Flexible networking-based ship electric power system power layering cooperative support method

Abstract

The invention discloses a ship electric power system power layering cooperative supporting method based on flexible networking, and relates to the technical field of ship electric power systems, and the method comprises the steps: firstly constructing a three-level power scheduling architecture, deploying controllers in a system layer, a region layer and a unit layer, dividing the priorities of an energy unit and a load unit, and carrying out the power scheduling of the system layer, the region layer and the unit layer; and then obtaining the optimal distribution power of each energy unit by using a system layer controller in a mode of solving a multi-objective optimization model, issuing the optimal distribution power to a region layer, constructing actual power, distribution power and schedulable power by using a region cooperative controller to determine a final power regulation scheme, and issuing the final power regulation scheme to a unit layer for execution. When the energy in the region cannot meet the power requirement, the global optimization controller is used for calling from other regions; according to the technical scheme, global power balance and power cooperation of energy and loads in the region can be ensured, optimal power distribution is achieved, system stability is maintained, and power fluctuation and sudden changes are rapidly coped with.

Description

Technical Field

[0001] This application relates to the technical field of ship power systems, and more specifically, to a power hierarchical collaborative support method for ship power systems based on flexible networking. Background Technology

[0002] As ships develop towards larger, more intelligent, and greener designs, the structure of ship power systems is becoming increasingly complex. Traditional ship power systems mostly adopt a centralized grid architecture, with a single energy source (mainly diesel generators), relatively fixed load characteristics, and power dispatch primarily relying on manual or simple automatic control strategies. However, in modern ships, to meet energy conservation and emission reduction requirements, a large number of new energy devices such as solar photovoltaic, energy storage batteries, and fuel cells are being integrated. At the same time, propulsion loads and precision operation loads on ships (such as drilling platform equipment and research vessel detection instruments) place extremely high demands on the power quality and stability of the power system.

[0003] Traditional centralized power dispatching methods have the following significant drawbacks: First, slow response speed. When ships encounter sudden load changes (such as propulsion system acceleration, large equipment start-up and shutdown) or fluctuations in renewable energy power (such as changes in photovoltaic irradiance), centralized controllers struggle to issue power allocation commands within milliseconds, easily leading to system frequency and voltage deviations exceeding permissible ranges. Second, poor flexibility. Different types of energy equipment (such as diesel generators, energy storage batteries, and supercapacitors) and loads (such as sensitive loads, ordinary loads, and impact loads) have different dynamic characteristics and priorities. Centralized dispatching cannot differentiate management based on these characteristics, resulting in low energy utilization efficiency. Third, low reliability. The centralized architecture carries the risk of single-point failure. Once the central controller fails, the power support of the entire power system will be paralyzed, seriously threatening the ship's navigation safety. Fourth, poor scalability. When new energy equipment or loads are added to a ship, the entire dispatching system needs to be redesigned and re-tested, increasing modification and time costs.

[0004] The emergence of flexible networking technology has made it possible to upgrade ship power systems. It enables flexible access and interconnection of different energy and load units through power electronic converters, possessing characteristics such as rapid control and bidirectional power flow. However, existing power support methods for ship power systems based on flexible networking still have shortcomings: most methods only achieve two-layer power scheduling (e.g., energy layer and load layer), failing to consider the dynamic response differences and priority allocation of different energy devices and loads; simultaneously, collaborative control strategies lack comprehensive optimization of multiple system objectives (e.g., power balance, frequency stability, voltage stability, and optimal energy efficiency), making it difficult to achieve efficient coordination among units under complex operating conditions. Therefore, developing a support method capable of power layering, multi-objective coordination, and high reliability has become crucial for solving the power management challenges of modern ship power systems. Summary of the Invention

[0005] The purpose of this application is to address the problems of slow response, poor flexibility, low reliability, and poor scalability in existing ship power system power support methods, and to provide a hierarchical collaborative power support method for ship power systems based on flexible networking. By constructing a multi-level power scheduling architecture and combining global power optimization, regional collaboration, and local power feedback control strategies, this method enables refined management of various energy units and load units within the ship power system, ensuring power balance, frequency stability, and voltage stability under various operating conditions, and improving energy utilization efficiency and system reliability.

[0006] The technical solution of this application is: to provide a power hierarchical collaborative support method for ship power systems based on flexible networking. The ship power system includes an energy layer, a flexible interconnection layer, and a load layer. The flexible interconnection layer contains multiple flexible interconnection nodes for flexible access and interconnection of different energy units and load units. The method includes:

[0007] Step 1: Construct a three-level power scheduling architecture including system layer, region layer, and unit layer. Deploy a global optimization controller at the system layer to control global power balance. Deploy a predetermined number of region coordination controllers at the region layer to control the power coordination of energy units and load units in each region. Deploy a local controller for each energy unit at the unit layer.

[0008] Step 2: Dynamically adjust the priority of each energy unit and each load unit according to the preset time interval. Calculate the priority of a single energy unit based on the dynamic response time, unit energy cost, and unit carbon emission of the energy unit. Calculate the priority of a single load unit based on the allowable voltage deviation and frequency deviation of the load unit.

[0009] Step 3: Establish a system-level multi-objective optimization model based on energy costs, carbon emissions, and allowable voltage and frequency deviations of load units. Calculate the total system power demand and total system dispatchable power using a global optimization controller. If the total system power demand is less than or equal to the total system dispatchable power, iteratively solve the model using an improved particle swarm optimization algorithm to obtain the optimal power allocation for each energy unit and transmit it to the regional collaborative controllers. If the total system power demand is greater than the total system dispatchable power, first cut off load units exceeding the system dispatchable power range according to priority from low to high, and then solve the model.

[0010] Step 4: For a single region, use the region coordination controller to calculate the region's total power demand and total schedulable power. If the region's total power demand is greater than its total schedulable power, use the global optimization controller to extract corresponding power from other regions as replenishment to this region. If the region's total power demand is less than or equal to its total schedulable power, then execute the following steps:

[0011] Step 41: Calculate the surplus power of each energy unit in this region based on the maximum output power and optimal allocation power of each energy unit in this region, and report it to the global optimization controller.

[0012] Step 42: Calculate the absolute difference between the actual total power of the region and the total allocated power of the region. If the absolute difference is less than or equal to a preset threshold, fine-tune the output power of the energy units in this region so that they reach the total power requirement within a preset time. If the absolute difference is greater than the preset threshold, directly adjust the output power of the energy units in this region so that they quickly reach the total power requirement. When calling on the energy units in this region to supplement power, they are selected in descending order of priority.

[0013] Furthermore, in step 3, the system-level multi-objective optimization model is expressed as:

[0014] min F=ω1|Δf j |+ω2(1 / p)Σ|ΔU j |+ω3Σ(P energy_i· C i )+ω4Σ(P energy_i· Em i )

[0015] The constraints of the system-level multi-objective optimization model are: P energy_i,min ≤P energy_i ≤P energy_i,max SOC i,min ≤SOC i ≤SOC i,max ,|P energy_i |≤P point P dem =P sup ;

[0016] In the formula, ω1, ω2, ω3, and ω4 are weighting coefficients, and Δf j Let ΔU be the allowable frequency deviation of the j-th load unit in the system. j Let P be the allowable voltage deviation of the j-th load unit in the system, where p is the proportional coefficient. energy_i Let C be the output power of the i-th energy unit in the system. i Let Em be the unit energy cost of the i-th energy unit in the system. i P represents the carbon emissions per unit of the i-th energy unit in the system. energy_i,min and P energy_i,max Let SOC be the upper and lower limits of the output power of the i-th energy unit in the system. i Let SOC be the charge state of the i-th energy unit in the system. i,min and SOC i,maxP represents the upper and lower limits of the charge state of the i-th energy unit in the system. point Due to the transmission capacity limitations of interconnected nodes, P dem For the total power requirement of the system, P sup This represents the total dispatchable power of the system.

[0017] Furthermore, in step 3, the total power demand and total dispatchable power of the system are calculated using the global optimization controller, specifically including:

[0018] Calculate the total system power demand P based on the power requirements of each load unit and the system line losses. dem The total dispatchable power P of the system is calculated based on the maximum output power of each energy unit. sup , is represented as:

[0019] P dem =ΣP load_j +P loss

[0020] P sup =ΣP energy_i,max

[0021] In the formula, P load_j Let P be the power requirement of the j-th load unit in the system. loss This refers to system line losses.

[0022] Furthermore, in step 4, the total regional power demand and total regional schedulable power are calculated using the regional cooperative controller, specifically including:

[0023] Calculate the total power demand P of the region based on the power requirements of each load unit in the region and the regional line losses. dem_q The total dispatchable power P of the region is calculated based on the maximum output power of each energy unit in the region. sup_q , is represented as:

[0024] P dem_q =ΣP load_m +P loss,q

[0025] P sup_q =ΣP energy_n,max

[0026] In the formula, P load_m For the power requirement of the m-th load unit in this region, P loss,q For the line loss in this area, P energy_n Let P be the output power of the nth energy unit in this region. energy_n,max Let q be the maximum output power of the nth energy unit, and q be the index of the region.

[0027] Furthermore, in step 4, the global optimization controller is used to retrieve corresponding power from other regions as replenishment and integrate it into this region. Specifically, this includes:

[0028] The regional collaborative controller generates a scheduling command to control the output power of each energy unit to be increased to the maximum value, and sends the scheduling command to the local controller corresponding to each energy unit. The local controller adjusts the output power of its corresponding energy unit to the maximum value through feedback.

[0029] Simultaneously, the regional collaborative controller is utilized based on the regional total power demand P. dem_q With the total dispatchable power P in the region sup_q Calculate the power supply amount, which is expressed as ΔP1 = P dem_q -P sup_q The system sends a power support request with a power supply amount of ΔP1 to the global optimization controller. Based on the power supply amount of ΔP1, the global optimization controller selects energy units with surplus power in other areas from high to low priority and generates corresponding power allocation instructions, which are sent to the local controllers corresponding to the selected energy units. The local controllers adjust the output power of the selected energy units to the corresponding allocation value through feedback adjustment. Then, the global optimization controller opens the power grid line from the output terminal of the selected energy unit to the input terminal of the load unit to be supplied in this area, so that the extra power output by the selected energy unit is transmitted to this area through the power grid line and finally flows into the input terminal of the load unit to be supplied in this area.

[0030] Furthermore, the power hierarchical collaborative support method for ship power systems based on flexible networking is characterized in that, in step 41, the surplus power of each energy unit in this region is expressed as:

[0031] P surplus_n =P energy_n,max -P allo_n

[0032] In the formula, P surplus_q,n P represents the surplus power of the nth energy unit in this region. energy_n,max P is the maximum output power of the nth energy unit. allo_n The optimal power allocation for the nth energy unit in this region.

[0033] Further, in step 42, the absolute difference between the actual total power of the region and the total distributed power of the region is calculated, specifically including:

[0034] The output voltage and current of each energy unit are collected by the Hall sensors built into the interconnected nodes in this region. The actual power of each energy unit is calculated based on the output voltage and current. The actual total power of the region is calculated using the regional collaborative controller based on the actual power of each energy unit. The total allocated power of the region is calculated based on the optimal power allocation of each energy unit. Finally, the absolute difference between the actual total power of the region and the total allocated power of the region is calculated and expressed as:

[0035] |ΔP2|=|ΣP allo_n -ΣP act_n |

[0036] In the formula, |ΔP2| is the absolute difference between the actual total power of the region and the total distributed power of the region, P act_n P represents the actual power of the nth energy unit in this region. allo_n The optimal power allocation for the nth energy unit in this region.

[0037] Furthermore, in step 42, the output power of the energy units in this region is fine-tuned to ensure that they reach the total power requirement within a preset time period, specifically including:

[0038] The preset threshold is set to 5% ΣP allo_n |ΔP2|≤5%ΣP allo_n For a single energy unit, an MPC model is established with a prediction time domain of N=10 and a control time domain of M=4. The objective function of the MPC model is set as follows:

[0039] min J=ΣΔP(t+k) 2 +ρΣΔu(t+j) 2

[0040] The constraints of the MPC model are: u min ≤u(t-1)+ΣΔu(t+j)≤u max ,Δu min ≤Δu≤Δu max ;

[0041] In the formula, J is the objective function of the MPC model, t is time, k = 1, 2, ..., N, j = 0, 1, ..., 4, ΔP(t+k) is the power gap at the k-th time step, Δu(t+j) is the control increment at the j-th future control step, u(t-1) is the output power of the energy unit at time t-1, u min and u max To control the upper and lower limits of the quantity, Δu min and Δu max To control the upper and lower limits of the increment, ρ is the penalty coefficient;

[0042] The predicted ΔP(t+k) is expressed as:

[0043] ΔP(t+k)=f k -Σc k,j Δu(t+j)

[0044] In the formula, f k c is the predicted power deficit value for the current energy unit when no power control is applied to it. k,j The influence coefficient of the control increment at the j-th control step on the power gap at the k-th time step;

[0045] The predicted ΔP(t+k) is substituted into the objective function of the MPC model. Under the premise of satisfying the constraints of the MPC model, the objective function of the MPC model is solved using the QP solver to minimize the J value, and finally the control increment sequence Δu(t), Δu(t+1), Δu(t+2), Δu(t+3), Δu(t+4) is obtained. Δu(t) is used as the power adjustment amount of the current control step. The corresponding scheduling instruction is generated by the regional cooperative controller and sent to the local controller corresponding to the current energy unit. The local controller adjusts the output power of the current energy unit so that the output power of the current energy unit at time t is updated to u(t-1)+Δu(t). The output power of the current energy unit is updated repeatedly until the output power of the current energy unit is adjusted to the corresponding optimal allocation power.

[0046] Furthermore, in step 42, the output power of the energy units within this region is directly adjusted to quickly reach the total power requirement, specifically including:

[0047] When |ΔP2|>5%ΣP allo_n At that time, the regional collaborative controller generates corresponding scheduling instructions based on the optimal power allocation of each energy unit in the region, and sends these scheduling instructions to the local controllers corresponding to each energy unit. The local controllers then use feedback adjustment to quickly adjust the output power of each energy unit to the corresponding optimal power allocation.

[0048] Furthermore, step 2 specifically includes:

[0049] Based on dynamic response time, unit energy cost, and unit carbon emissions, the priority coefficient R of each energy unit is calculated. Energy units with R ≥ 0.8 are classified as Level 1 support units, those with R ≤ 0.5 and R < 0.8 are classified as Level 2 support units, and those with R < 0.5 are classified as Level 3 support units. The priority coefficient R of a single energy unit is expressed as follows:

[0050] R = α·(1 / τ) i )+β·(1 / C i )+γ·(1 / Em i )

[0051] where τ i is the dynamic response time of the i-th energy unit, C i is the unit energy cost of the i-th energy unit, Em i is the unit carbon emission of the i-th energy unit, and α, β, and γ are weight coefficients;

[0052] Calculate the sensitivity coefficient S of a single load unit according to the allowed voltage deviation and frequency deviation of the single load unit. Classify the load unit with S ≤ 0.02 as a first-level sensitive load, classify the load unit with 0.02 < S ≤ 0.05 as a second-level ordinary load, and classify the load unit with S > 0.05 as a third-level adjustable load. The sensitivity coefficient S of a single load unit is expressed as:

[0053] S = λ·(ΔU j / U j ) + μ·(Δf j / f j )

[0054] where ΔU j is the allowed voltage deviation of the j-th load unit, U j is the rated voltage of the j-th load unit, Δf j is the allowed frequency deviation of the j-th load unit, f j is the rated frequency of the j-th load unit, and λ and μ are weight coefficients.

[0055] The beneficial effects of this application are:

[0056] First, the technical solution in this application constructs a three-level power dispatch architecture for ship power systems, comprising a system layer, a regional layer, and a unit layer. Different controllers and power regulation methods are set up at different levels. The system layer is responsible for global power balance, the regional layer is responsible for power coordination of energy units and load units within each region, and the unit layer is responsible for power control of each energy unit. This hierarchical architecture and control strategy enables collaborative work between the various levels, achieving multi-objective optimized dispatch based on energy cost, carbon emissions, and power demand at the global level, while also ensuring priority management and power coordination allocation of energy units and load units within each region. Furthermore, the intermediate regional layer is managed in zones, which is a significant improvement over traditional methods that rely solely on a global controller for overall management or employ a two-layer control system. The technical solution in this application combines real-time global optimization, independent control within a region, inter-regional coordination, and local feedback control to precisely regulate each energy unit and load unit in the ship's power system. The global controller only needs to manage multi-objective optimization scheduling and inter-regional power scheduling, without the need for detailed control of energy units and responsible units at the unit level. This effectively reduces the computational load and time of global optimization. Furthermore, the use of independent controllers for parallel control between regions improves the overall system's response speed and computational efficiency, reduces fluctuations in system frequency and voltage during power regulation, and controls the system frequency deviation within ±0.5Hz and the voltage deviation within ±3%, meeting the stringent power quality requirements of sensitive loads.

[0057] Furthermore, the technical solution in this application is designed for ship power systems based on flexible networking, which can ensure that the system operates efficiently under different load demands and changes in the external environment. When the number of energy units or load units changes, the system can respond quickly and readjust the power distribution strategy without changing the grid structure. There is no need to reconstruct the entire system, which reduces the cost of modification and time, effectively copes with emergencies and uncertainties, and thus achieves efficient, stable and flexible management of ship power systems.

[0058] Secondly, the technical solution in this application sets up a system-level multi-objective optimization model for global power scheduling. When performing global power optimization scheduling, it can simultaneously optimize multiple objectives such as energy cost, carbon emissions, and voltage and frequency deviations of load units. This ensures stable system operation while reducing energy consumption and carbon emissions, meeting the requirements of green energy. The technical solution in this application also sets up a dynamic priority adjustment mechanism for energy units and load units. When the system power supply is insufficient, load units can be cut off according to their priority to ensure the stable operation of important loads in the system. The priority of energy units is continuously updated within a preset time. When there is an emergency or sudden change in power demand in the system, high-priority energy units can be called up to provide power in a timely manner to maintain system stability. During system operation, new energy and energy storage equipment are used preferentially to reduce the usage time of diesel generators, thereby reducing carbon emissions and improving the environmental performance of the system.

[0059] Third, the technical solution in this application also sets up an optional adjustment scheme at the unit level. When the difference between the actual total power of the area and the total allocated power of the area is small, the output power of the energy unit is adjusted by fine adjustment so that it reaches the power demand within a preset time, thereby reducing the fluctuation of system voltage and frequency. When the difference between the actual total power of the area and the total allocated power of the area is large, the output power of the energy unit is adjusted by rapid adjustment so as to quickly adjust the power output to the required level, preventing the occurrence of system power shortage or overload, further ensuring the stability of the frequency and voltage of the ship's power system, and avoiding the impact of excessive fluctuations on the stable operation of sensitive loads. Attached Figure Description

[0060] The advantages of the above and / or additional aspects of this application will become apparent and readily understood in the description of the embodiments in conjunction with the following drawings, wherein:

[0061] Figure 1 This is a schematic flowchart of a power hierarchical collaborative support method for ship power systems based on flexible networking, according to an embodiment of this application.

[0062] Figure 2 This is a schematic diagram of a ship power system and a three-level power dispatch architecture according to an embodiment of this application. Detailed Implementation

[0063] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of this application can be combined with each other.

[0064] In the following description, many specific details are set forth in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below.

[0065] It should be noted that the ship's power system includes an energy layer, a flexible interconnection layer, a load layer, and a hierarchical collaborative control center. The energy layer contains multiple energy units, the load layer contains multiple load units, and the flexible interconnection layer contains multiple flexible interconnection nodes. These flexible interconnection nodes are used to enable flexible access and interconnection of different energy units and load units. The hierarchical collaborative control center is used to execute a pre-set power hierarchical collaborative support method for the ship's power system to uniformly monitor, coordinate, and optimize the scheduling of the energy layer, flexible interconnection layer, and load layer. This enables the coordinated operation of multiple energy units, dynamic management of flexible interconnection nodes (including the on / off state of functional paths between energy and load, power allocation, and dynamic topology reconstruction in the event of circuit failure), and orderly supply of loads, thereby ensuring the safety, reliability, and efficiency of the ship's power system.

[0066] like Figures 1 to 2 As shown, this embodiment provides a power hierarchical collaborative support method for ship power systems based on flexible networking, including:

[0067] Step 1: Construct a three-level power scheduling architecture comprising a system layer, a region layer, and a unit layer. At the system layer, deploy a Global Optimization Controller (GOC) responsible for global power balancing. At the region layer, deploy q region cooperative controllers (RCC1 to RCC1). q The regional coordination controller is used to manage the power coordination of n energy units and m load units within its region. Local controllers (LC / LC) are deployed at the unit level, and the local controllers are responsible for the power control of individual energy units.

[0068] It should be noted that before deploying the global optimization controller, regional collaborative controller, and local controller, the energy units and load units in the ship's electrical system can be divided into regions based on human experience. This ensures that the difference between the number of energy units and load units in each region and the number of energy units and load units in other regions does not exceed a predetermined value, and that there is a power supply relationship between energy units and load units within a single region. For example... Figure 2 As shown in the figure, the black dashed lines represent signal transmission channels, while the blue, orange, and green solid lines represent power transmission channels.

[0069] The global optimization controller, regional collaborative controller and local controller communicate with each other through a star-ring hybrid communication network constructed via industrial Ethernet, with a communication rate of ≥100Mbps and a transmission delay of ≤10ms.

[0070] Step 2: Dynamically adjust the priorities of each energy unit and each load unit at a preset time interval. Calculate the priority of a single energy unit based on the dynamic response time, unit energy cost, and unit carbon emissions of the energy unit, and calculate the priority of a single load unit based on the allowable voltage deviation and frequency deviation of the load unit. Specifically, it includes:

[0071] Calculate the priority coefficient R of each energy unit by weighted summation according to the dynamic response time, unit energy cost, and unit carbon emissions. Divide each energy unit into different priorities according to the magnitude of the priority coefficient R. Specifically, the energy unit with R≥0.8 is classified as a first-level support unit (PU), the energy unit with 0.5≤R<0.8 is classified as a second-level support unit (PU), and the energy unit with R<0.5 is classified as a third-level support unit (PU); where the priority coefficient R of a single energy unit is expressed as:

[0072] R = α·(1 / τ i ) + β·(1 / C i ) + γ·(1 / Em i )

[0073] In the formula, τ i is the dynamic response time of the i-th energy unit, C i is the unit energy cost of the i-th energy unit, Em i is the unit carbon emissions of the i-th energy unit, and α, β, and γ are weight coefficients and α + β + γ = 1.

[0074] Calculate its sensitivity coefficient S by weighted summation according to the allowable voltage deviation and frequency deviation of a single load unit. Divide each load unit into different priorities according to the magnitude of the sensitivity coefficient S. Specifically, the load unit with S≤0.02 is classified as a first-level sensitive load (LU), the load unit with 0.02 < S≤0.05 is classified as a second-level ordinary load (LU), and the load unit with S > 0.05 is classified as a third-level adjustable load (LU); where the sensitivity coefficient S of a single load unit is expressed as:

[0075] S = λ·(ΔU j / U j ) + μ·(Δf j / f j )

[0076] In the formula, ΔU j is the allowable voltage deviation of the j-th load unit, U j is the rated voltage of the j-th load unit, Δf<00​​Let λ be the rated frequency of the j-th load unit, and let λ and μ be weighting coefficients, with λ + μ = 1.

[0077] In this embodiment, R and S are calculated every 10 seconds. When SOC < 30%, the coefficient α of the energy storage battery decreases by 0.2, and the coefficient β increases by 0.2. When the photovoltaic power fluctuation is greater than 20% of the original output power, the coefficient α of the supercapacitor increases by 0.1. SOC < 30% indicates that the energy storage battery has too little power, so α is decreased and β is increased to reduce its priority. The photovoltaic power fluctuation is greater than 20% of the original output power, indicating that the photovoltaic fluctuation is too large and the output is unstable. The supercapacitor can quickly release electrical energy to smooth the fluctuation. Therefore, the control strategy increases the coefficient α of the supercapacitor (increasing its priority) to allow the supercapacitor to participate in regulation first and ensure stable power.

[0078] Step 3: Perform global power optimization scheduling at the system level using a global optimization controller. Specifically, establish a multi-objective optimization model at the system level based on energy cost, carbon emissions, and allowable voltage and frequency deviations of load units. Calculate the total system power demand and total system dispatchable power using the global optimization controller. If the total system power demand is less than or equal to the total system dispatchable power, use an improved particle swarm optimization algorithm (IPSO) to solve the model by iteratively adjusting the output power of each energy unit to simultaneously optimize multiple objectives and obtain the optimal power allocation for each energy unit. If the total system power demand is greater than the total system dispatchable power, first cut off load units exceeding the system dispatchable power range according to priority from low to high, and then solve the model.

[0079] A system-level multi-objective optimization model is established based on the energy cost and carbon emissions of the energy unit, as well as the allowable voltage and frequency deviations of the load unit. The system-level multi-objective optimization model is expressed as follows:

[0080] min F=ω1|Δf j |+ω2(1 / p)Σ|ΔU j |+ω3Σ(P energy_i· C i )+ω4Σ(P energy_i· Em i )

[0081] The constraints of the system-level multi-objective optimization model are: P energy_i,min ≤P energy_i ≤P energy_i,max SOC i,min ≤SOC i ≤SOC i,max ,|P energy_i |≤P point P dem =P sup ;

[0082] In the formula, F is the objective function of the system-level multi-objective optimization model, ω1, ω2, ω3, and ω4 are the weighting coefficients of the objective function F, used to determine the importance of different objectives, and Δf j Let ΔU be the allowable frequency deviation of the j-th load unit in the system. j Let P be the allowable voltage deviation of the j-th load unit in the system, and p be the proportional coefficient (a constant). energy_i Let C be the output power of the i-th energy unit in the system. i Let Em be the unit energy cost of the i-th energy unit in the system. i P represents the carbon emissions per unit of the i-th energy unit in the system. energy_i,min and P energy_i,max Let SOC be the upper and lower limits of the output power of the i-th energy unit in the system. i State of Charge (SOC) represents the state of charge (SOC) of the i-th energy unit in the system, which is the ratio between the current charge of the energy unit and its rated capacity, usually expressed as a percentage. i,min and SOC i,max P represents the upper and lower limits of the charge state of the i-th energy unit in the system. point Due to the transmission capacity limitations of interconnected nodes, P dem For the total power requirement of the system, P sup This represents the total dispatchable power of the system.

[0083] The global optimization controller is used to calculate the total power demand and total dispatchable power of the system, specifically including:

[0084] Calculate the total power demand P of the system based on the power requirements of each load unit and the system line losses. dem The total dispatchable power P of the system is calculated based on the maximum output power of each energy unit. sup Total system power requirement P dem and the total dispatchable power P of the system sup They are represented as follows:

[0085] P dem =ΣP load_j +P loss

[0086] P sup =ΣP energy_i,max

[0087] In the formula, P load_j Let P be the power requirement of the j-th load unit in the system. loss For system line losses, P energy_i Let P be the output power of the i-th energy unit in the system. energy_i,max Let P be the maximum output power of the i-th energy unit.dem ≤P sup Perform normal scheduling, if P dem >P sup Then, load units are gradually removed in order of priority from low to high (load units with the same priority are removed randomly) until P. dem ≤P sup Then, normal scheduling will be performed.

[0088] It should be noted that in an ideal system design, the total dispatchable power is usually greater than the total power demand of the system. However, in actual operation, there will be changes in load demand (such as a sudden increase in load power demand or the addition of new loads to the system) and changes in energy unit output (such as energy failure, changes in output power due to changes in state of charge, or the influence of external environmental factors such as weather and sunlight). The above power balance judgment is set up to ensure that the system can cope with uncertainties or emergencies during dynamic operation.

[0089] An improved particle swarm optimization (IPSO) algorithm is used to solve the model by iteratively adjusting the output power of each energy unit, thereby simultaneously optimizing multiple objectives and obtaining the optimal power allocation for each energy unit. Specifically, this includes:

[0090] The initial solution set is set based on the constraints of the system-level multi-objective optimization model. Each solution in the solution set corresponds to an array containing the output power of all energy units. The objective function F of each solution in the solution set is calculated. Poorly performing solutions are removed based on the objective function value. Excellent solutions are crossbred and mutated to generate new offspring. The offspring and parent are merged to form a new solution set. The objective function F of each solution in the new solution set is calculated again, and the solution set is updated again based on the calculation results. This process is repeated until the maximum number of iterations (100 generations) is reached. In each generation, the particles in the solution set continuously approach the optimal solution through local search and global search. The initial number of particles is set to 50, the maximum number of iterations is 100, and the inertia weight is linearly decreased (0.9→0.4). After the iteration is completed, a set of the best performing solutions is randomly selected from the final solution set as the output to obtain the optimal power allocation of each energy unit (i.e., the power allocation of each energy unit). The optimal power allocation of each energy unit is used as the output command of the global optimization controller and transmitted to the regional cooperative controllers respectively.

[0091] It should be noted that the Improved Particle Swarm Optimization (IPSO) algorithm is an optimization algorithm used to solve complex multi-objective optimization and constrained optimization problems. It can improve search efficiency, avoid premature convergence, and enhance global and local search capabilities by dynamically adjusting parameters such as inertia weights, learning factors, and particle velocities, ultimately finding the optimal solution to the problem.

[0092] Step 4: Utilize regional collaborative controllers to perform power collaborative control at the regional level, and use local controllers to adjust the power of each energy unit. Specifically, for a single region, the regional collaborative controller calculates the total regional power demand and the total regional dispatchable power. If the total regional power demand exceeds the total regional dispatchable power, the global optimization controller retrieves corresponding power from other regions as supplementary power and integrates it into this region. If the total regional power demand is less than or equal to the total regional dispatchable power, the following steps are executed:

[0093] Step 41: Calculate the surplus power of each energy unit in this region based on the maximum output power and optimal allocation power of each energy unit in this region, and report it to the global optimization controller.

[0094] Step 42: Calculate the absolute difference between the actual total power of the region and the total allocated power of the region. If the absolute difference is less than or equal to a preset threshold, fine-tune the output power of the energy units in this region so that they reach the total power requirement within a preset time. If the absolute difference is greater than the preset threshold, directly adjust the output power of the energy units in this region so that they quickly reach the total power requirement. When calling on the energy units in this region to supplement power, they are selected in descending order of priority.

[0095] In step 4 above, the regional cooperative controller is used to calculate the total regional power demand and the total regional dispatchable power within the region. This specifically includes:

[0096] Calculate the total power demand P of the region based on the power requirements of each load unit in the region and the regional line losses. dem_q The total dispatchable power P of the region is calculated based on the maximum output power of each energy unit in the region. sup_q Regional total power demand P dem_q and the total dispatchable power P of the region sup_q They are represented as follows:

[0097] P dem_q =ΣP load_m +P loss,q

[0098] P sup_q =ΣP energy_n,max

[0099] In the formula, P load_m For the power requirement of the m-th load unit in this region, P loss,q The line loss in this area (take the total power demand P of the area) dem_q (3%-5%), P energy_n Let P be the output power of the nth energy unit in this region. energy_n,max Let q be the maximum output power of the nth energy unit, and q be the index of the region.

[0100] In step 4 above, if the total power demand of a region exceeds the total dispatchable power of the region, the global optimization controller will draw power from other regions as a supplement to this region. Specifically, this includes:

[0101] If P dem_q >P sup_q This indicates that the total dispatchable power within this region cannot meet the total power demand within the region. Therefore, it is necessary to draw power from other regions with surplus power to supplement this region. The regional coordination controller generates dispatch instructions to increase the output power of each energy unit to its maximum value and sends these instructions to the local controllers corresponding to each energy unit. Each local controller then adjusts the output power of its corresponding energy unit to its maximum value through feedback, thereby improving the power supply within the region. Simultaneously, the regional coordination controller uses the total regional power demand P... dem_q With the total dispatchable power of the region

[0102] P sup_q Calculate the power supply amount, which is expressed as ΔP1 = P dem_q -P sup_q The system sends a power support request with a power supply amount of ΔP1 to the global optimization controller. Based on the power supply amount of ΔP1, the global optimization controller selects energy units with surplus power in other areas according to priority from high to low, and generates corresponding power allocation instructions, which are sent to the local controllers corresponding to the selected energy units. These local controllers adjust the output power of the selected energy units to the corresponding allocation value through feedback adjustment. Then, the global optimization controller opens the power grid line from the output end of the selected energy unit to the input end of the load unit to be supplied in this area. This allows the extra power output of the selected energy unit (extra power is the part of the power that is increased on the basis of the original power and used to supply power to the load units in other areas, i.e., the extra output power supply amount) to be transmitted to this area through the power grid line, and finally flows into the input end of the load unit to be supplied in this area to provide power support to the load unit to be supplied in this area, so that the total power demand in this area is met.

[0103] It should be noted that the flexible interconnection layer contains a complex power grid connecting various energy units and load units. The power grid lines between different areas are typically closed, only opening when needed. These power grid lines also incorporate components for power distribution. Under the control of the global optimization controller and the regional coordination controller, these components can adjust their respective parameters to achieve the required power distribution. Therefore, the system can dynamically adjust and transmit power based on power demand and supply conditions, thereby supporting the power requirements of loads in each area.

[0104] In step 41 above, the surplus power of each energy unit in the region is calculated based on the maximum output power and optimal allocation power of each energy unit in the region, and reported to the global optimization controller. Specifically, this includes:

[0105] The surplus power of each energy unit in this region is represented as follows:

[0106] P surplus_n =P energy_n,max -P allo_n

[0107] In the formula, P surplus_q,n P represents the surplus power of the nth energy unit in this region. energy_n,max P is the maximum output power of the nth energy unit. allo_n Optimize the power allocation for the nth energy unit in this region; use the regional collaborative controller to report the surplus power of each energy unit in this region to the global optimization controller.

[0108] Step 42 above specifically includes:

[0109] The output voltage and current of each energy unit are collected by the Hall sensors built into the interconnected nodes in this region. The actual power of each energy unit is calculated based on the output voltage and current. The actual total power of the region is calculated using the regional collaborative controller based on the actual power of each energy unit. The total allocated power of the region is calculated based on the optimal power allocation of each energy unit. Finally, the absolute difference between the actual total power of the region and the total allocated power of the region is calculated and expressed as:

[0110] |ΔP2|=|ΣP allo_n -ΣP act_n |

[0111] In the formula, |ΔP2| is the absolute difference between the actual total power of the region and the total distributed power of the region, P act_n P represents the actual power of the nth energy unit in this region. allo_n The optimal power allocation for the nth energy unit in this region;

[0112] The preset threshold is set to 5% ΣP allo_n If |ΔP2|≤5%ΣP allo_n Then, Model Predictive Control (MPC) is used to fine-tune the output power of the energy units in this region so that they reach the total power demand within a preset time. Specifically, for a single energy unit, an MPC model is established with a prediction time domain of N=10 and a control time domain of M=4. The objective function of the MPC model is set as follows:

[0113] min J=ΣΔP(t+k) 2 +ρΣΔu(t+j) 2

[0114] The constraints of the MPC model are: u min ≤u(t-1)+ΣΔu(t+j)≤u max ,Δu min ≤Δu≤Δu max ;

[0115] In the formula, J is the objective function of the MPC model, t is the current time, k is the index of the time step (k = 1, 2, ..., N), j is the index of the control step (j = 0, 1, ..., 4), ΔP(t+k) is the power gap at the k-th time step (a positive value indicates the amount of power that needs to be increased, and a negative value indicates the amount of power that needs to be reduced due to oversupply), Δu(t+j) is the control increment at the j-th future control step, u(t-1) is the output power of the energy unit at time t-1, u min and u max To control the upper and lower limits of the quantity, Δu min and Δu max To control the upper and lower limits of the increment, ρ is the penalty coefficient;

[0116] The formula for predicting ΔP(t+k) is expressed as follows:

[0117] ΔP(t+k)=f k -Σc k,j Δu(t+j)

[0118] In the formula, f k The predicted power deficit (i.e., the system's free response) for an energy unit when no power control is applied is given by c. k,j The influence coefficient (which is a constant) of the control increment at the j-th control step on the power gap at the k-th time step.

[0119] It should be noted that in practical systems, existing integrated modular energy management systems (EMS) are typically used to manage f. k The EMS system automatically calls the load forecasting module and the weather forecasting module to calculate the corresponding power deficit forecast value f. k .

[0120] The predicted ΔP(t+k) is substituted into the objective function of the MPC model. Under the premise of satisfying the constraints of the MPC model, the objective function of the MPC model is solved using a QP solver to minimize the J value, finally obtaining the control increment sequence Δu(t), Δu(t+1), Δu(t+2), Δu(t+3), Δu(t+4). The first step control increment Δu(t) in the control increment sequence is used as the power adjustment amount of the current control step. The corresponding scheduling instruction is generated by the regional cooperative controller and sent to the local controller corresponding to the current energy unit. The local controller adjusts the output power of the current energy unit through feedback adjustment, so that the output power of the current energy unit at time t is updated to u(t-1)+Δu(t). The process of predicting ΔP(t+k) and using the local controller for feedback adjustment is repeated until the output power of the current energy unit is adjusted to the corresponding optimal allocation power, so that the output power of the energy units in this region reaches the total power demand. The objective function of the MPC model is solved using a QP solver, and the formula is expressed as:

[0121]

[0122] In the formula, ΔU is the control increment sequence of the energy unit, ΔU=[Δu(t), Δu(t+1), Δu(t+2), Δu(t+3), Δu(t+4), 0, 0, 0, 0, 0] T Since the control time domain is M=4, this application directly sets the last five values ​​of ΔU to 0, and f is the predicted power gap vector sequence, f=[f1,f2,...,f N ] T C is the power gap influence coefficient matrix (derived from the pre-set calculation model in the system, and is a known quantity), used to describe the impact of ΔU on the future power gap.

[0123] In this embodiment, a QP solver is used to solve the objective function of the MPC model. The QP solver can be an operator-split quadratic programming solver (OSQP), a quadratic programming solver based on online active set strategy (qpOASES), a Gurobi optimizer, etc.

[0124] If |ΔP2|>5%ΣP allo_nThe system directly adjusts the output power of the energy units within the region to quickly reach the total power requirement. Specifically, the regional collaborative controller generates corresponding scheduling instructions based on the optimal power allocation for each energy unit in the region, and sends these instructions to the local controllers corresponding to each energy unit. The local controllers then use feedback adjustment to quickly adjust the output power of each corresponding energy unit to the corresponding optimal power allocation, ultimately enabling the output power of the energy units within the region to reach the total power requirement.

[0125] In this embodiment, when using the local controller for feedback control, the output voltage and current of each energy unit and each load unit can be collected by the Hall sensors built into the interconnect node to calculate the output power of each energy unit and the power of each load unit. In this embodiment, different energy units adopt different feedback control methods. For example, the supercapacitor adopts virtual impedance droop control; the energy storage battery adopts virtual synchronous machine (VSG) control to simulate the inertia and damping characteristics of a synchronous generator; and the diesel generator adopts PID speed regulation control, with the speed deviation adjusted by the PID to output a throttle control signal.

[0126] It should be noted that when changing the power demand of a single load unit, an external device can send a power adjustment command to the regional coordinating controller of the area where the load unit is located. The regional coordinating controller will then generate a power limit command based on the power adjustment command and adjust the output power of the load inverter by adjusting the PWM signal to change the power demand of the load unit.

[0127] In this embodiment, the hierarchical collaborative control center also has a built-in fault tolerance mechanism. Specifically, the hierarchical collaborative control center monitors the operating status of the global optimization controller, regional collaborative controller, and local controller in real time. When a controller failure is detected, the fault tolerance mechanism is automatically activated. For the global optimization controller, when its failure is detected, the backup global optimization controller is activated and switched on within 50ms. A preset power allocation scheme (e.g., supercapacitors bear 20%, energy storage batteries bear 30%, and two diesel generators bear 50% of the base load power) to maintain system operation. The adjustment process is the same as in step 4. For the regional collaborative controller, when its failure is detected... When the failure occurs, the region merging strategy is activated, merging the energy units and load units in the region corresponding to the failed region's coordinating controller into the adjacent region. The adjacent region's coordinating controller then takes over, and continues to adjust according to the power allocation instructions issued by the global optimization controller. The adjustment process is the same as in steps 3-4. For each energy unit's local controller, when a communication interruption is detected (no instructions received for 3 consecutive cycles), it is determined to be failed, and the region's coordinating controller in the region where the local controller is located takes over its control task. The region's coordinating controller adjusts the output power of the energy unit through feedback control.

[0128] Example 1:

[0129] A power system architecture comprising a system layer, a region layer, and a unit layer is established in the simulation platform. The energy layer in the target region (the experimental test area) includes: two diesel generators (rated power 1000kW, dynamic response time 500ms), one energy storage battery (capacity 500kWh, SOC range 20%-90%, dynamic response time 50ms), one supercapacitor (capacity 50kWh, dynamic response time 10ms), and one photovoltaic system (rated power 200kW, output power fluctuates due to sunlight). The load layer includes: propulsion load (rated power 1500kW, an impact load), navigation and communication equipment (power 50kW, a level-one sensitive load), residential electricity load (power 200kW, a level-two ordinary load), and an adjustable air conditioning system (power 100kW, a level-three adjustable load). The flexible interconnection node uses a three-phase four-arm inverter, possessing independent control capabilities for active and reactive power, with a power transmission capacity of 800kW.

[0130] Prioritization is performed according to the method in step 2: supercapacitors (fastest dynamic response, responsible for suppressing high-frequency power fluctuations) are primary support units; energy storage batteries (relatively fast dynamic response, responsible for smoothing mid-frequency power fluctuations) are secondary support units; diesel generators (slower dynamic response, but large power capacity, responsible for base load power support) are tertiary support units; photovoltaic systems (large output fluctuations, serving as auxiliary energy, with a lower priority than the above three) are quaternary auxiliary units. Navigation and communication equipment (related to ship navigation safety, highly sensitive to voltage and frequency) are primary sensitive loads; propulsion loads (ensuring ship navigation, a critical load, but allowing small power fluctuations in a short period) are secondary important loads; domestic electricity loads (generally have lower power quality requirements) are tertiary ordinary loads; adjustable air conditioning systems (can adjust operating power according to system power conditions) are quaternary adjustable loads.

[0131] Setting up operating condition 1: Supercapacitor output 0kW, energy storage battery 400kW, two diesel generators 400kW and 420kW respectively, photovoltaic system output 180kW, where the photovoltaic system power suddenly drops by 20kW (from 180kW to 160kW, Δ=-20kW); the power system provides layered coordinated support for operating condition 1, the specific steps are as follows:

[0132] S101: Calculate the total system power demand and total system dispatchable power using GOC.

[0133] P dem =1000+50+200+100+50=1400kW(P loss =50kW),

[0134] P sup =1000×2+500×0.8+50×1+180=2630kW, P dem ≤P sup Perform normal scheduling;

[0135] S102: The IPSO algorithm is used to iteratively adjust the output power of each energy unit to solve the system-level multi-objective optimization model. Let ω1 = 0.4, ω2 = 0.3, ω3 = 0.2, ω4 = 0.1, obtain the optimal power allocation of each energy unit and send it to RCC. The optimal power allocation is: supercapacitor 20kW, energy storage battery 400kW, two diesel generators 400kW and 420kW respectively, photovoltaic system 160kW;

[0136] S103: After receiving the command, the RCC reports the surplus power of each energy unit: 30kW for the supercapacitor, 100kW for the energy storage battery, and 600kW and 580kW for the two diesel generators respectively. It also calculates the absolute difference between the actual total power of the region and the total allocated power of the region.

[0137] |ΔP2|=20kW≤5%ΣP allo_n , ΣP allo_n =20+400+400+420+160=1400kW. The output power of the supercapacitor in this region is finely adjusted by MPC. The supercapacitor adopts virtual impedance droop control. The output power is adjusted from 0kW to 20kW within 10ms, and the power support is completed.

[0138] Simulation test results: The system frequency dropped from 50Hz to 49.95Hz and then quickly recovered to 50Hz. The system voltage fluctuation was controlled within ±2%, i.e., 110V×2%=2.2V. The first-level sensitive load navigation device was powered normally and was not affected by the voltage fluctuation, proving that the method of the present invention can meet the requirements of navigation devices and achieve stable power support.

[0139] Setting up operating condition 2: Supercapacitor output 20kW, energy storage battery 400kW, two diesel generators 400kW and 420kW respectively, photovoltaic system output 160kW, where the propulsion load suddenly increases by 300kW (from 1000kW to 1300kW, Δ = 300kW); the power system provides layered and coordinated support for operating condition 2, with the specific steps as follows:

[0140] S201: Calculate the total system power demand and total system dispatchable power using GOC.

[0141] P dem =1300+50+200+100+60=1710kW(P loss =60kW),

[0142] P sup =1000×2+500×0.8+50×1+180=2630kW, P dem ≤P sup Perform normal scheduling;

[0143] S202: The IPSO algorithm is used to iteratively adjust the output power of each energy unit to solve the system-level multi-objective optimization model. ω1 = 0.4, ω2 = 0.3, ω3 = 0.2, and ω4 = 0.1 are set to obtain the optimal power allocation for each energy unit and send it to the RCC. The optimal power allocation is as follows: supercapacitor 20kW, energy storage battery 400kW, two diesel generators 600kW (an increase of 200kW) and 530kW (an increase of 110kW) respectively, and photovoltaic system 160kW.

[0144] S203: After receiving the command, the RCC reports the surplus power of each energy unit: supercapacitor 30kW, energy storage battery 100kW, and two diesel generators 400kW and 470kW respectively, and calculates the absolute difference between the actual total power of the region and the total allocated power of the region |ΔP2|=300kW>5%ΣP allo_n , ΣP allo_n =20+400+400+420+160=1400kW. The RCC sends commands to the corresponding LC to set the output power of the two diesel generators to 600kW and 530kW respectively. The LC uses PID speed regulation to adjust the output power of the two diesel generators from 400kW and 420kW to 600kW and 530kW respectively within 500ms, and the power support is completed.

[0145] Simulation test results: The system frequency dropped from a minimum of 50Hz to 49.95Hz (within the allowable range), the system voltage stabilized at 380V±3%, and the power supply to the first-level sensitive load navigation device was normal and unaffected by voltage fluctuations, proving that the method of the present invention can meet the requirements of navigation devices and achieve stable power support.

[0146] In this example, the dynamic response time of the primary support unit is ≤50ms, the dynamic response time of the secondary support unit is 50ms-500ms, and the dynamic response time of the tertiary support unit is >500ms. The primary sensitive load requires voltage deviation ≤±2% and frequency deviation ≤±0.2Hz, while the secondary ordinary load requires voltage deviation ≤±5% and frequency deviation ≤±0.5Hz. The weighting coefficients ω1, ω2, ω3, and ω4 are dynamically adjusted according to the ship's navigation conditions. When the ship is in an emergency navigation condition, the weighting coefficients for system frequency deviation and voltage deviation increase, while when the ship is in an economical navigation condition, the weighting coefficients for energy consumption cost and carbon emissions increase.

[0147] The steps in this application can be rearranged, combined, or deleted according to actual needs.

[0148] The units in the device of this application can be merged, divided, or deleted according to actual needs.

[0149] Although this application has been disclosed in detail with reference to the accompanying drawings, it should be understood that these descriptions are merely exemplary and not intended to limit the application of this application. The scope of protection of this application is defined by the appended claims and may include various variations, modifications, and equivalents of the invention without departing from the scope and spirit of this application.

Claims

1. A power hierarchical collaborative support method for ship power systems based on flexible networking, wherein the ship power system includes an energy layer, a flexible interconnection layer, and a load layer, and the flexible interconnection layer contains multiple flexible interconnection nodes for flexible access and interconnection of different energy units and load units, characterized in that, The method includes: Step 1: Construct a three-level power scheduling architecture including system layer, region layer, and unit layer. Deploy a global optimization controller at the system layer to control global power balance. Deploy a predetermined number of region coordination controllers at the region layer to control the power coordination of energy units and load units in each region. Deploy a local controller for each energy unit at the unit layer. Step 2: Dynamically adjust the priority of each energy unit and each load unit according to the preset time interval. Calculate the priority of a single energy unit based on the dynamic response time, unit energy cost, and unit carbon emission of the energy unit. Calculate the priority of a single load unit based on the allowable voltage deviation and frequency deviation of the load unit. Step 3: Establish a multi-objective optimization model at the system level based on energy cost, carbon emissions, and allowable voltage and frequency deviations of load units. Calculate the total system power demand and total system dispatchable power using a global optimization controller. If the total system power demand is less than or equal to the total system dispatchable power, iteratively solve the model using an improved particle swarm optimization algorithm to obtain the optimal power allocation for each energy unit and transmit it to the regional collaborative controllers. If the total system power demand is greater than the total system dispatchable power, first cut off load units exceeding the system dispatchable power range according to priority from low to high, and then solve the model. Step 4: For a single region, use the region coordination controller to calculate the region's total power demand and total schedulable power. If the region's total power demand is greater than its total schedulable power, use the global optimization controller to extract corresponding power from other regions as replenishment to this region. If the region's total power demand is less than or equal to its total schedulable power, then execute the following steps: Step 41: Calculate the surplus power of each energy unit in this region based on the maximum output power and optimal allocation power of each energy unit in this region, and report it to the global optimization controller. Step 42: Calculate the absolute difference between the actual total power of the region and the total allocated power of the region. If the absolute difference is less than or equal to a preset threshold, fine-tune the output power of the energy units in this region so that they reach the total power requirement within a preset time. If the absolute difference is greater than the preset threshold, directly adjust the output power of the energy units in this region so that they quickly reach the total power requirement. When calling on the energy units in this region to supplement power, they are selected in descending order of priority.

2. The power hierarchical collaborative support method for ship power systems based on flexible networking as described in claim 1, characterized in that, In step 3, the system-level multi-objective optimization model is represented as follows: min F=ω1|Δf j |+ω2(1 / p)Σ|ΔU j |+ω3Σ(P energy_i· C i )+ω4Σ(P energy_i· Em i ) The constraints of the system-level multi-objective optimization model are: P energy_i,min ≤P energy_i ≤P energy_i,max SOC i,min ≤SOC i ≤SOC i,max ,|P energy_i |≤P point P dem =P sup ; In the formula, ω1, ω2, ω3, and ω4 are weighting coefficients, and Δf j Let ΔU be the allowable frequency deviation of the j-th load unit in the system. j Let P be the allowable voltage deviation of the j-th load unit in the system, where p is the proportional coefficient. energy_i Let C be the output power of the i-th energy unit in the system. i Let Em be the unit energy cost of the i-th energy unit in the system. i P represents the carbon emissions per unit of the i-th energy unit in the system. energy_i,min and P energy_i,max Let SOC be the upper and lower limits of the output power of the i-th energy unit in the system. i Let SOC be the charge state of the i-th energy unit in the system. i,min and SOC i,max P represents the upper and lower limits of the charge state of the i-th energy unit in the system. point Due to the transmission capacity limitations of interconnected nodes, P dem For the total power requirement of the system, P sup This represents the total dispatchable power of the system.

3. The power hierarchical collaborative support method for ship power systems based on flexible networking as described in claim 2, characterized in that, Step 3, which involves calculating the total power demand and total schedulable power of the system using a global optimization controller, specifically includes: Calculate the total system power demand P based on the power requirements of each load unit and the system line losses. dem The total dispatchable power P of the system is calculated based on the maximum output power of each energy unit. sup , is represented as: P dem =ΣP load_j +P loss P sup =ΣP energy_i,max In the formula, P load_j Let P be the power requirement of the j-th load unit in the system. loss This refers to system line losses.

4. The power hierarchical collaborative support method for ship power systems based on flexible networking as described in claim 1, characterized in that, Step 4, which involves using a regional collaborative controller to calculate the total regional power demand and the total regional schedulable power, specifically includes: Calculate the total power demand P of the region based on the power requirements of each load unit in the region and the regional line losses. dem_q The total dispatchable power P of the region is calculated based on the maximum output power of each energy unit in the region. sup_q , is represented as: P dem_q =ΣP load_m +P loss,q P sup_q =ΣP energy_n,max In the formula, P load_m For the power requirement of the m-th load unit in this region, P loss,q For the line loss in this area, P energy_n Let P be the output power of the nth energy unit in this region. energy_n,max Let q be the maximum output power of the nth energy unit, and q be the index of the region.

5. The power hierarchical collaborative support method for ship power systems based on flexible networking as described in claim 4, characterized in that, In step 4, the global optimization controller is used to retrieve corresponding power from other regions as a supplement and integrate it into this region. Specifically, this includes: The regional collaborative controller generates a scheduling command to control the output power of each energy unit to be increased to the maximum value, and sends the scheduling command to the local controller corresponding to each energy unit. The local controller adjusts the output power of its corresponding energy unit to the maximum value through feedback. Simultaneously, the regional collaborative controller is utilized based on the regional total power demand P. dem_q With the total dispatchable power P in the region sup_q Calculate the power supply amount, which is expressed as ΔP1 = P dem_q -P sup_q The system sends a power support request with a power supply amount of ΔP1 to the global optimization controller. Based on the power supply amount of ΔP1, the global optimization controller selects energy units with surplus power in other areas from high to low priority and generates corresponding power allocation instructions, which are sent to the local controllers corresponding to the selected energy units. The local controllers adjust the output power of the selected energy units to the corresponding allocation value through feedback adjustment. Then, the global optimization controller opens the power grid line from the output terminal of the selected energy unit to the input terminal of the load unit to be supplied in this area, so that the extra power output by the selected energy unit is transmitted to this area through the power grid line and finally flows into the input terminal of the load unit to be supplied in this area.

6. The power hierarchical collaborative support method for ship power systems based on flexible networking as described in claim 4, characterized in that, In step 41, the surplus power of each energy unit in this area is expressed as: P surplus_n =P energy_n,max -P allo_n In the formula, P surplus_q,n P represents the surplus power of the nth energy unit in this region. energy_n,max P is the maximum output power of the nth energy unit. allo_n The optimal power allocation for the nth energy unit in this region.

7. The power hierarchical collaborative support method for ship power systems based on flexible networking as described in claim 6, characterized in that, In step 42, calculate the absolute difference between the actual total power of the area and the total allocated power of the area, specifically including: Collect the output voltage and current of each energy unit through the Hall sensors built in the interconnection nodes of this area, calculate the actual power of each energy unit according to the output voltage and current, use the area coordination controller to calculate the actual total power of the area according to the actual power of each energy unit, calculate the total allocated power of the area according to the optimal allocated power of each energy unit, and then calculate the absolute difference between the actual total power of the area and the total allocated power of the area, which is expressed as: |ΔP2|=|ΣP allo_n -SP act_n | In the formula, |ΔP2| is the absolute difference between the actual total power of the region and the total distributed power of the region, P act_n P represents the actual power of the nth energy unit in this region. allo_n The optimal power allocation for the nth energy unit in this region.

8. The power hierarchical collaborative support method for ship power systems based on flexible networking as described in claim 7, characterized in that, In step 42, finely adjust the output power of the energy units in this area to make it reach the total power demand within the preset time, specifically including: The preset threshold is set to 5% ΣP allo_n |ΔP2|≤5%ΣP allo_n For a single energy unit, an MPC model is established with a prediction time domain of N=10 and a control time domain of M=4. The objective function of the MPC model is set as follows: min J=ΣΔP(t+k) 2 +ρΣΔu(t+j) 2 The constraints of the MPC model are: u min ≤u(t-1)+ΣΔu(t+j)≤u max ,Δu min ≤Δu≤Δu max ; In the formula, J is the objective function of the MPC model, t is time, k = 1, 2, ..., N, j = 0, 1, ..., 4, ΔP(t+k) is the power gap at the k-th time step, Δu(t+j) is the control increment at the j-th future control step, u(t-1) is the output power of the energy unit at time t-1, u min and u max To control the upper and lower limits of the quantity, Δu min and Δu max To control the upper and lower limits of the increment, ρ is the penalty coefficient; The predicted ΔP(t + k) is expressed as: ΔP(t+k)=f k -Σc k,j Δu(t+j) In the formula, f k The predicted power deficit value for the current energy unit when no power control is applied, c k,j The influence coefficient of the control increment at the j-th control step on the power gap at the k-th time step; Substitute the predicted ΔP(t + k) into the objective function of the MPC model. On the premise of satisfying the constraint conditions of the MPC model, use the QP solver to solve the objective function of the MPC model to minimize the J value. Finally, obtain the control increment sequence Δu(t), Δu(t + 1), Δu(t + 2), Δu(t + 3), Δu(t + 4). Take Δu(t) as the power adjustment amount of the current control step, use the area coordination controller to generate the corresponding scheduling instruction and send it to the local controller corresponding to the current energy unit, and use the local controller to adjust the output power of the current energy unit so that the output power of the current energy unit at time t is updated to u(t - 1)+Δu(t); repeat to update the output power of the current energy unit until the output power of the current energy unit is adjusted to the corresponding optimal allocated power.

9. The power hierarchical collaborative support method for ship power systems based on flexible networking as described in claim 8, characterized in that, In step 42, directly adjust the output power of the energy units in this area to make it quickly reach the total power demand, specifically including: When |ΔP2|>5%ΣP allo_n At that time, the regional collaborative controller generates corresponding scheduling instructions based on the optimal power allocation of each energy unit in the region, and sends these scheduling instructions to the local controllers corresponding to each energy unit. The local controllers then use feedback adjustment to quickly adjust the output power of each energy unit to the corresponding optimal power allocation.

10. The power hierarchical collaborative support method for ship power systems based on flexible networking as described in claim 1, characterized in that, Step 2 specifically includes: R=α·(1 / τ i )+β·(1 / C i )+γ·(1 / Em i ) In the formula, τ i Let C be the dynamic response time of the i-th energy unit. i Em represents the unit energy cost of the i-th energy unit. i Let be the unit carbon emission of the i-th energy unit, and α, β, and γ be weighting coefficients. Calculate the priority coefficient R of each energy unit according to the dynamic response time, unit energy cost and unit carbon emission. Divide the energy units with R≥0.8 into first-level support units, divide the energy units with 0.5≤R<0.8 into second-level support units, and divide the energy units with R<0.5 into third-level support units. The priority coefficient R of a single energy unit is expressed as: Calculate its sensitivity coefficient S according to the allowable voltage deviation and frequency deviation of a single load unit. Divide the load units with S≤0.02 into first-level sensitive loads, divide the load units with 0.02<S≤0.05 into second-level ordinary loads, and divide the load units with S>0.05 into third-level adjustable loads. The sensitivity coefficient S of a single load unit is expressed as: S=λ·(ΔU j / U j )+μ·(Δf j / f j ) In the formula, ΔU j U is the allowable voltage deviation for the j-th load unit. j Let Δf be the rated voltage of the j-th load unit. j f is the allowable frequency deviation for the j-th load unit. j Let λ be the rated frequency of the j-th load unit, and λ and μ be weighting coefficients.

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