A collaborative optimization method for private charging pile sharing mode operation and vehicle-to-grid connection

Abstract

A collaborative optimization method for the operation of a private charging pile sharing model and vehicle-to-grid coordination includes: building a private charging pile sharing network through a digital platform and opening up idle charging piles to other car owners. Based on the charging needs of electric vehicles and the spatiotemporal distribution of charging piles, a scheduling strategy is used to achieve precise matching of vehicles and charging piles, thereby improving resource utilization. Combined with vehicle-to-grid technology, electric vehicles reverse power during peak grid load periods to optimize grid load balance. A multi-objective robust optimization model is constructed, taking into account uncertainty information, low-carbon management, and demand response strategies to comprehensively optimize economic benefits, environmental benefits, and grid stability. The model is linearized and converted into a mixed integer programming model to improve solution efficiency. Using real data, the feasibility and optimization effect of the model are verified, providing decision support for actual operations and offering innovative solutions to address the shortage of public charging facilities, alleviate grid pressure, and promote low-carbon development.

Description

Technical Field

[0001] The present invention relates to charging pile operation optimization and two-way interaction technology between electric vehicles and energy systems, and in particular to a collaborative optimization method for shared mode operation of private charging piles and vehicle-to-grid connection. Background Art

[0002] Compared to the expansion of electric vehicles, charging infrastructure development has lagged, and existing charging resources are underutilized. The pace of charging infrastructure construction has not kept pace with the growth of electric vehicles, directly leading to an imbalance between supply and demand. Furthermore, the low utilization rate of existing charging infrastructure has further exacerbated the shortage of charging facilities. In some cities, private charging stations are used only once a day or less. This inefficient use is a significant waste of charging resources and exacerbates the severity of the charging problem. This phenomenon not only highlights the urgency of charging infrastructure construction, but also emphasizes the need for operational scheduling and optimized resource utilization.

[0003] Driven by practical needs and a variety of favorable factors, the private charging station sharing model has emerged globally. This model allows private charging station owners to make their idle charging stations available to other car owners through digital platforms (such as apps or websites). Addressing the tight supply and demand of charging facilities and inefficient utilization, private charging station sharing has become an effective way to optimize charging resources and alleviate the problem of charging difficulties.

[0004] By leveraging the potential of existing private charging stations, the sharing model can, to a certain extent, offset the shortage of public charging stations and expand the coverage of the charging network. Furthermore, the geographical distribution of private charging stations is more flexible and extensive, making the sharing model better suited to the diverse charging needs of electric vehicle owners. Vehicle-to-grid (V2G) energy management promotes bidirectional interaction between electric vehicles and the energy system, making it possible for them to be integrated into the private charging station sharing model. As a key supporting technology for this bidirectional interaction, V2G has moved from the experimental stage to global scale application. Using electric vehicles as carriers, V2G systems utilize scheduling strategies to dynamically balance electricity supply and demand at different times. The core of V2G systems lies in the bidirectional flow of information and energy, enabling electric vehicles to act not only as electricity consumers but also as mobile energy storage devices. Leveraging grid technology and advanced communication protocols, V2G systems can flexibly adjust charging and discharging behavior based on real-time electricity prices or grid conditions. This flexible energy exchange mechanism is crucial for balancing electricity supply and demand. Furthermore, the simultaneous charging of large numbers of electric vehicles, particularly during peak evening hours, can lead to peak-on-peak demand. Furthermore, the increasing integration of renewable energy sources into the grid is also exacerbating grid load pressure. Vehicle-to-grid technology, combined with a shared platform for private charging stations, can further optimize charging time and spatial distribution, effectively alleviating the challenges of load fluctuations. It not only effectively addresses current challenges such as electricity market reform, demand-side response, and energy mix optimization, but also promotes low-carbon transformation and enhances the resilience and adaptability of the power system.

[0005] It should be noted that the information disclosed in the above background technology section is only used to understand the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0006] The main purpose of the present invention is to overcome the defects existing in the above-mentioned background technology and provide a method for collaborative optimization of private charging pile sharing mode operation and vehicle-to-grid connection.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for collaboratively optimizing the operation of a private charging pile sharing mode and vehicle-to-grid connectivity includes the following steps:

[0009] S1. Private charging pile sharing network: Based on a digital platform, private charging piles are open to the public during idle periods for use by other electric vehicle owners, enabling the sharing of charging resources;

[0010] S2. Scheduling Strategy and Matching Mechanism: Based on the charging needs of electric vehicles and the spatiotemporal distribution of private charging piles, a scheduling strategy is used to accurately match electric vehicles with private charging piles, optimize vehicle-pile matching efficiency, and improve charging resource utilization.

[0011] S3. Vehicle-to-grid energy management: Integrating vehicle-to-grid technology enables electric vehicles to reversely supply power to the grid during peak load periods, enabling two-way energy interaction between electric vehicles and the grid, optimizing grid load balance, and reducing grid pressure.

[0012] S4. Multi-objective robust optimization model: Construct a multi-objective robust optimization model that considers uncertainty information, low-carbon management, and demand response strategies, comprehensively considers economic benefits, environmental benefits, and grid stability, and improves overall efficiency.

[0013] S5. Model linearization and solution: Use mathematical methods to linearize complex optimization models, transform them into mixed integer programming models, and use solvers to solve them efficiently to ensure the operability and practicality of the models.

[0014] The present invention has the following beneficial effects:

[0015] The present invention proposes an innovative private charging pile sharing model and its vehicle-to-grid energy management method, which realizes the efficient use of charging resources and two-way interaction with the power grid, significantly improving economic and environmental benefits. First, in response to the operational management issues of the private charging pile sharing model, the present invention proposes a vehicle-pile matching mechanism based on the spatiotemporal distribution characteristics of supply and demand, which optimizes the allocation efficiency of charging resources, makes it an important supplement to the existing charging network, and brings significant benefits to all parties involved. Secondly, by integrating vehicle-to-grid technology, the present invention realizes two-way energy interaction between electric vehicles and power grids, which not only meets the charging needs of electric vehicles, but also gives full play to the role of its distributed energy storage equipment, balances supply and demand through demand response strategies, smoothes the grid load curve, reduces the pressure on the power system, and at the same time helps low-carbon electricity use and promotes the green transformation of the power system. In addition, the present invention develops a multi-objective robust optimization model that takes into account uncertainty information, low-carbon management and demand response strategies, and simplifies the model complexity and improves the solution efficiency through scalarization and linear transformation, ensuring the operability and practicality of the model. Finally, experimental verification using real data verified the feasibility and optimization effect of the model and method of the present invention, demonstrated its potential in meeting charging needs, improving resource utilization, alleviating grid pressure, and promoting low-carbon development, and provided decision support for actual operations.

[0016] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a diagram of the vehicle-to-grid system in the private charging pile sharing mode according to an embodiment of the present invention.

[0018] Figure 2 For external living or other power needs at different times.

[0019] Figure 3 The car-to-pile ratio in private charging pile sharing models at different scales.

[0020] Figure 4 Share time and utilization of private charging stations.

[0021] Figure 5 Rated battery capacity utilization rate for electric vehicles.

[0022] Figure 6 is the total cost under ASAP and RCDR_C / D strategies.

[0023] Figure 7 is the return at different times under the RCDR_C / D strategy.

[0024] Figure 8 is the load fluctuation under ASAP and RCDR_C / D strategies.

[0025] Figure 9 is the load curve of electric vehicle at different times.

[0026] Figure 10 is the SOC of the electric vehicle at different times.

[0027] Figure 11 The carbon emissions per unit of electricity produced at different times.

[0028] Figure 12 is the total carbon emissions of electric vehicles under ASAP and RCDR_C / D strategies.

[0029] Figure 13 The distribution of carbon emissions of electric vehicles under ASAP and RCDR_C / D strategies.

[0030] Figure 14 This is an overall flow chart of the collaborative optimization method for private charging pile sharing mode operation and vehicle-to-grid connection of the present invention. DETAILED DESCRIPTION

[0031] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope of the present invention and its application.

[0032] The optimization of the shared operation model of private charging piles and the two-way interaction between electric vehicles and energy systems are crucial to improving resource utilization, supplementing the existing charging network, and enhancing economic and environmental benefits. However, there are some challenges in reality that restrict the efficient operation of this model. The present invention focuses on the shared operation model of private charging piles and its vehicle-to-grid energy management, which includes multiple dimensions such as the characteristics of the model, the integration of emerging technologies, and complex interactions. In addition, the key elements such as uncertainty information, low-carbon management and demand response strategies and their impacts are further taken into consideration in the proposed multi-objective robust optimization framework, and their potential impacts are analyzed. At the same time, the utilization rate of private charging resources is improved through scheduling strategies and matching mechanisms, the supply and demand relationship is balanced, and energy flow is effectively managed. Experimental verification using real data demonstrates the feasibility and development potential of the present invention, and verifies the improvement of multiple economic and environmental benefits, such as making up for the lack of public charging facilities, alleviating the pressure on the power grid, and improving the coverage and quality of charging services.

[0033] The present invention focuses on the operation of a private charging pile sharing model under uncertain information and its vehicle-to-grid energy management, which includes factors such as the characteristics of the model, the integration of emerging technologies, and complex interactions. This invention combines the private charging pile sharing model with vehicle-to-grid energy management for the first time. In addition, the present invention further incorporates key elements such as uncertainty information, low-carbon management, and demand response strategies into the optimization model. The optimization decision-making framework proposed in this invention comprehensively considers key factors and their influences, coordinates the interests of multiple parties in a complex system, and achieves a comprehensive improvement in economic and environmental benefits. The main innovative contributions of this invention include:

[0034] (1) The present invention focuses on the scheduling decision-making problem of the private charging pile sharing model, further improving the operational efficiency of this model. In combination with the characteristics of the spatiotemporal distribution of supply and demand in the private charging pile sharing model, the present invention matches vehicles and piles based on the supply and demand relationship in different time periods under the point-to-point sharing mode, taking into account the sharing time of private charging piles in different locations. The matching scheme not only optimizes the operational efficiency of the private charging pile sharing model, making it a better supplement to the existing charging network, but also brings significant multiple benefits to all parties involved.

[0035] (2) The present invention integrates vehicle-to-grid energy management into the operation of a private charging pile sharing model, and promotes two-way interaction between electric vehicles and the power system under the premise of meeting charging needs. Throughout the process, considering the influence of external factors such as life or other electricity demand, the present invention not only balances the supply and demand relationship through demand response strategies, but also gives full play to the role of electric vehicles as distributed energy storage devices. In addition to economic benefits, comprehensive benefits such as low-carbon management and smooth load fluctuations are also considered. By optimizing the charging and discharging behavior of electric vehicles, the load curve is smoothed, the pressure on the power system is reduced, and its safety and stability are maintained. In addition, the present invention focuses on the carbon emissions of electric vehicles throughout their life cycle, extends it to the low-carbon electricity use link, and helps the green transformation of the power system, thereby providing an example for sustainable energy utilization.

[0036] Figure 1 A vehicle-to-grid system diagram in a private charging pile sharing mode according to an embodiment of the present invention is shown.

[0037] (3) The present invention develops a multi-objective optimization model under uncertain information, and performs scalarization and linear transformation on the model. Based on uncertain information and multiple optimization directions, the present invention constructs a multi-objective robust optimization model for the operation of private charging pile sharing mode and its vehicle-to-grid energy management. Taking into account the complexity of the original model, the present invention uses an effective method to simplify the model, making it easier to solve and apply. First, the original model is restated using a scalarization method, that is, it is converted into an equivalent nonlinear form. On this basis, linear transformations are performed on uncertain parameters and non-convex nonlinear terms respectively, which reduces the computational complexity and improves the solution efficiency.

[0038] (4) The method of the present invention was experimentally verified using real data: (1) The private charging pile sharing model can fully meet the charging needs of electric vehicles under an appropriate vehicle-to-pile ratio. This model still has great development potential in terms of the level of shared time utilization and participation scale of private charging piles; (2) Vehicle-to-grid energy management can be introduced into the private charging pile sharing model, thereby achieving a two-way flow of energy and showing a large dispatch space; (3) Optimizing decisions on operation and management can achieve a comprehensive improvement in economic and environmental benefits, and promote efficient resource utilization and low-carbon development.

[0039] The embodiment of the present invention provides a method for collaborative optimization of private charging pile sharing mode operation and vehicle-to-grid connection, including the following steps (see Figure 14 ):

[0040] Step S1, private charging pile sharing network: Based on the digital platform, private charging piles are open to the public during idle periods for use by other electric vehicle owners, realizing the sharing of charging resources.

[0041] Step S2, scheduling strategy and matching mechanism: Based on the charging demand of electric vehicles and the spatiotemporal distribution of private charging piles, a scheduling strategy is used to achieve accurate matching of electric vehicles and private charging piles, optimize the efficiency of vehicle-pile matching, and improve the utilization rate of charging resources.

[0042] In a preferred embodiment, the scheduling strategy and matching mechanism includes: taking into account the electric vehicle's charging needs, remaining power, driving distance, and the opening hours of private charging piles to accurately match electric vehicles with private charging piles; optimizing the vehicle-pile matching scheme to improve the utilization rate of private charging piles and meet the electric vehicle's charging needs. It can further include: considering the electric vehicle's battery status and charging needs to optimize charging time; and comprehensively adjusting the electric vehicle's charging plan to adapt to the grid status and charging needs.

[0043] Step S3, vehicle-to-grid energy management: Combined with vehicle-to-grid technology, electric vehicles can reversely supply power to the grid when the grid load is peak, realizing two-way energy interaction between electric vehicles and the grid, optimizing grid load balance, and reducing grid pressure.

[0044] In a preferred embodiment, vehicle-to-grid energy management includes dynamically adjusting EV charging and discharging behavior based on grid conditions and EV demand; utilizing EVs as distributed energy storage devices to provide reverse power to the grid during peak grid loads; and further including flexibly adjusting EV charging and discharging strategies based on real-time electricity prices or grid conditions; optimizing the distribution of EV discharge revenue, and incentivizing private charging station owners to join a shared model.

[0045] Step S4, multi-objective robust optimization model: Construct a multi-objective robust optimization model that takes into account uncertainty information, low-carbon management, and demand response strategies, comprehensively consider economic benefits, environmental benefits, and grid stability, and improve overall efficiency.

[0046] In a preferred embodiment, the multi-objective robust optimization model includes: minimizing the total cost, including charging cost, driving distance penalty cost and external demand fluctuation penalty cost; minimizing grid load fluctuation and improving grid stability; minimizing carbon dioxide emissions and achieving low-carbon management.

[0047] Specifically, the multi-objective robust optimization model preferably includes the following objective functions and constraints:

[0048] The objective functions include: (1) minimizing the total cost, including charging cost, driving distance penalty cost, and external demand fluctuation penalty cost; (2) minimizing grid load fluctuation; and (3) minimizing carbon dioxide emissions.

[0049] The constraints specifically include: charging price constraint: the charging price is composed of the grid electricity price and the service price, ensuring the reasonable distribution of charging costs; private charging pile opening time constraint: all charging and discharging interactions must be carried out during the opening hours of private charging piles to ensure the effective use of charging resources; electric vehicle state constraint: electric vehicles can only be in one of the charging, discharging or idle states in the same time period to ensure the reasonable scheduling of charging and discharging behaviors; vehicle-pile matching constraint: each electric vehicle can only be matched with one private charging pile, and each charging pile can serve at most one electric vehicle in the same time period to ensure the rationality and efficiency of matching; battery power constraint: the charging and discharging behavior of electric vehicles must meet the upper and lower limits of their battery power to ensure the safe use and life of the battery; power supply and demand balance constraint: the power output of the power grid is consistent with all power demands to ensure the supply and demand balance of the power system; power grid load upper and lower limit constraints: the power supply load of the power grid in each time period must be within its upper and lower limits to ensure the safe operation of the power grid.

[0050] The multi-objective robust optimization model may further include: considering the uncertainty of external power demand and constructing an uncertainty set; converting the multi-objective optimization problem into a single-objective optimization problem through a scalarization method to improve the solution efficiency of the model.

[0051] Step S5, model linearization and solution: The complex optimization model is linearized by mathematical methods and converted into a mixed integer programming model, which is solved efficiently using a solver to ensure the operability and practicality of the model.

[0052] The model linearization and solution include: converting uncertainty parameters into linear form through the dual method; using the McCormick method to process non-convex nonlinear terms and linearize the model.

[0053] The present invention also uses real data for experimental verification, verifying the feasibility and optimization effect of the private charging pile sharing model and vehicle-to-grid energy management, providing decision support for actual operations.

[0054] Specifically, we conduct experimental case studies using real-world data to verify the feasibility and development potential of private charging station sharing models. We analyze the operational performance and dispatchable space of vehicle-to-grid energy management under different electric vehicle scales. We analyze electric vehicle costs, discharge benefits, and grid load fluctuations under different dispatch strategies. We also provide a lifecycle perspective on the effectiveness of low-carbon management to optimize the carbon emissions of electric vehicles.

[0055] By combining private charging pile sharing with vehicle-to-grid energy management, the present invention achieves efficient utilization of charging resources, optimized balance of grid load, and comprehensive improvement of economic and environmental benefits, providing an innovative solution for the operational optimization of private charging facilities for electric vehicles.

[0056] The following further describes specific embodiments of the present invention and experimental verification.

[0057] This invention involves an innovative model for operating shared private charging stations and managing vehicle-to-grid energy. It establishes a foundational model encompassing objective functions related to total cost, load fluctuation, and CO2 emissions, as well as constraints such as multi-party interaction, vehicle-to-pile matching, and charging and discharging scheduling.

[0058] This paper focuses on the operation of a shared private charging station model under uncertain information, and the energy dispatch management of the vehicle-to-grid system. The entire network mainly consists of key components such as the power grid, shared private charging stations, electric vehicles, an intelligent control platform, and external power demand. Figure 1 It shows these components and their interactions, energy flows and information flows, that is, information such as reality composition, matching mechanism, multi-party interaction, etc.

[0059] In the operational phase of the private charging station sharing model, the temporal and spatial distribution of charging supply and demand is considered to optimize the allocation of charging resources and balance supply and demand. Shared private charging stations are geographically flexible and widespread, with a distributed layout, and EV charging demand is also dispersed. Therefore, the vehicle-to-pile matching process requires a point-to-point matching mechanism that takes into account the varying remaining charge levels of EVs requiring charging. Furthermore, since private charging stations are not always available, all charging and discharging interactions must occur during their operating hours. Regarding the vehicle-to-grid energy dispatching component, a bidirectional energy dispatch scheme within the private charging station sharing model considers the impact of uncertain external electricity demand and develops an energy management solution from a demand response perspective that balances economic and environmental benefits. In this system, the utility acts as an aggregator, coordinating the bidirectional flow of energy between the power system and EVs through an intelligent control console to ensure that diverse demands are met. In addition to maximizing economic benefits, the system addresses the challenges of uncertain external electricity demand by optimizing EV charging and discharging behavior, thereby smoothing the load curve and mitigating load fluctuations, thereby alleviating pressure on the grid. In addition, in order to reduce carbon emissions throughout the life cycle of electric vehicles, emission reduction measures are extended to low-carbon electricity use to further promote low-carbon management.

[0060] This paper develops a multi-objective optimization model to characterize the operation of a private charging pile sharing model and its vehicle-to-grid energy dispatch management. The proposed optimization decision-making framework integrates key factors and their impacts, including uncertainty information, low-carbon management, and demand response strategies, to coordinate the interests of multiple parties in a complex system and achieve comprehensive improvements in economic and environmental benefits. The main questions addressed are: (i) Can the private charging pile sharing model complement the existing charging network through efficient point-to-point vehicle-to-pile matching to effectively meet the charging needs of electric vehicles? (ii) Can vehicle-to-grid energy management be incorporated into the private charging pile sharing model to achieve two-way interaction between electric vehicles and the energy system? (iii) What scheduling strategies and matching mechanisms can be used to improve economic and environmental benefits?

[0061] Tables 1 to 3 list the sets, parameters, and variable symbols used in the model and their definitions.

[0062] Table 1: List of collective symbols

[0063]

[0064] Table 2: Parameter symbol list

[0065]

[0066] Table 3: List of variable symbols

[0067]

[0068] The proposed model sets three objective functions, taking into account the private charging pile sharing model and its vehicle-to-grid energy management characteristics, as well as the demand-side management strategy. The first objective function is to minimize the total cost:

[0069]

[0070] The first of these is the cost of charging electric vehicles The second is the subsidy for charging in a shared model with private charging piles, to increase the attractiveness of private charging piles. The third item is the income generated by the discharge of electric vehicles

[0071] , the owner of the shared private charging pile will receive a certain percentage of the profit. At the same time, the distance between the electric vehicle and the matching charging pile is taken into account in the objective function. and fluctuations in external electricity demand In addition, formula (1) contains the RCDR_C / D (price-based and incentive-based converged demand response) scheduling strategy, where C jt represents the charging price of private charging pile j at different times, It represents the subsidy that electric vehicle i receives based on its charging volume in the private charging pile sharing model.

[0072] The second objective function is to minimize the load fluctuation in the region, that is, to maintain the stability of the power grid in the region.

[0073] (2)

[0074] The supply side and demand side The load difference is the smallest. Since there is energy loss during the discharge process of electric vehicles in reality, formula (2) takes into account the actual discharge amount of electric vehicles.

[0075] The third objective function is to minimize CO2 emissions, that is, to focus on environmental benefits while improving economic benefits.

[0076]

[0077] Considering the power consumption at different times , and the carbon emissions per unit of electricity produced at the corresponding moment (ε t Since the energy supply relationship is different in different periods, the discharge of electric vehicles also acts as an energy supplier.

[0078] The above formulas (1)–(3) are the objective functions of the basic model, and all the model constraints can be found in the following (4)–(20).

[0079] The charging price of electric vehicles is determined by the grid electricity price. and service prices The electricity price and service price are determined by the grid and the owner of the charging pile.

[0080]

[0081] In the private charging pile sharing mode, since private charging piles are not open all day, all charging and discharging interactions must be carried out within their opening hours. The sum of the interaction time between all electric vehicles and any charging pile does not exceed the duration of the private charging pile's open sharing, that is, it meets the sharing time limit set by the private charging pile owner, as shown in inequality (5). At the same time, the interaction between any electric vehicle i, i∈I, and the matched private charging pile shall not be earlier than the start time of its open sharing, and no later than the end time of open sharing, as shown in inequalities (6) and (7).

[0082]

[0083] Under the premise of meeting the user's charging needs, any electric vehicle i, i∈ I, can only maintain one of the three states of charging, idle and discharging in a time period, as shown in equation (8).

[0084]

[0085] Formulas (9), (10), and (11) represent the characteristics of vehicle-pile matching in the shared private charging pile model. Due to the distributed layout of shared charging piles, the vehicle-pile matching process takes into account the remaining power of the electric vehicle to be charged. Equation (9) indicates that the interaction behavior of the electric vehicle is completed at a single charging pile. At the same time, any private charging pile j, j∈J, can serve at most one electric vehicle in the same period, as shown in inequality (10). In addition, based on the remaining power of the battery of electric vehicle i, i∈I, private charging piles are reasonably allocated so that the electric vehicle has enough time to reach the matching charging pile, as shown in inequality (11).

[0086]

[0087] Equations (12) and (13) represent the nominal charge and discharge capacity of electric vehicle i, i∈I, in period t, respectively, without considering the transmission efficiency of the electric vehicle during the charging and discharging process. The battery capacity of any electric vehicle when leaving the charging location is not less than the charging value set by the owner, that is, the charging capacity requirement is met, as shown in inequality (14). In order to be closer to the real scene, the transmission efficiency of the electric vehicle charging and discharging is considered in the model, that is, .

[0088]

[0089] Equation (15) represents the load balance between power supply and demand, i.e., power supply (power grid output G t and electric vehicle discharge ) and electricity demand (electric vehicle charging capacity and life or other electricity needs E t ) one-to-one correspondence. Electric vehicles are charged under conditions of low electricity prices or low-carbon electricity, and reversely discharged when needed. In addition, due to the limitations of existing charging facilities and distribution facilities, the power supply load of the power grid in each period has upper and lower limits, namely 0 and U t , as shown in inequalities (16) and (17).

[0090]

[0091] Equation (18) represents the iterative process of EV charging and discharging, that is, the battery charge of the EV in period t depends on the battery charge in the previous period and the charging and discharging decision during period t.

[0092]

[0093] In order to preserve the service life of electric vehicle batteries, inequalities (19) and (20) strictly limit the upper and lower bounds of the charging and discharging processes. Inequality (19) indicates that during the entire charging process, the battery capacity of electric vehicle i, i∈I does not exceed its own rated battery capacity. Based on the actual situation, during the entire discharging process, the discharge depth of the electric vehicle is limited to Di, as shown in inequality (20).

[0094]

[0095] The operation of private charging pile sharing models and the management of vehicle-to-grid energy are complex and uncertain. Therefore, based on the basic model and uncertainty set, this paper establishes a multi-objective robust optimization model. Furthermore, the decision-making of multi-objective robust optimization problems is multidirectional. Therefore, the model generally requires scalarization. Using methods such as maximum-rank scalarization and robust enhanced weighted Chebyshev, the original model is converted into an equivalent nonlinear form.

[0096] Real-life electricity demand is full of uncertainty. Therefore, the present invention constructs an uncertainty set using partial moment information of real data. Based on the basic model, a multi-objective robust optimization model is established that considers the uncertainty of external life or other electricity demand.

[0097] Definition 1 Let the lower bound of the uncertain set be , interval length ζ∈ R T To simplify the mathematical expression, it is assumed that the nominal value of E is its minimum value. Therefore, the uncertainty set is as follows

[0098]

[0099] Therefore, we get the following uncertain set Multi-objective robust optimization model

[0100]

[0101] Scalar quantization

[0102] Due to the high complexity of multi-objective robust optimization problems, they are generally converted into single-objective optimization problems. Therefore, the multi-objective robust optimization model is scalarized to simplify the model structure. That is, by applying methods such as maximum ranking scalarization and robust enhanced weighted Chebyshev, it is converted into an equivalent nonlinear form.

[0103] Definition 2: For a multi-objective uncertain optimization problem, for a given weight vector and reference points , and its corresponding maximum sorting optimization problem can be expressed as

[0104]

[0105] Proposition 1 has an uncertain set The objective functions (21), (22), (23) can be written as follows form

[0106]

[0107] Proposition 2 Equivalent to the following nonlinear form

[0108]

[0109] Model linear transformation

[0110] Due to the presence of special forms of uncertainty and non-convex nonlinear terms in the original model, commercial solvers cannot solve it. Therefore, it is necessary to apply different mathematical methods to convert it. First, the duality method in discrete optimization is applied to convert the uncertain parameters into corresponding linear parameters. Then, the non-convex nonlinear terms in the model are processed using the method of McCormick et al. Finally, the original multi-objective robust optimization model of the present invention is converted into a mixed integer programming model that is easy to solve.

[0111] Linearization of uncertain parameters

[0112] Because both the objective function and the constraints contain uncertainties, solving the original model presents a significant challenge. This paper primarily utilizes duality and mathematical derivation to transform the uncertain parameters into equivalent linear forms, thereby improving the feasibility of the solution.

[0113] Proposition 3 Inequality (28) is equivalent to the following linear form

[0114]

[0115] Proposition 4 Inequality (29) is equivalent to the following linear form

[0116]

[0117] Proposition 5 Inequality (30) is equivalent to the following linear form

[0118]

[0119] Proposition 6: Equation (24) is equivalent to the following linear form

[0120]

[0121] Linearization of nonconvex nonlinear terms

[0122] Since the original model contains difficult-to-handle non-convex nonlinear terms that greatly slow down the solution process, the McCormick method is used to further transform the non-convex nonlinear terms into equivalent linear forms to improve the solution efficiency.

[0123] Proposition 7 Inequality (5) with nonlinear terms is equivalent to the following linear form

[0124]

[0125] The mixed integer programming model is finally simplified to

[0126] Finally, the multi-objective robust optimization model established by the present invention is transformed into the following mixed integer programming model which is easy to solve

[0127]

[0128] Experimental verification

[0129] Based on electric vehicle operation management, as well as electricity demand data from daily life or other situations, experimental verification is conducted. Specifically, the experimental verification includes: (i) verifying the feasibility and development potential of the private charging pile sharing model; (ii) exploring the application effect of vehicle-to-grid management in the private charging pile sharing model; and (iii) testing the performance of the proposed methods and strategies in improving multiple economic benefits on both the supply and demand sides, demand-side management, and low-carbon management.

[0130] The experimental example of the present invention divides a day into 96 time periods of 15 minutes each, and uses them as decision-making units. According to the existing actual situation, the time periods for the open sharing of private charging piles in this experimental verification can be divided into three categories, namely 07:00-22:00, 08:00-22:00 and 00:00-23:00. The battery capacity of the electric vehicle is 25kWh, and its charging and discharging power is 10kW and 8kW respectively. The battery power consumed by the electric vehicle per 100 kilometers is 15kWh. The driving distance of the electric vehicle to the private charging pile is randomly generated according to the real scenario. The attenuation rate of battery charging and discharging is about 5%, that is, the transmission efficiency Tr i C , Tr i D The maximum battery discharge depth of an electric vehicle is 90%. Figure 2 represents the average value of daily or other electricity demand at different times. Due to the equal emphasis on economic and environmental benefits, as well as the interests of multiple parties in the system, the weight vector θ and the reference point l are taken as 1 and 0, respectively.

[0131] The private charging pile sharing model not only meets the charging needs of electric vehicles, but also has great potential. Figure 3 It shows that when the ratio of electric vehicles to private charging piles is about 1.5, the private charging pile sharing model can fully meet the charging needs of electric vehicles. All electric vehicles that need to be charged can be matched with suitable shared private charging piles. When all charging needs are met, only part of the shared time of private charging piles is used. A new indicator is introduced to evaluate the potential of the private charging pile sharing model, namely , which represents the ratio of the time electric vehicles spend using a charging station to the time private charging stations are open for shared use. A higher STU (shared time utilization) indicates greater utilization of private charging stations, while a lower STU indicates more unused shared time. Figure 4 The data shows that when the number of electric vehicles increases from 30 to 60, the utilization rate of shared charging pile time decreases from 65.32% to 57.16%. This further demonstrates that in the private charging pile sharing model, as the number of participants increases, the available charging resources increase, and the potential to be tapped also increases.

[0132] Vehicle-to-grid energy management can be implemented in a private charging pile sharing model with a large dispatch space. The private charging pile sharing model not only fully meets the charging needs of electric vehicles, but also realizes two-way energy interaction. A new indicator is introduced to evaluate the operational level of vehicle-to-grid management, namely BPU = , which represents the ratio of the electric vehicle’s battery discharge to its rated battery capacity. A higher BPU (Battery Power Utilization) indicates a higher level of vehicle-to-grid management. Figure 5 The results show that the operational level of vehicle-to-grid management remains low at approximately 11% across all EV scales, indicating that there is still a large amount of dispatchable space for EVs that can be further explored and released.

[0133] The RCDR_C / D scheduling strategy significantly reduces the cost for electric vehicle owners. To further reduce charging costs, the present invention sets lower charging prices during idle time to attract charging demand during peak demand periods, and sets higher charging prices during busy times to guide electric vehicles to charge during low demand periods, which constitutes the RCDR_C / D scheduling strategy. Compared with disorderly charging (ASAP), that is, the actual charging situation, the RCDR_C / D scheduling strategy has a significant cost reduction effect, and the cost reduction rate under different electric vehicle scales exceeds 45%. When the electric vehicle scale is 60, the cost is reduced by 48.33%. Figure 6 In addition, the cost under disordered charging in the figure only includes the charging cost, while the cost under the RCDR_C / D scheduling strategy also includes the penalty cost caused by driving distance and external demand fluctuations.

[0134] The RCDR_C / D scheduling strategy enables private charging pile owners to obtain considerable benefits. The income of private charging pile owners is composed of the service fee for electric vehicle charging and the revenue share of electric vehicle discharge. The income brought by the private charging pile sharing model is jointly affected by factors such as the participation and matching degree of electric vehicles and private charging piles on both the supply and demand sides. For example, when the scale of electric vehicles increases from 30 to 60, the income of private charging pile owners increases from US$44.18 to US$84.46, and there is a certain linear relationship between the two. In addition, the income obtained at different times shows obvious characteristics. For example, Figure 7 The figure shows the specific distribution of daily revenue under the RCDR_C / D scheduling strategy when the number of electric vehicles is 60. The figure shows that the revenue of private charging pile sharing shows a clear peak-valley trend, which can provide a reference for private charging pile owners to set the opening time for sharing.

[0135] In the face of external factors, the vehicle-to-grid system in the private charging pile sharing model has a positive effect on alleviating load fluctuations. In this invention, load fluctuations are mainly affected by external factors such as electric vehicle charging and discharging, as well as daily life or other electricity demand. Figure 8 The results show that, compared to ASAP, the RCDR_C / D scheduling strategy mitigates regional load fluctuations to a certain extent. As the number of electric vehicles increases, the ratio of dispatchable space to residential or other electricity demand increases slightly, but still only accounts for approximately 6%. Therefore, at a given electric vehicle scale, the ability of electric vehicle charging and discharging scheduling to regulate load fluctuations remains limited.

[0136] In order to adapt to the changes in external environmental conditions, the RCDR_C / D scheduling strategy adjusts the load curve of electric vehicles and realizes demand-side management. According to the external environmental conditions, based on the RCDR_C / D scheduling strategy, the load curve of electric vehicles has changed significantly, such as Figure 9 shown. Figure 9 In the equation, (30), (40), (50), and (60) represent the number of electric vehicles in the experiment. The load curve of electric vehicles is adjusted through demand-side management, that is, the charging and discharging of each electric vehicle at different times is decided. In addition, Figure 10 The changes in SOC (battery state of charge) of electric vehicles at different times under the ASAP and RCDR_C / D scheduling strategies are shown.

[0137] Focus on carbon emissions in the electricity production stage from a life cycle perspective to further enhance the environmental benefits of electric vehicles. Grid electricity mainly comes from thermal power, wind power, nuclear power, solar power and other energy sources. There are significant differences in the carbon dioxide released by different electricity sources during the electricity production process. Therefore, the carbon emissions per unit of electricity at different times in the electricity production stage are different. According to previous studies, the carbon emissions per unit of electricity output at different times can be divided into three categories: 0.985kg / kWh, 0.562kg / kWh, and 0.005kg / kWh. The specific distribution of carbon emissions at different times is as follows: Figure 11 shown.

[0138] Taking the life cycle into consideration, the RCDR_C / D scheduling strategy allows for better scheduling flexibility of electric vehicles and promotes low-carbon management. The carbon dioxide emissions of electric vehicles are related to the amount of electric vehicle charging and the carbon emissions per unit of electricity produced over the corresponding time. Since CO2 emissions have already been calculated during the charging period of electric vehicles, the electricity provided by their discharge is considered clean energy. (1) The scheduling strategy based on RCDR_C / D enables electric vehicles to use more low-carbon electricity, broadening the stage of carbon emission reduction for electric vehicles. Figure 12 It shows that after the RCDR_C / D charging and discharging scheduling, compared with ASAP, the CO2 emissions of electric vehicles in different scale vehicle-to-grid systems have all been reduced to a certain extent. (2) The regularity of carbon emission distribution provides a scientific basis for further low-carbon management. Under RCDR_C / D, the charging and discharging decisions at different times make the carbon emissions of electric vehicles show obvious regularities. For example, in different scale vehicle-to-grid systems, the time period of 14:00-16:00 is the peak period of carbon dioxide emissions of electric vehicles. The specific distribution is as follows: Figure 13 shown. Figure 13 In the figure, (30), (40), (50), and (60) represent the number of electric vehicles in the experiment. The above results show that charging prices play a significant role in scheduling decisions. Therefore, if charging prices are scientifically set based on the carbon emissions per unit of electricity produced at different times, the level of low-carbon management will be higher and the environmental benefits of the scheduling strategy will be more prominent.

Claims

1. A method for collaborative optimization of private charging pile sharing mode operation and vehicle-to-grid connection, characterized in that: The following steps are involved: S1. Private charging pile sharing network: Based on a digital platform, private charging piles are open to the public during idle periods for use by other electric vehicle owners, enabling the sharing of charging resources; S2. Scheduling Strategy and Matching Mechanism: Based on the charging needs of electric vehicles and the spatiotemporal distribution of private charging piles, a scheduling strategy is used to accurately match electric vehicles with private charging piles, optimize vehicle-pile matching efficiency, and improve charging resource utilization. S3. Vehicle-to-grid energy management: Integrating vehicle-to-grid technology enables electric vehicles to reversely supply power to the grid during peak load periods, enabling two-way energy interaction between electric vehicles and the grid, optimizing grid load balance, and reducing grid pressure. S4. Multi-objective robust optimization model: Build a multi-objective robust optimization model that considers uncertainty information, low-carbon management, and demand response strategies, comprehensively considering economic benefits, environmental benefits, and grid stability to improve overall performance; The multi-objective robust optimization model includes: Minimize the total cost, including charging cost, driving distance penalty cost and external demand fluctuation penalty cost; Minimize grid load fluctuations and improve grid stability; Minimize carbon dioxide emissions and achieve low-carbon management; Considering the uncertainty of external electricity demand, an uncertainty set is constructed; The multi-objective optimization problem is transformed into a single-objective optimization problem through scalarization method to solve the model; S5. Model linearization and solution: Use mathematical methods to linearize complex optimization models, transform them into mixed integer programming models, and solve them using solvers; The model linearization and solution include: The uncertainty parameters are transformed into linear form through the dual method; The McCormick method is used to deal with non-convex nonlinear terms and the model is linearized; After step S4 and step S5, the constructed multi-objective robust optimization model is finally converted into a mixed integer programming model, which includes the following constraints and objective function: Constraints on the composition of electric vehicle charging prices, limiting charging prices to consist of grid electricity prices and service prices; The first constraint on the opening hours of private charging piles is that electric vehicles should start charging no earlier than the opening hours of the charging piles. The second constraint on the opening hours of private charging piles is that electric vehicles must finish charging no later than the closing time of the charging piles. The electric vehicle state constraint condition limits the electric vehicle to only be in one of the states of charging, discharging or idling at the same time; The first constraint for vehicle-pile matching is that each electric vehicle can only match one private charging pile; The second constraint of vehicle-pile matching is that each charging pile can only serve one electric vehicle at most during the same period. The constraints on the electric vehicle's endurance are limited to the remaining power of the electric vehicle being sufficient to support the matching charging pile; The first calculation constraint of the nominal charge of electric vehicles is to determine the charge based on the charging power, state variables and time span; The second calculation constraint of the nominal discharge capacity of electric vehicles is to determine the discharge capacity based on the discharge power, state variables and time span; The constraint condition of the electric vehicle charging end power is to limit the end power to meet the user's demand; The upper limit constraint of the power grid load limits the power grid load to not exceed the upper limit; The constraint condition of the lower limit of grid load is to limit the grid power supply load to no less than the lower limit; The constraints for electric vehicle battery power iteration limit the update of power based on the power of the previous period and the current charge and discharge capacity; The upper limit of battery power is limited to the rated capacity. The lower limit constraint of the battery power is to ensure that the power level is not less than the power level corresponding to the maximum depth of discharge; The constraints of the multi-objective optimization problem are scalarized, and the multi-objective is converted into a single objective through weights and reference points; A first objective function with an uncertainty set, scalarized, and with the uncertain parameters converted into an equivalent linear form by a dual method is used to minimize the total cost; wherein the scalarization process converts the total cost-related objective in the original multi-objective robust optimization problem into an equivalent nonlinear form by applying maximum ranking scalarization and robust enhanced weighted Chebyshev method; A second objective function with an uncertainty set, scalarized, and converted into an equivalent linear form using a duality method, is used to minimize grid load fluctuations. The scalarization transforms the objectives related to grid load fluctuations in the original multi-objective robust optimization problem into an equivalent nonlinear form by applying maximum-rank scalarization and a robust enhanced weighted Chebyshev method. A third objective function with an uncertainty set, scalarized, and converted into an equivalent linear form using a dual method to minimize CO2 emissions. The scalarization transforms the CO2 emission-related objective in the original multi-objective robust optimization problem into an equivalent nonlinear form by applying maximum-rank scalarization and a robust enhanced weighted Chebyshev method. Considering the power supply and demand balance constraints with the uncertainty of external power demand, the uncertain parameters are transformed into linear form through the dual method; The linearization constraints of the non-convex nonlinear terms of the vehicle-pile interaction time are processed using the McCormic method to transform the nonlinear relationship of the vehicle-pile interaction time into a linear form.

2. The method for collaborative optimization of private charging pile sharing mode operation and vehicle-to-grid connection according to claim 1, characterized in that: In step S2, the scheduling strategy and matching mechanism include: Considering the electric vehicle's charging needs, remaining power, driving distance, and the opening hours of private charging piles, it accurately matches electric vehicles with private charging piles; Optimize the car-pile matching solution to increase the utilization rate of private charging piles and meet the charging needs of electric vehicles.

3. The method for collaborative optimization of private charging pile sharing mode operation and vehicle-to-grid connection according to claim 1, characterized in that: In step S3, the vehicle-to-grid energy management includes: Dynamically adjust the charging and discharging behavior of electric vehicles based on the grid status and the needs of electric vehicles; When the grid load is at its peak, electric vehicles are used as distributed energy storage devices to supply reverse power to the grid.

4. The method for collaborative optimization of private charging pile sharing mode operation and vehicle-to-grid connection according to claim 1, characterized in that: In step S2, the scheduling strategy and matching mechanism further include: Optimize charging time by taking into account the battery status and charging needs of electric vehicles; Coordinate and adjust the charging plan of electric vehicles to adapt to the grid status and charging demand.

5. The method for collaborative optimization of private charging pile sharing mode operation and vehicle-to-grid connection according to claim 1, characterized in that: In step S3, the vehicle-to-grid energy management further includes: Flexibly adjust the charging and discharging strategies of electric vehicles based on real-time electricity prices or grid status; Optimize the distribution of discharge benefits of electric vehicles and encourage owners of private charging piles to join the sharing model.

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