New energy power generation project benefit evaluation method based on multi-dimensional coupling

Abstract

The present application belongs to the technical field of power generation project benefit evaluation, and discloses a new energy power generation project benefit evaluation method and system based on multi-dimensional coupling, comprising: performing compliance verification on the pre-acquired attenuation parameters and output data, combining a pre-set environmental operation correction factor to construct a device performance degradation dynamic model, and performing output correction on the verified output data; verifying the pre-acquired power grid power limiting parameters by using normative verification, constructing a power limiting strategy multi-dimensional constraint condition, and performing power limiting constraint on the corrected theoretical output data based on the power limiting strategy multi-dimensional constraint condition; performing physical constraint verification on the pre-acquired energy storage parameters, combining a pre-set safety constraint to construct an energy storage optimization scheduling model, and combining the actual output data to perform charging and discharging strategy optimization, and performing power generation project benefit evaluation according to the charging and discharging strategy optimization result. The present application can solve the economic prediction distortion problem under the power consumption capacity limited scenario.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power generation project benefit evaluation, and particularly relates to a new energy power generation project benefit evaluation method based on multi-dimensional coupling. BACKGROUND

[0002] Under the industrial background of continuous expansion of new energy power generation installed capacity, the forced power reduction measures taken by the power grid dispatching department to maintain the safe and stable operation of the power system have evolved into the main risk source restricting the project economy; the existing evaluation system has multiple defects in the technical implementation level, specifically in the insufficient coordination analysis ability in the time dimension, equipment dimension and system dimension.

[0003] In the time dimension level, the traditional evaluation method generally adopts a static processing mode, simplifying the power grid power reduction instruction into a single constraint parameter of annual average power reduction rate; however, production data analysis shows that the time and space distribution of power reduction behavior has significant non-uniform characteristics, especially in the peak period of photovoltaic output (usually corresponding to the three-hour interval before and after local noon) and the low load period (usually corresponding to the stage of sharp reduction of night electricity demand), the power reduction rate can be several times that of other periods. This temporal heterogeneity feature leads to systematic bias in the power generation prediction level of the traditional evaluation model.

[0004] In the equipment dimension level, the existing technical solutions fail to fully represent the performance degradation dynamic process of new energy power generation equipment; taking photovoltaic components as an example, the output power attenuation is not a simple linear decreasing process, but presents the dual characteristics of the initial light-induced attenuation stage and the long-term running attenuation stage; the initial light-induced attenuation is mainly caused by the activation of semiconductor material defects, and the main attenuation process is usually completed within the first year after the project is put into operation; the long-term running attenuation is related to material aging, environmental stress accumulation and other complex factors.

[0005] In the system dimension level, the existing technology lacks deep modeling of the dynamic interaction mechanism of the energy storage system and the power reduction strategy; especially in the market environment implementing the time-of-use electricity price mechanism, the economic benefit of the energy storage system depends on the matching degree of the electricity price difference arbitrage opportunity and the power reduction period distribution, and the fixed charging and discharging threshold strategy adopted by the traditional evaluation method cannot accurately reflect this dynamic coupling relationship, resulting in significant deviation between the benefit prediction result and the actual operation data.

[0006] Therefore, how to provide a new energy power generation project benefit evaluation method based on multi-dimensional coupling is a problem to be solved at present. SUMMARY

[0007] The embodiments of the present application provide a new energy power generation project benefit evaluation method based on multi-dimensional coupling to solve the above technical problems in the prior art.

[0008] The following presents a simplified summary of some aspects of the disclosed embodiments in order to provide a basic understanding of such embodiments. This summary is not an extensive overview of the embodiments and is intended neither to identify key / critical elements of the embodiments nor to delineate the scope of the embodiments. Its sole purpose is to present some concepts of the embodiments in a simplified form as a prelude to the more detailed description that is presented later.

[0009] According to a first aspect of embodiments of the present application, a new energy power generation project benefit evaluation method based on multi-dimensional coupling is provided.

[0010] In one embodiment, the new energy power generation project benefit evaluation method based on multi-dimensional coupling comprises:

[0011] The pre-acquired attenuation parameters and output data are subjected to compliance verification, and a device performance degradation dynamic model is constructed based on the verified attenuation parameters in combination with a pre-set environmental operation correction factor. The output data after verification is subjected to output correction using the device performance degradation dynamic model, to obtain theoretical output data after correction.

[0012] The pre-acquired power grid power limiting parameters are verified using the specification verification, and a power limiting strategy multi-dimensional constraint condition is constructed through the verified power grid power limiting parameters. The theoretical output data after correction is subjected to power limiting constraint based on the power limiting strategy multi-dimensional constraint condition, to obtain actual output data.

[0013] The pre-acquired energy storage parameters are subjected to physical constraint verification, and an energy storage optimization scheduling model is constructed based on the verified energy storage parameters in combination with a pre-set safety constraint. The energy storage optimization scheduling model is used in combination with the actual output data to optimize the charging and discharging strategy, and the power generation project benefit evaluation is performed according to the charging and discharging strategy optimization result.

[0014] In one embodiment, the pre-acquired attenuation parameters and output data are subjected to compliance verification, and a device performance degradation dynamic model is constructed based on the verified attenuation parameters in combination with a pre-set environmental operation correction factor. The output data after verification is subjected to output correction using the device performance degradation dynamic model, to obtain theoretical output data after correction, which comprises:

[0015] The pre-acquired attenuation parameters are subjected to monotonicity verification to obtain the verified attenuation parameters, and the pre-acquired output data are subjected to integrity verification to obtain the verified output data.

[0016] A two-stage attenuation model is constructed based on the verified attenuation parameters, and a device performance degradation dynamic model is constructed in combination with a pre-set environmental operation correction factor.

[0017] The output data after verification is subjected to output correction using the device performance degradation dynamic model, to obtain theoretical output data after correction.

[0018] In one embodiment, the environmental operation correction factors include: a temperature correction factor, an irradiance correction factor, and a pollution loss factor;

[0019] The temperature correction factor and the irradiance correction factor both satisfy a normal distribution; and the pollution loss factor satisfies a uniform distribution;

[0020] The temperature correction factor corrects a deviation between a working temperature of the power generation project and a standard test condition temperature;

[0021] The irradiance correction factor corrects a power output change caused by a solar irradiance intensity fluctuation;

[0022] The pollution loss factor corrects a light transmittance loss caused by a surface pollution accumulation of the power generation assembly.

[0023] In one embodiment, a specification verification is used to verify the pre-acquired power grid load shedding parameters, and a load shedding strategy multi-dimensional constraint condition is constructed based on the verified power grid load shedding parameters, and the actual output data is obtained by load shedding constraint on the corrected theoretical output data based on the load shedding strategy multi-dimensional constraint condition, including:

[0024] The pre-acquired power grid load shedding parameters are subjected to consistency verification to obtain consistent power grid load shedding parameters, and the consistent power grid load shedding parameters are subjected to rationality verification in combination with preset boundary conditions to obtain verified power grid load shedding parameters;

[0025] A load shedding strategy multi-dimensional constraint condition is constructed based on the verified power grid load shedding parameters, and an abnormality processing mechanism is established based on the load shedding strategy multi-dimensional constraint condition in combination with a preset priority;

[0026] The load shedding strategy multi-dimensional constraint condition and the abnormality processing mechanism are used to perform load shedding constraint on the corrected theoretical output data to obtain actual output data.

[0027] In one embodiment, the load shedding strategy multi-dimensional constraint condition includes: an annual unified load shedding rate constraint condition, a maximum instantaneous load shedding power constraint condition, a time period load shedding rate constraint condition, and a monthly time period load shedding rate constraint condition;

[0028] The annual unified load shedding rate constraint condition is obtained by the verified power grid load shedding parameters to constrain a ratio of an annual cumulative abandoned power to a theoretical total power generation;

[0029] The maximum instantaneous load shedding power constraint condition is obtained by the verified power grid load shedding parameters to constrain a maximum power that the power grid can withstand at any time;

[0030] The time period load shedding rate constraint condition is obtained by the verified power grid load shedding parameters to constrain a load shedding proportion of each hour of each day;

[0031] The monthly time-limited power rate constraint condition is obtained by verifying the power grid power limiting parameters to obtain the power limiting proportion of each month and each hour in each year.

[0032] In one embodiment, the power limiting constraint on the corrected theoretical output data by using the power limiting strategy multi-dimensional constraint condition and the abnormal processing mechanism includes:

[0033] The power limiting constraint on the corrected theoretical output data by using the power limiting strategy multi-dimensional constraint condition is simulated to obtain the limiting results of each constraint condition, and the limiting effect comparison of the limiting results of each constraint condition is performed.

[0034] The abnormal analysis is performed on the limiting effect comparison results, and the power limiting strategy constraint condition selection is performed based on the analysis results combined with the abnormal processing mechanism. The power limiting constraint is performed on the corrected theoretical output data according to the power limiting strategy constraint condition selection results.

[0035] In one embodiment, the pre-acquired energy storage parameters are physically constrained and verified, and the energy storage optimization scheduling model is constructed based on the verified energy storage parameters combined with the preset safety constraints. The charging and discharging strategy optimization is performed by using the energy storage optimization scheduling model combined with the actual output data, and the power generation project revenue evaluation is performed according to the charging and discharging strategy optimization results, including:

[0036] The physical feasibility of the pre-acquired energy storage parameters is verified, and the cycle life attenuation model is constructed by using the verified energy storage parameters;

[0037] Based on the cycle life attenuation model, a phased decision mechanism is established combined with the preset state of charge safe operation interval and the charging and discharging power physical constraint;

[0038] The energy storage optimization scheduling model is constructed by using the phased decision mechanism, and the charging and discharging strategy optimization and the energy storage state update are performed by using the energy storage optimization scheduling model combined with the actual output data;

[0039] The power generation project revenue difference comparison is performed according to the charging and discharging strategy optimization and the energy storage state update results, and the power generation project revenue evaluation is performed according to the comparison results.

[0040] In one embodiment, based on the cycle life attenuation model, a phased decision mechanism is established combined with the preset state of charge safe operation interval and the charging and discharging power physical constraint, including:

[0041] The optimization function of maximizing short-term revenue and minimizing long-term equipment loss is established based on the cycle life attenuation model, and the energy storage cycle attenuation model is constructed by using the optimization function;

[0042] The preset state of charge safe operation interval and the charging and discharging power physical constraint are taken as constraint conditions, and the phased decision mechanism is established combined with the energy storage cycle attenuation model and the particle swarm algorithm.

[0043] In one embodiment, the power generation project revenue difference comparison is performed according to the charge-discharge strategy optimization and the storage state update result, and the power generation project revenue evaluation is performed according to the comparison result, which includes:

[0044] The power generation project economic evaluation index is updated according to the charge-discharge strategy optimization and the storage state update result, and the economic evaluation index is statistically summarized to generate a time series distribution graph and a probability density function curve;

[0045] The time period distribution characteristics of power curtailment are analyzed and identified through the time series distribution graph and the probability density function curve, and the project net present value and the internal rate of return are calculated by using the discounted cash flow model in combination with the economic evaluation index and the time period distribution characteristics of power curtailment;

[0046] The power generation project revenue difference comparison is performed based on the project net present value and the internal rate of return, and the power generation project revenue evaluation is performed according to the comparison result.

[0047] According to a second aspect of the embodiment of the present application, a new energy power generation project revenue evaluation system based on multi-dimensional coupling is provided.

[0048] In one embodiment, the new energy power generation project revenue evaluation system based on multi-dimensional coupling includes an output correction module, a power curtailment constraint module, and a revenue evaluation module.

[0049] The output correction module is configured to perform compliance verification on the pre-acquired attenuation parameters and output data, and construct a device performance degradation dynamic model based on the verified attenuation parameters in combination with a pre-set environmental operation correction factor, and perform output correction on the verified output data by using the device performance degradation dynamic model to obtain corrected theoretical output data.

[0050] The power curtailment constraint module is configured to perform specification verification on the pre-acquired power grid power curtailment parameters, and construct a power curtailment strategy multi-dimensional constraint condition by using the verified power grid power curtailment parameters, and perform power curtailment constraint on the corrected theoretical output data based on the power curtailment strategy multi-dimensional constraint condition to obtain actual output data.

[0051] The revenue evaluation module is configured to perform physical constraint verification on the pre-acquired storage parameters, construct a storage optimization scheduling model based on the verified storage parameters in combination with a pre-set safety constraint, and perform charge-discharge strategy optimization by using the storage optimization scheduling model in combination with the actual output data, and perform power generation project revenue evaluation according to the charge-discharge strategy optimization result.

[0052] According to a third aspect of the embodiment of the present application, a computer device is provided.

[0053] In some embodiments, the computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the new energy power generation project benefit evaluation method based on multi-dimensional coupling when executing the computer program.

[0054] According to a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided.

[0055] In one embodiment, the computer readable storage medium stores a computer program, and the computer program implements the steps of the new energy power generation project benefit evaluation method based on multi-dimensional coupling when executed by a processor.

[0056] The technical solutions provided by the embodiments of the present application can include the following beneficial effects:

[0057] 1. The present application can solve the economic prediction distortion problem under the limited power consumption capacity scenario through the equipment performance degradation dynamic model, the multi-dimensional constraint condition of power limiting strategy and the energy storage optimization scheduling model, and can avoid the high prediction error rate problem caused by ignoring the key factors such as the time and space heterogeneity of the power limiting strategy, the nonlinear decay of equipment performance and the collaborative optimization of the energy storage system in the traditional evaluation method.

[0058] 2. The present application can accurately represent the differentiated characteristics of initial light-induced decay and long-term running decay through a two-stage decay model, integrate multi-dimensional constraint conditions such as annual unified power limiting rate, maximum instantaneous power limiting power, time-of-use power limiting rate vector and monthly time-of-use power limiting rate matrix, form a dynamic coupling analysis framework with priority judgment, and realize real-time adjustment of the charging and discharging strategy under the double constraints of cycle life decay and time-of-use price fluctuation, thereby providing a more accurate analysis tool for new energy project investment decision-making.

[0059] 3. The present application can significantly improve the power generation prediction accuracy through the dynamic correlation between equipment performance degradation and power limiting strategy, and can accurately simulate the actual operation logic of power grid dispatching in the constructed multi-dimensional constraint condition collaborative framework of power limiting strategy, support fine modeling of complex power limiting scenarios, and use the energy storage optimization scheduling model to couple energy storage scheduling with the integrated output analysis of new energy under the limited situation.

[0060] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0061] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the present application.

[0062] Figure 1It is a flow diagram of a new energy power generation project benefit evaluation method based on multi-dimensional coupling according to an exemplary embodiment.

[0063] Figure 2 It is a structural diagram of a new energy power generation project benefit evaluation system based on multi-dimensional coupling according to an exemplary embodiment.

[0064] Figure 3 It is a structural diagram of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0065] Figure 1 An embodiment of the new energy power generation project benefit evaluation method based on multi-dimensional coupling of the application is shown.

[0066] In this optional embodiment, the new energy power generation project benefit evaluation method based on multi-dimensional coupling comprises:

[0067] In step S101, the compliance verification is performed on the pre-acquired attenuation parameters and output data, and a device performance degradation dynamic model is constructed based on the verified attenuation parameters in combination with a pre-set environmental operation correction factor. The output data after verification is corrected by using the device performance degradation dynamic model to obtain corrected theoretical output data. The device performance degradation dynamic model can be used to realize accurate power generation prediction and benefit risk assessment under the condition of consumption limitation, accurately represent the differentiated characteristics of initial light-induced attenuation and long-term operation attenuation, and realize dynamic calibration of the output curve through the environmental parameter correction factor.

[0068] In step S102, the pre-acquired power grid power limitation parameters are verified by using the specification verification, and a power limitation strategy multi-dimensional constraint condition is constructed by using the verified power grid power limitation parameters. The corrected theoretical output data is subjected to power limitation constraint based on the power limitation strategy multi-dimensional constraint condition to obtain actual output data. The constructed power limitation strategy multi-dimensional constraint condition can be used to accurately simulate the actual operation logic of power grid dispatching and support fine modeling of complex power limitation scenarios.

[0069] In step S103, the physical constraint verification is performed on the pre-acquired energy storage parameters. An energy storage optimization scheduling model is constructed based on the verified energy storage parameters in combination with a pre-set safety constraint. The charge and discharge strategy optimization is performed by using the energy storage optimization scheduling model in combination with the actual output data. The power generation project benefit evaluation is performed according to the charge and discharge strategy optimization result. The energy storage scheduling can be coupled to the integrated output analysis of new energy under the limited condition by using the constructed energy storage optimization scheduling model.

[0070] Figure 2 An embodiment of the new energy power generation project benefit evaluation system based on multi-dimensional coupling of the application is shown.

[0071] In this optional embodiment, the new energy power generation project benefit assessment system based on multi-dimensional coupling includes: an output correction module 201, a power curtailment constraint module 202, and a benefit assessment module 203.

[0072] The output correction module 201 is used to verify the compliance of the pre-acquired attenuation parameters and output data, and to construct a dynamic model of equipment performance degradation based on the verified attenuation parameters and the preset environmental operation correction factor. The dynamic model of equipment performance degradation is used to correct the output data after verification to obtain the corrected theoretical output data.

[0073] The power curtailment constraint module 202 is used to verify the pre-acquired power curtailment parameters of the power grid using normative verification, and to construct multi-dimensional constraint conditions for the power curtailment strategy based on the verified power curtailment parameters. Based on the multi-dimensional constraint conditions for the power curtailment strategy, power curtailment constraints are applied to the corrected theoretical output data to obtain the actual output data.

[0074] The revenue assessment module 203 is used to verify the physical constraints of the pre-acquired energy storage parameters, construct an energy storage optimization scheduling model based on the verified energy storage parameters and preset safety constraints, optimize the charging and discharging strategy using the energy storage optimization scheduling model and actual output data, and evaluate the revenue of the power generation project based on the results of the charging and discharging strategy optimization.

[0075] It should be further explained that the core of this invention lies in constructing a multi-dimensional dynamic coupling evaluation system, including the following three aspects: establishing a model of equipment performance degradation and dynamic output (i.e., constructing a dynamic model of equipment performance degradation), constructing a synergistic framework of multi-mode power curtailment strategies (i.e., constructing multi-dimensional constraints of power curtailment strategies), and developing an energy storage system full life cycle optimization scheduling algorithm (i.e., an energy storage optimization scheduling model).

[0076] The model for equipment performance degradation and dynamic output is established by introducing a two-stage degradation theory model of photovoltaic modules (i.e., a two-stage degradation model) to accurately characterize the nonlinear degradation trajectory of equipment output power over operating time, and by using a temperature correction factor. T i Irradiance correction factor R i Pollution loss factors D i The correction is as follows:

[0077] ;

[0078] In the formula, i =1,…8760; where For original output; To adjust the dynamic output, the output curve is dynamically adjusted.

[0079] The synergistic framework of the multi-mode power limiting strategy breaks through the limitation of the traditional single power limiting rate model, integrates various power limiting instruction forms that the power grid dispatching department may adopt, including but not limited to annual unified power limiting rate instructions, maximum instantaneous power limiting power instructions, time period differentiated power limiting rate instructions and monthly time period matrix power limiting instructions. By establishing constraint condition priority determination rules and abnormal processing mechanisms, the organic synergy and dynamic coupling of different power limiting strategies are realized. When the calculation results of different power limiting instructions conflict, the optimal constraint condition is selected according to the preset decision level system, and the level system fully reflects the operation habits in the power grid dispatching practice.

[0080] The development of the whole life cycle optimization scheduling algorithm of the energy storage system is to construct a dynamic scheduling model (i.e. energy storage optimization scheduling model) with the core target of maximizing economic benefits under the premise of considering battery cycle life attenuation and time-of-use price fluctuation. The model establishes a phased decision mechanism to realize real-time optimization of charging and discharging strategies, and the dynamic scheduling model includes: data acquisition step, strategy formulation step and execution update step.

[0081] The data acquisition step is to collect wind power photovoltaic output data, power grid power limiting information and energy storage system state information; the strategy formulation step is to establish an optimization function, to maximize short-term benefits and minimize long-term equipment wear and tear as the objective function, to set up a battery cycle attenuation model, to introduce the state of charge safe operation interval and the charging and discharging power physical limit as the constraint condition, and to use the particle swarm algorithm to formulate the best charging and discharging capacity; the execution update step is to execute the predetermined charging and discharging capacity and update the energy storage system state.

[0082] The present application is suitable for single systems such as photovoltaic power generation systems and wind power generation systems, and is also suitable for multi-energy complementary systems such as wind-solar-storage integrated systems. DETAILED DESCRIPTION

[0084] The technical scheme of the present application is realized by a programmed module deployed in a computing device, and its running process strictly follows the operation rules of new energy power systems and the economic analysis criteria. The specific implementation process includes the following three main stages: input parameter preprocessing and data verification, time sequence simulation calculation and dynamic coupling analysis, and energy storage system optimization scheduling. Information transmission and state synchronization are realized between stages through data interface, forming a closed-loop processing mechanism.

[0085] The input parameter preprocessing and data verification include the structured reorganization and logical self-consistency test of multi-source heterogeneous data in the initialization phase. First, for the new energy power generation unit, the rated installed capacity parameter needs to be collected completely, which represents the theoretical maximum output capacity of the power station, usually measured in megawatts. At the same time, the theoretical output curve data set under standard test conditions (i.e. output data) needs to be imported, which covers the theoretical power generation power value every hour in the complete annual cycle, including 8760 continuous time point power record values. In addition, photovoltaic systems must obtain dynamic parameters of photovoltaic component attenuation rate over time (i.e. attenuation parameters), which need to be modeled according to the technical documents provided by the manufacturer. In particular, the photovoltaic component output power will decrease significantly due to the photo-induced attenuation effect in the first complete year of operation, with a decrease rate usually in the range of 1.5% to 3%; since the second year of operation, the component enters the stable attenuation period, with a power attenuation rate of 0.5% to 1% per year, and this attenuation process presents a linear cumulative characteristic. The expression of the two-stage attenuation model is:

[0086] ;

[0087] wherein, x is the running age, x =1,…25; f ( x ) is the annual attenuation in the operation period; a is the initial rapid attenuation; b is the long-term stable attenuation.

[0088] For the energy storage system, the rated energy storage capacity parameter (i.e. energy storage parameter) needs to be input, which reflects the maximum energy storage capacity of the energy storage device, usually measured in megawatt-hours. At the same time, the charging and discharging efficiency parameters of the energy storage system must be specified, especially the different efficiency characteristics of the charging and discharging processes. Usually, the charging and discharging efficiencies are in the numerical range of 85% to 95%, but they may have asymmetric differences.

[0089] In addition, the operating constraints of the energy storage system need to be set, including the safe operating threshold of the state of charge and the maximum charge and discharge power limit parameters.

[0090] For the input parameters of the grid power limiting strategy (i.e. grid power limiting parameters), a multi-dimensional constraint condition data system needs to be built. The annual unified power limiting rate parameter is defined as the ratio of the total annual abandoned power to the total theoretical power generation. This value needs to be set according to the annual consumption report published by the grid company. The maximum instantaneous power limiting parameter represents the maximum power that the grid can withstand at any time. This parameter is directly related to the capacity of the transformer in the substation and the current carrying capacity of the transmission line. The time-division power limiting rate parameter needs to form a vector structure containing 24 elements, each element corresponding to the power limiting rate of a specific hour each day. This data is usually obtained from historical operation records or future operation plans of the grid dispatching department. The monthly time-division power limiting rate parameter needs to build a matrix structure of 12 rows and 24 columns, each matrix element corresponding to the power limiting rate of a specific hour in a specific month. This parameter system can effectively reflect the differentiated impact of seasonal load fluctuations on the execution intensity of the power limiting strategy. All power limiting parameters must satisfy the global consistency principle, that is, the total annual abandoned power generated by the joint action of various power limiting rules must be consistent with the annual unified power limiting rate parameter.

[0091] The compliance verification includes integrity verification of the theoretical output curve data (i.e. output data) and verification modeling of the monotonic increasing property of the photovoltaic component attenuation rate parameter (i.e. monotonicity verification).

[0092] The specification verification includes consistency verification of the pre-obtained grid power limiting parameters and rationality verification of the consistent grid power limiting parameters combined with the preset boundary conditions.

[0093] The physical constraint verification includes physical feasibility verification and basic inspection of setting the operating constraint conditions of the energy storage system.

[0094] In the data verification link, multi-level logical self-consistency checks need to be performed. First, the integrity of the theoretical output curve data is verified to ensure that the power record values of each hour within the annual period are complete and have no missing values, and conform to the solar irradiance distribution characteristics of the project location. Second, the monotonic increasing property of the photovoltaic component attenuation rate parameter needs to be verified to avoid abnormal situations caused by incorrect parameter input. Third, the boundary condition review of various power limiting parameters is required to confirm that all power limiting rate values are within the effective and reasonable range. Finally, the physical feasibility verification of the energy storage system parameters is required, including the basic inspection that the product of the charging and discharging efficiency needs to be maintained above a certain threshold, and the effective span of the state of charge operating interval needs to meet the minimum requirement.

[0095] The time sequence simulation calculation and dynamic coupling analysis are specifically hourly iteration calculation in the whole life cycle of the project, each iteration cycle contains four sub-processes with strict time sequence relationship, including output correction calculation of sub-process one, power limiting scope determination of sub-process two, energy storage system optimization scheduling of sub-process three and economic index updating of sub-process four.

[0096] The output correction calculation of sub-process one is based on the input attenuation factor, which needs to calculate the output power attenuation of photovoltaic components caused by material aging during operation. This attenuation phenomenon shows a relatively sharp drop process at the beginning of the project operation, and then turns into a gentle linear accumulation process during the subsequent operation. The expression of the two-stage attenuation model is:

[0097] ;

[0098] In the formula, x represents the operation life, x =1,…25; f ( x ) represents the annual attenuation in the operation period; a represents the initial rapid attenuation; b represents the long-term stable attenuation.

[0099] In addition to the above attenuation parameters, the correction calculation of environmental operation parameters needs to be introduced, especially the correction of the deviation between the actual working temperature of the components and the standard test conditions, and the power output change caused by the fluctuation of solar radiation intensity. In addition, the loss of light transmittance caused by the accumulation of component surface pollution also needs to be considered. The dynamic correction process of the introduced temperature correction factor, irradiance correction factor and pollution loss factor and other variables is as follows:

[0100] ;

[0101] In the formula, i =1,…8760, wherein is the original output, is the adjusted dynamic output, which realizes the dynamic adjustment of the output curve. T i is the temperature correction factor, which satisfies the normal distribution; R i is the irradiance correction factor, which satisfies the normal distribution; D i is the pollution loss factor, which satisfies the uniform distribution.

[0102] The overall output correction is as follows:

[0103] ;

[0104] In the formula, x= 1,... 25; i = 1,... 8760; = 1,... 25;

[0105] The power limiting scope of sub-process two is determined after obtaining the corrected theoretical output value. The actual output is calculated according to the multi-dimensional constraint conditions of the power grid limiting strategy. This process can apply four types of constraint rules, including annual uniform limiting rate, maximum instantaneous limiting power, time period limiting rate, and monthly time period limiting rate. By comparing the limiting effects of each rule, the most stringent constraint condition is selected as the final execution standard.

[0106] 1) Annual uniform limiting rate constraint condition:

[0107] ;

[0108] In the formula, EAP year represents the annual online power generation; EAP i represents the power generation at a specific time, i = 1,... 8760; PRR fix represents the annual uniform limiting rate.

[0109] 2) Maximum instantaneous limiting power constraint condition:

[0110] ;

[0111] In the formula, EAP year represents the annual online power generation; EAP i represents the power generation at a specific time, i = 1,... 8760; PRR max represents the maximum power limit value.

[0112] 3) Time period limiting rate constraint condition

[0113] ;

[0114] In the formula, EAP year represents the annual online power generation; EAP i , j represents the power generation at a specific time; PRR j represents the limiting rate at a specific time; i represents a specific date, i = 1,... 365; j represents a specific time, j = 0,... 23.​

[0115] 4) Monthly time-limited power rate constraints:

[0116] ;

[0117] wherein, EAP year represents the annual online power generation; EAP ( i , j , k ) represents the power generation at a specific time; PRR ( j , k ) represents the time-limited power rate at a specific time in a specific month; i represents a specific date, i =1,…30or31or28; j represents a specific time, j =0,…23; k represents a specific month, k =1,…12.

[0118] An exception handling mechanism needs to be established in this process. When the calculation results of different power limiting rules conflict, the system will select the constraint condition according to the preset priority order. The priority system places the monthly time-limited power rate in the highest decision level, followed by the time-limited power rate, then the maximum instantaneous power limiting power, and finally the annual unified power limiting rate.

[0119] The third sub-process is the optimization scheduling of the energy storage system. After determining the available output, the charging and discharging strategy of the energy storage system is formulated to maximize economic benefits. This decision-making process needs to consider the arbitrage opportunities of time-of-use electricity price mechanism, the cost increase caused by the cycle life attenuation of the energy storage system, and the spatio-temporal distribution characteristics of the power grid limiting strategy, dynamically adjust the charging and discharging depth of the energy storage system, and balance the relationship between short-term benefits and long-term equipment wear and tear.

[0120] First, a cycle life attenuation model of the energy storage system is established. This model needs to reflect the nonlinear attenuation law of the battery capacity with the increase of charging and discharging cycles. The expression of the cycle life attenuation model is:

[0121] ;

[0122] wherein, C 0 represents the initial capacity of the energy storage; C n represents the capacity of the energy storage p after the second charging and discharging; w represents the attenuation coefficient (0.001-0.005); chg l represents the first lCharge (discharge) capacity per charge / discharge; q This represents the stress coefficient (1-2).

[0123] Considering battery cycle life degradation and time-of-use electricity price fluctuations, a dynamic scheduling model is constructed with maximizing economic benefits as its core objective. This model establishes a phased decision-making mechanism to achieve real-time optimization of the charging and discharging strategy. The dynamic scheduling model includes: data acquisition steps, strategy formulation steps, and execution update steps.

[0124] The data acquisition steps involve collecting wind and solar power output data, grid curtailment information, and energy storage system status information. The strategy formulation steps involve establishing an optimization function with the objective functions of maximizing short-term gains and minimizing long-term equipment losses. An energy storage cycle decay model is set up, and the safe operating range of the state of charge and the physical limits of charging and discharging power are introduced as constraints. The particle swarm optimization algorithm is used to solve for the optimal charging (discharging) capacity. The execution and update steps involve executing the predetermined charging (discharging) strategy and updating the energy storage system status.

[0125] Subprocess four involves updating the economic indicators based on the current grid-injected power and energy storage operation records. This includes key parameters such as cumulative grid-connected electricity, cumulative abandoned electricity, energy storage system cycle count, operation and maintenance costs, and cash flow changes.

[0126] Post-processing and uncertainty analysis are performed by the system after completing the full lifecycle time-series simulation, involving multi-dimensional data integration and analysis.

[0127] The system compiles and summarizes key indicators such as annual power generation, curtailment, and energy storage usage frequency, generating time series distribution maps and probability density function curves. It analyzes the periodic distribution characteristics of power curtailment and identifies overlaps between peak curtailment periods and peak photovoltaic output periods.

[0128] The discounted cash flow model is used to calculate the project's net present value (NPV) and internal rate of return (IRR). The NPV calculation is as follows:

[0129] ;

[0130] In the formula, PV Represents net present value; CF i Indicates the first i Annual cash flow; r Indicates the industry benchmark return; TV This represents the value at the end of the operating period; n Indicates the number of years of operation.

[0131] The internal rate of return is calculated as follows:

[0132] ;

[0133] In the formula, IRR Internal rate of return is represented.

[0134] By comparing the income difference under different power limiting strategies, the economic impact of the power grid constraint condition is evaluated. At the same time, the parameter sensitivity analysis is carried out, and the influence degree of the factors such as the fluctuation of the grid access price, the change of the power limiting rate, the acceleration of the equipment attenuation and the like on the project income is quantified. For example, the influence of the price fluctuation is calculated as follows:

[0135]

[0136] In the formula, price fix The benchmark price is represented; price sen The fluctuated price is represented; S price_pv The influence degree of the price on the net present value is represented; PV price fix The project net present value based on the benchmark price is represented; PV price sen The project net present value based on the fluctuated price is represented; S price_irr The influence degree of the price on the income rate is represented; IRR price fix The project income rate based on the benchmark price is represented; IRR price sen The project income rate based on the fluctuated price is represented.

[0137] An industry-standard technical and economic evaluation report is automatically generated, and the report content covers the power limiting loss analysis, the energy storage operation characteristics, the full life cycle cash flow prediction, the sensitivity analysis atlas and the like core elements, so that the conclusions can be directly displayed in the visual charts.

[0138] The present application relates to the technical innovation in the cross field of new energy power system planning and economic analysis, and in particular to the power consumption capacity limited evaluation under the high proportion renewable energy penetration scenario; the present application deeply integrates the power system operation physical law, the equipment performance degradation mechanism and the economic analysis model, is suitable for the full life cycle income prediction and risk evaluation of the photovoltaic power generation system, the wind power generation system and the matched energy storage device under the existence of the power grid dispatching constraint, and can effectively solve the technical bottlenecks of the traditional evaluation method in the dynamic coupling of the power limiting strategy, the nonlinear attenuation of the equipment performance and the optimization dispatching cooperation of the energy storage.

[0139] ​​​​​The application realizes accurate prediction of power generation and risk assessment of benefits in the consumption limited scene by establishing a coupling analysis mechanism of a device performance degradation dynamic model and multi-dimensional constraint conditions of power limiting strategies; the device performance degradation dynamic model comprises a photovoltaic component two-stage attenuation analysis module, which can distinguish the differentiated characteristics of the initial light-induced attenuation stage and the long-term operation attenuation stage, and realize dynamic calibration of the output curve through an environmental parameter correction factor; the multi-dimensional constraint conditions of power limiting strategies include but are not limited to annual unified power limiting rate, maximum instantaneous power limiting power threshold, time period power limiting rate vector, monthly time period power limiting rate matrix and various heterogeneous constraint forms, and priority determination rules and abnormal processing mechanisms of the constraint conditions are established; the energy storage system optimization scheduling algorithm comprises a cycle life attenuation compensation mechanism and a time-of-use price arbitrage analysis module, which can realize real-time optimization of charging and discharging strategies and economic evaluation of the whole life cycle.

[0140] In one embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in Figure 3 The computer device includes a processor, a memory and a network interface connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store static information and dynamic information data. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the steps in the above-mentioned new energy power generation project benefit evaluation method embodiment based on multi-dimensional coupling.

[0141] Those skilled in the art can understand that Figure 3 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the application, and does not constitute a limitation on the computer device to which the scheme of the application is applied. A specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0142] In addition, the application also provides a computer device including a memory and a processor, and the memory stores a computer program. The processor executes the computer program to implement the steps in the above-mentioned new energy power generation project benefit evaluation method embodiment based on multi-dimensional coupling.

[0143] In addition, the application also provides a computer readable storage medium having a computer program stored thereon. The computer program is executed by the processor to implement the steps in the above-mentioned new energy power generation project benefit evaluation method embodiment based on multi-dimensional coupling.

[0144] Those skilled in the art can understand that the implementation of all or part of the above-mentioned embodiments based on the multi-dimensional coupling new energy power generation project benefit evaluation method is completed by the computer program instructing the related hardware, the computer program can be stored in a non-volatile computer readable storage medium, and the computer program can include the processes of the above-mentioned embodiments of the multi-dimensional coupling new energy power generation project benefit evaluation method when executed. Wherein, any reference to memory, storage, database or other medium used in each embodiment provided by the present application can include at least one of non-volatile and volatile memory. The non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. The volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0145] The present application is not limited to the structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A revenue evaluation method for new energy power generation projects based on multi-dimensional coupling, characterized in that, include: The pre-acquired attenuation parameters and output data are verified for compliance. Based on the verified attenuation parameters and the preset environmental operation correction factor, a dynamic model of equipment performance degradation is constructed. The output data of the verified output data is corrected using the dynamic model of equipment performance degradation to obtain the corrected theoretical output data. The process involves verifying the compliance of pre-acquired attenuation parameters and output data, constructing a dynamic model of equipment performance degradation based on the verified attenuation parameters and a preset environmental operation correction factor, and then using this dynamic model to correct the output data after verification. The corrected theoretical output data includes: The monotonicity of the pre-acquired attenuation parameters is verified to obtain the verified attenuation parameters. The integrity of the pre-acquired output data is verified to obtain the verified output data. A two-stage attenuation model is constructed based on the verified attenuation parameters, and a dynamic model of equipment performance degradation is constructed by combining the preset environmental operation correction factor. The output data after verification is corrected by using a dynamic model of equipment performance degradation to obtain the corrected theoretical output data. The pre-acquired power grid curtailment parameters are verified using normative verification, and multi-dimensional constraints of the curtailment strategy are constructed using the verified power grid curtailment parameters. Based on the multi-dimensional constraints of the curtailment strategy, the modified theoretical output data is subjected to curtailment constraints to obtain the actual output data. The multi-dimensional constraints of the power curtailment strategy include: annual uniform power curtailment rate constraint, maximum instantaneous power curtailment constraint, time-based power curtailment rate constraint, and monthly time-based power curtailment rate constraint. The annual unified curtailment rate constraint is obtained by using the verified grid curtailment parameters to determine the ratio of the total annual curtailment to the theoretical total power generation. The maximum instantaneous power limitation constraint is obtained by using verified power limitation parameters to constrain the maximum power that the power grid can withstand at any given time. The time-based power curtailment rate constraint is obtained by using the verified power curtailment parameters to determine the power curtailment ratio for each hour of the day. The monthly time-based power curtailment rate constraint is obtained by using the verified power curtailment parameters to determine the power curtailment ratio for each hour of each month of the year. Physical constraints are verified on the pre-acquired energy storage parameters. Based on the verified energy storage parameters and the preset safety constraints, an energy storage optimization scheduling model is constructed. The energy storage optimization scheduling model is used in conjunction with actual output data to optimize the charging and discharging strategy. The revenue of the power generation project is evaluated based on the results of the charging and discharging strategy optimization. The process of verifying the pre-acquired energy storage parameters using physical constraints, constructing an energy storage optimization scheduling model based on the verified parameters and preset safety constraints, optimizing the charging and discharging strategy using the energy storage optimization scheduling model and actual output data, and evaluating the revenue of the power generation project based on the optimization results includes: Physical feasibility of the pre-acquired energy storage parameters is verified, and a cycle life decay model is constructed using the verified energy storage parameters. Based on the cycle life decay model, a phased decision-making mechanism is established by combining the preset safe operating range of state of charge and the physical constraints of charging and discharging power. An energy storage optimization scheduling model is constructed using a phased decision-making mechanism, and the energy storage optimization scheduling model is combined with actual output data to optimize charging and discharging strategies and update energy storage status. The differences in the revenue of power generation projects are compared based on the results of charging and discharging strategy optimization and energy storage status update, and the revenue of power generation projects is evaluated based on the comparison results.

2. The method for evaluating the profitability of new energy power generation projects based on multi-dimensional coupling as described in claim 1, characterized in that, The environmental operation correction factors include: temperature correction factor, irradiance correction factor, and pollution loss factor; Furthermore, both the temperature correction factor and the irradiance correction factor follow a normal distribution; the pollution loss factor follows a uniform distribution. The temperature correction factor is used to correct the deviation between the operating temperature of the power generation project and the standard test condition temperature. The power output variation caused by fluctuations in solar irradiance intensity is corrected by the aforementioned irradiance correction factor; The aforementioned pollution loss factor corrects for the light transmittance loss caused by the accumulation of pollution on the surface of the power generation components.

3. The method for evaluating the profitability of new energy power generation projects based on multi-dimensional coupling according to claim 1, characterized in that, The process involves verifying pre-acquired grid curtailment parameters using normative verification, constructing multi-dimensional constraints for the curtailment strategy based on the verified parameters, and applying these constraints to the corrected theoretical output data to obtain actual output data, including: The consistency of the pre-acquired power grid curtailment parameters is verified to obtain consistent power grid curtailment parameters. The rationality of the consistent power grid curtailment parameters is then verified in conjunction with the preset boundary conditions to obtain the verified power grid curtailment parameters. A multi-dimensional constraint condition for the power curtailment strategy is constructed based on the verified power curtailment parameters, and an anomaly handling mechanism is established based on the multi-dimensional constraint condition of the power curtailment strategy combined with the preset priority. By utilizing multi-dimensional constraints and anomaly handling mechanisms of the power curtailment strategy, power curtailment constraints are applied to the corrected theoretical power output data to obtain the actual power output data.

4. The method for evaluating the profitability of new energy power generation projects based on multi-dimensional coupling according to claim 3, characterized in that, The method of applying power-limiting constraints to the corrected theoretical output data using multi-dimensional constraints and anomaly handling mechanisms based on power-limiting strategies includes: The power curtailment strategy is used to simulate the power curtailment constraint on the corrected theoretical output data using multidimensional constraints. The constraint results of each constraint are obtained, and the constraint effects of each constraint are compared. Anomaly analysis is performed on the comparison results of the limiting effect, and the power limiting strategy constraint conditions are selected based on the analysis results and the anomaly handling mechanism. Power limiting constraints are then applied to the corrected theoretical output data according to the power limiting strategy constraint condition selection results.

5. The method for evaluating the profitability of new energy power generation projects based on multi-dimensional coupling according to claim 1, characterized in that, The phased decision-making mechanism established based on the cycle life decay model, combined with the preset safe operating range of state of charge and physical constraints on charging and discharging power, includes: An optimization function is established based on the cycle life decay model to maximize short-term benefits and minimize long-term equipment losses, and the optimization function is used to construct an energy storage cycle decay model. The preset safe operating range of the state of charge and the physical constraints of charging and discharging power are used as constraints, and a phased decision-making mechanism is established by combining the energy storage cycle decay model and the particle swarm algorithm.

6. The method for evaluating the profitability of new energy power generation projects based on multi-dimensional coupling according to claim 5, characterized in that, The comparison of power generation project revenue differences based on charging / discharging strategy optimization and energy storage state update results, and the evaluation of power generation project revenue based on the comparison results, include: The economic evaluation indicators of the power generation project are updated based on the results of the charging and discharging strategy optimization and energy storage status update. The economic evaluation indicators are statistically summarized to generate time series distribution plots and probability density function curves. By analyzing and identifying the time-period distribution characteristics of power rationing through time series distribution plots and probability density function curves, the net present value and internal rate of return of the project are calculated using a discounted cash flow model combined with economic evaluation indicators and the time-period distribution characteristics of power rationing. The differences in the profitability of power generation projects are compared based on the project's net present value and internal rate of return, and the profitability of power generation projects is evaluated based on the comparison results.

7. A new energy power generation project benefit evaluation system based on multi-dimensional coupling, characterized in that, The new energy power generation project benefit assessment system based on multi-dimensional coupling includes: an output correction module, a power curtailment constraint module, and a benefit assessment module; The output correction module is used to verify the compliance of the pre-acquired attenuation parameters and output data, and to construct a dynamic model of equipment performance degradation based on the verified attenuation parameters and a preset environmental operation correction factor. The dynamic model of equipment performance degradation is used to correct the output data after verification to obtain the corrected theoretical output data. The process involves verifying the compliance of pre-acquired attenuation parameters and output data, constructing a dynamic model of equipment performance degradation based on the verified attenuation parameters and a preset environmental operation correction factor, and then using this dynamic model to correct the output data after verification. The corrected theoretical output data includes: The monotonicity of the pre-acquired attenuation parameters is verified to obtain the verified attenuation parameters. The integrity of the pre-acquired output data is verified to obtain the verified output data. A two-stage attenuation model is constructed based on the verified attenuation parameters, and a dynamic model of equipment performance degradation is constructed by combining the preset environmental operation correction factor. The output data after verification is corrected by using a dynamic model of equipment performance degradation to obtain the corrected theoretical output data. The power curtailment constraint module is used to verify the pre-acquired power curtailment parameters using normative verification, and to construct multi-dimensional constraint conditions for the power curtailment strategy using the verified power curtailment parameters. Based on the multi-dimensional constraint conditions for the power curtailment strategy, power curtailment constraints are applied to the corrected theoretical output data to obtain the actual output data. The multi-dimensional constraints of the power curtailment strategy include: annual uniform power curtailment rate constraint, maximum instantaneous power curtailment constraint, time-based power curtailment rate constraint, and monthly time-based power curtailment rate constraint. The annual unified curtailment rate constraint is obtained by using the verified grid curtailment parameters to determine the ratio of the total annual curtailment to the theoretical total power generation. The maximum instantaneous power limitation constraint is obtained by using verified power limitation parameters to constrain the maximum power that the power grid can withstand at any given time. The time-based power curtailment rate constraint is obtained by using the verified power curtailment parameters to determine the power curtailment ratio for each hour of the day. The monthly time-based power curtailment rate constraint is obtained by using the verified power curtailment parameters to determine the power curtailment ratio for each hour of each month of the year. The revenue assessment module is used to verify the physical constraints of the pre-acquired energy storage parameters, construct an energy storage optimization scheduling model based on the verified energy storage parameters and preset safety constraints, optimize the charging and discharging strategy using the energy storage optimization scheduling model and actual output data, and evaluate the revenue of the power generation project based on the results of the charging and discharging strategy optimization. The process of verifying the pre-acquired energy storage parameters using physical constraints, constructing an energy storage optimization scheduling model based on the verified parameters and preset safety constraints, optimizing the charging and discharging strategy using the energy storage optimization scheduling model and actual output data, and evaluating the revenue of the power generation project based on the optimization results includes: Physical feasibility of the pre-acquired energy storage parameters is verified, and a cycle life decay model is constructed using the verified energy storage parameters. Based on the cycle life decay model, a phased decision-making mechanism is established by combining the preset safe operating range of state of charge and the physical constraints of charging and discharging power. An energy storage optimization scheduling model is constructed using a phased decision-making mechanism, and the energy storage optimization scheduling model is combined with actual output data to optimize charging and discharging strategies and update energy storage status. The differences in the revenue of power generation projects are compared based on the results of charging and discharging strategy optimization and energy storage status update, and the revenue of power generation projects is evaluated based on the comparison results.

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