A scheduling method and system for an electric vehicle based on a micro-grid

Abstract

The present application relates to the technical field of smart grid dispatching, and in particular to a scheduling method and system for electric vehicles based on microgrids, which solves the problem of how to fully consider the hybrid optimization of battery life and power loss when realizing the collaborative operation of microgrids and electric vehicle fleets. To solve the above problem, the present application provides a scheduling method, which comprises: establishing a transmission loss model between each set and a power distribution station, and establishing a line loss model for point-to-point transactions between sets; establishing an electric vehicle operation model based on charging and discharging power constraints, energy boundary constraints and state of charge constraints according to battery degradation costs; designing an enhanced crow search algorithm with a double-guided position update mechanism to generate a charging and discharging scheduling scheme that meets the operation model by attracting global optimal solutions and repelling global worst solutions.

Description

Technical Field

[0001] This invention relates to the field of smart grid dispatching technology, and more specifically, to a dispatching method and system for electric vehicles based on microgrids. Background Technology

[0002] The disorderly charging behavior of large-scale electric vehicles, especially the concentrated charging during peak electricity consumption periods, will significantly increase the peak load of the power grid, causing problems such as local power grid overload and voltage fluctuations, and seriously threatening the safe and stable operation of the power system.

[0003] In related technologies, there are significant shortcomings in the collaborative optimization of energy storage capacity of electric vehicle fleets and power trading between microgrids. Not only has it failed to jointly optimize power exchange with microgrids, thus failing to fully leverage the flexible adjustment role of electric vehicles as mobile energy storage and missing the opportunity to improve the overall system efficiency through collaborative optimization, but it has also ignored battery degradation costs, focusing only on short-term energy arbitrage gains. This may result in additional battery replacement burdens for vehicle owners in practical applications, reducing the actual promotion value of V2G technology and user participation. Furthermore, the calculation of benefits is inaccurate during the promotion process, and the transaction price is distorted, hindering the promotion of the dispatching method.

[0004] Therefore, it is necessary to fully consider the hybrid optimization of battery life and power loss when realizing the coordinated operation of microgrids and electric vehicle fleets. Summary of the Invention

[0005] The problem addressed by this invention is how to achieve a hybrid optimization of battery life and power loss while fully considering the coordinated operation of microgrids and electric vehicle fleets.

[0006] To address the aforementioned issues, this invention provides a microgrid-based electric vehicle scheduling method. The method includes: establishing a microgrid system architecture comprising multiple sets, each set containing a microgrid and its aggregated electric vehicle fleet; establishing a transmission loss model between each set and a distribution station, and establishing a line loss model for point-to-point transactions between sets; establishing an electric vehicle operation model based on charge / discharge power constraints, energy boundary constraints, and state of charge constraints according to battery degradation costs; designing an enhanced crow search algorithm with a dual-guided position update mechanism to generate a charge / discharge scheduling scheme that satisfies the operation model by attracting the globally optimal solution and rejecting the globally worst solution; determining the current net power of each set within a target time period based on the transmission loss model and the line loss model; matching electricity transactions between microgrids based on the current net power until all tradable electricity is traded, obtaining a transaction plan corresponding to the charge / discharge scheduling scheme; and obtaining an optimized scheme for the microgrid system architecture based on the charge / discharge scheduling scheme and the transaction plan.

[0007] Compared with existing technologies, the technical effects achieved by this solution are as follows: The transmission loss model and line loss model clearly distinguish between two scenarios: power transmission between microgrid clusters and distribution stations, and point-to-point power trading between microgrid clusters. The enhanced crow search algorithm with a dual-guided position update mechanism effectively solves the problems of high computational complexity and susceptibility to local optima in large-scale optimization problems. By attracting the global optimum and rejecting the global worst solution, the algorithm's global search capability and convergence speed are significantly improved. Compared with traditional swarm intelligence algorithms or algorithms with a single guiding mechanism, this application can find high-quality charging and discharging scheduling schemes that meet complex constraints more quickly, providing a solid foundation for subsequent transaction matching. Finally, combining these algorithms with the power trading mechanism achieves deep integration of technical scheduling and market trading, providing an optimization solution suitable for the working environment for the efficient collaborative operation of microgrids and electric vehicle fleets.

[0008] In one embodiment of the present invention, a transmission loss model is established between each set and the substation, and a line loss model is established for point-to-point transactions between sets. Specifically, this includes: designating the set that sells electricity as the seller set and the set that buys electricity as the buyer set; when the seller set and the buyer set transmit electricity to the substation, obtaining the transmission equivalent resistance of the connection line between the seller set and the buyer set and the substation, and obtaining the transmission loss model based on the transmission equivalent resistance and the operating parameters of the substation; when the seller set and the buyer set transmit electricity, obtaining the line equivalent resistance of the connection line between the seller set and the buyer set, and obtaining the line loss model based on the line equivalent resistance and the operating parameters of the microgrid.

[0009] Compared with existing technologies, the technical effects achieved by this solution are as follows: The division of the buyer and seller sets clarifies the role of the microgrid set as a seller or buyer in electricity trading, facilitating subsequent loss calculations. For different electricity transmission scenarios, corresponding transmission loss models and line loss models are accurately established. By differentiating parameter acquisition methods, the calculation of electricity transmission losses becomes more refined and accurate, avoiding scheduling and trading deviations caused by inaccurate loss models. Establishing accurate loss models provides a reliable basis for subsequent charging and discharging scheduling scheme generation and electricity trading matching, ensuring that energy losses during transmission are fully considered when determining the current net power of each set within the target time period. This makes electricity trading based on current net power more fair and reasonable, and ultimately, the optimized microgrid system architecture has greater practical operational value and economic benefits.

[0010] In one embodiment of the present invention, an operating model for electric vehicles based on charge / discharge power constraints, energy boundary constraints, and state of charge constraints is established according to battery degradation costs. Specifically, this includes: using Wöhler curves to determine the nonlinear relationship between battery discharge depth and the number of cycles that can be tolerated; establishing a dynamic evolution model of the battery energy storage state for each set of electric vehicle fleets at the target time; and constraining the dynamic evolution model of the battery energy storage state based on charge / discharge power constraints, energy boundary constraints, and state of charge constraints to obtain the operating model.

[0011] Compared with existing technologies, the technical effects achieved by adopting this solution are as follows: By introducing the Wöhler curve to accurately characterize the degradation cost of the battery, the operation model of electric vehicles fully considers the battery's lifespan loss. Combined with the dynamic evolution model of battery energy storage state, it can dynamically simulate the energy changes of electric vehicle batteries under different scheduling schemes. It strictly applies multiple constraints such as charging and discharging power, energy boundaries, and state of charge, ensuring that any scheduling scheme generated by the model conforms to the physical characteristics and operational requirements of the battery, effectively avoiding potential problems caused by ignoring battery degradation and operational limitations.

[0012] In one embodiment of the present invention, the dynamic evolution model of the battery energy storage state is constrained based on charge / discharge power constraints, energy boundary constraints, and state of charge constraints to obtain an operating model. Specifically, this includes: setting energy boundary constraints through the battery energy storage range; setting charge / discharge power constraints through the charging power range and the discharging power range; and setting state of charge constraints through the minimum state of charge threshold and the current energy storage of the battery.

[0013] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: setting energy boundary constraints by the battery energy storage range ensures that the scheduling scheme will not exceed the physical capacity limit of the battery; setting charging and discharging power constraints by the charging power range and discharging power range ensures that the charging and discharging operations are within the safe tolerance range of the battery and charging facilities; and setting state of charge constraints by the minimum state of charge threshold and the current energy storage of the battery effectively avoids deep discharge of the battery, thereby protecting the health of the battery.

[0014] In one embodiment of the present invention, an enhanced crow search algorithm with a dual-guided position update mechanism is designed. This algorithm generates a charging and discharging scheduling scheme that satisfies the operating model by attracting the global optimal solution and rejecting the global worst solution. Specifically, the algorithm includes: encoding the charging and discharging power sequences of all sets within a specific time period into the position vector of each crow individual to obtain an initial solution set that satisfies the operating model; when the random number is greater than or equal to the alert probability, the crow individual controls the search step size by following the historical best position of other crow individuals and combining it with the flight length parameter, iterating the initial solution set into an interval optimal solution; when the random number is less than the alert probability, the crow individual is simultaneously attracted by the global optimal solution and rejected by the global worst solution based on its own optimal position, iterating the initial solution set into an interval optimal solution; after the interval optimal solution is generated, it is determined whether the interval optimal solution can be iterated based on the charging and discharging power constraints, energy boundary constraints, and state of charge constraints.

[0015] Compared with existing technologies, the technical effects achieved by this solution are as follows: The initial solution set provides a solid foundation for effective optimization. During the algorithm's iteration, the position update strategy for individual crows is dynamically adjusted based on the comparison between a random number and the alertness probability. When the random number is greater than or equal to the alertness probability, the algorithm can effectively explore the solution space and learn from group experience, thus avoiding premature convergence to local optima. When the random number is less than the alertness probability, the attraction of the global optimal solution guides the algorithm towards the currently known best direction, while the repulsion of the global worst solution drives the algorithm away from undesirable solution regions, significantly enhancing the algorithm's ability to escape local optima and improving the efficiency and accuracy of global optimization. After generating interval optimal solutions in each iteration, the algorithm rigorously judges these solutions based on charging / discharging power constraints, energy boundary constraints, and state of charge constraints. This ensures that all generated charging / discharging scheduling schemes meet the actual operating requirements of electric vehicles and the physical characteristics of batteries, guaranteeing the feasibility and safety of the scheduling schemes.

[0016] In one embodiment of the present invention, electricity transactions between microgrids are matched based on the current net power until all tradable electricity is traded, resulting in a transaction plan corresponding to the charging and discharging scheduling scheme. Specifically, this includes: dividing the sets into roles based on the current net power within a target time period; the set with positive current net power is designated as the seller set, the set with negative current net power is designated as the buyer set, and the set with zero current net power does not participate in transactions during the target time period; obtaining the purchase price ceiling for the buyer set based on the electricity sales price of the distribution station, and obtaining the sales price floor for the seller set based on the electricity purchase price of the distribution station; processing individual sellers in the seller set based on the purchase price ceiling and sales price, checking the bids of all buyer individuals for each seller individual, and completing a single transaction; after each single transaction, updating the remaining net power of the seller set and the buyer set, and continuing to match transactions based on the remaining net power until all tradable electricity is traded.

[0017] Compared with existing technologies, the technical effects achieved by this solution are as follows: It clearly divides each set into seller, buyer, and non-participating sets based on the current net power, laying the foundation for subsequent electricity trading. It introduces the electricity sales and purchase prices of distribution stations, setting a ceiling price for the buyer set and a floor price for the seller set, thereby establishing a reasonable economic boundary in microgrid transactions and ensuring the rationality of transaction prices. Employing an iterative update strategy, after each transaction, the remaining net power of the participants is updated in real time, and transaction matching continues based on the new net power status. This dynamic iterative trading process ensures that the tradable electricity within the microgrid system is fully matched and utilized throughout the target time period until all tradable electricity is traded.

[0018] In one embodiment of the present invention, the sellers in the seller set are processed according to the ceiling price for purchasing electricity and the floor price for selling electricity. For each seller, the bids of all buyers are checked to complete a single transaction. Specifically, this includes: when more than one buyer bids higher than the floor price for selling electricity, the buyer with the highest bid and the seller complete the transaction using the second highest price rule; when only one buyer bids higher than the floor price for selling electricity, the buyer and the seller complete the transaction based on the average of their bids; when no buyer bids higher than the floor price for selling electricity, the seller directly transacts with the distribution station.

[0019] Compared to existing technologies, the technical effects of this solution are as follows: When multiple buyers offer prices higher than the seller's reserve price, to encourage honest bidding and improve market efficiency, the system selects the buyer with the highest bid for the transaction. However, the transaction price is not the highest bid, but the second highest among all valid bids. This mechanism effectively prevents buyers from maliciously underbidding or overbidding, promoting a more reasonable market price. If only one buyer offers a price higher than the seller's reserve price, to balance the interests of both parties, the system uses the average of the buyer's bid and the seller's reserve price as the transaction price. This is a fair compromise that helps facilitate transactions. When no buyer offers a price higher than the seller's reserve price, to prevent waste due to unused electricity, the system guides the seller to trade directly with the distribution station, ensuring that their surplus electricity is effectively utilized. Through these three complementary trading rules, this application ensures that electricity trading within the microgrid can be completed in a fair and efficient manner under different levels of market competition, thereby optimizing the overall operation of the microgrid system architecture, improving electricity absorption capacity and economic benefits, and facilitating the implementation of charging and discharging dispatching schemes.

[0020] In one embodiment of the present invention, this application also provides a dispatching system for electric vehicles based on a microgrid. The method described in the above embodiment is applied to the dispatching system. The dispatching system includes: a planning module for planning the microgrid system architecture; a calculation module for calculating battery degradation costs; a generation module for generating transmission loss models and line loss models; and an execution module for executing electricity transactions. The dispatching system has all the technical features of the above-described dispatching method, which will not be described in detail here. Attached Figure Description

[0021] Figure 1 This is one of the flowcharts for the electric vehicle scheduling method based on a microgrid according to the present invention;

[0022] Figure 2 This is the second flowchart of the electric vehicle scheduling method based on microgrid of the present invention;

[0023] Figure 3 This is the third flowchart of the electric vehicle scheduling method based on microgrid of the present invention;

[0024] Figure 4 This is an example diagram of the alliance structure of the microgrid system of the present invention;

[0025] Figure 5 This is the energy trajectory of a portion of the EV fleet when the scheduling method of this invention is executed;

[0026] Figure 6 This is a schematic diagram illustrating the comparison of power purchase and cost savings in power distribution stations according to the present invention;

[0027] Figure 7 This is a schematic diagram of the electric vehicle dispatching system based on microgrid of the present invention.

[0028] Explanation of reference numerals in the attached figures:

[0029] 100 - Scheduling system; 110 - Planning module; 120 - Calculation module; 130 - Generation module; 140 - Execution module. Detailed Implementation

[0030] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] First Embodiment

[0032] See Figure 1 This invention provides a scheduling method for electric vehicles based on a microgrid, the scheduling method comprising:

[0033] S100. Establish a microgrid system architecture that includes multiple sets, each set containing a microgrid and its aggregated electric vehicle fleet;

[0034] S200. Establish a transmission loss model between each set and the substation, and establish a line loss model for point-to-point transactions between sets.

[0035] S300: Establish an operating model for electric vehicles based on charge / discharge power constraints, energy boundary constraints, and state of charge constraints, according to battery degradation costs.

[0036] S400, an enhanced crow search algorithm with a dual-guided position update mechanism is designed to generate a charging and discharging scheduling scheme that satisfies the operating model by attracting the global optimal solution and rejecting the global worst solution.

[0037] S500: Determine the current net power of each set in the target time period based on the transmission loss model and the line loss model of the charging and discharging scheduling scheme;

[0038] S600: Match electricity transactions between microgrids based on the current net power until all tradable electricity is traded, and obtain the transaction plan corresponding to the charging and discharging scheduling scheme.

[0039] S700: Based on the charging and discharging scheduling scheme and the trading plan, an optimized scheme for the microgrid system architecture is obtained.

[0040] In steps S100 to S700, the microgrid system architecture refers to an interconnected system consisting of multiple independent microgrids and their aggregated electric vehicle fleet. Each microgrid, as an independent energy management unit, is capable of exchanging energy with the main grid and engaging in peer-to-peer transactions with other microgrids.

[0041] In this microgrid system architecture, a collection is defined as a basic unit, containing a microgrid and a fleet of electric vehicles aggregated within it. Each collection is considered an independent decision-making entity, responsible for managing its internal energy production, consumption, and the charging and discharging behavior of electric vehicles. The distribution station is the key node connecting the microgrid system to the main grid. It is responsible for distributing electricity from the main grid to the various microgrid collections and can also receive surplus electricity sold by the microgrid collections.

[0042] In this application, the establishment of a microgrid system architecture with multiple collections can be achieved in several ways. For example, a centralized data management platform can be used to aggregate information from all microgrids and electric vehicle fleets, forming a unified logical architecture. In this approach, each microgrid and its associated electric vehicle fleet are defined as an independent unit, with energy flow and management autonomously handled by that unit. Another approach is to employ a distributed information sharing mechanism, where each microgrid collection exchanges necessary information through a pre-defined communication protocol to jointly construct a virtual system architecture. In this case, each collection independently maintains its internal microgrid and electric vehicle fleet data and interacts with other collections through interfaces.

[0043] Transmission loss models can be established based on historical data analysis. By statistically analyzing losses under different transmission power levels, an empirical loss function can be fitted. For example, transmission loss can be simply expressed as a linear or quadratic function of transmission power. For line loss models, a fixed loss coefficient can be estimated based on the geographical distance between sets and the type of connecting lines. The loss is then calculated as the product of the transmitted power and this coefficient. In practice, sensors can be installed at key nodes to monitor voltage and current in real time, thereby calculating the power loss during transmission.

[0044] The establishment of the operational model can employ various mathematical modeling methods. For example, a simple linear programming model can be constructed to represent the battery's charging and discharging behavior, energy state changes, and related constraints as a series of linear equations and inequalities. Battery degradation costs can be simplified into a fixed penalty term related to the number of charge-discharge cycles or the depth of discharge. Charge-discharge power constraints can be reflected by setting the battery's maximum charging power and maximum discharging power. Energy boundary constraints can be represented by setting the battery's minimum and maximum state of charge. State of charge constraints can ensure vehicle availability by setting a minimum state of charge threshold.

[0045] Furthermore, an enhanced crow search algorithm with a dual-guided position update mechanism is designed to attract globally optimal solutions and exclude globally worst solutions, generating a charge / discharge scheduling scheme that satisfies the operating model. This algorithm can be evolved from basic swarm intelligence optimization algorithms. For example, a standard crow search algorithm can be used initially, where each individual crow represents a potential charge / discharge scheduling scheme, and the crow updates its position by mimicking the flight paths of other crows. To enhance the algorithm's performance, an additional guidance mechanism can be introduced during crow position updates. For example, in addition to following the historical best positions of other crows, a random perturbation term can be introduced to increase the diversity of the search. The initial scheduling scheme can be obtained by randomly generating a charge / discharge power sequence that satisfies the basic constraints.

[0046] Subsequently, the charge / discharge scheduling scheme determines the current net power of each set within the target time period based on transmission loss and line loss models. This determination process can be achieved through direct calculation. During the calculation, transmission and line losses need to be taken into account to reflect the actual available or required supplementary power.

[0047] Based on the current net power, electricity transactions between microgrids are matched until all tradable electricity is traded, resulting in the trading plan corresponding to the charging and discharging dispatch scheme. Various market mechanisms can be used for matching electricity transactions. For example, a simple bilateral negotiation model can be used, where sellers with surplus electricity and buyers with power shortages negotiate directly one-to-one until a transaction is reached. Another approach is to establish a virtual trading platform where each group publishes its buying and selling intentions and prices, and the platform matches transactions according to preset matching rules. The completion of a transaction can be set when all tradable electricity finds a buyer or seller, or when a preset transaction time limit is reached.

[0048] Finally, the charging and discharging scheduling scheme and the power trading plan obtained in the above steps are integrated to obtain an optimized scheme for the microgrid system architecture.

[0049] The transmission loss model and line loss model clearly distinguish between two scenarios: power transmission between microgrid clusters and distribution stations, and point-to-point power trading between microgrid clusters. The enhanced crow search algorithm with a dual-guided position update mechanism effectively solves the problems of high computational complexity and susceptibility to local optima in large-scale optimization problems. By attracting the global optimum and rejecting the global worst solution, the algorithm's global search capability and convergence speed are significantly improved. Compared with traditional swarm intelligence algorithms or algorithms with a single guiding mechanism, this application can find high-quality charging and discharging scheduling schemes that meet complex constraints more quickly, providing a solid foundation for subsequent transaction matching. Finally, combining these algorithms with the power trading mechanism achieves deep integration of technical scheduling and market trading, providing an optimization scheme suitable for the working environment for the efficient collaborative operation of microgrids and electric vehicle fleets.

[0050] Second Embodiment

[0051] See Figure 2 In a specific embodiment, a transmission loss model is established between each set and the substation, and a line loss model is established for point-to-point transactions between sets, specifically including:

[0052] S210. The set of those who sell electricity shall be denoted as the seller set, and the set of those who buy electricity shall be denoted as the buyer set;

[0053] S220. When the seller set and the buyer set transmit electricity to the substation, obtain the transmission equivalent resistance of the connection line between the seller set and the buyer set and the substation, and obtain the transmission loss model based on the transmission equivalent resistance and the operating parameters of the substation.

[0054] S230. When power is transferred between the seller set and the buyer set, obtain the equivalent resistance of the line connecting the seller set and the buyer set, and obtain the line loss model based on the equivalent resistance of the line and the operating parameters of the microgrid.

[0055] In step S220, a transmission loss model is established for the transmission power between the buyer set and the seller set and the substation. For the seller set... j The transmission loss when selling electricity to a power distribution station is:

[0056] .

[0057] In the formula, For sellers j At any moment t The power sent to the substation To connect the seller set j Equivalent resistance of the substation lines. This is the reference value for the bus voltage of the substation. This represents the loss factor of the transformer in the substation.

[0058] For buyer group i to purchase electricity from the distribution station, the transmission loss is:

[0059] .

[0060] In the formula, To connect the buyer set i Equivalent resistance of the substation lines. For the distribution station to be collected by the buyer i The transmitted power is determined by the following equation:

[0061] .

[0062] In the formula, For buyer collection i The actual power obtained.

[0063] In step S230, a line loss model for peer-to-peer transactions between the buyer set and the seller set is established. j Collect from buyers i When transmitting power, the seller set j Power required to be transmitted Determined by the following equation:

[0064] .

[0065] In the formula, For set i With sets j The equivalent resistance of the connecting lines between them. This is the reference value for the microgrid bus voltage. The transmission loss is:

[0066] .

[0067] It should be noted that the model parameters are dynamically updated based on real-time monitoring of network topology, line status, and voltage levels. , , , , For transactions between nodes at different voltage levels, an additional transformer loss term is introduced into the loss calculation to further improve the model's adaptability and accuracy. Each set needs to maintain and share the following key information to support distributed collaborative decision-making: real-time generation capacity and load demand, electric vehicle fleet status and availability, local transmission parameters ( , ) and voltage levels, trading preferences and market price data, and substation connection parameters ( , , This information structure operates autonomously while protecting the privacy of each set, sharing only necessary data with potential trading partners, minimizing communication overhead while maintaining system efficiency.

[0068] The distinction between buyer and seller sets clarifies the role of microgrid sets as sellers or buyers in electricity trading, facilitating subsequent loss calculations. For different electricity transmission scenarios, corresponding transmission loss models and line loss models are precisely established. By differentiating parameter acquisition methods, the calculation of electricity transmission losses becomes more refined and accurate, avoiding scheduling and trading deviations caused by inaccurate loss models. Establishing accurate loss models provides a reliable basis for generating subsequent charging and discharging scheduling schemes and matching electricity transactions. This ensures that when determining the current net power of each set within the target time period, energy losses during transmission have been fully considered, making electricity trading based on current net power more fair and reasonable. Ultimately, the optimized microgrid system architecture becomes more practically valuable and economically efficient.

[0069] Third Embodiment

[0070] See Figure 3 In one specific embodiment, an operating model for an electric vehicle is established based on charge / discharge power constraints, energy boundary constraints, and state of charge constraints, according to battery degradation costs. This specifically includes:

[0071] S310. The nonlinear relationship between the battery's depth of discharge and the number of cycles it can withstand is determined using the Wöhler curve.

[0072] S320. Establish a dynamic evolution model of the battery energy storage state of each electric vehicle fleet at the target time.

[0073] S330. The dynamic evolution model of battery energy storage state is constrained based on charging and discharging power constraints, energy boundary constraints, and state of charge constraints to obtain the operating model.

[0074] In step S310, the battery depth of discharge (DoD) and the number of cycles tolerable are described using Wöhler curves. The non-linear relationship between them.

[0075] ;

[0076] .

[0077] In the formula, a and b These are battery technical characteristic parameters; for lithium-ion batteries, typical values ​​are... SoC represents the state of charge of the battery. This curve shows that as the depth of discharge increases, the number of cycles the battery can withstand decreases exponentially, and deep discharge significantly accelerates battery life degradation.

[0078] The battery cycle life is converted into the monetary cost per unit depth of discharge by converting the unit capacity investment cost and nominal usable capacity, and an economic cost calculation model for battery degradation within a single discharge interval is established.

[0079] ;

[0080] in:

[0081]

[0082] In the formula, The unit capacity investment cost of batteries (yuan / kWh) Let be the nominal available battery capacity (kWh). The total degradation cost of the electric vehicle fleet in set i during the scheduling period is:

[0083]

[0084] in and Sets i At any moment t The depth of discharge at the start and end.

[0085] In step S320, a set is established. i A dynamic evolution model of the battery energy storage state of an electric vehicle fleet at a target time, denoted as time t. t .

[0086] .

[0087] In the formula, For a moment t Total battery energy storage (kWh), and These are the charging power and discharging power (kW), respectively. and These represent charge and discharge efficiencies, and It is a binary state variable (takes a value of 0 or 1), representing the charging and discharging states respectively.

[0088] In step S330, to ensure battery safety and vehicle range, multiple battery safety operation constraints are set, including energy boundaries, upper limits of charging and discharging power, charging and discharging mutual exclusion, and minimum state of charge, to prevent excessive losses caused by overcharging, over-discharging, and deep discharging of the battery.

[0089] By introducing the Wöhler curve to accurately characterize the degradation cost of the battery, the operating model of electric vehicles fully considers the battery's lifespan loss. Combined with the dynamic evolution model of the battery's energy storage state, it can dynamically simulate the energy changes of the electric vehicle battery under different scheduling schemes. Strict constraints such as charging and discharging power, energy boundaries, and state of charge are imposed to ensure that any scheduling scheme generated by the model conforms to the physical characteristics and operating requirements of the battery, effectively avoiding potential problems caused by ignoring battery degradation and operating limitations.

[0090] Fourth embodiment

[0091] In a specific embodiment, the dynamic evolution model of the battery energy storage state is constrained based on charge / discharge power constraints, energy boundary constraints, and state of charge constraints to obtain an operating model, which specifically includes:

[0092] S331, Set energy boundary constraints by the battery energy storage range;

[0093] S332, Set charging and discharging power constraints by charging power range and discharging power range;

[0094] S333, Set state of charge constraints by using the minimum state of charge threshold and the current energy storage of the battery.

[0095] The energy boundary constraints are shown in the following equation:

[0096] .

[0097] In the formula, and These are the minimum and maximum allowable energy storage of the battery, respectively. This constraint prevents the battery from being overcharged or over-discharged.

[0098] The charge / discharge power constraint is shown in the following formula:

[0099] ;

[0100] .

[0101] In the formula, The maximum allowable charging power for the battery is limited by the charging station capacity and the battery charging rate. This is the maximum allowable discharge power of the battery, limited by the inverter capacity and the battery discharge rate.

[0102] The charge-discharge mutual exclusion constraint ensures that the battery cannot be charged and discharged simultaneously at any time. The charge-discharge mutual exclusion constraint is shown in the following formula:

[0103] .

[0104] The minimum state of charge constraint is shown in the following equation:

[0105] .

[0106] In the formula, State of Charge This is the minimum state of charge threshold, typically set to 0.2. This constraint prevents excessive battery degradation caused by deep discharge.

[0107] Setting energy boundary constraints by using the battery energy storage range ensures that the scheduling scheme will not exceed the physical capacity limit of the battery. Setting charging and discharging power constraints by using the charging power range and discharging power range ensures that the charging and discharging operations are within the safe tolerance range of the battery and charging facilities. Setting state of charge constraints by using the minimum state of charge threshold and the current energy storage of the battery effectively avoids deep discharge of the battery, thereby protecting the health of the battery.

[0108] Fifth embodiment

[0109] In one specific embodiment, an enhanced crow search algorithm with a dual-guided position update mechanism is designed. This algorithm generates a charge / discharge scheduling scheme that satisfies the operational model by attracting the globally optimal solution and rejecting the globally worst solution. Specifically, it includes:

[0110] S410. Record the charging and discharging power sequence of all sets within a specific time period as the position vector encoding of each crow individual to obtain the initial solution set that satisfies the running model;

[0111] S420. When the random number is greater than or equal to the alert probability, individual crows control the search step size by following the historical best position of other individual crows and combining it with the flight length parameter, and iterate the initial solution set into the interval optimal solution.

[0112] S430. When the random number is less than the alert probability, make the crow individual be attracted by the global optimal solution and repelled by the global worst solution at the same time on the basis of its own optimal position, and iterate the initial solution set to the interval optimal solution.

[0113] S440. After the interval optimal solution is generated, determine whether the interval optimal solution can be iterated based on the charging and discharging power constraints, energy boundary constraints, and state of charge constraints.

[0114] In step S410, algorithm parameters are set, including but not limited to crow population size, maximum number of iterations, and flight length. fl and alertness probability AP The location vector of each crow is encoded as a sequence of charge and discharge power for all sets within a specific time period. The population is randomly initialized, and the initial solution is ensured to satisfy all battery safety operation constraints, providing a diverse set of starting solutions X for iterative optimization.

[0115] .

[0116] It should be noted that the duration of a specific time period is usually one hour.

[0117] In step S420, when the random number is greater than or equal to the alert probability... AP At this time, individual crows update their positions according to the standard crow search rules. By following the historical best positions of other randomly selected crows and controlling the search step size in combination with the flight length parameter, the algorithm simulates the crow's learning and imitation behavior of excellent solutions, thereby enhancing the algorithm's local search capability and the refinement of the solution. The specific formula for updating the position is as follows:

[0118] .

[0119] In the formula, and The crow i is in the first iter The second iteration and the first iter Position vector of +1 iterations For randomly selected crows j In the iter The best position remembered in the next iteration, i.e., the historical best solution. A random number within the interval [0,1]. fl The sailing length parameter is used to control the search step size.

[0120] In step S430, when the random number is less than the alert probability... AP At this time, a dual-guidance update strategy is adopted, so that the crow individual is simultaneously attracted by the global optimal solution and repelled by the global worst solution on the basis of its own historical best position. The attraction term guides the individual to move closer to the global optimal solution to enhance the development of high-quality areas, while the repulsion term drives the individual away from the global worst solution to avoid falling into inferior areas. The synergistic effect of the dual guidance forms an effective search direction. The specific formula for updating the position is as follows:

[0121] .

[0122] In the formula, and These are the global best position and the global worst position of the population in the current iteration, respectively. For crows i Its own historical best position, and It is a random number within the interval [0,1].

[0123] In step S440, after each position update, the new solution undergoes a constraint check. If it violates constraints such as battery energy boundaries, charge / discharge power limits, or minimum state of charge, repair strategies such as truncation, energy redistribution, and power adjustment are employed to ensure the solution meets all safe operating conditions. After repair, the individual memory is updated. If the new solution is better than the historical best solution, the memory is replaced to ensure the feasibility of the solution is maintained throughout the algorithm iteration process. The specific operation method is as follows:

[0124] For power exceeding limits: if or If the value exceeds the limit, truncation will be performed to restrict it to a certain value. or Within the range.

[0125] Regarding energy exceeding the limit: if Exceeding Energy is redistributed by adjusting the charging and discharging power before and after the charging and discharging periods.

[0126] For SOC constraint violations: increase charging power or decrease discharging power to ensure compliance at all times. .

[0127] After the fix, update the individual memory; if the new solution is better than the historical best solution... Then update the memory to If the new solution is not as good as the historical best solution Then there is no need to update the memory.

[0128] It should be noted that after the charging and discharging scheduling scheme is repaired, the corresponding charging and discharging scheduling scheme is input into the alliance game module to calculate the total system utility under the scheme and use it as the fitness of an individual. The higher the fitness, the higher the total system revenue brought by the scheduling scheme. The mapping relationship between electric vehicle charging and discharging scheduling and alliance transaction revenue is established through fitness evaluation, guiding the algorithm to search in the direction of high revenue.

[0129] The algorithm parameters are dynamically adjusted based on the convergence status. When the fitness improvement is less than a preset threshold for several consecutive generations, the travel length is increased. fl To increase search randomness or reduce the probability of alertness. AP To increase the frequency of using the dual-guidance mechanism, balance the algorithm's exploration and development capabilities, prevent the algorithm from converging prematurely to a local optimum, and improve the global quality search quality.

[0130] The algorithm terminates when the preset maximum number of iterations is reached or the fitness no longer improves significantly after several consecutive generations. It outputs the global optimal scheduling solution and its corresponding total system utility. The optimal solution includes the charging and discharging power of electric vehicles at every moment of the entire scheduling cycle for all sets, providing a basic scheme for subsequent alliance transactions and revenue distribution.

[0131] The initial solution set provides a solid foundation for the algorithm's efficient optimization. During the algorithm's iteration, the position update strategy for individual crows is dynamically adjusted based on the comparison between a random number and the alertness probability. When the random number is greater than or equal to the alertness probability, the algorithm can effectively explore the solution space and learn from group experience, thus avoiding premature convergence to local optima. When the random number is less than the alertness probability, the attraction of the global optimal solution guides the algorithm towards the currently known best direction, while the repulsion of the global worst solution drives the algorithm away from bad solution regions, significantly enhancing the algorithm's ability to escape local optima and improving the efficiency and accuracy of global optimization. After generating interval optimal solutions in each iteration, the algorithm rigorously evaluates these solutions based on charging / discharging power constraints, energy boundary constraints, and state of charge constraints. This ensures that all generated charging / discharging scheduling schemes meet the actual operating requirements of electric vehicles and the physical characteristics of batteries, guaranteeing the feasibility and safety of the scheduling schemes.

[0132] Sixth Embodiment

[0133] In a specific embodiment, electricity transactions between microgrids are matched based on the current net power until all tradable electricity is traded, resulting in a transaction plan corresponding to the charging and discharging scheduling scheme, which specifically includes:

[0134] S610. Within the target time period, the sets are divided into roles based on the current net power. The set with a positive current net power is denoted as the seller set, the set with a negative current net power is denoted as the buyer set, and the set with a current net power of 0 does not participate in the transaction within the target time period.

[0135] S620. Obtain the maximum purchase price for the buyer group based on the electricity sales price of the distribution station, and obtain the minimum purchase price for the seller group based on the electricity sales price of the distribution station.

[0136] S630. Process individual sellers in the seller set according to the purchase price ceiling and the sales price floor, check the bids of all buyer individuals for each seller, and complete a single transaction.

[0137] S640. After each single transaction ends, update the remaining net power of the seller set and the buyer set, and continue to match transactions based on the remaining net power until all tradable electricity is traded.

[0138] In step S610, roles are assigned based on the total net power of each set at each time point. Sets with positive net power are assigned to the seller set, indicating that they have surplus electricity available for sale. Sets with negative net power are assigned to the buyer set, indicating that they have a demand for electricity. Sets with zero net power do not participate in the transaction at the current time point. This establishes market entities for the auction-style transaction matching mechanism, with the target time period adjusted according to changes in the current net power. Typically, the target time period is 24 hours a day.

[0139] In step S620, for the buyer group, the highest price they are willing to pay for purchasing power from individual sellers should not exceed the cost of purchasing the same amount of power from a power distribution station. The specific calculation formula is as follows:

[0140] .

[0141] In the formula, For at any time t Buyer's microgrid i microgrids to seller j The highest bid price one is willing to pay when purchasing electricity; for t The price at which electricity is purchased from the distribution substation at all times; For at any time t Buyer's microgrid i In fact, from the seller's microgrid j The maximum effective power received at that location; In order to provide microgrids to the buyer i Provide with Equal effective power refers to the total power that a distribution substation needs to output from its own ports.

[0142] For a group of sellers, the lowest selling price they are willing to accept should not be lower than the revenue from selling electricity to the distribution station. The specific calculation formula is as follows:

[0143] .

[0144] In the formula, For at any time t Seller's microgrid j The lowest unit price you are willing to accept when selling electricity; for t The price at which electricity is sold to distribution substations at any given time; microgrid for seller j At any moment t Total remaining power; For the seller's microgrid j Power The power is sold to the distribution substation, and the effective power actually received by the substation.

[0145] In step S630, the system processes individual sellers in the seller set in a random order, checks the bids of all individual buyers for each individual seller, and executes three differentiated transaction rules based on the number of buyers whose bids are higher than the floor price.

[0146] In step S640, the tradable electricity includes the demand from the buyer set and the sales from the seller set. If either of these is 0, the tradable electricity is considered fully traded. After each transaction, the remaining net power of both buyers and sellers is updated. If the remaining net power of a set becomes zero, it is removed. The transaction matching is repeated until either the buyer or seller set is empty. The specific calculation formula is as follows:

[0147] .

[0148] Statistical Time t The total revenue from all transactions within the transaction, iterating through all possible seller processing orders. The order that maximizes the coalition utility is selected, and the optimal utility at all times is summed to obtain the total system utility F, which is used as the fitness evaluation index of the enhanced crow search algorithm. The specific calculation formula is as follows:

[0149] ;

[0150] ;

[0151] .

[0152] For example, suppose that during a certain target time period, a microgrid system comprises three sets: set A, set B, and set C. After the aforementioned charging and discharging scheduling steps, set A's current net power is +50 kWh (surplus), set B's current net power is -30 kWh (deficit), and set C's current net power is -20 kWh (deficit). At this point, based on the current net power, set A is classified as the seller set, and sets B and C are classified as the buyer sets. Assume the electricity selling price at the distribution station is 0.8 yuan / kWh, and the electricity purchase price is 0.5 yuan / kWh. Then, the ceiling price for electricity purchase for sets B and C is 0.8 yuan / kWh, and the floor price for electricity selling for set A is 0.5 yuan / kWh. During the transaction matching phase, set A, as the seller, can publish its available electricity volume and expected price (e.g., 0.6 yuan / kWh). Groups B and C, acting as buyers, can submit bids based on their demand and the ceiling price for electricity (e.g., Group B bids 0.7 yuan / kWh, and Group C bids 0.65 yuan / kWh). The trading mechanism will match these bids. For example, Group A may reach a single transaction with Group B, which has the highest bid, for a transaction volume of 30kWh. The transaction price may be determined according to specific rules. After the transaction, Group A's remaining net power will be updated to +20kWh, and Group B's remaining net power will be updated to 0kWh. At this point, the trading system will continue to match transactions between Group A (remaining +20kWh) and Group C (remaining -20kWh) until all tradable electricity has been traded.

[0153] By clearly dividing the current net power into seller, buyer, and non-participating sets, the system lays the foundation for subsequent electricity trading. It introduces the electricity sales and purchase prices of distribution stations, setting a ceiling price for the buyer set and a floor price for the seller set. This establishes a reasonable economic boundary within the microgrid's trading, ensuring the rationality of transaction prices. An iterative update strategy is employed, updating the remaining net power of participants in real time after each transaction and continuing to match transactions based on the new net power status. This dynamic iterative trading process ensures that the tradable electricity within the microgrid system is fully matched and utilized throughout the target time period, until all tradable electricity is traded.

[0154] Seventh Embodiment

[0155] In one specific embodiment, the sellers in the seller set are processed according to the ceiling price for purchasing electricity and the floor price for selling electricity. For each seller, the bids of all buyers are checked to complete a single transaction. Specifically, this includes:

[0156] S631. When more than one buyer offers a price higher than the reserve price for electricity, the buyer with the highest bid and the seller shall complete the transaction using the second highest price rule.

[0157] S632. When only one buyer offers a price higher than the reserve price for electricity, the buyer and seller complete the transaction based on the average of their respective offers.

[0158] S633. When no individual buyer offers a price higher than the reserve price for electricity, the individual seller directly transacts with the power distribution station.

[0159] In step S631, when at least two individual buyers offer prices higher than the seller's floor price, i.e. .

[0160] Transaction counterparty: Seller j With the highest bidder Transactions, of which .

[0161] Transaction price: The second highest price rule will be adopted, meaning the transaction price will be... ,in It was the second highest bid of all.

[0162] In step S632, if only one buyer offers a price higher than the seller's floor price, that is... .

[0163] Transaction counterparty: Seller j With the buyer make a deal.

[0164] Transaction price: To ensure fairness, the average of the bids from both the buyer and seller is used; that is, the transaction price is... .

[0165] In step S633, if no individual buyer offers a price higher than the seller's floor price, that is... .

[0166] When multiple buyers offer prices higher than the seller's reserve price, the system selects the highest bidder for the transaction to encourage honest bidding and improve market efficiency. However, the transaction price is not the highest bid, but the second highest among all valid bids. This mechanism effectively prevents buyers from maliciously underbidding or overbidding, promoting a more reasonable market price. If only one buyer offers a price higher than the seller's reserve price, the system uses the average of the buyer's bid and the seller's reserve price as the transaction price to balance the interests of both parties. This is a fair compromise that helps facilitate transactions. When no buyer offers a price higher than the seller's reserve price, to avoid waste due to unused electricity, the system guides the seller to trade directly with the distribution station, ensuring that their surplus electricity is effectively utilized. Through these three complementary trading rules, this application ensures that electricity trading within the microgrid can be completed fairly and efficiently under different levels of market competition, thereby optimizing the overall operation of the microgrid system architecture, improving electricity absorption capacity and economic benefits, and facilitating the implementation of charging and discharging dispatching schemes.

[0167] See Figure 4 The solutions described in the above embodiments can all be implemented in the following environment: a microgrid system comprising 12 collections is established within a 60km × 60km area. Each collection consists of a microgrid and a PEV fleet, with the substation located at the center coordinates (30, 30) of the area. The system operates for 24 hours with a time period resolution of 1 hour. The PEV operating window is set to 8:00-18:00, a total of 11 hours, and the maximum connection distance is 14.5km. The system adopts a hierarchical voltage structure: the substation uses a 50kV distribution level, and each microgrid uses a 25kV distribution level. Voltage transformation and power transmission are achieved through 50kV / 25kV transformers.

[0168] Figure 4 This example shows a consortium structure at 10:00, where markers of different shapes and colors represent the power status of the clusters during that time period: a green upper triangle represents a seller cluster with surplus power that can be sold to other clusters or DS; a blue lower triangle represents a buyer cluster with a power deficit that needs to purchase power from other clusters or distribution stations. Below each cluster marker are two values ​​displayed in the format "MG Net Power | EV Power," visually reflecting the power contribution of the microgrid and the electric vehicle fleet. Light gray connecting lines represent the effective transmission paths between clusters, with connection distances limited to within 14.5 km to ensure transmission economy. Figure 4 The geographical logic of alliance formation can be observed: adjacent complementary sets prioritize forming trading pairs to reduce transmission losses; sets that are far apart but have strong power complementarity can also establish alliance relationships; and isolated sets trade directly with power distribution stations.

[0169] See Figure 5 , Figure 5 This represents the energy trajectory of a portion of the electric vehicle fleet. Under the dispatch strategy, low-SOC fleets, such as PEV4, primarily charge during off-peak hours, while high-SOC fleets, such as PEV8 and PEV9, primarily discharge during peak hours. The energy trajectories of all fleets are strictly maintained between the red lower boundary and the black upper boundary.

[0170] See Figure 6 , Figure 6 The chart compares the electricity purchase costs and costs of distribution stations. The surplus electricity produced and sold by the seller pool to the distribution stations has decreased from 32302.11Wh during traditional operation to 10531.618kWh. This reduction in surplus electricity leads to lower electricity purchase costs for the distribution stations from the seller pool, with significant differences across different time periods: the cost savings are most pronounced during peak hours (9:00-12:00, 17:00-18:00), while savings are relatively moderate during off-peak hours.

[0171] Eighth embodiment

[0172] See Figure 7 This application also provides a dispatching system 100 for electric vehicles based on a microgrid. The method described in the above embodiments is applied to the dispatching system 100. The dispatching system 100 includes: a planning module 110 for planning the microgrid system architecture; a calculation module 120 for calculating battery degradation costs; a generation module 130 for generating transmission loss models and line loss models; and an execution module 140 for executing electricity transactions. The dispatching system 100 has all the technical features of the above-described dispatching method, which will not be described in detail here.

[0173] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A scheduling method for electric vehicles based on a microgrid, characterized in that, The scheduling method includes: Establish a microgrid system architecture comprising multiple sets, each set containing a microgrid and its aggregated fleet of electric vehicles; Establish a transmission loss model between each set and the substation, and establish a line loss model for point-to-point transactions between the sets; An operational model for electric vehicles based on battery degradation costs is established, incorporating constraints on charge / discharge power, energy boundary, and state of charge. An enhanced crow search algorithm with a dual-guided position update mechanism is designed to generate a charge / discharge scheduling scheme that satisfies the operating model by attracting the global optimal solution and rejecting the global worst solution. The charging and discharging scheduling scheme determines the current net power of each set within the target time period based on the transmission loss model and the line loss model. Based on the current net power, the electricity transactions between the microgrids are matched until all tradable electricity is traded, thus obtaining the transaction plan corresponding to the charging and discharging scheduling scheme. An optimized scheme for the microgrid system architecture is obtained based on the charging and discharging scheduling scheme and the trading plan; The establishment of an electric vehicle operation model based on battery degradation costs and constrained by charge / discharge power, energy boundary, and state of charge constraints specifically includes: The Wöhler curve is used to describe the battery's depth of discharge (DoD) and the number of cycles it can withstand. Nonlinear relationship between them; ； ； a and b These are battery technology characteristic parameters; SoC refers to the battery's state of charge. The battery cycle life is converted into the monetary cost per unit depth of discharge by converting the unit capacity investment cost and nominal usable capacity into the cost per unit depth of discharge, and an economic cost calculation model for battery degradation within a single discharge interval is established. ； in: ； In the formula, The cost per unit capacity of the battery is the investment cost. Given the nominal available battery capacity, the total degradation cost of the electric vehicle fleet in set i during the scheduling period is: ； and Sets i At any moment t The depth of discharge at the start and end.

2. The scheduling method according to claim 1, characterized in that, The establishment of a transmission loss model between each set and the substation, and the establishment of a line loss model for point-to-point transactions between the sets, specifically include: The set of those who sell electricity is denoted as the seller set, and the set of those who buy electricity is denoted as the buyer set; When the seller set and the buyer set transmit electricity to the power distribution station, the transmission equivalent resistance of the connection line between the seller set and the buyer set and the power distribution station is obtained, and the transmission loss model is obtained based on the transmission equivalent resistance and the operating parameters of the power distribution station. When the power transfer occurs between the seller set and the buyer set, the equivalent resistance of the connection line between the seller set and the buyer set is obtained, and the line loss model is obtained based on the equivalent resistance of the line and the operating parameters of the microgrid.

3. The scheduling method according to claim 2, characterized in that, The establishment of an electric vehicle operation model based on battery degradation costs and constrained by charge / discharge power, energy boundary, and state of charge includes: The nonlinear relationship between the depth of battery discharge and the number of cycles that can be tolerated was determined using Wöhler curves; Establish a dynamic evolution model of the battery energy storage state of the electric vehicle fleet in each set at the target time; The operating model is obtained by constraining the dynamic evolution model of the battery energy storage state based on the charging and discharging power constraints, the energy boundary constraints, and the state of charge constraints.

4. The scheduling method according to claim 3, characterized in that, The operational model is obtained by constraining the dynamic evolution model of the battery energy storage state based on the charging and discharging power constraints, the energy boundary constraints, and the state of charge constraints, specifically including: The energy boundary constraint is set by the battery energy storage range; The charging and discharging power constraints are set by the charging power range and the discharging power range; The state of charge constraint is set by using the minimum state of charge threshold and the current energy storage of the battery.

5. The scheduling method according to claim 4, characterized in that, The enhanced crow search algorithm with a dual-guided position update mechanism generates a charge / discharge scheduling scheme that satisfies the operational model by attracting the globally optimal solution and rejecting the globally worst solution. Specifically, it includes: The charging and discharging power sequences of all the aforementioned sets within a specific time period are denoted as the position vector encoding of each individual crow, thus obtaining the initial solution set that satisfies the aforementioned operating model; When the random number is greater than or equal to the alert probability, the crow individual controls the search step size by following the historical best position of other crow individuals and combining it with the flight length parameter, and iterates the initial solution set into the interval optimal solution. When the random number is less than the alert probability, the crow individual is simultaneously attracted by the global optimal solution and repelled by the global worst solution while in its own optimal position, and the initial solution set is iterated into the interval optimal solution. Once the optimal solution for the interval is generated, it is determined whether the optimal solution for the interval can be iterated based on the charging and discharging power constraint, the energy boundary constraint, and the state of charge constraint.

6. The scheduling method according to claim 5, characterized in that, The process of matching electricity transactions between the microgrids based on the current net power until all tradable electricity is traded, to obtain the trading plan corresponding to the charging and discharging scheduling scheme, specifically includes: Within the target time period, the sets are assigned roles based on the current net power. The sets with positive current net power are denoted as the seller set, the sets with negative current net power are denoted as the buyer set, and the sets with 0 current net power do not participate in the transactions within the target time period. The purchase price ceiling for the buyer group is obtained based on the electricity sales price of the substation, and the sale price floor for the seller group is obtained based on the electricity sales price of the substation. Process the individual sellers in the seller set according to the purchase price ceiling and the sales price floor, check the bids of all individual buyers for each individual seller, and complete a single transaction; After each transaction is completed, the remaining net power of the seller set and the buyer set is updated, and transactions are matched again based on the remaining net power until all the tradable electricity is traded.

7. The scheduling method according to claim 6, characterized in that, The process of processing individual sellers in the seller set based on the ceiling price for electricity purchase and the floor price for electricity sale, and checking the bids of all buyer individuals for each seller individual to complete a single transaction, specifically includes: When more than one of the buyers offers a price higher than the reserve price for electricity, the buyer with the highest bid and the seller complete the transaction using the second highest price rule. When only one of the aforementioned buyer individuals offers a price higher than the reserve price for electricity, the buyer individual and the seller individual complete the transaction based on the average of their respective offers; When no buyer offers a price higher than the reserve price for electricity, the seller directly transacts with the power distribution station.

8. A dispatching system for electric vehicles based on a microgrid, characterized in that, The scheduling method according to any one of claims 1 to 7 is applied to the scheduling system, the scheduling system comprising: Planning module, the planning module is used to plan the microgrid system architecture; A calculation module is used to calculate the battery degradation cost; The generation module is used to generate the transmission loss model and the line loss model; An execution module is used to execute the electricity transaction.

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