New energy delivery economy analysis method and equipment under multiple variable influence factors

Abstract

The invention provides a new energy delivery economy analysis method and equipment under multiple variable influence factors. Wherein a dual-objective economical efficiency optimization model is created, and aims to realize the lowest total power generation cost and the maximum new energy benefit at the same time, so that the characteristics of intermittency and instability of new energy power generation are fully considered. And then, through model application, the specific influence of influence factors such as seasonal factors, electricity price fluctuation and diversified working conditions on the new energy delivery economy can be analyzed and determined. And finally, according to an analysis result, making a corresponding strategy suggestion and optimizing a scheduling scheme. Therefore, energy waste can be effectively reduced, and stable and sustainable development of the new energy industry can be promoted.

Description

Technical Field

[0001] The present application relates to a method and device for analyzing the economic efficiency of new energy delivery under multiple variable influencing factors, and belongs to the technical field of energy economic analysis. Background Art

[0002] With the transformation of the global energy structure and the growing demand for sustainable energy, the development and utilization of new energy has become an important part of the national energy strategy. New energy, such as wind power and solar energy, has gradually become an emerging force in power supply with its clean and renewable characteristics. However, the intermittent and unstable nature of new energy has brought technical and economic challenges to large-scale grid connection and effective transmission. In the context of power marketization, how to achieve the optimal configuration and economic transmission of new energy has become an urgent problem to be solved in the power industry.

[0003] In existing technologies, energy storage technology is widely considered to be the key to solving the intermittent problem of renewable energy. By configuring an appropriate energy storage system, energy can be stored during peak renewable energy generation and released during peak demand, thereby balancing supply and demand. In order to improve the utilization rate of renewable energy, some technical solutions focus on optimizing the dispatching strategy of the power system. This includes reasonably arranging power generation plans, using forecasting tools to adapt to the volatility of energy supply, and balancing supply and demand through market mechanisms. In order to solve the problem of uneven distribution of renewable energy resources, some technical solutions focus on cross-regional energy transmission. By building ultra-high voltage transmission lines or submarine cables, renewable energy can be transported from resource-rich areas to demand centers.

[0004] Although existing technical solutions have solved some of the problems of renewable energy transmission to a certain extent, they usually do not fully consider the impact of seasonal changes, price fluctuations and different operating conditions. For example, some solutions may focus on the configuration of energy storage systems or the optimization of power dispatch, but do not conduct in-depth analysis on how these factors work together to affect the economic feasibility of renewable energy transmission. Summary of the invention

[0005] In order to solve the above problems, the present application provides a method and equipment for analyzing the economic efficiency of new energy delivery under multiple variable influencing factors.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] In a first aspect, the present application provides a method for analyzing the economic efficiency of new energy delivery under multiple variable influencing factors, which includes:

[0008] Based on multiple key variables that affect economic efficiency, a piecewise linear programming method is used to construct an economic optimization model, which aims to minimize the total power generation cost and maximize the benefits of new energy. The constraints include power balance constraints, upper and lower limit constraints on peak load capacity, and new energy output forecast value constraints.

[0009] Based on the economic optimization model, the economic efficiency of new energy transmission under various variable influencing factors is analyzed to determine the impact of various influencing factors on the economic efficiency of new energy transmission, including: comparing the economic efficiency of new energy transmission in different seasons, analyzing and determining the impact of seasonal factors on power generation costs and benefits; comparing the economic efficiency of new energy transmission under different electricity prices, determining the impact of electricity price fluctuations on the economic efficiency of new energy transmission; comparing the strategies and economic benefits of new energy transmission under different working conditions of the power grid, determining the impact of different working conditions on the economic efficiency of new energy transmission;

[0010] Based on the impact of various influencing factors on the economic feasibility of renewable energy transmission, the power resource allocation and dispatching plans under various influencing factors are generated.

[0011] Based on the above method, optionally, the objective function of the economic optimization model specifically includes:

[0012] Minimize the total electricity generation cost:

[0013]

[0014] Maximizing the benefits of new energy:

[0015]

[0016] In the formula, x 1 、x 2 、x 3 and x 4 They are the renewable energy grid-connected power within the province, renewable energy power transmission and thermal power output, peak is the peak load capacity; p 1 、p 2 、p 3 and p 4 They are the on-grid price of new energy within the province, the price for new energy transmission, the on-grid electricity price of thermal power units and the peak-shaving cost sharing standard.

[0017] Based on the above method, optionally, the constraint conditions specifically include:

[0018]

[0019] Where peak lb 、peak ub They are the upper and lower limits of the peak-shaving capacity, which are used to constrain or relax whether the peak-shaving capacity is allowed; load preNe is the load forecast value within the province, which is used to ensure that the optimization result meets the load demand; pre is the predicted value of new energy output, peak th is the peak shaving trigger threshold.

[0020] Based on the above method, optionally, it also includes:

[0021] Reasonable assumptions are made on the preset parameters and conditions to simplify the economic optimization model; wherein the preset parameters and conditions include the stability of new energy electricity prices, the uniformity of peak-shaving costs, the difference in seasonal operating costs and the fixedness of thermal power on-grid electricity prices.

[0022] Based on the above method, optionally, making reasonable assumptions about the preset parameters and conditions includes:

[0023] Assumption of stability of electricity prices for renewable energy: It is assumed that the provincial on-grid electricity prices and the external electricity prices of renewable energy remain constant during the study period;

[0024] Peak load cost uniformity assumption: Assume that the peak load electricity allocation cost remains unchanged at 96 time points throughout the day;

[0025] Seasonal operating cost difference assumption: It is assumed that there is a difference in operating costs between the heating period and the non-heating period;

[0026] Assumption of fixed on-grid electricity price for thermal power generation: The on-grid electricity price for thermal power generation units in the province is assumed to be fixed throughout the year to simplify the model and highlight the impact of the new energy transmission strategy on economic efficiency.

[0027] Based on the above method, optionally, the comparison of the economic feasibility of new energy delivery in different seasons and the analysis and determination of the impact of seasonal factors on power generation costs and benefits include:

[0028] Seasonal data collection and preprocessing: Collect detailed data on specific areas during the heating period and non-heating period, including peak-shaving capacity, renewable energy transmission capacity, power demand and renewable energy power generation; clean and preprocess the collected data to ensure data accuracy and consistency;

[0029] Calculation of economic indicators: Based on the preprocessed data, the total power generation cost for each season is calculated, including the cost of renewable energy power generation, thermal power generation, power transmission loss, and peak load operation cost; considering the seasonal fluctuation of electricity prices, the revenue from renewable energy transmission and the costs associated with peak load services are calculated;

[0030] Model parameter setting and solution: The calculated seasonal economic indicators are input as parameters into the economic optimization model. According to the set objective function, an appropriate optimization algorithm is used to solve the model to determine the optimal new energy delivery strategy in different seasons. The model solution results are analyzed to identify the economic differences in new energy delivery in different seasons.

[0031] Based on the above method, optionally, comparing the economic efficiency of renewable energy transmission under different electricity prices and determining the impact of electricity price fluctuations on the economic efficiency of renewable energy transmission includes:

[0032] Electricity price data collection: Collect new energy on-grid electricity price data in different time periods and regions, including peak and valley electricity prices and seasonally adjusted electricity prices; clean and standardize the collected electricity price data to ensure data quality and consistency;

[0033] Model solution and analysis: adjust input parameters and solve the economic optimization model to obtain the changes in power, cost and revenue under the objective function of minimizing power generation cost;

[0034] Calculation of sensitivity indicators: Calculate the sensitivity indicators of electricity price changes, including the on-grid electricity price, the on-grid electricity price for transmission and the per-kilowatt-hour allocation level of peak-shaving costs.

[0035] Based on the above method, optionally, comparing the strategies and economic benefits of the new energy transmission of the power grid under different working conditions to determine the impact of different working conditions on the economic efficiency of the new energy transmission includes:

[0036] Working condition definition: Three typical working conditions during the heating period and the non-heating period are defined, namely, the provincial working condition G1 where new energy is fully consumed, the working condition G2 where new energy is considered to avoid peak load regulation and possible power abandonment, and the curve working condition G3 where the power generation cost is optimized;

[0037] Model application and analysis: Taking minimizing power generation cost as the optimization goal, the economic optimization model is applied to compare and analyze three typical operating conditions;

[0038] Calculation and analysis: Calculate the power generation cost, new energy income and new energy per kilowatt-hour income indicators respectively, and conduct analysis.

[0039] Based on the above method, optionally, the power resource configuration and scheduling scheme includes:

[0040] Seasonal decision-making reference: During the heating period, focus on reducing costs and increasing profits, while during the non-heating period, focus on maximizing the profits from new energy sources and respond to load changes by adjusting the external delivery ratio;

[0041] Price decision reference: Determine whether to consume or export new energy within the province based on electricity prices;

[0042] Reference for operating condition decision-making: Avoid forced consumption within the province or triggering peak load regulation. Based on the uncertainty of renewable energy output and the lack of optimization tools, appropriate consideration should be given to power curtailment strategies.

[0043] In a second aspect, an embodiment of the present application further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor calls and executes the computer program, it implements the method for analyzing the economic efficiency of new energy delivery under multiple variable influencing factors as described in any one of the first aspects.

[0044] In the economic analysis method and equipment for new energy transmission under multiple variable influencing factors provided in this application, a dual-objective economic optimization model is created, aiming to simultaneously achieve the lowest total power generation cost and the maximum new energy revenue, thereby fully considering the intermittent and unstable characteristics of new energy power generation. Then, through the application of the model, the specific impact of influencing factors such as seasonal factors, electricity price fluctuations and diversified operating conditions on the economic efficiency of new energy transmission can be analyzed and determined. Finally, based on the analysis results, corresponding strategic recommendations and optimized scheduling plans can be formulated. In this way, energy waste can be effectively reduced, and the stability and sustainable development of the new energy industry can be promoted. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. In addition, these drawings and text descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application for those skilled in the art by referring to specific embodiments.

[0046] Figure 1 A schematic flow chart of a method for analyzing the economic efficiency of new energy delivery under multiple variable influencing factors provided in one embodiment of the present application;

[0047] Figure 2 A schematic diagram of the structure of an electronic device provided for one embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0049] Existing economic analysis methods for renewable energy transmission often ignore the impact of seasonal changes, market price fluctuations and different grid operating conditions on renewable energy power systems, resulting in the inability to accurately predict and optimize renewable energy power transmission strategies in practical applications, thereby failing to maximize the economic benefits and market competitiveness of renewable energy. In addition, existing methods lack flexibility and adaptability when dealing with the intermittent and unstable nature of renewable energy, making it difficult to achieve a balance between the economy and reliability of the power system.

[0050] In response to the above problems, the present invention provides a multi-time charging vehicle virtual power plant scheduling and operation scheme, which can effectively evaluate the economic feasibility of new energy under different circumstances and provide decision support for power system planning and operation management. First, the method establishes an economic optimization model, which takes into account the challenges brought by the intermittent and instability of new energy from the two perspectives of pursuing the lowest total power generation cost and pursuing the maximum new energy benefits. Then, through in-depth analysis of actual cases, the impact of seasonal changes, price fluctuations and different operating conditions on the economic feasibility of new energy transmission is explored. Finally, based on the application results of the model, a series of strategic recommendations are developed to optimize the electricity supply and demand structure, reduce energy waste, and promote the sustainable development of the new energy industry. The following is a non-restrictive description of the specific implementation scheme through several examples or embodiments.

[0051] Some embodiments of the present application provide a method for analyzing the economic efficiency of new energy delivery under multiple variable influencing factors, referring to Figure 1 , Figure 1 A flow chart of a method for analyzing the economic efficiency of new energy delivery under multiple variable influencing factors provided in one embodiment of the present application. In the specific implementation, the solution of this embodiment can be a processing system configured in an electronic device such as a computer or server. That is, the solution of this embodiment can be implemented by a software system in an electronic device.

[0052] like Figure 1 As shown, the economic analysis method of new energy delivery under multiple variable influencing factors of this embodiment includes the following steps:

[0053] Step S101: Based on multiple key variables that affect economic efficiency, a piecewise linear programming method is used to construct an economic optimization model. The economic optimization model aims to minimize the total power generation cost and maximize the new energy revenue. The constraints include power balance relationship constraints, upper and lower limit constraints of peak load capacity, and new energy output forecast value constraints.

[0054] Specifically, in practice, there are many variables that affect economic efficiency. In order to improve practicality and accuracy, this embodiment selects key variables to construct an economic optimization model, and at the same time, a piecewise linear programming method is used to construct the model. Key variables include but are not limited to new energy power consumption within the province, power transmission, thermal power unit output and peak load capacity.

[0055] In addition, the economic optimization model is a dual-objective optimization model that pursues the lowest total power generation cost and the maximum new energy benefit, thereby fully considering the intermittent and unstable characteristics of new energy power generation.

[0056] In some embodiments, the objective function of the economic optimization model specifically includes:

[0057] Minimize the total electricity generation cost:

[0058]

[0059] Maximizing the benefits of new energy:

[0060]

[0061] In the formula, x 1 、x 2 、x 3 and x 4 They are the renewable energy grid-connected power within the province, renewable energy power transmission and thermal power output, peak is the peak load capacity; p 1 、p 2 、p 3 and p 4 They are the on-grid price of new energy within the province, the price for new energy transmission, the on-grid electricity price of thermal power units and the peak-shaving cost sharing standard.

[0062] In some embodiments, the constraints specifically include:

[0063]

[0064] Where peak lb 、peak ub They are the upper and lower limits of the peak-shaving capacity, which are used to constrain or relax whether the peak-shaving capacity is allowed; load pre Ne is the load forecast value within the province, which is used to ensure that the optimization result meets the load demand; pre is the predicted value of new energy output, peak th is the peak shaving trigger threshold.

[0065] Among them, the above constraints are used to ensure that the optimization results meet the actual power load demand.

[0066] In addition, in order to improve the efficiency of the model, in some embodiments, the above method may also include: making reasonable assumptions on preset parameters and conditions to simplify the economic optimization model; wherein the preset parameters and conditions include the stability of new energy electricity prices, the uniformity of peak-shaving costs, the differences in seasonal operating costs, and the fixedness of the thermal power grid-connected electricity price.

[0067] Specifically, reasonable assumptions are made about the preset parameters and conditions, including:

[0068] Assumption of stability of electricity prices for renewable energy: It is assumed that the provincial on-grid electricity prices and the external electricity prices of renewable energy remain constant during the study period, that is, they are not affected by market fluctuations;

[0069] Peak load cost uniformity assumption: It is assumed that the peak load electricity allocation cost remains unchanged at 96 time points throughout the day, that is, intraday or seasonal fluctuations are not considered;

[0070] Seasonal operating cost difference assumption: It is assumed that there are differences in operating costs between the heating period and the non-heating period. Although the electricity price and peak-shaving cost are assumed to be stable, the difference in operating costs between the heating period and the non-heating period is still taken into account to reflect the impact of different seasons on the operating costs of the power system.

[0071] Assumption of fixed on-grid electricity price for thermal power generation: The on-grid electricity price for thermal power generation units in the province is assumed to be fixed throughout the year to simplify the model and highlight the impact of the new energy transmission strategy on economic efficiency.

[0072] Step S102: Based on the economic optimization model, the economic efficiency of new energy transmission under various variable influencing factors is analyzed to determine the impact of various influencing factors on the economic efficiency of new energy transmission, including: comparing the economic efficiency of new energy transmission in different seasons, analyzing and determining the impact of seasonal factors on power generation costs and benefits; comparing the economic efficiency of new energy transmission under different electricity prices, and determining the impact of electricity price fluctuations on the economic efficiency of new energy transmission; comparing the strategies and economic benefits of new energy transmission under different operating conditions of the power grid, and determining the impact of different operating conditions on the economic efficiency of new energy transmission.

[0073] Specifically, in this embodiment, the influencing factors considered include seasonal changes, price fluctuations and different operating conditions, and the economic feasibility of new energy delivery for each influencing factor is analyzed separately to facilitate the determination of corresponding response strategies.

[0074] Among them, the application analysis of parameter setting and optimization model is carried out using actual operation data:

[0075] To provide accurate input parameters for the optimization model, the model parameters are shown in Table 1:

[0076] Table 1 Parameter values

[0077]

[0078] Furthermore, the economic feasibility of renewable energy transmission in different seasons is compared, and the impact of seasonal factors on power generation costs and benefits is analyzed and determined, including:

[0079] Seasonal data collection and preprocessing: Collect detailed data on specific areas during the heating period and non-heating period, including peak-shaving capacity, renewable energy transmission capacity, power demand and renewable energy power generation; clean and preprocess the collected data to ensure data accuracy and consistency; cleaning and preprocessing include filling missing values, smoothing outliers, and format unification;

[0080] Calculation of economic indicators: Based on the preprocessed data, the total power generation cost for each season is calculated, including the cost of renewable energy power generation, thermal power generation, power transmission loss, and peak load operation cost; considering the seasonal fluctuation of electricity prices, the revenue from renewable energy transmission and the costs associated with peak load services are calculated;

[0081] Model parameter setting and solution: The calculated seasonal economic indicators are input as parameters into the economic optimization model. According to the set objective function, an appropriate optimization algorithm is used to solve the model and determine the optimal new energy delivery strategy in different seasons. The model solution results are analyzed to identify the economic differences in new energy delivery in different seasons and explore the reasons.

[0082] Furthermore, the economic feasibility of renewable energy transmission under different electricity prices is compared to determine the impact of electricity price fluctuations on the economic feasibility of renewable energy transmission, including:

[0083] Electricity price data collection: Collect new energy on-grid electricity price data in different time periods and regions, including peak and valley electricity prices and seasonally adjusted electricity prices; clean and standardize the collected electricity price data to ensure data quality and consistency;

[0084] Model solution and analysis: adjust input parameters and solve the economic optimization model to obtain the changes in power, cost and revenue under the objective function of minimizing power generation cost;

[0085] Calculation of sensitivity indicators: Calculate the sensitivity indicators of electricity price changes, including price factors such as the on-grid electricity price, the on-grid electricity price for transmission, and the level of peak-shaving cost per kilowatt-hour.

[0086] Furthermore, the strategies and economic benefits of renewable energy transmission under different working conditions of the power grid are compared to determine the impact of different working conditions on the economic efficiency of renewable energy transmission, including:

[0087] Working condition definition: Three typical working conditions during the heating period and the non-heating period are defined, namely, the provincial working condition G1 where new energy is fully consumed, the working condition G2 where new energy is considered to avoid peak load regulation and possible power abandonment, and the curve working condition G3 where the power generation cost is optimized;

[0088] Model application and analysis: Taking the minimization of power generation cost as the optimization goal, the economic optimization model is applied to compare and analyze three typical operating conditions;

[0089] Calculation and analysis: Calculate the power generation cost, new energy income and new energy per kilowatt-hour income indicators respectively, and conduct analysis.

[0090] Step S103: Based on the impact of various influencing factors on the economic efficiency of renewable energy transmission, generate power resource configuration and scheduling plans under various influencing factors.

[0091] Specifically, through the above analysis results, appropriate power resource allocation and scheduling schemes can be generated, as well as corresponding strategic recommendations. These recommendations are aimed at optimizing power resource allocation and scheduling, reducing energy waste, and promoting the sustainable development of the new energy industry.

[0092] More specifically, the power resource allocation and dispatching scheme includes:

[0093] Seasonal decision-making reference: During the heating period, focus on reducing costs and increasing profits, while during the non-heating period, focus on maximizing the benefits of new energy and respond to load changes by adjusting the delivery ratio.

[0094] Price decision reference: The consumption or transmission of new energy within the province is determined based on the electricity price; for example, when the on-grid electricity price within the province is lower than 0.19 yuan / kW.h, it is prioritized for consumption, and if it is higher, it is transmitted. When the on-grid electricity price for transmission is lower than 0.48 yuan / kW.h, transmission is more economical. When the peak-shaving cost is lower than 0.34 yuan / kW.h, consider using peak-shaving measures.

[0095] Reference for working condition decision-making: Avoid forced consumption within the province or triggering peak load regulation, and appropriately consider power abandonment strategies based on the uncertainty of new energy output and the lack of optimization tools.

[0096] Based on the above scheme, a dual-objective economic optimization model was created, aiming to simultaneously achieve the lowest total power generation cost and the maximum new energy benefits, thereby fully considering the intermittent and unstable characteristics of new energy power generation. Then, through the application of the model, the specific impact of seasonal factors, electricity price fluctuations, and diversified operating conditions on the economic efficiency of new energy transmission can be analyzed and determined. Finally, according to the analysis results, corresponding strategic recommendations and optimized scheduling plans can be formulated. In this way, energy waste can be effectively reduced and the stable and sustainable development of the new energy industry can be promoted.

[0097] Compared with the prior art, the economic optimization model proposed in the embodiment of the present application includes two objective functions, namely "minimizing the total power generation cost" and "maximizing the new energy benefits", which is innovative in the prior art and can reflect the differences in strategies under different goal orientations. In addition, the economic analysis of outbound delivery comprehensively considers multiple factors such as seasonal changes, price fluctuations, and different operating conditions. These factors are often considered or ignored separately in the prior art, and this solution integrates them into a unified framework, improving the practicality and accuracy of the model.

[0098] In addition, an embodiment of the present application provides an electronic device, such as Figure 2 As shown, the electronic device includes a memory 21 and a processor 22; wherein the memory 21 stores a computer program, and when the processor 22 calls and executes the computer program, the new energy transmission economic analysis method under multiple variable influencing factors in any of the above-mentioned embodiments is implemented.

[0099] The electronic device may be a desktop computer, a notebook computer or a server.

[0100] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0101] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0102] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0103] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0104] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0105] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0106] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0107] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for analyzing the economic performance of new energy delivery under multiple variable influencing factors, characterized in that: include: Based on multiple key variables that affect economic efficiency, a piecewise linear programming method is used to construct an economic optimization model, which aims to minimize the total power generation cost and maximize the benefits of new energy. The constraints include power balance constraints, upper and lower limit constraints on peak load capacity, and new energy output forecast value constraints. Based on the economic optimization model, the economic efficiency of new energy transmission under various variable influencing factors is analyzed to determine the impact of various influencing factors on the economic efficiency of new energy transmission, including: comparing the economic efficiency of new energy transmission in different seasons, analyzing and determining the impact of seasonal factors on power generation costs and benefits; comparing the economic efficiency of new energy transmission under different electricity prices, determining the impact of electricity price fluctuations on the economic efficiency of new energy transmission; comparing the strategies and economic benefits of new energy transmission under different working conditions of the power grid, determining the impact of different working conditions on the economic efficiency of new energy transmission; Based on the impact of various influencing factors on the economic feasibility of renewable energy transmission, the power resource allocation and dispatching plans under various influencing factors are generated.

2. The method according to claim 1, characterized in that The objective function of the economic optimization model specifically includes: Minimize the total electricity generation cost: min: Maximizing the benefits of new energy: max: Where x1, x2, x3 and x4 are the provincial grid-connected power of renewable energy, the power transmitted to other regions of renewable energy and the output of thermal power respectively; peak is the peak-shaving capacity; p1, p2, p3 and p4 are the provincial grid-connected price of renewable energy, the price of new energy transmission to other regions, the grid-connected price of thermal power units and the kilowatt-hour allocation standard for peak-shaving costs respectively.

3. The method according to claim 2, characterized in that The constraints specifically include: glued: Where peak lb 、peak ub They are the upper and lower limits of the peak-shaving capacity, which are used to constrain or relax whether the peak-shaving capacity is allowed; load pre Ne is the load forecast value within the province, which is used to ensure that the optimization result meets the load demand; pre is the predicted value of new energy output, peak th is the peak shaving trigger threshold.

4. The method according to claim 1, characterized in that: Also includes: Reasonable assumptions are made on the preset parameters and conditions to simplify the economic optimization model; wherein the preset parameters and conditions include the stability of new energy electricity prices, the uniformity of peak-shaving costs, the difference in seasonal operating costs and the fixedness of thermal power on-grid electricity prices.

5. The method according to claim 4, characterized in that The reasonable assumptions made about the preset parameters and conditions include: Assumption of stability of electricity prices for renewable energy: It is assumed that the provincial on-grid electricity prices and the external electricity prices of renewable energy remain constant during the study period; Peak load cost uniformity assumption: Assume that the peak load electricity allocation cost remains unchanged at 96 time points throughout the day; Seasonal operating cost difference assumption: It is assumed that there is a difference in operating costs between the heating period and the non-heating period; Assumption of fixed on-grid electricity price for thermal power generation: The on-grid electricity price for thermal power generation units in the province is assumed to be fixed throughout the year to simplify the model and highlight the impact of the new energy transmission strategy on economic efficiency.

6. The method according to claim 1, characterized in that The above-mentioned comparison of the economic feasibility of renewable energy transmission in different seasons and analysis of the impact of seasonal factors on power generation costs and benefits include: Seasonal data collection and preprocessing: Collect detailed data on specific areas during the heating period and non-heating period, including peak-shaving capacity, renewable energy transmission capacity, power demand and renewable energy power generation; clean and preprocess the collected data to ensure data accuracy and consistency; Calculation of economic indicators: Based on the preprocessed data, the total power generation cost for each season is calculated, including the cost of renewable energy power generation, thermal power generation, power transmission loss, and peak load operation cost; considering the seasonal fluctuation of electricity prices, the revenue from renewable energy transmission and the costs associated with peak load services are calculated; Model parameter setting and solution: The calculated seasonal economic indicators are input as parameters into the economic optimization model. According to the set objective function, an appropriate optimization algorithm is used to solve the model to determine the optimal new energy delivery strategy in different seasons. The model solution results are analyzed to identify the economic differences in new energy delivery in different seasons.

7. The method according to claim 1, characterized in that The comparison of the economic efficiency of renewable energy transmission under different electricity prices and the determination of the impact of electricity price fluctuations on the economic efficiency of renewable energy transmission include: Electricity price data collection: Collect new energy on-grid electricity price data in different time periods and regions, including peak and valley electricity prices and seasonally adjusted electricity prices; clean and standardize the collected electricity price data to ensure data quality and consistency; Model solution and analysis: adjust input parameters and solve the economic optimization model to obtain the changes in power, cost and revenue under the objective function of minimizing power generation cost; Calculation of sensitivity indicators: Calculate the sensitivity indicators of electricity price changes, including the on-grid electricity price, the on-grid electricity price for transmission and the per-kilowatt-hour allocation level of peak-shaving costs.

8. The method according to claim 1, characterized in that The comparison of strategies and economic benefits of renewable energy transmission under different working conditions of the power grid determines the impact of different working conditions on the economic efficiency of renewable energy transmission, including: Working condition definition: Three typical working conditions during the heating period and the non-heating period are defined, namely, the provincial working condition G1 where new energy is fully consumed, the working condition G2 where new energy is considered to avoid peak load regulation and possible power abandonment, and the curve working condition G3 where the power generation cost is optimized; Model application and analysis: Taking minimizing power generation cost as the optimization goal, the economic optimization model is applied to compare and analyze three typical operating conditions; Calculation and analysis: Calculate the power generation cost, new energy income and new energy per kilowatt-hour income indicators respectively, and conduct analysis.

9. The method according to claim 1, characterized in that: The power resource configuration and dispatching scheme includes: Seasonal decision-making reference: During the heating period, focus on reducing costs and increasing profits, while during the non-heating period, focus on maximizing the profits from new energy sources and respond to load changes by adjusting the external delivery ratio; Price decision reference: Determine whether to consume or export new energy within the province based on electricity prices; Reference for operating condition decision-making: Avoid forced consumption within the province or triggering peak load regulation. Based on the uncertainty of renewable energy output and the lack of optimization tools, appropriate consideration should be given to power curtailment strategies.

10. An electronic device, characterized in that: It includes a memory and a processor, the memory stores a computer program, and when the processor calls and executes the computer program, it implements the new energy transmission economic analysis method under multiple variable influencing factors as described in any one of claims 1 to 9.

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