CN111784409B - 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 PDFInfo
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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 interval information in the time-of-use electricity price data; setting the peak-valley electricity price ratio, the load response proportion and the response time length as variables to be optimized; constructing an objective function between peak clipping cost and peak clipping benefit, and setting constraint conditions and cost benefit relation formulas of the variables to be optimized according to the objective function so as to establish a linear optimization model; and solving the linear optimization model through a linear programming solver. The invention builds the model for determining the optimal configuration scheme of the peak electricity price and the demand response peak clipping measures, and can assist the peak clipping measure planning and analysis work of the large power grid.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to a model construction method, device, equipment and medium for configuring peak clipping measures.
Background
At present, due to the fact that time difference exists in the electricity utilization characteristics of different types of user loads, certain peak-valley difference exists in the overall load characteristics of the power grid. When the peak-valley difference is too large, sufficient power supply is required to be configured and a strong net rack is required to be built so as to ensure safe and stable operation of the power grid, however, a large number of devices are in an idle state. For this reason, it is necessary to take some peak clipping measures to flatten the load characteristics as much as possible and reduce peak-to-valley differences.
In the prior art, the study on peak clipping measures mainly aims at a power distribution network and a micro-grid, and focuses on how to construct peak clipping measures and peak clipping mechanisms by using modes such as energy storage, load control and the like. However, for large-scale power grids such as provincial power grids and regional power grids, the current peak clipping measures mainly comprise two means of time-of-use electricity price and demand response based on controllable load, calculation research on the peak clipping measures is limited to analysis and evaluation after implementation, and researches on how to optimally determine what peak clipping measures are adopted or how to combine peak clipping measures are adopted are fresh, so that an effective, convenient and reasonable method for assisting peak clipping measure planning and analysis work of a large power grid is needed to be provided.
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 peak electricity price and demand response of two peak clipping measures, and can assist peak clipping measure planning and analysis work of a large power grid.
In order to solve the above technical problems, an embodiment of the present invention provides a model building method for configuring peak clipping measures, including the steps of:
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-valley electricity price ratio, the load response proportion and the response time length as variables to be optimized;
constructing an objective function between peak clipping cost and peak clipping benefit, and setting constraint conditions and cost benefit relation formulas of the variables to be optimized according to the objective function so as 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:
Wherein epsilon ii is the self-elasticity coefficient, epsilon ij is the mutual-elasticity coefficient, Q i is the original electric quantity at the moment i, deltaQ i is the changed electric quantity at the moment i, rho i is the original electric price at the moment i, deltaρ i is the changed electric price at the moment i.
In one embodiment of the present invention, the objective function is:
Wherein C represents cost and B represents benefit;
Delta Q d is the electric quantity change value of each period of the day d after the time-sharing electricity price is adopted; The original electric quantity of each period of the day d; epsilon is an elastic coefficient matrix; ρ 0 is an original electricity price vector, and the period division into ρ on、ρmid、ρoff;|Toff | according to the period of the time-of-use electricity price represents the total number of periods of the valley period.
In one embodiment of the present invention, the cost-benefit relationship includes a power source side cost and benefit relationship, a grid side cost and benefit relationship, a user side cost and benefit relationship, a social cost and benefit relationship.
The embodiment of the invention also provides a model construction device for configuring peak clipping measures, which comprises the following steps:
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 the time period information in the time-of-use electricity price data;
The variable to be optimized determining module is used for setting the peak-valley cost ratio, the load response proportion and the response time length as variables to be optimized;
The linear optimization model building module is used for constructing an objective function between peak clipping cost and peak clipping benefit, and setting constraint conditions and cost benefit relation of the variable to be optimized according to the objective function so as to build 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:
Wherein epsilon ii is the self-elasticity coefficient, epsilon ij is the mutual-elasticity coefficient, Q i is the original electric quantity at the moment i, deltaQ i is the changed electric quantity at the moment i, rho i is the original electric price at the moment i, deltaρ i is the changed electric price at the moment i.
In one embodiment of the present invention, the objective function is:
Wherein C represents cost and B represents benefit;
Delta Q d is the electric quantity change value of each period of the day d after the time-sharing electricity price is adopted; The original electric quantity of each period of the day d; epsilon is an elastic coefficient matrix; ρ 0 is an original electricity price vector, and the period division into ρ on、ρmid、ρoff;|Toff | according to the period of the time-of-use electricity price represents the total number of periods of the valley period.
In one embodiment of the present invention, the cost-benefit relationship includes a power source side cost and benefit relationship, a grid side cost and benefit relationship, a user side cost and benefit relationship, a social cost and benefit relationship.
The embodiment of the invention also provides a model construction terminal device for configuring the peak clipping measures, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor realizes the model construction method for configuring the peak clipping measures when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program is used for controlling equipment where the computer readable storage medium is located to execute the model construction method for configuring peak clipping measures.
Compared with the prior art, the embodiment of the invention has the beneficial effects that the model construction method for configuring peak clipping measures 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-valley electricity price ratio, the load response proportion and the response time length as variables to be optimized; constructing an objective function between peak clipping cost and peak clipping benefit, and setting constraint conditions and cost benefit relation formulas of the variables to be optimized according to the objective function so as to establish a linear optimization model; and solving the linear optimization model through a linear programming solver. The linear optimization model is solved by using the linear programming solver to obtain a model result by establishing a linear optimization model considering two key parameters of peak electricity price, peak valley electricity price ratio and user response proportion of demand response, and the method has important significance for guiding the research and implementation of peak clipping measures of provincial and regional large-scale power grids.
Drawings
FIG. 1 is a step diagram of a model building method for configuring peak clipping measures in an embodiment of the invention;
FIG. 2 is a flow chart of a model building method for configuring peak clipping measures in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 and 2, an embodiment of the present invention provides a method for constructing a model 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 time-of-use electricity price and time period information in the time-of-use electricity price data;
In this embodiment, the peak period T on, the normal period T mid and the valley period T off in one day are determined according to the time-of-use electricity price mechanism. Illustratively, peak periods 9:00-12:00 and 14:00-17:00 are set, and peak periods 8:00-9:00, 12:00-14:00 and 17:00-24:00 are set, and valley periods 0:00-8:00 are set. Thus, peak, normal and valley electricity prices are entered in the computing system as ρ on,ρmid and ρ off, respectively. Study year D day T hour load data was entered, typically D is 365 or 366, T is 24, and P t d is day D T hour load data. And according to the definition of the required elastic matrix, obtaining the electricity price elastic matrix as follows:
Wherein epsilon ii is the self-elasticity coefficient, epsilon ij is the mutual-elasticity coefficient, Q i is the original electric quantity at the moment i, deltaQ i is the changed electric quantity at the moment i, rho i is the original electric price at the moment i, deltaρ i is the changed electric price at the moment i.
S2, setting the peak-valley cost ratio, the load response proportion and the response time length as variables to be optimized;
in the present embodiment, assuming that the peak period T c is 10:00-12:00 and the peak-to-valley electricity price ratio λ is a variable to be optimized, the peak electricity price ρ c may be determined by the peak-to-valley electricity price ratio and the valley period electricity price, that is ρ c＝λρoff.
Let P L be the current maximum load, let the interruptible load response proportion be eta, and the average response time T R, the interruptible load response scale and the response electric quantity be respectively
In the equation (2), the response electric quantity correction coefficient k is a constant, and approximately represents the user's willingness to participate in the demand response.
In summary, the peak-to-valley cost ratio lambda, the load response proportion eta and the average response time length T R are respectively used as variables to be optimized.
S3, constructing an objective function between peak clipping cost and peak clipping benefit, and setting constraint conditions and cost benefit relation formulas of the variables to be optimized according to the objective function so as to establish a linear optimization model;
In this embodiment, the step of establishing the linear optimization model is specifically as follows:
<1> establishing an objective function
The objective function is to minimize peak clipping cost and maximize peak clipping benefit, and can be expressed as
In the formula (3), C represents cost and B represents benefit.
<2> Time-of-use price of electricity cost
The electricity quantity transferred in one day can be obtained by the electricity price elastic matrix to be
In the formula (4), the amino acid sequence of the compound,The electric quantity change value of each period of the day d after the time-sharing electricity price is adopted; The original electric quantity of each period of the day d; epsilon is an elastic coefficient matrix; ρ 0 is an original electricity price vector, divided according to the time period of the time-of-use electricity price, and is composed of ρ on,ρmid and ρ off. The peak clipping cost after time-of-use electricity price is taken by averagely shifting the electricity demand of deltaq d to the valley period,
In the expression (5), T off represents the total number of periods of the valley period.
<3> Power supply side cost and benefit
The reduced power generation cost C 1 is calculated as
In the formula (6), ρ 1 is the internet power price, and q 1 and q 2 are the line loss rate and the station service power rate respectively.
The capacity-exemptable cost B 1 and the electricity-exemptable cost B 2 are calculated as:
in the formulas (6) and (7), sigma 1 is the unit cost of the power generation enterprise free capacity; σ 2 is the unit cost of the electricity-free quantity of the power generation enterprise.
<4> Grid side cost benefits
Cost of equipment investment C 2
In the formula (9), ρ 2 is the investment unit cost of the power grid equipment; alpha is a discount coefficient, and when the operation period of the demand side response project is considered to be l years and the discount rate is r, alpha can be expressed as
Project management fee C 3 is calculated as
In the formula (11), ρ 3 is the project management fee unit cost.
The user equipment subsidy cost C 4 is calculated as
C4＝PR·ρ4·β·α (12)
In the formula (12), ρ 4 is the investment unit cost of the user equipment; beta is the investment percentage of the subsidized user equipment of the power grid enterprise.
The user compensation cost C 5 is calculated as
In equation (13), ρ 5 is the user interruptible load response subsidy price.
Reduced electricity revenue C 6 is calculated as
In the formula (14), ρ 6 is the power transmission and distribution price.
The capacity-free cost B 3 of the power grid enterprise is calculated as
In the formula (15), sigma 3 is the unit cost of the power grid enterprise free capacity.
<5> User side cost effectiveness
The equipment investment cost C 7 is calculated as
C7＝PR·ρ4·(1-β)·α (16)
The user operation and maintenance cost C 8 is calculated as
C8＝PR·ρ8 (17)
In the formula (17), ρ 8 is the user operation unit cost.
The user compensation benefit B 4 is calculated as
B4＝PR·ρ5 (18)
The user saving electricity cost B 5 is calculated as
B5＝QR·σ5 (19)
In the formula (19), σ 5 is the average electricity purchase price.
<6> Social cost benefits
The propaganda management cost C 9 is calculated as
C9＝PR·ρ9 (20)
In the formula (20), ρ 9 is social propaganda management cost.
The carbon dioxide emission reduction amount B 6 is calculated as
In formula (21), σ 6 is the carbon dioxide emission factor.
Sulfur dioxide emission reduction B 7 is calculated as
In formula (22), σ 7 is the sulfur dioxide emission factor.
The emission reduction of nitrogen oxides is calculated as
In formula (23), σ 8 is the nox emission factor.
<7> Constraint
The invention requires that the values of all the optimization variables are not less than zero, and the invention also needs to meet the following requirements
And S4, solving the linear optimization model through a linear programming solver.
In this embodiment, the linear programming model may use a GLPK, CPLEX, gurobi-like mature linear programming solver or perform the solution without having to re-establish the solution algorithm.
For ease of understanding, as an example, peak clipping measures determination and cost benefit analysis are performed in 2025 of a certain province by using the above-mentioned optimization model, and peak clipping cost is lowest and peak clipping efficiency is highest when the peak-to-valley electricity valence ratio λ is 4:1, the load response ratio η is 2% and the response time period T R is 7.2 hours, respectively. The specific calculations for each cost and benefit are shown in table 1.
TABLE 1 optimal cost-effectiveness statistics of peak electricity prices and demand response
The embodiment of the invention also provides a model construction device for configuring peak clipping measures, which comprises the following steps:
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 the time period information in the time-of-use electricity price data;
The variable to be optimized determining module is used for setting the peak-valley cost ratio, the load response proportion and the response time length as variables to be optimized;
The linear optimization model building module is used for constructing an objective function between peak clipping cost and peak clipping benefit, and setting constraint conditions and cost benefit relation of the variable to be optimized according to the objective function so as to build 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:
Wherein epsilon ii is the self-elasticity coefficient, epsilon ij is the mutual-elasticity coefficient, Q i is the original electric quantity at the moment i, deltaQ i is the changed electric quantity at the moment i, rho i is the original electric price at the moment i, deltaρ i is the changed electric price at the moment i.
In one embodiment of the present invention, the objective function is:
Wherein C represents cost and B represents benefit;
Delta Q d is the electric quantity change value of each period of the day d after the time-sharing electricity price is adopted; q d is the original power for each period on day d; ε Is an elastic coefficient matrix; ρ0 is an original electricity price vector, and the period division into ρ on、ρoff、ρoff;|Toff | representing the total number of periods of the valley period according to the period of the time-of-use electricity price.
In one embodiment of the present invention, the cost-benefit relationship includes a power source side cost and benefit relationship, a grid side cost and benefit relationship, a user side cost and benefit relationship, a social cost and benefit relationship.
The embodiment of the invention also provides a model construction terminal device for configuring the peak clipping measures, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor realizes the model construction method for configuring the peak clipping measures when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program is used for controlling equipment where the computer readable storage medium is located to execute the model construction method for configuring peak clipping measures.
While the foregoing is directed to the preferred embodiments of the present invention, it should be noted that modifications and variations could be made by those skilled in the art without departing from the principles of the present invention, and such modifications and variations are to be regarded as being within the scope of the invention. Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps 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 (Random Access Memory, RAM), or the like.
Claims (10)
1. A model construction method for configuring peak clipping measures, comprising the steps of:
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-valley electricity price ratio, the load response proportion and the response time length as variables to be optimized;
constructing an objective function between peak clipping cost and peak clipping benefit, and setting constraint conditions and cost benefit relation formulas of the variables to be optimized according to the objective function so as to establish a linear optimization model;
solving the linear optimization model through a linear programming solver;
setting constraint conditions and cost-effective relation of the variables to be optimized according to the objective function to establish a linear optimization model, wherein the constraint conditions and the cost-effective relation comprise:
Establishing an objective function, wherein the objective function is used for minimizing peak clipping cost and maximizing peak clipping benefit;
calculating the time-sharing electricity price cost;
Calculating a power side cost and benefit, the power side cost comprising: reduced power generation costs, the power side benefit comprising: the capacity cost and the electricity quantity cost can be avoided;
Calculating grid-side costs and benefits, the grid-side costs comprising: equipment investment costs, project management costs, user equipment subsidy costs, user compensation costs and reduced electricity sales revenue, the grid-side benefits including: the power grid enterprise can avoid capacity cost;
Calculating a user side cost and benefit, wherein the user side cost comprises: equipment investment cost and user maintenance cost, wherein the user side benefit comprises: user compensation income and user saving electricity cost;
Calculating social costs and benefits, the social costs comprising: propaganda management cost, the social benefit includes: carbon dioxide emission reduction, sulfur dioxide emission reduction and nitrogen oxide emission reduction;
Setting constraint conditions, wherein the constraint conditions are as follows:
where λ represents the peak-to-valley cost ratio, the η load response ratio, and T R represents the average response time.
2. The model construction method for configuring peak clipping measures according to claim 1, wherein the electricity price elastic matrix is:
Wherein epsilon ii is the self-elasticity coefficient, epsilon ij is the mutual-elasticity coefficient, Q i is the original electric quantity at the moment i, deltaQ i is the changed electric quantity at the moment i, rho i is the original electric price at the moment i, deltaρ i is the changed electric price at the moment i.
3. The model construction method for configuring peak clipping measures according to claim 2, wherein the objective function is:
Wherein C represents cost and B represents benefit;
Delta Q d is the electric quantity change value of each period of the day d after the time-sharing electricity price is adopted; The original electric quantity of each period of the day d; epsilon is an elastic coefficient matrix; ρ 0 is an original electricity price vector, and the period division into ρ on、ρmid、ρoff;|Toff | according to the period of the time-of-use electricity price represents the total number of periods of the valley period.
4. The model building method for configuring peak clipping measures of claim 1, wherein the cost-benefit relation includes a power source side cost and benefit relation, a grid side cost and benefit relation, a user side cost and benefit relation, a social cost and benefit relation.
5. A model construction apparatus for configuring peak clipping measures, 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 the time period information in the time-of-use electricity price data;
The variable to be optimized determining module is used for setting the peak-valley cost ratio, the load response proportion and the response time length as variables to be optimized;
The linear optimization model building module is used for constructing an objective function between peak clipping cost and peak clipping benefit, and setting constraint conditions and cost benefit relation of the variable to be optimized according to the objective function so as to build a linear optimization model;
the optimization model calculation module is used for solving the linear optimization model through a linear programming solver;
setting constraint conditions and cost-effective relation of the variables to be optimized according to the objective function to establish a linear optimization model, wherein the constraint conditions and the cost-effective relation comprise:
Establishing an objective function, wherein the objective function is used for minimizing peak clipping cost and maximizing peak clipping benefit;
calculating the time-sharing electricity price cost;
Calculating a power side cost and benefit, the power side cost comprising: reduced power generation costs, the power side benefit comprising: the capacity cost and the electricity quantity cost can be avoided;
Calculating grid-side costs and benefits, the grid-side costs comprising: equipment investment costs, project management costs, user equipment subsidy costs, user compensation costs and reduced electricity sales revenue, the grid-side benefits including: the power grid enterprise can avoid capacity cost;
Calculating a user side cost and benefit, wherein the user side cost comprises: equipment investment cost and user maintenance cost, wherein the user side benefit comprises: user compensation income and user saving electricity cost;
Calculating social costs and benefits, the social costs comprising: propaganda management cost, the social benefit includes: carbon dioxide emission reduction, sulfur dioxide emission reduction and nitrogen oxide emission reduction;
Setting constraint conditions, wherein the constraint conditions are as follows:
where λ represents the peak-to-valley cost ratio, the η load response ratio, and T R represents the average response time.
6. The model construction apparatus for configuring peak clipping measures according to claim 5, wherein the electricity price elastic matrix is:
Wherein epsilon ii is the self-elasticity coefficient, epsilon ij is the mutual-elasticity coefficient, Q i is the original electric quantity at the moment i, deltaQ i is the changed electric quantity at the moment i, rho i is the original electric price at the moment i, deltaρ i is the changed electric price at the moment i.
7. The model construction device for configuring peak clipping measures according to claim 6, wherein the objective function is:
Wherein C represents cost and B represents benefit;
Delta Q d is the electric quantity change value of each period of the day d after the time-sharing electricity price is adopted; The original electric quantity of each period of the day d; epsilon is an elastic coefficient matrix; ρ 0 is an original electricity price vector, and the period division into ρ on、ρmid、ρoff;|Toff | according to the period of the time-of-use electricity price represents the total number of periods of the valley period.
8. The model building apparatus for configuring peak clipping measures of claim 5, wherein the cost-benefit relation includes a power source side cost and benefit relation, a grid side cost and benefit relation, a user side cost and benefit relation, a social cost and benefit relation.
9. Model building terminal device for configuring peak clipping measures, characterized in that it comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, which processor, when executing the computer program, implements the model building method for configuring peak clipping measures according to any of claims 1 to 4.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored computer program, wherein the computer program, when run, controls a device in which the computer-readable storage medium is located to execute the model building method for configuring peak clipping measures according to any one of claims 1 to 4.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103400199A (en) * | 2013-07-09 | 2013-11-20 | 国家电网公司 | Power demand side optimization method combining market demand response with physical demand response |
| CN103679357A (en) * | 2013-12-06 | 2014-03-26 | 国网山东省电力公司 | Power demand response intelligent decision method based on price and excitation |
| CN104156581A (en) * | 2014-08-01 | 2014-11-19 | 重庆大学 | Peak-valley time-of-use power price determining method taking both system reliability and power purchase risk into consideration |
| CN104809294A (en) * | 2015-04-30 | 2015-07-29 | 贵州大学 | Establishing method for responsivity model of user for time-of-use electricity price |
| CN105405061A (en) * | 2015-05-06 | 2016-03-16 | 国家电网公司 | Evaluation method of power supply reliability of distribution network based on demand response |
| CN107944630A (en) * | 2017-12-01 | 2018-04-20 | 华北电力大学 | A kind of seasonality tou power price optimization formulating method |
| CN107967533A (en) * | 2017-11-08 | 2018-04-27 | 国网冀北电力有限公司 | A kind of meter and distributed generation resource and the load forecasting method of Demand Side Response |
| CN108599157A (en) * | 2018-05-17 | 2018-09-28 | 东北电力大学 | A kind of alternating current-direct current cooperation optimal dispatch method considering electricity price type demand response |
| CN110417062A (en) * | 2019-07-31 | 2019-11-05 | 广东电网有限责任公司 | A kind of electrical integrated energy system Optimization Scheduling |
| CN111127081A (en) * | 2019-12-05 | 2020-05-08 | 深圳供电局有限公司 | Time-of-use electricity price determining method |
-
2020
- 2020-07-13 CN CN202010671724.4A patent/CN111784409B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103400199A (en) * | 2013-07-09 | 2013-11-20 | 国家电网公司 | Power demand side optimization method combining market demand response with physical demand response |
| CN103679357A (en) * | 2013-12-06 | 2014-03-26 | 国网山东省电力公司 | Power demand response intelligent decision method based on price and excitation |
| CN104156581A (en) * | 2014-08-01 | 2014-11-19 | 重庆大学 | Peak-valley time-of-use power price determining method taking both system reliability and power purchase risk into consideration |
| CN104809294A (en) * | 2015-04-30 | 2015-07-29 | 贵州大学 | Establishing method for responsivity model of user for time-of-use electricity price |
| CN105405061A (en) * | 2015-05-06 | 2016-03-16 | 国家电网公司 | Evaluation method of power supply reliability of distribution network based on demand response |
| CN107967533A (en) * | 2017-11-08 | 2018-04-27 | 国网冀北电力有限公司 | A kind of meter and distributed generation resource and the load forecasting method of Demand Side Response |
| CN107944630A (en) * | 2017-12-01 | 2018-04-20 | 华北电力大学 | A kind of seasonality tou power price optimization formulating method |
| CN108599157A (en) * | 2018-05-17 | 2018-09-28 | 东北电力大学 | A kind of alternating current-direct current cooperation optimal dispatch method considering electricity price type demand response |
| CN110417062A (en) * | 2019-07-31 | 2019-11-05 | 广东电网有限责任公司 | A kind of electrical integrated energy system Optimization Scheduling |
| CN111127081A (en) * | 2019-12-05 | 2020-05-08 | 深圳供电局有限公司 | Time-of-use electricity price determining method |
Non-Patent Citations (8)
| Title |
|---|
| 一种日前发电调度与日前分时电价联合优化模型;赵晋泉;胡佳;王珂;姚建国;杨胜春;苏大威;徐春雷;;电力系统保护与控制;20190501(第09期);全文 * |
| 国家发展和改革委员会经济研究所 编.《观大势 谋大局 出大策：国家发展和改革委员会经济研究所获奖成果集：1975-2015》.中国计划出版社,2015,454-455. * |
| 基于用户响应的尖峰电价模型研究;梁甜甜;王磊;高赐威;;华东电力(第01期);全文 * |
| 基于需求响应的分时电价主从博弈建模与仿真研究;胡鹏;艾欣;张朔;潘玺安;;电网技术(第02期);全文 * |
| 峰谷分时电价下用户需求响应行为模型的研究;马永武;赵国生;黄明山;李如意;;郑州大学学报(理学版)(第04期);全文 * |
| 考虑电价激励需求响应下多主体微电网电源容量优化;马国龙 等;电力自动化设备;第39卷(第5期);96-101 * |
| 计及价格型需求响应负荷响应量与风电出力相关性的含风电电力系统优化调度研究;原睿萌;范绚然;姬广龙;凌开元;袁越;;可再生能源;20180320(第03期);全文 * |
| 计及用户需求响应的分时电价优化模型;李春燕;许中;马智远;;电力系统及其自动化学报(第03期);全文 * |
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