Time-of-use electricity price division method considering capacity demand fee

Abstract

The invention discloses a time-of-use electricity price division method considering capacity demand fees, and relates to the technical field of vehicle network interaction, and the method comprises the steps: obtaining peak and valley time period data, constructing a first constraint condition, and building a first model with peak load shifting as a target; acquiring charging response offset data to construct a second constraint condition, and establishing a second model by taking the minimum charging cost as a target; based on the Stackelberg game theory, a first time-of-use electricity price double-layer model is obtained in combination with the first model and the second model; and introducing the capacity demand fee to optimize the first time-of-use electricity price double-layer model to obtain a second time-of-use electricity price double-layer model which is applied to determining the peak-valley time-of-use electricity price and the unit price of the capacity demand fee.

Description

Technical Field

[0001] The present invention relates to the field of vehicle-grid interaction technology, and more specifically, to a time-of-use electricity price division method taking into account capacity demand charges. Background Art

[0002] At present, most residential charging piles and daily life loads use the same peak and valley time division in electricity price setting. However, in order to ensure the convenience of residents' electricity use, the actual daily life load peak and valley time does not fully reflect the supply and demand status of the power system.

[0003] As the penetration rate of electric vehicles continues to rise, in the context of implementing the peak-valley time-of-use electricity price policy, the phenomenon of widespread residential charging piles responding to the valley electricity price at the same time may lead to over-response, thus triggering a new peak in electricity consumption, and then causing the distribution network infrastructure to overload. Therefore, it is urgent to design a new charging and discharging price mechanism based on the existing peak-valley electricity price mechanism.

[0004] For example, the invention patent announcement with announcement number: CN109376970A discloses a method and system for forming a dynamic real-time electricity price mechanism applicable to the energy Internet, including: receiving the basic electricity price issued; formulating the optimal operation strategy of the power equipment according to the dispatching command and the user's comprehensive electricity demand; judging whether the user's actual comprehensive electricity consumption deviates from the dispatching command, if so, executing the point-to-point transaction electricity consumption step and adopting the direct purchase electricity price; at the same time, if there is spare capacity, executing the spare capacity investment step; receiving the updated comprehensive electricity consumption dispatching command, judging whether the actual comprehensive electricity consumption deviates from the updated dispatching command, if deviated, controlling the comprehensive electricity consumption equipment to track the dispatching command, and adopting the penalty electricity price for the amount of actual comprehensive electricity consumption that deviates from the updated dispatching command, and adjusting the user's electricity consumption behavior through the dynamic real-time electricity price mechanism, ensuring that the power load operates according to the optimal energy interconnection optimization dispatching strategy, and stimulating the enthusiasm of power users to actively participate in the coordinated control of the power grid.

[0005] For example, the invention patent with announcement number CN116091102A announces a multi-agent planning method for a distribution system taking into account multiple uncertainties, including the following steps: S1: obtain distribution system data, initialize calculation parameters, set Lagrange multipliers, penalty parameters, original convergence thresholds and dual convergence thresholds; S2: calculate the optimal capacity configuration of distributed power sources under different investment strategies, and obtain the output power and installed capacity of distributed power sources; S3: coordinate the operation of distribution network operators and calculate the electricity price and profit risk of distribution nodes, and obtain the auxiliary dual variables corresponding to the output power; S4: determine whether the planning result converges: if not, update the Lagrange multipliers and the electricity price of distribution nodes, and repeat steps S2-S4; if converged, output the distributed power planning result. The present invention can meet the different nature and interest needs of potential investors, so that distribution system operators can flexibly manage the trade-off between expected profits and risks according to different risk perception preferences.

[0006] The above disclosed technical solutions have at least the following technical problems: The time-of-use electricity price for residential charging piles cannot adapt to the scenario of large-scale charging pile access, which can easily cause over-response, form a new peak in electricity consumption, and cause overload of the residential distribution network.

[0007] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0008] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for time-of-use electricity price division taking into account capacity demand fees. By constructing a two-layer model of time-of-use electricity prices, an independent time-of-use electricity price mechanism taking into account capacity demand fees is determined, thereby guiding residents' charging piles to charge and discharge in an orderly manner, achieving peak shaving and valley filling to avoid overload of the distribution network, and solving the problem of over-response during valley periods caused by large-scale access of residents' charging piles.

[0009] To achieve the above object, the present invention provides the following technical solutions: A time-of-use electricity price division method taking into account capacity demand charges comprises the following steps: obtaining peak-valley period data to construct a first constraint condition, and establishing a first model with the goal of shaving the peak and filling the valley; obtaining charging response offset data to construct a second constraint condition, and establishing a second model with the goal of minimizing charging costs; based on the Stackelberg game theory, combining the first model and the second model to obtain a first time-of-use electricity price double-layer model; introducing the capacity demand charge to optimize the first time-of-use electricity price double-layer model, and obtaining a second time-of-use electricity price double-layer model, which is applied to determine the peak-valley time-of-use electricity price and the capacity demand charge unit price.

[0010] In a preferred embodiment, the first model includes an upper-level power grid enterprise model, and the second model includes a lower-level resident charging load model; the peak-valley period data includes the start time of the valley period, the end time of the valley period, the peak-valley period continuity and the valley period range; the charging response offset data includes the charging period shift, the shift period uniqueness, the shift period directionality and the user response decision value.

[0011] In a preferred embodiment, the first time-of-use electricity price double-layer model is obtained by combining the first model and the second model based on the Stackelberg game theory, specifically: the first model passes the time-of-use electricity price after optimizing the charging period to the second model; the second model passes the optimized charging load to the first model.

[0012] In a preferred embodiment, the introduction of capacity demand fee optimizes the first time-of-use electricity price two-layer model to obtain a second time-of-use electricity price two-layer model, specifically: add the capacity demand fee to the second model, if it exceeds the preset capacity, add a penalty fee to the excess part, adjust the objective function of the second model to obtain a third objective function; obtain third data, combine with the second constraint condition, construct the third constraint condition, the third data includes the required charging time of electricity, the maximum charging power and the preset capacity; construct the third model according to the third objective function and the third constraint condition, the third model includes the charging load optimization model of the lower residents; combine the first model and the third model to obtain the second time-of-use electricity price two-layer model.

[0013] In a preferred embodiment, the first model and the third model are combined to obtain a second time-of-use electricity price two-layer model, specifically: the first model transfers the time-of-use electricity price after optimizing the charging period to the third model; the third model transfers the optimized charging load to the first model; based on the Stackelberg game theory, the second time-of-use electricity price two-layer model is iterated until the electricity price residual between adjacent iterations is lower than the preset first threshold and the load residual is lower than the preset second threshold, and the iteration is stopped.

[0014] The technical effects and advantages of the time-of-use electricity price division method taking into account the capacity demand fee of the present invention are as follows: The present invention constructs an upper-level power grid enterprise model and a lower-level resident charging load model, and constructs a time-of-use electricity price double-layer model based on the game relationship between power grid enterprises and resident users, thereby effectively optimizing the off-peak period, guiding residents to change their charging habits and thus alleviating the pressure of power grid expansion.

[0015] The present invention introduces a capacity demand fee, based on the restrictions of charging time, predetermined charging capacity and maximum charging power, to optimize the lower-level residential charging load model with the goal of minimizing charging costs, and further combines the upper-level power grid enterprise model and iteratively solves to obtain the optimal capacity demand fee and penalty electricity price, so that the peak load of the total load is as close to the peak value of the basic load as possible, effectively avoiding excessive load peaks caused by the growth of electric vehicles, and effectively reducing the risk of overload of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flow chart of a method for dividing time-of-use electricity prices taking into account capacity demand charges.

[0017] Figure 2 The figure is a schematic diagram of a time-of-use electricity price according to a method for dividing time-of-use electricity prices taking into account capacity demand charges in the present invention.

[0018] Figure 3 A diagram of the front load situation during the shift valley period on a typical summer day with low penetration rate of new energy vehicles provided by an embodiment of the present invention.

[0019] Figure 4 A diagram showing the load situation after the shift valley period on a typical summer day with low penetration rate of new energy vehicles provided by an embodiment of the present invention.

[0020] Figure 5 A diagram of the front load situation during the shift valley period on a typical summer day with a high penetration rate of new energy vehicles provided by an embodiment of the present invention.

[0021] Figure 6 This is a diagram of the load situation after the shift valley period on a typical summer day with a high penetration rate of new energy vehicles provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] 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.

[0023] Embodiment 1, Figure 1 The present invention provides a method for dividing time-of-use electricity prices taking into account capacity demand fees, comprising the following steps: S1, obtaining peak and valley period data to construct a first constraint condition, and establishing a first model with the goal of shaving the peak and filling the valley; S2, acquiring charging response offset data to construct a second constraint condition, and establishing a second model with the goal of minimizing charging costs; S3, based on Stackelberg game theory, the first model and the second model are combined to obtain the first time-of-use electricity price double-layer model; S4, introducing capacity demand fee to optimize the first time-of-use electricity price two-layer model, and obtaining the second time-of-use electricity price two-layer model, which is used to determine the peak and valley time-of-use electricity price and the capacity demand fee unit price.

[0024] The present invention designs a time-of-use electricity price mechanism that takes into account the capacity demand fee. Compared with a single peak-valley time-of-use electricity price, it can avoid too high a peak value caused by the growth of electric vehicles, and maximize the peak load of the total load close to the peak of the basic load, thereby alleviating the pressure of capacity expansion. In the time-of-use electricity price that takes into account the capacity demand fee, users need to reserve the charging capacity in advance and pay the capacity reservation fee; after each charging, users need to pay the electricity cost according to the peak-valley flat time-of-use electricity price, and whether they need to pay a penalty fee depends on whether the user's charging curve exceeds the predetermined capacity.

[0025] In scenarios with high penetration rates, a single peak-valley time-of-use electricity price is difficult to effectively guide peak shaving and valley filling. Introducing capacity demand fees on the basis of optimizing valley periods can avoid excessive peaks caused by the growth of electric vehicles, maximize the total load peak load close to the peak load of the base load, and alleviate the pressure of capacity expansion.

[0026] S1, obtaining peak and valley period data to construct a first constraint condition, and establishing a first model with the goal of shaving the peak and filling the valley.

[0027] In this embodiment, the first model includes an upper-layer power grid enterprise model, and the peak-valley period data includes a valley period start time, a valley period end time, a peak-valley period continuity, and a valley period range.

[0028] In this embodiment, the first constraint condition is specifically formulated as follows:

[0029]

[0030]

[0031]

[0032]

[0033] In the formula, , and is a binary variable, 1 means The time period is the valley period. 1 indicates the start time of the valley period. 1 indicates the end of the valley period. is the total number of valley periods, is the total number of time periods during the peak period; The specific formula of the objective function of the first model is:

[0034] in, For a typical day The value of the ideal flat load curve is:

[0035] In the formula, is the objective function of peak shaving and valley filling, For a typical day The weight of For a typical day exist The base load of the period, Charging stations for residents On a typical day of Optimized load for each time period, A total collection of charging piles for residents. is the total number of time periods in a typical day, A collection of typical days, It is the load data of residential charging piles that are not involved in optimization and is estimated from historical data.

[0036] It should be noted that in the first model, the residents' charging piles optimize the load during the typical day period, and its value is transferred from the second model.

[0037] Furthermore, the load data of residential charging piles that do not participate in the optimization is predicted based on historical data and implemented based on machine learning.

[0038] Furthermore, the base load during a typical day and time period refers to the minimum power demand load that exists continuously in the power system with small fluctuations.

[0039] S2, acquiring charging response deviation data to construct a second constraint condition, and establishing a second model with the goal of minimizing charging costs.

[0040] In this embodiment, the second model includes a lower-level resident charging load model, and the charging response offset data includes charging period shift, shift period uniqueness, shift period directionality, and user response decision value.

[0041] In this embodiment, the second constraint condition is specifically formulated as follows:

[0042]

[0043]

[0044]

[0045] In the formula, Charging stations for residents On a typical day of The load of the period, is a binary variable representing the load shift period, To ensure that the time index is Inner loop The expression of is a binary variable representing the load shift time period, A collection of residential charging stations in response to time-of-use electricity prices; The specific formula of the objective function of the second model is:

[0046] In the formula, For the first charging fee, To optimize the valley period, and They are the valley hour electricity price and peak hour electricity price in the time-of-use electricity price.

[0047] It should be noted that the user response decision value is divided into responding to the time-of-use electricity price and not responding to the time-of-use electricity price.

[0048] It should be noted that the uniqueness of the shifting period means that only one period can be selected to shift the charging period as a whole, and the directionality of the shifting period means that the period can only be shifted backwards.

[0049] S3, based on the Stackelberg game theory, the first model and the second model are combined to obtain the first time-of-use electricity price double-layer model.

[0050] In this embodiment, based on the Stackelberg game theory, the first model and the second model are combined to obtain the first time-of-use electricity price double-layer model, which is specifically: The first model transmits the time-of-use electricity price after optimizing the charging period to the second model; The second model transmits the optimized charging load to the first model.

[0051] S4, introducing capacity demand fee to optimize the first time-of-use electricity price two-layer model, and obtaining the second time-of-use electricity price two-layer model, which is used to determine the peak and valley time-of-use electricity price and the capacity demand fee unit price.

[0052] In this embodiment, the introduction of capacity demand fee optimizes the first time-of-use electricity price double-layer model to obtain the second time-of-use electricity price double-layer model, which is specifically: The second model is made to add a capacity demand fee. If the capacity exceeds the preset capacity, a penalty fee is added to the excess portion. The objective function of the second model is adjusted to obtain a third objective function. Acquire third data, and construct a third constraint condition in combination with the second constraint condition, wherein the third data includes required power charging time, maximum charging power, and preset capacity; According to the third objective function and the third constraint condition, a third model is constructed, and the third model includes a charging load optimization model for lower-level residents; Combining the first model with the third model, a second time-of-use electricity price double-layer model is obtained.

[0053] In this embodiment, the third constraint condition is specifically formulated as follows:

[0054]

[0055]

[0056]

[0057]

[0058]

[0059] In the formula, is the duration of a period, is the maximum charging power, is a binary variable indicating whether to use penalty electricity price. For users at residential charging piles typical day Reserved capacity.

[0060] In this embodiment, the third objective function is specifically formulated as follows:

[0061] In the formula, For the second charging fee, is the unit price of capacity demand charge, is the time-of-use electricity price for charging load, To punish the unit price of electricity.

[0062] It should be noted that the demand power charging time limit is reflected in the fact that the total charging capacity after optimization is consistent with the predicted total charging capacity value without participating in the optimization.

[0063] In this embodiment, the first model and the third model are combined to obtain a second time-of-use electricity price double-layer model, which is specifically: The first model transmits the time-of-use electricity price after optimizing the charging period to the third model; The third model transmits the optimized charging load to the first model; Based on the Stackelberg game theory, the second time-of-use electricity price double-layer model is iterated until the electricity price residual between adjacent iterations is lower than a preset first threshold and the load residual is lower than a preset second threshold, and the iteration is stopped.

[0064] In this embodiment, the initial values ​​of the time-of-use electricity price, the unit price of the capacity demand fee and the penalty electricity price variables are set, and the model is solved by an iterative method.

[0065] This embodiment constructs an upper-level grid enterprise model and a lower-level residential charging load model, and constructs a two-layer model of time-of-use electricity prices based on the game relationship between grid enterprises and residential users, thereby effectively optimizing off-peak hours, guiding residents to change their charging habits and thus alleviating the pressure on grid expansion.

[0066] This embodiment introduces a capacity demand fee, based on the restrictions of charging time, predetermined charging capacity and maximum charging power, to optimize the lower-level residential charging load model with the goal of minimizing charging costs, and further combines the upper-level power grid enterprise model and iteratively solves to obtain the optimal capacity demand fee and penalty electricity price, so that the peak load of the total load is as close to the peak value of the basic load as possible, effectively avoiding excessive load peaks caused by the growth of electric vehicles, and effectively reducing the risk of overload of the distribution network.

[0067] Embodiment 2, According to the current time-of-use electricity price for residential electric vehicles in a certain province, the peak time is 8:00-22:00, the price is 0.6153 yuan / kWh, and the valley time is the time period other than the peak time, the price is 0.3353 yuan / kWh. Same as current electricity price; unit price of capacity demand fee Set as 2 yuan / kW; penalty multiplier The response rates were set to 30% and 80% respectively.

[0068] The iterative solution model obtains the following results: Figure 3 , Figure 4 , Figure 5 and Figure 6As shown. According to the existing forecast results, the penetration rate of new energy vehicles in a certain province will reach 9.7% in 2025; and the penetration rate will further increase to 20.6% in 2030. In the low penetration scenario in 2025, regardless of whether the valley period is 22:00-8:00 the next day or 0:00-8:00, the load peak is almost unchanged under the time-of-use electricity price mechanism considering the capacity demand fee. Under the time-of-use electricity price mechanism considering the capacity demand fee, the corresponding load curve of the valley period is flatter. In the high penetration scenario in 2030, regardless of whether the valley period is 22:00-8:00 the next day or 0:00-8:00, the peak load under the time-of-use electricity price mechanism considering the capacity demand fee is lower than the time-of-use electricity price without the capacity demand fee. Under the time-of-use electricity price mechanism considering the capacity demand fee, the corresponding load curve of the valley period is flatter.

[0069] Table 1 shows the load characteristic indicators under different scenarios, including maximum load, minimum load, load rate, and peak-to-valley difference. It can be seen from the table that when the penetration rate of electric vehicles is relatively low, the maximum load of the time-of-use electricity price after optimizing the valley period and the time-of-use electricity price considering the capacity demand fee are tied for the smallest, which is 27.819 million kilowatts, while the peak-to-valley difference of the time-of-use electricity price after optimizing the valley period is the lowest; when the penetration rate of electric vehicles is relatively high, the maximum load of the time-of-use electricity price considering the capacity demand fee is the smallest among other electricity prices, which is 41.333 million kilowatts, and the peak-to-valley difference is also the smallest.

[0070] Table 1

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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 dividing time-of-use electricity prices taking into account capacity demand charges, characterized in that: The steps include: Obtain peak and valley period data to construct the first constraint condition, and establish the first model with the goal of reducing the peak and filling the valley; Acquire charging response offset data to construct a second constraint condition, and establish a second model with the goal of minimizing charging costs; Based on Stackelberg game theory, the first model and the second model are combined to obtain the first time-of-use electricity price double-layer model; The capacity demand fee is introduced to optimize the first time-of-use electricity price two-layer model, and the second time-of-use electricity price two-layer model is obtained, which is used to determine the peak and valley time-of-use electricity price and the capacity demand fee unit price.

2. The method for dividing time-of-use electricity prices taking into account capacity demand fees according to claim 1, characterized in that: The first model includes an upper-level power grid enterprise model, and the second model includes a lower-level resident charging load model; the peak-valley period data includes the start time of the valley period, the end time of the valley period, the peak-valley period continuity and the valley period range; the charging response offset data includes the charging period shift, the shift period uniqueness, the shift period directionality and the user response decision value.

3. The method for dividing time-of-use electricity prices taking into account capacity demand fees according to claim 2, characterized in that: The first constraint condition is specifically formulated as follows: In the formula, , and is a binary variable, 1 means The time period is the valley period. 1 indicates the start time of the valley period. 1 indicates the end of the valley period. is the total number of valley periods, is the total number of time periods during the peak period; The specific formula of the objective function of the first model is: in, For a typical day The value of the ideal flat load curve is: In the formula, is the objective function of peak shaving and valley filling, For a typical day The weight of For a typical day exist The base load of the period, Charging stations for residents On a typical day of Optimized load for each time period, A total collection of charging piles for residents. is the total number of time periods in a typical day, A collection of typical days, It is the load data of residential charging piles that are not involved in optimization and is estimated from historical data.

4. The method for dividing time-of-use electricity prices taking into account capacity demand fees according to claim 3, characterized in that: The specific formula of the second constraint condition is: In the formula, Charging stations for residents On a typical day of The load of the period, is a binary variable representing the load shift period, To ensure that the time index is Inner loop The expression of is a binary variable representing the load shift time period, A collection of residential charging stations in response to time-of-use electricity prices; The specific formula of the objective function of the second model is: In the formula, For the first charging fee, To optimize the valley period, and They are the valley hour electricity price and peak hour electricity price in the time-of-use electricity price.

5. The method for dividing time-of-use electricity prices taking into account capacity demand fees according to claim 4, characterized in that: Based on the Stackelberg game theory, the first model and the second model are combined to obtain the first time-of-use electricity price double-layer model, which is specifically: The first model transmits the time-of-use electricity price after optimizing the charging period to the second model; The second model transmits the optimized charging load to the first model.

6. The method for dividing time-of-use electricity prices taking into account capacity demand charges according to claim 5, characterized in that: The introduction of capacity demand fee optimizes the first time-of-use electricity price double-layer model to obtain the second time-of-use electricity price double-layer model, which is specifically: The second model is made to add a capacity demand fee. If the capacity exceeds the preset capacity, a penalty fee is added to the excess portion. The objective function of the second model is adjusted to obtain a third objective function. Acquire third data, and construct a third constraint condition in combination with the second constraint condition, wherein the third data includes required power charging time, maximum charging power, and preset capacity; According to the third objective function and the third constraint condition, a third model is constructed, and the third model includes a charging load optimization model for lower-level residents; Combining the first model and the third model, a second time-of-use electricity price double-layer model is obtained.

7. The method for dividing time-of-use electricity prices taking into account capacity demand fees according to claim 6, characterized in that: The second time-of-use electricity price double-layer model is obtained by combining the first model and the third model, which is specifically: The first model transmits the time-of-use electricity price after optimizing the charging period to the third model; The third model transmits the optimized charging load to the first model; Based on the Stackelberg game theory, the second time-of-use electricity price double-layer model is iterated until the electricity price residual between adjacent iterations is lower than a preset first threshold and the load residual is lower than a preset second threshold, and the iteration is stopped.

8. The method for dividing time-of-use electricity prices taking into account capacity demand fees according to claim 7, characterized in that: The specific formula of the third constraint condition is: In the formula, is the duration of a period, is the maximum charging power, is a binary variable indicating whether to use penalty electricity price. For users at residential charging piles typical day Reserved capacity.

9. The method for dividing time-of-use electricity prices taking into account capacity demand fees according to claim 8, characterized in that: The third objective function has the following specific formula: In the formula, For the second charging fee, is the unit price of capacity demand charge, is the time-of-use electricity price for charging load, To punish the unit price of electricity.

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