Storage and utilization collaborative optimization method and device based on virtual power plant and storage medium

Abstract

The invention discloses a storage and utilization collaborative optimization method and device based on a virtual power plant and a storage medium. According to the scheme, power consumption data of a target user is acquired; constructing indexes of the power utilization flexibility of the target user, wherein the indexes of the power utilization flexibility comprise a daytime fluctuation index, a single-time fluctuation index and a power consumption level index; calculating the power utilization flexibility of the target user based on the daytime fluctuation index, the single-time fluctuation index and the power consumption level index; the virtual power plant storage and utilization collaborative optimization model solves the target function of the virtual power plant storage and utilization collaborative optimization model according to the power utilization flexibility of the target user, the energy storage equipment constraint condition and the user power utilization and power supply balance constraint condition, and a collaborative optimization result enabling the value of the target function to be minimized is obtained. Different from the response of the user to demand side management in the prior art, the virtual power plant is utilized to enable the energy storage equipment and the power grid to cooperatively operate, peak load shifting is realized under the condition of not influencing the power utilization of the user, and the method has obvious advantages.

Description

Technical Field

[0001] The present application relates to the technical field of power system dispatching, and in particular to a storage-use collaborative optimization method, device and storage medium based on a virtual power plant. Background Art

[0002] Demand-side management, as an important means of balancing electricity supply and demand, is an important component of helping to achieve the dual carbon goals. At present, according to the relevant characteristics of the power grid load, the load peak is increasing sharply, the peak-to-valley difference is widening, the system load rate is gradually decreasing, the power gap is difficult to make up, and the balance of electricity supply and demand cannot be achieved. The problem of power shortage is mainly load-related power shortage. Under the current power industry rules, optimizing resource allocation through demand-side management to play a role in peak shaving and valley filling is an important method to deal with the contradiction between power supply and demand.

[0003] Electricity demand-side response is a concrete manifestation of demand-side management methods. It is very worthy of study to achieve peak load reduction and reasonable scheduling of power generation capacity through the flexible use of intelligent demand response technology.

[0004] A virtual power plant is a system that integrates and coordinates multiple dispersed distributed energy resources through advanced information and communication technology and smart grid technology. Introducing demand-side resources into the optimal scheduling of virtual power plants and coordinating them with energy storage resources will effectively improve the operating efficiency and economy of the power system. At present, research on the collaborative optimization of virtual power plants is difficult to guarantee the electricity consumption of users. The scheduling model adjusts the demand-side response and the participation of some energy storage equipment by interrupting the user's electricity consumption and adjusting the electricity consumption. However, the operating effect depends on the user's response to demand-side management and is not suitable for long-term planning.

[0005] Therefore, how to use virtual power plants to coordinately optimize energy storage equipment and power grids and achieve peak shaving and valley filling without affecting users' electricity consumption is a technical problem that needs to be solved urgently. Summary of the invention

[0006] Based on the above problems, the present application provides a storage-use collaborative optimization method, device and storage medium based on a virtual power plant. The purpose is to use the virtual power plant to collaboratively optimize the energy storage equipment and the power grid, so that the energy storage equipment and the power grid can operate in coordination and achieve peak shaving and valley filling without affecting the user's electricity consumption.

[0007] The embodiments of the present application disclose the following technical solutions:

[0008] In a first aspect, the present application provides a storage-use collaborative optimization method based on a virtual power plant, the method being applied to a virtual power plant, the method comprising:

[0009] Obtain target user's electricity consumption data;

[0010] Based on the acquired target user's electricity consumption data, construct indicators of target user's electricity consumption flexibility; the indicators of electricity consumption flexibility include daily fluctuation index, single-hour fluctuation index and electricity consumption level index;

[0011] Calculating the power consumption flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the power consumption level index;

[0012] The virtual power plant storage and use collaborative optimization model solves the objective function of the virtual power plant storage and use collaborative optimization model according to the target user's electricity consumption flexibility, energy storage equipment constraints and user electricity consumption and power supply balance constraints to obtain a collaborative optimization result that minimizes the value of the objective function; the collaborative optimization result includes the discharge capacity of the power grid and the charge and discharge capacity of the energy storage equipment.

[0013] In an optional implementation, the method of constructing the intraday volatility index includes:

[0014] Normalize the target user's 24-hour daily electricity consumption data;

[0015] Based on the normalized 24-hour daily electricity consumption data of the target users, the electricity consumption corresponding to the first quantile and the electricity consumption corresponding to the second quantile of the target users are calculated;

[0016] The difference between the power consumption corresponding to the second quantile of the target user and the power consumption corresponding to the first quantile is calculated as the diurnal fluctuation index of the target user; the second quantile is greater than the first quantile.

[0017] In an optional implementation, the method of constructing the single-time volatility index includes:

[0018] The single-time fluctuation index h of the target user is calculated based on the power consumption of the target user c at time t and the average power consumption of the target user c at time t in the typical power consumption mode. c,t , the specific formula is as follows:

[0019]

[0020] x c,t,d represents the electricity consumption of user c at time t on day d, x c,t ′ represents the average power consumption of user c at time t in the typical power consumption pattern, n represents the total number of days, and n is a positive integer;

[0021] The power consumption of a user in the typical power consumption mode refers to the average power consumption after removing abnormal values ​​in the user's actual power consumption data.

[0022] In an optional implementation, the method of constructing the power consumption level indicator includes:

[0023] Arrange the electricity consumption of all users in the data set at time t in ascending order and divide them into multiple electricity consumption intervals in geometric proportion;

[0024] Based on the power consumption of the target user at time t, determine the target power consumption range;

[0025] The quantile corresponding to the upper limit value of the target power consumption interval is used as the power consumption level indicator of the target user; the quantile corresponding to the upper limit value of the target interval is obtained according to a preset ratio.

[0026] In an optional implementation, calculating the power consumption flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the power consumption level index includes:

[0027] The electricity flexibility f for target user c at time t c,t The specific calculation formula is as follows:

[0028]

[0029] where h c,t is the single-time fluctuation index of target user c at time t, m c,t is the power consumption level indicator of target user c at time t, d c,t is the daily fluctuation index of the target user c at time t; t is an integer in the interval [0,23].

[0030] In an optional implementation, the objective function of the virtual power plant storage and utilization collaborative optimization model is:

[0031]

[0032] Among them, p j The electricity flexibility of the target user in the jth hour; i,j is the power grid output at the jth hour on the i-th day, p battery is the battery cost per 1 kWh, nc is the nominal capacity of the battery; d i,j and nc are obtained by minimizing the value of the objective function; N is the total number of days, and its value range is a positive integer; i is a positive integer, and j is an integer in the interval [0,23].

[0033] In an optional implementation, after calculating the power consumption flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the power consumption level index, the method further includes: setting energy storage device constraints according to the following rules;

[0034] Maintain a balance between the charge and discharge of the energy storage device and the amount of electricity stored in the energy storage device;

[0035] The charging and discharging of energy storage devices are not performed simultaneously;

[0036] The minimum stored power of the energy storage device is set to be no less than the discharge depth requirement; the energy storage device includes a battery.

[0037] In an optional implementation, after calculating the power consumption flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the power consumption level index, it also includes: setting the user's power consumption and power supply balance constraint conditions according to the following rules;

[0038] When the energy storage equipment and the power grid work together, the discharge amount matches the user's power consumption;

[0039] Based on the peak-to-valley difference before the coordinated operation of the energy storage equipment is achieved, the preset amplitude is adjusted downward.

[0040] The second aspect of the present application provides a storage-use collaborative optimization device based on a virtual power plant, the device comprising:

[0041] An electricity consumption data acquisition module is used to acquire electricity consumption data of target users;

[0042] An indicator construction module is used to construct indicators of target users' electricity consumption flexibility based on the acquired target users' electricity consumption data; the indicators of electricity consumption flexibility include daily fluctuation indicators, single-hour fluctuation indicators and electricity consumption level indicators;

[0043] The collaborative optimization result calculation module is used to calculate the electricity flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the electricity consumption level index; the virtual power plant storage and use collaborative optimization model solves the objective function of the virtual power plant storage and use collaborative optimization model according to the electricity flexibility of the target user, the energy storage device constraints and the user's electricity consumption and power supply balance constraints to obtain a collaborative optimization result that minimizes the value of the objective function; the collaborative optimization result includes the discharge capacity of the power grid and the charge and discharge capacity of the energy storage device.

[0044] A third aspect of the present application provides a computer-readable storage medium, in which a computer program is stored. When the program is executed by a processor, a storage and utilization collaborative optimization method based on a virtual power plant as provided in any implementation of the first aspect is implemented.

[0045] Compared with the prior art, this application has the following beneficial effects:

[0046] This application obtains the electricity consumption data of the target user to construct the index of the target user's electricity consumption flexibility. The index of electricity consumption flexibility includes a daily fluctuation index, a single-time fluctuation index and an electricity consumption level index. Based on the daily fluctuation index, the single-time fluctuation index and the electricity consumption level index, the target user's electricity consumption flexibility is calculated. In this way, the target user's electricity consumption behavior characteristics are analyzed from multiple dimensions such as time and magnitude. The virtual power plant storage and use collaborative optimization model solves the objective function of the virtual power plant storage and use collaborative optimization model according to the target user's electricity consumption flexibility, energy storage device constraints and user electricity consumption and power supply balance constraints, and obtains the collaborative optimization result that minimizes the value of the objective function. The collaborative optimization result includes the discharge amount of the power grid and the charge and discharge amount of the energy storage device. According to the collaborative optimization result, when the user's electricity consumption is at its peak, the energy storage device discharges to supplement the power grid, and when the user's electricity consumption is at its low point, the energy storage device charges, so that the peak-to-valley difference is reduced, and the pressure on the power grid during peak electricity consumption is relieved. This method can achieve peak shaving and valley filling by only adjusting the time and magnitude of the power supply of the power supplier without affecting the user's electricity consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 A flowchart of a storage and utilization collaborative optimization method based on a virtual power plant provided in an embodiment of the present application;

[0049] Figure 2 The actual electricity consumption diagram of user A and the box diagram of normalized data provided in the embodiment of the present application;

[0050] Figure 3 The actual electricity consumption diagram of user B and the box diagram of normalized data provided in the embodiment of the present application;

[0051] Figure 4 The average electricity consumption diagram for four seasons provided in the embodiment of the present application;

[0052] Figure 5 A four-season peak-to-valley difference diagram provided in an embodiment of the present application;

[0053] Figure 6 Battery discharge diagrams for spring and summer provided for the embodiments of this application

[0054] Figure 7 Autumn and winter battery discharge diagrams provided for embodiments of the present application;

[0055] Figure 8 Battery charging time diagram for spring and summer provided for the embodiment of the present application

[0056] Fig. 9 A battery charging time diagram for fall and winter provided for an embodiment of the present application;

[0057] Fig.10 A peak shaving and valley filling effect diagram provided in an embodiment of the present application;

[0058] Fig.11 The power plant output diagrams for spring and summer provided for the embodiments of the present application;

[0059] Fig.12 Autumn and winter power plant output diagrams provided for embodiments of the present application;

[0060] Fig.13 A schematic diagram of the structure of a storage-use collaborative optimization device based on a virtual power plant provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] As described above, current research on collaborative optimization of virtual power plants can hardly guarantee users’ electricity supply, and the operating results depend on users’ response to demand-side management, which is not suitable for long-term planning.

[0062] In view of the above problems, the inventors have proposed a storage-use collaborative optimization method, device and storage medium based on a virtual power plant after research. By acquiring the electricity consumption data of the target users, the indicators of the target users' electricity consumption flexibility are constructed. The indicators of electricity consumption flexibility include daily fluctuation indicators, single-time fluctuation indicators and electricity consumption level indicators. Based on the daily fluctuation indicators, the single-time fluctuation indicators and the electricity consumption level indicators, the electricity consumption flexibility of the target users is calculated. The virtual power plant storage-use collaborative optimization model solves the objective function of the virtual power plant storage-use collaborative optimization model according to the target users' electricity consumption flexibility, energy storage equipment constraints and user electricity consumption and power supply balance constraints to obtain a collaborative optimization result that minimizes the value of the objective function. The collaborative optimization result includes the discharge of the power grid and the charge and discharge of the energy storage equipment.

[0063] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0064] See also Figure 1, which is a flow chart of a storage and utilization collaborative optimization method based on a virtual power plant provided in an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0065] S101. Obtain electricity consumption data of a target user.

[0066] Public data sets are usually used to analyze the characteristics of users' electricity consumption behavior. Commonly used data sets include PecanStreet Dataport, CER Smart Metering Data, and UCI Machine Learning Repository. Users' electricity consumption data are obtained in a legal and compliant manner.

[0067] S102: construct an indicator of the target user's electricity consumption flexibility based on the acquired target user's electricity consumption data.

[0068] Among them, the indicators of electricity consumption flexibility include daily fluctuation index, single-hour fluctuation index and electricity consumption level index.

[0069] The construction method of the intraday volatility index includes:

[0070] Normalize the target user's 24-hour daily electricity consumption data;

[0071] Based on the normalized 24-hour daily electricity consumption data of the target users, the electricity consumption corresponding to the first quantile and the electricity consumption corresponding to the second quantile of the target users are calculated;

[0072] The difference between the power consumption corresponding to the second quantile of the target user and the power consumption corresponding to the first quantile is calculated as the diurnal fluctuation index of the target user; the second quantile is greater than the first quantile.

[0073] For example, the power consumption corresponding to the 20% quantile and the power consumption corresponding to the 60% quantile are calculated, and the difference between the power consumption corresponding to the 60% quantile and the power consumption corresponding to the 20% quantile is used as the user's daily fluctuation index. The effectiveness of describing daily fluctuations is illustrated by two cases: peak non-stable users and peak stable users. Figure 2 and Figure 3 As shown, Figure 2 They are the actual power consumption diagram of peak non-stable user A and the box diagram of normalized data. Figure 3 They are the actual electricity consumption graph of peak-stable user B and the box plot of normalized data. Figure 2 and Figure 3 The 24 hours of a day are used as the horizontal axis, each hour is used as a scale, and the vertical axis is the power consumption in kilowatt-hours (kwh).

[0074] from Figure 2 It can be seen that the nighttime electricity consumption of user A varies greatly between 18:00 and 22:00, that is, 18:00 to 22:00 is sometimes the peak time for electricity consumption. Overall, the specific time of the nighttime peak electricity consumption is unclear, so the difference between the 60% quantile and the 20% quantile in the nighttime electricity consumption trend between 18:00 and 22:00 in the box chart is large. For user B, who is relatively stable during the peak period, the box of the box chart is relatively short. Figure 3 It can be observed that the nighttime peaks are at 23:00 and 1:00 at night, with higher electricity consumption. However, due to the obvious trend, the difference between the 60% quantile and the 20% quantile of the trends at 24:00 and 1:00 is small.

[0075] The daily fluctuation index reflects the consistency of electricity consumption of users at each time point, for example, whether the peak time of electricity consumption between different users is stable. From a computational perspective, the advantage of calculating daily volatility based on the quantile of the normalized result is that on the one hand, the computational cost is low, and on the other hand, it can effectively reflect the characteristics of peaks and troughs of electricity consumption. Peak and trough times are the key moments for regulating electricity consumption.

[0076] The construction method of the single-time volatility index includes:

[0077] The single-time fluctuation index h of the target user is calculated based on the power consumption of the target user c at time t and the average power consumption of the target user c at time t in the typical power consumption mode. c,t , the specific formula is as follows:

[0078]

[0079] x c,t,d represents the electricity consumption of user c at time t on day d, x c,t ′ represents the average power consumption of user c at time t in a typical power consumption pattern, n represents the total number of days, and n is a positive integer.

[0080] The power consumption of a user in the typical power consumption mode refers to the average power consumption after removing abnormal values ​​in the user's actual power consumption data.

[0081] The specific method of identifying outliers based on quantiles is: Let IQR = q 0.75 -q 0.25 , when the power consumption value is greater than q 0.75 +1.5IQR or less than q 0.25 When IQR is -1.5, the point is considered an outlier. 0.75 represents the 75% quantile, q 0.25 It represents the 25% quantile, and IQR is the difference between the value corresponding to the 75% quantile and the value corresponding to the 25% quantile.

[0082] The method for constructing the electricity consumption level indicator includes:

[0083] Arrange the electricity consumption of all users in the data set at time t in ascending order and divide them into multiple electricity consumption intervals in geometric proportion;

[0084] Based on the power consumption of the target user at time t, determine the target power consumption range;

[0085] The quantile corresponding to the upper limit value of the target power consumption interval is used as the power consumption level indicator of the target user; the quantile corresponding to the upper limit value of the target interval is obtained according to a preset ratio.

[0086] Exemplarily, the electricity consumption of all users in the data set at time t is arranged in ascending order and geometrically divided into 20 electricity consumption intervals. If the electricity consumption of the target user is in the interval [45%, 50%], the electricity consumption level index of the target user is 50%.

[0087] Replacing the user's original electricity consumption with quantiles can smooth out the random fluctuations of the user's electricity consumption data. The electricity consumption levels of different users vary greatly. If the original electricity consumption data is used, the range of the data is larger. When it is included in the flexibility calculation, the flexibility indicator will tend to one part. The quantile controls the magnitude between 0 and 1 to ensure the balance of different parts.

[0088] S103: Calculate the power consumption flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the power consumption level index.

[0089] The electricity flexibility f for target user c at time t c,t The specific calculation formula is as follows:

[0090]

[0091] where h c,t is the single-time fluctuation index of target user c at time t, m c,t is the power consumption level indicator of target user c at time t, d c,t is the daily fluctuation index of the target user c at time t; t is an integer in the interval [0,23].

[0092] The user's electricity flexibility is calculated based on the user's daily fluctuation index, single-hour fluctuation index and electricity consumption level index, and the user's electricity consumption pattern is analyzed in detail from multiple dimensions such as time and magnitude. The multi-dimensional electricity consumption characteristics in the user's electricity consumption pattern are integrated to evaluate the user's electricity adjustment space.

[0093] S104. The virtual power plant storage and use collaborative optimization model solves the objective function of the virtual power plant storage and use collaborative optimization model according to the target user's electricity consumption flexibility, energy storage equipment constraints, and user electricity consumption and power supply balance constraints to obtain a collaborative optimization result that minimizes the value of the objective function.

[0094] The collaborative optimization results include the discharge capacity of the power grid and the charge and discharge capacity of the energy storage device.

[0095] Virtual power plants can allocate and dispatch distributed energy sources, such as energy storage devices. In this application, batteries are selected as energy storage devices, and only the power grid supplies power to users before coordinated operation. The objective function design additionally considers the cost of energy storage equipment and the hourly demand-side electricity consumption characteristics.

[0096] The virtual power plant storage and use collaborative optimization model mainly involves two aspects of parameters, one is the parameters that characterize the energy storage device, and the other is the parameters that characterize the user's electricity consumption behavior. In this embodiment, the battery is used as the energy storage device. The symbols, parameters and variables involved in the virtual power plant storage and use collaborative optimization model are shown in Table 1.

[0097] Table 1

[0098]

[0099]

[0100] Set energy storage device constraints according to the following rules:

[0101] Maintain a balance between the charge and discharge of the energy storage device and the amount of electricity stored in the energy storage device;

[0102] The charging and discharging of energy storage devices are not performed simultaneously;

[0103] The minimum stored power of the energy storage device shall not be less than the discharge depth requirement.

[0104] Exemplarily, the specific energy storage device constraints are as follows:

[0105]

[0106] (1-DOD)×nc≤ bv(i,j) ≤nc (4-5)

[0107] Formula (4-1) represents the battery capacity calculation formula. The battery capacity at the jth hour is equal to the battery capacity at j-1 hours minus the discharge amount plus the charge amount. Formula (4-2) represents the battery operation mode. During operation, the battery can only be charged or discharged, and cannot be charged and discharged at the same time. Formulas (4-3) and (4-4) represent the upper and lower limits of the charge amount and discharge amount. Formula (4-5) represents the constraints on the battery storage capacity. In order to ensure that the battery can operate for a longer time, the lower limit is set based on the maximum depth of discharge.

[0108] Set the user power consumption and power supply balance constraints according to the following rules:

[0109] When the energy storage equipment and the power grid work together, the discharge amount matches the user's power consumption;

[0110] Based on the peak-to-valley difference before the coordinated operation of the energy storage equipment is achieved, the preset amplitude is adjusted downward.

[0111] For example, the specific user power consumption and power supply balance constraints are as follows:

[0112]

[0113] Formula (4-6) indicates that the coordinated output must meet the user's electricity demand, that is, the power consumption in the jth hour is equal to the difference between the power output of the grid and the battery discharge and battery charge in the jth hour. Formula (4-7) and formula (4-8) indicate the maximum or minimum power consumption on the i-th day. Formula (4-9) indicates the constraint range of the maximum peak-valley power consumption difference on the day, thereby achieving peak shaving and valley filling.

[0114] In order to widen the gap in user power flexibility at different times, the user power flexibility is normalized by linear function and then processed as the coefficient p of power grid output at different times in the objective function. j , exemplarily, the specific formula is as follows:

[0115]

[0116] Among them, f j represents the user's electricity flexibility in the jth hour, and f represents the user's electricity flexibility in 24 hours.

[0117] There are three advantages to using user electricity flexibility as a coefficient: first, user electricity flexibility is scored for 24 hours, and more detailed scoring will lead to more refined adjustments; second, user electricity flexibility takes into account the electricity consumption level, trend, and change range at different times from a design perspective, leaving room for future coordination of demand-side management policies and energy storage equipment; third, energy storage equipment coordination is a high-cost scheduling method. Through this method, energy storage equipment can be used in a coordinated manner in time periods when the demand-side regulation capacity is weak to form a supplement.

[0118] The objective function of the virtual power plant storage and utilization collaborative optimization model is:

[0119]

[0120] Among them, p j The electricity flexibility of the target user in the jth hour; i,j is the power grid output at the jth hour on the i-th day, p battery is the battery cost per 1 kWh, nc is the nominal capacity of the battery; d i,j and nc are obtained by minimizing the value of the objective function; N is the total number of days, and its value range is a positive integer; i is a positive integer, and j is an integer in the interval [0,23].

[0121] The constraints and objective functions of the virtual power plant storage and utilization collaborative optimization model are both in linear form, which contain variables that only take integers, so the problem is a mixed integer linear programming problem (MILP). This embodiment uses the classic branch and bound method to minimize the objective function for solution.

[0122] The embodiment of the present application provides a storage-use collaborative optimization method based on a virtual power plant. By acquiring the electricity consumption data of the target user, the index of the target user's electricity consumption flexibility is constructed. The index of electricity consumption flexibility includes a daily fluctuation index, a single-time fluctuation index and an electricity consumption level index. Based on the daily fluctuation index, the single-time fluctuation index and the electricity consumption level index, the target user's electricity consumption flexibility is calculated. This method can analyze the target user's electricity consumption behavior characteristics. The virtual power plant storage-use collaborative optimization model solves the objective function of the virtual power plant storage-use collaborative optimization model according to the target user's electricity consumption flexibility, energy storage device constraints and user electricity consumption and power supply balance constraints, and obtains a collaborative optimization result that minimizes the value of the preset objective function. When the user's electricity consumption is at its peak, the energy storage device discharges to supplement the power grid, and when the user's electricity consumption is at its low point, the energy storage device charges, so that the peak-to-valley difference is reduced, and the pressure on the power grid during peak electricity consumption is relieved. This method can achieve peak shaving and valley filling by only adjusting the time and magnitude of the power supply of the power supplier without affecting the user's electricity consumption.

[0123] Based on the storage-use collaborative optimization method based on virtual power plants introduced above, this application conducts scheduling experiments on real electricity consumption in four quarters under the user's electricity flexibility score, and compares the battery capacity changes and objective function changes when the peak-to-valley difference decreases by 70%, 80%, and 90% compared with the original peak-to-valley difference before and after the optimization of the energy storage equipment. In the experimental results described in the embodiments of this application, spring is March-May, summer is June-August, autumn is September-November, and winter is December-February. The experimental results are shown in Table 2.

[0124] As the peak-to-valley difference gradually decreases from the original 90% to the original 70%, the battery capacity and the objective function value also gradually increase. The increase in battery capacity required for the four seasons from the power difference to 90% of the original power difference and further to 80% is significantly less than the increase in battery capacity required for the power difference to decrease from 80% to 70%, and the objective function also shows the same pattern. From the comparison of the four seasons, it can be seen that the battery capacity required to adjust the peak-to-valley difference in summer and winter is significantly higher than that in other seasons, especially from 80% to the original 70%. One reason is that the peak-to-valley difference and power consumption level in summer and winter are larger (such as Figure 4 The magnitude change brought by 10% is greater than that of other seasons. Secondly, the peak-to-valley difference between summer and winter changes greatly over time (such as Figure 5 The peak-to-valley variation and overall electricity consumption variation lead to increased uncertainty.

[0125] Table 2

[0126]

[0127]

[0128] Based on the storage-use collaborative optimization method based on virtual power plants introduced above, this application compares the battery discharge time and discharge magnitude in different seasons. Battery discharge is mainly to supplement the power grid output during peak power consumption, thereby alleviating the problems of power grid instability and cost increase caused by power plant output during peak power consumption. Figure 6 and Figure 7 As shown, Figure 6 Battery discharge diagram for spring and summer, Figure 7 The battery discharge diagrams for autumn and winter. There are two time periods when users use more electricity. One is the small morning peak from 7 to 12 o'clock, and the other is the night peak from 20 to 22 o'clock. By comparing the power diagram of user electricity consumption and the battery discharge time diagram, it can be observed that the battery generates electricity at the corresponding peak positions to supplement the power grid. Autumn and spring are similar in terms of battery discharge time and discharge magnitude. The battery will discharge and supplement during the peak of user electricity consumption.

[0129] The overall discharge levels and active discharge times in summer and winter are similar. The power consumption level is higher than that in spring and autumn, and the active time is also longer than that in spring and autumn. The reason is that the power consumption level in summer and winter is higher than that in spring and autumn due to the use of refrigeration and heating equipment, and the peak-to-valley difference is also relatively large, resulting in the need for more battery discharge for coordinated operation.

[0130] There are certain similarities in the four seasons. The first is that during the period from 0 to 5 o'clock, except for the concentrated discharge of the battery between 1 and 2 o'clock in winter, there is almost no discharge record of the battery, which shows that the battery will reasonably adjust the charging and discharging time during the period of low electricity consumption of users. The second is that the peak time at night usually gradually increases from 17 o'clock, and the battery discharge also shows a gradual upward trend, which shows the reasonable distribution of the model and actual electricity consumption, and the concentrated discharge will not be concentrated in a certain time period, resulting in the weakening of the ability of subsequent supplementary power plants to operate in coordination.

[0131] Based on the storage-use collaborative optimization method based on virtual power plants introduced above, this application compares the battery charging time and discharge level in different seasons. Batteries are usually charged during the user's power consumption valley time, which reduces the peak-valley difference and alleviates the power pressure during peak hours. Figure 8 and Fig. 9 As shown, Figure 8 Battery discharge diagram for spring and summer, Fig. 9 The battery discharge diagrams for autumn and winter. In the spring virtual power plant storage and utilization collaborative optimization model, the most stable charging times are at 3:00, 13:00 and 15:00, which are the valley times when users are usually resting or working outside, indicating that the model has the potential to access renewable energy. The stable charging times in summer are at 4:00 and 15:00, which are slightly later than in spring.

[0132] The charging pattern in autumn is similar to that in spring, while the charging in winter is more unstable than in the other three seasons, with the charging times at 3:00, 5:00, 10:00, 13:00 and 18:00 respectively, which basically coincides with the low and middle times of the actual power consumption curve. Charging is completed before the peak power consumption time and the low power consumption is supplemented.

[0133] Based on the storage and utilization collaborative optimization method based on virtual power plants introduced above, this application compares the power grid output before and after collaborative output in different seasons. It will analyze the differences in power grid output time, magnitude, etc. under collaborative operation and non-cooperative operation when the peak-to-valley difference is reduced to 70% of the original power difference in different seasons, presenting the peak-to-valley filling simulation effect (such as Fig.10 (shown)

[0134] The overall power consumption level in spring is low, and the power consumption during the daytime peak hours changes little. By comparing the original grid discharge curve with the power plant discharge curve after coordinated operation, the battery takes on part of the output function at peak hours under coordinated operation, raising the valley power consumption and reducing the peak power consumption to reduce the peak-to-valley difference. Comparing the curve shapes (such as Fig.11 As shown in the figure, the discharge curve after coordinated power supply is closer to a sawtooth shape, the time period of stable discharge of the power grid has increased, and the duration has been extended to a certain extent. For example, the original power grid discharge curve shows an upward trend from 3:00 to 7:00 in spring, but it becomes horizontal after coordinated operation.

[0135] In summer, the original power grid discharge curve has a higher power consumption level, with an obvious peak power consumption at 21:00-22:00 (such as Fig.11 As shown). After solving the objective function and adjusting the power based on the obtained collaborative optimization results through the storage and utilization collaborative optimization method based on the virtual power plant proposed in this application, there are two obvious improvements: one is that the battery is charged when the user's power consumption decreases or is relatively low. For example, during the process of user power consumption decreasing from 0 to 4 o'clock, the power grid maintains stable discharge to replenish the battery power reserve; the second is to smooth the trend of power consumption during peak periods, so that it is flattened from a peak shape. For example, the original 20-23 o'clock is peak-shaped, while the 20-23 o'clock after collaborative operation is flatter.

[0136] The overall electricity consumption level in autumn is low. In the early stage, it is affected by the high temperature in summer, which leads to high electricity consumption. In the later stage, it gradually decreases and maintains. The smoothing of the night peak of electricity consumption in autumn is more obvious (such as Fig.12 As shown in the figure, especially the electricity consumption from 19:00 to 22:00, the electricity consumption level can be effectively reduced, the electricity consumption time can be kept stable for a long time, and the peak output can be transferred to the low electricity consumption periods of 12:00 and 5:00.

[0137] After coordinated operation, the peak power consumption level in winter can be effectively controlled, especially the peak power consumption between 20:00 and 22:00 at night (such as Fig.12 As shown in the figure, the grid discharge platform period is longer after coordinated operation, and the main discharge time is dispersed to the low periods of 5:00, 10:00 and 13:00, effectively shifting the night peak.

[0138] Based on the storage-use collaborative optimization method based on a virtual power plant introduced in the previous embodiment, the present application also provides a storage-use collaborative optimization device based on a virtual power plant. Fig.13 Figure 1 is a schematic diagram of the structure of the device. Fig.13 As shown, the storage and utilization collaborative optimization device based on the virtual power plant includes:

[0139] The power consumption data acquisition module 1301 is used to acquire the power consumption data of the target user;

[0140] The indicator construction module 1302 is used to construct an indicator of the target user's electricity consumption flexibility based on the acquired target user's electricity consumption data; the electricity consumption flexibility indicator includes a daily fluctuation index, a single-time fluctuation index and an electricity consumption level index;

[0141] In an optional implementation, the method of constructing the intraday volatility index includes:

[0142] Normalize the target user's 24-hour daily electricity consumption data;

[0143] Based on the normalized 24-hour daily electricity consumption data of the target users, the electricity consumption corresponding to the first quantile and the electricity consumption corresponding to the second quantile of the target users are calculated;

[0144] The difference between the power consumption corresponding to the second quantile of the target user and the power consumption corresponding to the first quantile is calculated as the diurnal fluctuation index of the target user; the second quantile is greater than the first quantile.

[0145] In an optional implementation, the method of constructing the single-time volatility index includes:

[0146] The single-time fluctuation index h of the target user is calculated based on the power consumption of the target user c at time t and the average power consumption of the target user c at time t in the typical power consumption mode. c,t , the specific formula is as follows:

[0147]

[0148] x c,t,d represents the electricity consumption of user c at time t on day d, x c,t ′ represents the average power consumption of user c at time t in the typical power consumption pattern, n represents the total number of days, and n is a positive integer;

[0149] The power consumption of a user in the typical power consumption mode refers to the average power consumption after removing abnormal values ​​in the user's actual power consumption data.

[0150] In an optional implementation, the method of constructing the power consumption level indicator includes:

[0151] Arrange the electricity consumption of all users in the data set at time t in ascending order and divide them into multiple electricity consumption intervals in geometric proportion;

[0152] Based on the power consumption of the target user at time t, determine the target power consumption range;

[0153] The quantile corresponding to the upper limit value of the target power consumption interval is used as the power consumption level indicator of the target user; the quantile corresponding to the upper limit value of the target interval is obtained according to a preset ratio.

[0154] The collaborative optimization result calculation module 1303 is used to calculate the electricity flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the electricity consumption level index; the virtual power plant storage and use collaborative optimization model solves the objective function of the virtual power plant storage and use collaborative optimization model according to the electricity flexibility of the target user, the energy storage device constraints and the user's electricity consumption and power supply balance constraints to obtain a collaborative optimization result that minimizes the value of the objective function; the collaborative optimization result includes the discharge capacity of the power grid and the charge and discharge capacity of the energy storage device.

[0155] In an optional implementation, calculating the power consumption flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the power consumption level index includes:

[0156] The electricity flexibility f for target user c at time t c,t The specific calculation formula is as follows:

[0157]

[0158] where h c,t is the single-time fluctuation index of target user c at time t, m c,t is the power consumption level indicator of target user c at time t, d c,t is the daily fluctuation index of the target user c at time t; t is an integer in the interval [0,23].

[0159] In an optional implementation, the objective function of the virtual power plant storage and utilization collaborative optimization model is:

[0160]

[0161] Among them, p j The electricity flexibility of the target user in the jth hour; i,j is the power grid output at the jth hour on the i-th day, p battery is the battery cost per 1 kWh, nc is the nominal capacity of the battery; d i,j and nc are obtained by minimizing the value of the objective function; N is the total number of days, and its value range is a positive integer; i is a positive integer, and j is an integer in the interval [0,23].

[0162] After calculating the target user's electricity consumption flexibility based on the daily fluctuation index, the single-hour fluctuation index and the electricity consumption level index, the method further includes: setting energy storage device constraints according to the following rules;

[0163] Maintain a balance between the charge and discharge of the energy storage device and the amount of electricity stored in the energy storage device;

[0164] The charging and discharging of energy storage devices are not performed simultaneously;

[0165] The minimum stored power of the energy storage device is set to be no less than the discharge depth requirement; the energy storage device includes a battery.

[0166] After calculating the power consumption flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the power consumption level index, the method further includes: setting the user's power consumption and power supply balance constraint conditions according to the following rules;

[0167] When the energy storage equipment and the power grid work together, the discharge amount matches the user's power consumption;

[0168] Based on the peak-to-valley difference before the coordinated operation of the energy storage equipment is achieved, the preset amplitude is adjusted downward.

[0169] In addition, an embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored. When the program is executed by a processor, a storage and utilization collaborative optimization method based on a virtual power plant as described in any of the method embodiments is implemented.

[0170] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely schematic, in which the unit described as a separate component may or may not be physically separated, and the component prompted as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0171] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A storage and utilization collaborative optimization method based on a virtual power plant, characterized in that: The method is applied to a virtual power plant, and the method comprises: Obtain the target user's electricity consumption data; Based on the acquired target user's electricity consumption data, construct indicators of target user's electricity consumption flexibility; the indicators of electricity consumption flexibility include daily fluctuation index, single-hour fluctuation index and electricity consumption level index; Calculating the power consumption flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the power consumption level index; The virtual power plant storage and use collaborative optimization model solves the objective function of the virtual power plant storage and use collaborative optimization model according to the target user's electricity consumption flexibility, energy storage equipment constraints and user electricity consumption and power supply balance constraints to obtain a collaborative optimization result that minimizes the value of the objective function; the collaborative optimization result includes the discharge capacity of the power grid and the charge and discharge capacity of the energy storage equipment.

2. The method according to claim 1, characterized in that The construction method of the intraday volatility index includes: Normalize the target user's 24-hour daily electricity consumption data; Based on the normalized 24-hour daily electricity consumption data of the target users, the electricity consumption corresponding to the first quantile and the electricity consumption corresponding to the second quantile of the target users are calculated; The difference between the power consumption corresponding to the second quantile of the target user and the power consumption corresponding to the first quantile is calculated as the diurnal fluctuation index of the target user; the second quantile is greater than the first quantile.

3. The method according to claim 1, characterized in that The construction method of the single-time volatility index includes: The single-time fluctuation index h of the target user is calculated based on the power consumption of the target user c at time t and the average power consumption of the target user c at time t in the typical power consumption mode. c,t , the specific formula is as follows: x c,t,d represents the electricity consumption of user c at time t on day d, x c,t ′ represents the average power consumption of user c at time t in the typical power consumption pattern, n represents the total number of days, and n is a positive integer; The power consumption of a user in the typical power consumption mode refers to the average power consumption after removing abnormal values ​​in the user's actual power consumption data.

4. The method according to claim 1, characterized in that The method for constructing the electricity consumption level indicator includes: Arrange the electricity consumption of all users in the data set at time t in ascending order and divide them into multiple electricity consumption intervals in geometric proportion; Based on the power consumption of the target user at time t, determine the target power consumption range; The quantile corresponding to the upper limit value of the target power consumption interval is used as the power consumption level indicator of the target user; the quantile corresponding to the upper limit value of the target interval is obtained according to a preset ratio.

5. The method according to claim 1, characterized in that Calculating the power consumption flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the power consumption level index includes: The electricity flexibility f for target user c at time t c,t The specific calculation formula is as follows: where h c,t is the single-time fluctuation index of target user c at time t, m c,t is the power consumption level indicator of target user c at time t, d c,t is the daily fluctuation index of the target user c at time t; t is an integer in the interval [0,23].

6. The method according to claim 1, characterized in that The objective function of the virtual power plant storage and utilization collaborative optimization model is: Among them, p j The electricity flexibility of the target user in the jth hour; i,j is the power grid output at the jth hour on the i-th day, p battery is the battery cost per 1 kWh, nc is the nominal capacity of the battery; d i,j and nc are obtained by minimizing the value of the objective function; N is the total number of days, and its value range is a positive integer; i is a positive integer, and j is an integer in the interval [0,23].

7. The method according to any one of claims 1 to 6, characterized in that After calculating the target user's electricity consumption flexibility based on the daily fluctuation index, the single-hour fluctuation index and the electricity consumption level index, the method further includes: setting energy storage device constraints according to the following rules; Maintain a balance between the charge and discharge of the energy storage device and the amount of electricity stored in the energy storage device; The charging and discharging of energy storage devices are not performed simultaneously; The minimum stored power of the energy storage device is set to be no less than the discharge depth requirement; the energy storage device includes a battery.

8. The method according to any one of claims 1 to 6, characterized in that After calculating the power consumption flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the power consumption level index, the method further includes: setting the user's power consumption and power supply balance constraint conditions according to the following rules; When the energy storage equipment and the power grid work together, the discharge amount matches the user's power consumption; Based on the peak-to-valley difference before the coordinated operation of the energy storage equipment is achieved, the preset amplitude is adjusted downward.

9. A storage and utilization collaborative optimization device based on a virtual power plant, characterized in that: include: An electricity consumption data acquisition module is used to acquire electricity consumption data of target users; An indicator construction module is used to construct indicators of target users' electricity consumption flexibility based on the acquired target users' electricity consumption data; the indicators of electricity consumption flexibility include daily fluctuation indicators, single-hour fluctuation indicators and electricity consumption level indicators; The collaborative optimization result calculation module is used to calculate the electricity flexibility of the target user based on the daily fluctuation index, the single-hour fluctuation index and the electricity consumption level index; the virtual power plant storage and use collaborative optimization model solves the objective function of the virtual power plant storage and use collaborative optimization model according to the electricity flexibility of the target user, the energy storage device constraints and the user's electricity consumption and power supply balance constraints to obtain a collaborative optimization result that minimizes the value of the objective function; the collaborative optimization result includes the discharge capacity of the power grid and the charge and discharge capacity of the energy storage device.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the program is executed by the processor, the storage-use collaborative optimization method based on a virtual power plant as described in any one of claims 1 to 6 is implemented.

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