Port low-carbon scheduling method based on cloud edge architecture and two-dimensional consistency

Abstract

The application discloses a port low-carbon scheduling method based on a cloud edge-end architecture and double-dimension consistency, which is applied to a cloud edge-end three-layer distributed collaborative scheduling architecture comprising a port control center, a quay crane distribution unit and a terminal device group, and comprises the following steps: the port control center calculates a dynamic time-of-use electricity price through a nonlinear dynamic electricity price updating model and calculates a global power deviation signal according to a real-time running state of the system; the quay crane distribution unit generates a port quay crane distribution scheme with the lowest power consumption cost of the port quay crane group as the target in response to the dynamic time-of-use electricity price; and the terminal device group comprises a generator set group and a quay crane operation group, and the generator set group and the quay crane operation group are distributed and allocated in double dimensions with a generator cost incremental factor and a quay crane loading and unloading efficiency factor as consistency variables, and the global power deviation signal is received by the main intelligent agents elected by the generator set group and the quay crane operation group, and the consistency variables of the generator set group and the quay crane operation group are symmetrically compensated. The application realizes low-carbon economic collaboration and optimal scheduling.

Description

Technical Field

[0001] This invention belongs to the field of power system automation and port energy management technology, specifically involving a port low-carbon scheduling method based on cloud-edge-device architecture and dual-dimensional consistency. Background Technology

[0002] The rapid development of electrification in global ports has led to high energy consumption and large power fluctuations in large equipment such as quay cranes, posing challenges to the stability of power supply systems. Low-carbon and efficient scheduling has become a crucial issue. Existing research mainly falls into two categories: centralized and distributed optimization methods. Centralized optimization involves constructing a globally refined model for scheduling, but it suffers from inherent drawbacks such as rapidly increasing computational complexity, difficulty in scaling, and slow dynamic response. Distributed optimization (such as the alternating direction multiplier method and distributed model predictive control) can alleviate computational pressure, but it still has limitations in handling multi-objective non-convex coupling constraints in ports and coordinating economic efficiency with operational efficiency.

[0003] The "cloud-edge-device" collaborative architecture offers a new approach to port scheduling, employing a division of labor where the cloud layer integrates data, the edge layer performs regional optimization, and the terminal layer enables rapid decision-making. However, when applied to ports, this architecture still faces key challenges: the lack of a clear mathematical coupling model between quay crane power flexibility and scheduling objectives makes it difficult to balance cost and efficiency; and the loose integration of dynamic electricity pricing mechanisms with distributed algorithms limits the guiding role of price signals. These unresolved issues restrict the reliable application of existing technologies in engineering. Summary of the Invention

[0004] The main objective of this invention is to provide a port low-carbon scheduling method based on cloud-edge-device architecture and dual-dimensional consistency, aiming to achieve economic, low-carbon, and robust operation of multi-port systems while ensuring operational efficiency.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: In a first aspect, this invention provides a port low-carbon scheduling method based on a cloud-edge-device architecture and dual-dimensional consistency, applied to a three-layer distributed collaborative scheduling architecture including a port control center, quay crane allocation units, and terminal equipment groups. The method includes: Based on the real-time operating status of the system, the port control center calculates the dynamic time-of-use electricity price using a nonlinear dynamic electricity price update model, and also calculates the system's global power deviation signal. The quay crane allocation unit responds to dynamic time-of-use pricing and generates a quay crane allocation scheme for the port area with the goal of minimizing the electricity cost of the port area quay crane group. The terminal equipment group includes a generator set group and a quay crane operation group. The generator set group and the quay crane operation group respectively use the generator cost increment factor and the quay crane loading and unloading efficiency factor as consistency variables for two-dimensional consistent distributed allocation. Each group's elected master agent receives the global power deviation signal and performs symmetrical compensation on its respective consistency variables, specifically including: The generator group uses the generator cost increment factor as the first consistency variable. The elected master generator agent compensates for the first consistency variable based on the global power deviation signal, guiding the generator group to make the generator cost increment factor of all generators tend to the first consistency variable. The quay crane operation group uses the quay crane loading and unloading efficiency factor as the second consistency variable. The elected master quay crane agent compensates for the second consistency variable based on the global power deviation signal, guiding the quay crane operation group to make the quay crane loading and unloading efficiency factors of all quay cranes tend to the second consistency variable.

[0006] Following the above technical solution, the nonlinear dynamic electricity price update model generates a dynamic time-of-use electricity price that is closely coupled with the real-time operating status of the system by iteratively calculating based on the deviation between the total system load and the baseline load, the absorption elasticity of the actual and predicted output of renewable energy, and the impact function of sudden events.

[0007] Following the above technical solution, the nonlinear dynamic electricity price update model includes: ; In the formula, For dynamic time-of-use electricity pricing, For time, This represents the number of iterations. Based on forecasting electricity prices; This is the penalty coefficient for exceeding the threshold load. and These are the indicator functions and their impact weights for sudden events; Deviation in output from renewable energy sources; This is the amplification factor for the impact of renewable energy fluctuations on electricity prices; Standard deviation of renewable energy fluctuations; , The elastic coefficient; This represents the total load of the previous iteration; Baseline load; ; ; ; In the formula, , These are renewable energy forecasts and actual output, respectively. This refers to the basic load excluding the quay cranes in each port area; Adjust the weights for the over-threshold load penalty coefficient; This represents the total load of the quay cranes in the previous iteration. The dynamic time-of-use electricity price is calculated iteratively based on the above formula, and the convergence criterion for iterative calculation is as follows: As shown below: ; That is, when the dynamic time-of-use electricity price difference calculated in two consecutive iterations satisfies the iteration convergence criterion. If the iteration terminates, then the iteration ends.

[0008] Based on the above technical solution, and with the goal of minimizing the electricity cost of the port area's quay crane group, a port area quay crane allocation plan is generated, including: ; In the formula, S The total number of vessels served; Dynamic time-of-use pricing; , Port Area w Average unloading power per unit of a single quay crane and the quantity of cargo it unloads; For the port area w Mid-term t Internal allocation to ships s The number of quay bridges; ; ; ; ; ; In the formula, For time sets; For the port area w China Shipbuilding s Total number of containers to be unloaded; M For a sufficiently large number; For the port area w China Shipbuilding s The berthing state variable is 1 if the ship is berthing, and 0 otherwise. For the port area w Total number of quay cranes; For the port area w China Shipbuilding s During the period t Adjust the number of quay cranes; , Port Area w The ships allocated by China s The maximum and minimum number of quay bridges.

[0009] Following the above technical solution, the global power deviation signal is the difference between the total power generation of the system and the total load demand.

[0010] Following the above technical solution, the main power generation agent and the main quay crane agent perform symmetrical compensation for their respective consistency variables, specifically as follows: When the global power deviation signal is positive, that is, when the total power generation of the system is greater than the total load demand, the main power generation agent lowers the reference value of the first consistency variable to guide the power generation to decrease, while the main quay crane agent raises the reference value of the second consistency variable to guide the power consumption to increase. When the global power deviation signal is negative, meaning the total power generation of the system is less than the total load demand, the main power generation agent increases the reference value of the first consistency variable to guide the increase in power generation, while the main quay crane agent decreases the reference value of the second consistency variable to guide the decrease in power consumption.

[0011] Following the above technical solution, the generator cost increment factor This is the first derivative of the generator's quadratic cost function, i.e., the marginal cost. First, the generator's secondary cost function for: ; In the formula, , and Port Area w medium generator g The constant term, coefficient of the first term, and coefficient of the quadratic term of the power generation cost function; For the port area w medium generator g contribution; Then the generator cost increment factor as follows: ; The main power generation agent compensates for the first consistency variable based on the global power deviation signal as follows: ; In the formula, The first consistency variable after compensation; The first random matrix One of them, For the port area w The power generation deviation adjustment factor is a negative scalar that controls the convergence speed of the consistency theory. For the port area w In the k The global power deviation signal of the next iteration.

[0012] Based on the above technical solution, the quay crane loading and unloading efficiency factor The ratio of the mass of containers unloaded by the quay crane per unit time to its power consumption: ; In the formula, and Port Area w The Middle q The unloading capacity and container weight of each quay crane; and Port Area w The Middle q Unit unloading capacity and minimum value of each quay crane; The main quay bridge agent compensates for the second consistency variable based on the global power deviation signal as follows: ; In the formula, The second consistency variable after compensation; For the second random matrix One of them; For the port area w The quay crane power deviation adjustment factor is a positive scalar that controls the convergence speed of the consistency theory; For the port area w In the k The global power deviation signal of the next iteration.

[0013] In a second aspect, the present invention provides a computer device / apparatus / system, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the first aspect.

[0014] Thirdly, the present invention provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0015] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: Traditional centralized optimization methods suffer from exponentially increasing computational load when dealing with large-scale, multi-port systems, resulting in optimization speeds that cannot meet real-time scheduling requirements and poor system scalability. To address this, this invention innovatively proposes a three-layer distributed collaborative architecture: cloud-edge-device. By decomposing the globally complex optimization problem into layers and parallel solutions at the cloud, edge, and terminal device levels, it fundamentally breaks through the bottleneck of centralized computing, achieving a leap in computational efficiency and enabling elastic system scalability.

[0016] Addressing the challenge of synergistically optimizing economic efficiency, operational efficiency, and low-carbon goals in port scheduling, existing distributed algorithms or consensus theories have limited effectiveness in handling such strongly coupled multi-objective problems. This invention proposes a "two-dimensional consensus theory" for terminal equipment groups. By introducing two core collaborative variables—the "generator cost increment factor" and the "quay crane loading / unloading efficiency factor"—it drives distributed agents to achieve optimal consensus in both the economic and operational domains. This elegantly achieves simultaneous optimization of economic cost and system efficiency while ensuring that loading / unloading operations remain unaffected by hard constraints.

[0017] Finally, addressing the shortcomings of traditional static or simple time-of-use pricing in guiding flexible loads and effectively promoting renewable energy consumption, this invention constructs a nonlinear dynamic pricing update model in the cloud. This model deeply integrates real-time load, renewable energy output deviations, and sudden event impact signals, generating accurate and dynamic price guidance. This price signal permeates the entire "cloud-edge-device" architecture from top to bottom, guiding flexible loads such as quay cranes to adjust their electricity consumption behavior in real time and with sensitivity. This achieves excellent peak shaving and valley filling effects at the global level and maximizes the port's local absorption capacity of renewable energy. Attached Figure Description

[0018] Figure 1 This is a diagram of the operation of a quay crane according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a cloud-edge distributed low-carbon scheduling architecture for a multi-port system according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the port power generation and consumption scheduling solution process according to an embodiment of the present invention; Figure 4 This is a comparison diagram of the energy consumption of different scheduling methods according to an embodiment of the present invention; Figure 5 This is a graph showing the iterative change in electricity prices according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments provided by this invention without inventive effort are within the scope of protection of this invention.

[0020] Obviously, the accompanying drawings described below are merely some examples or embodiments of the present invention. Those skilled in the art can apply the present invention to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this invention, modifications to design, manufacturing, or production based on the technical content disclosed in this invention are merely conventional technical means and should not be construed as insufficient disclosure of the present invention.

[0021] In this invention, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this invention may be combined with other embodiments without conflict.

[0022] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "a," "an," "an," "the," and similar words used in this invention do not indicate quantity limitation and may indicate singular or plural. The terms "comprising," "including," "having," and any variations thereof used in this invention are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms "connected," "linked," "coupled," and similar words used in this invention are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "A plurality" used in this invention refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships may exist; for example, "A and / or B" can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects have an "or" relationship. The terms "first," "second," and "third" used in this invention are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0023] This invention provides a port low-carbon scheduling method based on a cloud-edge-device architecture and dual-dimensional consistency, aiming to systematically solve the core contradictions existing in port energy scheduling technologies. Traditional centralized optimization methods experience exponential growth in computational load when dealing with large-scale, multi-port systems, resulting in optimization speeds that cannot meet real-time scheduling requirements and poor system scalability. To address this, this invention innovatively proposes a three-layer distributed collaborative architecture of "cloud-edge-device," decomposing the globally complex optimization problem into layered and parallel solutions at the cloud, edge, and terminal device levels. This fundamentally breaks through the bottleneck of centralized computing, achieving a leap in computational efficiency and enabling elastic system scalability.

[0024] Addressing the challenge of synergistically optimizing economic efficiency, operational efficiency, and low-carbon goals in port scheduling, existing distributed algorithms or consensus theories have limited effectiveness in handling such strongly coupled multi-objective problems. This invention proposes a "two-dimensional consensus theory" for terminal equipment groups. By introducing two core collaborative variables—the "generator cost increment factor" and the "quay crane loading / unloading efficiency factor"—it drives distributed agents to achieve optimal consensus in both the economic and operational domains. This elegantly achieves simultaneous optimization of economic cost and system efficiency while ensuring that loading / unloading operations remain unaffected by hard constraints.

[0025] Finally, addressing the shortcomings of traditional static or simple time-of-use pricing in guiding flexible loads and effectively promoting renewable energy consumption, this invention constructs a nonlinear dynamic pricing update model in the cloud. This model deeply integrates real-time load, renewable energy output deviations, and sudden event impact signals, generating accurate and dynamic price guidance. This price signal permeates the entire "cloud-edge-device" architecture from top to bottom, guiding flexible loads such as quay cranes to adjust their electricity consumption behavior in real time and with sensitivity. This achieves excellent peak shaving and valley filling effects at the global level and maximizes the port's local absorption capacity of renewable energy.

[0026] This invention provides a port low-carbon scheduling system and method based on a "cloud-edge-device" distributed architecture and a two-dimensional consistency theory of terminal device clusters. It aims to achieve economical, low-carbon, and robust operation of multi-port systems while ensuring operational efficiency. The core of this solution lies in constructing a hierarchical collaborative scheduling framework and solving the challenges of information coupling and real-time response through an innovative algorithm model.

[0027] (1) Power characteristics analysis of quay bridge.

[0028] This solution focuses on the core of the scheduling object—the quay crane, a major energy-consuming device—and conducts key modeling of its power characteristics. By analyzing the three stages of quay crane operation—ascending, translating, and descending—especially the power-time trapezoidal curve during the ascending stage, the potential for flexible adjustment between the average operating power and operation time of the quay crane when completing a fixed loading / unloading volume is revealed. Based on this, this solution establishes a geometric programming model for quay crane demand response with average power as the control variable, transforming the flexibility of the physical equipment into standardized decision variables that can be used for optimized scheduling, thus laying a mathematical foundation for subsequent collaborative optimization.

[0029] like Figure 1 As shown, the operation of the quay crane can be divided into the following three parts: the loading and unloading process of the quay crane can be divided into three stages: rising, moving horizontally and falling.

[0030] (2) Multi-port area system “cloud-edge-device” distributed architecture.

[0031] This invention divides the multi-port area system's "cloud-edge-device" distributed low-carbon scheduling architecture into three layers, such as... Figure 2 As shown in the diagram, this solution first establishes a three-layer distributed scheduling architecture for the port: cloud, edge, and terminal. The top layer is the Port Control Center (PCC), which acts as the system's "cloud brain," responsible for aggregating global information and generating key guidance signals. The middle layer consists of Quay Crane Distribution Units (QCDUs) deployed in various port areas, serving as "edge computing nodes" responsible for receiving instructions and solving logistics resource allocation problems within their respective port areas. The bottom layer comprises equipment clusters (ECs) distributed across various port areas, including generator sets and quay crane operation groups, which act as "terminal execution units," forming a multi-intelligent system through local communication networks.

[0032] (3) Port Control Center (PCC) Electricity Price Update Model.

[0033] To optimize global resource allocation, this solution employs a nonlinear dynamic electricity price update mechanism within the cloud-based PCC (Power, Control, and Consumption) architecture. This mechanism breaks away from traditional fixed or simple time-of-use pricing models, deeply integrating three key dynamic factors into its update formula: the deviation between the system's total load and baseline load, the absorption elasticity of actual and predicted renewable energy output, and the impact function of unforeseen events. Through iterative calculations, this pricing model generates price signals closely coupled with the system's real-time operating status, thereby accurately and dynamically guiding electricity consumption behavior at the edge and end-user levels, proactively achieving peak shaving and valley filling, and promoting the absorption of renewable energy.

[0034] By utilizing a dynamic time-of-use pricing mechanism to guide electricity consumption by quay cranes, peak shaving and valley filling of port energy consumption can be achieved. Considering peak shaving and valley filling, renewable energy consumption, and the impact of unforeseen events, the electricity price update calculation formula is as follows: ; In the formula, Based on forecasting electricity prices; , The elastic coefficient; This represents the total load of the previous iteration; Baseline load; Deviation in output from renewable energy sources; This is the amplification factor for the impact of renewable energy fluctuations on electricity prices; Standard deviation of renewable energy fluctuations; This is the penalty coefficient for exceeding the threshold load. and These are the indicator functions for sudden events (0 for occurrence, 1 for non-occurrence) and their impact weights.

[0035] ; ; ; In the formula, , These are renewable energy forecasts and actual output, respectively. Adjust the weights for the over-threshold load penalty coefficient; This represents the total load of the quay cranes in the previous iteration. This refers to the basic load excluding the quay cranes in each port area.

[0036] Each iteration recalculates the electricity price based on the four equations above, and the convergence criterion is as follows: As shown in the following formula: ; That is, when the dynamic time-of-use electricity price difference calculated in two consecutive iterations satisfies the iteration convergence criterion. The iteration terminates, and we obtain... t Dynamic time-of-use electricity pricing.

[0037] (4) Port area quay crane allocation strategy.

[0038] After obtaining the time-of-use pricing, QCDU formulates a dispatch strategy with the goal of minimizing the electricity cost of the port area's quay crane cluster: ; In the formula, S The total number of vessels served; , Port Area w Average unloading power per unit of a single quay crane and the quantity of cargo it unloads; For the port area w Mid-term t Internal allocation to ships s The number of quay cranes. During the multi-port area quay crane allocation phase, and The value is a constant, and the specific value is derived from the historical data of each port area.

[0039] ; ; ; ; ; In the formula, For time sets; For the port area w China Shipbuilding s Total number of containers to be unloaded; M For a sufficiently large number; For the port area w China Shipbuilding s The berthing state variable is 1 if the ship is berthing, and 0 otherwise. For the port area w Total number of quay cranes; For the port area w China Shipbuilding s During the period t Adjust the number of quay cranes; , Port Area w The ships allocated by China s The maximum and minimum number of quay bridges.

[0040] The first formula indicates that sufficient quay cranes are allocated to handle containers on each ship. The second formula indicates that quay cranes are allocated only when a ship is berthed; otherwise, no allocation is made. The third formula indicates that the number of quay cranes allocated to all ships berthed in port does not exceed the total number of available quay cranes. The fourth formula indicates the number of quay cranes that need to be adjusted for each ship in each time period; if the number of quay cranes used by a ship changes within consecutive time periods, the quay crane allocation needs to be adjusted. The fifth formula indicates the number of quay cranes allocated to a ship... i The number of quay bridges allocated is within the allocation interval. As can be seen from the above five equations, this model is a mixed-integer linear programming problem. This paper uses the CPLEX solver to solve the established model.

[0041] Among the constraints mentioned above, the workload coupled with the ship's berthing status / time window should ensure that each ship completes its predetermined operational requirements within its berthing window, thereby eliminating the possibility of "delaying operations for peak shaving" at the model level. Therefore, the throughput and ship turnaround in this paper are directly guaranteed by the constraints, and cost and low-carbon optimizations are only carried out within this feasible domain.

[0042] (5) Terminal multi-agent two-dimensional consistency theory.

[0043] At the terminal execution layer, the core of this solution is the two-dimensional consistent distributed allocation of the unit group and the quay crane group.

[0044] For a group of generating units, their coordination is based on the "generator cost increment factor". This is a consistent variable, which is essentially the first derivative of the generator's secondary cost function, i.e., the marginal cost.

[0045] Cost function of generators in the port It is a quadratic function: ; In the formula, , and Port Area w medium generator g The constant term, coefficient of the first term, and coefficient of the quadratic term of the power generation cost function; For the port area w medium generator g of effort.

[0046] Generator cost increment factor As a consistency variable, it is calculated and neighborhood information is exchanged according to the following rules: .

[0047] When performing consistent allocation, the source-load balance in the power grid should be considered. Therefore, the update formula for the main generator agent is as follows: ; ; In the formula, For random matrices One of them, For the port area w The power generation deviation adjustment factor is a negative scalar that controls the convergence speed of the consistency theory. For the port area w In the k The power command difference in the next iteration; , Port Area w In the k The total generator output and total load of the next iteration.

[0048] The primary generator agent elected in the cluster is responsible for receiving the global power deviation signal from the upper layer of the system. Based on this, the reference values ​​of the global consistency variables are compensated and adjusted to guide the entire power generation cluster to quickly converge to the optimal economic allocation state with consistent system marginal costs, under the premise of meeting the upper and lower limits of output and ramping constraints.

[0049] For quay crane groups, their coordination is based on the "quay crane loading and unloading efficiency factor". For consistency variables: ; In the formula, and Port Area w The Middle q The unloading capacity and container weight of each quay crane; and Port Area w The Middle q The unit unloading capacity of each quay crane and its minimum value. The quay crane loading and unloading efficiency factor is defined as the ratio of the mass of containers unloaded by the quay crane per unit time to its power consumption, which is directly related to logistics efficiency and energy consumption.

[0050] and The update rules are the same, and the update formula is as follows: ; In the formula, For random matrices One of them, This is the adjustment factor for the power deviation of the quay cranes in port area w.

[0051] Similarly, the master quay crane agent selected by the quay crane cluster receives the same global power deviation signal. Furthermore, it performs reverse compensation on the reference values ​​of its consistency variables. This design enables the quay crane group to dynamically adjust the operating power of each quay crane to achieve load tracking while ensuring that the operating efficiency of all quay cranes in the group remains consistent, thereby maintaining the balance and stability of the overall loading and unloading capacity when responding to system scheduling instructions.

[0052] The coordination between the generator group and the quay crane group does not rely on complex information interweaving, but rather on two main intelligent agents coordinating the same global power deviation. This is achieved through a response mechanism. When the system's power generation exceeds the load demand, the main power generation agent lowers the consistency reference value to reduce power generation, while the main quay crane agent raises the reference value to guide the quay crane to moderately increase power consumption; conversely, the response is reversed. This bias-based symmetrical feedback mechanism tightly couples the distributed consistency iteration processes on both the power generation and load sides, ultimately driving the system to achieve real-time global power balance and multi-objective optimization while satisfying all equipment operating constraints.

[0053] (6) Distributed low-carbon scheduling process of “cloud-edge-device”.

[0054] The multi-port area "cloud-edge-device" distributed low-carbon scheduling optimization process based on the two-dimensional consistency theory is as follows: Figure 3As shown, the entire process forms a closed loop: the PCC issues an initial electricity price signal to each QCDU; the QCDU solves the quay crane allocation scheme based on the electricity price and issues power commands to the EC; the EC layer's generation and quay crane intelligent agent cluster performs distributed iteration based on the two-dimensional consistency theory, calculates the specific power generation and consumption of each device, and feeds it back to the PCC; the PCC then updates the electricity price according to the new system state. This process iterates cyclically until key indicators such as system power deviation meet the convergence conditions, thereby outputting the final optimal low-carbon dispatch scheme for the entire system.

[0055] The strategy proposed in this invention has achieved quantifiable and significant results on several key performance indicators through numerical examples.

[0056] (1) Analysis of total port load scheduling.

[0057] To verify that the proposed scheduling strategy has the ability to guide load "peak shaving and valley filling," the energy consumption curve under the port's disorderly electricity consumption strategy is compared with it, such as... Figure 4 As shown.

[0058] (1) The total electricity consumption is the same under the “cloud-edge-device” distributed low-carbon scheduling strategy and the disordered electricity consumption strategy proposed in this paper, indicating that the amount of cargo handled by the port remains unchanged during the scheduling period. Therefore, under the “cloud-edge-device” distributed low-carbon scheduling strategy, the container throughput does not affect the normal operation of port logistics. (2) Under the disordered power consumption strategy, the power consumption of the quay crane is relatively evenly distributed, resulting in the total load increasing with the increase of the base load, with a peak-to-valley difference of 52.50MW; (3) Under the “cloud-edge-terminal” distributed low-carbon scheduling strategy, during the period from 6:00 to 9:00, the power consumption of the quay crane is only 48.63MW-56.75MW because the power consumption of the basic load is at its peak; during the period from 23:00 to 3:00, the power consumption of the quay crane increases to 77.62-90.87MW because the power consumption of the basic load is at its valley. The total load peak-valley difference is 8.60MW, which is 83.62%, and the fluctuation of the total load of the port is greatly improved.

[0059] In terms of scheduling quality and operational assurance, this method achieves the optimization objective while strictly ensuring the completion of the port's core production tasks. Simulation results show that, with the total power consumption of the port (i.e., the total loading and unloading volume) remaining constant within the scheduling cycle, the peak-to-valley load difference of the system decreases from 52.50MW under the disordered power consumption strategy to 8.60MW, a reduction of 83.62%, achieving a significant peak shaving and valley filling effect.

[0060] (2) PCC dynamic electricity price analysis.

[0061] Under the "cloud-edge-device" distributed low-carbon scheduling strategy, the optimal solution can be obtained in only 4 iterations, and the electricity price for different iterations is as follows: Figure 5 As shown.

[0062] (1) In the initial stage of iteration (m=1): the basic electricity price fluctuates little and cannot guide the load; (2) In the intermediate stage of the iteration (m=2 and m=3): it can only affect the electricity consumption behavior of the total load during the valley period (11:00-15:00). However, during the peak electricity consumption period of the quay crane (6:00-9:00), due to the small increase in electricity price, it cannot fully guide its electricity consumption behavior and it is difficult to achieve comprehensive "peak shaving and valley filling". (3) During the iterative convergence phase (m=4): the electricity price changes in the opposite direction to the total load of the port, and compared with the intermediate phase, the peak (6:00-9:00 period) electricity price increases significantly, which can correctly guide the load to "shave peaks and fill valleys".

[0063] In terms of overall benefits, this method demonstrates superior guiding capabilities compared to other electricity pricing mechanisms. Comparative analysis shows that static time-of-use pricing, decoupled real-time pricing, and this method reduced the load peak-valley difference by 35.05%, 55.05%, and 83.62%, respectively. This is attributed to the strong coupling between the cloud-based dynamic pricing model and distributed consistency iteration, enabling the pricing signal to guide load transfer more accurately and promptly. Simultaneously, by promoting renewable energy consumption and optimizing generator output, this method reduces total system operating costs while simultaneously decreasing carbon emissions, achieving a balance between economic and environmental benefits.

[0064] In summary, this invention provides a port low-carbon scheduling method based on cloud-edge-device architecture and dual-dimensional consistency. It relates to a distributed low-carbon scheduling system and its optimization method applied to the collaborative operation of multiple port areas, and is particularly suitable for the collaborative optimization and real-time control of the port's "energy flow-logistics" system under a high proportion of new energy access.

[0065] Furthermore, the present invention also provides a computer device / apparatus / system, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described method.

[0066] The present invention also provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0067] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or block diagrams.

[0068] These computer program instructions may also be stored in a computer-readable storage medium capable of directing a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more blocks in a block diagram.

[0069] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, thereby providing steps for implementing the functions specified in one or more flowcharts and / or one or more blocks in a block diagram.

[0070] It should be noted that, depending on the implementation needs, the various steps / components described in this invention can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0071] Those skilled in the art will readily understand that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A port low-carbon scheduling method based on cloud-edge-device architecture and dual-dimensional consistency, characterized in that, This method, applied to a three-layer distributed collaborative scheduling architecture encompassing a port control center, quay crane allocation units, and terminal equipment clusters, includes: Based on the real-time operating status of the system, the port control center calculates the dynamic time-of-use electricity price using a nonlinear dynamic electricity price update model, and also calculates the system's global power deviation signal. The quay crane allocation unit responds to dynamic time-of-use pricing and generates a quay crane allocation scheme for the port area with the goal of minimizing the electricity cost of the port area quay crane group. The terminal equipment group includes a generator set group and a quay crane operation group. The generator set group and the quay crane operation group respectively use the generator cost increment factor and the quay crane loading and unloading efficiency factor as consistency variables for two-dimensional consistent distributed allocation. Each group's elected master agent receives the global power deviation signal and performs symmetrical compensation on its respective consistency variables, specifically including: The generator group uses the generator cost increment factor as the first consistency variable. The elected master generator agent compensates for the first consistency variable based on the global power deviation signal, guiding the generator group to make the generator cost increment factor of all generators tend to the first consistency variable. The quay crane operation group uses the quay crane loading and unloading efficiency factor as the second consistency variable. The elected master quay crane agent compensates for the second consistency variable based on the global power deviation signal, guiding the quay crane operation group to make the quay crane loading and unloading efficiency factors of all quay cranes tend to the second consistency variable.

2. The port low-carbon scheduling method based on cloud-edge-device architecture and dual-dimensional consistency as described in claim 1, characterized in that, The nonlinear dynamic electricity price update model generates a dynamic time-of-use electricity price that is closely coupled with the real-time operating status of the system by iteratively calculating based on the deviation between the total system load and the baseline load, the absorption elasticity of the actual and predicted output of renewable energy, and the impact function of sudden events.

3. The port low-carbon scheduling method based on cloud-edge-device architecture and dual-dimensional consistency as described in claim 1 or 2, characterized in that, Nonlinear dynamic electricity price update models include: ； In the formula, For dynamic time-of-use electricity pricing, For time, This represents the number of iterations. Based on forecasting electricity prices; This is the penalty coefficient for exceeding the threshold load. and These are the indicator functions and their impact weights for sudden events; Deviation in output from renewable energy sources; This is the amplification factor for the impact of renewable energy fluctuations on electricity prices; Standard deviation of renewable energy fluctuations; , The elastic coefficient; This represents the total load of the previous iteration; Baseline load; ； ； ； In the formula, , These are renewable energy forecasts and actual output, respectively. This refers to the load on the foundation excluding the quay cranes in each port area; Adjust the weights for the over-threshold load penalty coefficient; This represents the total load of the quay cranes in the previous iteration. The dynamic time-of-use electricity price is calculated iteratively based on the above formula, and the convergence criterion for iterative calculation is as follows: As shown below: ； That is, when the dynamic time-of-use electricity price difference calculated in two consecutive iterations satisfies the iteration convergence criterion. If the iteration terminates, then the iteration ends.

4. The port low-carbon scheduling method based on cloud-edge-device architecture and dual-dimensional consistency as described in claim 1, characterized in that, With the goal of minimizing the electricity cost of the port area's quay crane cluster, a port area quay crane allocation plan is generated, including: ； In the formula, S The total number of vessels served; Dynamic time-of-use pricing; , Port Area w Average unloading power per unit of a single quay crane and the quantity of cargo it unloads; For the port area w Mid-term t Internal allocation to ships s The number of quay bridges; ； ； ； ； ； In the formula, For time sets; For the port area w China Shipbuilding s Total number of containers to be unloaded; M For a sufficiently large number; For the port area w China Shipbuilding s The berthing state variable is 1 if the ship is berthing, and 0 otherwise. For the port area w Total number of quay cranes; For the port area w China Shipbuilding s During the period t Adjust the number of quay cranes; , Port Area w The ships allocated by China s The maximum and minimum number of quay bridges.

5. The port low-carbon scheduling method based on cloud-edge-device architecture and dual-dimensional consistency as described in claim 1, characterized in that, The global power deviation signal is the difference between the total power generation of the system and the total load demand.

6. The port low-carbon scheduling method based on cloud-edge-device architecture and dual-dimensional consistency as described in claim 1, characterized in that, The main generator agent and the main quay crane agent perform symmetrical compensation on their respective consistency variables, specifically as follows: When the global power deviation signal is positive, that is, when the total power generation of the system is greater than the total load demand, the main power generation agent lowers the reference value of the first consistency variable to guide the power generation to decrease, while the main quay crane agent raises the reference value of the second consistency variable to guide the power consumption to increase. When the global power deviation signal is negative, meaning the total power generation of the system is less than the total load demand, the main power generation agent increases the reference value of the first consistency variable to guide the increase in power generation, while the main quay crane agent decreases the reference value of the second consistency variable to guide the decrease in power consumption.

7. The port low-carbon scheduling method based on cloud-edge-device architecture and dual-dimensional consistency as described in claim 1, characterized in that, Generator cost increment factor This is the first derivative of the generator's quadratic cost function, i.e., the marginal cost. First, the generator's secondary cost function for: ； In the formula, , and Port Area w medium generator g The constant term, coefficient of the first term, and coefficient of the quadratic term of the power generation cost function; For the port area w medium generator g contribution; Then the generator cost increment factor as follows: ； The main power generation agent compensates for the first consistency variable based on the global power deviation signal as follows: ； In the formula, The first consistency variable after compensation; The first random matrix One of them, For the port area w The power generation deviation adjustment factor is a negative scalar that controls the convergence speed of the consistency theory. For the port area w In the k The global power deviation signal of the next iteration.

8. The port low-carbon scheduling method based on cloud-edge-device architecture and dual-dimensional consistency according to claim 1, characterized in that, Quay crane loading and unloading efficiency factor The ratio of the mass of containers unloaded by the quay crane per unit time to its power consumption: ； In the formula, and Port Area w The Middle q The unloading capacity and container weight of each quay crane; and Port Area w The Middle q Unit unloading capacity and minimum value of each quay crane; The main quay bridge agent compensates for the second consistency variable based on the global power deviation signal as follows: ； In the formula, The second consistency variable after compensation; For the second random matrix One of them; For the port area w The quay crane power deviation adjustment factor is a positive scalar that controls the convergence speed of the consistency theory; For the port area w In the k The global power deviation signal of the next iteration.

9. A computer device / equipment / system, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 8.

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