A power source configuration method for new energy base considering the characteristics of the receiving end and the resource end

Abstract

The application discloses a new energy large base power supply configuration method which comprehensively considers the characteristics preference of the receiving end and the resource endowment of the source end. The method first constructs the hourly output model of wind power, photovoltaic and photo-thermal power based on meteorological data, and analyzes the multi-energy complementary operation characteristics combined with energy storage constraints. Secondly, the demand preference of the receiving end system is identified, that is, the support type or the high proportion of green electricity type, and the corresponding outgoing target power curve is constructed. Then, the hourly production simulation of the whole year is carried out, and the utilization hours of the outgoing channel, the peak guarantee rate and the new energy utilization rate and other key indicators are calculated. Finally, the entropy weight TOPSIS method is used to comprehensively evaluate and optimize various power supply configuration schemes. The application can realize the accurate collaborative matching of the source end resource and the demand of the receiving end, and effectively improve the new energy consumption level and the utilization efficiency of the outgoing channel.

Description

Technical Field

[0001] This invention relates to the field of new energy power system planning and operation analysis technology, and in particular to a power configuration method for large-scale new energy bases that comprehensively considers the demand preferences of the receiving-end power system and the characteristics of the new energy resource endowment at the sending end. Specifically, it involves technologies such as output modeling of multiple types of power sources, including wind power, photovoltaic, pumped storage, solar thermal and electrochemical energy storage, multi-energy complementary operation analysis, hourly production simulation and comprehensive evaluation of power configuration schemes. Background Technology

[0002] Against the backdrop of "dual-carbon" goals and the construction of new power systems, large-scale new energy bases built in desert, Gobi, and arid regions have become an important form of new energy development and inter-regional power transmission in my country. These large-scale new energy bases typically possess abundant wind and solar energy resources, large power generation capacity, and long transmission distances. Their power configuration schemes directly affect the level of new energy absorption, the safety of power system operation, and the utilization efficiency of transmission channels. However, new energy output exhibits significant randomness, volatility, and intermittency, and different types of new energy show clear complementary relationships on intraday and intrayear scales. Simultaneously, receiving-end power systems differ significantly in load structure, peak-shaving capacity, and clean energy consumption targets, resulting in different preferences for the power form and electricity structure of transmitted power. How to coordinate the resource conditions at the sending end with the demand preferences at the receiving end is a key issue that urgently needs to be addressed in the power configuration of large-scale new energy bases.

[0003] Existing planning methods for renewable energy bases often focus on the development potential of resources at the sending end or a single economic indicator, failing to fully reflect the random fluctuations in renewable energy output and the differentiated demands of receiving systems for transmitted power. This leads to problems such as severe wind and solar curtailment, low utilization efficiency of transmission channels, or insufficient supply guarantee capacity in some renewable energy bases during actual operation. Therefore, it is necessary to propose a power configuration method for large-scale renewable energy bases that can simultaneously characterize both the renewable energy output characteristics and the demand preferences of receiving systems.

[0004] 1. Existing technical solutions: "Research on Optimization and Long-Term Planning of Multi-Energy Complementary Capacity Configuration in Clean Energy Bases" (Liao Chaohao. Research on Optimization and Long-Term Planning of Multi-Energy Complementary Capacity Configuration in Clean Energy Bases [D]. South China University of Technology, 2024. DOI:10.27151 / d.cnki.ghnlu.2024.000947.) and "Research on the Dual-Layer Optimization Configuration of Wind, Solar, Thermal and Storage for Multi-Energy Complementary New Energy Bases" (Yan Xinrong, Xie Yurong, Huang Rong, et al. Research on the Dual-Layer Optimization Configuration of Wind, Solar, Thermal and Storage for Multi-Energy Complementary New Energy Bases [J]. Science in China: Technological Sciences, 2025, 55(11):1865-1874.), mainly refer to the power supply configuration method of new energy bases guided by resource endowment. These methods typically assess the theoretical reserves and exploitable scale of wind and solar power based on the wind and solar energy resources in the region where the new energy base is located. They utilize multi-year meteorological data or typical year data to determine the scale of new energy installed capacity and then allocate a certain proportion of peak-shaving power sources or energy storage facilities to meet the grid-connected operation requirements of the new energy base. These methods generally employ typical daily or weekly load curves to conduct simplified production simulations of new energy and conventional power sources. The schemes are evaluated using indicators such as average annual utilization hours and curtailment rate to determine the power configuration scheme for the new energy base. Limitations of existing technologies: While the above-mentioned resource-endowment-oriented new energy base configuration methods can reflect the development potential of new energy resources at the sending end, they still have the following shortcomings: 1) Insufficient characterization of receiving end demand: These methods mainly focus on the sending end and do not fully consider the load structure characteristics, peak-shaving capacity, and clean energy consumption targets of the receiving end power system, making it difficult to reflect the actual preferences of the receiving end for the power form and power structure of transmitted electricity; 2) Coarse characterization of new energy output fluctuation characteristics: Most methods use typical day or averaging processing methods, ignoring the random fluctuation characteristics of new energy hourly output, making it difficult to truly reflect the operating status of new energy bases under high proportion of new energy conditions; 3) Insufficient depth of multi-energy complementarity analysis: The complementary relationship between wind power, photovoltaics, and energy storage is mostly set through empirical ratios, lacking system analysis based on hourly output characteristics, resulting in insufficient rationality in the configuration of energy storage scale and operation mode.

[0005] The existing technical solution is: "Research on the Power Configuration Ideas of Multi-Energy Complementary Bases with Integrated Wind, Solar, Thermal and Storage and Integrated Wind, Solar and Storage" (Wu Di. Research on the Power Configuration Ideas of Multi-Energy Complementary Bases with Integrated Wind, Solar, Thermal and Storage and Integrated Wind, Solar and Storage [J]. Journal of Electric Power, 2024, 39(05):464-468.DOI:10.13357 / j.dlxb.2024.049.). Technical solution two is mainly a new energy base configuration and operation evaluation method based on hourly production simulation. This type of method constructs an 8760-hour hourly output sequence of power sources such as wind power, photovoltaic and energy storage, conducts joint production simulation of new energy bases on a yearly time scale, and analyzes the curtailment, power structure and operation indicators under different power configuration schemes by combining external transmission channel constraints. Some studies further introduce the operation model of pumped storage or electrochemical energy storage, improve the new energy absorption capacity by simulating the charging and discharging process of energy storage, and compare and analyze the power configuration schemes with the new energy utilization rate, curtailment rate or system operating cost as the main evaluation indicators.

[0006] Limitations of Existing Technology 2: Although Existing Technology 2 improves upon Technology 1 in terms of time scale and operational details, it still has the following shortcomings: 1) Insufficient consideration of receiving-end demand preferences. Such methods often take maximizing renewable energy consumption or minimizing system operating costs as a single objective, failing to distinguish between the receiving-end system's demand for supply support and demand for high-proportion green electricity consumption, making it difficult to support differentiated configuration schemes for different receiving-end markets; 2) Insufficient applicability of power output modeling in desert, Gobi, and arid regions. In areas with scarce meteorological observation stations, such as deserts, Gobi, and arid lands, some methods directly use extrapolation or simplified assumptions to construct renewable energy power output sequences, failing to fully consider regional meteorological statistical characteristics, resulting in insufficient randomness and correlation in power output simulation results; 3) Incomplete comprehensive evaluation system. It focuses on single indicators such as curtailment rate or renewable energy utilization rate, lacking a systematic comprehensive evaluation of key operational indicators such as the utilization efficiency of transmission channels and peak power supply guarantee capacity, making it difficult to form stable and reliable scheme optimization conclusions. Summary of the Invention

[0007] The purpose of this invention is to provide a power configuration method for large-scale renewable energy bases that can comprehensively reflect the characteristics of renewable energy resource endowment at the source end and the demand preferences of the power system at the receiving end, characterize the randomness of renewable energy output and the characteristics of multi-energy complementarity on an hourly scale, and is applicable to scenarios with a high proportion of renewable energy transmission. This method overcomes the problems in existing methods, such as focusing only on resource development conditions, ignoring differences in load preferences at the receiving end, insufficient characterization of wind and photovoltaic power output fluctuations, unclear basis for energy storage configuration and operation, and poor matching between the transmission power curve and the power structure. This solves the technical problems in the planning and operation of large-scale renewable energy bases, such as insufficient renewable energy absorption capacity, low utilization efficiency of transmission channels, and unstable system supply capacity.

[0008] To achieve the above objectives, the technical solution adopted by this invention is as follows: A power configuration method for large-scale new energy bases that comprehensively considers the characteristics and preferences of the receiving end and the resource endowment of the source end, comprising the following steps: Step 1: Resource endowment assessment and output modeling; Based on long-term meteorological data and new energy site development conditions, the wind, solar and pumped storage resources in the sending-end area are assessed, and hourly output models of wind power, photovoltaic and solar thermal power sources are constructed. Step 2: Multi-energy complementarity and energy storage operation analysis; Combining the operational constraints of pumped hydro storage and electrochemical energy storage, analyze the combined output characteristics of wind, solar and energy storage under the multi-energy complementarity operation mode, and identify the constraints of new energy consumption and system regulation requirements; Step 3: Identification of receiving-end demand preferences and construction of export target curves; For potential receiving-end electricity markets, analyze their power supply and demand structure and load curve characteristics, identify supply support-oriented demand preferences guided by system power supply reliability and high proportion of green electricity-oriented demand preferences guided by the proportion of clean energy consumption, and construct corresponding export target power curves accordingly. Step 4: Hourly production simulation; Based on the 8760-hour time scale throughout the year, joint hourly production simulations are conducted for wind power, photovoltaic, solar thermal, pumped storage, and electrochemical energy storage to form the output process and power transmission curves of the new energy base under different power configuration schemes. Step 5: Calculation of operational indicators; Based on the hourly production simulation results, calculate and statistically analyze the utilization hours of various power transmission channels, peak guarantee rate of new energy bases, proportion of clean electricity, and utilization rate of new energy. Step 6: Comprehensive evaluation and scheme selection; conduct multi-dimensional comprehensive evaluation of the operating indicators of different power supply configuration schemes, and select the power supply configuration scheme of the new energy large-scale base that meets the load preference constraints of the receiving end and has the best overall performance.

[0009] Furthermore, in step 1, when the new energy base is located in an area with scarce meteorological observation stations, the measured power output data is not used directly. Instead, based on the long-term meteorological statistical characteristics of the reference meteorological stations, an 8760-hour hourly power output sequence of wind power and photovoltaic power is constructed through numerical simulation or statistical reconstruction methods to characterize the randomness, volatility and correlation characteristics of new energy power output.

[0010] Furthermore, the construction of the wind power hourly output model in step 1 specifically includes: calculating the hourly wind speed at the hub height based on mesoscale reanalysis meteorological data, and calculating the hourly output power of the wind farm by combining the wind turbine power characteristic function and the comprehensive efficiency coefficient; The construction of the photovoltaic hourly output model in step 1 specifically includes: calculating the horizontal surface or optimal tilt angle radiation based on mesoscale reanalysis meteorological data, and combining the radiation intensity under standard test conditions with the comprehensive efficiency coefficient considering temperature correction and inverter efficiency to calculate the photovoltaic hourly output power.

[0011] Furthermore, in step 2, based on the power output structure characteristics of wind power being higher at night and photovoltaic power being concentrated during the day, the complementary relationship between wind power and photovoltaic power on the intraday and intrayear time scales is identified, and the complementary relationship and the equivalent utilization hours of new energy are used as the basic parameters for determining the continuous full-load hours of pumped storage, configuring the power generation capacity, and analyzing the high proportion of new energy transmission capacity.

[0012] Furthermore, in step 2, both pumped hydro storage and electrochemical energy storage are subject to charging and discharging power constraints, energy capacity constraints, and mutual exclusion constraints on charging and discharging states, in order to prevent different energy storage forms from charging and discharging simultaneously during the same period.

[0013] Furthermore, step 3, which involves constructing the corresponding target power curve for power transmission, specifically includes: Construct a target curve for power transmission that ensures supply and supports external power transmission, taking the peak load guarantee of the receiving system as a constraint, and prioritizing the stable output demand during the period of maximum system load; Construct a target curve for high-proportion green electricity transmission, aiming to maximize the proportion of clean energy consumption at the receiving end and strengthen the proportion of new energy electricity transmitted.

[0014] Furthermore, in step 4, the hourly production simulation module introduces the random fluctuation characteristics of new energy output and energy storage regulation strategies on an annual scale of 8760 hours, and conducts joint hourly production simulation of wind power, photovoltaic, pumped storage and electrochemical energy storage to achieve coordinated optimization of the power scale configuration of new energy base and the shape of the external transmission target curve.

[0015] Furthermore, in step 5, the formula for calculating the peak guarantee rate of the new energy base is as follows:

[0016] In the formula, For the new energy base in the first Actual power output per hour; For the receiving end system in the first Hourly target power demand; This refers to the set of peak periods for the receiving system.

[0017] Furthermore, in step 6, the comprehensive evaluation and scheme selection module adopts a multi-index comprehensive evaluation method to perform weighted analysis on the indicators of new energy utilization rate, curtailment rate, peak power supply guarantee capability and external transmission channel utilization efficiency, and form a comprehensive evaluation result and selection ranking of different power configuration schemes for the new energy large base.

[0018] Furthermore, step 6 specifically includes: The entropy weight method is used to determine the weight of each operational indicator in order to objectively reflect the information differences of the indicators in each scheme; The TOPSIS method is used to calculate the distance between each power supply configuration scheme and the positive and negative ideal solutions, and the comprehensive score is calculated. All candidate solutions are ranked according to their comprehensive scores, and the solution with the highest score is the preferred solution, while ensuring that it meets the constraints of end-user demand preferences and key indicator threshold requirements.

[0019] The beneficial effects of this invention are as follows: By introducing calculation methods such as new energy resource endowment assessment, hourly output modeling, multi-energy complementary operation analysis, and hourly production simulation based on MERRA2 mesoscale meteorological data, this invention systematically constructs a method system for evaluating the power configuration and transmission capacity of large-scale new energy bases, comprehensively considering the characteristics of source-end resources and the demand preferences of receiving-end. This not only improves the scientificity and accuracy of new energy output modeling and power configuration scheme calculation results, truly reflecting the randomness, volatility, and synergistic operation characteristics of new energy sources such as wind power and photovoltaics, but also possesses good engineering feasibility and prospects for widespread application. It can provide quantitative analysis basis for the optimized configuration of power scale in large-scale new energy bases, the demonstration of cross-regional transmission capacity, and the planning of transmission channels. It also provides scientific basis and technical support for improving the new energy consumption mechanism, promoting the safe and orderly development of high-proportion renewable energy, and supporting the improvement of power system flexibility. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the power configuration method for large-scale new energy bases that comprehensively considers the characteristics and preferences of the receiving end and the resource endowment of the source end, according to the present invention; wherein: 1---Resource endowment assessment and output modeling module; 2---Multi-energy complementarity and energy storage operation analysis module; 3---Receiving end demand preference identification and external transmission target curve construction module; 4---Hourly production simulation module; 5---Operation index calculation module; 6---Comprehensive evaluation and scheme optimization module, and the direction indicated by the arrow is the implementation direction of the technical method.

[0021] Figure 2 This is the hourly power output curve of a wind farm according to an embodiment of the present invention.

[0022] Figure 3 This is the annual typical daily power output curve of a wind farm according to an embodiment of the present invention.

[0023] Figure 4 This is the hourly photovoltaic power output curve of an embodiment of the present invention.

[0024] Figure 5 This is the annual typical daily power output curve of a photovoltaic power station according to an embodiment of the present invention.

[0025] Figure 6 This is a typical daily load characteristic curve of the receiving-end power grid in winter 2035 according to an embodiment of the present invention.

[0026] Figure 7 This is the power supply curve of an embodiment of the present invention.

[0027] Figure 8 This is a typical daily power system balance diagram of the 8760 hourly production simulation according to an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0029] The present invention provides a power configuration method for large-scale new energy bases that comprehensively considers both receiver-end characteristic preferences and source-end resource endowments, including: Step 1: The resource endowment assessment and output modeling module, based on long-term meteorological data and new energy site development conditions, classifies and assesses the theoretical reserves, exploitable reserves, and technically exploitable reserves of wind energy, solar energy, and pumped storage energy resources in the sending-end area. On this basis, it constructs hourly output models of wind power, photovoltaic power, and solar thermal power to characterize the randomness, volatility, and complementary characteristics of new energy output.

[0030] Preferably, (1) the wind power hourly output model for 8760 hours:

[0031] In the formula, For the first Hourly wind farm output power For wind power installed capacity, The comprehensive efficiency coefficient is used to characterize the impact of availability, power curtailment losses, etc. Wind turbine power characteristic function; The wind speed at a specific hub height is calculated based on MERRA2 data.

[0032] Preferably, (2) the photovoltaic 8760-hour hourly output model:

[0033] In the formula, For the first Hourly photovoltaic power output For photovoltaic installed capacity, To obtain the first based on MERRA2 data Hourly horizontal or optimal tilt angle radiation; Radiation intensity under standard test conditions; The overall efficiency coefficient takes into account factors such as temperature correction and inverter efficiency; Step 2: The multi-energy complementarity and energy storage operation analysis module combines the structural characteristics of wind power and photovoltaic power output with the charging and discharging efficiency, energy capacity and operation constraints of pumped storage and electrochemical energy storage to analyze the combined output characteristics of multiple types of power sources under the multi-energy complementarity operation mode, and identify the constraints of new energy consumption and system regulation requirements. Step 3: The receiving-end demand preference identification and transmission target curve construction module analyzes the load curve characteristics, power supply and demand structure and clean energy consumption demand of potential receiving-end power systems, identifies different types of electricity demand preferences, and constructs a matching transmission target power curve accordingly. Preferably, (1) the supply support objective function can be expressed as:

[0034]

[0035] Preferably, (2) the objective function for a high proportion of green electricity can be expressed as:

[0036] In the formula, For the first Hourly base external power, For the first Rated capacity of the hourly delivery channel.

[0037] Step 4: The hourly production simulation module performs joint hourly production simulations of various power sources, including wind power, photovoltaic power, solar thermal power, pumped storage, and electrochemical energy storage, on an annual scale of 8760 hours, to generate output processes and power transmission curves of the new energy base under different power configuration schemes. (1) System power balance constraints New energy base at any time Satisfy power balance relationship: ,

[0038] In the formula, For the first Hourly wind farm output power For the first Hourly photovoltaic power output For the first Pumped storage power generation output per hour For the first Hourly electrochemical energy storage discharge output power, For the first Purchase power from the main grid every hour. For the first Hourly power delivery curve For the first Hourly pumped storage charging power, For the first Hourly electrochemical energy storage charging power, For the first Hourly power consumption.

[0039] (2) Constraints on the evolution of energy storage The energy state of pumped storage in adjacent time periods is as follows:

[0040] And satisfy capacity constraints

[0041] (3) Charging and discharging mutual exclusion constraint

[0042]

[0043]

[0044]

[0045] (4) Mutual exclusion constraint between power purchase and power abandonment

[0046] Step 5: The operation index calculation module calculates key operation indicators such as new energy utilization rate, curtailment rate, peak power supply guarantee capacity, clean energy ratio and external transmission channel utilization efficiency based on hourly production simulation results. Step 5.1, New Energy Utilization Rate The renewable energy utilization rate is used to characterize the effective utilization level of renewable energy power generation, and its calculation formula is as follows:

[0047] Step 5.2, curtailment rate The curtailment rate measures the proportion of renewable energy that fails to connect to the grid due to limitations in its absorption capacity. The formula for calculating this rate is:

[0048] Step 5.3, Peak Power Supply Guarantee Capability Peak power supply guarantee capability is used to characterize the ability of a new energy base to provide stable power output during periods of high load on the receiving-end system. Its calculation formula is as follows:

[0049] Step 5.4, Equivalent Utilization Hours of Outbound Delivery Channel The equivalent utilization hours of the power transmission channel are used to characterize the equivalent full-load operation level of the power transmission channel within the statistical period. The calculation formula is as follows:

[0050] Step 5.5, Proportion of Clean Energy

[0051] In the formula, To improve the utilization rate of new energy sources, For the curtailment rate, For peak power supply guarantee capacity, T represents the equivalent utilization hours of the external transmission channel. This represents the proportion of clean energy. For the first Actual grid-connected power output of new energy sources in 24 hours; For the first Hourly renewable energy sources can generate electricity. For the new energy base in the first Actual power output per hour; For the receiving end system in the first The target power demand per hour. This refers to the electricity transmitted externally. This refers to the rated capacity of the external transmission channel.

[0052] The indicator calculation module stipulates that the utilization rate of new energy and the curtailment rate are based on the "Notice of the National Energy Administration on Doing a Good Job in New Energy Consumption and Ensuring the High-Quality Development of New Energy" (Guonengfa Dianli

[2024] No. 44), and the utilization rate of new energy should not be less than 90% in principle; the equivalent utilization hours of the transmission channel are based on the "Pricing Method for Transmission Prices of Cross-Provincial and Cross-Regional Special Projects" (Fagaijiage

[2021] No. 1455), and the annual utilization hours of the DC channel should not be less than 4500h.

[0053] Preferably, the peak power supply guarantee capability fully utilizes the supporting role of DC power, and the guarantee rate during peak hours should generally not be less than 90%. Preferably, the proportion of clean energy consumption is 100% in pure clean energy bases; Step 6: The comprehensive evaluation and scheme selection module adopts a multi-index comprehensive evaluation method to comprehensively evaluate the operating effects of different power configuration schemes and select the power configuration scheme of the new energy large-scale base that meets the constraints of the receiving end demand preferences and has the best overall performance.

[0054] The specific implementation methods include the following: Step 6.1, Preparation of indicator data The evaluation matrix is ​​constructed using the indicator data obtained in step S5, including the utilization rate of new energy sources, the curtailment rate, peak power supply guarantee capacity, the proportion of clean energy, and the equivalent utilization hours of the power transmission channels. ], where i represents the scheme number and j represents the indicator number.

[0055] Step 6.2, Calculate the index weights using the entropy weight method. The entropy method is used to determine the weights of each indicator in order to objectively reflect the information differences of the indicators in different schemes. Specific steps include: Indicator normalization involves converting indicators with different dimensions, such as renewable energy utilization rate, curtailment rate, peak power supply guarantee capacity, clean energy proportion, and equivalent utilization hours of power transmission channels, into uniform dimensionless evaluation values. This eliminates the impact of differences in the order of magnitude of the indicators on the evaluation results. For example, for positive indicators (higher values ​​are better), a normalization method is used...

[0056] For negative indicators (the smaller the value, the better, such as the curtailment rate), adopt...

[0057] Calculate the proportion of the indicators in each scheme. and entropy : =

[0058]

[0059] Calculate the indicator weights:

[0060] in, This refers to the entropy weight of index j.

[0061] Step 6.3, Calculate the overall score using the TOPSIS method. Obtaining the weights of each indicator Then, the TOPSIS method was used to calculate the comprehensive evaluation value that is closest to the ideal solution for each scheme: Determine the ideal solution and negative ideal solution

[0062] Calculate the Euclidean distance between the proposed solution and the ideal solution: ,

[0063] Calculate the overall score:

[0064] Step 6.4, Scheme Ranking and Optimization Based on the overall score All candidate solutions are ranked, and the solution with the highest score is selected as the preferred solution, while ensuring that it meets the constraints of the end-user demand preferences and the key indicator threshold requirements.

[0065] The method of the present invention: 1. Achieve coordinated allocation of source-end resource endowments and receiver-end demand preferences. This invention introduces a receiving-end demand preference identification and transmission target curve construction mechanism to combine the characteristics of new energy resources at the sending end with the characteristics of load and clean energy consumption at the receiving end, thereby improving the matching between the power configuration scheme of large-scale new energy bases and the operational requirements of the receiving-end system.

[0066] 2. It can accurately depict the randomness, volatility, and complementary characteristics of new energy output. This invention constructs an hourly output model for wind and solar power over 8760 hours based on MERRA2 mesoscale meteorological data. In areas with scarce meteorological observation stations, it generates output sequences through numerical simulation or statistical reconstruction methods. Compared with traditional typical day or average year methods, it can more realistically reflect the temporal fluctuation characteristics of new energy output and the wind-solar complementary relationship.

[0067] 3. Fully leverage the system regulation capabilities of pumped hydro storage and electrochemical energy storage. In the analysis of multi-energy complementarity and energy storage operation, this invention comprehensively considers the power constraints, energy capacity constraints, and charge-discharge mutual exclusion constraints of pumped hydro storage and electrochemical energy storage, effectively avoiding unreasonable simultaneous charge-discharge conditions and improving the physical rationality and engineering feasibility of energy storage configuration and operation strategies.

[0068] 4. Improve the power supply reliability and absorption rate of high-proportion renewable energy transmission schemes. By constructing target curves for both supply guarantee and high-proportion green electricity transmission, and guiding the joint operation of new energy and energy storage in hourly production simulations, this invention can improve the utilization rate of new energy, reduce the curtailment rate, and enhance the effective support capacity of new energy bases for transmission channels while meeting the peak load guarantee requirements of the receiving end.

[0069] 5. The evaluation indicator system is complete, and the results are comparable and have decision-support value. Based on hourly production simulation results, this invention systematically calculates key indicators such as new energy utilization rate, curtailment rate, peak power supply guarantee capacity, clean energy proportion, and equivalent utilization hours of external transmission channels, and achieves optimal solution through comprehensive evaluation of multiple indicators.

[0070] 6. The method is highly versatile and applicable to various planning scenarios for large-scale new energy bases. This invention is applicable to areas with concentrated new energy development such as deserts, Gobi, and wastelands, as well as different receiving-end power market scenarios. It has good versatility and scalability, and can provide technical support for the optimization of power structure and the planning of transmission channels for large-scale new energy bases.

[0071] The following description, in conjunction with the accompanying drawings, further illustrates the following: A large-scale new energy base in a desert area that adopted the technical solution of this invention. like Figure 1 As shown, the present invention provides a power configuration method for large-scale new energy bases that comprehensively considers the characteristics and preferences of the receiving end and the resource endowment of the source end. The configuration system includes a resource endowment assessment and output modeling module 1, a multi-energy complementarity and energy storage operation analysis module 2, a receiving end demand preference identification and transmission target curve construction module 3, an hourly production simulation module 4, an operation index calculation module, and a comprehensive evaluation and scheme optimization module 5; the method includes: Step S1: The resource endowment assessment and output modeling module employs a combination of map-based operations and field surveys to identify new energy sites and acquire parameters. Map-based operations, based on ArcGIS 10.2 and the Aowei Map platform, utilize spatial analysis tools such as Clip, Intersect, Erase, and Buffer to overlay and analyze basic data including ecological protection red lines, data from the Third National Land Survey, high-resolution remote sensing imagery, and planned converter stations and transmission lines with the proposed new energy sites. This process identifies and eliminates various restrictive areas, determining key planning parameters such as the developable wind and solar power land area, land use characteristics, spatial distribution features, and distance from the site to the converter station. Based on this, field surveys are conducted, including on-site inspections and drone aerial photography of typical sites, to verify topography and surface conditions, thereby improving the accuracy of new energy planning layout results and the feasibility of engineering projects.

[0072] Step S2: The multi-energy complementarity and energy storage operation analysis module, based on the 8760-hour hourly output sequence of wind power and photovoltaic power, uses the Pearson method of the stats module in the scipy library of Python 3.10 to calculate the Pearson correlation coefficient between wind power output and photovoltaic power output, which is used to characterize the correlation and inverse fluctuation characteristics of wind power and photovoltaic power over time. At the same time, the std function of the numpy library in Python 3.10 is used to calculate the standard deviation of the combined output sequence of wind power, photovoltaic power, and the combined output sequence after the introduction of pumped hydro storage and electrochemical energy storage. By comparing the fluctuation level of combined output with that of a single power source, the module quantitatively evaluates the ability of multi-energy complementarity and energy storage configuration to reduce the fluctuation of new energy output.

[0073] Step S3: The receiving-end demand preference identification and transmission target curve construction module identifies the peak-valley distribution characteristics under temperature-controlled load based on the daily load curve of the receiving-end power grid under typical summer and winter climate conditions, extracts the load peak value, valley value and time duration characteristics, and divides the system peak and valley periods; using the rated capacity of the DC channel as a benchmark, it constructs the transmission target power curve in a per-unit manner, and achieves adaptive adjustment of transmission power on intraday and seasonal scales by increasing the transmission power during periods of concentrated renewable energy output and reducing the transmission power during periods of low load or insufficient renewable energy output; Step S4: The hourly production simulation module, using 8760 hours per year as the time scale, constructs a multi-power joint hourly production simulation model for the new energy base based on the hourly output models of wind power, photovoltaic and solar thermal power sources, as well as the power constraints, energy capacity constraints and operating status constraints of pumped storage and electrochemical energy storage. Within each time period, with system power balance as the constraint, it coordinates decision variables such as new energy power generation, energy storage charging and discharging, external power transmission, and power purchase and curtailment, and forms the power generation output process under different power configuration schemes through hourly rolling calculations.

[0074] Step S5: The operation index calculation module, based on the hourly output results of 8760 hours throughout the year output by the hourly production simulation module 4, uses Python's NumPy, Pandas and other numerical calculation and data processing libraries to perform batch calculations on the hourly output matrix, thereby realizing the automated extraction and statistical analysis of key operation indicators. Step S6: The comprehensive evaluation and scheme selection module adopts a multi-index comprehensive evaluation method combining the entropy weight method and the TOPSIS method to comprehensively evaluate the key operating indicators obtained in step S5, and select the power configuration scheme of the new energy large-scale base that meets the end-end demand constraints and has the best overall performance.

[0075] A certain desert-based new energy base in a certain city has abundant wind and solar resources. At the end of 2024, the city's grid-connected power capacity reached 12,112,200 kW. Of this, thermal power capacity was 2,385,100 kW; wind and solar new energy capacity was 9,727,100 kW (wind power 6,451,400 kW and photovoltaic 3,275,700 kW), with new energy accounting for 80.31% of the total capacity. The base's wind farm site is located in the northwest, with a planned capacity of 7,000,000 kW. Based on MERRA2 mesoscale data at a height of 130 meters in the planned area, the average annual wind speed at 130 meters in the wind farm area is approximately 7.06 m / s, and the wind power density is 392 W / m³. 2 The typical daily hourly and annual power output characteristics of wind power are shown in the figure. Figures 2-3 Preliminary estimates suggest that the average annual equivalent utilization hours of wind turbines in the wind farm area are approximately 2960 hours, with an average annual power generation of approximately 20.72 billion kWh. The wind energy resources are significant and possess immense development and utilization value. The photovoltaic (PV) farm area is located in the north. The area boasts abundant solar energy resources, with concentrated exploitable areas. The land type is primarily sandy, with over 1786 hours of sunshine annually. The solar energy resources in the PV farm area exhibit a distribution pattern of gradually decreasing from the northwest to the southeast. Annual PV output characteristics and typical daily hourly output characteristics are shown in [reference needed]. Figures 3-4 The optimal tilt angle radiation at the site center is 2164 kWh / m². 2 Annual normal radiation 1928 kWh / m 2 The average utilization hours over 25 years is 2041 hours, and the average annual power generation is approximately 22.449 billion kilowatt-hours.

[0076] A potential receiving end of a power grid is affected by high temperatures in summer, resulting in a significant impact from air conditioning load. The daily load curve generally exhibits a bimodal shape. Figure 6 The peak load occurs around 22:00 during the evening peak lighting time, with the second peak occurring between 14:00 and 15:00 in the afternoon. The daily minimum load generally occurs between 5:00 and 6:00 in the morning. Peak periods typically last 6-7 hours, while off-peak periods generally last 6-8 hours. In winter, the daily peak load occurs between 17:00 and 18:00, with the second peak occurring between 11:00 and 13:00. The daily minimum load generally occurs between 3:00 and 6:00 in the morning, and off-peak periods generally last 6-8 hours. Based on the characteristics of the receiving-end grid load, and considering factors such as the resource conditions at the sending end and the seasonal distribution of wind and solar power output, a DC transmission curve is proposed, as shown below. Figure 7This aligns with the typical power transmission curves for high-proportion green energy bases. In the first quarter (0-9 AM), the power transmission rate was 0.35 PU; from 10 AM to 5 PM, it reached 1.0 PU; and from 6 PM to 11 PM, it was 0.3 PU. In the second quarter (April-June), the power transmission rate was 0.35 PU from 0-9 AM, 0.95 PU from 10 AM to 5 PM, 0.55 PU from 6 PM to 8 PM, and 0.35 PU from 9 PM to 11 PM. In the third quarter, the power transmission rate was 0.3 PU from 0-9 AM, 0.9 PU from 10 AM to 5 PM, and 0.3 PU from 6 PM to 11 PM. In the fourth quarter, the power transmission rate was 0.25 PU from 0-10 AM, 0.85 PU from 11 AM to 4 PM, 0.5 PU from 5 PM to 8 PM, and 0.25 PU from 9 PM to 11 PM.

[0077] Taking into account the base power supply configuration structure, supporting power supply scheme, power transmission terminal location, and external transmission channel type, the calculation model proposed in this invention is used to simulate channel operation for different schemes. Typical daily system operation simulation results are shown below. Figure 8 The results show that, under the configuration of 7 million kW wind power, 11 million kW photovoltaic power, 200,000 kW solar thermal power, 4.8 million kW pumped storage, and 1 million kW / 1 hour electrochemical energy storage, the annual transmission capacity of the transmission channel reaches 37.865 billion kWh, with an annual utilization of 4,733 hours. The average utilization rate of new energy reaches 90.10%, and the proportion of new energy power is 98.34%. Furthermore, it can achieve a stable transmission capacity of up to 8 million kW under the support of AC mutual assistance from the sending-end grid. The comprehensive evaluation and scheme optimization module adopts a multi-index comprehensive evaluation method combining the entropy weight method and the TOPSIS method to quantitatively analyze the operational effects of different power supply configuration schemes. The evaluation indicators include transmission channel hours, annual transmission capacity, clean energy proportion, and new energy curtailment rate. Among these, transmission channel hours, annual transmission capacity, and clean energy proportion are positive indicators, while the new energy curtailment rate is a negative indicator. First, the indicators were normalized and their weights were determined using the entropy weight method. Then, the distances to the positive and negative ideal solutions for each scheme were calculated using the TOPSIS method, ultimately obtaining the comprehensive evaluation score for each scheme. Calculations showed that among the three power configuration schemes, Scheme 2 performed best in terms of balanced power output, channel utilization efficiency, and renewable energy absorption rate, resulting in the highest comprehensive score. Therefore, Scheme 2 was selected as the preferred power configuration scheme for the large-scale renewable energy base.

[0078] The above results show that the method of the present invention can synergistically optimize the power supply configuration scale, energy storage support capacity and transmission channel operation efficiency under high proportion of new energy conditions, effectively improve the level of new energy consumption and channel utilization efficiency, and provide operable, quantifiable and engineering-applicable technical support for the planning and demonstration of the Shagohuang new energy base and inter-regional power transmission project. It is of great significance for ensuring the safe and stable transmission of new energy bases and improving the clean power supply capacity of receiving-end systems.

[0079] Table 1 Calculation Results of Power Supply Ratio Scheme

[0080] By adopting the above-disclosed technical solution of this invention, the following beneficial effects are obtained: This invention enables the systematic analysis and optimization of power configuration schemes for large-scale renewable energy bases, taking into full account the characteristics of renewable energy resource endowments, receiving-end demand preferences, and multi-energy complementarity operation constraints. The results demonstrate high engineering feasibility and practical value. The methodology constructed in this invention provides a scientific and quantitative basis for decision-making in the planning of high-proportion renewable energy bases, power structure optimization, and operational analysis of transmission channels. Simultaneously, it provides effective technical support for improving the safe and stable operation and regulation flexibility of the power system, playing a positive role in promoting the high-quality development of renewable energy and the sustainable transformation of the energy industry.

[0081] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for configuring power sources in large-scale new energy bases, comprehensively considering both receiver-end characteristic preferences and source-end resource endowments, characterized in that: Includes the following steps: Step 1: Resource endowment assessment and output modeling; Based on long-term meteorological data and new energy site development conditions, the wind, solar and pumped storage resources in the sending-end area are assessed, and hourly output models of wind power, photovoltaic and solar thermal power sources are constructed. Step 2: Multi-energy complementarity and energy storage operation analysis; Combining the operational constraints of pumped hydro storage and electrochemical energy storage, analyze the combined output characteristics of wind, solar and energy storage under the multi-energy complementarity operation mode, and identify the constraints of new energy consumption and system regulation requirements; Step 3: Identification of receiving-end demand preferences and construction of export target curves; For potential receiving-end electricity markets, analyze their power supply and demand structure and load curve characteristics, identify supply support-oriented demand preferences guided by system power supply reliability and high proportion of green electricity-oriented demand preferences guided by the proportion of clean energy consumption, and construct corresponding export target power curves accordingly. Step 4: Hourly production simulation; Based on the 8760-hour time scale throughout the year, joint hourly production simulations are conducted for wind power, photovoltaic, solar thermal, pumped storage, and electrochemical energy storage to form the output process and power transmission curves of the new energy base under different power configuration schemes. Step 5: Calculation of operational indicators; Based on the hourly production simulation results, calculate and statistically analyze the utilization hours of various power transmission channels, peak guarantee rate of new energy bases, proportion of clean electricity, and utilization rate of new energy. Step 6: Comprehensive evaluation and scheme selection; conduct multi-dimensional comprehensive evaluation of the operating indicators of different power supply configuration schemes, and select the power supply configuration scheme of the new energy large-scale base that meets the load preference constraints of the receiving end and has the best overall performance.

2. The power supply configuration method for a large-scale new energy base according to claim 1, characterized in that, In step 1, when the new energy base is located in an area with scarce meteorological observation stations, the measured power output data is not used directly. Instead, based on the long-term meteorological statistical characteristics of the reference meteorological stations, an 8760-hour hourly power output sequence of wind power and photovoltaic power is constructed through numerical simulation or statistical reconstruction methods to characterize the randomness, volatility and correlation characteristics of new energy power output.

3. The power supply configuration method for a large-scale new energy base according to claim 1, characterized in that, The construction of the wind power hourly output model in step 1 specifically includes: calculating the hourly wind speed at the hub height based on mesoscale reanalysis meteorological data, and calculating the hourly output power of the wind farm by combining the power characteristic function of the wind turbine and the comprehensive efficiency coefficient. The construction of the photovoltaic hourly output model in step 1 specifically includes: calculating the horizontal surface or optimal tilt angle radiation based on mesoscale reanalysis meteorological data, and combining the radiation intensity under standard test conditions with the comprehensive efficiency coefficient considering temperature correction and inverter efficiency to calculate the photovoltaic hourly output power.

4. The power supply configuration method for a large-scale new energy base according to claim 1, characterized in that, In step 2, based on the power output structure characteristics of wind power being higher at night and photovoltaic power being concentrated during the day, the complementary relationship between wind power and photovoltaic power on the intraday and intrayear time scales is identified, and the complementary relationship and the equivalent utilization hours of new energy are used as the basic parameters for determining the continuous full-load hours of pumped storage, configuring the power generation capacity, and analyzing the high proportion of new energy transmission capacity.

5. The power supply configuration method for a large-scale new energy base according to claim 1, characterized in that, In step 2, both pumped hydro storage and electrochemical energy storage are subject to charging and discharging power constraints, energy capacity constraints, and mutual exclusion constraints on charging and discharging states, in order to prevent different energy storage forms from charging and discharging simultaneously during the same period.

6. The power supply configuration method for a large-scale new energy base according to claim 1, characterized in that, Step 3, which involves constructing the corresponding target power curve for power transmission, specifically includes: Construct a target curve for power transmission that ensures supply and supports external power transmission, taking the peak load guarantee of the receiving system as a constraint, and prioritizing the stable output demand during the period of maximum system load; Construct a target curve for high-proportion green electricity transmission, aiming to maximize the proportion of clean energy consumption at the receiving end and strengthen the proportion of new energy electricity transmitted.

7. The power supply configuration method for a large-scale new energy base according to claim 1, characterized in that, In step 4, the hourly production simulation module introduces the random fluctuation characteristics of new energy output and energy storage regulation strategy on an annual scale of 8760 hours, and conducts joint hourly production simulation of wind power, photovoltaic, pumped storage and electrochemical energy storage to achieve coordinated optimization of the power scale configuration of new energy base and the shape of the external transmission target curve.

8. The power supply configuration method for a large-scale new energy base according to claim 1, characterized in that, In step 5, the formula for calculating the peak guarantee rate of the new energy base is as follows: In the formula, For the new energy base in the first Actual power output per hour; For the receiving end system in the first Hourly target power demand; This refers to the set of peak periods for the receiving system.

9. The power supply configuration method for a large-scale new energy base according to claim 1, characterized in that, In step 6, the comprehensive evaluation and scheme selection module adopts a multi-index comprehensive evaluation method to perform weighted analysis on the indicators of new energy utilization rate, curtailment rate, peak power supply guarantee capability and external transmission channel utilization efficiency, and form a comprehensive evaluation result and selection ranking of different power configuration schemes for new energy large base.

10. The power supply configuration method for a large-scale new energy base according to claim 9, characterized in that, Step 6 specifically includes: The entropy weight method is used to determine the weight of each operational indicator in order to objectively reflect the information differences of the indicators in each scheme; The TOPSIS method is used to calculate the distance between each power supply configuration scheme and the positive and negative ideal solutions, and the comprehensive score is calculated. All candidate solutions are ranked according to their comprehensive scores, and the solution with the highest score is the preferred solution, while ensuring that it meets the constraints of end-user demand preferences and key indicator threshold requirements.

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