CN114938013A - Method and device for electric vehicle cluster to participate in power distribution network coordination control - Google Patents
Method and device for electric vehicle cluster to participate in power distribution network coordination control Download PDFInfo
- Publication number
- CN114938013A CN114938013A CN202210503377.3A CN202210503377A CN114938013A CN 114938013 A CN114938013 A CN 114938013A CN 202210503377 A CN202210503377 A CN 202210503377A CN 114938013 A CN114938013 A CN 114938013A
- Authority
- CN
- China
- Prior art keywords
- charging
- discharging
- power
- charge
- electric
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
- Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
- Y04S10/126—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation the energy generation units being or involving electric vehicles [EV] or hybrid vehicles [HEV], i.e. power aggregation of EV or HEV, vehicle to grid arrangements [V2G]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses a method and a device for an electric vehicle cluster to participate in coordination control of a power distribution network. The method comprises the following steps: acquiring charge and discharge data of the electric automobiles in the power distribution area, and performing cluster classification on the electric automobiles in the power distribution area according to the acquired charge and discharge data to obtain an electric automobile layered cluster; establishing a network access model for accessing the electric automobile hierarchical cluster to the power distribution network; determining charging and discharging and scheduling constraint conditions of the electric automobile hierarchical cluster based on the established network access model; establishing an objective function with the minimum charge and discharge power variance as a target, and solving the objective function by utilizing a particle swarm algorithm; and determining a charging and discharging plan of the electric automobile in which the layered cluster participates in the coordination control of the power distribution network according to the charging and discharging power solved by the particle swarm optimization.
Description
Technical Field
The invention relates to the field of electric automobile charging, in particular to a method and a device for an electric automobile cluster to participate in power distribution network coordination control.
Background
The construction of a power grid with high-proportion new energy access is an important development trend of a Chinese power system, and due to the fact that a new energy power supply with the characteristics of intermittency, uncertainty and volatility is connected to a power distribution network in a large scale, the operation environment of the power distribution network changes remarkably, and the problem of stable frequency control of the power distribution network is increasingly complex and important. The electric automobile can be used as a mobile energy storage device after removing the load attribute of the electric automobile. By means of the huge reserved quantity and the development of electric automobile networking technology, the clustered electric automobiles can provide a large amount of capacity support for load regulation and frequency regulation of a power system, compared with the traditional generator set, the electric automobiles can achieve quicker response aiming at the requirements of load regulation, frequency change and the like of a platform area in a power grid, and are important flexible resources participating in coordination control of load and frequency regulation of the power system.
The electric automobile has many advantages when participating in the coordinated control of the power distribution network, such as: 1) the response speed is quick;
2) the access place is flexible; 3) economically, the construction investment of a special frequency modulation power plant can be reduced, and the investment of large-area transformation of a high-low voltage power grid is reduced; 4) the energy conversion efficiency is high, the available resources are sufficient, the network loss can be reduced, and the energy utilization rate is improved.
However, the behavior of disorderly accessing a large number of electric vehicles to the power grid causes problems of voltage drop, mismatching of supply capacity and the like to the existing power grid, and the access of the electric vehicles cannot provide auxiliary services for the power grid. Therefore, it is particularly important to perform orderly charge and discharge coordination control on the electric vehicle connected to the power grid.
Disclosure of Invention
The invention provides a method and a device for electric vehicle cluster to participate in power distribution network coordination control, aiming at the technical problems that the electric vehicle unordered access to a power grid in the prior art can cause voltage drop, unmatched supply capacity and the like to the existing power grid and cannot provide auxiliary service for the power grid.
According to one aspect of the invention, a method for participating in coordination control of a power distribution network by an electric automobile cluster is provided, which comprises the following steps:
acquiring charge and discharge data of the electric vehicles in the power distribution area, and performing cluster classification on the electric vehicles in the power distribution area according to the acquired charge and discharge data to obtain electric vehicle hierarchical clusters;
establishing a network access model for accessing the electric automobile hierarchical cluster to the power distribution network;
determining charging and discharging and scheduling constraint conditions of the electric automobile hierarchical cluster based on the established network access model;
establishing an objective function with the minimum charge and discharge power variance as a target, and solving the objective function by utilizing a particle swarm algorithm;
and determining a charge and discharge plan of the electric automobile hierarchical cluster participating in coordination control of the power distribution network according to the charge and discharge power solved by the particle swarm optimization.
Optionally, the electric vehicle in the distribution area is classified according to the obtained charging and discharging data to obtain an electric vehicle layered cluster, including:
processing the acquired charging and discharging data to determine the charging and discharging state distribution information of the electric automobile;
and according to the charging and discharging state distribution information, carrying out cluster classification on the electric automobiles in the power distribution area to obtain an electric automobile layered cluster.
Optionally, the processing the acquired charge and discharge data to determine the charge and discharge state distribution information of the electric vehicle includes:
processing data of initial charging and discharging time in the charging and discharging data to obtain probability distribution information about the initial charging and discharging time;
and processing the data of the charging and discharging ending time in the charging and discharging data to obtain probability distribution information about the charging and discharging ending time.
Optionally, according to the charging and discharging state distribution information, the electric vehicles in the distribution substation are classified into clusters, and an electric vehicle layered cluster is obtained, including:
determining the initial charging and discharging time and the ending charging and discharging time of the electric automobile according to the probability distribution information of the initial charging and discharging time and the probability distribution information of the ending charging and discharging time;
determining the charging and discharging time of the electric automobile according to the initial charging and discharging time and the ending charging and discharging time;
and dividing the electric automobiles with similar initial charging and discharging time and charging and discharging time into a cluster according to the initial charging and discharging time, the ending charging and discharging time and the charging and discharging time to obtain the layered cluster of the electric automobiles.
Alternatively, the charge-discharge time period of the electric vehicle is calculated by the following formula:
wherein, T c For the duration of charging and discharging, SOC e,c To end the state of charge, SOC, of the battery at the time of charging and discharging s,c B is the battery charge state at the beginning of charging and discharging, and P is the battery capacity of the electric vehicle c η is the charge-discharge efficiency for the charge-discharge power.
Optionally, the networking model is divided into four layers, the first layer is a power grid dispatching center, the second layer is a centralized control center for electric vehicles to access the power distribution network to participate in coordination control, the third layer is a local control center, and the fourth layer is each charging pile and connected electric vehicles.
Optionally, the local control center is configured to collect electric vehicle information, battery state of charge, and participate in power distribution network coordination control of electric vehicles in a layered cluster, and send the collected information to the centralized control center, the centralized control center calculates real-time power and start/stop for coordination control of electric vehicles in a local power distribution system, and sends the calculated real-time power adjustment and start/stop instructions to the grid scheduling center, the grid scheduling center determines whether the adjustment instruction of the electric vehicle meets the requirement according to the received power adjustment and start/stop instructions, and then sends the calculated adjustment correction instruction to the local control center in a platform area unit, and the local control center sends the power adjustment and start/stop instructions to each electric vehicle.
Optionally, the charging, discharging and scheduling constraints of the electric vehicle hierarchical cluster include:
the sum of the charging and discharging power of the electric automobile in each time period is equal to the total electric quantity demand of the electric automobile hierarchical cluster;
the battery state of charge of the electric vehicle is greater than the lowest battery state of charge;
the charge-discharge power of the electric automobile is less than the maximum charge-discharge power allowed by the electric automobile;
the load rate of the local power grid does not exceed the upper limit value of the safe and economic operation interval of the power distribution equipment.
Optionally, establishing an objective function targeting minimum charge-discharge power variance includes:
establishing an objective function with the minimum system frequency variance as a target:
wherein, f Li System frequency at time i, f av Is the average frequency involved in the frequency modulation, n is the number of electric vehicles involved in the frequency modulation, f ij Is the exchange frequency of the electric vehicle j and the power grid in the period i;
based on an objective function with the minimum system frequency variance as a target, establishing an objective function with the minimum charge-discharge load power mean square error as a target:
wherein, P Li Charging and discharging load power at time i, P av Is the average power when participating in the coordinated control and preferentially responding to the frequency modulation, n is the number of electric vehicles participating in the frequency modulation, P ij Is the exchange power of the electric vehicle j and the power grid in the period i.
Optionally, solving the objective function by using a particle swarm algorithm includes:
taking the exchange power and initial charging and discharging time of the electric automobile and a power grid as particles;
initializing a particle population;
calculating the initial fitness of the particle population, screening the individual optimal value and the population optimal value, repeatedly updating the speed and the position of the iterative particles, calculating the fitness value of the objective function after each iteration until the maximum iteration number is reached, and obtaining the optimal initial charging and discharging time and the optimal charging and discharging power of the electric automobile.
Optionally, the charging and discharging plan of the electric vehicle hierarchical cluster is as follows:
when the system frequency is adjusted downwards, the electric automobile serves as a power load to be charged until the system frequency returns to a normal state or meets a constraint condition, and the electric automobile is stopped to be charged;
when the system frequency is adjusted upwards, the electric automobile starts an energy storage battery mode to discharge to a power grid, and when the charge state of the electric automobile battery is smaller than or equal to a set lower limit threshold value or the system fluctuation time is up, the electric automobile stops discharging.
According to another aspect of the invention, an electric vehicle cluster participation power distribution network coordination control device is provided, which comprises:
the data acquisition and classification module is used for acquiring charge and discharge data of the electric automobiles in the power distribution area, and performing cluster classification on the electric automobiles in the power distribution area according to the acquired charge and discharge data to obtain an electric automobile layered cluster;
the model building module is used for building a network access model of the electric automobile hierarchical cluster access power distribution network;
the constraint condition determining module is used for determining charge-discharge and scheduling constraint conditions of the electric automobile hierarchical cluster based on the established network access model;
the target function establishing and solving module is used for establishing a target function taking the minimum charge-discharge power variance as a target and solving the target function by utilizing a particle swarm algorithm;
and the charge and discharge plan determining module is used for determining a charge and discharge plan of the electric automobile in which the layered cluster participates in the coordination control of the power distribution network according to the charge and discharge power solved by the particle swarm algorithm.
According to a further aspect of the invention, there is provided a computer readable storage medium having stored thereon a computer program for executing the method of any of the above aspects of the invention.
According to still another aspect of the present invention, there is provided an electronic apparatus including: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of the above aspects of the present invention.
Therefore, the method and the device firstly acquire the charge and discharge data of the electric automobiles in the power distribution area, and perform cluster classification on the electric automobiles in the power distribution area according to the acquired charge and discharge data to obtain the layered cluster of the electric automobiles. And then, establishing a network access model for the electric automobile hierarchical cluster to access the power distribution network. Secondly, determining charging and discharging and scheduling constraint conditions of the electric automobile hierarchical cluster based on the established network access model. And secondly, establishing an objective function with the minimum charge and discharge power variance as a target, and solving the objective function by utilizing a particle swarm algorithm. And finally, determining a charge and discharge plan of the electric automobile hierarchical cluster participating in power distribution network coordination control according to the charge and discharge power solved by the particle swarm optimization. The electric vehicles are classified into clusters according to the characteristics of operation characteristics, the characteristics of the connected charging piles and the like, and the electric vehicles with similar characteristics are coordinately controlled and managed according to the same cluster type. The invention is beneficial to information exchange of each layer by establishing a layered cluster network access mode, and ensures that information such as power regulation, frequency regulation and the like can be transmitted in time. When the constraint condition is established, the charge state of the battery, the maximum charge and discharge power limit of the electric automobile and the load rate in a local power grid are used as the constraint condition, the over-low electric quantity of the electric automobile is avoided, and meanwhile, the charge and discharge control scheme is more reasonable. Therefore, the invention realizes that the electric automobile cluster participates in the coordination control and the reasonable scheduling of the power distribution network through the layered coordination control, and the method is more reasonable and has strong feasibility.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
fig. 1 is a schematic flow chart of a method for an electric vehicle cluster to participate in coordination control of a power distribution network according to an exemplary embodiment of the present invention;
fig. 2 is a schematic structural diagram of a device for coordinating and controlling participation of an electric vehicle cluster in a power distribution network according to an exemplary embodiment of the present invention;
fig. 3 is a structure of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, example embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of embodiments of the invention and not all embodiments of the invention, with the understanding that the invention is not limited to the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
It will be understood by those of skill in the art that the terms "first," "second," and the like in the embodiments of the present invention are used merely to distinguish one element, step, device, module, or the like from another element, and do not denote any particular technical or logical order therebetween.
It should also be understood that in embodiments of the present invention, "a plurality" may refer to two or more and "at least one" may refer to one, two or more.
It is also to be understood that any reference to any component, data, or structure in the embodiments of the invention may be generally understood as one or more, unless explicitly defined otherwise or stated to the contrary hereinafter.
In addition, the term "and/or" in the present invention is only one kind of association relationship describing the associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In the present invention, the character "/" generally indicates that the preceding and following related objects are in an "or" relationship.
It should also be understood that the description of the embodiments of the present invention emphasizes the differences between the embodiments, and the same or similar parts may be referred to each other, so that the descriptions thereof are omitted for brevity.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
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.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Embodiments of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations, and with numerous other electronic devices, such as terminal devices, computer systems, servers, etc. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with electronic devices, such as terminal devices, computer systems, servers, and the like, include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, networked personal computers, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
Exemplary method
Fig. 1 is a schematic flow chart of a method for participating in coordination control of a power distribution network by an electric vehicle cluster according to an exemplary embodiment of the present invention. The embodiment can be applied to an electronic device, and as shown in fig. 1, the method 100 for participating in power distribution network coordination control based on an electric vehicle cluster includes the following steps:
In the embodiment of the invention, the charging and discharging operation data of the electric automobile can be obtained according to the obtained charging and discharging data and the ordered charging and discharging control condition of the electric automobile in the power distribution area.
Optionally, the electric vehicle in the distribution area is classified according to the obtained charging and discharging operation data to obtain an electric vehicle layered cluster, including: processing the obtained charging and discharging operation data to determine the charging and discharging state distribution information of the electric automobile; and according to the charging and discharging state distribution information, carrying out cluster classification on the electric automobiles in the power distribution area to obtain an electric automobile layered cluster.
Optionally, the processing the obtained charge and discharge operation data to determine the charge and discharge state distribution information of the electric vehicle includes: processing data of initial charging and discharging time in the charging and discharging data to obtain probability distribution information about the initial charging and discharging time; processing data of the charging and discharging ending time in the charging and discharging data to obtain probability distribution information about the charging and discharging ending time; and counting the accessed charging piles according to types.
Optionally, according to the charging and discharging state distribution information, the electric vehicles in the distribution substation are classified into clusters, and an electric vehicle layered cluster is obtained, including: determining the initial charging and discharging time and the ending charging and discharging time of the electric automobile according to the probability distribution information of the initial charging and discharging time and the probability distribution information of the ending charging and discharging time; determining the charging and discharging time of the electric automobile according to the initial charging and discharging time and the ending charging and discharging time; classifying charging piles accessed by the electric automobile according to types of slow charging piles, fast charging piles, charging and discharging piles, charging piles and the like; and dividing the electric automobiles with similar initial charging and discharging time and charging and discharging time into a cluster according to the initial charging and discharging time, the ending charging and discharging time, the charging and discharging time and the type of the accessed charging pile to obtain the layered cluster of the electric automobiles.
Alternatively, the charge-discharge time period of the electric vehicle is calculated by the following formula:
wherein, T c For the duration of charging and discharging, SOC e,c To end the state of charge, SOC, of the cell at the time of charging and discharging s,c For the state of charge of the battery at the start of charging and discharging, B isElectric vehicle Battery Capacity, P c η is the charge-discharge efficiency for the charge-discharge power.
And step 102, establishing a network access model of the electric automobile hierarchical cluster accessing to the power distribution network.
Optionally, the networking model is divided into four layers, the first layer is a power grid dispatching center, the second layer is a centralized control center for electric vehicles to access into the power distribution network to participate in system coordination control, the third layer is a local control center, and the fourth layer is each charging pile and the connected electric vehicles.
In the embodiment of the invention, the regional electric automobile is controlled by the upper control center to participate in grid frequency modulation as a whole.
Optionally, the local control center is configured to collect electric vehicle information, battery state of charge, and power distribution network coordination control of electric vehicles in a hierarchical cluster, and send the collected information to the centralized control center, the centralized control center calculates real-time power and start/stop instructions for the electric vehicles in the local power distribution system coordination control, and sends the calculated real-time power and start/stop instructions to the grid scheduling center, the grid scheduling center determines whether the adjustment instructions of the electric vehicles meet requirements according to the received power adjustment and start/stop instructions, and then sends the calculated adjustment correction instructions to the local control center in units of a platform area, and the local control center sends the power adjustment and start/stop instructions to each electric vehicle.
And 103, determining charging and discharging and scheduling constraint conditions of the electric automobile hierarchical cluster based on the established network access model.
Optionally, the charging, discharging and scheduling constraints of the electric vehicle hierarchical cluster include: the sum of the charging and discharging power of the electric automobile in each time period is equal to the total electric quantity demand of the electric automobile hierarchical cluster; the battery state of charge of the electric vehicle is greater than the lowest battery state of charge; the charge-discharge power of the electric automobile is less than the maximum charge-discharge power allowed by the electric automobile; the load rate of the local power grid does not exceed the upper limit value of the safe and economic operation interval of the power distribution equipment.
In the embodiment of the invention, the lowest battery state of charge of the electric automobile is generally 0.2, and the upper limit value of the safe and economic operation interval of the power distribution equipment is generally 0.85.
And 104, establishing an objective function with the minimum charge and discharge power variance as a target, and solving the objective function by utilizing a particle swarm algorithm.
Optionally, establishing an objective function targeting minimum charge-discharge power variance includes:
establishing an objective function taking the minimum of the system frequency variance as a target:
wherein f is Li System frequency at time i, f av Is the average frequency involved in the frequency modulation, n is the number of electric vehicles involved in the frequency modulation, f ij Is the exchange frequency of the electric vehicle j and the power grid in the period i;
in the embodiment of the invention, the system frequency change is closely related to the active power, and the objective function is visual analysis, namely the minimum mean square error of the load power. Therefore, based on the objective function with the minimum system frequency variance as the target, the objective function with the minimum charge-discharge load power mean square error as the target is established:
wherein, P Li Charging and discharging load power at time i, P av Is the average power when participating in the coordinated control and preferentially responding to the frequency modulation, n is the number of electric vehicles participating in the frequency modulation, P ij Is the exchange power of the electric vehicle j with the power grid during the period i.
Optionally, solving the objective function by using a particle swarm algorithm includes: taking the exchange power and the initial charging and discharging time of the electric automobile and the power grid as particles; initializing a particle population; calculating the initial fitness of the particle population, screening the individual optimal value and the population optimal value, repeatedly updating the speed and the position of iterative particles, calculating the fitness of a target function after each iteration until the maximum iteration times are reached, and obtaining the optimal initial charging and discharging time and the optimal charging and discharging power of the electric automobile.
In the embodiment of the invention, the particle swarm optimization algorithm is initialized to a group of random particles, and then an optimal solution is found through iteration. And (3) performing iterative optimization on the particle swarm, wherein the optimal position searched by the ith particle is called an individual extremum, the optimal solution searched by the whole swarm in the iterative process so far is called a global extremum, and the iteration is completed after the individual extremum and the global extremum are found in the whole swarm. Each particle updates its position and velocity in each iteration, which is expressed by the following formula:
v id k+1 ＝ωv id k +c 1 ζ(p id k -x id k )+c 2 η(p gd k -x id k )；
x id k+1 ＝x id k +εv id k+1 ；
wherein v is id k+1 Denotes the velocity, v, of the ith particle at the k +1 th iteration id k Representing the velocity, p, of the ith particle at the kth iteration id k Represents the individual optimum, p, of the ith particle at k iterations gd k Represents the global optimum at the k-th iteration, x id k Position of ith particle, x, at kth iteration id k+1 Denotes the position of the ith particle at the (k + 1) th iteration, ω is the inertial weight, c 1 C2 are learning factors, ζ and η are random numbers of 0 to 1, and ε represents a velocity coefficient.
And 105, determining a charging and discharging plan of the electric automobile hierarchical cluster participating in power distribution network coordination control according to the charging and discharging power solved by the particle swarm optimization.
Optionally, the charging and discharging plan of the electric vehicle hierarchical cluster is as follows: when the system frequency is adjusted downwards, the electric automobile serves as a power load and is charged and discharged until the system frequency returns to a normal state or meets a constraint condition, and the charging and discharging of the electric automobile are stopped; when the system frequency is adjusted upwards, the electric automobile starts an energy storage battery mode to discharge to a power grid, and when the state of charge (SOC) of the battery of the electric automobile is smaller than or equal to a set lower limit threshold value or the system fluctuation time is up, the electric automobile stops discharging.
Therefore, the method and the device firstly acquire the charge and discharge data of the electric automobiles in the power distribution area, and perform cluster classification on the electric automobiles in the power distribution area according to the acquired charge and discharge data to obtain the layered cluster of the electric automobiles. And then, establishing a network access model for the electric automobile hierarchical cluster to access the power distribution network. Secondly, determining charging and discharging and scheduling constraint conditions of the electric automobile hierarchical cluster based on the established network access model. And secondly, establishing an objective function with the minimum charge-discharge power variance as a target, and solving the objective function by utilizing a particle swarm algorithm. And finally, determining a charge and discharge plan of the electric automobile hierarchical cluster participating in power distribution network coordination control according to the charge and discharge power solved by the particle swarm optimization. The invention classifies the electric vehicles according to certain characteristics in a cluster manner, and performs coordination control and management on the electric vehicles with similar characteristics according to the same cluster type. The invention is beneficial to information exchange of each layer by establishing a layered cluster network access mode, and ensures that information such as power regulation, frequency regulation and the like can be transmitted in time. When the constraint condition is established, the charge state of the battery, the maximum charge-discharge power limit of the electric automobile and the load rate of a local power grid are taken as the constraint condition, so that the electric automobile is prevented from being damaged due to too low electric quantity, and meanwhile, the charge-discharge control scheme is more reasonable. Therefore, the invention realizes the cluster control of the electric automobiles to participate in the coordinated control of the power distribution network through the hierarchical coordinated control, realizes the frequency modulation of the power distribution network through the upper and lower layer coordinated control, realizes the reasonable scheduling of the electric automobiles, and has more reasonable method and strong feasibility.
Exemplary devices
Fig. 2 is a schematic structural diagram of a device for coordinating and controlling participation of an electric vehicle cluster in a power distribution network according to an exemplary embodiment of the present invention. As shown in fig. 2, the apparatus 200 includes:
the data acquisition and classification module 210 is configured to acquire charge and discharge data of the electric vehicles in the power distribution substation, and perform cluster classification on the electric vehicles in the power distribution substation according to the acquired charge and discharge data to obtain an electric vehicle hierarchical cluster;
the model establishing module 220 is used for establishing a network access model of the electric automobile hierarchical cluster access power distribution network;
the constraint condition determining module 230 is configured to determine, based on the established network access model, charging and discharging and scheduling constraint conditions of the electric vehicle hierarchical cluster;
an objective function establishing and solving module 240, configured to establish an objective function with the minimum charge/discharge power variance as a target, and solve the objective function by using a particle swarm algorithm;
and the charging and discharging plan determining module 250 is used for determining a charging and discharging plan of the electric automobile in which the layered cluster participates in the coordination control of the power distribution network according to the charging and discharging power solved by the particle swarm algorithm.
Optionally, the data obtaining and classifying module 210 is specifically configured to:
processing the acquired charging and discharging data to determine the charging and discharging state distribution information of the electric automobile;
and according to the charging and discharging state distribution information, carrying out cluster classification on the electric automobiles in the power distribution area to obtain an electric automobile layered cluster.
Optionally, the data obtaining and classifying module 210 is further specifically configured to:
processing data of initial charging and discharging time in the charging and discharging data to obtain probability distribution information about the initial charging and discharging time;
and processing the data of the charging and discharging ending time in the charging and discharging data to obtain probability distribution information about the charging and discharging ending time.
Optionally, the data obtaining and classifying module 210 is further specifically configured to:
determining the initial charging and discharging time and the ending charging and discharging time of the electric automobile according to the probability distribution information of the initial charging and discharging time and the probability distribution information of the ending charging and discharging time;
determining the charging and discharging time of the electric automobile according to the initial charging and discharging time and the ending charging and discharging time;
and dividing the electric vehicles with similar initial charging and discharging time and charging and discharging time into a cluster according to the initial charging and discharging time, the ending charging and discharging time and the charging and discharging time to obtain the layered cluster of the electric vehicles.
Alternatively, the charge-discharge time period of the electric vehicle is calculated by the following formula:
wherein, T c For the duration of charging and discharging, SOC e,c To end the state of charge, SOC, of the battery at the time of charging and discharging s,c B is the battery charge state at the beginning of charging and discharging, and P is the battery capacity of the electric vehicle c η is the charge-discharge efficiency for the charge-discharge power.
Optionally, the networking model is divided into four layers, the first layer is a power grid dispatching center, the second layer is a centralized control center for electric vehicles to access the power distribution network to participate in coordination control, the third layer is a local control center, and the fourth layer is each charging pile and connected electric vehicles.
Optionally, the local control center is configured to collect electric vehicle information, battery state of charge, and power distribution network coordination control of electric vehicles in a hierarchical cluster, and send the collected information to the centralized management center, the centralized management and control center calculates real-time power and start/stop instructions for the electric vehicles in the local power distribution system coordination control, and sends the calculated real-time power adjustment and start/stop instructions to the grid scheduling center, the grid scheduling center determines whether the frequency modulation information adjustment instruction of the electric vehicle meets the requirement according to the received power adjustment and start/stop instructions, and then sends the calculated adjustment correction instruction to the local control center in a cell unit, and the local control center sends the power adjustment and start/stop instructions to each electric vehicle.
Optionally, the charging, discharging and scheduling constraint conditions of the electric vehicle hierarchical cluster include:
the sum of the charging and discharging power of the electric automobile in each time period is equal to the total electric quantity demand of the electric automobile hierarchical cluster;
the battery state of charge of the electric vehicle is greater than the lowest battery state of charge;
the charge-discharge power of the electric automobile is less than the maximum allowable charge-discharge power of the electric automobile;
the load rate of the local power grid does not exceed the upper limit value of the safe and economic operation interval of the power distribution equipment.
Optionally, the objective function establishing and solving module 240 is specifically configured to:
establishing an objective function taking the minimum of the system frequency variance as a target:
wherein, f Li System frequency at time i, f av Is the average frequency involved in the frequency modulation, n is the number of electric vehicles involved in the frequency modulation, f ij Is the exchange frequency of the electric vehicle j and the power grid in the period i;
based on an objective function with the minimum system frequency variance as a target, establishing an objective function with the minimum charge-discharge load power mean square error as a target:
wherein, P Li Charging and discharging load power at time i, P av Is the average power when participating in the coordinated control and preferentially responding to the frequency modulation, n is the number of electric vehicles participating in the frequency modulation, P ij Is the exchange power of the electric vehicle j with the power grid during the period i.
Optionally, the objective function establishing and solving module 240 is further specifically configured to:
taking the exchange power and initial charging and discharging time of the electric automobile and a power grid as particles;
initializing a particle population;
calculating the initial fitness of the particle population, screening the individual optimal value and the population optimal value, repeatedly updating the speed and the position of iterative particles, calculating the fitness of a target function after each iteration until the maximum iteration times are reached, and obtaining the optimal initial charging and discharging time and the optimal charging and discharging power of the electric automobile.
Optionally, the charging and discharging plan of the electric vehicle hierarchical cluster is as follows:
when the system frequency is adjusted downwards, the electric automobile serves as a power load to be charged until the system frequency returns to a normal state or meets a constraint condition, and the electric automobile is stopped to be charged;
when the system frequency is adjusted upwards, the electric automobile starts an energy storage battery mode to discharge to a power grid, and when the battery charge state of the battery of the electric automobile is smaller than or equal to a set lower limit threshold or the system fluctuation time is up, the electric automobile stops discharging.
The electric vehicle cluster participation distribution network coordination control device 200 in the embodiment of the present invention corresponds to the electric vehicle cluster participation distribution network coordination control method 100 in another embodiment of the present invention, and details thereof are not repeated herein.
Exemplary electronic device
Fig. 3 is a structure of an electronic device according to an exemplary embodiment of the present invention. The electronic device may be either or both of the first device and the second device, or a stand-alone device separate from them, which stand-alone device may communicate with the first device and the second device to receive the acquired input signals therefrom. FIG. 3 illustrates a block diagram of an electronic device in accordance with an embodiment of the present invention. As shown in fig. 3, the electronic device 30 includes one or more processors 31 and memory 32.
The processor 31 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
The input device 33 may also include, for example, a keyboard, a mouse, and the like.
The output device 34 can output various information to the outside. The output devices 34 may include, for example, a display, speakers, printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device that are relevant to the present invention are shown in fig. 3, omitting components such as buses, input/output interfaces, and the like. In addition, the electronic device may include any other suitable components, depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in the method of information mining of historical change records according to various embodiments of the present invention described in the "exemplary methods" section of this specification above.
The computer program product may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages, for carrying out operations according to embodiments of the present invention. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present invention may also be a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the steps in the method of information mining of historical change records according to various embodiments of the present invention described in the "exemplary methods" section above of this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present invention have been described above with reference to specific embodiments, but it should be noted that the advantages, effects, etc. mentioned in the present invention are only examples and are not limiting, and the advantages, effects, etc. should not be considered as being necessary for the various embodiments of the present invention. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the invention is not limited to the specific details described above.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
The block diagrams of devices, systems, apparatuses, and systems involved in the present invention are merely illustrative examples and are not intended to require or imply that the devices, systems, apparatuses, and systems must be connected, arranged, or configured in the manner shown in the block diagrams. These devices, systems, apparatuses, systems may be connected, arranged, configured in any manner, as will be appreciated by one skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably herein. As used herein, the words "or" and "refer to, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
The method and system of the present invention may be implemented in a number of ways. For example, the methods and systems of the present invention may be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above unless specifically indicated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
It should also be noted that in the systems, apparatus and methods of the present invention, the various components or steps may be broken down and/or re-combined. These decompositions and/or recombinations are to be regarded as equivalents of the present invention. The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the invention to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (14)
1. A method for an electric vehicle cluster to participate in power distribution network coordination control is characterized by comprising the following steps:
acquiring charge and discharge data of the electric automobiles in the power distribution area, and performing cluster classification on the electric automobiles in the power distribution area according to the acquired charge and discharge data to obtain an electric automobile layered cluster;
establishing a network access model for accessing the electric automobile hierarchical cluster to the power distribution network;
determining charging and discharging and scheduling constraint conditions of the electric automobile hierarchical cluster based on the established network access model;
establishing an objective function taking the minimum variance of the charge and discharge power as a target, and solving the objective function by utilizing a particle swarm algorithm;
and determining a charge and discharge plan of the electric automobile hierarchical cluster participating in coordination control of the power distribution network according to the charge and discharge power solved by the particle swarm optimization.
2. The method of claim 1, wherein the cluster classification of the electric vehicles in the distribution area is performed according to the obtained charging and discharging data to obtain an electric vehicle hierarchical cluster, and the cluster classification comprises:
processing the acquired charging and discharging data to determine the charging and discharging state distribution information of the electric automobile;
and according to the charging and discharging state distribution information, carrying out cluster classification on the electric vehicles in the power distribution area to obtain electric vehicle hierarchical clusters.
3. The method according to claim 2, wherein the step of processing the acquired charging and discharging data to determine the charging and discharging state distribution information of the electric vehicle comprises the following steps:
processing data of initial charging and discharging time in the charging and discharging data to obtain probability distribution information about the initial charging and discharging time;
and processing the data of the charging and discharging ending time in the charging and discharging data to obtain probability distribution information about the charging and discharging ending time.
4. The method according to claim 2, wherein the cluster classification of the electric vehicles in the distribution area is performed according to the charging and discharging state distribution information to obtain an electric vehicle hierarchical cluster, and the cluster classification comprises:
determining the initial charging and discharging time and the ending charging and discharging time of the electric automobile according to the probability distribution information of the initial charging and discharging time and the probability distribution information of the ending charging and discharging time;
determining the charging and discharging time of the electric automobile according to the initial charging and discharging time and the ending charging and discharging time;
and dividing the electric automobiles with similar initial charging and discharging time and charging and discharging time into a cluster according to the initial charging and discharging time, the ending charging and discharging time and the charging and discharging time to obtain the layered cluster of the electric automobiles.
5. The method according to claim 4, wherein the charge and discharge time period of the electric vehicle is calculated by the following formula:
wherein, T c For the duration of charging and discharging, SOC e,c To end the state of charge, SOC, of the battery at the time of charging and discharging s,c B is the battery capacity of the electric vehicle, P c η is the charge-discharge efficiency for the charge-discharge power.
6. The method according to claim 1, wherein the networking model is divided into four layers, the first layer is a power grid dispatching center, the second layer is a centralized control center for electric vehicles to access a power distribution network to participate in coordination control, the third layer is a local control center, and the fourth layer is each charging pile and the connected electric vehicles.
7. The method of claim 6, wherein the local control center is used for collecting electric vehicle information, battery charge states and participating in power distribution network coordination control of electric vehicles in a layered cluster of electric vehicles and then sending the electric vehicle information, battery charge states and power distribution network coordination control to the centralized control center, the centralized control center calculates real-time power and start and stop instructions for the local power distribution system to coordinate and control the electric vehicles and sends the calculated real-time power adjustment and start and stop instructions to the power grid dispatching center, the power grid dispatching center determines whether the adjustment instructions of the electric vehicles meet requirements according to the received power adjustment and start and stop instructions, the calculated adjustment correction instructions are sent to the local control center by taking a platform area as a unit, and the local control center sends the power adjustment and start and stop instructions to each electric vehicle.
8. The method of claim 1, wherein the charge-discharge and scheduling constraints of the hierarchical cluster of electric vehicles comprise:
the sum of the charging and discharging power of the electric automobile in each time period is equal to the total electric quantity demand of the electric automobile hierarchical cluster;
the battery state of charge of the electric vehicle is greater than the lowest battery state of charge;
the charge-discharge power of the electric automobile is less than the maximum allowable charge-discharge power of the electric automobile;
the load rate of the local power grid does not exceed the upper limit value of the safe and economic operation interval of the power distribution equipment.
9. The method of claim 1, wherein establishing an objective function that targets a minimum charge-discharge power variance comprises:
establishing an objective function with the minimum system frequency variance as a target:
wherein, f Li System frequency at time i, f av Is the average frequency involved in the frequency modulation, n is the number of electric vehicles involved in the frequency modulation, f ij Is the exchange frequency of the electric vehicle j and the power grid in the period i;
based on an objective function with the minimum system frequency variance as a target, establishing an objective function with the minimum charge-discharge load power mean square error as a target:
wherein, P Li Charging and discharging load power at time i, P av Is the average power when participating in the coordinated control and preferentially responding to the frequency modulation, n is the number of electric vehicles participating in the frequency modulation, P ij Is the exchange power of the electric vehicle j with the power grid during the period i.
10. The method of claim 1, wherein solving the objective function using a particle swarm algorithm comprises:
taking the exchange power and initial charging and discharging time of the electric automobile and a power grid as particles;
initializing a particle population;
calculating the initial fitness of the particle population, screening the individual optimal value and the population optimal value, repeatedly updating the speed and the position of the iterative particles, calculating the fitness value of the objective function after each iteration until the maximum iteration number is reached, and obtaining the optimal initial charging and discharging time and the optimal charging and discharging power of the electric automobile.
11. The method of claim 10, wherein the charging and discharging of the layered cluster of electric vehicles is planned as follows:
when the system frequency is adjusted downwards, the electric automobile serves as a power load to be charged until the system frequency returns to a normal state or meets a constraint condition, and the electric automobile is stopped to be charged;
when the system frequency is adjusted upwards, the electric automobile starts an energy storage battery mode to discharge to a power grid, and when the charge state of the battery of the electric automobile is smaller than or equal to a set lower limit threshold or the system fluctuation time is up, the electric automobile stops discharging.
12. The utility model provides an electric automobile cluster participates in distribution network coordinated control device which characterized in that includes:
the data acquisition and classification module is used for acquiring charge and discharge data of the electric automobiles in the power distribution area, and performing cluster classification on the electric automobiles in the power distribution area according to the acquired charge and discharge data to obtain an electric automobile layered cluster;
the model building module is used for building a network access model of the electric automobile hierarchical cluster access power distribution network;
the constraint condition determining module is used for determining charge-discharge and scheduling constraint conditions of the electric automobile hierarchical cluster based on the established network access model;
the target function establishing and solving module is used for establishing a target function taking the minimum charge-discharge power variance as a target and solving the target function by utilizing a particle swarm algorithm;
and the charge and discharge plan determining module is used for determining a charge and discharge plan of the electric automobile in which the layered cluster participates in the coordination control of the power distribution network according to the charge and discharge power solved by the particle swarm algorithm.
13. A computer-readable storage medium, characterized in that the storage medium stores a computer program for performing the method of any of the preceding claims 1-11.
14. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any one of claims 1-11.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210503377.3A CN114938013A (en) | 2022-05-09 | 2022-05-09 | Method and device for electric vehicle cluster to participate in power distribution network coordination control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210503377.3A CN114938013A (en) | 2022-05-09 | 2022-05-09 | Method and device for electric vehicle cluster to participate in power distribution network coordination control |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114938013A true CN114938013A (en) | 2022-08-23 |
Family
ID=82865155
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210503377.3A Pending CN114938013A (en) | 2022-05-09 | 2022-05-09 | Method and device for electric vehicle cluster to participate in power distribution network coordination control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114938013A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116911579A (en) * | 2023-09-14 | 2023-10-20 | 国网江苏省电力有限公司常州供电分公司 | Dispatching method and device for power distribution transformer area |
-
2022
- 2022-05-09 CN CN202210503377.3A patent/CN114938013A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116911579A (en) * | 2023-09-14 | 2023-10-20 | 国网江苏省电力有限公司常州供电分公司 | Dispatching method and device for power distribution transformer area |
| CN116911579B (en) * | 2023-09-14 | 2023-12-08 | 国网江苏省电力有限公司常州供电分公司 | Dispatching method and device for power distribution transformer area |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109816171B (en) | Double-layer distributed optimal scheduling method for electric vehicle regional micro-grid cluster | |
| CN109936128A (en) | A kind of dynamic need response method under scale electric car access conditions | |
| CN113103905B (en) | Intelligent charging distribution adjusting method, device, equipment and medium for electric automobile | |
| CN112131733B (en) | Distributed power supply planning method considering influence of charging load of electric automobile | |
| CN107069776B (en) | Energy storage look-ahead distributed control method for smooth microgrid tie line power | |
| CN111483331B (en) | Charging load aggregation scheduling control method and system based on scheduling instruction | |
| CN109878369A (en) | A kind of electric car charge and discharge Optimization Scheduling based on fuzzy Spot Price | |
| CN114039370B (en) | Electric automobile and intelligent charging and discharging station resource optimization method based on V2G mode | |
| CN109617099B (en) | Virtual energy storage coordination control system and method thereof | |
| CN108281989B (en) | Economic dispatching method and device for wind power system | |
| CN113269372A (en) | Cluster electric vehicle schedulable capacity prediction evaluation method considering user will | |
| CN113437754A (en) | Electric automobile ordered charging method and system based on platform area intelligent fusion terminal | |
| CN114899856A (en) | Method, system, equipment and medium for adjusting power of electric vehicle charging pile | |
| CN114938013A (en) | Method and device for electric vehicle cluster to participate in power distribution network coordination control | |
| CN115471044A (en) | Power distribution station electric automobile scheduling method and system with light storage and storage medium | |
| CN109693573B (en) | Electric automobile cluster charging power optimization method for promoting consumption of renewable energy | |
| CN114156951A (en) | Control optimization method and device of source network load storage system | |
| CN114358378A (en) | User side energy storage optimal configuration system and method for considering demand management | |
| KR20220088067A (en) | Method for managing aggrigation resource based on small distributed resource and apparuatus thereof | |
| CN117172486A (en) | Reinforced learning-based virtual power plant optical storage resource aggregation regulation and control method | |
| CN117691602A (en) | Source network load storage collaborative planning method and system considering flexibility and economy | |
| CN117639043A (en) | Electric automobile dispatching method and device under wind power cooperation, terminal equipment and medium | |
| CN117175579A (en) | Electric automobile charging and discharging group scheduling method and system | |
| CN117200334A (en) | Multi-energy scheduling method and device considering new energy uncertainty | |
| CN115776138A (en) | Micro-grid capacity planning method and device considering multi-dimensional uncertainty and energy management strategy |
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 |