A shore power-energy storage collaborative scheduling method, system, device, medium and product

Abstract

This application relates to the field of electrical digital data processing, and in particular to a method, system, equipment, medium, and product for coordinated dispatch of shore power and energy storage, aiming to address the technical problems of poor coordination between economic and environmental objectives, insufficient robustness to wind and solar power output, and the disconnect between day-ahead planning and intraday execution in existing port energy dispatching. The method includes: acquiring system forecast data and real-time status data; constructing a day-ahead multi-objective optimization model to formulate grid interaction power plans, energy storage system charging and discharging power plans, and a total power allocation budget for the shore power system; performing intraday rolling optimization steps based on multi-scenario robust optimization to generate executable instructions; and dynamically updating instructions through closed-loop correction using real-time monitoring and deviation triggering mechanisms. This invention achieves coordinated and optimized dispatching of port shore power systems, energy storage systems, local renewable energy sources, and the external power grid, primarily for improving the economic efficiency, low-carbon environmental protection, and power supply reliability of port operations.

Description

Technical Field

[0001] This application relates to the field of electrical digital data processing, and in particular to a shore power-energy storage coordinated dispatch method, system, equipment, medium and product. Background Technology

[0002] In the field of digital data processing, especially against the backdrop of the global port industry's transformation towards zero-carbon operations, the use of shore power systems to replace ship auxiliary machinery power generation and the coordinated scheduling of local clean energy sources such as photovoltaic, wind power and energy storage have become key technological approaches to achieving green and low-carbon development of ports.

[0003] However, the actual operation of a port's integrated energy system faces numerous complex challenges, and its scheduling can be viewed as a specific electrical digital data processing problem. On the load side, the electricity demand of berthed ships exhibits significant dynamism and randomness in terms of access time and power output. On the power supply side, the output of photovoltaic and wind power generation is affected by weather factors, inherently possessing intermittency and high uncertainty. On the market side, grid electricity prices typically adopt a time-of-use pricing model, and the cost of purchasing electricity fluctuates over time.

[0004] In such a complex environment, how to coordinate the scheduling of shore power, energy storage and grid power to simultaneously achieve the three goals of operational economy, low carbon and environmental protection and power supply reliability is the core challenge facing port energy management today.

[0005] Currently, energy dispatching methods in the port sector are mainly divided into two categories: The first category is simple heuristic dispatch strategies based on fixed rules, such as prioritizing self-consumption of photovoltaic power generation, using surplus power for energy storage charging, and purchasing electricity from the grid to fill load gaps. This type of method relies entirely on pre-set static rules and cannot be dynamically adjusted based on real-time information such as actual wind and solar power output, electricity price signals, and load changes. Therefore, it is difficult to make forward-looking decisions in the day-ahead electricity market, resulting in significant shortcomings in both economic and environmental aspects.

[0006] The second category is scheduling methods based on deterministic optimization, which typically use a single predicted value (such as the predicted average) of wind and solar power output as the future scenario for optimization. This method actually ignores the uncertainty and prediction error of wind and solar power output. When the actual output deviates from the predicted value, the pre-established scheduling plan often becomes infeasible or not optimal, which may lead to reduced power supply reliability, exacerbated wind and solar curtailment, and even problems such as exceeding the power limit for interaction with the grid, resulting in poor system robustness.

[0007] Furthermore, existing scheduling methods generally lack a precise quantification and internalization mechanism for carbon emission costs, making it difficult to directly and effectively coordinate economic costs and carbon emission reduction targets in optimization decision-making, and thus failing to provide core technical support for the precise operation of zero-carbon ports.

[0008] In summary, the existing technology mainly has the following problems: 1. The challenge of synergizing economic and environmental goals: How to simultaneously and quantitatively consider economic costs (such as electricity costs and equipment depreciation) and environmental goals (such as carbon emission reduction) in a unified optimization model, so as to avoid the separation of the two at the decision-making level and achieve the optimization of comprehensive benefits.

[0009] 2. The challenge of scheduling robustness under uncertain environments: How to effectively cope with the intermittency and volatility of wind and solar power output and the randomness of ship behavior, and formulate a robust scheduling scheme that can make full use of forecast information and resist the interference of various uncertain factors.

[0010] 3. The challenge of decision coordination across multiple time scales: How to organically connect and dynamically calibrate the macro-level, coarse-grained planning of the day-ahead phase with the micro-level, fine-grained control instructions of the intraday phase, to ensure the consistency between long-term optimization goals and short-term operational safety, and to build a closed-loop control system with feedback correction capabilities. Summary of the Invention

[0011] In view of the above-mentioned defects and shortcomings of existing technologies, it is desirable to provide a shore power-energy storage collaborative scheduling method, system, equipment, medium and product that can integrate accurate data perception, carbon cost internalization, uncertainty robust decision-making and event-driven adaptive correction to solve the multiple challenges of economy, low carbon, reliability and adaptability faced in the collaborative scheduling of shore power-energy storage in green ports.

[0012] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a shore power-energy storage coordinated dispatch method, including: Obtain forecast data and real-time status data of the port's integrated energy system within the future preset scheduling cycle; Based on the predicted data and the real-time status data, a day-ahead multi-objective optimization model is constructed with the objectives of minimizing the total port operating cost and maximizing the local renewable energy absorption rate. The day-ahead multi-objective optimization model is solved, and the obtained optimal solution is mapped to the day-ahead plan. The day-ahead plan includes the grid interaction power plan, the energy storage system charging and discharging power plan, and the shore power system total power allocation budget. The total port operating cost includes grid power purchase cost, energy storage system depreciation cost, and carbon trading cost. Repeat the following intraday rolling optimization steps at a preset rolling cycle: Based on wind and solar power output prediction data and prediction error distribution, multiple wind and solar power output scenarios are generated. With the goal of minimizing the expected operating cost under all wind and solar power output scenarios within the framework of tracking the grid interaction power plan and the energy storage system charging / discharging power plan and the shore power system total power allocation budget, an intraday rolling optimization model is constructed. The expected operating cost includes the expected electricity purchase cost under all wind and solar power output scenarios and the grid power plan tracking penalty term. The intraday rolling optimization model is solved, and the optimal decision value obtained in the current rolling period is used as the charging power command for each ship in port and the charging / discharging power command for the energy storage system. The charging power command is sent to the corresponding shore power facility, and the charging and discharging power command is sent to the energy storage converter; The system monitors the actual values ​​of grid interaction power, total shore power load, and energy storage output in real time; calculates the deviation between the actual values ​​and the corresponding instructions issued in the current time period; if any of the deviations exceeds its preset threshold, or if a sudden event affecting the reliability of port power supply is detected, the current process is interrupted, and the intraday rolling optimization step is triggered, which is re-executed based on the real-time system status, including the latest energy storage charge status, to update and issue the corrected instructions; otherwise, the currently issued instructions continue to be executed, and the system waits to enter the next rolling cycle.

[0013] Secondly, this application provides a shore power-energy storage coordinated dispatch system, comprising: The data acquisition and prediction input module is used to acquire prediction data and real-time status data of the port's integrated energy system within a future preset scheduling cycle. The day-ahead collaborative optimization module is used to construct a day-ahead multi-objective optimization model based on the predicted data and the real-time status data, with the objectives of minimizing the total port operating cost and maximizing the local renewable energy absorption rate, and to solve the day-ahead multi-objective optimization model, mapping the obtained optimal solution to the day-ahead plan; the day-ahead plan includes the grid interaction power plan, the energy storage system charging and discharging power plan, and the shore power system total power allocation budget; the total port operating cost includes grid power purchase cost, energy storage system depreciation cost, and carbon trading cost; The intraday rolling optimization module is used to repeatedly execute the following intraday rolling optimization steps at a preset rolling cycle: Based on wind and solar power output prediction data and prediction error distribution, multiple wind and solar power output scenarios are generated. With the goal of minimizing the expected operating cost under all wind and solar power output scenarios within the framework of tracking the grid interaction power plan and the energy storage system charging / discharging power plan and the shore power system total power allocation budget, an intraday rolling optimization model is constructed. The expected operating cost includes the expected electricity purchase cost under all wind and solar power output scenarios and the grid power plan tracking penalty term. The intraday rolling optimization model is solved, and the optimal decision value obtained in the current rolling period is used as the charging power command for each ship in port and the charging / discharging power command for the energy storage system. The charging power command is sent to the corresponding shore power facility, and the charging and discharging power command is sent to the energy storage converter; The execution monitoring and closed-loop correction module is used to monitor the actual values ​​of the system grid interaction power, total shore power load, and energy storage output in real time; calculate the deviation between the actual values ​​and the corresponding instructions issued in the current period; if any of the deviations exceeds its preset threshold, or if a sudden event affecting the reliability of port power supply is detected, the current process is interrupted and the intraday rolling optimization step is triggered, and the process is re-executed based on the real-time system status, including the latest energy storage charge status, to update and issue the corrected instructions; otherwise, the currently issued instructions continue to be executed, and the process waits to enter the next rolling cycle.

[0014] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the shore power-energy storage coordinated scheduling method described in any one of the above.

[0015] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the shore power-energy storage coordinated scheduling method described above.

[0016] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the shore power-energy storage coordinated scheduling method described above.

[0017] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, system, equipment, medium, and product for coordinated dispatch of shore power and energy storage in ports. By acquiring predictive and real-time status data, it achieves comprehensive perception and unified data modeling of the port's integrated energy system operation status, laying a precise and reliable foundation for optimization decisions. By constructing and solving a day-ahead multi-objective optimization model, it integrates the economic objectives of grid power purchase cost, energy storage system depreciation cost, and carbon trading cost, and coordinates optimization with the local renewable energy absorption rate as the objective. This solves the problem of the disconnect and difficulty in quantifying and coordinating economic costs and environmental protection objectives in port dispatching, realizing automatic trade-offs and integrated optimization of operational economy and low carbon emissions in the day-ahead planning stage, guiding the system to automatically tend towards a low-cost, low-emission operation mode. Through intraday rolling optimization steps, particularly the generation of wind and solar power output scenarios and the solution of intraday rolling optimization models, multiple wind and solar power output scenarios are used to quantitatively describe the uncertainty of wind and solar power output. Model predictive control decisions are made with the goal of minimizing the expected operating costs of multiple wind and solar power output scenarios and tracking the power grid interaction plan. This solves the problem of poor robustness of traditional deterministic scheduling schemes under intermittent and highly uncertain wind and solar power environments. It achieves a shift from "static planning" based on a single prediction to "dynamic robust scheduling" that adapts to multiple future possibilities, significantly improving the system's ability to withstand fluctuations. Through command issuance, monitoring, and closed-loop correction steps, a real-time control closed loop of "optimization-execution-monitoring-feedback-re-optimization" is established. This solves the problem of predetermined scheduling plans failing due to unexpected events such as actual operational deviations, changes in ship plans, or equipment failures. It achieves proactive monitoring of the system's operating status, rapid anomaly diagnosis, and adaptive real-time correction of control strategies, giving the system strong self-healing and autonomous operation capabilities. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a shore power-energy storage coordinated dispatch method for a port according to one embodiment of this application; Figure 2 A detailed flowchart illustrating the day-ahead collaborative optimization steps of a shore power-energy storage collaborative scheduling method for a port, provided as an embodiment of this application; Figure 3 This is a detailed flowchart illustrating the intraday rolling optimization steps of a shore power-energy storage coordinated scheduling method for ports, provided as an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] To make the technical solution of this application clearer, the main terms involved in this application are explained as follows: Green ports refer to ports that achieve environmental friendliness and sustainable development by applying clean energy technologies, improving energy efficiency, and reducing carbon emissions. In this application, it specifically refers to ports equipped with shore power systems, local photovoltaic and wind power generation equipment, and energy storage systems.

[0023] Shore power system: refers to a complete set of facilities that provide land power to ships berthed in port, enabling ships to shut down their auxiliary engines and thus eliminate exhaust emissions while in port.

[0024] Energy storage system: refers to a collection of devices that can store electrical energy and release it when needed. It usually consists of battery packs, power conversion systems and battery management systems, and is used for power regulation, peak shaving and valley filling and backup power supply.

[0025] Coordinated scheduling refers to the unified coordination of power allocation and timing of shore power load, energy storage charging and discharging, local renewable energy output, and external power grid purchase / sale in the port's integrated energy system, in order to optimize the overall system operation objectives.

[0026] Pre-operational optimization: refers to the optimization decision-making process based on forecast data the day before the actual operation day, used to formulate the baseline scheduling plan for the entire day of the following day.

[0027] Intraday rolling optimization refers to an online optimization process that is repeatedly executed at fixed short intervals during actual operating days. Based on updated system status and ultra-short-term forecasts, it performs refined adjustments and robust corrections to the day-ahead plan.

[0028] Renewable energy consumption rate: an indicator used to measure the actual utilization of local photovoltaic and wind power. It is calculated as: actual total consumption / predicted total output. Maximizing this ratio means minimizing wind and solar curtailment.

[0029] Scene generation and reduction technique: A stochastic optimization modeling method for handling uncertainties in wind and solar power output. "Scene generation" generates a large number of possible power output sequences based on the distribution of prediction error; "scene reduction" condenses these into a few typical scenes with probabilistic representativeness to control computational complexity.

[0030] Expected operating cost: The weighted average operating cost calculated considering the probability of different future scenarios, used as the optimization objective of the intraday rolling optimization model.

[0031] Unexpected constraint: A constraint used in multi-scenario stochastic optimization models that requires all scenarios to take the same control decision in the current decision period (i.e., the first optimization period) to ensure that the optimization result can be implemented immediately.

[0032] Carbon Flow Tracking Model: A method for calculating carbon emissions caused by electricity consumption, which traces the responsibility for carbon emissions back to electricity users by analyzing grid flow and carbon emission intensity on the generation side. In this application, it is used to quantify the carbon emissions corresponding to grid-purchased electricity.

[0033] Carbon trading costs refer to the expenses incurred by a port in purchasing corresponding carbon allowances or carbon credits under the carbon market mechanism due to carbon emissions generated by its operational activities. This application internalizes this cost into the objective function.

[0034] Energy storage system depreciation cost: The equivalent economic cost introduced by the life loss of a battery energy storage system due to charge and discharge cycles, which is usually related to charge and discharge power or energy throughput.

[0035] State of charge (SOC): refers to the percentage of the current remaining charge of an energy storage battery relative to its rated total capacity. It is a core parameter characterizing the energy state of an energy storage system.

[0036] This application provides an embodiment of a shore power-energy storage coordinated scheduling method. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, for example... Figure 1 As shown, the method is applied to a terminal as an example, including the following steps S100 to S400, wherein: S100, Data Acquisition and Input: Obtain forecast data and real-time status data of the port's integrated energy system within the future preset scheduling cycle; The forecast data includes the load power forecast sequence of all planned berthing vessels, the local renewable energy output forecast sequence, and the grid time-of-use electricity price sequence; The real-time status data includes the real-time state of charge of the energy storage system. It is understood that the port integrated energy system in this application refers to an overall system that includes local distributed photovoltaic power stations, wind turbines, energy storage systems, shore power facilities, and connection points with the upstream power grid for energy production, storage, consumption, and interaction.

[0037] S200, recently optimized collaboratively: Based on the predicted data and the real-time status data, a day-ahead multi-objective optimization model is constructed with the objectives of minimizing the total port operating cost and maximizing the local renewable energy absorption rate. The day-ahead multi-objective optimization model is solved, and the obtained optimal solution is mapped to the day-ahead plan. The day-ahead plan includes the grid interaction power plan, the energy storage system charging and discharging power plan, and the shore power system total power allocation budget. The total port operating cost includes the grid power purchase cost, the energy storage system depreciation cost, and the carbon trading cost. S300, Intraday Rolling Optimization: Repeat the following intraday rolling optimization steps at a preset rolling cycle: Based on wind and solar power output prediction data and prediction error distribution, multiple wind and solar power output scenarios are generated. With the goal of minimizing the expected operating cost under all wind and solar power output scenarios within the framework of tracking the power grid interaction plan and the energy storage system charging and discharging power plan and the total power allocation budget of the shore power system, an intraday rolling optimization model is constructed. The expected operating cost includes the expected electricity purchase cost under all wind and solar power output scenarios and the power grid power plan tracking penalty term. The intraday rolling optimization model is solved, and the optimal decision value obtained in the current rolling period is used as the charging power command for each ship in port and the charging and discharging power command for the energy storage system. The charging power command is sent to the corresponding shore power facility, and the charging and discharging power command is sent to the energy storage converter; S400, Monitoring and Closed-Loop Calibration: The system monitors the actual values ​​of grid interaction power, total shore power load, and energy storage output in real time. It calculates the deviation between these actual values ​​and the corresponding commands issued during the current time period. If any deviation exceeds a preset threshold, or if a sudden event affecting the port's power supply reliability is detected, the current process is interrupted, and the intraday rolling optimization step is triggered. This step is then re-executed based on the real-time system status, including the latest energy storage charge status, to update and issue corrected commands. Otherwise, the currently issued commands continue to be executed, and the system waits to enter the next rolling cycle. The real-time system status here includes information such as the latest energy storage charge status, equipment availability, and ship port status collected at the time of the anomaly trigger; this is the latest data set used for closed-loop correction.

[0038] Implementing steps S100 to S400, through data acquisition and input in step S100, integrates heterogeneous data from multiple sources, including ship load, local renewable energy output, grid time-of-use pricing, and energy storage status (real-time state of charge). This provides a precise and comprehensive input foundation for subsequent optimization, solving the problems of weak data support and single decision-making basis in traditional scheduling, and achieving comprehensive perception and reliable modeling of the port's integrated energy system operation status. Through day-ahead collaborative optimization in step S200, a unified economic objective integrating grid power purchase costs, energy storage depreciation costs, and carbon trading costs is constructed and collaboratively optimized with the local renewable energy consumption rate target. This solves the problem of the disconnect between economic costs and environmental protection goals at the decision-making level, achieving automatic trade-offs and integrated optimization of operational economy and low-carbon performance during the day-ahead planning stage. Through intraday rolling optimization in step S300, particularly the scenario generation and rolling optimization solution, multiple wind and solar power output scenarios are used to characterize the uncertainty of wind and solar power output. Rolling decisions are made with the goal of minimizing the expected operating costs of multiple wind and solar power output scenarios. This solves the problem of poor robustness of scheduling schemes under intermittent and highly uncertain wind and solar power environments, realizing a shift from static planning based on a single prediction to dynamic robust scheduling that adapts to multiple possibilities, significantly improving the system's anti-interference capability. Through the instruction issuance in step S300 and the monitoring and closed-loop correction in step S400, a real-time closed loop of "optimization-execution-monitoring-feedback-re-optimization" is established. This solves the problem of scheduling plan failure caused by plan deviations or sudden events (such as ship departures), achieving proactive monitoring of operational status, rapid anomaly diagnosis, and autonomous dynamic correction of scheduling strategies, giving the system strong self-healing and adaptive capabilities.

[0039] Furthermore, this application organically combines accurate prediction, carbon cost internalization, uncertainty robust optimization, and event-driven adaptive correction through the aforementioned multi-timescale collaborative optimization and closed-loop control mechanism. This addresses the multiple challenges of economic, low-carbon, reliable, and adaptive operation in green port energy dispatch, enabling the safe, economical, low-carbon, efficient, and autonomous operation of the port shore power-storage method under complex all-weather conditions.

[0040] In another exemplary embodiment of this application, S100 data acquisition and input: acquiring predicted data and real-time status data of the port's integrated energy system within a future preset scheduling period; specifically including steps 110 to 150. Wherein: S110: Obtain ship load power prediction sequence Obtain the predicted load power values ​​for each planned berthing vessel i within all time periods t divided by a fixed time interval Δt within a future preset scheduling period T, and form a load power prediction sequence. ,in, N is the total number of ships planned to berth, N≥1; M represents the total number of time periods, M = T / Δt.

[0041] S120: Obtaining Local Renewable Energy Output Forecast Sequence It is understood that, in this embodiment, the local renewable energy specifically refers to photovoltaic and wind power generation in the port area. The local renewable energy output forecast sequence correspondingly refers to the forecast data set composed of the photovoltaic output forecast sequence and the wind power output forecast sequence.

[0042] Obtain the same scheduling cycle from the port energy management platform or professional meteorological service system. The local renewable energy output forecast sequence includes a photovoltaic (PV) output forecast sequence and a wind power output forecast sequence. The PV output forecast sequence is used to obtain... Predicted power generation of distributed photovoltaic power stations in ports during the specified period The unit is kilowatts (kW); the wind power output prediction sequence is obtained from... Predicted power generation of distributed wind turbines at ports during the specified time period The unit is kilowatt (kW).

[0043] S130: Obtain the grid time-of-use electricity price sequence Obtain the same dispatch cycle from the electricity price information released by the power grid company or the results of electricity market transactions. Inside The unit electricity price at which the port purchases electricity from the power grid during a given period will be used as the time-of-use electricity price sequence for the power grid. The unit is yuan / kWh, and the port's electricity sales to the grid are defined as negative.

[0044] S140: Obtain the real-time status of the energy storage system The real-time state of charge (SOC) of the port energy storage system is obtained using a battery management system or energy storage converter. The real-time SOC includes the current SOC, rated energy capacity, and maximum charge / discharge power. The current SOC refers to the first optimization period within the optimization cycle. Initial state of charge of energy storage system This value is a dimensionless number between 0 and 1; rated energy capacity refers to the rated energy capacity of the energy storage system. Its unit is kilowatt-hour (kWh); maximum charge / discharge power refers to the maximum charging power of the energy storage system per unit time. (Negative values ​​represent charging) and maximum discharge power (A positive value represents discharge), or the absolute value of the maximum charge / discharge power. .

[0045] S150: Data Alignment and Preprocessing The load power prediction sequence obtained through the above steps Predicted power generation of photovoltaic power plants Predicted power generation of wind turbine generators and the time-of-use electricity pricing sequence of the power grid Alignment is performed during time period t. Simultaneously, the scheduling period is calculated. Total load power forecast within and total renewable energy forecast : In another exemplary embodiment of this application, such as Figure 2 As shown, S200 day-ahead collaborative optimization: Based on the predicted data and the real-time status data, a day-ahead multi-objective optimization model is constructed with the objectives of minimizing the total port operating cost and maximizing the local renewable energy absorption rate. The day-ahead multi-objective optimization model is then solved, and the resulting optimal solution is mapped to a day-ahead plan. The day-ahead plan includes a grid interaction power plan, an energy storage system charging and discharging power plan, and a shore power system total power allocation budget, specifically including steps 210 to 240. Wherein: S210, Constructing the objective function set of the current multi-objective optimization model. Based on the prediction data and real-time status data obtained in step S100, an optimization objective function set containing the following two objectives is constructed.

[0046] First objective function (minimizing total operating cost): Minimize the total operating cost F1 of the port within the scheduling period T. in, For the cost of purchasing electricity from the power grid, The power exchange between the port and the power grid during time period t (positive for power purchase, negative for power sale); For the depreciation cost of energy storage systems, The charging and discharging power of the energy storage system during time period t (discharging is positive, charging is negative). This is the power loss factor per unit, and its unit is yuan / kWh. For carbon trading costs, The carbon emissions associated with purchasing electricity from the grid are calculated using a carbon flow tracking model based on power flow. Carbon credits available at the port during period t; The unit carbon trading price; This indicates that the summation operation is performed on all time periods t within the scheduling period T; The second objective function (maximizing local renewable energy absorption rate): maximizes the ratio F2 of the actual total absorption of local photovoltaic and wind power in the port to the predicted total output of photovoltaic and wind power within the scheduling period T. in, The curtailment of wind and solar power during time period t represents the portion of the local renewable energy forecast that was not actually consumed. S220, Power balance constraints for constructing a current-day multi-objective optimization model: The total power consumption of the port (the sum of the predicted total load power and the charging power of the energy storage system) must be equal to the total power supply (the sum of the predicted power generation of the photovoltaic power station, the predicted power generation of the wind turbine generator, the power of grid interaction, and the discharge power of the energy storage system).

[0047] For each time period t, the predicted total load power of all ships and the charging power of energy storage systems With discharge power The following relationship must be satisfied: in, and All are non-negative variables. The charging power of the energy storage system during time period t. Let be the discharge power of the energy storage system during time period t, and the charge / discharge power of the energy storage system during time period t. ; S230, Constructing the grid interaction power constraints for the day-to-day multi-objective optimization model: in, (Positive value) indicates the upper limit of the power that the port can receive (purchase) from the power grid, and its value depends on the capacity of the substation transformer or the agreement with the power grid; (Negative or zero) indicates the upper limit of the power that the port can send back to the grid (sell electricity). When it is negative, its absolute value is... This represents the maximum allowable power return; when it is zero, it indicates that the system is not allowed to return power to the grid. S240, operational constraints for energy storage systems used in constructing a day-to-day multi-objective optimization model: A1) Power constraint: A2) Dynamic energy constraints: in, and The initial state of charge of the energy storage is shown for time period t and time period t+1, respectively. For overall efficiency, the charging and discharging power of the energy storage system during time period t is... Charge when <0, When the charging and discharging power of the energy storage system during time period t Discharge occurs when ≥0. , For the discharge efficiency of the energy storage system, , To improve the charging efficiency of energy storage systems. ; A3) Energy safety range constraints: in, This represents the minimum safe threshold for the state of charge of energy storage. Set the highest safe threshold for the state of charge of energy storage; set the lowest safe threshold. and the highest security threshold Used to maintain the state of charge of the energy storage system within a safe range, avoiding overcharging or over-discharging; A4) Periodic integrity constraints: Can guarantee scheduling cycle When the process ends, the energy storage system's state of charge returns to its initial value; S250, Constraints on Total Shore Power Demand for Constructing a Day-ahead Multi-Objective Optimization Model: Scheduling cycle The total shore power budget allocated to all berths must meet the projected total ship load power. And not exceeding the maximum total shore power capacity: in, Budget for total shore power; The maximum total shore power capacity is set; S260, the multi-objective optimization model is solved using a multi-objective particle swarm optimization algorithm, specifically including the following steps: When using the multi-objective particle swarm optimization algorithm, a clear mapping is established between the abstract parameters of the algorithm and the specific parameters of the day-ahead multi-objective optimization model: the position vector of each particle is encoded as a complete day-ahead scheduling scheme, and its dimension corresponds to the grid interaction power and energy storage charging and discharging power in each time period; the fitness of the particle is evaluated by calculating the total operating cost, green energy absorption rate, and constraint violation penalty value corresponding to the day-ahead multi-objective optimization model; the goal of the algorithm iteration is to search for the Pareto front, and each solution on it is decoded into a candidate scheduling scheme with optimal trade-offs. Among them, the penalty function method is specifically used to quantify constraint violations and treat the penalty term as an independent optimization objective.

[0048] S261, randomly generate a particle population of size G. For the g-th (g=1,2,…,G) particle in the population, its position vector... g represents a complete day-ahead scheduling scheme that covers the entire scheduling period T.

[0049] If the scheduling period T is discretized into M time periods, then the position vector of a particle is defined as: in, and Let g and g represent the grid interaction power and energy storage system charging and discharging power during time period t, respectively, in the scheduling scheme represented by particle g.

[0050] At the same time, an external archive (often called the external archive or Pareto solution set) is initialized to dynamically collect and update the discovered non-dominated solutions (i.e., the Pareto front approximation solution set) during the algorithm iteration process.

[0051] S262, the position vector of each particle Substitute g into the objective function set of the aforementioned multi-objective optimization model and calculate its first objective function value. (Minimize total operating cost) and the value of the second objective function (Maximize the local renewable energy absorption rate); simultaneously, the penalty function method is used to quantify the degree to which the particle violates the above constraints as a penalty term Penalty, and the penalty term Penalty is treated as an independent third objective function to be minimized: in, As a penalty factor, Let $j$ be the violation amount of the particle that violates the $j$-th constraint, where $j$ ≥ 1. S263, based on the first objective function value Second objective function value Based on the Pareto dominance relation and the third objective function value F3, the dominance state of particles in the current population is determined. Non-dominated particles are added to the external archive, and individuals dominated by new solutions are removed from the external archive to maintain the external archive as the current optimal set of non-dominated solutions. The crowding distance of particles in the external archive is calculated. Based on this, for each particle in the current population, an excellent guiding particle is selected from its neighborhood or from the external archive through a specific strategy (such as roulette), and its position is taken as the globally optimal guiding position. At the same time, the individual historical optimal position of each particle is updated (i.e., the position that the particle has reached in history and is determined to be optimal according to the Pareto dominance relation). S264, each particle updates its velocity and position (i.e., updates its position vector) according to the particle swarm optimization algorithm based on its current velocity, its own historical best position, and its globally best guiding position. ); S265, iterative convergence: Repeat steps S262 to S264 until the preset maximum number of iterations is reached or the convergence criterion is met; when the algorithm terminates, the final solution set stored in the external archive it maintains is the approximate solution set of the Pareto front. S270, Select and output the day-ahead schedule from the Pareto front approximate solution set: From the Pareto front approximate solution set, a final dispatch scheme is selected according to a preset decision preference (such as total operating cost priority or local renewable energy integration rate priority); this scheme is output as an executable day-ahead plan within the future dispatch period T, specifically including: grid interaction power plan. Energy storage system charging and discharging power plan and the total power allocation budget for shore power systems .

[0052] In another exemplary embodiment of this application, such as Figure 3 As shown, S300 intraday rolling optimization: Repeat the following intraday rolling optimization steps with a preset rolling cycle: Based on wind and solar power output prediction data and prediction error distribution, multiple wind and solar power output scenarios are generated. With the goal of minimizing the expected operating cost under all wind and solar power output scenarios within the framework of tracking the grid interaction power plan and the energy storage system charging / discharging power plan and the shore power system total power allocation budget, an intraday rolling optimization model is constructed. The expected operating cost includes the expected electricity purchase cost under all wind and solar power output scenarios and the grid power plan tracking penalty term. The intraday rolling optimization model is solved, and the optimal decision value obtained in the current rolling period is used as the charging power command for each ship in port and the charging / discharging power command for the energy storage system. The charging power command is sent to the corresponding shore power facility, and the charging and discharging power command is sent to the energy storage converter; Specifically, this includes the following steps 310 to 350, wherein: S310, Optimized Startup and Data Update The first optimization period of each preset rolling cycle Initiate intraday rolling optimization steps. First, obtain the latest forecast data and real-time state of charge, including: We obtain wind and solar power output forecast data, i.e., photovoltaic power output forecast sequences, from the meteorological service system. Wind power output prediction sequence Its prediction time domain covers the next few hours (e.g., 4 hours), and its time resolution is higher than that of the daytime prediction. The system obtains updated information on the precise arrival / departure times of ships from the port scheduling system, and adjusts the effective charging time window for each ship i accordingly. Obtain the latest real-time state of charge of the energy storage system from the battery management system. ; From the day-ahead optimization results (day-ahead plan) obtained in step S200 (day-ahead collaborative optimization), obtain the grid interaction power plan for the current day. Energy storage system charging and discharging power plan and the total power allocation budget for shore power systems This serves as a reference trajectory for intraday rolling optimization tracking.

[0053] S320, Uncertainty Modeling and Scene Generation of Wind and Solar Power Output, specifically includes the following steps: Based on wind and solar power output prediction data and historical prediction error distribution, a set of probabilistically representative wind and solar power output scenarios are generated using scene generation and reduction techniques to form a scene tree. S321, Based on the historical prediction error distribution, establish photovoltaic power output prediction errors respectively. Wind power output prediction error The probability distribution model (e.g., a normal distribution with a mean of 0); S322, employing Latin hypercube sampling technology, addresses the photovoltaic power output prediction error. Wind power output prediction error Future rolling optimization time domain The joint probability distribution within the range is sampled to generate K (e.g., K=1000) prediction error sequence samples; for the k-th sample, its corresponding prediction error sequence is... These are respectively superimposed onto the corresponding wind and solar power output prediction data to form the initial wind and solar power output scenario. : Where H is the optimized time domain length (e.g., 4 hours), K≥1, k∈K; and These are the photovoltaic and wind power prediction error values ​​for the k-th sample in time period t, respectively. S323 employs a forward scene reduction technique, based on the probability distance metric between scenes, to reduce the K initial landscape output scenes to a preset S (e.g., S=10) representative landscape output scenes; and assigns a probability to each representative landscape output scene s. ,satisfy , S≥1. S representative landscape power output scenarios and their probabilities constitute a scenario tree, used to describe the various possibilities of future landscape power output.

[0054] S330, constructing an intraday rolling optimization model, specifically includes the following steps: A rolling intraday optimization model considering multiple scenarios is constructed, with its decision variables, objective function, and constraints defined as follows: S331, for each generation scenario s and the optimization time domain ( For each time period t within the time frame, define scenario-related decision variables: The charging power of each ship is ; The power of grid interaction is ; The energy storage system's charging and discharging power is (Discharging is positive, charging is negative); At the same time, all scenarios are defined in the first optimization period. Decision variables that must remain consistent: The charging power command for vessels in port is: ; The charging and discharging power command of the energy storage system is ; S332, within the framework of tracking the grid interaction power plan and the energy storage system charging and discharging power plan and the shore power system total power allocation budget, aims to minimize the expected operating cost under all representative wind and solar power output scenarios, and constructs an intraday rolling optimization model. The objective function of the intraday rolling optimization model is: ; Among them, the first item Expected electricity purchase cost; second item This is a penalty item for grid power plan tracking, used to penalize the actual purchased power volume compared to the day-ahead plan in various scenarios. Deviation; α is a preset positive penalty coefficient used to adjust the penalty intensity for deviations from the day-ahead grid interaction power plan.

[0055] S333, Constraints for constructing the intraday rolling optimization model: B1) Unexpected constraints in constructing the intraday rolling optimization model: To ensure that the decisions generated by the intraday rolling optimization model can be executed immediately at the current moment, unexpected constraints need to be imposed, requiring all representative wind and solar power output scenarios to be implemented in the first optimization period. The decision variables remain consistent, that is: (For all scenarios s and all ships i) (For all scenarios s) in, and These are the decision variables for ship charging power and energy storage charging and discharging power under the representative wind and solar power output scenario s.

[0056] This constraint ensures that regardless of which wind and solar power output scenario actually occurs in the future, the system will remain in operation during the first optimization period. They all execute only one set of control commands. and This makes the optimization results executable.

[0057] B2) Constructing scenario-related power balance constraints for the intraday rolling optimization model: For each scenario s and the optimization time domain ( For each time period t within a given period, the system power must satisfy the following balance relationship; in, The total load power prediction value of all ships in port during time period t under scenario s; The charging power of the energy storage system during time period t under scenario s; The discharge power of the energy storage system during time period t under scenario s; The predicted power generation of the photovoltaic power station during time period t under scenario s; The predicted power generation of the wind turbine generator set in scenario s during time period t; The power exchange power between the port and the power grid during time period t under scenario s; And the net charging and discharging power of the energy storage system during time period t under scenario s is defined as follows: : B3) Scenario-related energy storage operation constraints for constructing an intraday rolling optimization model: For each scenario s and the optimization time domain ( For each time period t within a given period, the operation of the energy storage system must meet the following constraints; B31) Power Constraints: Energy storage system charging and discharging power It must not exceed the equipment's capacity limit.

[0058] B32) Dynamic Energy Constraints: The state of charge (SOC) of an energy storage system evolves with its charging and discharging behavior.

[0059] in, and These represent the initial energy storage state of charge during time period t and time period t+1 under scenario s, respectively. For overall efficiency, the charging and discharging power of the energy storage system during time period t under scenario s is... Charge when <0, When the charging and discharging power of the energy storage system during time period t under scenario s Discharge occurs when ≥0. , For the discharge efficiency of the energy storage system, The charging efficiency of the energy storage system; B33) Energy safety range constraints: The state of charge of an energy storage system must be maintained within a safe range to prevent overcharging or over-discharging.

[0060] B4) Constructing ship charging power constraints for the intraday rolling optimization model: For each scenario s, each vessel i in port, and the optimized time domain ( For each time period t within a given period, the charging power must satisfy the following inequality constraint: in, The upper limit of the rated power of the shore power facilities (or shore power connectors) connected to the vessel. B5) Constructing grid interaction power constraints for the intraday rolling optimization model: For each scenario s and the optimization time domain ( The power grid interaction between the port and the power grid at each time period t within the period. The following inequality constraints must be satisfied: B6) Constructing the total shore power constraint for the intraday rolling optimization model: For each scenario s and the optimization time domain ( For each time period t within ) The total charging power of all vessels in port must not exceed the total rated capacity of the shore power system. That is, the following inequality constraints must be satisfied: S340, Solving the intraday rolling optimization model and extracting the current instruction, specifically includes the following steps: The above intraday rolling optimization model is solved using a scenario-based model predictive control algorithm: S341, input the constructed intraday rolling optimization model into a commercial mathematical optimization solver (such as CPLEX, Gurobi) for solving; S342, the business mathematics optimization solver returns the values ​​of decision variables for all time periods t under all representative landscape power fields; S343, Based on the constraints defined in step S333, extract all representative wind and solar power output fields s in the first optimization period. The common optimal decision value is taken as the optimal executable instruction for the current rolling period: Optimal charging power instructions for each vessel in port: Optimal charge and discharge power command for energy storage system: S350, command issuance and execution, specifically includes the following steps: The optimal executable command for the current time period is sent to the corresponding physical device through the port's industrial IoT or fieldbus network: S351 will send the optimal charging power command The instruction is sent to the corresponding smart shore power pile controller; S352 will send the optimal charge / discharge power command. The instruction is issued to the energy storage converter control system; S353, after receiving the instruction, the intelligent shore power pile controller and energy storage converter control system will operate according to the instruction value in the next control cycle; S354, after the current rolling cycle ends, the system time advances to the starting point of the next rolling cycle, and steps S310 to S350 are repeated to achieve rolling optimization and closed-loop control; In another exemplary embodiment of this application, S400 monitoring and closed-loop correction involves: real-time monitoring of the actual values ​​of the system grid interaction power, total shore power load, and energy storage output; calculation of the deviation between the actual values ​​and the corresponding instructions issued in the current period; if any of the deviations exceeds its preset threshold, or if a sudden event affecting the reliability of port power supply is detected, the current process is interrupted, and the intraday rolling optimization step is triggered, re-executed based on the real-time system status including the latest energy storage charge state, to update and issue the corrected instructions; otherwise, the currently issued instructions continue to be executed, and the process waits to enter the next rolling cycle; specifically, this includes the following steps 410 to 440. Wherein: S410 monitors key operating parameters in real time, specifically including the following steps: The system uses smart sensors or data acquisition units deployed at grid connection points, energy storage converters, and shore power piles to collect the actual values ​​of the following key operating parameters in real time at a fixed sampling period (e.g., 1 second): C1) Actual power at the grid interaction point: denoted as This represents the real-time power exchange between the port and the power grid (positive for power purchase and negative for power sale). C2) Actual output of the energy storage system: denoted as This represents the real-time charging and discharging power of the energy storage system (discharging is positive, charging is negative). C3) Actual total load of the shore power system: The actual total load of the shore power system is obtained by summing the output power of all shore power piles supplying power in real time, denoted as: ; C4) Real-time state of charge of the energy storage system: denoted as This is provided directly by the battery management system.

[0061] S420, calculates the deviation between the instruction and the actual value, specifically including the following steps: The system compares the actual value monitored in step S410 with the deviation of the optimal executable instruction issued in step S350 during the current time period, and calculates the absolute deviation: S421, calculate the power deviation at the grid interaction point using the following formula: in, This is the reference value for the power grid interaction power within the current time period; S422, calculate the energy storage system output deviation using the following formula: in, The optimal charging and discharging power command for the energy storage system issued during the current time period; S423, calculate the total load deviation of the shore power system using the following formula: in, This is the sum of the optimal charging power instructions issued to all vessels in port during the current period; S430, determine the trigger condition: Determine if any of the following triggering conditions are met: D1) Power deviation exceeds limit: Any power deviation (i.e. , , Whether it exceeds its corresponding preset threshold, i.e.: in, Power deviation at grid interaction point The preset threshold; For the output deviation of the energy storage system The preset threshold; Total load deviation of shore power system The preset threshold; D2) Unexpected events: Receiving an alarm signal from the port dispatching system, equipment monitoring system, or manual control panel indicates the occurrence of one of the following emergencies: E1) The vessel's temporary emergency departure or berthing plan has been cancelled; E2) Sudden failure of shore power piles, energy storage converters, or photovoltaic / wind power equipment; E3) Anomalies occur on the power grid side (such as sudden voltage drop, frequency exceeding limits, or unplanned power outages). E4) Communication network interruption causes commands to fail to be issued or status acquisition to fail; If any of the above triggering conditions are met, proceed to step S440; otherwise, proceed to step S443. S440 triggers and executes closed-loop correction optimization, specifically including the following steps: If any of the above triggering conditions are met, the system will immediately perform the following closed-loop correction operation: S441, Interrupt the current process: Send a high-priority interrupt signal to the intraday rolling optimization model constructed in step S300 to pause the intraday rolling optimization step that is currently executed according to a fixed period. S442, Data Synchronization: The latest real-time system data is used as a new initial condition and passed to the intraday rolling optimization model constructed in step S300; the data includes: Current moment (i.e., the time of anomaly detection); Latest Energy Storage State of Charge ; Updated vessel status in port (such as the identification and original instructions of departing vessels). Current local renewable energy output forecast sequence; Applicable and up-to-date grid electricity price information.

[0062] S443, Re-execute optimization: Forcefully start the intraday rolling optimization model, and re-execute steps S310 to S350 (i.e., the complete process from data update to instruction issuance) based on the above new initial conditions. S444, Update instruction issued: Intraday rolling optimization model outputs the updated optimal new instruction. and The optimal new instruction will be immediately issued to the corresponding shore power piles and energy storage converters, overriding the original instruction; S445, Record and Restore: Record this correction event (including the triggering reason, time, and instructions before and after correction) to the system log; after the correction is completed, the system restores the regular rolling optimization process triggered at fixed intervals.

[0063] The shore power-energy storage coordinated scheduling method provided in this application embodiment internalizes carbon flow tracking and carbon trading costs into the optimization objective through the S100 data acquisition and input and the S200 day-ahead coordinated optimization steps. This effectively solves the problem of the separation and difficulty in quantifying economic and environmental objectives in port scheduling, and realizes the automatic trade-off and integrated optimization of operational economy and low carbon emissions. It can significantly reduce the net carbon emissions of port operations and effectively reduce the total operating cost by optimizing the power purchase period.

[0064] By employing the S300 intraday rolling optimization steps, using scenario trees to describe the uncertainty of wind and solar power output, and combining it with a model predictive control framework for rolling decision-making, the challenges posed by the intermittency and strong uncertainty of wind and solar power are effectively addressed, improving the robustness of the scheduling scheme and realizing the transformation from static planning to dynamic adaptive scheduling. Compared with traditional deterministic scheduling methods, it can significantly reduce the risk of excessive power deviation caused by wind and solar fluctuations or changes in ship schedules, thereby improving the stability and power supply reliability of the port power grid.

[0065] Through S400 monitoring and closed-loop correction steps, a self-healing mechanism based on real-time monitoring and deviation triggering has been established. This mechanism can effectively handle scheduling plan failures caused by operational deviations or emergencies, enabling proactive perception, rapid response, and autonomous correction of abnormal states. It significantly reduces reliance on manual intervention and improves operational efficiency and system security.

[0066] The three core steps described above work synergistically to streamline the entire decision-making process from long-term planning to real-time control: day-ahead collaborative optimization provides macro-level guidance for intraday rolling optimization, while intraday rolling optimization performs fine-grained calibration and dynamic tracking of day-ahead plans. This multi-timescale collaborative architecture effectively improves the utilization efficiency of shore power equipment and ensures the efficient absorption of local renewable energy.

[0067] This application also provides an application scenario in which the aforementioned shore power-energy storage coordinated dispatch method is applied. Specifically, the shore power-energy storage coordinated dispatch method provided in this application can be widely applied to various modern ports that pursue green, low-carbon, and efficient operations, especially ports with frequent ship berthing, large load fluctuations, and the deployment of distributed photovoltaic / wind power and energy storage systems. The following uses a typical large container hub port as an example. A certain international container hub port has multiple large deep-water berths, berthing multiple ultra-large container ships daily. In response to global emission reduction initiatives, the port has built a high-voltage shore power system covering its main berths, a multi-megawatt rooftop photovoltaic power station, coastal wind turbine generators, and a large-scale lithium iron phosphate battery energy storage system. The port operator faces multiple challenges, including high operating costs, power curtailment due to the intermittency of wind and solar power generation, and significant peak-valley electricity price differences. Therefore, an intelligent coordinated dispatch system is urgently needed to achieve safe, economical, and low-carbon energy supply.

[0068] The problems faced by intelligent collaborative dispatch systems established through traditional methods are as follows: dispatchers mainly rely on experience, prioritizing the use of wind and solar power, using surplus electricity to charge energy storage, and purchasing the remaining power from the grid. This approach cannot accurately utilize time-of-use electricity price differences for "low-storage, high-generation" arbitrage, nor does it incorporate carbon trading costs into decision-making, resulting in insufficient optimization of total operating costs and an inability to quantify carbon emission reduction benefits. When actual wind and solar output is lower than predicted, or when ships temporarily adjust their operating plans, the original power supply plan may not be able to meet load demand, forcing emergency purchases of electricity at high prices or the use of backup diesel generators, affecting power supply reliability and environmental goals. The currently formulated broad-based electricity consumption plan lacks a dynamic fine-tuning mechanism based on real-time information during daily execution, making it unable to quickly respond to sudden failures (such as a shore power pile tripping) or temporary departures of ships.

[0069] After the shore power-energy storage coordinated dispatch method of this application is deployed and implemented in the port, its operation process is as follows: Step 1: Comprehensive Perception and Forward-Looking Planning (Pre-Date Collaborative Optimization) Every afternoon, the system automatically acquires forecast data for the entire following day (schedule cycle T=24 hours): hourly photovoltaic and wind power output forecast curves provided by the meteorological department; expected power consumption curves for each scheduled container ship to berth provided by the port dispatch system; and time-of-use electricity price curves for the following day published by the electricity market. Simultaneously, it acquires the current state of charge (SOC) of the energy storage system.

[0070] Based on this data, the system constructs and solves a day-ahead multi-objective optimization model. Under the premise of satisfying all physical constraints (such as power balance constraints, grid interaction power constraints, energy storage system operation constraints, and total shore power demand constraints), this model automatically balances the two objectives of "minimizing total operating costs" and "maximizing local renewable energy integration rate." Innovatively, the total operating cost includes the grid-purchased carbon trading cost calculated based on a carbon flow tracking model.

[0071] After solving the day-ahead multi-objective optimization model, a refined day-ahead optimization plan for the entire following day is output, including: the power plan for purchasing / selling electricity from the grid for each time period, the charging and discharging power plan for the energy storage system for each time period, and the total power budget allocated to the shore power system. This plan achieves an optimal balance between cost and environmental protection and establishes a scientific "peak shaving and valley filling" strategy for energy storage.

[0072] Step 2: Dynamic Adjustment and Robust Execution (Intraday Rolling Optimization) On actual operating days, the system initiates intraday rolling optimization every 15 minutes (rolling cycle).

[0073] First, based on the latest wind and solar power output prediction data and prediction error distribution, a set (e.g., 10) of representative wind and solar power output scenarios and their probability of occurrence are generated using Latin hypercube sampling and forward reduction techniques to depict the uncertainty in the next few hours.

[0074] Next, the system tracks the daily plan and constructs and solves an intraday rolling optimization model with the objective of minimizing the expected operating costs under all the representative wind and solar power output scenarios. This model ensures that the current-period decision it outputs is unique and immediately executable through unexpected constraints.

[0075] After solving the intraday rolling optimization model, the optimal charging power command and the optimal charging and discharging power command of the energy storage system for each ship in port within the current 15 minutes are generated and immediately sent to the corresponding smart shore power piles and energy storage converters for execution.

[0076] Step 3: Active monitoring and self-healing correction (monitoring and closed-loop correction) The system monitors the actual operating power of the grid interaction points, shore power piles, and energy storage systems in real time.

[0077] Once the system detects that the deviation between the actual power consumption and the issued command exceeds the safety threshold (for example, because a ship's actual power consumption far exceeds the prediction), or receives an alarm for an emergency (for example, a shore power pile malfunctions or a ship leaves port in an emergency), it immediately determines that the system is abnormal.

[0078] At this point, the system interrupts the intraday rolling optimization process and urgently re-executes the intraday rolling optimization again, starting from the latest system status.

[0079] Within seconds, the system calculates the optimal correction command to adapt to the new situation and immediately issues it, quickly pulling the system back to a safe, economical, and efficient operating track, achieving closed-loop self-healing.

[0080] By applying the shore power-energy storage coordinated dispatch method for ports as described in this application, the container port has achieved the following: the system automatically utilizes energy storage for arbitrage and optimizes power purchase periods, effectively reducing overall electricity costs. Through internalizing carbon costs and multi-objective optimization, it maximizes the utilization of local green electricity, reducing dependence on high-carbon electricity from the external power grid and its own carbon quota expenditures. Through multi-scenario robust optimization and an event-driven closed-loop correction mechanism, the system's ability to cope with wind and solar power fluctuations, load surges, and equipment failures is significantly improved, ensuring the continuity and stability of power supply to ships. Furthermore, it achieves fully automated decision-making and execution from day-ahead planning to real-time control, greatly reducing manual intervention and enhancing the intelligence level of port energy management.

[0081] Based on the same inventive concept, this application also provides a shore power-energy storage coordinated dispatch system for implementing the shore power-energy storage coordinated dispatch method described above. The solution provided by this system is similar to the solution described in the above method; therefore, the specific limitations of one or more shore power-energy storage coordinated dispatch method system embodiments provided below can be found in the limitations of the shore power-energy storage coordinated dispatch method described above, and will not be repeated here.

[0082] The data acquisition and prediction input module is used to acquire prediction data and real-time status data of the port's integrated energy system within a future preset scheduling cycle. The day-ahead collaborative optimization module is used to construct a day-ahead multi-objective optimization model based on the predicted data and the real-time status data, with the objectives of minimizing the total port operating cost and maximizing the local renewable energy absorption rate, and to solve the day-ahead multi-objective optimization model, mapping the obtained optimal solution to the day-ahead plan; the day-ahead plan includes the grid interaction power plan, the energy storage system charging and discharging power plan, and the shore power system total power allocation budget; the total port operating cost includes grid power purchase cost, energy storage system depreciation cost, and carbon trading cost; The intraday rolling optimization module is used to repeatedly execute the following intraday rolling optimization steps at a preset rolling cycle: Based on wind and solar power output prediction data and prediction error distribution, multiple wind and solar power output scenarios are generated. With the goal of minimizing the expected operating cost under all wind and solar power output scenarios within the framework of tracking the grid interaction power plan and the energy storage system charging / discharging power plan and the shore power system total power allocation budget, an intraday rolling optimization model is constructed. The expected operating cost includes the expected electricity purchase cost under all wind and solar power output scenarios and the grid power plan tracking penalty term. The intraday rolling optimization model is solved, and the optimal decision value obtained in the current rolling period is used as the charging power command for each ship in port and the charging / discharging power command for the energy storage system. The charging power command is sent to the corresponding shore power facility, and the charging and discharging power command is sent to the energy storage converter; The execution monitoring and closed-loop correction module is used to monitor the actual values ​​of the system grid interaction power, total shore power load, and energy storage output in real time; calculate the deviation between the actual values ​​and the corresponding instructions issued in the current period; if any of the deviations exceeds its preset threshold, or if a sudden event affecting the reliability of port power supply is detected, the current process is interrupted and the intraday rolling optimization step is triggered, and the process is re-executed based on the real-time system status, including the latest energy storage charge status, to update and issue the corrected instructions; otherwise, the currently issued instructions continue to be executed, and the process waits to enter the next rolling cycle.

[0083] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a shore power-energy storage coordinated scheduling method.

[0084] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0085] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0086] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0087] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0088] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0089] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0090] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A shore power-energy storage coordinated dispatch method, characterized in that, include: Obtain forecast data and real-time status data of the port's integrated energy system within the future preset scheduling cycle; Based on the predicted data and the real-time status data, a day-ahead multi-objective optimization model is constructed with the objectives of minimizing the total port operating cost and maximizing the local renewable energy absorption rate. The day-ahead multi-objective optimization model is solved, and the obtained optimal solution is mapped to the day-ahead plan. The day-ahead plan includes the grid interaction power plan, the energy storage system charging and discharging power plan, and the shore power system total power allocation budget. The total port operating cost includes the grid power purchase cost, the energy storage system depreciation cost, and the carbon trading cost. Repeat the following intraday rolling optimization steps at a preset rolling cycle: Based on wind and solar power output prediction data and prediction error distribution, multiple wind and solar power output scenarios are generated. With the goal of minimizing the expected operating cost under all wind and solar power output scenarios within the framework of tracking the power grid interaction plan and the energy storage system charging and discharging power plan and the total power allocation budget of the shore power system, an intraday rolling optimization model is constructed. The expected operating cost includes the expected electricity purchase cost under all wind and solar power output scenarios and the power grid power plan tracking penalty term. The intraday rolling optimization model is solved, and the optimal decision value obtained in the current rolling period is used as the charging power command for each ship in port and the charging and discharging power command for the energy storage system. The charging power command is sent to the corresponding shore power facility, and the charging and discharging power command is sent to the energy storage converter; The system monitors the actual values ​​of grid interaction power, total shore power load, and energy storage output in real time; calculates the deviation between the actual values ​​and the corresponding instructions issued in the current time period; if any of the deviations exceeds its preset threshold, or if a sudden event affecting the reliability of port power supply is detected, the current process is interrupted, and the intraday rolling optimization step is triggered, which is re-executed based on the real-time system status, including the latest energy storage charge status, to update and issue the corrected instructions; otherwise, the currently issued instructions continue to be executed, and the system waits to enter the next rolling cycle.

2. The shore power-energy storage coordinated dispatch method according to claim 1, characterized in that, The carbon trading cost is calculated as follows: Based on the aforementioned power grid interaction plan, the corresponding carbon emissions are calculated using a carbon flow tracking model and determined in conjunction with the carbon credits held by the port and the unit carbon trading price.

3. The shore power-energy storage coordinated dispatch method according to claim 1, characterized in that, Multiple landscape power output scenes are generated using scene generation and reduction techniques, which include: Using Latin hypercube sampling technology, multiple initial landscape and solar power output scenarios are generated based on the prediction error distribution. Then, using forward scene reduction technology, the multiple initial landscape and solar power output scenarios are reduced to a preset number of representative landscape and solar power output scenarios, and a probability is assigned to each representative landscape and solar power output scenario.

4. The shore power-energy storage coordinated dispatch method according to claim 1, characterized in that: The intraday rolling optimization model includes unexpected constraints; the unexpected constraints require that the decision variables of all the wind and solar power output scenarios remain consistent in the first optimization period; the first optimization period is the starting period of the rolling optimization time domain.

5. The shore power-energy storage coordinated dispatch method according to claim 4, characterized in that, The unexpected constraints are specifically: For all representative scenic power output scenarios s and all vessels in port i, during the first optimization period satisfy: For all representative scenic output scenarios s, during the first optimization period satisfy: in, For the first optimization period The charging power instruction issued for vessels in port; For the first optimization period The issued charging and discharging power command for the energy storage system; The decision variable for ship charging power under a representative wind and solar power output scenario s is... S represents the total number of representative scenic power-generating scenes, where S≥1. N is the total number of ships planned to berth, N≥1; The decision variables for energy storage charging and discharging power under a representative wind and solar power output scenario s.

6. The shore power-energy storage coordinated dispatch method according to claim 1, characterized in that: The deviations include power deviation at the grid interaction point, output deviation of the energy storage system, and total load deviation of the shore power system.

7. The shore power-energy storage coordinated dispatch method according to claim 1, characterized in that: The emergencies include at least one of the following: temporary departure of a ship from port, failure of shore power facilities or energy storage systems, power grid anomalies, and communication outages.

8. The shore power-energy storage coordinated dispatch method according to claim 1, characterized in that, The solution to the aforementioned multi-objective optimization model specifically involves: The solution is obtained using a multi-objective particle swarm optimization algorithm, where: The position vector of each particle in the algorithm is mapped to a complete day-ahead scheduling scheme. Each dimension of the position vector corresponds to the power grid interaction power and energy storage charging and discharging power decision variables of each time period in the day-ahead multi-objective optimization model. The fitness of a particle is evaluated by calculating the total operating cost objective function value, the local renewable energy absorption rate objective function value, and the constraint violation penalty term for the scheduling scheme corresponding to each particle's position vector. Pareto non-dominated sorting and population iterative updates are then performed accordingly. After the algorithm converges, the Pareto front approximate solution set is obtained from its maintained external archive, and the final scheduling scheme is selected from the Pareto front approximate solution set according to preset decision preferences. The penalty function method is used to process the constraints of the day-ahead multi-objective optimization model. Specifically, the degree of particle constraint violation is quantified into a penalty term, which is then used as one of the optimization objectives or incorporated into the fitness function.

9. A shore power-energy storage coordinated dispatch system, characterized in that, The shore power-energy storage coordinated dispatch system includes: The data acquisition and prediction input module is used to acquire prediction data and real-time status data of the port's integrated energy system within a future preset scheduling cycle. The day-ahead collaborative optimization module is used to construct a day-ahead multi-objective optimization model based on the predicted data and the real-time status data, with the objectives of minimizing the total port operating cost and maximizing the local renewable energy absorption rate, and to solve the day-ahead multi-objective optimization model, mapping the obtained optimal solution to the day-ahead plan; the day-ahead plan includes the grid interaction power plan, the energy storage system charging and discharging power plan, and the shore power system total power allocation budget; the total port operating cost includes grid power purchase cost, energy storage system depreciation cost, and carbon trading cost; The intraday rolling optimization module is used to repeatedly execute the following intraday rolling optimization steps at a preset rolling cycle: Based on wind and solar power output prediction data and prediction error distribution, multiple wind and solar power output scenarios are generated. With the goal of minimizing the expected operating cost under all wind and solar power output scenarios within the framework of tracking the grid interaction power plan and the energy storage system charging / discharging power plan and the shore power system total power allocation budget, an intraday rolling optimization model is constructed. The expected operating cost includes the expected electricity purchase cost under all wind and solar power output scenarios and the grid power plan tracking penalty term. The intraday rolling optimization model is solved, and the optimal decision value obtained in the current rolling period is used as the charging power command for each ship in port and the charging / discharging power command for the energy storage system. The charging power command is sent to the corresponding shore power facility, and the charging and discharging power command is sent to the energy storage converter; The execution monitoring and closed-loop correction module is used to monitor the actual values ​​of the system grid interaction power, total shore power load, and energy storage output in real time; calculate the deviation between the actual values ​​and the corresponding instructions issued in the current period; if any of the deviations exceeds its preset threshold, or if a sudden event affecting the reliability of port power supply is detected, the current process is interrupted and the intraday rolling optimization step is triggered, and the process is re-executed based on the real-time system status, including the latest energy storage charge status, to update and issue the corrected instructions; otherwise, the currently issued instructions continue to be executed, and the process waits to enter the next rolling cycle.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the shore power-energy storage coordinated dispatch method according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the shore power-energy storage coordinated scheduling method as described in any one of claims 1-8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the shore power-energy storage coordinated scheduling method as described in any one of claims 1-8.

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