Source network load storage optimization scheduling method suitable for flexible supply and demand balance in extreme weather

Abstract

The invention relates to the technical field of power system optimization scheduling, and discloses a source network load storage optimization scheduling method suitable for flexible supply and demand balance in extreme weather. The method comprises the following steps: firstly, acquiring new energy power interval prediction data which aims at typical extreme weather scenes and is subjected to error correction; on the basis, the flexibility requirement of the system and the supply capability of multi-type resources are quantified; and furthermore, with maximization of the comprehensive benefit of the system as a target, a flexible vacancy virtual penalty term for stabilizing fluctuation risks caused by extreme weather is particularly introduced, an optimal scheduling model is constructed and solved, and a scheduling plan is generated. According to the method, by fusing extreme weather exclusive prediction information and an operation risk hedging mechanism, efficient coordination of source network load storage resources in extreme weather is realized, the new energy power abandoning rate is effectively reduced, the dependence on traditional thermal power is reduced, and the comprehensive operation benefit and safety margin of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of power system optimization dispatching technology, and in particular to a source-grid-load-storage optimization dispatching method applicable to flexible supply and demand balance under extreme weather conditions. Background Technology

[0002] With the large-scale grid connection of new energy sources and the accelerated clean transformation of the power system, renewable energy sources such as wind power and photovoltaics have become the main sources of power supply in new energy bases such as Hebei North and Yunnan. However, these regions are prone to extreme weather events, which are characterized by low probability, clustering, and complexity. This results in strong randomness and volatility in the output of new energy power, posing a severe challenge to the safe and stable operation of the power system, dispatch optimization, and the absorption of new energy.

[0003] Current power system dispatching technology has evolved from traditional single-source dispatching to multi-resource coordinated dispatching involving power generation, grid, load, and storage. The core objective is to address the supply-demand balance pressure caused by power source fluctuations by optimizing the allocation of various resources. However, under extreme weather scenarios, the fluctuation range of renewable energy power increases significantly, placing higher demands on the system's flexibility and adjustment capabilities. Existing dispatching technologies largely focus on conventional weather scenarios and do not fully consider the quantitative requirements for flexible supply and demand under extreme weather conditions, making it difficult to achieve precise coordination of multiple resources and hindering the safe, green, and economical operation of the power system.

[0004] In terms of dispatch optimization, although current technologies related to source-grid-load-storage dispatch have made some progress, there are still significant shortcomings in their adaptability to extreme weather scenarios. Specifically, these shortcomings include: a lack of precision in quantifying flexible supply and demand; existing methods do not incorporate demand modeling based on the range-bound fluctuations of renewable energy power under extreme weather conditions, nor do they fully integrate the regulation potential of traditional generating units, energy storage systems, and virtual power plants; a singular design of dispatch objectives, primarily focused on economic efficiency or emission minimization, without comprehensively considering the balance between renewable energy consumption and overall system benefits; a disconnect between forecast uncertainty and dispatch optimization, lacking risk response mechanisms based on range-bound forecasts; and an inadequate constraint system, with insufficient consideration given to key operational constraints such as power transmission, energy storage efficiency, and virtual power plant response boundaries under extreme weather conditions, affecting the feasibility and safety of dispatch schemes. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a source-grid-load-storage optimization scheduling method that is applicable to flexible supply and demand balance under extreme weather conditions.

[0006] The objective of this invention is achieved through the following technical solution: a source-grid-load-storage optimization scheduling method applicable to flexible supply and demand balance under extreme weather conditions. This method includes: acquiring preprocessed data for extreme weather scenarios; acquiring error-corrected renewable energy power range forecast data, load forecast data, and equipment and parameter data for typical extreme weather conditions, and verifying the data; modeling system flexibility supply and demand, calculating the overall upward and downward flexibility demand of the system based on renewable energy power range and load forecast data; quantifying the flexibility supply capacity of traditional generator sets, energy storage systems, and virtual power plants, summarizing them into the total system flexibility supply; constructing and solving an optimization scheduling model considering extreme weather risks, aiming to maximize the overall system benefit, introducing a virtual penalty term for flexibility deficit to offset the uncertainty of source-load fluctuations caused by extreme weather, constructing an optimization scheduling model with multiple resource constraints, and solving it using a mixed integer programming method; and outputting the scheduling plan and key operating indicators.

[0007] Specifically, the new energy power range prediction data includes the prediction center value, prediction upper limit, and prediction lower limit for wind power and photovoltaic power; the load prediction data includes the load prediction center value, the upper limit of the load prediction range, and the lower limit of the load prediction range.

[0008] Specifically, the system flexibility requirement modeling includes calculating the flexibility of wind power adjustments both upward and downward: ; In the formula, Increase the flexibility capacity of wind power; Reduce flexible capacity for wind power; Let t be the upper bound for wind power prediction; This represents the predicted central value for wind power. Let be the lower bound of the prediction interval for wind power at time t; Calculate the flexibility of photovoltaic adjustments, both upward and downward: ; In the formula, Increase the flexibility capacity for photovoltaic power generation; Reduce flexible capacity for photovoltaic systems; Let t be the upper bound for photovoltaic prediction. This is the predicted central value for photovoltaics; Let be the lower bound of the prediction interval for photovoltaic power at time t; Calculate load flexibility requirements: ; In the formula, This represents the upward adjustment capacity that the system needs to reserve at time t to cope with a possible reduction in load. This represents the downward adjustment capacity that the system needs to prepare at time t to cope with a potential increase in load. This is the load forecast center value; These represent the upper and lower bounds of the load forecast interval at time t, respectively. The overall system's flexibility requirements for both upward and downward adjustments are as follows: ; ; In the formula, The total upscaling flexibility capacity required by the system at time t; The total downscaling flexibility capacity required by the system at time t; Specifically, the power supply capacity of the traditional generator set is: ; ; In the formula, The adjustments are to increase or decrease the supply capacity of traditional generating units, respectively, to improve flexibility. These are the limits for adjusting the ramp rate of the generator unit, both upward and downward. These are the maximum and minimum power output limits for the generator unit, respectively. The step size is calculated; G is the set of traditional units in the system. This represents the actual output of a traditional generating unit at time t; the scheduling period T is 24 hours, and the above formula is applied time-by-time within the scheduling period. Calculations are performed to form a sequence of traditional unit flexibility supply throughout the entire lifecycle; Specifically, the energy storage system has the following supply capacity: The increased supply capacity (discharge) of the energy storage unit at time t: ; Reduced supply capacity (charging) of the energy storage unit at time t: ; In the formula, The maximum additional discharge power that can be provided to supply the upswing flexibility of the energy storage unit at time t; The maximum additional charging power that can be provided to supply the downsizing flexibility of the energy storage unit at time t; This represents the maximum capacity of the energy storage unit. This represents the minimum capacity of the energy storage unit.

[0009] These are the charge and discharge efficiencies, respectively. Let be the real-time capacity at time t; These are the maximum charging / discharging power, respectively. It is a collection of system energy storage units.

[0010] Specifically, the supply capacity of the virtual power plant is as follows: Flexibility supply potential under load interruption: ; In the formula, Provide flexibility under load interruption; This represents the load that has already been interrupted in the system. The original load of the system; This represents the maximum proportion of interruptible load. Flexibility supply potential under load shift: ; In the formula, The load transfer supply capacity is the potential margin of load transfer that can be transferred in or out at any given time. This represents the available load margin at each time point.

[0011] The potential margin is subject to the following constraints: The amount transferred at any given moment shall not exceed the preset upper and lower limits of the load transfer ratio; The total electricity consumption remains unchanged before and after the relocation; The power transferred at any given moment shall not exceed the maximum allowable power transfer limit of the system.

[0012] The total system flexibility supply is: The system as a whole is increasing its flexibility in supply: ; Overall system flexibility supply reduction: ; In the formula, To enhance the overall flexibility of the system's supply capacity; This reduces the overall flexibility of the system's supply capacity.

[0013] Specifically, the objective function of the optimized scheduling model is: ; In the formula, Revenue from the sale of electricity by traditional generating units; Revenue from the sale of electricity by new energy generating units; Compared to the operating costs of traditional generator sets; For energy storage operating costs; Cost of a virtual power plant; To mitigate supply and demand gap costs for flexibility.

[0014] Specifically, the operating cost of the traditional generator set is: ; In the formula, Here are the parameters for power generation cost, where the coefficient of the quadratic term is... This reflects the efficiency curve characteristics of the generator set, especially the rate of change of incremental coal consumption rate; the primary term coefficient Related to the benchmark coal consumption rate or average efficiency of the generator set; constant This represents the no-load operating cost or fixed operating cost of the generator set. For traditional generator units, the output power is the decision variable. The number of traditional generator sets; The units are respectively in Start and stop status variables for a given time period; These are the start-up and shutdown costs of the generating unit; The energy storage operating cost is: ; In the formula, This refers to the charging power. This refers to the discharge power. This refers to the unit price of energy storage loss. These are the charge and discharge efficiencies, respectively. The cost of the virtual power plant is: ; In the formula, This refers to the unit price of load transfer costs. This refers to the unit price for load interruption costs. Let t represent the amount of load that has been interrupted in the system at time t; This represents the amount of load power transferred through the virtual power plant at time t (positive values ​​represent load transfer in, and negative values ​​represent load transfer out). The cost of the supply and demand gap in flexibility is: ; In the formula, These are respectively the virtual penalty coefficients for increasing the supply-demand gap in flexibility and decreasing the flexible supply gap, to meet [the following needs]. ; The adjustments are respectively upward and downward to adjust the supply and demand gap for flexibility. ; Electricity sales revenue is: ; ; In the formula, These are the unit prices for electricity sold by traditional generating units, photovoltaic power, and wind power, respectively. Let t be the predicted power output of photovoltaic and wind power. These represent the curtailment power of solar and wind power at time t, respectively.

[0015] Specifically, the constraints of the optimized scheduling model include: Power constraints: ; In the formula, This is a Boolean variable representing the start-up and shutdown status of a traditional generator set. A value of 1 indicates that the unit is in the start-up and running state, and a value of 0 indicates that the unit is in the shutdown and stop state. These represent the minimum and maximum technical outputs of unit g, respectively.

[0016] Climbing constraints: ; Start-stop duration constraints are: ; ; In the formula, A Boolean variable characterizing whether unit g starts up at time t. ; A Boolean variable characterizing whether unit g experiences a shutdown at time t. ; To minimize the time required to keep the device powered on; To minimize downtime; The length of time that unit g has been continuously running at time t; The length of time that unit g has been continuously shut down at time t; Capacity constraints: ; Charge and discharge power constraints: ; ; In the formula, Let be the decision variable for the discharge power of the energy storage system at time t; Let t be the decision variable for the charging power of the energy storage system at time t (negative values ​​are taken during charging). These are the maximum allowable discharge power and the maximum allowable charging power of the energy storage system, respectively. Energy balance constraints: ; Load interruption constraints: ; In the formula, The original load forecast value at time t; This is the preset upper limit for the proportion of interruptible load; Let t be the initial load of the system at time t.

[0017] Load transfer constraints: ; In the formula, Let t be the decision variable for load transfer at time t (positive values ​​indicate load transfer in, and negative values ​​indicate load transfer out). This is the maximum allowable load transfer power limit for a single moment in the system. The original (before transfer) load forecast at time t; The net load value after load transfer adjustment at time t; the total electricity consumption remains unchanged before and after the transfer. External power constraints: ; In the formula, These represent the transmitted power at time t and the maximum transmitted power limit, respectively. Curtailment of wind and solar power: ; Power balance constraints: ; In the formula, Let t be the total load power that the system needs to satisfy. Let t be the system's outgoing power (power transmitted outside the region).

[0018] The present invention has the following advantages: This invention addresses the challenges of dispatching renewable energy power systems under extreme weather conditions. By integrating inter-regional forecast data to accurately quantify the supply-demand gap in flexibility, it constructs a source-grid-load-storage collaborative optimization model that aims to maximize comprehensive benefits while balancing renewable energy consumption and operational safety. Furthermore, it introduces a virtual penalty mechanism for flexibility deficits to balance economic efficiency and risk resilience. Ultimately, this invention achieves the technical effects of significantly reducing renewable energy curtailment rates, decreasing reliance on thermal power, and synergistically improving overall system benefits and operational flexibility under extreme weather conditions. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the optimized scheduling method of the present invention; Figure 2 A schematic diagram of the traditional unit output plan considering virtual penalties; Figure 3 A schematic diagram of the traditional unit output plan without considering virtual penalties; Figure 4 A schematic diagram of the system's power balance considering virtual penalties; Figure 5 This is a schematic diagram of the power balance of a system without considering virtual penalties. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] The present invention will be further described below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0024] like Figures 1 to 5As shown, this invention presents an optimized scheduling method for source-grid-load-storage systems to balance flexible supply and demand under extreme weather conditions. The core of this method lies in addressing the problem of significantly increased uncertainty in renewable energy power forecasting caused by extreme weather, employing a collaborative scheduling logic of "accurate forecasting - risk quantification - proactive defense." First, error correction technology improves the reliability of renewable energy power range forecasts under extreme weather conditions, providing accurate input for quantifying flexible supply and demand. Then, in the optimized scheduling model, forecast uncertainty is transformed and characterized as a potential shortage risk in flexible supply and demand, and this risk is proactively managed and weighed economically through a virtual penalty cost term, thereby guiding the generation of a scheduling plan that is both economically efficient and maintains sufficient safety margins. The specific steps are as follows: Preprocessing of multi-source input data to obtain new energy power range prediction data, load prediction data, and equipment and parameter data, and verifying the data; the new energy power range prediction data includes the prediction center value, upper limit, and lower limit of wind power and photovoltaic power; the load prediction data includes the load prediction center value, upper limit of the load prediction range, and lower limit of the load prediction range; the equipment and parameter data includes parameters such as traditional generator sets (thermal power, hydropower, nuclear power, including maximum / minimum power, ramp rate, start-up and shutdown costs, etc.), energy storage systems (including total capacity, real-time capacity, charge and discharge efficiency, maximum charge and discharge power, etc.), virtual power plants (including the maximum proportion of interruptible load, load transfer boundary, etc.), and external transmission channels (maximum external transmission power). Physical thresholding and logical consistency checks are used to eliminate abnormal data, ensuring the integrity and reliability of the input data.

[0025] System flexibility requirements are modeled based on renewable energy power range and load forecast data to calculate the overall upward and downward flexibility requirements of the system; based on the range fluctuation characteristics of renewable energy power and load under extreme weather conditions, the overall system flexibility requirements are calculated in layers. Calculate the flexibility of upward and downward adjustments for wind power: ; In the formula, Increase the flexibility capacity of wind power; Reduce flexible capacity for wind power; Let t be the upper bound for wind power prediction; This represents the predicted central value for wind power. Let be the lower bound of the prediction interval for wind power at time t; Calculate the flexibility of photovoltaic adjustments, both upward and downward: ; In the formula, Increase the flexibility capacity for photovoltaic power generation; Reduce flexible capacity for photovoltaic systems; Let t be the upper bound for photovoltaic prediction. This is the predicted central value for photovoltaics; Let be the lower bound of the prediction interval for photovoltaic power at time t; Calculate load flexibility requirements: The calculation of load flexibility requirements is performed in the opposite direction to that of wind and solar power. It calculates upward and downward adjustment flexibility requirements based on the characteristics of the load forecast interval. ; In the formula, This represents the upward adjustment capacity that the system needs to reserve at time t to cope with a possible reduction in load. This represents the downward adjustment capacity that the system needs to prepare at time t to cope with a potential increase in load. This is the load forecast center value; These represent the upper and lower bounds of the load forecast interval at time t, respectively. The overall system's flexibility requirements for both upward and downward adjustments are as follows: ; ; In the formula, The total upscaling flexibility capacity required by the system at time t; Let t be the total downscaling flexibility capacity required by the system at time t.

[0026] The system flexibility supply capacity is modeled, and the flexibility supply capacity of traditional generator sets, energy storage systems and virtual power plants is quantified separately and summarized into the total system flexibility supply. The power supply capacity of the traditional generator set is: ; ; In the formula, The adjustments are to increase or decrease the supply capacity of traditional generating units, respectively, to improve flexibility. These are the limits for adjusting the ramp rate of the generator unit, both upward and downward. These are the maximum and minimum power output limits for the generator unit, respectively. The step size is calculated; G is the set of traditional units in the system. This represents the actual output of a traditional generating unit at time t; the scheduling period T is 24 hours, and the above formula is applied time-by-time within the scheduling period. Calculations are performed to form a sequence of traditional unit flexibility supply throughout the entire lifecycle; The energy storage system has the following supply capacity: The increased supply capacity (discharge) of the energy storage unit at time t: ; Reduced supply capacity (charging) of the energy storage unit at time t: ; In the formula, The maximum additional discharge power that can be provided to supply the upswing flexibility of the energy storage unit at time t; The maximum additional charging power that can be provided to supply the downsizing flexibility of the energy storage unit at time t; This represents the maximum capacity of the energy storage unit. This represents the minimum capacity of the energy storage unit. These are the charge and discharge efficiencies, respectively. Let be the real-time capacity at time t; These are the maximum charging / discharging power, respectively. A collection of system energy storage units; The supply capacity of the virtual power plant is manifested in two types of adjustment behaviors: load interruption and load transfer. Its flexibility and supply potential are calculated respectively. Load interruption: ; In the formula, Provide flexibility under load interruption; This represents the load that has already been interrupted in the system. The original load of the system; This represents the maximum proportion of interruptible load. Load transfer: ; In the formula, The load transfer supply capacity is the potential margin of load transfer that can be transferred in or out at any given time. This represents the available load margin at each time point.

[0027] The potential margin is subject to the following constraints: The amount transferred at any given moment shall not exceed the preset upper and lower limits of the load transfer ratio; The total electricity consumption remains unchanged before and after the relocation; The power transferred at any single moment shall not exceed the maximum allowable power transfer limit of the system. The total system flexibility supply is: ; ; In the formula, To enhance the overall flexibility of the system's supply capacity; To reduce the overall flexibility of the system's supply capacity; This invention is based on the predictive characteristics of new energy power ranges to accurately calculate the flexibility requirements for upward / downward adjustments of wind power, photovoltaic power, and load; it comprehensively integrates the adjustment capabilities of traditional units, energy storage systems, and virtual power plants to construct a flexible supply model covering all resources, thereby achieving precise matching of supply and demand.

[0028] A source-grid-load-storage optimization scheduling model is constructed with the goal of maximizing the overall system benefits. A virtual penalty term for flexibility deficit is introduced to construct an optimization scheduling model with multiple resource constraints. The objective function for optimizing the scheduling model is: ; In the formula, Revenue from the sale of electricity by traditional generating units; Revenue from the sale of electricity by new energy generating units; Compared to the operating costs of traditional generator sets; For energy storage operating costs; Cost of a virtual power plant; To mitigate supply and demand gap costs for flexibility.

[0029] The operating cost of the traditional generator set is: ; In the formula, Here are the parameters for power generation cost, where the coefficient of the quadratic term is... This reflects the efficiency curve characteristics of the generator set, especially the rate of change of incremental coal consumption rate; the primary term coefficient Related to the benchmark coal consumption rate or average efficiency of the generator set; constant This represents the no-load operating cost or fixed operating cost of the generator set. For traditional generator units, the output power is the decision variable. The number of traditional generator sets; The units are respectively in Start and stop status variables for a given time period; These are the start-up and shutdown costs of the generating unit; The operating cost of energy storage is: ; In the formula, This refers to the charging power. This refers to the discharge power. This refers to the unit price of energy storage loss. These are the charge and discharge efficiencies, respectively. The cost of a virtual power plant is: ; In the formula, This refers to the unit price of load transfer costs. This refers to the unit price for load interruption costs. Let t represent the amount of load that has been interrupted in the system at time t; This represents the amount of load power transferred through the virtual power plant at time t (positive values ​​represent load transfer in, and negative values ​​represent load transfer out). The cost of supply and demand gaps in flexibility is: ; In the formula, These are respectively the virtual penalty coefficients for increasing the supply-demand gap in flexibility and decreasing the flexible supply gap, to meet [the following needs]. ; The adjustments are respectively upward and downward to adjust the supply and demand gap for flexibility. , ; Electricity sales revenue is: ; ; In the formula, These are the unit prices for electricity sold by traditional generating units, photovoltaic power, and wind power, respectively. Let t be the predicted power output of photovoltaic and wind power. These represent the curtailment power of solar and wind power at time t, respectively.

[0030] The constraints of the optimized scheduling model cover the entire resource constraint system, ensuring the feasibility of the scheduling plan, including: Power constraints: ; In the formula, This is a Boolean variable representing the start-up and shutdown status of a traditional generator set. A value of 1 indicates that the unit is in the start-up and running state, and a value of 0 indicates that the unit is in the shutdown and stop state. These represent the minimum and maximum technical outputs of unit g, respectively.

[0031] Climbing constraints: ; Start-stop duration constraints are: ; ; In the formula, A Boolean variable characterizing whether unit g starts up at time t. ; A Boolean variable characterizing whether unit g experiences a shutdown at time t. ; To minimize the time required to keep the device powered on; To minimize downtime; The length of time that unit g has been continuously running at time t; The length of time that unit g has been continuously shut down at time t; Capacity constraints: ; Charge and discharge power constraints: ; ; In the formula, Let be the decision variable for the discharge power of the energy storage system at time t; Let t be the decision variable for the charging power of the energy storage system at time t (negative values ​​are taken during charging). These are the maximum allowable discharge power and the maximum allowable charging power of the energy storage system, respectively. Energy balance constraints: ; In the formula, These refer to the charging efficiency and discharging efficiency of the energy storage system, respectively. The scheduling time step; Load interruption constraints: ; In the formula, The original load forecast value at time t; This is the preset upper limit for the proportion of interruptible load; Let t be the initial load of the system at time t.

[0032] Load transfer constraints: ; In the formula, Let t be the decision variable for load transfer at time t (positive values ​​indicate load transfer in, and negative values ​​indicate load transfer out). This is the maximum allowable load transfer power limit for a single moment in the system. The original (before transfer) load forecast at time t; The net load value after load transfer adjustment at time t; the total electricity consumption remains unchanged before and after the transfer. External power constraints: ; In the formula, These represent the transmitted power at time t and the maximum transmitted power limit, respectively. Curtailment of wind and solar power: ; The amount of wind and solar power curtailed should be less than the predicted output of wind and solar power. Power balance constraints: ; In the formula, Let t be the total load power that the system needs to satisfy. Let t be the system's outgoing power (power transmitted outside the region). The system operation must ensure that the power supply on the generation side and the power consumption side is balanced at all times.

[0033] The model solving and scheduling plan generation process employs a mixed-integer programming method to solve the optimization model, outputting the scheduling plan and key operational indicators. The mixed-integer programming method is used to solve the aforementioned optimization model, adapting to the real-time requirements of scheduling decisions under extreme weather conditions. It outputs the optimal output plan for traditional generating units, energy storage systems, and virtual power plants within 24 hours (calculation step size 15 minutes), as well as key indicators such as renewable energy consumption, power transmission, and curtailment rate, providing direct basis for scheduling execution.

[0034] To verify the effectiveness of the scheduling method described in this invention, a simulation test was conducted on a high-proportion new energy base in China (11.5GW of photovoltaic installed capacity and 11.2GW of wind power installed capacity) under six typical extreme weather conditions (high temperature, haze, snow cover, low temperature, icing, and strong wind). The scheduling model input used new energy power range prediction data (95% confidence level) that had been corrected for extreme weather errors.

[0035] The following highlights the key impact of the core scheduling mechanism of this invention—the virtual penalty for flexibility deficit—on system scheduling strategies and operational metrics in actual operation.

[0036] Figures 2 to 5 A direct comparison was made regarding whether a virtual penalty term C for flexibility deficit was introduced into the scheduling model during high-temperature weather. flex The resulting differences in scheduling plans.

[0037] Taking high temperatures as an example, considering C flex Under these circumstances, the output of the thermal power units is more conservative; for most of the operating time, the total power of the three traditional units is maintained at 1608.75MW; without considering C flex In this case, since the objective function tends to maximize total revenue, traditional units operate more aggressively, maintaining their total power at almost the rated power of 1950MW, which makes traditional units lack the ability to further increase their power.

[0038] Table 1 Virtual Penalty C flex Impact on scheduling

[0039] From the perspective of scheduling effectiveness, the virtual penalty mechanism C for activity shortage... flex The effective guidance and scheduling model shifts from simply pursuing the maximization of economic benefits to taking into account both safety margin and green consumption. Although the total system revenue decreases slightly by about 1.9%, this is a reasonable cost paid in exchange for a significant reduction in the renewable energy curtailment rate (relative reduction of 38.6%) and a significant improvement in system operational flexibility (safety margin), demonstrating the advantages of this invention in multi-objective collaborative optimization.

[0040] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any person skilled in the art can make many possible variations and modifications to the technical solution of the present invention, or modify it into equivalent embodiments, without departing from the scope of the present invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technology of the present invention without departing from the scope of the present invention are within the protection scope of the present invention.

Claims

1. A source-grid-load-storage optimization scheduling method applicable to flexible supply and demand balance under extreme weather conditions, characterized in that: Includes the following steps: S1. Obtain preprocessed data for extreme weather scenarios: Obtain new energy power range prediction data after error distribution correction for typical extreme weather conditions such as strong winds, icing, fog and haze, high temperature, low temperature and snow cover, as well as load prediction data and power system equipment and operation parameter data, and verify the data. S2. System flexibility supply and demand modeling: Based on the new energy power range prediction data and load prediction data, calculate the overall upward and downward flexibility demand of the system at each moment in the scheduling cycle; and quantify the flexibility supply capacity of traditional generator sets, energy storage systems, and virtual power plants at each moment in the scheduling cycle, and summarize them into the total system flexibility supply capacity. S3. Construct and solve an optimization scheduling model that considers extreme weather risks: With the goal of maximizing the overall system benefits, a virtual penalty cost term for flexibility deficit is introduced to offset the uncertainty of source load fluctuations under extreme weather conditions. A mixed integer programming model with multiple operational constraints is constructed and solved. S4. Output Scheduling Plan: Based on the model solution results, generate and output the scheduling plan and key operating indicators.

2. The method according to claim 1, characterized in that, In step S1, the error distribution correction specifically involves: based on the prediction error samples corresponding to various extreme weather events in historical data, fitting their error probability density distribution using the kernel density estimation method, and correcting the initial prediction results of the conventional prediction model under the corresponding extreme weather based on this distribution, so as to generate the new energy power range prediction data.

3. The source-grid-load-storage optimization scheduling method for flexible supply and demand balance under extreme weather conditions as described in claim 1, characterized in that: The system flexibility requirement modeling includes calculating the flexibility of wind power adjustments, both upward and downward: Increase flexibility capacity ; Reduce flexibility capacity: ; In the formula, Increase the flexibility capacity of wind power; Reduce flexible capacity for wind power; Let t be the upper bound for wind power prediction; This represents the predicted central value for wind power. Let be the lower bound of the prediction interval for wind power at time t; Calculate the flexibility of photovoltaic adjustments, both upward and downward: Increase flexibility capacity: ； Reduce flexibility capacity: ； In the formula, Increase the flexibility capacity for photovoltaic power generation; Reduce flexible capacity for photovoltaic systems; Let t be the upper bound for photovoltaic prediction. This is the predicted central value for photovoltaics; Let be the lower bound of the prediction interval for photovoltaic power at time t; Calculate load flexibility requirements: Flexibility requirements for load adjustments: ； Flexibility requirements for load reduction: ； In the formula, This represents the upward adjustment capacity that the system needs to reserve at time t to cope with a possible reduction in load. This represents the downward adjustment capacity that the system needs to prepare at time t to cope with a potential increase in load. This is the load forecast center value; , These represent the upper and lower bounds of the load forecast interval at time t, respectively. The overall system's flexibility requirements for both upward and downward adjustments include: The overall system has increased flexibility requirements: ； Overall system flexibility requirements have been reduced: ； In the formula, The total upscaling flexibility capacity required by the system at time t; Let t be the total downscaling flexibility capacity required by the system at time t.

4. The source-grid-load-storage optimization scheduling method for flexible supply and demand balance under extreme weather conditions as described in claim 1, characterized in that: The power supply capacity of the traditional generator set is: Traditional unit flexibility enhancement capabilities: ； Traditional unit flexibility reduction capability: ； In the formula, The adjustments are to increase or decrease the supply capacity of traditional generating units, respectively, to improve flexibility. These are the limits for adjusting the ramp rate of the generator unit, both upward and downward. These are the maximum and minimum power output limits for the generator unit, respectively. The step size is calculated; G is the set of traditional units in the system; This represents the actual output of a traditional generator unit at time t; the scheduling cycle T is 24 hours.

5. The source-grid-load-storage optimization scheduling method for flexible supply and demand balance under extreme weather conditions as described in claim 4, characterized in that: The energy storage system has the following supply capacity: Increased supply capacity of the energy storage unit at time t: ； Reduced supply capacity of the energy storage unit at time t: ； In the formula, The maximum additional discharge power that can be provided to supply the upswing flexibility of the energy storage unit at time t; The maximum additional charging power that can be provided to supply the downsizing flexibility of the energy storage unit at time t; This represents the maximum capacity of the energy storage unit. This represents the minimum capacity of the energy storage unit. These are the charge and discharge efficiencies, respectively. Let be the real-time capacity at time t; These are the maximum charging / discharging power, respectively. It is a collection of system energy storage units.

6. The source-grid-load-storage optimization scheduling method for flexible supply and demand balance under extreme weather conditions as described in claim 5, characterized in that: The supply capacity of the virtual power plant is: Flexibility supply potential under load interruption: ； In the formula, Provide flexibility under load interruption; This represents the load that has already been interrupted in the system. The original load of the system; This represents the maximum proportion of interruptible load. Flexibility supply potential under load shift: ； In the formula, The load transfer supply capacity is the potential margin of load transfer that can be transferred in or out at any given time. This represents the available load margin at each time point; Total system flexibility provision includes: The system as a whole is increasing its flexibility in supply: ； Overall system flexibility supply reduction: ； In the formula, To enhance the overall flexibility of the system's supply capacity; This reduces the overall flexibility of the system's supply capacity.

7. The source-grid-load-storage optimization scheduling method for flexible supply and demand balance under extreme weather conditions as described in claim 5, characterized in that: The objective function of the optimized scheduling model is: ； In the formula, Revenue from the sale of electricity by traditional generating units; Revenue from the sale of electricity by new energy generating units; Compared to the operating costs of traditional generator sets; For energy storage operating costs; Cost of a virtual power plant; To mitigate supply and demand gap costs for flexibility.

8. The source-grid-load-storage optimization scheduling method for flexible supply and demand balance under extreme weather conditions as described in claim 7, characterized in that: The operating cost of the traditional generator set is: ； In the formula, Here are the parameters for power generation cost, where the coefficient of the quadratic term is... This reflects the efficiency curve characteristics of the generator set; the coefficient of the primary term. Related to the benchmark coal consumption rate or average efficiency of the generator set; constant This represents the no-load operating cost or fixed operating cost of the generator set. For traditional generator units, the output power is the decision variable. The number of traditional generator sets; The units are respectively in Start and stop status variables for a given time period; These are the start-up and shutdown costs of the generating unit; The energy storage operating cost is: ； In the formula, This refers to the charging power. This refers to the discharge power. This refers to the unit price of energy storage loss. These are the charge and discharge efficiencies, respectively. The cost of the virtual power plant is: ； In the formula, This refers to the unit price of load transfer costs. This refers to the unit price for load interruption costs. Let t represent the amount of load that has been interrupted in the system at time t; Let t be the amount of load power transferred through the virtual power plant at time t; The cost of the supply and demand gap in flexibility is: ； In the formula, These are respectively the virtual penalty coefficients for increasing the supply-demand gap in flexibility and decreasing the flexible supply gap, to meet [the following needs]. ; The adjustments are respectively upward and downward to adjust the supply and demand gap for flexibility. ; Electricity sales revenue is: ； ； In the formula, These are the unit prices for electricity sold by traditional generating units, photovoltaic power, and wind power, respectively. for Real-time forecast of photovoltaic and wind power output; These represent the curtailment power of solar and wind power at time t, respectively.

9. The source-grid-load-storage optimization scheduling method for flexible supply and demand balance under extreme weather conditions as described in claim 8, characterized in that: The constraints of the optimized scheduling model include: Power constraints: ； In the formula, This is a Boolean variable representing the start-up and shutdown status of a traditional generator set. A value of 1 indicates that the unit is in the start-up and running state, and a value of 0 indicates that the unit is in the shutdown and stop state. These are the minimum and maximum technical outputs of unit g, respectively; Climbing constraints: ； In the formula, These are the upward and downward adjustment limits for the ramp rate of unit g, respectively. Start-stop duration constraints are: ； ； In the formula, A Boolean variable characterizing whether unit g starts up at time t; A Boolean variable characterizing whether unit g experiences a shutdown at time t; To minimize the time required to keep the device powered on; To minimize downtime; The length of time that unit g has been continuously running at time t; The length of time that unit g has been continuously shut down at time t; Capacity constraints: ； In the formula, These are the minimum and maximum allowable storage capacities of the energy storage system, respectively. Let be the real-time storage capacity of the energy storage system at time t; Charge and discharge power constraints: ； ； In the formula, Let be the decision variable for the discharge power of the energy storage system at time t; Let be the decision variable for the charging power of the energy storage system at time t; These are the maximum allowable discharge power and the maximum allowable charging power of the energy storage system, respectively. Energy balance constraints: ； In the formula, These refer to the charging efficiency and discharging efficiency of the energy storage system, respectively. The scheduling time step; Load interruption constraints: ； In the formula, The original load forecast value at time t; This is the preset upper limit for the proportion of interruptible load; The initial load of the system at time t; Load transfer constraints: ； In the formula, Let t be the decision variable for the load transfer amount; This is the maximum allowable load transfer power limit for a single moment in the system. The original load forecast value at time t; The net load value after load transfer adjustment at time t; External power constraints: ； In the formula, These represent the transmitted power at time t and the maximum transmitted power limit, respectively. Curtailment of wind and solar power: ； In the formula, Let t be the predicted wind power output at time t; Let be the wind curtailment power at time t; Let be the predicted photovoltaic power at time t; Let be the amount of photovoltaic curtailment at time t; Power balance constraints: ； In the formula, Let t be the total load power that the system needs to satisfy. Let t be the system's power output at time t.

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