AU2014101609A4 - A multi-objective stochastic programming method of electric vehicle charging load based on non-dominated sorting genetic algorithm - Google Patents
A multi-objective stochastic programming method of electric vehicle charging load based on non-dominated sorting genetic algorithm Download PDFInfo
- Publication number
- AU2014101609A4 AU2014101609A4 AU2014101609A AU2014101609A AU2014101609A4 AU 2014101609 A4 AU2014101609 A4 AU 2014101609A4 AU 2014101609 A AU2014101609 A AU 2014101609A AU 2014101609 A AU2014101609 A AU 2014101609A AU 2014101609 A4 AU2014101609 A4 AU 2014101609A4
- Authority
- AU
- Australia
- Prior art keywords
- charging
- load
- optimization
- power
- time interval
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired
Links
- 230000002068 genetic effect Effects 0.000 title claims abstract description 29
- 238000000034 method Methods 0.000 title claims description 9
- 238000005457 optimization Methods 0.000 claims abstract description 79
- 101100478173 Drosophila melanogaster spen gene Proteins 0.000 claims description 10
- 101100513476 Mus musculus Spen gene Proteins 0.000 claims description 10
- 210000000349 chromosome Anatomy 0.000 claims description 9
- 201000004569 Blindness Diseases 0.000 claims description 3
- 238000013178 mathematical model Methods 0.000 claims description 3
- 230000035772 mutation Effects 0.000 claims description 3
- 239000002699 waste material Substances 0.000 claims description 3
- 238000007476 Maximum Likelihood Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
OF THE INVENTION The invention discloses a multi-objective stochastic programming approach of EV charging load based on non-dominated sorting genetic algorithm. In combination with the requirements of the best operation of the distribution system and in consideration of the influence of multiple random factors, it establishes a new multi-objective stochastic optimization model of the distribution network based on EV charging load, which utilizes the improved non-dominated sorting genetic algorithm- II (non-dominated sorting genetic algorithm-2, NSGA-2) to solve, takes fully charged EV battery, charging power within limit and distribution network tide constraints as constraint conditions and takes distribution network loss, power node peak load and load fluctuation optimization as sub-goals to achieve multi-objective stochastic programming of EV charging load.
Description
A MULTI-OBJECTIVE STOCHASTIC PROGRAMMING METHOD OF ELECTRIC VEHICLE CHARGING LOAD BASED ON NON-DOMINATED SORTING GENETIC ALGORITHM
FIELD OF THE INVENTION
[0001] The application relates to the charging of the electric vehicle and specifically relates to a multi-objective stochastic programming method of electric vehicle charging load based on non-dominated sorting genetic algorithm.
BACKGROUND OF THE INVENTION
[0002] The state of charge (SOC) is an important parameter to describe the state of the battery capacity and has important significance for the operation of the operation vehicle as well as the maintenance and management of the battery. Since the capacity of the battery can be released is affected by the factors such as the discharge rate, the battery temperature and the cycles of battery charging and discharging, therefore, the parameter SOC inevitably has something to do with these factors. In the case of changes in the discharging current, there is difficulty in SOC estimation. SOC at the beginning of the battery charging directly affects the battery’s charging time.
[0003] The electric vehicle does not directly consume fossil energy and can effectively reduce CO2 emission and the cost of the owner, so it is favored by the government and the enterprise. Relevant researches have made tremendous progress, and it is expected that by 2015 there will be 10% hybrid cars in the newly purchased vehicles. The continuously growing electric vehicles will increase 18% load to the grid, and at the same time the large-scale promotion of EV will bring great impact on the access to the distribution network. Chinese and foreign researchers have made a lot of studies on the charging of electric vehicles, but the existing EV charging load optimization strategies still have a variety of problems. Fully controllable EV charging is supposed, but the randomness is ignored, or the strategy optimization target is single, or the initial SOC model can not reflect actual SOC.
SUMMARY OF THE INVENTION
[0004] The present invention provides a multi-objective stochastic programming approach of EV charging load based on non-dominated sorting genetic algorithm. In combination with the requirements of the best operation of the distribution system and in consideration of the influence of multiple random factors, it establishes a new multi-objective stochastic optimization model of the distribution network based on EV charging load, which utilizes the improved non-dominated sorting genetic algorithm- Π (non-dominated sorting genetic algorithm-2, NSGA-2) to solve, takes fully charged EV battery, charging power within limit and distribution network tide constraints as constraint conditions and takes distribution network loss, power node peak load and load fluctuation optimization as sub-goals to achieve multi-objective stochastic programming of EV charging load.
[0005] The said multi-objective stochastic programming approach of EV charging load based on non-dominated sorting genetic algorithm includes state of remaining charge and distribution of return time.
[0006] The mathematical estimation of statistical data shows that the daily distance conforms to the lognormal distribution
(1)
Where, μ and δ are respectively mean and variance, μ = 0.88 and S = 3.2, and d is daily distance of EY; [0007] State of remaining charge of EV battery:
(2)
Where, Stotal is the longest distance of EY with fully charged battery and S is the distance of EY from leaving time to return time; [0008] A mathematical model of state of remaining charge and distribution of return time is built;
(3)
Where, Stotal is the longest distajiee of EY with fully charged battery and Ssoc is the state of charge of the battery; δ = 0.88; μ = 3.2; [0009] If EV after the final return immediately starts charging at the intelligent car charging station, then from this moment to the leaving again, EV can be dispatched. The maximum likelihood estimation is used to estimate the statistical data, and EV’s final return time t satisfies the following distribution:
(4) [0010] This distribution conforms to the normal distribution, the standard deviation is 5t, and the expectation is //, ; 5t — 17.6, //( = 3.4; (2) Select sub-goal of charging load optimization EV charging load optimization objective is selected to reduce the impact of EV charging load to the distribution network and utilize its controllability to achieve load leveling and coordinated operation of new energy. (a) Minimum network loss in the entire optimization cycle is taken as EY charging load optimization sub-goal:
(5)
Where, M is the number of time intervals within the optimization cycle, N is the number of branches of the distribution network, pt j, qij , r and vt are respectively active power, reactive power, resistance and voltage of the end node of the ith branch in the jth time interval within the optimization cycle; in the case of minimum network loss without the network structure changed, to adjust the network running status, eliminate overload and balance load, the following inequality constraint should be satisfied:
(6)
Where, Vi, max and V,. are upper and lower limits of node voltage;
The following inequality constraints should be satisfied:
(7)
Where, and q""'1 are respectively active power and reactive power of the basic load at the end node of the ith branch in the jth time interval, and pfj and qfj are respectively active power and reactive power of EY charging overlap at the end node of the ith branch in the jth time interval;
(8)
Where, Nnumber is the number of vehicles connected to the end node of the ith branch in the jth time interval; pfvj k is the charging power of the kth EY connected to the end node of the ith branch in the jth time interval, which should satisfy following constraints:
(9)
Where, Pi, max and Pi, min are upper and lower limits of EY charging power; q is the probability of uncharged battery of EV within the time interval, 0 < q < 1; qfvjJc satisfies following constraint:
(10) Θ is the charging power factor angle of EV; power factorcos<9 = 0.95 is selected;
Considering the charger’s conversion rate 77 = 0.93, the electric quantity absorbed by EV battery under the action of pfvjk:
(11)
Where, t is the length of the jth time interval, and under the action of Qm (i,j,k), the battery’s state of charge satisfies:
(12)
Where, Ssoc(i,j,k) is the state of charge of the battery of the kth EV connected to the end node of the ith branch in the jth time interval within the optimization cycle, tk0 is the time when the kth EV is connected to the network. Ssoc(i,j,k) should satisfy following constraint:
Formula (13) indicates that the EY battery is charged to 90% to maximize the interest of the EY owner at the end of the A/th time interval, that is, at the end of the optimization. (b) The minimization of load peak passing the power node within the entire optimization cycle is taken as the sub-goal of the optimization of EV charging load: min max pTj j e [l,M] (14)
Where, pTj is the active power of power node in the jth time interval within the optimization cycle, and M is the number of time intervals within the entire optimization cycle; formula (12) can be digitized to indicate the network’s ultimate running status, optimize and utilize it to keep the network stable. (c) The minimization of load fluctuation of the power node within the entire optimization cycle is taken as the sub-goal of the optimization of EV charging load:
(15)
Where, PT is the mean value of load of the power node within the optimization cycle; the above formula is defined as the variance of load of the power node, and its value can reflect to some extent the load fluctuation.
Therefore, the optimization based on EV charging load is mathematically described as following multi-objective and multiconstrained optimization:
(16)
Where, M is the number of time intervals within the optimization cycle, M is the number of time intervals within the entire optimization cycle, pUj, qi j, /· and vt are respectively active power, reactive power, resistance and voltage of the end node of the ith branch in the jth time interval within the optimization cycle; pTj is active power of the power node in the jth time interval within the optimization cycle; PT is the mean value of load of the power node within the optimization cycle; [0011 ] Following constraints should be satisfied:
(17) [0012] Where, Vi, max and Vi, min are upper and lower limits of node voltage; p1.".'1 and q1""'1 are respectively active power and reactive power of the basic load at the end node of the i* branch in the jth time interval, and and qfVj are respectively active power and reactive power of EV charging overlap at the end node of the ith branch in the jlh time interval; Pi, max and Pi, min 3.1'C upper and lower limits of EV charging power; K is the Klh vehicle; Ssoc {i,Mis the capacity of fully charged EV battery at the end of the M* time interval, that is, at the end of the optimization.
[0013] (3) Select real-coded improved non-dominated sorting genetic algorithm NSGA-2 for Pareto solution set.
[0014] With respect of the multi-objective optimization problem, real-coded non-dominated sorting genetic algorithm NSGA-2 was selected for Pareto solution set, the optimized object is charging power pfVjk of the klh EV connected to the end node of the ilh branch in the jlh time interval.
[0015] (d) Polynomial mutation operator and simulated binary crossover (SBX) operator are selected to perform evolutionary operation and the league method is used to choose optimum individuals into the next generation. The league size is taken as half the population size. To avoid waste of machine time, under the condition of fixed genetic generations, add new loop termination conditions: if the average fitness of all individuals in the non-dominated solution set obtained after five consecutive generations of evolution operation to the three sub-goals is not less than the minimum fitness of Pareto solution set obtained previously, the algorithm has been converged to the optimal solution of Pareto, and the loop terminates.
[0016] (e) The starting time of optimization is from 7:00 to 22:00, when the original load curve maintains at a high level, so in this time interval greater probability qx should be used for zero EV charging power, while in the remaining time intervals smaller probability q2 should be used to accelerate EV charging. The heuristic operator is accordingly set:
(18) [0017] In the process of encoding initialization, both qx and q2 are encoded according to the heuristic operator in the above formula. This heuristic operator avoids the blindness of the encoding of the original genetic algorithm and effectively accelerates the convergence of the algorithm.
[0018] (f) Treatment of constraints: increase a constraint violation penalty position S on the original chromosome string: the larger penalty of the chromosome violating the node voltage constraint is Spen = inf , and the segmented penalties of the chromosome violating the constraint are: ® if S,oc > 1 or S™ < 1, Spen = inf ; © if 0.9 < Ssoe < 1, Spen = 0; © if 0 < 5soe < 0.9, Spen = / * e09 'S‘" , where f is penalty coefficient increasing with genetic generations; [0019] (4) Role of constraint violation penalty position in the algorithm: redefine Pareto Dominance: for individuals i and j, if and only if /' (n) < /.(n) is workable for all n sub-goals and there is at least one k Gn making /'(/:) < f.(k) workable, and in addition S‘pen = Spen is workable, then individual i can be superior to individual j; where, j] (n j is the fitness value of the Ith individual to the nth sub-goal.
DETAILED DESCRIPTION OF THE INVENTION
[0020] It is a multi-objective stochastic programming approach of EV charging load based on non-dominated sorting genetic algorithm. In combination with the requirements of the best operation of the distribution system and in consideration of the influence of multiple random factors, it establishes a new multi-objective stochastic optimization model of the distribution network based on EV charging load, which utilizes the improved non-dominated sorting genetic algorithm- II (non-dominated sorting genetic algorithm-2, NSGA-2) to solve, takes fully charged EV battery, charging power within limit and distribution network tide constraints as constraint conditions and takes distribution network loss, power node peak load and load fluctuation optimization as sub-goals to achieve multi-objective stochastic programming of EV charging load.
[0021] The said multi-objective stochastic programming approach of EV charging load based on non-dominated sorting genetic algorithm includes state of remaining charge and distribution of return time.
[0022] The mathematical estimation of statistical data shows that the daily distance conforms to the lognormal distribution
(1) [0023] Where, μ and δ are respectively mean and variance, // = 0.88 and δ = 3.2, and d is daily distance of EV; [0024] State of remaining charge of EV battery:
(2) [0025] Where, Stotal is the longest distance of EV with fully charged battery and S is the distance of EV from leaving time to return time; [0026] (1) A mathematical model of state of remaining charge and distribution of return time is built;
(3) [0027] Where, Stotal is the longest distance of EV with fully charged battery and Ssoc is the state of charge of the battery; δ = 0.88; // = 3.2; [0028] If EV after the final return immediately starts charging at the intelligent car charging station, then from this moment to the leaving again, EV can be dispatched. The maximum likelihood estimation is used to estimate the statistical data, and EV’s final return time t satisfies the following distribution: [0029] This distribution conforms to the normal distribution, the standard deviation is 5t, and λ/ the expectation is //, ; St = 17.6, //( = 3.4; [0030] (2) Select sub-goal of charging load optimization [0031] EV charging load optimization objective is selected to reduce the impact of EV charging load to the distribution network and utilize its controllability to achieve load leveling and coordinated operation of new energy.
[0032] (a) Minimum network loss in the entire optimization cycle is taken as EV charging load optimization sub-goal:
(5) [0033] Where, M is the number of time intervals within the optimization cycle, N is the number of branches of the distribution network, pj ., qj ., r and Vt are respectively active power, reactive power, resistance and voltage of the end node of the i* branch in the jlh time interval within the optimization cycle; in the case of minimum network loss without the network structure changed, to adjust the network running status, eliminate overload and balance load, the following inequality constraint should be satisfied:
(6) [0034] Where, Vi, max and Vi, min are upper and lower limits of node voltage; [0035] The following inequality constraints should be satisfied: [0036] Where, and q1°“ά are respectively active power and reactive power of the basic load at the end node of the ith branch in the jlh time interval, and //| and qfVj are respectively active power and reactive power of EV charging overlap at the end node of the i* branch in the jth time interval;
(8) [0037] Where, Nnumber is the number of vehicles connected to the end node of the ilh branch in the jth time interval; pfVjk is the charging power of the kth EV connected to the end node of the 1th branch in the jth time interval, which should satisfy following constraints:
(9) [0038] Where, Pi, max and Pi, min are upper and lower limits of EV charging power; q is the probability of uncharged battery of EV within the time interval, 0 < q < 1; qfvjJc satisfies following constraint:
(10) Θ is the charging power factor angle of EY; power faetorcosP = 0.95 is selected; [0039] Considering the charger’s conversion rate η = 0.93, the electric quantity absorbed by EV battery under the action of pfv. k : [0040] Where, t is the length of the j* time interval, and under the action of QM (i, j,k), the battery’s state of charge satisfies:
(12) [0041] Where, Ssoc (/, /, k) is the state of charge of the battery of the klh EV connected to the end node of the ith branch in the jth time interval within the optimization cycle, tk0 is the time when the kth EV is connected to the network. Ssoc (/, /, k) should satisfy following constraint:
(13) [0042] Formula (13) indicates that the EV battery is charged to 90% to maximize the interest of the EV owner at the end of the M* time interval, that is, at the end of the optimization.
[0043] (b) The minimization of load peak passing the power node within the entire optimization cycle is taken as the sub-goal of the optimization of EV charging load:
(14) [0044] Where, pTj is the active power of power node in the jth time interval within the optimization cycle, and M is the number of time intervals within the entire optimization cycle; formula (12) can be digitized to indicate the network’s ultimate running status, optimize and utilize it to keep the network stable.
[0045] (c) The minimization of load fluctuation of the power node within the entire optimization cycle is taken as the sub-goal of the optimization of EV charging load:
(15) [0046] Where, PT is the mean value of load of the power node within the optimization cycle; the above formula is defined as the variance of load of the power node, and its value can reflect to some extent the load fluctuation.
[0047] Therefore, the optimization based on EV charging load is mathematically described as following multi-objective and multi-constrained optimization:
(16) [0048] Where, M is the number of time intervals within the optimization cycle, M is the number of time intervals within the entire optimization cycle, pt ., qt ., r and Vt are respectively active power, reactive power, resistance and voltage of the end node of the ith branch in the jth time interval within the optimization cycle; pTj is active power of the power node in the j* time interval within the optimization cycle; PT is the mean value of load of the power node within the optimization cycle; [0049] Following constraints should be satisfied:
(17) [0050] Where, Vi, max and Vi, min are upper and lower limits of node voltage; p1.".'1 and q1""'1 are respectively active power and reactive power of the basic load at the end node of the i* branch in the jth time interval, and and qfVj are respectively active power and reactive power of EV charging overlap at the end node of the i* branch in the jlh time interval; Pi, max and P/, min axe upper and lower limits of EV charging power; K is the Klh vehicle; Ssoc {i,Mis the capacity of fully charged EV battery at the end of the M* time interval, that is, at the end of the optimization.
[0051] (3) Select real-coded improved non-dominated sorting genetic algorithm NSGA-2 for Pareto solution set.
[0052] With respect of the multi-objective optimization problem, real-coded non-dominated sorting genetic algorithm NSGA-2 was selected for Pareto solution set, the optimized object is charging power pfVjk of the klh EV connected to the end node of the ilh branch in the jlh time interval.
[0053] (d) Polynomial mutation operator and simulated binary crossover (SBX) operator are selected to perform evolutionary operation and the league method is used to choose optimum individuals into the next generation. The league size is taken as half the population size. To avoid waste of machine time, under the condition of fixed genetic generations, add new loop termination conditions: if the average fitness of all individuals in the non-dominated solution set obtained after five consecutive generations of evolution operation to the three sub-goals is not less than the minimum fitness of Pareto solution set obtained previously, the algorithm has been converged to the optimal solution of Pareto, and the loop terminates.
[0054] (e) The starting time of optimization is from 7:00 to 22:00, when the original load curve maintains at a high level, so in this time interval greater probability qx should be used for zero EV charging power, while in the remaining time intervals smaller probability q2 should be used to accelerate EV charging. The heuristic operator is accordingly set:
(18) [0055] In the process of encoding initialization, both ql and q2 are encoded according to the heuristic operator in the above formula. This heuristic operator avoids the blindness of the encoding of the original genetic algorithm and effectively accelerates the convergence of the algorithm.
[0056] (f) Treatment of constraints: increase a constraint violation penalty position on the original chromosome string: the larger penalty of the chromosome violating the node voltage constraint is ^*Π^ , and the segmented penalties of the chromosome violating the constraint are: ® if Ssoc > 1 or Ssox < 1; $pe„ = inf ^ .f 0.9 < Ssoc < 1 ^ Spen = 0 _ ^ .f 0 < Ssoc < 0.9 ^ S = f* e°'9~Ssoc pen , where f is penalty coefficient increasing with genetic generations; [0057] (4) Role of constraint violation penalty position in the algorithm: redefine Pareto Dominance: for individuals i and j, if and only if /' (n) < /.(n) is workable for all n sub-goals and there is at least one k Gn making /'(/:) < f.(k) workable, and in addition S‘pen = Spen is workable, then individual i can be superior to individual j; where, j] (n j is the fitness value of the Ith individual to the nth sub-goal.
[0058] In addition, the person skilled in the art can make other changes within the spirit of the present invention, and all these changes should be within the scope of protection of the present invention.
Claims (13)
- (1) Where, Stotal is the longest distance of EY with fully charged battery and Ssoc is the state of charge of the battery; δ = 0.88; μ = 3.2; If EV after the final return immediately starts charging at the intelligent car charging station, then EV’s final return time t satisfies the following distribution:1. A multi-objective stochastic programming approach of EV charging load based on non-dominated sorting genetic algorithm, which includes: (1) A mathematical model of state of remaining charge and distribution of return time is built;
- (2) This distribution conforms to the normal distribution, the standard deviation is δ,, and the expectation is //,; (2) Select sub-goal of charging load optimization (a) Minimum network loss in the entire optimization cycle is taken as EV charging load optimization sub-goal:
- (3) Where, M is the number of time intervals within the optimization cycle, N is the number of branches of the distribution network, pt , qt j, /· and vt are respectively active power, reactive power, resistance and voltage of the end node of the ith branch in the jth time interval within the optimization cycle; in the case of minimum network loss without the network structure changed, to adjust the network running status, eliminate overload and balance load, the following inequality constraint should be satisfied:
- (4) Where, V, max and Vi, min are upper and lower limits of node voltage; The following inequality constraints should be satisfied:
- (5) Where, and q""'1 are respectively active power and reactive power of the basic load at the end node of the ith branch in the jth time interval, and pfj and qfj are respectively active power and reactive power of EY charging overlap at the end node of the ith branch in the jth time interval;
- (6) Where, Nnumber is the number of vehicles connected to the end node of the ith branch in the jth time interval; pfvj k is the charging power of the kth EY connected to the end node of the ith branch in the jth time interval, which should satisfy following constraints:
- (7) Where, Pi, max and Pi, mm are upper and lower limits of EV charging power; q is the probability of uncharged battery of EV within the time interval, 0 < q < 1; qfv]k satisfies following constraint:
- (8) Θ is the charging power factor angle of EV; (b) The minimization of load peak passing the power node within the entire optimization cycle is taken as the sub-goal of the optimization of EV charging load: min max pTj
- (9) th Where, pTj is the active power of power node in the j time interval within the optimization cycle, and M is the number of time intervals within the entire optimization cycle; (c) The minimization of load fluctuation of the power node within the entire optimization cycle is taken as the sub-goal of the optimization of EV charging load:
- (10) Where, PT is the mean value of load of the power node within the optimization cycle; Therefore, the optimization based on EV charging load is mathematically described as following multi-objective and multiconstrained optimization:
- (11) Where, M is the number of time intervals within the optimization cycle, M is the number of time intervals within the entire optimization cycle, pUj, qi j, /· and vt are respectively active power, reactive power, resistance and voltage of the end node of the ith branch in the jth time interval within the optimization cycle; pTj is active power of the power node in the jth time interval within the optimization cycle; PT is the mean value of load of the power node within the optimization cycle; Following constraints should be satisfied:
- (12) Where, Vi,max and V,. are upper and lower limits of node voltage; pbad ancj qioad are reSpectiVely active power and reactive power of the basic load at the end node of the ith branch in the jth time interval, and //] and qfj are respectively active power and reactive power of EY charging overlap at the end node of the ith branch in the jth time interval; Pi, max and Pi, min are upper and lower limits of EY charging power; K is the Kth vehicle; Ssoc (i,M,k) is the capacity of fully charged EY battery at the end of the A/th time interval, that is, at the end of the optimization; (3) Select real-coded improved non-dominated sorting genetic algorithm NSGA-2 for Pareto solution set; (d) Polynomial mutation operator and simulated binary crossover (SBX) operator are selected to perform evolutionary operation and the league method is used to choose optimum individuals into the next generation. The league size is taken as half the population size. To avoid waste of machine time, under the condition of fixed genetic generations, add new loop termination conditions: if the average fitness of all individuals in the non-dominated solution set obtained after five consecutive generations of evolution operation to the three sub-goals is not less than the minimum fitness of Pareto solution set obtained previously, the algorithm has been converged to the optimal solution of Pareto, and the loop terminates. (e) The starting time of optimization is from 7:00 to 22:00, when the original load curve maintains at a high level, so in this time interval greater probability qx should be used for zero EY charging power, while in the remaining time intervals smaller probability q2 should be used to accelerate EV charging. The heuristic operator is accordingly set:
- (13) In the process of encoding initialization, both qx and q2 are encoded according to the heuristic operator in the above formula. This heuristic operator avoids the blindness of the encoding of the original genetic algorithm and effectively accelerates the convergence of the algorithm. (f) Treatment of constraints: increase a constraint violation penalty positions^ on the original chromosome string: the larger penalty of the chromosome violating the node voltage constraint is Spen = inf, and the segmented penalties of the chromosome violating the constraint are: © if Ssoc > 1 or S«*<1, Spen= inf; (D if 0.9 < Ssoc < 1, Spen= 0; (3) if 0 < Ssoc < 0.9, Spe„ = f* eM~Sm , where f is penalty coefficient increasing with genetic generations; (4) Role of constraint violation penalty position in the algorithm: redefine Pareto Dominance: for individuals i and j, if and only if ft(n)< fj(n)is workable for all n sub-goals and there is at least one k e n making /;.(£)< f^k) workable, and in addition S‘pen = SJpenis workable, then individual i can be superior to individual j; where, /;.(«) is the fitness value of the Ith individual to the nih sub-goal.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410523671.6A CN104281889B (en) | 2014-10-08 | 2014-10-08 | Electric automobile EV charging load multiple objective random programming method |
| CN201410523671.6 | 2014-10-08 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2014101609A4 true AU2014101609A4 (en) | 2017-07-27 |
Family
ID=52256749
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2014262212A Pending AU2014262212A1 (en) | 2014-10-08 | 2014-11-12 | A multi-objective stochastic programming method of electric vehicle charging load based on non-dominated sorting genetic algorithm |
| AU2014101609A Expired AU2014101609A4 (en) | 2014-10-08 | 2014-11-12 | A multi-objective stochastic programming method of electric vehicle charging load based on non-dominated sorting genetic algorithm |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2014262212A Pending AU2014262212A1 (en) | 2014-10-08 | 2014-11-12 | A multi-objective stochastic programming method of electric vehicle charging load based on non-dominated sorting genetic algorithm |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN104281889B (en) |
| AU (2) | AU2014262212A1 (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105976270B (en) * | 2016-05-25 | 2019-07-16 | 西南交通大学 | A kind of electric car under tou power price charges load dynamic probability distribution determination method |
| CN106253308A (en) * | 2016-08-23 | 2016-12-21 | 中国电力科学研究院 | A kind of var Optimization Method in Network Distribution |
| CN107067136B (en) * | 2016-12-22 | 2020-11-27 | 国家电网公司 | Electric vehicle charging distribution method and device |
| CN106777835A (en) * | 2017-02-15 | 2017-05-31 | 国网江苏省电力公司苏州供电公司 | The city electric car charging network planing method of multiple optimization aims is considered simultaneously |
| CN109214593B (en) * | 2018-10-19 | 2023-12-08 | 天津大学 | Multi-objective evaluation method for power supply capacity of active power distribution network |
| CN109978240B (en) * | 2019-03-11 | 2022-10-04 | 三峡大学 | Electric automobile ordered charging optimization method and system |
| CN110020505B (en) * | 2019-04-28 | 2022-11-08 | 三峡大学 | Small data-based large-scale charging pile planning model modeling method |
| CN112800547B (en) * | 2021-01-29 | 2024-03-08 | 中国科学院电工研究所 | Layout optimization method and device for electric vehicle motor controller and storage medium |
| CN113111584A (en) * | 2021-04-15 | 2021-07-13 | 天津大学 | Building storage battery configuration method considering charging load of electric automobile |
| DE102021113214A1 (en) | 2021-05-21 | 2022-11-24 | Audi Aktiengesellschaft | Method for planning a charging process, planning unit and motor vehicle |
| CN113629747B (en) * | 2021-09-16 | 2022-08-12 | 国网安徽省电力有限公司马鞍山供电公司 | Grid-connected reactive compensation control method for electric automobile |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101449801B1 (en) * | 2012-07-31 | 2014-10-28 | 제주대학교 산학협력단 | Method and system of scheduling in charing tasks for electric vehicles based on genetic algorithms and intial population selection heuristics |
| CN103840457B (en) * | 2014-03-20 | 2016-03-09 | 上海电力学院 | Consider DG Optimal Configuration Method in the power distribution network that electric automobile discharge and recharge affects |
-
2014
- 2014-10-08 CN CN201410523671.6A patent/CN104281889B/en active Active
- 2014-11-12 AU AU2014262212A patent/AU2014262212A1/en active Pending
- 2014-11-12 AU AU2014101609A patent/AU2014101609A4/en not_active Expired
Also Published As
| Publication number | Publication date |
|---|---|
| CN104281889A (en) | 2015-01-14 |
| AU2014262212A1 (en) | 2016-04-28 |
| CN104281889B (en) | 2016-02-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2014101609A4 (en) | A multi-objective stochastic programming method of electric vehicle charging load based on non-dominated sorting genetic algorithm | |
| CN107947231B (en) | Hybrid energy storage system control method for optimized operation of power distribution network | |
| CN110311396B (en) | Hybrid energy storage capacity optimization configuration method for AC/DC hybrid micro-grid | |
| CN106208090B (en) | A kind of voltage power-less optimized controlling method and system of photovoltaic generation access | |
| CN111619393B (en) | User-oriented orderly charging control method for electric vehicle in transformer area | |
| CN104037765B (en) | The method of active power distribution network service restoration scheme is chosen based on improved adaptive GA-IAGA | |
| CN105226694B (en) | The smooth generation of electricity by new energy control method of energy storage based on fuzzy empirical mode decomposition | |
| CN106786692A (en) | One kind is based on the orderly charge control method of distributed electric automobile | |
| CN105896575A (en) | Hundred megawatt energy storage power control method and system based on self-adaptive dynamic programming | |
| Yan et al. | Study on the whole life cycle energy management method of energy storage system with risk correction control | |
| Saber et al. | Smart micro-grid optimization with controllable loads using particle swarm optimization | |
| CN104281984B (en) | A kind of method of supplying power to for micro-capacitance sensor economical operation | |
| Li et al. | Optimal planning energy storage for promoting renewable power consumption in the urgent situation of UHV systems | |
| Kumar et al. | Smart home energy management with integration of PV and storage facilities providing grid support | |
| CN103368195A (en) | Method and system for power control of battery energy storage power station based on genetic algorithm | |
| CN115940284B (en) | Operation control strategy of new energy hydrogen production system considering time-of-use electricity price | |
| CN116169700A (en) | Power distribution network energy storage configuration model and solving method thereof | |
| CN109840621A (en) | Consider the grid type micro-capacitance sensor Multipurpose Optimal Method a few days ago that energy-storage system influences | |
| Daud et al. | An optimal state of charge feedback control strategy for battery energy storage in hourly dispatch of PV sources | |
| CN109510242A (en) | A kind of micro-grid energy storage system method for optimizing configuration and storage medium | |
| Shu-jun et al. | Distribution network reconfiguration with distributed power based on genetic algorithm | |
| Zhang et al. | The output optimization of multi-microgrids connected to distribution network during peak, flat and valley periods | |
| CN104578135B (en) | Power predictability control method based on heterogeneous energy storage system | |
| CN108667057A (en) | One kind accessing power distribution network multiple target electric energy administering method for high power density distributed photovoltaic | |
| Li et al. | Predictive Error Stable Distribution Modeling and Stochastic Optimization in Hybrid AC/DC Microgrid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FGI | Letters patent sealed or granted (innovation patent) | ||
| MK22 | Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry |