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

Abstract

The invention belongs to the technical field of power generation project income evaluation, and discloses a new energy power generation project income evaluation method and system based on multi-dimensional coupling, and the method comprises the steps: carrying out the compliance verification of a pre-obtained attenuation parameter and output data, and constructing an equipment performance degradation dynamic model through combining with a preset environment operation correction factor, performing output correction on the verified output data; verifying pre-acquired power grid power limiting parameters by using normalization 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; and carrying out physical constraint verification on pre-acquired energy storage parameters, constructing an energy storage optimization scheduling model in combination with preset security constraints, carrying out charging and discharging strategy optimization in combination with actual output data, and carrying out power generation project income evaluation according to a charging and discharging strategy optimization result. According to the method, the problem of economy prediction distortion in a power consumption capability limited scene can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation project benefit evaluation, and in particular to a new energy power generation project benefit evaluation method based on multi-dimensional coupling. Background Art

[0002] Against the backdrop of the continuous expansion of renewable energy power generation capacity, the mandatory power restrictions imposed by grid dispatching departments to maintain the safe and stable operation of the power system have become a major source of risk restricting the economic viability of projects. The current assessment system has multiple defects in terms of technical implementation, manifested in its insufficient collaborative analysis capabilities in terms of time, equipment, and system dimensions.

[0003] Traditional assessment methods generally adopt a static approach to time, simplifying grid curtailment orders to a single constraint parameter: the annual average curtailment rate. However, analysis of production data shows that curtailment behavior exhibits significant spatial and temporal heterogeneity. Curtailment rates can be several times higher during peak PV output periods (typically three hours before and after local noon) and peak load periods (typically when electricity demand plummets at night). This temporal heterogeneity leads to systematic biases in power generation forecasts in traditional assessment models.

[0004] At the equipment dimension level, existing technical solutions fail to fully characterize the dynamic process of performance degradation of new energy power generation equipment; taking photovoltaic modules as an example, their output power attenuation is not a simple linear decreasing process, but presents the dual characteristics of an initial photo-induced attenuation stage and a long-term operation attenuation stage; among them, the initial photo-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 operation attenuation is related to complex factors such as material aging and environmental stress accumulation.

[0005] At the system level, existing technologies lack in-depth modeling of the dynamic interaction mechanism between energy storage systems and power curtailment strategies. Especially in a market environment where time-of-use electricity pricing is implemented, the economic benefits of energy storage systems depend on the degree of match between electricity price arbitrage opportunities and the distribution of power curtailment periods. The fixed charge and discharge threshold strategy adopted by traditional evaluation methods cannot accurately reflect this dynamic coupling relationship, resulting in significant deviations between profit forecasts and actual operating data.

[0006] Therefore, how to provide a new energy power generation project benefit evaluation method based on multi-dimensional coupling is an urgent problem to be solved. Summary of the Invention

[0007] The embodiment of the present invention provides 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: Performing compliance verification on the pre-acquired attenuation parameters and output data, and constructing a device performance degradation dynamic model based on the verified attenuation parameters in combination with a pre-set environmental operation correction factor, performing output correction on the verified output data by using the device performance degradation dynamic model, and obtaining corrected theoretical output data; Performing verification on the pre-acquired power grid power limiting parameters by using the specification verification, constructing a power limiting strategy multi-dimensional constraint condition through the verified power grid power limiting parameters, performing power limiting constraint on the corrected theoretical output data based on the power limiting strategy multi-dimensional constraint condition, and obtaining actual output data; Performing physical constraint verification on the pre-acquired energy storage parameters, constructing an energy storage optimization scheduling model based on the verified energy storage parameters in combination with a pre-set safety constraint, and performing charging and discharging strategy optimization by using the energy storage optimization scheduling model in combination with the actual output data, and performing power generation project benefit evaluation according to the charging and discharging strategy optimization result.

[0011] In one embodiment, the compliance verification on the pre-acquired attenuation parameters and output data, and the construction of the device performance degradation dynamic model based on the verified attenuation parameters in combination with a pre-set environmental operation correction factor, the output correction on the verified output data by using the device performance degradation dynamic model, and the obtaining of the corrected theoretical output data comprise: Performing monotonicity verification on the pre-acquired attenuation parameters to obtain the verified attenuation parameters, and performing integrity verification on the pre-acquired output data to obtain the verified output data; Constructing a two-stage attenuation model based on the verified attenuation parameters, and constructing a device performance degradation dynamic model in combination with a pre-set environmental operation correction factor; Performing output correction on the verified output data by using the device performance degradation dynamic model, and obtaining corrected theoretical output data.

[0012] In one embodiment, the environmental operation correction factor comprises a temperature correction factor, an irradiance correction factor, and a pollution loss factor. And the temperature correction factor and the irradiance correction factor both satisfy normal distribution; the pollution loss factor satisfies uniform distribution; The temperature correction factor is used to correct the deviation between the working temperature of the power generation project and the standard test condition temperature; The irradiance correction factor is used to correct the power output change caused by the fluctuation of solar irradiance intensity; The pollution loss factor is used to correct the light transmittance loss caused by the accumulation of surface pollution of the power generation assembly.

[0013] In one embodiment, the pre-acquired power grid load shedding parameters are verified by normative verification, and a load shedding strategy multi-dimensional constraint condition is constructed based on the verified power grid load shedding parameters, and the modified theoretical output data is subjected to load shedding constraint based on the load shedding strategy multi-dimensional constraint condition, to obtain actual output data, including: 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 combined with preset boundary conditions, to obtain verified power grid load shedding parameters; 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 combined with preset priority; The modified theoretical output data is subjected to load shedding constraint by the load shedding strategy multi-dimensional constraint condition and the abnormality processing mechanism, to obtain actual output data.

[0014] In one embodiment, the load shedding strategy multi-dimensional constraint condition includes: annual unified load shedding rate constraint condition, maximum instantaneous load shedding power constraint condition, time period load shedding rate constraint condition, and monthly time period load shedding rate constraint condition; The annual unified load shedding rate constraint condition is obtained by the verified power grid load shedding parameters to constrain the proportion of annual cumulative abandoned power and theoretical total power generation; The maximum instantaneous load shedding power constraint condition is obtained by the verified power grid load shedding parameters to constrain the maximum power that the power grid can withstand at any time; The time period load shedding rate constraint condition is obtained by the verified power grid load shedding parameters to constrain the load shedding proportion of each hour of each day; The monthly time period load shedding rate constraint condition is obtained by the verified power grid load shedding parameters to constrain the load shedding proportion of each hour of each month of each year.

[0015] In one embodiment, the modified theoretical output data is subjected to load shedding constraint by the load shedding strategy multi-dimensional constraint condition and the abnormality processing mechanism, including: The modified theoretical output data is subjected to load shedding constraint simulation by the load shedding strategy multi-dimensional constraint condition, to obtain the limitation results of each constraint condition, and the limitation results of each constraint condition are subjected to limitation effect comparison; The comparison result is subjected to abnormality analysis, and the power curtailment strategy constraint condition is selected based on the analysis result and combined with the abnormality processing mechanism. The power curtailment constraint is performed on the corrected theoretical output data according to the power curtailment strategy constraint condition selection result.

[0016] In one embodiment, 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 and combined with preset safety constraints. The charging and discharging strategy optimization is performed by using the energy storage optimization scheduling model and combined with the actual output data. The power generation project revenue difference comparison is performed according to the charging and discharging strategy optimization result, and the power generation project revenue evaluation is performed according to the comparison result. The pre-acquired energy storage parameters are subjected to physical feasibility verification, and a cycle life attenuation model is constructed by using the verified energy storage parameters. Based on the cycle life attenuation model, a stage-by-stage decision mechanism is established by combining the preset state of charge safe operation interval and the charging and discharging power physical constraint; An energy storage optimization scheduling model is constructed by using the stage-by-stage 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 and combined with the actual output data. The power generation project revenue difference comparison is performed according to the charging and discharging strategy optimization and the energy storage state update result, and the power generation project revenue evaluation is performed according to the comparison result.

[0017] In one embodiment, based on the cycle life attenuation model, the stage-by-stage decision mechanism is established by combining the preset state of charge safe operation interval and the charging and discharging power physical constraint, including: An optimization function for maximizing short-term revenue and minimizing long-term equipment wear is established based on the cycle life attenuation model, and an energy storage cycle attenuation model is constructed by using the optimization function; The preset state of charge safe operation interval and the charging and discharging power physical constraint are taken as constraint conditions, and the stage-by-stage decision mechanism is established by combining the energy storage cycle attenuation model and the particle swarm algorithm.

[0018] In one embodiment, the power generation project revenue difference comparison is performed according to the charging and discharging strategy optimization and the energy storage state update result, and the power generation project revenue evaluation is performed according to the comparison result, including: The power generation project economic evaluation index is updated according to the charging and discharging strategy optimization and the energy 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; 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 discount cash flow model combined with the economic evaluation index and the time period distribution characteristics of power curtailment. 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.

[0019] According to a second aspect of the embodiments of the present application, a new energy power generation project benefit evaluation system based on multi-dimensional coupling is provided.

[0020] In one embodiment, the new energy power generation project benefit evaluation system based on multi-dimensional coupling comprises: an output correction module, a power cut constraint module and a benefit evaluation module. 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 and 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. The power cut constraint module is configured to perform verification on the pre-acquired power grid power cut parameters by using the specification verification, and construct a power cut strategy multi-dimensional constraint condition by using the verified power grid power cut parameters, and perform power cut constraint on the corrected theoretical output data based on the power cut strategy multi-dimensional constraint condition to obtain actual output data. The benefit evaluation module is configured to perform physical constraint verification on the pre-acquired energy storage parameters, construct an energy storage optimization scheduling model based on the verified energy storage parameters and a pre-set safety constraint, and perform charging and discharging strategy optimization by using the energy storage optimization scheduling model and the actual output data, and perform power generation project benefit evaluation according to the charging and discharging strategy optimization result.

[0021] According to a third aspect of the embodiments of the present application, a computer device is provided.

[0022] 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 above-mentioned new energy power generation project benefit evaluation method based on multi-dimensional coupling when executing the computer program.

[0023] According to a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided.

[0024] In one embodiment, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the above-mentioned new energy power generation project benefit evaluation method based on multi-dimensional coupling.

[0025] The technical scheme provided by the embodiments of the present application can include the following beneficial effects: 1. The device performance degradation dynamic model, the power cut strategy multi-dimensional constraint condition and the energy storage optimization scheduling model can solve the economic prediction distortion problem in the power consumption capacity limited scenario, and avoid the high prediction error rate problem caused by ignoring the time and space heterogeneity of the power cut strategy, the nonlinear attenuation of the device performance and the collaborative optimization of the energy storage system and other key factors in the traditional evaluation method.

[0026] 2、The application accurately characterizes the differentiated characteristics of initial light-induced attenuation and long-term running attenuation through a two-stage attenuation model, integrates multi-dimensional constraint conditions such as annual unified power curtailment rate, maximum instantaneous power curtailment power, time period power curtailment rate vector, and monthly time period power curtailment rate matrix, forms a dynamic coupling analysis framework with priority judgment, and realizes real-time adjustment of the charging and discharging strategy under the double constraints of considering the cycle life attenuation and time-of-use price fluctuation, thereby providing a more accurate analysis tool for new energy project investment decision-making.

[0027] 3、The application significantly improves the power generation prediction accuracy through the dynamic association of equipment performance degradation and power curtailment strategy, and can accurately simulate the actual operation logic of power grid dispatching in the constructed multi-dimensional constraint condition collaborative framework of power curtailment strategy, support fine modeling of complex power curtailment scenarios, and couple energy storage scheduling to the integrated output analysis of new energy under the limited situation by using the energy storage optimal scheduling model.

[0028] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the application. BRIEF DESCRIPTION OF DRAWINGS

[0029] The accompanying drawings, which are incorporated into and form part of the specification, illustrate an embodiment consistent with the application and, together with the specification, serve to explain the principles of the application.

[0030] Figure 1 is a flowchart of a new energy power generation project benefit evaluation method based on multi-dimensional coupling according to an exemplary embodiment; Figure 2 is a structural schematic diagram of a new energy power generation project benefit evaluation system based on multi-dimensional coupling according to an exemplary embodiment; Figure 3 is a structural schematic diagram of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0031] Figure 1 An embodiment of the new energy power generation project benefit evaluation method based on multi-dimensional coupling of the application is shown.

[0032] In this optional embodiment, the new energy power generation project benefit evaluation method based on multi-dimensional coupling includes: Step S101, the compliance verification is carried out on the pre-acquired attenuation parameter and output data, and a device performance degradation dynamic model is constructed based on the verified attenuation parameter and in combination with a preset environmental operation correction factor, the output data after verification is corrected by using the device performance degradation dynamic model, and the corrected theoretical output data is obtained;Under the action of the device performance degradation dynamic model, accurate power generation prediction and benefit risk assessment under the consumption limited scene can be realized, the differentiated characteristics of the initial light-induced attenuation and the long-term operation attenuation are accurately characterized, and the dynamic calibration of the output curve is realized through the environmental parameter correction factor.

[0033] Step S102, the pre-acquired power grid power limiting parameter is verified by using the specification verification, and a power limiting strategy multi-dimensional constraint condition is constructed through the verified power grid power limiting parameter, and the corrected theoretical output data is subjected to power limiting constraint based on the power limiting strategy multi-dimensional constraint condition, and the actual output data is obtained;Under the action of the constructed power limiting strategy multi-dimensional constraint condition, the actual operation logic of the power grid dispatching can be accurately simulated, and the fine modeling of the complex power limiting scene is supported.

[0034] Step S103, the pre-acquired energy storage parameter is subjected to physical constraint verification, an energy storage optimization scheduling model is constructed based on the verified energy storage parameter and in combination with a preset safety constraint, and the charge-discharge strategy optimization is carried out by using the energy storage optimization scheduling model in combination with the actual output data, and the power generation project benefit is evaluated according to the charge-discharge strategy optimization result;Under the action of the constructed energy storage optimization scheduling model, the energy storage scheduling is coupled to the integrated output analysis of new energy under the limited condition.

[0035] Figure 2 An embodiment of the new energy power generation project benefit evaluation system based on multi-dimensional coupling of the application is shown.

[0036] In this optional embodiment, the new energy power generation project benefit evaluation system based on multi-dimensional coupling comprises: an output correction module 201, a power limiting constraint module 202 and a benefit evaluation module 203. The output correction module 201 is used for performing compliance verification on the pre-acquired attenuation parameter and output data, and constructing a device performance degradation dynamic model based on the verified attenuation parameter and in combination with a preset environmental operation correction factor, and correcting the output data after verification by using the device performance degradation dynamic model, and obtaining the corrected theoretical output data. The power limiting constraint module 202 is used for verifying the pre-acquired power grid power limiting parameter by using the specification verification, and constructing a power limiting strategy multi-dimensional constraint condition through the verified power grid power limiting parameter, and performing power limiting constraint on the corrected theoretical output data based on the power limiting strategy multi-dimensional constraint condition, and obtaining the actual output data. The benefit evaluation module 203 is used to perform physical constraint verification on the pre-acquired energy storage parameters, build an energy storage optimization scheduling model based on the verified energy storage parameters combined with preset safety constraints, and use the energy storage optimization scheduling model in combination with actual output data to optimize the charging and discharging strategy. The power generation project benefit evaluation is performed based on the charging and discharging strategy optimization results.

[0037] It should be noted that the core of this invention lies in constructing a multi-dimensional dynamically coupled evaluation system, which includes the following three aspects: establishing a model of equipment performance degradation and dynamic output (i.e., constructing a dynamic model of equipment performance degradation), building a synergistic framework for multi-mode power-rationing strategies (i.e., constructing multi-dimensional constraints for power-rationing strategies), and developing an optimized scheduling algorithm for the entire life cycle of energy storage systems (i.e., an energy storage optimized scheduling model).

[0038] The equipment performance degradation and dynamic output model is established by introducing the photovoltaic module dual-stage attenuation theoretical model (i.e. dual-stage attenuation model), accurately describing the nonlinear attenuation trajectory of the equipment output power over the operating time, and through the temperature correction factor T i , irradiance correction factor R i , pollution loss factor D i The correction is: ; Where, i =1,…8760; where To contribute to the original; To adjust the dynamic output, the output curve can be adjusted dynamically.

[0039] Constructing a synergistic framework for multi-mode curtailment strategies transcends the limitations of traditional single curtailment rate models and integrates various curtailment instructions that grid dispatchers may employ, including but not limited to annual unified curtailment rate instructions, maximum instantaneous curtailment power instructions, time-of-day differentiated curtailment rate instructions, and monthly time-of-day matrix curtailment instructions. By establishing constraint priority determination rules and an exception handling mechanism, the organic coordination and dynamic coupling of different curtailment strategies are achieved. When the calculated results of different curtailment instructions conflict, the optimal constraint is selected based on a pre-defined decision-making hierarchy that fully reflects the operational practices of grid dispatchers.

[0040] The development of an optimized scheduling algorithm for the entire lifecycle of energy storage systems involves constructing a dynamic scheduling model (i.e., an energy storage optimization scheduling model) with the core goal of maximizing economic returns, taking into account battery cycle life degradation and time-of-use electricity price fluctuations. This model establishes a phased decision-making mechanism to achieve real-time optimization of charging and discharging strategies. The dynamic scheduling model includes data collection, strategy formulation, and execution and update steps.

[0041] The data collection step is to collect wind power and photovoltaic output data, power grid power limiting information and energy storage system state information; the strategy making step is to establish an optimization function, to maximize short-term revenue and minimize long-term equipment wear and tear as the objective function, to set an energy storage cycle attenuation model, to introduce the state of charge safe operation interval and the physical limit of charging and discharging power as the constraint condition, and to use the particle swarm algorithm to make the best charging and discharging capacity; the execution update step is to execute the established charging and discharging power, and to update the energy storage system state.

[0042] The 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 The technical scheme of the application is realized by a programmed module deployed on a computing device, and its running process strictly follows the operation rules and economic analysis criteria of new energy power systems, and 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.

[0044] The input parameter preprocessing and data verification include the initialization stage, and the multi-source heterogeneous data needs to be structured and reorganized and logically self-consistent. 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 needs to cover the theoretical power generation power value every hour in the complete annual cycle, a total of 8760 continuous time points of power record values. In addition, the photovoltaic system must obtain the dynamic parameters of the photovoltaic component attenuation rate over time (i.e. attenuation parameters), which need to be modeled according to the technical documents provided by the manufacturer, especially the need to distinguish the rapid attenuation of photovoltaic components in the early stage of operation from the stable attenuation in the long-term operation. Specifically, in the first complete year of the project, the output power of the photovoltaic component will decrease significantly due to the light-induced attenuation effect, and the decrease rate is usually in the range of 1.5% to 3%; since the second year of operation, the component enters the stable attenuation period, and the power attenuation rate in this stage is maintained at a low level of 0.5% to 1%, and this attenuation process presents a linear cumulative characteristic. The expression of the two-stage attenuation model is: ; In the formula, x is the running time, x =1,…25; f ( x ) is the annual attenuation in the operation period, a is the initial rapid attenuation;b To be long-term stable attenuation.

[0045] For 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, and is usually measured in megawatt-hours. At the same time, the charge and discharge efficiency parameters of the energy storage system must be clearly defined, especially the different efficiency characteristics of the charging and discharging processes. Usually, both the charging and discharging efficiencies are in the numerical range of 85% to 95%, but there may be asymmetric differences between them.

[0046] 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.

[0047] 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 amount of abandoned power to the theoretical total power generation in a year, which needs to be set according to the annual consumption report published by the grid company. The maximum instantaneous power limiting power parameter represents the maximum power that the grid can withstand at any time, which is directly related to the capacity of the transformer in the substation and the current carrying capacity of the transmission line. The time-of-use power limiting rate parameter needs to form a vector structure containing 24 elements, each element corresponding to the power limiting proportion of a specific hour each day. Such data is usually derived from historical operation records or future operation plans of the grid dispatching department. The monthly time-of-use power limiting rate parameter needs to build a 12-row 24-column matrix structure, each matrix element corresponding to the power limiting proportion 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 meet the global consistency principle, that is, the total amount of abandoned power generated by the joint action of all power limiting rules in a year must be consistent with the annual unified power limiting rate parameter.

[0048] Compliance verification includes integrity verification of the theoretical output curve data (i.e. output data) and monotonicity verification of the monotonicity of the photovoltaic component attenuation rate parameter.

[0049] Normative verification includes consistency verification of pre-acquired grid power limiting parameters and rationality verification of consistent grid power limiting parameters combined with preset boundary conditions.

[0050] Physical constraint verification includes physical feasibility verification and basic inspection of setting operating constraints for the energy storage system.

[0051] In the data verification link, multi-level logical self-consistency check needs to be performed. First, the integrity of the theoretical output curve data is verified to ensure that the hourly power record values are complete and have no missing values in the annual cycle, and meet the solar radiation distribution characteristics of the project location. Second, the monotonic increasing property of the photovoltaic module attenuation rate parameter needs to be verified to avoid abnormal calculation results caused by parameter input errors. Third, the boundary condition review of various power limiting parameters needs to be performed to confirm that all power limiting rate values are in the effective and reasonable interval. Finally, the physical feasibility verification of the energy storage system parameters needs to be performed, 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.

[0052] The time sequence simulation calculation and dynamic coupling analysis are specific to this stage, which implements hourly iteration calculation within the time span of the project's entire life cycle, with each iteration period containing four sub-processes with strict time sequence relationship, including sub-process one output correction calculation, sub-process two power limiting scope determination, sub-process three energy storage system optimization scheduling, and sub-process four economic index update.

[0053] 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 modules due to material aging during operation. This attenuation phenomenon is characterized by a sharp drop in the early stage of project operation, and a gradual linear accumulation process in the subsequent operation period. The expression of the two-stage attenuation model is as follows: ; In the formula, x represents the running life, x =1,…25; f ( x ) represents the annual attenuation during operation period; a represents the initial rapid attenuation; b represents the long-term stable attenuation.

[0054] In addition to the above attenuation parameters, environmental operating parameter correction calculation needs to be introduced, especially the correction of the deviation between the actual working temperature of the module and the standard test condition, 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 needs to be considered. The dynamic correction process of the introduced temperature correction factor, irradiance correction factor and pollution loss factor is as follows: ; In the formula, i =1,…8760, where is the original output, is the adjusted dynamic output, which realizes the dynamic adjustment of the output curve. Ti is the temperature correction factor, satisfying normal distribution; R i is the irradiance correction factor, satisfying normal distribution; D i is the pollution loss factor, satisfying uniform distribution.

[0055] The total output correction is as follows: ; wherein, x =1,…25; i =1,…8760; is the corrected total output.

[0056] The power limiting scope determination of sub-process two is to calculate the actual output according to the multi-dimensional constraint conditions of the power grid limiting strategy after obtaining the corrected theoretical output value. 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, and by comparing the limiting effects generated by each rule, the most stringent constraint condition is selected as the final execution standard.

[0057] 1) Annual uniform limiting rate constraint condition: ; wherein, EAP year represents the annual online power; EAP i represents the power at a specific time, i =1,…8760; PRR fix represents the annual uniform limiting rate.

[0058] 2) Maximum instantaneous limiting power constraint condition: ; wherein, EAP year represents the annual online power; EAP i represents the power at a specific time, i =1,…8760; PRR max represents the maximum power limit value.

[0059] 3) Time period limiting rate constraint condition ; wherein, EAP year represents the annual online power; EAP ( i , j ) represents the power at a specific time;PRR j denotes the power curtailment rate at specific time; i denotes the specific date, i =1,…365; j denotes the specific time, j =0,…23.

[0060] 4) Monthly time-of-use power curtailment constraint: ; In the formula, EAP year denotes the annual online power generation; EAP ( i , j , k ) denotes the power generation at specific time; PRR ( j , k ) denotes the power curtailment rate at specific time in specific month; i denotes the specific date, i =1,…30or31or28; j denotes the specific time, j =0,…23; k denotes the specific month, k =1,…12.

[0061] In this process, an exception handling mechanism needs to be established. When the calculation results of different power curtailment rules conflict, the system will select the constraint condition according to the preset priority order. The priority system places the monthly time-of-use power curtailment rate in the highest decision level, followed by the time-of-use power curtailment rate, then the maximum instantaneous power curtailment power, and finally the annual unified power curtailment rate.

[0062] 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 degradation of the energy storage system, and the spatio-temporal distribution characteristics of the power grid power curtailment strategy. The charging and discharging depth of the energy storage system is dynamically adjusted to balance the relationship between short-term benefits and long-term equipment wear and tear.

[0063] Firstly, a cycle life degradation model of the energy storage system is established. This model needs to reflect the nonlinear degradation law of battery capacity with the increase of charging and discharging cycles. The expression of the cycle life degradation model is: ; In the formula, 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; wrepresents the attenuation coefficient (0.001-0.005); chg l represents the first l charge (discharge) electric quantity; q represents the stress coefficient (1-2).

[0064] Under the premise of considering the attenuation of battery cycle life and the fluctuation of time-of-use electricity price, a dynamic scheduling model is constructed with the core goal of maximizing economic benefits. The model establishes a phased decision mechanism to realize real-time optimization of charging and discharging strategy, and the dynamic scheduling model includes: data collection step, strategy formulation step and execution update step.

[0065] The data collection step is to collect wind power and photovoltaic output data, power grid power limiting information and energy storage system state information; the strategy formulation step is to establish an optimization function, with the goal of maximizing short-term benefits and minimizing long-term equipment wear and tear, to set up an energy storage cycle attenuation model, to introduce the state of charge safe operation interval and the physical limit of charging and discharging power as constraint conditions, and to use particle swarm algorithm to solve the optimal charging (discharging) electric quantity; the execution update step is to execute the established charging (discharging) strategy and update the state of the energy storage system.

[0066] The economic index update of sub-process four is to update the core index of project economic evaluation according to the current grid injection power and energy storage operation record. It includes key parameters such as cumulative on-grid power, cumulative curtailment, energy storage system cycle number, operation and maintenance cost, and cash flow changes.

[0067] The result post-processing and uncertainty analysis is to perform multi-dimensional data integration and analysis processing after completing the whole life cycle time series simulation.

[0068] The core indicators such as annual power generation, curtailment, and energy storage usage frequency are statistically summarized to generate time series distribution graph and probability density function curve. The time period distribution characteristics of power limiting are analyzed to identify the overlap between the power limiting high incidence period and the photovoltaic output peak period.

[0069] The discounted cash flow model is used to calculate the net present value and internal rate of return indicators of the project. The net present value is calculated as follows: ; In the formula, PV represents the net present value; CF i represents the first i year cash flow; r represents the industry benchmark return rate; TV represents the end-of-operation value; n represents the number of years of operation.

[0070] The internal rate of return is calculated as follows: ; In the formula, IRR Internal rate of return is represented.

[0071] 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, parameter sensitivity analysis is carried out, and the influence degree of factors such as fluctuation of grid access price, change of power limiting rate, acceleration of equipment attenuation and the like on project income is quantified. For example, the influence of price fluctuation is calculated as follows: ; In the formula, price fix The base price is represented; price sen The fluctuated price is represented; S price_pv The influence degree of price on net present value is represented; PV price fix The project net present value based on the base 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 price on income rate is represented; IRR price fix The project income rate based on the base price is represented; IRR price sen The project income rate based on the fluctuated price is represented.

[0072] An industry-standard technical and economic evaluation report is automatically generated, and the report content covers power limiting loss analysis, energy storage operation characteristics, full life cycle cash flow prediction, sensitivity analysis atlas and the like core elements, so as to visually display the conclusion by means of visual charts.

[0073] The present application relates to the technical innovation in the cross field of new energy power system planning and economic analysis, and specifically is the power consumption capacity limited evaluation under the high proportion renewable energy penetration scenario; the present application deeply integrates the physical law of power system operation, the equipment performance degradation mechanism and the economic analysis model, is suitable for the full life cycle income prediction and risk evaluation of photovoltaic power generation system, wind power generation system and the matched energy storage device under the existence of power grid dispatching constraint, and can effectively solve the technical bottlenecks of traditional evaluation methods in dynamic coupling of power limiting strategy, nonlinear attenuation of equipment performance, optimization dispatching coordination of energy storage and the like.

[0074] ​​​​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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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 new energy power generation project benefit evaluation method based on multi-dimensional coupling, characterized by: include: The pre-acquired attenuation parameters and output data are verified for compliance. A dynamic model of equipment performance degradation is constructed based on the verified attenuation parameters combined with preset environmental operation correction factors. The verified output data is corrected using the dynamic model to obtain the corrected theoretical output data. The pre-acquired power-limiting parameters of the power grid are verified by normative verification, and the multi-dimensional constraints of the power-limiting strategy are constructed based on the verified power-limiting parameters. Based on the multi-dimensional constraints of the power-limiting strategy, the revised theoretical output data is subjected to power-limiting constraints to obtain the actual output data. The pre-acquired energy storage parameters are subject to physical constraint verification, and an energy storage optimization scheduling model is constructed based on the verified energy storage parameters and preset safety constraints. The energy storage optimization scheduling model is then used to optimize the charging and discharging strategy in combination with actual output data, and the power generation project benefits are evaluated based on the charging and discharging strategy optimization results.

2. The method for evaluating the benefits of new energy power generation projects based on multi-dimensional coupling according to claim 1 is characterized in that: The compliance verification of the pre-acquired attenuation parameters and output data is performed, and a dynamic model of equipment performance degradation is constructed based on the verified attenuation parameters combined with a preset environmental operation correction factor. The verified output data is corrected using the dynamic model of equipment performance degradation, and the corrected theoretical output data includes: Performing monotonicity verification on the pre-acquired attenuation parameter to obtain the verified attenuation parameter, and performing integrity verification on the pre-acquired output data 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 in combination with the preset environmental operation correction factors; The verified output data is corrected using the dynamic model of equipment performance degradation to obtain the corrected theoretical output data.

3. The method for evaluating the benefits of new energy power generation projects based on multi-dimensional coupling according to claim 2 is characterized in that: The environmental operation correction factors include: temperature correction factor, irradiance correction factor and pollution loss factor; The temperature correction factor and the irradiance correction factor both satisfy a normal distribution; the pollution loss factor satisfies a uniform distribution; Correcting the deviation between the operating temperature of the power generation project and the standard test condition temperature by using the temperature correction factor; Correcting power output changes caused by fluctuations in solar radiation intensity using the irradiance correction factor; The pollution loss factor is used to correct the transmittance loss caused by the accumulation of pollution on the surface of the power generation component.

4. The method for evaluating the benefits of new energy power generation projects based on multi-dimensional coupling according to claim 1 is characterized in that: The standardized verification is used to verify the pre-acquired power grid power restriction parameters, and the multi-dimensional constraint conditions of the power restriction strategy are constructed based on the verified power grid power restriction parameters. The revised theoretical output data is subjected to power restriction constraints based on the multi-dimensional constraint conditions of the power restriction strategy, and the actual output data obtained includes: Perform consistency verification on the pre-acquired power-limiting parameters of the power grid to obtain consistent power-limiting parameters, and perform rationality verification on the consistent power-limiting parameters in combination with preset boundary conditions to obtain verified power-limiting parameters of the power grid; The multi-dimensional constraints of the power-limiting strategy are constructed through the verified power-limiting parameters of the power grid, and an exception handling mechanism is established based on the multi-dimensional constraints of the power-limiting strategy and the preset priorities; The multi-dimensional constraints of the power restriction strategy and the exception handling mechanism are used to impose power restriction constraints on the revised theoretical output data to obtain the actual output data.

5. The method for evaluating the benefits of new energy power generation projects based on multi-dimensional coupling according to claim 4 is characterized in that: The multi-dimensional constraints of the power restriction strategy include: annual unified power restriction rate constraint, maximum instantaneous power restriction power constraint, time-based power restriction rate constraint and monthly time-based power restriction rate constraint; The annual unified power restriction rate constraint condition is to obtain the ratio of the annual cumulative power curtailment to the theoretical total power generation through the verified power restriction parameters of the power grid; The maximum instantaneous power limit constraint condition is to obtain the maximum power that the power grid can withstand at any time through the verified power limit parameters of the power grid; The time-based power restriction rate constraint condition is to obtain the power restriction ratio of each hour of each day by verifying the power restriction parameters of the power grid; The monthly time-of-use power restriction rate constraint condition is to obtain the power restriction ratio of each hour in each month of each year through the verified power restriction parameters of the power grid.

6. The method for evaluating the benefits of new energy power generation projects based on multi-dimensional coupling according to claim 4 is characterized in that: The method of using the multi-dimensional constraints of the power restriction strategy and the exception handling mechanism to restrict the power restriction of the revised theoretical output data includes: The power restriction constraint simulation is performed on the revised theoretical output data using the multi-dimensional constraint conditions of the power restriction strategy to obtain the constraint results of each constraint condition and compare the constraint effects of each constraint condition; An abnormal analysis is performed on the restriction effect comparison results, and based on the analysis results combined with the abnormal handling mechanism, the power restriction strategy constraint conditions are selected, and the power restriction constraints are applied to the revised theoretical output data according to the power restriction strategy constraint condition selection results.

7. The method for evaluating the benefits of new energy power generation projects based on multi-dimensional coupling according to claim 1 is characterized in that: The aforementioned physical constraint verification of pre-acquired energy storage parameters, building an energy storage optimization scheduling model based on the verified energy storage parameters combined with preset safety constraints, optimizing the charging and discharging strategy using the energy storage optimization scheduling model combined with actual output data, and evaluating the power generation project benefits based on the charging and discharging strategy optimization results include: Conduct physical feasibility verification on the pre-acquired energy storage parameters and use the verified energy storage parameters to build a cycle life attenuation model; Based on the cycle life attenuation model, a phased decision-making mechanism is established in combination with the preset state of charge safe operating range and the physical constraints of charge and discharge power; A phased decision-making mechanism is used to build an energy storage optimization scheduling model, which is then combined with actual output data to optimize charging and discharging strategies and update energy storage status. The benefits of power generation projects are compared based on the results of charge and discharge strategy optimization and energy storage status update, and the benefits of power generation projects are evaluated based on the comparison results.

8. The method for evaluating the benefits of new energy power generation projects based on multi-dimensional coupling according to claim 7 is characterized in that: The phased decision-making mechanism established based on the cycle life attenuation model and in combination with the preset state of charge safe operating range and charge and discharge power physical constraints includes: Based on the cycle life attenuation model, an optimization function is established to maximize short-term benefits and minimize long-term equipment losses, and the energy storage cycle attenuation model is constructed using the optimization function; The preset state of charge safe operating range and charge and discharge power physical constraints are taken as constraints, and a phased decision-making mechanism is established in combination with the energy storage cycle attenuation model and particle swarm algorithm.

9. The method for evaluating the benefits of new energy power generation projects based on multi-dimensional coupling according to claim 8 is characterized in that: Comparing the difference in power generation project benefits based on the charge and discharge strategy optimization and energy storage status update results, and evaluating the power generation project benefits based on the comparison results include: Update the economic evaluation indicators of power generation projects based on the results of charge and discharge strategy optimization and energy storage status update, and statistically summarize the economic evaluation indicators to generate time series distribution graphs and probability density function curves; The time series distribution diagram and probability density function curve are used to analyze and identify the distribution characteristics of power curtailment periods. The discounted cash flow model is used to combine economic evaluation indicators and the distribution characteristics of power curtailment periods to calculate the project's net present value and internal rate of return. Compare the difference in power generation project returns based on the project's net present value and internal rate of return, and evaluate the power generation project returns based on the comparison results.

10. The new energy power generation project benefit evaluation system based on multi-dimensional coupling is characterized by: The new energy power generation project benefit evaluation system based on multi-dimensional coupling includes: an output correction module, a power restriction constraint module and a benefit evaluation 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 combined with a preset environmental operation correction factor. The dynamic model of equipment performance degradation is used to perform output correction on the verified output data to obtain corrected theoretical output data; The power restriction constraint module is used to verify the pre-acquired power restriction parameters of the power grid by using normative verification, and to construct multi-dimensional constraint conditions of the power restriction strategy based on the verified power restriction parameters of the power grid. Based on the multi-dimensional constraint conditions of the power restriction strategy, the revised theoretical output data is subjected to power restriction constraints to obtain actual output data; The benefit evaluation module is used to perform physical constraint verification on pre-acquired energy storage parameters, construct an energy storage optimization scheduling model based on the verified energy storage parameters combined with preset safety constraints, and use the energy storage optimization scheduling model in combination with actual output data to optimize the charging and discharging strategy. The power generation project benefit evaluation is performed based on the charging and discharging strategy optimization results.

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