New energy station full life cycle dynamic evaluation method of coupled energy storage system

Abstract

The invention relates to a full-life-cycle dynamic evaluation method for a new energy station coupled with an energy storage system, computer equipment and a storage medium. The method comprises the following steps: firstly, respectively acquiring energy storage data of a new energy station coupled energy storage system and data of the new energy station coupled energy storage system participating in an electric power spot market, then constructing an energy storage influence factor to quantify the change of energy storage on generating capacity and electricity price, and constructing an electric power spot market influence factor to quantify the influence of the market on the two factors; then, dynamic analysis is carried out on the generating capacity and the electricity price of the new energy station in the full life cycle by using the two influence factors to obtain corresponding results; and finally, dynamic technical and economic evaluation is carried out on the new energy station in the full life cycle according to the results and a preset technical and economic evaluation process. According to the method, the coupling characteristics of the new energy station and the energy storage system and the dynamic change of the electric power spot market are comprehensively considered, so that comprehensive, accurate and dynamic evaluation of the full life cycle of the new energy station is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy power generation, and in particular relates to a full life cycle dynamic evaluation method for a renewable energy station coupled with an energy storage system. Background Art

[0002] As the global demand for clean energy continues to grow, new energy sites (such as wind power and photovoltaic sites) are developing rapidly. However, their inherent intermittent and volatile nature poses many challenges to the stable operation of the power system. As a key technical means to solve these problems, energy storage systems are increasingly being coupled with new energy sites.

[0003] In the existing technology, there are obvious deficiencies in the evaluation of projects coupling new energy sites and energy storage systems. On the one hand, there is a lack of effective methods to fully consider the characteristics of energy storage systems and their impact on power generation and electricity prices. Energy storage technologies are diverse, and different technical routes differ significantly in terms of energy conversion efficiency, charging and discharging rates, capacity attenuation, etc., and the mechanism of action on power generation and electricity prices when operating in coordination with new energy sites is complex. It is difficult for existing assessments to accurately quantify these impacts. On the other hand, in the electricity spot market environment, new energy sites are faced with new situations such as real-time electricity price fluctuations and changes in market supply and demand, but the current evaluation methods do not fully combine the dynamic characteristics of the electricity spot market to analyze its impact on the entire life cycle of the project.

[0004] In addition, with the advancement of technology and the evolution of the market environment, the performance changes of projects at different stages need to be dynamically evaluated. However, existing technologies are mostly static or phased evaluations, which cannot track in real time the impact of factors such as policy adjustments and technological innovations on the technical and economic performance of the full life cycle of new energy stations and energy storage system coupling projects, making it difficult to meet the needs of efficient project decision-making, optimized operation and sustainable development. Summary of the invention

[0005] Based on this, it is necessary to provide a new energy station full life cycle dynamic evaluation method, system, computer equipment and storage medium that can comprehensively and dynamically evaluate the coupled energy storage system in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for dynamic evaluation of the entire life cycle of a new energy station coupled with an energy storage system, including:

[0007] S1: Based on the energy storage system coupled with the new energy station, obtain energy storage data; based on the power spot market participated by the new energy station, obtain power spot market data;

[0008] S2: Construct energy storage impact factors based on energy storage data. The energy storage impact factors are used to quantify the changes in power generation and electricity prices caused by energy storage.

[0009] S3: Construct the power spot market impact factor based on the power spot market data. The power spot market impact factor is used to quantify the changes in power generation and electricity prices caused by the power spot market.

[0010] S4: Based on the influencing factors of energy storage and the influencing factors of the electricity spot market, a dynamic analysis is conducted on the power generation and electricity price of the new energy station throughout its life cycle to obtain the power generation analysis results and electricity price analysis results;

[0011] S5: Based on the results of power generation analysis and electricity price analysis, a dynamic technical and economic evaluation of the new energy station throughout its life cycle is conducted according to the preset technical and economic evaluation process.

[0012] In a second aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a dynamic evaluation method for the entire life cycle of a new energy station coupled with an energy storage system as in the first aspect is implemented.

[0013] In a third aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a full life cycle dynamic assessment method for a new energy station coupled with an energy storage system as in the first aspect.

[0014] The above-mentioned dynamic evaluation method for the whole life cycle of a new energy station coupled with an energy storage system realizes a comprehensive, accurate and dynamic evaluation of the whole life cycle of a new energy station by comprehensively considering the characteristics of the coupling between the new energy station and the energy storage system, as well as the dynamic changes in the electricity spot market. This can accurately quantify the impact of energy storage and the electricity spot market on the project, and achieve a comprehensive, dynamic and accurate evaluation of the technical and economic performance of the whole life cycle of the coupling project of the new energy station and the energy storage system, providing a scientific basis for project investment decisions, planning and design, operation and management, etc., improving the overall operation efficiency and economic benefits of the project, and promoting the coordinated and sustainable development of new energy stations and energy storage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A schematic diagram of a process for dynamic evaluation of the entire life cycle of a new energy station coupled with an energy storage system provided by the present invention;

[0017] Figure 2A schematic diagram of a flow chart of constructing an energy storage influencing factor based on energy storage data in an optional embodiment of the present invention;

[0018] Figure 3 The present invention is a flowchart of constructing an electricity spot market influencing factor based on electricity spot market data in an optional embodiment. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0020] refer to Figure 1 , which presents a flow chart of a method for dynamic evaluation of the whole life cycle of a new energy station coupled with an energy storage system provided by the present application, the method comprising the following steps:

[0021] S1: Based on the energy storage system coupled with the new energy station, obtain energy storage data; based on the power spot market in which the new energy station participates, obtain power spot market data.

[0022] Specifically, to obtain energy storage data, we must first determine the type of energy storage system coupled to the new energy station, such as lithium-ion battery energy storage, lead-acid battery energy storage, or other new energy storage technologies. The data acquisition device can be used to collect real-time change data on the energy storage system's charge and discharge power, charge and discharge time, and energy storage capacity, as well as the energy storage system's operating status information, such as whether it is in normal operation, fault warning, maintenance, etc. The frequency of collecting these data can be set according to actual needs, for example, every few minutes to ensure the timeliness and accuracy of the data. At the same time, it is also necessary to obtain parameter information such as the initial investment cost, operation and maintenance cost, and expected service life of the energy storage system.

[0023] In terms of obtaining electricity spot market data, data connections can be established with the trading platform of the electricity spot market or relevant data providers. Data such as the transaction volume, transaction price, and transaction time of new energy stations in the electricity spot market can be collected. In addition, attention should also be paid to information such as the price fluctuation patterns, changes in supply and demand relationships, and adjustments to market trading rules in the electricity spot market.

[0024] S2: Construct energy storage impact factors based on energy storage data. The energy storage impact factors are used to quantify the changes in power generation and electricity prices caused by energy storage.

[0025] Specifically, when constructing the energy storage impact factor, the impact of the charging and discharging behavior of the energy storage system on the power generation of new energy sites is analyzed. For example, when the energy storage system charges and stores electricity during the period of excess power generation of new energy, and discharges to supplement electricity during the period of insufficient power generation, it can reduce the phenomenon of wind and solar power abandonment, thereby increasing effective power generation. By comparing the power generation data of new energy sites with and without energy storage systems, the proportion of power generation increased by the energy storage system is calculated, and it is used as one of the factors affecting power generation by energy storage.

[0026] As for electricity prices, energy storage systems can indirectly affect electricity prices by adjusting the balance between electricity supply and demand. Discharging during peak electricity consumption can ease power shortages and may reduce the increase in electricity prices; charging during low electricity consumption can increase electricity demand and reduce the decrease in electricity prices. For example, a mathematical model can be established to combine the charging and discharging power and electricity volume of the energy storage system and the price fluctuation data of the electricity spot market to calculate the quantitative impact factors of the energy storage system on the stability or change of electricity prices. For example, regression analysis and other methods can be used to find the functional relationship between the operating parameters of the energy storage system and the change in electricity prices, thereby determining the factors that affect electricity prices through energy storage.

[0027] S3: Construct the electricity spot market impact factor based on the electricity spot market data. The electricity spot market impact factor is used to quantify the changes in power generation and electricity prices caused by the electricity spot market.

[0028] Specifically, when constructing the influencing factors of the electricity spot market, the impact of the price fluctuation characteristics of the electricity spot market on the power generation of new energy sites is considered. For example, when the price of the electricity spot market is high, the new energy site may adjust the power generation strategy and increase the output of power generation to obtain higher profits; when the price is low, the power generation may be appropriately reduced. By analyzing the changes in the power generation data of new energy sites in different price ranges, the elasticity coefficient of the electricity spot market price on the power generation can be determined as a factor affecting the power generation of the electricity spot market.

[0029] As for the factors affecting electricity prices, the supply and demand relationship, trading rules and other factors in the electricity spot market will directly lead to fluctuations in electricity prices. By studying the changing patterns of electricity prices in different time periods (such as peak, valley and flat periods), different seasons and different supply and demand balance states in the electricity spot market, a corresponding electricity price prediction model can be established. The deviation or fluctuation range between the electricity price predicted by the model and the actual electricity price is used as the factor affecting the electricity price in the electricity spot market, and the degree of change in the electricity price caused by the electricity spot market's own factors is quantified.

[0030] S4: Based on the influencing factors of energy storage and electricity spot market, a dynamic analysis is conducted on the power generation and electricity price of the new energy station throughout its life cycle to obtain the power generation analysis results and electricity price analysis results.

[0031] Specifically, when conducting dynamic analysis, the energy storage influencing factors and the electricity spot market influencing factors are incorporated into the prediction model of the power generation and electricity price of the new energy station. The entire life cycle of the new energy station is considered, from the initial construction to the different stages after several years of operation, because the equipment performance, energy storage system status, market environment, etc. of the new energy station will change at different stages.

[0032] For power generation analysis, the power generation in each time period can be predicted by combining the capacity attenuation curve of the power generation equipment of the new energy station, the change curve of the capacity and efficiency of the energy storage system, as well as the energy storage influencing factors and the electricity spot market influencing factors in different periods. For example, in the early stage of operation, the performance of the power generation equipment and energy storage system is good, and the demand in the electricity spot market is strong, so the power generation may be high at this time; as time goes by, the equipment ages and the energy storage performance decreases. If the market competition intensifies, the power generation may gradually decrease. Through year-by-year or stage-by-stage calculations, the power generation change curve of the new energy station throughout its life cycle is obtained, that is, the power generation analysis result.

[0033] For electricity price analysis, we can predict the electricity price levels faced by new energy sites at different stages of their life cycle based on the long-term development trend of the electricity spot market, policy adjustment expectations, and the dynamic changes in the impact of energy storage systems on electricity prices. Taking into account factors such as electricity market reform and the decline or adjustment of new energy subsidy policies, the parameters in the electricity price analysis model are dynamically adjusted. For example, in the early stages of new energy development, there may be higher subsidies that lead to relatively high electricity prices; in the later stages, subsidies are reduced, and electricity prices may be more dependent on market supply and demand and the regulation of energy storage, thereby obtaining a dynamic change curve of electricity prices throughout the life cycle, that is, the results of electricity price analysis.

[0034] S5: Based on the results of power generation analysis and electricity price analysis, a dynamic technical and economic evaluation of the new energy station throughout its life cycle is conducted according to the preset technical and economic evaluation process.

[0035] Specifically, firstly, the total power generation of the new energy station during its entire life cycle is calculated based on the power generation analysis results.

[0036] Then, combined with the results of the electricity price analysis, the total power generation revenue of the new energy station is calculated. When calculating the total power generation revenue, the price differences in different periods and possible revenue adjustment factors, such as electricity market transaction fees and ancillary service fees, can be considered.

[0037] Next, we can consider the construction cost of the new energy station, including the one-time investment costs such as the purchase and installation costs of power generation equipment, the construction costs of the energy storage system, and the land lease costs. At the same time, we can also calculate the operating costs, such as equipment maintenance costs, personnel management costs, and the annual operating expenses of the energy storage system's charge and discharge losses. Discount the construction costs and operating costs according to the time value to obtain the total cost present value of the entire life cycle.

[0038] Finally, the dynamic technical and economic evaluation of the new energy station over its entire life cycle can be conducted by calculating technical and economic indicators such as net present value (total power generation income present value minus total cost present value) and internal rate of return. If the net present value is greater than zero and the internal rate of return reaches the expected level, it means that the new energy station is technically and economically feasible and profitable; otherwise, it means that it is necessary to further optimize the operation strategy, adjust the energy storage system configuration, or find ways to reduce costs and increase profits to improve the overall benefits of the new energy station.

[0039] The above-mentioned dynamic evaluation method for the whole life cycle of a new energy station coupled with an energy storage system realizes a comprehensive, accurate and dynamic evaluation of the whole life cycle of a new energy station by comprehensively considering the characteristics of the coupling between the new energy station and the energy storage system, as well as the dynamic changes in the electricity spot market. This can accurately quantify the impact of energy storage and the electricity spot market on the project, and achieve a comprehensive, dynamic and accurate evaluation of the technical and economic performance of the whole life cycle of the coupling project of the new energy station and the energy storage system, providing a scientific basis for project investment decisions, planning and design, operation and management, etc., improving the overall operation efficiency and economic benefits of the project, and promoting the coordinated and sustainable development of new energy stations and energy storage systems.

[0040] refer to Figure 2 In an optional embodiment, constructing an energy storage impact factor based on energy storage data includes the following steps:

[0041] S21: Based on energy storage data, construct energy storage technology route factors, including:

[0042] Based on energy storage data, obtain characteristic data of different energy storage technology lines;

[0043] Based on the characteristic data of different energy storage technology lines, energy storage technology line factors are constructed;

[0044] Among them, the energy storage technology line factor is used to quantify the impact of different energy storage technology lines on power generation and electricity prices;

[0045] S22: Based on energy storage data, construct energy storage technology innovation factors, including:

[0046] Based on energy storage data, obtain characteristic data of energy storage technology development;

[0047] Based on the characteristic data of energy storage technology development, the uncertainty and risk of energy storage technology development are analyzed using the Monte Carlo model and learning curve model to obtain the energy storage technology innovation factor;

[0048] Among them, the energy storage technology innovation factor is used to quantify the changing trends of power generation and electricity prices under the influence of technological innovation;

[0049] S23: Construct energy storage impact factors based on energy storage technology route factors and energy storage technology innovation factors.

[0050] Specifically, step S21:

[0051] Common energy storage technology lines include liquid flow battery energy storage, long-term thermal energy storage, Carnot battery energy storage, lithium-ion battery energy storage, lead-acid battery energy storage, compressed air energy storage, flywheel energy storage, etc. For each energy storage technology line, various types of feature data related to it are extracted from the energy storage data.

[0052] For liquid flow battery energy storage: its characteristic data can be collected through the actual operation data collection of liquid flow battery energy storage projects and relevant scientific research literature, such as paying attention to the electrolyte characteristics (performance parameters of electrolytes with different compositions), battery stack power and capacity, changes in charge and discharge efficiency over time, system complexity and corresponding costs, adjustability of energy and power, storage performance decay rate, etc.

[0053] For long-term heat storage and energy storage: characteristic data such as the characteristics of the heat storage medium (heat capacity and thermal conductivity of different heat storage media), the capacity and power of the heat storage equipment, the change of charging and discharging efficiency over time, the system complexity and corresponding cost, and the storage performance decay rate can be collected (which can be obtained through actual operation project monitoring, relevant scientific research literature, etc.).

[0054] For Carnot battery energy storage: characteristic data such as the working medium characteristics of the Carnot battery (thermophysical properties of different working media, etc.), battery power and capacity, changes in charge and discharge efficiency over time, system complexity and corresponding costs, storage performance decay rate, etc. can be collected (which can be obtained through actual operation project monitoring, relevant scientific research literature, etc.).

[0055] For lithium-ion battery energy storage: data such as battery capacity, charge and discharge efficiency, cycle life, cost structure (including initial investment cost, cost loss per cycle, etc.), energy density, power density, storage performance decay rate, etc. can be obtained. These data can be obtained through monitoring equipment of lithium-ion battery energy storage systems in actual operation, or by referring to product specifications and other materials provided by relevant manufacturers.

[0056] For lead-acid battery energy storage: data can be collected on battery capacity range, charging and discharging efficiency characteristics, service life, maintenance cost frequency and amount, relative cost advantage (such as low cost characteristics in certain specific application scenarios), storage performance decay rate, etc. This can be obtained from the operating records of installed lead-acid battery energy storage systems and market research materials.

[0057] For compressed air energy storage: data on gas tank capacity and pressure parameters, performance data of air compressors and expanders (such as efficiency, power, etc.), energy loss during storage and release, construction costs and floor space requirements, storage performance attenuation rate, etc. can be obtained from the operation monitoring of compressed air energy storage demonstration projects and engineering design documents.

[0058] For flywheel energy storage: characteristic data such as the flywheel's speed range, moment of inertia, energy storage and release efficiency, mechanical losses, maintenance requirements and costs of high-speed rotating parts, system power response speed, storage performance decay rate, etc. can be collected from the operating test data of the flywheel energy storage device and the technical documents provided by the manufacturer.

[0059] By obtaining the characteristic data of different energy storage technology lines such as those mentioned above, the energy storage technology line factors can be constructed.

[0060] Regarding the impact on power generation: the impact of each energy storage technology line on the power generation of new energy sites under different operating conditions (such as different charging and discharging strategies, different power demand scenarios, etc.) can be analyzed. For example, lithium-ion battery energy storage, due to its high charging and discharging efficiency and good power density, can better cooperate with the new energy power generation system when dealing with power fluctuations and rapid charging and discharging needs in a short period of time, reduce wind and solar power abandonment, and thus increase power generation. By establishing a mathematical model, the power generation increase ratio of different energy storage technology lines in the same new energy power generation scenario is compared, and it is quantified as part of the energy storage technology line factor related to power generation.

[0061] Regarding the impact on electricity prices: the role of different energy storage technology lines in the electricity market can be considered. For example, liquid flow battery energy storage can more flexibly participate in auxiliary services such as frequency regulation and peak regulation in the electricity spot market due to its strong energy and power adjustability, which may affect the electricity price formation mechanism. By analyzing the inhibitory or promoting effects of different energy storage technology lines on electricity price fluctuations after participating in market transactions and auxiliary services, combined with market electricity price data, another part of the energy storage technology line factor related to electricity prices can be quantified.

[0062] Finally, the two factors related to power generation and electricity price are combined to form a complete energy storage technology line factor, which is used to accurately quantify the impact of different energy storage technology lines on power generation and electricity price.

[0063] Step S22:

[0064] Various characteristic data related to the development of energy storage technology can be mined from energy storage data. These data include but are not limited to:

[0065] Data on the development of new energy storage materials, such as the improvement of new material performance indicators (such as higher energy density, longer cycle life, etc.), R&D investment costs, and the progress of transformation from laboratory to industrialization. This can be obtained from research reports of scientific research institutions, technical seminar materials of related industries, etc.

[0066] Data on energy storage technology process improvements, such as cost reduction, production efficiency improvement, and product quality improvement resulting from manufacturing process optimization, are obtained by analyzing the production records and technology upgrade documents of energy storage equipment manufacturers.

[0067] Data on energy storage system integration innovation, such as performance improvements brought about by new system architecture designs (such as better energy management, more efficient charge and discharge control, etc.), changes in system costs, and collaborative innovation with other energy systems (such as new energy power generation systems), are collected from the implementation records of actual energy storage system integration projects and the release of related innovative results.

[0068] Using the Monte Carlo model: The Monte Carlo model is a method of simulating uncertainty through random sampling. Based on the characteristic data of the development of energy storage technology, the uncertain factors (such as the probability of successful research and development of new energy storage materials, the extent of cost reduction after process improvement, etc.) can be set as random variables. Then, through a large number of random sampling simulations, different possible scenarios are generated. For example, simulate the performance improvement and cost change of energy storage technology under different progress in the development of new energy storage materials. For each simulated scenario, calculate its impact on power generation and electricity prices, and finally combine the results of all simulation scenarios to obtain a preliminary distribution of the impact on power generation and electricity prices that takes into account uncertainty.

[0069] Using the learning curve model: The learning curve model is mainly used to analyze the changing patterns of cost and performance as experience accumulates (such as production experience, technology application experience, etc.). Relevant factors in the characteristic data of energy storage technology development (such as the trend of energy storage equipment cost reduction as production quantity increases, etc.) can be incorporated into the learning curve model. This model is used to analyze the impact of the downward trend in energy storage equipment costs and the upward trend in performance on power generation and electricity prices under the influence of technological innovation. For example, with the innovation of energy storage technology, the cost of energy storage equipment will decrease, which may enable more energy storage systems to be used, thereby affecting the power generation of new energy sites (such as reducing wind and solar power abandonment) and electricity prices (such as affecting market supply and demand by increasing energy storage regulation capabilities).

[0070] Finally, the analysis results of the Monte Carlo model and the learning curve model are combined and quantified into the energy storage technology innovation factor, which is used to quantify the changing trends of power generation and electricity prices under the influence of technological innovation.

[0071] Step S23:

[0072] Integrate the energy storage technology route factor and energy storage technology innovation factor obtained in the previous steps to construct the energy storage impact factor.

[0073] In terms of quantifying the impact on power generation: the impact of different energy storage technology routes on power generation in the energy storage technology route factor can be combined with the impact of technology innovation on power generation in the energy storage technology innovation factor. By establishing a suitable mathematical model (such as a weighted average model, etc.), the corresponding weights can be assigned according to the importance of different factors, and the comprehensive impact factor on power generation can be calculated as part of the energy storage impact factor.

[0074] In terms of quantifying the impact on electricity prices: Similarly, the part of the energy storage technology route factor related to electricity prices and the part of the energy storage technology innovation factor involving the impact of technological innovation on electricity prices can be combined. A similar mathematical model and weight allocation method can be used to derive a comprehensive impact factor on electricity prices as another part of the energy storage impact factor.

[0075] Ultimately, the factors affecting power generation and electricity prices will be integrated together to form a complete energy storage impact factor, which can be used for subsequent dynamic analysis of power generation and electricity prices throughout the life cycle of new energy stations and other related operations.

[0076] refer to Figure 3 ,In an optional embodiment, the electricity spot market information includes: policy documents, information disclosed by market operating institutions, electricity market historical data, primary energy price data, power grid data and new energy station data;

[0077] The construction of the power spot market influencing factors based on the power spot market data includes the following steps:

[0078] S31: Construct policy factors based on policy documents;

[0079] S32: Construct market maturity factors based on information disclosed by market operating institutions;

[0080] S33: Based on the power grid data and new energy station data, construct the power station transmission risk factor and power load distance factor;

[0081] S34: Based on the historical data of the electricity market and combined with historical meteorological data, meteorological factors are constructed;

[0082] S35: Construct primary energy price factors based on primary energy price data;

[0083] S36: Policy factors, market maturity factors, power plant transmission risk factors, power load distance factors, meteorological factors and primary energy price factors are used as factors affecting the electricity spot market;

[0084] Among them, the policy factor is used to quantify the impact of policies on new energy projects; the market maturity factor is used to quantify the degree of marketization at different stages of development of the electricity spot market; the power plant transmission risk factor is used to quantify the difficulty of new energy sites in transmitting electricity to the connected power grid; the power load distance factor is used to quantify the closeness of the supply and demand relationship between new energy sites and power loads; the meteorological factor is used to quantify the impact of meteorology on power generation, transmission capacity and power load; the primary energy price factor is used to quantify the comprehensive impact of primary energy price fluctuations on power generation and electricity prices.

[0085] Specifically, when obtaining policy documents, we can comprehensively collect policies and regulations related to the electricity spot market and the development of new energy issued by the national level, local governments, and various energy management departments. These include changes in new energy subsidy policies, relevant market access guidelines, and various types of restrictions on power production due to environmental protection requirements. For example, the new energy subsidy reduction policy will have a direct impact on the revenue estimates of new energy sites, so it is necessary to accurately analyze the key points in the policy, such as the time point of change in subsidy standards and the extent of subsidy reduction.

[0086] When obtaining information disclosed by market operating institutions, information such as detailed content of market trading rules, registration details of market entities, statistics on trading volume and trading prices, types of market transactions, and trading time plans can be obtained from the electricity market operators.

[0087] When obtaining historical data on the electricity market, transaction information on the electricity spot market in the past few years can be collected, covering the fluctuations in electricity prices in different time periods, the changing trends in trading volumes of different types of power sources, and historical records of electricity supply and demand balance in various regions.

[0088] When obtaining primary energy price data, market price information of primary energy such as coal, natural gas, and oil can be collected, including the changing trends of international and domestic market prices, price differences in different regions, and the relationship between price fluctuations and the macroeconomic environment and geopolitical factors.

[0089] When acquiring power grid data, you can obtain the topological architecture information of the power grid, such as the location of the substation, the length and capacity of the transmission line, etc.; power grid operation data, such as the power grid load rate in different time periods, power grid congestion conditions, power grid loss data, etc.; and power grid planning information, so as to understand the future expansion and upgrade plans of the power grid.

[0090] When acquiring data on new energy stations, you can obtain the installed capacity of the new energy stations, various parameters of the equipment (such as the specific model of the wind turbine, the conversion efficiency of the photovoltaic components, etc.), geographical location details (latitude and longitude, altitude, etc.), and historical data on station operation (time series data on power generation, equipment failure frequency data, operation and maintenance cost data, etc.).

[0091] Step S31:

[0092] The direction and degree of influence of various clauses in policy documents on new energy stations can be deeply analyzed. For example, if the policy encourages the development of new energy and grants land use concessions, then the corresponding positive weight should be given when constructing the policy factor; if the policy puts forward higher requirements for the grid connection standards of new energy stations and has strict punishment measures, it is necessary to consider giving a certain negative weight. Through comprehensive consideration of multiple influencing factors in the policy, the relative importance weight of each factor can be determined by using hierarchical analysis or expert scoring methods, so as to construct a quantitative policy factor that can accurately reflect the comprehensive impact of the policy.

[0093] Step S32:

[0094] Market maturity can be evaluated from multiple dimensions, such as the degree of perfection of market trading rules, the number and diversity of market participants, and the rationality of the market price formation mechanism. For example, the more detailed, transparent and fair the trading rules are, the more diverse the types of market participants are (including power generation companies, power sales companies, power users and other entities actively participating), and the price formation mechanism can fully reflect the power supply and demand relationship and cost factors, the higher the market maturity. A multi-indicator comprehensive evaluation method can be used to set corresponding evaluation indicators and weights for each dimension, and finally construct a market maturity factor.

[0095] Step S33:

[0096] The power plant transmission risk factor can be constructed by considering factors such as the length of the transmission line between the new energy station and the grid connection point, the matching degree of the line capacity and the station installed capacity, the frequency and duration of grid congestion, etc. If the transmission line is long and the capacity is relatively small, or the grid congestion is more serious, then the power plant transmission risk is higher, and a larger risk value should be reflected when constructing the factor. By establishing a transmission risk assessment model, these influencing factors can be used as input variables to output the power plant transmission risk factor.

[0097] The power load distance factor can be constructed based on the geographical distance between the new energy site and the main power load center, the regional power supply and demand balance and other factors. Generally speaking, the closer the distance to the power load center is and the greater the power demand in the region is while the supply is relatively insufficient, the closer the supply and demand relationship between the new energy site and the power load is, and the higher the value of the power load distance factor should be. The degree of correlation between the two is calculated by combining power supply and demand data through geographic information system (GIS) technology, thereby determining the quantitative value of this factor.

[0098] Step S34:

[0099] The correlation between historical meteorological data (such as light intensity, wind speed, temperature, precipitation, etc.) and historical data of the electricity market (power generation, electricity price) can be analyzed. For example, light intensity and wind speed directly affect photovoltaic power generation and wind power generation, while too high or too low temperature may affect the efficiency of power generation equipment, and precipitation may cause equipment failure or damage to transmission lines. By establishing a meteorological-power generation-electricity price association model, the influence coefficient of meteorological factors on power generation and electricity price is determined, and then the meteorological factors are constructed. For example, in a season with abundant wind resources, an increase in wind power generation may lead to a decrease in market electricity prices, so this negative correlation should be considered when constructing meteorological factors.

[0100] Step S35:

[0101] The transmission mechanism between primary energy price fluctuations and electricity production costs, power generation enterprise quotation strategies, and electricity market prices can be studied. When primary energy prices rise, the power generation costs of traditional thermal power companies increase, which may increase quotations, thereby affecting the electricity price level in the power market, and also changing the relative competitiveness of new energy sites in the market. By establishing a primary energy price-electricity cost-electricity price correlation model, the comprehensive impact of primary energy price changes on the power generation of new energy sites (such as changes in market share caused by changes in electricity prices, which in turn affect power generation) and electricity prices (the impact of market electricity price linkage) is analyzed, and a primary energy price factor is constructed.

[0102] Step S36:

[0103] The above-mentioned individually constructed factors are integrated to form a complete power spot market influencing factor. These factors quantify the impact of various factors in the power spot market on the power generation and electricity price of new energy stations from different angles.

[0104] In the subsequent dynamic analysis of the power generation and electricity prices of the new energy stations throughout their life cycle, the electricity spot market influencing factors will serve as important input parameters to help accurately evaluate the operation of new energy stations under different market environments and conditions.

[0105] In an optional embodiment, constructing a policy factor based on a policy file includes the following steps:

[0106] Obtain the policy level corresponding to each policy document, and set the level coefficient corresponding to the policy document according to the policy level;

[0107] Based on the historical data of the electricity market, we obtain the time lag from the introduction of different policies to their actual impact on new energy projects, conduct quantitative analysis on the time lag, and obtain the lag factor corresponding to each policy document;

[0108] Extract keywords from policy documents and convert them into quantitative data; analyze the relationship between quantitative data and market installed capacity, market power generation, and market electricity prices, and establish a policy-based forecast model for installed capacity, a policy-based forecast model for power generation, and a policy-based forecast model for electricity prices based on the relationship;

[0109] The policy factors are constructed based on the grade coefficient, lag factor, the policy prediction model for installed capacity, the policy prediction model for power generation and the policy prediction model for electricity price.

[0110] Specifically, the policy documents cover various laws, regulations, plans, subsidy policies and other text materials related to renewable energy power generation and the electricity market issued by the national, provincial and municipal levels.

[0111] In-depth analysis of the various clauses in the policy documents, extracting key information such as the average cost of power generation projects in the corresponding provinces and regions, unified pricing data, capacity electricity price data, installed capacity data, industrial distribution, national economic development data, electricity market reform measures, time-of-use electricity price policy, transmission and distribution price policy, capacity electricity price policy, energy storage policy, etc. The level coefficient is set according to the policy release level (national, provincial, and municipal), which can be 1, 0.5, and 0.25 respectively, and the lag factor is determined according to the nature of the policy (encouragement or constraint). The lag factor can be a numerical value, for example, the national level is set to 0.25, the provincial level is set to 0.5, and the municipal level is set to 1. The general equilibrium CGE model is used to simulate the comprehensive impact of economic policy changes on the supply and demand, price, industrial structure and other aspects of the electricity market, and the lag effect panel model is used to quantify the lag time and impact change curve of different policies based on historical data. The PMC index is constructed through system dynamics, and text mining technology is used to extract policy keywords and assign weights (the weight of keywords about encouragement is +1, and the weight of keywords about constraints is -1). A quantitative relationship model between energy policy and electricity market installed capacity, power generation, and electricity prices is established, and finally integrated to form policy factors.

[0112] In an optional embodiment, constructing a market maturity factor based on information disclosed by a market operation agency includes the following steps:

[0113] Determine the market operation stage based on the information disclosed by the market operation agency; set corresponding stage factors for different operation stages based on preset rules;

[0114] Based on the information disclosed by the market operating institutions, obtain the installed capacity of power stations participating in the electricity spot market, the installed capacity of all power stations, the electricity load participating in the electricity spot market and the total electricity load capacity;

[0115] Based on the ratio of installed capacity of power stations participating in the electricity spot market to the installed capacity of all power stations and the ratio of power generation of power stations participating in the electricity spot market to the power generation of all power stations, a marketization degree factor of power stations is constructed;

[0116] Based on the ratio of the electricity load participating in the electricity spot market to the total electricity load capacity and the ratio of the number of electricity loads participating in the electricity spot transaction to the total electricity load quantity, a factor of the marketization degree of electricity load is constructed;

[0117] The market maturity factor is constructed based on the stage factor, the power station marketization degree factor and the electricity load marketization degree factor.

[0118] Specifically, the information disclosed by market operating institutions includes real-time or periodic summary information such as the trading rules of the electricity market, operating hours, trading data of each participant, and market capacity.

[0119] The current operation stage of the market is accurately determined from the information disclosed by the market operation institutions, such as not yet in trial operation, simulated trial operation, power adjustment trial operation, settlement trial operation, formal operation, etc., and the stage factor is set accordingly. The stage factor can be expressed as a numerical value. For example, the stage factor of the stage of not yet in trial operation is 0, the stage factor of the stage of simulated trial operation is 0.01, the stage factor of the stage of power adjustment trial operation is 0.015, and the stage factor of the stage of settlement trial operation is The stage factor of the formal operation stage is 1. Calculate the marketization degree factor of the power station, that is, the ratio of the installed capacity of the power station participating in the electricity spot market to the installed capacity of all power stations in a province, and the ratio of the power generation of the power station participating in the electricity spot market to the power generation of all power stations in a province. For the marketization degree factor of the electricity load, calculate a1 = the ratio of the power load participating in the electricity spot market to the total power load capacity of a province (or a region) and a2 = the ratio of the number of power loads participating in the electricity spot transaction to the total number of power loads in a province, and combine the two to determine the marketization degree of the power load. Considering the key position of the power load side in the market maturity assessment, give it a greater weight. The marketization degree data of different provinces are normalized, and the development trend of the future marketization degree of the same power market is derived based on historical electricity price data through data mining and similarity analysis technology to construct a market maturity factor.

[0120] In an optional embodiment, based on the power grid data and the new energy station data, a power station transmission risk factor is constructed, including:

[0121] S3311: Obtain the site selection assessment results of the power station, including:

[0122] Based on the power grid data, the power flow of the existing power grid is analyzed to obtain the blocking probability distribution of the power station nodes;

[0123] The site selection of the power station is evaluated based on the distribution of the blocking probability, and the site selection evaluation result of the power station is obtained;

[0124] S3312: Construct node transmission risk factors, including:

[0125] Based on the data of new energy stations, obtain the transmission capacity of the grid node lines connected to the power station and the installed capacity of the power station;

[0126] The node transmission risk factor is constructed based on the ratio of the transmission capacity of the grid node line to which the power station is connected to and the installed capacity of the power station.

[0127] S3313: Obtaining power generation homogeneity factors, including:

[0128] Based on the data of new energy stations, the historical power generation time series data of all new energy stations of the power grid nodes connected to the new energy stations are obtained, and the power generation curve of the power station is obtained according to the historical power generation time series data;

[0129] Based on the total power generation time series data within the power grid node range, the power generation curves of each power station are analyzed for similarity to obtain the power generation homogeneity factor.

[0130] S3314: Construct transmission failure factors, including:

[0131] Based on the power grid data, the structural types of the power grid nodes where the new energy stations are located are classified;

[0132] Configure the corresponding initial average system availability index for different types of structures, correct the initial average system availability index according to the new energy station data, and obtain the actual average system availability index;

[0133] Obtain historical fault data based on the data of the new energy station, and calculate the ratio of the number of faults to the total number of faults of the power grid node where the new energy station is located according to the historical fault data;

[0134] The transmission fault factor is constructed based on the ratio of the actual average system availability index and the number of faults to the total number of faults of the grid node where the new energy station is located;

[0135] S3315: Construct power plant transmission risk factors, including:

[0136] The power plant transmission risk factor is constructed based on the site selection assessment results of the power station, the node transmission risk factor, the generation homogeneity factor and the transmission failure factor.

[0137] Specifically, the grid data includes the grid topology, transmission capacity of each node, historical flow data, fault records, etc., which are extracted by the grid company through interfaces or internal data systems. The new energy station data covers the station's installed capacity, geographical location, equipment parameters, historical power generation efficiency, operation and maintenance records, etc., which are summarized from the station's own operation management system and early construction materials.

[0138] First, the flow calculation is carried out, and the flow distribution of the existing power grid is simulated and analyzed using professional power system analysis software to obtain the blocking probability distribution data of each power station node, which is used to evaluate the rationality of the station selection. The node transmission risk factor is calculated, that is, the ratio of the transmission capacity of the power grid node line connected to the power station to the installed capacity of the power station. According to the voltage level of the power plant station, the voltage level of the provincial backbone network is investigated upward, and a maximum of three levels are investigated. The proportional relationship of the associated upper-level nodes is evaluated in a proportional manner. Based on the historical power generation time series data of all power stations in the power grid node connected to the power station, normalization is first performed to make the data in the same dimension, and then cluster analysis or curve fitting algorithm is used to perform similarity analysis on the power generation curve. The total power generation time series data within the node range is used as a benchmark to determine the power generation homogeneity factor of each station. The types of power grid nodes where the power station is located are classified (radial, ring, tree, mesh, hybrid, bus, star, etc.), and the corresponding average system availability index is configured according to the reliability theoretical model of different structural types. The ASAI is calculated based on historical data, and the historical data of relevant power generation stations and power grid failures in the power grid nodes connected to the power station are collected. The ratio of the number of failures to the number of node failures is calculated by time period, and the probability distribution function of the transmission failure factor is constructed. The transmission risk factor of the power station is obtained by combining the above results.

[0139] In an optional embodiment, based on the power grid data and the new energy station data, a distance factor of the power load is constructed, including:

[0140] S3321: Construct load concentration factors, including:

[0141] Based on the data of new energy stations, the concentration of power load in the grid node area is evaluated to obtain the concentration evaluation results;

[0142] Perform weighted calculation on the transmission line impedance between each independent power load and the new energy station to obtain a load impedance weighted value;

[0143] Based on the concentration evaluation results and the load impedance weighted value, the load concentration factor is constructed;

[0144] S3322: Construct electricity intensity factors, including:

[0145] Constructing an electricity intensity factor based on the ratio of the total electricity load of the area directly connected to the grid node to which the new energy station is connected to and the installed capacity of the new energy station connected to the grid node;

[0146] S3323: Construct the distance factor of the power load, including:

[0147] Based on the load concentration factor and the power intensity factor, the distance factor of the power load is constructed.

[0148] Specifically, the grid data includes the grid topology, transmission capacity of each node, historical flow data, fault records, etc., which are extracted by the grid company through interfaces or internal data systems. The new energy station data covers the station's installed capacity, geographical location, equipment parameters, historical power generation efficiency, operation and maintenance records, etc., which are summarized from the station's own operation management system and early construction materials.

[0149] Calculate the power intensity, that is, the ratio of the total power load of the area directly connected to the grid node connected to the power station to be investigated to the installed capacity of the power station connected to the node (the power generation time series data is used for dynamic evaluation). According to the voltage level of the power plant, the voltage level of the provincial backbone network is investigated upward, and a maximum of three levels are investigated. Different weights are set for the associated nodes and the upper nodes, and the more directly connected, the higher the weight. Use the HHI index to evaluate the concentration of power load in the node area, obtain the impedance data of the transmission line between the power load and the power plant through the geographic information system, and perform weighted calculation to comprehensively obtain the power load distance factor.

[0150] In an optional embodiment, based on the historical data of the electricity market and in combination with the historical meteorological data, a meteorological factor is constructed, including:

[0151] Based on the historical data of the power market, obtain the time series data of power market power generation, power grid transmission capacity and power load;

[0152] The power market power generation time series data and meteorological historical data are correlated to obtain the power generation influencing factors; the power grid transmission capacity time series data and meteorological historical data are correlated to obtain the transmission capacity influencing factors; the power load time series data and meteorological historical data are correlated to obtain the power load influencing factors;

[0153] Meteorological factors are constructed based on the factors affecting power generation, transmission capacity and power load.

[0154] Specifically, the historical data of the electricity market involves the power generation, electricity consumption, electricity price change curves, electricity load data in different time periods, etc. in the past years.

[0155] Comprehensively collect time series data such as temperature, wind speed, precipitation, etc. in historical meteorological data, and classify precipitation data into three types: rainfall, snowfall, and others. In-depth analysis of temperature data, including characteristics such as average temperature, temperature drop amplitude, and temperature time change rate, while considering the probability distribution function of extreme weather, combined with the distribution characteristics of wind and solar resources in different provinces and regions, and the differential impact of different time periods (high wind month, low wind month, flood season, dry season, heating season, summer supply period, etc.). Using the data association analysis algorithm, the historical meteorological data is correlated with the existing power market power generation time series data to determine the impact coefficient of meteorological data on power generation; the correlation mining is carried out with the power grid transmission capacity time series data to obtain the impact weight of meteorological data on transmission capacity; the fitting analysis is carried out with the power load time series data to obtain the impact relationship of meteorological data on power load, and a meteorological factor model is constructed to predict the trend of power generation, transmission capacity, and power load changes under future meteorological conditions.

[0156] In an optional embodiment, a primary energy price factor is constructed based on primary energy price data, including:

[0157] Based on the primary energy price data, obtain the changing pattern of the consumption ratio of primary energy to total energy over time, and set the time series factor of the primary energy consumption ratio based on the changing pattern;

[0158] Based on the primary energy price data, the degree of dependence of different types of new energy stations on primary energy is obtained, and the weight time series factor of the power station type is set based on the degree of dependence;

[0159] Based on the time series factor of primary energy consumption proportion and the time series factor of power station type weight, the primary energy price factor is constructed.

[0160] Specifically, we will collect data on the price of primary energy such as coal, natural gas, and oil from different provinces and regions, and build a database of the correlation between primary energy and the electricity market that is suitable for the energy structure of the region. Set the time series factor of the proportion of primary energy consumption, and dynamically adjust it according to the changes in the consumption proportion of each primary energy in different periods; determine the time series factor of the weight of the power station type, taking into account the different degrees of dependence on primary energy by different types of stations such as thermal power, wind power, and photovoltaic power. Use multiple linear regression or neural network algorithms to conduct in-depth correlation analysis between these factors and the historical time series data of electricity market electricity and prices, determine the transmission coefficient matrix of primary energy prices and electricity market electricity load and electricity prices, and construct a primary energy price factor to predict the comprehensive impact of primary energy price fluctuations on electricity and prices during the life cycle of the power station.

[0161] In an optional embodiment, based on the energy storage influencing factor and the electricity spot market influencing factor, the power generation and electricity price of the new energy station throughout its life cycle are dynamically analyzed to obtain power generation analysis results and electricity price analysis results, including:

[0162] S41: Multi-factor integration step:

[0163] Summarize energy storage impact factors, policy factors, market maturity factors, power plant transmission risk factors, power load distance factors, meteorological factors and primary energy price factors to obtain a historical comprehensive factor data set;

[0164] S42: Dynamic analysis steps of power generation:

[0165] Obtaining historical power generation time series data based on new energy station data, and using the historical power generation time series data to build a first quantitative analysis model integrating a neural network and an artificial intelligence algorithm; wherein the neural network and artificial intelligence algorithm in this step include LSTM and Transformer models;

[0166] Inputting the historical comprehensive factor data set into the first quantitative analysis model, training the quantitative analysis model, and obtaining a trained power generation prediction model;

[0167] According to different time periods, the real-time comprehensive factor data set is input into the trained power generation prediction model to calculate the power generation prediction values ​​of different time periods in the whole life cycle of the new energy station, forming the dynamic analysis results of power generation;

[0168] S43: Steps for dynamic analysis of electricity prices:

[0169] Obtain historical electricity price time series data based on new energy station data, and use the historical electricity price time series data to build a second quantitative analysis model that integrates a neural network and an artificial intelligence algorithm; wherein the neural network and artificial intelligence algorithm in this step include a CNN-LSTM model, a Transformer model, or an ARIMA-SSA-LSTM combination model;

[0170] Inputting the historical comprehensive factor data set into the second quantitative analysis model, training the quantitative analysis model, and obtaining a trained electricity price prediction model;

[0171] According to different time periods, the real-time comprehensive factor data set is input into the trained electricity price prediction model to calculate the electricity price forecast values ​​for different time periods in the entire life cycle of the new energy station, forming a dynamic analysis result of power generation.

[0172] Specifically, based on the analysis of new energy resources (such as wind energy resource assessment for wind power and light resource assessment for photovoltaic power, including the geographical location, topography, climate profile, meteorological station data, selection of representative meteorological stations, analysis of basic meteorological elements, theoretical annual power generation calculation and other data obtained in the early stage), the above-mentioned energy storage influencing factors and electricity spot market influencing factors (policy factors, market maturity factors, power station transmission risk factors, power load distance factors, meteorological factors, and primary energy price factors) are fully integrated into the analysis model. Using existing historical data (including historical time series data such as power generation of power stations, power grid transmission capacity, and power load), according to different time periods (year, quarter, month, ten days, week, day, etc.), a dynamic analysis model of electricity consumption is constructed that integrates neural networks and artificial intelligence algorithms (such as LSTM or Transformer models). The LSTM model can effectively capture long-term dependencies in time series data through its memory units, while the Transformer model uses a multi-head attention mechanism to better handle the complex interactions between multiple factors. It dynamically calculates the market trading electricity and station power generation throughout the life cycle of the station, obtains the power generation forecast curve and fluctuation range under different time periods, and forms the power generation analysis results.

[0173] Based on the above multi-factor fusion, the existing historical electricity price time series data of the electricity market is used to build an electricity price dynamic analysis model that integrates neural networks and artificial intelligence algorithms (such as CNN-LSTM, Transformer model or ARI MA-SSA-LSTM combination model), and the TGARCH-Vi neCopula model is used to predict electricity market risks. In the CNN-LSTM model, the CNN part extracts the local features of the electricity price data, and the LSTM part processes the long-term dependencies on the time series; the Transformer model processes the complex relationship between multiple factors and electricity prices; the ARI MA-SSA-LSTM combination model combines the processing ability of the autoregressive moving average model (ARIMA) for linear relationships, the advantages of singular spectrum analysis (SSA) for data denoising and trend extraction, and the modeling ability of LSTM for nonlinear relationships. Through the multi-factor electricity price dynamic calculation model to process time series data, the market electricity price and station node electricity price in the entire life cycle of the station are dynamically analyzed, and the electricity price forecast values ​​and risk assessment indicators under different time periods are obtained to form the electricity price analysis results.

[0174] In an embodiment of the present application, a method for dynamic evaluation of the whole life cycle of a new energy station coupled with an energy storage system is provided. The method obtains the energy storage data of the energy storage system coupled with the new energy station and the electricity spot market data in which the new energy station participates, and respectively constructs energy storage influencing factors (covering energy storage technology route factors, energy storage technology innovation factors, etc.) and electricity spot market influencing factors (including policy factors, market maturity factors, and other specific factors), and then integrates multiple factors based on these influencing factors, and uses a fusion neural network and an artificial intelligence algorithm to construct a quantitative analysis model to dynamically analyze and predict the power generation and electricity price of the new energy station throughout its life cycle. The technical effect achieved by this method is that it can comprehensively and dynamically evaluate the power generation and electricity price of the new energy station throughout its life cycle, and comprehensively considers the impact of various factors of the energy storage system and the electricity spot market on the new energy station, thereby providing a scientific, accurate and forward-looking basis for the planning, operation, and economic evaluation of the new energy station.

[0175] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0176] An embodiment of the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0177] The embodiments of the present application further provide a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0178] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial description of the method embodiments. The device embodiments described above are only schematic, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0179] The above-mentioned embodiments only express several implementation methods of the embodiments of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the embodiments of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the embodiments of the present application, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A dynamic evaluation method for the whole life cycle of a new energy station coupled with an energy storage system, characterized in that: The method comprises: S1: Based on the energy storage system coupled with the new energy station, energy storage data is obtained; based on the power spot market in which the new energy station participates, power spot market data is obtained; S2: constructing an energy storage impact factor based on the energy storage data, wherein the energy storage impact factor is used to quantify changes in power generation and electricity prices caused by energy storage; S3: constructing an electricity spot market impact factor based on the electricity spot market data, wherein the electricity spot market impact factor is used to quantify changes in power generation and electricity prices caused by the electricity spot market; S4: Based on the energy storage influencing factor and the electricity spot market influencing factor, dynamically analyze the power generation and electricity price of the new energy station throughout its life cycle to obtain power generation analysis results and electricity price analysis results; S5: Based on the power generation analysis results and the electricity price analysis results, a dynamic technical and economic evaluation of the new energy station over its entire life cycle is performed according to a preset technical and economic evaluation process.

2. The method according to claim 1, characterized in that The constructing the energy storage impact factor based on the energy storage data includes: S21: Based on the energy storage data, construct energy storage technology route factors, including: Based on the energy storage data, obtaining characteristic data of different energy storage technology lines; Based on the characteristic data of each of the different energy storage technology lines, construct an energy storage technology line factor; The energy storage technology line factor is used to quantify the impact of different energy storage technology lines on power generation and electricity prices; S22: Based on the energy storage data, construct energy storage technology innovation factors, including: Based on the energy storage data, acquiring characteristic data of energy storage technology development; Based on the characteristic data of the energy storage technology development, the uncertainty and risk of the energy storage technology development are analyzed using the Monte Carlo model and the learning curve model to obtain the energy storage technology innovation factor; The energy storage technology innovation factor is used to quantify the changing trend of power generation and electricity prices under the influence of technological innovation; S23: Constructing the energy storage impact factor based on the energy storage technology route factor and the energy storage technology innovation factor.

3. The method according to claim 2, characterized in that The electricity spot market information includes: Policy documents, information disclosed by market operators, historical data on the electricity market, primary energy price data, power grid data, and new energy station data; The constructing of the power spot market impact factor based on the power spot market data includes: S31: constructing a policy factor based on the policy document; S32: constructing a market maturity factor based on the information disclosed by the market operating institution; S33: constructing a power transmission risk factor of a power station and a power load distance factor based on the power grid data and the new energy station data; S34: constructing meteorological factors based on the electricity market historical data and in combination with historical meteorological data; S35: constructing a primary energy price factor based on the primary energy price data; S36: taking the policy factor, the market maturity factor, the power plant transmission risk factor, the power load distance factor, the meteorological factor and the primary energy price factor as the power spot market influencing factors; Among them, the policy factor is used to quantify the impact of policies on new energy projects; the market maturity factor is used to quantify the degree of marketization at different stages of development of the electricity spot market; the power station transmission risk factor is used to quantify the difficulty of new energy sites in transmitting electric energy to the connected power grid; the power load distance factor is used to quantify the closeness of the supply and demand relationship between new energy sites and power loads; the meteorological factor is used to quantify the impact of meteorology on power generation, transmission capacity and power load; the primary energy price factor is used to quantify the comprehensive impact of primary energy price fluctuations on power generation and electricity prices.

4. The method according to claim 3, characterized in that The step of constructing a policy factor based on the policy document includes: Obtaining the policy level corresponding to each of the policy documents, and setting the level coefficient corresponding to the policy document according to the policy level; Based on the historical data of the electricity market, the time lag from the introduction of different policies to their actual impact on new energy projects is obtained, and the time lag is quantitatively analyzed to obtain the lag factor corresponding to each policy document; Extract keywords from the policy document and convert the keywords into quantitative data; analyze the relationship between the quantitative data and market installed capacity, market power generation, and market electricity price, and establish a policy-to-installation capacity prediction model, a policy-to-power generation prediction model, and a policy-to-electricity price prediction model based on the relationship; The policy factor is constructed based on the grade coefficient, the hysteresis factor, the prediction model of the policy on installed capacity, the prediction model of the policy on power generation, and the prediction model of the policy on electricity price.

5. The method according to claim 3, characterized in that: The market maturity factor is constructed based on the information disclosed by the market operating institution, including: Determine the market operation stage according to the information disclosed by the market operation institution; set corresponding stage factors for different operation stages based on preset rules; Based on the information disclosed by the market operating institution, obtain the installed capacity of power stations participating in the electricity spot market, the installed capacity of all power stations, the electricity load participating in the electricity spot market and the total electricity load capacity; Constructing a power station marketization degree factor based on the ratio of the installed capacity of the power station participating in the power spot market to the installed capacity of all power stations and the ratio of the power generation of the power station participating in the power spot market to the power generation of all power stations; Based on the ratio of the power load participating in the power spot market to the total power load capacity and the ratio of the number of power loads participating in the power spot transaction to the total power load quantity, construct a power load marketization degree factor; The market maturity factor is constructed based on the stage factor, the power plant marketization degree factor and the electricity load marketization degree factor.

6. The method according to claim 3, characterized in that: The step of constructing a power station transmission risk factor based on the power grid data and the new energy station data includes: S3311: Obtain the site selection assessment results of the power station, including: Based on the power grid data, the power flow of the existing power grid is analyzed to obtain the blocking probability distribution of the power station nodes; Based on the blocking probability distribution, the site selection of the power station is evaluated to obtain the site selection evaluation result of the power station; S3312: Construct node transmission risk factors, including: Based on the new energy station data, the transmission capacity of the grid node line connected to the power station and the installed capacity of the power station are obtained; Constructing the node transmission risk factor based on the ratio of the power transmission capacity of the power grid node line to which the power station is connected to the installed capacity of the power station; S3313: Obtaining power generation homogeneity factors, including: Based on the new energy station data, historical power generation time series data of all new energy stations of the power grid node to which the new energy station is connected are obtained, and a power generation curve of the power station is obtained according to the historical power generation time series data; Based on the total power generation time series data within the range of the power grid node, similarity analysis is performed on the power generation curves of each power station to obtain the power generation homogeneity factor; S3314: Construct transmission failure factors, including: Based on the power grid data, classifying the structural type of the power grid node where the new energy station is located; Configuring corresponding initial average system availability indexes for the structural types of different classifications, and correcting the initial average system availability indexes according to the new energy station data to obtain actual average system availability indexes; Acquire historical fault data based on the new energy station data, and calculate the ratio of the number of faults to the total number of faults of the power grid node where the new energy station is located according to the historical fault data; Constructing the power transmission failure factor based on the ratio of the actual average system availability index and the number of failures to the total number of failures of the power grid node where the new energy station is located; S3315: Constructing the power transmission risk factor of the power station, including: The power station transmission risk factor is constructed based on the power station site selection assessment result, the node transmission risk factor, the power generation homogeneity factor and the transmission failure factor.

7. The method according to claim 3, characterized in that The step of constructing a distance factor of the power load based on the power grid data and the new energy station data includes: S3321: Construct load concentration factors, including: Based on the new energy station data, a concentration evaluation is performed on the power load in the grid node area to obtain a concentration evaluation result; Performing weighted calculation on the transmission line impedance between each independent power load and the new energy station to obtain a load impedance weighted value; constructing the load concentration factor based on the concentration evaluation result and the load impedance weighted value; S3322: Construct electricity intensity factors, including: Constructing the power intensity factor based on the ratio of the total power load of the area directly connected to the power grid node to which the new energy station is connected to and the installed capacity of the new energy station to which the power grid node is connected; S3323: constructing the distance factor of the power load, including: Based on the load concentration factor and the power intensity factor, a distance factor of the power load is constructed.

8. The method according to claim 3, characterized in that The meteorological factors are constructed based on the historical data of the power market and in combination with the historical meteorological data, including: Based on the electricity market historical data, obtain the electricity market power generation time series data, the power grid transmission capacity time series data and the electricity load time series data; Performing correlation analysis on the power market power generation time series data and the meteorological historical data to obtain the power generation influencing factor; performing correlation analysis on the power grid transmission capacity time series data and the meteorological historical data to obtain the transmission capacity influencing factor; performing correlation analysis on the power load time series data and the meteorological historical data to obtain the power load influencing factor; The meteorological factor is constructed based on the power generation influencing factor, the power transmission capacity influencing factor and the power load influencing factor.

9. The method according to claim 3, characterized in that: The constructing of a primary energy price factor based on the primary energy price data includes: Based on the primary energy price data, obtain the change pattern of the consumption ratio of primary energy to total energy over time, and set the primary energy consumption ratio time series factor based on the change pattern; Based on the primary energy price data, the degree of dependence of the different types of new energy stations on primary energy is obtained, and the weight time series factor of the power station type is set based on the degree of dependence; The primary energy price factor is constructed based on the primary energy consumption proportion time series factor and the power plant type weight time series factor.

10. The method according to claim 3, characterized in that: The method of dynamically analyzing the power generation and electricity price of the new energy station over the entire life cycle based on the energy storage influencing factor and the power spot market influencing factor to obtain power generation analysis results and electricity price analysis results includes: S41: Multi-factor integration step: Summarizing the energy storage impact factor, the policy factor, the market maturity factor, the power plant transmission risk factor, the power load distance factor, the meteorological factor and the primary energy price factor to obtain a historical comprehensive factor data set; S42: Dynamic analysis steps of power generation: Acquire historical power generation time series data based on the new energy station data, and use the historical power generation time series data to build a first quantitative analysis model integrating a neural network and an artificial intelligence algorithm; wherein the neural network and the artificial intelligence algorithm include LSTM and Transformer models; Inputting the historical comprehensive factor data set into the first quantitative analysis model, training the quantitative analysis model, and obtaining a trained power generation prediction model; According to different time periods, the real-time comprehensive factor data set is input into the trained power generation prediction model, and the power generation prediction values ​​of different time periods in the whole life cycle of the new energy site are calculated to form the power generation dynamic analysis results; S43: Steps for dynamic analysis of electricity prices: Based on the new energy station data, historical electricity price time series data is obtained, and the historical electricity price time series data is used to construct a second quantitative analysis model integrating a neural network and an artificial intelligence algorithm; wherein the neural network and the artificial intelligence algorithm include a CNN-LSTM model, a Transformer model or an ARIMA-SSA-LSTM combination model; Inputting the historical comprehensive factor data set into the second quantitative analysis model, training the quantitative analysis model, and obtaining a trained electricity price prediction model; According to different time periods, the real-time comprehensive factor data set is input into the trained electricity price prediction model, and the electricity price prediction values ​​for different time periods in the entire life cycle of the new energy site are calculated to form the dynamic analysis results of the power generation.

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