CN109094418B - Electric automobile active sequencing charging control method based on layer-by-layer optimization strategy - Google Patents
Electric automobile active sequencing charging control method based on layer-by-layer optimization strategy Download PDFInfo
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Abstract
The invention relates to a control method, in particular to an electric vehicle active sequencing charging control method based on a layer-by-layer optimization strategy. The electric automobile sequencing charging device can more effectively and reliably sequence and charge the electric automobile. The method comprises the steps of establishing dynamic programming and optimizing layer by considering battery characteristics and driving rules of different electric vehicles; and determining the optimal charging strategy of a single electric vehicle by an inner layer optimization method and an exhaustion method, carrying out outer layer optimization according to the inner layer optimization result, and carrying out active sequencing control on the electric vehicle charging by the outer layer by a dynamic programming method.
Description
Technical Field
The invention relates to a control method, in particular to an electric vehicle active sequencing charging control method based on a layer-by-layer optimization strategy.
Background
Under the background of the era of energy crisis and environmental pollution, human beings urgently need to adjust the energy structure. The primary energy is excessive and transformed from the traditional fossil energy as a main body to low-carbon clean renewable energy and new energy, and in the final consumption and utilization of the energy, the electric automobile is taken as a high-efficiency, low-carbon and environment-friendly vehicle, has a remarkable positive effect on the high-efficiency utilization of the energy, and the development and popularization of the electric automobile have profound and long-term influence on the human energy revolution.
Under the condition that the energy crisis and the environmental pollution problem become more serious at present, the large-scale use of the electric automobile becomes a necessary trend. Because the electric automobile load has the characteristic of space-time randomness, the large-scale charging load of the electric automobile can cause the adverse effects of increasing the peak-valley difference of a power grid, disturbing the stable operation of the power distribution network and the like. Therefore, in order to alleviate the above adverse effects, it is necessary to perform sequencing control on the charging load of the electric vehicle so as to restore the power grid to an economical and stable operating state.
Disclosure of Invention
The invention provides an electric vehicle active sequencing charging control method based on a layer-by-layer optimization strategy aiming at the defects in the prior art, and the method realizes that the charging load of an electric vehicle participates in power grid peak shaving by considering the battery characteristics and the driving rule of each electric vehicle and constructing a sequencing charging model. The method can more effectively and reliably carry out sequencing charging on the electric automobiles, and provides a technical basis and a practical method for the electric automobiles to participate in power grid peak shaving.
In order to achieve the purpose, the invention adopts the following technical scheme that dynamic programming layer-by-layer optimization is established by considering the battery characteristics and the driving rule of different electric vehicles; and determining the optimal charging strategy of a single electric vehicle by an inner layer optimization method and an exhaustion method, carrying out outer layer optimization according to the inner layer optimization result, and carrying out active sequencing control on the electric vehicle charging by the outer layer by a dynamic programming method.
As a preferable aspect of the present invention, the control method includes the steps of:
2, extracting relevant parameters of an electric automobile, and establishing an optimal charging strategy model for solving the single electric automobile by an exhaustion method in the inner layer;
and 3, according to the upper-layer optimization result and by combining the local load curve characteristic, establishing a dynamic programming model for the active sequencing charging of the electric automobile by using a dynamic programming method on the outer layer.
As another preferable aspect of the present invention, the step 2 includes the steps of:
step 2.1, generating a possible charging scheme set of the electric automobile, namely an initial charging time set, according to the use requirements of users and by combining the relevant parameters of the electric automobile;
step 2.2, one of the possible charging schemes is selected, and a 96-point charging load curve of the electric automobile under the charging scheme is calculated;
step 2.3, in the initial load state, superposing the charging load curve of the electric vehicle charging scheme, judging whether the starting and stopping time of the superposed load curve meets the requirements of a vehicle owner, if so, entering the next step, otherwise, returning to the step 2.2;
2.4, calculating a power grid influence coefficient under the scheme by using the related parameters of the electric vehicle and combining a charging scheme;
step 2.5, comparing the power grid influence coefficient with the power grid influence coefficient obtained by the previous round of calculation, and selecting the smaller one as a new power grid influence coefficient;
step 2.6, judging whether the collection elements are completely taken or not, if so, entering the next step, and if not, returning to the step 2.2;
and 2.7, outputting the optimal charging strategy of the electric vehicle (namely the charging scheme of the electric vehicle which enables the target function value to be minimum).
As another preferable aspect of the present invention, the step 3 includes the steps of:
step 3.1, acquiring 24-hour charging willingness information of a user, fully knowing the requirement of the user on the charging starting moment, and counting the number of electric vehicles participating in control;
3.2, establishing a dynamic planning model according to the number of the electric automobiles participating in the control;
3.3, selecting and marking the electric vehicle which benefits the power grid most through a dynamic planning model from the optimal charging strategy of each electric vehicle obtained from the inner layer;
step 3.4, superposing the load of the electric automobile to the initial load state to be used as a new initial load state;
step 3.5, calculating the power supply state of the load partition in the new state;
step 3.6, judging whether all the electric vehicles participating in the control are considered, if the next step (step 3.7) is carried out, otherwise, skipping to the step 3.3, and continuing to carry out calculation in the next decision stage;
and 3.7, finishing the calculation and outputting an active sequencing charging control strategy of the electric automobile.
As another preferable scheme of the present invention, the relevant parameters in step 1 include electric vehicle holding capacity; the reserve includes the battery charging characteristics of the electric vehicle and the driving rules of the electric vehicle.
As another preferable aspect of the present invention, the battery charging characteristics of the electric vehicle and the driving law of the electric vehicle include a charging power of the battery, a charging capacity of the battery, and a charging mode.
As another preferable scheme of the present invention, in step 2.3, the meeting of the requirement of the vehicle owner is to fully charge the electric vehicle in a time period in which the vehicle owner wishes to charge, and the following formula is adopted:
tstart-j>tstart-j min
tend-j<tend-j max
wherein, if:
tstart-j<tj<tend-j
then:
Ki(tj)＝1；
otherwise:
Ki(tj)＝0
wherein: t is tstart-j: the moment when the electric automobile is connected to a power grid; t is tend-j: the moment when the electric automobile leaves the power grid; t is tstart-j min: the electric vehicle which can be accepted by the vehicle owner is accessed to the power grid at the earliest moment; t is tend-j max: the latest moment when the electric vehicle which can be accepted by the vehicle owner leaves the power grid; ki(tj): the charging state of the ith electric vehicle in the time period j; pEi(tj): charging power of the ith electric vehicle in a time period j; ei: the charging capacity of the ith electric vehicle; ei0: initial state of charge of the ith electric vehicle.
As another preferred embodiment of the present invention, in step 2.5, the grid influence coefficient is a weighted average of the daily peak-to-valley difference and the mean square error of the load difference from the average load difference, and is expressed by the following formula:
PDt＝PLoadt+PEt；
in the formula, PD maxAnd PD minRespectively representing the daily maximum load and the daily minimum load, wherein the difference value is the peak-valley difference; the first term represents the mean square error of the difference between the load and the average load, with the aim of minimizing load fluctuations; in order to control the influence of the power grid, weighting processing is carried out on the influence of the power grid so as to enable the influence to become a power grid influence coefficient; in the formula, λ1And λ2Each is a weight coefficient of an influence function on the power grid, the bias point of the influence target on the power grid can be adjusted and controlled by changing the weight coefficient, and when one of the weight coefficients is 0, the control target is changed into a single target;
PLoadt: representing the daily load predicted value of residents; pEt: charging power plan values of all electric vehicles in a power supply area; wherein:
PEi: charging power for a single electric vehicle; ki(t): a function for judging whether a certain single electric vehicle is charged at the moment t; if in the charging state, then Ki(t) ═ 1; otherwise Ki(t)＝0。
As another preferred scheme of the present invention, the dynamic programming method is: decomposing a multivariable optimization problem into a plurality of univariate multi-stage optimization problems through stage division; the state transition equation of the dynamic programming method is the load state of the power supply subarea; (i.e., the total load state after charging i electric vehicles;) the state transition equation is:
PDi(t)＝PD(i-1)(t)+PEi(t)；
wherein:
PDi(t): the load state of the power supply partition; pD(i-1)(t): in the (i-1) stage, i-1 load state of the automobile after the automobile enters the enclosure; pEi(t): the ith stage, i the charging load of the vehicle; in the ith stage, the charging load of i cars is considered according to different initial charging time, and an exhaustion method is adopted for optimization calculation.
Compared with the prior art, the invention has the beneficial effects.
The invention can reduce the control difficulty of the power grid. The charging load of the electric vehicle is characterized by random distribution in time and space, which also makes the control of the power grid more difficult. According to the invention, through the active sequencing control on the charging load of the electric automobile, the adverse effect of the random distribution characteristic on the power grid can be relieved to a certain extent, and the control difficulty of the power grid after the electric automobile is connected to the grid is reduced.
The invention is easy to implement, the electric automobile load has controllability, and the implementation is easy in control; meanwhile, each function has a ready algorithm or software, and the control strategy is easy to implement.
The invention is convenient for improving the economy of the power grid. With the popularization of electric automobiles and the networking of a large number of charging loads, the method can effectively reduce the peak-valley difference of the load of the power grid, improve the stability of the operation of the power grid and reduce the operation cost of the power grid to a certain extent, so that the sequencing charging control becomes a necessary way for charging the electric automobiles, and the economy of the power grid can be better improved.
Drawings
The invention is further described with reference to the following figures and detailed description. The scope of the invention is not limited to the following expressions.
Fig. 1 is a flowchart for solving the optimal charging strategy by the inner layer exhaustion method of the present invention.
FIG. 2 is a flowchart of an electric vehicle active sequencing charging control method based on a dynamic programming layer-by-layer optimization strategy.
FIG. 3 is a schematic diagram of an outer dynamic programming-based active sequencing charging strategy for an electric vehicle according to the present invention.
Detailed Description
As shown in fig. 1 to 3, the present invention includes an active sequencing charging control method for electric vehicles based on a dynamic programming layer-by-layer optimization strategy, which is to establish dynamic programming layer-by-layer optimization in consideration of battery characteristics and driving rules of different electric vehicles. The optimal charging strategy of a single electric vehicle is determined by an inner layer optimization method and an exhaustion method, outer layer optimization is carried out according to the inner layer optimization result, and the outer layer performs active sequencing control on the charging of the electric vehicle by a dynamic programming method, so that the load peak-valley difference is reduced, and the running stability of a power grid is improved. The method comprises the following specific steps:
step 1) obtaining battery characteristics and relevant parameters of a driving rule;
step 2), establishing an optimal charging strategy model for solving the single electric automobile by using an exhaustion method in the inner layer;
(1) generating a possible charging scheme set of the electric automobile, namely an initial charging time set, according to the use requirements of users;
(2) taking a certain charging scheme, and calculating a 96-point (one point every twenty-four hours and fifteen minutes) charging load sequence of the electric automobile under the charging scheme;
(3) in an initial load state, the influence of the charging load of the electric automobile is taken into account;
(4) judging whether constraint conditions are met, if so, entering into the step (5), and if not, returning to the step (2);
(5) calculating an objective function value;
(6) comparing the initial target value with the target value, and selecting the smaller one as a new target value;
(7) judging whether the collection elements are completely taken or not, if so, entering (8), and if not, returning to (2);
(8) and outputting the optimal charging strategy.
Step 3) according to the upper-layer optimization result, combining the local load curve characteristic, and establishing a multi-stage decision model for actively sequencing and charging the electric automobile by using a dynamic programming method on the outer layer;
(1) acquiring 24-hour charging wish information of a user, and fully knowing the requirement of the user on the charging starting moment;
(2) predicting the generated power of the intra-area distributed power supply and predicting the area load;
(3) calculating an equivalent load curve and a target function value under an initial load;
(4) establishing a multi-stage decision model according to the number of the electric automobiles participating in the control;
(5) calculating an optimal charging strategy for the ith electric vehicle to be charged in a network under the initial state by using an exhaustion method;
(6) selecting an optimal charging strategy from the calculation results and marking the charging automobile;
(7) superposing the ith electric automobile load to the initial load state to serve as a new initial load state;
(8) calculating a target function value in a new state;
(9) judging whether all the electric vehicles participating in the control are considered, if so, skipping to (10), otherwise, skipping to (5), and continuing to perform calculation in the next decision stage;
(10) and finishing the calculation and outputting the charging strategy.
Preferably, the acquiring of the battery characteristics and the driving law related parameters includes: the battery charging characteristics of the electric automobile, including the battery charging power, the battery charging capacity, the charging mode and the like, and the driving rule of the electric automobile.
Preferably, the constraint condition is:
tstart-j>tstart-j min
tend-j<tend-j max
wherein if:
tstart-j<tj<tend-j
then:
Ki(tj)＝1；
otherwise:
Ki(tj)＝0
wherein:
tstart-j: the moment when the electric automobile is connected to a power grid; t is tend-j: the moment when the electric automobile leaves the power grid; t is tstart-j min: the electric vehicle which can be accepted by the vehicle owner is accessed to the power grid at the earliest moment; t is tend-j max: the latest moment when the electric vehicle which can be accepted by the vehicle owner leaves the power grid; ki(tj): the charging state of the ith electric vehicle in the time period j; pEi(tj): charging power of the ith electric vehicle in a time period j; ei: the charging capacity of the ith electric vehicle; ei0: the initial charge state of the ith electric vehicle;
preferably, the control objective function is:
PDt＝PLoadt+PWt+PHt+PEt
in the formula, PD maxAnd PD minRespectively representing the daily maximum load and the daily minimum load, wherein the difference value is the peak-valley difference; the first term represents the mean square error of the difference between the load and the average load, so that the load fluctuation is minimized. To facilitate the control of the above dual targets, they are now weighted so as to be an objective function, where λ1And λ2The weight coefficients of each of the objective functions,by changing the weight coefficient, the bias point of the control target can be adjusted, and when one of the weights is 0, the control target becomes a single target.
PLoadt: representing the daily load predicted value of residents; pWt: the wind power of the power supply area is accessed to a daily generated power predicted value; pHt: a photovoltaic access daily generated power predicted value in a power supply area; pEt: charging power plan values of all electric vehicles in a power supply area;
wherein:
PEi: charging power for a single electric vehicle;
Ki(t): a function for judging whether a certain single electric vehicle is charged at the moment t; if in the charging state, then Ki(t) ═ 1; otherwise Ki(t)＝0。
Preferably, the dynamic programming layer-by-layer optimization method is as follows: a multivariable optimization problem is decomposed into a plurality of univariate multi-stage optimization problems through stage division. The load characteristic is taken as a state quantity, the charging power of each electric automobile on the layer is taken as a decision quantity, and the state transition equation is as follows:
PDi(t)＝PD(i-1)(t)+PEi(t)
wherein:
PDi(t): the load state of the power supply partition; pD(i-1)(t): in the (i-1) stage, i-1 load state of the automobile after the automobile enters the enclosure; pEi(t): the ith stage, i the charging load of the vehicle; in the above-mentioned i-th stage, the charging load of i cars is optimized by an exhaustive method in consideration of the difference according to the initial charging time.
The weight defining the path between two states:
PDij(t)＝PD(i)(t)-PDj(t)；
wherein:
PDij(t): fromAfter the state i is transferred to the state j, the change value of the load characteristic objective function is considered due to the charging strategy of the electric vehicle j; pD(i)(t): after the ith electric vehicle is connected to the network, considering the charging behaviors of all the previous i vehicles, and then obtaining the objective function value of the system load characteristic; pDj(t): after the jth electric automobile is connected to the network, considering the charging behaviors of all the jth electric automobiles, and then obtaining the objective function value of the system load characteristic; the system load characteristic objective function value is obtained after the system objective function value is added to the charging strategy of the electric vehicle j under the state i which is equal to the value of the system objective function value in the previous state.
As shown in fig. 3, the dynamic planning decision process is as follows:
initial state → decision 1 → decision 2 →.. decision n → end state.
The electric vehicle sequencing charging control process can also be described as a multi-stage optimization process. If the system arranges one electric vehicle to charge each time, the system needs to complete the charging of all the electric vehicles after m times. In the first time, the system has m choices, and the scheme which should be selected is taken as the current optimal scheme according to the optimization calculation principle; the second phase of optimization selection is then performed until the last electric vehicle.
It should be noted that when the optimal solution is selected at each stage, i.e. which electric vehicle is selected to be in the enclosure, and the active sequencing control plan is participated, several possible solutions need to be calculated, which solution benefits the system most, and therefore, the charging plan of each electric vehicle which is in the enclosure is calculated. The charging strategy of each electric vehicle has multiple selection schemes and needs to be preferentially calculated, so that the problem is optimized layer by layer, the outer layer is solved by using a dynamic programming method, and each optimization stage enables the electric vehicle with the smallest power grid influence coefficient to enter the enclosure for sequencing charging control; the inner layer selects various charging schemes of each electric automobile, and finds out a charging mode which meets the requirements of an owner and enables the influence coefficient of a power grid to be minimum. The method is essentially different from other methods in that the sequencing control of the sequencing charging process of the electric automobile is carried out by utilizing a dynamic programming layer-by-layer optimization method.
The dynamic programming layer-by-layer optimization strategy is different from the traditional dynamic programming method in that the traditional dynamic programming method only converts the multi-stage optimization process into a series of single optimization processes, and the decision result of each stage is only influenced by the previous stage. The dynamic programming layer-by-layer optimization strategy is to optimize by taking a dynamic programming method as an outermost layer, namely, a decision result of each stage is not only influenced by the previous stage, but also related to an inner layer optimization result of the stage.
The active sequencing charging control method of the electric automobile comprises the following steps: and (4) carrying out active sequencing control on the electric automobile of a user who is willing to participate in power grid peak shaving by considering the charging and driving habits of the user. The charging load of the electric automobile charged in disorder has the defects of concentrated charging in the peak period of power utilization, increased load peak-valley difference, reduced power grid operation stability and the like. And the orderly control is to carry out optimized control on the electric automobile which is connected into the charging pile at the same moment through the charging pile.
It should be understood that the detailed description of the present invention is only for illustrating the present invention and is not limited by the technical solutions described in the embodiments of the present invention, and those skilled in the art should understand that the present invention can be modified or substituted equally to achieve the same technical effects; as long as the use requirements are met, the method is within the protection scope of the invention.
Claims (6)
1. The electric vehicle active sequencing charging control method based on the layer-by-layer optimization strategy is characterized by comprising the steps of considering battery characteristics and driving rules of different electric vehicles and establishing dynamic programming layer-by-layer optimization; determining the optimal charging strategy of a single electric vehicle by an inner layer optimization and exhaustion method, carrying out outer layer optimization according to the inner layer optimization result, and carrying out active sequencing control on the electric vehicle charging by the outer layer by a dynamic programming method;
step 1, acquiring battery characteristics and driving rule related parameters of all vehicles participating in an active sequencing charging control method of an electric vehicle;
2, extracting relevant parameters of an electric automobile, and establishing an optimal charging strategy model for solving the single electric automobile by an exhaustion method in the inner layer;
step 3, according to the optimization result of the inner layer and by combining the local load curve characteristics, the outer layer establishes a dynamic programming model for the active sequencing charging of the electric automobile by using a dynamic programming method;
the step 2 comprises the following steps:
step 2.1, generating a possible charging scheme set of the electric automobile, namely an initial charging time set, according to the use requirements of users and by combining the relevant parameters of the electric automobile;
step 2.2, one of the possible charging schemes is selected, and a 96-point charging load curve of the electric automobile under the charging scheme is calculated;
step 2.3, in the initial load state, superposing the charging load curve of the electric vehicle charging scheme, judging whether the starting and stopping time of the superposed load curve meets the requirements of a vehicle owner, if so, entering the next step, otherwise, returning to the step 2.2;
2.4, calculating a power grid influence coefficient under the scheme by using the related parameters of the electric vehicle and combining a charging scheme;
step 2.5, comparing the power grid influence coefficient with the power grid influence coefficient obtained by the previous round of calculation, and selecting the smaller one as a new power grid influence coefficient;
step 2.6, judging whether the collection elements are completely taken or not, if so, entering the next step, and if not, returning to the step 2.2;
step 2.7, outputting the optimal charging strategy of the electric automobile;
in step 2.5, the grid influence coefficient is a weighted average of the daily peak-to-valley difference and the mean square error of the load-to-average load difference, and is expressed by the following formula:
PDt＝PLoadt+PEt；
in the formula, min f1(PD) Representing a grid impact coefficient; pD maxAnd PD minRespectively representing the daily maximum load and the daily minimum load, wherein the difference value is the peak-valley difference; the first term represents the mean square error of the difference between the load and the average load, with the aim of minimizing load fluctuations; in order to control the influence of the power grid, weighting processing is carried out on the influence of the power grid so as to enable the influence to become a power grid influence coefficient; in the formula, λ1And λ2Each is a weight coefficient of an influence function on the power grid, the bias point of the influence target on the power grid can be adjusted and controlled by changing the weight coefficient, and when one of the weight coefficients is 0, the control target is changed into a single target;
PLt: load prediction at time t; n is a radical oft: the number of electric vehicles at time t;
PLoadt: representing the daily load predicted value of residents; pEt: charging power plan values of all electric vehicles in a power supply area; wherein:
PE(t): charging power values of all electric vehicles in a certain power supply area at the moment t; pEi(t): charging power for a single electric vehicle; n is a radical ofE: the number of electric vehicles; ki(t): a function for judging whether a certain single electric vehicle is charged at the moment t; if in the charging state, then Ki(t) ═ 1; otherwise Ki(t)＝0。
2. The active sequencing charging control method for the electric vehicle based on the layer-by-layer optimization strategy according to claim 1, characterized in that: the step 3 comprises the following steps:
step 3.1, acquiring 24-hour charging willingness information of a user, fully knowing the requirement of the user on the charging starting moment, and counting the number of electric vehicles participating in control;
3.2, establishing a dynamic planning model according to the number of the electric automobiles participating in the control;
3.3, selecting and marking the electric vehicle which benefits the power grid most through a dynamic planning model from the optimal charging strategy of each electric vehicle obtained from the inner layer;
step 3.4, superposing the load of the electric automobile to the initial load state to be used as a new initial load state;
step 3.5, calculating the power supply state of the load partition in the new state;
step 3.6, judging whether all the electric vehicles participating in the control are considered, if so, entering the next step, otherwise, skipping to the step 3.3, and continuing to perform calculation in the next decision stage;
and 3.7, finishing the calculation and outputting an active sequencing charging control strategy of the electric automobile.
3. The active sequencing charging control method for the electric vehicle based on the layer-by-layer optimization strategy according to claim 1, characterized in that: the relevant parameters in the step 1 comprise the holding capacity of the electric automobile; the reserve includes the battery charging characteristics of the electric vehicle and the driving rules of the electric vehicle.
4. The active sequencing charging control method for the electric vehicle based on the layer-by-layer optimization strategy according to claim 3, characterized in that: the battery charging characteristics of the electric automobile and the driving rule of the electric automobile comprise charging power of the battery, charging capacity of the battery and a charging mode.
5. The active sequencing charging control method for the electric vehicle based on the layer-by-layer optimization strategy according to claim 1, characterized in that: in step 2.3, the requirement of the owner is satisfied, that is, the electric vehicle is fully charged in the period of time that the owner wishes to charge, and the following formula is adopted:
tstart-j>tstart-j min
tend-j<tend-j max
wherein, if:
tstart-j<tj<tend-j
then:
Ki(tj)＝1；
otherwise:
Ki(tj)＝0
wherein: t is tstart-j: the moment when the electric automobile is connected to a power grid; t is tend-j: the moment when the electric automobile leaves the power grid; t is tstart-j min: the electric vehicle which can be accepted by the vehicle owner is accessed to the power grid at the earliest moment; t is tend-j max: the latest moment when the electric vehicle which can be accepted by the vehicle owner leaves the power grid; ki(tj): the charging state of the ith electric vehicle in the time period j; pEi(tj): charging power of the ith electric vehicle in a time period j; ei: the charging capacity of the ith electric vehicle; ei0: initial state of charge of the ith electric vehicle.
6. The active sequencing charging control method for the electric vehicle based on the layer-by-layer optimization strategy according to claim 1, characterized in that: the dynamic programming method comprises the following steps: decomposing a multivariable optimization problem into a plurality of univariate multi-stage optimization problems through stage division; the state transition equation of the dynamic programming method is the load state of the power supply subarea, and the state transition equation is as follows:
PDi(t)＝PD(i-1)(t)+PEi(t)；
wherein:
PDi(t): the load state of the power supply partition; pD(i-1)(t): in the (i-1) stage, i-1 load state of the automobile after the automobile enters the enclosure; pEi(t): the ith stage, i the charging load of the vehicle; in the i-th stage, the charging load of the i-vehicles is considered according to the initial charging timeThe difference of (2) is that the optimization calculation is carried out by adopting an exhaustion method.
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| CN105449684A (en) * | 2015-12-24 | 2016-03-30 | 合肥工业大学 | Large-scale electric vehicle trunking system based on MMC and control method thereof |
| CN105932741A (en) * | 2016-06-02 | 2016-09-07 | 中国南方电网有限责任公司电网技术研究中心 | Charging control method and system of electric automobile group |
| CN108099641A (en) * | 2017-11-27 | 2018-06-01 | 国网北京市电力公司 | The energy control method and device of charging station |
| CN108256674A (en) * | 2017-12-28 | 2018-07-06 | 国家电网公司 | A kind of active power distribution network load model construction method for participating in power grid peak load shifting |
| CN108528436A (en) * | 2018-01-18 | 2018-09-14 | 合肥工业大学 | A kind of ECMS multiple target dual blank-holders of ectonexine nesting |
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| CN105449684A (en) * | 2015-12-24 | 2016-03-30 | 合肥工业大学 | Large-scale electric vehicle trunking system based on MMC and control method thereof |
| CN105932741A (en) * | 2016-06-02 | 2016-09-07 | 中国南方电网有限责任公司电网技术研究中心 | Charging control method and system of electric automobile group |
| CN108099641A (en) * | 2017-11-27 | 2018-06-01 | 国网北京市电力公司 | The energy control method and device of charging station |
| CN108256674A (en) * | 2017-12-28 | 2018-07-06 | 国家电网公司 | A kind of active power distribution network load model construction method for participating in power grid peak load shifting |
| CN108528436A (en) * | 2018-01-18 | 2018-09-14 | 合肥工业大学 | A kind of ECMS multiple target dual blank-holders of ectonexine nesting |
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