Multi-energy vehicle and ship battery charging and replacing optimization scheduling method in port

Abstract

The invention relates to a multi-energy vehicle and ship battery charging and replacing optimization scheduling method in a port. The method comprises the steps of obtaining port charging requirements and collecting multi-source data; constructing a day-ahead scheduling optimization model and an intra-day rolling optimization model, and solving the day-ahead scheduling optimization model based on the port charging demand and the multi-source data to obtain an all-day scheduling strategic plan; based on the all-day scheduling strategy plan, the port charging demand and the multi-source data, solving the intra-day rolling optimization model by using a model prediction control method to obtain an optimal control sequence; and carrying out charging scheduling on the port vehicles and ships based on the optimal control sequence. Compared with the prior art, the multi-energy supply problem of electric ships and vehicles in a port area is effectively solved, and the economic operation efficiency and the green low-carbon level of a port energy system are improved.

Description

Technical Field

[0001] This invention relates to the field of port charging and swapping scheduling technology, and in particular to an optimized scheduling method for multi-energy vehicle and ship charging and swapping within a port. Background Technology

[0002] With the continued growth of global trade and the deepening implementation of the "dual-carbon" strategy, ports, as logistics hubs and energy-intensive areas, are facing an increasingly urgent need for green and low-carbon transformation. Traditional port energy supply methods mainly rely on municipal power grids and ship-mounted diesel generators, resulting in high carbon emissions, severe noise pollution, and high operating costs. In recent years, with the rapid popularization of new energy equipment such as electric ships and electric container trucks within ports, there is an urgent need to build an efficient and intelligent multi-energy supply system for ports to support the clean and intelligent upgrading of port operations.

[0003] Currently, existing technologies mainly include shore-based power supply systems and integrated photovoltaic-energy storage-charging / swapping stations. However, these solutions still have significant limitations in the complex operating environment of ports. For example, Chinese patent application CN115689215A provides a charging / swapping scheduling system and method for unmanned vehicle fleets in ports. By transferring the charging / swapping scheduling and control center of the unmanned vehicle fleet to the vehicle end and optimizing charging / swapping time using communication networks, it solves the problems of operational efficiency and production value requirements of port vehicles in existing technologies. However, its technical solution only targets electric trucks operating within the port and ignores berthed vessels. Alternatively, shore-based power supply systems provided by other technologies can only serve berthed vessels and cannot cover electric trucks operating within the port. Neither of these methods can achieve vehicle-ship energy synergy. Furthermore, existing fixed shore power systems are difficult to adapt to the changing routes and non-fixed berthing of vessels, especially those without shore power receiving capabilities. In addition, although integrated photovoltaic-energy storage-charging / swapping stations, as an advanced energy solution, have been successfully applied in scenarios such as highway service areas and urban charging stations, their application in the special scenario of ports is still lacking. Directly transplanting general photovoltaic-storage-charging solutions to ports faces numerous incompatibility issues. For example, port energy demand involves diverse scenarios such as ship battery swapping and vehicle charging, with strong load fluctuations and complex scheduling. Existing systems struggle to achieve coordinated optimization of multiple factors, including photovoltaic power generation, tidal power generation, wind power generation, grid power purchase, and vehicle and ship charging / swapping demands. Furthermore, existing technologies generally lack intelligent collaborative scheduling algorithms for the complex operational processes of ports, making it difficult to achieve efficient energy allocation and economical operation.

[0004] Therefore, providing a charging and battery swapping scheduling method that is suitable for port environments, integrating multiple energy sources, vehicles and ships, and enabling intelligent scheduling is a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a multi-energy vehicle and ship charging and swapping optimization scheduling method in ports. It incorporates photovoltaic power generation, tidal power generation, wind power generation, grid power purchase and energy storage batteries into the port charging scheduling optimization, and provides a scheduling method that can meet the energy replenishment needs of electric trucks and mobile machinery in ports.

[0006] The objective of this invention can be achieved through the following technical solutions: This invention provides an optimized scheduling method for multi-energy vehicle and ship charging and swapping within a port, comprising: The system acquires port charging demand and collects multi-source data; the charging demand includes marine battery charging demand and electric truck charging demand; the multi-source data includes measured and predicted data of photovoltaic power generation, tidal power generation and wind power generation, energy storage system operating status parameters, grid electricity price signals and charging pile status parameters. A day-ahead scheduling optimization model and an intraday rolling optimization model are constructed, and the day-ahead scheduling optimization model is solved based on the port charging demand and multi-source data to obtain the all-day scheduling strategic plan; Based on the all-day scheduling strategic plan, port charging demand, and multi-source data, the intraday rolling optimization model is solved using model predictive control methods to obtain the optimal control sequence. Port vehicle and vessel charging and swapping scheduling is carried out based on the aforementioned optimal control sequence.

[0007] As a preferred technical solution, the method for collecting the predicted data is as follows: For photovoltaic power generation: Obtain actual solar irradiance and temperature data, and calculate the predicted photovoltaic output value based on the actual solar irradiance and temperature data: , This indicates the rated power of the photovoltaic system under standard conditions; express Solar irradiance during a given time period; Indicates irradiance under standard test conditions; Indicates the power temperature coefficient; express Ambient temperature during the period; Indicates the standard test temperature; Indicates weather correction factor; Regarding the aforementioned tidal power generation: Obtain the tide table, and calculate the predicted tidal output value based on the tide table: , This represents a comprehensive coefficient, calculated based on seawater density, turbine data, and local characteristic flow velocity coefficients. Indicates tidal range; This indicates the duration of half a cycle in the current scheduling cycle; This indicates the time elapsed since the start of the current half-cycle. Regarding the aforementioned wind power generation: Obtain actual wind speed data, and calculate the predicted wind force output value based on the actual wind speed data, as follows: , Indicates the rated power of the fan; Indicates shape parameters; express Wind speed during the period; express Cut-in wind speed during the time period; This represents the nonlinear coefficient.

[0008] As a preferred technical solution, the method for obtaining the all-day scheduling strategic plan is as follows: Set the day-ahead scheduling optimization period, and use the predicted data and charging demand in the current day-ahead scheduling optimization period as the input to the day-ahead scheduling optimization model; Define the decision variables for the day-ahead scheduling optimization layer, including the grid power purchase during the time period, the charging and discharging power of the energy storage battery during the time period, and the state of charge (SOC) of the energy storage battery during the time period; A day-ahead optimization objective function and day-ahead optimization constraints are constructed. Based on the day-ahead optimization objective function and day-ahead constraint optimization conditions, the day-ahead scheduling optimization layer decision variables are solved to generate a day-ahead scheduling strategic plan. The day-ahead scheduling strategic plan includes the power grid purchase plan, energy storage battery SOC plan, and charge / discharge power plan for each time period.

[0009] As a preferred technical solution, the day-ahead optimization objective function is constructed to minimize the total operating cost of the day-ahead scheduling cycle, including the grid power purchase cost and the battery depreciation cost. The power grid purchase cost item is calculated based on the time-period electricity price and the corresponding power grid purchase power during the time period, while the battery depreciation cost item is calculated based on the battery depreciation cost coefficient and the time-period energy storage battery charging and discharging power.

[0010] As a preferred technical solution, the day-ahead optimization constraints include power balance constraints, energy storage battery dynamic balance constraints, grid power purchase constraints, energy storage battery charging and discharging power constraints, and energy storage battery SOC upper and lower limits constraints. The power balance constraint refers to the fact that the measured power generation of photovoltaic power, the measured power generation of tidal power, the measured power generation of wind power, the planned charging and discharging power of energy storage batteries, and the planned power purchase by the power grid are equal to the total power demand for the corresponding period at any given time. The dynamic balance constraint of the energy storage battery is as follows: , Indicates the first The SOC of the energy storage battery during each time period, Indicates the first The planned SOC of the energy storage battery for each time period, Indicates the first The planned charge and discharge power of the energy storage battery for each time period. Indicates time difference, Indicates the rated capacity of the energy storage battery; The aforementioned power purchase constraint refers to the power purchase of the grid at any given time period being greater than or equal to 0 and less than or equal to the maximum power purchase of the grid. The aforementioned energy storage battery charging and discharging power constraint means that the planned charging and discharging power of the energy storage battery at any given time period shall not exceed the maximum charging and discharging power value of the energy storage battery. The aforementioned upper and lower limits of SOC for energy storage batteries refer to the planned SOC value of energy storage batteries at any given time period being between the minimum and maximum values ​​of SOC for energy storage batteries.

[0011] As a preferred technical solution, the method for obtaining the optimal control sequence is as follows: Set an intraday optimization cycle, and use the real-time data and charging demand of the current period, the predicted data in the current intraday optimization cycle, and the all-day scheduling strategy plan as inputs to the intraday rolling optimization model; Define the decision variables for the intraday rolling optimization layer and construct a discrete-time state-space model; the decision variables for the intraday rolling optimization layer include the power purchase sequence of the power grid, the charging and discharging power sequence of the energy storage battery, the power allocation matrix of the marine power charging pile, the power allocation matrix of the electric truck charging pile, and the SOC state of the energy storage battery; Based on the aforementioned all-day scheduling strategic plan, construct the intraday rolling optimization objective function and intraday rolling optimization constraints. A finite-time optimal control problem is constructed based on the intraday rolling optimization objective function, and solved under the intraday rolling optimization constraints to obtain the optimal control sequence for the next N hours. Taking time period t as an example, the discrete-time state-space model of time period t is used as the initial state of the optimization problem, and the predicted data after feedback correction is used as the initial disturbance vector for the corresponding time period. The optimal control sequence includes the power setpoint of the grid interface, the power setpoint of the energy storage battery charging and discharging, the power allocation value of each marine container mobile power charging pile, and the power allocation value of each electric truck charging pile.

[0012] As a preferred technical solution, the intraday rolling optimization objective function is constructed to minimize the total operating cost of the intraday rolling cycle, including the grid power purchase cost item, the battery depreciation cost item, the tracking penalty item for the all-day dispatching strategic plan item, and the clean energy consumption reward item. The power grid purchase cost item is calculated based on the power grid price for the corresponding time period and the power grid purchase power for that time period. The battery depreciation cost item is calculated based on the battery depreciation cost coefficient and the charging and discharging power of the energy storage battery during the time period; The aforementioned tracking penalty item is: , , as well as These represent the grid power purchase weight, energy storage battery charge / discharge weight, and energy storage battery SOC weight, respectively. express Actual power purchased by the power grid during the specified time period; express Planned power purchase capacity of the power grid during the specified time period; express Actual charge and discharge power of the energy storage battery during the time period; express Planned charge and discharge power of energy storage batteries during specific time periods; express Actual SOC of the energy storage battery during the time period; express Time-of-use energy storage battery plan SOC; The aforementioned clean energy consumption incentive items are: , Indicates the clean energy incentive coefficient; This indicates the predicted output value of photovoltaic power. This indicates the predicted tidal output value; This indicates the predicted tidal output value; This indicates the predicted wind power output. It represents the clean energy consumption rate, which is calculated based on the actual power purchased by the power grid, the predicted tidal output, and the predicted wind power output. Indicates time difference; This represents the battery charging reward coefficient; This indicates the battery discharge power.

[0013] As a preferred technical solution, the intraday rolling optimization constraints include: intraday power balance constraints, intraday energy storage battery dynamic constraints, grid power purchase constraints, energy storage battery constraints, and charging pile allocation constraints. The intraday power balance constraint refers to the sum of the actual output of photovoltaic power, tidal power, wind power, actual charging and discharging power of energy storage batteries, and actual power purchased by the grid, plus the charging power of all truck charging piles, and the sum of the charging power of all marine battery charging piles. The sum of the charging power during each period is equal; The aforementioned intraday dynamic constraints for energy storage batteries refer to the conditions that must be met at any given time period: , Indicates the first The SOC of the energy storage battery during each time period, Indicates the first The planned SOC of the energy storage battery for each time period, Indicates the first The planned charge and discharge power of the energy storage battery for each time period. Indicates time difference, Indicates the rated capacity of the energy storage battery; The power purchase constraint of the power grid refers to the planned power purchase of the power grid in any time period being greater than or equal to 0 and less than or equal to the maximum power purchase of the power grid. The aforementioned energy storage battery constraints refer to the following: the actual charging and discharging power of the energy storage battery at any given time period shall not exceed the maximum charging and discharging power value of the energy storage battery; and the actual SOC value of the energy storage battery at any given time period shall be between the minimum and maximum SOC values ​​of the energy storage battery. The charging pile allocation constraints include: charging pile allocation constraints, charging pile equipment capacity constraints, and equipment charging capacity constraints.

[0014] As a preferred technical solution, the finite-time optimal control problem is: , in, Indicates the first The optimal control sequence obtained by solving the time interval problem has , Indicates the first The control quantity in the time-optimal control sequence. express The control quantity in the time-optimal control sequence. Indicates the intraday optimization cycle; Indicates the first The sequence of variables to be optimized over a given time period; Indicates the first The intraday rolling optimization objective function value for the time period; and the finite time domain optimal control problem satisfies the following constraints: , Indicates the first The first time period prediction Intraday rolling optimization layer decision variables for different time periods; Indicates the first The first time period prediction Control input vector for a given time period; Indicates the first The first time period prediction The perturbation vector for the time period; This represents the inequality constraints in the intraday rolling optimization constraints. This represents the equality constraint in the intraday rolling optimization constraint conditions.

[0015] As a preferred technical solution, the feedback correction method is as follows: for the current intraday optimization cycle, obtain the measured data at the end of the previous day's optimization cycle, and use the measured data at the end of the previous day as the initial value of the predicted data for the current intraday optimization cycle.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1) Addressing the limitations of existing technologies that only serve ships and cannot simultaneously cover electric container trucks within the port and vehicle-ship energy synergy, this invention constructs a unified scheduling framework that integrates vehicle-ship charging, grid power purchase, and energy storage batteries. By introducing a "vehicle-ship shared, parallel charging and swapping" replenishment mode, it breaks down the barriers between shore-based power supply systems and port vehicle charging and swapping systems. This allows a single station to simultaneously meet the three major needs of ship battery swapping, vehicle fast charging, and energy storage regulation, achieving full coverage of energy replenishment for all port vehicle and ship operation scenarios. Furthermore, the method provided by this invention significantly improves equipment utilization and effectively eliminates the shortcomings of traditional shore power, such as limited coverage and high equipment idle rate.

[0017] 2) This invention constructs a multi-energy charging scheduling framework that integrates photovoltaic power generation, tidal power generation, wind power generation, grid power purchase, and energy storage batteries. Through day-ahead and intraday multi-timescale optimization scheduling, and by adding a new energy consumption incentive during intraday rolling optimization, it directly replaces a large amount of high-priced grid power purchase by consuming clean power sources such as photovoltaic, tidal, and wind power on-site at a high proportion, significantly reducing port power purchase costs. At the same time, based on the characteristics of low-valley energy storage and peak-valley discharge of the energy storage system, it achieves peak shaving and valley filling, further reducing the dual expenditure of demand electricity costs and power consumption costs, comprehensively improving economic operation efficiency, realizing accurate prediction and real-time consumption of renewable energy output, and achieving multi-energy synergistic optimization of port charging scheduling, which is significantly better than existing shore power systems or single photovoltaic-storage-charging solutions. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a graph showing the actual output value of photovoltaic power generation according to the present invention; Figure 3 This is a graph showing the actual output value of wind power generation according to the present invention; Figure 4 This is a graph showing the actual power output of the tidal power generation according to the present invention. Figure 5 This is a time-of-use electricity price curve diagram of the present invention; Figure 6 This is a state-of-charge curve of the energy storage battery of the present invention; Figure 7 This is a graph showing the charge and discharge power curves of the energy storage battery of the present invention. Figure 8This is a charging progress curve diagram of the marine containerized mobile power supply of the present invention. Figure 9 This is a charging progress curve diagram of the electric truck of the present invention; Figure 10 This is a system component diagram of the port multi-energy vehicle and ship charging and swapping station system and configuration method in Embodiment 2 of the present invention; Figure 11 This is a system power supply flowchart in Embodiment 2 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] Example 1 To address the problems existing in the prior art, this invention provides an optimized scheduling method for multi-energy vehicle and ship charging and swapping within a port, the process of which is as follows: Figure 1 As shown, it includes: S1. Obtain port charging demand and collect multi-source data.

[0021] In this embodiment, the charging demand includes the charging demand for marine batteries and the charging demand for electric trucks. The charging demand for marine batteries includes the battery ID and the battery's rated capacity. Current state of charge Target state of charge Rated charging power And the calculated required charging amount, the calculation method for the charging amount is as follows: .

[0022] Electric truck charging requirements include the electric truck ID and the estimated start time of charging. Required charging amount and rated charging power .

[0023] Multi-source data includes measured and predicted data from photovoltaic power generation, tidal power generation, and wind power generation, operating status parameters of energy storage systems, grid electricity price signals, and charging pile status parameters.

[0024] In detail, the methods for obtaining measured and predicted data are as follows: For photovoltaic power generation: Directly obtain measured photovoltaic power output value The photovoltaic power generation is calculated based on the measured photovoltaic output value as follows: , express Time period.

[0025] We obtain actual solar irradiance and temperature data from meteorological services, and calculate the predicted photovoltaic output value based on this data: , This indicates the rated power of the photovoltaic system under standard conditions; express Solar irradiance during a given time period; Indicates irradiance under standard test conditions; Indicates the power temperature coefficient; express Ambient temperature during the period; Indicates the standard test temperature; Indicates the weather correction factor, and , Indicates the cloud cover coefficient. Indicates the percentage of cloud cover. Indicates the wind speed coefficient. Indicates wind speed. Indicates the humidity coefficient. This indicates relative humidity.

[0026] For tidal power generation: Directly obtain measured tidal output values The tidal power generation is calculated based on the measured tidal output value as follows: .

[0027] Obtain the tide table, and calculate the predicted tidal output value based on the tide table: , This represents a comprehensive coefficient based on seawater density. Gravitational acceleration Turbine swept area Turbine system efficiency and local characteristic velocity coefficient Calculated, i.e. , ; Indicates tidal range; This indicates the duration of half a cycle in the current scheduling cycle; This indicates the time elapsed since the start of the current half-cycle.

[0028] For wind power generation: Directly obtain the measured wind power output value The wind power generation is calculated based on the measured wind power output value as follows: .

[0029] Obtain actual wind speed data, and calculate the predicted wind power output value based on the actual wind speed data. , Indicates the rated power of the fan; Indicates shape parameters; express Wind speed during the period; express Cut-in wind speed during the time period; This represents the nonlinear coefficient.

[0030] For energy storage system operating status parameters, the following are included: real-time acquisition of energy storage system operating status parameters, including the state of charge of energy storage batteries. Real-time charging and discharging power (Positive values ​​indicate discharging, negative values ​​indicate charging), where: ; This indicates the current remaining capacity of the energy storage battery (kWh). Indicates the rated capacity (kWh) of the energy storage battery; This indicates the current actual maximum capacity (kWh) of the energy storage battery.

[0031] For grid electricity price signals: , express Electricity price signals for a given time period; , , as well as Both indicate time periods.

[0032] The charging pile status parameters include the currently charging battery ID or truck ID and the current charging power; and are set as follows: Assume a collection of ship-mounted containerized mobile power charging stations as follows: Truck charging stations are integrated into .

[0033] For each marine containerized mobile power charging station The power allocated to it during charging is: .

[0034] For each electric truck charging station The power allocated to it during charging is: .

[0035] S2. Construct a day-ahead scheduling optimization model and an intraday rolling optimization model, and obtain the all-day scheduling strategic plan by solving the day-ahead scheduling optimization model based on port charging demand and multi-source data.

[0036] S21. Construct a day-ahead scheduling optimization model.

[0037] The day-ahead scheduling optimization cycle is set to 24 hours, and the day-ahead scheduling optimization model is executed once every hour. The predicted data and charging demand in the current day-ahead scheduling optimization cycle are used as the input of the day-ahead scheduling optimization model.

[0038] S22, Generation of all-day scheduling strategic plan.

[0039] S221. Define the decision variables for the day-ahead scheduling optimization layer, including the grid power purchase capacity during the time period. Time-of-use energy storage battery charging and discharging power and time-of-use energy storage battery SOC Among them, a positive value for the charging and discharging power indicates discharging, and a negative value indicates charging.

[0040] S222. Construct the day-ahead optimization objective function and day-ahead optimization constraints. Based on the day-ahead optimization objective function, solve the day-ahead scheduling optimization layer decision variables under the day-ahead constraint optimization conditions to generate the all-day scheduling strategic plan. The all-day scheduling strategic plan includes the power grid purchase plan, energy storage battery SOC plan and charging and discharging power plan for each time period.

[0041] Specifically, a day-ahead optimization objective function is established to minimize the total operating cost of the day-ahead scheduling cycle, including grid power purchase cost and battery depreciation cost. The grid power purchase cost is calculated based on the time-period electricity price and the corresponding grid power purchase during that time period, while the battery depreciation cost is calculated based on the battery depreciation cost coefficient and the time-period energy storage battery charging and discharging power. The expression for the day-ahead optimization objective scheduling function is as follows: , express Electricity price during the specified time period; Indicates time difference; This represents the battery depreciation cost factor.

[0042] The recently optimized constraints include power balance constraints, dynamic balance constraints for energy storage batteries, grid power purchase constraints, charging and discharging power constraints for energy storage batteries, and upper and lower limits of state of charge (SOC) constraints for energy storage batteries. Among these, the power balance constraint refers to the measured photovoltaic power generation at any given time period. Measured power output of tidal power generation Actual wind power generation Planned charge and discharge power of energy storage batteries and the planned power purchase capacity of the power grid Total electrical power demand during the corresponding time period Equal, that is .

[0043] The dynamic balance constraints of energy storage batteries are: , Indicates the first The SOC of the energy storage battery during each time period, Indicates the first The planned SOC of the energy storage battery for each time period, Indicates the first The planned charge and discharge power of the energy storage battery for each time period. Indicates time difference, Indicates the rated capacity of the energy storage battery; Power purchase constraints refer to the requirement that the planned power purchase capacity of the power grid in any given time period must be greater than or equal to 0 and less than or equal to the maximum power purchase capacity of the power grid. ,Right now .

[0044] Energy storage battery charge / discharge power constraint refers to the requirement that the planned charge / discharge power of an energy storage battery at any given time period shall not exceed the maximum charge / discharge power value of the energy storage battery. ,Right now .

[0045] The upper and lower limits of SOC for energy storage batteries refer to the planned SOC values ​​of energy storage batteries at any given time period. Minimum SOC of energy storage battery and maximum value Between, that is .

[0046] S23. Construct an intraday rolling optimization model.

[0047] Set an intraday optimization cycle and use the real-time data and charging demand of the current period, the predicted data in the current intraday optimization cycle, and the all-day scheduling strategy plan as inputs to the intraday rolling optimization model.

[0048] Based on the all-day dispatching strategic plan, an intraday rolling optimization objective function and intraday rolling optimization constraints are constructed. Specifically, the objective function is designed to minimize the total operating cost of the intraday rolling cycle, including the power grid purchase cost. Battery depreciation cost item Tracking and penalty items for the all-day scheduling strategic plan Clean energy consumption incentives Among them, the grid purchase cost item is calculated based on the grid electricity price for the corresponding time period and the grid purchase power for that time period, i.e. The battery depreciation cost item is calculated based on the battery depreciation cost coefficient and the charging and discharging power of the energy storage battery during the time period, i.e. The tracking penalty items are as follows: , , as well as These represent the grid power purchase weight, energy storage battery charge / discharge weight, and energy storage battery SOC weight, respectively. express Actual power purchased by the power grid during the specified time period; express Planned power purchase capacity of the power grid during the specified time period; express Actual charge and discharge power of the energy storage battery during the time period; express Planned charge and discharge power of energy storage batteries during specific time periods; express Actual SOC of the energy storage battery during the time period; express Time-of-use battery storage program SOC; Clean energy consumption incentives are: , Indicates the clean energy incentive coefficient; This indicates the predicted output value of photovoltaic power. This indicates the predicted tidal output value; This indicates the predicted tidal output value; This indicates the predicted wind power output. The clean energy consumption rate is calculated based on the actual power purchased by the power grid, the predicted tidal output, and the predicted wind power output. ; Indicates time difference; This represents the battery charging reward coefficient; This represents the battery discharge power. The intraday rolling optimization objective function is: .

[0049] The intraday rolling optimization constraints include: intraday power balance constraints, intraday energy storage battery dynamic constraints, grid power purchase constraints, energy storage battery constraints, and charging pile allocation constraints.

[0050] Among them, the intraday power balance constraint refers to the sum of the actual output of photovoltaic power, actual tidal power, actual wind power, actual charging and discharging power of energy storage batteries, and actual power purchased by the grid, plus the charging power of all truck charging piles, and the sum of the charging power of all marine battery charging piles. The sum of the charging power during each period is equal, that is: .

[0051] Intraday dynamic constraints for energy storage batteries refer to the conditions that must be met at any given time period: , Indicates the first The SOC of the energy storage battery during each time period, Indicates the first The planned SOC of the energy storage battery for each time period, Indicates the first The planned charge and discharge power of the energy storage battery for each time period. Indicates time difference, Indicates the rated capacity of the energy storage battery; The power purchase constraint of the power grid refers to the requirement that the power purchase of the power grid in any given time period must be greater than or equal to 0 and less than or equal to the maximum power purchase of the power grid. ,Right now .

[0052] Energy storage battery constraints refer to the following: the actual charge / discharge power of the energy storage battery at any given time period does not exceed the maximum charge / discharge power value of the energy storage battery; and the actual SOC value of the energy storage battery at any given time period is between the minimum and maximum SOC values ​​of the energy storage battery, i.e.: ,and ; ,and ; .

[0053] Charging pile allocation constraints include: charging pile allocation constraints, charging pile equipment capacity constraints, and equipment charging capacity constraints. Specifically, the charging pile allocation constraint is: Battery ID. That is, one marine battery corresponds to one charging station; truck ID That is, one truck corresponds to one charging station.

[0054] The capacity constraints of charging pile equipment include those for marine battery charging pile equipment: And the capacity constraints of truck charging station equipment: .

[0055] Equipment charging capacity constraints include marine battery charging capacity constraints: And truck charging capacity constraints: .

[0056] S3. Based on the all-day scheduling strategic plan, port charging demand and multi-source data, the intraday rolling optimization model is solved using the model predictive control method to obtain the optimal control sequence.

[0057] S31. Define the decision variables for the intraday rolling optimization layer and construct a discrete-time state-space model.

[0058] S311. The decision variables for the intraday rolling optimization layer include: the power purchase sequence from the power grid, which is a continuous variable vector of length N, and can be represented as... Energy storage battery charge / discharge power sequence It is a continuous variable of length N; the power allocation matrix for marine power charging piles is a matrix with dimension equal to the number of marine charging piles. A continuous variable matrix can be represented as: ; The power allocation matrix for electric truck charging stations, which is a continuous variable matrix with dimension N representing the number of marine charging stations, can be represented as: ; And the SOC state of the energy storage battery, which is a continuous variable vector of length N, represented as: .

[0059] S312. Construct a discrete-time state-space model.

[0060] , in, , and All are constant matrices, representing the state matrix, control matrix, and disturbance matrix, respectively; Indicates the first The system state vector for a given time period includes the SOC state of the battery and the allocation of charging stations; This represents the control input vector at time t, which includes the power of the marine battery charging pile, the power of the truck charging pile, the power of the grid interface, and the charging and discharging power of the energy storage battery. Indicates the first The disturbance vector for the time period includes the predicted output values ​​of photovoltaic power, tidal power, wind power, and grid electricity price signals.

[0061] S32. Construct a finite-time optimal control problem based on the intraday rolling optimization objective function, and solve it under intraday rolling optimization constraints to obtain the optimal control sequence for the next N hours.

[0062] Taking time period t as an example, the discrete-time state-space model of time period t is... As the initial state of the optimization problem, the predicted data after feedback correction is used as the initial perturbation vector for the corresponding time period. It should be noted that the feedback correction method is as follows: for the current intraday optimization cycle, obtain the optimization cycle of the previous day. The measured data at the last moment will be used as the current intraday optimization cycle. The initial values ​​of the predicted data can be expressed as: ,in, Indicates the first The initial value of photovoltaic output during the time period; ,in, : No. Initial value of tidal output over a given time period; ,in, Indicates the first Initial wind power output over a given time period.

[0063] A finite-time optimal control problem is constructed, and a numerical optimization algorithm is used to solve the optimization problem in real time.

[0064] In detail, the finite-time optimal control problem can be expressed as: , in, Indicates the first The optimal control sequence obtained by solving the time interval problem has , Indicates the first The control quantity in the time-optimal control sequence. express The control quantity in the time-optimal control sequence. This indicates the daily optimization cycle, and the optimal control sequence includes the power setting value of the grid interface, the power setting value of the energy storage battery charging and discharging power, the power allocation value of each marine container mobile power charging pile, and the power allocation value of each electric truck charging pile. Indicates the first The sequence of variables to be optimized over a given time period; Indicates the first The intraday rolling optimization objective function value for the time period; and the finite-time optimal control problem satisfies the following constraints: , Indicates the first The first time period prediction Intraday rolling optimization layer decision variables for different time periods; Indicates the first The first time period prediction Control input vector for a given time period; Indicates the first The first time period prediction The perturbation vector for the time period; This represents the inequality constraints in the intraday rolling optimization constraints, including upper and lower limits of energy storage battery charging and discharging power, upper and lower limits of energy storage battery SOC, upper and lower limits of charging pile power, upper limit of grid power purchase, and equipment charging capacity constraints. The equation constraints representing intraday rolling optimization constraints include power balance constraints, energy storage battery dynamic constraints, and charging pile allocation constraints.

[0065] S4. Port vehicle and vessel charging and swapping scheduling based on optimal control sequence.

[0066] From the above optimal control sequence Extract the first control variable As The actual execution instructions for the specified time period.

[0067] After a time interval, step S1 is repeated to perform a new round of calculations and update the state.

[0068] Example 2 In this embodiment, in order to verify the feasibility of the technical solution provided in Embodiment 1, the above method is verified. Specifically, four marine containerized mobile power charging piles, six electric truck charging piles and one energy storage battery are set up. At the same time, a photovoltaic power generation system, a wind power generation system and a tidal power generation system are also equipped.

[0069] In this embodiment, the relevant configurations are as follows: Power generation capacity: Photovoltaic power generation system capacity is 2000kW, wind power generation system capacity is 1500kW, and tidal power generation system capacity is 80kW; Energy storage battery parameters: Energy storage battery capacity is 4000kWh, maximum charge / discharge power is 1000kW, and initial SOC is 60%; Charging facility configuration: 4 marine charging piles and 6 truck charging piles are installed, with a maximum output power of 350kW per charging pile. The collected time-of-use electricity price meets the following criteria: .

[0070] Based on the above conditions, the method steps provided in Example 1 are followed, including: S1. Obtain port charging demand and collect multi-source data.

[0071] Charging demand includes marine battery charging demand and electric truck charging demand. Marine charging demand is shown in Table 1, where the demand is calculated using the formula... = Calculate the amount of charge required for the power source.

[0072] Table 1 Marine Charging Demand Information The charging requirements for electric trucks are shown in Table 2.

[0073] Table 2 Electric Truck Charging Demand Information Multi-source data includes measured and predicted data from photovoltaic power generation, tidal power generation, and wind power generation, operating status parameters of energy storage systems, grid electricity price signals, and charging pile status parameters.

[0074] For photovoltaic power generation: Directly obtain measured photovoltaic power output value like Figure 2 As shown; actual solar irradiance and temperature data are obtained from meteorological services. Based on the actual solar irradiance and temperature data, the predicted photovoltaic output value is calculated as follows: , , ; , , , .

[0075] For wind power generation: Directly obtain the measured wind power output value like Figure 3 As shown; obtain actual wind speed data, and calculate the predicted wind power output value based on the actual wind speed data, as follows: , , and .

[0076] For tidal power generation: Directly obtain measured tidal output values like Figure 4 As shown; obtain the tide table, and calculate the predicted tidal output value based on the tide table, as follows: , This represents a comprehensive coefficient based on seawater density. Gravitational acceleration Turbine swept area Turbine system efficiency and local characteristic velocity coefficient Calculated, i.e. .

[0077] For grid electricity price signals such as Figure 5 As shown.

[0078] For energy storage system operating status parameters, the following are included: real-time acquisition of energy storage system operating status parameters, including the state of charge of energy storage batteries. (like Figure 6 As shown in the figure, an increase in SOC indicates a surplus in the output of photovoltaic, tidal, and wind power, at which point the energy storage battery is charged; a decrease in SOC indicates insufficient output from photovoltaic, tidal, and wind power, at which point the energy storage battery is discharged to make up the difference; real-time charging and discharging power... (Positive values ​​indicate discharging, negative values ​​indicate charging, such as...) Figure 7 As shown), in Figure 7 The charging and discharging power of the energy storage battery was positive from 10:00 to 10:30 and from 12:30 to 13:45, indicating that the energy storage battery was in a charging state. Figure 6 The state of charge (SOC) of the energy storage battery increases; from 10:45 to 12:15, the charging and discharging power of the energy storage battery is negative, indicating that the energy storage battery is in a discharging state. Figure 6 The state of charge (SOC) of the energy storage battery is reduced.

[0079] S2. Construct a day-ahead scheduling optimization model and an intraday rolling optimization model, and obtain the all-day scheduling strategic plan by solving the day-ahead scheduling optimization model based on port charging demand and multi-source data.

[0080] S3. Based on the all-day scheduling strategic plan, port charging demand and multi-source data, the intraday rolling optimization model is solved using the model predictive control method to obtain the optimal control sequence.

[0081] S4. Port vehicle and vessel charging and swapping scheduling based on optimal control sequence.

[0082] After the above steps of scheduling optimization, the final charging progress of the marine containerized mobile power supply in this scenario is as follows: Figure 8 As shown, the charging progress of the electric truck is as follows: Figure 9 As shown, the technical solution provided by the present invention is feasible.

[0083] Example 3 This invention also provides a multi-energy vehicle and ship charging and swapping station system in a port. The system includes an energy supply unit, an energy storage unit, a charging and swapping service unit, and an intelligent control and scheduling unit, used to implement the methods provided above. Its architecture is as follows: Figure 10 As shown, the power supply process is as follows Figure 11 As shown.

[0084] (1) Energy supply unit.

[0085] This unit includes: a photovoltaic power generation system: installed on the roof of the station building or in the surrounding open space to convert solar energy into electrical energy; a power grid connection interface: connected to the municipal power grid as a stable power source and energy supplement; a tidal power station: tidal turbine units are installed on the outside of the port channel or breakwater to generate electricity using the tidal energy of the waterways entering and leaving the port; and a wind power generation system: wind turbines are installed at the far end of the port yard, on the wharf shoreline, or on the breakwater to convert wind energy into electrical energy.

[0086] (2) Energy storage unit.

[0087] The unit includes a centralized energy storage battery for storing surplus electricity from photovoltaic, tidal, and wind power generation, and discharging it during peak hours to reduce electricity purchase costs.

[0088] (3) Charging and swapping service unit.

[0089] The unit includes: a marine containerized mobile power charging and swapping area, equipped with high-power DC charging piles and battery storage stations, enabling rapid replacement and charging of mobile power supplies; and a truck charging area, equipped with multiple DC fast charging piles, providing charging services for electric trucks in the port.

[0090] (4) Intelligent control and scheduling unit.

[0091] This unit includes the following modules: Data acquisition and input module: used to obtain port charging demand and collect multi-source data; charging demand includes marine battery charging demand and electric truck charging demand; multi-source data includes measured and predicted data of photovoltaic power generation, tidal power generation and wind power generation, energy storage system operating status parameters, grid electricity price signals and charging pile status parameters.

[0092] Day-ahead Optimization Scheduling Module: This module solves the day-ahead scheduling optimization model based on port charging demand and multi-source data to obtain the all-day scheduling strategic plan.

[0093] Intraday Rolling Optimization Scheduling Module: This module uses the model predictive control method to solve the intraday rolling optimization model based on the all-day scheduling strategic plan, port charging demand and multi-source data, to obtain the optimal control sequence.

[0094] Control execution module: Distributes the optimal control sequence to each charging pile, energy storage battery system and grid interface controller to carry out port vehicle and ship charging and swapping scheduling and achieve precise power allocation.

[0095] Furthermore, the control execution module obtains the optimal power setting values ​​for each device from the intraday rolling optimization scheduling module, including the power setting value for the marine battery charging pile. Power setting value of electric truck charging station The charging and discharging power settings of the energy storage battery Power setting value of the grid interface The power setting value is sent to each device execution unit through the corresponding communication protocol. After the current control cycle is completed, the system restarts a new round of data acquisition, optimization calculation and instruction generation after a fixed time interval, so as to realize the continuous update of system status and dynamic adjustment of scheduling strategy.

[0096] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimized scheduling of multi-energy vehicle and ship charging and swapping in a port, characterized in that, include: The system acquires port charging demand and collects multi-source data; the charging demand includes marine battery charging demand and electric truck charging demand; the multi-source data includes measured and predicted data of photovoltaic power generation, tidal power generation and wind power generation, energy storage system operating status parameters, grid electricity price signals and charging pile status parameters. A day-ahead scheduling optimization model and an intraday rolling optimization model are constructed, and the day-ahead scheduling optimization model is solved based on the port charging demand and multi-source data to obtain the all-day scheduling strategic plan; Based on the all-day scheduling strategic plan, port charging demand, and multi-source data, the intraday rolling optimization model is solved using model predictive control methods to obtain the optimal control sequence. Port vehicle and vessel charging and swapping scheduling is carried out based on the aforementioned optimal control sequence.

2. The optimized scheduling method for multi-energy vehicle and ship charging and swapping in a port according to claim 1, characterized in that, The method for collecting the aforementioned prediction data is as follows: For photovoltaic power generation: Obtain actual solar irradiance and temperature data, and calculate the predicted photovoltaic output value based on the actual solar irradiance and temperature data: , This indicates the rated power of the photovoltaic system under standard conditions; express Solar irradiance during a given time period; Indicates irradiance under standard test conditions; Indicates the power temperature coefficient; express Ambient temperature during the period; Indicates the standard test temperature; Indicates weather correction factor; Regarding the aforementioned tidal power generation: Obtain the tide table, and calculate the predicted tidal output value based on the tide table: , This represents a comprehensive coefficient, calculated based on seawater density, turbine data, and local characteristic flow velocity coefficients. Indicates tidal range; This indicates the duration of half a cycle in the current scheduling cycle; This indicates the time elapsed since the start of the current half-cycle. Regarding the aforementioned wind power generation: Obtain actual wind speed data, and calculate the predicted wind force output value based on the actual wind speed data, as follows: , Indicates the rated power of the fan; Indicates shape parameters; express Wind speed during the period; express Cut-in wind speed during the time period; This represents the nonlinear coefficient.

3. The optimized scheduling method for multi-energy vehicle and ship charging and swapping in a port according to claim 1, characterized in that, The method for obtaining the aforementioned all-day scheduling strategy plan is as follows: Set the day-ahead scheduling optimization period, and use the predicted data and charging demand in the current day-ahead scheduling optimization period as the input to the day-ahead scheduling optimization model; Define the decision variables for the day-ahead scheduling optimization layer, including the grid power purchase during the time period, the charging and discharging power of the energy storage battery during the time period, and the state of charge (SOC) of the energy storage battery during the time period; Construct a day-ahead optimization objective function and day-ahead optimization constraints. Based on the day-ahead optimization objective function and day-ahead constraint optimization conditions, solve the day-ahead scheduling optimization layer decision variables to generate a day-ahead scheduling strategic plan. The aforementioned all-day dispatching strategic plan includes power grid purchase plans for each time period, energy storage battery SOC plans, and charging and discharging power plans.

4. The optimized scheduling method for multi-energy vehicle and ship charging and swapping in a port according to claim 3, characterized in that, The day-ahead optimization objective function is constructed to minimize the total operating cost of the day-ahead scheduling cycle, including the grid power purchase cost and the battery depreciation cost. The power grid purchase cost item is calculated based on the time-period electricity price and the corresponding power grid purchase power during the time period, while the battery depreciation cost item is calculated based on the battery depreciation cost coefficient and the time-period energy storage battery charging and discharging power.

5. The optimized scheduling method for multi-energy vehicle and ship charging and swapping in a port according to claim 4, characterized in that, The day-ahead optimization constraints include power balance constraints, energy storage battery dynamic balance constraints, grid power purchase constraints, energy storage battery charging and discharging power constraints, and energy storage battery SOC upper and lower limits constraints. The power balance constraint refers to the fact that the measured power generation of photovoltaic power, the measured power generation of tidal power, the measured power generation of wind power, the planned charging and discharging power of energy storage batteries, and the planned power purchase by the power grid are equal to the total power demand for the corresponding period at any given time. The dynamic balance constraint of the energy storage battery is as follows: , Indicates the first The SOC of the energy storage battery during each time period, Indicates the first The planned SOC of the energy storage battery for each time period, Indicates the first The planned charge and discharge power of the energy storage battery for each time period. Indicates time difference, Indicates the rated capacity of the energy storage battery; The power purchase constraint of the power grid refers to the planned power purchase of the power grid in any time period being greater than or equal to 0 and less than or equal to the maximum power purchase of the power grid. The aforementioned energy storage battery charging and discharging power constraint means that the planned charging and discharging power of the energy storage battery at any given time period shall not exceed the maximum charging and discharging power value of the energy storage battery. The aforementioned upper and lower limits of SOC for energy storage batteries refer to the planned SOC value of energy storage batteries at any given time period being between the minimum and maximum values ​​of SOC for energy storage batteries.

6. The optimized scheduling method for multi-energy vehicle and ship charging and swapping in a port according to claim 1, characterized in that, The method for obtaining the optimal control sequence is as follows: Set an intraday optimization cycle, and use the real-time data and charging demand of the current period, the predicted data in the current intraday optimization cycle, and the all-day scheduling strategy plan as inputs to the intraday rolling optimization model; Define the decision variables for the intraday rolling optimization layer and construct a discrete-time state-space model; the decision variables for the intraday rolling optimization layer include the power purchase sequence of the power grid, the charging and discharging power sequence of the energy storage battery, the power allocation matrix of the marine power charging pile, the power allocation matrix of the electric truck charging pile, and the SOC state of the energy storage battery; Based on the aforementioned all-day scheduling strategic plan, construct the intraday rolling optimization objective function and intraday rolling optimization constraints. A finite-time optimal control problem is constructed based on the intraday rolling optimization objective function, and solved under the intraday rolling optimization constraints to obtain the optimal control sequence for the next N hours. Taking time period t as an example, the discrete-time state-space model of time period t is used as the initial state of the optimization problem, and the predicted data after feedback correction is used as the initial disturbance vector for the corresponding time period. The optimal control sequence includes the power setpoint of the grid interface, the power setpoint of the energy storage battery charging and discharging, the power allocation value of each marine container mobile power charging pile, and the power allocation value of each electric truck charging pile.

7. The optimized scheduling method for multi-energy vehicle and ship charging and swapping in a port according to claim 6, characterized in that, The intraday rolling optimization objective function is constructed to minimize the total operating cost of the intraday rolling cycle, including the grid power purchase cost, battery depreciation cost, tracking penalty for the all-day dispatching strategic plan, and clean energy consumption reward. The power grid purchase cost item is calculated based on the power grid price for the corresponding time period and the power grid purchase power for that time period. The battery depreciation cost item is calculated based on the battery depreciation cost coefficient and the charging and discharging power of the energy storage battery during the time period; The aforementioned tracking penalty item is: , , as well as These represent the grid power purchase weight, energy storage battery charge / discharge weight, and energy storage battery SOC weight, respectively. express Actual power purchased by the power grid during the specified time period; express Planned power purchase capacity of the power grid during the specified time period; express Actual charge and discharge power of the energy storage battery during the time period; express Planned charge and discharge power of energy storage batteries during specific time periods; express Actual SOC of the energy storage battery during the time period; express Time-of-use energy storage battery plan SOC; The aforementioned clean energy consumption incentive items are: , Indicates the clean energy incentive coefficient; This indicates the predicted output value of photovoltaic power. This indicates the predicted tidal output value; This indicates the predicted tidal output value; This indicates the predicted wind power output. It represents the clean energy consumption rate, which is calculated based on the actual power purchased by the power grid, the predicted tidal output, and the predicted wind power output. Indicates time difference; This represents the battery charging reward coefficient; This indicates the battery discharge power.

8. The optimized scheduling method for multi-energy vehicle and ship charging and swapping in a port according to claim 6, characterized in that, The intraday rolling optimization constraints include: intraday power balance constraints, intraday energy storage battery dynamic constraints, grid power purchase constraints, energy storage battery constraints, and charging pile allocation constraints. The intraday power balance constraint refers to the sum of the actual output of photovoltaic power, tidal power, wind power, actual charging and discharging power of energy storage batteries, and actual power purchased by the grid, plus the charging power of all truck charging piles, and the sum of the charging power of all marine battery charging piles. The sum of the charging power during each period is equal; The aforementioned intraday dynamic constraints for energy storage batteries refer to the conditions that must be met at any given time period: , Indicates the first The SOC of the energy storage battery during each time period, Indicates the first The planned SOC of the energy storage battery for each time period, Indicates the first The planned charge and discharge power of the energy storage battery for each time period. Indicates time difference, Indicates the rated capacity of the energy storage battery; The aforementioned power purchase constraint refers to the power purchase capacity of the power grid at any given time period being greater than or equal to 0 and less than or equal to the maximum power purchase capacity of the power grid. The aforementioned energy storage battery constraints refer to the following: the actual charging and discharging power of the energy storage battery at any given time period shall not exceed the maximum charging and discharging power value of the energy storage battery; and the actual SOC value of the energy storage battery at any given time period shall be between the minimum and maximum SOC values ​​of the energy storage battery. The charging pile allocation constraints include: charging pile allocation constraints, charging pile equipment capacity constraints, and equipment charging capacity constraints.

9. A method for optimized scheduling of multi-energy vehicle and ship charging and swapping in a port, as described in claim 6, is characterized in that... The aforementioned finite-time optimal control problem is: ， in, Indicates the first The optimal control sequence obtained by solving the time interval problem has , Indicates the first The control quantity in the time-optimal control sequence. express The control quantity in the time-optimal control sequence. Indicates the intraday optimization cycle; Indicates the first The sequence of variables to be optimized over a given time period; Indicates the first The intraday rolling optimization objective function value for the time period; and the finite time domain optimal control problem satisfies the following constraints: ， Indicates the first The first time period prediction Intraday rolling optimization layer decision variables for different time periods; Indicates the first The first time period prediction Control input vector for a given time period; Indicates the first The first time period prediction The perturbation vector for the time period; This represents the inequality constraints in the intraday rolling optimization constraints. This represents the equality constraint in the intraday rolling optimization constraint conditions.

10. A multi-energy vehicle and ship charging and swapping optimization scheduling method in a port according to claim 6, characterized in that, The feedback correction method is as follows: for the current intraday optimization cycle, obtain the measured data at the end of the previous day's optimization cycle, and use the measured data at the end of the previous day as the initial value of the predicted data for the current intraday optimization cycle.

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