New energy base capacity planning method and system considering two-stage source load matching

Abstract

The invention discloses a new energy base capacity planning method and system considering two-stage source load matching. The method specifically comprises the steps of constructing a new energy base capacity planning model considering two-stage source load matching and solving the planning model; according to the method, a load curve is scaled based on a proposed source load matching coefficient to realize preliminary source load matching and refined matching between a power supply and a matched load by a source load matching degree, so that two-stage source load matching is realized; and different objective functions in planning and operation stages are considered at the same time, the new energy utilization rate is improved, and a thought is provided for solving source-load mismatch for a power system.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to a method and system for capacity planning of new energy bases that considers two-stage source-load matching. Background Technology

[0002] The green and low-carbon energy transition has driven the large-scale development of new energy sources, with large-scale new energy bases becoming the mainstream model for large-scale development. However, the strong volatility and intermittency of new energy sources lead to difficulties in the absorption of new energy power generation and the operation of new power systems. As the proportion of new energy sources continues to increase, the power system is required to have greater flexibility and peak-shaving capabilities, while also facing the problem of source-load mismatch, further exacerbating the curtailment of new energy power. To address this, people have begun to explore new solutions for new energy development models and projects. The development and utilization of large-scale hybrid energy bases that integrate energy storage facilities and flexible power generation resources are gradually emerging due to their advantages in providing high-quality, sufficient, and stable new energy power.

[0003] Against this backdrop, how to improve system flexibility, reduce the curtailment of renewable energy, and ensure that the source and load are not imbalanced through multi-dimensional collaborative design in the planning stage has become a key issue for the sustainable development of renewable energy bases.

[0004] To address the source-load mismatch problem, existing research largely focuses on the complementarity between new energy sources and combining flexible resources such as energy storage and thermal power to reduce the volatility of new energy sources and improve the stability of their output. However, large-scale new energy bases, as the power supply end, target the receiving end load. Studying only the complementarity of new energy sources themselves is insufficient; source-load synergy should also be considered. Scholars have already conducted research on source-load synergy. For example, some studies introduce a source-load difference index to study the relationship between source-load difference and source-load matching; some consider a two-level optimal configuration model for source-load synergy, establishing source-load tracking coefficients and source-load proximity coefficients to achieve source-load matching; some studies analyze the difference between source-load curves to make the difference curves as close to a straight line as possible to achieve source-load matching, but this lacks the flexibility to consider deviations; some studies analyze the degree of source-load matching by measuring the information entropy of the source-load curves, increasing the difficulty of the solution.

[0005] Existing research on source-load matching is still insufficient, lacking multi-stage source-load matching design and research on source-load matching methods applicable to both system-level and single-site-level applications. Summary of the Invention

[0006] The present invention aims to at least partially solve one of the technical problems in the related art.

[0007] This invention aims to fully consider the impact of source-load matching on improving the utilization rate of new energy from the planning level. It proposes a new energy base capacity planning method that considers two-stage source-load matching. This method achieves preliminary source-load matching by scaling the load curve based on the proposed source-load matching coefficient, and performs fine matching between the power source and the matching load by the source-load matching degree, thereby realizing two-stage source-load matching. Furthermore, it considers different objective functions in the planning and operation stages to improve the utilization rate of new energy.

[0008] Another objective of this invention is to propose a new energy base capacity planning system that considers two-stage source-load matching.

[0009] To achieve the above objectives, this invention proposes a new energy base capacity planning method that considers two-stage source-load matching, comprising: S1. Obtain the power output curve and load curve of the new energy source in the target area, and scale the load curve based on the source-load matching coefficient to generate a matching load curve that initially matches the total power of the new energy source. S2, based on the mean normalization results of the new energy output curve and the matching load curve, calculate the source-load matching degree, and quantitatively evaluate the similarity between the new energy output and load curves at the system level and the station level respectively. S3. Construct a two-stage optimization model that includes a planning stage and an operation stage. The planning stage aims to minimize the construction cost of the new energy base, while the operation stage aims to minimize the source-load deviation and maximize the proportion of new energy output. The weight of each objective function is determined by the analytic hierarchy process, transforming the multi-objective optimization problem into a single-objective optimization problem. S4 uses the Monte Carlo sampling method to generate a weather scenario set for the operation phase, and transforms the two-stage optimization model into a one-stage optimization model based on the scenario expectation value. Then, the optimal installed capacity configuration of photovoltaic, wind power and solar thermal power plants is obtained by using a mixed integer linear programming solver.

[0010] The new energy base capacity planning method considering two-stage source-load matching in the embodiments of the present invention may also have the following additional technical features: In one embodiment of the present invention, S1 includes: S11. Based on the historical weather data and new energy resource characteristics of the target area, obtain the historical output curves of photovoltaic and wind power and the regional load curves respectively. S12 uses a source-load matching coefficient to scale the load curve, so that the scaled matching load curve matches the total output curve of new energy in terms of peak-valley difference rate.

[0011] In one embodiment of the present invention, S2 includes: S21, The system-level source-load matching degree is characterized by the mean normalized similarity between the combined output curves of multiple new energy power stations and the system-level matching load curve to represent the overall matching effect. S22, the source-load matching degree at the power station level is characterized by the mean normalized similarity between the output curve of a single new energy power station and the corresponding matching load curve at the power station level.

[0012] In one embodiment of the present invention, S3 includes: S31, by using the analytic hierarchy process, the three objective functions of construction cost in the planning stage, minimizing source-load deviation in the operation stage, and maximizing the proportion of new energy output are weighted and weighted to form a weighted comprehensive objective function. S32 combines the weighted objective function with power balance constraints, new energy output constraints, source-load matching constraints, positive and negative deviation constraints, and internal energy balance constraints of the solar thermal power plant to construct a mixed integer linear programming model.

[0013] In one embodiment of the present invention, it further includes: S5 dynamically adjusts the heat storage system's charging and discharging strategy based on the amount of wind and solar power wasted by the solar thermal power plant's power-to-heat conversion subsystem, in order to improve the overall utilization rate of new energy sources.

[0014] To achieve the above objectives, another aspect of the present invention proposes a new energy base capacity planning system that considers two-stage source-load matching, comprising: The data acquisition and scaling module is used to acquire the power output curve and load curve of the new energy source in the target area, and scale the load curve based on the source-load matching coefficient to generate a matching load curve that initially matches the total power of the new energy source. The source-load matching degree calculation module is used to calculate the source-load matching degree based on the mean normalization result of the new energy output curve and the matching load curve, and to quantitatively evaluate the similarity between the new energy output and load curves at the system level and the station level respectively. The two-stage optimization modeling module is used to construct a two-stage optimization model that includes a planning stage and an operation stage. The planning stage aims to minimize the construction cost of the new energy base, while the operation stage aims to minimize the source-load deviation and maximize the proportion of new energy output. The weight of each objective function is determined by the analytic hierarchy process, transforming the multi-objective optimization problem into a single-objective optimization problem. The model solving and capacity configuration module is used to generate a weather scenario set for the operation phase using the Monte Carlo sampling method, and transform the two-stage optimization model into a one-stage optimization model based on the scenario expectation value. Then, the optimal installed capacity configuration of photovoltaic, wind power and solar thermal power plants is obtained by using a mixed integer linear programming solver.

[0015] The new energy base capacity planning method and system considering two-stage source-load matching in the embodiments of the present invention can improve the source-load matching accuracy of new energy bases in the planning and operation stages, effectively reduce the new energy curtailment rate, and improve system flexibility and new energy utilization rate.

[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a new energy base capacity planning method considering two-stage source-load matching according to an embodiment of the present invention; Figure 2 This is another flowchart of a new energy base capacity planning method considering two-stage source-load matching according to an embodiment of the present invention; Figure 3 This is a structural diagram of a new energy base capacity planning system that considers two-stage source-load matching according to an embodiment of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] The following describes, with reference to the accompanying drawings, a new energy base capacity planning method and system that considers two-stage source-load matching according to an embodiment of the present invention.

[0021] Example 1 Figure 1 This is a flowchart of a new energy base capacity planning method considering two-stage source-load matching according to an embodiment of the present invention, such as... Figure 1 As shown, it includes: S1. Obtain the power output curve and load curve of the new energy source in the target area, and scale the load curve based on the source-load matching coefficient to generate a matching load curve that initially matches the total power of the new energy source. Specifically, this step aims to quantify the matching relationship between renewable energy output and load, and to reasonably scale the original load curve, thereby achieving a preliminary match between total renewable energy output and load demand during the planning stage, laying the foundation for subsequent refined optimization.

[0022] This step first involves obtaining the output curves of photovoltaic, wind, and solar thermal power plants in the target area using historical meteorological data and operational data from renewable energy stations. The total renewable energy power curve is a weighted superposition of the output curves for each type of power source, considering the ratio of rated power to actual output. Simultaneously, the load curve for the target area is obtained, typically with hourly or minute-level time resolution, covering load changes for typical days or weeks. In constructing the source-load matching coefficient, a normalization method is used to compare the load curve with the total renewable energy output curve and calculate their matching degree. The source-load matching coefficient is defined as the ratio of the matched load curve to the original load curve. This coefficient is used to linearly scale the load curve, ensuring that the scaled matched load curve is consistent with the total renewable energy output curve in terms of overall power level.

[0023] This step is applicable to system-level or station-level capacity planning for renewable energy bases. In system-level applications, it is necessary to integrate output data from multiple photovoltaic, wind, and solar thermal power plants to form a comprehensive renewable energy output curve. In station-level applications, matching analysis is performed on individual renewable energy stations. This method can be applied to renewable energy base planning under point-to-grid transmission models, and is particularly suitable for new power systems with a high proportion of renewable energy integration, in order to improve renewable energy absorption capacity and reduce curtailment rates.

[0024] This step enables an initial match between total renewable energy output and load demand at the overall level, providing a foundational input for the subsequent construction of a two-stage optimization model. This method effectively reduces the peak-to-valley difference between source and load curves, improves the temporal consistency between renewable energy output and load curves, thereby significantly enhancing renewable energy utilization during the planning phase, reducing wind and solar power curtailment caused by source-load mismatch, and providing the power system with a more flexible and economical renewable energy configuration scheme.

[0025] Furthermore, S1 includes: S11: Based on historical weather data and the characteristics of new energy resources in the target area, obtain the historical output curves of photovoltaic and wind power, as well as the regional load curves.

[0026] Specifically, this step is the foundation of the entire capacity planning methodology. Its core lies in modeling the matching relationship between renewable energy output and load using historical data, providing input for the subsequent two-stage source-load matching optimization model. At the technical implementation level, this step first involves collecting key weather parameters such as solar radiation intensity, wind speed, and temperature in the target area through meteorological databases or historical operational data from renewable energy power plants. The photovoltaic (PV) output curve is typically obtained based on the IV characteristic curve of PV modules, system efficiency (including inverter efficiency, temperature coefficient, shading, etc.), and historical irradiance data. This is calculated using PV power prediction models (such as the Sandia model or PVSyst model) to obtain hourly or minute-by-minute historical PV output sequences. Wind power output curves rely on the mapping relationship between wind speed and turbine power curves. Combining parameters such as the turbine's cut-in wind speed, rated wind speed, and cut-out wind speed, historical wind power output data is generated using wind power prediction models (such as Weibull distribution fitting or turbine power curve interpolation).

[0027] Obtaining regional load curves requires extracting historical load data for the target area from the power dispatch center or load forecasting system. This data is typically presented at an hourly time resolution, covering typical daily load variations, seasonal load changes, and holiday load fluctuations. The peak-to-valley ratio of the load curve is defined as the ratio of the difference between the maximum and minimum values ​​of the load curve to the maximum load, and is used to characterize the peak-shaving demand and fluctuation characteristics of the load.

[0028] At the parameter level, the historical output curves of photovoltaic and wind power must meet certain data quality standards, such as a time resolution of no less than 1 hour, data continuity of no less than 95%, and missing data must be supplemented through interpolation or prediction methods. The load curves must comply with the power system dispatch data specifications to ensure that they are strictly aligned with the renewable energy output curves in the time dimension.

[0029] In practical applications, this step is typically deployed in the data preprocessing stage of new energy base planning, and is applicable to capacity configuration analysis at the system or station level. By extracting the curve characteristics of the source and load ends, it provides data support for the subsequent calculation of source-load matching coefficients and matching degrees, thereby achieving synergistic optimization between the economy of new energy bases in the planning stage and the flexibility in the operation stage.

[0030] Furthermore, the technical effect of this step is to provide high-quality and high-precision input data for the two-stage optimization model, which helps to accurately assess the matching degree between new energy output and load, improve the utilization rate of new energy, reduce the curtailment rate of wind and solar power, and provide a scientific basis for the capacity configuration of new energy bases.

[0031] S12 uses a source-load matching coefficient to scale the load curve, so that the scaled matching load curve matches the total output curve of new energy in terms of peak-valley difference rate.

[0032] Specifically, in this invention, scaling the load curve using a source-load matching coefficient is a key step in achieving preliminary source-load matching. Its core lies in quantifying the degree of matching between the total output curve of new energy sources and the load curve, and proportionally adjusting the load curve so that its peak-valley difference rate is consistent with the total output curve of new energy sources, thereby laying the foundation for subsequent refined matching.

[0033] This step is applicable to system-level or site-level capacity planning of new energy bases, and has significant application value, especially in integrated wind-solar-storage projects. For example, in the planning of a large-scale wind-solar base in Northwest China, this method can be used to initially match the regional load curve with the combined wind and solar power output curve, thereby optimizing the configuration scale of solar thermal power plants and energy storage systems and improving the capacity for new energy consumption. In the point-to-grid transmission mode, this method helps to reduce the curtailment rate of new energy and improve the utilization rate of transmission channels.

[0034] By introducing a source-load matching coefficient, this step achieves initial alignment of the load curve and the renewable energy output curve in terms of fluctuation characteristics, reducing the peak-shaving pressure and curtailment risk caused by source-load mismatch. This method provides a basic load reference for subsequent operation optimization during the planning phase, improving the operational flexibility and economy of renewable energy bases, and is an important prerequisite for achieving two-stage source-load matching.

[0035] S2, based on the mean normalization results of the renewable energy output curve and the matching load curve, calculates the source-load matching degree, and quantitatively evaluates the similarity between the renewable energy output and load curves at both the system level and the plant level. Specifically, this step aims to calculate the source-load matching degree by normalizing the renewable energy output curve and the matching load curve, thereby quantitatively evaluating the similarity between the renewable energy output and load curves at the system and station levels. This process is a key step in the "two-stage source-load matching" method of this invention, used to achieve synergy between the refined configuration of renewable energy bases in the planning stage and the optimized scheduling in the operation stage.

[0036] Furthermore, based on the normalized curves, similarity calculation methods (such as cosine similarity, Euclidean distance, or dynamic time warping (DTW)) are used to quantify the source-load matching degree. At the system level, the matching degree reflects the degree of matching between the combined output curves of multiple renewable energy power plants and the system-level matched load; while at the power plant level, it reflects the degree of matching between the output curve of a single renewable energy power plant and the corresponding power plant-level matched load. The calculation results of this index can serve as a quantitative basis for the source-load synergy in the optimization model.

[0037] At the parameter level, the calculation of source-load matching degree requires setting key parameters such as time resolution (e.g., 15 minutes or 1 hour), statistical period (e.g., typical daily, monthly or annual load curves), and confidence interval of the renewable energy output curve (e.g., historical output data at a 95% confidence level). In addition, it is also necessary to set the scaling factor range of the matching load curve to ensure that it matches the renewable energy output curve within a reasonable range.

[0038] In application scenarios, this step is suitable for the capacity planning stage of new energy bases, especially in scenarios where multiple energy types (such as photovoltaic, wind power, and solar thermal) are configured in a coordinated manner. By evaluating the source-load matching degree at the system level and the site level, input variables can be provided for the subsequent two-stage optimization model, thereby balancing economic efficiency and operational flexibility in the planning.

[0039] By introducing mean normalization and matching quantification methods, the matching accuracy of the power output and load curves of new energy sources in terms of shape and fluctuation characteristics has been effectively improved. This provides a scientific basis for the capacity configuration of new energy bases, helps to reduce wind and solar curtailment rates, and improves the stability of system operation and the utilization rate of new energy sources.

[0040] Furthermore, S2 includes: S21, the system-level source-load matching degree is characterized by the mean normalized similarity between the combined output curves of multiple new energy power stations and the system-level matching load curve to represent the overall matching effect.

[0041] This step first involves weighted combining historical output data from multiple renewable energy plants (such as photovoltaic, wind, and solar thermal power plants) to form a system-level total renewable energy output curve. Then, the original load curve is scaled based on the source-load matching coefficient to generate a system-level matched load curve. On this basis, Mean Normalized Similarity (MNS) is used as the matching index to calculate the similarity between the total renewable energy output curve and the matched load curve. This similarity calculation method typically bases its assessment on the differences in the normalized curve shape, for example, by calculating the normalized mean square error (NRMSE) or correlation coefficient to evaluate the degree of matching.

[0042] This step is applicable to system-level capacity planning of new energy bases, especially in scenarios where multiple energy types operate in synergy, such as integrated wind, solar, and energy storage bases. By calculating the system-level matching degree, the matching effect between the overall output of new energy and the load curve under different installed capacity combinations can be evaluated, thereby providing input for the optimization objective function of the planning model and supporting the maximization of new energy utilization while meeting economic and operational flexibility requirements.

[0043] This step, by introducing a mean-normalized similarity index, effectively overcomes the sensitivity of traditional matching methods to absolute deviations and enhances the ability to judge the matching of curve shapes. Simultaneously, this method is applicable to system-level analysis of multi-site combinations, providing a quantitative basis for the subsequent construction of a two-stage optimization model. This helps improve the collaborative optimization level of new energy bases during the planning and operation phases, reduce wind and solar curtailment rates, and improve the stability and economy of system operation.

[0044] S22, the source-load matching degree at the power station level is characterized by the mean normalized similarity between the output curve of a single new energy power station and the corresponding matching load curve at the power station level.

[0045] Specifically, in some implementations, calculating the source-load matching degree at the power plant level is one of the key steps in achieving refined source-load matching in this invention. Its technical implementation principle is based on the similarity analysis of the power output curve of the renewable energy power plant and the matching load curve over time. Specifically, this step quantifies the degree of matching between the renewable energy power plant and load demand within a specific time period by calculating the mean-normalized similarity of the two curves, thereby evaluating the local matching effect.

[0046] At the technical implementation level, this step first obtains the historical output curves of new energy power plants (such as photovoltaic and wind power) and the matched load curves scaled according to the source-load matching coefficient. Then, these two curves are normalized by dividing them by their respective mean values ​​within the statistical period to eliminate differences in absolute power levels and highlight the similarity in curve shape. The normalized curves are used to calculate similarity indices, typically measured using statistical methods such as the Pearson Correlation Coefficient or Cosine Similarity.

[0047] Key parameters involved in this step include the sampling time interval (e.g., 15 minutes or 1 hour) of the renewable energy output curve, the statistical period length (e.g., 1 year or 1 month), the scaling factor of the matching load curve (calculated from the source-load matching factor), and the mathematical model and threshold setting used for similarity calculation. For example, in practical applications, a similarity threshold of 0.8 can be set. When the matching degree is lower than this value, it is considered that the power station deviates significantly from the load curve, and its installed capacity or operation strategy needs to be adjusted.

[0048] This step is applicable to the capacity planning and operation optimization of individual power stations in new energy bases, especially in integrated wind-solar-storage projects, where it can serve as a quantitative basis for assessing the load curve support capacity of each power station. Through this matching index, it is possible to identify which power stations have a low matching degree with the load curve during specific periods, thereby optimizing their installed capacity structure or configuring energy storage systems during the planning stage and improving the overall utilization rate of new energy.

[0049] Furthermore, the technical advantage of this step lies in its ability to effectively overcome the sensitivity of traditional matching methods to absolute power differences by introducing mean-normalized similarity, thereby enhancing the adaptability to changes in curve shape. Simultaneously, this index can serve as one of the constraints on the objective function of the operational phase in the two-stage optimization model, ensuring that while meeting economic requirements, the dynamic matching level between renewable energy output and load demand is improved, thereby reducing the curtailment rate and enhancing the stability and flexibility of system operation.

[0050] S3. A two-stage optimization model is constructed, which includes a planning stage and an operation stage. The planning stage aims to minimize the construction cost of the new energy base, while the operation stage aims to minimize the source-load deviation and maximize the proportion of new energy output. The weights of each objective function are determined by the analytic hierarchy process, transforming the multi-objective optimization problem into a single-objective optimization problem.

[0051] Specifically, this step constructs a two-stage optimization model encompassing the planning and operation phases, aiming to achieve synergistic optimization between the economic efficiency and operational flexibility of renewable energy base capacity configuration. In the planning phase, the model aims to minimize the construction cost of the renewable energy base, specifically including the installed capacity costs of photovoltaic, wind power, and solar thermal power plants.

[0052] This step plays a crucial role in the capacity planning of new energy bases, achieving system-level collaborative optimization from static planning to dynamic operation through two-stage modeling. In practical applications, it is suitable for system-level planning of large-scale new energy bases and can also be extended to the capacity configuration of individual power stations. Its technical value lies in improving the utilization rate of new energy, reducing the curtailment rate, and enhancing the power system's adaptability to renewable energy fluctuations, providing a quantifiable and operable modeling method for source-load collaborative optimization.

[0053] Furthermore, S3 includes: S31 uses the analytic hierarchy process to assign weights to the three objective functions of construction cost in the planning stage, minimizing source-load deviation in the operation stage, and maximizing the proportion of new energy output, forming a weighted comprehensive objective function.

[0054] Specifically, in the steps of this invention, the Analytic Hierarchy Process (AHP) is used to assign weights to three objective functions: construction cost in the planning phase, minimizing source-load deviation in the operation phase, and maximizing the proportion of new energy output. This results in the construction of a weighted comprehensive objective function, which is a key step in achieving multi-objective optimization decision-making. The technical implementation principle of this step is based on the structured decision-making framework of AHP, combining qualitative and quantitative indicators. By constructing a judgment matrix, calculating weight vectors, and performing consistency checks, the relative importance of each objective function in the comprehensive optimization is ultimately determined.

[0055] In practical applications, this step is suitable for capacity planning scenarios at the system or station level for new energy bases, especially in point-to-grid transmission modes, where both economic efficiency and source-load matching accuracy during operation must be considered in the planning stage. By introducing a weight allocation mechanism through the analytic hierarchy process (AHP), the optimization objectives at different stages can be effectively balanced, improving the utilization rate of new energy sources, reducing the curtailment rate, and enhancing the flexibility and stability of the power system.

[0056] The technical value of this step lies in transforming a multi-objective optimization problem into a computable single-objective optimization problem, providing a unified objective function form for subsequent mixed-integer linear programming solutions, thereby enabling scientific decision-making and optimal solution acquisition for the capacity allocation of new energy bases.

[0057] S32 combines the weighted objective function with power balance constraints, new energy output constraints, source-load matching constraints, positive and negative deviation constraints, and internal energy balance constraints of the solar thermal power plant to construct a mixed integer linear programming model.

[0058] Specifically, in constructing the Mixed Integer Linear Programming (MILP) model, this step combines the weighted objective function with various key constraints to achieve coordinated optimization of the new energy base during the planning and operation phases. Specifically, the model integrates power balance constraints, new energy output constraints, source-load matching constraints, positive and negative deviation constraints, and internal energy balance constraints of the solar thermal power plant, thereby achieving precise control over the scientific allocation of new energy installed capacity and source-load matching at the system level.

[0059] At the technical implementation level, firstly, the objective functions of the planning and operation phases are weighted using the Analytic Hierarchy Process (AHP), transforming the multi-objective optimization problem into a single-objective optimization problem. The objective functions include minimizing the construction cost of new energy sources (such as the unit investment cost of photovoltaic, wind, and solar thermal power plants), minimizing source-load matching deviation, and maximizing the proportion of new energy power generation. Secondly, the weighted objective functions are coupled with various constraints in a model. Power balance constraints ensure that the total output of new energy sources and the matching load remain balanced at any given time. New energy output constraints are based on historical resource data, and the output of photovoltaic and wind power is modeled in a per-unit manner to ensure that their output range is reasonable under different weather scenarios.

[0060] Furthermore, the source-load matching constraint ensures a high degree of consistency in the shape of the matching load and the new energy output curves by calculating the similarity between the new energy output and the matching load (such as the Pearson correlation coefficient or normalized mean square error). The internal energy balance constraint of the solar thermal power plant involves parameters such as its input and output energy, the charge and release power of the thermal storage system, and the heat loss rate, ensuring energy conservation and system stability under different operating conditions.

[0061] This step is central to the capacity planning of new energy bases. By constructing a MILP model that comprehensively considers economy, safety and flexibility, it provides a source-load collaborative optimization method applicable to both system-level and station-level power systems, effectively improving the utilization rate of new energy and reducing the curtailment rate, and has significant engineering application value.

[0062] S4 uses the Monte Carlo sampling method to generate a weather scenario set for the operation phase, and transforms the two-stage optimization model into a one-stage optimization model based on the scenario expectation value. Then, the optimal installed capacity configuration of photovoltaic, wind power and solar thermal power plants is obtained by using a mixed integer linear programming solver.

[0063] Specifically, in some implementations, this step uses Monte Carlo sampling to generate a set of weather scenarios for the operational phase, and transforms the two-stage optimization model into a one-stage optimization model based on the scenario expectation values. Finally, a mixed-integer linear programming (MILP) solver is used to solve for the optimal installed capacity configuration of photovoltaic, wind power, and solar thermal power plants. This step is a key link in this invention to achieve source-load coordinated optimization and uncertainty handling, and its technical implementation principle is based on probabilistic modeling and stochastic optimization theory.

[0064] At the technical implementation level, firstly, a multi-dimensional weather feature space is constructed by collecting historical meteorological data (such as wind speed, irradiance, and temperature) of the target area. Based on this, a Monte Carlo sampling method is used to randomly sample data, generating multiple representative weather scenario sets. Each scenario represents a possible operating condition, used to simulate the uncertainty of new energy output. In this invention, the sampling process may optionally employ Latin Hypercube Sampling (LHS) or Markov Chain Monte Carlo (MCMC) methods to improve the coverage and convergence of the scenario sets. The number of scenarios is typically set to 50 to 200 to achieve a balance between computational complexity and model accuracy.

[0065] Secondly, for each sampling scenario, the expected value of the objective function in the operation phase is calculated. The objective function in the operation phase includes minimizing the source-load matching deviation and maximizing the utilization rate of new energy sources, and its form is a multi-objective function. The Analytic Hierarchy Process (AHP) is used to assign appropriate weights to each objective function, transforming the multi-objective problem into a weighted single-objective function. Furthermore, the expected value of the second-stage objective function is embedded into the first-stage objective function, thus transforming the original two-stage optimization problem, which depended on the results of the operation phase, into a one-stage optimization problem that depends only on the planning variables.

[0066] This step is applicable to the planning phase of large-scale new energy bases, especially in point-to-grid transmission mode, effectively addressing source-load mismatch issues. By transforming operational uncertainties into a set of scenarios and embedding them into the planning model, robust optimization of the installed capacity of photovoltaic, wind, and solar thermal power plants can be achieved, enhancing system flexibility and new energy absorption capacity. This step significantly improves the adaptability and robustness of the new energy base planning model by introducing Monte Carlo sampling and expected value transformation mechanisms.

[0067] The new energy base capacity planning method considering two-stage source-load matching in the embodiments of the present invention improves the source-load matching level of new energy bases in the planning and operation stages, effectively reduces the curtailment rate of new energy, and improves system flexibility and new energy utilization rate.

[0068] Also includes: S5 dynamically adjusts the heat storage system's charging and discharging strategy based on the amount of wind and solar power wasted by the solar thermal power plant's power-to-heat conversion subsystem, in order to improve the overall utilization rate of new energy sources.

[0069] Specifically, in some implementations, dynamically adjusting the heat storage system's charging and discharging strategy based on the amount of wind and solar power curtailed by the solar thermal power plant's electro-thermal conversion subsystem is a key technical step in this invention for improving the overall utilization rate of new energy sources. The core principle of this step lies in real-time monitoring of the curtailment situation at wind and solar power plants, converting previously unabsorbable curtailed electricity into thermal energy and storing it in the thermal storage system. This achieves secondary energy utilization, alleviates source-load mismatch problems, and improves the system's economic efficiency and flexibility.

[0070] In its specific implementation, the Electro-Thermal Conversion Subsystem converts abandoned electricity into heat energy through electric heating devices. Its input power is related to the real-time abandoned power of the wind and solar power plant. In a solar thermal power plant, this heat energy can be injected into a Thermal Energy Storage (TES) system for subsequent heat release and power generation. The charging and discharging strategy of the TES needs to be dynamically adjusted according to the magnitude and duration of abandoned power, as well as the system's operating status. For example, when the abandoned power is high and the duration is long, the TES can enter a high-power charging mode to maximize heat storage efficiency; while when load demand increases and renewable energy output decreases, it switches to a heat release mode to supplement system output.

[0071] At the parameter level, the input power of the electrothermal subsystem is typically limited by its rated power (e.g., 5 MW to 20 MW), while the heat capacity of the thermal storage system is determined by the specific heat capacity of the storage medium (e.g., molten salt), the temperature range (e.g., 290℃ to 565℃), and the volume of the storage tank. The charge and discharge rates of the thermal storage system must meet ramp rate constraints (e.g., ±10 MW / h) to ensure system operational stability. Furthermore, the heat loss rate of the thermal storage system (e.g., 0.5%~1.2% / h) must also be included in the model to accurately assess the efficiency of thermal energy storage and release.

[0072] In application scenarios, this step is suitable for hybrid energy systems that include concentrated solar power (CSP) plants in large-scale renewable energy bases, especially in areas with abundant wind and solar resources but large load fluctuations. Through this strategy, CSP plants can absorb excess electricity and convert it into heat energy during peak periods of renewable energy curtailment, thereby releasing heat energy to generate electricity during low-output periods, achieving dynamic matching of source and load curves.

[0073] The technical benefits of this step are a significant improvement in the utilization rate of new energy sources, a reduction in wind and solar power curtailment, and an enhancement of the system's peak-shaving capacity and operational stability. By converting curtailed electricity into dispatchable thermal energy, solar thermal power plants not only serve as power generation units but also become crucial supports for system flexibility, providing an effective means to achieve the safe and economical operation of high-proportion renewable energy systems.

[0074] The new energy base capacity planning method considering two-stage source-load matching in this invention optimizes the source-load matching process of the new energy base by dynamically adjusting the charging and discharging strategy of the solar thermal power plant's thermal storage system to absorb the abandoned wind and solar power, thereby significantly improving the overall utilization rate of new energy and the economic efficiency of system operation.

[0075] Example 2 like Figure 2 As shown, the new energy base capacity planning method of the present invention, which considers two-stage source-load matching, is specifically as follows: In one embodiment of the present invention, the process includes source-load curve feature analysis, design of two-stage matching metrics, construction of a two-stage planning optimization model, solving the planning optimization model, and a system or device. S110) Source-Load Curve Feature Analysis For the power output curve at the source, the peak-to-valley difference rate can be used to characterize the fluctuation of new energy power output: (1) The peak-shaving demand and fluctuations of the load curve can be characterized by the peak-to-valley ratio, which is the ratio of the peak-to-valley difference of the load curve to the maximum load within a statistical time interval. The formula is as follows: (2) One of the goals of source-load matching is to reduce the peak-to-valley difference rate of the net load curve on the load side. At the same time, studying the peak-to-valley difference rate of the new energy output curve at the source end is beneficial to achieving source-load matching.

[0076] S120) Design two matching metrics: S120-1) Design source-load matching coefficient index : The original load is scaled to obtain the matched load based on system-level or station-level source-load matching requirements. The matched load is close to the total power of the renewable energy source. A single station refers to a single renewable energy station at the source end, which typically has a small rated output power. A system-level station refers to a system-level station composed of multiple single renewable energy stations at the source end, which typically has a larger rated output power. The source-load matching coefficient is defined as follows: (3) In the formula, To match the load curve, This is the load curve.

[0077] S120-2) Design Source-Load Matching Index : The similarity between the total power of new energy sources and the matching load is also divided into system-level and station-level indicators. The system-level source-load matching degree index is the similarity between the combined output curve of multiple individual new energy power stations and the aforementioned system-level matching load. The station-level source-load matching degree index is the similarity between the output curve of a single new energy power station and the corresponding station-level matching load. The formula is as follows: (4) in, This represents the power output curve of new energy sources. This represents the average output of new energy sources. Represents the load curve. This represents the average load.

[0078] S130) Construct a two-stage optimization model for new energy capacity planning with source-load matching: The two-stage optimization model for new energy capacity planning based on source-load matching is divided into a planning stage and an operation stage. The objective function expression for the planning stage is as follows: (5) The constraint expressions are as follows: (6) (7) (8) Where (1) is the objective function, i.e., minimizing the cost of new energy construction during the planning stage; in the formula, , , The figures represent the investment costs for photovoltaic, wind power, and solar thermal power plants, respectively, in yuan / MW. , , These represent the installed capacity of photovoltaic, wind power, and solar thermal power plants, respectively.

[0079] Equation (2) represents the photovoltaic installed capacity constraint, where This represents the lower limit of the planned photovoltaic capacity. This represents the upper limit of the planned photovoltaic capacity. Equation (3) represents the wind power installed capacity constraint, where This is the lower limit of the planned wind power capacity. This represents the upper limit of the planned wind power capacity. Equation (4) represents the installed capacity constraint of a solar thermal power plant, where This serves as the lower limit for the planned capacity of concentrated solar power (CSP) plants. This serves as the upper limit for the planned capacity of concentrated solar power (CSP) plants. The objective function for the runtime phase is as follows: (9) (10) (5) refers to achieving source-load matching by minimizing the deviation between the output value of new energy and the matching load. The portion of the new energy curve that exceeds the matching load curve is recorded as a positive deviation. The portion below the matching load is recorded as a negative deviation. ; The receiving-end load curve. (6) To maximize the proportion of renewable energy generation and reduce the amount of renewable energy wasted, among which... Contribute to the overall development of new energy.

[0080] The constraints are as follows: (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) Equation (11) is the power balance constraint, that is, the power balance relationship between new energy sources and matching loads; Equation (12) is the power equality constraint for new energy sources, that is, the output of new energy sources is defined as the sum of the outputs of wind, solar, and solar thermal power plants. Contribute to photovoltaic power plants Contribute to wind power stations To provide power for solar thermal power plants; Equation (13) is a constraint on the source-load matching degree, that is, the similarity between the output of new energy and the matching load should be within a certain demand range. Determined based on actual planning needs; Equation (14) is a non-negative constraint for positive and negative deviations, where both positive and negative deviations are positive values; Equation (15) is the inequality relationship between the output of new energy and the load at the receiving end. That is, for the transmission mode of large-scale new energy bases to the grid, the output of new energy should not exceed the load at the receiving end. Equation (16) represents the photovoltaic output constraint, where, It's a collection of different weather conditions. This is the per-unit value of the historical resource curve for the photovoltaic power station under this weather condition; Equation (17) represents the wind power output constraint, where, It's a collection of different weather conditions. This is the per-unit value of the historical resource curve for the wind power station under this weather condition; Equations (18) to (26) are the constraint conditions formulas for solar thermal power plants; Equation (18) represents the input constraint of the solar thermal power plant, i.e., the input of the solar thermal power plant is related to the solar radiation intensity. Thermal efficiency and the area of ​​the spotlight plate related; Equation (19) represents the thermal constraint of the heat storage subsystem, i.e., the heat storage subsystem under the condition of... Heat storage at all times The remaining heat storage of the system at the previous moment and the system's charging and discharging power , related, The heat loss rate of the heat storage subsystem; Equation (20) represents the initial heat storage capacity constraint of the heat storage subsystem, i.e., the initial heat storage capacity. equal Store heat at all times ; Equation (21) represents the heat storage capacity constraint of the heat storage subsystem. The heat storage subsystem has upper and lower capacity limits. Equation (22) represents the heat storage subsystem's charge and release capacity constraints; Equation (23) represents the constraints on the charging and discharging states of the thermal storage subsystem, meaning that the charging and discharging of the thermal storage subsystem cannot occur simultaneously. For 0-1 variables, It is a large random value; Equation (24) represents the power generation constraint of the power generation system. This refers to the rated power of the solar thermal power plant. Equation (25) represents the ramp constraint of the electronic system. The ramp rate of the electronic system. The downhill climbing rate; Equation (26) represents the energy balance constraint within a solar thermal power plant. The amount of wind and solar power wasted by the electro-thermal conversion subsystem.

[0081] Finally, the analytic hierarchy process (AHP) is used to determine different weights for the multi-objective function, transforming the multi-objective optimization problem into a single-objective optimization problem: (27) In the formula, , , They are respectively , , The corresponding weights.

[0082] S140) Solving the above planning optimization model includes three steps: transforming the two-stage optimization problem into a one-stage optimization problem; obtaining historical weather data, historical real resource curve data of wind power and photovoltaic power stations in the target area; and calculating the planning results.

[0083] S140-1) Transform the two-stage optimization problem into a one-stage optimization problem: Since the results of the operational phase will affect the construction cost of new energy sources in the planning phase, this is a two-stage multi-objective optimization problem, specifically in the following form: (28) in, In the feasible region The optimization variable for the first stage is the installed capacity of new energy sources; The objective function related to the first-stage optimization variables is to minimize the cost of new energy planning and construction. In the feasible region The optimization variables for the second stage, The objective function is related to the optimization variables in the second stage. This represents the expected value of the objective function for the second stage.

[0084] During the operation phase, the resource curve of new energy sources will change according to weather conditions, therefore the scenario for the second phase is uncertain. This represents resource curve scenarios under all possible weather conditions during the operational phase. Monte Carlo sampling is used to sample uncertain scenarios during the operational phase to solve the planning model at different time scales. The expected values ​​of these scenarios are then added to the objective function, as follows: (29) The above process transforms the two-stage optimization problem into a one-stage optimization problem.

[0085] S140-2) Obtain historical weather data and historical real resource curves for wind power and photovoltaic power plants in the target area: Historical weather data for the target region is obtained as input data for the concentrated solar power (CSP) plant. Historical real resource curves for wind and solar power plants in the target region are obtained to obtain historical scenarios, which serve as input data for wind and solar power. The corresponding variables for historical solar data are as follows: Historical wind power data corresponding variables Historical data on solar radiation intensity and corresponding variables .

[0086] S140-3) Calculation of planning results: The programming results can be obtained using a mixed-integer linear programming solver. , , The installed capacity of photovoltaic, wind power, and solar thermal power plants are respectively the planning results.

[0087] In summary, based on the proposed two-stage source-load matching metric, this invention performs two source-load matching operations on the capacity configuration and load curve of new energy bases to achieve reasonable capacity configuration at the system level or station level. It takes into account the economy, safety, and flexibility of the planning, which is conducive to making full use of the complementarity of new energy and the flexibility of solar thermal power plants, improving the utilization rate of new energy, and enhancing the power system's ability to cope with the volatility of renewable energy.

[0088] Example 3 To achieve the above embodiments, such as Figure 3 As shown, this embodiment also provides a new energy base capacity planning system 10 that considers two-stage source-load matching, including: The data acquisition and scaling module 100 is used to acquire the new energy output curve and load curve of the target area, and scale the load curve based on the source-load matching coefficient to generate a matching load curve that initially matches the total power of the new energy. The source-load matching degree calculation module 200 is used to calculate the source-load matching degree based on the mean normalization result of the new energy output curve and the matching load curve, and to quantitatively evaluate the similarity between the new energy output and load curves at the system level and the station level respectively. The two-stage optimization modeling module 300 is used to construct a two-stage optimization model that includes a planning stage and an operation stage. The planning stage aims to minimize the construction cost of the new energy base, while the operation stage aims to minimize the source-load deviation and maximize the proportion of new energy output. The weight of each objective function is determined by the analytic hierarchy process, transforming the multi-objective optimization problem into a single-objective optimization problem. The model solving and capacity configuration module 400 is used to generate a weather scenario set for the operation phase using the Monte Carlo sampling method, and transform the two-stage optimization model into a one-stage optimization model based on the scenario expectation value. Then, the optimal installed capacity configuration of photovoltaic, wind power and solar thermal power plants is obtained by using a mixed integer linear programming solver.

[0089] Furthermore, the data acquisition and scaling module is also used for: Based on historical weather data and the characteristics of new energy resources in the target area, historical output curves of photovoltaic and wind power, as well as regional load curves, are obtained respectively. The load curve is scaled using a source-load matching coefficient so that the scaled matching load curve matches the total output curve of new energy sources in terms of peak-valley difference.

[0090] Furthermore, the source-load matching degree calculation module is also used for: The system-level source-load matching degree is characterized by calculating the mean normalized similarity between the combined output curves of multiple new energy power stations and the system-level matching load curve to represent the overall matching effect. The source-load matching degree at the power station level is characterized by calculating the mean normalized similarity between the output curve of a single new energy power station and the corresponding matching load curve at the power station level to represent the local matching effect.

[0091] Furthermore, the two-stage optimization modeling module is also used for: The three objective functions of construction cost in the planning stage, minimizing source-load deviation in the operation stage, and maximizing the proportion of new energy output are weighted by the analytic hierarchy process to form a weighted comprehensive objective function. By combining the weighted objective function with power balance constraints, new energy output constraints, source-load matching constraints, positive and negative deviation constraints, and internal energy balance constraints of the solar thermal power plant, a mixed integer linear programming model is constructed.

[0092] Furthermore, it also includes: The dynamic adjustment module is used to dynamically adjust the heat storage system charging and discharging strategy of the solar thermal power plant based on the amount of wind and solar power curtailed by the power-to-heat conversion subsystem, so as to improve the overall utilization rate of new energy.

[0093] The new energy base capacity planning system of this invention, which considers two-stage source-load matching, further optimizes the source-load matching process of the new energy base by dynamically adjusting the charging and discharging strategy of the solar thermal power plant's thermal storage system to absorb the abandoned wind and solar power, thereby significantly improving the overall utilization rate of new energy and the economic efficiency of system operation.

[0094] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0095] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A capacity planning method for new energy bases considering two-stage source-load matching, characterized in that, include: S1. Obtain the power output curve and load curve of the new energy source in the target area, and scale the load curve based on the source-load matching coefficient to generate a matching load curve that initially matches the total power of the new energy source. S2, based on the mean normalization results of the new energy output curve and the matching load curve, calculate the source-load matching degree, and quantitatively evaluate the similarity between the new energy output and load curves at the system level and the station level respectively. S3. Construct a two-stage optimization model that includes a planning stage and an operation stage. The planning stage aims to minimize the construction cost of the new energy base, while the operation stage aims to minimize the source-load deviation and maximize the proportion of new energy output. The weight of each objective function is determined by the analytic hierarchy process, transforming the multi-objective optimization problem into a single-objective optimization problem. S4 uses the Monte Carlo sampling method to generate a weather scenario set for the operation phase, and transforms the two-stage optimization model into a one-stage optimization model based on the scenario expectation value. Then, the optimal installed capacity configuration of photovoltaic, wind power and solar thermal power plants is obtained by using a mixed integer linear programming solver.

2. The method as described in claim 1, characterized in that, S1 includes: S11. Based on the historical weather data and new energy resource characteristics of the target area, obtain the historical output curves of photovoltaic and wind power and the regional load curves respectively. S12 uses a source-load matching coefficient to scale the load curve, so that the scaled matching load curve matches the total output curve of new energy in terms of peak-valley difference rate.

3. The method as described in claim 1, characterized in that, S2 includes: S21, The system-level source-load matching degree is characterized by the mean normalized similarity between the combined output curves of multiple new energy power stations and the system-level matching load curve to represent the overall matching effect. S22, the source-load matching degree at the power station level is characterized by the mean normalized similarity between the output curve of a single new energy power station and the corresponding matching load curve at the power station level.

4. The method as described in claim 1, characterized in that, The S3 includes: S31, by using the analytic hierarchy process, the three objective functions of construction cost in the planning stage, minimizing source-load deviation in the operation stage, and maximizing the proportion of new energy output are weighted and weighted to form a weighted comprehensive objective function. S32 combines the weighted objective function with power balance constraints, new energy output constraints, source-load matching constraints, positive and negative deviation constraints, and internal energy balance constraints of the solar thermal power plant to construct a mixed integer linear programming model.

5. The method as described in claim 1, characterized in that, Also includes: S5 dynamically adjusts the heat storage system's charging and discharging strategy based on the amount of wind and solar power wasted by the solar thermal power plant's power-to-heat conversion subsystem, in order to improve the overall utilization rate of new energy sources.

6. A new energy base capacity planning system considering two-stage source-load matching, characterized in that, include: The data acquisition and scaling module is used to acquire the power output curve and load curve of the new energy source in the target area, and scale the load curve based on the source-load matching coefficient to generate a matching load curve that initially matches the total power of the new energy source. The source-load matching degree calculation module is used to calculate the source-load matching degree based on the mean normalization result of the new energy output curve and the matching load curve, and to quantitatively evaluate the similarity between the new energy output and load curves at the system level and the station level respectively. The two-stage optimization modeling module is used to construct a two-stage optimization model that includes a planning stage and an operation stage. The planning stage aims to minimize the construction cost of the new energy base, while the operation stage aims to minimize the source-load deviation and maximize the proportion of new energy output. The weight of each objective function is determined by the analytic hierarchy process, transforming the multi-objective optimization problem into a single-objective optimization problem. The model solving and capacity configuration module is used to generate a weather scenario set for the operation phase using the Monte Carlo sampling method, and transform the two-stage optimization model into a one-stage optimization model based on the scenario expectation value. Then, the optimal installed capacity configuration of photovoltaic, wind power and solar thermal power plants is obtained by using a mixed integer linear programming solver.

7. The system as described in claim 6, characterized in that, The data acquisition and scaling module is also used for: Based on historical weather data and the characteristics of new energy resources in the target area, historical output curves of photovoltaic and wind power, as well as regional load curves, are obtained respectively. The load curve is scaled using a source-load matching coefficient so that the scaled matching load curve matches the total output curve of new energy sources in terms of peak-valley difference.

8. The system as described in claim 6, characterized in that, The source-load matching degree calculation module is also used for: The system-level source-load matching degree is characterized by calculating the mean normalized similarity between the combined output curves of multiple new energy power stations and the system-level matching load curve to represent the overall matching effect. The source-load matching degree at the power station level is characterized by calculating the mean normalized similarity between the output curve of a single new energy power station and the corresponding matching load curve at the power station level to represent the local matching effect.

9. The system as described in claim 6, characterized in that, The two-stage optimization modeling module is also used for: The three objective functions of construction cost in the planning stage, minimizing source-load deviation in the operation stage, and maximizing the proportion of new energy output are weighted by the analytic hierarchy process to form a weighted comprehensive objective function. By combining the weighted objective function with power balance constraints, new energy output constraints, source-load matching constraints, positive and negative deviation constraints, and internal energy balance constraints of the solar thermal power plant, a mixed integer linear programming model is constructed.

10. The system as described in claim 6, characterized in that, Also includes: The dynamic adjustment module is used to dynamically adjust the heat storage system charging and discharging strategy of the solar thermal power plant based on the amount of wind and solar power curtailed by the power-to-heat conversion subsystem, so as to improve the overall utilization rate of new energy.

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