Private charging pile sharing mode operation and vehicle-to-power grid collaborative optimization method

Abstract

The invention discloses a private charging pile sharing mode operation and vehicle-to-power grid collaborative optimization method, and the method comprises the steps: constructing a private charging pile sharing network through a digital platform, and enabling idle charging piles to be used by other vehicle owners. Based on the charging demand of the electric vehicle and the space-time distribution of the charging piles, the scheduling strategy is utilized to realize accurate matching of the vehicle piles, and the resource utilization rate is improved. And by combining a vehicle-to-power grid technology, the electric vehicle reversely supplies power at the power grid load peak, and the power grid load balance is optimized. And a multi-target robust optimization model is constructed, uncertainty information, low-carbon management and a demand response strategy are considered, and economic benefits, environmental benefits and power grid stability are comprehensively optimized. And the model is linearized and converted into a mixed integer programming model, so that the solving efficiency is improved. The feasibility and optimization effect of the mode are verified by using real data, decision support is provided for actual operation, and an innovative solution is provided for solving the problem of insufficiency of public charging facilities, relieving power grid pressure and promoting 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 method for collaborative optimization of private charging pile sharing mode operation and vehicle-to-grid connection. Background Art

[0002] Compared with the expansion of electric vehicles, the construction of charging infrastructure has lagged behind, and the existing charging resources have not been fully utilized. The construction speed of charging infrastructure has not kept pace with the development of electric vehicles, which has directly led to an imbalance between supply and demand. In addition, the low utilization rate of existing charging infrastructure has further exacerbated the pressure of charging facility shortage. In some cities, the average daily use of private charging piles is only once or even lower. This inefficient use is a huge waste of charging resources and has exacerbated the severity of the charging problem. This phenomenon not only highlights the urgency of the construction of charging facilities, but also emphasizes the necessity of operational scheduling and optimized resource utilization.

[0003] Driven by practical needs and a variety of favorable factors, the private charging pile sharing model has emerged worldwide. This model refers to a new charging service mode in which the private charging pile owner opens the private charging pile to the public through a digital platform (such as an application or website) when the private charging pile is idle for other car owners to use. In response to the problems of tight supply and demand of charging facilities and low utilization efficiency, private charging pile sharing has become an effective way to optimize charging resources and alleviate the problem of "difficult charging".

[0004] The sharing model can make up for the shortage of public charging piles to a certain extent and expand the coverage of the charging network by tapping the potential of existing private charging pile resources. At the same time, the geographical distribution of private charging piles is more flexible and extensive, and the sharing model can better meet the diverse charging needs of electric vehicle owners. Vehicle-to-grid (V2G) energy management promotes two-way interaction between electric vehicles and energy systems, and provides the possibility for them to be integrated into the private charging pile sharing model. As an important supporting technology for two-way interaction, vehicle-to-grid has moved from the experimental stage to global large-scale application. The vehicle-to-grid system uses electric vehicles as carriers and uses scheduling strategies to dynamically balance the power supply and demand relationship at different times. The core of the vehicle-to-grid system lies in the two-way information flow and energy flow mode, which makes electric vehicles not only act as electricity consumers, but also become mobile energy storage devices. Relying on grid technology and advanced communication protocols, the vehicle-to-grid system can flexibly adjust the charging and discharging behavior according to the real-time electricity price or grid status. This flexible energy exchange mechanism is crucial to balancing the supply and demand of electricity. In addition, large-scale simultaneous charging of electric vehicles, especially during the peak power consumption period in the evening, may lead to the phenomenon of "peak on peak"; and the increasing access to the grid by renewable energy has also increased the load pressure on the grid. The vehicle-to-grid technology combined with the private charging pile sharing platform can further optimize the charging time and spatial distribution, effectively alleviating the challenges brought by load fluctuations. It can not only effectively solve many current problems, such as power market reform, demand-side response, and energy structure optimization, but also promote low-carbon transformation and enhance 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 for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the 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: A method for collaborative optimization of private charging pile sharing mode operation and vehicle-to-grid connection, comprising the following steps: 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, thus realizing the sharing of charging resources; S2. Scheduling strategy and matching mechanism: Based on the charging demand of electric vehicles and the spatial and temporal distribution of private charging piles, the scheduling strategy is used to achieve accurate matching between electric vehicles and private charging piles, optimize the matching efficiency between vehicles and piles, and improve the utilization rate of charging resources; 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; 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.

[0008] 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 model.

[0009] The present invention has the following beneficial effects: The present invention proposes an innovative private charging pile sharing mode 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 the economic and environmental benefits. First, in view of the operation and management problems of the private charging pile sharing mode, the present invention proposes a vehicle-pile matching mechanism based on the temporal and spatial distribution characteristics of supply and demand, 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 the 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 the supply and demand relationship through demand response strategies, smoothes the load curve of the power grid, reduces the pressure of the power system, and helps the use of low-carbon electricity, and promotes the green transformation of the power system. In addition, the present invention develops a multi-objective robust optimization model that considers 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.

[0010] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a vehicle-to-grid system diagram in a private charging pile sharing mode according to an embodiment of the present invention.

[0012] Figure 2 For external living or other power needs at different times.

[0013] Figure 3It is the car-to-pile ratio in the private charging pile sharing model at different scales.

[0014] Figure 4 Share time and utilization of private charging stations.

[0015] Figure 5 Rated battery capacity utilization for electric vehicles.

[0016] Figure 6 is the total cost under ASAP and RCDR_C / D strategies.

[0017] Figure 7 is the return at different times under the RCDR_C / D strategy.

[0018] Figure 8 is the load fluctuation under ASAP and RCDR_C / D strategies.

[0019] Fig. 9 The load curve of electric vehicle at different times.

[0020] Fig.10 is the SOC of the electric vehicle at different times.

[0021] Fig.11 The carbon emissions per unit of electricity produced at different times.

[0022] Fig.12 is the total carbon emissions of electric vehicles under ASAP and RCDR_C / D strategies.

[0023] Fig.13 The distribution of carbon emissions of electric vehicles under ASAP and RCDR_C / D strategies.

[0024] Fig.14 This is an overall flow chart of the collaborative optimization method of private charging pile sharing mode operation and vehicle to power grid in the present invention. DETAILED DESCRIPTION

[0025] 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 and application of the present invention.

[0026] The optimization of the shared mode operation 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 improving 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 mode operation 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, 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 grid pressure, and improving the coverage and quality of charging services.

[0027] The focus of this invention is the operation of the 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 uncertain 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: (1) The present invention focuses on the scheduling decision-making problem of the private charging pile sharing model, and further improves the operational efficiency of the model. Combined with the characteristics of the temporal and spatial distribution of supply and demand in the private charging pile sharing model, the present invention matches the vehicle and the pile according to the supply and demand relationship in different time periods under the point-to-point sharing mode, and considers 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.

[0028] (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 on the premise of meeting charging needs. Throughout the process, considering the impact of external factors on life or other electricity demand, the present invention not only balances the supply and demand relationship through a demand response strategy, 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, expands 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.

[0029] 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.

[0030] (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.

[0031] (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 car-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 realizing the two-way flow of energy and showing a large dispatchable space; (3) The optimization decision of operation management can achieve a comprehensive improvement in economic and environmental benefits, and help efficient resource utilization and low-carbon development.

[0032] 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 Fig.14 ): 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 to achieve sharing of charging resources.

[0033] 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.

[0034] In a preferred embodiment, the scheduling strategy and matching mechanism include: considering the charging demand, remaining power, driving distance and opening hours of private charging piles of electric vehicles, accurately matching electric vehicles with private charging piles; optimizing the vehicle-pile matching scheme to improve the utilization rate of private charging piles and meet the charging demand of electric vehicles. It can also further include: considering the battery status and charging demand of electric vehicles, optimizing the charging time; and comprehensively adjusting the charging plan of electric vehicles to adapt to the grid status and charging demand.

[0035] 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, realize two-way energy interaction between electric vehicles and the grid, optimize grid load balance, and reduce grid pressure.

[0036] In a preferred embodiment, the vehicle-to-grid energy management includes: dynamically adjusting the charging and discharging behavior of electric vehicles according to the grid status and the needs of electric vehicles; using electric vehicles as distributed energy storage devices to reversely supply power to the grid when the grid load is peak. It can also further include: flexibly adjusting the charging and discharging strategy of electric vehicles according to real-time electricity prices or grid status; optimizing the distribution of electric vehicle discharge benefits and encouraging private charging pile owners to join the sharing mode.

[0037] 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 strategy, comprehensively considers economic benefits, environmental benefits and grid stability, and improves overall efficiency.

[0038] 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.

[0039] Specifically, the multi-objective robust optimization model preferably includes the following objective functions and constraints: 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.

[0040] The constraints 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.

[0041] The multi-objective robust optimization model may further include: considering the uncertainty of external electricity 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.

[0042] Step S5, model linearization and solution: The complex optimization model is linearized by mathematical methods, converted into a mixed integer programming model, and solved efficiently using a solver to ensure the operability and practicability of the model.

[0043] The model linearization and solution include: converting the uncertainty parameters into a linear form through a dual method; using the McCormick method to process non-convex nonlinear terms and linearize the model.

[0044] The present invention also uses real data for experimental verification, verifies the feasibility and optimization effect of the private charging pile sharing mode and vehicle-to-grid energy management, and provides decision support for actual operations.

[0045] Specifically, we use real data to conduct experimental case studies to verify the feasibility and development potential of the private charging pile sharing model; analyze the operation level and dispatchable space of vehicle-to-grid energy management under different electric vehicle scales. We analyze the cost, discharge benefits and grid load fluctuations of electric vehicles under different dispatch strategies; provide a low-carbon management effect evaluation based on the life cycle perspective to optimize the carbon emission level of electric vehicles.

[0046] The present invention realizes efficient utilization of charging resources, optimal balance of grid load, and comprehensive improvement of economic and environmental benefits by combining private charging pile sharing with vehicle-to-grid energy management, providing an innovative solution for operational optimization of private charging facilities for electric vehicles.

[0047] The specific embodiments and experimental verification of the present invention are further described below.

[0048] The present invention relates to the operation of an innovative model, namely the operation of a private charging pile sharing model and its vehicle-to-grid energy management. The present invention establishes a basic model, including objective functions related to total cost, load fluctuation and carbon dioxide emissions, as well as constraints such as multi-party interaction, vehicle-pile matching and charging and discharging scheduling.

[0049] The present invention focuses on the operation of private charging pile sharing mode under uncertain information and the energy dispatch management of vehicle to power grid system. The entire network mainly consists of key components such as power grid, shared private charging piles, electric vehicles, 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 mechanisms, and multi-party interactions.

[0050] In the operation part of the private charging pile sharing mode, the characteristics of the temporal and spatial distribution of charging supply and demand are considered to optimize the allocation of charging resources and balance the supply and demand relationship. The geographical distribution of shared private charging piles is flexible and extensive, and their layout is distributed. At the same time, the charging demand of electric vehicles is also dispersed. Therefore, the vehicle-pile matching process should design a point-to-point matching mechanism and consider the differences in the remaining power of electric vehicles that need to be charged. In addition, since private charging piles are not open all day, all charging and discharging interactions must be carried out during their opening hours. In the energy dispatching part of the vehicle-to-grid system, as a bidirectional energy dispatching scheme in the private charging pile sharing mode, considering the impact of uncertain external electricity demand, an energy management scheme that takes into account both economic and environmental benefits is constructed from the perspective of demand response. In this system, utilities act as aggregators to coordinate the bidirectional flow of energy between the power system and electric vehicles through an intelligent console to ensure that diversified needs are met. In addition to maximizing economic benefits, by optimizing the charging and discharging behavior of electric vehicles, the challenges of uncertain external electricity demand are met, so that the load curve is smoothed and load fluctuations are alleviated to reduce the pressure on the power grid. In addition, in order to reduce carbon emissions throughout the life cycle of electric vehicles, emission reduction measures are extended to the use of low-carbon electricity to further promote low-carbon management.

[0051] This paper develops a multi-objective optimization model to characterize the operation of the private charging pile sharing mode and its vehicle-to-grid system energy dispatch management. The proposed optimization decision framework integrates key factors and their influences, including uncertainty information, low-carbon management, and demand response strategies, to coordinate the interests of multiple parties in a complex system and achieve comprehensive improvement in economic and environmental benefits. The following issues are mainly addressed: (i) Can the private charging pile sharing mode supplement the existing charging network through efficient point-to-point vehicle-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 mode and realize two-way interaction between electric vehicles and energy systems; (iii) What scheduling strategies and matching mechanisms are used to improve economic and environmental benefits.

[0052] Tables 1 to 3 list the sets, parameters, and variable symbols and their definitions used in the model.

[0053] Table 1: List of collective symbols

[0054] Table 2: Parameter symbol list

[0055] Table 3: List of variable symbols

[0056] The model of the present invention sets three objective functions, taking into account the private charging pile sharing mode 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:

[0057] The first of these is the cost of charging electric vehicles The second is a subsidy for charging in a shared model of private charging stations to increase the attractiveness of private charging stations. The third item is the income generated by the discharge of electric vehicles , 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, Represents the subsidy received by electric vehicle i based on its charging volume in the private charging pile sharing model.

[0058] 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.

[0059] (2)

[0060] The supply side and demand side The load difference is the smallest. Since there is energy loss in the discharge process of electric vehicles in reality, formula (2) takes into account the actual discharge amount of electric vehicles.

[0061] The third objective function is to minimize CO2 emissions, that is, to focus on environmental benefits while improving economic benefits.

[0062]

[0063] The power consumption at different times is considered , and the carbon emissions per unit of electricity produced at the corresponding moment (ε t ). Due to the different energy supply relationships in different periods, electric vehicle discharge also acts as an energy supplier.

[0064] 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).

[0065] 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.

[0066]

[0067] 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 open sharing time of the private charging pile, that is, it meets the sharing time limit set by the owner of the private charging pile, as shown in inequality (5). At the same time, the interaction between any electric vehicle i, i∈I, and the matched private charging pile is no 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).

[0068]

[0069] 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, idling and discharging in a period of time, as shown in equation (8).

[0070]

[0071] Formulas (9), (10) and (11) represent the characteristics of car-pile matching in the private charging pile sharing mode. Due to the distributed layout of shared charging piles, the car-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 the only 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, according to the remaining power of the battery of electric vehicle i, i∈I, the private charging piles are reasonably allocated so that the electric vehicle can reach the matching charging pile sufficiently, as shown in inequality (11).

[0072]

[0073] Equations (12) and (13) represent the nominal charge and discharge power of electric vehicle i, i∈I, in time period t, that is, the transmission efficiency of the electric vehicle during charging and discharging is not considered. The battery power 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), where, in order to be closer to the real scene, the transmission efficiency of electric vehicle charging and discharging is considered in the model, that is, .

[0074]

[0075] Equation (15) represents the load balance between power supply and demand, that is, power supply (power grid output G t and electric vehicle discharge ) and electricity demand (electric vehicle charging and life or other electricity needs E t ) one-to-one correspondence. Electric vehicles are charged under low-peak 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 an upper and lower limit, namely 0 and U t , as shown in inequalities (16) and (17).

[0076]

[0077] Equation (18) represents the iterative process of EV charging and discharging, that is, the battery power of the EV in period t depends on the battery power in the previous period and the charging and discharging decisions during period t.

[0078]

[0079] In order not to damage the service life of electric vehicle batteries, inequalities (19) and (20) strictly limit the upper and lower bounds during the charging and discharging process. 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).

[0080]

[0081] The operation of private charging pile sharing mode and its vehicle-to-grid energy management are complex and uncertain. Therefore, based on the basic model and uncertainty set, the present invention establishes a multi-objective robust optimization model. In addition, the decision-making of the multi-objective robust optimization problem is multi-directional. Therefore, the model generally needs to be scalarized. The original model is converted into an equivalent nonlinear form by applying methods such as maximum sort scalarization and robust enhanced weighted Chebyshev.

[0082] The electricity demand in real life is full of uncertainty. Therefore, the present invention constructs an uncertainty set using partial moment information of real data; and on the basis of the basic model, establishes a multi-objective robust optimization model that considers the uncertainty of external life or other electricity demand.

[0083] Definition 1 Let the lower bound of the uncertain set be , the interval length ζ∈ R T , to simplify the mathematical expression, assume that the nominal value of E is its minimum value. Therefore, the uncertainty set is as follows

[0084] Therefore, we get the following uncertain set Multi-objective robust optimization model

[0085] Scalar Quantization 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, it is converted into an equivalent nonlinear form by applying methods such as maximum ranking scalarization and robust enhanced weighted Chebyshev.

[0086] Definition 2: For a multi-objective uncertain optimization problem, for a given weight vector and reference point , and its corresponding maximum sorting optimization problem can be expressed as

[0087] Proposition 1 has an uncertain set The objective functions (21), (22), (23) can be written as follows form

[0088] Proposition 2 is equivalent to the following nonlinear form

[0089] Model linear transformation Due to the existence 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 dual method in discrete optimization is used to convert uncertain parameters into linear corresponding parameters. Then, the non-convex nonlinear terms in the model are processed using the method of McCormick, etc. 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.

[0090] Linearization of uncertain parameters Since both the objective function and the constraints contain uncertainties, solving the original model faces great challenges. The dual method and mathematical derivation method are mainly used to transform the uncertain parameters into equivalent linear forms to improve the feasibility of the solution.

[0091] Proposition 3 Inequality (28) is equivalent to the following linear form

[0092] Proposition 4 Inequality (29) is equivalent to the following linear form

[0093] Proposition 5 Inequality (30) is equivalent to the following linear form

[0094] Proposition 6 Equation (24) is equivalent to the following linear form

[0095] Linearization of nonconvex nonlinear terms Since the original model contains non-convex nonlinear terms that are difficult to handle, which greatly slows 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.

[0096] Proposition 7 Inequality (5) with nonlinear terms is equivalent to the following linear form

[0097] The mixed integer programming model is finally simplified to 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:

[0098] Experimental verification Based on the operation and management of electric vehicles, as well as the data of life or other electricity demand, experimental verification is carried out. The experimental verification specifically 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; (iii) verifying 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.

[0099] The experimental example of the present invention divides a day into 96 time periods of 15 minutes each, and uses it as a decision-making unit. According to the existing actual situation, the time periods for 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 from 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 It represents the average value of living or other electricity demand at different times. Due to the equal attention paid to 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.

[0100] 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 that electric vehicles use charging piles to the time that private charging piles are open for sharing. The higher the STU (sharing time utilization), the more fully the private charging piles are utilized, and the lower the STU, the more unused sharing time there is. Figure 4It shows that when the number of electric vehicles increases from 30 to 60, the utilization rate of shared time of private charging piles decreases from 65.32% to 57.16%. This further shows that in the private charging pile sharing model, as the scale of participation increases, the more charging resources can be dispatched, the greater the potential to be tapped.

[0101] Vehicle-to-grid energy management can be implemented in a private charging pile sharing mode with a large dispatchable space. The private charging pile sharing mode 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 operation level of vehicle-to-grid management, namely BPU = , which represents the ratio of the electric vehicle battery discharge to its rated battery capacity. The higher the BPU (battery power utilization rate), the higher the level of operation of vehicle-to-grid management. Figure 5 It shows that under different electric vehicle scales, the operation level of vehicle-to-grid management remains at a low level of about 11%, which means that there is still a lot of dispatchable space for electric vehicles that can be further explored and released.

[0102] The RCDR_C / D scheduling strategy significantly reduces the cost for electric vehicle owners. To further reduce charging costs, the present invention sets a lower charging price during idle time to attract charging demand during peak demand periods, and sets a higher charging price during busy time 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 scale of electric vehicles 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.

[0103] The RCDR_C / D scheduling strategy enables private charging pile owners to obtain considerable benefits. The income of private charging pile owners consists of the service fee for electric vehicle charging and the revenue share of electric vehicle discharge. The benefits brought by the private charging pile sharing model are 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 7The specific distribution of one-day revenue under the RCDR_C / D scheduling strategy is shown when the scale of electric vehicles is 60. It can be seen from the figure that the revenue of private charging pile sharing shows an obvious peak-valley trend, which can provide a reference for private charging pile owners to set the time for open sharing.

[0104] In the face of external factors, the vehicle-to-grid system in the private pile sharing mode has a positive effect on alleviating load fluctuations. In the present invention, load fluctuations are mainly affected by external factors such as electric vehicle charging and discharging, as well as life or other electricity demand. Figure 8 It shows that compared with ASAP, the RCDR_C / D dispatch strategy has alleviated regional load fluctuations to a certain extent. With the increase in the scale of electric vehicles, the ratio of dispatchable space to living or other electricity demand has increased to a certain extent, but it still only accounts for about 6%. Therefore, under the set scale of electric vehicles, the ability of electric vehicle charging and discharging dispatch to regulate load fluctuations is still limited.

[0105] 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 Fig. 9 shown. Fig. 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, decisions are made on the charging and discharging of each electric vehicle at different times. In addition, Fig.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.

[0106] 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: Fig.11 shown.

[0107] Considering the life cycle, the RCDR_C / D scheduling strategy makes better use of the 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 in the corresponding time. Since the CO2 emissions of electric vehicles have been calculated during charging, the electricity provided by their discharge is regarded as 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. Fig.12 It shows that after the charging and discharging scheduling of RCDR_C / D, 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 distribution regularity of carbon emissions 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 14:00-16:00 time period is the peak period of carbon dioxide emissions of electric vehicles. The specific distribution is as follows: Fig.13 shown. Fig.13 In the equation (30), (40), (50), and (60), respectively, we can see that charging price plays a big role in scheduling decision-making. Therefore, if the charging price is set scientifically with reference to the carbon emissions per unit of electricity output 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 the digital platform, private charging piles are open to the public during idle periods for use by other electric vehicle owners, thus realizing the sharing of charging resources; S2. Scheduling strategy and matching mechanism: Based on the charging demand of electric vehicles and the spatial and temporal distribution of private charging piles, the scheduling strategy is used to achieve accurate matching between electric vehicles and private charging piles, optimize the matching efficiency between vehicles and piles, and improve the utilization rate of charging resources; 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; 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; S5. Model linearization and solution: The complex optimization model is linearized through mathematical methods, converted into a mixed integer programming model, and solved using a solver.

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 charging needs, remaining power, driving distance and opening hours of private charging piles, the system can accurately match electric vehicles with private charging piles. Optimize the car-pile matching solution to improve 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 according to the state of the power grid 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 S4, 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.

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 S5, the model linearization and solution includes: 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.

6. 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.

7. 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 according to 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.

8. 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 S4, the multi-objective robust optimization model further includes: 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 the scalarization method to improve the solution efficiency of the model.

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