Peak-valley period demarcation method based on guiding charging load translation

Abstract

The invention discloses a peak-valley period demarcation method based on guiding charging load translation. The peak-valley period demarcation method based on guidance of charging load translation comprises the following steps: acquiring residential area data including private charging pile historical data and basic load data, and analyzing data features of the residential area data; establishing an upper-layer power grid total load model by taking peak load shifting as an objective function; establishing a lower-layer resident charging load model by taking the minimum charging cost as an objective function; according to the method, the charging load translation is guided, so that the defects in the aspect of charging period management in the prior art are overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of peak-valley period delineation, and in particular to a peak-valley period delineation method based on guiding charging load translation. Background Art

[0002] As fuel vehicles bring pressure on the environment and energy, countries are paying more and more attention to the research, development, application and promotion of new energy vehicles. my country's new energy vehicle sales have hit new highs and the number of vehicles in use has continued to increase. Against the backdrop of the gradual promotion of new energy vehicles, especially electric vehicles, the penetration rate of electric vehicles will increase significantly in the future, and the guiding role of time-of-use electricity prices and other policies on user charging behavior cannot be ignored.

[0003] Existing technologies often have problems such as concentrated charging demand during peak hours, charging fees failing to effectively motivate users to charge during off-peak hours, and lack of flexible load regulation mechanisms. Specifically, these problems have led to increased burden on the power grid, large fluctuations in charging fees, and inability to fully utilize renewable energy. The two-layer model of total grid load and residential charging load can more effectively achieve peak load shaving and valley filling by optimizing the charging period, thereby improving the operating efficiency and economy of the power grid. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a peak-valley period delineation method based on guiding charging load shifting, which solves the shortcomings of the prior art in charging period management by guiding charging load shifting.

[0005] To achieve the above object, the present invention provides the following technical solutions: A peak-valley period delineation method based on guiding charging load shifting comprises the following steps: acquiring residential area data, the residential area data including historical data of private charging piles and basic load data, and analyzing data characteristics of the residential area data; establishing an upper-layer power grid total load model with peak shaving and valley filling as the objective function; establishing a lower-layer resident charging load model with minimizing charging costs as the objective function; solving a double-layer optimization model to obtain optimal peak-valley period and charging load shifting results.

[0006] In a preferred embodiment, the specific steps of analyzing the data characteristics of residential areas are as follows: select a day that is closest to the seasonal average daily load as a typical day of the season; establish a plane rectangular coordinate system, with the horizontal axis variable being 24 time periods in a day and the vertical axis variable being the load; record the basic load amount and the original time-of-use electricity price for each time period in the typical day, and draw a line graph.

[0007] In a preferred embodiment, the two-layer optimization model is solved to obtain the optimal peak-valley time periods and charging load shifting results, specifically: clarifying the objective function and constraints of the two-layer optimization model; for the established two-layer optimization model, an iterative method is used to solve it to obtain the division of peak-valley time periods and the charging load shifting results of private charging piles.

[0008] In a preferred embodiment, the optimal peak-valley period is to add the time-of-use electricity price divided by this method to the line chart of the original load diagram of a typical day.

[0009] In a preferred embodiment, the charging load shifting results include load curve changes, charging time distribution, and economic benefit analysis, specifically: the load curve changes include the optimized load curve and the peak-to-valley load ratio after the charging load shift; the charging time distribution statistics the proportion of users charging during peak and valley periods, and evaluates the user's response to changes in electricity prices; the economic benefit analysis is based on the changes in the operating costs of the power grid after the load shift, and analyzes the overall economic benefits.

[0010] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. A peak-valley period demarcation method based on guiding charging load shift is adopted to guide charging load shift, thereby solving the shortcomings of the existing technology in charging period management.

[0011] 2. By optimizing the charging period, the load during peak hours can be effectively reduced, thereby balancing the grid load and improving the utilization efficiency of power resources.

[0012] 3. By rationally allocating electricity consumption periods, carbon emissions during peak hours can be reduced, thereby contributing to sustainable development and environmental protection.

[0013] 4. The stability of the power grid dispatch is ensured by setting continuity constraints and charging period constraints, thus avoiding power grid failures caused by sudden load changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of a method for defining peak and valley periods based on guiding charging load shifting provided in an embodiment of the present application; DETAILED DESCRIPTION The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0016] Embodiment 1, Figure 1 The present invention provides a peak-valley period delineation method based on guiding charging load translation, comprising the following steps: Acquire residential area data, the residential area data including historical data of private charging piles and basic load data, and analyze data characteristics of the residential area data; The total load model of the upper power grid is established with peak shaving and valley filling as the objective function; The charging load model of lower-class residents is established with minimizing charging costs as the objective function; Solve the two-layer optimization model and obtain the optimal peak-valley period and charging load shift results.

[0017] The analysis of residential area data characteristics includes estimating future load data conditions based on future load growth trends, and the specific steps are as follows: The day closest to the seasonal average daily load is selected as the typical day of the season; A rectangular coordinate system is established, with the horizontal axis variable representing the 24 time periods of a day and the vertical axis variable representing the load; Record the basic load and original time-of-use electricity price for each period of a typical day, and draw a line graph of the original load diagram of a typical day.

[0018] It should be noted that, when the number of days closest to the seasonal average daily load is not one, the average value of all days closest to the seasonal average daily load is taken.

[0019] ‌‌Time-of-use electricity price refers to dividing the 24 hours of a day into several time periods according to the average marginal cost of system operation, and charging different electricity prices for each time period. This electricity price system aims to optimize electricity consumption and encourage electricity users to use electricity reasonably at different times to achieve the effect of peak shaving and valley filling, and improve the utilization efficiency of electricity resources.‌

[0020] Time-of-use electricity prices have the following effects: Optimize electricity usage: By differentiating electricity prices at different times, users are encouraged to use electricity during periods of low electricity demand, thereby balancing the load on the power grid and reducing the pressure on power supply during peak hours; Improve the efficiency of power resource utilization: Time-of-use electricity prices help to make more efficient use of power resources, reduce power supply costs, and ensure the stability and reliability of power supply; Promote energy conservation and emission reduction: Through economic incentives, guide users to rationally arrange electricity usage time, reduce energy waste, and contribute to environmental protection and sustainable development.

[0021] The time-of-use electricity price divides the time periods of the day into peak periods with high electricity demand and tight power supply, normal periods with moderate electricity demand and moderate electricity prices, and valley periods with low electricity demand and sufficient power supply.

[0022] For the total load of the upper power grid, peak shaving and valley filling are guided by defining peak and valley periods for time-of-use electricity prices.

[0023] The upper-layer power grid total load model is established with peak shaving and valley filling as the objective function. The upper-layer power grid total load model is specifically:

[0024] Where: is the total load model of the upper power grid, is the value of the ideal flattest load curve in a typical day, d is a typical day, N is the total number of private pile residential users, T is the total number of time periods in a typical day, is a binary optimization variable, represents the weight of a typical day, represents the base load in the tth period of a typical day, represents the unoptimized private pile residential user load data estimated from historical data, It represents the optimized load of the nth private pile residential user in the tth period of a typical day.

[0025] It should be noted that a binary optimization variable of 1 indicates that period t is a valley period, otherwise it indicates that period t is a peak period. The value of the ideal flattest load curve in a typical day represents the unoptimized private pile load data estimated from historical data. The value of the optimized load of the nth private pile in the tth period of a typical day is transmitted from the lower-level residential user model.

[0026] The peak and valley periods for time-of-use electricity prices must be continuous, the valley period must be within the period when private charging piles are commonly charged, and the valley period must have a certain duration.

[0027] The upper grid total load model with peak shaving and valley filling as the objective function includes continuity constraints, private pile charging period constraints, and valley period duration constraints, as follows: The continuity constraint is specifically:

[0028] Where: is the time when the valley period begins. It indicates the time when the valley period ends, and T indicates the total number of periods in a typical day.

[0029] It should be noted that the continuity constraint means that a valley period can only have one start time and one end time. The continuity constraint can ensure that the peak period and valley period are continuous without interruption.

[0030] The private charging pile charging time constraints are specifically:

[0031] Where: is the total number of peak hours.

[0032] It should be noted that the private charging pile charging period constraint indicates that the valley period is within the period of private charging (such as the period from after get off work to before work the next day). The private charging pile charging period constraint can ensure that the valley period is within the period when private charging piles are generally charged.

[0033] The valley period duration constraints are specifically:

[0034] Where: is the total number of valley periods, and T represents the total number of periods in a typical day.

[0035] It should be noted that the valley period duration constraint can ensure that the valley period has a certain duration and meets the minimum duration requirement.

[0036] By setting continuity constraints, private charging period constraints, and valley period duration constraints, the effectiveness and feasibility of the model can be ensured, which has the following importance: Ensure logical consistency: Continuity constraints ensure that the start and end of the valley period are clear and unique, avoiding breaks between time periods. This is crucial for power dispatching, because the power grid needs to operate stably, and any interruption may lead to unstable power supply; Adapt to user behavior: By setting the charging time period reasonably, the off-peak period is ensured to match the user's actual charging behavior, reflecting the user's demand for electricity during a specific period of time. This can improve the efficiency of charging piles and optimize the overall electricity consumption structure; Balanced load curve: Reasonable valley time can effectively reduce peak load and improve the stability and operation efficiency of the power grid. This not only helps to reduce operating costs, but also has a positive impact on reducing carbon emissions and promoting the development of green electricity.

[0037] The charging load model for lower-level residents is established with minimizing charging costs as the objective function. The principle of establishing the model is to minimize the charging costs by shifting the charging period. The objective function is as follows:

[0038] Where: represents the optimized load of the nth private pile in the tth period of a typical day, represents the time period of load translation, which is a binary variable. represents the optimized valley period, represents the valley electricity price in the original time-of-use electricity price, Represents the peak electricity price in the original time-of-use electricity price.

[0039] It should be noted that the optimized valley period value is transmitted from the upper power grid total load model.

[0040] The charging load model for lower-class residents with minimizing charging costs as the objective function includes non-response constraints and post-translation constraints, as follows: The non-response constraints are specifically:

[0041] Where: represents the set of private charging pile residential users who respond to time-of-use electricity prices, It should be noted that the situation where private charging piles do not respond under time-of-use electricity prices is taken into consideration to ensure that charging shifting is only carried out under effective circumstances.

[0042] The post-translation constraint is specifically:

[0043] Where: represents the set of private charging pile residential users who respond to time-of-use electricity prices, Represents the time period of load translation.

[0044] It should be noted that the post-translation constraint means that only one time period can be selected to translate the entire charging, and it can only be translated backwards, ensuring that the translation of the private charging period can only be performed backwards to ensure that the electric vehicle has been connected to the private charging pile.

[0045] By setting non-response constraints and post-translation constraints, it is not only helpful to achieve the objective function of the model, that is, to minimize the charging cost, but also to ensure the operability and practicality of the model, ensuring that users charge under reasonable and controllable conditions, thereby promoting the popularization of electric vehicles and the efficient use of power resources. Through reasonable constraint settings, the safety and economy of the power system can be effectively improved, which is of the following importance: Ensure economy: Non-responsive constraints ensure that charging shifts only occur when users are willing to respond to time-of-use electricity prices. This avoids charging during peak hours, thereby minimizing electricity costs; Improve grid stability: Ensuring that charging is only carried out when it makes sense can help balance the grid load, reduce pressure during peak hours, and improve the overall efficiency of the grid; Charging time safety: The backward translation constraint ensures that charging only moves backward, which means that the user's electric vehicle is connected to the private charging pile when charging, avoiding the situation where the charging is incomplete or the electric vehicle is not connected due to the passage of time; Control charging time: This constraint stipulates that users can only choose one time period for charging shift, preventing users from frequently switching between multiple time periods. This can simplify the decision-making process and avoid operational errors caused by overly complex decisions; Optimize load management: By limiting the choice of charging time periods, the model can manage power load more effectively, ensure the rational use of power resources, reduce the impact on the power grid, and improve overall charging efficiency.

[0046] The two-layer optimization model is solved to obtain the optimal peak-valley period and charging load shift results, specifically: Clarify the objective function and constraints of the two-level optimization model; The established two-layer optimization model is solved by iterative method to obtain the division of peak and valley periods and the shift results of private charging pile charging load.

[0047] The optimal peak-valley period is to add the time-of-use electricity price divided by this method to the line chart of the original load chart of a typical day.

[0048] The charging load shift results include load curve changes, charging time distribution, and economic benefit analysis.

[0049] The load curve change includes the optimized load curve and the peak-to-valley load ratio after the charging load is shifted; The charging time distribution statistics the proportion of users charging during peak hours and off-peak hours, and evaluates the response of users to changes in electricity prices; The economic benefit analysis is based on the change in operating cost of the power grid after load shifting, and analyzes the overall economic benefit.

[0050] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0051] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0052] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0053] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0054] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which 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.

[0055] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for defining peak and valley periods based on guiding charging load translation, characterized in that: The following steps are involved: Acquire residential area data, the residential area data including historical data of private charging piles and basic load data, and analyze data characteristics of the residential area data; The total load model of the upper power grid is established with peak shaving and valley filling as the objective function; The charging load model of lower-class residents is established with minimizing charging costs as the objective function; Solve the two-layer optimization model and obtain the optimal peak-valley period and charging load shift results.

2. A method for defining peak and valley periods based on guiding charging load shifting as claimed in claim 1, characterized in that: The specific steps of analyzing the characteristics of residential area data are as follows: The day closest to the seasonal average daily load is selected as the typical day of the season; A rectangular coordinate system is established, with the horizontal axis variable representing the 24 time periods of a day and the vertical axis variable representing the load; Record the base load and original time-of-use electricity price in each period of a typical day and draw a line graph.

3. A peak-valley period delineation method based on guiding charging load translation as claimed in claim 2, characterized in that: The upper power grid total load model is established with peak shaving and valley filling as the objective function, specifically: Where: is the total load model of the upper power grid, is the value of the ideal flattest load curve in a typical day, d is a typical day, N is the total number of private pile residential users, T is the total number of time periods in a typical day, is a binary optimization variable, represents the weight of a typical day, represents the base load in the tth period of a typical day, represents the unoptimized private pile residential user load data estimated from historical data, It represents the optimized load of the nth private pile residential user in the tth period of a typical day.

4. A peak-valley period delineation method based on guiding charging load translation as claimed in claim 3, characterized in that: The charging load model for lower-class residents is established with minimizing charging costs as the objective function, specifically: Where: represents the optimized load of the nth private pile in the tth period of a typical day, represents the time period of load translation, which is a binary variable. represents the optimized valley period, represents the valley electricity price in the original time-of-use electricity price, Represents the peak electricity price in the original time-of-use electricity price.

5. A peak-valley period delineation method based on guiding charging load translation as claimed in claim 4, characterized in that: The upper grid total load model includes continuity constraints, private pile charging period constraints, and valley period duration constraints, specifically: The continuity constraint is formulated as follows: The private pile charging period constraint is as follows: The valley period duration constraint is as follows: Where: is the time when the valley period begins. represents the time when the valley period ends, T represents the total number of periods in a typical day, is the total number of peak hours, is the total number of valley periods, and T represents the total number of periods in a typical day.

6. A method for defining peak and valley periods based on guiding charging load shifting as claimed in claim 5, characterized in that: The lower-level residential charging load model includes non-response constraints and post-translation constraints, specifically: The non-response constraint is formulated as follows: The post-translation constraint is formulated as follows: Where: represents the set of private charging pile residential users who respond to time-of-use electricity prices, Represents the time period of load translation.

7. A peak-valley period delineation method based on guiding charging load translation as claimed in claim 6, characterized in that: The two-layer optimization model is solved to obtain the optimal peak-valley period and charging load shift results, specifically: Establish the objective function and constraints of the two-level optimization model; The established two-layer optimization model is solved by iterative method to obtain the division of peak and valley periods and the shift results of private charging pile charging load.

8. A peak-valley period delineation method based on guiding charging load translation as claimed in claim 7, characterized in that: The optimal peak-valley period is to add the time-of-use electricity price divided by this method to the line chart of the original load chart of a typical day.

9. A peak-valley period delineation method based on guiding charging load shifting as claimed in claim 8, characterized in that: The charging load shift results include load curve changes, charging time distribution, and economic benefit analysis, specifically: The load curve change includes the optimized load curve and the peak-to-valley load ratio after the charging load is shifted; The charging time distribution statistics the proportion of users charging during peak hours and off-peak hours, and evaluates the response of users to changes in electricity prices; The economic benefit analysis is based on the change in operating cost of the power grid after load shifting, and analyzes the overall economic benefit.

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