A method for adjusting the power generation performance of an energy system throughout its entire life cycle.

Abstract

This invention relates to the field of data processing technology for predictive purposes, and discloses a method for adjusting the power generation performance of an energy system throughout its entire life cycle. The method determines the parameter boundaries of a wind-solar-storage power supply system, divides the entire life cycle planning period into multiple consecutive life stages, and determines the load demand input for the entire life cycle. It sets a baseline power generation performance at the initial commissioning stage, calculates the degradation maintenance level for each stage, establishes a monotonic mapping relationship between the degradation maintenance level and maintenance intensity, constructs a stage-dependent model for calculating available power generation capacity coupled with degradation and maintenance, and obtains available power supply capacity parameters for each stage of the entire life cycle. It generates a maintenance time series distribution through Monte Carlo simulation and constructs a set of life cycle power generation performance evolution scenarios. With the goal of minimizing the total cost throughout the entire life cycle, a mixed-integer linear programming model is constructed to obtain the optimal capacity configuration and adjust the power generation performance throughout the entire life cycle. This method solves the problems of ignoring degradation maintenance and inaccurate planning, achieving the goal of accurate and reliable life cycle data at a low cost.

Description

Technical Field

[0001] This invention relates to the field of data processing technology for prediction purposes, and in particular to a method for adjusting the power generation performance of an energy system throughout its entire life cycle. Background Technology

[0002] In traditional energy system configuration planning, models typically assume that the power generation performance of renewable energy equipment such as wind power and photovoltaics is constant, neglecting the uncertainty of unit efficiency degradation (such as equipment aging and dust accumulation) and random shutdowns during maintenance and repairs over long-term operation. This can lead to planning results deviating from reality. For example, Chinese patent CN111967659B discloses a method for optimizing the configuration of a regional integrated energy system based on photovoltaic absorption, providing the following technical solutions: establishing a basic architecture of a regional integrated energy system based on photovoltaic absorption according to the regional electricity, cooling, and heating load demands; constructing a capacity configuration optimization model for the regional integrated energy system with the objective functions of maximizing system economy, maximizing system power supply reliability, and minimizing curtailment rate; setting the power generation strategy in sequence as photovoltaic power generation, combined cooling, heating, and power (CCHP) energy supply unit, energy storage unit, and external power grid energy supply unit; using MATLAB programming to implement the regional integrated energy system configuration optimization, obtaining the optimal capacity configuration, i.e., the number of units configured, and calculating the configured power; and outputting the annual operating results, including system power supply composition, photovoltaic absorption status, and power supply interruption status. The method of this invention can effectively promote photovoltaic (PV) consumption while ensuring energy supply reliability, thus effectively improving PV utilization and the optimization efficiency of regional integrated energy. However, the aforementioned method for optimizing the configuration of a regional integrated energy system based on PV consumption mainly focuses on improving PV consumption rate and short-term operational reliability. Its configuration optimization model is based on the assumption of a predetermined operational strategy sequence and constant equipment performance, emphasizing the trade-off between economy, reliability, and curtailment rate through multi-objective functions, and outputting typical annual operating results. The power generation capacity and energy storage capacity of equipment are usually considered to be unchanged with the number of years of operation during the planning period, and equipment aging and the resulting maintenance impact are not considered as core constraints on capacity configuration. In contrast, this invention starts from the configuration effectiveness at the entire life cycle scale of the energy system, explicitly introducing the decline in power generation performance caused by performance degradation and periodic maintenance of wind power, PV, and energy storage equipment during long-term operation into the configuration planning process. This makes the configuration decision no longer based solely on initial performance or the operating results of a single year, but rather a one-time determination based on the evolution of power generation performance of the equipment throughout its entire life cycle. Therefore, the present invention can effectively avoid the problem of insufficient power supply or decreased reliability caused by the degradation of equipment capacity in the later stages of system operation, and has more obvious technical advantages in terms of long-term operational reliability and life-cycle economy. Summary of the Invention

[0003] This invention solves the problems of neglecting degradation maintenance and inaccurate planning in the prior art, and proposes a method for adjusting the power generation performance of the energy system throughout its entire life cycle, achieving the goal of accurate and reliable full-life cycle data at low cost.

[0004] Furthermore, this invention establishes a power generation performance evolution model that couples equipment performance degradation with maintenance, and directly embeds the actual energy supply capacity changes of wind power, photovoltaics and energy storage throughout the entire life cycle into configuration optimization, aiming to achieve a reliable and economically optimal energy system capacity configuration that is determined once and is available throughout the entire life cycle.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for adjusting the power generation performance of an energy system throughout its entire life cycle, comprising: Determine the parameter boundaries of the wind-solar-storage power supply system, divide the entire life cycle planning period into multiple continuous life stages, and determine the load demand input for the entire life cycle. Set the baseline power generation performance at the initial stage of commissioning, calculate the degradation retention level at each stage, establish a monotonic mapping relationship between the degradation retention level and the maintenance intensity, construct a stage-based available power generation capacity calculation model that couples degradation and maintenance, and obtain the available energy supply capacity parameters for each stage of the entire cycle. The maintenance time sequence distribution is generated through Monte Carlo simulation, and a set of power generation performance evolution scenarios throughout the entire life cycle is constructed. With the goal of minimizing the total cost over the entire life cycle, a mixed-integer linear programming model is constructed to obtain the optimal capacity configuration and adjust the power generation performance over the entire life cycle.

[0006] By directly embedding the coupled impact of long-term equipment performance degradation and maintenance downtime into the energy system's full lifecycle configuration planning, unlike the idealized assumption of constant equipment performance in traditional planning, this approach avoids insufficient power supply and configuration mismatch issues caused by equipment capacity degradation in the later stages of system operation, while also taking into account the economy and operational reliability throughout the entire lifecycle.

[0007] Preferably, determining the parameter boundaries of the wind-solar-storage power supply system specifically includes limiting the upper limit of the installed capacity of wind power and photovoltaic, the rated power and capacity of energy storage, and the charging and discharging efficiency boundaries; dividing the entire life cycle planning period into multiple continuous life cycle stages includes the planning period covering the equipment design life, dividing the continuous life cycle stages on a single year basis, with the equipment power generation capacity and availability status being approximately stable in each stage, and determining the load time series data of the entire cycle as the demand-side input.

[0008] The system clearly defines the boundary range of system parameters and the rules for dividing life stages. Life stages are divided on a single year basis, which not only ensures the calculation stability of equipment power generation capacity and availability status in each stage, but also characterizes the performance changes of equipment as the years of operation increase. The full-cycle load time sequence input is clearly defined, providing boundary conditions and benchmarks for subsequent full-life-cycle capacity calculation and configuration optimization, ensuring that the planning results fit the actual engineering scenario.

[0009] Preferably, the setting of the initial benchmark power generation performance specifically includes determining the benchmark available power generation capacity per unit installed capacity of wind and solar power generation equipment in the initial stage of operation based on the conventional power generation efficiency of wind and solar power generation equipment and local resources, and determining the benchmark capacity level of energy storage equipment that can participate in the system energy balance under ideal conditions based on the initial charge and discharge efficiency.

[0010] The benchmark capacity for wind and solar power is determined by combining the power generation efficiency of the equipment itself with the wind and solar resources of the project site, and the benchmark capacity for energy storage is determined based on the initial charge and discharge efficiency. This ensures the authenticity and engineering applicability of the benchmark values ​​and avoids inaccurate full-cycle capacity calculations due to deviations in the benchmark value setting.

[0011] Preferably, the calculation of degradation retention level at each stage specifically includes calculating the degradation retention level of wind power, photovoltaic and energy storage equipment at each life stage based on the initial performance level of the equipment, the lower limit of performance at the end of the life, and the rate of performance decline of the equipment; the degradation retention level of wind power and photovoltaic represents how much capacity is left in this stage relative to the initial stage, and the degradation retention level of energy storage represents the retention ratio of the charging and discharging efficiency in this stage relative to the initial design efficiency.

[0012] By adapting the performance degradation characteristics of wind power, photovoltaic, and energy storage equipment to a unified calculation logic, the degree of performance degradation of equipment as the years of operation increase is quantified, and the ability retention of equipment at different stages relative to its initial state is characterized.

[0013] Preferably, establishing the monotonic mapping relationship between degradation retention level and maintenance intensity specifically includes establishing mapping rules for wind power, photovoltaic and energy storage equipment respectively, taking the degradation retention level of the corresponding life stage as input, and outputting the corresponding maintenance intensity. The maintenance intensity represents the proportion of time that the equipment is out of service due to maintenance or failure within the corresponding stage, which is jointly determined by the baseline maintenance level and the degradation retention level, increases according to a preset relationship as the degradation retention level decreases, and is constrained by the upper limit of maintenance intensity.

[0014] A monotonic mapping rule between degradation maintenance level and maintenance intensity was established. Adaptive mapping relationships were set for different equipment to depict the actual engineering law that the more severe the equipment performance degradation, the higher the maintenance demand. At the same time, through the benchmark maintenance level and upper limit constraint, the basic requirements of routine equipment maintenance are covered, while avoiding the insufficient equipment availability due to excessive maintenance intensity.

[0015] Preferably, the construction of the stage-based available power generation capacity calculation model that couples degradation and maintenance specifically includes: calculating the availability ratio of the corresponding equipment based on the maintenance intensity of each life stage; combining the baseline available power generation capacity at the initial stage of equipment commissioning with the degradation maintenance level of the corresponding stage to calculate the power generation capacity of wind power and photovoltaic equipment after degradation at each stage; and then combining the availability ratio of the corresponding stage to obtain the available power generation capacity of wind power and photovoltaic equipment at each life stage.

[0016] By superimposing the capacity reduction caused by performance degradation and the reduction in available time due to maintenance downtime, the system closely matches the actual output characteristics of the equipment during long-term operation from two dimensions. This quantifies the actual available power generation capacity of wind and solar equipment at each stage of its entire life cycle, avoiding the limitations of considering only a single degradation effect or a constant availability rate, and significantly improving the realism of the system output simulation.

[0017] Preferably, the model for calculating the available power generation capacity of the energy storage device in stages that couples degradation and maintenance also includes calculating the charge and discharge efficiency after degradation in each stage based on the baseline charge and discharge efficiency at the initial stage of the energy storage device's operation and the degradation retention level of the corresponding life stage. Combined with the availability ratio of the corresponding stage, the available power supply capacity parameters of each life stage of the energy storage device are determined.

[0018] Preferably, the construction of the full life cycle power generation performance evolution scenario set specifically includes, under the premise of ensuring that the total annual maintenance time of each life stage is equally distributed monthly, using Monte Carlo simulation to randomly generate the maintenance time distribution in each month, obtaining multiple sets of maintenance time sequence scenarios through multiple random sampling, forming a power generation performance evolution scenario set covering the entire life cycle.

[0019] By generating a multi-scenario set of maintenance schedules through Monte Carlo simulation, and ensuring the total annual maintenance duration is constrained, the random uncertainty of maintenance time distribution avoids the result deviation caused by fixed maintenance schedules in traditional planning. By covering multiple scenarios, the impact of maintenance schedule fluctuations on system power generation performance is considered, thereby improving the risk resistance and reliability of the configuration scheme in actual operation.

[0020] Preferably, after constructing the set of power generation performance evolution scenarios throughout the entire life cycle, the method further includes extracting the power generation performance parameters of wind power, photovoltaic, and energy storage devices for each set of evolution scenarios at each stage of the life cycle, using a single year as the rolling step, pairing them with the load characterization data of the corresponding stage, forming a combination input of power generation capacity and load throughout the entire life cycle, and summarizing all combination inputs to form a set of scenario inputs for configuration optimization.

[0021] The evolution results of multiple scenarios are transformed into a standardized set of configuration optimization inputs. The matching of power generation performance parameters and load data is completed with an annual rolling step, realizing the precise matching of energy supply capacity and load demand in stages throughout the entire life cycle. By summarizing multiple sets of scenario inputs, the limitations of making configuration decisions based solely on a single degradation assumption are avoided.

[0022] Preferably, the construction of the mixed integer linear programming model specifically includes setting wind and solar power installed capacity, rated power and rated capacity of energy storage as configuration decision variables, setting wind and solar power output, energy storage operation and system power supply balance constraints, and aiming to minimize the total cost throughout the entire life cycle, including equipment investment cost and full life cycle operation and maintenance cost, to solve for the optimal capacity configuration for stable operation throughout the entire life cycle.

[0023] With wind, solar and energy storage capacity as the core decision variables, multiple constraints were set to fit the operating characteristics of the equipment. At the same time, with the goal of minimizing the total investment and operation and maintenance costs throughout the entire life cycle, the system achieved coordinated optimization of operational reliability and life-cycle economy, and obtained the optimal configuration scheme, which greatly improved the practicality and economy of energy system planning.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows.

[0025] 1. This invention establishes a long-term performance degradation model for wind power, photovoltaic, and other equipment, and couples it with the equipment maintenance mechanism. This eliminates the assumption of constant equipment performance in energy system configuration planning. It accurately reflects the changes in available power generation capacity throughout the equipment's entire life cycle, thereby avoiding the problems of insufficient power supply or decreased reliability that occur in the later stages of system operation in traditional planning, and significantly improving the engineering applicability of the configuration scheme.

[0026] 2. This invention incorporates the inherent relationship between equipment degradation and maintenance intensity into the optimization framework, comprehensively considering investment costs and operation and maintenance costs throughout the entire life cycle during the capacity configuration phase. By solving for the optimal installed capacity and energy storage configuration through mixed-integer linear programming, the system achieves the lowest total cost while ensuring long-term power supply reliability, significantly improving the economic performance of the energy system throughout its entire life cycle.

[0027] 3. This invention introduces Monte Carlo simulation to generate multiple possible scenarios for the evolution of equipment power generation performance based on the degradation and maintenance coupled model, forming an input set covering the entire life cycle. This effectively characterizes the impact of uncertainties such as maintenance time distribution on system operation, ensuring the stability of the final configuration scheme under multiple scenarios and improving the scientific nature and adaptability of planning decisions. Attached Figure Description

[0028] Figure 1 This is an overall flowchart of an edge control method based on multi-scale prediction and game theory according to the present invention.

[0029] Figure 2 This diagram illustrates the typical monthly wind power capacity factor output of a method for adjusting the power generation performance of an energy system throughout its entire life cycle, as described in this invention.

[0030] Figure 3 This diagram illustrates the typical monthly photovoltaic capacity factor output of a method for adjusting the power generation performance of an energy system throughout its entire life cycle, as described in this invention.

[0031] Figure 4 This diagram illustrates a typical monthly electricity load for a method of adjusting the power generation performance of an energy system throughout its entire life cycle, as described in this invention.

[0032] Figure 5 This is a diagram showing the distribution of wind power, photovoltaic, and energy storage maintenance times in a certain month in one embodiment of a method for adjusting the power generation performance of an energy system throughout its entire life cycle, according to the present invention.

[0033] Figure 6 This is a typical monthly system operation diagram of the method for adjusting the power generation performance of an energy system throughout its entire life cycle, according to the present invention. Detailed Implementation

[0034] See Figures 1 to 6 As shown, a method for adjusting the power generation performance of an energy system throughout its entire life cycle includes: Determine the parameter boundaries of the wind-solar-storage power supply system, divide the entire life cycle planning period into multiple continuous life stages, and determine the load demand input for the entire life cycle. Set the baseline power generation performance at the initial stage of commissioning, calculate the degradation retention level at each stage, establish a monotonic mapping relationship between the degradation retention level and the maintenance intensity, construct a stage-based available power generation capacity calculation model that couples degradation and maintenance, and obtain the available energy supply capacity parameters for each stage of the entire cycle. The maintenance time sequence distribution is generated through Monte Carlo simulation, and a set of power generation performance evolution scenarios throughout the entire life cycle is constructed. With the goal of minimizing the total cost over the entire life cycle, a mixed-integer linear programming model is constructed to obtain the optimal capacity configuration and adjust the power generation performance over the entire life cycle.

[0035] like Figure 1 In one embodiment shown, the present invention is a method for adjusting the power generation performance of an energy system throughout its entire life cycle. The core of this method involves fine-grained modeling of the entire life cycle of coupled equipment performance degradation and maintenance behavior, combined with stochastic scenario simulation and mathematical optimization solutions, to achieve optimal capacity configuration of the wind-solar-storage combined power supply system. Ultimately, this achieves precise control and optimization of the system's power generation performance throughout its entire life cycle. The specific implementation process is as follows: First, the basic parameter boundaries of the wind-solar-storage combined power supply system and the time dimension decomposition of the entire life cycle planning period are completed. The upper limit of the installed capacity of wind power and photovoltaic equipment in the system, as well as the parameter constraints of rated power, rated capacity and charge-discharge efficiency of electrochemical energy storage equipment are first clarified. At the same time, a full life cycle planning period covering the design life of the equipment is selected, and this period is divided into multiple continuous life stages with a single year as the unit, to ensure that the power generation capacity and operational availability of the equipment in each stage can be approximately regarded as stable. In addition, load time series data within the entire planning period are obtained as the demand-side basic input for system supply and demand matching.

[0036] After defining the basic system boundaries, the baseline power generation performance levels of wind power, photovoltaic, and energy storage equipment were determined at the initial stage of operation. The baseline available power generation capacity of wind power and photovoltaic equipment was determined by combining the power generation efficiency of conventional wind and solar power generation devices with the wind and solar resource endowment conditions of the project site. This characterizes the stable power generation capacity per unit installed capacity when the equipment is free from aging, pollution, and maintenance impacts. The baseline capacity of energy storage equipment was verified based on its initial charge and discharge efficiency, corresponding to the basic capacity level at which the equipment can participate in system energy balance under ideal conditions.

[0037] Based on the baseline performance of the equipment at the initial stage of operation, the degradation retention levels of wind power, photovoltaic, and energy storage equipment at each stage of their lifespan are calculated. The core inputs are the initial performance level of the equipment, the lower limit of the performance at the end of the lifespan, and the rate of performance degradation of the equipment itself. The degradation retention level of wind power and photovoltaic represents the remaining proportion of the equipment's power generation capacity at the corresponding stage of its lifespan compared to the initial stage of operation. The degradation retention level of energy storage represents the proportion of the equipment's charge and discharge efficiency at the corresponding stage compared to the initial design efficiency. This quantifies the degree of performance degradation of the equipment as the years of operation increase.

[0038] Based on the equipment degradation retention level at each stage, a monotonic mapping rule between it and maintenance intensity is established. This leads to the construction of a stage-based available energy supply capacity calculation model that couples degradation and maintenance, completing the calculation of equipment power generation performance at each stage of the entire life cycle. Dedicated mapping rules are built for wind power, photovoltaic, and energy storage equipment, taking the degradation retention level of the corresponding life stage as input and outputting the maintenance intensity of that stage. The maintenance intensity is used to characterize the proportion of downtime due to maintenance or failure within the corresponding life stage. Its value is jointly determined by the equipment's baseline maintenance level and degradation retention level, and gradually increases according to preset rules as the degradation retention level decreases. At the same time, an upper limit for maintenance intensity is set to ensure the basic available operating time of the equipment.

[0039] Based on this, the availability ratio of corresponding equipment is first calculated according to the maintenance intensity of each stage, that is, the proportion of the equipment's normal operating time to the total time of the stage. For wind power and photovoltaic equipment, the degradation-after-degradation power generation capacity is calculated first, combined with the baseline available power generation capacity at the initial commissioning stage and the degradation maintenance level at the corresponding stage. Then, the availability ratio at that stage is used to finally obtain the available power generation capacity for the corresponding life stage. For energy storage equipment, the degradation-after-degradation charge and discharge efficiency is calculated based on the baseline charge and discharge efficiency at the initial commissioning stage and the degradation maintenance level at the corresponding stage. Then, the availability ratio at the corresponding stage is matched to determine the available energy supply capacity parameters for each life stage. Finally, the available power generation capacity of wind power and photovoltaic equipment and the available energy supply capacity parameters of energy storage at each stage of the entire life cycle are summarized to form a representation of the actual energy supply level of the system at each stage.

[0040] After completing the basic power generation performance calculation for each stage of the entire life cycle, the uncertainty of maintenance timing is introduced. A set of power generation performance evolution scenarios for the entire life cycle is constructed using the Monte Carlo simulation method. Under the constraint that the total annual maintenance time for each life cycle stage is equally distributed monthly, the maintenance time distribution within each month is randomly generated. Multiple sets of differentiated annual maintenance timing scenarios are obtained through multiple random sampling. The equipment power generation performance calculation process for each life cycle stage is repeatedly executed to obtain multiple sets of equipment capacity evolution results covering the entire life cycle. Each set of results corresponds to an evolution path of equipment degradation and maintenance, ultimately forming a set of power generation performance evolution scenarios for the entire life cycle.

[0041] Subsequently, using a single year as the rolling step, for each set of evolution scenarios, the power generation performance parameters of wind power, photovoltaic and energy storage equipment are extracted for each life stage, and paired with the load characterization data of the corresponding stage to form the power generation capacity and load combination input for the whole life cycle. After summarizing all the combination inputs, a set of scenario inputs for subsequent configuration optimization is formed, thereby avoiding the configuration decision bias caused by a single degradation assumption.

[0042] Finally, with the minimum total cost over the entire life cycle as the core objective, a mixed-integer linear programming model is constructed. The optimal capacity configuration scheme is obtained through model solving, ultimately achieving the regulation and optimization of the power generation performance throughout the energy system's life cycle. This optimization model uses wind power installed capacity, photovoltaic installed capacity, and rated power and capacity of energy storage as core configuration decision variables, while setting three types of core constraints: first, output constraints for wind and photovoltaic power, clarifying the correlation between real-time equipment output and installed capacity, baseline power generation capacity, and stage power generation performance coefficient; second, energy storage operation constraints, limiting the charging and discharging power of energy storage, the operating boundaries of the state of charge, and the energy conservation relationship of the state of charge in adjacent time periods, while prohibiting simultaneous charging and discharging of equipment at the same time; and third, system power supply balance constraints, ensuring the matching of system output and load demand in all time periods throughout the entire life cycle. The optimization objective of the model is to minimize the total cost over the entire life cycle. The total cost includes the initial investment cost of wind power, photovoltaic, and energy storage equipment, as well as the operation and maintenance cost of each piece of equipment throughout the entire life cycle. By solving the optimization model, the optimal capacity configuration scheme that can ensure the stable operation of the system throughout the entire life cycle is obtained, thereby completing the adjustment of the power generation performance of the energy system throughout its entire life cycle.

[0043] This invention proposes a wind-solar-storage energy system configuration planning method based on the life-cycle evolution of equipment's available power generation capacity. By characterizing the impact of equipment performance degradation and the resulting phased maintenance on available power generation capacity, it determines a wind power, solar power, and energy storage configuration scheme that meets a given load demand throughout the entire life-cycle. Specifically, it includes the following: S1: A power supply system comprised of wind power, solar power, and electrochemical energy storage aims to meet the power supply needs of a given load without relying on external power sources. First, the boundary conditions for wind power, solar power, and electrochemical energy storage within the system are defined. This includes: the upper limit of installed capacity for wind power and solar power, the upper limit of rated power and rated energy storage capacity, and the parameter boundaries for energy storage charge / discharge efficiency. Based on this, a full lifecycle configuration planning period (e.g., 10-25 years or covering the equipment design life) is further determined, and this period is divided into several lifecycle stages (e.g., one year per stage). This ensures that the power generation capacity and availability of wind power, solar power, and energy storage equipment can be approximated as stable within each stage, thus characterizing changes in actual power generation capacity due to equipment aging and maintenance in different years. Simultaneously, a load characterization method (e.g., 8760 hours of data per year) is determined as the demand-side input for the full lifecycle configuration planning.

[0044] S2: Set the initial available power generation capacity benchmark values ​​for wind power and photovoltaics. Based on the completion of system structure and full life cycle planning boundary determination, determine the benchmark power generation performance level of wind power, photovoltaics and electrochemical energy storage respectively in the initial stage of equipment commissioning.

[0045] Among them, the benchmark values ​​of the unit installed capacity of wind power and photovoltaic power can be determined based on the power generation efficiency of existing conventional wind and solar power generation equipment, and according to the local wind and solar resource endowment conditions, characterize the level of power generation capacity that wind power and photovoltaic equipment can stably provide per unit installed capacity when there is no aging, surface contamination and maintenance impact.

[0046] For electrochemical energy storage, its ability to participate in the system's energy balance under ideal conditions is determined based on its charge and discharge efficiency.

[0047] S3: Establish a method for calculating "phased available generating capacity" in the degradation-maintenance coupling process: (1) Lifespan stage division The entire life cycle of the energy system is divided into several stages, numbered as Stage 1, Stage 2, ..., Stage S; among which Stage 1 is the initial commissioning stage.

[0048] (2) Initial operational baseline capacity Based on steps 1 and 2, we know: the initial available power generation capacity of wind power; the initial available power generation capacity of photovoltaic power; and the initial standard charge and discharge efficiency of energy storage.

[0049] (3) Stage degradation maintains level For each lifespan stage, the degradation retention level for that stage can be given based on the performance degradation characteristics of the equipment over the corresponding number of years of operation: Wind power degradation maintenance level: indicates "how much capacity remains relative to the initial stage", with a value between 0 and 1; Photovoltaic degradation maintenance level: Same as above, with values ​​between 0 and 1; Energy storage degradation retention level: This indicates the proportion of the charge and discharge efficiency maintained relative to the initial design efficiency at this stage. It reflects the degree of performance degradation of the energy storage system due to cycle aging and aging. The value is between 0 and 1.

[0050] The set degradation retention level gradually decreases as the usage phase progresses; that is, the older the equipment, the lower its power generation capacity and performance. The degradation retention level for the equipment in phase S is determined as follows: Equipment degradation maintenance level (S stage) = Lower limit of equipment performance at the end of its life + (Initial performance level of equipment - Lower limit of equipment performance at the end of its life) / (1 + exp(Equipment performance degradation rate × (S stage - equipment life / 2))) Among them: the initial performance level is usually taken as 1, the lower limit of performance at the end of the life is greater than 0, and the rate of equipment performance degradation is usually taken in the range of 0.3 to 0.6.

[0051] In this embodiment, the maintenance intensity is monotonically determined by the degradation retention level, and the more severe the degradation, the greater the maintenance intensity. (4) Maintenance intensity is obtained from the equipment degradation retention level. Maintenance intensity refers to the average percentage of time that equipment is unavailable due to maintenance or malfunction during its lifespan; it can also be understood as the percentage of downtime.

[0052] Maintenance intensity mapping rules are established for wind power, photovoltaic and electrochemical energy storage respectively. The input is the performance degradation level of the life stage and the output is the corresponding maintenance intensity. The maintenance intensity is jointly determined by the equipment baseline maintenance level and the equipment degradation maintenance level. When the equipment degradation maintenance level decreases, the maintenance intensity can be increased synchronously according to a piecewise linear relationship.

[0053] Maintenance intensity = baseline maintenance intensity × (upper limit of maintenance intensity - baseline maintenance intensity) × (1 - equipment degradation retention level). When the equipment degradation retention level is close to 1, the maintenance intensity is close to the baseline maintenance intensity, which is used to characterize routine maintenance. As the degradation retention level decreases, the maintenance intensity gradually increases according to the preset sensitivity relationship and is constrained by the upper limit of maintenance intensity to ensure that the equipment still has usable operating time throughout the year.

[0054] (5) The availability ratio, i.e. the percentage of available time, can be obtained from the maintenance intensity. For wind power, solar power, and energy storage, calculate the availability ratio for each stage of their lifespan. This ratio represents the percentage of hours the equipment can operate normally within that time period, divided by the total number of hours. Availability ratio = 1 - Maintenance intensity If the maintenance intensity is 0.06, it means that the machine is shut down for maintenance an average of 6% of the time, and the availability ratio is 0.94.

[0055] (6) Calculate the available power generation capacity of wind power and photovoltaic power at each stage. For each lifespan stage, the available power generation capacity for both wind and solar power is calculated in the same way, starting with the calculated power generation capacity level after degradation: Degraded power generation capacity = Initial operational baseline usable power generation capacity × Stage S degradation maintenance level. Reflects the decrease in unit power generation capacity of wind and solar power equipment due to aging, pollution, and wear.

[0056] Available generating capacity of power generation equipment in Phase S = Degraded generating capacity × Availability ratio of power generation equipment in Phase S. This parameter reflects that even after the power generation equipment has degraded, it still has some generating capacity, but it is shut down for a period of time, so the actual usable capacity is further reduced.

[0057] Therefore, based on the initial baseline capability, the actual available power generation capacity of wind power and photovoltaic power in stage S can be obtained.

[0058] (7) Calculate the stage power generation performance of energy storage Energy storage must consider both charge and discharge efficiency. Calculations are performed for each lifespan stage: Charge and discharge efficiency after energy storage degradation Energy storage S-stage charge / discharge efficiency = initial energy storage charge / discharge efficiency × S-stage energy storage degradation maintenance level.

[0059] (8) Power generation performance of the energy system during the output lifespan stage S For the lifetime stage S, the following quantities are determined: The available power generation capacity of wind power during its lifespan S; The available power generation capacity of a photovoltaic system during its lifespan, stage S. The available charge / discharge efficiency of energy storage during its lifespan S.

[0060] The above quantities together constitute the available energy supply capacity parameters of the wind, solar and energy storage system under the life stage S, which are used to characterize the actual energy supply level of the energy system in this stage after considering the coupled effects of equipment degradation and maintenance.

[0061] Arranging the power generation performance of each stage of the life cycle in chronological order creates a sequence of stage power generation performance covering the entire life cycle planning period. This sequence can be used to reflect the gradual evolution of the energy supply capacity of wind power, photovoltaics, and energy storage over different operating years.

[0062] S4: Based on the degradation-maintenance coupled model, uncertainties are introduced and multiple lifetime capability evolution scenarios are generated. Building upon the degradation-maintenance coupled power generation performance model established in S3, after obtaining the total annual maintenance duration for year S, the specific maintenance time distribution within the 8760 hours needs to be further determined. This invention, while ensuring the total annual maintenance duration is equally distributed across 12 months, uses Monte Carlo simulation to randomly generate the maintenance distribution for each month, forming multiple possible annual maintenance time series scenarios while satisfying monthly maintenance duration constraints. Through multiple random samplings, the uncertainty of the impact of maintenance time distribution on system power generation performance can be characterized, and the generated scenarios can be used for subsequent full-lifecycle configuration planning analysis. Within a reasonable value range, multiple simulations generate the maintenance time distribution for wind power, photovoltaic, and electrochemical energy storage, generating a set of power generation performance evolution scenarios over the entire lifecycle for subsequent configuration planning decisions.

[0063] Let k = 1, 2, ..., k represent the annual maintenance distribution sample for the k-th simulation. During implementation, based on the degradation-maintenance coupled calculation process determined in S3, the calculations of equipment operation and power generation performance for each lifespan stage, as well as the maintenance allocation process, are repeated K times. In each calculation, the degradation retention level for each lifespan stage is continuously updated, and accordingly, the maintenance intensity, availability ratio, and available power generation and energy storage capacity of wind power, photovoltaic power, and energy storage for each lifespan stage are recalculated.

[0064] Through the above calculations, several sets of capability evolution results covering the entire life cycle can be obtained. Each set of results corresponds to a possible equipment degradation and maintenance evolution path, reflecting the actual power generation performance level that wind power, photovoltaics, and energy storage can provide to the system at different life stages under this path.

[0065] S5: Transform the results of multiple capability evolutions into annual inputs for "rolling optimization year by year". Using each lifetime configuration planning cycle as a rolling step, the entire lifecycle is divided into Year 1, Year 2, ..., Year Y. For each set of simulation results, the available wind power generation capacity, available photovoltaic power generation capacity, available energy storage capacity, and available power capacity are extracted for each lifecycle stage. These are then paired with the corresponding load characterization data for that lifecycle stage (e.g., an annual 8760-hour load sequence or a typical daily set with weights) to form a "lifecycle generation capacity-load combination input". All the "lifecycle generation capacity-load combination inputs" corresponding to the simulation results are summarized side-by-side as the scenario input set for configuration optimization, to avoid making configuration decisions based solely on a single degradation assumption.

[0066] S6: Based on the mixed-integer linear programming method, the optimal configuration is solved using Gurobi optimization. The wind power installed capacity, photovoltaic installed capacity, energy storage rated power and energy storage rated energy capacity are used as configuration decision variables. An energy system simulation model is established based on the mixed-integer linear programming method, and the configuration optimization model is solved using Gurobi. Finally, an optimal configuration scheme that can be determined in the early stage of planning and can guarantee the normal operation of the system throughout its entire life cycle can be obtained.

[0067] (1) Output constraints of wind power and photovoltaic power In each hour, the actual output of wind power and solar power The actual output of wind power is calculated as wind power installed capacity × benchmark value of power generation capacity per unit of wind power installed capacity × wind power S-stage power generation performance coefficient; The actual output of photovoltaic power is calculated as: photovoltaic installed capacity × benchmark value of power generation capacity per unit photovoltaic installed capacity × photovoltaic S-stage power generation performance coefficient; The energy storage charge and discharge efficiency is the standard charge and discharge efficiency at the initial stage of energy storage operation × the available charge and discharge efficiency in the S stage of energy storage. (2) Constraints on Energy Storage Operation The energy storage system can be charged or discharged every hour, and its operation must meet the following constraints: The charging and discharging power shall not exceed the rated power of the energy storage, and the state of charge of the energy storage shall not exceed the available energy storage capacity at any time. The energy storage state of charge is updated between adjacent hours according to the charge and discharge efficiency, reflecting the energy conservation relationship: the energy storage state of charge at hour t+1 = the energy storage state of charge at hour t - charging power × charging efficiency - discharging power ÷ discharging efficiency. Operational constraints also include the limitation that energy storage cannot be charged and discharged simultaneously at the same time.

[0068] (3) Power supply balance constraints Every hour, the system must maintain a power supply balance: Wind power output + photovoltaic power output + energy storage discharge - energy storage charging = hourly load demand + wind curtailment + solar curtailment.

[0069] (4) Construct the objective function Total cost = Investment cost + Lifetime maintenance cost of each piece of equipment Investment cost = (Wind power capacity × unit cost) + (Photovoltaic capacity × unit cost) + (Energy storage power × unit cost) + (Energy storage energy × unit cost) Total lifecycle maintenance cost of each device = ∑ (Years × Number of maintenance sessions × Cost per maintenance session) Through optimization calculations, the optimal configuration of the energy system (wind power capacity, photovoltaic capacity, energy storage power and energy) can be output. Simultaneously, the power supply satisfaction and key operational indicators of this configuration at each stage and under each scenario throughout its lifespan can be output.

[0070] In another embodiment, the application scenario is located on an island, and the annual electricity load with hourly accuracy is known, as well as the power generation capacity factors of wind power and photovoltaics, and the upper limit of wind power and photovoltaic installed capacity. The specific parameters are shown in Table 1 below.

[0071] Table 1:

[0072] Typical monthly wind power capacity factor, typical monthly photovoltaic capacity factor, typical monthly electricity load, etc. Figures 2 to 4 As shown, Monte Carlo simulations were performed on the maintenance dates of wind power, photovoltaics, and energy storage for each month. Taking a typical month as an example, the sampling results are as follows. Figure 5 As shown, in multiple simulations, the maximum values ​​for wind power capacity, photovoltaic capacity, and energy storage capacity were selected respectively, and the combination of the maximum values ​​of the three types of equipment was used as the final scheme. The final optimization results are shown in Table 2 below.

[0073] Table 2:

[0074] The results in the table show that, compared with the traditional configuration scheme that does not consider equipment aging and unit maintenance factors, the overall installed capacity required by the system is increased after introducing relevant constraints. Therefore, redundant equipment configuration is needed to offset the decline in power generation performance and the increase in maintenance time caused by equipment aging, so as to ensure the effective operation of the system throughout its entire life cycle.

[0075] The energy system operation and scheduling situation for a typical month is as follows: Figure 6 As shown in the diagram, green represents wind power, orange represents solar power, purple represents energy storage discharge, blue represents energy storage charging, and the black line represents the load. It is evident that the system's available power supply capacity decreases significantly during periods of maintenance for wind power, solar power, and energy storage equipment. To ensure continuous load fulfillment, a certain amount of redundant installed capacity needs to be reserved during the configuration phase to cope with the power supply capacity loss caused by equipment maintenance.

[0076] All data collection and extraction in this invention are carried out under compliant and legal conditions.

Claims

1. A method for adjusting the power generation performance of an energy system throughout its entire life cycle, characterized in that, include: Determine the parameter boundaries of the wind-solar-storage power supply system, divide the entire life cycle planning period into multiple continuous life stages, and determine the load demand input for the entire life cycle. Set the baseline power generation performance at the initial stage of commissioning, calculate the degradation retention level at each stage, establish a monotonic mapping relationship between the degradation retention level and the maintenance intensity, construct a stage-based available power generation capacity calculation model that couples degradation and maintenance, and obtain the available energy supply capacity parameters for each stage of the entire cycle. The maintenance time sequence distribution is generated through Monte Carlo simulation, and a set of power generation performance evolution scenarios throughout the entire life cycle is constructed. With the goal of minimizing the total cost over the entire life cycle, a mixed-integer linear programming model is constructed to obtain the optimal capacity configuration and adjust the power generation performance over the entire life cycle.

2. The method for adjusting the power generation performance of an energy system throughout its entire life cycle according to claim 1, characterized in that, The determination of the parameter boundaries of the wind-solar-storage power supply system specifically includes limiting the upper limit of the installed capacity of wind power and photovoltaic, the rated power and capacity of energy storage, and the charging and discharging efficiency boundaries; dividing the entire life cycle planning period into multiple continuous life cycle stages includes the planning period covering the equipment design life, dividing the continuous life cycle stages on a single year basis, keeping the equipment's power generation capacity and availability stable in each stage, and determining the load time series data of the entire cycle as the demand-side input.

3. The method for adjusting the power generation performance of an energy system throughout its entire life cycle according to claim 2, characterized in that, The specific provisions of setting the initial benchmark power generation performance include determining the benchmark available power generation capacity per unit of wind and solar power equipment in the initial stage of operation based on the conventional power generation efficiency of wind and solar power equipment and local resources, and determining the benchmark capacity level of energy storage equipment that can participate in the system energy balance under ideal conditions based on the initial charge and discharge efficiency.

4. The method for adjusting the power generation performance of an energy system throughout its entire life cycle according to claim 3, characterized in that, The calculation of degradation retention levels at each stage specifically includes calculating the degradation retention levels for wind power, photovoltaic, and energy storage equipment at each life stage based on the initial performance level of the equipment, the lower limit of performance at the end of the life, and the rate of performance degradation. The degradation retention levels of wind power and photovoltaic represent how much capacity remains in that stage relative to the initial stage, while the degradation retention level of energy storage represents the percentage of charge and discharge efficiency maintained relative to the initial design efficiency in that stage.

5. The method for adjusting the power generation performance of an energy system throughout its entire life cycle according to claim 4, characterized in that, The establishment of a monotonic mapping relationship between degradation retention level and maintenance intensity specifically includes establishing mapping rules for wind power, photovoltaic and energy storage equipment respectively. The degradation retention level of the corresponding life stage is used as input, and the corresponding maintenance intensity is output. The maintenance intensity represents the proportion of time that the equipment is out of service due to maintenance or failure within the corresponding stage. It is jointly determined by the baseline maintenance level and the degradation retention level. As the degradation retention level decreases, it increases according to a preset relationship and is constrained by the upper limit of maintenance intensity.

6. The method for adjusting the power generation performance of an energy system throughout its entire life cycle according to claim 5, characterized in that, The specific steps of constructing the stage-dependent available power generation capacity calculation model that couples degradation and maintenance include: calculating the availability ratio of corresponding equipment based on the maintenance intensity of each life stage; combining the baseline available power generation capacity at the initial stage of equipment commissioning with the degradation maintenance level of the corresponding stage to calculate the power generation capacity of wind power and photovoltaic equipment after degradation at each stage; and then combining the availability ratio of the corresponding stage to obtain the available power generation capacity of wind power and photovoltaic equipment at each life stage.

7. The method for adjusting the power generation performance of an energy system throughout its entire life cycle according to claim 6, characterized in that, The model for calculating the available power generation capacity of the energy storage device in stages that couples degradation and maintenance also includes calculating the charge and discharge efficiency after degradation in each stage based on the baseline charge and discharge efficiency at the initial stage of the energy storage device's operation and the degradation retention level of the corresponding life stage. Combined with the availability ratio of the corresponding stage, the available power supply capacity parameters of each life stage of the energy storage device are determined.

8. The method for adjusting the power generation performance of an energy system throughout its entire life cycle according to claim 7, characterized in that, The construction of the full life cycle power generation performance evolution scenario set specifically includes, under the premise of ensuring that the total annual maintenance time of each life stage is equally distributed on a monthly basis, using Monte Carlo simulation to randomly generate the maintenance time distribution in each month, obtaining multiple sets of maintenance time sequence scenarios through multiple random sampling, forming a power generation performance evolution scenario set covering the entire life cycle.

9. The method for adjusting the power generation performance of an energy system throughout its entire life cycle according to claim 8, characterized in that, After constructing the set of power generation performance evolution scenarios throughout the entire life cycle, the method further includes extracting the power generation performance parameters of wind power, photovoltaic, and energy storage devices for each set of evolution scenarios at each stage of the life cycle, using a single year as the rolling step, pairing them with the load characterization data of the corresponding stage, forming a combination input of power generation capacity and load throughout the entire life cycle, and summarizing all combination inputs to form a set of scenario inputs for configuration optimization.

10. A method for adjusting the power generation performance of an energy system throughout its entire life cycle according to any one of claims 1 to 9, characterized in that, The construction of the mixed integer linear programming model specifically includes using wind power and photovoltaic installed capacity, rated power and rated capacity of energy storage as configuration decision variables, setting constraints on wind and solar power output, energy storage operation and system power supply balance, and aiming to minimize the total cost throughout the entire life cycle, including equipment investment cost and full life cycle operation and maintenance cost, to solve for the optimal capacity configuration for stable operation throughout the entire life cycle.

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