CN116316752A - Electric vehicle ordered charging strategy optimization method considering power distribution network bearing capacity constraint - Google Patents
Electric vehicle ordered charging strategy optimization method considering power distribution network bearing capacity constraint Download PDFInfo
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- H—ELECTRICITY
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- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
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Abstract
The invention provides an electric vehicle ordered charging strategy optimization method considering the bearing capacity constraint of a power distribution network, which belongs to the technical field of operation control of power systems and comprises the following steps: step 1: establishing a distribution network bearing capacity constraint quantization model; step 2: setting constraint conditions with the aim of minimizing the comprehensive charging cost of the electric vehicle load, and establishing an ordered charging strategy optimization model of the electric vehicle; step 3: and solving an ordered charging strategy optimization model of the electric automobile based on the distribution network bearing capacity constraint quantization model, and evaluating. According to the method, through the guiding of price signals and the intervention of control measures, the translation of charging loads of the electric automobile in space time is realized, the bearing capacity space of the power distribution network is excavated, the utilization rate of the power distribution equipment is improved, the investment transformation period of the power distribution network is delayed, and the overload or overload of the power distribution equipment caused by overlarge load at peak moment is avoided.
Description
Technical Field
The invention relates to the technical field of operation control of power systems, in particular to an electric vehicle ordered charging strategy optimization method considering the bearing capacity constraint of a power distribution network.
Background
Along with the large-scale popularization and application of electric vehicles, electric vehicle charging piles become important power loads in power distribution networks. On one hand, the electric automobile is used as a technical carrier for implementing electric energy substitution in the field of energy consumption, and has remarkable advantages and large-scale effects in the aspects of energy conservation and emission reduction and the consumption of distributed clean energy power generation. On the other hand, the abrupt increase of the charging load of the electric automobile also puts forward higher requirements on the power supply bearing capacity of the power distribution network, and the power consumption requirement of the charging load of the electric automobile in peak time periods is difficult to be met by the bearing capacity of the power distribution network in some areas. The capacity expansion transformation cost of key equipment such as power distribution network lines and transformers is high, the time period is long, and if the power consumption requirement of peak period charging load is met through capacity expansion, the utilization rate of the power distribution network equipment is reduced.
Disclosure of Invention
The invention designs an electric vehicle ordered charging strategy optimization method considering the bearing capacity constraint of a power distribution network, which realizes the translation of charging load of the electric vehicle in time and space through the guiding of price signals and the intervention of control measures, digs the bearing capacity space of the power distribution network, improves the utilization rate of power distribution equipment, delays the investment transformation period of the power distribution network, and avoids overload or overload of the power distribution equipment caused by overlarge load at peak moment.
An electric vehicle ordered charging strategy optimization method considering the bearing capacity constraint of a power distribution network comprises the following steps:
step 1: establishing a distribution network bearing capacity constraint quantization model;
step 2: setting constraint conditions with the aim of minimizing the comprehensive charging cost of the electric vehicle load, and establishing an ordered charging strategy optimization model of the electric vehicle;
step 3: and solving an ordered charging strategy optimization model of the electric automobile based on the distribution network bearing capacity constraint quantization model, and evaluating.
Further, step 1 quantitatively describes the load capacity constraint of the power distribution network based on the limit values of the bus voltage, the line current and the running capacity of the transformer in the charging load power supply area of the electric automobile, and specifically comprises the following steps:
U i,min ≤U i ≤U i,max ,i＝1…N (1)
I j ≤I j,max ,j＝1…M (2)
S k ≤S k,max ,k＝1…K (3)
in the above, U i Bus voltage at node i; u (U) i,min A lower bus voltage limit for node i; u (U) i,max The upper bus voltage limit of the node i; n is the number of nodes; i j Current for line j; i j,max A current limit for line j; m is the number of lines; s is S k Is the operating capacity of the transformer k; s is S k,max For the operating capacity limit of the transformer K, K represents the number of transformers.
Further, step 2 is to minimize the electric vehicle load comprehensive charging cost, and based on the time-sharing price signal, the objective function is set as follows:
in the above formula, minf represents the minimum value of the comprehensive charging cost of the electric vehicle load, T represents the moment, and T is the optimization period; v is the number of charging piles; c (C) tou The time-sharing electricity price signal; p (P) v Charging power for the v-th charging pile; p (P) L Active power for a normal load in the target area; Δt is a unit period duration; c (C) ps Penalty cost for a unit amount of electricity; e (E) v To optimize the charge power requirements that are not met by the load-bearing capacity limitations during the cycle.
Further, the constraint condition of the step 2 comprises a distribution network bearing capacity constraint, wherein the distribution network bearing capacity constraint is expressed by a bearing capacity margin eta,
η j (t)≥η j,min ,j＝1…M (8)
η k (t)≥η k,min ,k＝1…K (9)
in the above, eta j Load capacity margin for the jth line; η (eta) j,min The load capacity margin limit value of the jth line; η (eta) k The bearing capacity margin of the kth transformer is obtained; η (eta) k,min And the load capacity margin limit value of the kth transformer.
Further, the constraint condition of the step 2 comprises capacity constraint of the charging pile, specifically:
0≤P v (t)≤P v,rate (10)
in the above, P v,rate And the rated charge capacity of the v-th charging pile.
Further, the constraint condition in the step 2 further includes a charging load constraint, specifically:
in the above, P v0 An initial charge demand for the v-th charge stake; e (E) v To optimize the charge power requirements that are not met by the load-bearing capacity limitations during the cycle.
Preferably, the step 3 adopts a genetic algorithm to solve the ordered charging strategy of the electric automobile.
Specifically, the genetic algorithm for solving the ordered charging strategy of the electric automobile comprises the following steps:
step 3.1A: defining an fitness function AF= -min f on a search space, wherein the smaller the comprehensive charging cost of the electric vehicle load is, the larger the fitness function value is; given population size N 1 Crossover rate Pc and mutation rate Pm, genetic algebra T 1 ；
Step 3.2A: binary-coded chromosome s for representing time sequence charging load P of electric automobile v (T) t=1, once again, the total number of components is equal, randomly generating N 2 Individuals on chromosome s1, s2, …, sN 2 Form an initial population s= { S1, S2, …, sN 2 Setting an algebraic counter t=1;
step 3.3A: calculating the fitness AF of each chromosome individual in the S;
step 3.4A: if the termination condition is met, selecting a chromosome individual with the maximum fitness in S;
step 3.5A: randomly selecting 1 individual from S each time according to the selection opportunity determined by the selection probability P (xi), copying the chromosomes of the selected individual, and forming the chromosomes obtained by copying into a group S1;
step 3.6A: c chromosomes which participate in crossing are randomly determined from S1 according to the chromosome number c which is determined by the crossing rate Pc, the crossing operation is performed in a pairing mode, and the generated new chromosomes are used for replacing the original chromosomes, so that a population S2 is obtained;
step 3.7A: randomly determining m chromosomes from the S2 according to the variation times m determined by the variation rate Pm, respectively performing variation operation, and replacing the original chromosomes with the generated new chromosomes to obtain a population S3;
step 3.8A: the population S3 is used as a new generation population, i.e. S3 is used instead of S, t=t+1, and step 3.3A is shifted.
Alternatively, step 3 may also use a neural network to solve the ordered charging strategy of the electric vehicle.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an electric vehicle ordered charging strategy optimization method considering the bearing capacity of a power distribution network, which realizes the translation of charging load of the electric vehicle in time and space through the intervention of price signal guiding and control measures, digs the bearing capacity space of the power distribution network, improves the utilization rate of power distribution equipment, delays the investment transformation period of the power distribution network, and avoids overload or overload of the power distribution equipment caused by overlarge load at peak moment.
1. According to the invention, through modeling based on the limit values of the running conditions of the power distribution network line, the transformer and the like, the constraint of the bearing capacity of the power distribution network under the condition that the charging load of the electric automobile is connected in is quantitatively described.
2. According to the invention, an ordered charging strategy optimization model of the electric automobile is established, constraint conditions such as the bearing capacity of a power distribution network, the number of charging piles, the capacity limit value, the electric power and electric quantity balance and the like are considered while the comprehensive charging cost of the electric automobile load under a time-sharing electricity price signal is minimized, the charging load power supply requirement of the electric automobile is met, the charging cost of the electric automobile is reduced, the idle capacity of the power distribution equipment in different time periods is fully utilized, and the utilization rate of the power distribution equipment is improved.
3. The method defines the quantization index of the distribution network bearing capacity margin, and provides the quantization index for evaluating the ordered charging strategy of the electric automobile considering the distribution network bearing capacity.
Drawings
FIG. 1 is a flow chart of an electric vehicle ordered charging strategy optimization method of the present invention;
FIG. 2 is an unoptimized pre-charge load supply-demand characteristic;
fig. 3 is an optimized post-charge load supply-demand characteristic.
Detailed Description
The invention relates to an electric vehicle ordered charging strategy optimization method considering the bearing capacity constraint of a power distribution network, which is further described in detail below with reference to the accompanying drawings and a specific implementation method.
The invention provides an electric vehicle ordered charging strategy optimization method considering the bearing capacity of a power distribution network. The method aims at minimizing the comprehensive charging cost of the electric vehicle load, and considers constraint conditions such as the bearing capacity of a power distribution network, the number of charging piles, the capacity and the like based on time-sharing price signals. As shown in fig. 1, the specific model and steps are as follows:
step 1: establishing a distribution network bearing capacity constraint quantization model
The load capacity constraint of the power distribution network is quantitatively described based on the limit values of bus voltage, line current and transformer operation capacity in an electric vehicle charging load power supply area, and the load capacity constraint is specifically shown as follows:
U i,min ≤U i ≤U i,max ,i＝1...N (1)
I j ≤I j,max ,j＝1...M (2)
S k ≤S k,max ,k＝1…K (3)
in the above, U i Bus voltage at node i; u (U) i,min A lower bus voltage limit for node i; u (U) i,max The upper bus voltage limit of the node i; n is the number of nodes; i j Current for line j; i j,max A current limit for line j; m is the number of lines; s is S k Is the operating capacity of the transformer k; s is S k,max The rated capacity is generally taken as the operating capacity limit of the transformer k.
Step 2: establishing an ordered charging strategy optimization model of an electric automobile
The model aims at minimizing the comprehensive charging cost of the electric vehicle load, and based on time-sharing price signals, takes constraint conditions such as the bearing capacity of a power distribution network, the number of charging piles, the capacity limit value, the electric power and electricity balance and the like into consideration, and specifically comprises the following steps:
1. objective function
In the above formula, T is an optimization period; v is the number of charging piles; c (C) tou The time-sharing electricity price signal; p (P) v Is v thCharging power of the charging pile; p (P) L Active power for a normal load in the target area; Δt is a unit period duration; c (C) ps Penalty cost for a unit amount of electricity; e (E) v To optimize the charge power requirements that are not met by the load-bearing capacity limitations during the cycle.
2. Electric power and electric quantity balance constraint
In the above, P PG Supplying power to a power distribution network in a target area; p (P) DG Active power for distributed generation within a target area; p (P) L Active power for a normal load in the target area; p (P) e Charging power for an e-th distributed energy storage facility within the target area; p (P) loss The network loss of the power distribution network in the target area.
3. Distribution network bearing capacity constraint
The distribution network load capacity constraint is expressed in terms of load capacity margin:
η j (t)≥η j,min ,j＝1...M (8)
η k (t)≥η k,min ,k＝1...K (9)
in the above, eta j Load capacity margin for the jth line; η (eta) j,min The load capacity margin limit value of the jth line; η (eta) k The bearing capacity margin of the kth transformer is obtained; η (eta) k,min And the load capacity margin limit value of the kth transformer. The load capacity margin reflects the current load state of the power distribution network: the larger the bearing capacity margin is, the more the power distribution equipment is in a light load state at present, and a large bearing capacity space can be utilized; the smaller the load bearing margin, the descriptionThe heavier the load of the power distribution equipment is, when the bearing capacity margin is smaller than 0, the power distribution equipment is in an overload state; therefore, when the bearing capacity margin is lower than a certain limit value, the system alarms so as to avoid safety accidents caused by overload of equipment.
4. Charging pile capacity constraint
0≤P v (t)≤P v,rate (10)
In the above, P v,rate And the rated charge capacity of the v-th charging pile.
5. Charging load constraint
In the above, P v0 An initial charge demand for the v-th charge stake; e (E) v To optimize the charge power requirements that are not met by the load-bearing capacity limitations during the cycle.
Step 3: ordered charging optimization strategy calculation and evaluation for electric automobile
And 3, solving an ordered charging strategy of the electric automobile by adopting a genetic algorithm.
Specifically, the genetic algorithm for solving the ordered charging strategy of the electric automobile comprises the following steps:
step 3.1A: defining an fitness function AF= -min f on a search space, wherein the smaller the comprehensive charging cost of the electric vehicle load is, the larger the fitness function value is; given population size N 1 Crossover rate Pc and mutation rate Pm, genetic algebra T 1 ；
Step 3.2A: binary-coded chromosome s for representing time sequence charging load P of electric automobile v (T) t=1, … T, randomly generating N 2 Individuals on chromosome s1, s2, …, sN 2 Form an initial population s= { S1, S2, …, sN 2 Setting an algebraic counter t=1;
step 3.3A: calculating the fitness AF of each chromosome individual in the S;
step 3.4A: if the termination condition is met, selecting a chromosome individual with the maximum fitness in S;
step 3.5A: randomly selecting 1 individual from S each time according to the selection opportunity determined by the selection probability P (xi), copying the chromosomes of the selected individual, and forming the chromosomes obtained by copying into a group S1;
step 3.6A: c chromosomes which participate in crossing are randomly determined from S1 according to the chromosome number c which is determined by the crossing rate Pc, the crossing operation is performed in a pairing mode, and the generated new chromosomes are used for replacing the original chromosomes, so that a population S2 is obtained;
step 3.7A: randomly determining m chromosomes from the S2 according to the variation times m determined by the variation rate Pm, respectively performing variation operation, and replacing the original chromosomes with the generated new chromosomes to obtain a population S3;
step 3.8A: the population S3 is used as a new generation population, i.e. S3 is used instead of S, t=t+1, and step 3.3A is shifted.
Alternatively, step 3 may also use a neural network to solve the ordered charging strategy of the electric vehicle. The method for solving the ordered charging strategy of the electric automobile by adopting the neural network comprises the following steps:
step 3.1B defining an augmented Lagrange-Hopfield neural network (ALHN: augmented Lagrange-Hopfield Neural network) for solving an electric vehicle charging strategy optimization problem targeting equation (4). The ALHN energy function is an augmented Lagrange function, which is augmented by Hopfield relation terms in the Hopfield neural network, thereby overcoming the defect of slow convergence speed of the traditional Hopfield network algorithm. The augmented Lagrange energy function is defined herein as
Wherein λ represents Lagrange multiplier; beta is a penalty factor.
Step 3.2B: the initialization ALHN neural network calculation parameters mainly comprise: 1) The shape parameter sigma of the ALHN neural network S-type function is generally 100; 2) The penalty factor beta in the energy function is generally 0001; 3) Continuous neuron step length updating coefficient a in algorithm i And the updated step size coefficient a of the multiplier neuron λ The value of (2) is adjusted according to the actual situation.
Step 3.3B: iterative computation is carried out based on the ALHN neural network, and the iterative computation formula is as follows:
in the method, in the process of the invention,
input for successive neurons at the nth iteration; v (V) i Is the output of a continuous neuron; />
Input to the multiplier neuron at the nth iteration; v (V) λ Is the output of the multiplier neuron.
Step 3.3B: when the maximum error is smaller than a preset value or the maximum iteration number is reached, the algorithm is iterated and terminated, the optimization calculation is finished, and a result is output. The maximum error at the nth iteration is defined herein as:
in the method, in the process of the invention,
is the maximum error at the nth iteration; ΔP n Is the error of the power balance at the nth iteration;
error of continuous neuron output at the nth iteration; />
The error is output for the multiplier neuron at the nth iteration.
Specifically, taking a certain community electric automobile charging system as an example, the community distribution capacity is 2500kVA; the power factor is calculated according to 0.9, the margin limit value of the distribution transformer bearing capacity is 5%, namely when the community power active load exceeds 2137.5kW, the distribution transformer bearing capacity reaches a safety margin alarm value; 30kW single-gun direct-current charging piles are arranged in the community, and the total charging capacity is 900kW; the network loss is calculated by 0.05; penalty coefficient is calculated according to 1.0 per kWh; the community power load scenario and time-of-use power rate information at a typical day are shown in the following table.
TABLE 1 typical daily Power load scenario and time-of-use price information
(1) When optimization of the charging strategy is not considered, the actual charging load characteristics are affected by the community distribution transformer bearing capacity, and are shown in fig. 2. The total charge load demand is 13501.6kWh; the actual charge amount is 11719.51kWh; the charging load demand 1782.085714kWh is not met and the total power supply cost for community consideration and penalty cost is 20620.91 yuan.
(2) When the charging strategy optimization method provided by the patent is adopted, the actual charging load characteristics are influenced by the community distribution transformer bearing capacity, and are shown in figure 3. The total charge load demand is 13501.6kWh; the actual charge amount is 13501.6kWh; through nimble regulation, charge load demand all satisfies, and the community takes into account the total power supply cost of punishment cost and is 19021.707 yuan, on the basis that satisfies all electric automobile charge load demand, practices thrift power supply cost 1599.203 yuan.
The load capacity margin of the distribution transformer at each period before and after the optimization is shown in table 2. From the table, it can be seen that: through the optimization of the charging strategy of the electric automobile, the reasonable distribution of the community distribution transformer bearing state is realized. The load capacity of the optimized distribution transformer is kept within the safety margin range of more than or equal to 5 percent under the limit of 5 percent of the safety margin of the load capacity, so that the load peak time is avoided, and the community distribution transformer is in overload and heavy load states.
Table 2 comparative analysis of load margin of the pre-and post-optimization distribution transformer
The above embodiments are provided to illustrate the technical concept and features of the present invention and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, and are not intended to limit the scope of the present invention. All equivalent changes or modifications made in accordance with the spirit of the present invention should be construed to be included in the scope of the present invention.
Claims (10)
1. An electric vehicle ordered charging strategy optimization method considering the bearing capacity constraint of a power distribution network is characterized by comprising the following steps:
step 1: establishing a distribution network bearing capacity constraint quantization model;
step 2: setting constraint conditions with the aim of minimizing the comprehensive charging cost of the electric vehicle load, and establishing an ordered charging strategy optimization model of the electric vehicle;
step 3: and solving an ordered charging strategy optimization model of the electric automobile based on the distribution network bearing capacity constraint quantization model, and evaluating.
2. The method for optimizing the ordered charging strategy of the electric automobile considering the bearing capacity constraint of the power distribution network according to claim 1, is characterized by comprising the following steps: step 1, quantitatively describing the bearing capacity constraint of a power distribution network based on the limit values of bus voltage, line current and transformer operation capacity in an electric vehicle charging load power supply area, wherein the bearing capacity constraint is specifically as follows:
U i,min ≤U i ≤U i,max ,i＝1…N (1)
I j ≤I j,max ,j＝1…M (2)
S k ≤S k,max ,k＝1…K (3)
on the upper partIn U i Bus voltage at node i; u (U) i,min A lower bus voltage limit for node i; u (U) i,max The upper bus voltage limit of the node i; n is the number of nodes; i j Current for line j; i j,max A current limit for line j; m is the number of lines; s is S k Is the operating capacity of the transformer k; s is S k,max For the operating capacity limit of the transformer K, K represents the number of transformers.
3. The method for optimizing ordered charging strategies of electric vehicles according to claim 1, wherein step 2 is performed with the aim of minimizing the comprehensive charging cost of the electric vehicle load, and the objective function is set based on the time-sharing price signal as follows:
in the above formula, min f represents the minimum value of the comprehensive charging cost of the electric vehicle load, T represents the moment, and T is the optimization period; v is the number of charging piles; c (C) tou The time-sharing electricity price signal; p (P) v Charging power for the v-th charging pile; p (P) L Active power for a normal load in the target area; Δt is a unit period duration; c (C) ps Penalty cost for a unit amount of electricity; e (E) v To optimize the charge power requirements that are not met by the load-bearing capacity limitations during the cycle.
4. An electric vehicle ordered charging strategy optimization method taking into account distribution network bearing capacity constraints, as claimed in claim 3, wherein the step 2 constraints include distribution network bearing capacity constraints, which are expressed in terms of bearing capacity margin η,
η j (t)≥η j,min ,j＝1…M (8)
η k (t)≥η k,min ,k＝1…K (9)
in the above, eta j Load capacity margin for the jth line; η (eta) j,m i n The load capacity margin limit value of the jth line; η (eta) k The bearing capacity margin of the kth transformer is obtained; η (eta) k,min And the load capacity margin limit value of the kth transformer.
5. The method for optimizing the ordered charging strategy of the electric automobile considering the bearing capacity constraint of the power distribution network according to claim 4, wherein the constraint condition of the step 2 comprises the capacity constraint of a charging pile, specifically:
0≤P v (t)≤P v,rate (10)
in the above, P v,rate And the rated charge capacity of the v-th charging pile.
6. The method for optimizing ordered charging strategies of electric vehicles according to claim 5, wherein the constraint conditions in step 2 include charging load constraints, specifically:
in the above, P v0 An initial charge demand for the v-th charge stake; e (E) v To optimize the charge power requirements that are not met by the load-bearing capacity limitations during the cycle.
7. The method for optimizing the ordered charging strategy of the electric automobile taking into account the bearing capacity constraint of the power distribution network as claimed in claim 6, wherein the step 3 is characterized in that the genetic algorithm is adopted to solve the ordered charging strategy of the electric automobile.
8. The method for optimizing the ordered charging strategy of the electric automobile considering the bearing capacity constraint of the power distribution network according to claim 7, is characterized in that: the genetic algorithm for solving the ordered charging strategy of the electric automobile comprises the following steps:
step 3.1A: defining an fitness function AF= -min f on a search space, wherein the smaller the comprehensive charging cost of the electric vehicle load is, the larger the fitness function value is; given population size N 1 Crossover rate Pc and mutation rate Pm, genetic algebra T 1 ；
Step 3.2A: binary-coded chromosome s for representing time sequence charging load P of electric automobile v (T) t=1, … T, randomly generating N 2 Individuals on chromosome s1, s2, …, sN 2 Form an initial population s= { S1, S2, …, sN 2 Setting an algebraic counter t=1;
step 3.3A: calculating the fitness AF of each chromosome individual in the S;
step 3.4A: if the termination condition is met, selecting a chromosome individual with the maximum fitness in S;
step 3.5A: randomly selecting 1 individual from S each time according to the selection opportunity determined by the selection probability P (xi), copying the chromosomes of the selected individual, and forming the chromosomes obtained by copying into a group S1;
step 3.6A: c chromosomes which participate in crossing are randomly determined from S1 according to the chromosome number c which is determined by the crossing rate Pc, the crossing operation is performed in a pairing mode, and the generated new chromosomes are used for replacing the original chromosomes, so that a population S2 is obtained;
step 3.7A: randomly determining m chromosomes from the S2 according to the variation times m determined by the variation rate Pm, respectively performing variation operation, and replacing the original chromosomes with the generated new chromosomes to obtain a population S3;
step 3.8A: the population S3 is used as a new generation population, i.e. S3 is used instead of S, t=t+1, and step 3.3A is shifted.
9. The method for optimizing the ordered charging strategy of the electric automobile considering the bearing capacity constraint of the power distribution network according to claim 8, is characterized in that: to ensure that the genetic algorithm converges to a globally optimal solution with a probability of 1, a retention operator β is introduced in the selection operation, i.e., the pre- β optimal individuals in the population are retained prior to the selection operation.
10. Ordered charging strategy optimization method for electric automobile considering bearing capacity constraint of power distribution network as claimed in claim 6
The method is characterized in that: and 3, solving an ordered charging strategy of the electric automobile by adopting a neural network.
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