A two-dimensional flexibility resource optimization configuration and scheduling method based on a screening curve model

Abstract

The present application belongs to the technical field of power system resource optimization allocation and dispatching, and discloses a two-dimensional flexible resource optimization allocation and dispatching method based on a screening curve model. According to the hourly load data and new energy predicted output data of a target region in a complete year, the upward and downward flexible demand persistence curves are extracted. The linear relationship between the total annual operation cost of the resource and the annual utilization hours is established. The technical and economic demarcation point set of any two resources is calculated, and the technical and economic ranking is generated by combining with the upward or downward flexible demand persistence curve. The expected annual average income per unit capacity and the annual operation cost gap are calculated, and then combined with the planning capacity of various resources in the optimized supply structure to determine the corresponding dispatch priority coefficient in system dispatching. The present application can preferentially direct the limited system regulation capacity to the key flexible resource with high technical and economic efficiency and high system regulation value, thereby realizing optimal dispatching efficiency.

Description

Technical Field

[0001] This invention belongs to the field of power system resource optimization and scheduling technology, and particularly relates to a two-dimensional flexible resource optimization and scheduling method based on a screening curve model. Background Technology

[0002] The rapid growth in power generation, while promoting cleaner energy, has also exacerbated the structural contradiction of "excess baseload coal power and insufficient peak-shaving" due to the randomness and intermittency of its output, posing a severe challenge to the capacity adequacy and operational flexibility of the power system. To ensure system security, an effective capacity support mechanism needs to be constructed to guide the rational planning and dispatch of flexibility resources. However, due to the lack of targeted design based on regional resource endowments and system operating characteristics, it is difficult to accurately match the differentiated and multi-dimensional flexibility needs of high-proportion renewable energy systems, easily leading to irrational resource allocation and mismatched dispatch instructions, and failing to effectively reverse the operational inertia of "emphasizing baseload and neglecting peak-shaving."

[0003] To address the shortage of system flexibility due to the high penetration of renewable energy, current capacity support mechanisms largely rely on fixed prices, cost parameters, or simple ranking. While fixed-price mechanisms are easy to implement, they struggle to dynamically reflect the true technical value and cost differences of various resources in system regulation, potentially leading to rigid dispatch instructions. Market-clearing or cost-plus models, while providing stronger techno-economic signals, require high-precision system data and models during the initial transition phase and may overlook the technical characteristics and response performance of emerging resources such as demand response and new energy storage. Therefore, there is an urgent need to develop a capacity support and dispatch decision-making method that can quantify the techno-economic boundaries of resources, adapt to regional system characteristics, and integrate with the evolution of operational mechanisms. This method would scientifically guide the optimal allocation of system regulation resources, ensuring the long-term reliability and operational sustainability of the power system. Summary of the Invention

[0004] To address the practical problems existing in current technologies, this invention proposes a two-dimensional flexible resource optimization and scheduling method based on a screening curve model. This method constructs a flexibility demand duration curve reflecting the system's dual needs for "upward peak shaving" and "downward absorption," and establishes corresponding technical and economic cost curves for the resources. The screening curve model identifies the resource types with the optimal cost and their technical and economic critical points within different duration intervals, thereby forming a hierarchical and collaborative resource optimization supply sequence. Furthermore, by quantifying the cost recovery gap of various resources and linking it to the scale of adjustable resources available to the system, differentiated and precise capacity support for flexible resources is ultimately achieved.

[0005] This invention provides the following technical solution: A two-dimensional flexible resource optimization and scheduling method based on a screening curve model includes the following steps: Step 1: Based on the hourly load data and new energy power output forecast data of the target area for the whole year, generate the system net load duration curve after preprocessing, and then extract the upward flexibility demand duration curve and the downward flexibility demand duration curve. Step 2: Based on the operational characteristic parameters of the flexible resource, establish a linear relationship between the annualized total operating cost of the resource and the annual utilization hours; Step 3: Based on the linear relationship, calculate the set of techno-economic boundary points for any two resources. By combining this with the upward flexibility demand continuity curve or the downward flexibility demand continuity curve, select the resource that minimizes the annualized total operating cost to form a techno-economic ranking. Step 4: Based on the techno-economic ranking, set the expected annual utilization hours for each type of target scheduling resource to obtain the expected annual revenue per unit capacity and the annualized operating cost gap. Then, combined with the planned capacity of each type of resource in the optimized supply structure, determine the corresponding scheduling priority coefficient in the system scheduling. The scheduling priority coefficient is used to set the resource call priority and duration in the scheduling system.

[0006] Preferably, in step one, the method for generating the system net load duration curve includes: Collect hourly load data and renewable energy forecast output data for at least one full year in the target area; Outlier data points are removed to form a typical load and renewable energy output sequence, and then the system net load value is calculated. By sorting the system net load values ​​from largest to smallest, the system net load duration curve can be generated.

[0007] Preferably, in step one, the method for extracting the upward flexibility demand persistence curve and the downward flexibility demand persistence curve includes: Set a baseline value to divide upward and downward demands; Extract the time period data that is greater than the benchmark value from the system net load duration curve, subtract the benchmark value from the system net load value, and obtain the upward flexibility demand capacity for each time period; Sort the upward flexibility demand capacity by value from largest to smallest, with the horizontal axis corresponding to its duration in hours and the vertical axis representing its demand capacity, thus forming an upward flexibility demand duration curve. Extract the time period data that is less than the benchmark value from the system net load duration curve, and subtract the system net load value from the benchmark value to obtain the downward flexibility demand capacity for each time period; Sort the downward flexibility demand capacity by value from largest to smallest, with the horizontal axis corresponding to its duration in hours and the vertical axis representing its demand capacity, thus forming the downward flexibility demand duration curve.

[0008] Preferably, in step two, the method for establishing a linear relationship between the annualized total operating cost of the resource and the annual utilization hours includes: Based on the operational characteristic parameters of flexible resources, calculate the annualized fixed operating resource consumption per unit capacity; Based on the annualized fixed operating resource consumption per unit capacity, the annualized total operating cost of the i-th type of resource when the annual utilization hours are h is obtained, which is the linear relationship between the annualized total operating cost of the resource and the annual utilization hours.

[0009] Preferably, in step three, the method for calculating the set of techno-economic boundary points includes: For any two resources and The annual utilization hours corresponding to the intersection of its operating characteristic curves This is called the techno-economic dividing line, which satisfies: ; in, Indicates the first Annual utilization hours of this type of resource Annualized total operating cost at that time Indicates the first Annual utilization hours of this type of resource Annualized total operating cost at that time; use After solving and simplifying, we get: ; in, and Representing resources With resources Annualized fixed cost per unit capacity This represents the annual utilization hours corresponding to the intersection of the operating characteristic curves. and Representing resources With resources The variable cost per unit of electricity; Under the guarantee At that time, calculate the annual utilization hours between all pairs of resources. This yields a set of techno-economic dividing points.

[0010] Preferably, in step three, the method for forming the techno-economic ranking includes: By combining the values ​​in the set of techno-economic dividing points with the duration range of the upward flexibility demand duration curve or the downward flexibility demand duration curve, multiple continuous intervals are defined on the demand duration axis. For any point on either the upward or downward flexibility demand curve, iterate through all resources and select the one that results in the annualized total operating cost. Minimal resources This allows the entire demand duration axis to be divided into several intervals, each interval corresponding to a cost-optimal resource type, thus forming a techno-economic ranking.

[0011] Preferably, in step four, the expected annual utilization hours are taken from the optimal technology adaptation range. The median, or determined based on historical operating data and future forecasts; The expected annual revenue per unit capacity that can be obtained through the energy dispatch and coordination mechanism is: ; in, The expected annual utilization hours, The expected relevant energy dispatch baseline consumption factor; The annualized operating cost gap for this type of resource, which cannot be recovered through the energy market and needs to be covered by system capacity support mechanisms, is as follows: , in, This refers to the annualized fixed operating resource consumption per unit capacity. This represents the amount of operating resources consumed per unit of electricity.

[0012] Preferably, the method for determining the corresponding scheduling priority coefficient in system scheduling includes: Assume the total amount of adjustable resources that the system can call upon annually is According to the Planning capacity of similar resources in optimizing supply structure and the gap in annualized operating cost per unit capacity Calculate its corresponding scheduling priority coefficient in system scheduling. : , in, For the set of all target resources that require scheduling support, represent One type of resource in For the first The planned capacity of similar resources in optimizing the supply structure The annualized operating cost gap per unit capacity That is, the first The scheduling support coefficient of resource class. The beneficial effects of this invention are as follows: This invention provides a two-dimensional flexible resource optimization and scheduling method based on a screening curve model, which has the following technical effects: 1) In terms of flexibility demand characterization, the constructed two-dimensional flexibility demand continuity curve can accurately reflect the spatiotemporal characteristics of the system's "concentrated peak shortage" and "persistent trough absorption pressure", providing a precise time scale basis for the timing matching and allocation of resources.

[0013] 2) In terms of resource optimization and allocation, the techno-economic ranking based on the screening curve model clearly reveals the comparative advantages of different resources in the very short, medium and long time adjustment ranges. It effectively solves the problem of unreasonable resource allocation caused by the traditional single scheduling mode, and provides a quantitative decision-making basis for establishing a hierarchical collaborative supply structure and scheduling order of "peak buffer layer - fast response layer - long-term guarantee layer".

[0014] 3) In terms of scheduling support mechanism design, by connecting the resource consumption gap of resource operation with the total amount of system callable adjustment resources, the proposed method can prioritize the limited system adjustment capacity to key flexible resources with good technical and economic performance and high system adjustment value, thereby achieving the optimization of scheduling efficiency and providing a direct and scientific accounting basis for formulating differentiated resource call priorities and support parameters. Attached Figure Description

[0015] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a schematic diagram of the two-dimensional flexible resource optimization and scheduling method based on the screening curve model according to an embodiment of the present invention. Figure 2 This is the upward flexibility requirement curve for embodiments of the present invention; Figure 3 This is a continuous curve representing the downward flexibility requirements of an embodiment of the present invention; Figure 4 This is the upward flexibility resource cost curve of an embodiment of the present invention; Figure 5 This is the downward flexibility resource cost curve of an embodiment of the present invention; Figure 6 This is an example of upward flexibility resource economy ranking in this invention. Figure 7This is an optimized upward flexibility resource supply structure for a certain region according to an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0017] Example 1 This invention provides a two-dimensional flexible resource optimization and scheduling method based on a screening curve model. The overall process is as follows: Figure 1 As shown, it includes the following steps: Step 1: Based on the hourly load data and predicted renewable energy output data for the target area throughout the year, a system net load duration curve is generated after preprocessing. Then, the upward flexibility demand duration curve and the downward flexibility demand duration curve are extracted. Specifically: S11. Collect hourly load data for at least one full year for the target area. With new energy power output forecast data .

[0018] S12. Remove abnormal data points caused by extreme weather or metering failures to form a typical load and renewable energy output sequence. System net load value The load is reduced by the output of new energy sources, and the reserve capacity required for system operation is taken into account. The calculation formula is as follows: (1), Among them, the reserve capacity It can be determined based on system reliability requirements. The net system load value for 8760 hours per year is... Sort the loads from largest to smallest to form the Net-Load Duration Curve (NLDC). This curve forms the basis for constructing the two-dimensional demand curve.

[0019] S13. Set a baseline value to divide upward and downward demands. This value can be selected as the system average load or a load level that meets a certain probability.

[0020] S14. Extracting all time periods exceeding the baseline value from the system net load duration curve NLDC satisfies the requirement. The upward flexibility demand capacity for each time period is obtained by subtracting the baseline value from the system net load value: (2) S15. Calculate the upward flexibility demand capacity for each time period. Sort by value from largest to smallest, with the horizontal axis corresponding to the duration in hours and the vertical axis representing the demand capacity, thus forming the Upward Flexibility Demand Duration Curve (UF-DDC), as shown below. Figure 2 As shown.

[0021] S16. Similarly, extracting all time periods with values ​​less than the baseline from the system net load duration curve NLDC satisfies the requirement. The downside flexibility requirement capacity for each time period is obtained by subtracting the system net load value from the baseline value. (3) S17. Determine the downward flexibility demand capacity for each time period. Sort by value from largest to smallest, with the horizontal axis corresponding to the duration in hours and the vertical axis representing the demand capacity, thus forming the Downward Flexibility Demand Duration Curve (DF-DDC), as shown below. Figure 3 As shown.

[0022] Step Two: Based on the operational characteristic parameters of flexible resources, establish a linear relationship between the annualized total operating cost of the resource and the annual utilization hours. This step aims to establish a techno-economic model for various flexible resources, characterizing their life-cycle operating cost as a function of annual utilization hours, providing a quantitative basis for subsequent scheduling decisions. The key is to obtain accurate operational characteristic parameters. Specifically: Determine the operational characteristic parameters of flexibility resources, for the first Key technical parameters of this type of resource include: resource input per unit capacity. (RMB / kW), Design service life (Year), Annual Fixed Operation and Maintenance Factor (%), Operating resource consumption per unit of electricity change (Yuan / kWh). Derive the formula for annualized operating resource consumption and calculate the annualized fixed operating resource consumption per unit capacity. The initial investment is discounted using the equal annuity method, and the calculation formula is as follows: (4) in, Let be the discount rate, then the th The number of hours of utilization for this type of resource per year is Annualized total operating cost for: (5) in, Representing resources Annualized fixed cost per unit capacity (RMB / kW·year). This represents the variable cost per unit of electricity (yuan / kWh). This is... Figure 4 , Figure 5 This model provides a mathematical representation of the operational characteristic curves of various resources. For each type of resource to be evaluated, a corresponding techno-economic curve can be plotted based on its operational parameters, providing a cost boundary reference for system scheduling. This model can be further integrated into energy management systems to support the generation and optimization of real-time scheduling strategies.

[0023] Step 3: Based on linear relationships, calculate the set of techno-economic boundary points for any two resources. By combining this with the upward or downward flexibility demand continuity curve, select the resource that minimizes the annualized total operating cost, forming a techno-economic ranking. This step determines the comparison range of operating costs for different resources by solving for the intersection points of the operating characteristic curves, providing a priority basis for scheduling decisions. Specifically: S31. Set of technological and economic dividing points: For any two resources and The annual utilization hours corresponding to the intersection of its operating characteristic curves This is called the techno-economic dividing line, which satisfies: (6) in, Indicates the first Annual utilization hours of this type of resource Annualized total operating cost at that time Indicates the first Annual utilization hours of this type of resource Annualized total operating cost.

[0024] The solution formula is: (7) in, and Representing resources With resources Annualized fixed cost per unit capacity (RMB / kW·year). This represents the annual utilization hours corresponding to the intersection of the operating characteristic curves. and Representing resources With resources The variable cost per unit of electricity (yuan / kWh).

[0025] After simplification, we get equation (8): (8) Must guarantee Calculate the pairwise relationships between all resources This yields a set of techno-economic dividing points. .

[0026] S32. Forming a techno-economic ranking: Set the technological and economic dividing line The values ​​in the curve are combined with the duration range of the upward or downward flexibility demand duration curve to define multiple continuous intervals on the demand duration axis (horizontal axis).

[0027] For any point on the demand curve, iterate through all resources and select the one that results in the annualized total operating cost. Minimal resources ,Right now: (9) Therefore, the entire demand duration axis can be divided into several intervals, each interval corresponding to a cost-optimal resource type, forming a sequence such as... Figure 6 The techno-economic ranking chart shown can be directly used as a quantitative basis for resource allocation priority in the system's hierarchical scheduling strategy.

[0028] Step 4: Based on the techno-economic ranking, assign the expected annual utilization hours to each type of target scheduling resource to obtain the expected annual revenue per unit capacity and the annualized operating cost gap. Then, combining this with the planned capacity of each type of resource in the optimized supply structure, determine the corresponding scheduling priority coefficient in the system scheduling. The scheduling priority coefficient is used to set the resource call priority and duration in the scheduling system. Specifically: S41. Based on the resource techno-economic ranking results, allocate resources for each type of objective. Set a reasonable expected number of annual utilization hours. This value is typically taken as its optimal technology adaptation range. The median, or determined based on historical operating data and future forecasts. The expected annual revenue per unit capacity of this resource, achievable through energy dispatch coordination mechanisms. Approximately: (10) in The expected relevant energy dispatch baseline consumption factor (yuan / kWh).

[0029] This means that the annualized operating cost gap for such resources, which cannot be recovered through the energy market, needs to be covered by the system capacity support mechanism. Yuan / kW is: (11) The max function ensures that scheduling support is provided only for resources that have runtime technology compatibility gaps.

[0030] S42. Let the total amount of adjustable resources that the system can call up annually be... According to the first Planning capacity of similar resources in optimizing supply structure (kW) and its unit capacity annualized operating cost gap This allows for the calculation of the corresponding scheduling priority coefficient in system scheduling. Thus, a scheduling support coefficient that embodies "differentiation" and "precise support" is created. The design method (yuan / kW / year) is as follows: (12) in, For the set of all target resources that require scheduling support, represent One type of resource in For the first The planned capacity of similar resources in optimizing the supply structure The annualized operating cost gap per unit capacity That is, the first The scheduling support coefficient for resource types can be used in the scheduling system to set the priority and duration of resource calls, enabling precise and hierarchical control of critical flexibility resources.

[0031] Example 2 This second embodiment uses a power system in a certain region of my country as an application scenario to demonstrate the specific implementation process and effects of the method of the present invention. The data mainly comes from the actual operation data of the power grid in that region in 2024, relevant planning documents, and industry cost statistics. The 2024 load data of the regional power grid (8760 hours) and the predicted wind and solar power output for the same period are used. Assuming a system reserve rate of 15%, the net load at each time point is calculated. After sorting, the net load duration curve is obtained. Selecting... As a baseline value, it is approximately the average annual load level. Following the method described in this invention, upward flexibility demand sustainability curves and downward flexibility demand sustainability curves are generated respectively, as follows: Figure 2 and Figure 3 As shown. Figure 2 The data shows that upward demand exceeded 4.2GW in the first 50 hours, with peak demand concentrated in certain areas; Figure 3 The data shows that in the 850-1982 hour range, downward demand remained above 9.2GW, indicating significant long-term pressure to absorb the load.

[0032] Resource cost parameters were determined, and upward and downward flexibility cost curves were plotted. Based on the screening curve model, and combined with the regional characteristics, policy requirements, and industry data of various core flexibility resources in the region, upward and downward flexibility cost curves were constructed to clarify the economic boundaries of various resources under different annual utilization hours. The upward and downward flexibility resource cost parameters are shown in Table 1 (Upward Flexibility Resource Cost Parameter Table) and Table 2 (Downward Flexibility Resource Cost Parameter Table).

[0033] Table 1 Table 2 Based on the core data of fixed costs and variable costs, cost curves for each flexible unit are constructed using a screening curve model. Figure 4 This paper illustrates the relationship between the annualized cost of five types of upward flexibility resources in the region and the annual utilization hours: the curve intercept represents the fixed cost, reflecting the initial cost of the resource, while the curve slope represents the variable cost, reflecting the marginal cost per unit of adjustment. The intersection of the curves for different resources is the economic critical point. For example, the intersection of demand response and thermal power flexibility retrofit corresponds to approximately 23 hours of annual utilization, indicating that in scenarios where the continuous upward flexibility demand hours are less than 23 hours, demand response is the more technically and economically superior dispatch option; however, for hours exceeding 23 hours, thermal power flexibility retrofit is more advantageous.

[0034] Taking upward flexibility resources as an example, according to Figure 6 By prioritizing the upward flexibility and economic efficiency of resources, and calculating the critical economic threshold, this invention further establishes a technology call sequence for resources in the time dimension, corresponding to the system adjustment needs for different duration intervals: Long-term capacity guarantee scenario (501–1000 hours): This scenario mainly addresses the high load pressure lasting for several days or even weeks during the winter heating season. The ranking results show that thermal power plant flexibility retrofitting ranks first due to its overall cost advantage. However, thermal power plant flexibility retrofitting also ranks highly in the model, mainly due to its lower fixed costs. However, this must be adjusted based on the specific circumstances of the region. The flexibility retrofitting potential of existing coal-fired power units in this region has a rigid upper limit both technically and economically. If it is used as the mainstay in this range, it will reinstate the inertia of "emphasizing base load and neglecting peak shaving." Therefore, although its model economics are acceptable, its capacity scale must be strictly limited in actual configuration. In contrast, pumped storage, as the core regulation means in the region's "clean energy base" plan, has irreplaceable advantages such as large capacity, long duration, reliable operation, and good coupling with wind and solar power, despite its higher fixed costs. Therefore, in this range and even the entire upward flexibility supply structure, pumped storage should be established as the core pillar of long-term peak capacity. By establishing pumped storage as the core regulation method in this scenario, its technical characteristics of large-capacity, long-cycle energy storage and synchronous generators are leveraged. By combining hydrological forecasting and medium-to-long-term load forecasting technologies, reservoir scheduling is optimized. Pumped storage is performed during off-peak hours at night, while power generation occurs during daytime and sustained peak periods. This provides reliable rotational inertia and voltage support for the system, effectively alleviating the pressure of deep peak shaving by coal-fired power plants and enhancing the grid's ability to cope with seasonal power shortages.

[0035] Short-to-medium-term regulation scenarios (42–501 hours): This scenario covers typical fluctuations such as sharp drops in intraday photovoltaic output, evening peak load ramp-up, and wind power counter-peak regulation. In this scenario, the cost curve of new energy storage exhibits a decisive advantage. The fundamental reason is that the fixed cost of new energy storage is far lower than that of pumped hydro storage and gas turbine units, while its variable costs, resulting in a lower total cost within this duration, are lower than the DR (return to load) level. Therefore, it should be prioritized for development and compensation as a flexible resource. By prioritizing the allocation of new energy storage, its technological advantages of power and energy decoupling and bidirectional flexible regulation can be utilized. Through model predictive control and adaptive scheduling algorithms, the charging and discharging sequence of energy storage can be optimized, enabling it to store electricity during periods of high renewable energy generation and discharge during peak load periods. This smooths rapid fluctuations in net load, provides precise power support for automatic generation control, and improves the smoothness and controllability of system operation.

[0036] Extremely short-duration peak scenario (0–42 hours): This scenario corresponds to Figure 2During extreme peak periods characterized by "demand capacity ≥ 4.2GW in the first 50 hours," demand response (DR) becomes the most economical solution due to its unique attribute of zero fixed investment. By prioritizing the use of demand response resources and leveraging its millisecond to minute-level response speed and zero start-up / shutdown losses, a short-term power buffer layer for the power grid is constructed through a combination of direct load control and incentive-based response techniques. This minimizes the impact on traditional power generation equipment and ensures frequency stability.

[0037] Combining the aforementioned technology call sequence with the system characteristics of this region, namely "high proportion of new energy sources and prominent winter peaks," this invention constructs as follows: Figure 7 The diagram illustrates a hierarchical, collaborative, and clearly defined upward-flexible resource supply structure with distinct technical functions. This structure, from a technical implementation perspective, systematically couples flexibility requirements at different time scales with matching resource and technical characteristics.

[0038] The optimized structure is specifically divided into three clearly defined functional layers: Peak Response Buffer Layer (0–42 hours): Composed of demand response. This layer is the first line of defense against extreme peak loads, aiming to address the reliability challenge of the shortest duration but largest power deficit with minimal system investment. Rapid Response and Intraday Regulation Layer (42–501 hours): Led by new energy storage technologies. This layer's resources are responsible for smoothing rapid fluctuations in renewable energy output, meeting intraday peak-shaving needs, and providing rapid power support at the millisecond to hour level. It is the backbone of ensuring real-time system balance and operational flexibility. Long-Term Capacity Guarantee and Seasonal Regulation Layer (501–1000 hours): Centered on pumped hydro storage, with gas turbine units as an important supplement. This layer provides reliable capacity adequacy for the system, specifically addressing long-term, high-power load gaps caused by seasonal factors. Clearly defining the core position of pumped hydro storage is an inevitable choice that aligns with the region's clean energy base positioning and achieves long-term low-carbon transformation. Thermal power plant flexibility retrofitting is only an auxiliary and reliable supplement within the established potential range.

[0039] This invention, through the aforementioned hierarchical technical configuration method based on a screening curve model, provides specific strategies and bases for power grid regulation resource allocation and capacity planning, realizing a shift from a "power compensation" mindset to a "capacity value and regulation capability pricing" technical path. This method not only provides technical screening standards for defining target resources for capacity compensation mechanisms, but its output hierarchical supply structure can also be directly applied to the operation mode arrangement and long-term planning of power grid dispatching agencies, providing an implementable technical solution for building a flexible resource regulation system with "multi-timescale coordination and multi-technology complementarity" in high-proportion renewable energy power systems.

[0040] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A two-dimensional flexible resource optimization and scheduling method based on a screening curve model, characterized in that, Includes the following steps: Step 1: Based on the hourly load data and new energy power output forecast data of the target area for the whole year, generate the system net load duration curve after preprocessing, and then extract the upward flexibility demand duration curve and the downward flexibility demand duration curve. Step 2: Based on the operational characteristic parameters of the flexible resource, establish a linear relationship between the annualized total operating cost of the resource and the annual utilization hours; Step 3: Based on the linear relationship, calculate the set of techno-economic boundary points for any two resources. By combining this with the upward flexibility demand continuity curve or the downward flexibility demand continuity curve, select the resource that minimizes the annualized total operating cost, forming a techno-economic ranking. The method for calculating the set of techno-economic boundary points includes: For any two resources and The annual utilization hours corresponding to the intersection of its operating characteristic curves This is called the techno-economic dividing line, which satisfies: ; in, Indicates the first Annual utilization hours of this type of resource Annualized total operating cost at that time Indicates the first Annual utilization hours of this type of resource Annualized total operating cost at that time; use After solving and simplifying, we get: ; in, and Representing resources With resources Annualized fixed cost per unit capacity The number of annual utilization hours corresponding to the intersection of the operating characteristic curves. and Representing resources With resources The variable cost per unit of electricity; Under the guarantee At that time, calculate the annual utilization hours between all pairs of resources. This yields a set of techno-economic boundary points; Methods for establishing techno-economic rankings include: By combining the values ​​in the set of techno-economic dividing points with the duration range of the upward flexibility demand duration curve or the downward flexibility demand duration curve, multiple continuous intervals are defined on the demand duration axis. For any point on either the upward or downward flexibility demand curve, iterate through all resources and select the one that results in the annualized total operating cost. Minimal resources The entire demand duration axis is divided into several intervals, each interval corresponding to a cost-optimal resource type, thus forming a techno-economic ranking. Step 4: Based on the techno-economic ranking, set the expected annual utilization hours for each type of target scheduling resource to obtain the expected annual revenue per unit capacity and the annualized operating cost gap. Then, combined with the planned capacity of each type of resource in the optimized supply structure, determine the corresponding scheduling priority coefficient in the system scheduling. The scheduling priority coefficient is used to set the resource call priority and duration in the scheduling system.

2. The method according to claim 1, characterized in that: In step one, the method for generating the system net load duration curve includes: Collect hourly load data and renewable energy forecast output data for at least one full year in the target area; Outlier data points are removed to form a load and new energy output sequence, and then the system net load value is calculated. The system net load values ​​are sorted from largest to smallest to form the system net load duration curve.

3. The method according to claim 2, characterized in that: In step one, the methods for extracting the upward flexibility demand persistence curve and the downward flexibility demand persistence curve include: Set a baseline value to divide upward and downward demands; Extract the time period data that is greater than the benchmark value from the system net load duration curve, subtract the benchmark value from the system net load value, and obtain the upward flexibility demand capacity for each time period; Sort the upward flexibility demand capacity by value from largest to smallest, with the horizontal axis corresponding to its duration in hours and the vertical axis representing its demand capacity, thus forming an upward flexibility demand duration curve. Extract the time period data that is less than the benchmark value from the system net load duration curve, and subtract the system net load value from the benchmark value to obtain the downward flexibility demand capacity for each time period; Sort the downward flexibility demand capacity by value from largest to smallest, with the horizontal axis corresponding to its duration in hours and the vertical axis representing its demand capacity, thus forming the downward flexibility demand duration curve.

4. The method according to claim 1, characterized in that: In step two, the method for establishing a linear relationship between the annualized total operating cost of the resource and the annual utilization hours includes: Based on the operational characteristic parameters of flexible resources, calculate the annualized fixed operating resource consumption per unit capacity; Based on the annualized fixed operating resource consumption per unit capacity, the annualized total operating cost of the i-th type of resource when the annual utilization hours are h is obtained, which is the linear relationship between the annualized total operating cost of the resource and the annual utilization hours.

5. The method according to claim 1, characterized in that: In step four, the expected annual utilization hours are taken from the optimal technology adaptation range. The median, or determined based on historical operating data and future forecasts; The expected annual revenue per unit capacity obtained through the energy dispatch coordination mechanism is: ； in, The expected annual utilization hours, The expected relevant energy dispatch baseline consumption factor; The annualized operating cost gap that cannot be recovered through the energy market and needs to be covered by the system capacity support mechanism is: ； in, This refers to the annualized fixed operating resource consumption per unit capacity. This represents the amount of operating resources consumed per unit of electricity.

6. The method according to claim 5, characterized in that: Methods for determining the corresponding scheduling priority coefficient in system scheduling include: Assume the total amount of adjustable resources that the system can call upon annually is According to the Planning capacity of similar resources in optimizing supply structure and the gap in annualized operating cost per unit capacity Calculate its corresponding scheduling priority coefficient in system scheduling. : ， in, For the set of all target resources that require scheduling support, represent One type of resource in For the first The planned capacity of similar resources in optimizing the supply structure This is the gap in the annualized operating cost per unit capacity.

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