A wharf electric device orderly charging and energy storage collaborative planning method and system

Abstract

The present application provides a wharf electric equipment orderly charging and energy storage collaborative planning method and system, relating to the technical field of charging and energy storage intelligent scheduling. In view of the poor collaboration between charging scheduling and energy storage site selection in the prior art, the present application collects multi-source heterogeneous operating state data of the wharf yard, determines the yard partition congestion index and charging congestion index; based on the equipment historical operating data and real-time operation tasks, predicts the charging demand urgency of the equipment within the future time window; according to the multi-source heterogeneous operating state data, the partition congestion index, the charging congestion index, the charging demand urgency and the power grid operation constraint, a double-layer optimization model of orderly charging scheduling is established; based on the partition congestion index, the charging congestion index and the charging load space-time distribution output by the double-layer optimization model, combined with the power grid operation constraint and the energy storage cost parameter, a collaborative planning model of energy storage site selection and capacity determination is established; the charging scheduling scheme and the energy storage planning scheme are output. The present application has good collaboration between charging and energy storage.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling technology for charging and energy storage, and in particular to a method and system for coordinated planning of orderly charging and energy storage for port electric equipment. Background Technology

[0002] With the accelerated green and low-carbon transformation of ports worldwide, terminal electrification has become a significant trend. Electric container trucks, automated guided vehicles (AGVs), and electric yard cranes are being deployed on a large scale at terminals, driving port operations towards zero emissions. The charging demands of this large-scale electric equipment have placed significant pressure on the terminal's power system: when multiple devices are charging simultaneously, the port area experiences noticeable demand spikes, potentially exceeding the design capacity of existing power infrastructure. There is a profound coupling between terminal yard congestion and the charging behavior of electric equipment.

[0003] In traditional scheduling models, equipment charging decisions are mostly triggered by fixed power thresholds, lacking awareness and utilization of real-time congestion status in the storage yard. When storage yard congestion is severe, a large number of devices are stuck in queues, leading to a serious mismatch between their charging needs and actual operating rhythm: on the one hand, devices in congested conditions cannot reach charging stations in time, resulting in increased charging station idle rates; on the other hand, after congestion eases, a large number of devices simultaneously flood into the charging area, causing charging congestion and grid load shocks. Existing technologies do not quantify storage yard congestion, resulting in a disconnect between charging scheduling and energy storage site selection.

[0004] Therefore, developing a method and system for coordinated planning of orderly charging and energy storage for port electric equipment is of great significance for improving the coordination between charging scheduling and energy storage site selection. Summary of the Invention

[0005] To address the problem of poor coordination between charging scheduling and energy storage site selection in existing technologies, this invention proposes a method for coordinated planning of orderly charging and energy storage for port electric equipment, which specifically includes the following steps: S1. Collect multi-source heterogeneous operation status data of the terminal yard through the vehicle-road cooperative perception network deployed in the terminal yard, wherein the multi-source heterogeneous operation status data includes equipment operation status, charging pile status and yard traffic status. S2. Determine the yard zoning congestion index and charging congestion index based on multi-source heterogeneous operating status data; S3. Based on the historical operating data and real-time operation tasks of the electric equipment in the terminal yard, predict the urgency of charging demand for the electric equipment in the future time window. S4. Based on multi-source heterogeneous operation status data, regional congestion index, charging congestion index, charging demand urgency and grid operation constraints, establish an orderly charging scheduling two-layer optimization model. S5. Based on the spatiotemporal distribution of charging load output by the partition congestion index, charging congestion index and orderly charging scheduling dual-layer optimization model, and combined with grid operation constraints and energy storage cost parameters, establish an energy storage site selection and capacity determination collaborative planning model. S6. Output the charging scheduling scheme generated by the ordered charging scheduling dual-layer optimization model, and the energy storage planning scheme generated by the energy storage location and capacity determination collaborative planning model.

[0006] Furthermore, in S2, the yard zoning congestion index and charging congestion index are determined based on multi-source heterogeneous operating status data, including: dividing the terminal yard into multiple sub-zones according to functional areas, including: gate area, main road area, container area and charging area; calculating the yard zoning congestion index based on the number of devices in the sub-zone, average passage time and queue length; and for the charging area, calculating the charging congestion index based on the proportion of devices waiting to be charged and the ratio of real-time total charging power to rated total power.

[0007] Furthermore, the formula for calculating the zone congestion index is as follows: ; in, This represents the congestion index of the k-th sub-region at time t. This represents the number of devices in sub-region k at time t. This represents the maximum capacity of devices within sub-region k. This represents the average travel time through sub-region k. This represents the baseline travel time for sub-region k under unobstructed conditions. This represents the queue length within sub-region k. This represents the maximum allowed queue length for sub-region k. , , These are the weighting coefficients.

[0008] Furthermore, the formula for calculating the charging congestion index is as follows: ; in, This represents the charging congestion index at time t. This represents the number of devices waiting to be charged at time t. This indicates the total number of charging stations in the charging area. Indicates the total charging power. Indicates the rated total power capacity of the charging area. This represents the weighting coefficient.

[0009] Furthermore, in S3, based on the historical operating data and real-time operation tasks of the electric equipment in the terminal yard, the urgency of charging demand for the electric equipment in the future time window is predicted, including: extracting the energy consumption pattern per unit mileage of the equipment based on the historical operating data, and predicting the amount of electricity required to complete the remaining tasks in the real-time operation tasks; calculating the task time urgency based on the task deadline in the real-time operation tasks and the equipment charging rate in the historical operating data; calculating the electricity urgency based on the current remaining electricity of the equipment in the real-time operation tasks; and integrating the required electricity, task time urgency, electricity urgency, and the regional congestion index of the area where the equipment is located to obtain the urgency of charging demand for the equipment in the future time window.

[0010] Furthermore, the formula for calculating the urgency of the charging demand is as follows: ; in, This indicates the urgency of device i's charging need at time t. The function representing the urgency of electricity usage. Represents the task requirement component function. Indicates the remaining task time. This indicates the remaining power required for the task. Represents the congestion adaptive component function. Indicates the location of device i in the storage yard. Sub-regional congestion index. Indicates the sub-area number k to which device i belongs. , , These are the weighting coefficients.

[0011] Furthermore, in step S4, based on multi-source heterogeneous operating status data, zonal congestion index, charging congestion index, charging demand urgency, and grid operation constraints, an ordered charging scheduling two-layer optimization model is established. This includes: based on the equipment and charging pile locations in the multi-source heterogeneous operating status data, the charging demand urgency, and the zonal congestion index, under equipment charging allocation constraints, an upper-layer optimization model is established to optimize the target charging piles and charging start times for each piece of equipment, aiming to minimize the overall operational efficiency loss of the yard and the charging congestion level; based on the rated power of the charging piles in the multi-source heterogeneous operating status data, the charging congestion index, and power distribution system operation constraints, a lower-layer optimization model is established to execute real-time charging power allocation for each charging pile, aiming to minimize power distribution system load fluctuations; and through iterative coordination between the upper and lower layers, a charging scheduling scheme is obtained.

[0012] Furthermore, the device charging allocation constraints include: a single device is allocated to one target charging pile, a single pile serves only one device at a time, and the remaining power of the device during the entire charging process is not lower than a preset safety threshold; the power distribution system operation constraints include: the charging power of a single pile does not exceed the rated power, the total load of the transformer does not exceed the rated capacity, and the node voltage is maintained within a preset safety range.

[0013] Furthermore, in S5, based on the zonal congestion index, the charging congestion index, and the spatiotemporal distribution of charging load output by the orderly charging scheduling two-layer optimization model, and combined with grid operation constraints and energy storage cost parameters, an energy storage site selection and capacity determination collaborative planning model is established. This includes: calculating energy storage investment cost, operation and maintenance cost, grid power purchase cost, and charging congestion penalty cost based on the energy storage cost parameters and the charging congestion index; constructing an objective function by minimizing the sum of energy storage investment cost, operation and maintenance cost, grid power purchase cost, and charging congestion penalty cost; introducing a congestion location reward term into the objective function, which is determined based on the zonal congestion index and the spatiotemporal distribution of charging load; and solving for the energy storage planning scheme by combining the distribution system operation constraints.

[0014] The present invention also provides a coordinated planning system for orderly charging and energy storage of port electric equipment, the system being used to execute the coordinated planning method for orderly charging and energy storage of port electric equipment described in any of the above claims, the system comprising: The data acquisition module is used to collect multi-source heterogeneous operating status data of the terminal yard through the vehicle-road cooperative sensing network deployed in the terminal yard. The multi-source heterogeneous operating status data includes equipment operating status, charging pile status and yard traffic status. The congestion sensing module determines the congestion index of the storage yard area and the charging congestion index based on multi-source heterogeneous operating status data. The charging demand forecasting module is used to predict the urgency of charging demand for electric equipment in the future time window based on historical operating data and real-time work tasks of electric equipment in the terminal yard. The orderly charging scheduling module is used to establish a two-layer optimization model for orderly charging scheduling based on multi-source heterogeneous operating status data, regional congestion index, charging congestion index, charging demand urgency and grid operation constraints. The energy storage collaborative planning module is used to establish an energy storage site selection and capacity determination collaborative planning model based on the spatiotemporal distribution of charging load output by the zonal congestion index, charging congestion index and orderly charging scheduling dual-layer optimization model, combined with grid operation constraints and energy storage cost parameters. The output module is used to output the charging scheduling scheme generated by the ordered charging scheduling dual-layer optimization model and the energy storage planning scheme generated by the energy storage location and capacity determination collaborative planning model.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention collects multi-source heterogeneous operational status data from a terminal yard; determines the yard's zone congestion index and charging congestion index based on this data; predicts the urgency of charging demand for electric equipment within future time windows based on historical operational data and real-time tasks of the terminal yard's electric equipment; establishes an orderly charging scheduling two-layer optimization model based on the multi-source heterogeneous operational status data, zone congestion index, charging congestion index, charging demand urgency, and grid operation constraints; establishes an energy storage site selection and capacity determination collaborative planning model based on the zone congestion index, charging congestion index, and the spatiotemporal distribution of charging load output by the orderly charging scheduling two-layer optimization model, combined with grid operation constraints and energy storage cost parameters; and outputs the charging scheduling scheme generated by the orderly charging scheduling two-layer optimization model and the energy storage planning scheme generated by the energy storage site selection and capacity determination collaborative planning model. By quantifying two types of congestion indicators—the storage yard and the charging area—using multi-source heterogeneous operating status data, and integrating the congestion index into the calculation of equipment charging urgency, a hierarchical orderly charging model is built to achieve fine-grained control of charging timing and power. Furthermore, by combining the spatiotemporal load of charging output with congestion parameters, a collaborative planning model for energy storage site selection and capacity determination is constructed to achieve integrated linkage between charging scheduling and energy storage configuration, thereby improving the collaborative optimization level of charging scheduling and energy storage configuration. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for coordinated planning of orderly charging and energy storage for dock electric equipment, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of the spatiotemporal coupling relationship between ordered charging scheduling and energy storage collaborative planning provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a coordinated planning system for orderly charging and energy storage of dock electric equipment provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0019] The specific embodiments of the present invention will be described below.

[0020] To address the poor coordination between charging scheduling and energy storage site selection in existing technologies, this invention collects multi-source heterogeneous operational status data from wharf yards to determine the yard's zonal congestion index and charging congestion index. Based on historical equipment operation data and real-time operational tasks, it predicts the urgency of charging demand within future time windows. Based on the multi-source heterogeneous operational status data, zonal congestion index, charging congestion index, charging demand urgency, and grid operation constraints, an ordered charging scheduling two-layer optimization model is established. Based on the zonal congestion index, charging congestion index, and the spatiotemporal distribution of charging load output by the two-layer optimization model, combined with grid operation constraints and energy storage cost parameters, an energy storage site selection and capacity determination collaborative planning model is established. Finally, it outputs a charging scheduling scheme and an energy storage planning scheme. This invention exhibits good coordination between charging and energy storage.

[0021] Example 1 This invention provides a method for coordinated planning of orderly charging and energy storage for port electric equipment. Figure 1 This is a flowchart of a method for coordinated planning of orderly charging and energy storage for dock electrical equipment provided in an embodiment of the present invention, such as... Figure 1 As shown, the specific steps include the following: S1. Collect multi-source heterogeneous operation status data of the terminal yard through the vehicle-road cooperative perception network deployed in the terminal yard, wherein the multi-source heterogeneous operation status data includes equipment operation status, charging pile status and yard traffic status.

[0022] Vehicle-road cooperative sensing networks are intelligent sensing infrastructure deployed in terminal yards, integrating vehicle-mounted terminals, roadside sensing devices, and communication systems to achieve real-time data collection and interaction across the entire area, including equipment, road environment, and facility status. Multi-source heterogeneous operational status data refers to real-time terminal operational information collected by different sensors, with varying data structures and formats. This includes equipment operational status data, charging pile status data, and yard traffic status data. For example, equipment operational status data includes the real-time location, remaining state of charge (SOC), current task status, current load, and historical energy consumption per unit mileage for electric trucks; charging pile status data includes the location coordinates, rated power, current occupancy status, and fault status of each charging pile; and yard traffic status data includes the number of devices, average driving speed, and queue length in each sub-area of ​​the yard, collected through visual sensing or vehicle-road cooperative devices.

[0023] Multi-source heterogeneous operational status data, bearing a unified timestamp, is transmitted to the collaborative control center via the port's 5G private network or industrial Ethernet. By aggregating multi-dimensional actual operating condition information from the yard, various types of operational data are standardized and collected, forming a standardized original data source.

[0024] S2. Determine the yard zoning congestion index and charging congestion index based on multi-source heterogeneous operation status data.

[0025] The yard zoning congestion index is a dimensionless indicator that quantifies the degree of traffic congestion in specific functional areas of a yard, while the charging congestion index is an indicator that quantifies the service saturation of a charging area.

[0026] Specifically, the yard congestion index and charging congestion index are determined based on multi-source heterogeneous operating status data. This includes: dividing the terminal yard into multiple sub-zones according to functional areas, including: gate area, main road area, container area, and charging area; calculating the yard congestion index based on the number of devices, average passage time, and queue length in the sub-zone; and calculating the charging congestion index for the charging area based on the proportion of devices waiting to charge and the ratio of real-time total charging power to rated total power.

[0027] Sub-zones refer to independent units within the physical space of a terminal yard, divided according to operational functions. These include gate areas, main road areas, container areas, and charging areas. Sub-zones are used for refined zoning management in case of congestion. The percentage of devices waiting to charge is the ratio of waiting devices to the total number of charging piles, reflecting the severity of queuing in the charging area. The ratio of real-time total charging power to rated total power is the ratio of the current total charging power to the maximum design capacity of the charging area, reflecting the power load saturation of the charging area.

[0028] The formula for calculating the zone congestion index is as follows: ; in, This represents the congestion index of the k-th sub-region at time t. This represents the number of devices in sub-region k at time t. This represents the maximum capacity of devices within sub-region k. This represents the average travel time through sub-region k. This represents the baseline travel time for sub-region k under unobstructed conditions. This represents the queue length within sub-region k. This represents the maximum allowed queue length for sub-region k. , , These are the weighting coefficients.

[0029] Weighting coefficient , , Used to adjust the degree of influence of equipment density, transit time, and queue length on congestion assessment, for example, taking =0.4, =0.35, =0.25. When When sub-region k is determined to be in a congested state, The threshold for determining congestion in different zones. =0.7.

[0030] The formula for calculating the charging congestion index is: ; in, This represents the charging congestion index at time t. This represents the number of devices waiting to be charged at time t. This indicates the total number of charging stations in the charging area. Indicates the total charging power. Indicates the rated total power capacity of the charging area. Represents the weighting coefficient, for example, Setting it to 0.5 can balance the contribution of queue size and power load to charging congestion.

[0031] By finely dividing the storage yard into zones and constructing congestion quantification formulas, the differentiated characteristics of storage yard traffic congestion and charging station power consumption congestion can be accurately characterized in different areas. This allows for standardized and quantitative expression of congestion levels, providing a more intuitive distinction between the severity of congestion in different areas. Furthermore, by subdividing charging area congestion into two dimensions—spatial queuing and power saturation—misjudgments caused by traditional single indicators are avoided, improving the alignment between charging decisions and actual storage yard operations.

[0032] S3. Based on historical operating data and real-time operation tasks of electric equipment in the terminal yard, predict the urgency of charging demand for electric equipment in the future time window.

[0033] Historical operating data refers to long-term recorded data accumulated by electric equipment during past operations, used to extract energy consumption patterns and performance characteristics, such as energy consumption per unit mileage and state of health (SOH) of the battery. Implemented work tasks refer to the details of the transportation or loading / unloading tasks currently assigned to the electric equipment, including task start and end times, remaining workload, and task deadline. Charging demand urgency is a dimensionless index that comprehensively quantifies the urgency of the equipment needing immediate charging at a given moment; a higher value indicates a greater risk of power shortage or greater task pressure.

[0034] Specifically, based on historical operating data and real-time work tasks of the electric equipment in the terminal yard, the urgency of charging demand for the electric equipment in the future time window is predicted. This includes: extracting the energy consumption pattern per unit mileage of the equipment based on the historical operating data, and predicting the amount of electricity required to complete the remaining tasks in the real-time work tasks; calculating the task time urgency based on the task deadline in the real-time work tasks and the equipment charging rate in the historical operating data; calculating the electricity urgency based on the current remaining electricity of the equipment in the real-time work tasks; and integrating the required electricity, task time urgency, electricity urgency, and the regional congestion index of the area where the equipment is located to obtain the urgency of charging demand for the equipment in the future time window.

[0035] Energy consumption per unit distance refers to a characteristic parameter derived from statistical analysis of historical operating data, representing the amount of electricity consumed by equipment per unit distance traveled under specific load and operating conditions. This parameter is used to predict energy consumption requirements for future tasks. Task time urgency reflects the pressure on the equipment to complete its assigned task within the remaining available time. Power urgency is a quantitative value of the urgency of power shortage, calculated based on the equipment's remaining power capacity.

[0036] Based on historical operating data, the energy consumption pattern of a single device is determined, and the total power shortage is calculated in combination with the remaining tasks. The time urgency is calculated based on the task deadline and charging rate, and the power urgency is obtained by combining the real-time remaining power. The charging demand urgency is obtained by weighted integration with the congestion index of the area where the device is located.

[0037] The formula for calculating the urgency of charging needs is: ; in, This indicates the urgency of device i's charging need at time t. The function representing the urgency of electricity usage. Let represent the remaining battery power of device i at time t. Represents the task requirement component function. Indicates the remaining task time. This indicates the remaining power required for the task. Represents the congestion adaptive component function. Indicates the location of device i in the storage yard. Sub-regional congestion index. Indicates the sub-area number k to which device i belongs. , , These are the weighting coefficients.

[0038] Among them, the urgency component function of electricity The expression is: ; In the formula, This represents the minimum battery level threshold, with a value of 0.2. This represents the critical power depletion threshold, with a value of 0.1. This represents a limit function.

[0039] Task requirement component function The expression is: ; Indicates the device's rated battery capacity. Indicates the task deadline. Indicates the start time of the task.

[0040] Congestion adaptive component function The Sigmoid function is used to characterize the impact of regional congestion on charging accessibility; the expression is: ; This represents the curve steepness coefficient, with a value of 8. This represents a congestion reference threshold, with a value of 0.5. When regional congestion is severe... Approaching 1, appropriately reduce the urgency of immediate charging of the device to alleviate pressure on the charging area.

[0041] By constructing three quantitative components—power consumption, task requirements, and congestion—in segments and combining them with fixed thresholds and weighting coefficients, the charging urgency is calculated. This accurately depicts the negative impact of yard congestion on equipment charging accessibility. When power is scarce or task deadlines are tight, the charging priority is increased; when area congestion worsens, the urgency of immediate charging is appropriately reduced. The multi-dimensional quantitative model can fit the actual operation of the terminal and the traffic conditions in the yard, enabling refined, standardized, and quantitative calculation of the urgency of equipment charging needs. This provides a reliable quantitative reference for differentiated scheduling of charging times.

[0042] S4. Based on multi-source heterogeneous operation status data, regional congestion index, charging congestion index, charging demand urgency, and grid operation constraints, establish an orderly charging scheduling two-layer optimization model.

[0043] Grid operation constraints refer to the physical limitations that a power distribution system must adhere to for safe and stable operation, including boundary conditions that restrict charging output, such as distribution network voltage, power limits, and transformer capacity. The ordered charging scheduling two-layer optimization model is a hierarchical decision-making architecture that includes an upper-layer charging task allocation model and a lower-layer real-time power allocation model.

[0044] Specifically, based on multi-source heterogeneous operating status data, zonal congestion index, charging congestion index, charging demand urgency, and grid operation constraints, a two-layer optimization model for orderly charging scheduling is established. This includes: based on the equipment and charging pile locations in the multi-source heterogeneous operating status data, the charging demand urgency, and the zonal congestion index, under equipment charging allocation constraints, an upper-layer optimization model is established to optimize the target charging piles and charging start times for each piece of equipment, aiming to minimize the overall operational efficiency loss of the yard and the degree of charging congestion; based on the rated power of the charging piles in the multi-source heterogeneous operating status data, the charging congestion index, and power distribution system operation constraints, a lower-layer optimization model is established to execute real-time charging power allocation for each charging pile, aiming to minimize power distribution system load fluctuations; and through iterative coordination between the upper and lower layers, a charging scheduling scheme is obtained.

[0045] Device charging allocation constraints refer to the rules that the upper-level optimization model must follow, including assigning one target charging pile to each device, allowing only one device per pile at a time, and ensuring that the remaining power of the device during the entire charging process does not fall below a preset safety threshold. Power distribution system operation constraints are the grid safety rules that the lower-level optimization model must follow, including ensuring that the charging power of a single pile does not exceed its rated power, the total load on transformers does not exceed their rated capacity, and that node voltages are maintained within a preset safety range. Iterative coordination between upper and lower layers refers to a solution strategy; for example, the upper layer provides a charging task plan, the lower layer verifies the grid feasibility and provides feedback for adjustments, iterating repeatedly until the optimal solution is found.

[0046] The upper-level optimization model aims to minimize operational efficiency loss and charging congestion. ; This indicates the optimization goal at the higher level. Let i represent the charging decision variable for device i. This indicates the urgency of charging device i at time t, and M represents the amplification factor. This represents the device charging position allocation variable. This represents the congestion detour loss coefficient. This represents the queuing time function for charging zone j. , , These represent the weighting coefficients.

[0047] Wherein, the queuing time function for charging zone j The expression is: ; This represents the set of queuing devices in charging area j at time t. This indicates the charging time of device i within the queue. This represents the charging congestion impact coefficient, with a value of 0.5. The queuing time function for charging area j takes into account the amplification effect of charging area congestion on charging time.

[0048] The lower-level optimization model performs power allocation while satisfying the security constraints of the power distribution system, with the objective function being to minimize load fluctuations. ; This represents the lower-level optimization objective, where T represents the total number of scheduling periods. , Indicates the load balancing weight. This represents the total charging power of the entire station at time t. This represents the average charging power across the entire station. This represents the real-time power of charging zone j. This represents the reference value of the power in charging zone j. Indicates the scheduling time step.

[0049] By incorporating zone congestion, charging congestion, equipment charging urgency, and grid constraints into a two-layer optimization model, the upper layer considers charging urgency, queuing losses, and congestion detour losses when arranging charging plans, prioritizing charging for high-demand equipment and reducing queuing delays in the yard. The lower layer balances charging power from both the overall station and zone perspectives, smoothing out peak electricity demand. This approach adapts to real-world yard operations and congested environments while also ensuring distribution network safety, thereby improving the practicality of the charging scheduling scheme and the stability of grid operation.

[0050] S5. Based on the spatiotemporal distribution of charging load output by the partition congestion index, charging congestion index and orderly charging scheduling dual-layer optimization model, and combined with grid operation constraints and energy storage cost parameters, an energy storage site selection and capacity determination collaborative planning model is established.

[0051] The spatiotemporal distribution of charging load refers to a matrix or dataset output by an ordered charging scheduling two-layer optimization model, describing the charging power demand of each area and time within the terminal yard over a future period. It serves as the basis for the capacity and power boundaries of energy storage configuration. Energy storage cost parameters refer to a set of quantitative indicators used to calculate the comprehensive cost of energy storage throughout its entire lifecycle, including fixed parameters for energy storage equipment purchase, operation and maintenance, and other costs that constitute the investment in energy storage.

[0052] Specifically, based on the spatiotemporal distribution of charging load output from the zonal congestion index, the charging congestion index, and the orderly charging scheduling dual-layer optimization model, and combined with grid operation constraints and energy storage cost parameters, an energy storage site selection and capacity determination collaborative planning model is established. This includes: calculating energy storage investment costs, operation and maintenance costs, grid power purchase costs, and charging congestion penalty costs based on the energy storage cost parameters and the charging congestion index; constructing an objective function by minimizing the sum of these costs; introducing a congestion location reward term into the objective function, which is determined based on the zonal congestion index and the spatiotemporal distribution of charging load; and solving for the energy storage planning scheme by combining the distribution system operation constraints.

[0053] The cost of charging congestion penalties refers to the monetized cost of operational efficiency loss caused by charging congestion, calculated using the following formula: ; This indicates the cost of penalties for charging congestion. This represents the congestion penalty coefficient, where the congestion penalty coefficient is... Take 0.5 yuan / kW.

[0054] The congested location incentive is This is used to reduce the overall cost of deploying energy storage in high-value areas, among which, The weighting coefficient for congested location rewards. This indicates the energy storage capacity configured at site m. This represents the location weighting coefficient determined by the zonal congestion index and the spatiotemporal distribution of charging load.

[0055] The goal is to minimize overall costs. ; This indicates the total cost of energy storage planning. Indicates the investment cost of energy storage. Indicates operation and maintenance costs. This indicates the cost of purchasing electricity from the power grid.

[0056] Energy storage investment costs Conversion using the equivalent annual value method: ; This indicates the unit price for purchasing energy storage capacity. This indicates the rated energy storage capacity of site m. This indicates the unit price per unit power of the energy storage converter. This indicates the rated charging and discharging power of the energy storage at site m. denoted as r, representing the one-time investment cost of fixed infrastructure for the energy storage site; r represents the benchmark discounted annual interest rate for the project; and Y represents the lifespan of the energy storage equipment.

[0057] Site selection weighting coefficient for energy storage candidate locations Coupled with the yard congestion index: ; This represents the typical average zone congestion index for the area where station m is located. This represents the sum of the typical average congestion indices for all candidate regions. The average charging load in the area where site m is located.

[0058] The above coupling mechanism ensures that the congestion index in the container area is within range. High and charging load Energy storage should be prioritized in densely populated areas to absorb peak charging loads nearby.

[0059] In this embodiment, the location reward coefficient is quantified by combining the dual indicators of area congestion level and charging load size. The higher the congestion and the larger the load, the higher the reward coefficient of the area. In the objective function, cost reduction guides energy storage to be deployed in high-congestion and high-load areas, so as to achieve precise linkage between congestion load and energy storage deployment.

[0060] S6. Output the charging scheduling scheme generated by the ordered charging scheduling dual-layer optimization model, and the energy storage planning scheme generated by the energy storage location and capacity determination collaborative planning model.

[0061] By outputting two implementation schemes—charging scheduling and energy storage planning—the integrated results of orderly charging management of front-end equipment and energy storage layout planning of back-end equipment are realized. The scheme is optimized in collaboration with real-world operating data such as site congestion, equipment power demand, and grid constraints. It can guide the daily time-sharing and orderly charging operation of electric vehicles at the site and alleviate charging queue congestion. It can also serve as a design basis for energy storage construction investment and site layout, realizing the overall management and control of charging operation and energy storage configuration.

[0062] For example, taking the daily operation data of an automated terminal as an example, assuming there are 60 electric container trucks, 20 AGVs, and 20 charging piles in the yard. In a control scenario without the method of this invention, during the peak operation period from 14:00 to 15:00 in the afternoon, the number of devices queuing in the charging area reaches 12, the charging congestion index exceeds 0.85, and the load rate of the distribution transformer reaches 110%. Based on the above embodiment, by shifting the charging time of some devices to the off-peak period of 13:00 to 14:00 through congestion-aware scheduling, the maximum number of devices queuing in the charging area is reduced to 5, and the peak load rate of the transformer is reduced to 92%. The energy storage planning model outputs that deploying a 500kW / 1000kWh energy storage system in the high-congestion area can further reduce the electricity purchase cost by approximately 8.5% daily through low-charge and high-discharge. Figure 2 This is a schematic diagram of the spatiotemporal coupling relationship between charging scheduling and energy storage. Compared with traditional disordered charging, the method in this embodiment can effectively alleviate charging congestion at the dock, smooth load fluctuations, reduce peak electricity demand, and reduce operating costs.

[0063] This invention collects multi-source heterogeneous operational status data from a terminal yard; determines the yard's zone congestion index and charging congestion index based on this data; predicts the urgency of charging demand for electric equipment within future time windows based on historical operational data and real-time tasks of the terminal yard's electric equipment; establishes an orderly charging scheduling two-layer optimization model based on the multi-source heterogeneous operational status data, zone congestion index, charging congestion index, charging demand urgency, and grid operation constraints; establishes an energy storage site selection and capacity determination collaborative planning model based on the zone congestion index, charging congestion index, and the spatiotemporal distribution of charging load output by the orderly charging scheduling two-layer optimization model, combined with grid operation constraints and energy storage cost parameters; and outputs the charging scheduling scheme generated by the orderly charging scheduling two-layer optimization model and the energy storage planning scheme generated by the energy storage site selection and capacity determination collaborative planning model. By quantifying two types of congestion indicators—the storage yard and the charging area—using multi-source heterogeneous operating status data, and integrating the congestion index into the calculation of equipment charging urgency, a hierarchical orderly charging model is built to achieve fine-grained control of charging timing and power. Furthermore, by combining the spatiotemporal load of charging output with congestion parameters, a collaborative planning model for energy storage site selection and capacity determination is constructed to achieve integrated linkage between charging scheduling and energy storage configuration, thereby improving the collaborative optimization level of charging scheduling and energy storage configuration.

[0064] Example 2 This invention also provides a coordinated planning system for orderly charging and energy storage of dock electrical equipment. Figure 3 This is a schematic diagram of a coordinated planning system for orderly charging and energy storage of dock electrical equipment provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the system includes: The data acquisition module is used to collect multi-source heterogeneous operating status data of the terminal yard through the vehicle-road cooperative sensing network deployed in the terminal yard. The multi-source heterogeneous operating status data includes equipment operating status, charging pile status and yard traffic status. The congestion sensing module determines the congestion index of the storage yard area and the charging congestion index based on multi-source heterogeneous operating status data. The charging demand forecasting module is used to predict the urgency of charging demand for electric equipment in the future time window based on historical operating data and real-time work tasks of electric equipment in the terminal yard. The orderly charging scheduling module is used to establish a two-layer optimization model for orderly charging scheduling based on multi-source heterogeneous operating status data, regional congestion index, charging congestion index, charging demand urgency and grid operation constraints. The energy storage collaborative planning module is used to establish an energy storage site selection and capacity determination collaborative planning model based on the spatiotemporal distribution of charging load output by the zonal congestion index, charging congestion index and orderly charging scheduling dual-layer optimization model, combined with grid operation constraints and energy storage cost parameters. The output module is used to output the charging scheduling scheme generated by the ordered charging scheduling dual-layer optimization model and the energy storage planning scheme generated by the energy storage location and capacity determination collaborative planning model.

[0065] The dock electric equipment orderly charging and energy storage collaborative planning system in this embodiment is used to execute the dock electric equipment orderly charging and energy storage collaborative planning method in any of the above embodiments, and has the beneficial effects of any of the above embodiments, which will not be repeated here.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for coordinated planning of orderly charging and energy storage for port electric equipment, characterized in that, include: S1. Collect multi-source heterogeneous operation status data of the terminal yard through the vehicle-road cooperative perception network deployed in the terminal yard, wherein the multi-source heterogeneous operation status data includes equipment operation status, charging pile status and yard traffic status. S2. Determine the yard zoning congestion index and charging congestion index based on multi-source heterogeneous operating status data; S3. Based on the historical operating data and real-time operation tasks of the electric equipment in the terminal yard, predict the urgency of charging demand for the electric equipment in the future time window. S4. Based on multi-source heterogeneous operation status data, regional congestion index, charging congestion index, charging demand urgency and grid operation constraints, establish an orderly charging scheduling two-layer optimization model. S5. Based on the spatiotemporal distribution of charging load output by the partition congestion index, charging congestion index and orderly charging scheduling dual-layer optimization model, and combined with grid operation constraints and energy storage cost parameters, establish an energy storage site selection and capacity determination collaborative planning model. S6. Output the charging scheduling scheme generated by the ordered charging scheduling dual-layer optimization model, and the energy storage planning scheme generated by the energy storage location and capacity determination collaborative planning model.

2. The method for coordinated planning of orderly charging and energy storage for dock electrical equipment according to claim 1, characterized in that, In step S2, the yard zoning congestion index and charging congestion index are determined based on multi-source heterogeneous operating status data, including: The terminal yard is divided into several sub-zones according to functional areas, including: gate area, main road area, container area and charging area; The yard zoning congestion index is calculated based on the number of equipment in the sub-zone, average transit time, and queue length. For charging areas, the charging congestion index is calculated based on the proportion of devices waiting to be charged and the ratio of real-time total charging power to rated total power.

3. The method for coordinated planning of orderly charging and energy storage for dock electrical equipment according to claim 2, characterized in that, The formula for calculating the zone congestion index is as follows: ； in, This represents the congestion index of the k-th sub-region at time t. This represents the number of devices in sub-region k at time t. This represents the maximum capacity of devices within sub-region k. This represents the average travel time through sub-region k. This represents the baseline travel time for sub-region k under unobstructed conditions. This represents the queue length within sub-region k. This represents the maximum allowed queue length for sub-region k. , , These are the weighting coefficients.

4. The method for coordinated planning of orderly charging and energy storage for dock electrical equipment according to claim 2, characterized in that, The formula for calculating the charging congestion index is as follows: ； in, This represents the charging congestion index at time t. This represents the number of devices waiting to be charged at time t. This indicates the total number of charging stations in the charging area. Indicates the total charging power. Indicates the rated total power capacity of the charging area. This represents the weighting coefficient.

5. The method for coordinated planning of orderly charging and energy storage for dock electrical equipment according to claim 1, characterized in that, In step S3, based on historical operating data and real-time work tasks of the electric equipment in the terminal yard, the urgency of charging demand for the electric equipment within a future time window is predicted, including: Based on the historical operating data, the energy consumption pattern per unit mileage of the equipment is extracted, and combined with the remaining task volume in the real-time operation task, the power required to complete the remaining task is predicted. The task time urgency is calculated based on the task deadline in the real-time job task and the device charging rate in the historical operation data. Calculate the power urgency based on the current remaining power of the equipment in the real-time task. By combining the required power, task time urgency, power urgency, and the regional congestion index of the area where the device is located, the charging demand urgency of the device within the future time window is obtained.

6. The method for coordinated planning of orderly charging and energy storage for dock electrical equipment according to claim 5, characterized in that, The formula for calculating the urgency of the charging demand is as follows: ； in, This indicates the urgency of device i's charging need at time t. The function representing the urgency of electricity usage. Let represent the remaining battery power of device i at time t. Represents the task requirement component function. Indicates the remaining task time. This indicates the remaining power required for the task. Represents the congestion adaptive component function. Indicates the location of device i in the storage yard. Sub-regional congestion index. Indicates the sub-area number k to which device i belongs. , , These are the weighting coefficients.

7. The method for coordinated planning of orderly charging and energy storage for dock electrical equipment according to claim 1, characterized in that, In step S4, based on multi-source heterogeneous operating status data, zonal congestion index, charging congestion index, charging demand urgency, and grid operation constraints, a two-layer optimization model for orderly charging scheduling is established, including: Based on the equipment location and charging pile location in the multi-source heterogeneous operation status data, the urgency of charging demand and the zone congestion index, an upper-level optimization model is established under the equipment charging allocation constraint. The model aims to minimize the overall operation efficiency loss of the yard and the degree of charging congestion, and optimizes the target charging pile and charging start time of each equipment. Based on the rated power of the charging piles, the charging congestion index, and the operating constraints of the power distribution system in the multi-source heterogeneous operating status data, a lower-level optimization model is established to perform real-time charging power allocation for each charging pile with the goal of minimizing the load fluctuation of the power distribution system. The charging scheduling scheme is obtained by iteratively coordinating solutions at the upper and lower levels.

8. The method for coordinated planning of orderly charging and energy storage for dock electrical equipment according to claim 7, characterized in that, The device charging allocation constraints include: one target charging pile is allocated to a single device, a single charging pile serves only one device at a time, and the remaining power of the device during the entire charging process is not lower than a preset safety threshold. The operating constraints of the power distribution system include: the charging power of a single pile does not exceed the rated power, the total load of the transformer does not exceed the rated capacity, and the node voltage is maintained within a preset safety range.

9. The method for coordinated planning of orderly charging and energy storage for dock electrical equipment according to claim 1, characterized in that, In S5, based on the spatiotemporal distribution of charging load output by the zonal congestion index, the charging congestion index, and the ordered charging scheduling dual-layer optimization model, and combined with grid operation constraints and energy storage cost parameters, an energy storage site selection and capacity determination collaborative planning model is established, including: Based on the energy storage cost parameters and the charging congestion index, calculate the energy storage investment cost, operation and maintenance cost, grid electricity purchase cost, and charging congestion penalty cost. The objective function is constructed by minimizing the sum of energy storage investment costs, operation and maintenance costs, grid electricity purchase costs, and charging congestion penalty costs. In the objective function, a congestion location reward term is introduced, which is determined based on the zonal congestion index and the spatiotemporal distribution of the charging load. By combining the operating constraints of the power distribution system, the energy storage planning scheme is obtained.

10. A coordinated planning system for orderly charging and energy storage of electrical equipment at a dock, characterized in that, The system is used to execute the coordinated planning method for orderly charging and energy storage of dock electric equipment according to any one of claims 1-9, and the system includes: The data acquisition module is used to collect multi-source heterogeneous operating status data of the terminal yard through the vehicle-road cooperative sensing network deployed in the terminal yard. The multi-source heterogeneous operating status data includes equipment operating status, charging pile status and yard traffic status. The congestion sensing module determines the congestion index of the storage yard area and the charging congestion index based on multi-source heterogeneous operating status data. The charging demand forecasting module is used to predict the urgency of charging demand for electric equipment in the future time window based on historical operating data and real-time work tasks of electric equipment in the terminal yard. The orderly charging scheduling module is used to establish a two-layer optimization model for orderly charging scheduling based on multi-source heterogeneous operating status data, regional congestion index, charging congestion index, charging demand urgency and grid operation constraints. The energy storage collaborative planning module is used to establish an energy storage site selection and capacity determination collaborative planning model based on the spatiotemporal distribution of charging load output by the zonal congestion index, charging congestion index and orderly charging scheduling dual-layer optimization model, combined with grid operation constraints and energy storage cost parameters. The output module is used to output the charging scheduling scheme generated by the ordered charging scheduling dual-layer optimization model and the energy storage planning scheme generated by the energy storage location and capacity determination collaborative planning model.

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