Offshore energy platform energy configuration planning method based on new energy volatility

Abstract

The invention discloses an offshore energy platform energy configuration planning method based on new energy volatility, and the method comprises the steps: firstly constructing a multi-dimensional volatility index system and a resource complementarity matrix, and achieving the comprehensive description of the multi-source new energy characteristics of a platform region; establishing a dynamic power model and an adjustment adaptability evaluation framework in combination with an equipment response capability index DRI; a structure-scheduling double-layer optimization model is constructed, the upper layer completes equipment type selection and configuration decision, the lower layer realizes time sequence operation scheduling under multiple scenes, and the supply and demand matching precision is improved on the premise of considering the investment cost and the operation risk; through energy gap analysis and extreme scene simulation, an energy storage and redundant resource collaborative matching mechanism is constructed, and a system toughness index is introduced to carry out quantitative evaluation and dynamic correction on a configuration effect; and finally, in combination with platform structure boundary conditions and equipment layout constraints, generating equipment type selection suggestions and scheduling strategies, and realizing a high-reliability, extensible and intelligent energy configuration scheme of the offshore energy platform driven by new energy.

Description

Technical Field

[0001] This invention belongs to the technical field of new energy integrated system planning and energy management optimization, and mainly relates to an energy configuration planning method for offshore energy platforms based on the volatility of new energy sources. Background Technology

[0002] With the continued deepening of the "dual-carbon" strategy and the accelerated development of the marine economy, more and more offshore platforms are being tasked with green and low-carbon transformation, gradually evolving towards intelligent platforms that are "energy self-sufficient and clean in operation and maintenance." Against this backdrop, various renewable energy sources, such as photovoltaic, wind, and wave energy, are being integrated and deployed on offshore platforms to build independent energy supply systems with island-like operation characteristics, driving the transformation of the traditional energy system towards a focus on multi-source collaboration and intelligent dispatch. However, the unique characteristics of the marine environment significantly increase the complexity of energy system design and operation. On the one hand, influenced by climate change and marine conditions, wind, solar, and wave resources exhibit strong volatility, low predictability, and weak complementarity, leading to unstable renewable energy output and a tendency for significant energy shortages or curtailment. On the other hand, limited platform space, restricted deployment capacity, and clearly defined structural load-bearing boundaries prevent the system from relying on a "stacking" capacity expansion approach for adjustment, further exacerbating the difficulty of system matching.

[0003] Existing energy configuration methods for offshore platforms are mostly based on "redundancy assurance," using over-deployment of photovoltaic panels or wind turbines in conjunction with traditional energy storage to improve power supply stability. However, these methods generally have the following shortcomings: (1) They do not establish a refined resource volatility modeling mechanism, ignoring the dynamic changes of resources at different scales such as minutes and hours, making it difficult to support precise scheduling and energy storage matching; (2) They do not consider the differences in response capabilities between equipment, and the use of a unified configuration logic leads to some resource waste or insufficient regulation capabilities; (3) They lack a multi-scenario robustness assessment mechanism, resulting in a sharp drop in system performance under extreme weather conditions and a high risk of operation; (4) Energy storage and redundancy configuration generally rely on empirical parameters, lacking scientific quantification and collaborative optimization methods, resulting in poor economic efficiency and low system efficiency. Summary of the Invention

[0004] This invention addresses the problems of high volatility of renewable resources, high redundancy in energy storage configuration, and low accuracy in load-demand matching in current isolated offshore energy systems. It provides an energy configuration planning method for offshore energy platforms based on the volatility of new energy sources. First, it introduces minute-level wind, solar, and wave resource data to construct a multi-dimensional volatility index system and resource complementarity matrix, achieving a comprehensive characterization of the multi-source renewable energy characteristics of the platform area. Then, combined with the equipment response capability index (DRI), it establishes a dynamic power model and adjustment adaptability evaluation framework including photovoltaic, wind power, wave energy, and energy storage equipment. Finally, it constructs a two-layer optimization model of structure layer and scheduling layer. The first layer completes equipment selection and configuration decisions, while the second layer implements time-series operation scheduling in multiple scenarios, improving the accuracy of supply and demand matching while taking into account investment costs and operational risks. Through energy gap analysis and extreme scenario simulation, a collaborative matching mechanism for energy storage and redundant resources is constructed, and a system resilience index is introduced to quantitatively evaluate and dynamically correct the configuration effect, significantly enhancing the system's ability to cope with extreme weather or continuous fluctuations. Finally, combined with the platform structure boundary conditions and equipment deployment constraints, deployable equipment selection suggestions and scheduling strategies are generated to realize a highly reliable, scalable, and intelligent energy configuration solution for offshore energy platforms driven by new energy sources.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is: an energy configuration planning method for offshore energy platforms based on the volatility of new energy sources, characterized by comprising at least the following steps:

[0006] S1: Collect minute-level meteorological data and perform volatility analysis on renewable resources such as photovoltaic, wind, and wave energy; construct power response models for various resources; and calculate complementarity indices and regulation adaptability. Establish a unified resource description dictionary to provide basic input for subsequent equipment matching and scheduling optimization.

[0007] S2: The energy consumption of offshore platforms is divided into three categories: basic load, periodic tasks and sudden load. Combined with historical operation data and task types, typical daily curves are extracted and multi-scenario load sets are constructed. Structured load labels and adjustable attributes are output for matching scheduling resources and evaluating configuration robustness.

[0008] S3: Construct power output models for photovoltaic, wind power, wave energy and various energy storage technologies, define equipment response capability index (DRI) by combining parameters such as adjustment slope and response delay, and establish deployability boundary constraints based on platform area, water depth, load-bearing capacity and other conditions, and output standardized equipment modeling structure;

[0009] S4: Upper-layer optimization determines the types and quantities of equipment, and strategies for minimizing investment costs. Lower-layer scheduling achieves time-series supply and demand matching and operating cost control in multiple scenarios. Through iterative optimization with coupling between the upper and lower layers, a unified improvement is achieved in system economy, operating efficiency, and robustness in dealing with multiple disturbance scenarios.

[0010] S5: Combine indicators such as maximum power shortage, duration and annual total power shortage to evaluate the lower limit of energy storage, combine short-term and long-term energy storage methods to carry out structural design, identify key bottleneck equipment and propose capacity expansion suggestions to improve the system's continuous power supply capability and expand the redundancy adjustment range under extreme resource fluctuations.

[0011] S6: Combine the platform's physical boundaries to generate equipment deployment schemes and verify deployment feasibility; conduct operational simulations based on extreme weather and continuous fluctuation scenarios to evaluate resilience indicators such as maximum power shortage rate, minimum SOC, and system recovery time, and finally form an engineering-ready configuration scheme and performance verification report.

[0012] As an improvement of the present invention, step S1 specifically includes the following steps:

[0013] S11: Extract historical meteorological and marine environmental data of the target sea area, including key resource parameters such as wind speed, solar irradiance, significant wave height and average wave period. Process the raw resource data into a time series with a fixed time step to construct a standardized resource dataset for subsequent power modeling and volatility analysis.

[0014] S12: Based on the power conversion mechanism of different renewable energy technologies such as photovoltaic, wind power and wave energy, a resource-power mapping model is established; among them, the photovoltaic system adopts the irradiance-temperature correction model, the wind power system adopts the segmented wind speed-power output curve, and the wave energy converter is modeled based on the two-dimensional wave energy power matrix, outputting the rated power value of the unit equipment at each time step, forming resource power time series data;

[0015] S13: Construct a multi-dimensional resource volatility index system, including standard deviation, coefficient of variation, sliding rate of change, volatility frequency and peak and valley duration, etc. Extract typical volatility characteristics of resource sequences through window filtering and statistical calculation to form a volatility feature vector, which is used to match the response characteristics and stability design requirements of different equipment.

[0016] S14: Perform complementarity analysis on different types of renewable resources, calculate the Pearson correlation coefficient between resource pairs, define the complementarity index based on this, construct the complementarity matrix, and evaluate the synergistic capabilities of various resources in time series; the results can be used for resource priority allocation, capacity redundancy adjustment, and weight setting in optimization strategies.

[0017] S15: Encapsulates the power output sequence, volatility index, complementarity coefficient, and operational constraint information of various resources into a unified data dictionary structure; this structure supports programmatic calls between the modeling module and the optimization scheduling module, provides a dynamically updatable resource base library, and provides input support for system equipment configuration and scheduling optimization.

[0018] As an improvement of the present invention, in step S2, the energy consumption of the offshore platform is divided into three categories: basic load, periodic tasks, and sudden load. Combined with historical operating data and task types, typical daily curves are extracted and a multi-scenario load set is constructed. Structured load labels and adjustable attributes are output for matching scheduling resources and evaluating configuration robustness. The multi-scenario load set constructed in step S2 includes, but is not limited to, corresponding resource fluctuation scenarios, platform task status, scenario probability weights, and scenario load curves. A load adjustability and flexible load modeling mechanism is established to identify time-shiftable, interruptible, and flexible load tasks, extract task parameters, and construct an elastic scheduling boundary model to achieve demand-side participation and system adjustment coordination.

[0019] As another improvement of the present invention, step S4 further includes incorporating a multi-scenario robustness modeling and extreme weather disturbance simulation mechanism, introducing extreme weather scenarios as strongly constrained scenarios at the scheduling layer, and adding a maximum energy shortage constraint:

[0020]

[0021] Among them, E miss (t) represents the energy gap at time t, ε max This indicates the maximum acceptable instantaneous power shortage tolerance of the system;

[0022] Introduce a minimum resource coverage redundancy constraint at the structural layer:

[0023]

[0024] Among them, RES installed RES represents the total installed capacity of the actual configured renewable energy system. peak required It is the maximum required renewable output observed during the scheduling process, r min It is the minimum resource installation guarantee coefficient.

[0025] As another improvement of the present invention, the toughness index in step S5 is specifically as follows:

[0026]

[0027] in, E represents the total energy deficit under extreme weather scenarios. load E represents the total energy demand for the entire year's load. sto For total energy storage configuration, DRI avg ω1 to ω3 represent the average response capability of the system, and are weighting coefficients.

[0028] Compared with existing technologies, this invention offers the following advantages: It provides an energy configuration planning method for offshore energy platforms based on the volatility of new energy sources. Addressing the technical bottlenecks in isolated offshore energy systems, such as severe fluctuations in renewable resources, redundant energy storage configurations, and low scheduling efficiency of multi-source equipment, this invention proposes an optimal energy configuration planning method that integrates resource volatility modeling, equipment response capability assessment, and coordinated optimization of structural scheduling. First, by constructing a minute-level weather-driven multi-source new energy volatility index system and a complementarity matrix, it achieves a detailed characterization of resources such as photovoltaic, wind power, and wave energy, enhancing the system's ability to recognize dynamic resource changes. Second, it proposes a DRI-based equipment response capability modeling framework to quantify the adjustment rate and start-stop response capability of various equipment, supporting the participation of flexible loads and optimal equipment configuration. Third, it constructs a two-layer optimization model coupling the structural and scheduling layers. Under the constraints of platform area, water depth, and load-bearing capacity, it achieves coordinated solutions for equipment selection, capacity configuration, and time-series scheduling strategies, significantly improving the system's supply-demand matching accuracy and operational economy. Furthermore, by introducing system resilience assessment indicators and simulations of typical extreme operating conditions, the redundant configuration and control strategies of energy storage are dynamically adjusted to enhance the system's continuous power supply capability and scalability under harsh sea conditions. This invention effectively improves the robustness, intelligence, and deployability of offshore platform energy systems in uncertain environments, providing a systematic technical path and engineering implementation scheme for the construction of green self-sufficient platforms. Attached Figure Description

[0029] Figure 1 This is a flowchart of the steps of the method of the present invention;

[0030] Figure 2 This is a schematic diagram illustrating the construction logic of each functional module in this invention and their interrelationships. Detailed Implementation

[0031] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0032] Example 1

[0033] An optimal energy allocation planning method for offshore energy platforms based on the volatility of new energy sources, such as Figure 1 As shown, it includes the following steps:

[0034] Step S1: Through processes such as resource identification, meteorological data processing, volatility index extraction, and complementarity assessment, the availability and dynamic characteristics of renewable resources such as photovoltaic, wind power, and wave energy in the target sea area are comprehensively characterized, providing basic input for subsequent equipment modeling and optimized configuration.

[0035] S11: Extract historical meteorological data for the area where the target offshore platform is located, including key variables such as wind speed, total solar irradiance, significant wave height, and mean period. Data sources include ERA5 reanalysis data, the Copernicus Marine database, and environmental monitoring devices deployed on the platform. After interpolation, cleaning, and standardization of time steps, the raw data is processed to form a year-round ordered dataset, and a resource time series matrix R(t) is constructed as the basic input for subsequent modeling.

[0036] S12: Based on the power conversion mechanisms of different renewable energy technologies such as photovoltaic, wind power, and wave energy, a resource-power mapping model is established. The photovoltaic system adopts an irradiance-temperature correction model, the wind power system adopts a segmented wind speed-power output curve, and the wave energy converter is modeled based on a two-dimensional wave energy power matrix, outputting the rated power value of the unit equipment at each time step, forming resource power time series data.

[0037] Based on the resource time series matrix R(t) obtained in S11, a standardized output power model is constructed for various types of energy equipment:

[0038] For photovoltaic systems, the unit panel output power model is as follows:

[0039] P PV (t)=η pv ·G(t)·A pv

[0040] Where, η pv Let G(t) be the photovoltaic conversion efficiency, G(t) be the total solar irradiance at time t, and A be the total solar irradiance at time t. pv This refers to the panel area.

[0041] For wind power systems, a typical piecewise wind speed-power curve model is used:

[0042]

[0043] Where v(t) is the real-time wind speed, v cut-in To cut off the wind speed, v cut-out It's the cutoff wind speed, v rated Represents the rated wind speed, α is the power coefficient, and P rated That is the rated power.

[0044] For wave energy systems, H is constructed through experiments or historical data. s -T e A two-dimensional wave power matrix, where each element corresponds to the power output under a specific combination of wave height and period:

[0045] P wave (t)=f(H s (t),T e (t))

[0046] Among them, H s (t) represents the significant wave height, T e (t) represents the average wave period.

[0047] S13: Construct a multi-dimensional resource volatility index system, including standard deviation, coefficient of variation, sliding rate of change, volatility frequency, and peak-valley duration. Extract typical volatility characteristics of resource sequences through window filtering and statistical calculations to form a volatility feature vector, which is used to match the response characteristics and stability design requirements of different equipment.

[0048] To quantify resource volatility characteristics, multidimensional volatility assessment indicators are introduced, including resource standard deviation, coefficient of variation, moving average rate of change, volatility frequency, and peak-valley duration. A typical volatility characteristic vector W for each resource type is calculated using time-domain statistics and window filtering methods. res It is used for device response adaptability matching and operation strategy design.

[0049] S14: Perform complementarity analysis on different types of renewable resources, calculate the Pearson correlation coefficient between resource pairs, define a complementarity index based on this, construct a complementarity matrix, and evaluate the temporal synergy of various resources. This result can be used for resource priority allocation, capacity redundancy adjustment, and weight setting in optimization strategies.

[0050] Complementarity analysis is performed on different types of resource combinations to quantify their collaborative capabilities during temporal changes. The Pearson correlation coefficient is used to measure the temporal correlation between the outputs of resource i and resource j.

[0051]

[0052] Where T is the total length of the time series, P i (t) represents the output power of the i-th type of resource at time t. Let be the average power output of resource type i over the entire cycle. Based on this, the resource complementarity index is defined as follows:

[0053] CI ij =1-|ρ ij |

[0054] Among them, when CI ij →1 indicates that the more inconsistent the resource fluctuation trends, the greater the potential for coordinated adjustment; when CI ij →0 indicates that resources fluctuate in the same direction and the ability to collaborate is weak.

[0055] S15: Encapsulates all resource parameters, output models, and volatility indicators into a unified dictionary data structure, including fields such as the original resource sequence, power model function, volatility eigenvector, and complementarity matrix. This structure supports on-demand invocation by subsequent equipment modeling and optimization scheduling modules, enabling a programmable data interface and a rapid modeling invocation mechanism. An example dictionary is shown below:

[0056]

[0057]

[0058] Step S2: Divide the energy consumption of offshore platforms into three categories: basic load, periodic tasks, and burst load. Combine historical operation data and task types to extract typical daily curves and construct multi-scenario load sets. Output structured load labels and adjustable attributes for matching scheduling resources and evaluating configuration robustness.

[0059] By mining historical data, identifying load types, and constructing scenarios, the energy demand expression of the platform under different time granularities and operating modes is established, and time-series load inputs and typical operating scenario sets are provided for subsequent optimization models.

[0060] S21: Identify the load types of offshore platforms and establish a structured load modeling framework. Based on the platform's operating conditions and mission characteristics, loads are divided into three categories: basic loads, cyclical loads, and peak burst loads. Basic loads are characterized by stability and continuity; cyclical loads are characterized by fixed time periods and regular power variations; and burst loads are characterized by short-duration high power and random response. The operating patterns and regulation attributes of each type of load are extracted to form load curves for each type.

[0061] Based on the process characteristics and energy consumption structure of the platform's operational tasks, the main load types of the platform are identified, which typically include the following three categories: 1) Basic sustaining load L base (t): such as traffic lights, communication modules, navigation, monitoring systems, etc., characterized by continuous, stable, and uninterrupted operation; 2) Periodic operating load L cyc (t): Such as daytime radar inspection, take-off and landing of UAVs on the sea surface, and platform environmental monitoring tasks, which have the characteristics of fixed time windows and periodic power changes; 3) Peak burst load L peak (t): Such as fault detection response, emergency lighting systems, and emergency communication activation, these have high power but short duration and often exhibit a degree of randomness. These three types of loads together constitute the total load curve.

[0062] L(t)=L base (t)+L cyc (t)+L peak (t)

[0063] To improve modeling clarity, different loads are classified and labeled with response characteristics, such as whether they can be shifted, whether they must be guaranteed, and whether they have flexible control attributes, forming a structured load label table.

[0064] S22: Construct a set of typical operating scenarios and generate multi-time-series load sequences. Through historical operating data analysis and clustering algorithms, extract several typical load days and construct an operating scenario set by combining resource fluctuations, task cycles, and seasonal variations. Each scenario includes a scenario label, scenario occurrence probability, and corresponding load time-series curve, used to support optimization modeling and robustness assessment under multiple operating conditions.

[0065] Considering the changes in energy consumption status of offshore platforms under different seasons, operational mission phases, and meteorological conditions, a set of multiple typical operational scenarios S = S1, S2, ..., S K This provides "multi-condition" scheduling conditions for subsequent model optimization. S for each scenario k This includes corresponding resource fluctuation scenarios (such as continuous low light, strong winds, etc.), platform task status (such as maintenance period, operation period, half-load period, etc.), and scenario probability weight ω. k (used for weighted calculation of the expected optimal value), scenario load curve L k (t)(based on historical data clustering or typical day extraction) and other elements.

[0066] S23: Establish a load adjustability and flexible load modeling mechanism. Identify time-shiftable, interruptible, and flexible load tasks, extract key parameters such as their operating window, minimum continuous working time, and maximum interruption time, and construct a flexible scheduling boundary model. Encapsulate load adjustment capabilities into a scheduling parameter dictionary structure to support demand-side participation and system adjustment coordination in subsequent scheduling optimization.

[0067] To facilitate subsequent energy storage system scheduling and equipment selection optimization, it is necessary to identify the portion of the load with regulation capabilities and model the following flexible boundaries: 1) Time-shifting task set T: such as L i (t)∈T, allowing t∈[t] min ,t max 1) Internal scheduling; 2) Interruptible tasks: Define the maximum interruptible time τ int 3) Flexible load modeling: A start / stop threshold modeling method is adopted. Various flexible load parameters and adjustment capacity boundaries are saved.

[0068] Step S3: Construct power output models for photovoltaic, wind power, wave energy and various energy storage technologies, define the device response capability index (DRI) by combining parameters such as adjustment slope and response delay, and establish deployability boundary constraints based on platform area, water depth, load-bearing capacity and other conditions to output a standardized device modeling structure.

[0069] S31: Establish a system of equipment response capability indicators and a dynamic characteristic model. Extract parameters such as the maximum power change rate, minimum stable operating time, and response delay of the equipment, and define the Device Response Index (DRI) to quantify the equipment's adaptability to resource fluctuations. Combine this with actual control characteristics to construct dynamic behavior models for charging and discharging rates, response delays, and start / stop boundaries.

[0070] To quantitatively characterize the adaptability of equipment to the volatility of new energy sources, a response index (DRI) is defined, which comprehensively considers the maximum adjustable output change rate γ. i Minimum stable operating time τ i and start / stop response delay d i The DRI is defined from three aspects:

[0071]

[0072] A higher DRI value indicates that the equipment is more responsive and adaptable to fluctuations. This indicator will be used in subsequent optimization models for equipment selection ranking and operational strategy weight allocation.

[0073] S32: Establish a state evolution and operational constraint model for the energy storage system. Construct an energy state update equation using charging / discharging power, charging / discharging efficiency, and time step, and set the capacity boundary, power limit, and charging / discharging mutual exclusion relationship of the energy storage system to form a complete energy dynamic model for state tracking and constraint embedding in the subsequent scheduling optimization process.

[0074] The state evolution of the energy storage system is represented using a standard battery dynamic model. The energy state update is expressed as:

[0075]

[0076] Where, η c ,η d These represent the charge / discharge efficiency, E bat (t) represents the energy state of the energy storage system at time t, P ch (t) and P dis (t) represents the charge / discharge power at time t. Furthermore, these parameters are subject to the following constraints:

[0077] 0≤E bat (t)≤E max ,

[0078]

[0079] P ch (t)·P dis (t)=0,

[0080] Among them, E max For the maximum capacity of the energy storage system, and This represents the maximum permissible value for charging and discharging power.

[0081] S33: Establish an engineering feasibility constraint model for equipment deployment. Combining the platform's available area, structural load-bearing capacity, water depth conditions, and the maximum number of equipment to be installed, construct boundary conditions such as area constraints, weight constraints, water depth adaptability, and deployment limit to ensure the optimization results are feasible for engineering implementation.

[0082] To ensure the deployability of the configuration results, it is necessary to model the deployment resources of the offshore platform, mainly including area constraints, weight constraints, water depth adaptability limitations, and maximum deployment quantity / number of units.

[0083] Area constraints (e.g., available layout area A at the top of the platform) avail ):

[0084]

[0085] Where A i N represents the area per unit area of ​​device i. i Configure the quantity for it.

[0086] Weight constraints (maximum load capacity of the platform W) max ):

[0087]

[0088] Among them, w i Let W be the weight of a single unit of the i-th type of equipment. max This represents the maximum structural load capacity allowed by the platform.

[0089] Water depth adaptability limitations (e.g., minimum water depth h for wave energy equipment) min ): For equipment layout schemes that do not meet the water depth requirements, they will be automatically set as infeasible solutions.

[0090] Maximum number of units / devices deployed (e.g., maximum number of devices per point):

[0091]

[0092] in This represents the maximum number of devices that can be deployed.

[0093] Step S4: Construct a two-layer optimization model to jointly solve the system structure configuration decision (upper layer) and the time-series operation scheduling strategy (lower layer) to achieve system-level coordinated configuration and dynamic supply-demand balance under the volatility of new energy sources. This step makes decisions on equipment type and scale through the structure layer, and achieves optimal energy flow allocation under different resource and load scenarios through the scheduling layer, so as to minimize system investment, energy waste, and gap costs, and achieve the optimal configuration of the renewable energy-dominated platform in terms of economy and robustness. The specific implementation is as follows:

[0094] S41: Construct the upper-level optimization model for the structural configuration layer. Using equipment selection and configuration capacity as decision variables, establish an upper-level planning model with the objective of minimizing system investment cost. Constraints include platform area, load-bearing capacity, water depth, lower limit of equipment response capability, and maximum installation quantity. Output the optimal equipment combination scheme and its corresponding deployment scale as the input basis for the scheduling layer.

[0095] The structural configuration decision (upper level) uses equipment selection and capacity configuration as core variables, with the objective of minimizing the system's annualized investment cost and worst-case operational losses while satisfying engineering constraints and adaptability to fluctuations. The core variables are represented as follows:

[0096] x i ∈{0,1},N i ∈￠≥0

[0097] Where, x i Indicates whether device type i, N is enabled. i Determine the number of installations. Based on the response capability filtering in S32 and the project deployment constraints in S34, minimize the following operational losses:

[0098]

[0099] in, and C unit i represents the fixed and unit investment costs of equipment i, respectively, and C represents the fixed and unit investment costs of equipment i. penalty This represents the penalty for energy shortages or waste in the scheduling layer. By minimizing operational losses, the output is a set of optimal configuration schemes (including equipment type, quantity, and corresponding parameters) used for energy flow optimization in the scheduling layer.

[0100] S42: Construct the lower-level optimization model of the operation and scheduling layer. Based on the configuration scheme output by the structure layer, and combined with the platform's annual load data and multi-scenario resource inputs, establish a scheduling model with the objectives of supply and demand balance, minimizing energy shortage, and minimizing energy curtailment. The model includes dynamic decision variables such as energy storage charging and discharging, load adjustment, and equipment start-up and shutdown, and the constraints cover energy storage state evolution, power output limits, and scheduling time windows.

[0101] The timing-based scheduling strategy (lower layer) is based on the device combination and capacity configuration output from the structure layer, targeting T steps throughout the year or typical scenarios S = S1,...,S K Fine-grained time-step operation scheduling is implemented to minimize operating costs, energy shortages, and fluctuation response costs. Scheduling variables include the output power P of various power sources. i (t), Energy storage charging and discharging power P ch (t),P dis (t), load gap E miss (t) and energy overflow E surp (t). The aim is to minimize the following operating costs, energy shortages, and fluctuation response costs:

[0102]

[0103] Where λ is the energy shortage penalty weight, β is the energy abandonment penalty weight, γ represents the cost of frequent start-stop operations, and f switch (t) represents the number of start / stop state transitions. Constraints include supply-demand balance, S33 energy storage state evolution, and load elastic dispatch window constraints, where supply-demand balance is expressed as:

[0104]

[0105] S43: Establish a structure-scheduling dual-layer coupling mechanism. A two-stage nested solution approach is used to achieve coordinated optimization of upper-layer structure configuration and lower-layer operational strategies. Upper-layer configuration influences the lower-layer operational boundary, and lower-layer operational feedback is used to correct structural investment assessments and resource selection schemes, thereby achieving coordinated adaptation and overall optimization of system structure and scheduling.

[0106] The configuration of the upper-level structure affects the feasible domain of the lower-level scheduling (whether the equipment is available, capacity boundaries, etc.), and the output of the lower-level scheduling affects the upper-level configuration evaluation (e.g., excessive energy shortage / waste feedback indicates insufficient configuration). Therefore, a two-stage iterative algorithm is used to solve the problem jointly.

[0107] S44: Incorporates multi-scenario robustness modeling and extreme weather disturbance simulation mechanisms. For typical extreme conditions such as prolonged low radiation and low wind speed, a set of disturbance scenarios with the strongest constraints is constructed to evaluate the system's maximum energy deficit, recovery time, and operational stability. Resource installation guarantee coefficients and maximum energy deficit constraints are introduced to improve the robustness and emergency adjustment capabilities of the configuration scheme under uncertain conditions.

[0108] Extreme weather scenarios (prolonged periods of no wind and no sunlight) are introduced as strong constraints at the scheduling layer, and a maximum energy shortage constraint is added:

[0109]

[0110] Among them, E miss (t) represents the energy gap at time t, òmax This represents the maximum acceptable instantaneous power shortage tolerance of the system. Furthermore, a minimum resource coverage redundancy constraint is introduced in the structural layer:

[0111]

[0112] Among them, RES installed RES represents the total installed capacity of the actual configured renewable energy system. peak required It is the maximum required renewable output observed during the scheduling process, r min It is the minimum resource installation guarantee coefficient.

[0113] Step S5: Evaluate the lower limit of energy storage by combining indicators such as maximum power shortage, duration and total annual power shortage, conduct structural design by combining short-term and long-term energy storage methods, identify key bottleneck equipment and propose capacity expansion suggestions to improve the system's continuous power supply capability and expand the redundancy adjustment range under extreme resource fluctuations.

[0114] Based on the already obtained optimal structural configuration, energy storage capacity and power generation redundancy resources are jointly optimized to improve the energy supply reliability and dispatch resilience of the offshore platform energy system under conditions of fluctuating renewable energy sources. This step determines the minimum energy storage capacity and minimum necessary redundancy level under the premise of minimizing costs through energy gap analysis, fluctuating load analysis, and resilience index construction, ensuring the safe operation of the system under typical adverse scenarios such as continuous resource depletion and extreme weather. The specific implementation is as follows:

[0115] S51: Conduct energy shortage analysis and assess energy storage compensation capacity. Based on the output of the dispatch layer, extract key indicators such as the system's annual energy shortage, maximum power shortage, and longest continuous energy shortage duration to assess the system's energy imbalance characteristics under typical and extreme operating conditions. Based on this, deduce the minimum capacity and power requirements of the energy storage system and establish corresponding lower limit constraints for configuration.

[0116] Based on the optimized output of step S4, the total missing energy and peak shortfall of the system throughout the year or in various typical scenarios are statistically analyzed. Based on this, a minimum coverage target for energy storage is set, where the total compensation under coverage condition 1 and the peak power compensation under coverage condition 2 are expressed as follows:

[0117]

[0118]

[0119] in, and θ1 and θ2 represent the minimum energy storage system capacity and the maximum discharge power of the energy storage system, respectively, and are redundancy guarantee factors. Furthermore, the continuous energy support capability under coverage condition 3 is expressed as:

[0120]

[0121] Where, τ miss,max This is the longest duration during which the system continuously experiences a power shortage state within the scheduling cycle.

[0122] S52: Optimize energy storage configuration structure and technology combination scheme. Taking into account the response speed, energy density, lifespan, and deployment conditions of various energy storage technologies such as lithium batteries, differential pressure storage, and supercapacitors, a hybrid energy storage structure synergistically designed for short-term and medium-to-long-term use is constructed. Under the premise of meeting system regulation capabilities, the optimal energy storage combination strategy is selected through annualized cost assessment and platform boundary constraints.

[0123] Based on the energy shortage analysis, the energy storage configuration structure needs further optimization. This step comprehensively considers the response speed, energy density, charge / discharge efficiency of energy storage technologies, and platform deployment conditions to construct a short-term and medium-to-long-term synergistic energy storage strategy. Priority is given to combinations such as lithium batteries and seawater differential pressure storage, and the annualized cost, operational efficiency, and platform deployment feasibility are evaluated to ultimately determine an economical, efficient, and adaptable energy storage configuration.

[0124] S53: Correct the redundancy configuration boundaries of critical power generation resources. Analyze the capacity expansion potential and response capabilities of resources during high-frequency periods of energy shortage or those prone to failure, and select wind power, photovoltaic, or wave energy equipment with priority for capacity expansion. Based on meeting economic constraints, propose minimum capacity expansion schemes and suggested expansion ranges to improve the system's safety redundancy capabilities under extreme fluctuations.

[0125] To enhance the system's power supply capacity under extreme weather conditions or continuous fluctuations, it is necessary to identify periods of frequent power shortages and resource bottlenecks during operation. Based on scheduling data backtracking, the capacity expansion potential of photovoltaic, wind power, and wave energy equipment is analyzed. Combining equipment response speed (DRI) and platform resource boundaries, suggestions for redundancy expansion of key equipment at the lowest cost are proposed, and the recommended expansion range is clarified as a reference for platform expansion and robustness improvement.

[0126] S54: Construct system resilience indices and conduct elastic assessments of configuration schemes. Define a system resilience index with energy shortage rate, energy storage support capacity, and equipment response capability as its core. Conduct multi-scenario elastic simulations and comparative analyses of different energy storage-redundancy configuration schemes to evaluate the system's recovery capability and operational stability under disturbances and shocks, providing quantitative feedback for configuration strategies.

[0127] A system-level resilience evaluation metric, the Resilience Index, is introduced to measure the adaptive capability of the configured system under different disturbance scenarios. The resilience index is defined as follows:

[0128]

[0129] in, E represents the total energy deficit under extreme weather scenarios. load E represents the total energy demand for the entire year's load. sto For total energy storage configuration, DRI avg ω1 to ω3 represent the average response capability of the system, and are weighting coefficients.

[0130] Step S6: As Figure 2 As shown, this method covers the entire process from supply and demand modeling, equipment and system construction, two-layer optimization decision-making to redundancy control, and finally adds a deployment verification step to test the actual effectiveness of the final solution. Specifically, it generates equipment deployment schemes based on the platform's physical boundaries to verify deployment feasibility; it conducts operational simulations based on extreme weather and continuous fluctuation scenarios to evaluate resilience indicators such as maximum power shortage rate, minimum SOC, and system recovery time, ultimately forming an engineering-ready configuration scheme and performance verification report.

[0131] To ensure the engineering feasibility and system stability of the optimized configuration results, the equipment deployment plan needs to be verified, and the system's resilience under extreme scenarios needs to be evaluated to improve the engineering adaptability and robustness of the plan.

[0132] Feasibility verification and layout design of equipment deployment: Based on the platform structure parameters and layout boundary constraints, conduct feasibility analysis on the equipment types and capacities in the optimization results, verify whether the photovoltaic panel installation angle, wind turbine tower layout, underwater wave energy anchoring conditions and energy storage compartment layout space meet the engineering requirements, and generate a platform equipment layout suggestion diagram and feasibility analysis report to ensure that the configuration scheme can be successfully deployed under actual platform conditions;

[0133] Conduct system resilience verification under typical extreme scenarios: Based on scenarios such as continuous windless and solar-free conditions and sudden load increases, use optimized configuration schemes for simulation tests to evaluate the system's maximum energy deficit rate, minimum state of energy storage (SOC), and recovery time under extreme disturbance conditions. Quantify the system's energy supply resilience and adjustment toughness, form system operation stability assessment results and configuration correction suggestions, and verify the stable operation capability of the configuration scheme in complex environments.

[0134] In summary, this invention introduces minute-level meteorological and load data for multi-source resource volatility assessment, establishes a multi-dimensional equipment model using the Device Response Index (DRI), and coordinates the platform's energy structure design and time-series operation strategies through a "two-layer optimization architecture," thereby improving supply-demand matching efficiency and resource utilization. Simultaneously, it integrates multi-scenario disturbance simulation and sensitivity analysis mechanisms to achieve dynamic matching of energy storage configuration and redundancy adjustment capabilities, effectively enhancing the robustness, scalability, and intelligent scheduling level of offshore platform energy systems under complex operating conditions. This provides theoretical support and engineering pathways for the subsequent construction of green energy offshore platforms, promoting the evolution of offshore energy platforms towards "clean, efficient, and intelligent scheduling."

[0135] It should be noted that the above content merely illustrates the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. For those skilled in the art, various improvements and modifications can be made without departing from the principle of the present invention, and all such improvements and modifications fall within the scope of protection of the claims of the present invention.

Claims

1. An energy allocation planning method for offshore energy platforms based on the volatility of new energy sources, characterized in that... It includes at least the following steps: S1: Based on standardized resource data, construct a multidimensional volatility index system and a resource complementarity matrix. Encapsulate the power output sequence, volatility index, complementarity coefficient and operational constraint information of each resource into a unified data dictionary structure, which becomes the resource base library. The resources include at least photovoltaic, wind energy and wave energy. The multidimensional volatility index system includes at least standard deviation, coefficient of variation, sliding rate of change, fluctuation frequency and peak-valley duration. S2: Based on historical operating data, load type identification, and task type, extract typical daily curves and construct multi-scenario load sets, output structured load labels and adjustable attributes, and establish the energy demand expression of the platform at different time granularities and operating modes; the load types include at least three categories: basic load, periodic tasks, and burst load; S3: Extract the maximum power change rate, minimum stable operating time and response delay of the equipment, and define the equipment response capability index DRI; construct the energy state update equation by charging / discharging power, charging / discharging efficiency and time step, set the capacity boundary, power limit and charging / discharging mutual exclusion relationship of the energy storage system, form an energy dynamic model, and establish an engineering feasibility constraint model for equipment deployment to ensure the feasibility of engineering implementation. S4: Construct a two-layer optimization model. The upper layer optimization of the model is used to determine the equipment type, configuration quantity and investment cost minimization strategy. The lower layer scheduling realizes the time-series supply and demand matching and operation cost control in multiple scenarios. Through the coupling iterative optimization of the upper and lower layers, the two-stage nested solution method realizes the linkage optimization of the upper layer structure configuration and the lower layer operation strategy. S5: Determine the minimum energy storage capacity and minimum necessary redundancy level under the premise of minimizing cost by energy gap analysis, fluctuating load analysis and resilience index construction; S6: Verify deployment feasibility by combining platform structural parameters and deployment boundary constraints; conduct operational simulations based on extreme weather and continuous fluctuation scenarios to evaluate the system's resilience under extreme conditions.

2. The energy allocation planning method for offshore energy platforms based on the volatility of new energy sources as described in claim 1, characterized in that: Step S1 specifically includes the following steps: S11: Extract the raw resource data of the target sea area, process the data into a time series with a fixed time step, and construct a standardized resource dataset; the raw resource data includes at least photovoltaic, wind power, and wave energy. S12: Establish a resource-power mapping model, in which the photovoltaic system adopts the irradiance-temperature correction model, the wind power system adopts the segmented wind speed-power output curve, and the wave energy system is modeled based on the two-dimensional wave energy power matrix, outputting the rated power value of the unit equipment at each time step to form resource power time series data; S13: Construct a multi-dimensional resource volatility index system, including at least standard deviation, coefficient of variation, sliding rate of change, volatility frequency and peak and valley duration, and calculate the typical volatility characteristic vector of each type of resource through time-domain statistics and window filtering methods; S14: Perform complementarity analysis on different types of resources, calculate the complementarity index, and construct a complementarity matrix; the complementarity index is determined based on the Pearson correlation coefficient between resource pairs, specifically: The Pearson correlation coefficient is used to measure the temporal correlation between the outputs of resource i and resource j: Where T is the total length of the time series, P i (t) represents the output power of the i-th type of resource at time t. The average power output of resource type i over the entire cycle; The resource complementarity index is as follows: IN ij =1-|ρ ij | Among them, when CI ij →1 indicates that the more inconsistent the resource fluctuation trends, the greater the potential for coordinated adjustment; when CI ij →0 indicates that resources fluctuate in the same direction and the ability to collaborate is weak; S15: Based on the above steps, the power output sequence, volatility index, complementarity coefficient and operation constraint information of various resources are encapsulated into a unified data dictionary structure to form a resource database.

3. The energy allocation planning method for offshore energy platforms based on the volatility of new energy sources as described in claim 1, characterized in that: In step S12, the photovoltaic system adopts an irradiation-temperature correction model, and its unit panel output power model is as follows: P PV (t)=η pv ·G(t)·A pv Where, η pv Let G(t) be the photovoltaic conversion efficiency, G(t) be the total solar irradiance at time t, and A be the total solar irradiance at time t. pv The panel area; The wind power system adopts a segmented wind speed-power output curve, specifically: Where v(t) is the real-time wind speed, v cut-in To cut off the wind speed, v cut-out It's the cutoff wind speed, v rated The rated wind speed is P, α is the power coefficient, and P is the rated wind speed. rated It is the rated power; Wave energy systems are modeled based on two-dimensional wave energy power matrices. For the constructed H... s -T e The two-dimensional wave power matrix, where each element corresponds to the power output under a specific combination of wave height and period, is as follows: P wave (t)=f(H s (t),T e (t)) Among them, H s (t) represents the significant wave height, T e (t) represents the average wave period.

4. The energy allocation planning method for offshore energy platforms based on the volatility of new energy sources as described in claim 1, characterized in that: The multi-scenario load set constructed in step S2 includes, but is not limited to, corresponding resource fluctuation scenarios, platform task status, scenario probability weights, and scenario load curves. A load adjustability and flexible load modeling mechanism is established to identify time-shiftable, interruptible, and flexible load tasks, extract task parameters, and construct an elastic scheduling boundary model to achieve demand-side participation and system adjustment coordination.

5. The energy allocation planning method for offshore energy platforms based on the volatility of new energy sources as described in claim 1, characterized in that: In step S3, the specific calculation method for the Device Response Index (DRI) is as follows: Where, γ i For the maximum adjustable output change rate, τ i For the minimum stable operating time, d i The start / stop response is delayed; The specific equation for updating the energy state is as follows: Where, η c ,η d These represent the charge / discharge efficiency, E bat (t) represents the energy state of the energy storage system at time t, P ch (t) and P dis (t) represents the charging and discharging power at time t; subject to the following constraints: 0≤E bat (t)≤E max , P ch (t)·P dis (t)=0, Among them, E max For the maximum capacity of the energy storage system, and This represents the maximum permissible value for charging and discharging power.

6. The energy allocation planning method for offshore energy platforms based on the volatility of new energy sources as described in claim 5, characterized in that: The engineering feasibility constraint model for equipment deployment in step S3 includes at least area constraints, weight constraints, water depth adaptability limitations, and the maximum number of devices per point. Area constraint A avail Specifically: Where A i N represents the area per unit area of ​​device i. i Configure the quantity for it; Weight constraint W max Specifically: Among them, w i Let W be the weight of a single unit of the i-th type of equipment. max This represents the platform's maximum allowable structural load capacity. In terms of water depth adaptability constraints: equipment deployment schemes that do not meet the water depth requirements will be automatically set as infeasible solutions; The maximum number of devices at a single point is as follows: in This represents the maximum number of devices that can be deployed.

7. The energy allocation planning method for offshore energy platforms based on the volatility of new energy sources as described in claim 1, characterized in that: Step S4 further includes incorporating multi-scenario robust modeling and extreme weather disturbance simulation mechanisms, introducing extreme weather scenarios as strongly constrained scenarios at the scheduling layer, and adding maximum energy deficit constraints: Among them, E miss (t) represents the energy gap at time t, ε max This indicates the maximum acceptable instantaneous power shortage tolerance of the system; Introduce a minimum resource coverage redundancy constraint at the structural layer: Among them, RES installed RES represents the total installed capacity of the actual configured renewable energy system. peak required It is the maximum required renewable output observed during the scheduling process, r min It is the minimum resource installation guarantee coefficient.

8. The energy allocation planning method for offshore energy platforms based on the volatility of new energy sources as described in claim 1, characterized in that: The toughness index in step S5 is specifically as follows: in, E represents the total energy deficit under extreme weather scenarios. load E represents the total energy demand for the entire year's load. sto For total energy storage configuration, DRI avg ω1 to ω3 represent the average response capability of the system, and are weighting coefficients.

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