Offshore energy platform cooperative scheduling method based on multi-energy complementation and layered optimization

Abstract

The invention relates to an offshore energy platform coordinated scheduling method based on multi-energy complementation and hierarchical optimization, which combines multi-energy complementation characteristic modeling, multi-target opportunity constraint optimization and rolling optimization, and realizes offshore multi-energy coordinated scheduling by constructing a hierarchical decoupling optimization and control system. Based on prediction and historical data of multiple types of energy such as offshore wind power, photovoltaic energy and tidal energy, complementarity and flexibility of the energy are quantified, high-quality data support is provided for scheduling optimization, a day-ahead layered optimization model containing renewable energy priority consumption and flexible standby configuration is constructed, and a medium-and-long-term output strategy is formulated. Output of various energy sources is dynamically adjusted through a rolling optimization mechanism, and flexible response to renewable energy fluctuation is achieved. And finally, second-level frequency and voltage support is realized by using a virtual synchronous machine and droop control, and the self-adaptive capability of the system is enhanced. According to the invention, the cooperative regulation capability and operation stability of the offshore platform multi-energy system can be effectively improved, and the dependence on a traditional standby power supply is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore energy scheduling, and in particular to a method for collaborative scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization. Background Art

[0002] With the rapid development of renewable energy technologies such as offshore wind power, photovoltaic power generation, and tidal power, offshore integrated energy platforms are gaining increasing attention as critical infrastructure supporting operations on remote islands and reefs, offshore industrial operations, communication relay, and ocean observation. To increase energy self-sufficiency and reduce fossil fuel consumption, an increasing number of offshore platforms are developing independent energy supply systems that primarily utilize renewable energy and integrate multiple energy sources. However, due to the complex and volatile offshore environment, a fragmented power grid structure, and limited communication resources, achieving the coordinated utilization and safe scheduling of renewable energy sources such as wind, solar, and tidal power has become a critical issue that needs to be addressed. Traditional multi-energy management strategies, often based on centralized optimization or empirical time-sharing control methods, can achieve energy sharing and redundant configuration to a certain extent, but lack systematic modeling and hierarchical design of the output patterns, complementary relationships, and responsiveness of multiple energy sources, making it difficult to fully realize the operational potential of renewable energy. Furthermore, since most offshore platforms are isolated or poorly connected, lacking external grid support, they are prone to power imbalances, frequency anomalies, and voltage instability when wind and solar power fluctuate significantly or tidal power is limited, seriously impacting the system's safe operation and energy efficiency.

[0003] To improve the operational efficiency and safety of multi-energy systems on offshore platforms, various optimization scheduling and control strategies have been proposed, including multi-timescale optimization methods based on predictive control, energy management strategies based on heuristic algorithms, and energy tracking methods with integrated intelligent controllers. While these methods have achieved some progress in model structure and operational mechanisms, they still face significant limitations: First, the prediction model accuracy is insufficient, making it unable to effectively characterize the short-term output fluctuations of renewable energy sources such as wind, solar, and tides; second, the optimization model lacks a hierarchical structure and flexible adjustment mechanism, resulting in a disconnect between scheduling strategies at different timescales, prone to frequent revisions or scheduling failures; third, in actual implementation, traditional PID or centralized control strategies struggle to rapidly support key indicators such as frequency and voltage, especially in isolated or weak grid conditions, posing significant risks to platform operational stability. Therefore, a multi-energy collaborative optimization method based on a multi-energy complementary mechanism and featuring hierarchical scheduling and control linkage is urgently needed to achieve efficient and stable operation of offshore energy platforms. Summary of the Invention

[0004] To overcome the problems of low multi-energy utilization efficiency and weak regulation capabilities in offshore energy platform scheduling, this paper discloses a collaborative scheduling method for offshore energy platforms based on multi-energy complementarity and hierarchical optimization. By effectively combining the multi-energy complementarity and hierarchical optimization, the system's clean energy utilization rate and operational stability are effectively improved.

[0005] The present invention discloses a method for collaborative scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization, comprising the following steps:

[0006] Based on the historical and forecast data of wind power, photovoltaic power, tidal power and load of offshore energy platforms, S1 analyzes the correlation and complementarity of different energy outputs, builds a multi-energy complementary characteristic model and a flexible supply and demand model, and provides an accurate data foundation for subsequent coordinated scheduling optimization. The specific implementation steps are as follows:

[0007] S11 collects environmental data such as wind speed v(t), solar irradiance G(t), tidal flow velocity u(t), and historical output data of wind power, photovoltaic power, and tide. wind (t), P solar (t), P tide (t), and the load forecast curve L(t) of the offshore energy platform. Wind power, photovoltaic, and tidal power prediction models are constructed to provide data for subsequent complementary analysis and optimized scheduling;

[0008] S12 quantitatively evaluates the output complementarity between different energy sources, reveals the synergistic characteristics of renewable energy output, and supports complementary priority scheduling design;

[0009] S13 evaluates the flexibility adjustment demand and flexibility resource supply capacity of offshore energy platforms in different time periods, determines whether there is a flexibility gap in the system in each time period, and ensures scheduling feasibility and system stability.

[0010] S2, based on the complementary relationships among multiple energy sources identified in S1 and the flexibility of each energy source, establishes a multi-objective day-ahead optimization model based on opportunity constraints, formulates a hierarchical scheduling plan centered on the priority utilization of renewable energy and the rational allocation of flexible backup resources, and formulates preliminary output instructions for stable system operation. The specific implementation steps are as follows:

[0011] S21 determines the multi-objective functions of the optimization model and the decision variables that need to be optimized, laying the foundation for the model solution. According to the actual operation requirements of the offshore energy platform, the optimization objectives mainly include three items: maximizing the total absorption of renewable energy (wind, solar and tide), minimizing diesel engine fuel consumption, and minimizing energy abandonment;

[0012] S22 builds a comprehensive, solvable, and fully constrained optimization model based on the objective function and decision variables defined in S21, combined with the energy complementarity characteristics, forecast data, and flexibility analysis results obtained in S1. This primarily involves constructing a weighted objective function and constraint system for the scheduling model.

[0013] Based on the hierarchical scheduling plan developed by S2, S3 obtains minute-level short-term forecast data of wind and solar power in real time. Combined with the complementary priority, it dynamically adjusts the output of the energy storage system, renewable energy, and diesel units through rolling optimization to achieve flexible response to fluctuations and efficient coordination of multiple energy sources. The specific implementation steps are as follows:

[0014] S31 builds a method that can identify in real time whether the current system operating status deviates from the planned benchmark set in S2, so as to redefine the target input data for scheduling optimization based on the degree and direction of deviation;

[0015] S32 establishes a multi-objective rolling optimization model with net load balance as the goal, achieving optimal coordinated scheduling of each energy unit in a short period of time, ensuring that the output matches the system demand, and taking into account economic efficiency and equipment life;

[0016] S33 builds a multi-energy response priority mechanism, which provides calling sequence and boundary settings for the rolling optimization module based on resource characteristics, complementary indicators and regulation capabilities, to avoid unreasonable resource waste or scheduling deviation.

[0017] Based on the dynamic complementary rolling optimization achieved in S3, S4 adopts virtual synchronous machine control and droop control strategies, and uses clean energy such as energy storage and tidal energy to provide second-level inertial response and frequency modulation support to ensure the frequency and voltage stability of the platform under isolated or weak network conditions. At the same time, a state feedback mechanism is formed to continuously correct the optimization plan and realize closed-loop adaptive scheduling. The specific implementation steps are as follows:

[0018] S41 introduces a virtual synchronous generator (VSG) to prevent frequency fluctuations caused by power disturbances, so that the energy storage system can not only follow the power dispatch plan but also actively provide virtual inertia and damping support to quickly respond to frequency changes and enhance system stability when there are sudden load disturbances or wind and solar fluctuations.

[0019] The S42 uses droop control as a control method for diesel generators and controllable tidal units, enabling it to automatically adjust active or reactive output when frequency or voltage fluctuates to achieve system balance support;

[0020] S43 establishes an information feedback channel between the S4 control layer and the S3 and S2 scheduling layers to achieve scheduling-control closed-loop linkage and continuously optimize system performance.

[0021] Beneficial effects of the present invention: The present invention has made innovative designs to address the problems of large fluctuations in energy output, insufficient complementary utilization, and weak regulation capabilities in the scheduling of offshore energy platforms, effectively solving the problems of low renewable energy utilization efficiency, poor system regulation capabilities, and slow scheduling response, and has achieved remarkable results. Specifically, in response to the problem of lack of systematic complementary modeling and flexibility assessment between multiple energy sources, the present invention first proposes a multi-energy complementarity analysis method based on historical and forecast data, constructs a complementary characteristic model and a flexibility supply and demand model, and provides accurate data support for scheduling optimization. Secondly, in response to the problem that traditional scheduling strategies are difficult to cope with uncertainty and multi-objective coordination, a multi-objective day-ahead hierarchical optimization model based on opportunity constraints is constructed to achieve the priority absorption of renewable energy and the reasonable allocation of backup resources, effectively taking into account clean utilization rate, economic cost and flexibility redundancy. Thirdly, in response to the frequent fluctuations in wind, solar and tidal output, a minute-level rolling optimization mechanism is designed, combined with a multi-energy response priority strategy, to dynamically adjust the output of each energy unit, significantly improving the system's adaptability to sudden disturbances and regulation accuracy. Finally, a virtual synchronous machine and droop control strategy are introduced at the control layer, and energy storage and tidal power generation systems are used to provide inertia and frequency modulation support, achieving second-level frequency and voltage regulation. A closed-loop linkage system is constructed through a state feedback mechanism, enhancing the platform's real-time operational stability under isolated or weak grid conditions. Overall, this invention achieves a hierarchical decoupling fusion of multi-energy collaborative optimization, scheduling, and control, and possesses strong robustness, scalability, and engineering practicality. It can be widely applied to various types of offshore independent energy supply platforms and has important promotion value and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of the method of the present invention;

[0023] Figure 2 This is a schematic diagram of the scheduling hierarchical optimization designed by the present invention;

[0024] Figure 3 This is a schematic diagram of the rolling optimization and real-time control collaboration designed by the present invention;

[0025] Figure 4 This is a logic flow chart of the multi-energy response priority dynamic collaborative control designed by the present invention. DETAILED DESCRIPTION

[0026] The specific embodiments of the present invention will be described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention.

[0027] Example: Figure 1-Figure 4 As shown in the figure, the collaborative scheduling method of offshore energy platforms based on multi-energy complementarity and hierarchical optimization includes the following steps:

[0028] Based on the historical and forecast data of wind power, photovoltaic power, tidal power and load of offshore energy platforms, S1 analyzes the correlation and complementarity of different energy outputs, builds a multi-energy complementary characteristic model and a flexible supply and demand model, and provides an accurate data foundation for subsequent coordinated scheduling optimization. The specific implementation steps are as follows:

[0029] S11 collects environmental data such as wind speed v(t), solar irradiance G(t), tidal flow velocity u(t), and historical output data of wind power, photovoltaic power, and tide. wind (t), P solar (t), P tide (t), and the load forecast curve L(t) of the offshore energy platform. Wind power, photovoltaic, and tidal power prediction models are constructed to provide data for subsequent complementary analysis and optimized scheduling.

[0030] (1) Wind power prediction modeling

[0031] According to the wind turbine power curve model, the predicted output power of the wind power unit at time t It can be expressed as:

[0032]

[0033] Among them, v cut-in Indicates the fan start-up wind speed. v rated Indicates rated wind speed. v cut-out Indicates the shutdown wind speed. P rated Indicates the rated power of the fan.

[0034] (2) Photovoltaic power prediction modeling

[0035] The output of photovoltaic modules is affected by irradiance and temperature. It can be expressed as:

[0036]

[0037] Among them, η PV Indicates the conversion efficiency of photovoltaic modules (generally 15% to 22%). PV represents the total area of ​​the photovoltaic array. G(t) represents the solar irradiance.

[0038] (3) Tidal power prediction modeling

[0039] Tidal energy output is calculated based on tidal flow velocity, and the predicted power of tidal power generation units It can be expressed as:

[0040]

[0041] Where ρ represents the density of seawater (about 1025 kg / m 3). A turb C represents the frontal area of ​​tidal turbine. p represents the power coefficient of the tidal turbine (usually 0.3 to 0.5). u(t) represents the tidal flow velocity.

[0042] S12 quantitatively evaluates the output complementarity between different energy sources, reveals the synergistic characteristics of renewable energy output, and supports complementary priority scheduling design.

[0043] First, normalize the output of each energy source to compare its changing trends:

[0044]

[0045] Among them, P i (t) represents the standardized predicted output of the i-th energy at time t. i Represents the mean output of energy source i. i represents the standard deviation of the output of energy source i.

[0046] Next, calculate the complementary correlation coefficient Corr between the output of energy i and energy j ij :

[0047]

[0048] Where T represents the total number of time steps. i (t) and P j (t) represents the normalized predicted output of the i-th and j-th energy at time t, respectively.

[0049] Using the complementary correlation coefficient Corr ij Define the complementarity index CI between energy sources ij :

[0050] CI ij =1-Corr ij

[0051] Among them, CI ij The larger the value, the stronger the complementarity between energy i and energy j. ij Approaching -1, the CI ij It approaches 2, and the complementarity is excellent.

[0052] The complementarity index can be used to obtain the complementarity matrix CI between wind, solar and tide, quantitatively identify which energy combinations are more suitable for priority coordinated scheduling, and clearly guide the priority ranking of energy output in hierarchical scheduling.

[0053] S13 evaluates the flexibility adjustment demand and flexibility resource supply capacity of offshore energy platforms in different time periods, determines whether there is a flexibility gap in the system in each time period, and ensures scheduling feasibility and system stability.

[0054] (1) Computational flexibility requirements

[0055] Flexibility requirement F d (t) is defined as the maximum positive or negative deviation caused by the forecast error of the combined wind, solar and tidal output:

[0056]

[0057] Among them, P RES (t) represents the total renewable energy forecast output. L(t) represents the load forecast curve of the offshore energy platform. Indicates the rate of change with respect to time.

[0058] (2) Calculating the flexibility supply

[0059] Flexibility supply F of energy storage system and diesel engine BESS (t) and F DG (t) is calculated as follows:

[0060]

[0061] F DG (t) = P DG,max -P DG (t)

[0062] Among them, P BESS,max Indicates the maximum charge and discharge power of the energy storage system. E BESS (t) represents the remaining energy of the energy storage system at time t. BESS (t) represents the charge and discharge power of the energy storage system at time t. Δt represents the scheduling time step. P DG,max Indicates the maximum output of the diesel engine. DG (t) represents the output of the diesel engine at time t.

[0063] (3) Calculation of flexibility margin

[0064] Flexibility margin F m (t) is defined as the difference between the quantity supplied and the quantity demanded:

[0065] F m (t) = F BESS (t)+F DG (t)-F d (t)

[0066] Among them, F BESS(t) is the flexibility supply of the energy storage system, F DG (t) is the flexibility supply of the diesel engine, F d (t) is the flexibility requirement.

[0067] S2, based on the complementary relationships among multiple energy sources identified in S1 and the flexibility of each energy source, establishes a multi-objective day-ahead optimization model based on opportunity constraints, formulates a hierarchical scheduling plan centered on the priority utilization of renewable energy and the rational allocation of flexible backup resources, and formulates preliminary output instructions for stable system operation. The specific implementation steps are as follows:

[0068] S21 determines the multi-objective functions of the optimization model and the decision variables that need to be optimized, laying the foundation for the model solution. Based on the actual operational requirements of the offshore energy platform, the optimization objectives mainly include maximizing the total absorption of renewable energy (wind, solar, and tidal), minimizing diesel engine fuel consumption, and minimizing wasted energy.

[0069] (1) Maximize the total absorption capacity of renewable energy (wind, solar, and tide)

[0070] The total consumption of renewable energy such as wind power, photovoltaic power, and tidal power in the dispatch results E RES The bigger the better, the calculation formula is as follows:

[0071]

[0072] in, represents the dispatch output of wind power at time t, represents the dispatch output of photovoltaic power at time t, represents the dispatched output of tidal energy at time t. Δt represents the dispatch time step. T represents the total number of time steps in the dispatch period.

[0073] (2) Minimize diesel engine fuel consumption

[0074] The diesel engine has high power generation cost and large carbon emissions, so its use should be minimized. The fuel consumption cost of the diesel engine is C fuel The calculation is as follows:

[0075]

[0076] Among them, c DG Represents the unit power generation cost of the diesel engine. represents the dispatch output of the diesel engine at time t. Δt represents the dispatch time step.

[0077] (3) Minimize wasted energy

[0078] For renewable energy that cannot be absorbed due to excess output or insufficient capacity, its waste should be reduced as much as possible, that is, the total amount of abandoned wind, solar and tidal power should be minimized.curt The calculation formula is as follows:

[0079]

[0080] in, Represent the predicted values ​​of wind power, photovoltaic power, and tidal power (provided by S1), respectively. Respectively represent the dispatch output of wind power, photovoltaic power, and tidal power. [·] The brackets represent the abandoned energy of renewable energy, [·] + Indicates that only positive values ​​are taken, that is, the part that is actually available but not scheduled.

[0081] Finally, set the decision variables of the dispatch model. The decision variables mainly include: dispatch instructions for wind power, photovoltaic power, and tidal power Power instructions for energy storage systems The state of charge SOC(t) of the energy storage system and the output dispatch instruction of the diesel engine

[0082] S22 is based on the objective function and decision variables defined in S21, and combines the energy complementary characteristics, forecast data and flexibility analysis results obtained in S1 to establish an optimization model with complete structure, solvability and complete constraints.

[0083] First, we construct a weighted objective function for the scheduling model. We linearly weight the objective functions to construct the overall scheduling optimization objective. The objective function is expressed as follows:

[0084] minJ=ω1·(-E RES )+ω2·C fuel +ω3·E curt

[0085] Where ω1, ω2, ω3∈[0,1] represent adjustable weight factors. By optimizing the objective function, the triple balance of green, economic, and coordinated offshore energy platform scheduling can be achieved.

[0086] Next, the constraint system of the dispatch model is constructed. It mainly includes power balance constraint, output upper and lower limit constraint, energy storage system dynamic constraint, and flexibility margin opportunity constraint.

[0087] (1) Power balance constraints

[0088] The system must maintain a balance between supply and demand at all times, that is, the total dispatch output of wind power, photovoltaic power, tidal power, energy storage system, and diesel generators must be balanced with the load forecast value. The formula is as follows:

[0089]

[0090] Where L(t) represents the load forecast value (from S1).

[0091] (2) Output upper and lower limit constraints

[0092] All energy units should operate within the limits of what is physically possible:

[0093]

[0094] Among them, P i min and P i max Respectively represent the lower and upper limits of the output of energy i. i sch (t) represents the dispatch output of energy source i at time t.

[0095] (3) Dynamic constraints of energy storage systems

[0096] Energy storage systems play key roles in multi-energy collaborative systems, including energy caching, output smoothing, and frequency regulation support. To ensure that energy storage can be charged and discharged, operate in a steady state, and avoid overload during scheduling, its energy change process, namely the SOC trajectory, must be accurately characterized, and the SOC dynamic update equation and boundary constraints must be introduced into the optimization model. The state of charge (SOC) of the energy storage system at a certain time t is determined by its state at the previous time, the charging power at the current time, and the discharging power at the current time. Specifically expressed as follows:

[0097]

[0098] Among them, SOC(t) represents the state of charge of the energy storage system at time t, and SOC(t+1) represents the state of charge of the energy storage system at the next moment after time t. and Respectively represent the charging power and discharging power of the energy storage system at time t. ch and η dis They represent the charging efficiency and discharging efficiency of the energy storage system respectively. Represents the rated capacity of the energy storage system (total energy storage capacity). Δt represents the scheduling time step.

[0099] When the energy storage system is charging, At this time, SOC increases because η ch <1, the input power will be lost. When the energy storage is discharged, At this time, SOC decreases because η dis <1, the released energy is less than the energy change. The energy storage system cannot be charged and discharged simultaneously, nor can it have unlimited power. Therefore, the following constraints need to be added to the scheduling:

[0100] 1) SOC operating boundary

[0101] The energy storage system's state of charge must be within the normal range at any time:

[0102]

[0103] Among them, SOC min Indicates the minimum value of energy storage capacity, usually 10% to 20% of the total capacity to prevent over-discharge damage to the battery. max Indicates the maximum value of energy storage capacity, usually 90% to 100% of the total capacity to prevent overcharging.

[0104] 2) Charge / discharge power boundary

[0105] The charging and discharging power of the energy storage system at any time must be within the rated power range:

[0106]

[0107] in, Indicates the maximum charging power of the energy storage system, Indicates the maximum discharge power of the energy storage system.

[0108] 3) Mutually exclusive constraints

[0109] The mutual exclusion constraint is to avoid the energy storage system from charging and discharging at the same time. The method used is to introduce the binary variable u ch (t) and u dis (t) indicates the charging and discharging status:

[0110] u ch (t)+u dis (t)≤1,u ch ,u dis ∈{0,1}

[0111] And set:

[0112]

[0113] (4) Opportunity constraints on flexibility margin

[0114] In most time periods (e.g., more than 95%), the flexibility of offshore energy platforms must cover output / load fluctuations:

[0115]

[0116] Where Pr(·) represents probability calculation. m (t) represents the flexibility margin at time t. γ represents the confidence level, which is usually set to 0.95, indicating that the system's adjustment capability should be able to cover fluctuations 95% of the time.

[0117] Based on the hierarchical scheduling plan developed by S2, S3 obtains minute-level short-term forecast data of wind and solar power in real time. Combined with the complementary priority, it dynamically adjusts the output of the energy storage system, renewable energy, and diesel units through rolling optimization to achieve flexible response to fluctuations and efficient coordination of multiple energy sources. The specific implementation steps are as follows:

[0118] S31 builds a method that can identify in real time whether the current system operating status deviates from the planned benchmark set in S2, so as to redefine the target input data for scheduling optimization based on the degree and direction of deviation.

[0119] First, collect the actual operating data of each energy unit in the platform, mainly including the actual output of the wind power system at the current moment Actual output of the photovoltaic system Actual output of the tidal system And the actual load value L of the platform real (t).

[0120] Next, calculate the difference between the actual output and the scheduled output:

[0121] ΔP i (t) = P i real (t)-P i sch (t),i∈{wind,solar,tide}

[0122] Where ΔP i (t) represents the actual dispatch deviation of the i-th type of renewable energy at time t. i sch (t) represents the day-ahead dispatch plan value of the i-th type of renewable energy generated in S2. i When (t)>0, it means that the actual output is higher than the scheduled output, and the output of other energy sources can be temporarily reduced. i When (t) < 0, it indicates a power shortage and requires compensation from an energy storage system or a diesel generator.

[0123] Finally, to facilitate the scheduler to quickly determine whether additional compensation power is needed, the current net load of the system is calculated:

[0124]

[0125] Among them, P net (t) represents the net required compensation power of the offshore energy platform at the current moment. If P net (t)>0, indicating that clean energy is insufficient and needs to be compensated by energy storage or diesel engines. net (t)<0, indicating that there is surplus power in clean energy, and you can choose to charge or reduce the output.

[0126] S32 establishes a multi-objective rolling optimization model with net load balance as the goal to achieve optimal coordinated scheduling of each energy unit in a short period of time, ensure that the output matches the system demand, and take into account economy and equipment life.

[0127] First, define the rolling scheduling time window. Optimize the period T r Set it as 15 minutes, divide the next 15 minutes into 3 time steps Δt, each step is 5 minutes. At each new time point t, re-optimize the scheduling decision of t, t+1, t+2 (generate P every 5 minutes) i sch (t), covering the corresponding value in S2 in real time), and only implementing the result of the first moment.

[0128] Next, define the optimization objective function J r :

[0129]

[0130] Among them, the first item indicates that the net load compensation is incomplete, the second item indicates that the diesel engine fuel cost should be reduced as much as possible, and the third item indicates that the frequent charging and discharging loss and aging of energy storage should be controlled to reflect the equipment life protection. net (τ) represents the net load at time τ. BESS (τ) represents the energy storage output at time τ. DG (τ) represents the diesel engine output at time τ. DG Represents the unit fuel cost of diesel engine. and They represent the charging power and discharging power of the energy storage at time τ, respectively. α1, α2, and α3 represent optimization weights, which are used to control the scheduling priority.

[0131] While achieving the above optimization goals, some constraints need to be met:

[0132] (1) Power balance constraint: used to ensure that the system clock maintains supply and demand balance at each time step

[0133]

[0134] (2) Energy storage SOC update formula: The energy storage dynamic update in S22 is hourly, while the SOC update here is minute-by-minute.

[0135]

[0136] Among them, SOC(τ) represents the state of charge of the energy storage system at time τ, and SOC(τ+1) represents the state of charge of the energy storage system at the next moment after time τ. and Respectively represent the charging power and discharging power of the energy storage system at time τ. ch and η dis They represent the charging efficiency and discharging efficiency of the energy storage system respectively. represents the rated capacity of the energy storage system (total energy storage capacity). Δτ represents the time step.

[0137] (3) Energy storage boundary constraints: Avoid overcharging or overdischarging of the energy storage system

[0138] SOC min ≤SOC(τ)≤SOC max

[0139] Among them, SOC min Indicates the minimum value of energy storage capacity. SOC max Indicates the maximum value of energy storage capacity.

[0140] (4) Charge / discharge mutual exclusion constraint: prevents the energy storage system from charging and discharging simultaneously

[0141]

[0142] in, and They represent the charging power and discharging power of the energy storage at time τ respectively.

[0143] S33 builds a multi-energy response priority mechanism, which provides calling sequence and boundary settings for the rolling optimization module based on resource characteristics, complementary indicators and regulation capabilities, to avoid unreasonable resource waste or scheduling deviation.

[0144] First, define priorities based on resource response characteristics. Energy storage systems are considered the preferred regulation resource due to their fast response speed, high precision, strong controllability, and low cost. Tidal energy adjustable units may have output limits limited by tidal currents, but they are highly stable and can be used to temporarily adjust their output (via current control). Therefore, they are considered the next-level, fast regulation resource. Diesel generators are expensive, have high start-up and shutdown costs, and respond slowly, but they have large capacity, so they are considered the last choice for regulation.

[0145] Next, the dynamic collaborative control logic is designed. In S31, the difference ΔP between the actual output of renewable energy and the output of the scheduling plan is calculated. i (t) = P i real (t)-P i sch (t), i∈{wind,solar,tide}, then the total difference between the actual output of the three renewable energy sources, wind power, photovoltaic power, and tidal power, and the dispatch plan can be expressed as ΔP RES (t) = ΔPwind (t)+ΔP solar (t)+ΔP tide (t). When ΔP RES When (t) is positive, it means that the actual renewable power output is higher than the plan, and there is a surplus output; when ΔP RES When (t) is negative, it means that the actual renewable power output is lower than the plan, and other resources are needed to compensate for the power. In the dynamic collaborative control logic, the dispatching system needs to quickly determine whether it is "power shortage" or "power abundance", which mainly depends on the total actual power output deviation ΔP RES (t). The specific control methods are as follows:

[0146] 1) If ΔP RES (t)<0, that is, the output decreases, then determine whether SOC(t) can support it. If it can be discharged, dispatch energy storage;

[0147] If the energy storage is insufficient, determine whether the tidal output has room for adjustment;

[0148] If the tidal output is still insufficient, the diesel engine will be dispatched to compensate.

[0149] 2) If ΔP RES (t)>0, that is, the output increases, then it is determined whether the energy storage can be charged (SOC is not full);

[0150] If it can be charged, it absorbs the surplus electricity;

[0151] If it is full, then abandon the energy or adjust the tide downward.

[0152] Based on the dynamic complementary rolling optimization achieved in S3, S4 adopts virtual synchronous machine control and droop control strategies, and uses clean energy such as energy storage and tidal energy to provide second-level inertial response and frequency modulation support to ensure the frequency and voltage stability of the platform under isolated or weak network conditions. At the same time, a state feedback mechanism is formed to continuously correct the optimization plan and realize closed-loop adaptive scheduling. The specific implementation steps are as follows:

[0153] In order to enable the energy storage system to not only follow the power dispatch plan but also actively provide virtual inertia and damping support to quickly respond to frequency changes and enhance system stability when there are sudden load disturbances or wind and solar fluctuations, S41 introduces a virtual synchronous generator (VSG) to resist frequency oscillations caused by power disturbances.

[0154] VSG can simulate the kinetic inertia response characteristics of traditional rotating generators. Its basic frequency dynamic response equation can be expressed as:

[0155]

[0156] Where J represents the virtual moment of inertia. ω(t) represents the grid frequency, and ω0 represents the rated angular frequency.m (t) represents the given mechanical input power, which is set to the energy storage output (P BESS (τ)). P e (t) represents the actual output power of the power grid. D represents the damping coefficient.

[0157] The frequency regulation result is converted into the controller target frequency or power regulation command. The output power regulation equation is expressed as follows:

[0158]

[0159] in, K represents the output power regulation instruction of the controller to the energy storage system at time t. ω This represents the virtual frequency regulation gain, which determines the energy storage's sensitivity to frequency deviations. When the frequency decreases, the system automatically increases the energy storage output (discharging) to support the frequency. When the frequency is too high, the system automatically reduces the output or even enters a charging state (absorbing energy).

[0160] The S42 uses droop control as a control method for diesel generators and controllable tidal units, enabling them to automatically adjust active or reactive output when frequency or voltage fluctuates to achieve system balance support. Droop control is achieved by operating traditional synchronous generators according to the "power-frequency" and "voltage-reactive power" droop curves, as shown in the following formula:

[0161] P i (t) = P i * (t)-K P,i (ω(t)-ω0)

[0162]

[0163] Among them, P i (t) and Q i (t) represents the active and reactive output of the i-th generating unit. K P,i and K Q,i Represent the active and reactive droop coefficients respectively. ω(t) and V(t) represent the grid frequency and bus voltage measurements respectively. ω0 and V0 represent the system rated frequency and voltage respectively. i * (t) represents the active reference output of the power generation unit, which comes from the dispatch plan output P given by S3 rolling optimization solution. i sch (t). Represents the reactive reference output of the generating unit, which is calculated using the constant power factor method:

[0164]

[0165] Among them, cosφ i Indicates the set equipment operating power factor.

[0166] With this control strategy, diesel generators can use power-frequency droop mode to respond to large frequency disturbances, while tidal power systems can utilize voltage-reactive power control logic to maintain terminal voltage stability. This maintains a dynamic balance in active power supply while simultaneously regulating reactive power to stabilize voltage, improving system frequency and voltage stability and self-recovery capabilities.

[0167] S43 establishes an information feedback channel between the S4 control layer and the S3 and S2 scheduling layers to achieve scheduling-control closed-loop linkage and continuously optimize system performance.

[0168] First, feedback information should be collected, including the current state of charge SOC(t) of the energy storage system, grid frequency ω(t), bus voltage V(t), actual output of energy storage and diesel engine. and Frequency deviation Δf(t)=ω(t)-ω0 and real-time flexibility margin index F m (t).

[0169] Next, these state parameters are packaged and uploaded to the S3 rolling optimization module every five minutes as input for the next round of optimization. If the system frequently experiences frequency deviation exceeding limits or the energy storage SOC enters the safety boundary, a "flexible boundary adjustment request" is sent to S2, automatically loosening or tightening the opportunity-constrained confidence interval of the flexibility margin. Through these steps, a complete closed-loop system of "prediction-dispatch-control-feedback" is implemented, completing the coordinated scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization.

[0170] Further explanation is needed:

[0171] The implementation method of the present invention involves executing the various steps described in the present invention by controlling hardware through a computer program. Specifically, all or part of the processes in the above method can be implemented by controlling the relevant hardware through computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, the corresponding operations can be completed in accordance with the processes of the above-mentioned method embodiments. Among them, any reference to the memory, storage, database or other media involved in the embodiments provided in this application may include non-volatile memory and / or volatile memory.

[0172] Those skilled in the art will clearly understand that for the sake of convenience and brevity, we use the above-mentioned division of functional units or modules for illustration. However, in actual applications, the above-mentioned functional units or modules can be divided into different functional units or modules as needed to complete all or part of the functions of the method of the present invention.

[0173] The above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the same. Although the specific embodiments of the present invention have been described in detail, those skilled in the art should understand that they may modify the technical solutions of the aforementioned embodiments or replace some of the technical features therein with equivalents; such modifications or replacements do not deviate from the core spirit and scope of the embodiments of the present invention and should be included within the scope of protection of the present invention.

Claims

1. A method for collaborative scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization, characterized in that: The following steps are involved: Based on historical and forecasted data on wind power, photovoltaic power, tidal energy, and load on offshore energy platforms, S1 analyzes the correlation and complementarity of different energy outputs, constructs a multi-energy complementary characteristic model and a flexible supply and demand model, and provides an accurate data foundation for subsequent coordinated scheduling optimization. S2 establishes a multi-objective day-ahead optimization model based on opportunity constraints based on the complementary relationships of multiple energy sources identified in S1 and the flexibility capabilities of each energy source. It formulates a hierarchical scheduling plan centered on the priority utilization of renewable energy and the rational allocation of flexible backup resources, and formulates preliminary output instructions for stable system operation. Based on the hierarchical scheduling plan developed in S2, S3 obtains real-time minute-level short-term forecast data for wind, solar, and tidal currents. Combining complementary priorities, it dynamically adjusts the output of the energy storage system, renewable energy, and diesel generators through rolling optimization, achieving flexible response to fluctuations and efficient coordination of multiple energy sources. Based on the dynamic complementary rolling optimization achieved in S3, S4 adopts virtual synchronous machine control and droop control strategies, and uses clean energy such as energy storage and tidal energy to provide second-level inertial response and frequency regulation support to ensure the frequency and voltage stability of the platform under isolated or weak network conditions. At the same time, a state feedback mechanism is formed to continuously correct the optimization plan and realize closed-loop adaptive scheduling.

2. According to the method for collaborative scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization in claim 1, step S1 comprises the following steps: S11 collects environmental data such as wind speed v(t), solar irradiance G(t), tidal flow velocity u(t), and historical output data of wind power, photovoltaic power, and tide. wind (t), P solar (t), P tide (t), as well as the load forecast curve L(t) of the offshore energy platform, and construct wind power, photovoltaic, and tidal power prediction models to provide data for subsequent complementary analysis and optimized scheduling; S12 quantitatively evaluates the output complementarity between different energy sources, reveals the synergistic characteristics of renewable energy output, and supports complementary priority scheduling design; S13 evaluates the flexibility adjustment demand and flexibility resource supply capacity of offshore energy platforms in different time periods, determines whether there is a flexibility gap in the system in each time period, and ensures scheduling feasibility and system stability.

3. According to the method for coordinated scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization in claim 1, step S2 comprises the following steps: S21 determines the multi-objective function of the optimization model and the decision variables that need to be optimized, laying the foundation for the model solution. According to the actual operation requirements of the offshore energy platform, the optimization objectives mainly include three items: maximizing the total absorption of renewable energy (wind, solar and tide), minimizing diesel engine fuel consumption, and minimizing abandoned energy. S22 is based on the objective function and decision variables defined in S21, and combines the energy complementary characteristics, forecast data and flexibility analysis results obtained in S1 to establish an optimization model with a complete structure, solvable and complete constraints, which mainly includes the weighted objective function and constraint system for constructing the scheduling model.

4. The method for collaborative scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization according to claim 1, wherein step S3 comprises the following steps: S31 builds a method that can identify in real time whether the current system operating status deviates from the planned benchmark set in S2, so as to redefine the target input data for scheduling optimization based on the degree and direction of deviation; S32 establishes a multi-objective rolling optimization model with net load balance as the goal, achieving optimal coordinated scheduling of each energy unit in a short period of time, ensuring that the output matches the system demand, and taking into account economic efficiency and equipment life; S33 builds a multi-energy response priority mechanism, which provides calling sequence and boundary settings for the rolling optimization module based on resource characteristics, complementary indicators and regulation capabilities, to avoid unreasonable resource waste or scheduling deviation.

5. According to the method for coordinated scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization in claim 1, step S4 comprises the following steps: S41 introduces a virtual synchronous generator (VSG) to prevent frequency fluctuations caused by power disturbances, so that the energy storage system can not only follow the power dispatch plan but also actively provide virtual inertia and damping support to quickly respond to frequency changes and enhance system stability when there are sudden load disturbances or wind and solar fluctuations. The S42 uses droop control as a control method for diesel generators and controllable tidal units, enabling it to automatically adjust active or reactive output when frequency or voltage fluctuates to achieve system balance support; S43 establishes an information feedback channel between the S4 control layer and the S3 and S2 scheduling layers to achieve scheduling-control closed-loop linkage and continuously optimize system performance.

6. The method for collaborative scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization according to claim 1, wherein step S1 comprises the following steps: S11 collects environmental data of wind speed v(t), solar irradiance G(t), and tidal flow velocity u(t), and also collects historical output data of wind power, photovoltaic power, and tide. wind (t), P solar (t), P tide (t), and the load forecast curve L(t) of the offshore energy platform, and build wind power, photovoltaic, and tidal power prediction models to provide data for subsequent complementary analysis and optimized scheduling. (1) Wind power prediction modeling According to the wind turbine power curve model, the predicted output power of the wind power unit at time t It can be expressed as: Among them, v cut-in Indicates the fan startup wind speed, v rated Indicates rated wind speed, v cut-out Indicates the shutdown wind speed, P rated Indicates the rated power of the fan. (2) Photovoltaic power prediction modeling, The output of photovoltaic modules is affected by irradiance and temperature. Expressed as: Among them, η PV Indicates the conversion efficiency of photovoltaic modules, A PV represents the total area of ​​the photovoltaic array, G(t) represents the solar irradiance, (3) Tidal power prediction modeling, Tidal energy output is calculated based on tidal flow velocity, and the predicted power of tidal power generation units Expressed as: Where ρ represents the density of seawater (about 1025 kg / m 3 ), A turb represents the frontal area of ​​tidal turbine, C p represents the tidal turbine power coefficient, u(t) represents the tidal flow velocity, S12 quantitatively evaluates the output complementarity between different energy sources and reveals the synergistic characteristics of renewable energy output to support complementary priority scheduling design. First, normalize the output of each energy source to compare its changing trends: Among them, P i (t) represents the standardized predicted output of the i-th energy at time t, μ i represents the mean output of energy source i, σ i represents the standard deviation of the output of energy source i, Next, calculate the complementary correlation coefficient Corr between the output of energy i and energy j ij : Where T represents the total number of time steps, P i (t) and P j (t) represents the standardized predicted output of the i-th and j-th energy at time t, Using the complementary correlation coefficient Corr ij Define the complementarity index CI between energy sources ij : I.E. ij =1-Corr ij Among them, CI ij The larger the value, the stronger the complementarity between energy i and energy j. If Corr ij Approaching -1, the CI ij Approaching 2, the complementarity is excellent, S13 evaluates the flexibility adjustment demand and flexibility resource supply capacity of offshore energy platforms in different time periods, determines whether there is a flexibility gap in the system in each time period, and ensures scheduling feasibility and system stability. (1) Computational flexibility requirements, Flexibility requirement F d (t) is defined as the maximum positive or negative deviation caused by the forecast error of the combined wind, solar and tidal output: Among them, P RES (t) represents the total renewable energy forecast output, L(t) represents the load forecast curve of the offshore energy platform, represents the rate of change with respect to time, (2) Calculate the flexibility supply, Flexibility supply F of energy storage system and diesel engine BESS (t) and F DG (t) is calculated as follows: F DG (t)=P DG,max -P DG (t) Among them, P BESS,max Indicates the maximum charge and discharge power of the energy storage system, E BESS (t) represents the remaining energy of the energy storage system at time t, P BESS (t) represents the charge and discharge power of the energy storage system at time t, Δt represents the scheduling time step, P DG,max Indicates the maximum output of the diesel engine, P DG (t) represents the output of the diesel engine at time t, (3) Calculate the flexibility margin, Flexibility margin F m (t) is defined as the difference between the quantity supplied and the quantity demanded: F m (t)=F BESS (t)+F DG (t)-F d (t) Among them, F BESS (t) is the flexibility supply of the energy storage system, F DG (t) is the flexibility supply of the diesel engine, F d (t) is the flexibility requirement.

7. The method for collaborative scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization according to claim 1, wherein step S2 comprises the following steps: S21 determines the multi-objective function of the optimization model and the decision variables that need to be optimized, laying the foundation for the model solution. According to the actual operation requirements of the offshore energy platform, the optimization objectives mainly include three items, namely maximizing the total absorption of renewable energy (wind, solar and tide), minimizing diesel engine fuel consumption, and minimizing abandoned energy. (1) Maximize the total absorption capacity of renewable energy (wind, solar and tide), The total consumption of renewable energy such as wind power, photovoltaic power, and tidal power in the dispatch results E RES The bigger the better, the calculation formula is as follows: in, represents the dispatch output of wind power at time t, represents the dispatch output of photovoltaic power at time t, represents the dispatch output of tidal energy at time t, Δt represents the dispatch time step, T represents the total number of time steps in the dispatch period, (2) Minimize diesel engine fuel consumption, Fuel consumption cost of diesel engine C fuel The calculation is as follows: Among them, c DG represents the unit power generation cost of the diesel engine, represents the dispatch output of the diesel engine at time t, Δt represents the dispatch time step, (3) Minimize the abandoned energy, Total abandoned energy value E curt The calculation formula is as follows: in, Represent the predicted values ​​of wind power, photovoltaic power and tidal power respectively, Respectively represent the dispatch output of wind power, photovoltaic power, and tidal power. The brackets [·] represent the abandoned energy of renewable energy. + Indicates that only positive values ​​are taken, that is, the part that is actually available but not scheduled. Finally, set the decision variables of the dispatch model, which mainly include: dispatch instructions for wind power, photovoltaic power, and tidal power Power instructions for energy storage systems The state of charge SOC(t) of the energy storage system and the output dispatch instruction of the diesel engine S22 is based on the objective function and decision variables defined in S21, combined with the energy complementarity characteristics, forecast data and flexibility analysis results obtained in S1, to establish an optimization model with complete structure, solvability and complete constraints. First, a weighted objective function of the scheduling model is constructed. The objective function is linearly weighted and combined to construct the overall scheduling optimization goal. The objective function is expressed as follows: minJ=ω1·(-E RES )+ω2·C fuel +ω3·E curt Among them, ω1, ω2, ω3∈[0,1] represent adjustable weight factors. By optimizing the objective function, the triple balance of green, economic and coordinated offshore energy platform scheduling can be achieved. Next, the constraint system of the dispatch model is constructed, which mainly includes power balance constraint, output upper and lower limit constraint, energy storage system dynamic constraint, and flexibility margin opportunity constraint. (1) Power balance constraints The system must maintain a balance between supply and demand at all times, that is, the total dispatch output of wind power, photovoltaic power, tidal power, energy storage system, and diesel generators must be balanced with the load forecast value. The formula is as follows: Where L(t) represents the load forecast value, (2) Output upper and lower limit constraints All energy units should operate within the limits of what is physically possible: Among them, P i min and P i max Represent the lower and upper limits of energy i’s output, P i sch (t) represents the dispatch output of energy source i at time t, (3) Dynamic constraints of energy storage systems, The state of charge (SOC) of an energy storage system at a certain time t is determined by its state at the previous time, the current charging power, and the current discharging power, which can be expressed as follows: Among them, SOC(t) represents the state of charge of the energy storage system at time t, and SOC(t+1) represents the state of charge of the energy storage system at the next moment after time t. and They represent the charging power and discharging power of the energy storage system at time t, η ch and η dis They represent the charging efficiency and discharging efficiency of the energy storage system respectively, represents the rated capacity of the energy storage system (total energy storage capacity), Δt represents the scheduling time step, When the energy storage system is charging, At this time, SOC increases because η ch <1, the input power will be lost, when the energy storage is discharged, At this time, SOC decreases because η dis <1, the released energy is less than the energy change. The energy storage system cannot charge and discharge simultaneously, nor can it have unlimited power. Therefore, the following constraints need to be added to the scheduling: 1) SOC operating boundary The energy storage system's state of charge must be within the normal range at any time: Among them, SOC min Indicates the minimum value of energy storage capacity, which is 10% to 20% of the total capacity. SOC max Indicates the maximum value of energy storage capacity, which is 90% to 100% of the total capacity to prevent overcharging. 2) Charge / discharge power boundary The charging and discharging power of the energy storage system at any time must be within the rated power range: in, Indicates the maximum charging power of the energy storage system, Indicates the maximum discharge power of the energy storage system, 3) Mutually exclusive constraints The mutual exclusion constraint is to avoid the energy storage system from charging and discharging at the same time. The method used is to introduce a binary variable u ch (t) and u dis (t) indicates the charging and discharging status: u ch (t)+u dis (t)≤1,u ch ,u dis ∈{0,1} And set: (4) Opportunity constraints on flexibility margin In most periods of time, the flexibility of offshore energy platforms must cover output / load fluctuations: Among them, Pr(·) represents probability calculation, F m (t) represents the flexibility margin at time t, γ represents the confidence level, which is 0.95, indicating that the system's adjustment capability must be able to cover fluctuations within 95% of the time.

8. The method for coordinated scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization according to claim 1, wherein step S3 comprises the following steps: S31 builds a method that can identify in real time whether the current system operation status deviates from the planned benchmark set in S2, so as to redefine the target input data of scheduling optimization according to the degree and direction of deviation. First, collect the actual operating data of each energy unit in the platform, mainly including the actual output of the wind power system at the current moment Actual output of the photovoltaic system Actual output of the tidal system And the actual load value L of the platform real (t), Next, calculate the difference between the actual output and the scheduled output: ΔP i (t)=P i real (t)-P i sch (t),i∈{wind,solar,tide} Where ΔP i (t) represents the actual dispatch deviation of the i-th type of renewable energy at time t, P i sch (t) represents the day-ahead dispatch plan value of the i-th type of renewable energy generated in S2. When ΔP i When (t)>0, it means that the actual output is higher than the dispatch plan output, and other energy outputs can be temporarily reduced. i When (t) < 0, it indicates a power shortage and requires energy storage system or diesel generator compensation. Finally, to facilitate the scheduler to quickly determine whether additional compensation power is needed, the current net load of the system is calculated: Among them, P net (t) represents the net required compensation power of the offshore energy platform at the current moment. If P net (t)>0, indicating that clean energy is insufficient and needs to be compensated by energy storage or diesel engines. net (t)<0, indicating that there is surplus power in clean energy, and you can choose to charge or reduce the output. S32 establishes a multi-objective rolling optimization model with net load balance as the goal, achieving the optimal coordinated scheduling of each energy unit in a short time, ensuring that the output matches the system demand, and taking into account the economy and equipment life. First, define the rolling scheduling time window and optimize the period T r Set it as 15 minutes, divide the next 15 minutes into 3 time steps Δt, each step is 5 minutes, and re-optimize the scheduling decisions of t, t+1, and t+2 at each new time point t (generate P every 5 minutes). i sch (t), real-time coverage of the corresponding value in S2), only the result of the first moment is implemented, Next, define the optimization objective function J r : Among them, the first item indicates that the net load compensation is incomplete, the second item indicates that the diesel engine fuel cost should be reduced as much as possible, and the third item indicates that the frequent charging and discharging loss and aging of energy storage should be controlled to reflect the equipment life protection. net (τ) represents the net load at time τ, P BESS (τ) represents the energy storage output at time τ, P DG (τ) represents the diesel engine output at time τ, c DG represents the unit fuel cost of diesel engine, and They represent the charging power and discharging power of the energy storage at time τ, α1, α2, and α3 represent the optimization weights used to control the scheduling priority. While achieving the above optimization goals, some constraints need to be met: (1) Power balance constraint: used to ensure that the system clock maintains supply and demand balance at each time step (2) Energy storage SOC update formula: The energy storage dynamic update in S22 is hourly, while the SOC update here is minute-by-minute. Among them, SOC(τ) represents the state of charge of the energy storage system at time τ, and SOC(τ+1) represents the state of charge of the energy storage system at the next moment after time τ. and They represent the charging power and discharging power of the energy storage system at time τ, η ch and η dis They represent the charging efficiency and discharging efficiency of the energy storage system respectively, represents the rated capacity of the energy storage system (total energy storage capacity), Δτ represents the time step, (3) Energy storage boundary constraints: Avoid overcharging or overdischarging of the energy storage system SOCIETY min ≤SOC(τ)≤SOC max Among them, SOC min Indicates the minimum value of energy storage capacity, SOC max Indicates the maximum value of energy storage capacity. (4) Charge / discharge mutual exclusion constraint: prevents the energy storage system from charging and discharging simultaneously in, and denote the charging power and discharging power of the energy storage at time τ, respectively. S33 builds a multi-energy response priority mechanism, which provides the calling sequence and boundary setting for the rolling optimization module based on resource characteristics, complementary indicators and adjustment capabilities, avoiding unreasonable resource waste or scheduling deviation. First, define priorities based on resource response characteristics. Next, design the dynamic collaborative control logic. The specific control method is as follows: 1) If ΔP RES If SOC(t)<0, that is, the output decreases, it is determined whether SOC(t) can support the output. If it can be discharged, the energy storage is dispatched. If the energy storage is insufficient, it is determined whether the tidal output has room for adjustment. If the tidal output is still insufficient, the diesel engine will be dispatched to compensate. 2) If ΔP RES (t)>0, that is, the output increases, then it is determined whether the energy storage can be charged; If it can be charged, it absorbs the surplus electricity; If it is full, then abandon the energy or adjust the tide downward.

9. The method for coordinated scheduling of offshore energy platforms based on multi-energy complementarity and hierarchical optimization according to claim 1, wherein step S4 comprises the following steps: The S41 VSG simulates the kinetic inertia response characteristics of a traditional rotating generator. Its basic frequency dynamic response equation is expressed as: Where J represents the virtual moment of inertia, ω(t) represents the grid frequency, ω0 represents the rated angular frequency, and P m (t) represents the given mechanical input power, which is set to the energy storage output (P BESS (τ)), P e (t) represents the actual output power of the power grid, D represents the damping coefficient, The frequency regulation result is converted into the controller target frequency or power regulation command. The output power regulation equation is expressed as follows: in, represents the output power regulation instruction of the controller to the energy storage system at time t, K ω It represents the virtual frequency adjustment gain, which determines the sensitivity of the energy storage to frequency deviation. When the frequency decreases, the system automatically increases the energy storage output (discharge) to support the frequency; when the frequency is too high, it automatically reduces the output or even enters the charging state (absorbs energy). The S42 uses droop control as a control method for diesel generators and controllable tidal units, enabling them to automatically adjust active or reactive output when frequency or voltage fluctuates to achieve system balance support. Droop control is implemented by operating traditional synchronous generators according to the "power-frequency" and "voltage-reactive power" droop curves. The formula is as follows: P i (t)=P i * (t)-K P,i ·(ω(t)-ω0) Among them, P i (t) and Q i (t) represents the active and reactive output of the i-th generating unit, K P,i and K Q,i denote the active and reactive droop coefficients respectively, ω(t) and V(t) denote the grid frequency and bus voltage measurements respectively, ω0 and V0 denote the system rated frequency and voltage respectively, P i * (t) represents the active reference output of the power generation unit, which comes from the dispatch plan output P given by S3 rolling optimization solution. i sch (t), Represents the reactive reference output of the generating unit, which is calculated using the constant power factor method: Among them, cosφ i Indicates the set equipment operating power factor, S43 establishes an information feedback channel between the S4 control layer and the S3 and S2 scheduling layers to achieve scheduling-control closed-loop linkage and continuously optimize system performance. First, feedback information should be collected, including the current state of charge SOC(t) of the energy storage system, grid frequency ω(t), bus voltage V(t), actual output of energy storage and diesel engine. and Frequency deviation Δf(t)=ω(t)-ω0 and real-time flexibility margin index F m (t).

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the offshore energy platform collaborative scheduling method based on multi-energy complementarity and hierarchical optimization as described in any one of claims 1 to 9 above.

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