CN111080082B - Power grid planning method suitable for low-carbon power supply development - Google Patents

Power grid planning method suitable for low-carbon power supply development Download PDF

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CN111080082B
CN111080082B CN201911178614.8A CN201911178614A CN111080082B CN 111080082 B CN111080082 B CN 111080082B CN 201911178614 A CN201911178614 A CN 201911178614A CN 111080082 B CN111080082 B CN 111080082B
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CN111080082A (en
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张家宁
刘晓明
杨斌
田鑫
曹相阳
杨思
高效海
王男
魏鑫
张丽娜
薄其滨
王轶群
孙东磊
付一木
魏佳
刘冬
牟颖
张玉跃
张栋梁
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Jinan Jingwei Electric Power Engineering Consulting Co ltd
Shandong Zhiyuan Electric Power Design Consulting Co ltd
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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Jinan Jingwei Electric Power Engineering Consulting Co ltd
Shandong Zhiyuan Electric Power Design Consulting Co ltd
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention discloses a power grid planning method suitable for low-carbon power supply development, which comprises the following steps: constructing a power grid planning model adapting to low-carbon power supply development; determining an objective function of the power grid planning model as minimizing the investment cost and the running cost of the power grid; generating constraint conditions of a power grid planning model suitable for low-carbon power supply development; and carrying out linearization solution on the power grid planning model by adopting a constraint cost variable method. The carbon emission cost and the economic benefit of the system can be comprehensively considered, the carbon emission reduction potential of the power grid side is fully considered under the low-carbon background, and a new thought is provided for power grid planning decision under the low-carbon background.

Description

Power grid planning method suitable for low-carbon power supply development
Technical Field
The invention relates to the field of power grid planning and operation, in particular to a power grid planning method suitable for low-carbon power supply development.
Background
The electric power industry has obvious carbon locking effect, and the economy and carbon emission reduction benefits of the system are required to be considered in the planning level. The power grid is a carrier for power transmission and management and plays an important role in reasonably optimizing and configuring power generation resources and power utilization requirements. The reasonable power grid structure can not only reduce the power grid operation energy consumption and indirectly reduce the carbon emission of the power generation side, but also promote the consumption of intermittent renewable energy sources and reduce the comprehensive carbon emission intensity of the power generation side. The implementation of the low-carbon target-oriented power planning can relieve the carbon locking effect and effectively promote the low-carbon development of the power industry.
At present, a common power grid planning method does not establish a traditional analysis model capable of flexibly adjusting the coordination and optimization operation of a power supply and an intermittent power supply, lacks the related analysis of the influence of low-carbon power supply access on a power grid, has a dislocation between the low-carbon power supply and the power grid planning, and cannot maximally realize the low-carbon benefit of a power system.
Disclosure of Invention
In order to solve the above problems, the present invention provides a power grid planning method adapted to low-carbon power supply development, and in order to achieve the above purposes, the present invention adopts the following technical scheme:
a power grid planning method suitable for low-carbon power supply development comprises the following steps:
constructing a power grid planning model adapting to low-carbon power supply development;
determining an objective function of the power grid planning model as minimizing the investment cost and the running cost of the power grid;
generating constraint conditions of a power grid planning model suitable for low-carbon power supply development;
and carrying out linearization solution on the power grid planning model by adopting a constraint cost variable method.
Further, the building of the power grid planning model adapting to the development of the low-carbon power supply specifically comprises the following steps:
within the target planning period, calculating 365 net load scene libraries each year according to the load prediction result and the hour-level output prediction result of the new energy;
scene reduction is carried out based on a K-means method to obtain S typical scenes of each planning year and the probability p corresponding to the S typical scenes s ；
And respectively carrying out power and electricity balance simulation on the S typical scenes.
Further, the power and electricity balance simulation is performed on the S typical scenes respectively, and specifically includes:
adding system operation constraint to realize power balance simulation;
according to the unit optimization output arrangement of different scenes, annual electricity contribution probability of the different scenes is considered, annual utilization hours of the unit and annual power generation operation cost index of the system are calculated, and annual electricity contribution probability eta of the scenes is calculated s The calculation formula is as follows:
wherein:representing the predicted load of the d-th day period t; />Representing the load of the t period of the s-th scene.
Further, the objective function is specifically
Wherein: y represents the planning consideration time length; s is S y A scene number representing the y-th year; η (eta) s,y Representing the power contribution rate of the scene s in the y year;the carbon emission price;
the objective function comprises investment cost and system operation cost, wherein the investment cost comprises the investment cost of a circuit in a planning period and the carbon emission cost of the whole life cycle; the system running cost comprises the power generation cost and the carbon emission cost of different types of units, and the wind abandoning punishment cost and the load shedding cost;
and->Respectively the investment cost and the full life cycle carbon emission column vector of the circuit to be built; u (U) B,y A construction state column vector representing the components of the circuit to be constructed, wherein the circuit state is 1 after the circuit is constructed, otherwise, the circuit state is 0; subscripts c, h, n and w respectively represent a conventional unit, a hydro-generator unit, a nuclear power unit and a wind power unit, < ->P i,s,y Respectively representing the optimized output, the power generation cost and the carbon emission intensity column vector of the ith group of units, wherein i is { c, h, n, w };
and->Column vectors of unit load shedding cost and unit wind curtailment cost respectively, < >>And->Optimizing the cut load and the abandoned wind row vector; line 4 represents economic loss and carbon emission costs corresponding to transmission loss>And->Respectively representing the average power generation cost and the average carbon emission intensity of the system, E E,s,y And E is B,s,y The line loss column vectors of the established line and the line to be established are respectively.
Further, the generating constraint conditions of the power grid planning model suitable for low-carbon power supply development specifically includes:
1) And (3) constructing constraint of a line to be constructed:
U B,y ≤U B,y+1 ,y＝1,2,...,Y-1 (3)
U B,y (i)＝U B,y (j),y＝1,2,...,Y (4)
U B,y (i)+U B,y (j)≤1,y＝1,2,...,Y (6)
the formula (3) is line production state constraint; the formulas (4), (5) and (6) are respectively the construction constraints of the line i and the line j simultaneously, successively and mutually exclusive;
2) And (3) power balance constraint of each scene node in each year in the planning period:
wherein: a is that E And A B Node-branch correlation matrices of the established line and the line to be established respectively; b (B) E A diagonal matrix formed by the reactance of the established line; w (W) i Is a unit-node association matrix, wherein i is { c, h, n, w }; theta (theta) s,y A node voltage phase angle column vector;
3) In each scene of each year in the planning period, for the line to be built, which is selected to be put into operation in the year, a tide equation of the line is added as a constraint condition, and for the line which is not selected to be put into operation, the tide of the line is limited to 0, and the tide constraint of the line is as follows
Wherein: f (F) E,max ，F B,max The transmission capacities of the established and to-be-established lines respectively; b (B) B A diagonal matrix composed of reactance of the line to be built is represented;
for the line flow equation constraint to be built of the 2 nd line nonlinearity, converting the line flow equation constraint to be linear constraint by a separation inequality method:
wherein delta is a positive number large enough, and U is used when the line I to be selected is put into operation B,y (1) =1, at which time the constraint becomes a flow equation constraint; when the line l to be selected is not put into operation, U B,y (1) =0, at which time the constraint relaxes;
4) Maximum and minimum output constraint of unit in each scene of each year in a planning period:
wherein: p (P) i,min And P i,max Respectively representing the upper and lower limits of the output of the ith class of unit, i epsilon { c, h, n };representing wind power predicted force;
5) The unit utilizes hour constraint:
in the middle ofAnnual utilization of the ith class of units respectivelyAn upper limit and a lower limit of the number of hours;
6) Load shedding constraint of each scene in each year in a planning period:
7) The network loss equation of each scene in each year in the planning period:
equation (13) is a matrix of the loss equation (12) that will reduce the loss E of the established line E,s,y Expressed as a function of the phase angle difference of the node, and the network loss E of the line to be built B,s,y Expressed as a function of line active power flow, where G E And G B Representing a diagonal matrix of the conductance composition of the lines to be constructed.
Further, the method for solving the power grid planning model in a linearization manner by adopting the constraint cost variable method specifically comprises the following steps:
relaxing the network loss equation constraint into a series of piecewise linear inequality constraints by adopting a constraint cost variable method;
and after linearizing the model, converting the power grid planning model into a mixed integer linear planning model, and calling a commercial planning software package Cplex solver to solve.
The effects provided in the summary of the invention are merely effects of embodiments, not all effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
according to the power grid planning method suitable for low-carbon power supply development, a power grid planning model suitable for low-carbon power supply development is built by analyzing low-carbon elements of a power grid link, and nonlinear constraint is converted into linear constraint by using a separation inequality method and a constraint cost variable method, so that the model is further converted into a standard large-scale mixed integer linear planning model, and the model is solved. The carbon emission cost and the economic benefit of the system can be comprehensively considered, the carbon emission reduction potential of the power grid side is fully considered under the low-carbon background, and a new thought is provided for power grid planning decision under the low-carbon background.
Drawings
FIG. 1 is a flow chart of the steps of an embodiment of the method of the present invention.
Detailed Description
In order to clearly illustrate the technical features of the present solution, the present invention will be described in detail below with reference to the following detailed description and the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different structures of the invention. In order to simplify the present disclosure, components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and processes are omitted so as to not unnecessarily obscure the present invention.
As shown in fig. 1, a power grid planning method suitable for low-carbon power supply development includes the following steps:
s1, constructing a power grid planning model suitable for low-carbon power supply development;
s2, determining an objective function of a power grid planning model as minimizing the investment cost and the operation cost of the power grid;
s3, generating constraint conditions of a power grid planning model suitable for low-carbon power supply development;
and S4, carrying out linearization solution on the power grid planning model by adopting a constraint cost variable method.
As an embodiment of the present invention, in step S1, a power grid planning model adapted to low-carbon power supply development is constructed, which specifically includes:
s11, calculating 365 payload scene libraries each year according to a load prediction result and an hour-level output prediction result of new energy in a target planning period;
s12, scene reduction is carried out based on a K-means method, and S typical fields of each planning year are obtainedScene and corresponding probability p s ；
S13, respectively carrying out electric power and electric quantity balance simulation on S typical scenes.
As an embodiment of the present invention, in step S13, power and electricity balance simulation is performed on S typical scenarios, which specifically includes:
adding system operation constraint to realize power balance simulation;
according to the unit optimization output arrangement of different scenes, annual electricity contribution probability of the different scenes is considered, annual utilization hours of the unit and annual power generation operation cost index of the system are calculated, and annual electricity contribution probability eta of the scenes is calculated s The calculation formula is as follows:
wherein:representing the predicted load of the d-th day period t; />Representing the load of the t period of the s-th scene.
In step S2, as an embodiment of the present invention, the objective function is specifically
Wherein: y represents the planning consideration time length; s is S y A scene number representing the y-th year; η (eta) s,y Representing the power contribution rate of the scene s in the y year;the carbon emission price;
the objective function comprises investment cost and system operation cost, wherein the investment cost comprises the investment cost of a circuit in a planning period and the carbon emission cost of the whole life cycle; the system running cost comprises the power generation cost and the carbon emission cost of different types of units, and the wind abandoning punishment cost and the load shedding cost;
and->Respectively the investment cost and the full life cycle carbon emission column vector of the circuit to be built; u (U) B,y A construction state column vector representing the components of the circuit to be constructed, wherein the circuit state is 1 after the circuit is constructed, otherwise, the circuit state is 0; subscripts c, h, n and w respectively represent a conventional unit, a hydro-generator unit, a nuclear power unit and a wind power unit, < ->P i,s,y Respectively representing the optimized output, the power generation cost and the carbon emission intensity column vector of the ith group of units, wherein i is { c, h, n, w };
and->Column vectors of unit load shedding cost and unit wind curtailment cost respectively, < >>And->Optimizing the cut load and the abandoned wind row vector; line 4 represents economic loss and carbon emission costs corresponding to transmission loss>And->Respectively representing the average power generation cost and the average carbon emission intensity of the system, E E,s,y And E is B,s,y The line loss column vectors of the established line and the line to be established are respectively.
As an embodiment of the present invention, in step S3, constraint conditions of a power grid planning model adapted to low-carbon power supply development are generated, which specifically include:
1) And (3) constructing constraint of a line to be constructed:
U B,y ≤U B,y+1 ,y＝1,2,...,Y-1 (3)
U B,y (i)＝U B,y (j),y＝1,2,...,Y (4)
U B,y (i)+U B,y (j)≤1,y＝1,2,...,Y (6)
the formula (3) is line production state constraint; the formulas (4), (5) and (6) are respectively the construction constraints of the line i and the line j simultaneously, successively and mutually exclusive;
2) And (3) power balance constraint of each scene node in each year in the planning period:
wherein: a is that E And A B Node-branch correlation matrices of the established line and the line to be established respectively; b (B) E A diagonal matrix formed by the reactance of the established line; w (W) i Is a unit-node association matrix, wherein i is { c, h, n, w }; theta (theta) s,y A node voltage phase angle column vector;
3) In each scene of each year in the planning period, for the line to be built, which is selected to be put into operation in the year, a tide equation of the line is added as a constraint condition, and for the line which is not selected to be put into operation, the tide of the line is limited to 0, and the tide constraint of the line is as follows
Wherein: f (F) E,max ，F B,max The transmission capacities of the established and to-be-established lines respectively; b (B) B A diagonal matrix composed of reactance of the line to be built is represented;
for the line flow equation constraint to be built of the 2 nd line nonlinearity, converting the line flow equation constraint to be linear constraint by a separation inequality method:
wherein delta is a positive number large enough, and U is used when the line I to be selected is put into operation B,y (1) =1, at which time the constraint becomes a flow equation constraint; when the line l to be selected is not put into operation, U B,y (1) =0, at which time the constraint relaxes;
4) Maximum and minimum output constraint of unit in each scene of each year in a planning period:
wherein: p (P) i,min And P i,max Respectively representing the upper and lower limits of the output of the ith class of unit, i epsilon { c, h, n };representing wind power predicted force;
5) The unit utilizes hour constraint:
in the middle ofThe upper limit and the lower limit of annual utilization hours of the ith group of units are respectively set;
6) Load shedding constraint of each scene in each year in a planning period:
7) The network loss equation of each scene in each year in the planning period:
equation (13) is a matrix of the loss equation (12) that will reduce the loss E of the established line E,s,y Expressed as a function of the phase angle difference of the node, and the network loss E of the line to be built B,s,y Expressed as a function of line active power flow, where G E And G B Representing a diagonal matrix of the conductance composition of the lines to be constructed.
In step S4, as an embodiment of the present invention, a constraint cost variable method is used to perform linear solution on a power grid planning model, which specifically includes:
relaxing the network loss equation constraint into a series of piecewise linear inequality constraints by adopting a constraint cost variable method;
and after linearizing the model, converting the power grid planning model into a mixed integer linear planning model, and calling a commercial planning software package Cplex solver to solve.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.

Claims (3)

1. The power grid planning method suitable for low-carbon power supply development is characterized by comprising the following steps of:
constructing a power grid planning model adapting to low-carbon power supply development;
determining an objective function of the power grid planning model as minimizing the investment cost and the running cost of the power grid;
generating constraint conditions of a power grid planning model suitable for low-carbon power supply development;
carrying out linearization solution on a power grid planning model by adopting a constraint cost variable method;
the construction of the power grid planning model adapting to the development of the low-carbon power supply specifically comprises the following steps:
within the target planning period, calculating 365 net load scene libraries each year according to the load prediction result and the hour-level output prediction result of the new energy;
scene reduction is carried out based on a K-means method to obtain S typical scenes of each planning year and the probability p corresponding to the S typical scenes s ；
Respectively carrying out electric power and electric quantity balance simulation on S typical scenes;
the power and electricity balance simulation is performed on S typical scenes respectively, and specifically comprises the following steps:
adding system operation constraint to realize power balance simulation;
according to the unit optimization output arrangement of different scenes, annual electricity contribution probability of the different scenes is considered, annual utilization hours of the unit and annual power generation operation cost index of the system are calculated, and annual electricity contribution probability eta of the scenes is calculated s The calculation formula is as follows:
wherein:representing the predicted load of the d-th day period t; />A load representing a t period of the s-th scene;
the objective function is specifically
Wherein: y represents the planning consideration time length; s is S y A scene number representing the y-th year; η (eta) s,y Representing the power contribution rate of the scene s in the y year;the carbon emission price;
the objective function comprises investment cost and system operation cost, wherein the investment cost comprises the investment cost of a circuit in a planning period and the carbon emission cost of the whole life cycle; the system running cost comprises the power generation cost and the carbon emission cost of different types of units, and the wind abandoning punishment cost and the load shedding cost;
and->Respectively the investment cost and the full life cycle carbon emission column vector of the circuit to be built; u (U) B,y A construction state column vector representing the components of the circuit to be constructed, wherein the circuit state is 1 after the circuit is constructed, otherwise, the circuit state is 0; subscripts c, h, n and w respectively represent a conventional unit, a hydro-generator unit, a nuclear power unit and a wind power unit, < ->P i,s,y Respectively representing the optimized output, the power generation cost and the carbon emission intensity column vector of the ith group of units, wherein i is { c, h, n, w };
and->Column vectors of unit load shedding cost and unit wind disposal cost respectively，/>And->Optimizing the cut load and the abandoned wind row vector; line 4 represents economic loss and carbon emission costs corresponding to transmission loss>And (3) withRespectively representing the average power generation cost and the average carbon emission intensity of the system, E E，s，y And E is B，s，y The line loss column vectors of the established line and the line to be established are respectively.
2. The method for planning a power grid adapted to low-carbon power supply development according to claim 1, wherein the constraint condition for generating the power grid planning model adapted to low-carbon power supply development specifically comprises:
1) And (3) constructing constraint of a line to be constructed:
U B，y ≤U B，y+1 ，y＝1，2，...，Y-1 (3)
U B，y (i)＝U B，y (j)，y＝1，2，...，Y (4)
U B，y (i)+U B，y (j)≤1，y＝1，2，...，Y (6)
the formula (3) is line production state constraint; the formulas (4), (5) and (6) are respectively the construction constraints of the line i and the line j simultaneously, successively and mutually exclusive;
2) And (3) power balance constraint of each scene node in each year in the planning period:
wherein: a is that E And A B Node-branch correlation matrices of the established line and the line to be established respectively; b (B) E A diagonal matrix formed by the reactance of the established line; w (W) i Is a unit-node association matrix, wherein i is { c, h, n, w }; theta (theta) s，y A node voltage phase angle column vector;
3) In each scene of each year in the planning period, for the line to be built, which is selected to be put into operation in the year, a tide equation of the line is added as a constraint condition, and for the line which is not selected to be put into operation, the tide of the line is limited to 0, and the tide constraint of the line is as follows
Wherein: f (F) E,max ，F B,max The transmission capacities of the established and to-be-established lines respectively; b (B) B A diagonal matrix composed of reactance of the line to be built is represented;
for the line flow equation constraint to be built of the 2 nd line nonlinearity, converting the line flow equation constraint to be linear constraint by a separation inequality method:
wherein delta is a positive number large enough, and U is used when the line I to be selected is put into operation B,y (1) =1, at which time the constraint becomes a flow equation constraint; when the line l to be selected is not put into operation, U B,y (1) =0, at which time the constraint relaxes;
4) Maximum and minimum output constraint of unit in each scene of each year in a planning period:
wherein: p (P) i,min And P i,max Respectively representing the upper and lower limits of the output of the ith class of unit, i epsilon { c, h, n };representing wind power predicted force;
5) The unit utilizes hour constraint:
in the middle ofThe upper limit and the lower limit of annual utilization hours of the ith group of units are respectively set;
6) Load shedding constraint of each scene in each year in a planning period:
7) The network loss equation of each scene in each year in the planning period:
equation (13) is a matrix of the loss equation (12) that will reduce the loss E of the established line E,s,y Expressed as a function of the phase angle difference of the node, and the network loss E of the line to be built B,s,y Expressed as a function of line active power flow, where G E And G B Representing a diagonal matrix of the conductance composition of the lines to be constructed.
3. The power grid planning method for adapting to low-carbon power supply development according to claim 2, wherein the method for linearly solving the power grid planning model by adopting a constraint cost variable method specifically comprises the following steps:
relaxing the network loss equation constraint into a series of piecewise linear inequality constraints by adopting a constraint cost variable method;
and after linearizing the model, converting the power grid planning model into a mixed integer linear planning model, and calling a commercial planning software package Cplex solver to solve.
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Publication number Priority date Publication date Assignee Title
CN111612266B (en) * 2020-05-28 2021-11-30 国家电网公司西南分部 Hydropower cluster delivery net rack optimization method considering clean energy utilization rate
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CN115907155B (en) * 2022-11-22 2023-07-18 南方电网能源发展研究院有限责任公司 Generator set planning method and device for introducing carbon emission cost
CN115933503B (en) * 2023-03-10 2023-05-30 山东盛日电力集团有限公司 Intelligent regulation control method and system for power generation equipment

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102156815A (en) * 2011-04-07 2011-08-17 国电南瑞科技股份有限公司 Method for optimizing power generation plan in mode of tracing scheduling of annual electric quantity progress
CN102545258A (en) * 2012-01-12 2012-07-04 西安交通大学 Power grid optimal planning method of large-scale grid-connected wind farm
CN103208086A (en) * 2013-01-21 2013-07-17 云南电网公司电网规划研究中心 Electric power and energy balance method for accurately calculating wind power contributions
CN105976046A (en) * 2016-04-27 2016-09-28 中国电力科学研究院 Low carbon power grid planning method considering demand side management
CN106099993A (en) * 2016-06-28 2016-11-09 中国电力工程顾问集团西南电力设计院有限公司 A kind of adapt to the power source planning method that new forms of energy access on a large scale
CN106410852A (en) * 2016-11-24 2017-02-15 国家电网公司 Evaluation method and equipment for absorption of power grid for new energy power generation
CN107301472A (en) * 2017-06-07 2017-10-27 天津大学 Distributed photovoltaic planing method based on scene analysis method and voltage-regulation strategy
CN107591830A (en) * 2017-10-23 2018-01-16 国网能源研究院有限公司 A kind of electricity power planing method for being suitable to Renewable Energy Development at high proportion
CN109193668A (en) * 2018-10-31 2019-01-11 四川大学 A kind of contract rolling method based on distribution robust optimization
CN109447510A (en) * 2018-11-13 2019-03-08 国电南瑞科技股份有限公司 Medium-term and long-term power secure check method, apparatus and system based on SCUC
CN109787259A (en) * 2019-01-23 2019-05-21 西安交通大学 A kind of polymorphic type energy storage joint planing method based on new energy random fluctuation
CN110071505A (en) * 2019-06-04 2019-07-30 清华大学 The power transmission network enlarging of the access containing large-scale wind power configures joint planing method with energy storage
CN110163450A (en) * 2019-05-31 2019-08-23 国网山东省电力公司经济技术研究院 A kind of distribution network planning bi-level optimal model construction method limited based on operation

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102156815A (en) * 2011-04-07 2011-08-17 国电南瑞科技股份有限公司 Method for optimizing power generation plan in mode of tracing scheduling of annual electric quantity progress
CN102545258A (en) * 2012-01-12 2012-07-04 西安交通大学 Power grid optimal planning method of large-scale grid-connected wind farm
CN103208086A (en) * 2013-01-21 2013-07-17 云南电网公司电网规划研究中心 Electric power and energy balance method for accurately calculating wind power contributions
CN105976046A (en) * 2016-04-27 2016-09-28 中国电力科学研究院 Low carbon power grid planning method considering demand side management
CN106099993A (en) * 2016-06-28 2016-11-09 中国电力工程顾问集团西南电力设计院有限公司 A kind of adapt to the power source planning method that new forms of energy access on a large scale
CN106410852A (en) * 2016-11-24 2017-02-15 国家电网公司 Evaluation method and equipment for absorption of power grid for new energy power generation
CN107301472A (en) * 2017-06-07 2017-10-27 天津大学 Distributed photovoltaic planing method based on scene analysis method and voltage-regulation strategy
CN107591830A (en) * 2017-10-23 2018-01-16 国网能源研究院有限公司 A kind of electricity power planing method for being suitable to Renewable Energy Development at high proportion
CN109193668A (en) * 2018-10-31 2019-01-11 四川大学 A kind of contract rolling method based on distribution robust optimization
CN109447510A (en) * 2018-11-13 2019-03-08 国电南瑞科技股份有限公司 Medium-term and long-term power secure check method, apparatus and system based on SCUC
CN109787259A (en) * 2019-01-23 2019-05-21 西安交通大学 A kind of polymorphic type energy storage joint planing method based on new energy random fluctuation
CN110163450A (en) * 2019-05-31 2019-08-23 国网山东省电力公司经济技术研究院 A kind of distribution network planning bi-level optimal model construction method limited based on operation
CN110071505A (en) * 2019-06-04 2019-07-30 清华大学 The power transmission network enlarging of the access containing large-scale wind power configures joint planing method with energy storage

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