CN112734077A - Day-ahead optimized operation method and system for electric vehicle charging station - Google Patents

Day-ahead optimized operation method and system for electric vehicle charging station Download PDF

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CN112734077A
CN112734077A CN202011372542.3A CN202011372542A CN112734077A CN 112734077 A CN112734077 A CN 112734077A CN 202011372542 A CN202011372542 A CN 202011372542A CN 112734077 A CN112734077 A CN 112734077A
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charging
demand response
electric vehicle
charging station
time
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刘亚骑
崔晓青
韩为民
王晶
马桂荣
丁鹤
张斌荣
全雯琳
李�昊
张昌栋
杨阳
李斌
刘畅
张静
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Jinan Power Supply Co of State Grid Shandong Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Jinan Power Supply Co of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention relates to the technical field of cooperative control of energy storage and electric vehicles in a charging station, and particularly provides a day-ahead optimized operation method and system for an electric vehicle charging station, aiming at solving the technical problem of how to optimize the operation of the charging station under the condition of considering energy storage configuration of the charging station and response of charging requirements of the electric vehicle. In the embodiment, the method comprises the steps of predicting the charging load demand of the electric automobile at each moment in the future period; and substituting the predicted charging load requirements of the electric automobile at each moment in the future time period into a pre-established charging station optimization model, solving the pre-established charging station optimization model, and obtaining the optimization parameters of the electric automobile charging station. According to the scheme, energy storage is configured in the charging station, a target with the maximum benefit of the charging station is adopted, a demand response measure is taken, how to adjust and optimize parameters is determined in the day ahead, and the electric vehicle user is guided to charge orderly.

Description

Day-ahead optimized operation method and system for electric vehicle charging station
Technical Field
The invention relates to the field of cooperative control of energy storage and electric vehicles in a charging station, in particular to a day-ahead optimized operation method and system for an electric vehicle charging station.
Background
In recent years, the electric automobile industry is rapidly developed, and the number of electric automobiles in various cities in China is remarkably increased. Charging of electric vehicles is a key technical problem. The electric automobile is reasonably arranged to be charged orderly, which is beneficial to improving the load of the power distribution network; the disordered charging may impact the power grid, resulting in the situation of peak-to-peak. The distribution network can utilize peak valley price of electricity, carries out the regulation and control of demand of charging to the charging station. At present, the utilization of energy storage devices is increasingly widespread, and under the condition of considering demand response and energy storage configured in a charging station, how to arrange energy storage operation and electric vehicle charging to optimize the economy of the charging station is a problem worthy of research.
There are some researches on the operation method of the electric vehicle charging station:
a microgrid economic dispatching strategy and a model comprising wind, light, storage and electric vehicles are proposed in the wind/light/storage microgrid economic analysis comprising electric vehicles for anchoring Meiqin, grand tree and Su Jian emblem, but the influence of demand response on electric vehicle charging is not considered;
in the optimized operation research of the Ru-reformed electric vehicle charging station based on the demand response, the optimized operation strategy of the electric vehicle is researched under the condition of considering the demand response, but the influence of energy storage participation on the charging station is not considered;
an energy management strategy considering photovoltaic output, electric vehicle charging and discharging power, power grid power price time interval division and energy storage states is proposed in the micro-grid energy optimization management of Su millet, Jiang Xiao Chao, Wang valuable, Jiang Jiuchun, V.G. AGELIDI, Jingjing, electric vehicles and photovoltaic-energy storage. The above studies have some disadvantages, or only the demand response or only the energy storage configuration is considered, and both are not considered; or the operation method is multi-station in the micro-grid, rather than the main goal of charging station profit maximization.
The demand response of the electric automobile is considered in the multi-time-scale random optimization scheduling of the electric automobile charging station considering the demand response, namely the Yandes, the Ma Ru Xiang, the Liu Shi navigation, the Zhu Xiao Peng and the Wei Shi nong, and the energy storage and the photovoltaic are configured in the electric automobile charging station, so that the multi-time-scale random optimization scheduling model of the electric automobile charging station considering the demand response is provided. The reference has a reference meaning and also has disadvantages. The goal is to minimize the operating cost of the charging station, without considering the revenue situation of the charging station. Within the constraints of demand response, this document considers that the load is only shifted over time and that the total amount does not change. The electric automobile with the actual condition influenced by the price can adjust the charging requirement of the electric automobile, the charging total amount can fluctuate, the exciting requirement response is adopted, the charging total amount of the electric automobile directly controlled by the charging station to be charged can not change, and the effect of load transfer can be achieved. The optimization objects of the document are V2G, the charging and discharging amount of stored energy and the electric quantity purchased from a power grid, and the charging station is taken as an operation individual, and the charging price can be adjusted by time intervals to adjust the charging demand so as to achieve the optimal economy.
In summary, how to optimize the operation of the charging station in consideration of the charging station energy storage configuration and the electric vehicle charging demand response needs to be further studied.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks, the present invention is proposed to provide a method and a system for optimizing the operation of an electric vehicle charging station in the future, which solve or at least partially solve the technical problem of how to optimize the operation of the charging station in consideration of the charging station energy storage configuration and the electric vehicle charging demand response.
In a first aspect, a day-ahead optimized operation method for an electric vehicle charging station is provided, and the day-ahead optimized operation method for the electric vehicle charging station includes:
predicting the charging load demand of the electric automobile at each moment in the future period;
and substituting the predicted charging load requirements of the electric automobile at each moment in the future time period into a pre-established charging station optimization model, solving the pre-established charging station optimization model, and obtaining the optimization parameters of the electric automobile charging station.
Preferably, the optimization parameters include: the charging power price at each time of the future time period, the valley time power price, the normal time power price and the peak time power price of the electric vehicle charging at each time of the future time period, the charging power of the electric vehicle with the excitation type demand response as the demand response mode at each time of the future time period, and the charging and discharging power of the energy storage device at each time of the future time period.
Preferably, the calculation formula of the objective function in the pre-established charging station optimization model is as follows:
maxC＝C1-C2-C3
in the above formula, C is the total profit, C1For the sale of electricity at charging stations, C2For charging station's own electricity consumption, C3To take advantage of the cost of stored energy.
Further, the electricity selling income C of the charging station1Is calculated as follows:
Figure BDA0002806555240000021
charging station's self power consumption charges C2Is calculated as follows:
Figure BDA0002806555240000022
cost C of using stored energy3Is calculated as follows:
Figure BDA0002806555240000031
in the above formula, λtFor charging electricity prices, P, at time t of future time periodt evpThe charging load demand, lambda, of the electric vehicle at the future time t moment with the demand response mode being electricity price type demand responsejPreferential electricity prices for interruptible charging loads, Pt evjThe charging load demand of the electric vehicle at the future time t moment with the demand response mode being the excitation type demand response, ctFor the future time t, the price of electricity purchased from the higher-level grid, ptFor the overall load of the charging station at time t of the future time period, CsFor the total investment construction cost of energy storage, SchAnd the capacity of energy storage utilization for the future time period, n is the total number of times of complete charging and discharging of the energy storage device, and S is the capacity of energy storage.
Preferably, the pre-established charging station optimization model further includes any one or more of the following constraints;
the pre-established demand response mode in the charging station optimization model is the charging load adjustment quantity constraint of the electric vehicle with excitation type demand response:
Figure BDA0002806555240000032
the pre-established demand response mode in the charging station optimization model is the charging load change regulating quantity constraint of the electric vehicle with electricity price demand response:
Figure BDA0002806555240000033
the energy storage constraints in the pre-established charging station optimization model are as follows:
Figure BDA0002806555240000034
Figure BDA0002806555240000035
Figure BDA0002806555240000036
μ1+μ2≤1，μ1，μ2∈{0,1}
0≤Sch≤S
the electric vehicle charging constraint in the pre-established charging station optimization model is as follows:
Figure BDA0002806555240000037
Figure BDA0002806555240000038
the demand response electricity price constraint in the pre-established charging station optimization model is as follows:
λmin＜λlow＜λnorm＜λhigh＜λmax
the power balance constraints in the pre-established charging station optimization model are as follows:
Figure BDA0002806555240000041
Figure BDA0002806555240000042
in the above formula,. DELTA.Pt evjThe method comprises the steps of adjusting the charging load of the electric vehicle with the excitation type demand response as the demand response mode, wherein epsilon is the elastic coefficient of the electricity price of the charging electricity quantity, lambda is the original charging electricity price, and lambda istFor charging electricity price at time t of future time period, Δ Pt evpThe charging load adjustment quantity of the electric vehicle with the demand response mode of electricity price type demand response, alpha is the ratio of the electric vehicle with the demand response mode of excitation type demand response, and Pt evCharging load demand for original electric vehicle, Pt SdIs the discharge power, P, of the energy storage device at time tt ScFor charging power, η, of the energy storage device at time tSdIs the discharge efficiency of the energy storage device, etaScFor the charging efficiency of the energy storage device, SchCapacity for energy storage utilization for future periods, mu1，μ2∈{0,1}，
Figure BDA0002806555240000043
Is the maximum discharge power of the energy storage device,
Figure BDA0002806555240000044
is the maximum charging power of the energy storage device, S is the energy storage capacity,
Figure BDA0002806555240000045
the charging power of the electric vehicle i with the excitation type demand response as the demand response mode at the time t,
Figure BDA0002806555240000046
the state of charge of the electric vehicle i before charging,
Figure BDA0002806555240000047
appointing the required charge state for the electric automobile i,
Figure BDA0002806555240000048
for maximum charging power, lambda, of an electric vehicle with an excitation-type demand response to the demand response modemin、λlow、λnorm、λhighAnd λmaxThe lowest electricity price, the valley time electricity price, the normal time electricity price, the peak time electricity price and the highest electricity price, P, for charging the electric automobile respectivelytFor power, P, obtained from the grid at time t in the futuret evpThe charging load demand, P, of the electric automobile i with the demand response mode of electricity price type demand response at the time tt SPower for storing charge and discharge, teve,iEnd of charge time, t, for electric vehicle ievb,iThe charging start time of the electric automobile I is, and the I is the total number of the electric automobiles needing to be charged in the future time period.
In a second aspect, an electric vehicle charging station day-ahead optimized operation system is provided, and the electric vehicle charging station day-ahead optimized operation system includes:
the prediction module is used for predicting the charging load demand of the electric automobile at each moment in the future period;
and the solving module is used for substituting the predicted charging load demand of the electric automobile at each moment in the future time period into the pre-established charging station optimization model, solving the pre-established charging station optimization model and obtaining the optimization parameters of the electric automobile charging station.
Preferably, the optimization parameters include: the charging power price at each time of the future time period, the valley time power price, the normal time power price and the peak time power price of the electric vehicle charging at each time of the future time period, the charging power of the electric vehicle with the excitation type demand response as the demand response mode at each time of the future time period, and the charging and discharging power of the energy storage device at each time of the future time period.
Preferably, the calculation formula of the objective function in the pre-established charging station optimization model is as follows:
maxC＝C1-C2-C3
in the above formula, C is the total profit, C1For the sale of electricity at charging stations, C2For charging station's own electricity consumption, C3To take advantage of the cost of stored energy.
Further, the electricity selling income C of the charging station1Is calculated as follows:
Figure BDA0002806555240000051
charging station's self power consumption charges C2Is calculated as follows:
Figure BDA0002806555240000052
cost C of using stored energy3Is calculated as follows:
Figure BDA0002806555240000053
in the above formula, λtFor charging electricity prices, P, at time t of future time periodt evpFor electric vehicle with demand response mode of electricity price type demand response at time tCharging load requirement, λjPreferential electricity prices for interruptible charging loads, Pt evjThe charging load demand of the electric vehicle at the future time t moment with the demand response mode being the excitation type demand response, ctFor the future time t, the price of electricity purchased from the higher-level grid, ptFor the overall load of the charging station at time t of the future time period, CsFor the total investment construction cost of energy storage, SchAnd the capacity of energy storage utilization for the future time period, n is the total number of times of complete charging and discharging of the energy storage device, and S is the capacity of energy storage.
Preferably, the pre-established charging station optimization model further includes any one or more of the following constraints;
the pre-established demand response mode in the charging station optimization model is the charging load adjustment quantity constraint of the electric vehicle with excitation type demand response:
Figure BDA0002806555240000054
the pre-established demand response mode in the charging station optimization model is the charging load change regulating quantity constraint of the electric vehicle with electricity price demand response:
Figure BDA0002806555240000055
the energy storage constraints in the pre-established charging station optimization model are as follows:
Figure BDA0002806555240000056
Figure BDA0002806555240000057
Figure BDA0002806555240000061
μ1+μ2≤1，μ1，μ2∈{0,1}
0≤Sch≤S
the electric vehicle charging constraint in the pre-established charging station optimization model is as follows:
Figure BDA0002806555240000062
Figure BDA0002806555240000063
the demand response electricity price constraint in the pre-established charging station optimization model is as follows:
λmin＜λlow＜λnorm＜λhigh＜λmax
the power balance constraints in the pre-established charging station optimization model are as follows:
Figure BDA0002806555240000065
Figure BDA0002806555240000066
in the above formula,. DELTA.Pt evjThe method comprises the steps of adjusting the charging load of the electric vehicle with the excitation type demand response as the demand response mode, wherein epsilon is the elastic coefficient of the electricity price of the charging electricity quantity, lambda is the original charging electricity price, and lambda istFor charging electricity price at time t of future time period, Δ Pt evpThe charging load adjustment quantity of the electric vehicle with the demand response mode of electricity price type demand response, alpha is the ratio of the electric vehicle with the demand response mode of excitation type demand response, and Pt evCharging load demand for original electric vehicle, Pt SdIs the discharge power, P, of the energy storage device at time tt ScFor charging power, η, of the energy storage device at time tSdIs the discharge efficiency of the energy storage device, etaScFor the charging efficiency of the energy storage device, SchCapacity for energy storage utilization for future periods, mu1，μ2∈{0,1}，
Figure BDA0002806555240000067
Is the maximum discharge power of the energy storage device,
Figure BDA0002806555240000068
is the maximum charging power of the energy storage device, S is the energy storage capacity,
Figure BDA0002806555240000069
the charging power of the electric vehicle i with the excitation type demand response as the demand response mode at the time t,
Figure BDA00028065552400000610
the state of charge of the electric vehicle i before charging,
Figure BDA00028065552400000611
appointing the required charge state for the electric automobile i,
Figure BDA00028065552400000612
for maximum charging power, lambda, of an electric vehicle with an excitation-type demand response to the demand response modemin、λlow、λnorm、λhighAnd λmaxThe lowest electricity price, the valley time electricity price, the normal time electricity price, the peak time electricity price and the highest electricity price, P, for charging the electric automobile respectivelytFor power, P, obtained from the grid at time t in the futuret evpThe charging load demand, P, of the electric automobile i with the demand response mode of electricity price type demand response at the time tt SPower for storing charge and discharge, teve,iEnd of charge time, t, for electric vehicle ievb,iThe charging start time of the electric automobile I is, and the I is the total number of the electric automobiles needing to be charged in the future time period.
In a third aspect, a storage device is provided, wherein a plurality of program codes are stored in the storage device, and the program codes are suitable for being loaded and executed by a processor to execute the day-ahead optimization operation method of the electric vehicle charging station according to any one of the above technical solutions.
In a fourth aspect, a control device is provided, which comprises a processor and a storage device, wherein the storage device is adapted to store a plurality of program codes, and the program codes are adapted to be loaded and executed by the processor to execute the day-ahead optimization operation method of the electric vehicle charging station according to any one of the above technical solutions.
One or more technical schemes of the invention at least have one or more of the following beneficial effects:
the invention provides a day-ahead optimized operation method of an electric vehicle charging station, which comprises the following steps: predicting the charging load demand of the electric automobile at each moment in the future period; and substituting the predicted charging load requirements of the electric automobile at each moment in the future time period into a pre-established charging station optimization model, solving the pre-established charging station optimization model, and obtaining the optimization parameters of the electric automobile charging station. This scheme disposes the energy storage in the charging station to the biggest target of charging station profit takes demand response measure, decides how to adjust the optimization parameter in the future, and the guide electric automobile user charges in order, can realize the load peak clipping and fill the millet, and the economic nature of make full use of millet time section price improves electric automobile charging station.
Drawings
FIG. 1 is a schematic flow chart of the main steps of a method for optimizing the operation of an electric vehicle charging station in the future according to one embodiment of the invention;
fig. 2 is a main structural block diagram of an electric vehicle charging station day-ahead optimal operation system according to an embodiment of the invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Some terms to which the invention relates are explained here:
the electric vehicle charging station refers to but is not limited to devices such as a distribution transformer, a charger and an energy storage device;
the incentive type demand response means that the charging station directly controls the charging power of the electric automobile, even the charging can be interrupted, and the user is given a preferential price;
the electricity price type demand response means that the time-of-use electricity price is adopted for charging, and different electricity prices at different moments are used for guiding the electric automobile user to carry out off-peak charging;
the conventional day-ahead optimized operation method of the electric vehicle charging station cannot consider the energy storage configuration of the charging station and the charging demand response condition of the electric vehicle;
in an embodiment of the present invention, referring to fig. 1, fig. 1 is a schematic flow chart of main steps of a method for optimizing an operation of an electric vehicle charging station in the future according to an embodiment of the present invention. As shown in fig. 1, the method for day-ahead optimized operation of an electric vehicle charging station in the embodiment of the present invention mainly includes the following steps:
step S101: predicting the charging load demand of the electric automobile at each moment in the future period;
step S102: and substituting the predicted charging load requirements of the electric automobile at each moment in the future time period into a pre-established charging station optimization model, solving the pre-established charging station optimization model, and obtaining the optimization parameters of the electric automobile charging station.
Wherein the optimization parameters include: the charging power price at each moment in the future time period, the valley time power price, the normal time power price and the peak time power price of the electric vehicle charging at each moment in the future time period, and the charging power of the electric vehicle with the excitation type demand response as the demand response mode at each moment in the future time period and the charging and discharging power of the energy storage device at each moment in the future time period;
the future time period may be a future day; the charging station comprises devices such as but not limited to a distribution transformer, a charger and an energy storage device;
specifically, in one embodiment, the step S101 may be implemented by:
and (3) obtaining the probability density function of the charging start time and the charging duration of one electric vehicle by utilizing the existing statistical data and a maximum likelihood estimation method. And randomly obtaining the charging start time and the charging duration of n electric vehicles in the region according to a probability density function, wherein n is the total number of the electric vehicles owned by the region. Under the condition of uncontrolled, each electric automobile is charged with constant power, and the charging load condition of each electric automobile in future time period can be obtained. And repeating the operation for 100 times, and taking an average value as the charging load condition of the electric automobile in each final time period.
In this case, the objective function in the pre-established charging station optimization model is calculated as follows:
maxC＝C1-C2-C3
in the above formula, C is the total profit, C1For the sale of electricity at charging stations, C2For charging station's own electricity consumption, C3To take advantage of the cost of stored energy.
Wherein the electricity selling profit C of the charging station1Is calculated as follows:
Figure BDA0002806555240000081
charging station's self power consumption charges C2Is calculated as follows:
Figure BDA0002806555240000091
cost C of using stored energy3Is calculated as follows:
Figure BDA0002806555240000092
in the above formula, λtFor charging electricity prices, P, at time t of future time periodt evpThe charging load demand, lambda, of the electric vehicle at the future time t moment with the demand response mode being electricity price type demand responsejPreferential electricity prices for interruptible charging loads, Pt evjThe charging load demand of the electric vehicle at the future time t moment with the demand response mode being the excitation type demand response, ctFor the future time t, the price of electricity purchased from the higher-level grid, ptFor the overall load of the charging station at time t of the future time period, CsFor the total investment construction cost of energy storage, SchAnd the capacity of energy storage utilization for the future time period, n is the total number of times of complete charging and discharging of the energy storage device, and S is the capacity of energy storage.
Specifically, the present embodiment further provides the following constraint conditions of the charging station optimization model:
the demand response mode in the pre-established charging station optimization model is the charging load adjustment quantity constraint of the electric vehicle with incentive demand response:
Figure BDA0002806555240000093
the demand response mode in the pre-established charging station optimization model is the charging load change regulating quantity constraint of the electric vehicle with electricity price demand response:
Figure BDA0002806555240000094
the energy storage constraint in the pre-established charging station optimization model is as follows:
Figure BDA0002806555240000095
Figure BDA0002806555240000096
Figure BDA0002806555240000097
μ1+μ2≤1，μ1，μ2∈{0,1}
0≤Sch≤S
the electric vehicle charging constraint in the pre-established charging station optimization model is as follows:
Figure BDA0002806555240000098
Figure BDA0002806555240000099
the demand response electricity price constraint in the pre-established charging station optimization model is as follows:
λmin＜λlow＜λnorm＜λhigh＜λmax
the power balance constraints in the pre-established charging station optimization model are as follows:
Figure BDA0002806555240000101
Figure BDA0002806555240000102
in the above formula,. DELTA.Pt evjThe method comprises the steps of adjusting the charging load of the electric vehicle with the excitation type demand response as the demand response mode, wherein epsilon is the elastic coefficient of the electricity price of the charging electricity quantity, lambda is the original charging electricity price, and lambda istFor charging electricity price at time t of future time period, Δ Pt evpThe demand response mode is a power price type demand responseThe charging load adjustment quantity of the corresponding electric automobile, alpha is the ratio of the electric automobile with the demand response mode being the excitation type demand response, and Pt evCharging load demand for original electric vehicle, Pt SdIs the discharge power, P, of the energy storage device at time tt ScFor charging power, η, of the energy storage device at time tSdIs the discharge efficiency of the energy storage device, etaScFor the charging efficiency of the energy storage device, SchCapacity for energy storage utilization for future periods, mu1，μ2∈{0,1}，
Figure BDA0002806555240000103
Is the maximum discharge power of the energy storage device,
Figure BDA0002806555240000104
is the maximum charging power of the energy storage device, S is the energy storage capacity,
Figure BDA0002806555240000105
the charging power of the electric vehicle i with the excitation type demand response as the demand response mode at the time t,
Figure BDA0002806555240000106
the state of charge of the electric vehicle i before charging,
Figure BDA0002806555240000107
appointing the required charge state for the electric automobile i,
Figure BDA0002806555240000108
for maximum charging power, lambda, of an electric vehicle with an excitation-type demand response to the demand response modemin、λlow、λnorm、λhighAnd λmaxThe lowest electricity price, the valley time electricity price, the normal time electricity price, the peak time electricity price and the highest electricity price, P, for charging the electric automobile respectivelytFor power, P, obtained from the grid at time t in the futuret evpCharging load demand of electric vehicle i with demand response mode being electricity price type demand response at time t，Pt SPower for storing charge and discharge, teve,iEnd of charge time, t, for electric vehicle ievb,iThe charging start time of the electric automobile I is, and the I is the total number of the electric automobiles needing to be charged in the future time period.
In one embodiment, the step S102 may be implemented by:
solving the optimal value of the objective function by using a Genetic Algorithm (GA):
firstly, randomly generating an initial population with the size of N, and calculating the fitness of each individual; secondly, sequencing each individual according to the fitness, giving weight according to the fitness, and selecting the individual as a parent population by using a roulette method; and finally, performing operations such as crossing, mutation and the like to generate a next generation population. Repeating the operation until the maximum population generation number is reached, and respectively obtaining the charging electricity price at each moment in the future period, the valley-time electricity price, the ordinary-time electricity price and the peak-time electricity price of the electric vehicle charging at each moment in the future period, and the optimal value of the charging power of the electric vehicle with the excitation type demand response and the optimal value of the charging and discharging power of the energy storage device at each moment in the future period;
based on the method, an optimal electric vehicle charging control scheme is found, so that the benefit of the charging station in the future time period is maximized.
In the embodiment of the invention, the energy storage is configured in the charging station, and with the goal of the maximum profit of the charging station, demand response measures are taken, how to adjust and optimize parameters is determined in the future, and electric vehicle users are guided to charge in order, so that load peak clipping and valley filling can be realized, the electricity price in the valley period is fully utilized, and the economy of the electric vehicle charging station is improved.
It should be noted that, although the foregoing embodiments describe each step in a specific sequence, those skilled in the art will understand that, in order to achieve the effect of the present invention, different steps do not necessarily need to be executed in such a sequence, and they may be executed simultaneously (in parallel) or in other sequences, and these changes are all within the protection scope of the present invention.
Further, the invention also provides a day-ahead optimized operation system of the electric vehicle charging station.
Referring to fig. 2, fig. 2 is a main structural block diagram of a day-ahead optimal operation system of an electric vehicle charging station according to an embodiment of the invention. As shown in fig. 2, the system for optimizing the operation of an electric vehicle charging station in the embodiment of the present invention mainly includes a prediction module and a solution module, and in some embodiments, one or more of the prediction module and the solution module may be combined together into one module. In some embodiments, the prediction module is configured to predict a charging load demand of the electric vehicle at each time of the future time period; the solving module can be configured to substitute the charging load demand of the electric vehicle at each moment of the predicted future time period into a pre-established charging station optimization model, solve the pre-established charging station optimization model, and obtain an optimization parameter of the electric vehicle charging station;
in one embodiment, the optimization parameters include: the charging power price at each time of the future time period, the valley time power price, the normal time power price and the peak time power price of the electric vehicle charging at each time of the future time period, the charging power of the electric vehicle with the excitation type demand response as the demand response mode at each time of the future time period, and the charging and discharging power of the energy storage device at each time of the future time period.
Wherein the objective function in the pre-established charging station optimization model is calculated as follows:
maxC＝C1-C2-C3
in the above formula, C is the total profit, C1For the sale of electricity at charging stations, C2For charging station's own electricity consumption, C3To take advantage of the cost of stored energy.
Further, the electricity selling income C of the charging station1Is calculated as follows:
Figure BDA0002806555240000111
the charging station's own power consumption chargesC2Is calculated as follows:
Figure BDA0002806555240000121
cost C of using stored energy3Is calculated as follows:
Figure BDA0002806555240000122
in the above formula, λtFor charging electricity prices, P, at time t of future time periodt evpThe charging load demand, lambda, of the electric vehicle at the future time t moment with the demand response mode being electricity price type demand responsejPreferential electricity prices for interruptible charging loads, Pt evjThe charging load demand of the electric vehicle at the future time t moment with the demand response mode being the excitation type demand response, ctFor the future time t, the price of electricity purchased from the higher-level grid, ptFor the overall load of the charging station at time t of the future time period, CsFor the total investment construction cost of energy storage, SchAnd the capacity of energy storage utilization for the future time period, n is the total number of times of complete charging and discharging of the energy storage device, and S is the capacity of energy storage.
Preferably, the demand response mode in the pre-established charging station optimization model is a charging load adjustment quantity constraint of an electric vehicle with incentive type demand response:
Figure BDA0002806555240000123
the demand response mode in the pre-established charging station optimization model is the charging load change regulating quantity constraint of the electric vehicle with electricity price demand response:
Figure BDA0002806555240000124
the energy storage constraint in the pre-established charging station optimization model is as follows:
Figure BDA0002806555240000125
Figure BDA0002806555240000129
Figure BDA0002806555240000126
μ1+μ2≤1，μ1，μ2∈{0,1}
0≤Sch≤S
the electric vehicle charging constraint in the pre-established charging station optimization model is as follows:
Figure BDA0002806555240000127
Figure BDA0002806555240000128
the demand response electricity price constraint in the pre-established charging station optimization model is as follows:
λmin＜λlow＜λnorm＜λhigh＜λmax
the power balance constraints in the pre-established charging station optimization model are as follows:
Figure BDA0002806555240000131
Figure BDA0002806555240000132
in the above formula, the first and second carbon atoms are,ΔPt evjthe method comprises the steps of adjusting the charging load of the electric vehicle with the excitation type demand response as the demand response mode, wherein epsilon is the elastic coefficient of the electricity price of the charging electricity quantity, lambda is the original charging electricity price, and lambda istFor charging electricity price at time t of future time period, Δ Pt evpThe charging load adjustment quantity of the electric vehicle with the demand response mode of electricity price type demand response, alpha is the ratio of the electric vehicle with the demand response mode of excitation type demand response, and Pt evCharging load demand for original electric vehicle, Pt SdIs the discharge power, P, of the energy storage device at time tt ScFor charging power, η, of the energy storage device at time tSdIs the discharge efficiency of the energy storage device, etaScFor the charging efficiency of the energy storage device, SchCapacity for energy storage utilization for future periods, mu1，μ2∈{0,1}，
Figure BDA0002806555240000133
Is the maximum discharge power of the energy storage device,
Figure BDA0002806555240000134
is the maximum charging power of the energy storage device, S is the energy storage capacity,
Figure BDA0002806555240000135
the charging power of the electric vehicle i with the excitation type demand response as the demand response mode at the time t,
Figure BDA0002806555240000136
the state of charge of the electric vehicle i before charging,
Figure BDA0002806555240000137
appointing the required charge state for the electric automobile i,
Figure BDA0002806555240000138
for maximum charging power, lambda, of an electric vehicle with an excitation-type demand response to the demand response modemin、λlow、λnorm、λhighAnd λmaxThe lowest electricity price, the valley time electricity price, the normal time electricity price, the peak time electricity price and the highest electricity price, P, for charging the electric automobile respectivelytFor power, P, obtained from the grid at time t in the futuret evpThe charging load demand, P, of the electric automobile i with the demand response mode of electricity price type demand response at the time tt SPower for storing charge and discharge, teve,iEnd of charge time, t, for electric vehicle ievb,iThe charging start time of the electric automobile I is, and the I is the total number of the electric automobiles needing to be charged in the future time period.
It will be understood by those skilled in the art that all or part of the flow of the method according to the above-described embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used to implement the steps of the above-described embodiments of the method when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, media, usb disk, removable hard disk, magnetic diskette, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunication signals, software distribution media, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
Furthermore, the invention also provides a storage device. In one embodiment of the storage device according to the present invention, the storage device may be configured to store a program for executing the electric vehicle charging station day-ahead optimized operation method of the above-described method embodiment, and the program may be loaded and executed by the processor to implement the electric vehicle charging station day-ahead optimized operation method. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and details of the specific techniques are not disclosed. The storage device may be a storage device apparatus formed by including various electronic devices, and optionally, a non-transitory computer-readable storage medium is stored in the embodiment of the present invention.
Furthermore, the invention also provides a control device. In an embodiment of the control device according to the present invention, the control device includes a processor and a storage device, the storage device may be configured to store a program for executing the electric vehicle charging station day-ahead optimized operation method of the above-mentioned method embodiment, and the processor may be configured to execute the program in the storage device, the program including but not limited to the program for executing the electric vehicle charging station day-ahead optimized operation method of the above-mentioned method embodiment. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and details of the specific techniques are not disclosed. The control device may be a control device apparatus formed including various electronic apparatuses.
Further, it should be understood that, since the modules are only configured to illustrate the functional units of the system of the present invention, the corresponding physical devices of the modules may be the processor itself, or a part of software, a part of hardware, or a part of a combination of software and hardware in the processor. Thus, the number of individual modules in the figures is merely illustrative.
Those skilled in the art will appreciate that the various modules in the system may be adaptively split or combined. Such splitting or combining of specific modules does not cause the technical solutions to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or combining will fall within the protection scope of the present invention.
So far, the technical solution of the present invention has been described with reference to one embodiment shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.

Claims (12)

1. A day-ahead optimized operation method for an electric vehicle charging station is characterized by comprising the following steps:
predicting the charging load demand of the electric automobile at each moment in the future period;
and substituting the predicted charging load demand of the electric automobile at each moment in the future time period into a pre-established charging station optimization model, solving the pre-established charging station optimization model, and obtaining the optimization parameters of the electric automobile charging station.
2. The method of claim 1, wherein the optimization parameters comprise: the charging power price at each time of the future time period, the valley time power price, the normal time power price and the peak time power price of the electric vehicle charging at each time of the future time period, the charging power of the electric vehicle with the excitation type demand response as the demand response mode at each time of the future time period, and the charging and discharging power of the energy storage device at each time of the future time period.
3. The method of claim 1, wherein the objective function in the pre-established charging station optimization model is calculated as follows:
max C＝C1-C2-C3
in the above formula, C is the total profit, C1For the sale of electricity at charging stations, C2For charging station's own electricity consumption, C3To take advantage of the cost of stored energy.
4. The method of claim 3, wherein the electricity selling profit C of the charging station1Is calculated as follows:
Figure FDA0002806555230000011
the charging station is self-poweredElectricity and electricity fee C2Is calculated as follows:
Figure FDA0002806555230000012
cost C of using stored energy3Is calculated as follows:
Figure FDA0002806555230000013
in the above formula, λtFor charging electricity prices, P, at time t of future time periodt evpThe charging load demand, lambda, of the electric vehicle at the future time t moment with the demand response mode being electricity price type demand responsejPreferential electricity prices for interruptible charging loads, Pt evjThe charging load demand of the electric vehicle at the future time t moment with the demand response mode being the excitation type demand response, ctFor the future time t, the price of electricity purchased from the higher-level grid, ptFor the overall load of the charging station at time t of the future time period, CsFor the total investment construction cost of energy storage, SchAnd the capacity of energy storage utilization for the future time period, n is the total number of times of complete charging and discharging of the energy storage device, and S is the capacity of energy storage.
5. The method of claim 3, wherein the pre-established charging station optimization model further comprises any one or more of the following constraints;
the pre-established demand response mode in the charging station optimization model is the charging load adjustment quantity constraint of the electric vehicle with excitation type demand response:
Figure FDA0002806555230000021
the pre-established demand response mode in the charging station optimization model is the charging load change regulating quantity constraint of the electric vehicle with electricity price demand response:
Figure FDA0002806555230000022
the energy storage constraints in the pre-established charging station optimization model are as follows:
Figure FDA0002806555230000023
Figure FDA0002806555230000024
Figure FDA0002806555230000025
μ1+μ2≤1，μ1，μ2∈{0,1}
0≤Sch≤S
the electric vehicle charging constraint in the pre-established charging station optimization model is as follows:
Figure FDA0002806555230000026
Figure FDA0002806555230000027
the demand response electricity price constraint in the pre-established charging station optimization model is as follows:
λmin＜λlow＜λnorm＜λhigh＜λmax
the power balance constraints in the pre-established charging station optimization model are as follows:
Figure FDA0002806555230000028
Figure FDA0002806555230000029
in the above formula,. DELTA.Pt evjThe method comprises the steps of adjusting the charging load of the electric vehicle with the excitation type demand response as the demand response mode, wherein epsilon is the elastic coefficient of the electricity price of the charging electricity quantity, lambda is the original charging electricity price, and lambda istFor charging electricity price at time t of future time period, Δ Pt evpThe charging load adjustment quantity of the electric vehicle with the demand response mode of electricity price type demand response, alpha is the ratio of the electric vehicle with the demand response mode of excitation type demand response, and Pt evCharging load demand for original electric vehicle, Pt SdIs the discharge power, P, of the energy storage device at time tt ScFor charging power, η, of the energy storage device at time tSdIs the discharge efficiency of the energy storage device, etaScFor the charging efficiency of the energy storage device, SchCapacity for energy storage utilization for future periods, mu1，μ2∈{0,1}，
Figure FDA0002806555230000031
Is the maximum discharge power of the energy storage device,
Figure FDA0002806555230000032
is the maximum charging power of the energy storage device, S is the energy storage capacity,
Figure FDA0002806555230000033
the charging power of the electric vehicle i with the excitation type demand response as the demand response mode at the time t,
Figure FDA0002806555230000034
the state of charge of the electric vehicle i before charging,
Figure FDA0002806555230000035
appointing the required charge state for the electric automobile i,
Figure FDA0002806555230000036
for maximum charging power, lambda, of an electric vehicle with an excitation-type demand response to the demand response modemin、λlow、λnorm、λhighAnd λmaxThe lowest electricity price, the valley time electricity price, the normal time electricity price, the peak time electricity price and the highest electricity price, P, for charging the electric automobile respectivelytFor power, P, obtained from the grid at time t in the futuret evpThe charging load demand, P, of the electric automobile i with the demand response mode of electricity price type demand response at the time tt SPower for storing charge and discharge, teve,iEnd of charge time, t, for electric vehicle ievb,iThe charging start time of the electric automobile I is, and the I is the total number of the electric automobiles needing to be charged in the future time period.
6. An electric vehicle charging station day-ahead optimization operation system, comprising:
the prediction module is used for predicting the charging load demand of the electric automobile at each moment in the future period;
and the solving module is used for substituting the predicted charging load demand of the electric automobile at each moment in the future time period into the pre-established charging station optimization model, solving the pre-established charging station optimization model and obtaining the optimization parameters of the electric automobile charging station.
7. The system of claim 6, wherein the optimization parameters include: the charging power price at each time of the future time period, the valley time power price, the normal time power price and the peak time power price of the electric vehicle charging at each time of the future time period, the charging power of the electric vehicle with the excitation type demand response as the demand response mode at each time of the future time period, and the charging and discharging power of the energy storage device at each time of the future time period.
8. The system of claim 6, wherein the objective function in the pre-established charging station optimization model is calculated as follows:
max C＝C1-C2-C3
in the above formula, C is the total profit, C1For the sale of electricity at charging stations, C2For charging station's own electricity consumption, C3To take advantage of the cost of stored energy.
9. The system of claim 8, wherein the electricity selling profit C of the charging station1Is calculated as follows:
Figure FDA0002806555230000037
charging station's self power consumption charges C2Is calculated as follows:
Figure FDA0002806555230000041
cost C of using stored energy3Is calculated as follows:
Figure FDA0002806555230000042
in the above formula, λtFor charging electricity prices, P, at time t of future time periodt evpThe charging load demand, lambda, of the electric vehicle at the future time t moment with the demand response mode being electricity price type demand responsejPreferential electricity prices for interruptible charging loads, Pt evjThe charging load demand of the electric vehicle at the future time t moment with the demand response mode being the excitation type demand response, ctFor the future time t, the price of electricity purchased from the higher-level grid, ptFor the overall load of the charging station at time t of the future time period, CsFor the total investment construction cost of energy storage, SchAnd the capacity of energy storage utilization for the future time period, n is the total number of times of complete charging and discharging of the energy storage device, and S is the capacity of energy storage.
10. The system of claim 6, wherein the pre-established charging station optimization model further comprises any one or more of the following constraints;
the pre-established demand response mode in the charging station optimization model is the charging load adjustment quantity constraint of the electric vehicle with excitation type demand response:
Figure FDA0002806555230000043
the pre-established demand response mode in the charging station optimization model is the charging load change regulating quantity constraint of the electric vehicle with electricity price demand response:
Figure FDA0002806555230000044
the energy storage constraints in the pre-established charging station optimization model are as follows:
Figure FDA0002806555230000045
Figure FDA0002806555230000046
Figure FDA0002806555230000047
μ1+μ2≤1，μ1，μ2∈{0,1}
0≤Sch≤S
the electric vehicle charging constraint in the pre-established charging station optimization model is as follows:
Figure FDA0002806555230000048
Figure FDA0002806555230000051
the demand response electricity price constraint in the pre-established charging station optimization model is as follows:
λmin＜λlow＜λnorm＜λhigh＜λmax
the power balance constraints in the pre-established charging station optimization model are as follows:
Figure FDA0002806555230000052
Figure FDA0002806555230000053
in the above formula,. DELTA.Pt evjThe method comprises the steps of adjusting the charging load of the electric vehicle with the excitation type demand response as the demand response mode, wherein epsilon is the elastic coefficient of the electricity price of the charging electricity quantity, lambda is the original charging electricity price, and lambda istFor charging electricity price at time t of future time period, Δ Pt evpThe charging load adjustment quantity of the electric vehicle with the demand response mode of electricity price type demand response, alpha is the ratio of the electric vehicle with the demand response mode of excitation type demand response, and Pt evCharging load demand for original electric vehicle, Pt SdIs the discharge power, P, of the energy storage device at time tt ScFor charging power, η, of the energy storage device at time tSdIs the discharge efficiency of the energy storage device, etaScFor the charging efficiency of the energy storage device, SchCapacity for energy storage utilization for future periods, mu1，μ2∈{0,1}，
Figure FDA0002806555230000054
Is the maximum discharge power of the energy storage device,
Figure FDA0002806555230000055
is the maximum charging power of the energy storage device, S is the energy storage capacity,
Figure FDA0002806555230000056
the charging power of the electric vehicle i with the excitation type demand response as the demand response mode at the time t,
Figure FDA0002806555230000057
the state of charge of the electric vehicle i before charging,
Figure FDA0002806555230000058
appointing the required charge state for the electric automobile i,
Figure FDA0002806555230000059
for maximum charging power, lambda, of an electric vehicle with an excitation-type demand response to the demand response modemin、λlow、λnorm、λhighAnd λmaxThe lowest electricity price, the valley time electricity price, the normal time electricity price, the peak time electricity price and the highest electricity price, P, for charging the electric automobile respectivelytFor power, P, obtained from the grid at time t in the futuret evpThe charging load demand, P, of the electric automobile i with the demand response mode of electricity price type demand response at the time tt SPower for storing charge and discharge, teve,iEnd of charge time, t, for electric vehicle ievb,iThe charging start time of the electric automobile I is, and the I is the total number of the electric automobiles needing to be charged in the future time period.
11. A storage device having a plurality of program codes stored therein, wherein the program codes are adapted to be loaded and executed by a processor to perform the method of optimizing the operation of an electric vehicle charging station in the future according to any of claims 1 to 5.
12. A control device comprising a processor and a memory device, the memory device adapted to store a plurality of program codes, wherein the program codes are adapted to be loaded and executed by the processor to perform the electric vehicle charging station day-ahead optimization method of operation of any one of claims 1 to 5.
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CN113283807A (en) * 2021-06-23 2021-08-20 阳光电源股份有限公司 Operation scheduling method and device for optical storage charging station
CN113335127A (en) * 2021-05-14 2021-09-03 南方电网电动汽车服务有限公司 Charging load scheduling method and device, computer equipment and storage medium
CN114493329A (en) * 2022-02-14 2022-05-13 国网河北省电力有限公司营销服务中心 Demand response regulation and control method and device for electric automobile
CN115018379A (en) * 2022-07-18 2022-09-06 东南大学溧阳研究院 Electric vehicle in-day response capability assessment method and system and computer storage medium
CN114493329B (en) * 2022-02-14 2024-05-10 国网河北省电力有限公司营销服务中心 Demand response regulation and control method and device for electric automobile

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Publication number Priority date Publication date Assignee Title
CN113335127A (en) * 2021-05-14 2021-09-03 南方电网电动汽车服务有限公司 Charging load scheduling method and device, computer equipment and storage medium
CN113283807A (en) * 2021-06-23 2021-08-20 阳光电源股份有限公司 Operation scheduling method and device for optical storage charging station
CN113283807B (en) * 2021-06-23 2024-04-05 阳光慧碳科技有限公司 Operation scheduling method and device of optical storage charging station
CN114493329A (en) * 2022-02-14 2022-05-13 国网河北省电力有限公司营销服务中心 Demand response regulation and control method and device for electric automobile
CN114493329B (en) * 2022-02-14 2024-05-10 国网河北省电力有限公司营销服务中心 Demand response regulation and control method and device for electric automobile
CN115018379A (en) * 2022-07-18 2022-09-06 东南大学溧阳研究院 Electric vehicle in-day response capability assessment method and system and computer storage medium

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