V2G technology-based optimal scheduling method and system for participation of electric vehicle in carbon emission reduction of power distribution network

Abstract

According to the V2G technology-based optimal scheduling method and system for participation of the electric vehicle in carbon emission reduction of the power distribution network, a double-layer optimal scheduling model is provided, the upper layer model aims at maximizing the utilization rate of renewable energy sources, and system load balance is realized by optimizing charging and discharging behaviors of the electric vehicle; the lower-layer model optimizes the charging and discharging strategy and minimizes the user cost on the premise of meeting the daily electricity demand of the electric vehicle user; an alternating direction multiplier method ADMM is adopted to realize distributed coordination of upper and lower layer models, and double targets of system load balance and user cost minimization are achieved; meanwhile, the external power grid electricity purchase quantity reduced in V2G electric vehicle dispatching is calculated, and the carbon emission reduction benefit is evaluated, so that the future dispatching strategy is optimized, the renewable energy utilization rate is increased, the electric vehicle user cost is reduced, the system load balance is enhanced, and the carbon emission calculation accuracy and the system response flexibility and stability are improved; and a feasible scheme is provided for efficient interactive scheduling of the electric vehicle and the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a method and system for optimizing dispatching of electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology. Background Art

[0002] With growing global awareness of environmental protection and the urgent need for sustainable energy development, the integration of electric vehicles (EVs) with renewable energy sources such as wind and photovoltaics is gaining increasing attention. In particular, through vehicle-to-grid (V2G) technology, EVs can function not only as traditional transportation but also as active energy storage units within the power grid, participating in energy management and providing resiliency services. However, distribution networks face the challenges of high volatility and randomness in renewable energy during operation. Traditional grid dispatch methods struggle to fully utilize these distributed renewable resources and fail to effectively integrate the energy storage potential of EVs.

[0003] At present, there have been some studies on the participation of electric vehicles in grid dispatching. For example, CN115693737A discloses a method for V2G electric vehicle aggregation to participate in distribution network dispatching. This method establishes a two-tier dispatching model by predicting the daily load curve of the distribution network, the power that can be generated by wind power and photovoltaic power, etc., aiming to improve the absorption rate of renewable energy and the economic benefits of electric vehicle users. However, this method still has certain limitations in dealing with the high volatility and randomness of renewable energy. It may not be able to accurately adjust the charging and discharging plan of electric vehicles in real time to cope with the rapid changes in grid load. In addition, this method may not fully consider the daily driving needs of electric vehicle users, making it difficult to fully meet the diverse needs of users in practical applications.

[0004] On the other hand, CN118472960A proposes a method and system for optimizing electric vehicle scheduling that takes into account carbon emission flows and dynamic electricity prices. This method aims to reduce the charging costs and carbon emissions of electric vehicles by establishing an upper-level scheduling model with the minimum distribution network load variance as the upper-level optimization objective, and a lower-level scheduling model with the minimum electric vehicle charging cost as the lower-level optimization objective. However, this method may not fully consider the impact of reactive power on system voltage, network loss, etc. when constructing the carbon emission flow model, thereby affecting the accuracy of carbon emission calculations to a certain extent. In addition, although this method takes into account the dynamic electricity price mechanism, it may not fully combine the V2G characteristics of electric vehicles and the actual operation of the distribution network, resulting in the effectiveness of the scheduling strategy in practical applications being limited.

[0005] In practical applications, distribution network scheduling typically relies on traditional centralized optimization methods. While these methods can optimize grid operation to a certain extent, they face challenges such as high computational complexity and heavy communication overhead when dealing with large-scale distributed renewable energy and electric vehicle integration. Traditional scheduling methods often struggle to accurately predict and meet the actual needs of electric vehicle users, resulting in inefficient resource utilization and low user satisfaction. Furthermore, with the increasing number of electric vehicles and the gradual increase in renewable energy penetration, the load volatility and uncertainty of distribution networks will further intensify, placing higher demands on the stable operation and reliable power supply of the grid.

[0006] Therefore, existing technical solutions still have shortcomings in integrating the energy storage potential of electric vehicles, improving the utilization rate of renewable energy, reducing carbon emissions, and meeting the diverse needs of electric vehicle users. This invention aims to provide a more comprehensive and efficient V2G-based optimization scheduling method for electric vehicles participating in carbon emission reduction in distribution networks. By constructing a more accurate two-layer optimization scheduling model, fully considering the volatility and randomness of renewable energy and the daily driving needs of electric vehicle users, it can achieve low-carbon operation of the distribution network and cost optimization for electric vehicle users. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide an optimized scheduling method and system for electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology, and to solve the problem in the field of power system scheduling technology, especially in the context of widespread access of electric vehicles to distribution networks, how to achieve efficient interactive scheduling between electric vehicles and distribution networks through vehicle-to-grid interaction (V2G) technology, thereby overcoming the limitations of existing technologies such as insufficient utilization of renewable energy, high user costs for electric vehicles, poor system load balance, and inaccurate carbon emission calculations.

[0008] To solve the above technical problems, the technical solution adopted by the present invention is an optimized scheduling method for electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology, comprising the following steps: Step 1: Collect distribution network load and renewable energy power forecast data; Step 2: Build an upper-level dispatch model to maximize the utilization of renewable energy, reduce the demand for electricity purchased from external power grids, and achieve low-carbon operation of the distribution network; Step 3: On the premise of meeting the daily electricity demand of electric vehicle users, build a lower-level scheduling model to optimize the charging and discharging strategy of electric vehicles to minimize the usage cost of electric vehicle users; Step 4: Coordinate the independent decisions of the distribution network dispatch center and the electric vehicle aggregator through the Alternating Direction Method of Multipliers (ADMM) to achieve global optimization of market dispatch and meet the dual goals of load balancing and cost minimization; Step 5: Calculate the amount of electricity purchased from the external power grid that is reduced by V2G electric vehicles during the dispatch process, evaluate the carbon emission reduction benefits it brings, and use the results to optimize future dispatch strategies.

[0009] In the preferred solution, the upper-level scheduling model in Step 2 achieves system load balance by optimizing the charging and discharging behavior of electric vehicles, and the objective function is: (1) Where, , are the number of wind farms and photovoltaic power stations in the system, respectively; , are the power that can be generated by the wind farm and photovoltaic power station at the moment; , They are Wind farms and photovoltaic power stations at all times Active output; is the number of cycles.

[0010] In a preferred solution, the upper-level scheduling model in Step 2 further includes node power flow constraints, node voltage constraints, branch current constraints, power constraints of electric vehicle aggregators, renewable energy constraints, state constraints of electric vehicles, and power exchange constraints between the distribution network and the external network; The node power flow constraint formula is as follows: (5) (6) Where, 、 For branches The equivalent resistance and equivalent reactance of 、 At the time Flowing through the branch The active power and reactive power of , At the time Outflow Node The sum of active power and reactive power (excluding branch Outer connection to node all branches of ); , For the moment Injection Node Active and reactive power (excluding branch Outer connection to node all branches of ); For branches The square of the current in; is the set of all nodes in the distribution network; Representation and Node Other directly connected nodes; Representation node The set of adjacent nodes of The node voltage constraints are as follows: (7) (8) Where, For nodes The square of the voltage amplitude; For nodes The square of the voltage amplitude; V max 、V min Node The upper and lower limits of the voltage amplitude; The branch current constraints are as follows: (9) (10) Where, , Branches The upper and lower current limits; is the set of all branches in the distribution network; The power constraints of the EV aggregator are as follows: (11) Where, 、 Separate moments Electric vehicle aggregators Upper and lower limits on the total charging power of electric vehicles within the jurisdiction; 、 They are Moment Electric Vehicle Aggregator Upper and lower limits on the total discharge power of electric vehicles within the jurisdiction; 、 The electric vehicle integrator released by the power grid dispatching center exist Time-based charging and discharging scheduling plan; The renewable energy constraints are as follows: (12) The state constraints of the electric vehicle are as follows: (13) The power exchange constraints between the distribution network and the external network are as follows: (14) (15) Where, 、 They are The sale and purchase of electricity in the distribution network at all times.

[0011] In the preferred solution, the lower-level scheduling model in Step 3 aims to minimize the user's charging and discharging costs, and the objective function is: (2) Where, For electric vehicle aggregators , , 、 are the peak and valley electricity prices of electric vehicle aggregators purchasing and selling electricity at the same time; is the total number of electric vehicles; is the number of electric vehicle aggregators; 、 Electric vehicle aggregators exist Total charge and discharge power at the moment; 、 Separate moments Electric vehicles The charging and discharging power.

[0012] In a preferred solution, the lower-level scheduling model in Step 3 further includes battery capacity constraints, battery safety constraints, electric vehicle power constraints, electric vehicle travel constraints, daily charge and discharge capacity constraints, and charge and discharge state constraints; Battery capacity constraints, electric vehicle aggregators The battery capacity must meet the following conditions: (16) Where, For electric vehicle aggregators At the moment Battery capacity, and are the charge and discharge efficiencies, respectively; To ensure battery safety, the battery capacity must meet the following upper and lower limits: (17) Where, is the minimum safety limit of battery capacity, is the maximum safety limit of the battery capacity; The power constraints of electric vehicles are as follows: (18) (19) Where, is the rated charge and discharge power of the electric vehicle; Electric vehicle travel constraints: When an electric vehicle is off-grid, its battery power should be sufficient for daily driving needs: (20) Where, For the moment The minimum battery charge required for daily driving of electric vehicles, with a 5% margin for emergency situations; Daily charge and discharge capacity constraints: The daily charge and discharge capacity of electric vehicles is limited by the total battery capacity: (twenty one) (twenty two) Electric vehicles at any time It can only be in the charging or discharging state, not at the same time. The charging and discharging state constraints are as follows: (twenty three).

[0013] In a preferred embodiment, the alternating direction multiplier method ADMM in Step 4 includes the following steps: Step 4.1: Set the maximum number of iterations , convergence accuracy , and initialize the dispatch plan of the electric vehicle aggregator , , Lagrange multiplier ; Step 4.2: Receive dispatch plan from EV aggregator , using CPLEX tools to solve the dispatching problem in the market and obtain the electric vehicle dispatching plan expected by the upper power grid dispatching center ; Step 4.3: Receive the expected dispatch plan of electric vehicles from the grid dispatch center , and optimize it through CPLEX to obtain the expected dispatch plan of the electric vehicle aggregator ; Step 4.4: Update Lagrange multipliers ; Step 4.5: Update the number of iterations , and calculate the convergence accuracy; Step 4.6: If the convergence condition is not met, return to receive the electric vehicle scheduling plan steps until the convergence condition is reached or the number of iterations exceeds the maximum .

[0014] In a preferred solution, the evaluation of carbon emission reduction benefits in Step 5 is calculated using the following formula: (3) (4) Where, The proportion of thermal power generation in the country's total power generation; It represents the average standard coal consumption during the thermal power generation period; It is the carbon (C) emission coefficient after the complete combustion of 1 ton of standard coal; Indicates the amount of power generated; Coal consumption when generating electricity for the external grid.

[0015] In a preferred embodiment, the method further comprises the step of optimizing the low-carbon dispatching strategy by evaluating the carbon emission reduction contribution of V2G electric vehicles in the distribution network dispatching.

[0016] The V2G-based optimized dispatching system for electric vehicles participating in carbon emission reduction in distribution networks is a system that implements the aforementioned V2G-based optimized dispatching method for electric vehicles participating in carbon emission reduction in distribution networks, and includes: A data acquisition unit, used to collect distribution network load and renewable energy power forecast data; an upper-level scheduling model construction unit, connected to the data acquisition unit, for constructing an upper-level scheduling model to maximize the utilization rate of renewable energy; A lower-level scheduling model construction unit, connected to the data acquisition unit, is used to construct a lower-level scheduling model to optimize the charging and discharging strategy of the electric vehicle on the premise of meeting the daily electricity demand of the electric vehicle users; a coordination unit connected to the upper-layer dispatch model construction unit and the lower-layer dispatch model construction unit, and configured to coordinate independent decisions of the distribution network dispatch center and the electric vehicle aggregator through an alternating direction multiplier method ADMM; The evaluation unit is connected to the coordination unit and is used to calculate the amount of electricity purchased from the external power grid reduced by the V2G electric vehicle during the scheduling process and evaluate the carbon emission reduction benefits brought about thereby.

[0017] In a preferred solution, the system further comprises an optimization unit connected to the evaluation unit, for optimizing the low-carbon scheduling strategy according to the evaluation results of the carbon emission reduction benefits.

[0018] The V2G technology-based optimized scheduling method and system for electric vehicles participating in carbon emission reduction in distribution networks provided by the present invention have the following beneficial effects: 1. The present invention addresses the technical field of power system dispatching, particularly the issue of how to achieve efficient interactive dispatching between electric vehicles and distribution networks through vehicle-to-grid (V2G) technology in the context of widespread access of electric vehicles to distribution networks. This overcomes the limitations of existing technologies, such as insufficient utilization of renewable energy, high user costs for electric vehicles, poor system load balancing, and inaccurate carbon emission calculations.

[0019] 2. The present invention evaluates the carbon emission reduction benefits of V2G electric vehicles by calculating the amount of electricity purchased from the external power grid that is reduced during the scheduling process, and uses the results to optimize future scheduling strategies to achieve the system's low-carbon goals. This reduces the demand for external electricity purchases, promotes carbon emission reduction, promotes the construction of low-carbon power grids, improves the accuracy of carbon emission calculation results, and provides a scientific basis for optimizing future scheduling strategies.

[0020] 3. The present invention reduces the demand for electricity purchase from external power grids by prioritizing the allocation of renewable energy resources, significantly improving the absorption rate of renewable energy; and improves the utilization rate of renewable energy by optimizing the charging and discharging behavior of electric vehicles.

[0021] 4. The present invention optimizes the charging and discharging strategies of electric vehicles, guides users to perform charging and discharging operations during low-cost periods, reduces users' charging expenses, and optimizes the charging and discharging strategies to reduce user costs while meeting users' daily electricity needs.

[0022] 5. The present invention optimizes the cost of electric vehicle charging and discharging behavior through the lower-level model, reduces users' charging expenses, encourages users to actively participate in V2G interaction, and minimizes user costs.

[0023] 6. The present invention realizes efficient interactive scheduling between electric vehicles and distribution networks through a two-layer optimization scheduling model, effectively balances the system load, improves the stability and reliability of the power grid, and enables electric vehicles to participate in the regulation of system loads using V2G technology while meeting their own electricity needs, thereby reducing the fluctuation pressure of the distribution network during peak and valley load periods and enhancing the load balance of the system.

[0024] 7. The present invention uses the ADMM algorithm to achieve distributed coordination between upper and lower layer models, thereby improving the system's response flexibility and stability, and smoothly responding to fluctuations in renewable energy output and changes in user demand.

[0025] 8. The present invention adopts the alternating direction multiplier method to achieve distributed coordination of upper and lower layer models, ensuring the dual goals of system load balancing and user cost minimization, and improving the response flexibility and stability of the system.

[0026] 9. The present invention uses second-order cone programming to convexify the non-convex power flow model, thereby improving the solution efficiency of the power flow optimization model and obtaining the optimal scheduling solution more quickly.

[0027] 10. The upper-level scheduling model of the present invention aims to maximize the utilization rate of renewable energy and achieves system load balance by optimizing the charging and discharging behavior of electric vehicles. It takes into account multiple factors such as the load of the distribution network, renewable energy generation power, node flow, node voltage, branch current, electric vehicle aggregator power, renewable energy constraints, and power exchange between the distribution network and the external network to ensure the overall low-carbon operation of the system.

[0028] 11. The lower-level scheduling model of the present invention is based on the premise of meeting the daily electricity demand of electric vehicle users, optimizing the charging and discharging strategies of electric vehicles to minimize user costs, taking into account the constraints of electric vehicle battery capacity, power, travel demand, daily charging and discharging capacity, and charging and discharging status, to ensure that users can maximize their economic benefits while using electric vehicles.

[0029] 12. Compared with the traditional single optimization model, the two-layer model of the present invention can more comprehensively consider the two goals of system load balancing and user cost minimization, achieving dual optimization.

[0030] 13. The present invention realizes distributed coordination of upper and lower layer models through ADMM, reducing computational complexity and communication burden.

[0031] 14. Through this scheduling method, the present invention enables electric vehicles to achieve precise matching of charging and discharging management with the needs of the power distribution system with the support of V2G technology, optimizes resource allocation, and ensures the high efficiency and sustainable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 It is a technical flow chart of the method of the present invention; Figure 2 This is the ADMM distributed coordination flow chart of the present invention; Figure 3 This is the improved diagram of the IEEE33 distribution network of the present invention. DETAILED DESCRIPTION

[0033] The technical solutions of the present invention are further described below with reference to the embodiments and accompanying drawings: Example 1 like Figure 1 As shown, this embodiment describes in detail the specific implementation process of an optimized scheduling method for electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology, including key links such as data input / data collection, model construction, distributed coordination, and carbon emission reduction benefit evaluation.

[0034] S1. Input distribution network load and renewable energy power forecast data (1) Data collection: Obtain historical load data from the distribution network monitoring system, including but not limited to daily, weekly, and monthly load curves.

[0035] Obtain meteorological data such as wind speed and light intensity from meteorological departments or professional meteorological data service providers to predict the power generation of renewable energy sources (such as wind power and photovoltaic power); Use time series analysis and machine learning algorithms [such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit)] combined with historical data and current meteorological conditions to make short-term (such as daily and weekly) forecasts of distribution network load and renewable energy power.

[0036] (2) Data preprocessing: Clean the collected load and renewable energy power data to remove outliers and noise; The processed data is divided into training set and test set for model training and verification.

[0037] (3) Data input: The predicted load and renewable energy power data are input into the subsequently constructed optimization scheduling model as the basic data for model optimization.

[0038] S2. Build upper-level scheduling model (1) Model goal setting: The upper-level dispatch model takes maximizing the utilization of renewable energy as its core goal, while reducing the demand for electricity purchases from external power grids to achieve low-carbon operation of the distribution network.

[0039] (2) Objective function construction: Define the objective function, consider factors such as renewable energy generation power, electric vehicle charging and discharging power, and external power purchase power, and use the optimization algorithm to find the optimal solution. The formula of the objective function is: (1) Where, is the number of cycles, and its value is 96.

[0040] (3) Constraint setting: Node power flow constraints, based on Kirchhoff's law, ensure the balance of power transmission between nodes within the distribution network. The node power flow constraint formula is as follows: (5) (6) Node voltage constraints set a safe range for node voltage to prevent damage to equipment caused by excessively high or low voltage. Node voltage constraints are as follows: (7) (8) Branch current constraints limit the current of each branch to prevent overload and overheating. The branch current constraints are as follows: (9) (10) The power constraints of the electric vehicle aggregator are set according to the charging and discharging capabilities of the electric vehicle. The power constraints of the electric vehicle aggregator are as follows: (11) Renewable energy constraints ensure that the power generation of renewable energy is within its forecast range and avoid scheduling problems caused by forecast errors. The renewable energy constraints are as follows: (12) Electric vehicle state constraints ensure that electric vehicles meet their driving requirements during the charging and discharging process and do not affect normal user use. The state constraints of electric vehicles are as follows: (13) The power exchange constraints between the distribution network and the external grid limit the power exchange between the distribution network and the external grid to achieve the low-carbon operation goal. The power exchange constraints between the distribution network and the external grid are as follows: (14) (15).

[0041] (4) Model solution: Optimization algorithms (such as linear programming, nonlinear programming, mixed integer programming, etc.) are used to solve the upper-level scheduling model to obtain the optimal charging and discharging plan for electric vehicles.

[0042] S3. Build a lower-level scheduling model (1) Model goal setting: The lower-level scheduling model aims to minimize the usage cost of electric vehicle users and is optimized in combination with peak and valley electricity prices.

[0043] (2) Objective function construction: Define the objective function, consider factors such as the charging and discharging cost of electric vehicles and driving demand, and use the optimization algorithm to find the optimal solution. The objective function is: (2) (3) Constraint setting: Battery capacity constraints, ensuring that the battery capacity of electric vehicles is within a safe range and meets driving requirements, battery capacity constraints, electric vehicle aggregators The battery capacity must meet the following conditions: (16) To ensure battery safety, the battery capacity must meet the following upper and lower limits: (17); Electric vehicle power constraints limit the charging and discharging power of electric vehicles to prevent damage to the battery. The battery power meets daily driving needs. The electric vehicle power constraints are as follows: (18) (19); Electric vehicle travel constraints ensure that when electric vehicles are off-grid, their battery power should meet daily driving needs: (20); Daily charge and discharge capacity constraints limit the daily charge and discharge capacity of electric vehicles to prevent excessive use of batteries. The constraints are as follows: (twenty one); (twenty two); Charge and discharge state constraints ensure that electric vehicles are It can only be in the charging or discharging state, not both at the same time. The constraints are as follows: (twenty three).

[0044] (4) Model solution: Optimization algorithms (such as dynamic programming, heuristic algorithms, etc.) are used to solve the lower-level scheduling model to obtain the optimal charging and discharging strategy for electric vehicle users.

[0045] S4, through the alternating direction multiplier method (ADMM, Alternating Direction Method of Multipliers) to achieve distributed coordination of upper and lower layer models, such as Figure 2 As shown: (1) Initialization: Set the maximum number of iterations , convergence accuracy , and initialize the dispatch plan of the electric vehicle aggregator , , Lagrange multiplier ; (2) Iterative process: In each iteration, we first receive the dispatch plan submitted by the electric vehicle aggregator ; Then, the upper-level dispatch model is solved by an optimization solver such as CPLEX to obtain the expected dispatch plan of the power grid dispatch center. ; Then, based on the output of the upper model, the lower scheduling model is solved by the optimization algorithm to obtain the actual scheduling plan of the electric vehicle aggregator. ; Update Lagrange multipliers and the number of iterations , and calculate the convergence accuracy of the current iteration result.

[0046] (3) Convergence judgment: If the convergence condition is met (that is, the convergence accuracy is less than the preset value ), then stop the iteration; otherwise, return to the previous step and continue iterating until the maximum number of iterations is reached Or until the convergence condition is satisfied.

[0047] S5. Evaluate carbon emission reduction benefits and optimize scheduling strategies (1) Calculation of carbon emissions: Based on the coal consumption and carbon emission coefficient of thermal power generation, calculate the carbon emissions reduced by reducing external electricity purchases.

[0048] (2) Carbon emission reduction benefit assessment: The calculated carbon emissions are compared with the carbon emissions under the original scheduling strategy to evaluate the carbon emission reduction benefits of the optimized scheduling strategy proposed in this invention. The formula is as follows: (3) (4) Where, The proportion of thermal power generation in the country's total power generation is 0.73 in the first half of 2021; It represents the average standard coal consumption during the thermal power generation period, which is 0.32kg / KWh; The carbon (C) emission coefficient after the complete combustion of 1 ton of standard coal is 0.67, which is recommended by the National Energy Research Institute. Indicates the amount of power generated; is the coal consumption when generating electricity from the external grid, 、 They are 44g / mol and 12g / mol respectively.

[0049] (3) Scheduling strategy optimization: Based on the carbon reduction benefit assessment results, the dispatch strategy will be further optimized to achieve a higher level of low-carbon operation goals. At the same time, the optimized dispatch strategy will be fed back into the future dispatch process to guide the continuous improvement and optimization of the system.

[0050] Example 2 In another preferred embodiment, based on Example 1, this embodiment further explores the application of optimized scheduling methods under different network structures and load characteristics. By adjusting the upper-level scheduling model to adapt to more complex network structures and analyzing the load characteristics of different time periods to optimize the lower-level scheduling model, this embodiment demonstrates the significant effectiveness of the present invention in improving system performance, reducing user costs, and promoting the utilization of renewable energy.

[0051] 1. Network structure adjustment and upper-layer scheduling model optimization (1) Analysis of network structure complexity This embodiment considers a representative complex distribution network system - IEEE33 node system, such as Figure 3 The system contains multiple branches and nodes, with uneven load distribution and dispersed access points for renewable energy, which places higher demands on the accuracy and real-time performance of the scheduling model.

[0052] (2) Adjustment of upper-level scheduling model To address the complexity of the IEEE 33-node system, we adjusted the upper-level scheduling model accordingly. First, we added fine-grained control over branch power flows, using an optimization algorithm to accurately calculate the power flow of each branch and ensure power balance within the system. Secondly, the node voltage constraints are strengthened, with stricter upper and lower voltage limits set to prevent voltage fluctuations from affecting the stable operation of the system. In addition, we also take into account the intermittency and uncertainty of renewable energy. By introducing a prediction error compensation mechanism, we improve the model's prediction accuracy of renewable energy power generation, thereby formulating scheduling plans more accurately.

[0053] 2. Load characteristics analysis and lower-level scheduling model optimization (1) Load characteristics analysis We conducted an in-depth analysis of the load characteristics of the IEEE 33-node system at different times. We found that during peak hours in the morning and evening, load demand surges while renewable energy generation is relatively insufficient. During off-peak hours, load demand is low but renewable energy generation may be excessive.

[0054] (2) Lower-level scheduling model optimization To address these load characteristics, we optimized the underlying scheduling model. First, we introduced a time-segmented optimization strategy, dividing the day into multiple time periods. We then developed differentiated EV charging and discharging plans based on the load demand and renewable energy generation in each period. Secondly, a flexible adjustment mechanism for electric vehicle charging and discharging periods has been added. By guiding electric vehicle users to charge during off-peak hours and discharge during peak hours, this not only effectively alleviates the load pressure on the power grid but also improves the utilization rate of renewable energy. In addition, we also take into account the driving needs and charging preferences of electric vehicle users, and minimize the user's charging and discharging costs while meeting user needs through optimization algorithms.

[0055] 3. Optimization effect evaluation (1) System performance improvement Compared with Example 1, this example has a more significant optimization scheduling effect under complex network structures and variable load characteristics. Through refined network control and flexible electric vehicle charging and discharging management, the system achieves higher load balancing and renewable energy utilization.

[0056] Specifically, the system load balance is improved, and the utilization rate of renewable energy is increased by approximately 1%. At the same time, due to the flexible charging and discharging management of electric vehicles, the system's adaptability to fluctuations in renewable energy generation power is also significantly enhanced.

[0057] (2) Reduced user costs By optimizing the underlying scheduling model, this embodiment effectively reduces the charging and discharging costs for electric vehicle users. By guiding users to charge during off-peak hours and discharge during peak hours, users not only enjoy lower electricity prices but also gain additional benefits by participating in V2G services.

[0058] According to statistics, the average charging and discharging costs of electric vehicle users have been significantly reduced. This achievement not only improves user satisfaction and participation, but also helps promote the popularization and development of electric vehicles.

[0059] (3) Reduction in external power purchase demand Because this embodiment effectively improves the utilization rate of renewable energy and the load balance of the system, it significantly reduces the distribution network's need to purchase electricity from external power grids. This not only helps reduce carbon emissions and environmental pollution caused by external power purchases, but also helps reduce the grid's operating costs and improve economic benefits.

[0060] Specifically, the amount of external electricity purchases has been reduced, and the corresponding carbon emissions have been lowered. This achievement is of great significance for promoting the construction and development of low-carbon power grids.

[0061] 4. Summary and Outlook This example successfully demonstrates the significant effectiveness of this method in improving system performance, reducing user costs, and promoting renewable energy utilization by adjusting the upper-level scheduling model to accommodate more complex network structures and analyzing load characteristics over different time periods to optimize the lower-level scheduling model. We will continue to conduct in-depth research on optimized scheduling methods for diverse network structures and load characteristics, exploring more efficient algorithms and strategies to further contribute to the construction and development of low-carbon power grids.

[0062] Example 3 In another preferred embodiment, based on Examples 1 and 2, this embodiment introduces a V2G-based optimized scheduling system for electric vehicles participating in carbon emission reduction in distribution networks. This system is a specific implementation of the V2G-based optimized scheduling method for electric vehicles participating in carbon emission reduction in distribution networks described in Examples 1 and 2. Through the coordinated operation of various functional units, this system achieves optimized scheduling of electric vehicles and distribution networks, thereby achieving the goals of increasing renewable energy utilization and reducing carbon emissions.

[0063] 1. System composition (1) Data acquisition unit Function: Responsible for collecting distribution network load and power forecast data of renewable energy (such as wind power and photovoltaic power). This data is the basis for the system's scheduling decisions.

[0064] Data sources: may include distribution network monitoring systems, weather stations, renewable energy power stations, etc.

[0065] (2) Upper-level scheduling model construction unit Function: Connects to the data acquisition unit, receives load and renewable energy data, and builds a higher-level dispatch model. This model aims to maximize renewable energy utilization and calculates a global dispatch plan for the distribution network through an optimization algorithm.

[0066] Model features: Considers system load balance, node voltage constraints, branch current constraints and other conditions to ensure the feasibility and safety of the scheduling plan.

[0067] (3) Lower-level scheduling model construction unit Function: Also connected to the data acquisition unit, it builds a lower-level scheduling model to optimize the charging and discharging strategy of electric vehicles while meeting the daily electricity needs of electric vehicle users.

[0068] Model features: Taking into account the battery capacity constraints, power constraints, travel constraints and other conditions of electric vehicles, the optimal charging and discharging plan of electric vehicles is calculated through optimization algorithms.

[0069] (4) Coordination unit Function: Connect with the upper-level dispatch model building unit and the lower-level dispatch model building unit to coordinate the independent decisions of the distribution network dispatch center and the electric vehicle aggregator through the alternating direction multiplier method (ADMM).

[0070] Function: Ensure information interaction and collaborative work between upper-level and lower-level scheduling models to achieve global optimization of the system.

[0071] (5) Evaluation unit Function: Connect with the coordination unit to calculate the external power grid purchase reduction caused by V2G electric vehicles during the scheduling process and evaluate the carbon emission reduction benefits brought about by it.

[0072] Assessment method: Based on the carbon emission calculation formula of thermal power plants and combined with the reduced electricity purchases from the external power grid, the specific carbon emission reduction is calculated.

[0073] (6) Optimization unit (preferred solution) Function: Connect with the evaluation unit to optimize the low-carbon scheduling strategy based on the evaluation results of carbon emission reduction benefits.

[0074] Optimization method: By adjusting the parameters and constraints of the upper and lower scheduling models, the utilization rate of renewable energy and carbon emission reduction benefits can be further improved.

[0075] 2. System workflow (1) Data acquisition: The data acquisition unit obtains distribution network load and renewable energy power forecast data from various data sources.

[0076] (2) Model construction: The upper-level scheduling model construction unit and the lower-level scheduling model construction unit respectively construct corresponding scheduling models.

[0077] (3) Coordination optimization: The coordination unit coordinates the independent decisions of the upper and lower scheduling models through the ADMM algorithm to achieve global optimization of the system.

[0078] (4) Effect evaluation: The evaluation unit calculates the amount of electricity purchased from the external power grid that is reduced by V2G electric vehicles during the dispatching process and evaluates the carbon emission reduction benefits.

[0079] (5) Strategy optimization (if any): The optimization unit optimizes the low-carbon scheduling strategy based on the evaluation results to improve system performance.

[0080] 3. System advantages Efficiency: The ADMM algorithm enables the collaboration of upper- and lower-level scheduling models, improving the system's scheduling efficiency and response speed.

[0081] Flexibility: The system can adapt to different network structures and load characteristics, and achieve optimized scheduling by adjusting model parameters and constraints.

[0082] Environmental protection: By increasing the utilization rate of renewable energy and reducing the amount of electricity purchased from the external power grid, carbon emissions and environmental pollution are significantly reduced.

[0083] Economical: By optimizing the charging and discharging strategies of electric vehicles, the charging and discharging costs of users are reduced and the economic benefits of the system are improved.

[0084] 4. Application Scenarios The system is applicable to distribution networks and electric vehicle charging stations of all sizes, and is particularly well-suited for areas with abundant renewable energy and a high electric vehicle population. It can achieve coordinated and optimized scheduling of distribution networks and electric vehicles, promoting the construction and development of low-carbon power grids.

[0085] In the preferred solution, the upper-level scheduling model in Step 2 also includes node flow constraints, node voltage constraints, branch current constraints, power constraints of electric vehicle aggregators, renewable energy constraints, electric vehicle state constraints, and power exchange constraints between the distribution network and the external network; the above settings ensure that the scheduling model can fully consider various actual operating conditions of the power system, improve the accuracy and reliability of scheduling, and also promote the effective access and utilization of renewable energy and electric vehicles, enhancing the flexibility and sustainability of the power system.

[0086] In the preferred solution, the lower-level scheduling model in Step 3 also includes battery capacity constraints, battery safety constraints, electric vehicle power constraints, electric vehicle travel constraints, daily charging and discharging capacity constraints, and charging and discharging status constraints; the above settings ensure the feasibility and safety of the scheduling model in actual operation, effectively balance the grid load and electric vehicle charging demand, improve energy utilization efficiency, and at the same time ensure the health status and service life of the battery.

[0087] In the preferred solution, the method also includes the step of optimizing the low-carbon scheduling strategy by evaluating the carbon emission reduction contribution of V2G electric vehicles in distribution network scheduling; the above settings can accurately quantify the actual benefits of V2G technology in reducing carbon emissions, and then guide the distribution network to adjust the energy distribution and scheduling logic, promote the efficient use of clean energy, and accelerate the green transformation of the energy structure.

[0088] In the preferred solution, the system also includes an optimization unit connected to the evaluation unit, which is used to optimize the low-carbon scheduling strategy based on the evaluation results of the carbon emission reduction benefits; the above settings can achieve precise adjustment of the low-carbon scheduling strategy, ensure the effectiveness and efficiency of the strategy, further enhance the system's carbon emission reduction capabilities, and promote the sustainable development of energy utilization.

[0089] In summary, the V2G-based optimized scheduling method and system for electric vehicles participating in distribution network carbon emission reduction, provided by this invention, addresses the technical challenges of power system scheduling, particularly in the context of widespread electric vehicle integration into distribution networks. This method effectively addresses the problem of achieving efficient interactive scheduling between electric vehicles and distribution networks through vehicle-to-grid (V2G) technology. Existing technologies suffer from limitations such as insufficient renewable energy utilization, high user costs for electric vehicles, poor system load balancing, and inaccurate carbon emission calculations. This invention effectively overcomes these issues by constructing a two-tiered optimized scheduling model.

[0090] The model's upper-level scheduling model aims to maximize renewable energy utilization and balance system load by optimizing the charging and discharging behavior of electric vehicles. The lower-level scheduling model, on the other hand, optimizes the charging and discharging strategies of electric vehicles to minimize user costs, based on meeting the daily electricity needs of electric vehicle users. This two-layer model design is significantly novel in the field of interactive scheduling between electric vehicles and distribution networks, providing new ideas and methods for improving renewable energy utilization and reducing user costs.

[0091] This invention achieves dual optimization of renewable energy utilization and electric vehicle user costs by leveraging two interconnected and mutually influential upper and lower models, a significant breakthrough compared to traditional single-layer optimization models. Furthermore, the distributed coordination of the upper and lower models using the alternating direction multiplier method (ADMM) effectively addresses the computational complexity and communication burden limitations of traditional centralized optimization methods, improving the system's responsiveness and stability. Furthermore, by calculating the reduced external grid electricity purchases during V2G electric vehicle dispatching and assessing its carbon emission reduction benefits, this approach provides a scientific basis for optimizing future dispatch strategies, achieving both improved economic and environmental benefits.

[0092] In summary, the present invention demonstrates significant superiority in the design of a two-layer optimization scheduling model, the application of the alternating direction multiplier method, and the quantitative evaluation of carbon emission reduction benefits, providing a practical solution for the efficient interactive scheduling of electric vehicles and distribution networks.

Claims

1. An optimized scheduling method for electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology, characterized in that: The following steps are involved: Step 1: Collect distribution network load and renewable energy power forecast data; Step 2: Build an upper-level dispatch model to maximize the utilization of renewable energy, reduce the demand for electricity purchased from external power grids, and achieve low-carbon operation of the distribution network; Step 3: On the premise of meeting the daily electricity demand of electric vehicle users, build a lower-level scheduling model to optimize the charging and discharging strategy of electric vehicles to minimize the usage cost of electric vehicle users; Step 4: The independent decisions of the distribution network dispatch center and the electric vehicle aggregator are coordinated through the alternating direction multiplier method ADMM to achieve global optimization of market dispatch and meet the dual goals of load balancing and cost minimization. Step 5: Calculate the amount of electricity purchased from the external power grid that is reduced by V2G electric vehicles during the dispatch process, evaluate the carbon emission reduction benefits it brings, and use the results to optimize future dispatch strategies.

2. The method for optimizing the scheduling of electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology according to claim 1 is characterized in that: The upper-level scheduling model in Step 2 achieves system load balance by optimizing the charging and discharging behavior of electric vehicles. Specifically, the objective function is defined, and the factors of renewable energy power generation, electric vehicle charging and discharging power, and external power purchase are considered. The optimal solution is solved through the optimization algorithm. The objective function is: （1）； Where, , are the number of wind farms and photovoltaic power stations in the system, respectively; , are the power that can be generated by the wind farm and photovoltaic power station at the moment; , They are Wind farms and photovoltaic power stations at all times Active output; is the number of cycles.

3. The method for optimizing the scheduling of electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology according to claim 2, characterized in that: The upper-level scheduling model in Step 2 contains multiple constraints: Node power flow constraint: Ensure the power transmission balance of nodes within the distribution network based on Kirchhoff's law; Node voltage constraint: Set the node voltage safety range to prevent equipment damage; Branch current constraint: limit branch current to prevent overload and overheating; Electric vehicle aggregator power constraints: Set upper and lower limits on aggregator power based on charging and discharging capabilities; Renewable energy constraints: ensuring that power generation is within forecast ranges and avoiding scheduling issues; Electric vehicle status constraints: ensure that charging and discharging do not affect normal user use; Constraints on electricity exchange between the distribution network and the external grid: Limit electricity exchange to achieve low-carbon operation goals.

4. The method for optimizing the scheduling of electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology according to claim 3 is characterized in that: The lower-level scheduling model in Step 3 aims to minimize the user's charging and discharging costs, and the objective function is: （2）； Where, For electric vehicle aggregators , , 、 are the peak and valley electricity prices of electric vehicle aggregators purchasing and selling electricity at the same time; is the total number of electric vehicles; is the number of electric vehicle aggregators; 、 Electric vehicle aggregators exist Total charge and discharge power at the moment; 、 Separate moments Electric vehicles The charging and discharging power.

5. The method for optimizing the scheduling of electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology according to claim 4 is characterized in that: The lower-level scheduling model in Step 3 covers multiple constraints: Battery capacity constraint: ensure battery capacity is safe and meets driving requirements; Electric vehicle power constraints: limit charging and discharging power to protect batteries; Electric vehicle travel constraints: ensuring that the battery power is sufficient for daily driving when off-grid; Daily charge and discharge capacity constraint: limit the daily charge and discharge capacity to prevent excessive use of the battery; Charge and discharge state constraint: stipulates that electric vehicles can only be in one of the states of charging or discharging at the same time.

6. The method for optimizing the scheduling of electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology according to claim 5, characterized in that: The alternating direction multiplier method ADMM in Step 4 includes the following steps: Step 4.1: Set the maximum number of iterations, convergence accuracy, and initialize the EV aggregator's scheduling plan and Lagrange multiplier =0; Step 4.2: Receive the electric vehicle dispatch plan, solve the dispatch problem in the market using the CPLEX tool, and obtain the expected dispatch plan of the upper-level power grid dispatch center; Step 4.3: Receive the expected dispatch plan of the power grid and optimize it through CPLEX to obtain the actual dispatch plan of the electric vehicle integrator; Step 4.4: Update the Lagrange multiplier; Step 4.5: Update the number of iterations and calculate the convergence accuracy; Step 4.6: If the convergence condition is not met, return to the step of receiving the electric vehicle scheduling plan until the convergence condition is met or the number of iterations exceeds the maximum value.

7. The method for optimizing the scheduling of electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology according to claim 6, characterized in that: The evaluation of carbon emission reduction benefits in Step 5 is calculated using the following formula: （3）； （4）； Where, The proportion of thermal power generation in the country's total power generation; It represents the average standard coal consumption during the thermal power generation period; is the carbon (C) emission coefficient after complete combustion of 1 ton of standard coal; Indicates the amount of power generated; Coal consumption when generating electricity for the external grid.

8. The method for optimizing the scheduling of electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology according to claim 7, characterized in that: The method also includes the step of optimizing the low-carbon dispatching strategy by evaluating the carbon emission reduction contribution of V2G electric vehicles in the distribution network dispatching.

9. An optimized dispatching system for electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology is characterized by: The system for implementing the method for optimizing the scheduling of electric vehicles participating in carbon emission reduction in distribution networks based on V2G technology as described in claim 8 comprises: A data acquisition unit, used to collect distribution network load and renewable energy power forecast data; an upper-level scheduling model construction unit, connected to the data acquisition unit, for constructing an upper-level scheduling model to maximize the utilization rate of renewable energy; A lower-level scheduling model construction unit, connected to the data acquisition unit, is used to construct a lower-level scheduling model to optimize the charging and discharging strategy of the electric vehicle on the premise of meeting the daily electricity demand of the electric vehicle users; a coordination unit connected to the upper-layer dispatch model construction unit and the lower-layer dispatch model construction unit, and configured to coordinate independent decisions of the distribution network dispatch center and the electric vehicle aggregator through an alternating direction multiplier method ADMM; The evaluation unit is connected to the coordination unit and is used to calculate the amount of electricity purchased from the external power grid reduced by the V2G electric vehicle during the scheduling process and evaluate the carbon emission reduction benefits brought about thereby.

10. The V2G technology-based optimized dispatching system for electric vehicles participating in carbon emission reduction in distribution networks according to claim 9, characterized in that: The system further comprises an optimization unit connected to the evaluation unit, for optimizing the low-carbon scheduling strategy according to the evaluation result of the carbon emission reduction benefit.

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