CN109066752A - Based on the orderly charging schedule method and system of electric car for improving GRASP algorithm - Google Patents
Based on the orderly charging schedule method and system of electric car for improving GRASP algorithm Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention discloses a kind of based on the orderly charging schedule method and system of electric car for improving GRASP algorithm, based on improved GRASP algorithm to formulate charging optimisation strategy, it is applied to Path-Relinking as a kind of reinforcement strategy in the locally optimal solution that the local search stage obtains, update is advanced optimized to obtained locally optimal solution and obtains last solution, and charging schedule is carried out to electric car accordingly, can to avoid traditional GRASP algorithm there are the problem of, power distribution network operation cost is effectively reduced, reduce via net loss and improves node voltage level, be conducive to maintain system the safe and economic operation and load " peak load shifting " under high permeability electric car access grid condition, there is certain help for solving the orderly charging problems of electric car.
Description
Technical field
The present invention relates to electric car charging techniques more particularly to a kind of electric car based on improvement GRASP algorithm to have
Sequence charging schedule method and system.
Background technique
Electric car is more and more favored by the majority of users as a kind of environment-friendly type vehicles.But extensive electricity
The problems such as grid-connected charging meeting centering pressure of electrical automobile or low-voltage network cause a degree of impact, cause " on peak plus peak ".It is logical
It crosses and is orderly charged using a variety of different charging strategies guidance electric car, the impact to power grid can be reduced.
How to formulate electric car and coordinate charging strategy, has many domestic and foreign scholars at present and this is studied.Have
Establish electric car coordinate charging linear restriction convex quadratic programming model, to realize the mesh of peak load shifting to greatest extent
Mark；Some establishes multi-agent system, using the algorithm for having trained mechanism, achievees the purpose that reduce peak-valley difference.Method mentioned above
It is all building single object optimization model, although negative effect of the electric car to power grid can be reduced in terms of certain, optimization
Scheme it is comprehensive not as good as multi-objective Model.Some establishes a kind of electric car harmonious orderly charge model of multiple-objection optimization,
Reach the requirement for reducing network loss, improving node voltage level, but the user that cannot preferentially charge to expectation provides right.
Summary of the invention
Present invention is primarily aimed at provide a kind of based on the orderly charging schedule side of electric car for improving GRASP algorithm
Method and system are taken into account and realize that multiple-objection optimization and the user preferentially to charge to expectation are preferentially charged.
The present invention is achieved through the following technical solutions:
A kind of orderly charging schedule method of electric car based on improvement GRASP algorithm, comprising:
Step 1: creation initial solution；
Step 2: calculating power distribution network steady-state operation parameter；
Step 3: calculating sensitivity indexes；
Step 4: creation has conditional candidate list, includes several in the candidate list for being combined into the member of solution
Element；
Step 5: calculating the assessed value of candidate's element in the candidate list by greedy valuation functions, and according to calculating
To assessed value select next element to be added in the initial solution；
Step 6: evaluation goal function judges whether to meet and stops iterated conditional, such as meets, then stop iteration and obtain can
Row solution, otherwise, return step 3；
Step 7: the continuous loop iteration in the neighborhood of the feasible solution, the higher solution of search quality is to obtain local optimum
Disaggregation；
Step 8: the local optimum disaggregation being updated based on Path-Relinking algorithm, obtains final disaggregation；
Step 9: according to the final disaggregation, charging schedule being carried out to electric car.
Further, the feasible solution is indicated by encoder matrix, and every a line in the encoder matrix represents node company
The electric vehicle quantity connect, each column represent electric vehicle charge period, and the element in encoder matrix represents needed for electric car charging
Period.
Further, the step of creating the candidate list include:
It is assessed by greedy valuation functions and each element is combined to required increased cost in solution；
The candidate list is added lower than the element of given threshold in cost.
Further, the element in the conditional candidate list of tool is ascending order arrangement.
A kind of orderly charging schedule system of electric car based on improvement GRASP algorithm, comprising:
Initial solution creation module, for creating initial solution；
Steady-state operation parameter calculating module, for calculating power distribution network steady-state operation parameter；
Sensitivity indexes computing module, for calculating sensitivity indexes；
Candidate list creation module has conditional candidate list for creating, and includes several use in the candidate list
In the element for being combined into solution；
Iteration module, for calculating the assessed value of candidate's element in the candidate list, and root by greedy valuation functions
Next element is selected to be added in the initial solution according to the assessed value being calculated；
Objective function evaluation module is used for evaluation goal function, judges whether to meet stopping iterated conditional, such as meet, then
Stop iteration and obtains feasible solution, otherwise, return step 3；
Locally optimal solution search module, for loop iteration continuous in the neighborhood of the feasible solution, search quality is higher
Solution to obtain local optimum disaggregation；
Locally optimal solution update module, for being carried out more based on Path-Relinking algorithm to the local optimum disaggregation
Newly, final disaggregation is obtained；
Electric car charging schedule module, for carrying out charging schedule to electric car according to the final disaggregation.
Further, the feasible solution is indicated by encoder matrix, and every a line in the encoder matrix represents node company
The electric vehicle quantity connect, each column represent electric vehicle charge period, and the element in encoder matrix represents needed for electric car charging
Period.
Further, the candidate list creation module is specifically used for:
It is assessed by greedy valuation functions and each element is combined to required increased cost in solution；
The candidate list is added lower than the element of given threshold in cost.
Further, the element in the conditional candidate list of tool is ascending order arrangement.
Compared with prior art, provided by the invention based on the orderly charging schedule side of electric car for improving GRASP algorithm
Method and system, based on improved GRASP algorithm to formulate charging optimisation strategy, using Path-Relinking as a kind of reinforcement plan
It is slightly applied in the locally optimal solution that the local search stage obtains, obtained locally optimal solution is advanced optimized and is updated
To last solution, and accordingly to electric car carry out charging schedule, can to avoid traditional GRASP algorithm there are the problem of, effectively drop
Low power distribution network operation cost reduces via net loss and improves node voltage level, is conducive to high permeability electric car access electricity
System the safe and economic operation and load " peak load shifting " are maintained in the case of net, are had for solving the orderly charging problems of electric car
Certain help.
Detailed description of the invention
Fig. 1 is the functional block diagram of distribution network operation business control electric car charging；
Fig. 2 is encoder matrix schematic diagram；
Fig. 3 is construction phase flow diagram；
Fig. 4 is neighborhood organigram；
Fig. 5 a is distribution network load change curve schematic diagram under 32% permeability；
Fig. 5 b is distribution network load change curve schematic diagram under 63% permeability；
Fig. 6 a is distribution network system network loss situation schematic diagram under permeability 32%；
Fig. 6 b is distribution network system network loss situation schematic diagram under permeability 63%；
Fig. 7 is provided in an embodiment of the present invention based on the orderly charging schedule method of electric car for improving GRASP algorithm
Flow diagram；
Fig. 8 is provided in an embodiment of the present invention based on the orderly charging schedule system of electric car for improving GRASP algorithm
Composition schematic diagram.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below with reference to embodiment and attached drawing, to this
Invention is described in further detail.
Electric car orderly charging schedule method and system provided by the invention based on improvement GRASP algorithm are based on following
Theoretical basis.
1. electric car charge control method
After electric car enters charging station, intelligent detector obtains the parameters such as its state-of-charge (SOC), charge power, and
It calculates full of the time required to battery.User can be according to " the urgent degree " of charging, manual setting pick-up time and preliminary filling electricity.
These informations parameter are transferred to distribution network operation business by charging station, and operator formulates the orderly charging schedule strategy of economical rationality,
And it is assigned to corresponding charging end agent by order is executed, power network safety operation and maximum profit etc. are maintained to reach
Purpose.Distribution network operation business control block diagram is as shown in Figure 1.
The target that electric car orderly charges is that reduction power grid operation cost, reduction network loss and reduction voltage are inclined as far as possible
It moves, there is preferential charge requirement in view of user, objective function is answered are as follows:
In formula: number of segment when T is total；L is bus number；α is cost of losses；β is that conventional load and electric automobile load are taken
With；δ is the rejection penalty of voltage out-of-limit；For the out-of-limit rejection penalty of electric current；ψ is the punishment expense that electric car does not complete charging
With；σ is preferential charging encouragement expense；RijFor branch ij resistance；IIj, tFor branch ij the t period current effective value；For section
The voltage reference value of point i；For the maximum voltage deviant of node i.
It include three objective functions in objective function (1).Wherein, f1With the minimum target of power distribution network operation cost, f2To match
Minimum target, f is lost in electric network active3With the minimum target of variation.To realize that three's synthesis is optimal, herein using linear
Multiple-objection optimization is converted single object optimization by weighted sum method.It is different in view of the dimension of three objective functions, it needs to carry out respectively
Normalized, normalized processing formula are shown in formula (2).
F=min (ω1f1/f1max+ω2f2/f2max+ω3f3/f3max) (2)
In formula: f1maxFor original power distribution network operation cost；f2maxFor the network loss of original power distribution network；f3maxFor original power distribution network
Variation；ω1、ω2、ω3Respectively f1、f2、f3Corresponding weight coefficient, and have ω1+ω2+ω3=1.
f1In each expense calculation formula it is as follows:
In formula: N is total node number；μtFor t period electricity price；Δ t is charging duration；It is node j in the t period
Conventional load and electric automobile load；θ is the penalty factor of voltage out-of-limit；For voltage lower bound；σ is the out-of-limit punishment of electric current
The factor；For electric current high limit；Π is the penalty factor that electric car does not complete charging；πjFor the difference apart from preliminary filling electricity；κj
For preferentially charge " urgent degree "；ρtFor the factor that preferentially charges.
Although above-mentioned algorithm has, computational efficiency is high, the strong advantage of climbing capacity, since its is without memory, searches locally
In the iterative process in rope stage, the information that can not be obtained using previous iteration, the optimal solution that algorithm terminates is easily trapped into office
Portion is optimal, rather than global optimum.Therefore, it as shown in fig. 7, the present invention is directed to the defect of traditional algorithm, proposes a kind of based on improvement
The orderly charging schedule method of the electric car of GRASP algorithm, specifically comprises the following steps:
Step 1: creation initial solution；
Step 2: calculating power distribution network steady-state operation parameter；
Step 3: calculating sensitivity indexes；
Step 4: creation has conditional candidate list, includes several in candidate list for being combined into the element of solution；
Step 5: calculating the assessed value of candidate's element in candidate list by greedy valuation functions, and according to being calculated
Assessed value selects next element to be added in initial solution；
Step 6: evaluation goal function judges whether to meet and stops iterated conditional, such as meets, then stop iteration and obtain can
Row solution, otherwise, return step 3；
Step 7: the continuous loop iteration in the neighborhood of feasible solution, the higher solution of search quality is to obtain locally optimal solution
Collection；
Step 8: local optimal solution set being updated based on Path-Relinking algorithm, obtains final disaggregation；
Step 9: according to final disaggregation, charging schedule being carried out to electric car.
Above steps is described in detail below:
Before formally executing above-mentioned steps 1 to step 9, an encoder matrix can be initially formed and be used to indicate feasible solution.It compiles
For code matrix as shown in Fig. 2, every a line of encoder matrix represents the electric vehicle quantity of node connection, each column represent electric vehicle charging
Period.Element in encoder matrix is all integer, represents the period needed for electric car charges.For example, in Fig. 2, encoder matrix
The second row first row number 3, expression be electric vehicle 2 charging need three periods the case where.Needed for electric vehicle charging
Time can be calculated by following formula:
Above-mentioned steps 1 to step 6 is the construction phase of feasible solution.Construction phase is an iterative process, and each iteration is all
Certain member is selected usually to be combined into solution, from RCL list until it becomes complete solution.Here solution is by many small elements
It constitutes.When iteration, the assessed value of candidate's element is calculated by greedy valuation functions first, is chosen further according to these values
Next element is added in solution.The step of creating candidate list includes: to assess to combine each element by greedy valuation functions
The required increased cost into solution；Candidate list is added in element by cost lower than given threshold.This is GRASP algorithm greed
Place.And the random place of the algorithm, being embodied in the element being added in solution is that random selection comes out from RCL.Once choosing
Element be added in the solution of part, the element in RCL is with regard to be updated, and corresponding assessed value also can be by revaluation, this is exactly the calculation
The adaptable place of method.
Has the element in conditional candidate list for ascending order arrangement, candidate list (RCL) is usually and sensitivity indexes
Φk,tIt is related with coefficient ε, between the value 0 and 1 of ε.When there is the grid-connected charging of new electric vehicle, Φk,tBe conducive to evaluation goal letter
Number, and do not have to carry out entire Load flow calculation.RCL is obtained by following formula:
RCL=k, t ∈ X | Φmin≤Φk,t≤Φmin+ε(Φmax-Φmin)} (10)
In formula: X indicates to allow to form the element set solved, ΦminAnd ΦmaxRespectively indicate sensitivity indexes minimum value and
Maximum value.The size of coefficient ε can be adjusted by emulation experiment, obtain least member Φ as ε=0min, embody the algorithm
Greed.As ε ≠ 0, ε can arbitrarily be chosen, and embody the randomness of the algorithm.The process of construction phase is specifically as shown in Figure 3.
Step 7 to step 8 is the local search stage based on Path-Relinking.The local search stage is to improve
The feasible solution that construction phase generates, general procedure are the continuous loop iterations in the neighborhood of feasible solution, the higher solution of search quality,
Then replace current solution, algorithm stop condition is to can not find better solution in neighborhood.But traditional local search procedure obtains
Solution be likely to locally optimal solution, local search performance is heavily dependent on neighbour structure, neighbor search techniques and rises
The quality etc. for the solution that begins.The local search stage generally uses simple neighbour structure, as shown in Figure 4.Certainly for traditional GRASP algorithm
Body there are the shortcomings that, be applied to the local optimum that the local search stage obtains for Path-Relinking as a kind of strategy of reinforcing
Xie Zhong, can to avoid traditional GRASP algorithm there are the problem of.Local search stage general procedure based on Path-Relinking
It is: selects two solutions from locally optimal solution concentration, one is used as starting solution, then another detects two solutions as target solution
Relationship, formed one from starting solution to the path of target solution, for solution when moving on this paths, starting solution is gradually directed toward target
Solution, the feature of target solution, which is gradually introduced into starting solution, forms a series of new explanations, these are deconstructed into an optimal solution pond, Xie Chi's
Size can be set, and algorithm operation is initial, and optimal solution pond is sky.When optimal solution pond is not expired, obtained every time by local search
Locally optimal solution may be added to optimal solution pond, when optimal solution pond has been expired, if candidate solution is better than therein worst
Solution, by constantly updating, obtains final disaggregation then the candidate solution just replaces the worst solution.
After obtaining final disaggregation, according to final disaggregation, so that it may carry out charging schedule to electric car.
It is verified using 33 node power distribution net system of IEEE, distribution network system power reference value SBFor 10MVA, electricity
Press a reference value UBFor 12.66kV, at daily load top, total burden with power is 3715.0kW, and total load or burden without work is
2300.0kvar.Node 0 connects with major network, as reference mode, it is assumed that the node keeps voltage magnitude constant.Remaining node is equal
For PQ node.
MATLAB emulation platform is built to be tested.The period of emulation testing is from 18:00 to 07:00, is per hour 1
Period.The case where electricity price and load of each period, is as shown in appendix 1.Objective function normalized parameter f1max=1301918.5,
f2max=103.76, f3max=0.0194.It is optimal that each objective function takes identical weight coefficient to can reach synthesis, therefore this is tested
Take ω1=ω2=ω3=1/3.
The electricity price hourly of table 1 and load condition
1) distribution network load changes experimental result
The definition of electric car permeability is the ratio of electric car charge power Yu route peak load.32% permeability
Lower distribution network load change curve as shown in attached drawing 5a, analysis result as it can be seen that three kinds of charging strategies all do not make power grid overload,
Tradition and improvement GRASP algorithm have certain effect in terms of stabilizing load variations, but effect is essentially identical.For permeability
63% the case where, Fig. 5 b show that unordered charging leads to problems such as overload, peak-valley difference increase.Using traditional GRASP algorithm energy
Reduce peak load to a certain extent, but is ordered into the effect of charging not as good as improvement GRASP algorithm.GRASP algorithm is improved to meet
Under conditions of power distribution network operation constraint, utmostly achieve the purpose that " peak load shifting ", most beneficial for distribution safe and stable operation.
2) power distribution network operation cost experimental result
Permeability is respectively 32% and 63% electric car charging required power distribution network operation cost such as table 2,3 institute of table
Show, the Comparative result orderly to be charged according to unordered charging, tradition GRASP algorithm and improvement GRASP algorithm is it is found that improve GRASP
Algorithm can amplitude peak reduce power distribution network operation cost, and the bigger power distribution network income of permeability is bigger, is conducive to power distribution network
Operator's Rational Decision achievees the purpose that maximize profit.
The operation cost that the electric car of 2 permeability 32% of table charges in power distribution network
The operation cost that the electric car of 3 permeability 63% of table charges in power distribution network
3) distribution network system via net loss experimental result
The lower distribution network system network loss situation of permeability 32% is as shown in Figure 6 a.Network loss size is in load caused by unordered charging
(18:00-21:00) is big compared with other times for peak period, and tradition and improvement GRASP algorithm policy can reduce network loss, but this by a small margin
Two kinds of strategies are almost the same in 00:00-07:00 period network loss.Network loss size is as shown in Figure 6 b in the case of permeability 63%,
Unordered charging has increased considerably via net loss, is less than unordered charging using network loss caused by traditional GRASP algorithm policy, and changes
Network loss can be further decreased into GRASP algorithm.It is orderly charged using GRASP algorithm is improved, the network loss of day part is relatively average, always
Network loss is less than tradition GRASP algorithm and unordered charging.
4) power distribution network node voltage experimental result
32%, minimum node voltage (per unit value) situation such as table 4 He of the distribution network systems in each period under 63% permeability
Shown in table 5.There is voltage out-of-limit in 18:00 and 19:00 in the case where 4 permeability 32% of analytical table, unordered charging, and tradition and change
It can guarantee that quality of voltage is good into GRASP strategy, but innovatory algorithm is more preferably than conventional effects.By the feelings of 5 permeability 63% of table
Condition, unordered charging cross lower bound in 18:00-22:00 peak times of power consumption voltage, the horizontal too low situation of node voltage occur.It passes
There is voltage out-of-limit in 18:00 and 19:00 in system GRASP strategy.And GRASP strategy lower node voltage magnitude is improved in each period
It is held in normal range (NR), only low-voltage occurs in 19:00, but without more overvoltage lower bound, other moment power qualities
It is higher, power grid power supply reliability highest.
The per unit value of 4 permeability of table, 32% situation lower node minimum voltage
The per unit value of 5 permeability of table, 63% lower node minimum voltage
Based on above-mentioned charging schedule method, as shown in figure 8, another embodiment of the present invention additionally provides one kind based on improvement
The orderly charging schedule system of the electric car of GRASP algorithm, comprising:
Initial solution creation module 1, for creating initial solution；
Steady-state operation parameter calculating module 2, for calculating power distribution network steady-state operation parameter；
Sensitivity indexes computing module 3, for calculating sensitivity indexes；
Candidate list creation module 4 has conditional candidate list for creating, and includes several in candidate list for group
Synthesize the element of solution；
Iteration module 5, for calculating the assessed value of candidate's element in candidate list by greedy valuation functions, and according to meter
Obtained assessed value selects next element to be added in initial solution；
Objective function evaluation module 6 is used for evaluation goal function, judges whether to meet stopping iterated conditional, such as meet, then
Stop iteration and obtains feasible solution, otherwise, return step 3；
Locally optimal solution search module 7, for loop iteration continuous in the neighborhood of feasible solution, the higher solution of search quality
To obtain local optimum disaggregation；
Locally optimal solution update module 8, for being updated based on Path-Relinking algorithm to local optimal solution set,
Obtain final disaggregation；
Electric car charging schedule module 9, for carrying out charging schedule to electric car according to final disaggregation.
Feasible solution indicates that every a line in encoder matrix represents the electric vehicle quantity of node connection, often by encoder matrix
One column represent electric vehicle charge period, and the element in encoder matrix represents the period needed for electric car charges.
Candidate list creation module 4 is specifically used for:
It is assessed by greedy valuation functions and each element is combined to required increased cost in solution；
Candidate list is added in element by cost lower than given threshold.
Has the element in conditional candidate list for ascending order arrangement.
Each step in the charging schedule system in each module and above-mentioned charging schedule method corresponds, for executing
Each step in charging schedule method is stated, the concrete operating principle of each module can refer to the respective description in charging schedule method,
Details are not described herein.
The present invention considers the preferential charge requirement of user, establishes Model for Multi-Objective Optimization, and propose that a kind of improvement GRASP is asked
Resolving Algorithm.Simulation Example is made using 33 system node of IEEE, GRASP will be improved and coordinate charging result and tradition GRASP and nothing
Sequence charging compares, it was demonstrated that innovatory algorithm charging strategy can effectively reduce power distribution network operation cost, reduce via net loss and raising
Node voltage is horizontal, is conducive to maintain system the safe and economic operation and load under high permeability electric car access grid condition
" peak load shifting " has certain help for solving the orderly charging problems of electric car.
Above-described embodiment is only preferred embodiment, the protection scope being not intended to limit the invention, in spirit of the invention
With any modifications, equivalent replacements, and improvements made within principle etc., should all be included in the protection scope of the present invention.
Claims (8)
1. a kind of based on the orderly charging schedule method of electric car for improving GRASP algorithm characterized by comprising
Step 1: creation initial solution；
Step 2: calculating power distribution network steady-state operation parameter；
Step 3: calculating sensitivity indexes；
Step 4: creation has conditional candidate list, includes several in the candidate list for being combined into the element of solution；
Step 5: calculating the assessed value of candidate's element in the candidate list by greedy valuation functions, and according to being calculated
Assessed value selects next element to be added in the initial solution；
Step 6: evaluation goal function judges whether to meet stopping iterated conditional, such as meets, then stop iteration and obtain feasible
Solution, otherwise, return step 3；
Step 7: the continuous loop iteration in the neighborhood of the feasible solution, the higher solution of search quality is to obtain locally optimal solution
Collection；
Step 8: the local optimum disaggregation being updated based on Path-Relinking algorithm, obtains final disaggregation；
Step 9: according to the final disaggregation, charging schedule being carried out to electric car.
2. as described in claim 1 based on the orderly charging schedule method of electric car for improving GRASP algorithm, feature exists
In, the feasible solution is indicated by encoder matrix, and every a line in the encoder matrix represents the electric vehicle quantity of node connection,
Each column represent electric vehicle charge period, and the element in encoder matrix represents the period needed for electric car charges.
3. as claimed in claim 2 based on the orderly charging schedule method of electric car for improving GRASP algorithm, feature exists
Include: in, the step of creating the candidate list
It is assessed by greedy valuation functions and each element is combined to required increased cost in solution；
The candidate list is added lower than the element of given threshold in cost.
4. as described in claim 1 based on the orderly charging schedule method of electric car for improving GRASP algorithm, feature exists
In the element in the conditional candidate list of tool is ascending order arrangement.
5. a kind of based on the orderly charging schedule system of electric car for improving GRASP algorithm characterized by comprising
Initial solution creation module, for creating initial solution；
Steady-state operation parameter calculating module, for calculating power distribution network steady-state operation parameter；
Sensitivity indexes computing module, for calculating sensitivity indexes；
Candidate list creation module has conditional candidate list for creating, and includes several in the candidate list for group
Synthesize the element of solution；
Iteration module, for calculating the assessed value of candidate's element in the candidate list by greedy valuation functions, and according to meter
Obtained assessed value selects next element to be added in the initial solution；
Objective function evaluation module is used for evaluation goal function, judges whether to meet stopping iterated conditional, such as meets, then stop
Iteration simultaneously obtains feasible solution, otherwise, return step 3；
Locally optimal solution search module, for loop iteration continuous in the neighborhood of the feasible solution, the higher solution of search quality
To obtain local optimum disaggregation；
Locally optimal solution update module, for being updated based on Path-Relinking algorithm to the local optimum disaggregation,
Obtain final disaggregation；
Electric car charging schedule module, for carrying out charging schedule to electric car according to the final disaggregation.
6. as claimed in claim 5 based on the orderly charging schedule system of electric car for improving GRASP algorithm, feature exists
In, the feasible solution is indicated by encoder matrix, and every a line in the encoder matrix represents the electric vehicle quantity of node connection,
Each column represent electric vehicle charge period, and the element in encoder matrix represents the period needed for electric car charges.
7. as claimed in claim 6 based on the orderly charging schedule system of electric car for improving GRASP algorithm, feature exists
In the candidate list creation module is specifically used for:
It is assessed by greedy valuation functions and each element is combined to required increased cost in solution；
The candidate list is added lower than the element of given threshold in cost.
8. as claimed in claim 5 based on the orderly charging schedule system of electric car for improving GRASP algorithm, feature exists
In the element in the conditional candidate list of tool is ascending order arrangement.
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| CN112182963A (en) * | 2020-09-24 | 2021-01-05 | 中国人民解放军空军工程大学 | Multi-sensor scheduling scheme optimization method based on projection spiral clustering eddy current search algorithm |
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