CN114511156B - Ordered charging optimization method and device containing partial disordered charging - Google Patents
Ordered charging optimization method and device containing partial disordered charging Download PDFInfo
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Abstract
The invention discloses an ordered charging optimization method and device comprising partial unordered charging, which are used for determining ordered charging electric vehicles and unordered charging electric vehicles connected in a distribution network according to the permeability of the electric vehicles in the distribution network and the responsivity of ordered charging users, taking the unordered charging electric vehicles as new conventional loads, constructing an ordered charging optimization model of the electric vehicles aiming at the minimum total network loss of the distribution network, carrying out ordered charging optimization on the electric vehicles based on the model, orderly controlling the large-scale electric vehicles connected into a power grid, guiding users to charge scientifically and reasonably, thereby reducing the charging cost of the electric vehicle owners, reducing the peak-valley difference of the loads, stabilizing the load fluctuation, improving the running economy, reliability and safety of the power grid, improving the power quality of the power distribution network and the like, and realizing benign safety interaction of the charging load of the electric vehicles and the power grid.
Description
Technical Field
The invention relates to an ordered charging optimization method and device containing partial unordered charging, and belongs to the technical field of ordered charging of electric automobiles.
Background
The continuous development of the energy internet and new energy technology gradually diversifies the energy forms in the power grid, reduces the environmental pollution speed, and increases the instability of the power system. With the increase of the charging demand of the electric automobile, the large-scale electric automobile connected with the power grid can have non-negligible influence on the aspects of stability, reliability, electric energy quality and the like of the power distribution network. In an actual power distribution network, the situation that part of ordered parts are unordered exists in electric automobile charging, and a charging optimization method suitable for the situation is not available at present.
Disclosure of Invention
The invention provides an ordered charging optimization method and device containing partial unordered charging, which solve the problems disclosed in the background technology.
In order to solve the technical problems, the invention adopts the following technical scheme:
An ordered charge optimization method including partial unordered charging, comprising:
determining ordered charging electric vehicles and disordered charging electric vehicles which are connected in the distribution network according to the permeability of the electric vehicles in the distribution network and the responsiveness of ordered charging users; the responsiveness of the orderly charging user is the proportion of the number of the electric vehicles participating in orderly charging to the total number of the electric vehicles;
Taking the unordered charged electric automobile as a new conventional load, and constructing an ordered charging optimization model of the electric automobile aiming at the minimum total network loss of the distribution network according to the new conventional load, the original conventional load of the distribution network node and the ordered charged electric automobile;
And according to the ordered charging optimization model of the electric automobile, carrying out ordered charging optimization of the electric automobile.
According to the electric automobile ordered charging optimization model, electric automobile ordered charging optimization is carried out, and the method comprises the following steps:
and taking the starting charging time of the electric automobile as an optimization variable, and solving an ordered charging optimization model of the electric automobile by adopting a particle swarm optimization algorithm to obtain the minimum total network loss of the distribution network.
The objective function of the ordered charging optimization model of the electric automobile is as follows:
Wherein W is an objective function of an ordered charging optimization model of the electric automobile, L is the number of distribution network branches, deltat is the length of an ordered charging time period of the electric automobile, R l is the resistance of the first branch, and I l,t is the current of the first branch at the moment t;
Wherein U l,t is the voltage of the first branch at time t, For the active power flowing into the first branch at time t under the original normal load,The active power variable quantity of the first branch when the electric automobile is in disordered charge at the moment t,The active power variation quantity of the first branch caused by ordered charging optimization of the electric automobile with disordered charging at the t moment,Reactive power flowing into the first branch at time t under the original normal load.
Constraint conditions of the ordered charging optimization model of the electric automobile are as follows:
TS≤T≤TE-TC
Wherein, T S is charging start time, T E is charging end time, T is ordered charging time of the electric vehicle, and T C is ordered charging time of the electric vehicle;
SOCmin≤SOC≤SOCmax
The SOC is the state of charge of the power battery of the electric automobile, the SOC min is the minimum value of the SOC, and the SOC max is the maximum value of the SOC;
The SOC le is the expected state of charge of the power battery when the electric vehicle starts, the SOC re is the expected state of charge of the power battery when the electric vehicle returns, Δt is the length of the ordered charging time period of the electric vehicle, E is the capacity of the electric battery, P s,t is the ordered charging power of the electric vehicle at the time t, and η is the charging efficiency;
Wherein N is the number of electric vehicles orderly charged in the distribution network, gamma is the response of orderly charged users, M i is the number of orderly charged electric vehicles distributed to the ith node, N i,t is the sum of the numbers of electric vehicles arranged at each charging moment of the ith node in the optimization process, The method comprises the steps that a node set for ordered charging is obtained, t S is an ordered charging optimization starting time, and t E is an ordered charging optimization ending time;
Wherein P i,t is the active power injected by the ith node at the time t, Q i,t is the reactive power injected by the ith node at the time t, U i,t is the voltage of the ith node at the time t, U j,t is the voltage of the jth node at the time t, N' is the total number of all nodes connected with the ith node, G ij is the conductance of a branch ij, B ij is the susceptance of the branch ij, and theta ij,t is the phase angle difference between the ith node and the jth node at the time t;
Vmin≤Uk≤Vmax,k＝1,2,…,Z
Wherein U k is the voltage of the kth node in the distribution network, Z is the maximum node number in the distribution network, V min is the lowest voltage limit of U k, and V max is the highest voltage limit of U k.
An in-order charge optimization device including partial unordered charging, comprising:
The load determining module is used for determining ordered charging electric vehicles and disordered charging electric vehicles which are connected in the distribution network according to the permeability of the electric vehicles in the distribution network and the responsiveness of ordered charging users; the responsiveness of the orderly charging user is the proportion of the number of the electric vehicles participating in orderly charging to the total number of the electric vehicles;
The model construction module is used for taking the unordered charged electric automobile as a new conventional load and constructing an ordered charging optimization model of the electric automobile aiming at the minimum total network loss of the distribution network according to the new conventional load, the original conventional load of the distribution network node and the ordered charged electric automobile;
and the optimizing module is used for carrying out ordered charging optimization of the electric automobile according to the ordered charging optimizing model of the electric automobile.
And the optimization module is used for taking the starting charging time of the electric automobile as an optimization variable, and solving an ordered charging optimization model of the electric automobile by adopting a particle swarm optimization algorithm to obtain the minimum total network loss of the distribution network.
In the model construction module, the objective function of the ordered charging optimization model of the electric automobile is as follows:
Wherein W is an objective function of an ordered charging optimization model of the electric automobile, L is the number of distribution network branches, deltat is the length of an ordered charging time period of the electric automobile, R l is the resistance of the first branch, and I l,t is the current of the first branch at the moment t;
Wherein U l,t is the voltage of the first branch at time t, For the active power flowing into the first branch at time t under the original normal load,The active power variable quantity of the first branch when the electric automobile is in disordered charge at the moment t,The active power variation quantity of the first branch caused by ordered charging optimization of the electric automobile with disordered charging at the t moment,Reactive power flowing into the first branch at time t under the original normal load.
In the model building module, constraint conditions of the ordered charging optimization model of the electric automobile are as follows:
TS≤T≤TE-TC
Wherein, T S is charging start time, T E is charging end time, T is ordered charging time of the electric vehicle, and T C is ordered charging time of the electric vehicle;
SOCmin≤SOC≤SOCmax
The SOC is the state of charge of the power battery of the electric automobile, the SOC min is the minimum value of the SOC, and the SOC max is the maximum value of the SOC;
The SOC le is the expected state of charge of the power battery when the electric vehicle starts, the SOC re is the expected state of charge of the power battery when the electric vehicle returns, Δt is the length of the ordered charging time period of the electric vehicle, E is the capacity of the electric battery, P s,t is the ordered charging power of the electric vehicle at the time t, and η is the charging efficiency;
Wherein N is the number of electric vehicles orderly charged in the distribution network, gamma is the response of orderly charged users, M i is the number of orderly charged electric vehicles distributed to the ith node, N i,t is the sum of the numbers of electric vehicles arranged at each charging moment of the ith node in the optimization process, The method comprises the steps that a node set for ordered charging is obtained, t S is an ordered charging optimization starting time, and t E is an ordered charging optimization ending time;
Wherein P i,t is the active power injected by the ith node at the time t, Q i,t is the reactive power injected by the ith node at the time t, U i,t is the voltage of the ith node at the time t, U j,t is the voltage of the jth node at the time t, N' is the total number of all nodes connected with the ith node, G ij is the conductance of a branch ij, B ij is the susceptance of the branch ij, and theta ij,t is the phase angle difference between the ith node and the jth node at the time t;
Vmin≤Uk≤Vmax,k＝1,2,…,Z
Wherein U k is the voltage of the kth node in the distribution network, Z is the maximum node number in the distribution network, V min is the lowest voltage limit of U k, and V max is the highest voltage limit of U k.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform an ordered charge optimization method comprising partial unordered charging.
A computing device comprising one or more processors, one or more memories, and one or more programs, wherein one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing an ordered charge optimization method comprising partially unordered charging.
The invention has the beneficial effects that: according to the permeability and the responsiveness of ordered charging users of the electric vehicles in the distribution network, the ordered charging electric vehicles and the unordered charging electric vehicles which are connected in the distribution network are determined, the unordered charging electric vehicles are used as new conventional loads, an ordered charging optimization model of the electric vehicles with the minimum total network loss of the distribution network as a target is constructed, the ordered charging optimization of the electric vehicles is carried out based on the model, the large-scale electric vehicles can be connected into a power grid for ordered control, the users are guided to charge scientifically and reasonably, and therefore charging cost of owners of the electric vehicles is reduced, peak-valley difference of the loads is reduced, load fluctuation is stabilized, running economy, reliability and safety of the power grid are improved, power quality of the power distribution network is improved, and the like, so that benign safe interaction between the charging load of the electric vehicles and the power grid is realized.
Drawings
FIG. 1 is a flow chart of an ordered charge optimization method;
Fig. 2 is a system structure diagram of a two-node power distribution network for load access of an electric automobile;
FIG. 3 is a block diagram of an IEEE-33 node power distribution network;
FIG. 4 is a graph of load change at different permeabilities;
FIG. 5 is a graph of load change at different user responsivities;
FIG. 6 is a graph of network loss variation for different user responsivities;
FIG. 7 is a graph of typical node charge load change at 0 responsivity;
FIG. 8 is a graph of typical node charge load change at 50% responsiveness;
Fig. 9 is a graph of typical node charge load change at 90% responsiveness.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
As shown in fig. 1, an ordered charge optimization method including partial unordered charging includes the following steps:
Step 1, determining ordered charging electric vehicles and disordered charging electric vehicles which are connected in a distribution network according to the permeability of the electric vehicles in the distribution network and the responsiveness of ordered charging users; the responsiveness of the orderly charging user is the proportion of the number of the electric vehicles participating in orderly charging to the total number of the electric vehicles;
Step 2, taking the unordered charged electric automobile as a new conventional load, and constructing an ordered charging optimization model of the electric automobile aiming at the minimum total network loss of the distribution network according to the new conventional load, the original conventional load of the distribution network node and the ordered charged electric automobile;
and 3, according to the ordered charging optimization model of the electric automobile, carrying out ordered charging optimization of the electric automobile.
According to the method, the ordered charging electric vehicles and the unordered charging electric vehicles which are connected in the distribution network are determined according to the permeability of the electric vehicles in the distribution network and the responsiveness of ordered charging users, the unordered charging electric vehicles are used as new conventional loads, an ordered charging optimization model of the electric vehicles with the minimum total network loss of the distribution network as a target is built, the ordered charging optimization of the electric vehicles is carried out based on the model, the large-scale electric vehicles can be connected into a power grid for ordered control, the users are guided to charge scientifically and reasonably, and therefore charging cost of owners of the electric vehicles is reduced, load peaks and valleys are reduced, load fluctuation is stabilized, power grid operation economy, reliability and safety are improved, power quality of a power distribution network is improved, and the like, so that benign safe interaction between the charging load of the electric vehicles and the power grid is realized.
Before optimization, the influence of disordered charging of the electric automobile on the network loss of the power distribution network is analyzed, taking the structure of fig. 2 as an example, wherein V i EV is the voltage of a node i after the electric automobile is connected to the network, V j EV is the voltage of a node j after the electric automobile is connected to the network, Z ij is the impedance of a line ij, and P ij L,EV is the active loss generated on Z ij after the electric automobile is connected to the network.
The power on the line is ignored, and the active power and the reactive power transmitted on the line are respectively:
Wherein, P ij EV is the active power of the node i flowing to the node j after the electric automobile is connected to the distribution network, Q ij EV is the reactive power of the node i flowing to the node j after the electric automobile is connected to the distribution network, N1 is the total number of nodes between the nodes i and j, P EV,n is the active power generated by the electric automobile connected to the node N between the nodes i and j, Q EV,n is the reactive power generated by the electric automobile connected to the node N between the nodes i and j, P L,n is the active power generated by the normal load connected to the node N between the nodes i and j, and Q L,n is the reactive power generated by the normal load connected to the node N between the nodes i and j.
Neglecting the phase offset between the nodes i and j, the active power loss generated after the electric automobile is connected to the distribution network is as follows:
Where R ij+jXij, Z ij,Rij, is the resistance on line ij and X ij is the reactance on line ij.
If the voltage level of the node n is low, the voltage of the node after being connected to the charging load of the large-scale electric automobile can be further reduced, so that the network loss of the distribution network is increased, and the safety and economy operation of the distribution network are adversely affected.
Based on the analysis, under the condition of determining the topology structure of the distribution network and the size of the conventional load, a distribution network model can be obtained, and ordered charging nodes and unordered charging nodes are arranged; according to the permeability of the electric vehicles in the distribution network, a certain amount of electric vehicles are distributed to each node according to the proportion of the conventional load to the total load, and the response of ordered charging users can be set in consideration of the unordered charging load, namely the proportion of the number of the electric vehicles participating in ordered charging to the total number of the electric vehicles.
According to the permeability of the electric vehicles in the distribution network and the response of ordered charging users, the ordered charging electric vehicles and disordered charging electric vehicles connected in the distribution network can be determined.
Taking the unordered charged electric automobile as a new conventional load, constructing and solving an electric automobile ordered charging optimization model aiming at the minimum total network loss of the distribution network according to the new conventional load, the original conventional load of the distribution network node and the ordered charged electric automobile, and carrying out ordered charging optimization on the electric automobile, wherein the method specifically can be as follows:
and superposing the original conventional loads of all nodes in the electric vehicle participating in disordered charging and the distribution network to obtain a new conventional load, optimizing the charging starting time of the electric vehicle subjected to ordered charging on the basis, superposing the ordered charging load of the electric vehicle on the new conventional load, taking the charging starting time of the electric vehicle as an optimization variable, and solving an ordered charging optimization model by adopting a particle swarm optimization algorithm to obtain the minimum total network loss of the distribution network.
When the electric automobile is connected into the distribution network, the distribution network power flow can be influenced, so that the line loss can be increased, the electric automobile ordered charging can be realized by taking the minimum distribution network total network loss as an optimization target in consideration of the economic operation of the power grid, and the objective function of the electric automobile ordered charging optimization model can be as follows:
Wherein W is an objective function of an ordered charging optimization model of the electric automobile, L is the number of distribution network branches, deltat is the length of an ordered charging time period of the electric automobile, one hour is taken, R l is the resistance of the first branch, and I l,t is the current of the first branch at the moment t;
Wherein U l,t is the voltage of the first branch at time t, For the active power flowing into the first branch at time t under the original normal load,The active power variable quantity of the first branch when the electric automobile is in disordered charge at the moment t,The active power variation quantity of the first branch caused by ordered charging optimization of the electric automobile with disordered charging at the t moment,Reactive power flowing into the first branch at time t under the original normal load.
The constraints may be:
1) Charging time period constraints;
The randomness of the charging load of the electric automobile is mainly influenced by the travel rule of a user, and the charging time is determined according to the travel rule:
TS≤T≤TE-TC
Wherein, T S is charging start time, T E is charging end time, T is ordered charging time of the electric vehicle, and T C is ordered charging time of the electric vehicle;
2) State of charge constraints of the power battery;
SOCmin≤SOC≤SOCmax
The SOC is the state of charge of the power battery of the electric automobile, the SOC min is the minimum value of the SOC, and the SOC max is the maximum value of the SOC;
3) Electric quantity balance constraint;
based on travel demands, the electric automobile user needs to fully charge or charge the power battery to a desired state in a chargeable time period, and the expression is as follows:
The SOC le is the expected state of charge of the power battery when the electric vehicle starts, the SOC re is the expected state of charge of the power battery when the electric vehicle returns, Δt is the length of the ordered charging time period of the electric vehicle, E is the capacity of the electric battery, P s,t is the ordered charging power of the electric vehicle at the time t, and η is the charging efficiency;
4) The number of the electric vehicles to be charged is restricted;
assuming that N electric vehicles are connected to the ordered charging nodes in the distribution network area, ensuring that the sum of the number of electric vehicles arranged at each charging moment by each node in the charging optimization process is equal to the total number of electric vehicles on the ordered charging nodes, namely
Wherein N is the number of electric vehicles orderly charged in the distribution network, gamma is the response of orderly charged users, M i is the number of orderly charged electric vehicles distributed to the ith node, N i,t is the sum of the numbers of electric vehicles arranged at each charging moment of the ith node in the optimization process,The method comprises the steps that a node set for ordered charging is obtained, t S is an ordered charging optimization starting time, and t E is an ordered charging optimization ending time;
5) A power balance constraint;
Wherein P i,t is the active power injected by the ith node at the time t, Q i,t is the reactive power injected by the ith node at the time t, U i,t is the voltage of the ith node at the time t, U j,t is the voltage of the jth node at the time t, N' is the total number of all nodes connected with the ith node, G ij is the conductance of a branch ij, B ij is the susceptance of the branch ij, and theta ij,t is the phase angle difference between the ith node and the jth node at the time t;
6) Node voltage constraints;
After the distribution network is connected to the electric automobile, the voltage of each node in the distribution network still needs to meet the node voltage constraint:
Vmin≤Uk≤Vmax,k＝1,2,…,Z
Wherein U k is the voltage of the kth node in the distribution network, Z is the maximum node number in the distribution network, V min is the lowest voltage limit of U k, and V max is the highest voltage limit of U k.
The PSO algorithm is adopted to solve the model, and the core idea of the algorithm is that individuals in a certain population search for the optimal position, namely the optimal solution of the problem, through mutual cooperation and information sharing.
Based on the optimization model, taking the starting charging time of the electric automobile as an optimization variable, solving the model by adopting a PSO algorithm, ensuring that each electric automobile battery can be fully charged and meet the traveling requirement of a user on the next day, and finally optimizing the output optimal solution to be the total system loss and ensuring that the voltage level of the power grid is in a reasonable range; in the PSO algorithm, the dimension (variable number) of each particle is the total number of electric vehicles participating in orderly charging; the specific solution process may be as follows:
S1) initializing basic parameters of a distribution network, wherein the basic parameters comprise line parameters, regular load data of each node, ordered charging positions, unordered charging positions, the number of electric vehicles of each node and the like;
S2) setting the responsivity of ordered charging users, simulating unordered charging load of the electric automobile, and updating the conventional load of each node;
S3) setting particle swarm simulation parameters, initializing a swarm, and randomly generating charging starting moments of the electric vehicles participating in ordered charging;
s4) carrying out distribution network power flow calculation, solving a fitness value corresponding to each particle, namely the total network loss of the system, and finding out an individual optimal value and a group optimal value;
S5) starting iteration, updating the speed and the position of each particle, recalculating the fitness value, and finding out new individual extremum and population extremum by comparing the fitness value with the previously calculated fitness value;
s6) judging whether the program is converged, if the convergence condition is not met, increasing the iteration times, and returning to S15 to continue to execute calculation; and if the convergence condition is met, namely, finding the global optimal solution or reaching the maximum iteration number, ending the operation.
In order to verify the effectiveness of the method, the simulation is carried out by taking IEEE-33 as an example shown in fig. 3, the reference capacity of a distribution network system is 10MVA, the rated voltage of a power supply end is 12.66kV, the total active load of each node is 3715kW, and the reactive load is 2300kVar. The node 1 is directly connected with a power supply and is used as a balance node, and the other nodes are all used as PQ nodes; nodes 2, 3, 4, 7, 8, 14, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31 and 32 are selected as ordered charging nodes, and the rest are unordered charging nodes.
As can be seen from fig. 4, as the permeability of the electric vehicles increases, i.e. the number of electric vehicles increases, the peak-valley difference of the load of the distribution network system increases, and the load fluctuation increases, so that the difficulty of peak clipping and valley filling of the power grid increases.
1) Load analysis under user responsiveness for different ordered charges
Taking 50% permeability of the electric automobile as an example for analysis, user responsivity participating in ordered charging is respectively set to be 0%, 30%, 50%, 70% and 90%, wherein 0% of user responsivity indicates that the electric automobile is subjected to disordered charging, and system load change curves under different user responsivities are shown in fig. 5.
When the electric vehicles are all charged in disorder, the load reaches a peak value around 19 hours at night. As more and more users participate in orderly charging of the electric automobile, the load peak Gu Chalv is smaller and smaller, and the effects of peak clipping, valley filling and load fluctuation stabilization are more obvious. But the peak load is still high, mainly because some users can only participate in unordered charging and some users are reluctant to participate in ordered charging.
2) Network loss analysis under responsivity of different ordered charging users
As can be seen from fig. 6, as the user responsiveness of the electric vehicle increases, the network loss gradually decreases in one day compared with the network loss during all disorder charging, and the economic benefit generated by the optimization method is very considerable.
3) Ordered charge node charge load comparison analysis
Nodes 3, 7 and 14 are selected to give load curves at the responsivity of the user of the 0%, 50% and 90% ordered charging on the corresponding nodes, as shown in fig. 7, 8 and 9.
After the electric automobile participates in orderly charging, the peak value of the charging power in the electricity consumption low peak period is higher in 0-8 hours, the peak value is higher along with the improvement of user responsiveness, and the electricity consumption high peak period is almost not charged in the evening to the early 0 hours, so that the optimization method can reduce the network loss of the system, avoid the electric automobile from being charged in the electricity consumption high peak period, and effectively reduce the peak-valley difference of the load of the power distribution network.
Based on the same technical scheme, the invention also discloses a software device of the method, and an ordered charging optimization method comprising partial unordered charging, which comprises the following steps:
The load determining module is used for determining ordered charging electric vehicles and disordered charging electric vehicles which are connected in the distribution network according to the permeability of the electric vehicles in the distribution network and the responsiveness of ordered charging users; the user responsiveness of orderly charging is the proportion of the number of electric vehicles participating in orderly charging to the total number of the electric vehicles.
The model construction module is used for taking the unordered charged electric automobile as a new conventional load, and constructing an ordered charging optimization model of the electric automobile aiming at the minimum total network loss of the distribution network according to the new conventional load, the original conventional load of the distribution network node and the ordered charged electric automobile.
In the model construction module, the objective function of the ordered charging optimization model of the electric automobile is as follows:
Wherein W is an objective function of an ordered charging optimization model of the electric automobile, L is the number of distribution network branches, deltat is the length of an ordered charging time period of the electric automobile, R l is the resistance of the first branch, and I l,t is the current of the first branch at the moment t;
Wherein U l,t is the voltage of the first branch at time t, For the active power flowing into the first branch at time t under the original normal load,The active power variable quantity of the first branch when the electric automobile is in disordered charge at the moment t,The active power variation quantity of the first branch caused by ordered charging optimization of the electric automobile with disordered charging at the t moment,Reactive power flowing into the first branch at the moment t under the original conventional load;
constraint conditions of the ordered charging optimization model of the electric automobile are as follows:
TS≤T≤TE-TC
Wherein, T S is charging start time, T E is charging end time, T is ordered charging time of the electric vehicle, and T C is ordered charging time of the electric vehicle;
SOCmin≤SOC≤SOCmax
The SOC is the state of charge of the power battery of the electric automobile, the SOC min is the minimum value of the SOC, and the SOC max is the maximum value of the SOC;
The SOC le is the expected state of charge of the power battery when the electric vehicle starts, the SOC re is the expected state of charge of the power battery when the electric vehicle returns, Δt is the length of the ordered charging time period of the electric vehicle, E is the capacity of the electric battery, P s,t is the ordered charging power of the electric vehicle at the time t, and η is the charging efficiency;
Wherein N is the number of electric vehicles orderly charged in the distribution network, gamma is the response of orderly charged users, M i is the number of orderly charged electric vehicles distributed to the ith node, N i,t is the sum of the numbers of electric vehicles arranged at each charging moment of the ith node in the optimization process, The method comprises the steps that a node set for ordered charging is obtained, t S is an ordered charging optimization starting time, and t E is an ordered charging optimization ending time;
Wherein P i,t is the active power injected by the ith node at the time t, Q i,t is the reactive power injected by the ith node at the time t, U i,t is the voltage of the ith node at the time t, U j,t is the voltage of the jth node at the time t, N' is the total number of all nodes connected with the ith node, G ij is the conductance of a branch ij, B ij is the susceptance of the branch ij, and theta ij,t is the phase angle difference between the ith node and the jth node at the time t;
Vmin≤Uk≤Vmax,k＝1,2,…,Z
Wherein U k is the voltage of the kth node in the distribution network, Z is the maximum node number in the distribution network, V min is the lowest voltage limit of U k, and V max is the highest voltage limit of U k.
And the optimization module is used for taking the starting charging time of the electric automobile as an optimization variable, and solving an ordered charging optimization model of the electric automobile by adopting a particle swarm optimization algorithm to obtain the minimum total network loss of the distribution network.
Based on the same technical solution, the present invention also discloses a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform an ordered charge optimization method comprising partial unordered charging.
Based on the same technical solution, the invention also discloses a computing device comprising one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, and the one or more programs comprise instructions for executing the ordered charge optimization method comprising partial unordered charging.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather as providing for the use of additional embodiments and advantages of all such modifications, equivalents, improvements and similar to the present invention are intended to be included within the scope of the present invention as defined by the appended claims.
Claims (6)
1. An ordered charge optimization method including partial unordered charging, comprising:
determining ordered charging electric vehicles and disordered charging electric vehicles which are connected in the distribution network according to the permeability of the electric vehicles in the distribution network and the responsiveness of ordered charging users; the responsiveness of the orderly charging user is the proportion of the number of the electric vehicles participating in orderly charging to the total number of the electric vehicles;
Taking the unordered charged electric automobile as a new conventional load, and constructing an ordered charging optimization model of the electric automobile aiming at the minimum total network loss of the distribution network according to the new conventional load, the original conventional load of the distribution network node and the ordered charged electric automobile;
The objective function of the ordered charging optimization model of the electric automobile is as follows:
Wherein W is an objective function of an ordered charging optimization model of the electric automobile, L is the number of distribution network branches, deltat is the length of an ordered charging time period of the electric automobile, R l is the resistance of the first branch, and I l,t is the current of the first branch at the moment t;
Wherein U l,t is the voltage of the first branch at time t, For the active power flowing into the first branch at time t under the original normal load,The active power variable quantity of the first branch when the electric automobile is in disordered charge at the moment t,The active power variation quantity of the first branch caused by ordered charging optimization of the electric automobile with disordered charging at the t moment,Reactive power flowing into the first branch at the moment t under the original conventional load;
constraint conditions of the ordered charging optimization model of the electric automobile are as follows:
TS≤T≤TE-TC
Wherein, T S is charging start time, T E is charging end time, T is ordered charging time of the electric vehicle, and T C is ordered charging time of the electric vehicle;
SOCmin≤SOC≤SOCmax
The SOC is the state of charge of the power battery of the electric automobile, the SOC min is the minimum value of the SOC, and the SOC max is the maximum value of the SOC;
The SOC le is the expected power battery charge state when the electric automobile starts, the SOC re is the expected power battery charge state when the electric automobile returns, E is the electric battery capacity, P s,t is the ordered charging power of the electric automobile at the time t, and eta is the charging efficiency;
Wherein N is the number of electric vehicles orderly charged in the distribution network, gamma is the response of orderly charged users, M i is the number of orderly charged electric vehicles distributed to the ith node, N i,t is the sum of the numbers of electric vehicles arranged at each charging moment of the ith node in the optimization process, The method comprises the steps that a node set for ordered charging is obtained, t S is an ordered charging optimization starting time, and t E is an ordered charging optimization ending time;
Wherein P i,t is the active power injected by the ith node at the time t, Q i,t is the reactive power injected by the ith node at the time t, U i,t is the voltage of the ith node at the time t, U j,t is the voltage of the jth node at the time t, N' is the total number of all nodes connected with the ith node, G ij is the conductance of a branch ij, B ij is the susceptance of the branch ij, and theta ij,t is the phase angle difference between the ith node and the jth node at the time t;
Vmin≤Uk≤Vmax,k＝1,2,…,Z
Wherein U k is the voltage of the kth node in the distribution network, Z is the maximum node number in the distribution network, V min is the lowest voltage limit of U k, and V max is the highest voltage limit of U k;
And according to the ordered charging optimization model of the electric automobile, carrying out ordered charging optimization of the electric automobile.
2. The method for optimizing ordered charging including partial disordered charging according to claim 1, wherein the optimizing the ordered charging of the electric vehicle is performed according to an ordered charging optimizing model of the electric vehicle, comprising:
and taking the starting charging time of the electric automobile as an optimization variable, and solving an ordered charging optimization model of the electric automobile by adopting a particle swarm optimization algorithm to obtain the minimum total network loss of the distribution network.
3. An ordered charge optimization device comprising a partially disordered charge, comprising:
The load determining module is used for determining ordered charging electric vehicles and disordered charging electric vehicles which are connected in the distribution network according to the permeability of the electric vehicles in the distribution network and the responsiveness of ordered charging users; the responsiveness of the orderly charging user is the proportion of the number of the electric vehicles participating in orderly charging to the total number of the electric vehicles;
The model construction module is used for taking the unordered charged electric automobile as a new conventional load and constructing an ordered charging optimization model of the electric automobile aiming at the minimum total network loss of the distribution network according to the new conventional load, the original conventional load of the distribution network node and the ordered charged electric automobile;
The objective function of the ordered charging optimization model of the electric automobile is as follows:
Wherein W is an objective function of an ordered charging optimization model of the electric automobile, L is the number of distribution network branches, deltat is the length of an ordered charging time period of the electric automobile, R l is the resistance of the first branch, and I l,t is the current of the first branch at the moment t;
Wherein U l,t is the voltage of the first branch at time t, For the active power flowing into the first branch at time t under the original normal load,The active power variable quantity of the first branch when the electric automobile is in disordered charge at the moment t,The active power variation quantity of the first branch caused by ordered charging optimization of the electric automobile with disordered charging at the t moment,Reactive power flowing into the first branch at the moment t under the original conventional load;
constraint conditions of the ordered charging optimization model of the electric automobile are as follows:
TS≤T≤TE-TC
Wherein, T S is charging start time, T E is charging end time, T is ordered charging time of the electric vehicle, and T C is ordered charging time of the electric vehicle;
SOCmin≤SOC≤SOCmax
The SOC is the state of charge of the power battery of the electric automobile, the SOC min is the minimum value of the SOC, and the SOC max is the maximum value of the SOC;
The SOC le is the expected power battery charge state when the electric automobile starts, the SOC re is the expected power battery charge state when the electric automobile returns, E is the electric battery capacity, P s,t is the ordered charging power of the electric automobile at the time t, and eta is the charging efficiency;
Wherein N is the number of electric vehicles orderly charged in the distribution network, gamma is the response of orderly charged users, M i is the number of orderly charged electric vehicles distributed to the ith node, N i,t is the sum of the numbers of electric vehicles arranged at each charging moment of the ith node in the optimization process, The method comprises the steps that a node set for ordered charging is obtained, t S is an ordered charging optimization starting time, and t E is an ordered charging optimization ending time;
Wherein P i,t is the active power injected by the ith node at the time t, Q i,t is the reactive power injected by the ith node at the time t, U i,t is the voltage of the ith node at the time t, U j,t is the voltage of the jth node at the time t, N' is the total number of all nodes connected with the ith node, G ij is the conductance of a branch ij, B ij is the susceptance of the branch ij, and theta ij,t is the phase angle difference between the ith node and the jth node at the time t;
Vmin≤Uk≤Vmax,k＝1,2,…,Z
Wherein U k is the voltage of the kth node in the distribution network, Z is the maximum node number in the distribution network, V min is the lowest voltage limit of U k, and V max is the highest voltage limit of U k;
and the optimizing module is used for carrying out ordered charging optimization of the electric automobile according to the ordered charging optimizing model of the electric automobile.
4. The ordered charging optimization device comprising partial unordered charging according to claim 3, wherein the optimization module is configured to solve an ordered charging optimization model of the electric vehicle by using a particle swarm optimization algorithm with a charging start time of the electric vehicle as an optimization variable, so as to obtain a minimum total network loss of the distribution network.
5. A computer readable storage medium storing one or more programs, characterized by: the one or more programs include instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-2.
6. A computing device, comprising:
one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-2.
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