Renewable energy coordinated consumption scheduling method based on time shift of electric vehicle charging

Abstract

The application discloses a renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift, relates to the technical field of optimal scheduling of electric vehicles participating in distribution networks, and comprises the following steps: identifying renewable energy node residual output by using distribution partition and day-ahead scheduling data, and acquiring maximum delayable charging power in a previous time period; screening local delayable vehicles based on the maximum delayable charging power in the previous time period and the distribution partition and day-ahead scheduling data, and determining an optimal delayable start charging time by combining a consumption matching degree index; and performing three-layer progressive collaborative consumption scheduling according to the local delayable vehicles and the optimal delayable start charging time, solving by using a day-ahead layered mixed integer linear programming method, and acquiring a renewable energy collaborative consumption scheduling result. The application can improve the renewable energy consumption rate in the distribution partition on the basis of fully meeting the basic travel demand of electric vehicle users, and simultaneously improve the operation income of charging stations and the economy of the user side.

Description

Technical Field

[0001] This invention relates to the field of optimized scheduling technology for electric vehicles participating in power distribution networks, and particularly to a renewable energy collaborative consumption scheduling method based on the charging time shift of electric vehicles. Background Technology

[0002] With the development of new power systems and the advancement of the "dual carbon" target, the proportion of renewable energy sources such as wind power and photovoltaics in the power system is constantly increasing. However, due to their randomness, intermittency and volatility, their output is difficult to match with the load demand in the time dimension, which easily leads to wind and solar curtailment during periods of high renewable energy output. During peak load periods, traditional thermal power units are still needed to compensate, thus restricting the absorption of renewable energy and affecting the economic efficiency and stability of the system.

[0003] Meanwhile, with the rapid growth of electric vehicle ownership, their charging load is gradually becoming an important part of the power distribution network. Electric vehicle charging has a certain degree of time-series adjustability, and without guidance, concentrated charging can easily occur, causing a rise in load peaks and potentially leading to local overload and voltage exceeding limits. However, by rationally scheduling the charging process, the load transferability can be used to smooth the system load curve, thereby improving the absorption capacity of renewable energy.

[0004] Currently, existing dispatching methods for renewable energy consumption mainly include traditional optimization dispatching methods and demand response-based control methods. Optimization-based methods typically model the renewable energy consumption problem as an optimization problem with grid operation constraints, which can improve renewable energy utilization to some extent. However, they focus more on dispatching at the power source side or the overall system level, failing to fully explore the charging time-shifting potential of electric vehicle loads. Furthermore, they do not adequately consider the electrical connections between different distribution nodes, making it difficult to achieve reasonable coordinated dispatching of flexible loads across nodes. While demand response-based methods can guide user-side loads to participate in regulation, most methods primarily aim at peak shaving and valley filling or reducing charging costs, lacking deep coupling with the surplus output characteristics of renewable energy, making it difficult to achieve precise matching between renewable energy surplus periods and the charging window of electric vehicles.

[0005] Furthermore, existing electric vehicle scheduling methods typically use aggregated loads for modeling, which do not adequately consider single-vehicle level constraints such as vehicle arrival time, departure time, target state of charge, latest start charging time, and maximum delay duration. At the same time, they lack unified consideration of node electrical distance, cross-node calling order, and distribution network safety operation constraints, which limits the feasibility of scheduling results in vehicle-level execution and actual distribution network operation.

[0006] Therefore, there is an urgent need for a renewable energy coordinated consumption and scheduling method based on the charging time shift of electric vehicles, which can achieve spatiotemporal coordinated optimization matching between renewable energy output and electric vehicle charging load, while fully considering the load shift potential of electric vehicles and the electrical constraints of the distribution network. Summary of the Invention

[0007] The purpose of this invention is to propose a renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift, which fully explores the transferability of electric vehicle charging load in the time dimension, and establishes spatial collaborative calling relationship by combining the electrical distance between nodes, so as to solve the technical problems of insufficient local resource utilization, disordered cross-node resource calling, insufficient single-vehicle level execution constraints, and insufficient matching between delayed charging and renewable energy surplus time periods in existing methods.

[0008] To achieve the above objectives, this invention provides a renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift, comprising:

[0009] S1. Identify the remaining output of renewable energy nodes using distribution zone and day-ahead dispatch data, and obtain the maximum delayable charging capacity for the preceding period.

[0010] S2. Based on the maximum delayable charging capacity of the preceding period and the power distribution zone and day-ahead scheduling data, select local vehicles that can be delayed, and combine the consumption matching index to determine the optimal time to start charging.

[0011] S3. Perform a three-layer progressive collaborative consumption scheduling based on the local delayable vehicles and the optimal delayed start charging time, and use the day-ahead hierarchical mixed integer linear programming method to solve the problem and obtain the renewable energy collaborative consumption scheduling result.

[0012] Optionally, S1, using distribution zone and day-ahead dispatch data to identify the remaining output of renewable energy nodes and obtain the maximum delayable charging capacity for the preceding period, including:

[0013] Read the distribution zone and day-ahead dispatch data, which includes the basic load of each node in the distribution zone, the predicted output of renewable energy, the original charging load of electric vehicles, the electrical distance between each node, the arrival time of electric vehicles, the departure time of electric vehicles, the state of charge at the arrival point, the target state of charge, the battery capacity, the maximum charging power, the charging efficiency, and the adjustable boundary of the virtual energy storage of electric vehicles.

[0014] The remaining renewable energy power is calculated based on the power distribution zones and day-ahead dispatch data, thereby identifying the remaining output of renewable energy nodes;

[0015] Based on the remaining output of the renewable energy node and the source time delay constraint, the maximum delayable charging power of the preceding time period is obtained, and the maximum delayable charging capacity of the preceding time period is calculated.

[0016] Optionally, the formula for calculating the source time delay constraint is:

[0017]

[0018] in, Based on the load, For the original charging load of local electric vehicles, Contribute to renewable energy.

[0019] Optionally, S2, based on the maximum delayable charging capacity of the preceding time period and the power distribution zone and day-ahead scheduling data, local vehicles that can be delayed are screened, and combined with the absorption matching index, the optimal delayed charging start time is determined, including:

[0020] By combining the maximum delayable charging capacity of the preceding time period with the power distribution zone and the day-ahead scheduling data, the local delayable vehicles and the maximum delayable time period of the local delayable vehicles are obtained through filtering.

[0021] Set the remaining renewable energy power for resource absorption, and obtain the judgment result of calling ordinary nodes based on the local delayable vehicles and the maximum delayable time period of the local delayable vehicles through node-level delayed charging constraints.

[0022] Based on the judgment result of calling the ordinary node and the remaining renewable energy power absorbed by the resource, the adjustable resources of the ordinary node in the current period are calculated to obtain the remaining renewable energy power to be consumed.

[0023] The optimal time to start charging is determined by combining the remaining renewable energy power to be consumed with the local available vehicles for consumption matching degree.

[0024] Optionally, the maximum delayable charging capacity of the preceding time period is combined with the power distribution zone and day-ahead scheduling data for filtering to obtain the locally delayable vehicles and the maximum delayable time period for locally delayable vehicles, including:

[0025] Based on the maximum delayable charging capacity of the preceding period and the power distribution zone and day-ahead scheduling data, the vehicle basic parameters are set. The vehicle basic parameters include battery capacity, arrival state of charge, target state of charge, maximum charging power, charging efficiency, arrival time and departure time.

[0026] The required additional power for the vehicle is calculated using the vehicle's basic parameters, and the vehicles that need charging are identified.

[0027] Based on the vehicles that need charging and their basic parameters, calculate the number of charging periods and the number of parking periods for the vehicles that need charging.

[0028] The number of charging periods for the vehicles that need charging is compared with the number of parking periods for the vehicles that need charging to obtain vehicles that meet the conditions for delayed charging.

[0029] Based on the vehicles that meet the conditions for delayed charging and their basic parameters, the maximum number of time periods that can be delayed for vehicles that meet the conditions for delayed charging is obtained.

[0030] Based on the maximum number of delayable time periods for vehicles with delayed charging conditions and the vehicle's basic parameters, a minimum safe state of charge threshold is set to obtain the local delayable vehicles and the maximum number of delayable time periods for local delayable vehicles.

[0031] Optionally, the formula for calculating the absorption matching degree is:

[0032]

[0033] in, Indicates that vehicle k is from the candidate The degree of matching between the charging window at the start of charging and the surplus output of renewable energy. Let n be the actual charging start time for vehicle k after a delay. Given any node n, the minimum number of charging sessions required for vehicle k. Let n be the remaining renewable energy power of any node n in time period t. For the length of each scheduling cycle, For any node n, vehicle k is in The theoretical charging capacity corresponding to continuous charging at maximum charging power within the window period.

[0034] Optionally, S3, a three-layer progressive coordinated consumption scheduling is performed based on the locally delayable vehicles and the optimal delayed start charging time, and a day-ahead hierarchical mixed integer linear programming method is used to solve the problem to obtain the renewable energy coordinated consumption scheduling result, including:

[0035] Based on the locally delayable vehicles and the optimal delayed start charging time, a three-layer progressive renewable energy collaborative consumption scheduling model is constructed.

[0036] The actual absorption of renewable energy is calculated based on the three-layer progressive renewable energy collaborative consumption and scheduling model.

[0037] The renewable energy absorption rate is calculated using the actual amount of renewable energy absorbed, and an optimization objective function is constructed.

[0038] Based on the three-layer progressive renewable energy collaborative consumption and scheduling model and the adjustable boundary of electric vehicle virtual energy storage, scheduling constraints are constructed.

[0039] Based on the optimization objective function and the scheduling constraints, the three-layer progressive renewable energy collaborative consumption scheduling model is solved using the day-ahead hierarchical mixed integer linear programming method to obtain the renewable energy collaborative consumption scheduling results.

[0040] Optionally, based on the locally delayable vehicles and the optimal delayed start charging time, a three-layer progressive renewable energy collaborative consumption scheduling model is constructed, including:

[0041] Based on the locally delayable vehicle, the optimal delayed start charging time, and the remaining renewable energy power absorbed by the resource, the remaining renewable energy power after the first layer of scheduling is obtained by using the local delay absorption of renewable energy nodes.

[0042] By combining the remaining renewable energy power after the first-level scheduling with the electrical distance between nodes, and using the current time period coordinated consumption by ordinary nodes, the remaining renewable energy power after the second-level scheduling is obtained.

[0043] Based on the remaining renewable energy power after the second-level scheduling and the deferred charging task of the preceding period of the ordinary node, the remaining renewable energy power after the third-level scheduling is obtained by using the deferred time-shifting of the preceding period of the ordinary node for consumption, and then the three-level progressive renewable energy collaborative consumption scheduling model is obtained.

[0044] Optionally, the method further includes: evaluating and verifying the results of the coordinated consumption and scheduling of renewable energy, and obtaining the evaluation results of the scheduling effect.

[0045] Optionally, the results of the coordinated consumption and dispatch of renewable energy are evaluated and verified to obtain the evaluation results of the dispatch effect, including:

[0046] The renewable energy integration rate is calculated using the results of the coordinated renewable energy consumption and scheduling.

[0047] Based on the results of the coordinated consumption and scheduling of renewable energy and the total revenue of the site, the net revenue of the site is obtained.

[0048] Based on the renewable energy collaborative consumption and scheduling results, the total user-side cost is obtained by using the total user charging cost and delay compensation revenue.

[0049] The renewable energy absorption rate, the net revenue of the site, and the total cost on the user side are evaluated and verified to obtain the scheduling effect evaluation results.

[0050] Compared with the closest existing technology, the present invention has the following advantages:

[0051] (1) This invention establishes a time-shifted collaborative consumption framework for electric vehicle charging in distribution zones with renewable energy nodes. Based on existing node zones, electrical distances, electric vehicle charging loads, and virtual energy storage adjustable capabilities, it orderly matches the surplus output of renewable energy with the flexible charging resources of electric vehicles. First, from the perspective of power balance, the surplus renewable energy of renewable energy nodes in each time period is identified, and collaborative scheduling is initiated only for target time periods with actual surplus output. At the same time, by setting the necessary reserved charging power for the source time period, the maximum delayable charging capacity is determined under the condition that the original renewable energy in the source time period continues to be consumed, thereby avoiding new power curtailment caused by simply removing the electric vehicle load from the source time period.

[0052] (2) This invention performs joint screening of the delay capability of electric vehicles at both the node level and the vehicle level. The node-level model is used to determine the maximum allowable charging energy to be removed in the preceding time period. The single-vehicle-level model further combines the vehicle's arrival time, departure time, arrival SOC, target SOC, required charging power, dwell time, and latest start charging time to screen specific delayable vehicles, so that the aggregated scheduling results can be implemented to specific vehicles and improve the engineering feasibility of the scheduling scheme.

[0053] (3) This invention introduces a renewable energy consumption matching index. Within the allowable delay time range of the vehicle, the optimal delay start time is determined based on the degree of overlap between the vehicle's continuous charging window and the surplus output period of renewable energy. This is different from the scheduling method that simply adopts the maximum delay time. It can further improve the time matching degree between the limited flexible resources of electric vehicles and the surplus renewable energy.

[0054] (4) This invention establishes a three-layer progressive scheduling mechanism of “local delayed consumption of renewable energy nodes – coordinated consumption of ordinary nodes in the current time period – delayed time-shifted consumption of ordinary nodes in the preceding time period”, and uses the electrical distance between nodes to determine the calling order of ordinary nodes from near to far, so that the flexible resource call of electric vehicles has clear time priority and spatial priority relationship at the same time. By solving the sub-layers sequentially through hierarchical mixed integer linear programming, and updating the remaining renewable energy, vehicle resources and adjustable capacity between each layer, the scale of a single optimization problem can be reduced while ensuring the resource call logic.

[0055] In summary, this invention can improve the renewable energy absorption rate within a region while fully meeting the basic travel needs of electric vehicle users, and at the same time improve the operating revenue of charging stations and the economic efficiency on the user side, providing a reliable technical means for electric vehicles to participate in the flexible dispatch of the distribution network under the condition of high proportion of renewable energy access. Attached Figure Description

[0056] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0057] Figure 1 This is a flowchart of a renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift, as described in an embodiment of the present invention.

[0058] Figure 2 This is a flowchart of the renewable energy collaborative consumption scheduling based on electric vehicle charging time shift proposed in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram illustrating the identification of renewable energy consumption demand according to an embodiment of the present invention;

[0060] Figure 4 This is a schematic diagram illustrating the participation of electric vehicles in renewable energy consumption and dispatch according to an embodiment of the present invention;

[0061] Figure 5 This is a schematic diagram of the delayed absorption source period and target period proposed in an embodiment of the present invention;

[0062] Figure 6 This is a schematic diagram of delayed charging for electric vehicles proposed in an embodiment of the present invention;

[0063] Figure 7 This is a diagram illustrating the renewable energy consumption configuration of node 38 as proposed in an embodiment of the present invention.

[0064] Figure 8 This is a diagram illustrating the renewable energy consumption structure of node 39 as proposed in an embodiment of the present invention.

[0065] Figure 9 This is a power decomposition diagram of the collaborative absorption of each ordinary node to node 38 in the current time period, as proposed in this embodiment of the invention.

[0066] Figure 10 This is a power decomposition diagram of delayed time shift and coordinated absorption from each node to node 38, as proposed in an embodiment of the present invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0068] The terminology used in the embodiments section of this invention is for the purpose of explaining specific embodiments of the invention only, and is not intended to limit the invention.

[0069] The purpose of this invention is to overcome the shortcomings of the prior art and provide a renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift, so as to solve the following technical defects in existing methods for renewable energy consumption scheduling of electric vehicles participating in the distribution network:

[0070] (1) Existing methods are mostly focused on flexible load scheduling of a single charging station or a single node, lacking a collaborative scheduling mechanism for multiple nodes in a distribution zone. When the local adjustable resources of renewable energy nodes are insufficient, it is difficult to effectively utilize the electric vehicle charging resources of other nodes.

[0071] (2) Existing methods mainly focus on the charging power adjustment of electric vehicles in the current period, and do not make full use of the charging tasks that can be delayed in the preceding period, thus failing to give full play to the transferability of electric vehicle charging load in the time dimension.

[0072] (3) When calling the charging load of ordinary nodes across nodes, there is a lack of quantitative basis to characterize the electrical coupling relationship between different nodes, making it difficult to determine a reasonable order of node resource calls;

[0073] (4) Some methods mainly use electric vehicle aggregation models for scheduling, without fully considering constraints such as arrival time, departure time, target SOC, minimum safe SOC, latest start charging time and maximum delay time of a single vehicle, which makes it difficult to directly implement the aggregation optimization results at the vehicle level;

[0074] (5) In cases where there are multiple vehicles that can be delayed, there is a lack of a matching evaluation method between vehicle charging windows and the surplus output period of renewable energy, which makes it impossible to guarantee that the limited delay resources will be prioritized for the period that is more conducive to the consumption of renewable energy.

[0075] This invention takes into account the following key factors:

[0076] (1) Temporal fluctuation characteristics of renewable energy output: Renewable energy output fluctuates in a time period during the day, with surplus output concentrated in specific periods such as midday; electric vehicle charging load has time-shiftable characteristics, and the two have space for coordinated matching in time sequence;

[0077] (2) Spatial electrical coupling characteristics of distribution network nodes: There are differences in electrical coupling strength between different nodes. Power transmission across nodes is constrained by line impedance and voltage sensitivity. Coordinated dispatch must follow the "proximity principle" to avoid the adverse effects of power flow transfer on the safe operation of the power grid.

[0078] (3) Individual travel constraints of electric vehicle users: There are differences in the arrival time, departure time, target state of charge and battery capacity of each vehicle. The scheduling decision must be made on the premise of meeting the basic travel needs of users, and the maximum absorption rate cannot be pursued at the expense of user convenience.

[0079] (4) Synergy of hierarchical progressive scheduling: The distribution network is divided into zones based on electrical distance to enhance spatial coupling consistency. The charging time-shifting characteristics of electric vehicles are used to achieve time matching with renewable energy output. Power balance and global optimization scheduling of each zone are achieved through system-level coordination.

[0080] By comprehensively considering the above key factors, this invention designs a three-layer progressive scheduling architecture of "local delayed consumption of renewable energy nodes - coordinated consumption of ordinary nodes in the current period - delayed time-shifted consumption of ordinary nodes in the preceding period". This fully taps the potential for time-series adjustment of electric vehicle charging load, and achieves efficient consumption of remaining renewable energy within the region while ensuring user travel needs and grid safety. This provides reliable technical support for distribution network operation in scenarios with high proportion of renewable energy access.

[0081] like Figure 1-2 As shown, this embodiment of the invention provides a renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift. Through a process of reading day-ahead data of distribution zones and identifying remaining renewable energy sources—calculating the delayed charging capacity and consumption matching degree of electric vehicles—three-layer progressive hierarchical MILP collaborative scheduling—scheduling result evaluation and verification, the method achieves collaborative consumption of remaining renewable energy sources within distribution zones. The specific steps are as follows:

[0082] S1. Identify the remaining output of renewable energy nodes using distribution zone and day-ahead dispatch data, and obtain the maximum delayable charging capacity for the preceding period.

[0083] This step reads day-ahead dispatch data such as the distribution zone division results, the basic load of each node, the original charging load of electric vehicles, the predicted output of renewable energy, and the electrical distance between nodes. It calculates the remaining renewable energy power of each renewable energy node during different dispatch periods and locates the target period with surplus output to be consumed. Then, it verifies whether the preceding period corresponding to the target period meets the delayed access condition that the renewable energy has been fully consumed under the original operating conditions. It calculates the minimum charging power that the preceding period needs to retain to ensure its own new energy consumption, and then solves for the maximum delayable charging amount of the preceding period. This amount is the theoretical upper limit threshold at the node level, providing a power constraint boundary for subsequent vehicle selection.

[0084] S2. Based on the maximum delayable charging capacity of the preceding period and the power distribution zone and day-ahead scheduling data, select local vehicles that can be delayed, and combine the consumption matching index to determine the optimal time to start charging.

[0085] This step uses the maximum delayable charging capacity of the preceding time period as the total capacity constraint. Combined with individual vehicle parameters such as electric vehicle arrival time, departure time, arrival state of charge, target state of charge, and battery capacity from the day-ahead scheduling data, it selects locally delayable vehicles that simultaneously meet the requirements of dwell time margin and minimum safe state of charge, avoiding scheduling results that violate user travel needs. Furthermore, it constructs a consumption matching degree index to measure the degree of overlap between the vehicle's candidate charging window and the surplus output period of renewable energy. The time point with the optimal consumption matching degree is selected as the optimal delayed start charging time for the vehicle, completing the preliminary determination of the charging time shift scheme at the individual vehicle level, and providing vehicle set and time parameter input for subsequent optimized scheduling.

[0086] S3. Perform a three-layer progressive collaborative consumption scheduling based on the local delayable vehicle and the optimal delayed start charging time, and use the day-ahead hierarchical mixed integer linear programming method to solve the problem and obtain the renewable energy collaborative consumption scheduling result.

[0087] Using the obtained set of locally delayable vehicles and the optimal delayed start time of charging as model input, a three-layer progressive collaborative consumption architecture is constructed, which includes local delayed consumption of renewable energy nodes, collaborative consumption of ordinary nodes in the current time period, and time-shifted consumption of ordinary nodes in the preceding time period. According to the scheduling principles of local priority, proximity coordination, and current before delay, flexible charging resources are called across nodes. The day-ahead hierarchical mixed integer linear programming is used to solve the problem layer by layer. The output results of the upper-level scheduling are updated and used as the input parameters of the lower-level model. Iterative calculations are completed for all nodes and all scheduling time periods, and finally, the complete renewable energy collaborative consumption scheduling results of electric vehicle charging time shift are output.

[0088] In summary, the methods described in steps S1 to S3 are geared towards distribution zones containing renewable energy nodes. Based on the calculation results of existing node zones, electrical distances, electric vehicle charging loads, and virtual energy storage adjustable capacity, the remaining output of renewable energy nodes is identified. Utilizing the time-transferable characteristics of electric vehicle charging tasks under the condition of meeting user travel needs, a progressive scheduling method is constructed: "local delayed absorption of renewable energy nodes—coordinated absorption of ordinary nodes in the current time period—delayed time-shifted absorption of ordinary nodes in the preceding time period." The method is solved using a day-ahead hierarchical mixed integer linear programming method to achieve spatiotemporal coordinated matching between the remaining output of renewable energy and the flexible charging load of electric vehicles within the distribution zone.

[0089] As one possible implementation, in the above embodiments, step S1 may specifically include the following steps:

[0090] S1-1, Read power distribution zone and day-ahead dispatch data;

[0091] Distribution zone and day-ahead dispatch data reading: Read the pre-obtained distribution network zoning results and day-ahead dispatch data, including the basic load of each node in the distribution zone, the predicted output of renewable energy, the original charging load of electric vehicles, the electrical distance between each node, the arrival time of electric vehicles, the departure time of electric vehicles, the state of charge (SOC) at the station, the target SOC, the battery capacity, the maximum charging power, the charging efficiency, and the adjustable boundary of the virtual energy storage of electric vehicles.

[0092] Let the set of nodes in a certain partition be . The set of nodes connected to renewable energy sources is denoted as . The set of ordinary nodes that are not connected to renewable energy sources is denoted as ,satisfy , The set of scheduling periods is denoted as T, and the length of each scheduling period is... .

[0093] S1-2. Calculate the remaining renewable energy power based on the power distribution zone and day-ahead dispatch data, and then identify the remaining output of renewable energy nodes;

[0094] Remaining renewable energy power identification: for renewable energy nodes During the period Let its renewable energy output, base load, and local electric vehicle initial charging load be respectively... , and Then the remaining renewable energy power of node i in time period t. The definition is as follows:

[0095] (1)

[0096] The remaining renewable energy energy at renewable energy node i during time period t for:

[0097] (2)

[0098] like If this indicates that the renewable energy at that node during that time period has been fully absorbed by the local load and no external coordinated dispatch is required; if If this occurs, it indicates that the node has remaining renewable energy output to be consumed during time period t, which should trigger the subsequent scheduling process.

[0099] S1-3. Based on the remaining output of the renewable energy node and the source time delay constraint, obtain the maximum delayable charging power of the preceding time period, and calculate the maximum delayable charging capacity of the preceding time period.

[0100] Source time delay condition judgment: When renewable energy node i has remaining renewable energy in the target time period t, select charging tasks that can be delayed from the preceding time periods before the target time period.

[0101] For the preceding period Only when the renewable energy in that period has been fully absorbed by the base load and the original electric vehicle charging load in its original state can a portion of the charging load be delayed. The source period delay constraint should be met, and the calculation formula is as follows:

[0102] (3)

[0103] This constraint is designed to prevent the re-emergence of renewable energy curtailment during the source period after the charging load is moved out of the source period.

[0104] Define node i in the preceding time period Minimum electric vehicle charging capacity required to ensure the continued full utilization of existing renewable energy sources. for:

[0105] (4)

[0106] The maximum delayable charging power during the preceding period. for:

[0107] (5)

[0108] The corresponding maximum delayable charging capacity for:

[0109] (6)

[0110] Through the above steps, at the node-time level, we first determine "which time periods can be removed from the charging load" and "the maximum amount of charging can be removed", providing capacity boundaries for subsequent vehicle-level delayed task screening.

[0111] In summary, steps S1-1 to S1-3 define the theoretical upper limit of the charging load that can be shifted to the later time period at the node-time period level. This not only accurately identifies the scheduling objects that need to be coordinated for consumption, but also avoids new power abandonment problems caused by the migration of charging load through source time period delay constraints. This provides reliable data input and power constraint boundaries for subsequent selection of vehicles with delay per vehicle and hierarchical optimization scheduling.

[0112] As one possible implementation, in the above embodiments, step S2 may specifically include the following steps:

[0113] S2-1. Using the maximum delayable charging capacity of the preceding time period combined with the power distribution zone and the day-ahead scheduling data, filter to obtain the local delayable vehicles and the maximum delayable time period of the local delayable vehicles;

[0114] The pre-charge period for renewable energy nodes cannot be delayed for electric vehicles solely at the node aggregation level; specific vehicles eligible for delay must also be screened at the individual vehicle level. These vehicles, originally planned to begin charging during the pre-charge period, can have their charging start time appropriately postponed to the current renewable energy surplus period, provided that user demand and off-site constraints are met. The specific implementation steps are as follows:

[0115] S2-1-1. Set the vehicle's basic parameters based on the maximum delayable charging capacity of the preceding time period and the power distribution zone and day-ahead scheduling data.

[0116] Read the power distribution zone information and the day-ahead scheduling raw data, and use the maximum delayable charging capacity of the preceding time period as the prerequisite for subsequent node power constraints. The k-th electric vehicle in the system, let its battery capacity be... Arrival SOC is The target SOC is Maximum charging power is Charging efficiency is Arrival time is Departure time is This process collects all the parameters required for single-vehicle calculations, providing parameter input for subsequent quantitative calculations of charging capacity, charging time, and parking time.

[0117] S2-1-2. Calculate the required additional power for the vehicle using the vehicle's basic parameters, and obtain the vehicles that need to be charged;

[0118] The amount of electricity required to charge the vehicle from its arrival SOC to the target SOC is:

[0119] (7)

[0120] in, This indicates the total amount of charge required to bring the vehicle from its initial state of charge at the destination to its target state of charge. If... If the vehicle's charge level is positive, it means that the vehicle has already met the target charge requirement upon arrival and does not need to participate in subsequent charging scheduling. Therefore, by filtering out vehicles that actually need additional charge (greater than 0) and excluding vehicles that do not need additional charge, we can obtain the vehicles that need charging and narrow down the scope of vehicles subject to subsequent delayed charging conditions.

[0121] S2-1-3. Based on the vehicles that need charging and the basic parameters of the vehicles, calculate the number of charging periods and the number of parking periods for the vehicles that need charging.

[0122] For the selected vehicles that need charging, within a scheduling cycle of [length missing]... Under these conditions, the minimum number of charging sessions required for this vehicle. for:

[0123] (8)

[0124] in, This indicates rounding up. The formula shows that the number of charging periods required by a vehicle depends on the target charging capacity, maximum charging power, and charging efficiency. In essence, it describes the shortest charging window that a vehicle must occupy to complete a predetermined charging task. The larger the value, the higher the vehicle's dependence on available dwell time, and the more limited the corresponding delay scheduling space.

[0125] Furthermore, the permitted parking time for vehicles within the station is as follows:

[0126] (9)

[0127] The minimum number of charging periods and the number of parking periods corresponding to each vehicle that needs to be charged are obtained to determine whether the vehicle has a time delay margin.

[0128] S2-1-4. Compare the number of charging periods for the vehicles that need charging with the number of parking periods for the vehicles that need charging to obtain vehicles that meet the conditions for delayed charging.

[0129] For each vehicle requiring charging, a time margin check is performed by comparing the minimum number of charging periods with the number of parking periods. This indicates that the vehicle still has time leeway after meeting the target charging demand, thus meeting the basic conditions for delayed charging; if This indicates that the vehicle needs to be charged almost the entire time while parked, or there is simply no time leeway, making it unsuitable as a vehicle for delayed charging.

[0130] S2-1-5. Based on the vehicles with delayed charging conditions and the vehicle's basic parameters, obtain the maximum number of time periods that can be delayed for vehicles with delayed charging conditions.

[0131] For vehicles with delayed charging capabilities, the latest time to start charging can be determined. :

[0132] (10)

[0133] The above formula shows that if the vehicle starts charging later than If the vehicle fails to meet its target charging needs before leaving the station, it will be unable to complete the charging process. Therefore, the vehicle's charging start time... Must meet:

[0134] (11)

[0135] Assuming the electric vehicle begins charging immediately upon arrival at the station, what is the initial charging start time? It can be recorded as:

[0136] (12)

[0137] The maximum number of delay periods for a vehicle Defined as:

[0138] (13)

[0139] This parameter represents the upper limit of the time during which the charging sequence of a single vehicle can be shifted backward, thus defining the feasible time range for delayed charging of the vehicle.

[0140] S2-1-6. Based on the maximum number of delayable time periods for vehicles with delayed charging conditions and the vehicle's basic parameters, set a minimum safe state of charge threshold to obtain the local vehicles that can be delayed and the maximum number of delayable time periods for local vehicles that can be delayed.

[0141] To prevent low-battery vehicles from being further delayed, the minimum safe state of charge threshold for a vehicle to participate in delay scheduling is set as follows: This represents the minimum safe state of operation (SOC) required to participate in delayed scheduling. Therefore, when... and At that time, the vehicle is deemed eligible to participate in the delayed charging scheduling. The above conditions together ensure that the vehicle eligible for delayed charging has the time to extend its service life, but the delayed charging will not disrupt the user's basic travel needs.

[0142] S2-2. Set the remaining renewable energy power for resource absorption, and obtain the judgment result of calling ordinary nodes based on the local delayable vehicles and the maximum delayable time period of the local delayable vehicles through node-level delayed charging constraints.

[0143] Based on the obtained local set of vehicles with delay capabilities and the maximum number of delay periods for each vehicle, a node-level delay charging constraint is introduced: Let node i start from the previous time period The actual total charging energy delayed to the target time period t is Then the following must be satisfied:

[0144] (14)

[0145] After determining the movable load in the preceding time period and screening the delayable vehicles within the node, it is not guaranteed that the remaining renewable energy in the target time period can be fully absorbed by local resources. Therefore, before entering the ordinary node scheduling, it is necessary to further determine whether the renewable energy node's own available resources are sufficient to cover the remaining renewable energy in the current time period. Let the remaining renewable energy power of renewable energy node i after absorption by local delay resources in the target time period t be... Remaining renewable energy capacity Then we have:

[0146] (15)

[0147] like If this indicates that the local delay resources generated by the renewable energy node in the preceding period are sufficient to absorb all the remaining renewable energy in that period, then there is no need to further call upon other ordinary nodes to participate in coordinated scheduling; if This indicates that after prioritizing the use of local delay resources, there is still a surplus of renewable energy that has not yet been absorbed during the target time period. In this case, it is necessary to further utilize other ordinary nodes to participate in coordinated consumption. For the ordinary nodes being utilized, priority should be given to their directly transferable loads currently being charged; if the directly transferable loads are still insufficient to cover the current time period... Then, the electric vehicle charging loads with delay capabilities in the preceding time period of the ordinary node are further called up and allocated in order of electrical distance from near to far.

[0148] S2-3. Based on the judgment result of calling the ordinary node and the remaining renewable energy power absorbed by the resource, calculate the adjustable resources of the ordinary node in the current time period to obtain the remaining renewable energy power to be consumed.

[0149] When the obtained judgment result indicates that a regular node needs to be called, the adjustable resources of the regular node in the current time period are calculated: For the regular node When renewable energy node i has surplus renewable energy in time period t, priority should be given to the electric vehicle load of ordinary node j that is already charging in the same time period.

[0150] Let the original total charging power of the electric vehicle at ordinary node j in time period t be . Then, the remaining renewable energy power to be consumed after the electric vehicle load of ordinary node j in time period t participates in the consumption. for:

[0151] (16)

[0152] in, The remaining renewable energy power of renewable energy node i after local delay resource absorption in time period t. For the local electric vehicle original charging load of renewable energy node j in time period t.

[0153] like If this indicates that the directly transferable load currently being charged by node j is sufficient to absorb the remaining renewable energy, then there is no need to further utilize the delayed charging load from the preceding period of ordinary nodes or the charging load from other nodes; if This indicates that after prioritizing the use of local delay resources and the load that can be directly transferred from ordinary nodes in the current time period, there is still a surplus of renewable energy that has not yet been absorbed in the target time period t. At this time, in order to further improve the level of renewable energy consumption, it is necessary to call upon the electric vehicle charging load with delay capability in the preceding time period of node j and the charging load of other nodes, so as to obtain the remaining renewable energy power to be consumed after the electric vehicle load of ordinary nodes participates in the consumption.

[0154] S2-4. Based on the remaining renewable energy power to be consumed and the local delayable vehicle consumption matching degree, obtain the optimal delayed start charging time;

[0155] When the remaining renewable energy power to be absorbed after the acquisition of electric vehicle loads from ordinary nodes has been absorbed is still greater than zero, it indicates that it is necessary to further utilize electric vehicle charging loads with delay capabilities from previous periods. At this time, combined with the acquired locally available vehicles that can delay, a absorption matching index is established to quantify the degree of overlap between the vehicle's delayed charging window and the surplus period of renewable energy. Based on this index, the optimal delayed start time for a single vehicle is determined, so that the delayed scheduling and the time-domain distribution of renewable energy output achieve optimal matching. The specific implementation process is as follows:

[0156] When multiple vehicles meeting the delay conditions exist within the same node, a matching index is established to measure the overlap between different delayed charging windows and the target renewable energy surplus period. Let the actual charging start time of vehicle k after the delay at any node n be denoted as . To measure the overlap between the vehicle charging process and the period of abundant renewable energy, we define the vehicle k at renewable energy node i as... Start charging at any time and charge continuously. The matching degree of delayed charging windows for each time period is used to characterize the degree to which surplus renewable energy output supports the continuous charging demand of vehicles within the candidate charging window. This is then compared with the renewable energy node... The degree of absorption matching is defined as:

[0157] (17)

[0158] in, Indicates that vehicle k is from the candidate The degree of matching between the charging window at the start of charging and the surplus output of renewable energy. Given any node n, the minimum number of charging sessions required for vehicle k. Let n be the remaining renewable energy power of any node n in time period t. For any node n, vehicle k is in The theoretical charging capacity corresponding to continuous charging at maximum charging power within the window period. The numerator represents the vehicle's initial charge from... After charging begins, in continuous The denominator represents the surplus renewable energy power within a given time period; the denominator represents the theoretical charging capacity of the vehicle when continuously charging at maximum charging power within that window. Clearly, The larger the value, the more likely the vehicle will be delayed until the scheduled time. The result indicates that when a vehicle begins charging during this period, its charging process is more likely to coincide with periods of abundant renewable energy.

[0159] Therefore, the optimal delayed start time for this vehicle is... Defined as:

[0160] (18)

[0161] Corresponding actual delay time for:

[0162] (19)

[0163] in, Let be the arrival time of vehicle k at any node n.

[0164] As can be seen from the formula, the present invention does not directly adopt the maximum delay time of the vehicle, but selects the delay start time that best matches the surplus output of renewable energy within the feasible range.

[0165] In summary, steps S2-1 to S2-4, based on the maximum delayable charging capacity of the preceding time period and the day-ahead scheduling data of the distribution zone, firstly, locally delayable vehicles and their maximum delayable time periods are selected. Then, the local resource absorption capacity is calculated through node-level delayed charging capacity constraints. The results of whether to call ordinary nodes and the remaining renewable energy power after local absorption are output. Subsequently, the remaining renewable energy power to be absorbed after the participation of ordinary node load is updated by combining the original total charging power of electric vehicles at ordinary nodes. Finally, the optimal delayed start charging time for each vehicle is obtained based on the absorption matching degree index. The entire process pays attention to completing the identification of locally schedulable resources, the verification of local absorption capacity, the judgment of cross-node resource call, and the optimization of charging time shift time. This provides reliable input for the subsequent three-layer progressive system absorption optimization model, which can fully explore the potential of electric vehicle charging time shift, improve the matching absorption level of surplus renewable energy output within the zone, and constrain the charging migration boundary to avoid the problem of vehicles being unable to complete the replenishment on time.

[0166] As one possible implementation, in the above embodiments, step S3 may specifically include the following steps:

[0167] S3-1. Based on the locally delayable vehicle and the optimal delayed start charging time, construct a three-layer progressive renewable energy collaborative consumption scheduling model;

[0168] Based on locally available vehicles and their optimal delayed charging start times as adjustable resources, a three-layer progressive renewable energy collaborative consumption scheduling model is constructed according to the principles of "local priority, proximity coordination, and prioritizing current needs over delayed ones." This model divides the process of electric vehicles participating in the consumption of remaining renewable energy into three progressive scheduling layers, and solves them sequentially using a day-ahead hierarchical mixed-integer linear programming method. The specific process of constructing the three-layer progressive renewable energy collaborative consumption scheduling model is as follows:

[0169] S3-1-1. Based on the locally delayable vehicle, the optimal delayed start charging time, and the remaining renewable energy power absorbed by the resource, the remaining renewable energy power after the first layer of scheduling is obtained by using the local delay absorption of the renewable energy node.

[0170] The first layer of the three-layer progressive renewable energy collaborative consumption scheduling model is the local delay consumption layer of renewable energy nodes. This layer serves as the first priority adjustment method, using locally delayable vehicles and their optimal delay start charging times as the adjustable resource base, and the remaining renewable energy power after resource absorption as the consumption demand input. When renewable energy node i has remaining renewable energy in time period t, it first calls the electric vehicle charging task that meets the delay condition in the previous time period of its own node. This layer only changes the execution time of electric vehicle charging tasks within the same node and does not call other node resources first, so it serves as the first priority adjustment method. The remaining renewable energy after the first layer scheduling is updated according to equation (15). If the remaining renewable energy after local delay scheduling is zero, the collaborative scheduling of the current node in the current time period ends; if there is still a remainder, it enters the second layer.

[0171] S3-1-2. Using the remaining renewable energy power after the first-level scheduling and the electrical distance between nodes, ordinary nodes are used to collaboratively consume the power during the current time period to obtain the remaining renewable energy power after the second-level scheduling.

[0172] The second layer of the three-layer progressive renewable energy collaborative consumption scheduling model is the collaborative consumption layer of ordinary nodes in the current time period. This layer takes the remaining renewable energy power that is still greater than zero from the first layer as the consumption demand, and determines the calling order of ordinary nodes based on the electrical distance between nodes, as follows:

[0173] For the remaining renewable energy sources, a candidate node sequence is established based on the electrical distance between ordinary nodes and renewable energy nodes:

[0174] (20)

[0175] Ordinary node resources are invoked sequentially from nearest to farthest in terms of electrical distance. For the currently invoked ordinary node, its currently executing electric vehicle charging load is utilized for collaborative consumption. This layer does not initially change the original charging start time of the ordinary node's vehicles. Compared to the previous delayed charging method, it causes less disturbance to the original charging plan of the vehicles, and therefore takes precedence over the third layer.

[0176] The method for determining the order of cross-node resource calls based on electrical distance calculates the electrical distance matrix through node voltage sensitivity and completes the distribution network partitioning. During cross-node scheduling, ordinary node resources are called in order of electrical distance from near to far, so that the scheduling process follows the principle of "nearest coordination".

[0177] S3-1-3. Based on the remaining renewable energy power after the second-level scheduling and combined with the deferred charging tasks of the preceding time periods of ordinary nodes, the remaining renewable energy power after the third-level scheduling is obtained by using the deferred time-shifting consumption of the preceding time periods of ordinary nodes. This leads to the acquisition of a three-layer progressive renewable energy collaborative consumption scheduling model. The third layer of the three-layer progressive renewable energy collaborative consumption scheduling model, namely the deferred time-shifting consumption layer of the preceding time periods of ordinary nodes, is constructed. This layer takes the remaining renewable energy power, which is still greater than zero, output from the second layer as the consumption demand, and combines it with the deferred charging tasks of the preceding time periods of ordinary nodes for final consumption, as detailed below:

[0178] If the charging load of a regular node in the current time period is still insufficient to fully absorb the remaining renewable energy, then the deferred charging tasks of the previous time period of the regular node are further invoked. First, the maximum deferred charging capacity of the previous time period of the regular node is determined using equations (3) to (6). Then, specific deferred vehicles are screened using equations (7) to (13), and the optimal deferred start time for each vehicle is determined according to equations (17) to (19). If the deferred resources of the current regular node are still insufficient to meet the demand for absorbing the remaining renewable energy, then the next regular node is invoked according to the electrical distance sequence until the remaining renewable energy is fully absorbed or all candidate regular nodes are invoked.

[0179] After the scheduling of this layer is completed, the remaining renewable energy power after the scheduling of the third layer is obtained. At this point, the local delay consumption layer of renewable energy nodes, the current time-slot collaborative consumption layer of ordinary nodes, and the previous time-slot delay time-shift consumption layer of ordinary nodes are executed in sequence. The three layers are executed in sequence according to a fixed priority order of "local priority, nearby collaboration, current before delay". The remaining renewable energy power after the scheduling of the previous layer is used as the consumption demand input of the next layer, forming a complete three-layer progressive renewable energy collaborative consumption scheduling model with sequential transmission of inter-layer status and clear priority of resource call. This model is different from the existing single-layer or disordered scheduling methods.

[0180] S3-2. Calculate the actual absorption of renewable energy based on the three-layer progressive renewable energy collaborative consumption and scheduling model.

[0181] Based on the established three-layer progressive collaborative consumption and scheduling model, the renewable energy consumption capacity of each level of resources is calculated step by step according to the hierarchical scheduling order. The actual amount of electricity absorbed by the distribution network from surplus renewable energy by the delayed electric vehicle load during the entire scheduling cycle is accurately calculated. The specific implementation process is as follows:

[0182] Define the electric vehicle charging power of ordinary node j in time period t that is transferred to renewable energy node i for consumption as follows: Define node n in the preceding time period. The delayed charging energy that is postponed to the target time period t and supported by the remaining renewable energy of renewable energy node i is denoted as . Then the amount of renewable energy electricity actually absorbed by electric vehicles at node i during time period t. for:

[0183] (twenty one)

[0184] S3-3. Calculate the renewable energy consumption rate using the actual amount of renewable energy absorbed, and construct an optimization objective function;

[0185] Using the actual absorption of renewable energy as the core calculation parameter, and combining it with the total surplus power output of renewable energy in each time period of the distribution zone, the renewable energy absorption rate of the zone is obtained by calculating the ratio of actual absorbed power output to total surplus power output. To maximize the utilization efficiency of renewable energy in the distribution network and minimize wind and solar curtailment, the core optimization objective is to maximize the global renewable energy absorption rate, while also considering the rationality of electric vehicle charging timing and the economic efficiency of load migration. A day-ahead optimization objective function adapted to a three-layer progressive scheduling model is constructed, and the specific implementation process is as follows:

[0186] Total renewable energy output during the entire dispatch cycle Contribute to renewable energy at each renewable energy node The sum is:

[0187] (twenty two)

[0188] in, Provide renewable energy output to renewable energy node i during time period t.

[0189] Total amount of renewable energy actually absorbed during the entire dispatch cycle for:

[0190] (twenty three)

[0191] in, The power output of renewable energy node i during time period t is the amount of renewable energy actually consumed.

[0192] The renewable energy absorption rate is defined as follows: :

[0193] (twenty four)

[0194] Therefore, the objective function can be written as:

[0195] (25)

[0196] It should be noted that this invention uses the maximization of renewable energy absorption rate as the optimization objective, and site revenue and user cost as economic evaluation indicators of the scheduling results, instead of constructing a second optimization objective parallel to the renewable energy absorption rate.

[0197] S3-4. Based on the three-layer progressive renewable energy collaborative consumption scheduling model and the adjustable boundary of electric vehicle virtual energy storage, a scheduling constraint is constructed.

[0198] Based on the operational logic of the three-layer progressive collaborative energy consumption scheduling model and combined with the adjustable boundary parameters of electric vehicle virtual energy storage, a comprehensive and multi-dimensional scheduling constraint system is established. The amount of electricity received by a renewable energy node for collaborative charging of electric vehicles in time period t must not exceed the actual remaining renewable energy in that time period.

[0199] (26)

[0200] Meanwhile, the electric vehicle charging resources called upon must not exceed the upper limit of the adjustable capacity of the electric vehicle calculated by the preceding virtual energy storage model:

[0201] (27)

[0202] in, This indicates the amount of electricity allocated for electric vehicle charging by the dispatcher. This represents the upper limit of adjustable energy for the corresponding time period, as determined by the electric vehicle virtual energy storage model.

[0203] All coordinated power and delayed charging energy requirements are met:

[0204] (28)

[0205] The main constraints are the remaining renewable energy constraint, the virtual energy storage adjustable capacity constraint, and the non-negativity constraint.

[0206] S3-5. Solve the three-layer progressive renewable energy collaborative consumption scheduling model using the day-ahead hierarchical mixed integer linear programming method based on the optimization objective function and the scheduling constraints to obtain the renewable energy collaborative consumption scheduling results;

[0207] Guided by the objective function of optimal absorption rate and using multi-dimensional scheduling constraints as boundary conditions, a day-ahead hierarchical mixed integer linear programming algorithm adapted to time-series optimization and hierarchical scheduling scenarios is adopted to solve the three-layer progressive renewable energy collaborative absorption scheduling model in a hierarchical iterative manner.

[0208] Since the scheduling process includes three sub-processes with a clear sequential relationship: local delay, current coordination of ordinary nodes, and preceding delay of ordinary nodes, a hierarchical mixed-integer linear programming (MILP) method is adopted. Hierarchical MILP is a method that divides a complex optimization problem into multiple sub-layers according to decision logic, variable coupling relationships, or scheduling priorities, and establishes a mixed-integer linear programming model for solving each sub-layer. Its basic idea is to decompose the original large-scale optimization problem into several smaller, more clearly structured MILP sub-problems, and solve each sub-problem sequentially according to a pre-defined hierarchical order. The solution result of the previous layer serves as the input condition or state parameter of the next layer, thereby realizing the recursive solution of the complex system optimization problem.

[0209] Suppose the hierarchical scheduling process contains K sub-layers, and the MILP subproblem of the k-th layer is... It can be uniformly represented as:

[0210] (29)

[0211] (30)

[0212] (31)

[0213] in, This represents the continuous decision variables in the k-th layer model; Represents the 0-1 decision variables in the k-th layer model; The coefficients are those of the objective function. and These are the objective function coefficient vectors for continuous variables and 0-1 variables, respectively; and This is the constraint coefficient matrix; The right-hand side of the k-th level constraint takes the same value as the system state obtained after solving the previous level constraint. related; The number of continuous variables in the k-th layer model; The number of 0-1 variables in the k-th layer model; is the dimension of continuous variables in the k-th layer model.

[0214] The optimal solution of the k-th layer model It can be represented as:

[0215] (32)

[0216] After solving the problem at level k, the system state is updated based on the optimal solution for that level:

[0217] (33)

[0218] in, This represents the state update function corresponding to the k-th layer, which describes the impact of the current layer's optimization result on the model parameters, available resources, constraint boundaries, or system state of subsequent layers.

[0219] Therefore, the entire hierarchical MILP solution process can be written in recursive form:

[0220] (34)

[0221] As can be seen from the above expressions, hierarchical MILP does not incorporate all variables and constraints into the same global optimization model at once. Instead, it breaks them down into multiple hierarchical MILP sub-models based on the problem structure. That is, hierarchical MILP does not incorporate all nodes, all vehicles, and all time periods into the same optimization problem at once. Instead, it makes the scheduling results of the previous layer the input state of the next layer. This can both express the resource allocation priority of the present invention and reduce the scale of variables and constraints in a single sub-problem.

[0222] In summary, steps S3-1 to S3-5 integrate hierarchical scheduling logic into optimization modeling, with the absorption rate as the optimization guide. At the same time, multi-dimensional constraints lock the adjustable operating boundary of electric vehicles, ensuring that the scheduling scheme meets the requirements of vehicle travel, charging safety and grid operation, and can reasonably allocate absorption resources at each level, effectively improving the absorption level of surplus renewable energy in the distribution area, and obtaining a feasible day-ahead charging time-shifting scheduling scheme.

[0223] As one possible implementation, in the above embodiments, the method further includes evaluating and verifying the results of the coordinated consumption and scheduling of renewable energy to obtain the evaluation results of the scheduling effect.

[0224] After the hierarchical MILP scheduling is completed, the scheduling effect of the proposed electric vehicle charging time-shift coordinated absorption method is evaluated. To comprehensively measure the effectiveness of the proposed method in terms of renewable energy absorption and economics, indicators such as renewable energy absorption rate, site net revenue, and total user-side cost are selected for evaluation. The specific steps are as follows:

[0225] S4-1. Calculate the renewable energy consumption rate using the aforementioned renewable energy collaborative consumption scheduling results;

[0226] Renewable energy absorption rate: Read the actual renewable energy output data of each node in each time period in the renewable energy collaborative absorption scheduling results, count the total renewable energy output of all renewable energy in the region during the scheduling period and the total amount of renewable energy actually absorbed, use formula (24) to calculate the renewable energy absorption rate of a single renewable energy node and the entire distribution region, and compare the results before and after scheduling. Among them, the more obvious the increase in absorption rate, the more remaining renewable energy is obtained through electric vehicle charging time shift and cross-node coordination.

[0227] S4-2. Based on the renewable energy collaborative consumption and scheduling results and the total revenue of the site, obtain the net revenue of the site;

[0228] Net revenue per site: Based on the actual charging power of electric vehicles at each node and time period output by the renewable energy collaborative consumption dispatch, let the actual charging power of electric vehicles at node n in time period t after dispatch be... Then the total revenue of the site It can be represented as:

[0229] (35)

[0230] Among them, charging service revenue for:

[0231] (36)

[0232] in, This represents the unit charging service price for node n during time period t.

[0233] Electric vehicle and renewable energy consumption revenue for:

[0234] (37)

[0235] in, The unit price for subsidies to the renewable energy consumption of the site.

[0236] Cost of purchasing electricity from the grid for:

[0237] (38)

[0238] in, This represents the price at which renewable energy node n purchases electricity from the grid during time period t. This represents the actual amount of electricity purchased from the grid by node n during time period t.

[0239] User compensation costs for:

[0240] (39)

[0241] in, The unit is the price for delayed compensation.

[0242] Cross-node scheduling cost for:

[0243] (40)

[0244] in, Price is the unit price for electrical distance dispatching.

[0245] S4-3. Based on the renewable energy collaborative consumption and scheduling results, use the total user charging cost and delay compensation revenue to obtain the total user-side cost;

[0246] Total User Cost: Based on the results of renewable energy collaborative consumption and scheduling, the total original charging cost for users is calculated. The total user cost is determined by both the original charging cost of the vehicle and the compensation received by the vehicle for participating in delayed scheduling. Let the total user charging cost be... The delayed compensation benefit obtained is ,but:

[0247] (41)

[0248] The lower this indicator, the lower the actual charging cost borne by the user after participating in collaborative scheduling.

[0249] S4-4. Evaluate and verify the renewable energy absorption rate, the site net revenue, and the user-side total cost to obtain the scheduling effect evaluation results.

[0250] The calculated renewable energy absorption rate, site net revenue, and total user-side cost are comprehensively evaluated and verified. The renewable energy absorption rate reflects the effect of improving the absorption of surplus renewable energy from the perspective of renewable energy utilization level. The site net revenue reflects the improvement effect of the dispatch scheme on the site operation efficiency from the perspective of charging station economic benefits. The total user-side cost represents the actual economic burden of users after participating in dispatch from the perspective of actual user costs. The above three indicators verify the proposed method from the perspectives of renewable energy utilization level, charging station economic benefits, and actual user-side costs, respectively. Together, they constitute the dispatch effect evaluation system of the electric vehicle charging time-shifted collaborative absorption method. By comparing the changes of each indicator before and after dispatch, a complete dispatch effect evaluation result is obtained, thereby verifying the overall effectiveness of the dispatch method.

[0251] In summary, steps S4-1 to S4-4 take the results of renewable energy collaborative consumption and dispatch as input, and sequentially complete the calculation of renewable energy consumption rate, site net revenue calculation, and user-side total cost statistics. A multi-dimensional evaluation system including renewable energy utilization level, site operation efficiency, and user economic burden is constructed for comprehensive evaluation and verification. The quantitative consumption rate directly reflects the actual utilization degree of surplus renewable energy in the distribution area. The site net revenue reflects the improvement effect of charging station participation in collaborative dispatch. The user-side total cost represents the actual economic burden of users after participating in charging time-shift dispatch. The comprehensive evaluation results of dispatch effect are obtained by integrating multiple indicators, which can effectively verify the technical effect of this dispatch method in improving renewable energy consumption capacity while taking into account the economic benefits of both the site and user sides.

[0252] This invention constructs a three-layer progressive scheduling architecture: "local delayed consumption of renewable energy nodes—coordinated consumption of ordinary nodes during the current time period—time-shifted consumption of ordinary nodes during the preceding time period." Following the scheduling principles of "local priority, proximity coordination, and current-first-delay," it achieves efficient consumption of remaining renewable energy within a designated area while meeting individual vehicle travel demands, state-of-charge safety thresholds, and node power boundary constraints. The method first calculates the electrical distance matrix between nodes based on the voltage sensitivity of distribution network nodes and completes the distribution network partitioning, providing a quantitative basis for cross-node resource allocation. Secondly, a complete delay condition screening mechanism is established at the individual vehicle level, comprehensively considering individual constraints such as vehicle arrival / departure time, target replenishment capacity, state-of-charge safety threshold, and maximum delay duration to screen vehicles with delayed charging conditions. A consumption matching index is introduced to determine the optimal delayed start time for vehicle charging. Finally, day-ahead hierarchical mixed integer linear programming (MILP) is used for optimization, improving the renewable energy consumption rate while considering the economic efficiency of site operation. This solves the technical problems of existing scheduling methods, such as a single adjustment method, lack of quantitative basis for cross-node coordination, and insufficient consideration of individual vehicle-level feasibility.

[0253] The method of the present invention will be described in detail below with specific examples:

[0254] A road-grid coupled system consisting of an IEEE 69-node distribution system and a 68-node transportation system was used as the simulation object. Based on existing node electrical distances and distribution network zoning results, the 69 distribution network nodes were divided into four zones. Zone 2 was selected as the analysis object, including nodes 4, 36, 37, 38, and 39. Nodes 38 and 39 are renewable energy nodes, while nodes 4, 36, and 37 are conventional nodes. The electrical distances between the nodes are shown in Table 1. Basic loads for each node were generated according to different functional areas, and original charging loads were generated based on an electric vehicle charging load model that considers charging queues. The number of electric vehicles was set to 5000, the day-ahead scheduling cycle to be 24 hours, and the time resolution to be 15 minutes.

[0255] Table 1

[0256] node 4 36 37 38 39 4 0 0.0616 0.2543 0.3549 0.36856 36 0.0616 0 0.1927 0.2933 0.3070 37 0.2543 0.1927 0 0.1006 0.1143 38 0.3549 0.2933 0.1006 0 0.0137 39 0.3686 0.3070 0.1143 0.0137 0

[0257] According to the method described in this invention, the remaining renewable energy at each renewable energy node is identified, and electric vehicles with delayed charging capabilities are selected. The process for identifying renewable energy consumption demand is as follows: Figure 3 As shown, the ways in which electric vehicles participate in the coordinated consumption of renewable energy are as follows: Figure 4 As shown, the relationship between the delayed consumption source period and the target period, and the delayed charging process of electric vehicles are respectively as follows: Figure 5-6 As shown. Then follow Figure 2The scheduling process shown follows the principles of "local priority, proximity coordination, and current-first, delay-later," sequentially executing local delay absorption by renewable energy nodes, coordinated absorption by ordinary nodes during the current time period, and time-shifted absorption by ordinary nodes during the preceding time period. A day-ahead hierarchical mixed-integer linear programming method is used for the solution. This scheduling logic is consistent with the actual process described in Chapter 4 of the paper.

[0258] In the scheduling results, the renewable energy consumption composition of nodes 38 and 39 is as follows: Figure 7-8 As shown; the current time period collaborative absorption power provided by each ordinary node to node 38 is as follows: Figure 9 As shown, the delay-shift cooperative absorption power provided by each node to node 38 is as follows: Figure 10 As shown in the figure. The results show that the present invention can fully utilize the time-shifting adjustment capability of electric vehicle charging load through local delay, coordination of ordinary nodes in the current time period, and time shifting of the preceding delay of ordinary nodes, thereby improving the absorption level of surplus renewable energy.

[0259] Experimental results show that, after adopting the method of this invention, the renewable energy absorption rate of node 38 increased from 67.90% to 77.54%, and that of node 39 increased from 69.60% to 85.98%, with the overall renewable energy absorption rate of the region increasing from 68.78% to 81.89%. Simultaneously, the net revenue of the charging station increased from RMB 2117.23 to RMB 2836.76, and the total cost on the user side decreased from RMB 3097.32 to RMB 2851.31. The renewable energy absorption rate and economic indicators before and after the scheduling are shown in Table 2. The results demonstrate that this invention can improve the renewable energy absorption level within a region while simultaneously considering the economic efficiency of both the charging station and user sides.

[0260] Table 2

[0261] index Before scheduling After scheduling Node 38 Renewable Energy Consumption Rate 67.90% 77.54% Node 39 Renewable Energy Consumption Rate 69.60% 85.98% Total absorption rate 68.78% 81.89% Site net revenue 2117.23 yuan 2836.76 yuan Total cost on the user side 3097.32 yuan 2851.31 yuan

[0262] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0263] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0264] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0265] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0266] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift, characterized in that, include: S1. Identify the remaining output of renewable energy nodes using distribution zone and day-ahead dispatch data, and obtain the maximum delayable charging capacity for the preceding period. S2. Based on the maximum delayable charging capacity of the preceding period and the power distribution zone and day-ahead scheduling data, select local vehicles that can be delayed, and combine the consumption matching index to determine the optimal time to start charging. S3. Perform a three-layer progressive collaborative consumption scheduling based on the local delayable vehicles and the optimal delayed start charging time, and use the day-ahead hierarchical mixed integer linear programming method to solve the problem and obtain the renewable energy collaborative consumption scheduling result.

2. The renewable energy collaborative consumption and scheduling method based on electric vehicle charging time shift according to claim 1, characterized in that, S1. Identify the remaining output of renewable energy nodes using distribution zone and day-ahead dispatch data, and obtain the maximum delayable charging capacity for the preceding period, including: Read the distribution zone and day-ahead dispatch data, which includes the basic load of each node in the distribution zone, the predicted output of renewable energy, the original charging load of electric vehicles, the electrical distance between each node, the arrival time of electric vehicles, the departure time of electric vehicles, the state of charge at the arrival point, the target state of charge, the battery capacity, the maximum charging power, the charging efficiency, and the adjustable boundary of the virtual energy storage of electric vehicles. The remaining renewable energy power is calculated based on the power distribution zones and day-ahead dispatch data, thereby identifying the remaining output of renewable energy nodes; Based on the remaining output of the renewable energy node and the source time delay constraint, the maximum delayable charging power of the preceding time period is obtained, and the maximum delayable charging capacity of the preceding time period is calculated.

3. The renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift according to claim 2, characterized in that, The formula for calculating the source time delay constraint is: ， in, Based on the load, For the original charging load of local electric vehicles, Contribute to renewable energy.

4. The renewable energy collaborative consumption and scheduling method based on electric vehicle charging time shift according to claim 2, characterized in that, S2. Based on the maximum delayable charging capacity of the preceding time period and the power distribution zone and day-ahead scheduling data, filter locally delayable vehicles, and combine with the absorption matching index to determine the optimal delay start time for charging, including: By combining the maximum delayable charging capacity of the preceding time period with the power distribution zone and the day-ahead scheduling data, the local delayable vehicles and the maximum delayable time period of the local delayable vehicles are obtained through filtering. Set the remaining renewable energy power for resource absorption, and obtain the judgment result of calling ordinary nodes based on the local delayable vehicles and the maximum delayable time period of the local delayable vehicles through node-level delayed charging constraints. Based on the judgment result of calling the ordinary node and the remaining renewable energy power absorbed by the resource, the adjustable resources of the ordinary node in the current period are calculated to obtain the remaining renewable energy power to be consumed. The optimal time to start charging is determined by combining the remaining renewable energy power to be consumed with the local available vehicles for consumption matching degree.

5. The renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift according to claim 4, characterized in that, By combining the maximum delayable charging capacity of the preceding time period with the power distribution zone and day-ahead scheduling data, local delayable vehicles and the maximum delayable time period for local delayable vehicles are obtained, including: Based on the maximum delayable charging capacity of the preceding period and the power distribution zone and day-ahead scheduling data, the vehicle basic parameters are set, including battery capacity, arrival state of charge, target state of charge, maximum charging power, charging efficiency, arrival time and departure time. The required additional power for the vehicle is calculated using the vehicle's basic parameters, and the vehicles that need charging are identified. Based on the vehicles that need charging and their basic parameters, calculate the number of charging periods and the number of parking periods for the vehicles that need charging. The number of charging periods for the vehicles that need charging is compared with the number of parking periods for the vehicles that need charging to obtain vehicles that meet the conditions for delayed charging. Based on the vehicles that have the conditions for delayed charging and the vehicle's basic parameters, the maximum number of time periods that can be delayed for vehicles with the conditions for delayed charging is obtained. Based on the maximum number of delayable time periods for vehicles with delayed charging conditions and the vehicle's basic parameters, a minimum safe state of charge threshold is set to obtain the local delayable vehicles and the maximum number of delayable time periods for local delayable vehicles.

6. The renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift according to claim 4, characterized in that, The formula for calculating the absorption matching degree is: ， in, Indicates that vehicle k is from the candidate The degree of matching between the charging window at the start of charging and the surplus output of renewable energy. Let n be the actual charging start time for vehicle k after a delay. Given any node n, the minimum number of charging sessions required for vehicle k. Let n be the remaining renewable energy power of any node n in time period t. For the length of each scheduling cycle, For any node n, vehicle k is in The theoretical charging capacity corresponding to continuous charging at maximum charging power within the window period.

7. The renewable energy collaborative consumption and scheduling method based on electric vehicle charging time shift according to claim 4, characterized in that, S3. Based on the locally delayable vehicles and the optimal delayed start charging time, a three-layer progressive coordinated consumption scheduling is performed, and a day-ahead hierarchical mixed integer linear programming method is used to solve the problem to obtain the renewable energy coordinated consumption scheduling result, including: Based on the locally delayable vehicles and the optimal delayed start charging time, a three-layer progressive renewable energy collaborative consumption scheduling model is constructed. The actual absorption of renewable energy is calculated based on the three-layer progressive renewable energy collaborative consumption and scheduling model. The renewable energy absorption rate is calculated using the actual amount of renewable energy absorbed, and an optimization objective function is constructed. Based on the three-layer progressive renewable energy collaborative consumption and scheduling model and the adjustable boundary of electric vehicle virtual energy storage, scheduling constraints are constructed. Based on the optimization objective function and the scheduling constraints, the three-layer progressive renewable energy collaborative consumption scheduling model is solved using the day-ahead hierarchical mixed integer linear programming method to obtain the renewable energy collaborative consumption scheduling results.

8. The renewable energy collaborative consumption and scheduling method based on electric vehicle charging time shift according to claim 7, characterized in that, Based on the locally delayable vehicles and the optimal delayed start charging time, a three-layer progressive renewable energy collaborative consumption scheduling model is constructed, including: Based on the locally delayable vehicle, the optimal delayed start charging time, and the remaining renewable energy power absorbed by the resource, the remaining renewable energy power after the first layer of scheduling is obtained by using the local delay absorption of renewable energy nodes. By combining the remaining renewable energy power after the first-level scheduling with the electrical distance between nodes, and using the current time period coordinated consumption by ordinary nodes, the remaining renewable energy power after the second-level scheduling is obtained. Based on the remaining renewable energy power after the second-level scheduling and the deferred charging task of the preceding period of the ordinary node, the remaining renewable energy power after the third-level scheduling is obtained by using the deferred time-shifting of the preceding period of the ordinary node for consumption, and then the three-level progressive renewable energy collaborative consumption scheduling model is obtained.

9. The renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift according to claim 1, characterized in that, The method further includes: evaluating and verifying the results of the coordinated consumption and scheduling of renewable energy, and obtaining the evaluation results of the scheduling effect.

10. The renewable energy collaborative consumption scheduling method based on electric vehicle charging time shift according to claim 9, characterized in that, The results of the coordinated consumption and scheduling of renewable energy are evaluated and verified to obtain the evaluation results of the scheduling effect, including: The renewable energy integration rate is calculated using the results of the coordinated renewable energy consumption and scheduling. Based on the results of the coordinated consumption and scheduling of renewable energy and the total revenue of the site, the net revenue of the site is obtained. Based on the renewable energy collaborative consumption and scheduling results, the total user-side cost is obtained by using the total user charging cost and delay compensation revenue. The renewable energy absorption rate, the net revenue of the site, and the total cost on the user side are evaluated and verified to obtain the scheduling effect evaluation results.

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