CN105207241A - Electric automobile frequency modulation optimizing control method based on charge state detection - Google Patents

Abstract

The invention discloses an electric vehicle frequency modulation optimization control method based on state of charge detection, comprising the steps of: acquiring real-time electricity price data and frequency modulation price data of a distribution network; acquiring electric vehicle charging load characteristic data and an electric vehicle SOC curve during a charging period; Taking the SOC curve of the electric vehicle as a constraint condition, establish an electric vehicle charging cost model; establish a frequency modulation revenue model for electric vehicles participating in frequency modulation; combine the electric vehicle charging cost model and frequency modulation revenue model to establish a user final revenue model, and according to the The optimal solution of the user's final revenue model is obtained from the real-time electricity price data of the distribution network and the frequency modulation price data; according to the optimal solution of the model, the charging strategy for electric vehicles participating in frequency modulation is obtained and the execution of the charging strategy is controlled. The present invention satisfies the user's travel needs and at the same time reasonably allocates the charging and frequency regulation time periods of the electric vehicle within the rechargeable time period, so that the user can obtain the maximum benefit and effectively reduce the life loss cost of the battery.

Description

A frequency modulation optimization control method for electric vehicles based on state of charge detection

technical field

The invention relates to an electric vehicle frequency modulation optimization control method based on charge state detection, and belongs to the field of electric vehicle charging technology.

Background technique

With the rapid development of the economy, society and the automobile industry, problems such as energy shortage, environmental pollution, and global climate change caused by the widespread use of traditional internal combustion engine vehicles have become increasingly prominent, and the transformation of the automobile industry has become an inevitable trend. Electric vehicles have shown advantages that traditional means of transportation do not have in alleviating energy and environmental crises, and have received extensive attention. At present, electric vehicles have developed rapidly in the world.

The charging and discharging of electric vehicles has strong randomness. If the charging and discharging process of electric vehicles is controllable, a large number of battery packs can be used as distributed energy storage devices to provide backup when the active power of renewable energy units fluctuates, and participate in grid peak regulation and frequency regulation. Get extra income. The survey shows that for the vast majority of electric vehicles, 96% of the time in a day is idle, which can provide auxiliary services for the grid, such as frequency regulation, voltage regulation, spinning reserve, system stability regulation, etc. If electric vehicles charge and discharge disorderly, it will increase the peak load and peak-valley difference of the system, which will have a greater impact on the stability of the power grid.

The charging and discharging characteristics of electric vehicles enable it to adjust the frequency up and down. However, when the conventional unit participates in the frequency deviation adjustment, it needs to consider the active power change corresponding to the frequency deviation and the limitation of the speed change, and the power of the conventional unit can only be 1 Therefore, the optimal operation of grid-connected electric vehicles has great research potential. As the penetration rate of electric vehicles in the power grid continues to deepen, their potential to provide ancillary services such as frequency regulation will gradually increase. Electric vehicles can obtain certain frequency regulation benefits while completing driving functions, which can further encourage users to participate in ancillary services.

Contents of the invention

The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, to provide an electric vehicle frequency modulation optimization control method based on state-of-charge detection, to solve the problem that the power of the existing electric vehicle can only be changed in one direction in the charging and discharging characteristics, and cannot The problem of reasonably allocating the charging and frequency regulation time slots of electric vehicles in the rechargeable time period.

The present invention specifically adopts the following technical solutions to solve the above technical problems:

A method for optimal control of frequency modulation of electric vehicles based on state of charge detection, the method comprising the following steps:

Step 1. Obtain real-time electricity price data and frequency regulation price data of the distribution network;

Step 2. Obtain the charging load characteristic data of the electric vehicle during the charging period, and obtain the SOC curve of the electric vehicle according to the charging load characteristic data of the electric vehicle;

Step 3, taking the SOC curve of the electric vehicle as a constraint condition, establishing an electric vehicle charging cost model;

Step 4. Perform frequency regulation during non-charging periods, and establish a frequency regulation revenue model for electric vehicles participating in frequency regulation;

Step 5. Combining the electric vehicle charging cost model and the frequency modulation revenue model to establish the user's final revenue model, and obtain the optimal solution of the user's final revenue model according to the real-time electricity price data of the distribution network and the frequency modulation price data;

Step 6. According to the optimal solution of the model, obtain the charging strategy for the electric vehicle to participate in frequency regulation and control the execution of the charging strategy.

Further, as a preferred technical solution of the present invention: said step 2 electric vehicle charging load characteristic data includes charging initial SOC value, charging end expected SOC value, battery capacity, charging power, charging efficiency and charging period.

Further, as a preferred technical solution of the present invention: said step 3 establishes the electric vehicle charging cost model as:

${\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}\underset{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}{<span\; class="notranslate">\; \&Integral;</span>}\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; t</span>}$

Among them, M is the rated charging power; r(t) is the ratio of the actual charging power to the rated charging power at time t; P _c (t) is the charging electricity price at time t; T _c is the charging period.

Further, as a preferred technical solution of the present invention: the frequency modulation revenue model of electric vehicles participating in frequency modulation established in step 4 is:

P _R ＝P _RU (t)W _U (x(t))+P _RD (t)W _D (x(t))

Among them, P _RU (t) and P _RD (t) are the prices of up-frequency adjustment and down-frequency adjustment respectively; x(t) is the SOC value at time t; W _U (x(t)) and W _D (x(t) )) represent the weights of up-regulation and down-regulation at x(t) respectively.

Further, as a preferred technical solution of the present invention: the user final revenue model established in step 5 is:

$\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; =</span>\underset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; \&Integral;</span>}}<span\; class="notranslate">\; \&lsqb;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; R</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; R</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

Among them, t ₁ and t ₂ are the initial and end time of charging respectively; P _R is the frequency modulation revenue model; c(t) is a variable whose value is 1 during charging and 0 during frequency modulation; D _c is the charging cost of electric vehicles Model; x(t ₂ ) is the actual SOC value at the end of charging; x ^T is the expected SOC value at the end of charging; β is the compensation coefficient.

Furthermore, as a preferred technical solution of the present invention: the step 5 uses a dynamic programming algorithm to obtain the model optimal solution of the user's final revenue model.

The present invention adopts above-mentioned technical scheme, can produce following technical effect:

(1), the electric vehicle frequency modulation optimization control method based on the state of charge detection provided by the present invention, combined with the charging load characteristic data of the electric vehicle, and considering the SOC curve constraint when the battery is charged, constructs the mathematics for the optimal operation of the grid connection of the electric vehicle Model, and solve the model to obtain the optimal solution, while satisfying the user's travel needs, reasonably allocate the charging and frequency adjustment time period of the electric vehicle in the rechargeable time period, so that the user can obtain the maximum benefit. It effectively reduces the life loss cost of the battery, maximizes the user's benefits, and improves the safe operation level and power quality of the power grid.

(2) The present invention further uses a dynamic programming algorithm to convert multi-stage decision-making problems into a series of relatively simple single-stage optimization problems, making the frequency modulation process more optimized and accurate.

Description of drawings

FIG. 1 is a schematic flow chart of an electric vehicle frequency modulation optimization control method based on charge state detection according to the present invention.

Fig. 2 is a schematic diagram of the electric vehicle SOC curve in the electric vehicle frequency modulation optimization control method based on the state of charge detection of the present invention.

Figure 3 is a diagram of the income corresponding to each arrangement of the electric vehicle charging period and the results of sorting the income corresponding to different arrangements.

Detailed ways

Embodiments of the present invention will be described below in conjunction with the accompanying drawings.

The present invention provides an electric vehicle frequency modulation optimization control method based on state of charge detection, as shown in Figure 1, taking a private car as an example to illustrate the electric vehicle frequency modulation optimization control process, specifically as follows:

Step 1. Obtain real-time electricity price data and frequency regulation price data of the distribution network, wherein the frequency regulation price data includes different situations of upward frequency regulation and downward frequency regulation. Specifically, as shown in Table 1, it is the electricity price data of each period of the day and the frequency regulation price of each period of the day, and the unit is Euro/MWh (only the 12-hour price information of EV grid connection is given); Table 2

Weights for up-frequency modulation and down-frequency modulation defined for different SOCs.

Table 1:

Table 2:

Step 2. Obtain the charging load characteristic data of the electric vehicle during the charging period, and obtain the SOC curve of the electric vehicle according to the charging load characteristic data of the electric vehicle. End expected load, battery capacity, charging power, charging efficiency and charging period.

Obtain the charging load characteristics of the electric vehicle during the charging period, it can be obtained that the SOC value x(t ₁ )=10% at the beginning of charging; the expected SOC value x ^T =90% at the end of charging; the battery capacity C=30KW; the rated charging power M= 7KW; the charging efficiency is 90%; the charging period is 12 hours from 19:00 to 7:00, which is composed of the frequency modulation period and the charging period.

Step 3. Using the SOC curve of the electric vehicle as a constraint condition, a charging cost model of the electric vehicle is established. The electric vehicle charging cost model is:

${\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}\underset{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}{<span\; class="notranslate">\; \&Integral;</span>}\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Among them, M is the rated charging power; r(t) is the ratio of the actual charging power to the rated charging power at time t, and 0≤r(t)≤1; P _c (t) is the charging price at time t; T _c is charging period. Required charging capacity $Q=C\&Center\; Dot;(x^T-x(t_1\left)\right)=m\underset{T_c}{\&Integral;}r\left(t\right)dt.$ Considering the SOC curve constraints, the charging time is 5 hours.

In the electric vehicle charging cost model, considering the electric vehicle SOC curve as a constraint condition, namely:

I(t)=I ₀ e ^-αt (2)

Among them, I(t) represents the real-time charging current; I ₀ is the maximum charging current at the initial moment; α is the charging acceptance rate.

Therefore, the ratio of r(t) between the actual charging power and the rated charging power at time t can be expressed as:

r(t)=I(t)/I ₀ ＝e ^-αt (3)

According to formula (3), it can be known that as the charging time increases, the SOC value of the battery is also increasing, while the charging power of the battery is decreasing.

Step 4. Perform frequency regulation during non-charging periods, and establish a frequency regulation revenue model for electric vehicles participating in frequency regulation;

Electric vehicle frequency regulation is divided into upward regulation and downward regulation, and the expression of the frequency regulation benefit model is:

P _R =P _RU (t)W _U (x(t))+P _RD (t)W _D (x(t))(4)

Among them, P _RU (t) and P _RD (t) are the prices of up-frequency adjustment and down-frequency adjustment respectively, as shown in Table 1; x(t) is the SOC value at time t; W _U (x(t)) and W _D (x(t)) respectively represent the weights of up-regulation and down-regulation at x(t), as shown in Table 2. The time for electric vehicles to be connected to the grid is 12 hours, and the time for participating in charging is 5 hours, that is, the time for frequency modulation is 7 hours.

Step 5. Combining the electric vehicle charging cost model and frequency regulation revenue model to establish a user final revenue model, and obtain the optimal solution of the user final revenue model based on the distribution network real-time electricity price data and frequency modulation price data. The user's final revenue model is:

$\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; =</span>\underset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; \&Integral;</span>}}<span\; class="notranslate">\; \&lsqb;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; R</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; R</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

Among them, t ₁ and t ₂ are the initial and end time of charging respectively; P _R is the frequency modulation revenue model; c(t) is a variable whose value is 1 during charging and 0 during frequency modulation; D _c is the charging cost of electric vehicles Model; x(t ₂ ) is the actual SOC value at the end of charging; x ^T is the expected SOC value at the end of charging; β is the compensation coefficient.

Then, the optimal solution of the user's final revenue model is obtained by using the dynamic programming algorithm, and the multi-stage decision-making problem is transformed into a series of relatively simple single-stage optimization problems. Specifically, the charging period of an electric vehicle is In the middle combination method, the real-time electricity price data of the distribution network and the frequency regulation price data are imported into the model to obtain the results in various situations, as shown in Figure 3, the income situation corresponding to each arrangement method of the electric vehicle charging period and the corresponding income ranking of different arrangement methods The final results are arranged accordingly, and whether the arrangement and combination is completed is judged in the arrangement, and the maximum value of the user's income under the charging and frequency modulation period is selected as the optimal solution.

Step 6. According to the optimal solution of the model, obtain the charging strategy for the electric vehicle to participate in the frequency regulation and control the execution of the charging strategy, so that the electric vehicle participates in the frequency regulation to obtain the optimal charging strategy.

Therefore, the electric vehicle frequency modulation optimization control method based on state of charge detection provided by the present invention combines the electric vehicle charging load characteristic data and considers the SOC curve constraints during battery charging to construct a mathematical model for electric vehicle grid-connected optimal operation , and solve the model to obtain the optimal solution, while meeting the travel needs of users, reasonably allocate the charging and frequency modulation periods of electric vehicles in the rechargeable period, and improve the frequency modulation optimization function.

The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments, and can also be made without departing from the gist of the present invention within the scope of knowledge possessed by those of ordinary skill in the art. Variations.

Claims (6)

1., based on the electric automobile frequency modulation optimal control method that state-of-charge detects, it is characterized in that, the method comprises the following steps:

Step 1, acquisition power distribution network Spot Price data and frequency modulation price data;

Step 2, obtain charging electric vehicle part throttle characteristics data at charge period, and according to charging electric vehicle part throttle characteristics data acquisition electric automobile SOC curve;

Step 3, with described electric automobile SOC curve for constraints, set up charging electric vehicle cost model;

Step 4, carry out frequency modulation at non-charge period, and set up the frequency modulation earnings pattern that electric automobile participates in frequency modulation;

Step 5, set up user's ultimate yield model in conjunction with described charging electric vehicle cost model and frequency modulation earnings pattern, and obtain the optimal solution of user's ultimate yield model according to described power distribution network Spot Price data and frequency modulation price data;

Step 6, obtain electric automobile according to described model optimal solution and participate in the charging strategy of frequency modulation and control the execution of charging strategy.

2. according to claim 1 based on the electric automobile frequency modulation optimal control method that state-of-charge detects, it is characterized in that: described step 2 charging electric vehicle part throttle characteristics data comprise charging initial SOC value, charging terminates to expect SOC value, battery capacity, charge power, charge efficiency and chargeable period.

3., according to claim 1 based on the electric automobile frequency modulation optimal control method that state-of-charge detects, it is characterized in that: described step 3 is set up charging electric vehicle cost model and is:

$D_c=M\underset{T_c}{\&Integral;}r\left(t\right)P_c\left(t\right)dt$

Wherein, M is specified charge power; R (t) is the ratio of the actual charge power of t and specified charge power; P _ct () is the charging electricity price of t; T _cit is charge period.

4. according to claim 1 based on the electric automobile frequency modulation optimal control method that state-of-charge detects, it is characterized in that: the frequency modulation earnings pattern that the electric automobile that described step 4 is set up participates in frequency modulation is:

P _R＝P _RU(t)W _U(x(t))+P _RD(t)W _D(x(t))

Wherein, P _rU(t) and P _rDt () is the price of upwards frequency modulation and downward frequency modulation respectively; X (t) is the SOC value of t; W _u(x (t)) and W _d(x (t)) upwards regulates and the weight regulated downwards when representing x (t) respectively.

5. according to claim 1 based on the electric automobile frequency modulation optimal control method that state-of-charge detects, it is characterized in that: user's ultimate yield model that described step 5 is set up is:

$R=\underset{t_1}{\overset{t_2}{\&Integral;}}\&lsqb;P_R-c\left(t\right)(P_R+D_c)\&rsqb;dt-\mathrm{\&beta;}{\left(x\right(t_2)-x^T)}^2$

Wherein, t ₁and t ₂be respectively the initial of charging and end time; P _rit is frequency modulation earnings pattern; C (t) is variable, value be charging time be 1 and frequency modulation time be 0; D _cit is charging electric vehicle cost model; X (t ₂) be charging at the end of actual soc-value; x ^tfor expecting SOC value at the end of charging; β is penalty coefficient.

6., according to claim 1 based on the electric automobile frequency modulation optimal control method that state-of-charge detects, it is characterized in that: described step 5 adopts dynamic programming algorithm to try to achieve the optimal solution of user's ultimate yield model.