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CN109193623B - Day-ahead market and real-time market combined optimization method combined with electric automobile - Google Patents

Day-ahead market and real-time market combined optimization method combined with electric automobile Download PDF

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CN109193623B
CN109193623B CN201811052069.3A CN201811052069A CN109193623B CN 109193623 B CN109193623 B CN 109193623B CN 201811052069 A CN201811052069 A CN 201811052069A CN 109193623 B CN109193623 B CN 109193623B
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CN109193623A (en
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武小梅
汤伟成
胡俊灵
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Guangdong University of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/008—Circuit arrangements for AC mains or AC distribution networks involving trading of energy or energy transmission rights
    • H02J2103/30—
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations

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  • Power Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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Abstract

本发明公开一种结合电动汽车的日前市场和实时市场联合优化方法，包括以下步骤，S1:售电商与所负责地区的电动汽车所有者签订调度协议，S2:将参与调度的电动汽车考虑在日前市场和实时市场的购买计划中，结合储能装置以及电动汽车建立一个统一的电能交易模式，所述的电动汽车在该电能交易模式下即充当负荷也充当电源，S3:利用粒子群优化算法求解结合电动汽车的日前市场和实时市场联合优化的购电方案。本发明利用电动汽车的即可充当负荷亦可充当电源的特性，结合粒子群算法计算获取售电商的最佳购电方案，减少隐形资源的浪费，是运营成本最低。

Figure 201811052069

The present invention discloses a method for joint optimization of day-ahead market and real-time market combining electric vehicles, comprising the following steps: S1: the electricity seller signs a scheduling agreement with the owner of the electric vehicle in the responsible area, S2: considers the electric vehicles participating in the scheduling in the In the purchase plan of the day-ahead market and the real-time market, a unified electric energy transaction mode is established in combination with energy storage devices and electric vehicles, and the electric vehicles act as both loads and power sources in this electric energy transaction mode. Solve the power purchase scheme that combines the joint optimization of the day-ahead market and the real-time market for electric vehicles. The invention utilizes the characteristics of the electric vehicle that can act as a load or a power source, and combines the particle swarm algorithm calculation to obtain the best electricity purchase plan for the electricity retailer, reduces the waste of invisible resources, and has the lowest operating cost.

Figure 201811052069

Description

Day-ahead market and real-time market combined optimization method combined with electric automobile
Technical Field
The invention relates to the field of power market power transmission and transmission, in particular to a day-ahead market and real-time market joint optimization method for an electric automobile.
Background
The main purpose of the electric power market reform is to introduce a competition system into the electric power market, so as to stimulate market activity and reduce the electricity consumption cost of users. The electricity vendors appear in the market as a new trading body, and the trading mode of the electricity vendors is carried out according to the principles of fairness, justice and disclosure. Under the large environment of the power market, the electricity vendors buy the electric energy in batches in the electricity generators and then sell the electric energy to the users, so that the profit gained from the electric energy vendors becomes a new economic transaction mode. In the current stage, the operation mode of independent trading of 2 markets of the current market and the real-time market in the electric power market or the electric energy purchase amount of some electric power selling companies to the current market or the real-time market independently reduces the operation cost. However, these are modes of two markets operating separately and independently, and there may be a situation that the power purchased by the market at a certain time is larger than the actual demand or the power purchased is smaller than the actual demand. The electricity selling company needs to sell the surplus electric energy or buy the lacking electric energy from the real-time market, which undoubtedly increases the operation cost of the electricity selling company greatly.
Disclosure of Invention
The invention provides a combined optimization method for the day-ahead market and the real-time market of an electric automobile, aiming at overcoming at least one defect in the prior art.
The present invention aims to solve the above technical problem at least to some extent.
The invention aims to break through the limitation that the purchasing quantity of the electricity in the day-ahead market is determined according to the load prediction in the day-ahead market, and reduce the operation cost of an electricity selling company in the electric energy transaction of the electricity market.
The invention further aims to fully utilize the characteristic that the electric automobile can serve as a load and a power supply, combine the day-ahead market and the real-time market for joint optimization, reduce the operation cost per se and simultaneously enable the owner of the electric automobile to obtain profits so as to achieve the win-win situation.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a day-ahead market and real-time market combined optimization method combined with electric automobiles comprises the following steps:
s1, the electricity seller signs a dispatching agreement with the owner of the electric vehicle in the region in charge;
s2, considering the electric automobile participating in dispatching in the purchase plan of the day-ahead market and the real-time market, and establishing a uniform electric energy transaction mode by combining the energy storage device and the electric automobile, wherein the electric automobile serves as a load and also serves as a power supply in the electric energy transaction mode;
s3, solving a power purchase scheme combining the day-ahead market and the real-time market of the electric automobile by utilizing a particle swarm optimization algorithm;
in the scheme, by utilizing the characteristic that the electric automobile can be a load or a power supply capable of providing electric energy, the electric automobile can be combined with an energy storage device of an electricity vendor and electric energy storage of the electric automobile, and combined with the joint optimization of the current market and the real-time market, so that the operator can operate in a low-cost environment.
Preferably, the scheduling protocol of step S1 is as follows:
s1.1 setting the minimum discharge capacityQuantity Si,minAnd maximum charge capacity Si,max，Si,max＞Si,min；
S1.2 when the battery capacity S of the electric automobilei(t) is greater than Si,maxAt the moment, the electric automobile only allows the discharging operation;
s1.3 when Si(t) is less than Si,minAt the moment, the electric automobile only allows charging operation;
s1.4 when Si(t) is greater than Si,minAnd is less than Si,maxAnd at the moment, the electric automobile carries out charging and discharging operation according to the requirement.
The specific steps of step S2 are as follows:
s2.1 defining the day-ahead market electric energy purchase amount to be B 'in the unified trading mode'DA(t), the real-time market electric energy purchase amount in the unified trading mode is B'RT(t)；
S2.2, the cost of the market at the day before is as follows:
C'DA(t)＝PDA(t)(B'DA(t)+S(t))
of formula (II) to C'DA(t) represents the electricity vendor operating cost of the market at the day-ahead, PDA(t) the electricity price of each hour in the market at the day before, S (t) the total electricity quantity of all the electric automobiles, selling the electric energy of the electric automobiles in the electric power market when S (t) is less than zero, and buying the electric energy of the electric power selling company in the electric power market when S (t) is more than zero;
s2.3, the cost of the real-time market is as follows:
C'RT(t)＝PRT(t)(B'RT(t)+S(t))
of formula (II) to C'RT(t) represents the electricity vendor operating cost of the real-time market, PRT(t) the electricity price of each hour in the market before the day;
s2.4, considering the compensation of the electricity vendor to the owner of the electric automobile, the following steps are included:
X＝ηS(t)
wherein, X is the compensation amount of the electric power vendor to the owner of the electric vehicle, eta is the compensation price coefficient of the electric power vendor to the owner of the electric vehicle, and eta is (P)S(t)-PB(t))·β,PS(t)、PB(t) each isIs the electricity price when selling and purchasing the electric quantity of the electric automobile, beta is a compensation factor, 0<β<1；
S2.5, the operation cost of the electricity selling company combined with the joint optimization of the day-ahead market and the real-time market of the electric automobile is as follows:
C'＝C'DA+C'RT+X
c' is the operation cost of the electricity vendor which is optimized by combining the day-ahead market and the real-time market of the electric automobile;
when the electricity selling company signs a dispatching agreement with the owner of the electric automobile, if the electric automobile is charged when the electricity price is low, the charging cost is borne by the electricity selling company, and when the electricity price is high, the electricity selling company sells the electric automobile electricity quantity to the market, which is equivalent to the electricity selling company selling the electric energy, so that the cost of the electricity selling company is relatively reduced.
The specific step of step S3:
s3.1, determining the population number of the particle swarm, wherein one particle comprises a day-ahead market, a real-time market and the electric energy purchase amount of the electric automobile per hour per day;
s3.2, setting initial parameters of the particle swarm algorithm, wherein the initial parameters comprise a weight factor w, learning factors c1 and c2, a maximum speed Vmax, iteration times T and the like;
s3.3, randomly generating the initial speed and the initial position of each particle;
s3.4, judging the fitness of each particle;
s3.5, after each particle is iterated once, comparing the iterated once with the optimal value and the overall optimal value, and accordingly changing the advancing speed and the advancing direction of each particle and enabling the particles to approach the optimal value gradually;
s3.6, judging whether the set precision is met or whether the maximum iteration number is reached, and returning to S3.4 if the maximum iteration number is not met;
and S3.7, outputting an optimization result when the set precision is reached or the maximum iteration number is reached.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the characteristic that the electric automobile can be used as a load and a power supply is utilized, the operation strategy of the electricity selling company is optimized in combination with the day-ahead market and the real-time market of the electric automobile, the limit that the electricity purchasing quantity of the day-ahead market is determined according to the day-ahead load prediction is broken through, the operation cost of the electricity selling company in the electricity market electricity trading is reduced, and the competitiveness and the flexibility of the electricity selling company in the electricity market are improved.
Drawings
Fig. 1 is a flowchart of a combined optimization method for the day-ahead market and the real-time market of an electric vehicle according to the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
The day-ahead market and real-time market combined optimization method combining the electric automobile provided by the embodiment comprises the following steps of:
s1, the electricity seller signs a dispatching agreement with the owner of the electric vehicle in the region in charge;
s2, considering the electric automobile participating in dispatching in the purchase plan of the day-ahead market and the real-time market, and establishing a uniform electric energy transaction mode by combining the energy storage device and the electric automobile, wherein the electric automobile serves as a load and also serves as a power supply in the electric energy transaction mode;
s3, solving a power purchase scheme combining the day-ahead market and the real-time market of the electric automobile by utilizing a particle swarm optimization algorithm;
the scheduling protocol of step S1 is as follows:
s1.1 setting discharge minimum volume Si,minAnd maximum charge capacity Si,max，Si,max＞Si,min；
S1.2 when the battery capacity S of the electric automobilei(t) is greater than Si,maxAt the moment, the electric automobile only allows the discharging operation;
s1.3 when Si(t) is less than Si,minAt the moment, the electric automobile only allows charging operation;
s1.4 when Si(t) is greater than Si,minAnd is less than Si,maxAnd at the moment, the electric automobile carries out charging and discharging operation according to the requirement.
The specific steps of step S2 are as follows:
s2.1 defining the day-ahead market electric energy purchase amount to be B 'in the unified trading mode'DA(t), the real-time market electric energy purchase amount in the unified trading mode is B'RT(t)；
S2.2, the cost of the market at the day before is as follows:
C'DA(t)＝PDA(t)(B'DA(t)+S(t))
of formula (II) to C'DA(t) represents the electricity vendor operating cost of the market at the day-ahead, PDA(t) the electricity price of each hour in the market at the day before, S (t) the total electricity quantity of all the electric automobiles, selling the electric energy of the electric automobiles in the electric power market when S (t) is less than zero, and buying the electric energy of the electric power selling company in the electric power market when S (t) is more than zero;
s2.3, the cost of the real-time market is as follows:
C'RT(t)＝PRT(t)(B'RT(t)+S(t))
of formula (II) to C'RT(t) represents the electricity vendor operating cost of the real-time market, PRT(t) the electricity price of each hour in the market before the day;
s2.4, considering the compensation of the electricity vendor to the owner of the electric automobile, the following steps are included:
X＝ηS(t)
wherein, X is the compensation amount of the electric power vendor to the owner of the electric vehicle, eta is the compensation price coefficient of the electric power vendor to the owner of the electric vehicle, and eta is (P)S(t)-PB(t))·β,PS(t)、PB(t) the price of electricity for selling and purchasing the electric quantity of the electric vehicle, beta is a compensation factor, 0<β<1；
S2.5, the operation cost of the electricity selling company combined with the joint optimization of the day-ahead market and the real-time market of the electric automobile is as follows:
C'＝C'DA+C'RT+X
c' is the operation cost of the electricity vendor which is optimized by combining the day-ahead market and the real-time market of the electric automobile;
the specific step of step S3:
s3.1, determining the population number of the particle swarm, wherein one particle comprises a day-ahead market, a real-time market and the electric energy purchase amount of the electric automobile per hour per day;
s3.2, setting initial parameters of the particle swarm algorithm, wherein the initial parameters comprise a weight factor w, learning factors c1 and c2, a maximum speed Vmax, iteration times T and the like;
s3.3, randomly generating the initial speed and the initial position of each particle;
s3.4, judging the fitness of each particle;
s3.5, after each particle is iterated once, comparing the iterated once with the optimal value and the overall optimal value, and accordingly changing the advancing speed and the advancing direction of each particle and enabling the particles to approach the optimal value gradually;
s3.6, judging whether the set precision is met or whether the maximum iteration number is reached, and returning to S3.4 if the maximum iteration number is not met;
and S3.7, outputting an optimization result when the set precision is reached or the maximum iteration number is reached.
In the specific implementation process, an electricity selling company counts the total load of the electric vehicle subjected to dispatching in the region in charge of the electricity selling company, the total load of the electric vehicle subjected to dispatching is compared with the current power price and the predicted real-time power price, the combined optimization model of the current market and the real-time market of the electric vehicle is mainly used for enabling the electricity selling company to regard the current purchased electric energy as the quantity to be optimized, the current purchased electric energy is compared with the predicted real-time power price on the day according to the current trading power price, a unified electric energy trading mode is established by combining an energy storage device and the electric vehicle, the current purchased electric energy in the current market per hour and the charge and discharge time period of the electric vehicle subjected to dispatching are calculated through a particle swarm algorithm, the optimal electricity purchasing scheme of the electricity selling company is obtained, and the operation cost of the company is reduced.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (2)

1.一种结合电动汽车的日前市场和实时市场联合优化方法，其特征在于，包括以下步骤:1. a kind of before the market in conjunction with electric vehicle and real-time market joint optimization method, is characterized in that, comprises the following steps: S1:售电商与所负责地区的电动汽车所有者签订调度协议；S1: The e-commerce seller signs a scheduling agreement with the electric vehicle owner in the responsible area; S2:将参与调度的电动汽车考虑在日前市场和实时市场的购买计划中，结合储能装置以及电动汽车建立一个统一的电能交易模式，所述的电动汽车在该电能交易模式下即充当负荷也充当电源；S2: Consider the electric vehicles participating in the dispatch in the purchase plan of the day-ahead market and the real-time market, and combine the energy storage device and the electric vehicle to establish a unified electric energy transaction mode. act as a power source; S3:利用粒子群优化算法求解结合电动汽车的日前市场和实时市场联合优化的购电方案；S3: Use the particle swarm optimization algorithm to solve the power purchase plan combined with the day-ahead market and real-time market optimization of electric vehicles; 所述步骤S1的调度协议具体如下：The scheduling protocol of the step S1 is specifically as follows: S1.1:设定放电最低容量Si,min以及充电最大容量Si,max，Si,max＞Si,min；S1.1: Set the minimum discharge capacity Si ,min and the maximum charge capacity Si ,max , Si ,max > Si ,min ; S1.2:当电动汽车的电池容量Si(t)大于Si,max时，此时电动汽车只允许放电操作；S1.2: When the battery capacity S i (t) of the electric vehicle is greater than S i,max , the electric vehicle only allows discharge operation; S1.3:当Si(t)小于Si,min时，此时电动汽车只允许充电操作；S1.3: When S i (t) is less than S i,min , the electric vehicle only allows charging operation at this time; S1.4:当Si(t)大于Si,min且小于Si,max时，此时电动汽车按照售电商需求进行充放电操作；S1.4: When S i (t) is greater than S i,min and smaller than S i,max , the electric vehicle is charged and discharged according to the needs of the e-commerce business; 所述步骤S2的具体步骤如下：The specific steps of the step S2 are as follows: S2.1:定义在统一交易模式下的日前市场电能购买量为B'DA(t)，在统一交易模式下的实时市场电能购买量为B'RT(t)；S2.1: Define the electricity purchase volume in the day-ahead market under the unified transaction mode as B' DA (t), and the real-time market electricity purchase volume under the unified transaction mode as B' RT (t); S2.2:日前市场的成本为：S2.2: The cost of the day-ahead market is: C'DA(t)＝PDA(t)(B'DA(t)+S(t)) C'DA (t)= PDA (t)( B'DA (t)+S(t)) 式中，C'DA(t)表示日前市场的售电商运营成本，PDA(t)为日前市场各小时电价，S(t)为所有电动汽车的总电量，当S(t)小于零时为电动汽车在电力市场中售出电能，当大于零时表示售电公司在电力市场中购买电能；In the formula, C' DA (t) is the operating cost of the e-commerce retailer in the day-ahead market, P DA (t) is the hourly electricity price in the day-ahead market, and S(t) is the total power of all electric vehicles. When S(t) is less than zero When the electric vehicle sells electricity in the electricity market, when it is greater than zero, it means that the electricity sales company purchases electricity in the electricity market; S2.3:实时市场的成本为：S2.3: The cost of the real-time market is: C'RT(t)＝PRT(t)(B'RT(t)+S(t))C' RT (t)=P RT (t)(B' RT (t)+S(t)) 式中，C'RT(t)表示实时市场的售电商运营成本，PRT(t)为日前市场各小时电价；In the formula, C' RT (t) represents the operating cost of e-commerce retailers in the real-time market, and P RT (t) is the hourly electricity price in the day-ahead market; S2.4:考虑售电商对电动汽车所有者的补偿，有：S2.4: Consider compensation for electric vehicle owners from e-commerce retailers, including: X＝ηS(t)X=ηS(t) 式中，X为售电商对电动汽车所有者的补偿金额，η为售电商对电动汽车所有者的补偿价格系数，其中η＝(PS(t)-PB(t))·β,PS(t)、PB(t)分别是出售和购买电动汽车电量时的电价，β为补偿因子，0<β<1；In the formula, X is the compensation amount of the electric vehicle owner from the electricity seller, η is the compensation price coefficient of the electricity seller to the electric vehicle owner, where η=(P S (t)-P B (t)) β , P S (t), P B (t) are the electricity prices when selling and purchasing electric vehicle electricity, respectively, β is the compensation factor, 0<β<1; S2.5:结合电动汽车的日前市场和实时市场的联合优化的售电商运营成本为：S2.5: Combined with the joint optimization of the day-ahead market and real-time market of electric vehicles, the operating cost of e-commerce sales is: C′＝C′DA+C′RT+XC'=C' DA +C' RT +X 式中C'为结合电动汽车的日前市场和实时市场的联合优化的售电商运营成本。In the formula, C' is the operating cost of e-commerce that combines the joint optimization of the day-ahead market and the real-time market of electric vehicles. 2.根据权利要求1所述的结合电动汽车的日前市场和实时市场联合优化方法，其特征在于，所述步骤S3的具体步骤：2. the day-ahead market and real-time market joint optimization method in conjunction with electric vehicle according to claim 1, is characterized in that, the concrete steps of described step S3: S3.1:确定粒子群的种群个数，其中一个粒子包括日前市场、实时市场以及电动汽车一天每小时的电能购买量；S3.1: Determine the population number of the particle swarm, one of which includes the day-ahead market, the real-time market, and the electric vehicle's electricity purchase per hour per day; S3.2:对粒子群算法进行初始参数设置包括权重因子w、学习因子c1和c2、最大速度Vmax、以及迭代次数T等；S3.2: Initial parameter settings for particle swarm algorithm include weight factor w, learning factors c1 and c2, maximum speed Vmax, and iteration times T, etc.; S3.3:随机产生每个粒子的初始速度及初始位置；S3.3: Randomly generate the initial velocity and initial position of each particle; S3.4:判断每个粒子的适应度；S3.4: Judge the fitness of each particle; S3.5:每个粒子迭代一次后与自身最优值和全局最优值进行比较，从而改变自身的前进速度和前进方向，逐步接近最优值；S3.5: After each particle iterates once, compare it with its own optimal value and the global optimal value, thereby changing its own forward speed and direction, and gradually approaching the optimal value; S3.6:判断是否满足设定精度或是否达到最大迭代次数，否则返回S3.4；S3.6: judge whether the set accuracy is met or whether the maximum number of iterations is reached, otherwise return to S3.4; S3.7:当达到设定精度或达到最大迭代次数输出优化结果。S3.7: When the set accuracy is reached or the maximum number of iterations is reached, the optimization result is output.
CN201811052069.3A 2018-09-10 2018-09-10 Day-ahead market and real-time market combined optimization method combined with electric automobile Active CN109193623B (en)

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