A mobile energy storage scheduling method and system based on virtual power distribution network modeling

Abstract

The application discloses a mobile energy storage scheduling method and system based on virtual power distribution network modeling, and relates to the technical field of mobile energy storage scheduling. The method comprises the following steps: acquiring state data of each energy storage node and operation data of a mobile energy storage device in a scheduling area; establishing a virtual energy channel among the energy storage nodes according to total predicted energy consumption power and available supply power, and constructing a virtual power distribution network model; taking energy flow constraints, energy storage node power balance constraints and virtual channel capacity constraints as constraints, taking minimization of the scheduling cost of the mobile energy storage device as an objective, constructing and solving an energy flow optimization model to obtain an initial ideal energy flow; determining the efficiency of the virtual energy channel according to road distance, and calculating ideal inflow energy flow of each energy storage node in combination with the initial ideal energy flow; taking the ideal inflow energy flow as a tracking objective, constructing and solving a mobile energy storage device schedulable model to generate scheduling instructions, and realizing mobile energy storage scheduling.

Description

Technical Field

[0001] This application relates to the field of mobile energy storage dispatching technology, and in particular to a mobile energy storage dispatching method and system based on virtual distribution network modeling. Background Technology

[0002] With the integration of distributed renewable energy, the large-scale development of electric transportation, the construction of charging and battery swapping infrastructure, and the application of mobile energy storage equipment, the supply and demand relationship of multi-energy storage node power distribution systems within the dispatch area exhibits stronger spatiotemporal imbalances. There are often differences in available supply capacity, load demand, renewable energy output, and critical load guarantee requirements among different energy storage nodes. Some energy storage nodes may have surplus power supply or renewable energy absorption capacity at certain times, while others may experience concentrated charging demand, insufficient local power supply capacity, or pressure to guarantee critical loads. In multi-energy storage node power distribution scenarios with road accessibility, mobile energy storage devices, characterized by mobility, chargeability, reusability, and variable response location, can transport energy between different energy storage nodes, thereby providing cross-energy storage node energy support for the dispatch area. Typical application scenarios for this include highway energy networks, park distribution networks, station cluster power supply networks, charging and battery swapping station clusters, and emergency power supply scenarios.

[0003] Existing mobile energy storage scheduling methods make scheduling decisions by directly constructing a vehicle-level optimization scheduling model for mobile energy storage devices among energy storage nodes within the scheduling area. This model uses the location, charging and discharging power, and state of charge of the mobile energy storage device as decision variables, and directly solves and outputs the executable scheduling actions of the mobile energy storage device.

[0004] However, the aforementioned scheduling model for mobile energy storage devices only focuses on the scheduling actions of mobile energy storage devices between energy storage nodes. It cannot explicitly represent the energy transfer amount of each energy storage node during the scheduling decision-making process, and therefore cannot explicitly quantify the energy mutual assistance demand between each energy storage node during the scheduling process. This makes it difficult for schedulers to flexibly adjust energy storage scheduling strategies according to the energy mutual assistance demand between each node, and it is impossible to form a traceable explanation of scheduling actions. Summary of the Invention

[0005] Therefore, it is necessary to provide a mobile energy storage dispatching method and system based on virtual distribution network modeling to address the above-mentioned technical problems.

[0006] The present invention adopts the following technical solution: This invention provides a mobile energy storage dispatch method based on virtual distribution network modeling, comprising: Acquire status data of each energy storage node and operational data of mobile energy storage devices within the energy storage dispatch area; Based on the status data of each energy storage node, a virtual distribution network model of the energy storage dispatch area is constructed; In the virtual distribution network model, the energy flow constraints of each virtual energy channel, the power balance constraints of the energy storage node, and the capacity constraints of the virtual channel are used as constraints. The optimization objective is to minimize the scheduling cost of the mobile energy storage device in each virtual energy channel. An energy flow optimization model is constructed and solved to obtain the initial ideal energy flow of each virtual energy channel. Among them, the energy flow is used to characterize the amount of power transfer when the mobile energy storage device moves from the starting energy storage node to the ending energy storage node in each virtual energy channel. The ideal inflow energy flow to each energy storage node is determined based on the road distance between energy storage nodes and the initial ideal energy flow. Based on the operational data of the mobile energy storage device, a dispatchable model of the mobile energy storage device among various energy storage nodes is constructed. Taking the ideal inflow energy flow as the target inflow energy flow of each energy storage node, the dispatchable model is solved to obtain the dispatch instructions of the mobile energy storage device, so as to realize the dispatch of mobile energy storage within the dispatch area.

[0007] Preferably, the status data of each energy storage node includes: total predicted energy consumption, available supply power, energy supply cost of the energy storage node, road distance data, and road traffic speed data; the operation data of the mobile energy storage device includes: the location of the mobile energy storage device, the state of charge of the mobile energy storage device, and operation constraint parameters; the operation constraint parameters include maximum energy storage capacity, minimum energy storage capacity, maximum charging power, maximum discharging power, charging efficiency, and discharging efficiency.

[0008] Preferably, based on the status data of each energy storage node, a virtual energy channel is established between the energy storage nodes, specifically including: Based on the difference between the total predicted energy consumption and the available supply power in the status data of each energy storage node, determine the energy storage nodes with surplus power and the energy storage nodes with power load gap. Establish a virtual energy channel between energy storage nodes with surplus power and energy storage nodes with power load gaps; The virtual energy channel is oriented from the power surplus energy storage node to the power load gap energy storage node, and is used to characterize the modeling channel for equivalent energy transfer from the power surplus energy storage node to the power load gap energy storage node.

[0009] Preferably, a virtual distribution network model of the energy storage dispatch area is constructed based on the status data of each energy storage node, specifically including: Based on the operational data of each energy storage node, a virtual energy channel is established between the energy storage nodes; and based on the road connection relationship within the scheduling area, a road arc is established between the energy storage nodes. Each energy storage node is taken as the set of energy storage nodes in the virtual distribution network model, each virtual energy channel is taken as the set of virtual energy channels in the virtual distribution network model, each road arc is taken as the set of road arcs in the virtual distribution network model, and each time period in the scheduling time domain is taken as the set of time slices. The virtual power distribution network model is constructed based on the set of energy storage nodes, the set of virtual energy channels, the set of road arcs, and the set of time slices. The direction of the virtual energy channel indicates the direction of energy flow, and the direction of the road arc indicates the direction of travel of the mobile energy storage device.

[0010] Preferably, the road arc has a starting energy storage node, an ending energy storage node, a road distance, a travel delay, and a travel energy consumption; the travel delay is determined based on the road distance, road traffic speed, and the time slice length of the scheduling period; the travel energy consumption is determined based on the road distance and the travel energy consumption coefficient per unit distance of the mobile energy storage device.

[0011] Preferably, based on the virtual distribution network model, an energy flow optimization model is constructed with constraints on energy flow in each virtual energy channel, power balance constraints at energy storage nodes, and capacity constraints in the virtual channel, and with the optimization objective of minimizing the energy supply cost of mobile energy storage devices in each virtual energy channel. Specifically, this includes: For each energy storage node within the scheduling area and for each time period within the scheduling time domain, the upper limit of the actual power supplied by each energy storage node is set as the available power supplied by each energy storage node, and the actual power supplied is not less than zero, which serves as the source output constraint. Based on the actual power supplied by each energy storage node within the dispatch area, the unmet load power, the energy flow transferred from other energy storage nodes to that energy storage node via the virtual energy channel corresponding to that energy storage node, the total predicted energy consumption, and the energy flow transferred from that energy storage node to other energy storage nodes via all virtual energy channels flowing out of that energy storage node, the power balance constraint of the energy storage node is determined, expressed as: ; In the formula, For energy storage nodes within the dispatch area During the period The actual power supplied within, For energy storage nodes within the dispatch area During the period Unmet load power within, For virtual energy channels Channel efficiency, For energy storage nodes Flow to energy storage nodes Ideal energy flow, For energy storage nodes within the dispatch area During the period Total predicted energy consumption within the area For energy to flow into energy storage nodes A collection of virtual energy channels Energy outflow storage node A collection of virtual energy channels; For each virtual energy channel and each time period, the upper limit of the ideal energy flow on the virtual energy channel is set as the upper limit of the capacity of the virtual energy channel in that time period, and the ideal energy flow is not less than zero, which serves as the capacity constraint of the virtual channel. With constraints including source output, energy storage node power balance, and virtual channel capacity, an energy flow optimization model is constructed based on the actual utilized power supply and unmet load power of each energy storage node within the scheduling area. The model aims to minimize the scheduling cost of mobile energy storage devices in each virtual energy channel. The scheduling cost includes source output cost and supply shortage penalty cost, expressed as: ; In the formula, For the set of scheduling time segments, The total number of energy storage nodes in the scheduling area. and Energy storage nodes Actual utilization of power supply and unmet load power The cost coefficient, where, Greater than This is used to prioritize avoiding load shortages. This refers to a time segment for scheduling.

[0012] Preferably, the ideal inflow energy flow to each energy storage node is determined based on the road distance between energy storage nodes and the initial ideal energy flow, specifically including: The efficiency of the virtual energy channel is determined based on the road distance between the two energy storage nodes connected by the virtual energy channel and the preset energy loss coefficient per unit distance. Wherein, the efficiency of the virtual energy channel is 1 minus the product of the energy loss coefficient per unit distance and the road distance, and the efficiency of the virtual energy channel is greater than or equal to zero; The ideal inflow energy flow to each energy storage node is determined by multiplying the virtual energy channel efficiency by the initial ideal energy flow.

[0013] Preferably, the ideal inflow energy flow is used as the target inflow energy flow for each energy storage node, and the schedulable model of the mobile energy storage device is solved to obtain the scheduling instructions for the mobile energy storage device, specifically including: The constraints for the schedulable model of mobile energy storage devices are set, including: energy storage node power balance constraints, docking uniqueness constraints, location recursion constraints, departure and docking location binding constraints, charging and discharging actions binding docking location constraints, action mutual exclusion constraints, state of charge recursion constraints, state of charge upper and lower limit constraints, terminal energy constraints, and terminal docking constraints. Based on the constraints of mobile energy storage devices, and taking the departure, docking, charging, discharging, and state-of-charge trajectories of the mobile energy storage devices as decision-making objects, an executable scheduling model for the mobile energy storage devices is constructed, expressed as follows: ; In the formula, This serves as the fundamental runtime cost function for the executable scheduling model. The tracking deviation weight for the energy flow received by the energy storage node. and For mobile energy storage devices at energy storage nodes During the period Introduced positive and negative deviation variables; Using the ideal inflow energy flow as the target inflow energy flow of the mobile energy storage device at each energy storage node, we solve the executable scheduling model of the mobile energy storage device to obtain the actual inflow energy flow, departure decision, charging power, discharging power and state of charge trajectory of the mobile energy storage device at each energy storage node in the virtual distribution network model. Based on the actual inflow of energy, departure decisions, charging power, discharging power, and state of charge trajectory of each energy storage node in the virtual distribution network model, scheduling instructions for the mobile energy storage device in the virtual distribution network model are generated.

[0014] Preferably, after obtaining the actual inflow of energy into each energy storage node of the mobile energy storage device in the virtual distribution network model, the method further includes: For each energy storage node within the dispatch area, the energy inflow tracking shortage of the energy storage node is determined based on the difference between the ideal inflow energy flow of the energy storage node within the dispatch area and the actual inflow energy flow formed by the mobile energy storage device at the energy storage node. If the energy flow tracking deficit of any energy storage node in any time period is greater than a preset threshold, then that time period is marked as the deficit period of that energy storage node, and adjacent deficit periods are merged into a continuous error time window. For each continuous error time window, the cumulative energy capacity constraint of the energy storage node within the continuous error time window is determined based on the set of virtual energy channels into which energy flows into the energy storage node and the channel efficiency of each virtual energy channel in the set of virtual energy channels. The cumulative energy capacity constraint is added to the energy flow optimization model, and the energy flow optimization model, the mobile energy storage device schedulable model, the tracking shortage amount of the incoming energy flow are solved again, and the continuous error time window is determined until the tracking shortage amount of all energy storage nodes in all time periods is less than the preset threshold, and the final mobile energy storage device scheduling instruction is output.

[0015] This invention provides a mobile energy storage dispatch system based on virtual distribution network modeling, comprising: The data acquisition module is used to acquire the status data of each energy storage node within the energy storage dispatch area and the operation data of mobile energy storage devices; The energy flow solution module is used to construct a virtual distribution network model of the energy storage dispatch area based on the state data of each energy storage node. In the virtual distribution network model, the energy flow constraints of each virtual energy channel, the power balance constraints of the energy storage node, and the capacity constraints of the virtual channel are used as constraints. The optimization objective is to minimize the dispatch cost of the mobile energy storage device in each virtual energy channel. The energy flow is used to characterize the amount of power transferred when the mobile energy storage device moves from the starting energy storage node to the ending energy storage node in each virtual energy channel. An executable scheduling module is used to determine the ideal inflow energy flow of each energy storage node based on the road distance between energy storage nodes and the initial ideal energy flow; to construct a schedulable model of the mobile energy storage device among each energy storage node based on the operating data of the mobile energy storage device; and to solve the schedulable model with the ideal inflow energy flow as the target inflow energy flow of each energy storage node to obtain the scheduling instructions of the mobile energy storage device, so as to realize the scheduling of mobile energy storage within the scheduling area.

[0016] This invention provides a mobile energy storage dispatch system based on virtual distribution network modeling, comprising: The data acquisition module is used to acquire the status data of each energy storage node within the energy storage dispatch area and the operation data of mobile energy storage devices; The energy flow solution module is used to establish virtual energy channels between energy storage nodes based on the state data of each energy storage node; and to establish road arcs between energy storage nodes based on the road connection relationship within the scheduling area; based on the virtual energy channels and road arcs between energy storage nodes, a virtual distribution network model of the energy storage scheduling area is constructed; in the virtual distribution network model, the energy flow constraints of each virtual energy channel, the power balance constraints of energy storage nodes, and the capacity constraints of virtual channels are used as constraints, and the optimization objective is to minimize the scheduling cost of mobile energy storage devices in each virtual energy channel. An energy flow optimization model is constructed and solved to obtain the initial ideal energy flow of each virtual energy channel; whereby, energy flow is used to characterize the amount of power transfer when a mobile energy storage device moves from the starting energy storage node to the ending energy storage node on each virtual energy channel; An executable scheduling module is used to determine the efficiency of the virtual energy channel based on the road distance between energy storage nodes; and to determine the ideal inflow energy flow of each energy storage node based on the virtual energy channel efficiency and the initial ideal energy flow; using the ideal inflow energy flow of each energy storage node as the tracking target of the inflow energy flow of the mobile energy storage device, and constructing and solving the schedulable model of the mobile energy storage device based on the operating data of the mobile energy storage device to obtain the scheduling instructions of the mobile energy storage device, so as to realize the scheduling of mobile energy storage within the scheduling area.

[0017] The above-mentioned at least one technical solution adopted in this invention can achieve the following beneficial effects: This invention is based on an energy flow optimization model constructed using a virtual distribution network model. During energy storage scheduling, it ensures energy supply and demand balance among energy storage nodes at the global level through energy flow constraints on virtual energy channels, power balance constraints of energy storage nodes, and capacity constraints of virtual channels. Simultaneously, it quantitatively represents the energy of each energy storage node and the inflow and outflow of energy from all associated virtual energy channels through the energy flow optimization model. Guided by minimizing scheduling costs, it rationally allocates energy transfer amounts on each channel. The previously implicit and difficult-to-quantify energy mutual assistance needs between energy storage nodes during scheduling are transformed into energy flow variables that can be explicitly expressed and globally optimized on each channel in the virtual distribution network. This gives the energy transfer amounts between energy storage nodes during scheduling a clear mathematical form and a solvable path, solving the problem of the inability to explicitly quantify energy transfer during scheduling in existing technologies. It achieves an explicit explanation of the energy mutual assistance needs between energy storage nodes within the scheduling area, thus providing a traceable explanation for the scheduling actions of mobile energy storage devices. This guides dispatchers to flexibly adjust energy storage scheduling strategies based on the energy mutual assistance needs between nodes during scheduling. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a mobile energy storage dispatching method based on virtual distribution network modeling provided by the present invention; Figure 2 The diagram shows the connection relationship of energy storage nodes and the initial distribution of mobile energy storage devices in a typical application scenario of a mobile energy storage scheduling method based on virtual distribution network modeling, which is provided by the present invention. Figure 3 A schematic diagram illustrating the change in load shortage power before and after dispatching of a mobile energy storage device based on virtual distribution network modeling, which is provided by the present invention. Figure 4 A schematic diagram of the charge state trajectory and upper and lower limit constraints of a mobile energy storage device in a mobile energy storage scheduling method based on virtual distribution network modeling provided by the present invention. Figure 5 A timing diagram illustrating the docking and on-the-way status of a mobile energy storage device node in a mobile energy storage scheduling method based on virtual distribution network modeling, provided by the present invention. Figure 6 This invention provides a schematic diagram of a mobile energy storage dispatching system based on virtual distribution network modeling. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in the specification without creative effort are within the scope of protection of this application.

[0020] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic diagram of a mobile energy storage dispatching method based on virtual distribution network modeling in this invention, which specifically includes the following steps: S101: Obtain the status data of each energy storage node and the operation data of mobile energy storage devices within the energy storage dispatch area; Specifically, the status data of each energy storage node includes: total predicted energy consumption, available supply power, energy supply cost of the energy storage node, road distance data, and road traffic speed data; the operation data of the mobile energy storage device includes: mobile energy storage device location, mobile energy storage device state of charge, and operation constraint parameters; the operation constraint parameters include maximum energy storage capacity, minimum energy storage capacity, maximum charging power, maximum discharging power, charging efficiency, and discharging efficiency.

[0022] S102: Based on the status data of each energy storage node, establish virtual energy channels between each energy storage node; and based on the road connection relationship within the scheduling area, establish road arcs between each energy storage node.

[0023] Optionally, based on the state data of each energy storage node, a virtual energy channel is established between the energy storage nodes. Specifically, this includes: determining energy storage nodes with surplus power and energy storage nodes with power load gaps based on the difference between the total predicted energy consumption and the available supply power of each energy storage node; establishing a virtual energy channel between the energy storage nodes with surplus power and the energy storage nodes with power load gaps; wherein the direction of the virtual energy channel is from the energy storage nodes with surplus power to the energy storage nodes with power load gaps, and is used to characterize the modeling channel for equivalent energy transfer from the energy storage nodes with surplus power to the energy storage nodes with power load gaps.

[0024] Optionally, based on the road connectivity within the scheduling area, a road arc is established between each energy storage node. Specifically, this includes: establishing a bidirectional road arc between each energy storage node according to the road connectivity, road distance data, and road traffic speed data within the scheduling area; the road arc has a starting energy storage node, an ending energy storage node, road distance, travel delay, and travel energy consumption; the travel delay is determined based on the road distance, road traffic speed, and the time slice length of the scheduling period; the travel energy consumption is determined based on the road distance and the unit distance travel energy consumption coefficient of the mobile energy storage device.

[0025] S103: Construct a virtual distribution network model for the energy storage dispatch area based on the virtual energy channels and road arcs between each energy storage node; based on the virtual distribution network model, with the energy flow constraints of each virtual energy channel, the power balance constraints of the energy storage node, and the capacity constraints of the virtual channel as constraints, and with the optimization objective of minimizing the dispatch cost of the mobile energy storage device in each virtual energy channel, construct and solve an energy flow optimization model to obtain the initial ideal energy flow of each virtual energy channel; whereby, energy flow is used to characterize the amount of power transfer when the mobile energy storage device moves from the starting energy storage node to the ending energy storage node on each virtual energy channel.

[0026] Optionally, based on the status data of each energy storage node, a virtual power distribution network model of the energy storage dispatch area is constructed, specifically including: establishing virtual energy channels between each energy storage node based on the operating data of each energy storage node; and establishing road arcs between each energy storage node based on the road connection relationship within the dispatch area; taking each energy storage node as the set of energy storage nodes in the virtual power distribution network model, taking each virtual energy channel as the set of virtual energy channels in the virtual power distribution network model, taking each road arc as the set of road arcs in the virtual power distribution network model, and taking each time period in the dispatch time domain as the set of time slices; and constructing the virtual power distribution network model according to the set of energy storage nodes, the set of virtual energy channels, the set of road arcs, and the set of time slices; wherein, the direction of the virtual energy channel represents the direction of energy flow, and the direction of the road arc represents the direction of travel of the mobile energy storage device.

[0027] The road arc has a starting energy storage node, an ending energy storage node, a road distance, a travel delay, and a travel energy consumption; the travel delay is determined based on the road distance, road traffic speed, and the time slice length of the scheduling period; the travel energy consumption is determined based on the road distance and the travel energy consumption coefficient per unit distance of the mobile energy storage device.

[0028] Optionally, based on the virtual distribution network model, an energy flow optimization model is constructed with constraints on energy flow of each virtual energy channel, power balance of energy storage nodes, and capacity of virtual channels as constraints, and minimizing the energy supply cost of mobile energy storage devices in each virtual energy channel as the optimization objective. Specifically, this includes: for each energy storage node within the dispatch area and for each time period within the dispatch time domain, setting the upper limit of the actual utilized supply power of each energy storage node as the available supply power of each energy storage node, and ensuring that the actual utilized supply power is not less than zero, as a source output constraint; determining the power balance constraint of each energy storage node based on its actual utilized supply power, unmet load power, energy flow from other energy storage nodes to that energy storage node via the virtual energy channel corresponding to that energy storage node, total predicted energy consumption, and energy flow from that energy storage node to other energy storage nodes via all virtual energy channels flowing out of that energy storage node, expressed as: ; In the formula, For energy storage nodes within the dispatch area During the period The actual power supplied within, For energy storage nodes within the dispatch area During the period Unmet load power within, For virtual energy channels Channel efficiency, For energy storage nodes Flow to energy storage nodes Ideal energy flow, For energy storage nodes within the dispatch area During the period Total predicted energy consumption within the area For energy to flow into energy storage nodes A collection of virtual energy channels Energy outflow storage node A collection of virtual energy channels.

[0029] For each virtual energy channel and each time period, the upper limit of the ideal energy flow on that virtual energy channel is set as the upper limit of the channel's capacity during that time period, and the ideal energy flow is not less than zero, serving as the virtual channel capacity constraint. Using source output constraints, energy storage node power balance constraints, and virtual channel capacity constraints as conditions, and based on the actual utilized power supply and unmet load power of each energy storage node within the scheduling area, an energy flow optimization model is constructed with the objective of minimizing the scheduling cost of mobile energy storage devices in each virtual energy channel. The scheduling cost includes source output cost and supply shortage penalty cost, expressed as: ; In the formula, For the set of scheduling time segments, The total number of energy storage nodes in the scheduling area. and Energy storage nodes Actual utilization of power supply and unmet load power The cost coefficient, where, Greater than This is used to prioritize avoiding load shortages. This refers to a time segment for scheduling.

[0030] S104: Determine the ideal inflow energy flow to each energy storage node based on the road distance between energy storage nodes and the initial ideal energy flow.

[0031] Specifically, the efficiency of the virtual energy channel is determined based on the road distance between the two energy storage nodes connected by the virtual energy channel and the preset energy loss coefficient per unit distance; wherein, the efficiency of the virtual energy channel is 1 minus the product of the energy loss coefficient per unit distance and the road distance, and the efficiency of the virtual energy channel is greater than or equal to zero; the ideal inflow energy flow of each energy storage node is determined based on the product of the efficiency of the virtual energy channel and the initial ideal energy flow.

[0032] S105: Based on the operation data of the mobile energy storage device, construct a schedulable model of the mobile energy storage device among various energy storage nodes; take the ideal inflow energy flow as the target inflow energy flow of each energy storage node, solve the schedulable model, and obtain the scheduling instructions of the mobile energy storage device to realize the scheduling of mobile energy storage within the scheduling area.

[0033] Optionally, the ideal inflow energy flow is used as the target inflow energy flow for each energy storage node. The schedulable model of the mobile energy storage device is solved to obtain the scheduling instructions for the mobile energy storage device. Specifically, this includes setting constraints on the schedulable model of the mobile energy storage device, including: energy storage node power balance constraints, docking uniqueness constraints, position recursion constraints, departure and docking position binding constraints, charging / discharging action and docking position binding constraints, action mutual exclusion constraints, state of charge recursion constraints, upper and lower limits of state of charge constraints, terminal energy constraints, and terminal docking constraints. Based on the constraints of the mobile energy storage device, an executable scheduling model of the mobile energy storage device is constructed, taking the departure, docking, charging, discharging, and state of charge trajectory of the mobile energy storage device as the decision objects. The expression is: ; In the formula, This serves as the fundamental runtime cost function for the executable scheduling model. The tracking deviation weight for the energy flow received by the energy storage node. and For mobile energy storage devices at energy storage nodes During the period Positive and negative deviation variables were introduced.

[0034] Using the ideal inflow energy flow as the target inflow energy flow of the mobile energy storage device at each energy storage node, we solve the executable scheduling model of the mobile energy storage device to obtain the actual inflow energy flow, departure decision, charging power, discharging power and state of charge trajectory of the mobile energy storage device at each energy storage node in the virtual distribution network model. Based on the actual inflow of energy, departure decisions, charging power, discharging power, and state of charge trajectory of each energy storage node in the virtual distribution network model, scheduling instructions for the mobile energy storage device in the virtual distribution network model are generated.

[0035] In addition, after obtaining the actual inflow energy flow of each energy storage node in the virtual distribution network model, the process further includes: for each energy storage node within the scheduling area, determining the inflow energy flow tracking shortage of the energy storage node based on the difference between the ideal inflow energy flow of the energy storage node within the scheduling area and the actual inflow energy flow formed by the mobile energy storage device at the energy storage node; if the inflow energy flow tracking shortage of any energy storage node in any time period is greater than a preset threshold, then the time period is marked as the shortage period of the energy storage node, and adjacent shortage periods are merged into a continuous error time window; for each continuous error time window, determining the cumulative energy capacity constraint of the energy storage node within the continuous error time window based on the set of virtual energy channels into which energy flows into the energy storage node and the channel efficiency of each virtual energy channel in the set of virtual energy channels; adding the cumulative energy capacity constraint to the energy flow optimization model, and resolving the energy flow optimization model, solving the schedulable model of the mobile energy storage device, calculating the inflow energy flow tracking shortage, and determining the continuous error time window, until the tracking shortage of all energy storage nodes in all time periods is less than the preset threshold, and outputting the final mobile energy storage device scheduling instruction.

[0036] To illustrate the technical solution of the present invention in detail, the following specific embodiments are provided: This invention aims to solve the following technical problems existing in current mobile energy storage dispatch methods: First, existing vehicle-level scheduling models struggle to explicitly express the energy transfer demands between different energy storage nodes, resulting in insufficient interpretability of scheduling results. Second, simple energy flow optimization models fail to consider constraints on the feasibility of mobile energy storage, such as road travel delay, initial location of mobile energy storage devices, state of charge, and charging / discharging power, making it difficult for ideal energy flows to be actually realized by mobile energy storage devices. Third, there is a lack of a mechanism to update the capacity constraints of the virtual energy channel set corresponding to the energy storage node based on the continuous tracking deviation between the ideal energy flow into the energy storage node and the actual energy flow into the mobile energy storage device, making it difficult to coordinate the deviation between the ideal energy transfer demands between energy storage nodes and the feasibility of energy transport by mobile energy storage devices.

[0037] To address the aforementioned technical problems, this invention provides a mobile energy storage dispatching method based on virtual distribution network modeling. This method treats real or engineering-locatable energy supply points, load points, charging / swapping stations, service areas, toll stations, park load points, distributed power access points, or critical backup load points within the dispatching area as energy storage nodes. The equivalent energy transfer relationships between energy storage nodes, achieved by mobile energy storage devices, are abstracted as virtual energy channels, and the road connections that allow mobile energy storage devices to actually travel are abstracted as road arcs. The energy storage nodes themselves are real or engineering-locatable dispatching objects; what is virtual is the equivalent energy transfer channels between energy storage nodes and the dispatching network model they constitute.

[0038] This invention first obtains the total predicted energy consumption, available supply power, energy supply cost, road distance, traffic speed, and status of mobile energy storage devices for each energy storage node in the future scheduling time domain. The total predicted energy consumption of the energy storage nodes can be obtained from external short-term prediction modules, historical energy consumption curves, real-time monitoring data, manually set scenarios, or a combination thereof. The prediction process itself is not a necessary limitation of this invention; it is used as input data in the scheduling model.

[0039] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be understood that the described embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Unless otherwise specified, the embodiments and features described in this specification can be combined with each other.

[0040] I. Basic Definition Let the set of energy storage nodes be: ; The time slice set is as follows: ; The mobile energy storage device collection consists of: ; The virtual energy channels are set as follows: ; Energy flows into energy storage nodes The set of virtual energy channels is as follows: ; Energy outflow from energy storage node The set of virtual energy channels is as follows: ; The set of road arcs is: ; It should be noted that, The direction in the middle represents the direction of the ideal energy flow. The direction in the text indicates the direction of travel of the mobile energy storage device. The energy storage node is a real or engineered location-based scheduling object, while the virtual one refers to the equivalent energy transfer channels between energy storage nodes and the scheduling network model they form. In this invention, the scheduling network model is called a virtual distribution network model, and is not limited to a physical distribution network formed by real electrical lines.

[0041] Virtual Energy Channel Used to represent the energy storage nodes in the energy flow optimization model. To energy storage nodes A modeling channel for equivalent energy transfer is provided. This virtual energy channel has channel efficiency. and capacity limit The efficiency of the channel can be determined based on road distance, travel losses of the mobile energy storage device, traffic conditions, or historical operating data.

[0042] Each energy storage node With energy storage nodes available to supply power Total predicted energy consumption of energy storage nodes .in, The forecasts can be generated by external short-term forecasting modules, historical energy consumption curves, real-time monitoring data, or manually set scenarios. These forecasts can correspond to load points, energy supply points, charging / swapping stations, service areas, toll stations, park load points, distributed power access points, or key backup energy storage nodes within the dispatch area during specific time periods. Total energy demand.

[0043] Road arc With starting energy storage node Reaching the energy storage node Road distance Traffic speed Travel delay and driving energy consumption Mobile energy storage devices With maximum energy storage capacity Minimum energy storage capacity Maximum charging power Maximum discharge power Charging efficiency Discharge efficiency Initial docking of energy storage nodes and initial state of charge. The scheduling period is divided into equal-length time slices, with a time slice length of [missing information]. .

[0044] II. Energy Flow Optimization Model In the ideal energy flow model, an energy flow optimization model based on a virtual distribution network model is constructed. This model is used to determine the expected energy transfer scale between different energy storage nodes from the perspective of supply and demand imbalance at energy storage nodes.

[0045] For any energy storage node and time period The source output constraint is: ; in, For energy storage nodes During the period Actual utilization of power supply, For energy storage nodes During the period Available power supply.

[0046] For any energy storage node and time period The power balance constraint of energy storage nodes can be expressed as: ; The left side represents the energy storage node. Actual utilization of supplied power, unmet load power And flowing into the energy storage node via the virtual energy channel Ideal energy flow; the right side represents the energy storage node. Total predicted energy consumption and energy storage nodes Ideal energy flow to other energy storage nodes , For virtual energy channels Efficiency.

[0047] For any virtual energy channel and time period The ideal energy flow capacity constraint is: ; in, For virtual energy channels During the period The maximum capacity.

[0048] The efficiency of a virtual channel can be determined by the following formula: ; in, This is the equivalent loss coefficient per unit distance. For energy storage nodes and energy storage nodes The road distance between them.

[0049] The objective function of the energy flow optimization model can be set as minimizing the sum of the source output cost and the shortage penalty cost: ; in, Greater than This is used to prioritize avoiding load shortages. Through the above objective function and constraints, the ideal energy flow of each virtual energy channel in each time period can be obtained. .

[0050] III. Mobile energy storage device execution scheduling model The mobile energy storage device execution scheduling layer is used to convert ideal energy flow into executable actions of the mobile energy storage device. The mobile energy storage device layer takes the departure, docking, charging, discharging and state of charge trajectory of the mobile energy storage device as the decision object, and takes the ideal inflow of energy into the energy storage node as the tracking target.

[0051] The definitions of docking variables, tripping variables, charging power, discharging power, and stored energy capacity for mobile energy storage devices are shown in Table 1. Table 1. Variable Definitions for Mobile Energy Storage Devices

[0052] in, .

[0053] The power balance of energy storage nodes in the mobile energy storage device layer is as follows: ; The uniqueness constraint for docking of mobile energy storage devices is: ; like: ; This indicates a mobile energy storage device. During the period It is en route.

[0054] The recursive constraint for the location of mobile energy storage devices is: ; The above constraints indicate that if a mobile energy storage device originates from an energy storage node... When departing, it leaves the energy storage node within the corresponding time period. If the mobile energy storage device is in A period of time ago, along the road arc from other energy storage nodes Departure and arrival at the energy storage node Then it will enter the energy storage node during the current time period. .

[0055] The departure and docking location binding constraints are as follows: ; The constraint linking charging / discharging power to action variables is as follows: ; ; The binding constraint between charging / discharging actions and docking positions is as follows: ; ; The mutual exclusion constraint for the operation of mobile energy storage devices is: ; This constraint means that the same mobile energy storage device cannot simultaneously perform charging, discharging, and starting actions at the same time.

[0056] The recursive constraint for the state of charge of mobile energy storage devices is: ; The upper and lower limits of the state of charge and the terminal energy constraints for mobile energy storage devices are as follows: ; ; Terminal energy constraints are used to prevent mobile energy storage devices from merely consuming initial electricity to form a superficial power supply capacity, so that mobile energy storage devices mainly play the role of energy transport during the dispatch cycle.

[0057] Mobile energy storage device terminal docking constraints: ; Terminal docking constraints are used to ensure that mobile energy storage devices are docked at a certain energy storage node at the end of the scheduling cycle, rather than being in transit.

[0058] IV. Coupling between Ideal Energy Flow and Actual Executable Energy Flow The mobile energy storage layer tracks the ideal inflow energy flow to each energy storage node. For any given energy storage node... Define the flow of energy into the energy storage node. The set of virtual energy channels is as follows: ; Energy storage nodes During the period The ideal inflow energy flow is defined as: ; Mobile energy storage devices at energy storage nodes The actual inflow of energy formed by the discharge is defined as: ; The mobile energy storage device layer tracks the ideal inflow of energy into the energy storage node, that is: ; To facilitate linear optimization solutions, a non-negative deviation variable is introduced. and And establish the following linear relationship: ; Substituting into the above definition, we get: ; The basic operating cost function of the executable scheduling model for mobile energy storage devices is defined as follows: ; in, For energy storage nodes Energy supply cost coefficient, The penalty coefficient for supply shortage. The cost factor for charge-discharge cycle losses of mobile energy storage devices. This represents the unit distance travel loss cost coefficient for mobile energy storage devices. For road arc distance, This represents the energy consumption cost coefficient for mobile energy storage devices during operation. For mobile energy storage devices along road arcs Energy consumption during driving.

[0059] The complete objective function for the mobile energy storage device layer is: ; in, For energy storage nodes to receive energy flow tracking deviation weights, and For energy storage nodes During the period Positive and negative non-negative deviation variables.

[0060] V. Identification of Continuous Error Time Window and Update of Capacity Constraints After solving for the ideal energy flow and the executable scheduling of the mobile energy storage device, for any energy storage node Calculate the energy flow into the energy storage nodes for each time period to track the shortage: ; like Greater than the preset threshold If the actual energy flow into the mobile energy storage device during that period is insufficient to meet the energy storage node requirements, then it is considered that the energy flow into the mobile energy storage device during that period was insufficient. The ideal inflow energy flow requirement. Adjacent shortage periods are combined into a continuous error time window: ; For any energy storage node and its corresponding continuous error time window The following energy storage node-level time window capacity constraints are generated: ; in: ; This constraint states that within the continuous error time window... Internally, the energy flow optimization model requires that the energy flowing into the energy storage node... The cumulative transmitting energy of the mobile energy storage device must not exceed the actual energy that the device can transmit to the energy storage node within that window. The equivalent capability of the transmitter after adjusting for channel efficiency based on the cumulative energy released.

[0061] VI. Iterative Solution Process An initial virtual channel capacity limit is established, and a time window capacity constraint set is initialized. The initial virtual channel capacity limit can be determined based on the total discharge power of the mobile energy storage device, the total predicted energy consumption of the energy storage node, the available supply capacity, the number of virtual energy channels, road traffic conditions, and historical scheduling experience.

[0062] Solve the energy flow optimization model to obtain the ideal energy flow of each virtual energy channel at each time period. And calculate the ideal inflow energy flow to each energy storage node based on the efficiency of the virtual energy channel. .

[0063] Ideally, energy storage nodes should flow into the energy stream. As the energy flow tracking target of the energy storage node in the mobile energy storage device layer, the executable scheduling model of the mobile energy storage device is solved to obtain the trajectory of the mobile energy storage device's departure, docking, charging, discharging and charging states.

[0064] Extract the actual inflow of energy generated by the mobile energy storage device at each energy storage node. And calculate the energy flow received by the energy storage node to track the shortage. .

[0065] If the maximum tracking deviation or cumulative tracking deviation of all energy storage nodes in all time periods is less than a preset threshold, the iteration is terminated; otherwise, continuous error time windows are identified for energy storage nodes with tracking shortages.

[0066] For continuous error time window Based on the set of virtual energy channels that flow into the corresponding energy storage nodes, a time window capacity constraint for energy storage nodes is generated, and an energy flow optimization model is added.

[0067] Repeat the above steps until the tracking deviation termination condition is met or the preset iteration limit is reached.

[0068] Output dispatch instructions for mobile energy storage devices, and send the departure time, departure energy storage node, arrival energy storage node, location, charging power, discharging power, and state of charge trajectory of the mobile energy storage device to the mobile energy storage device dispatch platform or energy management system.

[0069] VII. Dispatch Instruction Output The final output mobile energy storage device dispatch instructions include, but are not limited to, the fields shown in Table 2: Table 2 Dispatch Instructions for Mobile Energy Storage Devices

[0070] The following uses a typical application scenario of a highway energy network as an example to illustrate the present invention, aiming to explain the specific implementation of the virtual distribution network modeling method in a road-accessible multi-energy storage node energy dispatch scenario. It should be understood that this embodiment is only used to illustrate the specific implementation process of the present invention and does not limit the present invention to highway energy networks, nor does it limit the number of energy storage nodes, the number of virtual energy channels, the number of road arcs, and the number of mobile energy storage devices.

[0071] In this embodiment, see Figure 2 This is a schematic diagram showing the connection relationship of energy storage nodes and the initial distribution of mobile energy storage devices in a typical application scenario of highway energy networks. The set of energy storage nodes is as follows: ; The mobile energy storage device collection consists of: ; The scheduling cycle is 2 hours, and the time slice length is 10 minutes. Therefore, the number of scheduling periods is: ; The available power output and the total predicted power consumption of each energy storage node are as follows: ; ; Therefore, the net supply and demand status of each energy storage node is as follows: ; Among them, energy storage nodes 1, 3, and 4 are energy surplus energy storage nodes, while energy storage nodes 2, 5, and 6 are load gap energy storage nodes. Energy storage nodes 3 and 4 are connected to photovoltaic resources, and their unit energy supply cost or energy storage node electricity price is set at 0.3 yuan / kWh. The unit energy supply cost or energy storage node electricity price of the remaining energy storage nodes is set at 1 yuan / kWh. This setting is intended to reflect scenarios where some energy storage nodes have lower energy supply costs due to their connection to distributed photovoltaic power.

[0072] Road arc set Generated from highway road connectivity relationships. In this embodiment, the basic road connections include: ; ; ; ; ; ; Each basic road connection can be expanded into a two-way road arc. Road travel delay is determined by road distance, road traffic speed, mobile energy storage device speed, and time slice length; road travel energy consumption is determined by energy consumption per unit mileage and road distance.

[0073] Virtual energy channel collection Used for energy flow optimization models. In this embodiment, the virtual energy channel includes: ; The aforementioned virtual energy channel represents the equivalent energy transfer relationship from surplus energy storage nodes to load deficit energy storage nodes. The efficiency of the virtual energy channel is determined based on the equivalent distance and the energy loss coefficient per unit distance. ; in, For virtual energy channels The equivalent distance.

[0074] The parameters for the mobile energy storage devices are set as follows: The capacity of each mobile energy storage device is: ; The upper and lower limits of the state of charge are as follows: ; ; Both the maximum charging power and the maximum discharging power are: ; Both charging efficiency and discharging efficiency are: ; The initial docking node for the mobile energy storage device is: ; The corresponding initial state of charge is: ; Mobile energy storage devices 1 to 3 initially dock at energy storage node 1; mobile energy storage devices 4 to 5 initially dock at energy storage node 3; mobile energy storage device 6 initially docks at energy storage node 5; and mobile energy storage devices 7 to 8 initially dock at energy storage node 6. (See also...) Figure 5 This is a timeline diagram illustrating the docking and en route status of energy storage nodes.

[0075] In this embodiment, the energy flow optimization model first solves for the ideal energy flow on each virtual energy channel based on the supply and demand status of the energy storage nodes, the energy supply cost of the energy storage nodes, the efficiency of the virtual energy channels, and the capacity of the virtual channels. See [link to relevant documentation]. Figure 3This diagram illustrates the changes in load shortage power before and after the scheduling of a mobile energy storage device. Since an energy storage node may have multiple virtual energy channels into which energy flows, for example, energy storage node 2 may have two virtual energy channels into which energy flows, 1→2 and 3→2. Therefore, the mobile energy storage layer does not track each virtual energy channel individually, but rather aggregates the ideal inflow energy flow by energy storage node.

[0076] For any energy storage node The equivalent energy flow at its ideal receiving end is: ; The actual inflow of energy into the mobile energy storage device layer at this energy storage node is as follows: ; The mobile energy storage device can execute a scheduling model by tracking the departure, docking, charging, discharging, and state-of-charge trajectories of the mobile energy storage device. Try to track When a certain energy storage node experiences a shortage in tracking the inflow of energy over a continuous period, it is based on the set of virtual energy channels that allow energy to flow into that energy storage node. Generate a time window capacity constraint at the energy storage node level and feed this constraint back to the energy flow optimization model for iterative correction.

[0077] This embodiment demonstrates that the present invention can coordinate the ideal energy transfer demand with the actual achievable capability of mobile energy storage devices in situations where there is a spatial mismatch between energy storage node supply and demand, some energy storage nodes are connected to low-cost distributed energy sources, the initial distribution of mobile energy storage devices is uneven, and there is road travel delay.

[0078] During the engineering implementation process, the total predicted energy consumption of the energy storage nodes The forecast can be provided by an external short-term forecast module, historical energy consumption curves, real-time monitoring data, traffic flow monitoring data, or manually set operating scenarios. This invention does not limit the specific forecast algorithm, but uses the total predicted energy consumption of the energy storage nodes as the input data for the scheduling model.

[0079] Road travel delay The speed limit of the mobile energy storage device can be determined by a combination of factors, including road distance, speed limit of the mobile energy storage device, real-time traffic speed, or historical traffic speed. For example, the road arc can be determined using the following formula. Discrete driving delay: ; in, For road arc distance, For mobile energy storage devices on road arcs Available driving speed on This represents the time slice length.

[0080] Road driving energy consumption The energy consumption per unit distance can be determined by factors such as road distance, the mass of the mobile energy storage device, road gradient, ambient temperature, traffic congestion, or historical energy consumption per unit distance. In one implementation, it can be calculated as follows: ; in, This represents the energy consumption coefficient per unit distance traveled by the mobile energy storage device.

[0081] In practical engineering, the executable scheduling model for mobile energy storage devices can be further supplemented with constraints such as road traffic capacity, energy storage node docking capacity, mobile energy storage access capacity, charging interface capacity, mobile energy storage device maintenance window, driver shift constraints, and priority constraints for ensuring key energy storage nodes. These constraints can be configured according to the management needs of the scheduling area and the actual constraints of the mobile energy storage operation platform. See also... Figure 4 This is a schematic diagram of the charge state trajectory and upper and lower limit constraints of a mobile energy storage device.

[0082] In the rolling scheduling scenario, the energy flow optimization model can receive the rolling updates of the total predicted energy consumption of energy storage nodes, road traffic conditions, and mobile energy storage device conditions, and re-execute the method of the present invention in each rolling scheduling cycle to adapt to the dynamic changes in supply and demand conditions and road conditions.

[0083] The mobile energy storage devices include, but are not limited to, mobile energy storage vehicles, mobile charging vehicles, trailer-mounted energy storage power supply units, special vehicles with energy storage and charging / discharging interfaces, or other energy storage power supply devices with road mobility capabilities.

[0084] After adopting the method of this invention, the ideal energy flow layer can provide the energy transfer requirements between energy storage nodes, and the mobile energy storage device execution scheduling layer can provide the departure, docking, charging, discharging, and charging state trajectory of the mobile energy storage device. Because this invention adopts an energy flow tracking method at the energy storage node level, it is applicable to scenarios where one energy storage node corresponds to multiple virtual energy channels flowing into that node, avoiding the repeated attribution of the discharge power of the mobile energy storage device at the same energy storage node to multiple virtual energy channels.

[0085] In practical applications, the effectiveness of the method of the present invention can be verified by comparing indicators such as the ideal inflow energy flow curve of the energy storage node, the actual inflow energy flow curve of the mobile energy storage device, the iterative change of the tracking deviation at the energy storage node level, the location or en route status of the mobile energy storage device, the state of charge curve, the change of the energy storage node's supply shortage, and the number of road departures.

[0086] Compared to methods that reduce ideal energy flow based solely on a single time period or a single virtual energy channel, this invention generates energy storage node-level capacity constraints based on continuous error time windows. This more accurately reflects the impact of the mobile energy storage device's initial location, road travel delay, road travel energy consumption, state of charge constraints, and charging / discharging power limitations on the mobile energy storage device's energy transport capacity. By feeding this capacity constraint back to the energy flow optimization model, this invention enables the ideal energy transfer demand to gradually converge within the feasible range of the mobile energy storage device.

[0087] It should be noted that the virtual energy flow described in this invention is used for equivalent energy transfer modeling in mobile energy storage scheduling based on virtual distribution network modeling, and is not limited to power flow calculation of real distribution lines; the energy storage node-level inflow energy flow tracking and time window capacity constraints are used to achieve coordination between ideal energy flow and the executability of mobile energy storage devices, and are not limited to a specific solver or a specific decomposition algorithm.

[0088] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this invention.

[0089] The above describes a mobile energy storage dispatching method based on virtual distribution network modeling, provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding mobile energy storage dispatching system based on virtual distribution network modeling, such as... Figure 6 As shown.

[0090] Figure 6 A schematic diagram of a mobile energy storage dispatch system based on virtual distribution network modeling provided by the present invention is shown. The system includes: The data acquisition module 601 is used to acquire the status data of each energy storage node within the energy storage dispatch area and the operation data of the mobile energy storage device; The energy flow solving module 602 is used to construct a virtual distribution network model of the energy storage dispatch area based on the state data of each energy storage node. In the virtual distribution network model, the energy flow constraints of each virtual energy channel, the power balance constraints of the energy storage node, and the capacity constraints of the virtual channel are used as constraints, and the optimization objective is to minimize the dispatch cost of the mobile energy storage device in each virtual energy channel. The energy flow is used to characterize the amount of power transfer when the mobile energy storage device moves from the starting energy storage node to the ending energy storage node in each virtual energy channel. The executable scheduling module 603 is used to determine the ideal inflow energy flow of each energy storage node based on the road distance between energy storage nodes and the initial ideal energy flow; to construct a schedulable model of the mobile energy storage device among each energy storage node based on the operating data of the mobile energy storage device; and to solve the schedulable model with the ideal inflow energy flow as the target inflow energy flow of each energy storage node to obtain the scheduling instructions of the mobile energy storage device, so as to realize the scheduling of mobile energy storage within the scheduling area.

[0091] Specific limitations regarding the mobile energy storage dispatch system based on virtual distribution network modeling can be found in the limitations of the mobile energy storage dispatch method based on virtual distribution network modeling described above, and will not be repeated here. Each module in the aforementioned mobile energy storage dispatch system based on virtual distribution network modeling can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

Claims

1. A mobile energy storage dispatch method based on virtual distribution network modeling, characterized in that, include: Acquire status data of each energy storage node and operational data of mobile energy storage devices within the energy storage dispatch area; Based on the status data of each energy storage node, a virtual distribution network model of the energy storage dispatch area is constructed; In the virtual distribution network model, the energy flow constraints of each virtual energy channel, the power balance constraints of the energy storage node, and the capacity constraints of the virtual channel are used as constraints. The optimization objective is to minimize the scheduling cost of the mobile energy storage device in each virtual energy channel. An energy flow optimization model is constructed and solved to obtain the initial ideal energy flow of each virtual energy channel. Among them, the energy flow is used to characterize the amount of power transfer when the mobile energy storage device moves from the starting energy storage node to the ending energy storage node in each virtual energy channel. The ideal inflow energy flow to each energy storage node is determined based on the road distance between energy storage nodes and the initial ideal energy flow. Based on the operational data of the mobile energy storage device, a dispatchable model of the mobile energy storage device among various energy storage nodes is constructed. Taking the ideal inflow energy flow as the target inflow energy flow of each energy storage node, the dispatchable model is solved to obtain the dispatch instructions of the mobile energy storage device, so as to realize the dispatch of mobile energy storage within the dispatch area.

2. The mobile energy storage dispatch method based on virtual distribution network modeling as described in claim 1, characterized in that, The status data of each energy storage node includes: total predicted energy consumption, available supply power, energy supply cost of energy storage node, road distance data, and road traffic speed data; the operation data of the mobile energy storage device includes: mobile energy storage device location, mobile energy storage device state of charge, and operation constraint parameters; the operation constraint parameters include maximum energy storage capacity, minimum energy storage capacity, maximum charging power, maximum discharging power, charging efficiency, and discharging efficiency.

3. The mobile energy storage dispatch method based on virtual distribution network modeling as described in claim 1, characterized in that, Based on the status data of each energy storage node, a virtual energy channel is established between the energy storage nodes, specifically including: Based on the difference between the total predicted energy consumption and the available supply power in the status data of each energy storage node, determine the energy storage nodes with surplus power and the energy storage nodes with power load gap. Establish a virtual energy channel between energy storage nodes with surplus power and energy storage nodes with power load gaps; The virtual energy channel is oriented from the power surplus energy storage node to the power load gap energy storage node, and is used to characterize the modeling channel for equivalent energy transfer from the power surplus energy storage node to the power load gap energy storage node.

4. The mobile energy storage dispatch method based on virtual distribution network modeling as described in claim 1, characterized in that, The construction of a virtual distribution network model for the energy storage dispatch area based on the status data of each energy storage node specifically includes: Based on the operational data of each energy storage node, a virtual energy channel is established between the energy storage nodes; and based on the road connection relationship within the scheduling area, a road arc is established between the energy storage nodes. Each energy storage node is taken as the set of energy storage nodes in the virtual distribution network model, each virtual energy channel is taken as the set of virtual energy channels in the virtual distribution network model, each road arc is taken as the set of road arcs in the virtual distribution network model, and each time period in the scheduling time domain is taken as the set of time slices. The virtual power distribution network model is constructed based on the set of energy storage nodes, the set of virtual energy channels, the set of road arcs, and the set of time slices. The direction of the virtual energy channel indicates the direction of energy flow, and the direction of the road arc indicates the direction of travel of the mobile energy storage device.

5. The mobile energy storage dispatch method based on virtual distribution network modeling as described in claim 4, characterized in that, The road arc has a starting energy storage node, an ending energy storage node, a road distance, a travel delay, and a travel energy consumption; the travel delay is determined based on the road distance, road traffic speed, and the time slice length of the scheduling period; the travel energy consumption is determined based on the road distance and the travel energy consumption coefficient per unit distance of the mobile energy storage device.

6. The mobile energy storage dispatch method based on virtual distribution network modeling as described in claim 1, characterized in that, Based on a virtual distribution network model, an energy flow optimization model is constructed, with constraints on energy flow in each virtual energy channel, power balance in energy storage nodes, and capacity in virtual channels. The optimization objective is to minimize the energy supply cost of mobile energy storage devices in each virtual energy channel. Specifically, this model includes: For each energy storage node within the scheduling area and for each time period within the scheduling time domain, the upper limit of the actual power supplied by each energy storage node is set as the available power supplied by each energy storage node, and the actual power supplied is not less than zero, which serves as the source output constraint. Based on the actual power supplied by each energy storage node within the dispatch area, the unmet load power, the energy flow transferred from other energy storage nodes to that energy storage node via the virtual energy channel corresponding to that energy storage node, the total predicted energy consumption, and the energy flow transferred from that energy storage node to other energy storage nodes via all virtual energy channels flowing out of that energy storage node, the power balance constraint of the energy storage node is determined, expressed as: ； In the formula, For energy storage nodes within the dispatch area During the period The actual power supplied within, For energy storage nodes within the dispatch area During the period Unmet load power within, For virtual energy channels Channel efficiency, For energy storage nodes Flow to energy storage nodes Ideal energy flow, For energy storage nodes within the dispatch area During the period Total predicted energy consumption within the area For energy to flow into energy storage nodes A collection of virtual energy channels Energy outflow storage node A collection of virtual energy channels; For each virtual energy channel and each time period, the upper limit of the ideal energy flow on the virtual energy channel is set as the upper limit of the capacity of the virtual energy channel in that time period, and the ideal energy flow is not less than zero, which serves as the capacity constraint of the virtual channel. With constraints including source output, energy storage node power balance, and virtual channel capacity, an energy flow optimization model is constructed based on the actual utilized power supply and unmet load power of each energy storage node within the scheduling area. The model aims to minimize the scheduling cost of mobile energy storage devices in each virtual energy channel. The scheduling cost includes source output cost and supply shortage penalty cost, expressed as: ； In the formula, For the set of scheduling time segments, The total number of energy storage nodes in the scheduling area. and Energy storage nodes Actual utilization of power supply and unmet load power The cost coefficient, where, Greater than This is used to prioritize avoiding load shortages. This refers to a time segment for scheduling.

7. The mobile energy storage dispatch method based on virtual distribution network modeling as described in claim 1, characterized in that, The process of determining the ideal inflow energy flow to each energy storage node based on the road distance between the energy storage nodes and the initial ideal energy flow specifically includes: The efficiency of the virtual energy channel is determined based on the road distance between the two energy storage nodes connected by the virtual energy channel and the preset energy loss coefficient per unit distance. Wherein, the efficiency of the virtual energy channel is 1 minus the product of the energy loss coefficient per unit distance and the road distance, and the efficiency of the virtual energy channel is greater than or equal to zero; The ideal inflow energy flow to each energy storage node is determined by multiplying the virtual energy channel efficiency by the initial ideal energy flow.

8. The mobile energy storage dispatch method based on virtual distribution network modeling as described in claim 1, characterized in that, The method of using the ideal inflow energy flow as the target inflow energy flow for each energy storage node to solve the schedulable model of the mobile energy storage device yields the scheduling instructions for the mobile energy storage device, specifically including: The constraints for the schedulable model of mobile energy storage devices are set, including: energy storage node power balance constraints, docking uniqueness constraints, location recursion constraints, departure and docking location binding constraints, charging and discharging actions binding docking location constraints, action mutual exclusion constraints, state of charge recursion constraints, state of charge upper and lower limit constraints, terminal energy constraints, and terminal docking constraints. Based on the constraints of mobile energy storage devices, and taking the departure, docking, charging, discharging, and state-of-charge trajectories of the mobile energy storage devices as decision-making objects, an executable scheduling model for the mobile energy storage devices is constructed, expressed as follows: ； In the formula, This serves as the fundamental runtime cost function for the executable scheduling model. The tracking deviation weight for the energy flow received by the energy storage node. and For mobile energy storage devices at energy storage nodes During the period Introduced positive and negative deviation variables; Using the ideal inflow energy flow as the target inflow energy flow of the mobile energy storage device at each energy storage node, we solve the executable scheduling model of the mobile energy storage device to obtain the actual inflow energy flow, departure decision, charging power, discharging power and state of charge trajectory of the mobile energy storage device at each energy storage node in the virtual distribution network model. Based on the actual inflow of energy, departure decisions, charging power, discharging power, and state of charge trajectory of each energy storage node in the virtual distribution network model, scheduling instructions for the mobile energy storage device in the virtual distribution network model are generated.

9. The mobile energy storage dispatch method based on virtual distribution network modeling as described in claim 8, characterized in that, After obtaining the actual inflow of energy into each energy storage node of the mobile energy storage device in the virtual distribution network model, the following is also included: For each energy storage node within the dispatch area, the energy inflow tracking shortage of the energy storage node is determined based on the difference between the ideal inflow energy flow of the energy storage node within the dispatch area and the actual inflow energy flow formed by the mobile energy storage device at the energy storage node. If the energy flow tracking deficit of any energy storage node in any time period is greater than a preset threshold, then that time period is marked as the deficit period of that energy storage node, and adjacent deficit periods are merged into a continuous error time window. For each continuous error time window, the cumulative energy capacity constraint of the energy storage node within the continuous error time window is determined based on the set of virtual energy channels into which energy flows into the energy storage node and the channel efficiency of each virtual energy channel in the set of virtual energy channels. The cumulative energy capacity constraint is added to the energy flow optimization model, and the energy flow optimization model, the mobile energy storage device schedulable model, the tracking shortage amount of the incoming energy flow are solved again, and the continuous error time window is determined until the tracking shortage amount of all energy storage nodes in all time periods is less than the preset threshold, and the final mobile energy storage device scheduling instruction is output.

10. A mobile energy storage dispatch system based on virtual distribution network modeling, characterized in that, include: The data acquisition module is used to acquire the status data of each energy storage node within the energy storage dispatch area and the operation data of mobile energy storage devices; The energy flow solution module is used to construct a virtual distribution network model of the energy storage dispatch area based on the state data of each energy storage node. In the virtual distribution network model, the energy flow constraints of each virtual energy channel, the power balance constraints of the energy storage node, and the capacity constraints of the virtual channel are used as constraints. The optimization objective is to minimize the dispatch cost of the mobile energy storage device in each virtual energy channel. The energy flow is used to characterize the amount of power transferred when the mobile energy storage device moves from the starting energy storage node to the ending energy storage node in each virtual energy channel. An executable scheduling module is used to determine the ideal inflow energy flow of each energy storage node based on the road distance between energy storage nodes and the initial ideal energy flow; to construct a schedulable model of the mobile energy storage device among each energy storage node based on the operating data of the mobile energy storage device; and to solve the schedulable model with the ideal inflow energy flow as the target inflow energy flow of each energy storage node to obtain the scheduling instructions of the mobile energy storage device, so as to realize the scheduling of mobile energy storage within the scheduling area.

Images