CN116258345A - Load aggregator operation optimization control method considering participation of electric automobile - Google Patents
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Abstract
The invention relates to an optimization management and control method, in particular to a load aggregator operation optimization management and control method considering participation of an electric automobile. The method comprises the steps of establishing a load aggregator operation optimization model considering reactive power demand uncertainty in demand side response and a solving algorithm thereof. Step 1, carrying out probability density analysis on daily mileage of a vehicle; selecting the number of mileage in daily driving; calculating the charging time of the electric automobile; step 2, performing normal function distribution fitting on the final return time of the vehicle; step 3, aiming at the charging and discharging behavior characteristics of the electric automobile, the load aggregator participates in management and operation; step 4, adjusting the electricity price interval and the electricity price dividing span again, and determining the price dividing span; step 5, determining reactive adjustable capacity of a demand side; step 6, selecting an objective function; step 7, establishing constraint conditions; and step 8, quantifying uncertainty of reactive power demand and adjustment capacity thereof in the response process of the load aggregator on the demand side.
Description
Technical Field
The invention relates to an optimization management and control method, in particular to a load aggregator operation optimization management and control method considering participation of an electric automobile.
Background
The electric automobile is a novel power load with randomness in space-time distribution, the impact is higher than that of common household appliances when the electric automobile is connected to a power distribution network for charging in a short time, and the electric automobile is a great test on whether a power system can normally and stably run under high load.
Along with the expansion of the power consumption load and the irregularity of the power consumption load, the economic, reliable and efficient power supply and demand balance of the power grid can not be ensured only by the traditional peak shaving unit and the common energy storage equipment. Thus, taking into account the controllable nature of the user, they can be guided into the grid demand side supply by means of electricity price incentives or the like and provide the grid with the ability to regulate. The load aggregator can aggregate the demand sides and then link the aggregate with the power grid as a whole, and is an effective carrier for realizing the management of the demand sides. Therefore, the operation optimization research of the load aggregator is needed, the electricity consumption behavior of the user is optimized, and more response capacity on the demand side is obtained while the aggregated load electricity consumption quality is ensured.
And when the electric automobile load regulated by the load aggregator is subjected to a demand side response process, various load reactive demands change and have certain uncertainty. This uncertainty has an impact on both the power supply and the participation demand side response in the actual use of the load aggregator. At present, further research is still needed on the aspects of the relationship and influence of reactive power demand uncertainty on reactive power interaction balance between the operation of a load aggregator and a power grid in the demand side response process.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a load aggregator operation optimization management and control method considering the participation of an electric automobile. The method is based on the running characteristics of the electric automobile load, and an adaptive genetic algorithm of elite operators is adopted to establish a load aggregator running optimization model considering reactive power demand uncertainty in demand side response and a solving algorithm thereof.
In order to achieve the above purpose, the present invention adopts the following technical scheme that:
and 1, carrying out probability density analysis on the daily mileage of the vehicle.
When the vehicle is initially charged, the state of charge of the vehicle battery is selected from the daily mileage probability density function of the vehicle.
And calculating the charging time of the electric automobile.
And 2, performing normal function distribution fitting on the final return time Tr of the vehicle.
And approximates a normal distribution of probability density for the user away from home time Td.
And 3, in the response process of the electric automobile on the participation demand side, the charge and discharge processes are included, and the load aggregator participates in management and operation aiming at the charge and discharge behavior characteristics of the electric automobile.
Step 5, determining reactive adjustable capacity of the demand side (the reactive demand of the reactive power demand has uncertainty due to fluctuation of various loads of the demand side).
And 6, selecting an objective function.
And 7, establishing constraint conditions.
And step 8, quantifying uncertainty of reactive power demand and regulation capacity thereof in the response process of the load aggregator at the demand side, and regulating and controlling the electric automobile aggregation model with the maximum income of the load aggregator as a target.
Further, in step 1, the probability density function of performing the probability density analysis is as follows.
wherein ,γD ＝3.20，λ D ＝0.88。
The selecting the daily mileage number from the daily mileage probability density function of the automobile comprises.
wherein ,SOCp To initiate charge percentage, SOC r To end the desired charge of the charging user, E is the consumed charge per kilometer, C is the capacity of the power battery, R d The number of driving strokes per day.
The calculating the charging time length of the electric automobile comprises the following steps:
wherein ,Tc For the duration of charging, P c 、η c Charging power and energy conversion efficiency of the charging pile are respectively.
Further, in step 2, the normal function distribution fitting is performed on the last return time Tr of the vehicle, where the probability density function is the same.
wherein ,
η r ＝2.41。
the probability density for the user away from home time Td approximates a normal distribution as a function.
wherein ,
η d ＝1.90。
in step 3, the electric vehicle charge-discharge polymerization model is as follows.
Further, in step 4, the span is calculated as:
wherein ,ΔWL To divide the span parameter ΔW L，max ，ΔW L，min Respectively predicting the highest load and the lowest load of the daily basis load, wherein H is the number of segments;
wherein ,Smax and Smin The peak value and the valley value of the original time-of-use electricity price are calculated by membership function to obtain the mapping S of each load period in the price interval min ；
S i ＝μ·ΔL+S min (10)
wherein ,Si For the charging unit price at the i-th period, μ is an intermediate variable.
Further, in step 5, determining the reactive adjustable capacity of the demand side includes:
reactive adjustable capacity on demand side is
in the formula ：Qm -demand side m load reactive adjustable capacity; p (P) k，m，t -probability corresponding to the kth state at moment i of the load m on the demand side; q (Q) k，m，t -reactive power corresponding to the kth state at moment i of the load m on the demand side; ΔQ E -an indeterminate capacity of the demand side reactive output; k (K) m -the operating state of the demand side m; a z-z transform;
delta Q E The probability distribution of (2) is:
in the formula ：σE -the maximum uncertainty value that can occur in the demand side reactive regulation process.
Further, step 6 includes the sub-steps of:
step 6-1, power grid load variance:
the smaller the load fluctuation, the more advantageous the stable operation of the system, the objective function can be expressed as
P k ＝P k,c +P k,v (14)
wherein ,Pk P is the total load at the kth period of the day k,c For EV charging load at the kth period of the day, P k,v For the base load at the kth period of the day,
is the expected total load average; l is the total time period number in valley, M is the optimized objective function;
step 6-2, user satisfaction:
assuming that the user satisfaction G is positively correlated with the total charge cost, the user's total charge condition is considered in the optimization process.
G＝μS t (15)
Wherein μ is a conversion coefficient between price and satisfaction, and total cost S t Calculated from equation (16):
step 6-3, linear weighted summation method:
based on a linear weighted summation method, the objective functions M and P are calculated i,c Normalization processing is performed as shown in formula (17):
s is a multi-objective optimization function of the vehicle; m is M max ,S max Respectively single objective functions M, S t Is the maximum value of (2); psi phi type k，1 、ψ k，2 Respectively single objective functions M, S t Is used to optimize the weights of (a).
Further, step 7 includes the sub-steps of:
step 7-1, power balance constraint:
P a,t ＝θ con-ev,t P eva-all,t +θ con-ac,t P aca-all,t (18)
in the formula ：Pa,t -total power of the load aggregator to the grid power delivery or storage at time t;
step 7-2, electric automobile polymerization equilibrium constraint:
in the formula 
-upper/lower limit of aggregate charge power and discharge power of electric car, +.>
-the capacity of the electric vehicle at time t and its upper/lower limits;
step 7-3, the load aggregator and the power grid are constrained in an interactive way:
both electricity purchasing and electricity selling for load aggregation are within the limit range of the interactive power:
in the formula ：Psell，t 、
-the power of the power grid sold by the load aggregator at time t and its upper/lower limits; p (P) buy，t 、/>
-the power of the grid purchased by the load aggregator at time t and its upper/lower limits;
step 7-4, reactive power constraint of the system:
in the formula ：Qt 、Q min 、Q max Reactive power emitted by the system at the moment t and the upper limit and the lower limit thereof;
U i，t 、/>
the voltage at node i in the system at time t and its upper and lower limits.
Compared with the prior art, the invention has the beneficial effects.
(1) The load aggregator operation optimization model which is built by the invention and takes the uncertainty of reactive power demand into consideration when the demand side responds can effectively improve the operation income of the load aggregator when the load aggregator participates in the response of the demand side;
(2) Aiming at the problems of peak-valley mismatch and the like in a time-sharing electricity price strategy, the invention integrates demand response resources through a load aggregator, combines a multi-period dynamic electricity price to regulate and control a charging load, effectively guides the load to transfer to a valley, and improves various indexes along with the improvement of the responsiveness of a user to an ordered charging strategy under the existing 40% permeability compared with an unordered charging condition.
(3) According to the load aggregator operation optimization model provided by the invention, the risk caused by uncertain reactive power requirements during response of the demand side is considered, the peak-valley regulation requirements of the power grid can be more accurately met, and the phenomena of wind abandoning and light abandoning are reduced.
Drawings
The invention is further described below with reference to the drawings and the detailed description. The scope of the present invention is not limited to the following description.
Fig. 1 is a schematic diagram of an electric vehicle with disordered charge hazard.
Fig. 2 is a schematic flow chart of an electric vehicle charging process.
FIG. 3 is a flow chart of load aggregator operation optimization solution.
Fig. 4 is a diagram of electric vehicle load aggregator resource integration and regulation.
Fig. 5 is a load aggregator operation optimization comparison graph.
Detailed Description
As shown in fig. 1-5, the load aggregator operation optimization management and control method considering participation of the electric automobile comprises the following steps:
step one, carrying out probability density analysis on daily mileage of a vehicle driven by a user, wherein the probability density function is as follows:
wherein γD ＝3.20,λ D ＝0.88。
When the automobile is initially charged, the state of charge of the automobile battery can be calculated by selecting the daily mileage from the daily mileage probability density function of the automobile as follows:
wherein ,SOCp To initiate charge percentage, SOC r To end the desired charge of the charging user (if simplified, it can be set to a full state of charge), E is the power consumed per kilometer, C is the capacity of the power battery, R d The number of driving strokes per day.
The charging time of the electric automobile is calculated as
wherein ,Tc For the duration of charging, P c ,η c Charging power and energy conversion efficiency of the charging pile are respectively.
Step two, the final return time T of the vehicle r Fitting normal function distribution, wherein the probability density function is that
wherein ,
η r ＝2.41。
for the time T of leaving home of the user d Is approximately normal, as a function of:
wherein ,
η d ＝1.90。
and thirdly, in the response process of the electric automobile on the participation demand side, the method mainly comprises 2 processes of charging and discharging, and a load aggregator participates in management and operation aiming at the charging and discharging behavior characteristics of the electric automobile. The electric automobile charge-discharge aggregation model is:
And fourthly, adjusting the electricity price interval and the electricity price dividing span again, dividing the 24h load into a plurality of segments, and then calculating the price of the current time period according to the actual load of each time period, so as to accurately and effectively guide the load transfer by combining the actual different base load conditions, and improve the benefit. Price repartitioning is carried out, a price partition span needs to be determined, and the span is calculated as follows:
wherein ,ΔWL To divide the span parameter ΔW L，max ，ΔW L，min The highest load and the lowest load predicted by the daily basis load are respectively, and H is the number of segments.
wherein ,Smax and Smin The peak value and the valley value of the original time-of-use electricity price are calculated by membership function to obtain the mapping S of each load period in the price interval min 。
S i ＝μ·ΔL+S min (10)
wherein ,Si For the charging unit price at the i-th period, μ is an intermediate variable.
Step five, because of fluctuation of various loads at the demand side, the reactive power demand has uncertainty, and the reactive power adjustable capacity at the demand side is as follows:
in the formula ：Qm -demand side m load reactive adjustable capacity; p (P) k，m，t -probability corresponding to the kth state at moment i of the load m on the demand side; q (Q) k，m，t -reactive power corresponding to the kth state at moment i of the load m on the demand side; ΔQ E -an indeterminate capacity of the demand side reactive output; k (K) m -the operating state of the demand side m; and z-z transformation.
Delta Q E The probability distribution of (2) is:
in the formula ：σE -the maximum uncertainty value that can occur in the demand side reactive regulation process.
Step six, selecting an objective function:
1. step 6-1, power grid load variance:
the smaller the load fluctuation, the more advantageous the stable operation of the system, the objective function can be expressed as
P k ＝P k,c +P k,v (14)
wherein ,Pk P is the total load at the kth period of the day k,c For EV charging load at the kth period of the day, P k,v For the base load at the kth period of the day,
is the expected total load average; l is the total time period number at the valley, and M is the optimized objective function.
2. Step 6-2, user satisfaction:
assuming that the user satisfaction G is positively correlated with the total charge cost, the user's total charge condition is considered in the optimization process.
G＝μS t (15)
Wherein μ is a conversion coefficient between price and satisfaction, and total cost S t Calculated from equation (16):
3. step 6-3, linear weighted summation method:
based on a linear weighted summation method, the objective functions D and P are calculated i,c Normalized as shown in formula (17)
S is a multi-objective optimization function of the vehicle; m is M max ,S max Respectively single objective functions M, S t Is the maximum value of (2); psi phi type k，1 、ψ k，2 Respectively single objective functions M, S t Is used to optimize the weights of (a).
Step seven, constraint conditions:
1. step 7-1, power balance constraint:
P a,t ＝θ con-ev,t P eva-all,t +θ con-ac,t P aca-all,t (18)
in the formula ：Pa,t -total power delivered or stored by the load aggregator to the grid at time t.
2. Step 7-2, electric automobile polymerization equilibrium constraint:
in the formula 
-upper/lower limit of aggregate charge power and discharge power of electric car, +.>
E eva，t 、/>
-the capacity of the electric car at time t and its upper/lower limits.
3. Step 7-3, the load aggregator and the power grid are constrained in an interactive way:
for load aggregation, whether electricity purchase or electricity selling is carried out with a power grid is within the limit range of the interactive power:
in the formula ：Psell，t 、
-the power of the power grid sold by the load aggregator at time t and its upper/lower limits; p (P) buy，t 、/>
The load aggregator purchases power and its upper/lower limits from the grid at time t.
4. Step 7-4, reactive power constraint of the system:
in the formula ：Qt 、Q min 、Q max Reactive power emitted by the system at the moment t and the upper limit and the lower limit thereof;
U i，t />
the voltage at node i in the system at time t and its upper and lower limits.
And step 8, quantifying uncertainty of reactive power demand and regulation capacity thereof in the response process of the load aggregator at the demand side, and regulating and controlling the electric automobile aggregation model with the maximum income of the load aggregator as a target.
As shown in fig. 1, in the charging process of the electric automobile, voltage and current are converted through a power electronic device, so that harmonic waves are increased, and the production, transmission and utilization efficiency of electric energy are reduced; the voltage offset increases, so that the operation efficiency of the device is lowered, and damage to the device may be caused in severe cases. The power equipment runs under high load and generates a great amount of heat loss, so that the running efficiency is reduced, and the running cost is increased. The large-scale electric automobile is connected into the distribution network for charging in a short time, a large amount of load is accumulated, higher peak values are superposed, the peak-valley gap is further enlarged, and the power supply equipment can bear larger pressure, so that the running performance of the electric automobile is influenced, and the stability of a power system is reduced.
As shown in fig. 4, the electric vehicle load aggregator is a participant for integrating resources specifically aiming at the single adjustable load of electric vehicle charging, and can be used as a main body for two-way communication between system power supply and electric vehicle user charging, so that the resource allocation level is improved, and the limitation that the existing electricity price guiding strategy depends on users to participate by themselves is well solved.
As shown in fig. 5, the dotted line in the figure represents the load of the load aggregator, which means that the electric automobile and the air conditioner temperature control load added into the load aggregator participate in the operation curve managed on the demand side on the basis of the conventional load operation, and the total load optimization operation curve of the regional distribution network is obtained through superposition. The utilization of renewable energy sources can be improved due to the consideration of the addition of load aggregator operations.
It should be understood that the foregoing detailed description of the present invention is provided for illustration only and is not limited to the technical solutions described in the embodiments of the present invention, and those skilled in the art should understand that the present invention may be modified or substituted for the same technical effects; as long as the use requirement is met, the invention is within the protection scope of the invention.
Claims (8)
1. The utility model provides a load aggregator operation optimization management and control method considering electric automobile participation, which is characterized in that: step 1, carrying out probability density analysis on daily mileage of a vehicle;
when the automobile is initially charged, the state of charge of the automobile battery is selected from the daily driving mileage probability density function of the automobile;
calculating the charging time of the electric automobile;
step 2, performing normal function distribution fitting on the final return time Tr of the vehicle;
and approximates the normal distribution of probability density of the user away from home time Td;
step 3, in the response process of the electric automobile at the participation demand side, the charge and discharge processes are included, and a load aggregator participates in management and operation aiming at the charge and discharge behavior characteristics of the electric automobile;
step 4, the electricity price interval and the electricity price dividing span are adjusted again, the 24h load is divided into a plurality of segments, and then the price of the current time period is calculated according to the actual load of each time period; performing price repartitioning, and determining price partition spans;
step 5, determining reactive adjustable capacity of a demand side;
step 6, selecting an objective function;
step 7, establishing constraint conditions;
and step 8, quantifying uncertainty of reactive power demand and regulation capacity thereof in the response process of the load aggregator at the demand side, and regulating and controlling the electric automobile aggregation model with the maximum income of the load aggregator as a target.
2. The method according to claim 1, characterized in that: in step 1, the probability density function for performing the probability density analysis is:
wherein ,γD ＝3.20，λ D ＝0.88；
The selecting the daily mileage number from the daily mileage probability density function of the automobile comprises the following steps:
wherein ,SOCp To initiate charge percentage, SOC r To end the desired charge of the charging user, E is the consumed charge per kilometer, C is the capacity of the power battery, R d The number of the driving strokes is the number of the driving strokes per day;
the calculating the charging time length of the electric automobile comprises the following steps:
wherein ,Tc For the duration of charging, P c 、η c Charging power and energy conversion efficiency of the charging pile are respectively.
3. The method according to claim 1, characterized in that: in step 2, the final return time Tr of the vehicle is fitted with a normal function distribution, and the probability density function is
wherein ,
η r ＝2.41；
the probability density of the user leaving time Td is approximately normal distribution, and the function is that
wherein ,
η d ＝1.90。
4. the method according to claim 1, characterized in that: in the step 3, the electric automobile charge-discharge polymerization model is as follows:
5. The method according to claim 1, characterized in that: in step 4, the span is calculated as:
wherein ,ΔWL To divide the span parameter ΔW L，max 、ΔW L，min Respectively predicting the highest load and the lowest load of the daily basis load, wherein H is the number of segments;
wherein ,Smax and Smin The peak value and the valley value of the original time-of-use electricity price are calculated by membership function to obtain the mapping S of each load period in the price interval min ；
S i ＝μ·ΔL+S min (10)
wherein ,Si For the charging unit price at the i-th period, μ is an intermediate variable.
6. The method according to claim 1, characterized in that: in step 5, determining the reactive adjustable capacity of the demand side comprises:
reactive adjustable capacity on demand side is
in the formula ：Qm -demand side m load reactive adjustable capacity; p (P) k，m，t -probability corresponding to the kth state at moment i of the load m on the demand side; q (Q) k，m，t -reactive power corresponding to the kth state at moment i of the load m on the demand side; ΔQ E -an indeterminate capacity of the demand side reactive output; k (K) m -the operating state of the demand side m; a z-z transform;
delta Q E The probability distribution of (2) is:
in the formula ：σE -the maximum uncertainty value that can occur in the demand side reactive regulation process.
7. The method according to claim 1, characterized in that: step 6 comprises the sub-steps of:
step 6-1, power grid load variance:
the smaller the load fluctuation, the more advantageous the stable operation of the system, the objective function can be expressed as
P k ＝P k,c +P k,v (14)
wherein ,Pk P is the total load at the kth period of the day k,c For EV charging load at the kth period of the day, P k,v For the base load at the kth period of the day,
is the expected total load average; l is the total time period number in valley, M is the optimized objective function;
step 6-2, user satisfaction:
assuming that the user satisfaction G is positively correlated with the total charge cost, the user's total charge condition is considered in the optimization process.
G＝μS t (15)
Wherein μ is a conversion coefficient between price and satisfaction, and total cost S t Calculated from equation (16):
step 6-3, linear weighted summation method:
based on a linear weighted summation method, the objective functions M and P are calculated i,c Normalization processing is performed as shown in formula (17):
s is a multi-objective optimization function of the vehicle; m is M max ,S max Respectively single objective functions M, S t Is the maximum value of (2); psi phi type k，1 、ψ k，2 Respectively single objective functions M, S t Is used to optimize the weights of (a).
8. The method according to claim 1, characterized in that: step 7 comprises the sub-steps of:
step 7-1, power balance constraint:
P a,t ＝θ con-ev,t P eva-all,t +θ con-ac,t P aca-all,t (18)
in the formula ：Pa,t -total power of the load aggregator to the grid power delivery or storage at time t;
step 7-2, electric automobile polymerization equilibrium constraint:
in the formula 
-upper/lower limit of aggregate charge power and discharge power of electric car, +.>
E eva，t 、/>
-the capacity of the electric vehicle at time t and its upper/lower limits;
step 7-3, the load aggregator and the power grid are constrained in an interactive way:
both electricity purchasing and electricity selling for load aggregation are within the limit range of the interactive power:
in the formula ：
-the load aggregator sells the grid at time tThe power of electricity and its upper/lower limits;
-the power of the grid purchased by the load aggregator at time t and its upper/lower limits;
step 7-4, reactive power constraint of the system:
in the formula ：Qt 、Q min 、Q max Reactive power emitted by the system at the moment t and the upper limit and the lower limit thereof;
U i，t 、/>
the voltage at node i in the system at time t and its upper and lower limits. />
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