CN106803130A - Distributed power source accesses the planing method of power distribution network - Google Patents
Distributed power source accesses the planing method of power distribution network Download PDFInfo
- Publication number
- CN106803130A CN106803130A CN201611157411.7A CN201611157411A CN106803130A CN 106803130 A CN106803130 A CN 106803130A CN 201611157411 A CN201611157411 A CN 201611157411A CN 106803130 A CN106803130 A CN 106803130A
- Authority
- CN
- China
- Prior art keywords
- distributed power
- power supply
- planning
- power source
- genetic algorithm
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000002068 genetic effect Effects 0.000 claims abstract description 24
- 230000005611 electricity Effects 0.000 claims abstract description 7
- 238000011234 economic evaluation Methods 0.000 claims abstract description 3
- 238000005457 optimization Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 7
- 239000000243 solution Substances 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000002347 injection Methods 0.000 claims description 2
- 239000007924 injection Substances 0.000 claims description 2
- 238000012423 maintenance Methods 0.000 claims description 2
- 238000013178 mathematical model Methods 0.000 claims description 2
- 230000005855 radiation Effects 0.000 claims description 2
- 230000035772 mutation Effects 0.000 abstract 1
- 238000011160 research Methods 0.000 description 5
- 230000007613 environmental effect Effects 0.000 description 4
- 238000010248 power generation Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000002759 chromosomal effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/043—Optimisation of two dimensional placement, e.g. cutting of clothes or wood
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses the planing method that a kind of distributed power source accesses power distribution network, it is characterized in that setting up the power distribution network Expansion Planning model of distributed power source, it is circuit operating cost, the Mathematical Modeling of the purchase of electricity expense that the access that distributed power source builds expense and distributed power source is reduced, solved using genetic algorithm, the planning of distributed power source is first carried out with self-adapted genetic algorithm, for per generation produces in genetic algorithm distributed power source capacity and the individuality of position, the Expansion Planning of network is carried out using genetic algorithm, add the influence of distributed power source, and to due to the intersection in genetic algorithm, the infeasible solution that mutation operation is produced is repaired, unified plan result to distributed power source and network carries out economic evaluation.The present invention according to the Distributing network structure and the capacity of distributed power source in area, the actual conditions of distributing position, the problems such as solve distribution system planning, substation site selection and constant volume and distributed power source addressing and constant volume using suitable algorithm.
Description
Technical Field
The invention relates to the field of new energy and power distribution network planning, in particular to a planning method for a Distributed Generation (DG) to be connected into a power distribution network.
Background
In recent years, new energy power generation mainly based on wind power and photovoltaic power is rapidly developed. The vigorous development of wind power and photovoltaic is beneficial to reducing fossil fuel consumption and reducing carbon emission level. Photovoltaic power stations and wind power plants in the power distribution network are generally accessed in a distributed power supply mode, and because the wind speed and the illumination are greatly influenced by weather, the output of the wind power plants and the photovoltaic power stations has randomness, so that the traditional power distribution network planning method cannot be completely suitable for the grid connection condition of the distributed power supplies. When a large number of distributed power sources are present in a planning scheme, the large number of random variations adds significantly to the complexity of the power system. The traditional planning method has insufficient capability to solve the planning problem including the distributed power supply, which is mainly because the traditional planning method simplifies the planning problem to different degrees and lacks a better processing method for uncertain factors which objectively exist in the planning and are difficult to quantitatively express. The occurrence of distributed power generation brings substantial challenges to traditional power distribution network planning, so that power distribution network planning personnel must consider influences brought by the optimal scheme when selecting the optimal scheme, and therefore, the power distribution network planning becomes a focus of power distribution network planning research.
In terms of power distribution network planning algorithms, linear planning and approximate optimization algorithms usually make several simplifications to a power distribution network planning model, such as separate processing of a substation and a feeder system, linearization of nonlinear cost and operation constraints, no consideration of radial constraints and reliability, and the like. All these simplifications make the solution found only be a locally optimal solution under a certain condition, thus affecting its practical application. The document 'distributed power generation multi-target optimization configuration considering environmental factors' combines the active and reactive network loss micro-increment rates of nodes, proposes the concept of equivalent micro-increment rate, determines the optimal position of a distributed power supply by calculating and sequencing the micro-increment rates, and comprehensively considers the network loss, the voltage improvement degree and the environmental benefit factors to fix the volume of the distributed power supply. According to a document 'distributed power supply constant volume location considering voltage quality' and models of different types of distributed power supplies in load flow calculation, the influence of distributed power supply access on voltage stability and voltage quality is analyzed, and distributed power supply constant volume location aiming at reducing network loss and improving voltage quality is provided. The document, "microgrid multi-type distributed power supply location and volume planning considering environmental cost and time sequence characteristics", establishes a distributed power supply location and volume planning model considering environmental cost based on different load types and time sequence characteristics of distributed power generation. Most of the above researches only perform a single planning on power distribution, and relatively few researches on coordinated planning of a power distribution network.
In the actual power distribution network planning, planning personnel often need to accept or cut decision variables according to the planning purpose and requirements, when the load changes are small and no large equipment is put in, the power distribution planning personnel need to solve the problem of power distribution network reconstruction, and the planning model does not relate to the decision of a transformer substation and a feeder line; when reliability is not considered, decisions of the switching devices, etc. are not involved in the model. This requires that the power distribution network planning model and algorithm have both comprehensiveness and integrity, as well as flexibility and tailorability.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a planning method for accessing a distributed power supply to a power distribution network, analyzes and researches the relevant characteristics of the distributed power supply technology and the influence of the distributed power supply technology on a regional power system, researches the reasonable scale, the distribution point planning and the power grid extension planning of the distributed power supply, and finally provides a planning optimization scheme for accepting the distributed power supply by the regional power distribution network.
The method of the invention is characterized in that:
1. establishing a power distribution network extension planning model of the distributed power supply, wherein the model is a mathematical model of line operation cost, distributed power supply construction cost and electricity purchasing cost reduced by the access of the distributed power supply;
2. and solving by adopting a genetic algorithm.
The solving by adopting the genetic algorithm is the mutual combination of the calculation processes of the genetic algorithm.
The mutual combination is to combine network optimization and the position and capacity optimization of the distributed power supply, firstly, the self-adaptive genetic algorithm is used for planning the distributed power supply, for individuals of the capacity and the position of the distributed power supply generated in each generation of the genetic algorithm, the genetic algorithm is used for expanding and planning the network, the influence of the distributed power supply is added into the individual capacity and the position, the infeasible solution generated by the crossing and variation operations in the genetic algorithm is repaired, and the economic evaluation is carried out on the comprehensive planning result of the distributed power supply and the network so as to measure the quality of the individual scheme.
The invention has the main advantages that: and combining network optimization and the position and capacity optimization of the distributed power supply by using a genetic algorithm to finally obtain a comprehensive optimization scheme of the distributed power supply and the power distribution network. In the specific implementation process of the algorithm, firstly, the position and capacity scheme of the distributed power supply is determined, and then the network expansion planning is performed on the individual of the position and capacity scheme of the distributed power supply determined by the external circulation.
The invention has the beneficial effects that: according to the actual conditions of the distribution network structure of the region and the capacity and distribution position of the distributed power supply, the problems of distribution network planning, site selection and constant volume of a transformer substation, site selection and constant volume of the distributed power supply and the like are solved by adopting a proper algorithm, and reference is provided for the construction and planning of a regional power grid.
Drawings
FIG. 1 is a flow chart of the calculation process of the genetic algorithm of the method of the present invention.
Detailed Description
The method comprises the following specific steps: firstly, determining the type, capacity and position of the distributed power supply according to the distribution condition of natural resources and the energy policy of a country, wherein the process only considers which types and capacities of distributed power supplies can be installed at which positions, namely only considering the factors in terms of environment and policy; and then, modeling the distributed power supply from the technical perspective by combining an actual power grid accessed by the distributed power supply, and planning the optimal capacity and the DG position by using a genetic algorithm.
Firstly, establishing a power distribution network extension planning model of a distributed power supply: for the existing power distribution network, the increase of the load is considered, and the requirement of the increase of the load can be met by reasonably accessing the distributed power supply under the condition of not changing the network structure, namely the distribution planning of the distributed power supply. The present invention assumes that the load demand increases at a rate of 1% per year, while assuming that the substation capacity can meet the load growth requirements, so the substation extension costs are not considered in the model.
The invention adopts a power distribution network planning economy model with distributed power supply operation cost.
The model comprises the following steps:
1. objective function
。
。
Wherein,is an objective function;to minimize the objective function;the investment and operation cost of the distributed power supply are reduced to each year;the number of distributed power supplies for accessing a power distribution network;is as followsThe fixed annual average cost coefficient of the distributed power supply;is as followsFixed investment cost (ten thousand yuan) of each distributed power supply;in units of electricity prices (dollars/kWh);is as followsThe total annual power loss value (ten thousand kWh) of each distributed power supply;is as followsMaintenance and overhaul costs (ten thousand dollars) for individual distributed power supplies;for conversion to annual line operating costs;the cost is the electricity purchasing cost.
2. Constraint of inequality
(1) Node voltage constraint
。
Wherein,is the node voltage;、the upper and lower limits of the node voltage are respectively;the penalty factor is a node voltage, and is used as a penalty for deviating from the operation voltage limit, the value is generally larger, and the value is 0 when the operation condition is met.
(2) Branch current constraint
。
Is a branch current;the maximum current allowed to pass for the jth branch;the punishment factor is a punishment factor of the branch current, is used for punishment of deviation from the operation limit, generally has a larger value, and is 0 when the operation condition is met;
(3) distributed power supply operation constraints
。
Wherein,the total capacity of the distributed power supply connected to the power grid;the load is 10% of the total load of the power grid;the penalty factor for the injection amount of the distributed power supply is generally large in value and is 0 when the operation condition is met.
All inequality constraints are merged into a normalized objective function in the form of penalty factors, and the obtained new objective function is as follows:
。
3. constraint of equality
The solving method of the power distribution network power flow adopts a traditional power flow calculation method, namely a Newton-Raphson iteration method. The power flow equation in the radiation line is an equality constraint condition, and the following is:
。
。
。
wherein,；the active power of the branch is the active power,is reactive power.
Secondly, the specific implementation process of the genetic algorithm: on the basis of the first step of model establishment, the genetic algorithm is used for solving the model to minimize the objective function, namelyThe solution of (a) is the optimal solution.
The calculation process is shown in fig. 1.
1. Chromosomal coding: the expansion planning process of the network mainly relates to the problem of selection of lines to be built, and for each line to be built, the selection is only two: and building and not building. Therefore, integer coding is used here, the number 1 indicating build and the number 0 indicating no build.
2. Generation of initial population:
(1) and (5) initializing. The marked power supply node is G, and other nodes and lines are not marked. All nodes labeled "G" are referred to as root nodes, which form a root node cluster.
(2) Randomly selecting a branch i from the unmarked branches connected with the power supply point, and selecting a variable corresponding to the branch iMarked 1 (i.e. branch is illustrated)Selected) while marking its other end node also as "G" i.e., joining the end node to the root node group.
(3) An unmarked branch j is arbitrarily selected. If one end node of the branch is the root node and the other end node is not marked as 'G', the corresponding variant is carried outMeasurement ofLabeled 1; if the two end nodes of the branch are root nodes, the variable corresponding to the branch is usedLabeled 0 (indicating that the branch was not selected); if neither end node of the branch is marked, the branch is reselected and the step is repeated.
(4) It is checked whether all nodes are all marked as "G". If yes, the process is ended, otherwise, the process goes to the step (3).
Claims (4)
1. A planning method for accessing a distributed power supply to a power distribution network is characterized by comprising the following steps:
(1) establishing a power distribution network extension planning model of the distributed power supply, wherein the model is a mathematical model of line operation cost, distributed power supply construction cost and electricity purchasing cost reduced by the access of the distributed power supply;
(2) and solving by adopting a genetic algorithm.
2. A method for planning the access of a distributed power source to a power distribution network according to claim 1, wherein said model comprises:
(1) an objective function:
wherein,is an objective function;to minimize the objective function;the investment and operation cost of the distributed power supply are reduced to each year;the number of distributed power supplies for accessing a power distribution network;is as followsThe fixed annual average cost coefficient of the distributed power supply;is as followsFixed investment cost (ten thousand yuan) of each distributed power supply;in units of electricity prices (dollars/kWh);is as followsThe total annual power loss value (ten thousand kWh) of each distributed power supply;is as followsMaintenance and overhaul costs (ten thousand dollars) for individual distributed power supplies;for conversion to annual line operating costs;the cost for purchasing electricity;
(2) the inequality constrains:
1) node voltage constraint
Wherein,is the node voltage;、the upper and lower limits of the node voltage are respectively;the penalty factor is node voltage, is used as the penalty for deviating from the operation voltage limit, generally has a larger value, and is 0 when the operation condition is met;
2) branch current constraint
Is a branch current;the maximum current allowed to pass for the jth branch;the punishment factor is a punishment factor of the branch current, is used for punishment of deviation from the operation limit, generally has a larger value, and is 0 when the operation condition is met;
3) distributed power supply operation constraints
In (1),the total capacity of the distributed power supply connected to the power grid;the load is 10% of the total load of the power grid;injection penalty factors for distributed power supplies, in generalThe value is large and is 0 when the operation condition is met;
all inequality constraints are merged into a normalized objective function in the form of penalty factors, and the obtained new objective function is as follows:
(3) and (3) constraint of an equation:
the solving method of the power distribution network power flow adopts a traditional power flow calculation method, namely a Newton-Raphson iteration method; the power flow equation in the radiation line is an equality constraint condition, and the following is:
wherein,；the active power of the branch is the active power,is reactive power.
3. The method for planning the access of a distributed power source to a power distribution network according to claim 1, characterized in that: the solving by adopting the genetic algorithm is the mutual combination of the calculation processes of the genetic algorithm.
4. The method for planning the access of a distributed power source to a power distribution network according to claim 1, characterized in that: the mutual combination is to combine network optimization and the position and capacity optimization of the distributed power supply, firstly, the self-adaptive genetic algorithm is used for planning the distributed power supply, for individuals of the capacity and the position of the distributed power supply generated in each generation of the genetic algorithm, the genetic algorithm is used for expanding and planning the network, the influence of the distributed power supply is added into the individual capacity and the position, the infeasible solution generated by the crossing and variation operations in the genetic algorithm is repaired, and the economic evaluation is carried out on the comprehensive planning result of the distributed power supply and the network so as to measure the quality of the individual scheme.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611157411.7A CN106803130B (en) | 2016-12-15 | 2016-12-15 | Planning method for distributed power supply to be connected into power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611157411.7A CN106803130B (en) | 2016-12-15 | 2016-12-15 | Planning method for distributed power supply to be connected into power distribution network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106803130A true CN106803130A (en) | 2017-06-06 |
| CN106803130B CN106803130B (en) | 2020-12-25 |
Family
ID=58984087
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611157411.7A Active CN106803130B (en) | 2016-12-15 | 2016-12-15 | Planning method for distributed power supply to be connected into power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106803130B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107196297A (en) * | 2017-06-28 | 2017-09-22 | 国网天津市电力公司 | Flexible Distributed Generation in Distribution System maximum penetration level computational methods based on SNOP |
| CN107392350A (en) * | 2017-06-08 | 2017-11-24 | 国网宁夏电力公司电力科学研究院 | Power distribution network Expansion Planning comprehensive optimization method containing distributed energy and charging station |
| CN107612016A (en) * | 2017-08-08 | 2018-01-19 | 西安理工大学 | The planing method of Distributed Generation in Distribution System based on voltage maximal correlation entropy |
| CN107944631A (en) * | 2017-12-01 | 2018-04-20 | 国家电网公司 | A kind of power distribution network distributed generation resource planing method based on the optimization of vectorial sequence |
| CN111027769A (en) * | 2019-12-10 | 2020-04-17 | 中国电建集团福建省电力勘测设计院有限公司 | Distributed power supply site selection and volume fixing optimization method based on genetic-simulated annealing hybrid algorithm |
| CN111525557A (en) * | 2020-05-08 | 2020-08-11 | 南京恺隆电力科技有限公司 | Planning method for power distribution network with distributed power supply |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2528182A2 (en) * | 2011-05-26 | 2012-11-28 | General Electric Company | Power distribution network load forecasting |
| CN105552965A (en) * | 2016-02-18 | 2016-05-04 | 中国电力科学研究院 | Chance constraint planning based optimal configuration method of distributed energy source |
-
2016
- 2016-12-15 CN CN201611157411.7A patent/CN106803130B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2528182A2 (en) * | 2011-05-26 | 2012-11-28 | General Electric Company | Power distribution network load forecasting |
| CN105552965A (en) * | 2016-02-18 | 2016-05-04 | 中国电力科学研究院 | Chance constraint planning based optimal configuration method of distributed energy source |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107392350A (en) * | 2017-06-08 | 2017-11-24 | 国网宁夏电力公司电力科学研究院 | Power distribution network Expansion Planning comprehensive optimization method containing distributed energy and charging station |
| CN107392350B (en) * | 2017-06-08 | 2021-08-13 | 国网宁夏电力公司电力科学研究院 | Comprehensive optimization method for power distribution network extension planning containing distributed energy and charging stations |
| CN107196297A (en) * | 2017-06-28 | 2017-09-22 | 国网天津市电力公司 | Flexible Distributed Generation in Distribution System maximum penetration level computational methods based on SNOP |
| CN107196297B (en) * | 2017-06-28 | 2020-03-24 | 国网天津市电力公司 | SNOP-based method for calculating maximum access capacity of distributed power supply in flexible power distribution network |
| CN107612016A (en) * | 2017-08-08 | 2018-01-19 | 西安理工大学 | The planing method of Distributed Generation in Distribution System based on voltage maximal correlation entropy |
| CN107612016B (en) * | 2017-08-08 | 2020-01-14 | 西安理工大学 | Planning method of distributed power supply in power distribution network based on maximum voltage correlation entropy |
| CN107944631A (en) * | 2017-12-01 | 2018-04-20 | 国家电网公司 | A kind of power distribution network distributed generation resource planing method based on the optimization of vectorial sequence |
| CN107944631B (en) * | 2017-12-01 | 2021-08-24 | 国家电网公司 | Power distribution network distributed power supply planning method based on vector sequence optimization |
| CN111027769A (en) * | 2019-12-10 | 2020-04-17 | 中国电建集团福建省电力勘测设计院有限公司 | Distributed power supply site selection and volume fixing optimization method based on genetic-simulated annealing hybrid algorithm |
| CN111525557A (en) * | 2020-05-08 | 2020-08-11 | 南京恺隆电力科技有限公司 | Planning method for power distribution network with distributed power supply |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106803130B (en) | 2020-12-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106803130B (en) | Planning method for distributed power supply to be connected into power distribution network | |
| Reddy | Optimal scheduling of thermal-wind-solar power system with storage | |
| CN103903073B (en) | A kind of micro-capacitance sensor Method for optimized planning containing distributed power source and energy storage and system | |
| Zhao et al. | Flexible transmission network planning considering distributed generation impacts | |
| Rastgou et al. | Improved harmony search algorithm for electrical distribution network expansion planning in the presence of distributed generators | |
| Rajesh et al. | Least cost generation expansion planning with wind power plant incorporating emission using differential evolution algorithm | |
| CN109242177B (en) | Active power distribution network planning method | |
| Okinda et al. | A review of techniques in optimal sizing of hybrid renewable energy systems | |
| CN109598377B (en) | AC/DC hybrid power distribution network robust planning method based on fault constraint | |
| CN109508823A (en) | A kind of Distributed Generation in Distribution System planing method of method based on scene analysis | |
| Wang et al. | Power system decarbonisation with Global Energy Interconnection–a case study on the economic viability of international transmission network in Australasia | |
| Neumann | Costs of regional equity and autarky in a renewable European power system | |
| Aliari et al. | Planning for integration of wind power capacity in power generation using stochastic optimization | |
| CN111799777A (en) | Comprehensive energy planning method considering coupling of natural gas and electric power | |
| CN106529737A (en) | Planning and distribution method for peak load regulation power source on supply side of power distribution network | |
| CN106786801A (en) | One kind is based on micro-capacitance sensor operation method in a balanced way of bidding | |
| CN105552896B (en) | A kind of power distribution network peak load control method based on distributed photovoltaic power generation | |
| Moraes et al. | A probabilistic approach to assess the impact of wind power generation in transmission network expansion planning | |
| Evangeline et al. | A real-time multi-objective optimization framework for wind farm integrated power systems | |
| CN115660343A (en) | Urban comprehensive energy development planning method for carbon neutralization | |
| Li et al. | A novel framework for integrating solar renewable source into smart cities through digital twin simulations | |
| Alipour et al. | An efficient optimization framework for distribution network planning by simultaneous allocation of photovoltaic distributed generations and transformers | |
| CN117239792A (en) | Regional distributed energy storage, site selection and volume determination method and device | |
| Eghlimi et al. | Decision model for cross-border electricity trade considering renewable energy sources | |
| Jasemi et al. | Optimal operation of micro-grids considering the uncertainties of demand and renewable energy resources generation |
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 | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 235000 Anhui Province, Huaibei City Xiangshan District South Road No. 99 Applicant after: HUAIBEI POWER SUPPLY COMPANY OF STATE GRID ANHUI ELECTRIC POWER Co.,Ltd. Applicant after: STATE GRID CORPORATION OF CHINA Address before: 235000 Anhui Province, Huaibei City Xiangshan District South Road No. 99 Applicant before: HUAIBEI POWER SUPPLY COMPANY OF STATE GRID ANHUI ELECTRIC POWER Co. Applicant before: State Grid Corporation of China |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |