V2G grid-connected power configuration suitability evaluation method for multiple scenes

Abstract

A multi-scene-oriented V2G grid-connected power configuration suitability evaluation method relates to the technical field of new energy power grids, and comprises the following steps: inputting scene parameters including scene types, power grid data and user behaviors; setting a power configuration scheme to be evaluated; constructing evaluation sets, and setting weights of evaluation indexes in the evaluation sets according to scene types; based on the evaluation set and the weight corresponding to each evaluation index, constructing an evaluation equation of each power configuration scheme; calculating an evaluation score of each evaluation equation by adopting a quantitative analysis model according to the scene parameters; the scheme with the highest evaluation score is used as the final power configuration scheme, and the method is used for solving the problems that in a traditional charging pile power configuration scheme, the dimensionality of considering the problem is single, only a configuration curve can be given, and quantitative configuration cannot be achieved.

Description

Technical Field

[0001] This invention relates to the field of new energy grid technology, and specifically to a method for evaluating the adaptability of V2G grid-connected power configuration for multiple scenarios. Background Technology

[0002] The development of V2G (Vehicle-to-Grid) technology is rapid. As a key means to promote the deep integration of electric vehicles and the power grid, it shows broad prospects in improving grid flexibility and promoting the consumption of new energy. However, with the promotion and deployment of V2G in different application scenarios, the issue of selecting charging pile power configuration schemes is becoming increasingly prominent. At present, the industry generally lacks a scientific, systematic, and quantifiable evaluation system, resulting in the selection of schemes relying heavily on the market share or local experience of equipment manufacturers, lacking a comprehensive consideration of the technical solutions in terms of distribution network carrying capacity, user economy, grid friendliness, technical feasibility, and system flexibility.

[0003] In practical applications, the demand for V2G grid connection varies significantly across different scenarios, such as residential areas, business parks, and public charging stations. For example, residential areas are more concerned about the impact on the existing power distribution network, avoiding voltage overruns or transformer overloads caused by large-scale V2G access; while business parks prioritize the economic benefits of V2G participation in the electricity market. Existing decision-making mechanisms often unilaterally emphasize one aspect of performance, neglecting the coordination and trade-offs between multiple objectives, which can easily lead to unreasonable resource allocation and even prematurely reaching the power distribution network's capacity bottleneck in some scenarios, affecting power supply security. Furthermore, due to the lack of refined modeling of user-side revenue models, some solutions, while technically feasible, are economically unacceptable to users, resulting in low V2G resource utilization and hindering large-scale application.

[0004] On the other hand, existing assessment methods mostly remain at the level of qualitative analysis or single-indicator comparison, lacking dynamic assessment models that can integrate multi-dimensional and multi-timescale data. With the continuous evolution of V2G dispatch strategies and electricity pricing mechanisms, static assessment results are insufficient to reflect the true operating status of the system, further limiting the effective use of V2G as a flexible resource. Therefore, there is an urgent need to construct a V2G power configuration adaptability assessment method that can adapt to multiple scenarios, integrate multi-dimensional quantitative indicators, and support dynamic weight adjustment. This method would provide scientific and objective decision support for V2G grid connection schemes in different application scenarios, thereby achieving synergistic optimization of grid security, user benefits, and social benefits.

[0005] Chinese patent with publication number CN119009978A discloses a method, device and equipment for evaluating V2G dispatch schemes for electric vehicles. It focuses on the post-event evaluation of dispatch schemes, and the indicators include renewable energy consumption ratio, dispatchable capacity, dispatch response time, load stability and economic benefits of dispatch operation. It is biased towards the evaluation of grid operation effect, and the key point of the method is the generation and evaluation of dispatch strategy, which belongs to the post-event analysis of the dispatch stage.

[0006] Therefore, we propose a dynamic evaluation method with multiple dimensions and quantification. Summary of the Invention

[0007] The purpose of this invention is to provide a V2G grid-connected power configuration adaptability evaluation method for multiple scenarios, which solves the problems of traditional charging pile power configuration schemes that consider only one dimension of the problem and can only provide configuration curves but cannot provide quantitative configuration.

[0008] This invention is achieved through the following technical solution: A method for evaluating the adaptability of V2G grid-connected power configuration for multiple scenarios, specifically including: The input includes scenario parameters such as scenario type, power grid data, and user behavior; Define the power configuration scheme to be evaluated; Construct evaluation sets and set the weights of evaluation indicators in each evaluation set according to the scenario type; Based on the evaluation set and the corresponding weights of each evaluation index, the evaluation equations for each power configuration scheme are constructed. Based on the scenario parameters, a quantitative analysis model is used to calculate the evaluation scores of each evaluation equation; The scheme with the highest evaluation score will be used as the final power configuration scheme.

[0009] Furthermore, the setting of the power configuration scheme to be evaluated specifically includes: Construct a hardware layer solution that includes the number of AC charging piles and DC charging piles; A system-level solution for constructing charging pile installation locations and grid connection node locations; Construct a control layer scheme for charging and discharging strategies; Based on the hardware layer scheme, system layer scheme, and control layer scheme, multiple power configuration schemes are randomly combined.

[0010] Furthermore, the evaluation set includes dimensions of distribution network carrying capacity, grid friendliness, user economics, technical feasibility, and system flexibility.

[0011] Furthermore, the indicator of the carrying capacity dimension is the maximum number of electric vehicles that can be connected. The process of obtaining this indicator specifically includes: Construct a power grid security constraint model and an electric vehicle cluster power model; Set the search range for the number of electric vehicles; Based on the power grid security constraint model and the electric vehicle cluster power model, a bisection method is used for iterative search to obtain the probability of violation that does not meet the power grid security constraint model and the electric vehicle cluster power model. The maximum number of electric vehicles that can be connected is determined based on the probability of violations.

[0012] Furthermore, the indicators of the grid friendliness dimension include peak load change rate. and three-phase imbalance .

[0013] Furthermore, the formula for calculating the peak load change rate is as follows:

[0014] In the formula, The total grid load is the baseline scenario. The total load of the power grid after the introduction of V2G dispatch.

[0015] Furthermore, the formula for calculating the three-phase unbalance is as follows:

[0016] In the formula, , and These are the effective values ​​of the three-phase currents. This represents the average current.

[0017] Furthermore, the indicator for the user economic dimension is the average daily V2G revenue per vehicle. and investment payback period .

[0018] Furthermore, the indicator for the technical feasibility dimension is the cost of power grid transformation. .

[0019] Furthermore, the indicator for the system flexibility dimension is the maximum peak shaving and valley filling capability. .

[0020] The technical solution of the present invention has at least the following advantages and beneficial effects: This invention discloses a V2G grid-connected power configuration adaptability evaluation method for multiple scenarios. By constructing an evaluation set containing five core dimensions and defining precise quantitative indicators and calculation models for each dimension, the basis for scheme selection is transformed from vague factors such as subjective experience and market share to objective scoring based on simulation calculation and data models, which greatly improves the scientificity and reliability of decision-making.

[0021] In addition, by dynamically setting the weights of each evaluation dimension according to the scenario type, the same evaluation system can flexibly adapt to the core needs of different scenarios such as residential communities, industrial parks, and public facilities (e.g., residential areas prioritize carrying capacity, while industrial parks prioritize economic efficiency), outputting truly "locally tailored" optimal power configuration solutions, and achieving precise optimization of resource allocation.

[0022] Furthermore, through a multi-dimensional comprehensive scoring mechanism, the distribution network carrying capacity, grid friendliness, user economy, technical feasibility, and system flexibility can be evaluated simultaneously and quantitatively. The final selected solution is the equilibrium solution with the best overall benefits, thereby effectively ensuring grid security, enhancing user participation, promoting the overall optimization of social resources, and quantitatively evaluating V2G grid-connected power. Attached Figure Description

[0023] Figure 1 This is a flowchart of a V2G grid-connected power configuration adaptability evaluation method according to the present invention; Figure 2 This is a flowchart of the method for setting a power configuration scheme to be evaluated according to the present invention; Figure 3 This is a flowchart of the method for obtaining the load-bearing capacity dimension index of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0025] Example 1 like Figures 1-2 The method for evaluating the adaptability of V2G grid-connected power configuration for multiple scenarios, as shown, specifically includes: The input includes scenario parameters such as scenario type, power grid data, and user behavior; scenarios include residential communities, commercial parks, or parking lots, power grid data includes distribution transformer capacity, line parameters, and allowable range of node voltage, and user behavior includes electric vehicle travel patterns and charging / discharging behavior preferences. Define the power configuration scheme to be evaluated, specifically including: Construct a hardware layer solution that includes the number of AC charging piles and DC charging piles; For example: Option A involves deploying 20 AC V2G charging piles with a power of 7kW; Option B involves deploying 10 DC V2G charging piles of 60kW each; Option C involves a hybrid deployment of five 120kW DC charging piles and fifteen 7kW AC charging piles. A system-level solution for constructing charging pile installation locations and grid connection node locations; For example, in the hardware layer solution, the charging piles are centrally located in a corner of the parking lot or scattered in each parking space, and all the charging piles in the hardware layer solution are connected to the low-voltage side of the community transformer or to a specific feeder. Construct a control layer scheme for charging and discharging strategies; For example, the charging piles deployed in the hardware layer can adopt disordered charging or ordered charging. On the basis of ordered charging, schemes based on electricity price, grid command and aggregator scheduling can also be adopted. Based on the hardware layer scheme, system layer scheme, and control layer scheme, multiple power configuration schemes are randomly combined. Assuming we are planning V2G infrastructure for an office park with 100 parking spaces, we might propose the following three alternatives for comparison and evaluation: plan : Deploy 100 bidirectional AC charging piles of 7kW each, one for each parking space; Total power: 700kW; Strategy: The main approach is to adopt an orderly charging and discharging system based on time-of-use pricing, encouraging employees to discharge electricity when prices are high at midday and charge it at night when prices are low.

[0026] The scheme is characterized by wide coverage, low power consumption per vehicle, minimal impact on the power grid, and potentially low barriers to user participation.

[0027] plan : Composition: Ten 60kW bidirectional DC charging piles are centrally deployed in the central area of ​​the parking lot.

[0028] Total power: 600kW.

[0029] Strategy: Adopt a V2G dispatch strategy that responds to grid peak shaving, and release battery energy in a concentrated manner to support the grid during peak electricity consumption periods in the region.

[0030] The solution is characterized by high power, fast response, and small footprint, but it has a limited number of vehicles covered and high requirements for grid access points.

[0031] plan : Composition: Deployment of 80 7kW AC charging piles + 5 120kW DC charging piles.

[0032] Total power: 80 * 7kW + 5 * 120kW = 1160kW.

[0033] Strategy: A hybrid strategy is adopted, with AC charging piles serving daily employee vehicles for orderly charging and discharging; DC charging piles serve as a "rapid response force" for rapid energy replenishment and emergency grid support.

[0034] The key feature of this scheme is that it balances coverage and power flexibility, resulting in the largest total power capacity, but also the most complex system and potentially the highest initial investment.

[0035] Construct evaluation sets and set the weights of evaluation indicators in each evaluation set according to the scenario type; For example, for residential community scenarios, the distribution network carrying capacity and grid friendliness dimensions in the evaluation set are given higher weights, while for industrial parks, the user economic efficiency and system flexibility dimensions are given more emphasis. The specific allocation can be manually made according to the actual application scenario. Based on the evaluation set and the corresponding weights of each evaluation index, the evaluation equations for each power configuration scheme are constructed. The evaluation equation can be constructed by multiplying each dimension in the evaluation set by the weight corresponding to the index of that dimension and then summing them. Based on the scenario parameters, a quantitative analysis model is used to calculate the evaluation scores of each evaluation equation; Since there is no quantitative data to support the calculation of each dimension of the evaluation set, it is necessary to use the Monte Carlo algorithm in the quantitative analysis model to simulate the random behavior of the electric vehicle cluster, and then analyze the power grid status based on the time-series power flow algorithm. The scheme with the highest evaluation score will be used as the final power configuration scheme.

[0036] Example 2 As one embodiment, the evaluation set includes distribution network carrying capacity dimension, grid friendliness dimension, user economic dimension, technical feasibility dimension, and system flexibility dimension; wherein the distribution network carrying capacity dimension is used to evaluate the physical safety limits of the existing distribution network (transformers, lines, etc.) under this V2G power configuration scheme; The grid-friendliness dimension is used to assess the impact of V2G grid connection on the quality and stability of grid operation; The user economic dimension assesses the financial attractiveness of participating in V2G from the perspective of electric vehicle users; The technical feasibility dimension assesses the additional grid-side modification costs required to implement this power configuration scheme; The system flexibility dimension is used to quantify the ability of this V2G solution to serve as a distributed energy storage resource and provide ancillary services such as peak shaving and frequency regulation for the power grid.

[0037] In addition, the load-bearing capacity dimension index is the maximum number of electric vehicles that can be connected. The process of obtaining this indicator, combined with Figure 3 Specifically, it includes: Construct a power grid security constraint model and an electric vehicle cluster power model; The power grid security constraint model includes node voltage constraints, specifically:

[0038] in, For nodes At any moment voltage, and These are the lower and upper limits of the permissible voltage, respectively. ; And line / transformer load factor constraints, specifically:

[0039] In the formula, For lines or transformers At any moment Apparent power For rated capacity, This represents the maximum allowable load rate, such as 80%.

[0040] The electric vehicle cluster power model includes the power of a single electric vehicle, specifically:

[0041] And the total power of the cluster, specifically:

[0042] In the formula, The total number of electric vehicles connected; Set the search range for the number of electric vehicles, for example, for ; Based on the power grid security constraint model and the electric vehicle cluster power model, a bisection method is used for iterative search to obtain the probability of violation that does not meet the power grid security constraint model and the electric vehicle cluster power model. The process is as follows: First, let the current number of electric vehicles being tested be the median of the search range. ; Then based on The behavior of the electric vehicle swarm was generated using Monte Carlo simulation, and the calculation was performed at each time step. Total power of the electric vehicle cluster: Will As a load (or power source) injection model into the power grid, a time-series power flow calculation is performed over a 24-hour period (or one scheduling cycle), which yields the results for each moment. The voltage at each node and the load rate of each line / transformer; Finally, check whether the power flow calculation results generated by Monte Carlo simulation for all scenarios meet the power grid security constraints, and count the proportion of scenarios that violate any of the above constraints in all simulation scenarios, i.e., the "probability of violation".

[0043] The maximum number of electric vehicles that can be connected is determined based on the probability of violations, specifically including: If the probability of violation is less than or equal to the preset risk threshold (e.g., 5%), it indicates that the current... If it's safe, then raise the search threshold, that is... ; If the probability of violation is greater than the preset risk threshold, it indicates that the current... This has led to an unsafe power grid. Therefore, the search limit will be lowered, i.e. ; Repeat the iterative search steps until... If it is less than a very small tolerance value, then... This is the maximum number of electric vehicles that can be connected that we are looking for. .

[0044] In addition, the indicators of the grid friendliness dimension include peak load change rate. and three-phase imbalance The formula for calculating the peak load change rate is:

[0045] In the formula, The total grid load under the baseline scenario (without V2G) The total load of the power grid after the introduction of V2G dispatch; The formula for calculating the three-phase unbalance is:

[0046] In the formula, , and These are the effective values ​​of the three-phase currents. This represents the average current.

[0047] The indicator for the user economic dimension is the average daily V2G revenue per vehicle. and investment payback period ,in:

[0048] In the formula, and They are respectively Discharge power and charging power during the time period and They are respectively Time-of-use electricity prices and purchase prices for electricity during specific time periods. For time intervals, The average daily battery wear cost is related to factors such as the number of cycles and the depth of discharge. The total number of time periods in a day;

[0049] In the formula, For the initial investment on the user side, This represents the average annual net income.

[0050] The indicator for the technical feasibility dimension is the cost of power grid transformation. Specifically:

[0051] In the formula, To increase the capacity of the transformer, For line replacement / laying, Upgrade the switch cabinet Cost of building a communication system.

[0052] The system flexibility dimension is measured by its maximum peak shaving and valley filling capability. Specifically: First, calculate the real-time upward adjustment capability:

[0053] In the formula, This refers to the rated discharge power of a single vehicle. The current state of charge, This is the minimum allowable discharge value; Then adjust the capability downwards in real time:

[0054] In the formula, The rated charging power for a single vehicle, The maximum allowable charging limit; Finally, calculate the overall system flexibility:

[0055] This indicator is usually represented as a curve over a period of time. Its maximum value or reliable capacity is the evaluation value.

[0056] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the adaptability of V2G grid-connected power configuration for multiple scenarios, characterized in that, Specifically, it includes: The input includes scenario parameters such as scenario type, power grid data, and user behavior; Define the power configuration scheme to be evaluated; Construct evaluation sets and set the weights of evaluation indicators in each evaluation set according to the scenario type; Based on the evaluation set and the corresponding weights of each evaluation index, the evaluation equations for each power configuration scheme are constructed. Based on the scenario parameters, a quantitative analysis model is used to calculate the evaluation scores of each evaluation equation; The scheme with the highest evaluation score will be used as the final power configuration scheme.

2. The V2G grid-connected power configuration adaptability evaluation method for multiple scenarios as described in claim 1, characterized in that: The power configuration scheme to be evaluated specifically includes: Construct a hardware layer solution that includes the number of AC charging piles and DC charging piles; A system-level solution for constructing charging pile installation locations and grid connection node locations; Construct a control layer scheme for charging and discharging strategies; Based on the hardware layer scheme, system layer scheme, and control layer scheme, multiple power configuration schemes are randomly combined.

3. The V2G grid-connected power configuration adaptability evaluation method for multiple scenarios as described in claim 1, characterized in that: The evaluation set includes the dimensions of distribution network carrying capacity, grid friendliness, user economy, technical feasibility, and system flexibility.

4. The V2G grid-connected power configuration adaptability evaluation method for multiple scenarios as described in claim 3, characterized in that: The indicator for the carrying capacity dimension is the maximum number of electric vehicles that can be connected. The process of obtaining this indicator specifically includes: Construct a power grid security constraint model and an electric vehicle cluster power model; Set the search range for the number of electric vehicles; Based on the power grid security constraint model and the electric vehicle cluster power model, a bisection method is used for iterative search to obtain the probability of violation that does not meet the power grid security constraint model and the electric vehicle cluster power model. The maximum number of electric vehicles that can be connected is determined based on the probability of violations.

5. The V2G grid-connected power configuration adaptability evaluation method for multiple scenarios as described in claim 3, characterized in that: The indicators for the grid friendliness dimension include peak load change rate. and three-phase imbalance .

6. The V2G grid-connected power configuration adaptability evaluation method for multiple scenarios as described in claim 5, characterized in that: The formula for calculating the peak load change rate is: In the formula, The total grid load under the baseline scenario (without V2G) The total load of the power grid after the introduction of V2G dispatch.

7. The V2G grid-connected power configuration adaptability evaluation method for multiple scenarios as described in claim 5, characterized in that: The formula for calculating the three-phase unbalance is: In the formula, , and These are the effective values ​​of the three-phase currents. This represents the average current.

8. The V2G grid-connected power configuration adaptability evaluation method for multiple scenarios as described in claim 3, characterized in that: The indicator for the user economic dimension is the average daily V2G revenue per vehicle. and investment payback period .

9. The V2G grid-connected power configuration adaptability evaluation method for multiple scenarios according to claim 3, characterized in that: The indicator for the technical feasibility dimension is the cost of power grid transformation. .

10. The V2G grid-connected power configuration adaptability evaluation method for multiple scenarios according to claim 3, characterized in that: The system flexibility dimension is measured by its maximum peak shaving and valley filling capability. .

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