CN115882494A - Hierarchical V2G scheduling optimization method considering vehicle grid-connected service duration difference - Google Patents
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Abstract
The invention discloses a hierarchical V2G scheduling optimization method considering the difference of vehicle grid-connected service time length and considering the difference of vehicle grid-connected service time length. The method comprises a V2G demand response and scheduling process and a vehicle charging and discharging scheduling model with optimal benefits for an electric vehicle service main body. Under the condition that various constraint conditions of a V2G dispatching system are met, the vehicle cost can be saved after the electric vehicle responds to the demand through a V2G technology, the stability of the dispatchable load and the capacity of the electric vehicle is ensured through resource integration by a V2G load aggregator, the whole load fluctuation of a power grid is effectively reduced, and the operation benefit of the power grid is improved. The method is different from V2G scheduling with minimized load fluctuation of a power system, takes the aim of optimal economic benefit into consideration from the service cost and benefit of vehicles and the schedulable load characteristic of an electric vehicle cluster, fully utilizes the charge and discharge resources of the electric vehicle, improves the enthusiasm of users participating in V2G, and improves the benefits of the electric vehicle users, operators and a power grid.
Description
Technical Field
The invention relates to the technical field of energy Internet, in particular to a hierarchical V2G scheduling optimization method considering vehicle grid-connected service time difference.
Background
With the development of social economy, environmental pollution has not only gained much attention, but also the demand of people on energy and environment is higher, and the popularization of electric vehicles has become a future trend. Active policy measures have also been taken by countries to encourage the development of electric vehicles. However, with the large-scale development of electric vehicles, because the charging behavior of vehicle owners is often random, the insertion of a large number of electric vehicles into the power grid for charging will certainly cause huge pressure on the power grid consumption structure and operation. The V2G (Vehicle to Grid) technology uses an electric automobile as a load storage resource to realize bidirectional interaction between the electric automobile and a power Grid. The large-scale electric automobile participates in the operation of the power grid in the role of the distributed energy storage unit, on the premise that the basic running requirements of electric automobile users are met, when the power grid needs to adjust the load of the power grid, the electric automobile can realize energy conversion (charging and discharging) with the power grid, the buffer is provided for the power grid and the power generation of renewable energy sources, the permeability of the renewable energy sources is improved, the load fluctuation of the power grid is reduced, and the comprehensive operation efficiency of the power grid is improved.
The dispatching is an important problem in the operation control process of the power distribution system, and means that benefits of electric vehicle users, operators and a power grid are coordinated under the condition that load requirements and various constraint conditions are met, and charging and discharging operations of vehicles are optimized and dispatched by combining energy storage characteristics of the vehicles, so that the benefit of the power distribution system is maximum. The pain points of mutual conflict of benefits of multiple main bodies under the large-scale electric automobile dispatching application scene are solved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a hierarchical V2G scheduling optimization method considering the difference of vehicle grid-connected service time length.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a hierarchical V2G scheduling optimization method considering vehicle grid-connected service time difference comprises the following steps:
the method comprises the following steps: the dispatching center imports initial information of electric vehicle charge-discharge control and economic dispatching control, and performs matching analysis on power supply, load demand and electric vehicle capacity among the main bodies;
step two: the dispatching center optimizes the demand of the power grid load balance by taking the minimum response cost of the regional demand side as a target, and sends an optimization result to a V2G load aggregator;
step three: AGs send V2G service offers to the EV cluster;
step four: AGs acquire offer information returned by EV users, and distribute EV vehicle groups to corresponding V2G service points for vehicle grid connection;
step five: AGs construct a vehicle optimization scheduling model based on mixed integers according to the conditions of respective areas, and set a user profit maximization target equation of an area V2G scheduling system;
step six: AGs issue a charge and discharge operation instruction of a specified vehicle to a charging pile;
step seven: the EV starts to execute charge and discharge invitations, the AGs adjusts the charge and discharge power of the EVs in real time according to the load, and the V2G invitations are continuously sent to the off-network EV cluster;
step eight: updating the EVs execution condition, and feeding back the load total response execution condition to the dispatching center; and returning to the first step for continuous execution, and performing loop iteration updating.
Compared with the prior art, the invention has the beneficial effects that:
the invention can not only save the cost of the vehicle owner, but also reduce the overall load fluctuation of the power grid and improve the benefits of the power grid. Compared with economic dispatching without electric automobiles, the method provided by the invention fully considers the charging cost and the service cost of the electric automobile owner and the load characteristic of the electric automobile cluster, and improves the benefits of electric automobile users, operators and a power grid.
Drawings
FIG. 1 is a schematic diagram of a hierarchical V2G scheduling optimization method taking into account differences in vehicle grid-connected service durations according to the present invention;
FIG. 2 is a diagram illustrating a dispatch control center, agents and EVs in accordance with an embodiment of the present invention;
FIG. 3 is a model diagram of a scheduling framework in an embodiment of the invention;
FIG. 4 is a node distribution diagram of an electrical power system according to an embodiment of the present invention;
FIG. 5 is a standard of electricity price for EV charging and discharging service under peak-valley time-of-use electricity price excitation in an embodiment of the present invention;
fig. 6 is a charging/discharging electricity price standard of the EV service under the time-of-use electricity price mechanism for promoting the consumption of the renewable energy in the embodiment of the present invention;
FIG. 7 is a diagram illustrating a distribution of EV charge/discharge behavior quantity in each time period according to a peak-valley time-of-use price mechanism in the embodiment of the present invention;
fig. 8 is a distribution diagram of the number of EV charge/discharge behaviors at each time period in the electricity price mechanism for promoting the consumption of renewable energy in the embodiment of the present invention.
Detailed Description
Example (b):
in order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the following detailed description of the present invention is provided with reference to the accompanying drawings and detailed description. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings.
In the present application, DC refers to a dispatch center, AG(s) refers to an agent (s stands for plural), and EV(s) refers to an electric vehicle (s stands for plural).
The application discloses a hierarchical V2G scheduling optimization method considering vehicle grid-connected service duration difference, wherein the main charging cost, the discharging benefit and the charging and discharging service expenditure of an electric vehicle are used as target functions of a target model, and the electric vehicle participates in vehicle network interaction in a mode of responding to a scheduling command. And solving the model by adopting a multilayer optimization scheduling method under the condition of meeting various constraint conditions of the V2G scheduling system. After the electric automobile adopts the V2G technology to participate in economic dispatching, the cost of an automobile owner can be saved, the overall load fluctuation of a power grid can be reduced, and the benefits of the power grid are improved. Compared with economic dispatching without electric vehicles, the method provided by the invention fully considers the charging cost and the service cost of the electric vehicle owner and the load characteristics of the electric vehicle cluster, and improves the benefits of three parties, namely an electric vehicle user, an operator (equivalent AG) and a power grid. Specifically, as shown in fig. 1, the hierarchical V2G scheduling optimization method considering the vehicle grid-connected service time difference includes the following steps:
the method comprises the following steps: the dispatching center imports initial information of electric vehicle charge-discharge control and economic dispatching control, and performs matching analysis on power supply, load demand and electric vehicle capacity among the main bodies; each subject refers to a subject participating in electric power, that is, an electric vehicle user, AGs, and a grid
Step two: the dispatching center optimizes the demand of the power grid load balance by aiming at minimizing the response cost of the regional demand side, and sends the optimization result to a V2G load aggregation operator (equivalent AGs);
step three: AGs send V2G service offers to the EV cluster;
step four: AGs acquire offer information returned by EV users, and distribute EV vehicle groups to corresponding V2G service points for vehicle grid connection;
step five: AGs construct a vehicle optimization scheduling model based on mixed integers according to vehicle information, price information, participation subject load information and the like of respective areas, and set a user profit maximization target equation of a V2G scheduling system of the areas;
step six: AGs issue a charge and discharge operation instruction of a specified vehicle to a charging pile;
step seven: the EV starts to execute charge and discharge invitations, the AGs adjusts the charge and discharge power of the EVs in real time according to the load, and the V2G invitations are continuously sent to the off-network EV cluster;
step eight: and updating the EVs execution condition, and feeding back the load overall response execution condition to the dispatching center. And returning to the first step for continuous execution, and performing loop iteration updating.
Specifically, in the first step, the initial information of the electric vehicle charge/discharge control and the economic dispatch control includes vehicle information of an electric vehicle cluster participating in the V2G offer, various price information, information of each participating body load, and feedback information of AGs.
The vehicle information of the electric automobile cluster comprises: number N of vehicles participating in V2G service, and charging rated power of electric automobile
Electric automobile discharges rated power->
Rated capacity Cap of battery i Upper battery charge limit >>
Lower limit of battery discharge->
Vehicle participating in V2G service i =1,2,3 \ 8230N, scheduling time T =1,2,3 \ 8230 |, T |, where T | i For the time of the i-th vehicle's start of the merging>
The off-grid time of the ith vehicle;
the various price information includes: charging time-sharing price of electric automobile
Electric automobile discharge time-sharing price
Service charge SU for electric vehicle charging at each time i Service charge SD for each discharge of electric vehicle i ；
The participating subject load information includes: with principal participation in the gridSupporting power
Output power of renewable energy source->
Regular load power of the system->
The feedback information of the AGs includes: the quantity and the information of the current grid-connected electric vehicles of the charging piles below the AGs and the load demand information below the AGs.
In the fifth step, the vehicle optimized dispatching model based on the mixed integers is constructed, and a user profit maximization target equation of the regional V2G dispatching system is set as follows:
in the above formula, i =1,2,3 \ 8230n, and N denotes a vehicle with reference to V2G service. T =1,2,3 \8230, | T | represents a participation schedule period;
the charging time-sharing price of the electric automobile is represented; />
The time-sharing price of electric vehicle discharge is represented; SU i Representing the service charge of each charging of the electric automobile; SD i And represents the service charge of each discharge of the electric automobile.
The vehicle optimal scheduling model of the mixed integer should satisfy the following constraint conditions:
V2G system with vehicle service status constraints
In the above formula, the decision variables include
Wherein it is present>
Indicates the charging status of the i-th electric vehicle during the period t>
Indicates a charging status, is asserted>
Indicating a non-charging state; />
Indicating the discharge state of the ith electric vehicle in the t period, wherein 1 indicates discharge and 0 indicates no discharge; />
The change of the ith electric automobile from idle to charging in the t period is represented, 1 represents that the ith electric automobile is converted from idle to charging in the t period, and 0 represents that the ith electric automobile is not charged in the t period; />
The change of the ith electric automobile from idle to discharge in the t period is represented, 1 represents that the i electric automobile is converted from idle to discharge in the t period, and 0 represents that the i electric automobile is not discharged in the t period; />
The change of the ith electric vehicle from charging to idle rotation in the t period is represented, 1 represents that the i electric vehicles are converted from charging to idle rotation in the t period, and 0 represents that the i electric vehicles are continuously charged in the t period; />
Indicating the ith vehicle motorThe vehicle is converted from discharging to an idle state in a time period t, 1 represents that i electric vehicles are converted from discharging to idle in the time period t, and 0 represents that i electric vehicles continuously discharge in the time period t;
the charging and discharging operation process of the electric automobile is restrained:
in the above formula, the state variables include XXX
Representing the charging power of the ith electric automobile in the t period; />
Represents the discharge power of the ith electric vehicle in the t period>
Representing the maximum SOC value of the ith electric vehicle, default to 100 percent, cap i Indicating the maximum electrical energy storage capacity of the ith electric vehicle. s it Represents the SOC value of the ith electric automobile in the t period>
Indicates an ith electric vehicle maximum hill climb limit on charge, based on the maximum hill climb limit determined by the vehicle status of the vehicle>
Indicating the discharge maximum climbing limit of the ith electric vehicle.
And electric quantity balance constraint:
in the above formula, the first and second carbon atoms are,
represents the incoming power of node m->
Representing the outgoing power of node m.
The invention is further explained below with reference to an application scenario:
the total 100 electric vehicles in the system are set to be distributed at 33 nodes, 10 vehicle types are contained, and the parameters are shown in table 1.
TABLE 1 electric vehicle parameters
The grid connection time of the electric automobile is 0:00-23:00 for a total of 24 periods. The V2G control system receives data such as renewable energy output, residential electricity load, and the like, and table 2 shows regional base load parameters and renewable energy output data.
TABLE 2 regional base load parameters and renewable energy output data
All electric vehicle users participating in scheduling receive scheduling arrangement of the scheduling center and report electric vehicle charging and discharging plans in advance. The example includes parameters such as battery capacity of 100 electric vehicles, state of charge during grid connection, maximum number of charge/discharge switches per day, and the like. The system comprises data of residential electricity load, renewable energy output, distributed power generation and the like of an IEEE-33 node power system in each time period.
Assuming that the charging prices are consistent in each time period, price =1 yuan/kilowatt hour, and the charging is only once in one day, and the charging service fee is SU i =1 yuan/time.
And calculating the charge and discharge income of the electric vehicle, wherein the charge and discharge income comprises EV charge and discharge electricity charge and service charge of each charge and discharge of the EV, which are collected by an operator. The model assumes that the charging and discharging electricity prices and the service fees of the electric automobiles at all nodes are consistent, and because electric automobile users participate in the electric power market and obtain benefits by the charging and discharging difference, the charging electricity price is set to be 0.5 yuan/kilowatt hour at all times, and the discharging electricity price is set to be 1 yuan/kilowatt hour.
The peak-valley time-of-use electricity price mechanism divides 24 hours a day into three periods of peak-valley average according to the size of the electricity load, and the corresponding charge-discharge electricity price of the electric automobile is customized in each period, so that the peak clipping and valley filling functions of the electric automobile are realized.
The EV resource earnings are charging price earnings obtained by an operator from the initial state of charge to full charge of each vehicle with the service fee deducted, and the expression is as follows:
in the formula, G 1 And representing the charging resource income under the disordered charging situation of the electric automobile.
For each vehicle charged state that is full, is charged>
The state of charge is switched on for each vehicle when charging.
Obtaining charging resource income G under the disordered charging situation of the electric automobile according to a formula (19) 1 =2560 yuan. Under the excitation of a peak-valley time-of-use electricity price mechanism, the charge and discharge resource yield after the optimized scheduling of 100 electric vehicles is G 3 =25119 yuan.
The example results show that the benefit is remarkably increased by adopting the electric automobile charge-discharge optimization scheduling model in the research compared with the disordered charge of the electric automobile. The effectiveness of the model in fully mining the electric automobile load storage potential and utilizing the load storage resource to the maximum extent is verified.
Effect analysis of peak-valley time-of-use electricity price mechanism on guiding of charging and discharging behaviors of electric vehicle
By analyzing and optimizing the aggregation of the EV charge-discharge behaviors under regulation, the guiding effect of the model on the EV charge-discharge behaviors is reflected, and the statistics of the number of the charge-discharge behaviors of the electric vehicle at each time period under the peak-valley time-of-use electricity price mechanism is shown in FIG. 4.
In fig. 4, it is shown that under peak-to-valley level of valence excitation, the occurrence of electric vehicle charging behavior is concentrated at 2:00 to 7: during the valley period of 00, the discharge behavior occurs centrally between 0 and 00 and 2 and between 7 and 00 and 18:00, mostly centered on the peak period. Therefore, the peak-valley flat electrovalence excitation measures of the part can effectively guide the charging and discharging behaviors of the electric automobile, so that the electric automobile is charged at the valley time and discharged at the peak time, and the standby service is provided for the operation of a power grid system.
Fig. 5 and 6 show the system load curves for the baseline scenario and the peak-to-valley power rate incentive scheme, respectively.
Under the reference electricity price, the peak value of the system base load when the electric automobile does not participate in charge-discharge scheduling is 6600kWh, the valley value is 1470kWh, and the peak-valley difference is 5130kWh. When 100 electric vehicles participate in charge-discharge optimization scheduling, the load peak value is 6456kWh, which is compared with 144kWh of front peak clipping. And the valley value is 1900kWh, and 430kWh is filled before the electric vehicle participates in charge and discharge optimization scheduling. The peak-to-valley difference was 4556kWh, which is reduced by 574kWh compared to that before the schedule.
Under a peak-valley average price mechanism, when the electric automobile does not participate in charge-discharge scheduling, the peak value of the system base load is 6600kWh, the valley value is 1470kWh, and the peak-valley difference is 5130kWh. When 100 electric vehicles participate in charge-discharge optimization scheduling, the load peak value is 5900kWh, compared with 700kWh before peak clipping. And the valley value is 1966kWh, and the valley value is filled with 496kWh before the electric automobile participates in charge and discharge optimization scheduling. The peak-to-valley difference was 3934kWh, which is 1196kWh smaller than before scheduling.
Under the excitation of a peak-valley flat electricity price mechanism, the load peak-valley difference 1196kWh of the system of 100 electric automobiles is reduced, and compared with the reduced 574kWh under the reference electricity price, the peak clipping and valley filling effects are enhanced by nearly one time. Analysis results show that the peak-valley time-of-use electrovalence excitation mechanism proposed by the research can play a role in peak clipping and valley filling of system loads.
The impact of the pricing strategy on the payload load peak clipping and valley filling effect is seen in fig. 7-8. In the case of three waveforms, V2G scheduling brings a significant peak clipping and valley filling effect to the peak valley of the payload, and the peak segments of 9-11 in all three waveforms are clipped below the peak line, and other peak periods are significantly shifted. For the valley section of payload 1.
V2G operators participate in investment and construction of the distributed renewable energy system, mobile energy storage resources are fully utilized, and coordination benefits of renewable energy and electric vehicles are effectively excavated. The model is in a V2G scheduling mechanism with participation of renewable energy sources.
Therefore, the method can save the cost of the vehicle owner, reduce the overall load fluctuation of the power grid and improve the benefits of the power grid. Compared with economic dispatching without electric vehicles, the method provided by the invention fully considers the charging cost and the service cost of the electric vehicle owner and the load characteristic of the electric vehicle cluster, and improves the benefits of electric vehicle users, operators and a power grid.
The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention accordingly, and not to limit the protection scope of the present invention accordingly. All equivalent changes or modifications made in accordance with the spirit of the present disclosure are intended to be covered by the scope of the present disclosure.
Claims (9)
1. A hierarchical V2G scheduling optimization method considering vehicle grid-connected service duration difference is characterized by comprising the following steps:
the method comprises the following steps: the dispatching center imports initial information of electric vehicle charge-discharge control and economic dispatching control, and performs matching analysis on power supply, load demand and electric vehicle capacity among the main bodies;
step two: the dispatching center optimizes the demand of the power grid load balance by taking the minimum response cost of the regional demand side as a target, and sends an optimization result to a V2G load aggregation operator;
step three: AGs send a V2G service invitation to the EV cluster;
step four: AGs acquire offer information returned by EV users, and distribute EV vehicle groups to corresponding V2G service points for vehicle grid connection;
step five: AGs construct a vehicle optimization scheduling model based on mixed integers according to the information of respective areas, and set a user profit maximization target equation of an area V2G scheduling system;
step six: AGs issue a charge and discharge operation instruction of a specified vehicle to a charging pile;
step seven: the EV starts to execute charge and discharge invitations, the AGs adjusts the charge and discharge power of the EVs in real time according to the load, and the V2G invitations are continuously sent to the off-network EV cluster;
step eight: updating the EVs execution condition, and feeding back the load total response execution condition to the dispatching center; and returning to the first step for continuous execution, and performing loop iteration updating.
2. The method according to claim 1, wherein in step one, the initial information of the electric vehicle charge-discharge control and economic dispatch control comprises vehicle information of electric vehicle clusters participating in the V2G offer, various price information, various participation subject load information, and feedback information of AGs.
3. The method according to claim 2, wherein the vehicle information of the electric vehicle cluster comprises: number N of vehicles participating in V2G service, and charging rated power of electric automobile
Electric automobile discharges rated power->
Rated capacity Cap of battery i Upper battery charge limit >>
Lower limit of battery discharge->
Vehicle participating in V2G service i =1,2,3 \ 8230N, scheduling time T =1,2,3 \ 8230 |, T |, where T | i Of the i-th vehicleOn the start time of the connection>
The off-grid time of the ith vehicle;
the various price information includes: charging time-sharing price of electric automobile
Electric automobile time-sharing price of discharging
Electric automobile service charge SU of charging each time i Service charge SD for each discharge of electric vehicle i ；
The participating subject load information includes: the main body participates in the supporting power of the power grid
Output power of renewable energy
Regular load power of the system->
The feedback information of the AGs comprises: the quantity and the information of the current grid-connected electric vehicles of the charging piles below the AGs and the load demand information below the AGs.
4. The method for optimizing hierarchical V2G scheduling considering difference of vehicle grid-connected service durations according to claim 1, wherein in the fifth step, the information of each region includes vehicle information, price information and participation subject load information of each region.
5. The method according to claim 1, wherein in step five, the building of the hybrid integer-based vehicle optimal scheduling model sets a user profit maximization objective equation of a regional V2G scheduling system to be:
in the above formula, i =1,2,3 \ 8230n denotes a vehicle participating in V2G service. T =1,2,3 \8230, | T | represents a participation schedule period;
representing the charging time-sharing price of the electric automobile; />
The time-sharing price of electric vehicle discharge is represented; SU i The service charge of each charging of the electric automobile is represented; SD i And represents the service charge of each discharge of the electric automobile.
6. The method of claim 5, wherein the mixed integer vehicle optimal scheduling model comprises the following constraints:
the V2G system has vehicle service state constraint, charge and discharge operation process constraint of the electric automobile and electric quantity balance constraint.
7. The method of claim 6, wherein the V2G system with vehicle service state constraints comprises:
in the above formula, the decision variables include
Wherein +>
Indicates the charging status of the i-th electric vehicle during the period t>
Indicates a charging status, is asserted>
Indicating a non-charging state; />
Indicating the discharge state of the ith electric vehicle in the t period, wherein 1 indicates discharge and 0 indicates no discharge; />
The change of the ith electric automobile from idle to charging in the t period is represented, 1 represents that the ith electric automobile is converted from idle to charging in the t period, and 0 represents that the ith electric automobile is not charged in the t period; />
The change of the ith electric vehicle from idle to discharge in the t period is represented, 1 represents that the i electric vehicles are converted from idle to discharge in the t period, and 0 represents that the i electric vehicles are not discharged in the t period; />
The change of the ith electric vehicle from charging to idle rotation in the t period is represented, 1 represents that the i electric vehicles are converted from charging to idle rotation in the t period, and 0 represents that the i electric vehicles are continuously charged in the t period; />
Indicating that the ith electric vehicle turns to an idle state from discharging in the period t, 1 indicating that the ith electric vehicle turns to idle from discharging in the period t, and 0 indicating that the ith electric vehicle continues to discharge in the period t. />
8. The method for optimizing hierarchical V2G scheduling considering vehicle grid-connected service duration differences according to claim 6, wherein the charging and discharging operation process constraints of the electric vehicle comprise:
in the state variable of the above-mentioned formula,
representing the charging power of the ith electric automobile in the t period; />
Represents the discharge power of the ith electric vehicle in the t period>
Representing the maximum SOC value of the ith electric vehicle, default to 100 percent, cap i Representing the maximum electric energy storage capacity of the ith electric automobile; s is it Represents the SOC value of the ith electric automobile in the t period>
Indicates the ith electric vehicle charging maximum hill climbing limit and is based on the status of the electric vehicle>
Indicating the discharge maximum climbing limit of the ith electric vehicle.
9. The method of claim 6, wherein the electrical balance constraints comprise:
in the above formula, the first and second carbon atoms are,
represents the incoming power of node m->
Representing the outgoing power of node m. />
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