Power plant station full life cycle dynamic technology economic evaluation method

Abstract

The invention relates to a power station full-life-cycle dynamic technical economic evaluation method, device and equipment and a medium. The method comprises the following steps: setting a quantification factor, obtaining historical data of a station in a specific region of a corresponding province, and carrying out dynamic analysis on an officially operated electric power spot market based on the data and the quantification factor so as to predict the electric quantity and the electricity price of the station in a full life cycle. And on the basis of the prediction data, constructing an electric power spot market feature number, and using a feature equation to solve to predict the electric quantity and the electricity price of the electric power spot market in the pilot run stage to obtain the prediction data of the corresponding pilot run stage. And finally, performing full-life-cycle dynamic technical and economic evaluation on the electric power spot market based on the prediction data in the trial operation stage to obtain a comprehensive technical and economic evaluation result. According to the method, the influence of the power station participating in the commissioning stage of the electric power spot market on the income of the power station is considered, and dynamic technical and economic evaluation of the whole life cycle of the electric power spot market is realized.

Description

Technical Field

[0001] The present invention belongs to the field of electric power economic evaluation, and in particular relates to a method, device, computer equipment and storage medium for dynamic technical and economic evaluation of a power station throughout its life cycle. Background Art

[0002] With the development of power economic evaluation technology, a dynamic technical and economic evaluation technology for the entire life cycle of power plants has emerged, whose operation period includes the trial operation stage of the power spot market. One of the core methods of technical and economic evaluation of existing power plants is to evaluate the power plant's electricity fee income. The operation period of existing power plant projects usually includes the trial operation stage of the power spot market. Under the conditions of the power spot market, the power plant's electricity volume and electricity price fluctuations are highly correlated with market transactions. However, in the analysis of electricity prices and electricity volume in the technical and economic evaluation of the project, the impact of the power plant's participation in the trial operation stage of the power spot market on the power plant's income has not yet been considered. Summary of the invention

[0003] Based on this, it is necessary to provide a dynamic technical and economic evaluation method, device, computer equipment and storage medium for the entire life cycle of a power station in the trial operation stage, which can take into account the impact of the power station's participation in the trial operation stage of the electricity spot market on the power station's revenue in the analysis of electricity prices and electricity volume in the project's technical and economic evaluation, in response to the above-mentioned technical problems.

[0004] In the first aspect, the present application provides a method for dynamic technical and economic evaluation of a power station throughout its life cycle, including:

[0005] Set quantitative factors; quantitative factors include policy factors, electricity market maturity factors, meteorological factors, primary energy price factors, and energy storage impact factors.

[0006] Obtain the existing historical data of specific stations in the corresponding areas of the corresponding provinces; conduct dynamic analysis on the officially operating electricity spot market based on quantitative factors and historical data to obtain the predicted electricity volume and predicted electricity price data for the entire life cycle of the station.

[0007] The characteristic numbers of the electricity spot market are constructed according to the quantitative factors and the predicted electricity quantity and electricity price data of the station throughout its life cycle. Based on the characteristic numbers, the characteristic equations of the electricity quantity and electricity price in the electricity spot market during the trial operation phase are solved to obtain the predicted electricity quantity and electricity price data of the electricity spot market.

[0008] Based on the forecasted electricity volume and forecasted electricity price data of the target power market in the trial operation phase obtained by solving the characteristic equation, a dynamic technical and economic evaluation of the power spot market over the entire life cycle is carried out to obtain the dynamic technical and economic evaluation results of the entire life cycle.

[0009] In one embodiment, the policy factor is set by:

[0010] Policy documents are divided into different levels, and corresponding level coefficients are set to obtain level coefficients corresponding to policy documents of different levels; the levels of policy documents include national, provincial, and municipal levels, and the level coefficients are 1, 0.5, and 0.25, respectively.

[0011] The lag effect panel model is used to quantitatively analyze the lag effects of policy documents of different levels, and the lag factors corresponding to policy documents of different levels are obtained; the lag factors corresponding to national, provincial, and municipal policy documents are 0.25, 0.5, and 0.25, respectively.

[0012] Text mining is used to extract keywords from policy documents of different levels to obtain the support attitudes of different policy documents towards the power market; support attitudes include encouragement or restriction, and the corresponding coefficients are +1 and -1 respectively.

[0013] In one embodiment, the electricity market maturity factor is set by:

[0014] Obtain market operation information.

[0015] Determine the market operation stage based on market operation information and set the corresponding market stage factor.

[0016] If the market operation is not yet in the trial operation stage, the corresponding market stage factor is 0.

[0017] If the market operation is in the simulation trial operation stage, the corresponding market stage factor is 0.01.

[0018] If the market operation is in the power regulation trial operation stage, the corresponding market stage factor is 0.015.

[0019] If the market operation is in the settlement trial operation stage, the corresponding market stage factor expression is min(0.9, continuous trial operation days / max(number of operation days in the same period, 366)).

[0020] If the market operation is in the formal operation stage, the corresponding market stage factor is 1.

[0021] A power station marketization degree factor is set, and the power station marketization degree factor includes at least one of the installed capacity of power stations participating in the electricity spot market, the installed capacity of all power stations in a province, the power generation of power stations participating in the electricity spot market, and the power generation of all power stations in a province.

[0022] A factor for the degree of marketization of electricity load is set, and the factor for the degree of marketization of electricity load includes at least one of the electricity load participating in the electricity spot market, the total electricity load of a province, and the capacity and quantity of the electricity load participating in the electricity spot transaction.

[0023] In one embodiment, the meteorological factor is set by:

[0024] Obtain historical meteorological data, power generation time series data, and power load time series data in a specific area in the electricity spot market.

[0025] Based on the correlation analysis of historical meteorological data and power generation time series data, the impact factors of meteorological data on power generation are obtained.

[0026] The prediction method using meteorological data is used to predict future power generation based on influencing factors to obtain the future power generation prediction results.

[0027] Based on the correlation analysis of historical meteorological data and electricity load time series data, the impact factors of meteorological data on electricity load are obtained.

[0028] The prediction method using meteorological data is used to predict the future power load based on the influencing factors to obtain the future power load prediction results.

[0029] In one embodiment, the primary energy price factor is set by:

[0030] Set the correlation factor between primary energy price and electricity market electricity load and electricity price.

[0031] Based on the correlation factors, a correlation model between primary energy and electricity market applicable to the energy structure of different provinces and regions is constructed; the correlation model includes a price prediction model for primary energy and a primary energy price factor.

[0032] Set the timing factor of primary energy consumption proportion and the timing factor of power station type weight.

[0033] Based on the time series factors of primary energy consumption proportion and power plant type weight, a correlation analysis is conducted on the historical time series data of electricity market electricity volume and price. The price prediction model of primary energy and primary energy price factors are used to obtain the predicted electricity volume and predicted price for the entire life cycle of the power plant.

[0034] In one embodiment, the energy storage impact factor is set by:

[0035] Construct energy storage technology route factors for different energy storage technology routes to participate in the electricity spot market.

[0036] Based on the energy storage technology route factors, projects combining independent energy storage power stations, shared energy storage power stations, wind farms, photovoltaic power stations, and thermal power plants are set up to obtain different influencing factors.

[0037] Based on the influencing factors, a quantitative analysis is conducted on the power generation, electricity price, carbon emissions and carbon price brought by energy storage, and the corresponding quantitative analysis results are obtained.

[0038] Based on the results of quantitative analysis, the energy storage technology innovation factor is constructed, and the technical and economic feasibility, energy storage capacity, time span and other parameters of the energy storage project are quantitatively analyzed to obtain the corresponding change trends under the influence of technological innovation.

[0039] In one embodiment, a dynamic analysis of the officially operating power spot market is performed based on quantitative factors and historical data to obtain the predicted power and electricity price data for the entire life cycle of the station, including:

[0040] Based on the historical data of power generation in corresponding provinces, regions and specific stations, a quantitative analysis model integrating neural networks and artificial intelligence algorithms is constructed according to different time periods to obtain different time series data for corresponding stations.

[0041] Based on the time series data, a multi-factor electricity quantity dynamic calculation model and an electricity price dynamic calculation model are processed, and the market trading electricity quantity, station power generation, market electricity price and station node electricity price throughout the life cycle of the station are dynamically analyzed to obtain the predicted electricity quantity and predicted electricity price data for the entire life cycle of the station.

[0042] Dynamic analysis includes dynamic analysis of power generation of new energy power stations, dynamic analysis of power generation of thermal power plants and dynamic analysis of electricity prices.

[0043] The dynamic analysis of power generation of new energy power stations is based on the analysis of new energy resources, coupled with policy factors, electricity spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors to obtain a multi-factor electricity dynamic calculation model.

[0044] The dynamic analysis of thermal power plant power generation takes the supporting role of thermal power plants participating in electricity spot trading in the new power system as the analysis basis, coupling policy factors, electricity spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors to obtain a multi-factor electricity dynamic calculation model.

[0045] The dynamic analysis of electricity prices is based on the power generation form of power plants, installed capacity, coupling policy factors, electricity spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors to obtain a multi-factor electricity price dynamic calculation model.

[0046] In the second aspect, the present application also provides a device for dynamic technical and economic evaluation of the entire life cycle of a power station, including:

[0047] The factor setting module is used to set quantitative factors; quantitative factors include policy factors, electricity market maturity factors, meteorological factors, primary energy price factors, and energy storage impact factors.

[0048] The data acquisition module is used to obtain the existing historical data of specific stations in the corresponding areas of the corresponding provinces; based on the quantitative factors and historical data, the officially operated electricity spot market is dynamically analyzed to obtain the predicted electricity volume and predicted electricity price data for the entire life cycle of the station; the characteristic number of the electricity spot market is constructed according to the quantitative factors and the predicted electricity volume and predicted electricity price data for the entire life cycle of the station, and based on the characteristic number, the characteristic equation of the electricity volume and electricity price in the electricity spot market during the trial operation stage is solved to obtain the predicted electricity volume and predicted electricity price data for the electricity spot market.

[0049] The economic evaluation module is used to obtain the predicted electricity volume and predicted electricity price data of the target power market in the trial operation phase based on the characteristic equation, and to use the technical and economic evaluation process to conduct a dynamic technical and economic evaluation of the power spot market throughout its life cycle to obtain the dynamic technical and economic evaluation results of the entire life cycle.

[0050] In a third 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, the following steps are implemented:

[0051] Set quantitative factors; quantitative factors include policy factors, electricity market maturity factors, meteorological factors, primary energy price factors, and energy storage impact factors.

[0052] Obtain the existing historical data of specific stations in the corresponding areas of the corresponding provinces; conduct dynamic analysis on the officially operating electricity spot market based on quantitative factors and historical data to obtain the predicted electricity volume and predicted electricity price data for the entire life cycle of the station.

[0053] The characteristic numbers of the electricity spot market are constructed according to the quantitative factors and the predicted electricity quantity and electricity price data of the station throughout its life cycle. Based on the characteristic numbers, the characteristic equations of the electricity quantity and electricity price in the electricity spot market during the trial operation phase are solved to obtain the predicted electricity quantity and electricity price data of the electricity spot market.

[0054] Based on the forecasted electricity volume and forecasted electricity price data of the target power market in the trial operation phase obtained by solving the characteristic equation, a dynamic technical and economic evaluation of the power spot market over the entire life cycle is carried out to obtain the dynamic technical and economic evaluation results of the entire life cycle.

[0055] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0056] Set quantitative factors; quantitative factors include policy factors, electricity market maturity factors, meteorological factors, primary energy price factors, and energy storage impact factors.

[0057] Obtain the existing historical data of specific stations in the corresponding areas of the corresponding provinces; conduct dynamic analysis on the officially operating electricity spot market based on quantitative factors and historical data to obtain the predicted electricity volume and predicted electricity price data for the entire life cycle of the station.

[0058] The characteristic numbers of the electricity spot market are constructed according to the quantitative factors and the predicted electricity quantity and electricity price data of the station throughout its life cycle. Based on the characteristic numbers, the characteristic equations of the electricity quantity and electricity price in the electricity spot market during the trial operation phase are solved to obtain the predicted electricity quantity and electricity price data of the electricity spot market.

[0059] Based on the forecasted electricity volume and forecasted electricity price data of the target power market in the trial operation phase obtained by solving the characteristic equation, a dynamic technical and economic evaluation of the power spot market over the entire life cycle is carried out to obtain the dynamic technical and economic evaluation results of the entire life cycle.

[0060] The above-mentioned dynamic technical and economic evaluation method, device, computer equipment and storage medium for the whole life cycle of power stations first set quantitative factors including policy factors, power market maturity factors, meteorological factors, primary energy price factors and energy storage impact factors. Then, the historical data of the stations in the specific area of ​​the corresponding province are obtained, and the officially operated power spot market is dynamically analyzed based on these data and quantitative factors to predict the power and electricity prices of the whole life cycle of the station. Based on these predicted data, the characteristic number of the power spot market is constructed, and the characteristic equation is solved to predict the power and electricity prices of the power spot market in the test operation stage, and the prediction data of the corresponding trial operation stage are obtained. Finally, based on the prediction data of the trial operation stage, the power spot market is dynamically evaluated for the whole life cycle, and a comprehensive technical and economic evaluation result is obtained. This method takes into account the impact of the power station participating in the trial operation stage of the power spot market on the power station revenue, provides scientific price and power forecast data for the market in the trial operation stage, realizes the dynamic technical and economic evaluation of the whole life cycle of the power spot market, and improves the scientificity and accuracy of market analysis and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] 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.

[0062] Figure 1 A flow chart of a method for dynamic technical and economic evaluation of the entire life cycle of a power station is provided for an embodiment of the present invention;

[0063] Figure 2A flow chart of the steps for setting the power market maturity factor provided by an embodiment of the present invention;

[0064] Figure 3 A flow chart of the meteorological factor setting steps provided in an embodiment of the present invention;

[0065] Figure 4 A flow chart of steps for setting a primary energy price factor provided by an embodiment of the present invention;

[0066] Figure 5 A flowchart of the steps for setting the energy storage influence factor provided by an embodiment of the present invention;

[0067] Figure 6 A structural block diagram of a dynamic technical and economic evaluation device for the entire life cycle of a power station provided by an embodiment of the present invention; DETAILED DESCRIPTION

[0068] 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.

[0069] Next, the implementation environment of the embodiment of the present application is described. The implementation environment of the method for dynamic technical and economic evaluation of the entire life cycle of a power plant provided in the embodiment of the present application includes, illustratively, a terminal 101 and a server 102 .

[0070] Among them, the terminal 101 communicates with the server 102 through the network. The terminal can be equipped with running application software, or it can simply provide a display method in the form of a web page. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. In the dynamic technical and economic evaluation process of the entire life cycle of the power station, the server 102 first sets the quantitative factors including policy factors, power market maturity factors, meteorological factors, primary energy price factors and energy storage impact factors, and then the terminal 101 obtains the historical data of the station in the specific area of ​​the corresponding province and uploads it to the server 102. Based on these quantitative factors and historical data, the server 102 dynamically analyzes the officially operated power spot market and predicts the power and electricity prices of the entire life cycle of the station. Then, the server 102 uses these predicted data to construct the characteristic numbers of the power spot market, and solves the characteristic equation of the power spot market in the trial operation stage to obtain the predicted power and electricity prices. Finally, the server 102 conducts a dynamic technical and economic evaluation of the electricity spot market over the entire life cycle based on these forecast data, and the terminal 101 displays the dynamic technical and economic evaluation results of the entire life cycle. Among them, the terminal 101 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 102 can be implemented as an independent server or a server cluster consisting of multiple servers.

[0071] In combination with the above-mentioned explanations of terms and implementation environment, the application scenarios of the embodiments of the present application are explained.

[0072] The dynamic technical and economic evaluation method for the entire life cycle of a power plant provided in the embodiments of the present application can comprehensively and accurately evaluate the economic and technical aspects of a project at various stages such as planning, construction, operation and decommissioning, help decision makers to rationally allocate resources, optimize investment decisions, and reduce investment risks, and provide a scientific basis for the government to formulate relevant policies and enterprises to formulate operating strategies, thereby promoting the sustainable development of power plants. Schematically, the dynamic technical and economic evaluation method for the entire life cycle of a power plant provided in the embodiments of the present application can be applied to at least one of the following scenarios, including but not limited to.

[0073] First, the dynamic technical and economic evaluation method of the power station throughout its life cycle is applied to the combination of new energy power generation and energy storage technology. For example, in the field of new energy power generation, especially wind power and photovoltaic power generation, the dynamic technical and economic evaluation method of the whole life cycle is used to evaluate the application effect and economic benefits of energy storage technology. Energy storage technology can help solve the intermittent and instability problems of new energy power generation and improve the stability and reliability of the power grid. For example, by configuring an energy storage system, excess electricity can be stored for use in the evening or during peak demand periods, thereby improving the self-sufficiency of new energy and reducing dependence on traditional energy.

[0074] Second, the dynamic technical and economic evaluation method of the power plant's entire life cycle is applied to the evaluation of clean coal-fired power generation technology. In the field of clean coal-fired power generation, the life cycle evaluation method is used to evaluate the environmental impact and economic feasibility of different clean coal-fired technologies. This includes a comprehensive consideration of the resource consumption, environmental impact, and economic cost of the power plant's entire life cycle. Through this method, a sustainability evaluation of the power plant can be proposed, and a sensitivity analysis can be performed to identify the key factors that affect the evaluation indicators.

[0075] Third, the dynamic technical and economic evaluation method of the power station's full life cycle is applied to the evaluation of biomass energy power generation technology. For example, in the field of biomass energy power generation, the dynamic technical and economic evaluation method of the full life cycle is used to evaluate the feasibility of straw biogasification power generation technology. This includes not only environmental impact assessments, such as global warming potential, environmental acidification and human toxicity, but also economic feasibility analysis, such as dynamic payback period, internal rate of return and net present value. Through this method, the environmental and economic benefits of biomass energy power generation technology can be comprehensively evaluated.

[0076] In one embodiment, if Figure 1 As shown, a method for dynamic technical and economic evaluation of the entire life cycle of a power station is provided. By way of example, in this embodiment, the following steps may be included:

[0077] Step 201, setting quantitative factors; the quantitative factors include policy factors, power market maturity factors, meteorological factors, primary energy price factors, and energy storage impact factors.

[0078] This embodiment sets a series of quantitative factors, which play a key role in comprehensively and accurately evaluating the technical and economic conditions of power plants. Specifically, the quantitative factors cover policy factors, electricity market maturity factors, meteorological factors, primary energy price factors, and energy storage impact factors. Among them, the policy factors are intended to extract key information and set reasonable grade coefficients and lag factors through in-depth analysis of policy documents of different levels, and at the same time, with the help of keyword variables, accurately quantify the multi-faceted impact of policies on power plant projects, thereby realizing a dynamic evaluation of the impact of different policies throughout the life cycle of the power plant; the electricity market maturity factor obtains information disclosed by the market operating agency, determines the market operation stage and sets the corresponding stage factors, and proposes the marketization degree factor of the power plant and the marketization degree factor of the electricity load, and reasonably allocates weights according to different markets, regions and time periods, so as to accurately quantify the marketization degree of different spot market development stages, and then realize accurate dynamic calculation of power generation and electricity price fluctuation risks at different stages of power spot market development; the meteorological factor uses the correlation between time series data such as temperature, wind speed, precipitation, etc. in meteorological data and power generation and electricity load in a specific area in the power spot market. The data are analyzed by using similar analysis methods, taking into full account the distribution of wind and solar resources in different provinces and regions and the impact of different time periods, and comprehensively analyzing and accurately predicting the impact of meteorological factors on the power market; the primary energy price factor constructs a correlation model for the energy structure in different provinces and regions, sets relevant factors and conducts correlation analysis with the historical time series data of electricity volume and price in the power market, so that the price prediction model of primary energy and this factor can be used to scientifically predict the electricity volume and price of the power station throughout its life cycle; the energy storage impact factor constructs technical route factors and technological innovation factors for different energy storage technology routes to participate in the electricity spot market, sets unique impact factors for different types of energy storage power stations and projects combined with other power stations, and then quantifies the changes in power generation, electricity price, carbon emissions and carbon price due to energy storage, as well as the changing trends of related parameters under the influence of technological innovation.

[0079] Step 202, obtain the existing historical data of the specific station in the corresponding area of ​​the corresponding province; based on the quantitative factors and historical data, dynamically analyze the officially operating power spot market to obtain the predicted electricity volume and predicted electricity price data for the entire life cycle of the station.

[0080] Based on historical data, for the power generation of corresponding provinces, corresponding regions and specific stations, a quantitative analysis model integrating neural networks and artificial intelligence algorithms is constructed according to different time periods to obtain different time series data for the corresponding stations. Then, these time series data are processed using a multi-factor electricity dynamic calculation model and an electricity price dynamic calculation model, and then a dynamic analysis is conducted on the market transaction electricity, station power generation, market electricity price, and station node electricity price throughout the life cycle of the station, and finally the predicted electricity and predicted electricity price data for the entire life cycle of the station are obtained. Among them, the dynamic analysis covers the dynamic analysis of power generation of new energy power stations, the dynamic analysis of power generation of thermal power plants, and the dynamic analysis of electricity prices.

[0081] Step 203, constructing the characteristic number of the electricity spot market according to the quantitative factors and the predicted electricity quantity and predicted electricity price data of the station throughout its life cycle, and solving the characteristic equation for the electricity quantity and electricity price of the electricity spot market during the trial operation phase based on the characteristic number, to obtain the predicted electricity quantity and predicted electricity price data of the electricity spot market.

[0082] The characteristic number is constructed based on the above quantitative factors, and the quantitative factors for formal operation are added with the subscript F: X1 F , X2 F 、X3 F 、X4 F 、X5 F ; Quantization factor in the trial operation phase with subscript T: X1 T , X2 T 、X3 T 、X4 T 、X5 T .

[0083] The characteristic numbers of the quantization factors are as follows:

[0084] Number of policy features: X1 F / X1 T

[0085] Market maturity characteristic number: X2 F / X2 T

[0086] Meteorological characteristics number: X3 F / X3 T

[0087] Primary energy price characteristic number: X4 F / X4 T

[0088] Energy storage characteristic number: X5 F / X5 T .

[0089] Step 204 , based on the predicted electricity quantity and predicted electricity price data of the target electricity market in the trial operation phase obtained by solving the characteristic equation, a dynamic technical and economic evaluation of the electricity spot market over the entire life cycle is performed to obtain a dynamic technical and economic evaluation result over the entire life cycle.

[0090] Based on the predicted electricity volume and electricity price data of the target power market in the trial operation phase obtained by solving the characteristic equation, a dynamic technical and economic evaluation of the power generation project over its entire life cycle is conducted using a standardized technical and economic evaluation process. This can be used for project pre-investment decisions, planning, design and construction, as well as power production during the operation period, decisions on whether to participate in the trial operation of spot electricity, formulation of electricity market trading strategies, and mergers, acquisitions or sales of power stations.

[0091] In the above-mentioned dynamic technical and economic evaluation method for the whole life cycle of power stations, quantitative factors including policy factors, power market maturity factors, meteorological factors, primary energy price factors and energy storage impact factors are first set. Then, the historical data of the stations in the specific area of ​​the corresponding province are obtained, and the officially operated power spot market is dynamically analyzed based on these data and quantitative factors to predict the power and electricity prices of the whole life cycle of the station. Based on these predicted data, the characteristic number of the power spot market is constructed, and the characteristic equation is solved to predict the power and electricity prices of the power spot market in the test operation phase, and the prediction data of the corresponding trial operation phase are obtained. Finally, based on the prediction data of the trial operation phase, the power spot market is dynamically evaluated for the whole life cycle, and a comprehensive technical and economic evaluation result is obtained. This method takes into account the impact of the power station participating in the trial operation phase of the power spot market on the power station revenue, provides scientific price and power forecast data for the market in the trial operation phase, realizes the dynamic technical and economic evaluation of the whole life cycle of the power spot market, and improves the scientificity and accuracy of market analysis and decision-making.

[0092] In one embodiment, the policy factor can be set by:

[0093] Policy documents are divided into different levels, and corresponding level coefficients are set to obtain level coefficients corresponding to policy documents of different levels; the levels of policy documents include national, provincial, and municipal levels, and the level coefficients are 1, 0.5, and 0.25, respectively.

[0094] The lag effect panel model is used to quantitatively analyze the lag effects of policy documents of different levels, and the lag factors corresponding to policy documents of different levels are obtained; the lag factors corresponding to national, provincial, and municipal policy documents are 0.25, 0.5, and 0.25, respectively.

[0095] Text mining is used to extract keywords from policy documents of different levels to obtain the support attitudes of different policy documents towards the power market; support attitudes include encouragement or restriction, and the corresponding coefficients are +1 and -1 respectively.

[0096] Through policy documents, we extract unified pricing data on average costs of power generation projects corresponding to market types, provinces, regions and specific sites, capacity electricity price data, installed capacity data, industrial distribution, national economic development data, electricity market reform measures, time-of-use electricity price policies, transmission and distribution price policies, capacity electricity price policies, and energy storage policies.

[0097] General equilibrium CGE is used to analyze the comprehensive effects of economic policy changes or external shocks on the electricity market and carbon market, and the lag effect panel model is used to quantitatively analyze the lag effects that may occur due to different policies.

[0098] The PMC index is constructed through system dynamics, and text mining is used to quantitatively evaluate electricity carbon policies. The relationship between energy policies and variables such as electricity market installed capacity, electricity market power generation, electricity spot market electricity prices, carbon market carbon quotas, CCER amounts, and carbon prices is established. A prediction model for the impact of policy factors on market installed capacity, electricity market power generation, electricity market electricity prices, total carbon quotas in the carbon market, CCER development, and carbon prices is established.

[0099] The PMC model is used to establish a quantitative evaluation relationship between policies and economic development speed, and then predict future economic development.

[0100] By setting policy factors, setting level coefficients and hysteresis factors for policy documents of different levels, and extracting keywords to quantify the impact of policies on power station projects, a dynamic evaluation of the impact of different policies in the entire life cycle of the power plant is achieved. This effectively achieves a dynamic and accurate evaluation of the impact of policies in the entire life cycle of the power plant, providing strong data support for strategic adjustments and decision-making optimization of power station projects.

[0101] In one embodiment, if Figure 2 As shown, the electricity market maturity factor can be set by the following methods:

[0102] Step 301, obtaining market operation information.

[0103] Step 302: determine the market operation stage based on the market operation information and set the corresponding market stage factor.

[0104] If the market operation is not yet in the trial operation stage, the corresponding market stage factor is 0.

[0105] If the market operation is in the simulation trial operation stage, the corresponding market stage factor is 0.01.

[0106] If the market operation is in the power regulation trial operation stage, the corresponding market stage factor is 0.015.

[0107] If the market operation is in the settlement trial operation stage, the corresponding market stage factor expression is min(0.9, continuous trial operation days / max(number of operation days in the same period, 366)).

[0108] If the market operation is in the formal operation stage, the corresponding market stage factor is 1.

[0109] Step 303, setting the marketability factor of the power station, which includes at least one of the installed capacity of power stations participating in the electricity spot market, the installed capacity of all power stations in a province, and the power generation of power stations participating in the electricity spot market and the power generation of all power stations in a province.

[0110] Step 304, setting the electric load marketization degree factor, the electric load marketization degree factor includes at least one of the electric load participating in the electric power spot market, the total electric load of a province, and the capacity and quantity of the electric load participating in the electric power spot transaction.

[0111] Among them, in step 304, the more the market-oriented electricity load accounts for the total electricity load of the whole province, the greater the number of electricity loads, the lower the individual electricity load, and the higher the degree of marketization.

[0112] As for the weights of both the power station marketization degree factor and the electricity load marketization degree factor, the electricity load side can better reflect the marketization maturity and the weight can be set larger.

[0113] The TGARCH model and VineCopula theory are used to analyze the electricity price fluctuation and risk measurement of the existing market data. The marginal distribution of day-ahead, real-time and ancillary service transaction electricity prices is established through the TGARCH model, and the multi-dimensional dependency structure of each transaction electricity price is fitted using VineCopula, so as to analyze the dynamic fluctuation law between each transaction electricity price and determine the dynamic fluctuation risk of electricity prices in different market stages.

[0114] By setting the spot market maturity factor and quantifying the degree of marketization at different stages of spot market development, accurate dynamic calculation of power generation and electricity price fluctuation risks at different stages of electricity spot market development is achieved.

[0115] In one embodiment, if Figure 3 As shown, meteorological factors can be set by:

[0116] Step 401, obtaining historical meteorological data, power generation time series data and power load time series data in a specific area in the power spot market.

[0117] Step 402: perform correlation analysis based on historical meteorological data and power generation time series data to obtain the impact factor of meteorological data on power generation.

[0118] Step 403, using the prediction method of meteorological data to predict the future power generation based on the influencing factors, to obtain the future power generation prediction result.

[0119] Step 404: perform correlation analysis based on historical meteorological data and power load time series data to obtain the impact factor of meteorological data on power load.

[0120] Step 405: forecast the future power load based on the influencing factors using the forecasting method of meteorological data to obtain the future power load forecast result.

[0121] Among them, in step 401, the electricity load time series data is directly related to the transaction types of the electricity market, and is divided into annual, quarterly, monthly, ten-day, weekly and daily.

[0122] Precipitation data is classified into three types: rainfall, snowfall, and others; temperature trends are analyzed according to average temperature, temperature drop amplitude, and temperature time change rate; extreme weather probability functions are also considered. The meteorological factor model is constructed to take into account the distribution of wind and solar resources in different provinces and regions, and also consider the impact and conversion of different time periods, such as strong wind months and weak wind months, flood season and dry season, heating season, and summer supply guarantee period.

[0123] This embodiment proposes a method based on the correlation coefficient between the temperature, wind speed, and precipitation time series data in the historical meteorological data and the power generation and power load in a specific area in the power spot market, using a similarity analysis method to analyze and predict the impact of meteorological factors on the power market in different provinces, and to analyze the power and electricity prices of the future power stations throughout their life cycle. This effectively improves the accuracy and reliability of the prediction of power and electricity prices throughout the life cycle of future power stations, and provides solid data support for the stable operation of the power market and the scientific decision-making of power generation companies.

[0124] In one embodiment, if Figure 4 As shown, the primary energy price factor can be set by:

[0125] Step 501, setting the correlation factor between the primary energy price and the electricity load and electricity price in the electricity market.

[0126] Step 502, based on the correlation factor, construct a correlation model between primary energy and the power market applicable to the energy structure of different provinces and regions; the correlation model includes a price prediction model for primary energy and a primary energy price factor.

[0127] Step 503, setting the primary energy consumption proportion time series factor and the power station type weight time series factor.

[0128] Step 504, based on the time series factor of the proportion of primary energy consumption and the time series factor of the power plant type weight, a correlation analysis is performed on the historical time series data of electricity market electricity quantity and price, and the price prediction model of primary energy and the price factor of primary energy are used to obtain the predicted electricity quantity and predicted price of the power plant throughout its life cycle.

[0129] By setting the correlation factors between primary energy prices and electricity load and electricity prices in the power market, a primary energy and electricity market correlation model suitable for regional energy structures in different provinces is constructed, and the primary energy consumption proportion time series factors and power station type weight time series factors are introduced. This method can deeply analyze the correlation between electricity market electricity and price historical time series data, and use the primary energy price prediction model and factors to accurately predict the electricity and price of the power station throughout its life cycle, providing scientific decision-making basis for electricity market participants, optimizing resource allocation, and promoting the stable operation and development of the power market.

[0130] In one embodiment, if Figure 5 As shown, the energy storage impact factor can be set by the following methods:

[0131] Step 601, constructing energy storage technology route factors for different energy storage technology routes to participate in the electricity spot market.

[0132] Step 602, based on the energy storage technology route factor, set up the project of independent energy storage power station, shared energy storage power station, wind farm, photovoltaic power station, and thermal power plant combination to obtain different influencing factors.

[0133] Step 603, based on the influencing factors, quantitative analysis is performed on the power generation, electricity price, carbon emissions and carbon price brought by the energy storage to obtain corresponding quantitative analysis results.

[0134] Step 604, constructing energy storage technology innovation factors based on the quantitative analysis results, conducting quantitative analysis on parameters such as the technical economy, energy storage capacity, and time span of the energy storage project, and obtaining corresponding change trends under the influence of technological innovation.

[0135] By constructing factors covering different energy storage technology routes and setting corresponding influencing factors for independent energy storage power stations, shared energy storage power stations, wind farms, photovoltaic power stations and thermal power plant combination projects, this method can accurately quantify the impact of energy storage on power generation, electricity prices, carbon emissions and carbon prices. Furthermore, based on these quantitative analysis results, energy storage technology innovation factors are constructed to deeply analyze key parameters such as the technical and economic feasibility, energy storage capacity and time span of energy storage projects, revealing the future trend of energy storage projects driven by technological innovation.

[0136] In one embodiment, a dynamic analysis of the officially operating power spot market is performed based on quantitative factors and historical data to obtain the predicted power and power price data for the entire life cycle of the station, which may include the following steps:

[0137] Based on the historical data of power generation in corresponding provinces, regions and specific stations, a quantitative analysis model integrating neural networks and artificial intelligence algorithms is constructed according to different time periods to obtain different time series data for corresponding stations.

[0138] Based on the time series data, a multi-factor electricity quantity dynamic calculation model and an electricity price dynamic calculation model are processed, and the market trading electricity quantity, station power generation, market electricity price and station node electricity price throughout the life cycle of the station are dynamically analyzed to obtain the predicted electricity quantity and predicted electricity price data for the entire life cycle of the station.

[0139] Dynamic analysis includes dynamic analysis of power generation of new energy power stations, dynamic analysis of power generation of thermal power plants and dynamic analysis of electricity prices.

[0140] The dynamic analysis of power generation of new energy power stations is based on the analysis of new energy resources, coupled with policy factors, electricity spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors to obtain a multi-factor electricity dynamic calculation model.

[0141] The dynamic analysis of thermal power plant power generation takes the supporting role of thermal power plants participating in electricity spot trading in the new power system as the analysis basis, coupling policy factors, electricity spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors to obtain a multi-factor electricity dynamic calculation model.

[0142] The dynamic analysis of electricity prices is based on the power generation form of power plants, installed capacity, coupling policy factors, electricity spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors to obtain a multi-factor electricity price dynamic calculation model.

[0143] The dynamic analysis method of power generation over the entire life cycle of a new energy power plant project requires the fusion analysis of the aforementioned multiple factors, based on new energy resource analysis, coupled policy factors, electricity spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors, and uses existing historical data to analyze the power generation of corresponding provinces, regions, and specific stations. According to different time periods, such as years, quarters, months, ten days, weeks, and days, a quantitative analysis model integrating neural networks and artificial intelligence algorithms is constructed. Deep learning architectures such as LSTM or Transformer models are used to process time series data through a multi-factor power dynamic calculation model, and dynamically calculate the market trading power and station power generation over the entire life cycle of the station.

[0144] The dynamic analysis method of power generation over the entire life cycle of a thermal power plant project requires the fusion analysis of the aforementioned multiple factors, with the supporting role of thermal power plants participating in electricity spot trading in the new power system as the analysis basis, coupled policy factors, electricity spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors. The existing historical data is used to analyze the power generation of corresponding provinces, regions, and specific stations according to different time periods, such as years, quarters, months, ten days, weeks, and days. A quantitative analysis model that integrates neural networks and artificial intelligence algorithms is constructed. A deep learning architecture such as the LSTM or Transformer model is used to process time series data through a multi-factor electricity dynamic calculation model, and dynamically calculate the market trading electricity and station power generation over the entire life cycle of the station.

[0145] The dynamic analysis method of electricity prices throughout the life cycle of a power plant project requires the fusion analysis of the aforementioned multiple factors, including the power generation form of the power plant, installed capacity, coupling policy factors, electricity spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors. The existing historical data is used to analyze the power generation of the corresponding province, region, and specific station. According to different time periods, such as years, quarters, months, ten days, weeks, and days, a quantitative analysis model integrating neural networks and artificial intelligence algorithms is constructed. Deep learning architectures such as CNN-LSTM, Transformer models, or ARIMA-SSA-LSTM combination models are used. The TGARCH-VineCopula model is used to predict electricity market risks. The time series data is processed through a multi-factor electricity price dynamic calculation model, and a rolling dynamic analysis is performed on the market electricity price and station node electricity price throughout the life cycle of the station.

[0146] The dynamic calculation model of electricity prices needs to establish adaptive adjustments for different provinces. For example, the pricing unit types in the electricity spot market in different provinces are different, and the price formation mechanisms in different provinces are not exactly the same. For this reason, the electricity price calculation formula is proposed:

[0147] The bidding space for thermal power as a pricing unit = provincial dispatching power load in the spot market + inter-provincial transmission power load - wind farm power generation - photovoltaic power station power generation - hydropower and other new energy units power generation.

[0148] The demand-supply ratio of the electricity spot market = the bidding space of thermal power as a pricing unit / the total installed capacity of SCUC thermal power online units.

[0149] The existing historical electricity price time series data of the power market is used to build a calculation model for bidding space and electricity price, and combined with the above-mentioned electricity price and electricity quantity analysis model, the electricity price of the power station throughout its life cycle is predicted. This process not only significantly improves the accuracy of predicted electricity quantity and electricity price, but also provides valuable decision-making support for station operators, helping them optimize power generation plans, electricity price strategies and resource allocation.

[0150] 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.

[0151] Based on the same inventive concept, the embodiment of the present application also provides an evaluation device for implementing the above-mentioned dynamic technical and economic evaluation method for the whole life cycle of a power plant. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more evaluation device embodiments provided below can refer to the above-mentioned limitations on the dynamic technical and economic evaluation method for the whole life cycle of a power plant, and will not be repeated here.

[0152] In an exemplary embodiment, Figure 6 As shown, a dynamic technical and economic evaluation device for the entire life cycle of a power station is provided, comprising:

[0153] The factor setting module 701 is used to set quantitative factors; the quantitative factors include policy factors, power market maturity factors, meteorological factors, primary energy price factors, and energy storage impact factors.

[0154] The data acquisition module 702 is used to obtain the existing historical data of specific stations in the corresponding area of ​​the corresponding province; based on the quantitative factors and historical data, a dynamic analysis is performed on the officially operating electricity spot market to obtain the predicted electricity volume and predicted electricity price data for the entire life cycle of the station; based on the quantitative factors and the predicted electricity volume and predicted electricity price data for the entire life cycle of the station, the characteristic number of the electricity spot market is constructed, and based on the characteristic number, the characteristic equation is solved for the electricity volume and electricity price of the electricity spot market in the trial operation stage to obtain the predicted electricity volume and predicted electricity price data of the electricity spot market.

[0155] The economic evaluation module 703 is used to obtain the predicted electricity volume and predicted electricity price data of the target electricity market in the trial operation phase based on the characteristic equation, and to use the technical and economic evaluation process to conduct a dynamic technical and economic evaluation of the electricity spot market throughout its life cycle to obtain dynamic technical and economic evaluation results throughout its life cycle.

[0156] In summary, the device for dynamic technical and economic evaluation of the entire life cycle of a power station provided in the embodiment of the present application, by setting a setting factor module including multi-dimensional quantitative factors such as policies, electricity market maturity, meteorology, primary energy prices and energy storage impact, and a data acquisition module that collects historical data of specific stations and performs dynamic analysis to predict the electricity volume and electricity price throughout the life cycle, can accurately construct the characteristic numbers of the electricity spot market, and solve the characteristic equation of the market in the trial operation stage to obtain forecast data. Finally, the economic evaluation module uses these forecast data to realize a dynamic technical and economic evaluation of the entire life cycle of the electricity spot market through a comprehensive technical and economic evaluation process. This device not only significantly improves the accuracy and comprehensiveness of the evaluation, but also provides strong data support and scientific basis for the operational decisions of power stations, which helps to optimize resource allocation and improve economic benefits and market competitiveness.

[0157] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for dynamic technical and economic evaluation of the entire life cycle of a power plant as described above are implemented.

[0158] In one embodiment, a computer-readable storage medium is provided, 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.

[0159] 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.

[0160] 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 technical and economic evaluation method for the entire life cycle of a power station, characterized in that: The method comprises: Setting quantitative factors; the quantitative factors include policy factors, power market maturity factors, meteorological factors, primary energy price factors, and energy storage impact factors; Obtain the existing historical data of specific stations in the corresponding region of the corresponding province; conduct a dynamic analysis of the officially operating power spot market based on the quantitative factors and the historical data to obtain the predicted power volume and predicted electricity price data for the entire life cycle of the station; Constructing a power spot market characteristic number according to the quantitative factor and the predicted power quantity and predicted power price data of the station throughout its life cycle, and solving a characteristic equation for the power quantity and power price of the power spot market during the trial operation phase based on the characteristic number to obtain the predicted power quantity and predicted power price data of the power spot market; Based on the predicted electricity quantity and predicted electricity price data of the target electricity market in the trial operation phase obtained by solving the characteristic equation, a dynamic technical and economic evaluation of the electricity spot market is performed throughout its life cycle to obtain a dynamic technical and economic evaluation result throughout its life cycle.

2. The method according to claim 1, characterized in that The policy factor is set by: The policy documents are divided into different levels, and corresponding level coefficients are set to obtain the level coefficients corresponding to the policy documents of different levels; the levels of the policy documents include national level, provincial level, and prefecture-level, and the level coefficients are 1, 0.5, and 0.25 respectively; The hysteresis effect panel model is used to quantitatively analyze the hysteresis effects of policy documents at different levels, and the hysteresis factors corresponding to policy documents at different levels are obtained; the hysteresis factors corresponding to policy documents at the national, provincial, and prefecture-level are 0.25, 0.5, and 0.25 respectively; Text mining is used to extract keywords from policy documents of different levels to obtain the support attitudes of different policy documents towards the power market; the support attitudes include encouragement or restriction, and the corresponding coefficients are +1 and -1 respectively.

3. The method according to claim 1, characterized in that The electricity market maturity factor is set by: Obtain market operation information; Determine the market operation stage based on the market operation information, and set a corresponding market stage factor; If the market operation is in the stage of not yet in trial operation, the corresponding market stage factor is 0; If the market operation is in the simulation trial operation stage, the corresponding market stage factor is 0.01; If the market operation is in the power adjustment trial operation stage, the corresponding market stage factor is 0.015; If the market operation is in the settlement trial operation stage, the corresponding market stage factor expression is min(0.9, continuous trial operation days / max(same period operation days, 366)); If the market operation is in the formal operation stage, the corresponding market stage factor is 1; Setting a power station marketability factor, wherein the power station marketability factor includes at least one of the installed capacity of power stations participating in the electricity spot market, the installed capacity of all power stations in a province, and the power generation of power stations participating in the electricity spot market and the power generation of all power stations in a province; A power load marketization degree factor is set, and the power load marketization degree factor includes at least one of the power load participating in the power spot market, the total power load of a province, and the capacity and quantity of the power load participating in the power spot transaction.

4. The method according to claim 1, characterized in that: The meteorological factors are set by: Obtain historical meteorological data, power generation time series data, and power load time series data in a specific area in the power spot market; Performing correlation analysis based on the historical meteorological data and the power generation time series data to obtain the impact factor of the meteorological data on the power generation; Using a forecasting method of meteorological data to forecast future power generation based on the influencing factors, and obtaining a forecast result of future power generation; Based on the historical meteorological data and the power load time series data, a correlation analysis is performed to obtain the impact factor of the meteorological data on the power load. The forecasting method using meteorological data is used to forecast the future power load based on the influencing factors to obtain the future power load forecasting result.

5. The method according to claim 1, characterized in that The primary energy price factor is set by the following method: Set the correlation factor between primary energy price and electricity market load and electricity price; Based on the correlation factor, a correlation model between primary energy and the power market applicable to the energy structure of different provinces and regions is constructed; the correlation model includes a price prediction model of the primary energy and a primary energy price factor; Set the time series factor of primary energy consumption ratio and the time series factor of power station type weight; Based on the time series factor of the proportion of primary energy consumption and the time series factor of the power plant type weight, a correlation analysis is performed on the historical time series data of electricity market electricity volume and price, and the price prediction model of the primary energy and the primary energy price factor are used to obtain the predicted electricity volume and predicted price for the entire life cycle of the power plant.

6. The method according to claim 1, characterized in that The energy storage impact factor is set by the following method: Construct energy storage technology route factors for different energy storage technology routes to participate in the electricity spot market; Based on the energy storage technology route factors, the projects of independent energy storage power stations, shared energy storage power stations, wind farms, photovoltaic power stations, and thermal power plants are set to obtain different influencing factors; Based on the influencing factors, a quantitative analysis is performed on the power generation, electricity price, carbon emissions and carbon price brought by energy storage to obtain corresponding quantitative analysis results; Based on the quantitative analysis results, the energy storage technology innovation factor is constructed, and the technical and economic performance, energy storage capacity, time span and other parameters of the energy storage project are quantitatively analyzed to obtain the corresponding change trend under the influence of technological innovation.

7. The method according to claim 1, characterized in that The dynamically analyzing the officially operating electricity spot market based on the quantitative factors and the historical data to obtain the predicted electricity quantity and predicted electricity price data for the entire life cycle of the station includes: Based on the historical data, the power generation of the corresponding province, the corresponding region, and the specific station is constructed according to different time periods by a quantitative analysis model integrating a neural network and an artificial intelligence algorithm to obtain different time series data of the corresponding station; Based on the time series data, a multi-factor electricity quantity dynamic calculation model and an electricity price dynamic calculation model are processed, and the market transaction electricity quantity, station power generation, market electricity price, and station node electricity price in the whole life cycle of the station are dynamically analyzed to obtain the predicted electricity quantity and predicted electricity price data of the whole life cycle of the station; The dynamic analysis includes dynamic analysis of power generation of new energy power stations, dynamic analysis of power generation of thermal power plants and dynamic analysis of electricity prices; The dynamic analysis of the power generation of the new energy power station is based on the analysis of new energy resources, coupled with policy factors, power spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors to obtain the multi-factor power dynamic calculation model; The dynamic analysis of the power generation of thermal power plants is based on the supporting role of thermal power plants participating in power spot trading in the new power system, coupled with policy factors, power spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors, to obtain the multi-factor power dynamic calculation model; The electricity price dynamic analysis is based on the power generation form of the power station, installed capacity, coupling policy factors, electricity spot market maturity factors, carbon market maturity factors, meteorological factors, and primary energy price factors to obtain the multi-factor electricity price dynamic calculation model.

8. A dynamic technical and economic evaluation device for the entire life cycle of a power station, characterized in that: The device comprises: Setting factor module, used to set quantitative factors; the quantitative factors include policy factors, power market maturity factors, meteorological factors, primary energy price factors, and energy storage impact factors; A data acquisition module is used to obtain the existing historical data of a specific station in a corresponding region of a corresponding province; based on the quantitative factors and the historical data, a dynamic analysis is performed on the officially operated power spot market to obtain the predicted power and predicted electricity price data for the entire life cycle of the station; based on the quantitative factors and the predicted power and predicted electricity price data for the entire life cycle of the station, a characteristic number of the power spot market is constructed, and based on the characteristic number, a characteristic equation is solved for the power and electricity price of the power spot market in the trial operation stage to obtain the predicted power and predicted electricity price data of the power spot market; The economic evaluation module is used to conduct a dynamic technical and economic evaluation of the power spot market over the entire life cycle based on the predicted electricity volume and predicted electricity price data of the target power market in the trial operation phase obtained by solving the characteristic equation, and to obtain a dynamic technical and economic evaluation result over the entire life cycle by using a technical and economic evaluation process.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images