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CN114285029A - Scheduling control method and system for exciting electric vehicle to participate in vehicle network interaction - Google Patents

Scheduling control method and system for exciting electric vehicle to participate in vehicle network interaction Download PDF

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CN114285029A
CN114285029A CN202111558532.3A CN202111558532A CN114285029A CN 114285029 A CN114285029 A CN 114285029A CN 202111558532 A CN202111558532 A CN 202111558532A CN 114285029 A CN114285029 A CN 114285029A
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electric vehicle
power
electric
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electric automobile
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CN114285029B (en
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李鹏
王剑晓
张云天
田春筝
李慧璇
李庚银
周明
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North China Electric Power University
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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North China Electric Power University
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems
    • 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

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Abstract

The invention relates to a scheduling control method and a system for exciting an electric automobile to participate in vehicle network interaction, wherein the method comprises the following steps: acquiring power information of a comprehensive energy system in a period to be scheduled and market prices corresponding to the period; inputting the power information and the market price corresponding to the time interval into a pre-established comprehensive energy system optimization scheduling model to obtain the charging and discharging demand data of the electric vehicle at each moment in the time interval to be scheduled; determining the contribution degree of the electric automobile participating in the vehicle network interaction in the period to be scheduled by utilizing the Nash game principle and the electric automobile charging and discharging demand data at each moment in the period to be scheduled; and formulating a control signal for stimulating the electric automobile to participate in the vehicle network interaction based on the contribution degree of the electric automobile. According to the technical scheme provided by the invention, the contribution degree of the electric automobile participating in the vehicle network interaction is calculated, and the enthusiasm of the electric automobile participating in the vehicle network interaction is better mobilized through a perfect scheduling control strategy, so that the safe, stable and economic operation of a power grid is realized.

Description

Scheduling control method and system for exciting electric vehicle to participate in vehicle network interaction
Technical Field
The invention relates to the technical field of stimulating electric automobiles to participate in vehicle network interaction, in particular to a scheduling control method and system for stimulating electric automobiles to participate in vehicle network interaction.
Background
In the existing power system in China, the electric automobile is attracted to be charged orderly through a peak-valley electricity price system. In a novel high-proportion new energy power system, many researches are initiated on the problem of overlarge peak-to-valley difference, and the effective arrangement of orderly charging of electric vehicles is a good strategy which is favorable for safe, stable and economic operation of a power grid. Through the peak-valley electricity price set in advance, a user can be attracted to select to charge in the electricity utilization low valley period, and the electricity utilization high peak period is avoided, so that the purposes of orderly charging the electric automobile and reducing the peak-valley difference of a power grid are achieved.
However, the prior art mostly adopts a peak-valley electricity price system which is quite rigid and needs to be executed for a long time even years once being established. There are obvious seasonal characteristics of the power system load and a large number of newly grid-connected distributed power generation will also have a large impact on the grid load. If the peak-valley electricity price is still used for guiding the charging of the electric automobile, the requirement of rapid load change of a novel power system is difficult to meet in many times, even the peak and the valley of actual power load are difficult to reflect really, and the peak counter-regulation effect is achieved in certain specific periods. Secondly, for the electric automobile, the battery property provides the basic condition as equivalent energy storage, and the peak-valley electricity price strategy only excavates the potential as load, and the energy storage capacity cannot be effectively mobilized, so that the waste of precious demand side response resources is avoided. Moreover, the peak-valley electricity price system seriously underestimates the benefits brought to the power grid by the orderly charging of the electric automobile, so that the reward given to the owner is too little, the incentive for the owner to participate in the orderly charging is not powerful enough, and the interactive enthusiasm of the electric automobile in participating the power grid is low.
Disclosure of Invention
The application provides a scheduling control method and system for stimulating electric vehicles to participate in vehicle network interaction, so as to at least solve the technical problem that the enthusiasm for electric vehicles to participate in vehicle network interaction is low in the related technology.
An embodiment of a first aspect of the present application provides a scheduling control method for stimulating an electric vehicle to participate in vehicle network interaction, where the method includes:
acquiring power information of a comprehensive energy system in a period to be scheduled and market prices corresponding to the period;
inputting the power information and the market price corresponding to the time interval into a pre-established comprehensive energy system optimization scheduling model to obtain the charging and discharging demand data of the electric vehicle at each moment in the time interval to be scheduled;
determining the contribution degree of the electric automobile participating in the vehicle network interaction in the period to be scheduled by utilizing the Nash game principle and the electric automobile charging and discharging demand data at each moment in the period to be scheduled;
and formulating a control signal for stimulating the electric automobile to participate in the vehicle network interaction based on the contribution degree of the electric automobile, and carrying out scheduling control on the electric automobile to participate in the vehicle network interaction based on the control signal.
In a second aspect of the present application, an embodiment provides a dispatch control system for stimulating an electric vehicle to participate in vehicle network interaction, where the dispatch control system includes:
the first acquisition module is used for acquiring power information of the comprehensive energy system in a period to be scheduled and market prices corresponding to the period;
the second acquisition module is used for inputting the power information and the market price corresponding to the time interval into a pre-established comprehensive energy system optimization scheduling model to acquire the charge and discharge demand data of the electric automobile at each moment in the time interval to be scheduled;
the determining module is used for determining the contribution degree of the electric automobile participating in the vehicle network interaction in the period to be scheduled by utilizing the Nash game principle and the electric automobile charging and discharging demand data at each moment in the period to be scheduled;
and the control module is used for formulating a control signal for stimulating the electric automobile to participate in the vehicle network interaction based on the contribution degree of the electric automobile and carrying out scheduling control on the electric automobile participating in the vehicle network interaction based on the control signal.
In a third aspect of the present application, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the prediction method according to the first aspect of the present application is implemented.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
the invention provides a dispatching control method and a system for exciting an electric automobile to participate in vehicle network interaction, wherein the method comprises the following steps: acquiring power information of a comprehensive energy system in a period to be scheduled and market prices corresponding to the period; inputting the power information and the market price corresponding to the time interval into a pre-established comprehensive energy system optimization scheduling model to obtain the charging and discharging demand data of the electric vehicle at each moment in the time interval to be scheduled; determining the contribution degree of the electric automobile participating in the vehicle network interaction in the period to be scheduled by utilizing the Nash game principle and the electric automobile charging and discharging demand data at each moment in the period to be scheduled; and formulating a control signal for stimulating the electric automobile to participate in the vehicle network interaction based on the contribution degree of the electric automobile, and carrying out scheduling control on the electric automobile to participate in the vehicle network interaction based on the control signal. According to the technical scheme provided by the invention, the contribution degree of the electric automobile participating in the vehicle network interaction is calculated, and the enthusiasm of the electric automobile participating in the vehicle network interaction is better mobilized through a perfect scheduling control strategy, so that the safe, stable and economic operation of a power grid is realized.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a dispatch control method for incentivizing electric vehicles to participate in vehicle network interaction according to one embodiment of the present application;
FIG. 2 is a block diagram of a dispatch control system for incentivizing electric vehicles to participate in vehicle network interactions, according to one embodiment of the present application;
fig. 3 is a block diagram of a control module in a dispatch control system for incentivizing participation of electric vehicles in vehicle network interaction, according to one embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The application provides a scheduling control method and a system for exciting an electric automobile to participate in vehicle network interaction, wherein the method comprises the following steps: acquiring power information of a comprehensive energy system in a period to be scheduled and market prices corresponding to the period; inputting the power information and the market price corresponding to the time interval into a pre-established comprehensive energy system optimization scheduling model to obtain the charging and discharging demand data of the electric vehicle at each moment in the time interval to be scheduled; determining the contribution degree of the electric automobile participating in the vehicle network interaction in the period to be scheduled by utilizing the Nash game principle and the electric automobile charging and discharging demand data at each moment in the period to be scheduled; and formulating a control signal for stimulating the electric automobile to participate in the vehicle network interaction based on the contribution degree of the electric automobile, and carrying out scheduling control on the electric automobile to participate in the vehicle network interaction based on the control signal. According to the technical scheme provided by the invention, the contribution degree of the electric automobile participating in the vehicle network interaction is calculated, and the enthusiasm of the electric automobile participating in the vehicle network interaction is better mobilized through a perfect scheduling control strategy, so that the safe, stable and economic operation of a power grid is realized.
Example 1
Fig. 1 is a flowchart of a scheduling control method for stimulating an electric vehicle to participate in vehicle-grid interaction according to an embodiment of the present disclosure, and as shown in fig. 1, the scheduling control method for stimulating the electric vehicle to participate in vehicle-grid interaction includes:
step 1: acquiring power information of a comprehensive energy system in a period to be scheduled and market prices corresponding to the period;
in an embodiment of the present disclosure, the power information of the integrated energy system in the period to be scheduled includes: the day-ahead power and the real-time power of a micro gas turbine, a distributed photovoltaic and electric vehicle charging station and a load in the comprehensive energy system.
Step 2: inputting the power information and the market price corresponding to the time interval into a pre-established comprehensive energy system optimization scheduling model to obtain the charging and discharging demand data of the electric vehicle at each moment in the time interval to be scheduled;
the building process of the pre-built comprehensive energy system optimization scheduling model comprises the following steps:
constructing an objective function of an optimization scheduling model of the comprehensive energy system based on the day-ahead power and the real-time power of a micro gas turbine, a distributed photovoltaic, an electric vehicle charging station and a load in the comprehensive energy system, wherein the maximum income objective function of the comprehensive energy system is established by taking the maximum income of the comprehensive energy system as a target;
constructing a constraint condition for an objective function of the model: the method comprises the following steps of distributed photovoltaic power generation constraint, comprehensive energy system load power flexible load constraint, micro gas turbine power constraint, electric vehicle charging and discharging power constraint in an electric vehicle charging station and charging and discharging upper and lower limit constraint of the electric vehicle charging station.
It should be noted that the calculation formula of the maximum profit objective function of the integrated energy system is as follows:
Figure BDA0003417816740000051
where μ is the yield of the integrated energy system, γsThe corresponding weight under the s electric vehicle parameter,
Figure BDA0003417816740000052
Figure BDA0003417816740000053
the sum of the day-ahead income and the real-time income corresponding to the t moment under the s type of electric vehicle parameters,
Figure BDA0003417816740000054
the day-ahead income corresponding to the t moment under the s type electric vehicle parameters,
Figure BDA0003417816740000055
the real-time income corresponding to the t moment under the s type of electric vehicle parameters, aiFor the electricity generation price of the ith gas turbine, phiSIs the total number of the electric vehicle parameter types, phiTIs the total number of the time moments in the period to be scheduled,
Figure BDA0003417816740000056
Figure BDA0003417816740000057
for the day-ahead market electricity selling price at time t, Pt DAIs at t timeThe power of the corresponding day ahead is measured,
Figure BDA0003417816740000058
Figure BDA0003417816740000059
the real-time market electricity selling price corresponding to the time t,
Figure BDA00034178167400000510
is the real-time power corresponding to the t moment under the s type of electric vehicle parameters,
Figure BDA00034178167400000511
the real-time power of the ith micro gas turbine at the t moment under the s electric vehicle parameters,
Figure BDA00034178167400000512
Figure BDA00034178167400000513
the day-ahead power, phi, of the ith micro gas turbine at time tMTIs the total number of micro gas turbines,
Figure BDA00034178167400000514
the day-ahead power, phi, of the alpha-th distributed photovoltaic at time tPVIs the total number of distributed photovoltaics, Pt CSD,DAThe day-ahead discharge power, P, of the electric vehicle charging station at time tt CSC,DAThe day-ahead charging power, P, of the electric vehicle charging station at time tt L,DAThe day-ahead load of the system at time t,
Figure BDA00034178167400000515
Figure BDA00034178167400000516
is the real-time power of the alpha distributed photovoltaic power at the t moment under the s type of electric vehicle parameters,
Figure BDA00034178167400000517
the real-time discharge power of the electric vehicle charging station at the moment t under the s type of electric vehicle parameters,
Figure BDA00034178167400000518
the real-time charging power of the electric vehicle charging station at the moment t under the s type of electric vehicle parameters,
Figure BDA00034178167400000519
and the real-time load of the system at the t moment under the s type of electric vehicle parameters.
Further, the calculation formula of the distributed photovoltaic power generation power constraint is as follows:
Figure BDA00034178167400000520
in the formula,
Figure BDA0003417816740000061
power of the alpha-th distributed photovoltaic at time t, PPV,maxMaximum power for distributed photovoltaic;
the calculation formula of the load power flexible load constraint of the integrated energy system is as follows:
Figure BDA0003417816740000062
in the formula, PL,minIs the minimum value, P, of the load power flexible load of the integrated energy systemt LFor the load power of the integrated energy system at time t, PL,maxThe maximum value of the load power flexible load of the comprehensive energy system;
the power constraint of the micro gas turbine is calculated as follows:
Figure BDA0003417816740000063
in the formula,
Figure BDA0003417816740000064
the maximum climbing rate of the ith micro gas turbine,
Figure BDA0003417816740000065
the maximum power of the ith micro gas turbine;
the calculation formula of the charge and discharge power constraint of the electric vehicle in the electric vehicle charging station is as follows:
Figure BDA0003417816740000066
Figure BDA0003417816740000067
Figure BDA0003417816740000068
Figure BDA0003417816740000069
in the formula,
Figure BDA00034178167400000610
the maximum charging power of the q-th electric automobile at the time t,
Figure BDA00034178167400000611
the maximum charging power of the q-th electric automobile,
Figure BDA00034178167400000612
the battery power of the q-th electric automobile at the time t,
Figure BDA00034178167400000613
for the q electric automobile under the constant current charging mode battery state of charge threshold, when the charge reaches the threshold and then converts to the constant voltage charging mode, can describeIs described as
Figure BDA00034178167400000614
And part represents the charging process in the constant voltage mode of the electric vehicle,
Figure BDA00034178167400000615
the state of the electric automobile during charging is constant current charging or constant voltage charging,
Figure BDA00034178167400000616
the maximum discharge power of the q-th electric automobile at the time t,
Figure BDA00034178167400000617
the maximum discharge power of the q-th electric automobile,
Figure BDA00034178167400000618
a threshold value for switching the q-th electric automobile from a constant-current charging mode to a constant-voltage charging mode; wherein,
Figure BDA0003417816740000071
Figure BDA0003417816740000072
the battery power of the q-th electric automobile at the time t-1,
Figure BDA0003417816740000073
the rated capacity of the q-th electric vehicle battery,
Figure BDA0003417816740000074
for the loss of the French holy in the charging and discharging process of the q-th electric automobile,
Figure BDA0003417816740000075
the charging power of the q-th electric automobile at the time of t-1,
Figure BDA0003417816740000076
the discharge power of the q-th electric automobile at the time of t-1;
the calculation formula of the charging and discharging upper and lower limit constraints of the electric vehicle charging station is as follows:
Figure BDA0003417816740000077
Figure BDA0003417816740000078
in the formula,
Figure BDA0003417816740000079
for the time when the q electric vehicle arrives at the electric vehicle charging station
Figure BDA00034178167400000710
The corresponding amount of battery charge is determined,
Figure BDA00034178167400000711
the electric quantity when the q-th electric automobile starts to be charged,
Figure BDA00034178167400000712
for the q electric automobile leaving the electric automobile charging station
Figure BDA00034178167400000713
The corresponding amount of battery charge is determined,
Figure BDA00034178167400000714
the required electric quantity is the set electric quantity when the q-th electric automobile finishes charging.
It should be noted that the process for determining the electric vehicle parameters includes:
respectively determining the average value and the variance of the arrival time, the departure time and the initial electric quantity of the electric automobile by utilizing normal distribution;
and obtaining each electric vehicle parameter based on the average value and the variance of the arrival time, the departure time and the initial electric quantity of the electric vehicle.
It should be noted that research shows that uncertainty of EV arrival time, departure time and initial charge follows a normal distribution, and is expressed as a probability density function:
Figure BDA00034178167400000715
where μ represents the average value, σ represents the standard deviation, x may represent the actual value of EV arrival time, departure time, and initial charge, respectively, and f (x) represents the probability of obtaining the value.
Through the normal distribution formula, as long as the parameters of the average value and the standard deviation of the mu and the standard deviation of the initial electric quantity of the EV arrival time and the departure time are determined, the parameters of different electric vehicles can be randomly generated by a computer.
And step 3: determining the contribution degree of the electric automobile participating in the vehicle network interaction in the period to be scheduled by utilizing the Nash game principle and the electric automobile charging and discharging demand data at each moment in the period to be scheduled;
in the embodiment of the present disclosure, the determining, by using the nash game principle and the electric vehicle charging and discharging demand data at each time in the period to be scheduled, a contribution degree of the electric vehicle participating in the vehicle network interaction in the period to be scheduled includes:
and establishing a comparison model of the electric vehicle not participating in the vehicle network interaction based on the constraint condition established for the objective function of the energy system optimization scheduling model and the electric vehicle charging and discharging power of the electric vehicle between the time of reaching the electric vehicle charging station and the time of leaving the electric vehicle determined by normal distribution, and comparing to obtain the contribution of the electric vehicle network interaction to the regional energy system.
Specifically, the electric vehicle comparison model is formulated by using the obtained electric vehicle charging and discharging power between the arrival time and the departure time and the system constraint conditions, and comprises the following steps: in order to better determine the contribution of the electric automobile network interaction, reasonably establish a control strategy and return profits, the Nash game principle is applied in the paper. A comparison model is needed to be established according to the Nash game principle, and the contribution of the electric automobile network interaction to the regional energy system is obtained through comparison. The method is mainly characterized in that in the non-interactive model, the electric automobile adopts a plug-and-play mode, and is charged with the maximum power allowed by power constraint until the charging is finished after the charging is started. In the vehicle network interaction model, the charging power of the electric vehicle can be switched between the allowed maximum charging power and the maximum discharging power through the regulation and control of the regional energy system.
The optimal electric vehicle dispatching control signal is generated by collecting electric load data and distributed power generation data in the comprehensive energy system and electric vehicle arrival time, departure time, required electric quantity and other data of the charging station. Meanwhile, according to a comparison model, the income obtained by the electric automobile placed in the charging pile of the owner is definitely calculated, the contribution degree of the electric automobile to the vehicle-grid interaction is judged by taking the actual charging strategy adjustment amount of the electric automobile according to the power grid requirement as a basis, and the contribution degree is taken as the basis for obtaining the income, wherein the formula expression is as follows:
Figure BDA0003417816740000081
in the formula, SCRqThe contribution degree of the q electric automobile participating in the interaction with the vehicle network in the period to be dispatched,
Figure BDA0003417816740000082
the charging strategy variation quantity of the q electric automobile in the period to be scheduled in comparison of the non-interactive model and the interactive model of the electric automobile under the s electric automobile parameters,
Figure BDA0003417816740000091
and comparing the non-interactive model with the interactive model of the q electric vehicle in the period to be scheduled under the s electric vehicle parameters to obtain the discharge strategy change quantity.
And 4, step 4: and formulating a control signal for stimulating the electric automobile to participate in the vehicle network interaction based on the contribution degree of the electric automobile, and carrying out scheduling control on the electric automobile to participate in the vehicle network interaction based on the control signal.
In an embodiment of the present disclosure, the formulating a control signal for stimulating an electric vehicle to participate in a vehicle network interaction based on the contribution degree of the electric vehicle includes:
determining the reward expected to be obtained by the electric vehicle user by using the contribution degree of the electric vehicle;
and formulating a control signal for motivating the electric automobile to participate in the vehicle network interaction based on the reward.
Specifically, the total value of the vehicle-network interaction is determined by calculating the total profit of the electric vehicle not participating in the vehicle-network interaction when the electric vehicle participates in the vehicle-network interaction and comparing the total profit with the total profit obtained by the support of software and hardware of the vehicle-network interaction condition provided by the comprehensive energy system, and the contribution degree of the electric vehicle to the vehicle-network interaction is SCRqDetermining the specific income thereof;
wherein f isq＝(1-ε)SCRqfR，fR＝fR,1-fR,0Wherein f isR,1And fR,0And respectively representing the benefits of the electric vehicle under the vehicle-grid interaction condition, and obtaining the cooperation surplus of the electric vehicle participating in power grid dispatching through calculation. Describing the amount of revenue that a particular electric vehicle user can obtain, epsilon represents the return that the platform providing the vehicle network interaction service should charge, fRCooperative remainder, f, obtained through vehicle network interactive cooperationqIndicating that the q-th electric vehicle user can be rewarded.
Furthermore, after the reward is obtained through calculation, the charging station is informed of information interconnection in real time through the information platform to inform that the electric automobile obtains the reward due, the income information is conducted to the charging pile responding, the charge generated by charging is reduced, and if the obtained income is larger than the charge required, the extra amount of money can be used for the next charging.
In summary, the scheduling control method for stimulating the electric vehicle to participate in the vehicle-grid interaction provided by the embodiment of the disclosure calculates the contribution degree of the electric vehicle to participate in the vehicle-grid interaction, and better mobilizes the enthusiasm of the electric vehicle to participate in the vehicle-grid interaction through a perfect scheduling control strategy, so as to realize safe, stable and economic operation of a power grid.
Example 2
Fig. 2 is a structural diagram of a dispatch control system for stimulating an electric vehicle to participate in vehicle network interaction according to an embodiment of the present disclosure, and as shown in fig. 2, the dispatch control system for stimulating an electric vehicle to participate in vehicle network interaction includes:
the first obtaining module 100 is configured to obtain power information of the integrated energy system in a period to be scheduled and a market price corresponding to the period;
the second obtaining module 200 is configured to input the power information and the market price corresponding to the time period into a pre-established comprehensive energy system optimization scheduling model, and obtain charge and discharge demand data of the electric vehicle at each time in the time period to be scheduled;
the determining module 300 is configured to determine the contribution degree of the electric vehicle participating in the vehicle network interaction in the period to be scheduled by using the Nash game principle and the electric vehicle charging and discharging demand data at each moment in the period to be scheduled;
and the control module 400 is used for formulating a control signal for stimulating the electric automobile to participate in the vehicle network interaction based on the contribution degree of the electric automobile and carrying out scheduling control on the electric automobile participating in the vehicle network interaction based on the control signal.
In an embodiment of the present disclosure, the process of building the pre-established integrated energy system optimized scheduling model includes:
constructing an objective function of an optimization scheduling model of the comprehensive energy system based on the day-ahead power and the real-time power of a micro gas turbine, a distributed photovoltaic, an electric vehicle charging station and a load in the comprehensive energy system, wherein the maximum income objective function of the comprehensive energy system is established by taking the maximum income of the comprehensive energy system as a target;
constructing a constraint condition for an objective function of the model: the method comprises the following steps of distributed photovoltaic power generation constraint, comprehensive energy system load power flexible load constraint, micro gas turbine power constraint, electric vehicle charging and discharging power constraint in an electric vehicle charging station and charging and discharging upper and lower limit constraint of the electric vehicle charging station.
Further, the calculation formula of the maximum profit objective function of the integrated energy system is as follows:
Figure BDA0003417816740000101
where μ is the yield of the integrated energy system, γsThe corresponding weight under the s electric vehicle parameter,
Figure BDA0003417816740000102
Figure BDA0003417816740000103
the sum of the day-ahead income and the real-time income corresponding to the t moment under the s type of electric vehicle parameters,
Figure BDA0003417816740000104
the day-ahead income corresponding to the t moment under the s type electric vehicle parameters,
Figure BDA0003417816740000105
the real-time income corresponding to the t moment under the s type of electric vehicle parameters, aiFor the electricity generation price of the ith gas turbine, phiSIs the total number of the electric vehicle parameter types, phiTIs the total number of the time moments in the period to be scheduled,
Figure BDA0003417816740000111
Figure BDA0003417816740000112
for the day-ahead market electricity selling price at time t, Pt DAIs the power in the day before the moment t,
Figure BDA0003417816740000113
Figure BDA0003417816740000114
the real-time market electricity selling price corresponding to the time t,
Figure BDA0003417816740000115
is the real-time power corresponding to the t moment under the s type of electric vehicle parameters,
Figure BDA0003417816740000116
the real-time power of the ith micro gas turbine at the t moment under the s electric vehicle parameters,
Figure BDA0003417816740000117
Figure BDA0003417816740000118
the day-ahead power, phi, of the ith micro gas turbine at time tMTIs the total number of micro gas turbines,
Figure BDA0003417816740000119
the day-ahead power, phi, of the alpha-th distributed photovoltaic at time tPVIs the total number of distributed photovoltaics, Pt CSD,DAThe day-ahead discharge power, P, of the electric vehicle charging station at time tt CSC,DAThe day-ahead charging power, P, of the electric vehicle charging station at time tt L,DAThe day-ahead load of the system at time t,
Figure BDA00034178167400001110
Figure BDA00034178167400001111
is the real-time power of the alpha distributed photovoltaic power at the t moment under the s type of electric vehicle parameters,
Figure BDA00034178167400001112
the real-time discharge power of the electric vehicle charging station at the moment t under the s type of electric vehicle parameters,
Figure BDA00034178167400001113
the real-time charging power of the electric vehicle charging station at the moment t under the s type of electric vehicle parameters,
Figure BDA00034178167400001114
and the real-time load of the system at the t moment under the s type of electric vehicle parameters.
Wherein the calculation formula of the distributed photovoltaic power generation power constraint is as follows:
Figure BDA00034178167400001115
in the formula,
Figure BDA00034178167400001116
power of the alpha-th distributed photovoltaic at time t, PPV,maxMaximum power for distributed photovoltaic;
the calculation formula of the load power flexible load constraint of the integrated energy system is as follows:
Figure BDA00034178167400001117
in the formula, PL,minIs the minimum value, P, of the load power flexible load of the integrated energy systemt LFor the load power of the integrated energy system at time t, PL,maxThe maximum value of the load power flexible load of the comprehensive energy system;
the power constraint of the micro gas turbine is calculated as follows:
Figure BDA0003417816740000121
in the formula,
Figure BDA0003417816740000122
the maximum climbing rate of the ith micro gas turbine,
Figure BDA0003417816740000123
the maximum power of the ith micro gas turbine;
the calculation formula of the charge and discharge power constraint of the electric vehicle in the electric vehicle charging station is as follows:
Figure BDA0003417816740000124
Figure BDA0003417816740000125
Figure BDA0003417816740000126
Figure BDA0003417816740000127
in the formula,
Figure BDA0003417816740000128
the maximum charging power of the q-th electric automobile at the time t,
Figure BDA0003417816740000129
the maximum charging power of the q-th electric automobile,
Figure BDA00034178167400001210
the battery power of the q-th electric automobile at the time t,
Figure BDA00034178167400001211
for the q-th electric vehicle, the threshold value of the state of charge of the battery in the constant-current charging mode is set, and when the charge reaches the threshold value, the charging mode is switched to the constant-voltage charging mode, which can be described as
Figure BDA00034178167400001212
And part represents the charging process in the constant voltage mode of the electric vehicle,
Figure BDA00034178167400001213
the state of the electric automobile during charging is constant current charging or constant voltage charging,
Figure BDA00034178167400001214
is time tThe maximum discharge power of the qth electric vehicle,
Figure BDA00034178167400001215
the maximum discharge power of the q-th electric automobile,
Figure BDA00034178167400001216
a threshold value for switching the q-th electric automobile from a constant-current charging mode to a constant-voltage charging mode; wherein,
Figure BDA00034178167400001217
Figure BDA00034178167400001218
the battery power of the q-th electric automobile at the time t-1,
Figure BDA00034178167400001219
the rated capacity of the q-th electric vehicle battery,
Figure BDA00034178167400001220
for the loss of the French holy in the charging and discharging process of the q-th electric automobile,
Figure BDA00034178167400001221
the charging power of the q-th electric automobile at the time of t-1,
Figure BDA00034178167400001222
the discharge power of the q-th electric automobile at the time of t-1;
the calculation formula of the charging and discharging upper and lower limit constraints of the electric vehicle charging station is as follows:
Figure BDA00034178167400001223
Figure BDA0003417816740000131
in the formula,
Figure BDA0003417816740000132
for the time when the q electric vehicle arrives at the electric vehicle charging station
Figure BDA0003417816740000133
The corresponding amount of battery charge is determined,
Figure BDA0003417816740000134
the electric quantity when the q-th electric automobile starts to be charged,
Figure BDA0003417816740000135
for the q electric automobile leaving the electric automobile charging station
Figure BDA0003417816740000136
The corresponding amount of battery charge is determined,
Figure BDA0003417816740000137
the required electric quantity is the set electric quantity when the q-th electric automobile finishes charging.
Further, the process for determining the electric vehicle parameters comprises:
respectively determining the average value and the variance of the arrival time, the departure time and the initial electric quantity of the electric automobile by utilizing normal distribution;
and obtaining each electric vehicle parameter based on the average value and the variance of the arrival time, the departure time and the initial electric quantity of the electric vehicle.
In an embodiment of the present disclosure, the determining module 300 is specifically configured to:
and establishing a comparison model of the electric vehicle not participating in the vehicle network interaction based on the constraint condition established for the objective function of the energy system optimization scheduling model and the electric vehicle charging and discharging power of the electric vehicle between the time of reaching the electric vehicle charging station and the time of leaving the electric vehicle determined by normal distribution, and comparing to obtain the contribution of the electric vehicle network interaction to the regional energy system.
The calculation formula of the contribution degree of the electric vehicle network interaction to the regional energy system is as follows:
Figure BDA0003417816740000138
in the formula, SCRqThe contribution degree of the q electric automobile participating in the interaction with the vehicle network in the period to be dispatched,
Figure BDA0003417816740000139
the charging strategy variation quantity of the q electric automobile in the period to be scheduled in comparison of the non-interactive model and the interactive model of the electric automobile under the s electric automobile parameters,
Figure BDA00034178167400001310
and comparing the non-interactive model with the interactive model of the q electric vehicle in the period to be scheduled under the s electric vehicle parameters to obtain the discharge strategy change quantity.
In the embodiment of the present disclosure, the control module 400, as shown in fig. 3, includes:
a determining unit 401, configured to determine, by using the contribution degree of the electric vehicle, a reward expected to be obtained by a user of the electric vehicle;
the formulating unit 402 is used for formulating a control signal for motivating the electric automobile to participate in vehicle network interaction based on the reward;
and a control unit 403, configured to perform scheduling control of the electric vehicle participating in vehicle network interaction based on the control signal.
In summary, the scheduling control system for stimulating the electric vehicle to participate in the vehicle-grid interaction provided by the embodiment of the disclosure calculates the contribution degree of the electric vehicle to participate in the vehicle-grid interaction, and better mobilizes the enthusiasm of the electric vehicle to participate in the vehicle-grid interaction through a perfect scheduling control strategy, so as to realize safe, stable and economic operation of a power grid.
Example 3
In order to implement the above embodiments, the present disclosure also provides a computer device.
The computer device provided in this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method in embodiment 1 is implemented.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims (10)

1. A scheduling control method for exciting an electric automobile to participate in vehicle network interaction is characterized by comprising the following steps:
acquiring power information of a comprehensive energy system in a period to be scheduled and market prices corresponding to the period;
inputting the power information and the market price corresponding to the time interval into a pre-established comprehensive energy system optimization scheduling model to obtain the charging and discharging demand data of the electric vehicle at each moment in the time interval to be scheduled;
determining the contribution degree of the electric automobile participating in the vehicle network interaction in the period to be scheduled by utilizing the Nash game principle and the electric automobile charging and discharging demand data at each moment in the period to be scheduled;
and formulating a control signal for stimulating the electric automobile to participate in the vehicle network interaction based on the contribution degree of the electric automobile, and carrying out scheduling control on the electric automobile to participate in the vehicle network interaction based on the control signal.
2. The method of claim 1, wherein the pre-established integrated energy system optimization scheduling model establishing process comprises:
constructing an objective function of an optimization scheduling model of the comprehensive energy system based on the day-ahead power and the real-time power of a micro gas turbine, a distributed photovoltaic, an electric vehicle charging station and a load in the comprehensive energy system, wherein the maximum income objective function of the comprehensive energy system is established by taking the maximum income of the comprehensive energy system as a target;
constructing a constraint condition for an objective function of the model: the method comprises the following steps of distributed photovoltaic power generation constraint, comprehensive energy system load power flexible load constraint, micro gas turbine power constraint, electric vehicle charging and discharging power constraint in an electric vehicle charging station and charging and discharging upper and lower limit constraint of the electric vehicle charging station.
3. The method of claim 2, wherein the integrated energy system maximum gain objective function is calculated as follows:
Figure FDA0003417816730000011
in the formula,mu is the yield of the comprehensive energy system, gammasThe corresponding weight under the s electric vehicle parameter,
Figure FDA0003417816730000012
Figure FDA0003417816730000013
the sum of the day-ahead income and the real-time income corresponding to the t moment under the s type of electric vehicle parameters,
Figure FDA0003417816730000014
the day-ahead income corresponding to the t moment under the s type electric vehicle parameters,
Figure FDA0003417816730000015
the real-time income corresponding to the t moment under the s type of electric vehicle parameters,
Figure FDA0003417816730000016
for the electricity generation price of the ith gas turbine, phiSIs the total number of the electric vehicle parameter types, phiTIs the total number of the time moments in the period to be scheduled,
Figure FDA0003417816730000021
Figure FDA0003417816730000022
for the day-ahead market electricity selling price at time t, Pt DAIs the power in the day before the moment t,
Figure FDA0003417816730000023
Figure FDA0003417816730000024
the real-time market electricity selling price corresponding to the time t,
Figure FDA0003417816730000025
is the real-time power corresponding to the t moment under the s type of electric vehicle parameters,
Figure FDA0003417816730000026
the real-time power of the ith micro gas turbine at the t moment under the s electric vehicle parameters,
Figure FDA0003417816730000027
Figure FDA0003417816730000028
the day-ahead power, phi, of the ith micro gas turbine at time tMTIs the total number of micro gas turbines,
Figure FDA0003417816730000029
the day-ahead power, phi, of the alpha-th distributed photovoltaic at time tPVIs the total number of distributed photovoltaics, Pt CSD,DAThe day-ahead discharge power, P, of the electric vehicle charging station at time tt CSC,DAThe day-ahead charging power, P, of the electric vehicle charging station at time tt L,DAThe day-ahead load of the system at time t,
Figure FDA00034178167300000210
Figure FDA00034178167300000211
is the real-time power of the alpha distributed photovoltaic power at the t moment under the s type of electric vehicle parameters,
Figure FDA00034178167300000212
the real-time discharge power of the electric vehicle charging station at the moment t under the s type of electric vehicle parameters,
Figure FDA00034178167300000213
the real-time charging power of the electric vehicle charging station at the moment t under the s type of electric vehicle parameters,
Figure FDA00034178167300000214
and the real-time load of the system at the t moment under the s type of electric vehicle parameters.
4. The method of claim 2, wherein the distributed photovoltaic power generation power constraint is calculated as follows:
Figure FDA00034178167300000215
in the formula,
Figure FDA00034178167300000216
power of the alpha-th distributed photovoltaic at time t, PPV,maxMaximum power for distributed photovoltaic;
the calculation formula of the load power flexible load constraint of the integrated energy system is as follows:
PL,min≤Pt L≤PL,max
in the formula, PL,minIs the minimum value, P, of the load power flexible load of the integrated energy systemt LFor the load power of the integrated energy system at time t, PL,maxThe maximum value of the load power flexible load of the comprehensive energy system;
the power constraint of the micro gas turbine is calculated as follows:
Figure FDA0003417816730000031
in the formula,
Figure FDA0003417816730000032
the maximum climbing rate of the ith micro gas turbine,
Figure FDA0003417816730000033
the maximum power of the ith micro gas turbine;
the calculation formula of the charge and discharge power constraint of the electric vehicle in the electric vehicle charging station is as follows:
Figure FDA0003417816730000034
Figure FDA0003417816730000035
Figure FDA0003417816730000036
Figure FDA0003417816730000037
in the formula,
Figure FDA0003417816730000038
the maximum charging power of the q-th electric automobile at the time t,
Figure FDA0003417816730000039
the maximum charging power of the q-th electric automobile,
Figure FDA00034178167300000310
the battery power of the q-th electric automobile at the time t,
Figure FDA00034178167300000311
for the q-th electric vehicle, the threshold value of the state of charge of the battery in the constant-current charging mode is set, and when the charge reaches the threshold value, the charging mode is switched to the constant-voltage charging mode, which can be described as
Figure FDA00034178167300000312
Electric automobileThe charging process in the constant-voltage mode,
Figure FDA00034178167300000313
the state of the electric automobile during charging is constant current charging or constant voltage charging,
Figure FDA00034178167300000314
the maximum discharge power of the q-th electric automobile at the time t,
Figure FDA00034178167300000315
the maximum discharge power of the q-th electric automobile,
Figure FDA00034178167300000316
a threshold value for switching the q-th electric automobile from a constant-current charging mode to a constant-voltage charging mode; wherein,
Figure FDA00034178167300000317
Figure FDA00034178167300000318
the battery power of the q-th electric automobile at the time t-1,
Figure FDA00034178167300000319
the rated capacity of the q-th electric vehicle battery,
Figure FDA00034178167300000320
the q-th electric automobile is subjected to loss in the charging and discharging processes,
Figure FDA00034178167300000321
the charging power of the q-th electric automobile at the time of t-1,
Figure FDA00034178167300000322
the discharge power of the q-th electric automobile at the time of t-1;
the calculation formula of the charging and discharging upper and lower limit constraints of the electric vehicle charging station is as follows:
Figure FDA00034178167300000323
Figure FDA0003417816730000041
in the formula,
Figure FDA0003417816730000042
for the time when the q electric vehicle arrives at the electric vehicle charging station
Figure FDA0003417816730000043
The corresponding amount of battery charge is determined,
Figure FDA0003417816730000044
the electric quantity when the q-th electric automobile starts to be charged,
Figure FDA0003417816730000045
for the q electric automobile leaving the electric automobile charging station
Figure FDA0003417816730000046
The corresponding amount of battery charge is determined,
Figure FDA0003417816730000047
the required electric quantity is the set electric quantity when the q-th electric automobile finishes charging.
5. The method of claim 3, wherein the determining of the electric vehicle parameter comprises:
respectively determining the average value and the variance of the arrival time, the departure time and the initial electric quantity of the electric automobile by utilizing normal distribution;
and obtaining each electric vehicle parameter based on the average value and the variance of the arrival time, the departure time and the initial electric quantity of the electric vehicle.
6. The method as claimed in claim 4, wherein the determining the contribution degree of the electric vehicle participating in the vehicle network interaction in the period to be scheduled by using the Nash game principle and the electric vehicle charging and discharging demand data at each moment in the period to be scheduled comprises:
and establishing a comparison model of the electric vehicle not participating in the vehicle network interaction based on the constraint condition established for the objective function of the energy system optimization scheduling model and the electric vehicle charging and discharging power of the electric vehicle between the time of reaching the electric vehicle charging station and the time of leaving the electric vehicle determined by normal distribution, and comparing to obtain the contribution of the electric vehicle network interaction to the regional energy system.
7. The method of claim 6, wherein the contribution of the electric vehicle grid interaction to the regional energy system is calculated as follows:
Figure FDA0003417816730000048
in the formula, SCRqThe contribution degree of the q electric automobile participating in the interaction with the vehicle network in the period to be dispatched,
Figure FDA0003417816730000049
the charging strategy variation quantity of the q electric automobile in the period to be scheduled in comparison of the non-interactive model and the interactive model of the electric automobile under the s electric automobile parameters,
Figure FDA00034178167300000410
and comparing the non-interactive model with the interactive model of the q electric vehicle in the period to be scheduled under the s electric vehicle parameters to obtain the discharge strategy change quantity.
8. The method of claim 1, wherein formulating a control signal that incentivizes electric vehicle to participate in vehicle network interactions based on the electric vehicle's contribution comprises:
determining the reward expected to be obtained by the electric vehicle user by using the contribution degree of the electric vehicle;
and formulating a control signal for motivating the electric automobile to participate in the vehicle network interaction based on the reward.
9. A dispatch control system for incentivizing electric vehicles to participate in vehicle network interactions, the system comprising:
the first acquisition module is used for acquiring power information of the comprehensive energy system in a period to be scheduled and market prices corresponding to the period;
the second acquisition module is used for inputting the power information and the market price corresponding to the time interval into a pre-established comprehensive energy system optimization scheduling model to acquire the charge and discharge demand data of the electric automobile at each moment in the time interval to be scheduled;
the determining module is used for determining the contribution degree of the electric automobile participating in the vehicle network interaction in the period to be scheduled by utilizing the Nash game principle and the electric automobile charging and discharging demand data at each moment in the period to be scheduled;
and the control module is used for formulating a control signal for stimulating the electric automobile to participate in the vehicle network interaction based on the contribution degree of the electric automobile and carrying out scheduling control on the electric automobile participating in the vehicle network interaction based on the control signal.
10. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, which when executed by the processor implements the method of any one of claims 1 to 8.
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