Dispatching method and device based on micro-grid coupling traffic system, equipment and medium

Abstract

This application provides a scheduling method, apparatus, device, and medium for a microgrid-coupled transportation system, belonging to the field of microgrid technology. The method includes: acquiring data from the microgrid itself to obtain microgrid-owned data; acquiring interaction data between the microgrid and other microgrids to obtain first grid interaction data; acquiring interaction data between the microgrid and electric transportation units to obtain second grid interaction data; constructing a function based on the microgrid-owned data, the first grid interaction data, and the second grid interaction data to obtain a utility function; solving for the utility function, multiple candidate feasible actions, and a reinforcement learning algorithm to obtain a target feasible action; and controlling the microgrid to execute the target feasible action, so that energy is transferred between the microgrid and at least one of the other microgrids. This application embodiment enables energy scheduling of microgrids in non-cooperative game theory under conditions of incomplete information.

Description

Technical Field

[0001] This application relates to the field of microgrid technology, and in particular to a scheduling method, apparatus, equipment and medium based on a microgrid coupled transportation system. Background Technology

[0002] A microgrid-coupled transportation system is a system consisting of multiple microgrids and interconnected electric transportation devices (such as connected electric vehicles) connected to each microgrid. In this system, each microgrid generates energy, and the connected electric transportation devices consume energy. The number of connected electric vehicles connected to each microgrid is not fixed, leading to energy surplus or shortage. Therefore, surplus energy can be transferred between multiple microgrids to achieve energy dispatch.

[0003] Currently, during energy dispatch, each microgrid needs to obtain information from other microgrids (such as production capacity, surplus energy, etc.), revenue, and energy dispatch strategies (such as which microgrids to transfer energy to and how much energy to transfer) before determining its own energy dispatch strategy. However, the goal of existing microgrid energy dispatch methods is to maximize the overall revenue of all microgrids; that is, existing microgrid energy dispatch methods belong to cooperative game theory. But each microgrid actually tends to maximize its own revenue, which belongs to non-cooperative game theory. Furthermore, existing microgrid energy dispatch methods require microgrids to be able to obtain each other's revenue and energy dispatch strategies, which is actually a game of complete information, requiring complete information exchange between microgrids. However, the revenue and energy dispatch strategies of microgrids are private information, and microgrids often do not disclose this information, resulting in information isolation between microgrids. This situation belongs to incomplete information game theory.

[0004] Therefore, how to perform energy dispatching for microgrid-coupled transportation systems in non-cooperative game theory under conditions of incomplete information has become an urgent technical problem to be solved. Summary of the Invention

[0005] The main objective of this application is to propose a scheduling method, apparatus, device, and medium based on a microgrid-coupled transportation system, which aims to perform energy scheduling in a non-cooperative game under incomplete information conditions to meet the charging requirements of the microgrid.

[0006] To achieve the above objectives, a first aspect of this application proposes a scheduling method based on a microgrid-coupled transportation system, the method comprising:

[0007] Data from the microgrid itself is obtained from the microgrid of the microgrid coupled to the transportation system, thus obtaining the microgrid's own data; wherein, the microgrid coupled to the transportation system includes the microgrid and at least one electric transportation unit, and each electric transportation unit is electrically connected to one of the microgrids;

[0008] The interaction data between the microgrid and each of the microgrids is obtained to obtain the first grid interaction data;

[0009] The interaction data between the microgrid and the electric transportation unit is acquired to obtain the second grid interaction data;

[0010] For each microgrid, a utility function is obtained by constructing a function based on the microgrid's own data, the first grid interaction data, and the second grid interaction data.

[0011] The target feasible action is obtained by solving the utility function, multiple preset candidate feasible actions, and a preset reinforcement learning algorithm.

[0012] Control the microgrid to perform the target feasible action to enable energy transfer between the microgrid and at least one of the individual microgrids.

[0013] This application proposes a scheduling method, apparatus, equipment, and medium based on a microgrid coupled with a transportation system. It acquires data from the microgrid itself and the interaction data between various microgrids. Furthermore, considering the charging and discharging operations of electric transportation units within the microgrid coupled with the transportation system, it acquires interaction data between the microgrid and the electric transportation units to obtain second grid interaction data. This takes into account the impact of each electric transportation unit's charging and discharging operations on the microgrid, increasing the influencing factors and constraints for energy scheduling of the microgrid in this method, thus improving its accuracy. Then, for each microgrid, a utility function is constructed based on its own data, the first grid interaction data, and the second grid interaction data. The target feasible action is obtained by solving the utility function, multiple pre-set candidate feasible actions, and a pre-set reinforcement learning algorithm. Each microgrid focuses only on its own utility function, without considering the benefits of other microgrids or the overall system benefit. Each microgrid independently decides its own energy scheduling action to maximize its own benefit, which is a non-cooperative game. Furthermore, since each microgrid's feasible action is calculated based on its own utility function without accessing the utility functions of other microgrids, this aligns with the reality that microgrids do not disclose their own utility functions or other privacy data; thus, this method falls under the category of incomplete information game theory. To solve the game under incomplete information conditions and obtain the feasible action, this method employs a reinforcement learning algorithm to determine the feasible action. Each microgrid executes its own feasible action, thereby enabling energy scheduling for each microgrid within the microgrid-coupled transportation system. Therefore, this method can perform energy scheduling for microgrid-coupled transportation systems under non-cooperative game theory conditions with incomplete information, and fully considers the charging and discharging of electric transportation units on the microgrid, improving the accuracy and reliability of the scheduling method. Attached Figure Description

[0014] Figure 1 This is a flowchart of a scheduling method for a microgrid-coupled transportation system provided in an embodiment of this application;

[0015] Figure 2 This is a schematic diagram of the structure of a dispatching device based on a microgrid-coupled traffic system provided in the embodiments of this application;

[0016] Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0020] First, let's analyze some of the terms used in this application:

[0021] Coupled Microgrid-Transportation (CMT) systems are systems where microgrids and transportation networks exchange energy closely. A CMT system includes microgrids (MGs) and connected electric vehicles (CEVs). Energy exchange occurs between multiple microgrids, and there is charging and discharging behavior between the microgrids and the connected electric vehicles.

[0022] A microgrid (MG) is a small, independent power system that manages electricity to balance loads and perform energy dispatch. A microgrid typically includes different types of loads (i.e., electrical appliances), distributed generators, and energy storage devices. The distributed generators can generate electricity using renewable energy sources. For transportation networks, microgrids are also associated with connected electric vehicles (CEVs), which can be powered via wired or wireless charging.

[0023] Connected Electric Vehicles (CEVs) are electric vehicles that utilize vehicle-to-everything (V2X) technology. CEVs can exchange data and energy with microgrids. Energy management and scheduling of CEVs are typically required, along with the selection of feasible charging strategies. CEVs can coordinate charging of microgrids via V2G (vehicle-to-grid) communication.

[0024] V2G (Vehicle-to-Grid) coordination refers to the method of using the batteries of electric vehicles to charge the power system. In V2G coordination, the batteries of electric vehicles can be regarded as distributed energy storage devices that provide electrical energy.

[0025] Incomplete information games: In incomplete information games, decision-makers are unaware of certain information about their opponents, such as the payoff functions, costs, or action strategies of other decision-makers.

[0026] Non-cooperative game theory refers to a game in which each decision-maker makes autonomous decisions without considering the actions of other decision-makers. In non-cooperative games, each decision-maker needs to choose a strategy to maximize their own payoff, thereby reaching a Nash equilibrium.

[0027] Nash equilibrium is a set of strategies in which no decision-maker can improve their own returns by unilaterally changing their strategy.

[0028] Stackelberg games are a type of non-cooperative game theory. The main idea is that both decision-makers in a game choose their own strategy based on the other's possible strategies to maximize their own payoff under those strategies. In a Stackelberg game, there are two decision-makers: a leader and a follower. The leader first determines the price, and the follower determines their own strategy after learning the leader's price.

[0029] Artificial intelligence (AI) is a new technological science that studies, develops, and applies theories, methods, technologies, and application systems to simulate, extend, and expand human intelligence. The main components of AI include deep learning and reinforcement learning (RL).

[0030] Reinforcement learning (RL) is a machine learning method used to describe and solve problems where an agent learns policies to maximize gains or achieve specific goals through interaction with its environment. A common model for reinforcement learning is the Markov Decision Process (MDP).

[0031] A Markov Decision Process (MDP) is used to simulate stochastic policies and gains that an agent can achieve in an environment where the system state has the Markov property. A stochastic process possesses the Markov property when, given the present state and all past states, the conditional probability distribution of its future states depends only on the current state. In MDP simulations, the agent performs actions according to the policy based on the system state, thereby receiving a reward. The accumulation of this reward over time is called the gain.

[0032] Deep Deterministic Policy Gradient (DDPG) is a deep reinforcement learning-based algorithm that selects achievable actions based on the agent's policy. DDPG can be used for the allocation of computational resources.

[0033] The scheduling method, apparatus, equipment, and medium based on microgrid-coupled transportation systems provided in this application are specifically described through the following embodiments. First, the scheduling method based on microgrid-coupled transportation systems in this application embodiment is described.

[0034] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0035] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0036] The scheduling method based on a microgrid-coupled transportation system provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the scheduling method based on a microgrid-coupled transportation system, etc., but is not limited to the above forms.

[0037] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0038] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0039] Figure 1 This is an optional flowchart of a scheduling method based on a microgrid-coupled transportation system provided in the embodiments of this application. Figure 1 The methods include, but are not limited to, steps 101 to 106.

[0040] Step 101: Obtain the microgrid's own data from the microgrid of the microgrid coupled to the transportation system, and obtain the microgrid's own data; wherein, the microgrid coupled to the transportation system includes a microgrid and at least one electric transportation unit, and each electric transportation unit is electrically connected to a microgrid;

[0041] Step 102: Obtain the interaction data between the microgrid and each microgrid to obtain the first grid interaction data;

[0042] Step 103: Obtain the interaction data between the microgrid and the electric transportation unit to obtain the second grid interaction data;

[0043] Step 104: For each microgrid, construct a function based on the microgrid's own data, the first grid interaction data, and the second grid interaction data to obtain the utility function;

[0044] Step 105: Solve the problem based on the utility function, multiple pre-defined candidate feasible actions, and a pre-defined reinforcement learning algorithm to obtain the target feasible action;

[0045] Step 106: Control the microgrid to perform the target feasible action to enable energy transfer between the microgrid and at least one of the individual microgrids.

[0046] The beneficial effects of this application's embodiments include, but are not limited to: acquiring data from the microgrid itself and the interaction data between various microgrids; and, specifically considering the charging and discharging operations of electric transportation units in a microgrid-coupled transportation system, acquiring interaction data between the microgrid and the electric transportation units to obtain second grid interaction data. This takes into account the impact of each electric transportation unit's charging and discharging operations on the microgrid in the microgrid-coupled transportation system, increasing the influencing factors and constraints on energy scheduling of the microgrid in this method, and improving the accuracy of this method. Then, for each microgrid, a function is constructed based on the microgrid's own data, the first grid interaction data, and the second grid interaction data to obtain a utility function; the target feasible action is obtained by solving the utility function, multiple preset candidate feasible actions, and a preset reinforcement learning algorithm. Each microgrid only focuses on its own utility function, without considering the benefits of other microgrids or the overall benefits of the system. Each microgrid independently decides its own energy scheduling action and maximizes its own benefits, which belongs to non-cooperative game theory. Furthermore, since each microgrid's feasible action is calculated based on its own utility function without accessing the utility functions of other microgrids, this aligns with the reality that microgrids do not disclose their own utility functions or other privacy data; thus, this method falls under the category of incomplete information game theory. To solve the game under incomplete information conditions and obtain the feasible action, this method employs a reinforcement learning algorithm to determine the feasible action. Each microgrid executes its own feasible action, thereby enabling energy scheduling for each microgrid within the microgrid-coupled transportation system. Therefore, this method can perform energy scheduling for microgrid-coupled transportation systems under non-cooperative game theory conditions with incomplete information, and fully considers the charging and discharging of electric transportation units on the microgrid, improving the accuracy and reliability of the scheduling method.

[0047] It should be noted that while microgrids can generate electricity from renewable energy sources (such as solar or wind power), this generation is intermittent and uncertain. Furthermore, the charging and discharging behavior of connected electric vehicles on microgrids is also random. These factors can lead to random power fluctuations in the microgrid, and excessive power fluctuations may damage the microgrid. The scheduling method based on a microgrid coupled with a transportation system provided in this application allows microgrids with excess energy to exchange energy with those with insufficient capacity, thereby stabilizing the power of the microgrid. Furthermore, it determines feasible actions for each microgrid to achieve energy scheduling of the microgrid coupled with the transportation system.

[0048] In step 101 of some embodiments, the microgrid's own data may include the microgrid's power generation, load power consumption, road network, etc. The microgrid's road network can be described using a directed graph. For example, the edge set of the directed graph represents multiple road segments in the road network, and the node set of the directed graph represents the boundary locations of multiple road segments. Electric transportation units can travel on the road network.

[0049] In step 102 of some embodiments, the first grid interaction data may include the energy transfer between the various microgrids and the connection relationships between the various microgrids, which can be represented by an adjacency matrix. For example, an adjacency matrix... The element A in the i-th row and j-th column i,j A value of 1 indicates that the i-th microgrid in the microgrid-coupled transportation system is connected to the j-th microgrid. Element A i,j A value of 0 indicates that the two microgrids are not connected. All diagonal elements of the adjacency matrix are 0, indicating that the microgrid cannot exchange energy with itself.

[0050] In step 103 of some embodiments, the second grid interaction data may include the real-time number of electric transport units in the microgrid, a wireless charging influence factor characterizing whether an electric transport unit participates in wireless charging, a wired charging influence factor characterizing whether an electric transport unit participates in wired charging, and so on. Each electric transport unit can exchange energy with at most one microgrid at a time.

[0051] In some embodiments, the road network of a microgrid includes wireless power tracks and wired charging stations. A wireless power track is defined as an energy supply unit based on wireless power transmission. The wireless power track can supply power to electric transport units via electromagnetic induction. A wireless charging influence factor of 1 indicates that the electric transport unit participates in wireless charging, for example, if the electric transport unit is traveling on the microgrid's wireless power track; a wireless charging influence factor of 0 indicates that it does not participate in wireless charging. Similarly, a wired charging influence factor of 1 indicates that the electric transport unit participates in wired charging, for example, if the electric transport unit is connected to a wired charging station; a wired charging influence factor of 0 indicates that it does not participate in wired charging.

[0052] In step 104 of some embodiments, the function value of the utility function represents the revenue of the corresponding microgrid. In some embodiments, if the function value of the utility function is negative, it may represent the operating cost of the microgrid.

[0053] In step 105 of some embodiments, candidate feasible actions can be substituted into the utility function for calculation to obtain the benefit (utility function value) corresponding to each candidate feasible action. The target feasible action can be the feasible action with the largest corresponding utility function value among multiple candidate feasible actions, that is, the feasible action with the largest benefit.

[0054] In some embodiments, the microgrid-coupled transportation system also includes a public power grid, which is electrically connected to multiple microgrids.

[0055] Prior to step 104, the scheduling method based on a microgrid-coupled transportation system further includes:

[0056] The interaction data between the microgrid and the public power grid is obtained to obtain the interaction data of the third power grid;

[0057] Step 104 includes:

[0058] For each microgrid, a utility function is constructed based on the microgrid's own data, the first grid interaction data, the second grid interaction data, and the third grid interaction data.

[0059] The advantage of this embodiment is that, considering the impact of the public power grid on the energy dispatching actions of the microgrid in the microgrid-coupled transportation system, it obtains the interaction data between the microgrid and the public power grid to obtain the interaction data of the third power grid, and constructs a utility function based on the interaction data of the third power grid so that the utility function can reflect the energy exchange process between the microgrid and the public power grid, thereby improving the accuracy of this method. For the case where there is a public power grid in the system, it selects the target feasible action corresponding to the microgrid, thus realizing the energy dispatching of the microgrid-coupled transportation system.

[0060] It should be noted that in the process of energy transfer between the microgrid and the public grid, the public grid determines the energy transfer cost, specifically by setting cost influencing factors (such as the price of electricity transfer). Therefore, this game belongs to the Stackelberg game, where the public grid is the leader in determining the price, and the microgrid is the follower, determining its own strategy (i.e., the feasible action) after knowing the leader's price.

[0061] In some embodiments, for each microgrid, a function is constructed based on the microgrid's own data, first grid interaction data, second grid interaction data, and third grid interaction data to obtain a utility function including:

[0062] The microgrid's own data, the first grid interaction data, the second grid interaction data, and the third grid interaction data are discretized using a pre-defined Markov decision process model.

[0063] The utility function is obtained by constructing a function based on the discretized microgrid's own data, the first grid interaction data, the second grid interaction data, and the third grid interaction data;

[0064] Among them, the reinforcement learning algorithm is a deep deterministic policy gradient algorithm;

[0065] The target feasible action is obtained by solving the problem based on the utility function, multiple pre-defined candidate feasible actions, and a pre-defined reinforcement learning algorithm, including:

[0066] The utility function and multiple candidate feasible actions are input into the deep deterministic policy gradient algorithm to calculate the gain value corresponding to each candidate feasible action;

[0067] The candidate feasible action corresponding to the largest gain value is determined as the target feasible action.

[0068] The advantage of this embodiment is that, by using a Markov decision process model, the interaction data of the first, second, and third power grids are discretized. Then, a utility function is constructed based on the discretized data, and the utility function is calculated using a deep deterministic policy gradient algorithm to determine the maximum gain value. The candidate feasible action corresponding to the maximum gain value is then determined as the target feasible action, thereby maximizing the microgrid's revenue in the game and realizing the system's energy scheduling.

[0069] It should be noted that the utility function represents the gain value that changes over time. In the embodiments of this application, the deep deterministic policy gradient algorithm calculates based on the data corresponding to different time periods (including the aforementioned first power grid interaction data, second power grid interaction data, and third power grid interaction data). Therefore, before using the deep deterministic policy gradient algorithm to solve for the feasible actions of the target, it is necessary to first discretize the data in time using a Markov decision process model.

[0070] It should be noted that the data exchanged between the first, second, and third power grids are all based on data within a preset total time period. For example, the total time period... This includes time periods t1 and t2. Discretizing the microgrid's own data, first grid interaction data, second grid interaction data, and third grid interaction data using a pre-defined Markov decision process model refers to discretizing the data over time; for example, dividing the total time periods... The above data within the time frame is discretized to obtain the first, second, and third power grid interaction data for time period t1, and the first, second, and third power grid interaction data for time period t2.

[0071] In some embodiments, a utility function is constructed based on the discretized microgrid's own data, first grid interaction data, second grid interaction data, and third grid interaction data, including:

[0072] Based on the interaction data of the first power grid, construct the first sub-formula;

[0073] Based on the microgrid's own data, a second sub-formula is constructed;

[0074] A third sub-formula is constructed based on the microgrid's own data and the interaction data between the second power grid;

[0075] Based on the interaction data from the third power grid, a fourth sub-formula is constructed;

[0076] By integrating the first, second, third, and fourth sub-formulas, we obtain the utility function.

[0077] The advantage of this embodiment is that by constructing a function based on the microgrid's own data, the first grid interaction data, the second grid interaction data, and the third grid interaction data, the resulting utility function can characterize the microgrid's gain value, thereby more accurately determining the feasible action for energy exchange in the microgrid and improving the accuracy and reliability of this method.

[0078] In some embodiments, the second grid interaction data includes the charging status of the electric transport unit, the number of electric transport units, and the set of charging and discharging power; the third sub-formula includes a first sub-item, a second sub-item, and a third sub-item.

[0079] Based on the microgrid's own data and the interaction data with the second power grid, a third sub-formula is constructed, including:

[0080] Substitute the set of charging and discharging power into the preset function of charging and discharging loss of the transportation unit to construct the first sub-term;

[0081] Substitute the number of electric transport units and the set of charging and discharging power into the preset transport unit power adjustment gain function to construct the second sub-term;

[0082] Substitute the charging status and charging / discharging power of the electric transport unit into the preset insufficient power penalty function to construct the third sub-term.

[0083] In some embodiments, the first grid interaction data is the total energy transmission of the microgrid; the first sub-formula includes a fourth sub-item and a fifth sub-item.

[0084] Based on the interaction data of the first power grid, the first sub-formula is constructed, including:

[0085] Substitute the total energy transmission of the microgrid into the preset energy transmission gain function to construct the fourth sub-term;

[0086] Substitute the total energy transmission of the microgrid into the preset energy transmission loss function to construct the fifth sub-term.

[0087] In some embodiments, specifically, the microgrid's own data includes power generation G. i,t Power consumption per load (L) i,t and renewable energy utilization R i,t The first grid interaction data is the total energy transmission of the microgrid, including S. ij,t and S ji,t The second grid interaction data includes the electric transport unit's State of Charge (SOC). n,t Number of electric transport units N and set of charging and discharging power The third-party grid interaction data is the energy transmission volume of the public power grid. The definitions of the above data can be found in the formula for the utility function (as shown below).

[0088] In some embodiments, the utility function is shown in the following formula:

[0089]

[0090] U i,t This represents the gain value of the microgrid MGi within time period t; where t represents the total time period. The t-th time period in the total number of time periods is T; i represents the microgrid set. The i-th microgrid MGi in the set of microgrids Includes M microgrids;

[0091] In the above formula, the first term is: When the value of the first term is greater than zero, it represents the energy output benefit; when the value of the first sub-formula is less than zero, it represents the energy acquisition cost; where, Represents the energy transfer gain coefficient; ∑ j≠i S ij,t ∑ represents the total energy transferred from other microgrids (excluding microgrid MGi) to microgrid MGi during time period t. j≠i S ji,t This represents the total amount of energy transferred from microgrid MGi to other microgrids during time period t;

[0092] The second term is: ∑ j≠i (S ji,t ) 2 The second term characterizes the first energy transfer loss between microgrids; where S ji,t This represents the energy transferred from microgrid MGi to microgrid MGj during time period t; ∑ j≠i (S ji,t ) 2 This represents the sum of the squares of the energy transferred from microgrid MGi to other microgrids during time period t;

[0093] The third item is: The third item characterizes the power generation losses of renewable energy sources; among which, R represents the renewable energy generation loss factor of the microgrid MGi. i,t This represents the amount of renewable energy utilized by the microgrid MGi during time period t;

[0094] The fourth item is: The fourth term characterizes the second energy transfer loss between the microgrid and the public grid; among which, This represents the energy loss coefficient of the microgrid MGi from the public grid during time period t. This represents the energy that the microgrid MGi receives from the public grid during time period t;

[0095] The fifth item is: The fifth term characterizes the charging loss of the electric transport unit; where n represents the set of electric transport units. The nth electric transport unit CEVn; the set of electric transport units Includes N electric transport units; P represents the charging and discharging loss coefficient of the electric transport unit CEVn within time period t. n,t Let P represent the charging and discharging power of the electric transport unit CEVn during time period t, and P n,t Belongs to the charging and discharging power collection Where N represents the number of electric transport units in the microgrid MGi;

[0096] The sixth item is: The sixth term characterizes the power regulation gain of the electric transport unit; where F represents the gain coefficient of the electric transport unit charging the microgrid MGi. Indicates the total time period The duration; ξ represents the battery capacity penalty factor, ξ∈(0,1]; time period t and time period t-1 both belong to the total time period. This represents the active power of the microgrid MGi during time period t-1; This represents the active power of the microgrid MGi during time period t;

[0097] The definition of is:

[0098]

[0099] in This represents the power imbalance of the microgrid-coupled transportation system during time period t; This represents the sum of the charging and discharging power of the electric transport units in the microgrid MGi during time period t;

[0100] It should be noted that the battery capacity penalty factor ξ reflects a reduction in the upper limit of the battery capacity of the electric transport unit. For example, the battery degradation effect leads to a reduction in the upper limit of the battery capacity of the electric transport unit. The battery degradation effect refers to the phenomenon that battery materials gradually deteriorate and the battery capacity decreases during use.

[0101] The seventh item is: The seventh term characterizes the energy surplus penalty when the energy supply of the microgrid MGi exceeds its energy demand during time period t; where σ1 represents the first penalty factor; C i,t L represents the charging and discharging amount of the microgrid MGi within time period t; i,t S represents the load power consumption of the microgrid MGi during time period t; i,t G represents the total energy transferred from microgrid MGi to other microgrids during time period t; i,t This represents the power generation of the microgrid MGi during time period t; ∑ represents the energy obtained by the microgrid MGi from the public grid during time period t; j≠i β ji,tThis represents the energy that microgrid MGi receives from other microgrids during time period t; max(·) represents taking the maximum value, and The value of is greater than or equal to 0;

[0102] The eighth item is:

[0103]

[0104] The eighth term represents the penalty for the microgrid MGi failing to meet its charging requirements within time period t; where σ2 represents the second penalty factor. This indicates the target charging state of the electric transport unit CEVn, which is also the charging requirement of the electric transport unit CEVn. Indicates the buffer factor; SOC n,t P represents the charging state of the electric transport unit CEVn during time period t; n,t η represents the charging and discharging power of the electric transport unit CEVn within time period t; η(·) represents the energy efficiency of the charging and discharging power of the electric transport unit CEVn. The total duration of the time period is represented by Δt; Δt represents the total duration of the time period. The duration of each time period; η ch η represents the charging efficiency of the electric transport unit CEVn. ch ∈(0,1]; This represents the charging price adjustment factor for the electric transport unit CEVn;

[0105] Buffer factor Defined as:

[0106]

[0107] Where μ represents the safety margin, μ∈[0,1]; This indicates the upper limit threshold of the electric transport unit CEVn's power capacity;

[0108] The eighth condition satisfies the charging requirement constraint, which is shown in the following formula:

[0109]

[0110] in, This indicates that the duration of time period t is less than the total duration of the time period;

[0111] Charge / discharge efficiency η(P) n,t The definition of ) is:

[0112]

[0113] η ch η represents the charging efficiency of the electric transport unit CEVn. dchη represents the discharge efficiency of the electric transport unit CEVn. ch ∈(0,1] and η dch ∈(0,1).

[0114] The advantage of this embodiment is that, through a utility function, it characterizes the energy transmission gain of the microgrid, the first energy transmission loss between the microgrids, the renewable energy generation loss of the microgrid, the second energy transmission loss between the microgrid and the public grid, the charging and discharging loss of the electric transport unit, the power regulation gain of the electric transport unit, the energy surplus penalty of the microgrid, and the power shortage penalty of the microgrid; and determines the target feasible actions based on the utility function, thereby more accurately calculating the benefits of energy exchange by the microgrid, improving the accuracy and reliability of this method.

[0115] In one application example, assume the total time period is 24 hours, comprising 96 time periods, which is the total time period. Total duration of the period The number of time periods T = 96, where the duration of each time period Δt = 0.25h = 15min. Charging price adjustment factor. The setting is 0.59. This charging price adjustment factor represents the charging price per kilowatt-hour for an electric transport unit as 0.59. A kilowatt-hour, also called a "kilowatt-hour," represents the energy consumed by an appliance with a power output of one kilowatt after one hour of use. The gain value U is calculated based on the utility function. i,t The gain value is 40. This gain value can be interpreted as a bonus of 40 per megawatt-hour, where each megawatt-hour represents the amount of energy used or produced in one hour.

[0116] In some embodiments, the set of constraints specifically includes: renewable energy utilization constraints, microgrid energy transmission constraints, public grid energy transmission constraints, power balance constraints, power balance constraints, energy trading constraints, charge / discharge power constraints, state of charge constraints, and charge / discharge energy constraints.

[0117] The constraint on renewable energy utilization rate is shown in the following formula:

[0118]

[0119] This represents a set of microgrids, which includes M microgrids; i represents the i-th microgrid MGi; Represents the total time period, which includes T time periods; t represents the t-th time period; R i,t G represents the amount of renewable energy utilized by the microgrid MGi during time period t. i,tThis represents the power generation of the microgrid MGi during time period t.

[0120] Specifically, power generation can be the electricity obtained by the microgrid through renewable energy generation, and the utilization of renewable energy can be the energy obtained from power generation in the form of G. i,t In the case of a microgrid, the total amount of energy used within time period t (e.g., the microgrid's own electricity consumption, or the transmission of electricity to other microgrids).

[0121] The energy transmission constraints of a microgrid are shown in the following formula:

[0122]

[0123] i represents the i-th microgrid MGi, and j represents the j-th microgrid MGj; α represents the upper limit threshold for energy transfer between microgrids MGi and MGj; ij,t Let α represent the energy that microgrid MGj is expected to obtain from microgrid MGi during time period t, and let α be the energy that microgrid MGj is expected to obtain from microgrid MGi. ij,t Energy purchase vector belonging to microgrid MGi Purchase energy vector α j,t This represents the total amount of energy that microgrid MGi is expected to obtain from other microgrids during time period t.

[0124] It should be noted that other microgrids refer to a collection of microgrids. This includes all microgrids except for the MGi microgrid. Specifically, the upper limit threshold for energy transfer. It can be 300 kW. In some embodiments, due to energy transmission losses between microgrids, the energy expected to be acquired by the microgrid is not equal to the energy actually acquired by the microgrid.

[0125] The energy transmission constraints of the public power grid are shown in the following formula:

[0126]

[0127] This represents the energy that the microgrid MGi receives from the public grid during time period t; This represents the maximum energy that the microgrid MGi obtains from the public power grid.

[0128] The power balance constraints are shown in the following formula:

[0129]

[0130] G i,t This represents the power generation of the microgrid MGi during time period t; ∑ represents the energy obtained by the microgrid MGi from the public grid during time period t;j≠i β ji,t β represents the total energy obtained by microgrid MGi from other microgrids during time period t. ji,t L represents the energy obtained by microgrid MGi from microgrid MGj during time period t; i,t C represents the load power consumption of the microgrid MGi during time period t; i,t S represents the charging and discharging amount of the microgrid MGi within time period t; i,t This represents the total amount of energy transferred from microgrid MGi to other microgrids during time period t;

[0131] β ji,t The definition of is:

[0132]

[0133] Where, α ji,t γ represents the energy that microgrid MGi is expected to obtain from microgrid MGj during time period t; ij,t γ represents the energy transfer loss between microgrids MGi and MGj during time period t; ij,t Energy transmission loss vector belonging to microgrid coupled transportation system

[0134] C i,t The definition of is:

[0135]

[0136] in, P represents the sum of the charging and discharging power of all electric transport units in the microgrid MGi; n,i,t This represents the charging and discharging power of the electric transport unit CEVn in the microgrid MGi during time period t; Let N represent the set of electric transport units in the microgrid MGi, which includes N electric transport units; n represents the nth electric transport unit CEVn.

[0137] S i,t The definition of is:

[0138]

[0139] Among them, R i,t G represents the amount of renewable energy utilized by the microgrid MGi during time period t; i,t >R i,t This indicates that the microgrid has surplus renewable energy.

[0140] The power balance constraint is shown in the following formula:

[0141]

[0142] R i,t This represents the amount of renewable energy utilized by the microgrid MGi during time period t; ∑ represents the energy obtained by the microgrid MGi from the public grid during time period t; j≠i β ji,t C represents the energy that microgrid MGi receives from other microgrids during time period t; i,t L represents the charging and discharging amount of the microgrid MGi within time period t; i,t S represents the load power consumption of the microgrid MGi during time period t; i,t This represents the total amount of energy transferred from microgrid MGi to other microgrids during time period t.

[0143] The constraints for energy trading are shown in the following formula:

[0144]

[0145] ∑ j≠i a ij,t S represents the total energy purchased by other microgrids from microgrid MGi during time period t; i,t This represents the total amount of energy transferred from microgrid MGi to other microgrids during time period t.

[0146] The charging and discharging power constraint conditions are shown in the following formula:

[0147]

[0148] P n,t This represents the charging and discharging power of the electric transport unit CEVn during time period t; This represents the discharge power threshold of the electric transport unit CEVn, and This represents the charging power threshold of the electric transport unit CEVn, and This represents the set of electric transport units in the microgrid MGi, which includes N electric transport units; n represents the set of electric transport units. The nth electric transport unit CEVn; k n,t Indicates whether the electric transport unit CEVn participates in wireless charging and discharging scheduling; if k n,t =0 indicates that the electric transport unit CEVn did not participate in wireless charging and discharging scheduling during time period t. In this case, P n,t =0; if k n,t =1 indicates that the electric transport unit CEVn participates in wireless charging and discharging scheduling during time period t;

[0149] When the electric transport unit CEVn is charging, it is connected to either the wired charging unit CEVAk or the wireless charging unit PTs; the wired charging unit CEVAk belongs to the set of wired charging units of the microgrid MGi. The wireless charging unit PTS is a collection of wireless charging units belonging to the microgrid MGi. The electric transport unit CEVn has a charging state, and the corresponding formula is:

[0150]

[0151] SOC n,t+Δt This represents the charging state of the electric transport unit CEVn during the time period (t+Δt); SOC n,t The charging state of the electric transport unit CEVn during time period t is represented; Δt represents the total time period. The duration of each time period; This indicates the battery capacity of the electric transport unit CEVn, and η(·) represents the energy efficiency of the charging and discharging power of the electric transport unit CEVn, and η(·) is defined as follows:

[0152]

[0153] η ch η represents the charging efficiency of the electric transport unit CEVn. dch η represents the discharge efficiency of the electric transport unit CEVn. ch ∈(0,1] and η dch ∈(0,1];

[0154] SOC n,t+Δt The definition of is:

[0155]

[0156] λ n Let λ represent the energy consumption coefficient per unit of the electric transport unit CEVn, and λ represent the energy consumption coefficient per unit of the electric transport unit CEVn. n ≥0; This represents the distance traveled by the electric transport unit CEVn from location s to location v within the microgrid MGi during time period t; the microgrid MGi includes the road network. Where the edge set ε i This represents a set of nodes representing multiple road segments in a road network. This represents the boundary location of multiple road segments; road segment (s,v)∈ε i , and

[0157] The corresponding formula is in The electric transport unit CEVn represents the average speed of the electric transport unit from position s to position v during time period t; Δt represents the total time period. The duration of each time period, that is, the duration of time period t.

[0158] Specifically, the unit energy consumption coefficient λ n It could be 1.112 kilowatt-hours per kilometer.

[0159] The state of charge constraint is shown in the following formula:

[0160]

[0161] This indicates the final charging state of the electric transport unit CEVn, that is, the state of charge of the electric transport unit CEVn after the total time period. Subsequent charging status; This indicates the initial charging state of the electric transport unit CEVn. This represents the target charging state of the electric transport unit CEVn, i.e., the charging requirement of the electric transport unit CEVn; n represents the set of electric transport units. The nth electric transport unit CEVn in the system.

[0162] The energy constraint for charging and discharging is shown in the following formula:

[0163]

[0164] SOC n This represents the lower limit threshold of the electrical charge of the electric transport unit CEVn. This indicates the upper limit threshold of the electric transport unit's (CEVn) energy capacity; SOC n,t This indicates the charging status of the electric transport unit CEVn during time period t.

[0165] In one application example, the specific values ​​of the aforementioned charging states follow a uniform distribution U[a,b] over the interval [a,b]. For example, the initial charging state of an electric transport unit. For U[0%, 10%], the target state of charge For U[40%, 50%], the lower limit threshold of battery power. SOC n For U[10%, 20%], the upper limit threshold for battery power is... For U[90%, 99%].

[0166] In some embodiments, the method further includes, prior to inputting the utility function and multiple candidate feasible actions into a deep deterministic policy gradient algorithm:

[0167] Historical datasets and historical action sets are obtained from a pre-defined microgrid status database;

[0168] Based on historical datasets and historical action sets, gradient training is performed on the deep deterministic policy gradient algorithm, and the deep deterministic policy gradient algorithm is updated.

[0169] The advantage of this embodiment is that it trains the deterministic policy gradient algorithm based on historical data, thereby improving the accuracy and reliability of the method and realizing energy scheduling of the microgrid coupled transportation system.

[0170] In some embodiments, specifically, data such as the microgrid's power generation, load power consumption, total energy transmission of the microgrid, charging status of electric transport units, number of electric transport units, and time period can be obtained from preset information states. The information state is defined as:

[0171]

[0172] Indicates the information status of the microgrid MGi; G i,t L represents the power generation of the microgrid MGi during time period t; u,t α represents the load power consumption of the microgrid MGi during time period t; uj,t S represents the energy transferred from microgrid MGi to microgrid MGj during time period t, which is also the energy purchased by microgrid MGj from microgrid MGi during time period t; i,t This represents the total amount of energy transferred from microgrid MGi to other microgrids during time period t; This represents the charging and discharging regulation signal of the microgrid MGi within time period t, such as the charging and discharging price. This represents the set of electric transport units of the microgrid MGi during time period t. The charging status; N represents the number of electric transport units in the microgrid MGi; t represents the total time period. The t-th time period in the data.

[0173] In some embodiments, it is assumed that in the total time period In the middle, the t-1 time period and the time period before it The corresponding microgrid-specific data, first grid interaction data, second grid interaction data, third grid interaction data, and target feasible actions are all known historical data. Therefore, a deterministic policy gradient algorithm can be trained based on this historical data, and the trained algorithm can be used to solve the problem, thereby evaluating the target feasible actions for a future time period (e.g., time period t). The target feasible action is defined as:

[0174]

[0175] Hi,t S represents the target feasible action of the microgrid MGi, which satisfies the set of constraints; ij,t This represents the energy transferred from microgrid MGj to microgrid MGi during time period t; i represents the set of microgrids. The i-th microgrid MGi;j represents the set of microgrids. The j-th microgrid MGj; R i,t This represents the amount of renewable energy utilized by the microgrid MGi during time period t. This represents the energy that the microgrid MGi receives from the public grid during time period t. This represents the set of charging and discharging power of the electric transport units in the microgrid MGi. Where P n,t Represents the set of electric transport units of the microgrid MGi The charging and discharging power of the electric transport unit CEVn.

[0176] Please see Figure 2 This application also provides a scheduling device based on a microgrid-coupled transportation system, which can implement the above-mentioned scheduling method based on a microgrid-coupled transportation system. The device includes:

[0177] The proprietary data acquisition module 201 is used to acquire data from the microgrid itself in the microgrid of the microgrid coupled to the transportation system, and obtain the proprietary data of the microgrid; wherein, the microgrid coupled to the transportation system includes a microgrid and at least one electric transportation unit, and each electric transportation unit is electrically connected to a microgrid;

[0178] The first interactive data acquisition module 202 is used to acquire interactive data between the microgrid and each microgrid to obtain the first grid interactive data.

[0179] The second interactive data acquisition module 203 is used to acquire the interactive data between the microgrid and the electric transportation unit to obtain the second grid interactive data.

[0180] The function construction module 204 is used to construct a utility function for each microgrid based on the microgrid's own data, the first grid interaction data, and the second grid interaction data.

[0181] Action solving module 205 is used to solve for the target feasible action based on the utility function, multiple preset candidate feasible actions and preset reinforcement learning algorithm;

[0182] Action execution module 206 is used to control the microgrid to perform target feasible actions to enable energy transfer between the microgrid and at least one of the individual microgrids.

[0183] The specific implementation of the dispatching device based on the microgrid coupled traffic system is basically the same as the specific implementation of the dispatching method based on the microgrid coupled traffic system described above, and will not be repeated here.

[0184] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned scheduling method based on a microgrid-coupled transportation system. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0185] Please see Figure 3 , Figure 3 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0186] The processor 301 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0187] The memory 302 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 302 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 302 and called and executed by the processor 301 using the scheduling method based on a microgrid coupled transportation system according to the embodiments of this application.

[0188] Input / output interface 303 is used to implement information input and output;

[0189] The communication interface 304 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0190] Bus 305 transmits information between various components of the device (e.g., processor 301, memory 302, input / output interface 303, and communication interface 304);

[0191] The processor 301, memory 302, input / output interface 303, and communication interface 304 are connected to each other within the device via bus 305.

[0192] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described scheduling method based on a microgrid-coupled transportation system.

[0193] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0194] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0195] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0196] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0197] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0198] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0199] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0200] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, or indirect coupling or communication connection between the apparatus or units, and may be electrical, mechanical, or other forms.

[0201] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0202] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0203] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0204] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A scheduling method for a microgrid-coupled transportation system, characterized in that, The method includes: Data from the microgrid itself is obtained from the microgrid of the microgrid coupled to the transportation system, thus obtaining the microgrid's own data; wherein, the microgrid coupled to the transportation system includes the microgrid and at least one electric transportation unit, and each electric transportation unit is electrically connected to one of the microgrids; The interaction data between the microgrid and each of the microgrids is obtained to obtain the first grid interaction data; The interaction data between the microgrid and the electric transportation unit is acquired to obtain the second grid interaction data; For each microgrid, a utility function is obtained by constructing a function based on the microgrid's own data, the first grid interaction data, and the second grid interaction data. The target feasible action is obtained by solving the utility function, multiple preset candidate feasible actions, and a preset reinforcement learning algorithm. Control the microgrid to perform the target feasible action to enable energy transfer between the microgrid and at least one of the individual microgrids; The utility function is shown in the following formula: U i,t This represents the gain value of the microgrid MGi within time period t; where t represents the total time period. The t-th time period in the total time period includes T time periods; i represents the microgrid set. The i-th microgrid MGi in the set of microgrids Includes M microgrids; In the formula for the utility function, the first term is: in, Represents the energy transfer gain coefficient; ∑ j≠i S ij,t ∑ represents the total energy transferred from other microgrids (excluding microgrid MGi) to microgrid MGi during time period t. j≠i S ji,t This represents the total amount of energy transferred from microgrid MGi to other microgrids during time period t; The second term is: ∑ j≠i (S ji,t ) 2 Among them, S ji,t This represents the energy transferred from microgrid MGi to microgrid MGj during time period t; ∑ j≠i (S ji,t ) 2 This represents the sum of the squares of the energy transferred from microgrid MGi to other microgrids during time period t; The third item is: in, R represents the renewable energy generation loss factor of the microgrid MGi. i,t This represents the amount of renewable energy utilized by the microgrid MGi during time period t; The fourth item is: in, This represents the energy loss coefficient of the microgrid MGi from the public grid during time period t. This represents the energy that the microgrid MGi receives from the public grid during time period t; The fifth item is: Where n represents the set of electric transport units The nth electric transport unit CEVn in the set of electric transport units; Includes N electric transport units; P represents the charging and discharging loss coefficient of the electric transport unit CEVn within time period t. n,t Let P represent the charging and discharging power of the electric transport unit CEVn during time period t, and P n,t Belongs to the charging and discharging power collection Where N represents the number of electric transport units in the microgrid MGi; The sixth item is: Wherein, F represents the gain coefficient of the electric transport unit charging the microgrid MGi; Indicates the total time period The duration; ξ represents the battery capacity penalty factor, ξ∈(0,1]; time period t and time period t-1 both belong to the total time period. This represents the active power of the microgrid MGi during time period t-1; This represents the active power of the microgrid MGi during time period t; The definition of is: in This represents the power imbalance of the microgrid-coupled transportation system during time period t; This represents the sum of the charging and discharging power of the electric transport units in the microgrid MGi during time period t; The seventh item is: Where σ1 represents the first penalty factor; C i，t L represents the charging and discharging amount of the microgrid MGi within time period t; i，t S represents the load power consumption of the microgrid MGi during time period t; i，t G represents the total energy transferred from microgrid MGi to other microgrids during time period t; i，t This represents the power generation of the microgrid MGi during time period t; ∑ represents the energy obtained by the microgrid MGi from the public grid during time period t; j≠i β ji,t This represents the energy that microgrid MGi receives from other microgrids during time period t; max(·) indicates taking the maximum value; The eighth item is: Where σ2 represents the second penalty factor; This indicates the target charging state of the electric transport unit CEVn, which is also the charging requirement of the electric transport unit CEVn. Indicates the buffer factor; SOC n,t P represents the charging state of the electric transport unit CEVn during time period t; n,t η represents the charging and discharging power of the electric transport unit CEVn within time period t; η(·) represents the energy efficiency of the charging and discharging power of the electric transport unit CEVn. The total duration of the time period is represented by Δt; Δt represents the total duration of the time period. The duration of each time period; η ch η represents the charging efficiency of the electric transport unit CEVn. ch ∈(0,1]; This indicates the charging power of the electric transport unit CEVn; Buffer factor Defined as: Where μ represents the safety margin, μ∈[0,1]; This indicates the upper limit threshold of the electric transport unit CEVn's power capacity; The eighth item satisfies the charging requirement constraint, and the formula corresponding to the charging requirement constraint is: in, This indicates that the duration of time period t is less than the total duration of the time period; η(P) n,t The definition of ) is: η ch η represents the charging efficiency of the electric transport unit CEVn. dch η represents the discharge efficiency of the electric transport unit CEVn. ch ∈(0,1] and η dch ∈(0,1).

2. The scheduling method according to claim 1, characterized in that, The microgrid-coupled transportation system also includes a public power grid, which is electrically connected to multiple microgrids; before constructing a utility function for each microgrid based on its own data, the first power grid interaction data, and the second power grid interaction data, the method further includes: The interaction data between the microgrid and the public power grid is acquired to obtain the third power grid interaction data; For each microgrid, a utility function is constructed based on the microgrid's own data, the first grid interaction data, and the second grid interaction data, including: For each microgrid, a utility function is constructed based on the microgrid's own data, the first grid interaction data, the second grid interaction data, and the third grid interaction data.

3. The scheduling method according to claim 2, characterized in that, For each microgrid, a utility function is constructed based on the microgrid's own data, the first grid interaction data, the second grid interaction data, and the third grid interaction data, including: The microgrid's own data, the first grid interaction data, the second grid interaction data, and the third grid interaction data are discretized using a preset Markov decision process model. The utility function is obtained by constructing a function based on the discretized microgrid data, the first grid interaction data, the second grid interaction data, and the third grid interaction data. The reinforcement learning algorithm is a deep deterministic policy gradient algorithm; the step of solving the problem based on the utility function, multiple preset candidate feasible actions, and the preset reinforcement learning algorithm to obtain the target feasible action includes: The utility function and the multiple candidate feasible actions are input into the deep deterministic policy gradient algorithm to calculate the gain value corresponding to each candidate feasible action; The candidate feasible action corresponding to the largest gain value is determined as the target feasible action.

4. The scheduling method according to claim 3, characterized in that, The utility function is obtained by constructing a function based on the discretized microgrid's own data, the first grid interaction data, the second grid interaction data, and the third grid interaction data, including: Based on the first power grid interaction data, construct the first sub-formula; Based on the microgrid's own data, construct a second sub-formula; A third sub-formula is constructed based on the microgrid's own data and the second grid's interactive data. Based on the third power grid interaction data, a fourth sub-formula is constructed; The utility function is obtained by integrating the first sub-formula, the second sub-formula, the third sub-formula, and the fourth sub-formula.

5. The scheduling method according to claim 4, characterized in that, The second grid interaction data includes the charging status of the electric transport unit, the number of electric transport units, and the set of charging and discharging power; the third sub-formula includes a first sub-item, a second sub-item, and a third sub-item; The step of constructing a third sub-formula based on the microgrid's own data and the second grid's interactive data includes: Substitute the set of charging and discharging power into the preset function of charging and discharging loss of the transportation unit to construct the first sub-term; Substitute the number of electric transport units and the set of charging and discharging power into a preset transport unit power adjustment gain function to construct the second sub-item; The third sub-item is constructed by substituting the charging state of the electric transport unit and the set of charging and discharging power into a preset power deficiency penalty function.

6. The scheduling method according to claim 4, characterized in that, The first grid interaction data is the total energy transmission volume of the microgrid; The first sub-formula includes the fourth and fifth sub-terms; The step of constructing the first sub-formula based on the first power grid interaction data includes: Substitute the total energy transmission of the microgrid into the preset energy transmission gain function to construct the fourth sub-term; The fifth sub-item is constructed by substituting the total energy transmission of the microgrid into a preset energy transmission loss function.

7. A dispatching device based on a microgrid-coupled traffic system, characterized in that, The device includes: The proprietary data acquisition module is used to acquire data from the microgrid itself in the microgrid coupled to the transportation system, thereby obtaining proprietary data of the microgrid; wherein, the microgrid coupled to the transportation system includes the microgrid and at least one electric transportation unit, and each electric transportation unit is electrically connected to one of the microgrids; The first interactive data acquisition module is used to acquire interactive data between the microgrid and each of the microgrids to obtain the first grid interactive data. The second interactive data acquisition module is used to acquire the interactive data between the microgrid and the electric transportation unit to obtain the second grid interactive data. The function construction module is used to construct a utility function for each microgrid based on the microgrid's own data, the first grid interaction data, and the second grid interaction data; wherein the utility function is shown in the following formula: U i,t This represents the gain value of the microgrid MGi within time period t; where t represents the total time period. The t-th time period in the total time period includes T time periods; i represents the microgrid set. The i-th microgrid MGi in the set of microgrids Includes M microgrids; In the formula for the utility function, the first term is: in, Represents the energy transfer gain coefficient; ∑ j≠i S ij,t ∑ represents the total energy transferred from other microgrids (excluding microgrid MGi) to microgrid MGi during time period t. j≠i S ji,t This represents the total amount of energy transferred from microgrid MGi to other microgrids during time period t; The second term is: ∑ j≠i (S ji,t ) 2 Among them, S ji,t This represents the energy transferred from microgrid MGi to microgrid MGj during time period t; ∑ j≠i (S ji,t ) 2 This represents the sum of the squares of the energy transferred from microgrid MGi to other microgrids during time period t; The third item is: in, R represents the renewable energy generation loss factor of the microgrid MGi. i,t This represents the amount of renewable energy utilized by the microgrid MGi during time period t; The fourth item is: in, This represents the energy loss coefficient of the microgrid MGi from the public grid during time period t. This represents the energy that the microgrid MGi receives from the public grid during time period t; The fifth item is: Where n represents the set of electric transport units The nth electric transport unit CEVn in the set of electric transport units; Includes N electric transport units; P represents the charging and discharging loss coefficient of the electric transport unit CEVn within time period t. n,t Let P represent the charging and discharging power of the electric transport unit CEVn during time period t, and P n,t Belongs to the charging and discharging power collection Where N represents the number of electric transport units in the microgrid MGi; The sixth item is: Wherein, F represents the gain coefficient of the electric transport unit charging the microgrid MGi; Indicates the total time period The duration; ξ represents the battery capacity penalty factor, ξ∈(0,1]; time period t and time period t-1 both belong to the total time period. This represents the active power of the microgrid MGi during time period t-1; This represents the active power of the microgrid MGi during time period t; The definition of is: in This represents the power imbalance of the microgrid-coupled transportation system during time period t; This represents the sum of the charging and discharging power of the electric transport units in the microgrid MGi during time period t; The seventh item is: Where σ1 represents the first penalty factor; C i，t L represents the charging and discharging amount of the microgrid MGi within time period t; i，t S represents the load power consumption of the microgrid MGi during time period t; i，t G represents the total energy transferred from microgrid MGi to other microgrids during time period t; i,t This represents the power generation of the microgrid MGi during time period t; ∑ represents the energy obtained by the microgrid MGi from the public grid during time period t; j≠i β ji,t This represents the energy that microgrid MGi receives from other microgrids during time period t; max(·) indicates taking the maximum value; The eighth item is: Where σ2 represents the second penalty factor; This indicates the target charging state of the electric transport unit CEVn, which is also the charging requirement of the electric transport unit CEVn. Indicates the buffer factor; SOC n,t P represents the charging state of the electric transport unit CEVn during time period t; n,t η represents the charging and discharging power of the electric transport unit CEVn within time period t; η(·) represents the energy efficiency of the charging and discharging power of the electric transport unit CEVn. The total duration of the time period is represented by Δt; Δt represents the total duration of the time period. The duration of each time period; η ch η represents the charging efficiency of the electric transport unit CEVn. ch ∈(0,1]; This indicates the charging power of the electric transport unit CEVn; Buffer factor Defined as: Where μ represents the safety margin, μ∈[0,1]; This indicates the upper limit threshold of the electric transport unit CEVn's power capacity; The eighth item satisfies the charging requirement constraint, and the formula corresponding to the charging requirement constraint is: in, This indicates that the duration of time period t is less than the total duration of the time period; η(P) n,t The definition of ) is: η ch η represents the charging efficiency of the electric transport unit CEVn. dch η represents the discharge efficiency of the electric transport unit CEVn. ch ∈(0,1] and η dch ∈(0,1]; The action-solving module is used to solve for the target feasible action based on the utility function, multiple preset candidate feasible actions, and a preset reinforcement learning algorithm. An action execution module is used to control the microgrid to perform the target feasible action to enable energy transfer between the microgrid and at least one of the individual microgrids.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the scheduling method for a microgrid-coupled transportation system as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the scheduling method for a microgrid-coupled transportation system as described in any one of claims 1 to 6.

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