CN114266445A - Coordinated planning method for distributed power supply and electric vehicle charging station - Google Patents
Coordinated planning method for distributed power supply and electric vehicle charging station Download PDFInfo
- Publication number
- CN114266445A CN114266445A CN202111457294.7A CN202111457294A CN114266445A CN 114266445 A CN114266445 A CN 114266445A CN 202111457294 A CN202111457294 A CN 202111457294A CN 114266445 A CN114266445 A CN 114266445A
- Authority
- CN
- China
- Prior art keywords
- electric vehicle
- cost
- carbon
- formula
- power
- 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
Images
Classifications
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
- Y02P90/845—Inventory and reporting systems for greenhouse gases [GHG]
-
- 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
-
- 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
- Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
- Y04S50/10—Energy trading, including energy flowing from end-user application to grid
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a coordinated planning method for a distributed power supply and an electric vehicle charging station, which comprises the following steps: modeling distributed power supplies of a photovoltaic power station and a gas turbine; establishing a carbon trading cost model based on a carbon trading market mechanism; constructing a distributed power supply and electric vehicle charging station coordination planning model considering carbon transaction cost; and carrying out complex constraint linearization processing on the planning model, and solving the model by using an optimization algorithm to obtain a distributed power supply and electric vehicle charging station coordinated planning scheme considering the carbon transaction cost. According to the planning method for the electric vehicle charging station, the requirements of economic efficiency and environmental protection optimization planning can be considered, the utilization rate of clean distributed energy resources is improved, and the electric vehicle industry is promoted to cooperatively participate in carbon reduction and emission reduction.
Description
Technical Field
The invention belongs to the field of planning of electric vehicle charging facilities, and relates to a planning method of an electric vehicle charging station.
Background
The charging station is an important support system in an electric automobile industry chain and an important supporting facility of an electric automobile, so that scientific and reasonable planning of the charging station of the electric automobile is performed, the carbon emission right trading market in China is taken as a trigger, the penetration proportion of clean energy in a traffic system is improved, and the charging station has important significance for promoting the rapid development of the electric automobile, striving for realizing zero-carbon traffic as early as possible and realizing green sustainable development. At present, the problem that only economical efficiency is concerned and environmental protection requirements are ignored still exists in electric vehicle charging station planning, and no deep research is conducted on how to promote the electric vehicle industry and the carbon trading market to cooperatively develop in the charging station planning combined with the carbon trading market mechanism. Under the background, how to introduce a carbon trading market mechanism and meet the requirements of economy and environmental protection to plan a charging station becomes a key problem.
In order to scientifically and reasonably plan an electric vehicle charging station, many researchers have conducted substantial research from different perspectives. The predecessors proposed a planning method considering economic and social environmental benefits, however, without effectively combining key elements of the carbon trading market, the configuration of the carbon trading market cannot be exerted, and the cooperative development with the national carbon trading market is realized.
Disclosure of Invention
The invention aims to provide a coordinated planning method for a distributed power supply and an electric vehicle charging station, which can effectively plan the electric vehicle charging station with consideration of the requirements on economy and low-carbon environmental protection.
In order to solve the technical problems, the invention adopts the following technical scheme:
the coordinated planning method for the distributed power supply and the electric vehicle charging station comprises the following steps:
1) modeling distributed power supplies of a photovoltaic power station and a gas turbine;
2) establishing a carbon trading cost model based on a carbon trading market mechanism;
3) constructing a distributed power supply and electric vehicle charging station coordination planning model considering carbon transaction cost;
4) and carrying out complex constraint linearization processing on the planning model, and solving the model by using an optimization algorithm to obtain a distributed power supply and electric vehicle charging station coordinated planning scheme considering the carbon transaction cost.
Preferably, modeling is performed on the photovoltaic power station, and the relation between the active power output of the photovoltaic module and the solar illumination intensity is characterized as follows:
in the formula: psThe intensity of solar illumination; s the corresponding photovoltaic module has active power output; ps,ratedA rated active power output for the photovoltaic module; sratedThe rated solar illumination intensity of the photovoltaic module;
the adjustable range of the actual output of the gas turbine is shown as follows:
0≤PMT≤SMT,rated
in the formula, PMT，QMTActive and reactive power output of the micro gas turbine are respectively provided; sMT,ratedThe installed capacity of the micro gas turbine.
Preferably, the step of establishing the carbon transaction cost model is as follows:
1) initial carbon emission quota
The carbon emission intensity is used as a reference index of initial quota allocation, a carbon emission baseline method is adopted, and the initial quota is described as follows:
in the formula, omega is a power grid unit electric quantity emission quota coefficient, eta is a gas turbine unit electric quantity quota coefficient, PEVdemand(t) Power requirement for charging electric vehicle at time t of time section, PPMdemand(t) is the output of the gas turbine at time t of the time section;
2) carbon emissions calculation
For carbon emission generated by power grid power and gas turbine power generation, carbon emission factors are adopted to approximately calculate carbon emission mainly generated for meeting the power requirement of an electric automobile, and the specific characteristics are as follows:
in the formula, alphaco2For grid baseline emission factor, gammaco2Is a gas turbine carbon emission factor;
3) cost of carbon transaction
The total carbon transaction cost is:
in the formula, muco2Is the trade price per carbon emission.
Preferably, the coordination planning model of the distributed power supply and the electric vehicle charging station, which takes the carbon transaction cost into account, comprises the following steps:
the objective function is:
in the formula, CINFor the total investment cost of the system, CPLFor system loss cost, CO∝MFor the system operation and maintenance cost, CCLThe cost is lost for the charging action of the electric vehicle;
wherein each cost is characterized by the formula:
1) total investment cost of system
In the formula, cPVRepresents the unit investment cost of the photovoltaic power station, cPMRepresenting the unit investment cost of the gas turbine, cCFThe unit investment cost of the charging pile is shown,
representing the number of charging piles at the node i;
2) system loss cost
In the formula, cPLTo the unit cost of loss, Iij(t) is the square of the current flowing in branch ij in time section t, RijResistance for branch ij;
3) system operation and maintenance cost
In the formula (I), the compound is shown in the specification,
respectively representing the unit operation and maintenance costs of the photovoltaic power station, the gas turbine and the charging pile;
4) loss cost of charging behavior of electric automobile
In the formula, cCLUnit loss cost for electric vehicle charging;
constraint conditions are as follows:
1) system power flow constraint
Wherein u (j)/v (j) represents a set of all nodes connected to and downstream/upstream of node j, Pt,ij、Qt,ijRespectively representing the active power and reactive power, U, of a branch ij flowing through a time section tt,i/jIs the voltage amplitude, X, of node i or j in time section tijReactance of branch ij, ΩN、ΩLRespectively a node set and a branch set in the system;
2) voltage amplitude constraint
In the formula of UminAnd UmaxIs the lower limit and the upper limit of the set voltage fluctuation allowable range; u shapet,iThe voltage of a power distribution network node i at a moment t;
3) branch power constraint
In the formula, SijMaximum capacity allowed to flow on leg ij; pijActive power, Q, for branch ijijIs the reactive power of branch ij;
4) branch current constraint
In the formula Iij,maxThe maximum current allowed to flow on branch ij;
5) charging pile installation quantity constraint
Preferably, the complex constraint linearization process for the planning model is as follows: aiming at the nonlinear quadratic constraint of branch power, a linearization method of circular constraint is adopted, and the method is characterized in that:
-Sij≤Pij,t≤Sij
-Sij≤Qij,t≤Sij
the coordinated planning method for the distributed power supply and the electric vehicle charging station, which is provided by the invention and takes the carbon transaction cost into account, can give consideration to the requirements of economy and environmental protection compared with a conventional economic single-target planning method aiming at the planning problem of the electric vehicle charging station, effectively combines key elements of a carbon transaction market, promotes the efficient fusion of carbon transaction and related energy industries, enables a planning scheme to be more suitable for regional society and economic development requirements, and provides theoretical reference for the planning of the electric vehicle charging station under the background of the carbon transaction market.
The following detailed description will explain the present invention and its advantages.
Drawings
The invention is further described with reference to the accompanying drawings and the detailed description below:
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is an electrical topology of an electric vehicle charging station planning target area;
fig. 3 is a graph comparing results of planning with and without carbon trading.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments of the present invention, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. 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.
FIG. 1 is a schematic flow chart of the method of the present invention, which describes the basic steps of the method of the present invention, and specifically includes the following steps:
1) modeling distributed power supplies of a photovoltaic power station and a gas turbine;
the mathematical models of the photovoltaic power station and the gas turbine constructed in the step 1) are as follows:
the relationship between active power output and solar illumination intensity for a photovoltaic module can be characterized as:
in the formula: psThe active power output of the photovoltaic module corresponding to the solar illumination intensity s; ps,ratedA rated active power output for the photovoltaic module; sratedIs the rated solar irradiance of the photovoltaic module.
The adjustable range of the actual output of the micro gas turbine is shown as the formula:
0≤PMT≤SMT,rated
in the formula, PMT，QMTActive and reactive power output of the micro gas turbine are respectively provided; sMT,ratedThe installed capacity of the micro gas turbine.
2) Based on a carbon trading market mechanism, a carbon trading cost model is established:
the carbon transaction cost model established in step 2) is as follows:
(1) initial carbon emission quota
The carbon emission intensity is used as a reference index of initial quota allocation, a carbon emission baseline method is adopted, and the initial quota is described as follows:
in the formula, omega is a power grid unit electric quantity emission quota coefficient, eta is a gas turbine unit electric quantity quota coefficient, PEVdemand(t) Power requirement for charging electric vehicle at time t of time section, PPMdemand(t) is the output of the gas turbine at time t.
(2) Carbon emissions calculation
For carbon emission generated by power grid power and gas turbine power generation, carbon emission factors are adopted to approximately calculate carbon emission mainly generated for meeting the power requirement of an electric automobile, and the specific characteristics are as follows:
in the formula, alphaco2For grid baseline emission factor, gammaco2Is a gas turbine carbon emission factor.
(3) Cost of carbon transaction
The total carbon transaction cost should be:
in the formula, muco2Is the trade price per carbon emission.
3) Constructing a distributed power supply and electric vehicle charging station coordination planning model considering carbon transaction cost:
the planning model constructed in step 3) is as follows:
the objective function is:
in the formula, CINFor the total investment cost of the system, CPLFor system loss cost, CO∝MFor the system operation and maintenance cost, CCLThe cost is lost for the charging action of the electric automobile. The specific characterization form of each cost is shown as the formula:
(1) total investment cost of system
In the formula, cPVRepresenting photovoltaic power stationsUnit investment cost of cPMRepresenting the unit investment cost of the gas turbine, cCFThe unit investment cost of the charging pile is shown,
the number of the charging piles is shown.
(2) System loss cost
In the formula, cPLTo the unit cost of loss, Iij(t) is the square of the current flowing in branch ij in time section t, RijIs the resistance of branch ij.
(3) System operation and maintenance cost
In the formula (I), the compound is shown in the specification,
respectively show the unit operation and maintenance cost of photovoltaic power plant, gas turbine, fill electric pile.
(4) Loss cost of charging behavior of electric automobile
In the formula, cCLThe unit loss cost caused by the charging action of the electric automobile.
Constraint conditions are as follows:
(a) system power flow constraint
Wherein u (j)/v (j) represents a set of all nodes connected to and downstream/upstream of node j, Pt,ij、Qt,ijRespectively representing the active power and reactive power, U, of a branch ij flowing through a time section tt,i/jIs the voltage amplitude, X, of node i or j in time section tijReactance of branch ij, ΩN、
ΩLRespectively a node set and a branch set in the system.
(b) Voltage amplitude constraint
In the formula of UminAnd UmaxIs the lower limit and the upper limit of the set voltage fluctuation allowable range. U shapet,iThe voltage of the distribution network node i at the time t.
(c) Branch power constraint
In the formula SijThe maximum capacity allowed to flow on branch ij. PijActive power, Q, for branch ijijIs the reactive power of branch ij.
(d) Branch current constraint
In the formulaIij,maxThe maximum current allowed to flow on branch ij.
(e) Charging pile installation quantity constraint
4) Carrying out complex constraint linearization processing on a planning model, solving the model by using an optimization algorithm, specifically, obtaining a distributed power supply and electric vehicle charging station coordination planning scheme considering carbon transaction cost by adopting an intelligent algorithm such as a particle swarm algorithm, simulated annealing and the like or a mathematical method such as a simplex method, a branch and bound method and the like:
the complex constraint linearization processing of the planning model in the step 4) is as follows:
aiming at the nonlinear quadratic constraint of branch power, a linearization method of circular constraint is adopted, which can be characterized as follows:
-Sij≤Pij,t≤Sij
-Sij≤Qij,t≤Sij
example 1 is exemplified below.
In order to verify the effectiveness of the proposed distributed power supply and electric vehicle charging station coordinated planning method considering the carbon transaction cost, research is carried out on electric vehicle charging station planning, and the candidate installation node sets of electric vehicle charging stations, photovoltaic power stations and gas turbines are respectively set as {2,7,10,14,17,25,32}, {6,12,15,17,21,24,30,31}, {4,7,11,16,22,25,29 and 31 }; the economic life of the charging pile is 10 years, the purchasing and construction cost is 2 ten thousand yuan/set, and the annual operation and maintenance cost is 2000 yuan/set; the unit carbon emission price is 0.15 yuan/kg; the power grid baseline emission factor is 0.7921kg/kWh, and the gas turbine carbon emission factor is 2.1622 kg/kWh; the carbon quota coefficient of the unit electric quantity of the power grid is 0.7567kg/kWh, and the unit electric quantity quota coefficient of the gas turbine is 0.385 kg/kWh; the battery capacity of the electric vehicle is assumed to be 100 kWh; the unit network loss cost value is 0.8 yuan/kWh; the lower limit and the upper limit of the node voltage fluctuation range in the system are respectively set to be 0.9p.u. and 1.05p.u., and the maximum current allowed to flow on the line is 500A. For the established model, including but not limited to, solving by a Gurobi solver, an electric vehicle planning scheme is obtained as follows.
Table 1 example 1 electric vehicle charging station planning scheme
Referring to fig. 3, the electric vehicle charging station planning scheme is relatively decentralized, and a certain number of charging facilities are established on each candidate node. The system plans 116 electric automobile charging piles in total to satisfy electric automobile user's demand. Compared with the comparative case, the carbon transaction cost planning method provided by the embodiment does not change the total installation number of the charging piles, because the requirements of the electric vehicle users are not changed. However, in the carbon trading cost model, the number of gas turbines installed is small, and the number of photovoltaic power generation installations is large. The method is beneficial to improving the utilization rate of clean energy, meets the requirement of low-carbon traffic, and has outstanding social attribute while considering the economy. The comparison and analysis of different schemes prove that the electric vehicle charging station planning method considering the carbon transaction cost is effective, and the carbon transaction market configuration can be fully considered in the planning process, so that the planning result has economy and low-carbon environmental protection.
Claims (5)
1. The coordinated planning method for the distributed power supply and the electric vehicle charging station is characterized by comprising the following steps:
1) modeling distributed power supplies of a photovoltaic power station and a gas turbine;
2) establishing a carbon trading cost model based on a carbon trading market mechanism;
3) constructing a distributed power supply and electric vehicle charging station coordination planning model considering carbon transaction cost;
4) and carrying out complex constraint linearization processing on the planning model, and solving the model by using an optimization algorithm to obtain a distributed power supply and electric vehicle charging station coordinated planning scheme considering the carbon transaction cost.
2. The distributed power supply and electric vehicle charging station coordinated planning method according to claim 1, characterized in that: modeling is carried out on the photovoltaic power station, and the relation representation between the active power output of the photovoltaic module and the solar illumination intensity is as follows:
in the formula: psThe intensity of solar illumination; s the corresponding photovoltaic module has active power output; ps,ratedA rated active power output for the photovoltaic module; sratedThe rated solar illumination intensity of the photovoltaic module;
the adjustable range of the actual output of the gas turbine is shown as follows:
0≤PMT≤SMT,rated
in the formula, PMT，QMTActive and reactive power output of the micro gas turbine are respectively provided; sMT,ratedFor micro gas turbinesMachine capacity.
3. The distributed power supply and electric vehicle charging station coordinated planning method according to claim 1, characterized in that: the steps of establishing the carbon transaction cost model are as follows:
1) initial carbon emission quota
The carbon emission intensity is used as a reference index of initial quota allocation, a carbon emission baseline method is adopted, and the initial quota is described as follows:
in the formula, omega is a power grid unit electric quantity emission quota coefficient, eta is a gas turbine unit electric quantity quota coefficient, PEVdemand(t) Power requirement for charging electric vehicle at time t of time section, PPMdemand(t) is the output of the gas turbine at time t of the time section;
2) carbon emissions calculation
For carbon emission generated by power grid power and gas turbine power generation, carbon emission factors are adopted to approximately calculate carbon emission mainly generated for meeting the power requirement of an electric automobile, and the specific characteristics are as follows:
in the formula, alphaco2For grid baseline emission factor, gammaco2Is a gas turbine carbon emission factor;
3) cost of carbon transaction
The total carbon transaction cost is:
in the formula, muco2Is the trade price per carbon emission.
4. The distributed power supply and electric vehicle charging station coordinated planning method according to claim 1, characterized in that: considering a coordination planning model of a distributed power supply and an electric vehicle charging station of carbon transaction cost:
the objective function is:
in the formula, CINFor the total investment cost of the system, CPLFor system loss cost, CO∝MFor the system operation and maintenance cost, CCLThe cost is lost for the charging action of the electric vehicle;
wherein each cost is characterized by the formula:
1) total investment cost of system
In the formula, cPVRepresents the unit investment cost of the photovoltaic power station, cPMRepresenting the unit investment cost of the gas turbine, cCFThe unit investment cost of the charging pile is shown,
representing the number of charging piles at the node i;
2) system loss cost
In the formula, cPLTo the unit cost of loss, Iij(t) is the square of the current flowing in branch ij in time section t, RijResistance for branch ij;
3) system operation and maintenance cost
In the formula (I), the compound is shown in the specification,
respectively representing the unit operation and maintenance costs of the photovoltaic power station, the gas turbine and the charging pile;
4) loss cost of charging behavior of electric automobile
In the formula, cCLUnit loss cost for electric vehicle charging;
constraint conditions are as follows:
1) system power flow constraint
Wherein u (j)/v (j) represents a set of all nodes connected to and downstream/upstream of node j, Pt,ij、Qt,ijRespectively representing the active power and reactive power, U, of a branch ij flowing through a time section tt,i/jIs the voltage amplitude, X, of node i or j in time section tijReactance of branch ij, ΩN、ΩLRespectively a node set and a branch set in the system;
2) voltage amplitude constraint
In the formula of UminAnd UmaxIs a set voltage waveThe lower limit and the upper limit of the dynamic allowable range; u shapet,iThe voltage of a power distribution network node i at a moment t;
3) branch power constraint
In the formula, SijMaximum capacity allowed to flow on leg ij; pijActive power, Q, for branch ijijIs the reactive power of branch ij;
4) branch current constraint
In the formula Iij,maxThe maximum current allowed to flow on branch ij;
5) charging pile installation quantity constraint
5. The distributed power supply and electric vehicle charging station coordinated planning method according to claim 1, characterized in that: the complex constraint linearization process for the planning model is as follows: aiming at the nonlinear quadratic constraint of branch power, a linearization method of circular constraint is adopted, and the method is characterized in that:
-Sij≤Pij,t≤Sij
-Sij≤Qij,t≤Sij
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111457294.7A CN114266445A (en) | 2021-12-02 | 2021-12-02 | Coordinated planning method for distributed power supply and electric vehicle charging station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111457294.7A CN114266445A (en) | 2021-12-02 | 2021-12-02 | Coordinated planning method for distributed power supply and electric vehicle charging station |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114266445A true CN114266445A (en) | 2022-04-01 |
Family
ID=80826037
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111457294.7A Pending CN114266445A (en) | 2021-12-02 | 2021-12-02 | Coordinated planning method for distributed power supply and electric vehicle charging station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114266445A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117081059A (en) * | 2023-08-24 | 2023-11-17 | 国网北京市电力公司 | Optimal control method, device, equipment and medium for charging and replacing power station cluster |
| CN117391311A (en) * | 2023-12-07 | 2024-01-12 | 国网湖北省电力有限公司经济技术研究院 | Charging station and power distribution network collaborative planning method considering carbon emission and uncertainty |
-
2021
- 2021-12-02 CN CN202111457294.7A patent/CN114266445A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117081059A (en) * | 2023-08-24 | 2023-11-17 | 国网北京市电力公司 | Optimal control method, device, equipment and medium for charging and replacing power station cluster |
| CN117081059B (en) * | 2023-08-24 | 2024-06-11 | 国网北京市电力公司 | Optimal control method, device, equipment and medium for charging and replacing power station cluster |
| CN117391311A (en) * | 2023-12-07 | 2024-01-12 | 国网湖北省电力有限公司经济技术研究院 | Charging station and power distribution network collaborative planning method considering carbon emission and uncertainty |
| CN117391311B (en) * | 2023-12-07 | 2024-03-08 | 国网湖北省电力有限公司经济技术研究院 | Charging station and power distribution network collaborative planning method and device considering carbon emission and uncertainty |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Fang | Life cycle cost assessment of wind power–hydrogen coupled integrated energy system | |
| CN106786603B (en) | A kind of regional complex energy resource system multiobjective optimization mixed current algorithm | |
| CN103840457B (en) | Consider DG Optimal Configuration Method in the power distribution network that electric automobile discharge and recharge affects | |
| CN110739725B (en) | Optimal scheduling method for power distribution network | |
| CN103903073B (en) | A kind of micro-capacitance sensor Method for optimized planning containing distributed power source and energy storage and system | |
| CN110866627B (en) | Multi-zone electricity-gas coupling comprehensive energy system optimal scheduling method considering step gas price | |
| Wu et al. | Carbon-oriented expansion planning of integrated electricity-natural gas systems with EV fast-charging stations | |
| Moein et al. | Finding the minimum distance from the national electricity grid for the cost-effective use of diesel generator-based hybrid renewable systems in Iran | |
| Yi et al. | Joint optimization of charging station and energy storage economic capacity based on the effect of alternative energy storage of electric vehicle | |
| CN105375507A (en) | Power two-stage interactive optimization scheduling system of virtual power plant in haze environment | |
| CN114266445A (en) | Coordinated planning method for distributed power supply and electric vehicle charging station | |
| Arandian et al. | Effects of environmental emissions on optimal combination and allocation of renewable and non-renewable CHP technologies in heat and electricity distribution networks based on improved particle swarm optimization algorithm | |
| CN111445107A (en) | Multi-objective optimization configuration method for cold-heat-power combined supply type micro-grid | |
| Dong et al. | Hierarchical multi-objective planning for integrated energy systems in smart parks considering operational characteristics | |
| Chupradit et al. | Modeling and Optimizing the Charge of Electric Vehicles with Genetic Algorithm in the Presence of Renewable Energy Sources | |
| CN105550770A (en) | Active power distribution network multi-source optimized configuration method based on integrated resource strategy planning | |
| CN106709611A (en) | Microgrid optimization configuration method in whole life period | |
| CN104598991A (en) | Unit combination acquiring method considering out-going power transaction, transprovincial or interregional line transaction and security constraint | |
| CN110414805A (en) | Low-carbon comprehensive estimation method neural network based under a kind of cross-border electricity trading environment | |
| CN109840621A (en) | Consider the grid type micro-capacitance sensor Multipurpose Optimal Method a few days ago that energy-storage system influences | |
| Li et al. | The expansion planning of wind-thermal co-generation system based on harmony search algorithm under smart grid | |
| Li et al. | Optimal configuration for distributed generations in micro-grid system considering diesel as the main control source | |
| Azaroual et al. | Optimal energy management strategy in a grid-tied system with PV, wind, and ESS considering environmental aspect | |
| Zhang et al. | A Two‐Stage Optimization Model of Capacity Allocation and Regulation Operation for Virtual Power Plant | |
| Feng et al. | Economic research on application of electric heating system based on game theory |
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 |