
in the embodiment, the peak time T in one day is determined according to the time-of-use electricity price mechanismonNormal time segment TmidAnd off-peak time period Toff. Illustratively, peak hours 9:00-12:00 and 14:00-17:00, normal hours 8:00-9:00, 12:00-14:00 and 17:00-24:00, and valley hours 0:00-8:00 are set. Therefore, the electricity prices are respectively rho in the peak period, the ordinary period and the off-peak period of the input in the computing systemon，ρmidAnd ρoff. Inputting load data of T hours on D days of the research year, wherein D is 365 or 366 generally, T is 24 generally, and P is sett dDay d, hour t load data. According to the definition of the demand elasticity matrix, the electricity price elasticity matrix is obtained as follows:
Figure BDA0002581920600000051
wherein the content of the first and second substances,iiin order to have a self-elastic coefficient,ijis a coefficient of mutual elasticity, QiFor the original quantity of electricity at time i, Δ QiFor i moment varying electric quantity, piFor the original electricity price at time i, Δ ρiThe electricity prices are changed for i moment.
S2, setting the peak-valley electricity price ratio, the load response proportion and the response duration as variables to be optimized;
in the present embodiment, it is assumed that the spike period Tc10:00-12:00, the peak-to-valley electrovalence ratio lambda is a variable to be optimized, and the peak electrovalence rhocCan be determined by the peak-to-valley electrovalence ratio and the valley-time electrovalence, i.e. rhoc＝λρoff。
Let PLExpressed as the current maximum load, the interruptible load response ratio is set to η, and the average response time length TRThe interruptible load response scale and the response electric quantity are respectively
Figure BDA0002581920600000052
In the equation (2), the response electric quantity correction coefficient k is a constant, and approximately represents the willingness of the user to participate in the demand response.
In summary, the peak-to-valley electrovalence ratio λ,Load response ratio η and average response time duration TRAre variables to be optimized.
S3, constructing an objective function between peak clipping cost and peak clipping benefit, and setting a constraint condition and a cost benefit relational expression of the variable to be optimized according to the objective function to establish a linear optimization model;
in this embodiment, the steps of establishing the linear optimization model are specifically as follows:
<1> creation of objective function
The objective function is to minimize peak clipping cost and maximize peak clipping benefit, which can be expressed as
Figure BDA0002581920600000061
In the formula (3), C represents cost and B represents benefit.
<2> time-of-use electricity price cost
The electricity quantity transferred in one day can be obtained by the electricity price elastic matrix
Figure BDA0002581920600000062
In the formula (4), the reaction mixture is,
Figure BDA0002581920600000063
after the time-of-use electricity price is adopted, the electricity quantity change value of each time period on the d day is adopted;
Figure BDA0002581920600000064
the original electric quantity of each time interval on the day d; is an elastic coefficient matrix; rho0Is the original price vector of electricity, divided according to the time interval of time-of-use price of electricity, and is divided by rhoon，ρmidAnd ρoffAnd (4) forming. Will be delta QdThe average shift of the demand to the valley period, the peak clipping cost after the time-of-use electricity price is taken can be expressed as,
Figure BDA0002581920600000065
in the formula (5, | Toff| represents the total number of periods of the valley period.
<3> Power supply side cost and benefit
Reduced power generation cost C1Is calculated as
Figure BDA0002581920600000066
In the formula (6), ρ1For the price of electricity on the internet, q1And q is2Respectively the line loss rate and the station service power rate.
Free capacity cost B1And can avoid the electric cost B2The calculation is as follows:
Figure BDA0002581920600000067
Figure BDA0002581920600000071
in the formulae (6) and (7), σ1The unit cost of capacity can be avoided for power generation enterprises; sigma2The unit cost of the electric quantity can be avoided for power generation enterprises.
<4> grid side cost efficiency
Investment cost of equipment C2
Figure BDA0002581920600000072
In the formula (9), ρ2α is a discount coefficient, and α can be expressed as the investment unit cost of the power grid equipment, considering that the operation period of the demand side response project is l years and the discount rate is r
Figure BDA0002581920600000073
Project management cost C3Is calculated as
Figure BDA0002581920600000074
In the formula (11), rho3The unit cost is managed for the project.
User equipment subsidy cost C4Is calculated as
C4＝PR·ρ4·β·α (12)
In the formula (12), p4The investment cost of the user equipment is shown as β, and the investment percentage of the user equipment is subsidized for the power grid enterprises.
User compensation cost C5Is calculated as
Figure BDA0002581920600000075
In the formula (13), rho5Subsidizing the price for user interruptible load responses.
Reduced electricity sales revenue C6Is calculated as
Figure BDA0002581920600000076
In the formula (14), p6For the transmission and distribution of electricity.
Free capacity cost B of power grid enterprise3Is calculated as
Figure BDA0002581920600000081
In formula (15), σ3The capacity unit cost can be avoided for power grid enterprises.
<5> user-side cost-effectiveness
Investment cost of equipment C7Is calculated as
C6＝PR·ρ4·(1-β)·α (16)
User operation and maintenance cost C8Is calculated as
C8＝PR·ρ8(17)
In the formula (17), rho8The unit cost is the operation and maintenance cost of the user.
User compensation gain B4Is calculated as
B4＝PR·ρ5(18)
User power saving cost B5Is calculated as
B5＝QR·σ5(19)
In formula (19), σ5Is the average electricity purchase price.
<6> social cost benefits
Propaganda management cost C9Is calculated as
C9＝PR·ρ9(20)
In the formula (20), p9And the cost is publicized and managed for the society.
Carbon dioxide emission reduction B6Is calculated as
Figure BDA0002581920600000082
In the formula (21), σ6Is a carbon dioxide emission factor.
Sulfur dioxide emission reduction B7Is calculated as
Figure BDA0002581920600000091
In the formula (22), σ7Is a sulfur dioxide emission factor.
The nitrogen oxide reduction is calculated as
Figure BDA0002581920600000092
In formula (23), σ8Is a nitrogen oxide emission factor.
<7> constraint conditions
In the present invention, the values of the optimized variables are not less than zero, and the following requirements are satisfied
Figure BDA0002581920600000093
And S4, solving the linear optimization model through a linear programming solver.
In this embodiment, the linear programming model may adopt or solve a mature linear programming solver such as GLPK, CPLEX, Gurobi, and the like, without re-establishing a solving algorithm.
For the convenience of understanding, as an example, the peak clipping measure determination and the cost-benefit analysis are performed on 2025 years of a certain province by using the optimization model, and a peak-to-valley electricity-price ratio λ of 4:1, a load response proportion η of 2% and a response time length T are respectively obtainedRWhen the peak clipping time is 7.2 hours, the peak clipping cost is the lowest, and the peak clipping benefit is the highest. Specific calculated values of the respective costs and benefits are shown in table 1.
TABLE 1 Peak Electricity prices and optimal cost-benefit statistics for demand response
Figure BDA0002581920600000094
Figure BDA0002581920600000101
The embodiment of the present invention further provides a model building apparatus for configuring peak clipping measures, including:
the data input module is used for acquiring time-of-use electricity price data and constructing an electricity price elastic matrix according to the time-of-use electricity price and time period information in the time-of-use electricity price data;
the optimization control system comprises a to-be-optimized variable determination module, a load response module and a control module, wherein the to-be-optimized variable determination module is used for setting a peak-to-valley electricity price ratio, a load response ratio and response duration as to-be-optimized variables;
the linear optimization model establishing module is used for constructing an objective function between peak clipping cost and peak clipping benefit, and setting a constraint condition and a cost benefit relational expression of the variable to be optimized according to the objective function so as to establish a linear optimization model;
and the optimization model calculation module is used for solving the linear optimization model through a linear programming solver.
In one embodiment of the present invention, the electricity price elastic matrix is:
Figure BDA0002581920600000102
wherein the content of the first and second substances,iiin order to have a self-elastic coefficient,ijis a coefficient of mutual elasticity, QiFor the original quantity of electricity at time i, Δ QiFor i moment varying electric quantity, piFor the original electricity price at time i, Δ ρiThe electricity prices are changed for i moment.
In one embodiment of the present invention, the objective function is:
Figure BDA0002581920600000103
wherein C represents cost and B represents benefit;
Figure BDA0002581920600000111
ΔQdafter the time-of-use electricity price is adopted, the electricity quantity change value of each time period on the d day is adopted; qdThe original electric quantity of each time interval on the day d; is an elastic coefficient matrix; rho0Dividing into rho according to time-of-use electricity price period for original electricity price vectoron、ρoff、ρoff；|Toff| represents the total number of periods of the valley period.
In one embodiment of the present invention, the cost-benefit relationship includes a power-side cost and benefit relationship, a power-grid-side cost and benefit relationship, a user-side cost and benefit relationship, and a social cost and benefit relationship.
The embodiment of the present invention further provides a model building terminal device for configuring peak clipping measures, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the processor implements the model building method for configuring peak clipping measures as described above.
The embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the above model building method for configuring peak clipping measures.
The foregoing is directed to the preferred embodiment of the present invention, and it is understood that various changes and modifications may be made by one skilled in the art without departing from the spirit of the invention, and it is intended that such changes and modifications be considered as within the scope of the invention. It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

Claims (10)

1. A model construction method for configuring peak clipping measures is characterized by comprising the following steps:
acquiring time-of-use electricity price data, and constructing an electricity price elastic matrix according to time-of-use electricity price and time period information in the time-of-use electricity price data;
setting the peak-to-valley electricity price ratio, the load response ratio and the response time length as variables to be optimized;
constructing an objective function between peak clipping cost and peak clipping benefit, and setting a constraint condition and a cost benefit relational expression of the variable to be optimized according to the objective function to establish a linear optimization model;
and solving the linear optimization model through a linear programming solver.
2. The model building method for configuring peak clipping measures according to claim 1, characterized in that the electricity price elastic matrix is:
Figure FDA0002581920590000011
wherein the content of the first and second substances,iiin order to have a self-elastic coefficient,ijis a coefficient of mutual elasticity, QiFor the original quantity of electricity at time i, Δ QiFor i moment varying electric quantity, piFor the original electricity price at time i, Δ ρiThe electricity prices are changed for i moment.
3. The model building method for configuring peak reduction measures according to claim 2, characterized in that the objective function is:
Figure FDA0002581920590000012
wherein C represents cost and B represents benefit;
Figure FDA0002581920590000013
ΔQdafter the time-of-use electricity price is adopted, the electricity quantity change value of each time period on the d day is adopted; qdThe original electric quantity of each time interval on the day d; is an elastic coefficient matrix; rho0Dividing into rho according to time-of-use electricity price period for original electricity price vectoron、ρoff、ρoff；|Toff| represents the total number of periods of the valley period.
4. The model building method for configuring peak clipping measures according to claim 1, wherein the cost-benefit relation includes a power-side cost and benefit relation, a grid-side cost and benefit relation, a user-side cost and benefit relation, and a social cost and benefit relation.
5. A model building apparatus for configuring a peak clipping measure, comprising:
the data input module is used for acquiring time-of-use electricity price data and constructing an electricity price elastic matrix according to the time-of-use electricity price and time period information in the time-of-use electricity price data;
the optimization control system comprises a to-be-optimized variable determination module, a load response module and a control module, wherein the to-be-optimized variable determination module is used for setting a peak-to-valley electricity price ratio, a load response ratio and response duration as to-be-optimized variables;
the linear optimization model establishing module is used for constructing an objective function between peak clipping cost and peak clipping benefit, and setting a constraint condition and a cost benefit relational expression of the variable to be optimized according to the objective function so as to establish a linear optimization model;
and the optimization model calculation module is used for solving the linear optimization model through a linear programming solver.
6. The model building apparatus for configuring peak clipping measures according to claim 5, characterized in that the electricity price elastic matrix is:
Figure FDA0002581920590000021
wherein the content of the first and second substances,iiin order to have a self-elastic coefficient,ijis a coefficient of mutual elasticity, QiFor the original quantity of electricity at time i, Δ QiFor i moment varying electric quantity, piFor the original electricity price at time i, Δ ρiThe electricity prices are changed for i moment.
7. The model building apparatus for configuring peak reduction measures according to claim 6, characterized in that the objective function is:
Figure FDA0002581920590000031
wherein C represents cost and B represents benefit;
Figure FDA0002581920590000032
ΔQdafter the time-of-use electricity price is adopted, the electricity quantity change value of each time period on the d day is adopted; qdThe original electric quantity of each time interval on the day d; is an elastic coefficient matrix; rho0Dividing into rho according to time-of-use electricity price period for original electricity price vectoron、ρoff、ρoff；|Toff| represents the total number of periods of the valley period.
8. The model building apparatus for configuring peak clipping measures according to claim 5, wherein the cost-benefit relation includes a power-side cost and benefit relation, a grid-side cost and benefit relation, a user-side cost and benefit relation, and a social cost and benefit relation.
9. A model building terminal device for configuring peak clipping measures, comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the model building method for configuring peak clipping measures according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the model construction method for configuring peak reduction measures according to any one of claims 1 to 4.
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