CN112132453A - Method, system and device for evaluating optimal admission scale of renewable energy sources of regional power grid - Google Patents
Method, system and device for evaluating optimal admission scale of renewable energy sources of regional power grid Download PDFInfo
- Publication number
- CN112132453A CN112132453A CN202010999210.1A CN202010999210A CN112132453A CN 112132453 A CN112132453 A CN 112132453A CN 202010999210 A CN202010999210 A CN 202010999210A CN 112132453 A CN112132453 A CN 112132453A
- Authority
- CN
- China
- Prior art keywords
- index
- membership
- subsystem
- power grid
- matrix
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- 239000011159 matrix material Substances 0.000 claims abstract description 82
- 230000008901 benefit Effects 0.000 claims abstract description 15
- 238000009434 installation Methods 0.000 claims abstract description 9
- 238000011156 evaluation Methods 0.000 claims description 65
- 230000006870 function Effects 0.000 claims description 52
- 238000011160 research Methods 0.000 claims description 14
- 238000010248 power generation Methods 0.000 claims description 11
- 238000013178 mathematical model Methods 0.000 claims description 10
- 238000003860 storage Methods 0.000 claims description 8
- 239000003245 coal Substances 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 6
- 238000013210 evaluation model Methods 0.000 claims description 6
- 230000015556 catabolic process Effects 0.000 claims description 4
- 238000006731 degradation reaction Methods 0.000 claims description 4
- 230000006866 deterioration Effects 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 2
- 238000011161 development Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 230000005611 electricity Effects 0.000 description 5
- 239000007789 gas Substances 0.000 description 4
- 238000005457 optimization Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000003034 coal gas Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000003209 petroleum derivative Substances 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method, a system and a device for evaluating the optimal admission scale of renewable energy sources of a regional power grid, wherein the method comprises the following steps: constructing a physical model of a tree-structured four-layer hierarchical system for evaluating the optimal receiving scale of renewable energy of a regional power grid, evaluating the state grade of each index in an index layer of the physical model, establishing a membership function of each state grade, and acquiring a membership matrix of the index; correcting by adopting an entropy weight resisting method to finally obtain a mixed weight vector of each index; calculating to obtain a membership matrix of each subsystem of the control layer according to the membership matrix of each index of the subsystem of the control layer and the mixed weight vector; according to the membership matrix of each subsystem, the comprehensive grading value combination is maximized into an objective function, constraint conditions are added, the comprehensive benefit of the regional power grid power supply combination is maximized, the objective function is established, the objective function is solved, and the future horizontal year power supply installation structure of the regional power grid is obtained.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method, a system and a device for evaluating the optimal admission scale of renewable energy sources of a regional power grid.
Background
In recent decades, the rapid development of world economy is supported by traditional fossil energy mainly comprising petroleum, coal and natural gas, the environment bearing capacity and the sustainable resource supply capacity are also sharply weakened, and countries and regions in the world face energy crisis caused by resource and environment constraints. The Paris climate convention clearly proposes that the global temperature rise is controlled within 2 ℃ at the end of the century, and the effort is made to control the global temperature rise within 1.5 ℃. This goal cannot be achieved without converting fossil energy-based energy production and consumption modes. In order to meet three challenges of resource shortage, environmental pollution and climate change and meet the sustainable development requirements of human beings, the method is fundamentally developed to establish a safe, clean and sustainable energy supply system and is also an important direction of energy transformation in China.
In a whole view, the development of renewable energy with high quality is the core support of green and low-carbon transformation of an energy system, and the evaluation of the optimal acceptance capability of renewable energy of a regional power grid is the basis for promoting scientific planning, reasonable layout and efficient consumption of renewable energy. The evaluation of the optimal admission capacity of renewable energy of the regional power grid has great complexity and uncertainty, is closely related to factors such as regional power generation energy resources, a power grid topological structure, economic and social development, scientific and technical progress, environmental bearing capacity, policy regulation and the like, and the essence of the optimization of the regional power grid power supply structure is the process of mutual coupling and coordinated development of various related elements. Therefore, a method for evaluating the optimal admission scale of renewable energy sources in a regional power grid is needed.
Disclosure of Invention
The invention aims to provide a method, a system and a device for evaluating the optimal admission scale of renewable energy sources of a regional power grid, and aims to solve the problems in the prior art.
The invention provides a regional power grid renewable energy optimal admission scale evaluation method, which comprises the following steps:
the method comprises the following steps of constructing a physical model of a tree-structure four-layer hierarchical system for regional power grid renewable energy optimal admission scale evaluation, wherein the tree-structure four-layer hierarchical system specifically comprises the following steps: describing a target layer for evaluating the total target, an object layer for developing a research object around the target layer, a control layer of a subsystem formed by a plurality of main influence factors of the total target, and an index layer for setting each specific index for the evaluation target of each subsystem;
evaluating the state grade of each index in the index layer of the physical model, establishing a membership function of each state grade, and acquiring a membership matrix of the index according to the membership function; calculating the weight of the evaluation index based on a fuzzy logarithm theory, and correcting by using information provided by the established judgment matrix of each subsystem and adopting an entropy weight resisting method to finally obtain a mixed weight vector of each index;
calculating to obtain a membership matrix of each subsystem of the control layer according to the membership matrix of each index of the subsystem of the control layer and the mixed weight vector; according to the membership matrix of each subsystem, maximizing the comprehensive grading value combination into an objective function, adding constraint conditions, maximizing the comprehensive benefit of the regional power grid power supply combination, establishing the objective function, and solving the objective function to obtain the future horizontal year power supply installation structure of the regional power grid.
The invention provides a regional power grid renewable energy optimal admission scale evaluation system, which comprises:
the physical model module is used for constructing a physical model of a tree-structured four-layer hierarchical system for regional power grid renewable energy optimal admission scale evaluation, wherein the tree-structured four-layer hierarchical system specifically comprises: describing a target layer for evaluating the total target, an object layer for developing a research object around the target layer, a control layer of a subsystem formed by a plurality of main influence factors of the total target, and an index layer for setting each specific index for the evaluation target of each subsystem;
the mathematical model module is used for evaluating the state grades of all indexes in the index layer of the physical model, establishing membership function of each state grade, and acquiring the membership matrix of the indexes according to the membership function; calculating the weight of the evaluation index based on a fuzzy logarithm theory, and correcting by using information provided by the established judgment matrix of each subsystem and adopting an entropy weight resisting method to finally obtain a mixed weight vector of each index; calculating to obtain a membership matrix of each subsystem of the control layer according to the membership matrix of each index of the subsystem of the control layer and the mixed weight vector; according to the membership matrix of each subsystem, maximizing the comprehensive grading value combination into an objective function, adding constraint conditions, maximizing the comprehensive benefit of the regional power grid power supply combination, establishing the objective function, and solving the objective function to obtain the future horizontal year power supply installation structure of the regional power grid.
The embodiment of the present invention further provides a device for evaluating an optimal admission scale of renewable energy sources of a regional power grid, including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the steps of the method for evaluating the optimal acceptance scale of the renewable energy sources of the regional power grid when being executed by the processor.
The embodiment of the invention also provides a computer-readable storage medium, where an implementation program for information transfer is stored on the computer-readable storage medium, and when the program is executed by a processor, the method for evaluating the optimal admission scale of renewable energy resources in the regional power grid is implemented.
By adopting the embodiment of the invention, the optimization of the renewable energy source admission scale of the regional power grid is realized.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic diagram of a regional power grid renewable energy optimal admission size evaluation framework according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for evaluating an optimal admission size of renewable energy sources in a regional power grid according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a physical model of an embodiment of the invention;
fig. 4 is a schematic diagram of a regional power grid renewable energy optimal admission size evaluation system according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an evaluation apparatus for an optimal admission size of renewable energy sources in a regional power grid according to an embodiment of the present invention.
Detailed Description
The evaluation model for the optimal admission scale of the renewable energy sources of the regional power grid is mainly divided into two parts: physical and mathematical models, as shown in fig. 1, wherein:
physical model: in the physical model, a plurality of energy power development influence factors are subjected to layered classification, and key influence factors for evaluating the optimal receiving scale of the renewable energy are extracted from a target layer, an object layer, a control layer and an index layer in sequence. Wherein the target layer describes the task of evaluating the model. The object layer is composed of research objects, including renewable energy power generation (hydroelectric power generation, wind power generation, solar energy and other power generation) and non-renewable energy (coal power, gas power and nuclear power). In addition, by referring to domestic and foreign documents, influence factor indexes of regional power grid renewable energy admission evaluation are summarized in total, the influence factors are summarized into a plurality of subsystems and defined as control layers, and each subsystem of the control layers directly influences the evaluation of object layers. The bottommost layer is a marker layer, specific indexes are set according to different evaluation targets of corresponding subsystems, and the specific indexes are the basis for carrying out quantitative comprehensive evaluation. Thus, a physical model of the power supply development structure evaluation index system with clear target level and definite target is constructed.
The mathematical model is as follows: the mathematical model is a quantitative process of a physical model, the first step of the mathematical model is to assign values to all indexes of an evaluation system, and the main method comprises energy and power literature data research and energy and power experience expert scoring, wherein the embodiment of the invention provides an improved renewable energy power generation development prediction method based on Logistic and a learning curve model to assign values to renewable energy resources and economic related indexes. The key of evaluation decision is to quantify the importance of each index, so that the embodiment of the invention provides a fuzzy logarithm-inverse entropy weight combination weighting method, which not only fully considers the advantage of depending on expert experience in a fuzzy logarithm priority planning theory, but also draws all information in original data, and greatly improves the reasonability of weight and the objectivity of an evaluation result. After index assignment and weight calculation are completed, comprehensive evaluation is carried out on the renewable energy accepting scale of the regional power grid based on a fuzzy theory, and optimization of the renewable energy accepting scale of the regional power grid is achieved by solving the proportional weight of the object layer power supply.
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. 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.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise. Furthermore, the terms "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Method embodiment
According to an embodiment of the present invention, there is provided a method for evaluating an optimal admission scale of renewable energy sources of a regional power grid, fig. 2 is a flowchart of the method for evaluating an optimal admission scale of renewable energy sources of a regional power grid according to an embodiment of the present invention, and as shown in fig. 2, the method for evaluating an optimal admission scale of renewable energy sources of a regional power grid according to an embodiment of the present invention specifically includes:
step 201, constructing a physical model of a tree-structured four-layer hierarchical system for regional power grid renewable energy optimal admission scale evaluation, wherein the tree-structured four-layer hierarchical system specifically comprises: describing a target layer for evaluating the total target, an object layer for developing a research object around the target layer, a control layer of a subsystem formed by a plurality of main influence factors of the total target, and an index layer for setting each specific index for the evaluation target of each subsystem;
specifically, as shown in fig. 3, in the physical model, a four-layer hierarchical structure system with a tree structure is constructed, which is a target layer, an object layer, a control layer and an index layer from top to bottom. The first layer is a total target layer of the system and describes a total target of evaluation, namely evaluation on the renewable energy admission scale of the regional power grid; the second layer is a research object which is developed around a target layer, namely, the proportion of different power supplies of a regional power grid is taken, and six factors of coal power, gas power, hydroelectric power, nuclear power, wind power, solar energy and other power generation are selected as object layers; the third layer is a control layer which directly influences the general target, namely, the evaluation of the renewable energy source admission ratio of the regional power grid needs to be developed from 5 aspects of resources, economy, technology, environment and policy, the 5 aspects form 5 subsystems, and main influencing factors forming an index system are reflected from different sides; the fourth layer is a index layer, and each specific index is set according to different evaluation targets of the corresponding subsystem, and the specific indexes are the basis for carrying out quantitative comprehensive evaluation.
Step 202, evaluating the state grade of each index in the index layer of the physical model, establishing a membership function of each state grade, and acquiring a membership matrix of the index according to the membership function; calculating the weight of the evaluation index based on a fuzzy logarithm theory, and correcting by using information provided by the established judgment matrix of each subsystem and adopting an entropy weight resisting method to finally obtain a mixed weight vector of each index;
specifically, in the mathematical model, the first step is to evaluate each index of the regional power grid renewable energy optimal admission scale evaluation model with a rating of { excellent, good, medium, and poor }, and the set of ratings can be expressed as P ═ P1, P2, P3, and P4 }. And giving fuzzy boundary intervals of 4 state grades according to each index data, and establishing membership functions of the state grades, which are shown in formulas (1) - (4).
Wherein z represents a numerical value of the degree of deterioration of the index.
And obtaining a membership matrix Li in which the degradation degree values zij of the n secondary indexes in the ith primary index belong to a state space [ p1, p2, p3 and p4] according to the membership function, wherein the membership matrix Li is shown as the formula (5).
And the second step is a fuzzy logarithm-inverse entropy weight combination weighting method, the weight of the evaluation index is calculated by using a fuzzy logarithm theory, the information provided by the established judgment matrix of each subsystem is utilized, and an inverse entropy weight method is adopted for correction, so that the mixed weight is finally obtained.
Obtaining a subjective weight matrix W ' ═ W ' by fuzzy logarithm '1,w′2,...,w′n]And simultaneously obtaining an objective weight matrix W' ═ W by using an inverse entropy weight method1′,w2′,,...,wn′]Calculating the important coefficient alpha of the subjective and objective weight of each index according to the moment estimation theoryiAnd betaiThe final calculated combining weight is as follows.
The fuzzy logarithm-inverse entropy weight theory is an analysis method which utilizes the combination of the importance scale determination weight of fuzzy trigonometric numbers in the fuzzy logarithm theory and an inverse entropy weight method to finally determine an evaluation result. The method not only fully considers all information in the original data, but also absorbs the advantage of depending on expert experience in the fuzzy logarithm priority planning theory, thereby overcoming the defects of subjective uncertainty in the fuzzy logarithm priority planning theory and incapability of reflecting the expert experience in an anti-entropy weight method. Therefore, the method greatly improves the reasonability of the weight and the objectivity of the evaluation result.
Step 203, calculating to obtain a membership matrix of each subsystem of the control layer according to the membership matrix and the mixed weight vector of each index of the subsystem of the control layer; according to the membership matrix of each subsystem, maximizing the comprehensive grading value combination into an objective function, adding constraint conditions, maximizing the comprehensive benefit of the regional power grid power supply combination, establishing the objective function, and solving the objective function to obtain the future horizontal year power supply installation structure of the regional power grid.
In the embodiment of the invention, specifically, the membership matrix L according to n indexes of the kth control layer subsystemkAnd an index weight vector WkAnd calculating to obtain membership matrix G of each subsystem of the control layerk。
Gk＝WkLk＝[gk(p1),gk(p2),gk(p3),gk(p4)] (8)
Wherein, gk (pi) (i ═ 1, 2, 3, 4) is the membership value corresponding to the kth subsystem.
And calculating a comprehensive evaluation membership degree matrix C of each power supply according to the membership degree matrix and the control layer weight coefficient of each subsystem of the control layer.
And cij is a membership numerical value corresponding to the ith power supply, namely a comprehensive fuzzy evaluation result of the power supply.
By utilizing the evaluation results C of various power supplies of the regional power grid, the problem of solving the optimal admission scale of the renewable energy sources of the regional power grid can be converted into the problem of solving how to distribute the weights of various power supplies of the regional power grid. Let the weight of the various power supplies in the power supply configuration be i (i ═ 1, 2, 3, 4, 5, 6) in the next horizontal year. The comprehensive grading value combination is maximized to be an objective function, constraint conditions such as resources, environments, policies and the like are added, the comprehensive benefit of the regional power supply combination is maximized, and the objective function is established as follows:
wherein q isiFor membership grade, 90, 70, 50 and 30 are respectively taken from q 1-q 4.
The above-described technical means will be exemplified below.
Taking a regional power grid as an example, firstly, a physical layer index assignment specification matrix Z is constructed in a data research and expert grading modeeuroAs shown in table 1.
TABLE 1 specification matrix Z for physical layer index assignment of power grid in certain areaeuro
In the index specification matrix ZeuroThe fuzzy transformation is carried out on the basis, and membership degree matrixes of coal electricity, gas electricity, nuclear electricity, water electricity, wind electricity and solar power generation of a certain area of a power grid are respectively as follows:
and determining the weight of each energy power factor, wherein the weight is the core content of the regional power grid renewable energy optimal admission scale evaluation model. Firstly, a fuzzy decision matrix of the European power grid is determined by using the fuzzy logarithm-inverse entropy weight method provided by the invention, and then the weight value of each index is obtained according to the fuzzy matrix. Taking 5 subsystems of the control layer as an example, according to the size of the influence surface of each subsystem, taking reference to the authoritative research conclusion, setting a fuzzy decision matrix as follows:
similarly, a fuzzy judgment matrix of the index layer can be obtained, and an index weight matrix W is obtained by using the fuzzy logarithm-inverse entropy weight combination weighting method provided herein, as shown in table 2.
TABLE 2 some regional grid index weight matrix Weuro
And finally, calculating a membership matrix Gk of the kth system of the control layer according to the membership matrix Lk of the n indexes of the kth control layer subsystem and the index weight vector Wk determined in the table 3. And then calculating to obtain a membership matrix C of various power supply comprehensive evaluations according to the membership matrix and the weight coefficient of each subsystem, as shown in formula (18):
then, the objective function is solved, and the installed structure of the power supply in the future horizontal year of the power grid of a certain area can be obtained, as shown in table 3.
TABLE 3 installation structure calculation results of power supply of certain area
Thus, empirical analysis of the optimal acceptance scale of renewable energy sources of the regional power grid is completely completed.
System embodiment
According to an embodiment of the present invention, there is provided a system for evaluating an optimal admission scale of renewable energy sources for a local grid, fig. 4 is a schematic diagram of the system for evaluating an optimal admission scale of renewable energy sources for a local grid according to an embodiment of the present invention, and as shown in fig. 4, the system for evaluating an optimal admission scale of renewable energy sources for a local grid according to an embodiment of the present invention specifically includes:
the physical model module 40 is configured to construct a physical model of a tree-structured four-layer hierarchical system for regional power grid renewable energy optimal admission scale evaluation, where the tree-structured four-layer hierarchical system specifically includes: describing a target layer for evaluating the total target, an object layer for developing a research object around the target layer, a control layer of a subsystem formed by a plurality of main influence factors of the total target, and an index layer for setting each specific index for the evaluation target of each subsystem; wherein the total evaluation target in the target layer is as follows: evaluating the renewable energy source admission scale of the regional power grid; research objects in the object layer comprise coal power, gas power, hydroelectric power, nuclear power, wind power, solar energy and other power generation objects; the subsystem of the control layer comprises: resources, economics, technology, environment, and policies.
The mathematical model module 42 is configured to perform state level evaluation on each index in the index layer of the physical model, establish a membership function of each state level, and obtain a membership matrix of the index according to the membership function; calculating the weight of the evaluation index based on a fuzzy logarithm theory, and correcting by using information provided by the established judgment matrix of each subsystem and adopting an entropy weight resisting method to finally obtain a mixed weight vector of each index; calculating to obtain a membership matrix of each subsystem of the control layer according to the membership matrix of each index of the subsystem of the control layer and the mixed weight vector; according to the membership matrix of each subsystem, maximizing the comprehensive grading value combination into an objective function, adding constraint conditions, maximizing the comprehensive benefit of the regional power grid power supply combination, establishing the objective function, and solving the objective function to obtain the future horizontal year power supply installation structure of the regional power grid.
The mathematical model module 42 is specifically configured to:
evaluating each index of the regional power grid renewable energy optimal admission scale evaluation model by a comment grade, wherein the comment grade is { excellent, good, medium and poor }, and a comment set is represented as P ═ P1, P2, P3 and P4 };
according to formulas 1-4, based on each index data, giving fuzzy demarcation intervals of 4 state grades, and establishing a membership function of each state grade:
wherein z represents a numerical value of the degree of deterioration of the index
According to the membership function and formula 5, calculating the degradation degree values z of n secondary indexes in the ith primary indexijBelonging to a state space [ p1, p2, p3, p4]]Is given by the membership matrix Li：
Obtaining a subjective weight matrix W ' ═ W ' by fuzzy logarithm '1,w′2,...,w′n]And simultaneously obtaining an objective weight matrix W' ═ W by using an inverse entropy weight method1′,w2′,,...,wn′]Calculating the important coefficient alpha of the subjective and objective weight of each index according to the moment estimation theoryiAnd betaiFinally, the mixed weight vector of each index is calculated according to the formulas 6 to 7:
membership degree matrix L according to n indexes of k control layer subsystemkAnd a hybrid weight vector WkCalculating to obtain a membership matrix G of each subsystem of the control layer according to a formula 8k。
Gk＝WkLk＝[gk(p1),gk(p2),gk(p3),gk(p4)]Equation 8;
wherein, gk(pi) The value of the membership degree corresponding to the kth subsystem is i ═ 1, 2, 3 and 4;
calculating a comprehensive evaluation membership degree matrix C of each power supply according to a formula 9 according to the membership degree matrix and the control layer weight coefficient of each subsystem of the control layer:
wherein, cijThe membership degree value corresponding to the ith power supply is a comprehensive fuzzy evaluation result of the power supply;
converting the problem of solving the optimal admission scale of renewable energy sources of the regional power grid into the problem of solving how to distribute the weights of various power sources of the regional power grid by utilizing the evaluation result C of various power sources of the regional power grid, setting the weights of various power sources in a power source structure in the future horizontal year as i, wherein i is 1, 2, 3, 4, 5 and 6, maximizing the comprehensive benefit of the power source combination of the regional power grid by using the comprehensive credit value combination as an objective function, adding the constraint conditions of resources, environments and policies, and establishing the objective function according to a formula 10:
wherein q isiScoring degree of membership, q1～q4Respectively 90, 70, 50 and 30.
The embodiment of the present invention is a system embodiment corresponding to the above method embodiment, and specific operations of each module may be understood with reference to the description of the method embodiment, which is not described herein again.
Apparatus embodiment one
An embodiment of the present invention provides an evaluation apparatus for an optimal admission scale of renewable energy sources in a regional power grid, as shown in fig. 5, including: a memory 50, a processor 52 and a computer program stored on the memory 50 and executable on the processor 52, wherein the computer program, when executed by the processor 52, implements step 201 and step 203 in the method embodiment.
Device embodiment II
The embodiment of the present invention provides a computer-readable storage medium, where an implementation program for information transmission is stored on the computer-readable storage medium, and when the program is executed by the processor 52, step 201 and step 203 in the embodiment of the method are implemented.
The computer-readable storage medium of this embodiment includes, but is not limited to: ROM, RAM, magnetic or optical disks, and the like.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A regional power grid renewable energy optimal admission size evaluation method is characterized by comprising the following steps:
the method comprises the following steps of constructing a physical model of a tree-structure four-layer hierarchical system for regional power grid renewable energy optimal admission scale evaluation, wherein the tree-structure four-layer hierarchical system specifically comprises the following steps: describing a target layer for evaluating the total target, an object layer for developing a research object around the target layer, a control layer of a subsystem formed by a plurality of main influence factors of the total target, and an index layer for setting each specific index for the evaluation target of each subsystem;
evaluating the state grade of each index in the index layer of the physical model, establishing a membership function of each state grade, and acquiring a membership matrix of the index according to the membership function; calculating the weight of the evaluation index based on a fuzzy logarithm theory, and correcting by using information provided by the established judgment matrix of each subsystem and adopting an entropy weight resisting method to finally obtain a mixed weight vector of each index;
calculating to obtain a membership matrix of each subsystem of the control layer according to the membership matrix of each index of the subsystem of the control layer and the mixed weight vector; according to the membership matrix of each subsystem, maximizing the comprehensive grading value combination into an objective function, adding constraint conditions, maximizing the comprehensive benefit of the regional power grid power supply combination, establishing the objective function, and solving the objective function to obtain the future horizontal year power supply installation structure of the regional power grid.
2. The method of claim 1,
the total evaluation target in the target layer is as follows: evaluating the renewable energy source admission scale of the regional power grid;
research objects in the object layer comprise coal power, gas power, hydroelectric power, nuclear power, wind power, solar energy and other power generation objects;
the subsystem of the control layer comprises: resources, economics, technology, environment, and policies.
3. The method of claim 1, wherein evaluating the state grades of each index in the index layer of the physical model, establishing a membership function of each state grade, and obtaining a membership matrix of the index according to the membership function specifically comprises:
evaluating each index of the regional power grid renewable energy optimal admission scale evaluation model by a comment grade, wherein the comment grade is { excellent, good, medium and poor }, and a comment set is represented as P ═ P1, P2, P3 and P4 };
according to formulas 1-4, based on each index data, giving fuzzy demarcation intervals of 4 state grades, and establishing a membership function of each state grade:
wherein z represents a numerical value of the degree of deterioration of the index;
according to the membership function and formula 5, calculating the degradation degree values z of n secondary indexes in the ith primary indexijBelonging to a state space [ p1, p2, p3, p4]]Is given by the membership matrix Li：
4. The method according to claim 1, wherein the calculating of the weight of the evaluation index based on the fuzzy logarithm theory and the correcting by using the information provided by the established judgment matrix of each subsystem and the entropy-resisting method to finally obtain the mixed weight vector of each index specifically comprises:
obtaining a subjective weight matrix W ' ═ W ' by fuzzy logarithm '1,w′2,...,w′n]And simultaneously obtaining an objective weight matrix W ═ W ″, by using an entropy weight resisting method1,w″2,,...,w″n]Calculating the important coefficient alpha of the subjective and objective weight of each index according to the moment estimation theoryiAnd betaiFinally, the mixed weight vector of each index is calculated according to the formulas 6 to 7:
5. the method according to claim 1, wherein the membership degree matrix of each subsystem of the control layer is obtained by calculation according to the membership degree matrix and the mixed weight vector of each index of the subsystem of the control layer; according to the sub-system membership matrix, maximizing a comprehensive score value combination into an objective function, adding constraint conditions, maximizing the comprehensive benefits of the regional power grid power supply combination, establishing the objective function, and solving the objective function to obtain a power supply installation structure of the regional power grid in the future horizontal year specifically comprises the following steps:
membership degree matrix L according to n indexes of k control layer subsystemkAnd a hybrid weight vector WkCalculating to obtain a membership matrix G of each subsystem of the control layer according to a formula 8k。
Gk＝WkLk＝[gk(p1),gk(p2),gk(p3),gk(p4)]Equation 8;
wherein, gk(pi) The value of the membership degree corresponding to the kth subsystem is i ═ 1, 2, 3 and 4;
calculating a comprehensive evaluation membership degree matrix C of each power supply according to a formula 9 according to the membership degree matrix and the control layer weight coefficient of each subsystem of the control layer:
wherein, cijThe membership degree value corresponding to the ith power supply is a comprehensive fuzzy evaluation result of the power supply;
converting the problem of solving the optimal admission scale of renewable energy sources of the regional power grid into the problem of solving how to distribute the weights of various power sources of the regional power grid by utilizing the evaluation result C of various power sources of the regional power grid, setting the weights of various power sources in a power source structure in the future horizontal year as i, wherein i is 1, 2, 3, 4, 5 and 6, maximizing the comprehensive benefit of the power source combination of the regional power grid by using the comprehensive credit value combination as an objective function, adding the constraint conditions of resources, environments and policies, and establishing the objective function according to a formula 10:
wherein q isiScoring degree of membership, q1～q4Respectively 90, 70, 50 and 30.
6. An optimal admission size evaluation system for renewable energy sources of a regional power grid is characterized by comprising the following components:
the physical model module is used for constructing a physical model of a tree-structured four-layer hierarchical system for regional power grid renewable energy optimal admission scale evaluation, wherein the tree-structured four-layer hierarchical system specifically comprises: describing a target layer for evaluating the total target, an object layer for developing a research object around the target layer, a control layer of a subsystem formed by a plurality of main influence factors of the total target, and an index layer for setting each specific index for the evaluation target of each subsystem;
the mathematical model module is used for evaluating the state grades of all indexes in the index layer of the physical model, establishing membership function of each state grade, and acquiring the membership matrix of the indexes according to the membership function; calculating the weight of the evaluation index based on a fuzzy logarithm theory, and correcting by using information provided by the established judgment matrix of each subsystem and adopting an entropy weight resisting method to finally obtain a mixed weight vector of each index; calculating to obtain a membership matrix of each subsystem of the control layer according to the membership matrix of each index of the subsystem of the control layer and the mixed weight vector; according to the membership matrix of each subsystem, maximizing the comprehensive grading value combination into an objective function, adding constraint conditions, maximizing the comprehensive benefit of the regional power grid power supply combination, establishing the objective function, and solving the objective function to obtain the future horizontal year power supply installation structure of the regional power grid.
7. The system of claim 6,
the total evaluation target in the target layer is as follows: evaluating the renewable energy source admission scale of the regional power grid;
research objects in the object layer comprise coal power, gas power, hydroelectric power, nuclear power, wind power, solar energy and other power generation objects;
the subsystem of the control layer comprises: resources, economics, technology, environment, and policies.
8. The system of claim 6, wherein the mathematical model module is specifically configured to:
evaluating each index of the regional power grid renewable energy optimal admission scale evaluation model by a comment grade, wherein the comment grade is { excellent, good, medium and poor }, and a comment set is represented as P ═ P1, P2, P3 and P4 };
according to formulas 1-4, based on each index data, giving fuzzy demarcation intervals of 4 state grades, and establishing a membership function of each state grade:
wherein z represents a numerical value of the degree of deterioration of the index;
according to the membership function and formula 5, calculating the degradation degree values z of n secondary indexes in the ith primary indexijBelonging to a state space [ p1, p2, p3, p4]]Is given by the membership matrix Li：
Obtaining a subjective weight matrix W ' ═ W ' by fuzzy logarithm '1,w′2,...,w′n]And simultaneously obtaining an objective weight matrix W ═ W ″, by using an entropy weight resisting method1,w″2,,...,w″n]Calculating the important coefficient alpha of the subjective and objective weight of each index according to the moment estimation theoryiAnd betaiFinally, the mixed weight vector of each index is calculated according to the formulas 6 to 7:
membership degree matrix L according to n indexes of k control layer subsystemkAnd a hybrid weight vector WkCalculating to obtain a membership matrix G of each subsystem of the control layer according to a formula 8k。
Gk＝WkLk＝[gk(p1),gk(p2),gk(p3),gk(p4)]Equation 8;
wherein, gk(pi) The value of the membership degree corresponding to the kth subsystem is i ═ 1, 2, 3 and 4;
calculating a comprehensive evaluation membership degree matrix C of each power supply according to a formula 9 according to the membership degree matrix and the control layer weight coefficient of each subsystem of the control layer:
wherein, cijThe membership degree value corresponding to the ith power supply is a comprehensive fuzzy evaluation result of the power supply;
converting the problem of solving the optimal admission scale of renewable energy sources of the regional power grid into the problem of solving how to distribute the weights of various power sources of the regional power grid by utilizing the evaluation result C of various power sources of the regional power grid, setting the weights of various power sources in a power source structure in the future horizontal year as i, wherein i is 1, 2, 3, 4, 5 and 6, maximizing the comprehensive benefit of the power source combination of the regional power grid by using the comprehensive credit value combination as an objective function, adding the constraint conditions of resources, environments and policies, and establishing the objective function according to a formula 10:
wherein q isiScoring degree of membership, q1～q4Respectively 90, 70, 50 and 30.
9. An evaluation device for optimal admission size of renewable energy sources in a regional power grid is characterized by comprising the following components: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the regional power grid renewable energy optimal admission size assessment method according to any one of claims 1 to 5.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores thereon an information transfer implementation program, which when executed by a processor implements the steps of the regional power grid renewable energy optimal admission size evaluation method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010999210.1A CN112132453A (en) | 2020-09-22 | 2020-09-22 | Method, system and device for evaluating optimal admission scale of renewable energy sources of regional power grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010999210.1A CN112132453A (en) | 2020-09-22 | 2020-09-22 | Method, system and device for evaluating optimal admission scale of renewable energy sources of regional power grid |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112132453A true CN112132453A (en) | 2020-12-25 |
Family
ID=73841975
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010999210.1A Pending CN112132453A (en) | 2020-09-22 | 2020-09-22 | Method, system and device for evaluating optimal admission scale of renewable energy sources of regional power grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112132453A (en) |
Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105243513A (en) * | 2015-11-09 | 2016-01-13 | 国家电网公司 | Optimal configuration method of county power grid funds from bottom to top |
| CN105976257A (en) * | 2015-12-17 | 2016-09-28 | 国家电网公司 | Power grid vulnerability evaluation method based on membership function fuzzy comprehensive evaluation method |
| CN106127377A (en) * | 2016-06-21 | 2016-11-16 | 国家电网公司 | A kind of intelligent grid multiple-energy-source comprehensive coordination level evaluation method |
| CN106327006A (en) * | 2016-08-09 | 2017-01-11 | 国网四川省电力公司经济技术研究院 | Comprehensive benefit analysis-based micro-power-grid optimal configuration method |
| CN106651656A (en) * | 2016-12-28 | 2017-05-10 | 国网江西省电力公司经济技术研究院 | Power supply reliability fuzzy comprehensive evaluation method based on improved membership degree function |
| CN106651636A (en) * | 2016-10-11 | 2017-05-10 | 四川大学 | Multi-energy resource optimum allocation method for global energy internet |
| CN106650988A (en) * | 2016-09-23 | 2017-05-10 | 国网山东省电力公司经济技术研究院 | High-voltage distribution network planning project fuzzy combination optimization method |
| CN107273968A (en) * | 2017-06-30 | 2017-10-20 | 广东工业大学 | A kind of Multiobjective Scheduling method and device based on dynamic fuzzy Chaos-Particle Swarm Optimization |
| CN107609790A (en) * | 2017-09-29 | 2018-01-19 | 南方电网科学研究院有限责任公司 | Comprehensive benefit evaluation method, device, medium and computer equipment for smart power grid |
| CN107609693A (en) * | 2017-08-31 | 2018-01-19 | 安徽大学 | Multi-objective optimization method for micro-grid based on Pareto archive particle swarm algorithm |
| CN109118067A (en) * | 2018-07-27 | 2019-01-01 | 华北电力大学 | A kind of Renewable Energy Development potential evaluation method |
| CN109193732A (en) * | 2018-09-13 | 2019-01-11 | 国网山东省电力公司电力科学研究院 | The unit that meter and multi-infeed HVDC participate in restores sequence optimizing method and system |
| CN109446630A (en) * | 2018-10-23 | 2019-03-08 | 宁夏大学 | Multiple target water resource optimal allocation method based on Hybrid Particle Swarm |
| CN109523128A (en) * | 2018-10-22 | 2019-03-26 | 上海理工大学 | A kind of renewable energy capacity collocation method promoting consumption |
| CN109670720A (en) * | 2018-12-26 | 2019-04-23 | 国网河南省电力公司经济技术研究院 | A kind of active distribution network evaluation method containing distributed energy based on graded index |
| CN109858843A (en) * | 2019-04-10 | 2019-06-07 | 国网新疆电力有限公司经济技术研究院 | A kind of power distribution network investment decision evaluation method |
| CN110163537A (en) * | 2019-06-25 | 2019-08-23 | 北京工商大学 | Water eutrophication evaluation method based on trapezoidal cloud model |
| CN110428168A (en) * | 2019-07-31 | 2019-11-08 | 广东电网有限责任公司 | It is a kind of meter and energy storage multiple-energy-source distribution system coordinated scheduling integrated evaluating method |
| CN110460090A (en) * | 2019-07-22 | 2019-11-15 | 广东工业大学 | A kind of water scene micro-capacitance sensor power supply capacity configuration method |
| AU2019101413A4 (en) * | 2019-11-18 | 2020-01-02 | China Electric Power Research Institute Company Limited | Method for evaluating and regulating consumption capacity of regional power grid for renewable energy sources |
| CN111160732A (en) * | 2019-12-14 | 2020-05-15 | 国网浙江省电力有限公司 | Method suitable for comprehensive evaluation of multi-station fusion safety and benefit |
| CN111552925A (en) * | 2020-04-23 | 2020-08-18 | 国网江苏省电力有限公司经济技术研究院 | Method for constructing comprehensive evaluation index system of alternating current-direct current hybrid power distribution network |
-
2020
- 2020-09-22 CN CN202010999210.1A patent/CN112132453A/en active Pending
Patent Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105243513A (en) * | 2015-11-09 | 2016-01-13 | 国家电网公司 | Optimal configuration method of county power grid funds from bottom to top |
| CN105976257A (en) * | 2015-12-17 | 2016-09-28 | 国家电网公司 | Power grid vulnerability evaluation method based on membership function fuzzy comprehensive evaluation method |
| CN106127377A (en) * | 2016-06-21 | 2016-11-16 | 国家电网公司 | A kind of intelligent grid multiple-energy-source comprehensive coordination level evaluation method |
| CN106327006A (en) * | 2016-08-09 | 2017-01-11 | 国网四川省电力公司经济技术研究院 | Comprehensive benefit analysis-based micro-power-grid optimal configuration method |
| CN106650988A (en) * | 2016-09-23 | 2017-05-10 | 国网山东省电力公司经济技术研究院 | High-voltage distribution network planning project fuzzy combination optimization method |
| CN106651636A (en) * | 2016-10-11 | 2017-05-10 | 四川大学 | Multi-energy resource optimum allocation method for global energy internet |
| CN106651656A (en) * | 2016-12-28 | 2017-05-10 | 国网江西省电力公司经济技术研究院 | Power supply reliability fuzzy comprehensive evaluation method based on improved membership degree function |
| CN107273968A (en) * | 2017-06-30 | 2017-10-20 | 广东工业大学 | A kind of Multiobjective Scheduling method and device based on dynamic fuzzy Chaos-Particle Swarm Optimization |
| CN107609693A (en) * | 2017-08-31 | 2018-01-19 | 安徽大学 | Multi-objective optimization method for micro-grid based on Pareto archive particle swarm algorithm |
| CN107609790A (en) * | 2017-09-29 | 2018-01-19 | 南方电网科学研究院有限责任公司 | Comprehensive benefit evaluation method, device, medium and computer equipment for smart power grid |
| CN109118067A (en) * | 2018-07-27 | 2019-01-01 | 华北电力大学 | A kind of Renewable Energy Development potential evaluation method |
| CN109193732A (en) * | 2018-09-13 | 2019-01-11 | 国网山东省电力公司电力科学研究院 | The unit that meter and multi-infeed HVDC participate in restores sequence optimizing method and system |
| CN109523128A (en) * | 2018-10-22 | 2019-03-26 | 上海理工大学 | A kind of renewable energy capacity collocation method promoting consumption |
| CN109446630A (en) * | 2018-10-23 | 2019-03-08 | 宁夏大学 | Multiple target water resource optimal allocation method based on Hybrid Particle Swarm |
| CN109670720A (en) * | 2018-12-26 | 2019-04-23 | 国网河南省电力公司经济技术研究院 | A kind of active distribution network evaluation method containing distributed energy based on graded index |
| CN109858843A (en) * | 2019-04-10 | 2019-06-07 | 国网新疆电力有限公司经济技术研究院 | A kind of power distribution network investment decision evaluation method |
| CN110163537A (en) * | 2019-06-25 | 2019-08-23 | 北京工商大学 | Water eutrophication evaluation method based on trapezoidal cloud model |
| CN110460090A (en) * | 2019-07-22 | 2019-11-15 | 广东工业大学 | A kind of water scene micro-capacitance sensor power supply capacity configuration method |
| CN110428168A (en) * | 2019-07-31 | 2019-11-08 | 广东电网有限责任公司 | It is a kind of meter and energy storage multiple-energy-source distribution system coordinated scheduling integrated evaluating method |
| AU2019101413A4 (en) * | 2019-11-18 | 2020-01-02 | China Electric Power Research Institute Company Limited | Method for evaluating and regulating consumption capacity of regional power grid for renewable energy sources |
| CN111160732A (en) * | 2019-12-14 | 2020-05-15 | 国网浙江省电力有限公司 | Method suitable for comprehensive evaluation of multi-station fusion safety and benefit |
| CN111552925A (en) * | 2020-04-23 | 2020-08-18 | 国网江苏省电力有限公司经济技术研究院 | Method for constructing comprehensive evaluation index system of alternating current-direct current hybrid power distribution network |
Non-Patent Citations (3)
| Title |
|---|
| 傅毓维等: ""基于模糊模式识别法的我国电源结构优化评价"", 《统计与决策》, no. 9, 31 May 2000 (2000-05-31), pages 53 - 55 * |
| 向思阳: ""考虑低碳约束的微电网容量优化与能量管理策略"", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》, 15 January 2020 (2020-01-15), pages 042 - 2377 * |
| 蔡静颖: "《模糊聚类算法及应用》", 31 August 2015, 冶金工业出版社, pages: 24 - 25 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107688879B (en) | Active power distribution network distributed power supply planning method considering source-load matching degree | |
| Zhang et al. | Renewable energy project performance evaluation using a hybrid multi-criteria decision-making approach: case study in Fujian, China | |
| CN105389633B (en) | It is a kind of meter and distributed generation resource Substation Optimization planing method | |
| Ghadimi et al. | PSO based fuzzy stochastic long-term model for deployment of distributed energy resources in distribution systems with several objectives | |
| Yi et al. | Coordinated operation strategy for a virtual power plant with multiple DER aggregators | |
| Liang et al. | Probability-driven transmission expansion planning with high-penetration renewable power generation: A case study in northwestern China | |
| CN109193748B (en) | Evaluation method and computing device for photovoltaic absorption capacity | |
| CN107947164A (en) | It is a kind of to consider multiple uncertain and correlation electric system Robust Scheduling method a few days ago | |
| Zhang et al. | Evaluation model of black-start schemes based on optimal combination weights and improved VIKOR method | |
| CN108199404A (en) | The spectral clustering assemblage classification method of high permeability distributed energy resource system | |
| CN107706921A (en) | Micro-capacitance sensor voltage adjusting method and device based on Nash Game | |
| Li et al. | Optimal planning of Electricity–Hydrogen hybrid energy storage system considering demand response in active distribution network | |
| CN112150025A (en) | Economic evaluation method and system for comprehensive energy service project | |
| CN114638502A (en) | Power distribution network line reinforcement strategy considering flexibility of demand side resources | |
| CN105046354A (en) | Multi-agent power distribution network planning scene simulation generation method and system | |
| CN109544006A (en) | A method of evaluation electric automobile charging station economic benefit | |
| CN107492887B (en) | Region division method and system for wide area energy Internet | |
| CN105574632A (en) | Method for evaluating comprehensive benefits of AC/DC hybrid urban distribution network | |
| CN109615246B (en) | Method for determining economic operation state of active power distribution network | |
| CN113887809A (en) | Power distribution network supply and demand balance method, system, medium and computing equipment under double-carbon target | |
| CN114037209A (en) | Comprehensive benefit analysis method and device for distributed photovoltaic access direct-current power distribution system | |
| CN112132453A (en) | Method, system and device for evaluating optimal admission scale of renewable energy sources of regional power grid | |
| CN113033886B (en) | Power distribution network planning construction evaluation method | |
| CN105046594A (en) | Balanced scorecard method for comprehensive benefit evaluation on user energy sources | |
| CN110474338A (en) | A kind of alternating current-direct current mixing micro-capacitance sensor Optimal Configuration Method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |