Vehicle-storage capacity matching method considering two-system electricity price

Abstract

The invention discloses a vehicle-storage capacity matching method considering two-part electricity price, which comprises the following steps: collecting park new energy power generation capacity data, energy storage capacity data, historical charging and feeding behaviors and feeding willingness of an electric vehicle user, and calculating the minimum feeding capacity of the user according to the feeding willingness of the user; establishing a two-part system charging price mechanism, and determining two-part system electricity price pricing parameters by taking the condition that the two-part system electricity price electric charge is equal to the time-of-use electricity price electric charge as a constraint; according to the method, the optimal energy storage ratio of the park is determined by using a particle swarm algorithm with the total income maximization as the optimization target, the relationship between the minimum feed capacity of a user and the optimal energy storage ratio of the park is established through sensitivity analysis, and finally the vehicle-storage capacity matching method considering the two-part electricity price is given.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method for matching vehicle-storage capacity considering two-part tariffs. Background Technology

[0002] According to the "Notice on Establishing a Coal-fired Power Capacity Pricing Mechanism" issued by the National Development and Reform Commission and the National Energy Administration, the current single-part coal-fired power price will be adjusted to a two-part pricing mechanism. The capacity pricing level will be reasonably determined and gradually adjusted based on the actual situation, such as the progress of the transition. The introduction of the two-part pricing mechanism addresses the issue of insufficient power supply during peak electricity consumption periods.

[0003] Electric vehicles, as excellent mobile energy storage units, can effectively reduce user-side energy storage construction costs and further improve the stability of power distribution networks by rationally utilizing them in conjunction with energy storage batteries for grid feeding. With the increasing penetration rate of new energy sources in distribution networks, power supply shortages are gradually emerging, making the installation of sufficient energy storage a key measure to ensure the stability of power supply for distribution network users.

[0004] Therefore, it is necessary to provide a new method for matching vehicle-storage capacity based on two-part tariffs to solve the above-mentioned technical problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for matching vehicle-storage capacity based on two-part tariffs.

[0006] This invention provides a method for matching vehicle-to-storage capacity considering two-part tariffs, comprising the following steps: S1. Collect park configuration data: new energy power generation capacity data, energy storage capacity data; collect electric vehicle user charging and discharging data: historical charging and discharging behavior, discharging intentions. S2. Calculate the minimum power supply capacity for users based on their power supply intentions; S3. Based on the two-part charging pricing mechanism, taking into account the incentives for the minimum power supply capacity of electric vehicles, the incentives for the power supply of electric vehicles, and the penalties for default, the pricing incentives and penalties for the two-part electricity price are determined with the constraint that the two-part electricity price is equal to the time-of-use electricity price. S4. Based on the overall revenue maximization mentioned above, consider the electricity purchase expenditure, energy storage construction expenditure, minimum power supply capacity incentive expenditure for electric vehicle users, power supply incentive expenditure for electric vehicles, electricity sales revenue, and penalty revenue. S5. Taking the premise that electric vehicle users can choose the optimal charging method independently, the constraint that the power supply of the distribution network is sufficient and stable, and the optimization objective of maximizing overall benefits, the particle swarm algorithm is used to determine the optimal energy storage ratio of the park. S6. By establishing the relationship between the minimum power supply capacity of users and the optimal energy storage ratio in the park through sensitivity analysis, a universally applicable vehicle-storage capacity ratio method for two-part tariffs is finally given.

[0007] Preferably, the data is configured based on the following: new energy power generation capacity data, energy storage capacity data; electric vehicle user charging and discharging data: historical charging and discharging behavior, and discharging intention. The minimum discharging capacity for each user is calculated based on their discharging intention, specifically including: The minimum power supply capacity guaranteed by a user to the charging aggregator is the minimum power supply capacity that the user guarantees to the grid. Upon receiving a power supply notification from the charging aggregator, the user must immediately respond and supply the corresponding capacity to the grid. If the user is unable to supply the corresponding capacity, they must pay the corresponding penalty to the charging aggregator. The specific formula for calculating the minimum power supply capacity is as follows:

[0008] Where Vi is the minimum power supply capacity of the i-th user, and WSi is the power supply capacity intention of the i-th user.

[0009] Preferably, based on the two-part charging pricing mechanism, the incentives for minimum electric vehicle charging capacity, the incentives for electric vehicle charging capacity, and the penalties for default are determined by using the constraint that the two-part electricity price is equal to the time-of-use electricity price, specifically including: The two-part tariff mainly consists of: electricity price, minimum charging capacity incentive for electric vehicles, charging capacity incentive for electric vehicles, and penalty for default. The specific calculation formula is as follows:

[0010] Where Fi is the two-part electricity price for the i-th user, Ui is the electricity price for the i-th user, VMi is the minimum feeder capacity incentive for the i-th user, QMi is the feeder capacity incentive for the i-th user, Pi is the penalty for default for the i-th user; pi,t is the electricity load of the i-th user at time t, α and β are the weighting coefficients of VMi and QMi respectively, prih is the local peak-hour electricity price, prl is the local off-peak electricity price, j is the charging / feeder number, and m is the total number of feeding operations. The capacity for power supply to the i-th user during the j-th time; pris is the price per unit capacity of energy storage; l is the maximum number of charge-discharge cycles for energy storage. The time-of-use electricity price implemented at local time t.

[0011] Preferably, based on the overall revenue, considering electricity purchase expenditure, energy storage construction expenditure, minimum feed capacity incentive expenditure for electric vehicle users, feed power incentive expenditure for electric vehicles, electricity sales revenue, and penalty revenue, the overall revenue of the vehicle-storage collaborative feeder network is calculated. The specific calculation formula is as follows:

[0012] Where M represents total revenue, M1 represents revenue from charging and power supply, M2 represents revenue from energy storage construction, and Vs represents planned energy storage capacity. This is the original energy storage capacity.

[0013] Preferably, with the goal of maximizing overall revenue, the particle swarm optimization algorithm is used to determine the optimal energy storage ratio for the park. The specific formula is as follows:

[0014] Where f is the objective function of the particle swarm optimization algorithm, and x is the optimization variable of the particle swarm optimization algorithm.

[0015] Compared with related technologies, the vehicle-storage capacity allocation method considering two-part tariffs provided by this invention has the following advantages: This invention provides a vehicle-storage capacity allocation method considering two-part tariffs. It collects data on renewable energy generation capacity, energy storage capacity, historical charging and discharging behavior of electric vehicle users, and their willingness to discharge electricity. Based on user willingness to discharge, it calculates the minimum discharge capacity for each user. A two-part charging price mechanism is established, using the equality of the two-part tariff and the time-of-use tariff as a constraint to determine the pricing parameters. With maximizing overall revenue as the optimization objective, a particle swarm optimization algorithm is used to determine the optimal energy storage allocation for the park. Sensitivity analysis is used to establish the relationship between the minimum discharge capacity for users and the optimal energy storage allocation for the park. Finally, a vehicle-storage capacity allocation method considering two-part tariffs is presented. This is the first time that the parameter of the minimum discharge capacity for users has been used to represent the real-time operational stability of the distribution network. The two-part tariff is transferred to the scenario of vehicle-storage coordinated grid feeding. A formula for the relationship between the minimum discharge capacity for users and the optimal energy storage allocation is established. Attached Figure Description

[0016] Figure 1 Flowchart of the vehicle-storage capacity allocation method considering two-part tariffs provided by the present invention; Figure 2 This is a schematic diagram illustrating the regional power grid stability for comparing time-of-use electricity pricing and two-part electricity pricing for feeder billing, as provided by the present invention. Detailed Implementation

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] In the specific implementation process, such as Figure 1 As shown, a method for matching vehicle-to-storage capacity considering two-part tariffs includes the following steps: S1. Collect park configuration data: new energy power generation capacity data, energy storage capacity data; collect electric vehicle user charging and discharging data: historical charging and discharging behavior, discharging intentions. S2. Calculate the minimum power supply capacity for users based on their power supply intentions; S3. Based on the two-part charging pricing mechanism, taking into account the incentives for the minimum power supply capacity of electric vehicles, the incentives for the power supply of electric vehicles, and the penalties for default, the pricing incentives and penalties for the two-part electricity price are determined with the constraint that the two-part electricity price is equal to the time-of-use electricity price. S4. Based on the overall revenue maximization mentioned above, consider the electricity purchase expenditure, energy storage construction expenditure, minimum power supply capacity incentive expenditure for electric vehicle users, power supply incentive expenditure for electric vehicles, electricity sales revenue, and penalty revenue. S5. Taking the premise that electric vehicle users can choose the optimal charging method independently, the constraint that the power supply of the distribution network is sufficient and stable, and the optimization objective of maximizing overall benefits, the particle swarm algorithm is used to determine the optimal energy storage ratio of the park. S6. By establishing the relationship between the minimum power supply capacity of users and the optimal energy storage ratio in the park through sensitivity analysis, a universally applicable vehicle-storage capacity ratio method for two-part tariffs is finally given.

[0019] Based on the park's configuration data: new energy power generation capacity data, energy storage capacity data; electric vehicle user charging and discharging data: historical charging and discharging behavior, and discharging intentions. And based on users' discharging intentions, the minimum discharging capacity for each user is calculated, specifically including: The minimum power supply capacity guaranteed by a user to the charging aggregator is the minimum power supply capacity that the user guarantees to the grid. Upon receiving a power supply notification from the charging aggregator, the user must immediately respond and supply the corresponding capacity to the grid. If the user is unable to supply the corresponding capacity, they must pay the corresponding penalty to the charging aggregator. The specific formula for calculating the minimum power supply capacity is as follows: in, V i For the first i Minimum power supply capacity per user WS i For the first i Individual user's power supply capacity intentions.

[0020] Based on the two-part charging pricing mechanism, the incentives for minimum electric vehicle charging capacity, the incentives for electric vehicle charging capacity, and the penalties for default are determined by using the constraint that the two-part electricity price is equal to the time-of-use electricity price to determine the pricing incentive and penalty parameters, specifically including: The two-part tariff mainly consists of: electricity price, minimum charging capacity incentive for electric vehicles, charging capacity incentive for electric vehicles, and penalty for default. The specific calculation formula is as follows:

[0021] in, F i For the first i Two-part electricity pricing for individual users U i For the first i Electricity price per user VM i For the first i Minimum power supply capacity incentive for each user QM i For the first i Incentives for individual user power supply P i For the first i Penalties for individual users' breach of contract; p i,t For the first i Electricity load of a user at time t α and β They are respectively VM i and QM i The weighting coefficients, pri h This refers to the local peak-hour electricity price. pri l This refers to the off-peak electricity pricing implemented locally. j For charging and power supply numbering, m This represents the total number of power-feeding cycles. For the first i The user j The capacity for subsequent power supply; pri s The price for energy storage capacity built by the unit. l This represents the maximum number of charge-discharge cycles for energy storage. For the local t Time-of-use pricing is implemented at specific times.

[0022] Based on the overall revenue, considering electricity purchase expenditure, energy storage construction expenditure, incentive expenditure for minimum feeder capacity of electric vehicle users, incentive expenditure for electric vehicle feeder capacity, revenue from electricity sales, and penalty revenue, the overall revenue of the vehicle-storage collaborative feeder network is calculated. The specific calculation formula is as follows:

[0023] in, M For overall benefits, M 1 is the revenue from charging and recharging. M 2. Revenue from energy storage construction. V s In order to plan energy storage capacity, This is the original energy storage capacity.

[0024] With maximizing overall revenue as the optimization objective, the optimal energy storage ratio for the park is determined using the particle swarm optimization algorithm. The specific formula is as follows:

[0025] in, f Let be the objective function of the particle swarm optimization algorithm. x These are the optimization variables for the particle swarm optimization algorithm.

[0026] By establishing the relationship between the minimum power supply capacity of users and the optimal energy storage ratio in the park through sensitivity analysis, a universally applicable vehicle-storage capacity matching method for two-part tariffs is finally presented.

[0027] To simulate and verify the vehicle-to-storage capacity allocation method considering two-part tariffs proposed in this embodiment of the invention, the simulation scenario is set as follows: (1) Considering that the new energy industry is developing rapidly and its capacity is constantly increasing, the original energy storage capacity of the park should be less than 10% of the new energy installed capacity. Here, the new energy installed capacity of the park is set at 20MWh and the energy storage capacity of the park is 1.5MWh. (2) The travel time of electric vehicle users follows a normal distribution with a mean of 32 and a standard deviation of 4, and the return time of users follows a normal distribution with a mean of 72 and a standard deviation of 4. Based on the probability function obtained by fitting, the travel situation of electric vehicles is simulated, and the charging and discharging behavior of electric vehicles is obtained by Monte Carlo sampling method. (3) The charging device is a constant power slow charging mode with a charging and discharging power of 8kW and a charging and discharging efficiency of 90%. Considering safety, the upper and lower limits of SOC are set to 90% and 10%, respectively. (4) Users are profit-driven. By default, users will choose a more suitable charging and discharging method based on the electricity price adjustment, that is, to ensure their charging comfort while minimizing charging costs. (5) Park users are also profit-driven. Without sufficient incentives, user parks will not take the initiative to undertake the task of building energy storage and maintaining the stable operation of the regional power grid. (6) This invention simulates two innovative settings: comparing the regional power grid stability of time-of-use electricity pricing with that of two-part electricity pricing for feeder billing, such as... Figure 2 As shown in Table 1, the operational economy of comparing the original energy storage ratio with the reasonable vehicle-storage ratio is as follows.

[0028] Compared with time-of-use pricing, two-part pricing has the function of stabilizing regional power grid load. The mean square error of the regional load curve for two-part pricing is 1.49, while that for time-of-use pricing is 1.53, resulting in a 2.6% decrease in the mean square error of the regional power grid load curve.

[0029] Table 1

[0030] Compared with pure energy storage, vehicle-storage collaborative energy storage has higher economic benefits in terms of construction cost and daily profit. The construction cost of pure energy storage mode is 314,125 yuan and the daily profit is 717 yuan, while the construction cost of vehicle-storage collaborative mode is 0 yuan and the daily profit is 896 yuan. In addition, both modes can guarantee the normal travel needs of electric vehicle users, and neither scheme has resulted in insufficient electric vehicle power.

[0031] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for vehicle-to-storage capacity allocation considering two-part tariffs, characterized in that, Includes the following steps: S1. Collect park configuration data: new energy power generation capacity data, energy storage capacity data; collect electric vehicle user charging and discharging data: historical charging and discharging behavior, discharging intentions. S2. Calculate the minimum power supply capacity for users based on their power supply intentions; S3. Based on the two-part charging pricing mechanism, taking into account the incentives for the minimum power supply capacity of electric vehicles, the incentives for the power supply of electric vehicles, and the penalties for default, the pricing incentives and penalties for the two-part electricity price are determined with the constraint that the two-part electricity price is equal to the time-of-use electricity price. S4. Based on the overall revenue maximization mentioned above, consider the electricity purchase expenditure, energy storage construction expenditure, minimum power supply capacity incentive expenditure for electric vehicle users, power supply incentive expenditure for electric vehicles, electricity sales revenue, and penalty revenue. S5. Taking the premise that electric vehicle users can choose the optimal charging method independently, the constraint that the power supply of the distribution network is sufficient and stable, and the optimization objective of maximizing overall benefits, the particle swarm algorithm is used to determine the optimal energy storage ratio of the park. S6. Through sensitivity analysis, establish the relationship between the minimum power supply capacity of users and the optimal energy storage ratio in the park, and finally give a universally applicable vehicle-storage capacity ratio method for two-part tariff electricity.

2. The vehicle-to-storage capacity allocation method considering two-part tariffs according to claim 1, characterized in that: The data is based on the following: new energy power generation capacity data and energy storage capacity data; electric vehicle user charging and discharging data: historical charging and discharging behavior and discharging intentions. And calculate the minimum power supply capacity for users based on their power supply intentions, including: The minimum power supply capacity guaranteed by a user to the charging aggregator is the minimum power supply capacity that the user guarantees to the grid. Upon receiving a power supply notification from the charging aggregator, the user must immediately respond and supply the corresponding capacity to the grid. If the user is unable to supply the corresponding capacity, they must pay the corresponding penalty to the charging aggregator. The formula for calculating the minimum power supply capacity is as follows: Where Vi is the minimum power supply capacity of the i-th user, and WSi is the power supply capacity intention of the i-th user.

3. The vehicle-to-storage capacity allocation method considering two-part tariffs according to claim 1, characterized in that: Based on the two-part charging pricing mechanism, the incentives for minimum electric vehicle charging capacity, electric vehicle charging capacity, and penalties for default are determined by the constraint that the two-part electricity price is equal to the time-of-use electricity price. The two-part electricity price consists of: the electricity price, the minimum electric vehicle charging capacity incentive, the electric vehicle charging capacity incentive, and the penalty for default. The calculation formula is as follows: Where Fi is the two-part electricity price for the i-th user, Ui is the electricity price for the i-th user, VMi is the minimum feeder capacity incentive for the i-th user, QMi is the feeder capacity incentive for the i-th user, Pi is the penalty for default for the i-th user; pi,t is the electricity load of the i-th user at time t, α and β are the weighting coefficients of VMi and QMi respectively, prih is the local peak-hour electricity price, prl is the local off-peak electricity price, j is the charging / feeder number, and m is the total number of feeding operations. The capacity for power supply to the i-th user during the j-th time; pris is the price per unit capacity of energy storage; l is the maximum number of charge-discharge cycles for energy storage. The time-of-use electricity price implemented at local time t.

4. The vehicle-to-storage capacity allocation method considering two-part tariffs as described in claim 1, characterized in that: Based on the overall revenue, considering electricity purchase expenditure, energy storage construction expenditure, incentive expenditure for minimum feeder capacity of electric vehicle users, incentive expenditure for electric vehicle feeder power, revenue from electricity sales, and penalty revenue, the overall revenue of the vehicle-storage collaborative feeder network is calculated using the following formula: Where M represents total revenue, M1 represents revenue from charging and power supply, M2 represents revenue from energy storage construction, and Vs represents planned energy storage capacity. This is the original energy storage capacity.

5. The vehicle-to-storage capacity allocation method considering two-part tariffs according to claim 1, characterized in that: With maximizing overall revenue as the optimization objective, the optimal energy storage ratio for the park is determined using the particle swarm optimization algorithm. The specific formula is as follows: Where f is the objective function of the particle swarm optimization algorithm, and x is the optimization variable of the particle swarm optimization algorithm.

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