New energy power system regulation capability analysis method and device in severe weather process

Abstract

The invention discloses a new energy power system regulation capability analysis method and device in a major weather process, and the method comprises the steps: obtaining the structure and operation data of a target power grid, and the structure and operation data of the target power grid comprise a wind-solar load output typical scene in a typical major weather process of the current year; determining a power supply operation constraint and a system operation constraint associated with the long-term regulation capability analysis model in the new energy power system by using the obtained target power grid structure and operation data, and establishing a target function with a minimum regulation capability gap of the new energy power system as an optimization target; solving a long-term regulation capability analysis model in the new energy power system by using the determined power supply operation constraint, the system operation constraint and the established objective function and adopting a multi-cut Benders decomposition method; and constructing a plurality of medium-and-long-term regulation capability analysis statistical indexes to analyze the medium-and-long-term regulation capability of the new energy power system in the typical major weather process of the current year. The calculation efficiency is high and the availability is strong.

Description

Technical Field

[0001] The present invention relates to the field of power system regulation technology, and in particular to a method and device for analyzing the regulation capability of a new energy power system under severe weather processes. Background Art

[0002] In recent years, extreme weather has occurred frequently and has become normalized. Major weather processes represented by cold waves and high temperatures have directly changed the characteristics of new energy power generation, and in turn put forward higher requirements on the climbing ability and medium- and long-term regulation capabilities of the power system.

[0003] Existing power system regulation capacity analysis methods are mainly based on deterministic scenarios and typical operating modes, which are difficult to reflect the system's insufficient ramping capacity or regulation capacity gap under major weather processes.

[0004] Therefore, it is necessary to establish a technical solution for analyzing the regulation capacity of new energy power systems taking into account major weather processes. Summary of the Invention

[0005] In view of this, the present invention proposes a method and device for analyzing the regulation capacity of a new energy power system under a major weather process, aiming to solve the problems of low efficiency and insufficient accuracy in the existing method for analyzing the regulation capacity of a new energy power system.

[0006] In a first aspect, the present invention provides a method for analyzing the regulation capability of a new energy power system under a major weather process, comprising:

[0007] Obtaining target grid structure and operation data, including typical wind and solar load output scenarios under typical major weather events in the current year, wherein the typical major weather events include: cold waves and high temperatures;

[0008] Using the acquired target grid structure and operation data, the power supply operation constraints and system operation constraints associated with the medium- and long-term regulation capacity analysis model of the new energy power system are determined, and an objective function is established with the goal of minimizing the regulation capacity gap of the new energy power system.

[0009] Using the determined power supply operation constraints, the system operation constraints, and the established objective function, combined with the typical wind and solar load output scenario under the typical major weather process of the current year, a multi-cut Benders decomposition method is adopted to solve the medium- and long-term regulation capability analysis model of the new energy power system, and obtain the medium- and long-term regulation time series simulation solution results of the new energy power system under the typical major weather process of the current year;

[0010] Using the obtained results of the medium- and long-term regulation time series simulation of the new energy power system, a number of medium- and long-term regulation capacity analysis statistical indicators are constructed to analyze the medium- and long-term regulation capacity of the new energy power system under the typical major weather processes of the current year.

[0011] Furthermore, the obtaining of target power grid structure and operation data includes:

[0012] Obtain information about various power sources in the power grid, including thermal power units, hydropower units, wind power units, photovoltaic units, pumped storage power stations, and energy storage;

[0013] Obtain the annual utilization hours of renewable energy and annual load electricity in the target power grid area in the previous year;

[0014] Obtain historical data on wind speed under normal weather conditions in the previous year in the target power grid area, historical data on sunlight under normal weather conditions in the previous year, historical data on load under normal weather conditions in the previous year, wind-solar-load output curves under the previous year's cold wave process, and wind-solar-load output curves under the previous year's high temperature process.

[0015] Furthermore, determining the power supply operation constraints for the medium- and long-term regulation capability analysis model of the new energy power system includes:

[0016] Determine thermal power unit constraints, wherein the thermal power unit constraints include: thermal power output upper / lower limit constraints, thermal power unit start / stop constraints, and thermal power unit step-type ramp constraints;

[0017] Determine the constraints of the hydropower unit, wherein the constraints of the hydropower unit include: hydropower unit output constraints, hydropower unit power generation constraints and reservoir capacity constraints;

[0018] Determining energy storage constraints, wherein the energy storage constraints include: charge and discharge state and charge and discharge number constraints, charge and discharge power constraints, and state of charge constraints;

[0019] Determining constraints of a pumped storage power station, wherein the constraints of the pumped storage power station include: power generation constraint, pumping power constraint, water pumping and discharge state constraint, and pumped storage capacity constraint;

[0020] The determining of the system operation constraints for the medium- and long-term regulation capability analysis model of the new energy power system includes:

[0021] Determine power constraints of power supply loads, renewable energy output constraints and interconnection line constraints.

[0022] Furthermore, the multi-cut Benders decomposition method is used to solve the medium- and long-term regulation capability analysis model of the new energy power system, and obtain the medium- and long-term regulation time series simulation solution results of the new energy power system under the current typical major weather process, including:

[0023] The long-term regulation capability analysis model of the renewable energy power system is used as the original problem. It is decomposed into an MP model and n SP models according to a main problem and multiple sub-problems. The MP model is a mixed integer programming problem used to optimize the status of multiple types of power units. Each SP model is a small linear programming problem used to calculate the actual output of multiple types of power units.

[0024] Among them, the n SP models SP1 to SP n According to the time division, time t1 to t s is the s moments corresponding to the sub-model SP1, and the moment t (n-1)s+1 to t ns For sub-model SP n The corresponding s moments, s is a positive integer, n is a positive integer and an integer multiple of s;

[0025] During the interactive iteration, the actual output of each type of generator set at s moments obtained by sub-models SP1 to SPn are returned to the MP model as Benders cuts; the states of multiple types of power units determined by the MP model are used as known quantities of each sub-model to solve the actual output of each type of generator set at s moments.

[0026] Furthermore, the obtained results of the medium- and long-term regulation time series simulation of the new energy power system are used to construct a plurality of medium- and long-term regulation capability analysis statistical indicators to analyze the medium- and long-term regulation capability of the new energy power system under the typical major weather process of the current year, including:

[0027] Calculate the system's up-climbing demand to characterize the up-climbing power required by the system at time t; Count the system's down-climbing demand to characterize the down-climbing power required by the system at time t;

[0028] According to the system's up-climbing demand and down-climbing demand, the system's up-climbing capacity shortfall and down-climbing capacity shortfall are calculated to represent the power gap when the system's up-climbing capacity is insufficient and the power gap when the system's down-climbing capacity is insufficient, respectively;

[0029] Calculate the upward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the downward regulation capabilities of adjustable loads, to determine the system's upward regulation capabilities; Calculate the downward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the upward regulation capabilities of adjustable loads, to determine the system's downward regulation capabilities;

[0030] Determining a maximum shortfall in the system's upward regulation capacity and a maximum shortfall in the system's downward regulation capacity based on the system's upward ramping demand and system downward ramping demand, the system's upward regulation capacity, and the system's downward regulation capacity, respectively representing the maximum power when the system's upward regulation capacity is insufficient and the maximum power when the system's downward regulation capacity is insufficient;

[0031] Collect statistics on the percentage of time when the system's upward adjustment capacity is insufficient and the percentage of time when the system's downward adjustment capacity is insufficient.

[0032] In a second aspect, the present invention provides a device for analyzing the regulation capability of a new energy power system under a major weather process, comprising:

[0033] a data acquisition unit, configured to acquire target grid structure and operation data, wherein the target grid structure and operation data include typical wind and solar load output scenarios under typical major weather processes of the current year, wherein the typical major weather processes include: cold waves and high temperatures;

[0034] A regulation capacity analysis model establishment unit is used to use the acquired target grid structure and operation data to determine the power supply operation constraints and system operation constraints associated with the medium- and long-term regulation capacity analysis model of the new energy power system, and to establish an objective function with the minimum regulation capacity gap of the new energy power system as the optimization goal;

[0035] A regulation capability analysis model solving unit is configured to utilize the determined power supply operation constraints, the system operation constraints, and the established objective function, and in combination with a typical scenario of wind and solar load output under a typical major weather process of the current year, employ a multi-cut Benders decomposition method to solve the medium- and long-term regulation capability analysis model of the new energy power system, thereby obtaining a simulation solution result of the medium- and long-term regulation of the new energy power system under a typical major weather process of the current year;

[0036] The regulation capability index analysis unit is used to construct a plurality of medium- and long-term regulation capability analysis statistical indicators using the obtained medium- and long-term regulation time series simulation solution results of the new energy power system to analyze the medium- and long-term regulation capability of the new energy power system under the typical major weather processes of the current year.

[0037] Furthermore, the regulation capacity analysis model solving unit is used to solve the medium- and long-term regulation capacity analysis model of the new energy power system using a multi-cut Benders decomposition method, and obtain a simulation solution result of the medium- and long-term regulation of the new energy power system under the typical major weather process of the current year, including:

[0038] The long-term regulation capability analysis model of the renewable energy power system is used as the original problem. It is decomposed into an MP model and n SP models according to a main problem and multiple sub-problems. The MP model is a mixed integer programming problem used to optimize the status of multiple types of power units. Each SP model is a small linear programming problem used to calculate the actual output of multiple types of power units.

[0039] Among them, the n SP models SP1 to SP n According to the time division, time t1 to t s is the s moments corresponding to the sub-model SP1, and the moment t(n-1)s+1 to t ns For sub-model SP n The corresponding s moments, s is a positive integer, n is a positive integer and an integer multiple of s;

[0040] During the interactive iteration, the actual output of each type of generator set at s moments obtained by sub-models SP1 to SPn are returned to the MP model as Benders cuts; the states of multiple types of power units determined by the MP model are used as known quantities of each sub-model to solve the actual output of each type of generator set at s moments.

[0041] Furthermore, the regulation capability index analysis unit is used to construct a plurality of medium- and long-term regulation capability analysis statistical indicators using the obtained medium- and long-term regulation time series simulation solution of the new energy power system to analyze the medium- and long-term regulation capability of the new energy power system under the typical major weather process of the current year, including:

[0042] Calculate the system's up-climbing demand to characterize the up-climbing power required by the system at time t; Count the system's down-climbing demand to characterize the down-climbing power required by the system at time t;

[0043] According to the system's up-climbing demand and down-climbing demand, the system's up-climbing capacity shortfall and down-climbing capacity shortfall are calculated to represent the power gap when the system's up-climbing capacity is insufficient and the power gap when the system's down-climbing capacity is insufficient, respectively;

[0044] Calculate the upward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the downward regulation capabilities of adjustable loads, to determine the system's upward regulation capabilities; Calculate the downward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the upward regulation capabilities of adjustable loads, to determine the system's downward regulation capabilities;

[0045] Determining a maximum shortfall in the system's upward regulation capacity and a maximum shortfall in the system's downward regulation capacity based on the system's upward ramping demand and system downward ramping demand, the system's upward regulation capacity, and the system's downward regulation capacity, respectively representing the maximum power when the system's upward regulation capacity is insufficient and the maximum power when the system's downward regulation capacity is insufficient;

[0046] Collect statistics on the percentage of time when the system's upward adjustment capacity is insufficient and the percentage of time when the system's downward adjustment capacity is insufficient.

[0047] In a third aspect, the present invention provides a terminal comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect.

[0048] In a fourth aspect, the present invention provides a computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method described in the first aspect.

[0049] The proposed method for analyzing the regulation capacity of a new energy power system under severe weather events establishes typical wind and solar load output scenarios for typical severe weather events. Taking minimizing the regulation capacity gap as the regulation goal, the method comprehensively considers various operational constraints of the new energy power system (including power supply and system operation constraints), specifically introducing the step-by-step ramping constraints of deep peak-shaving operation characteristics of thermal power units, to establish a system medium- and long-term regulation capacity analysis model. The method uses a multi-cut Benders decomposition method to solve the system medium- and long-term regulation capacity analysis model and generate system medium- and long-term time series simulation data. Using the generated system medium- and long-term time series simulation data, the method determines the system net load change, ramping capacity, and regulation capacity indicators to quantitatively evaluate the moment-by-moment regulation capacity of the new energy power system over medium- and long-term time scales. This method has high computational efficiency and strong usability.

[0050] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0052] Figure 1 Schematic diagram of the flow of a method for analyzing the regulation capability of a new energy power system under a major weather process according to an embodiment of the present invention;

[0053] Figure 2 This is a flow chart of a method for analyzing the regulation capability of a new energy power system during a major weather process according to another embodiment of the present invention;

[0054] Figure 3 A schematic diagram of solving Benders decomposition based on multi-cuts according to an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of the composition of a device for analyzing the regulation capability of a new energy power system under a major weather process according to an embodiment of the present invention;

[0056] Figure 5 A schematic diagram of the composition of a terminal to which the method according to an embodiment of the present invention is applied;

[0057] Figure 6 A schematic diagram of a program product applying the method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features therein can be combined with each other.

[0059] A cold wave is a widespread weather event that can occur across the country, triggering a variety of natural disasters, including frost and freezing damage. Cold waves typically occur in late autumn, winter, and early spring. Due to my country's vast territory, the climate differs significantly between the north and south. Generally speaking, the criteria for a cold wave in the north are: a temperature drop of more than 10°C in 24 hours, or more than 12°C in 48 hours, with the minimum temperature below 4°C. The criteria for a cold wave in the south are: a temperature drop of more than 8°C in 24 hours, or more than 10°C in 48 hours, with the minimum temperature below 5°C.

[0060] In meteorology, the standard for high temperature is generally when the maximum daily temperature reaches or exceeds 35℃. High temperature heat wave, also known as high temperature heat wave, is a meteorological term, which usually refers to high temperature weather above 35℃ that lasts for many days. Due to the long duration of high temperature, it causes people, animals and plants to be unable to adapt and has adverse effects. It is a meteorological disaster.

[0061] With the rapid growth of renewable energy installed capacity, the contradiction between midday power consumption and evening peak supply faced by power systems with a high proportion of renewable energy (hereinafter referred to as the new energy power system) has become increasingly prominent. Furthermore, in recent years, extreme weather events have become more frequent and normalized. Major weather events, such as cold waves and high temperatures, have directly altered the characteristics of renewable energy power generation, placing higher demands on the power system's ramping capacity and medium- and long-term regulation capabilities. Consequently, the impact of major weather events, such as cold waves and high temperatures, on the ramping capacity and regulation capabilities of the new energy power system has become more pronounced.

[0062] The present invention proposes a technical solution for analyzing the regulation capability of a new energy power system under major weather processes, including a method and device for analyzing the regulation capability of a new energy power system under major weather processes, and introduces a step-by-step climbing constraint during the deep peak-shaving operation characteristics of thermal power units, which can better reflect the changing trend of the system regulation capability under major weather processes; in response to the climbing demand and regulation capability gap, the system's medium- and long-term regulation strategy adopts effective flexible scheduling measures, such as reducing the number of unit starts and stops, reducing wind and solar power abandonment or load shedding scenarios, which is conducive to improving the system's new energy penetration rate and reliable power supply capability.

[0063] The method for analyzing the regulation capacity of a new energy power system under major weather processes proposed in the present invention establishes a typical scenario of wind and solar load output for typical major weather processes; takes minimizing the regulation capacity gap as the regulation target, comprehensively considers various operating constraints of the new energy power system (including power supply operation constraints and system operation constraints), and especially introduces the step-by-step climbing constraints during the deep peak-shaving operation characteristics of thermal power units to establish a system medium- and long-term regulation capacity analysis model; adopts a multi-cut Benders decomposition method to solve the system medium- and long-term regulation capacity analysis model, and generates system medium- and long-term time series simulation data; uses the generated system medium- and long-term time series simulation data to determine the system net load change, climbing capacity and regulation capacity indicators, so as to quantitatively evaluate the moment-by-moment regulation capacity of the new energy power system under medium- and long-term scales.

[0064] like Figure 1 As shown, the method for analyzing the regulation capability of a new energy power system under a major weather process proposed by the present invention includes:

[0065] S10: Acquire target grid structure and operation data, wherein the target grid structure and operation data include typical wind and solar load output scenarios under typical major weather processes in the current year, wherein the typical major weather processes include: cold waves and high temperatures;

[0066] S20: Using the acquired target grid structure and operation data, determine the power supply operation constraints and system operation constraints associated with the medium- and long-term regulation capability analysis model of the new energy power system, and establish an objective function with the minimum regulation capability gap of the new energy power system as the optimization goal;

[0067] S30: using the determined power supply operation constraints, the system operation constraints, and the established objective function, combined with the typical wind and solar load output scenario under the typical major weather process of the current year, using a multi-cut Benders decomposition method to solve the medium- and long-term regulation capability analysis model of the new energy power system, and obtain a time series simulation solution result of the medium- and long-term regulation of the new energy power system under the typical major weather process of the current year;

[0068] S40: Using the obtained results of the medium- and long-term regulation time series simulation solution of the new energy power system, a plurality of medium- and long-term regulation capability analysis statistical indicators are constructed to analyze the medium- and long-term regulation capability of the new energy power system under the typical major weather process of the current year.

[0069] In step S10, obtaining the target power grid structure and operation data includes:

[0070] Obtain information about various power sources in the power grid, including thermal power units, hydropower units, wind power units, photovoltaic units, pumped storage power stations, and energy storage;

[0071] Obtain the annual utilization hours of renewable energy and annual load electricity in the target power grid area in the previous year;

[0072] Obtain historical data on wind speed under normal weather conditions in the previous year in the target power grid area, historical data on sunlight under normal weather conditions in the previous year, historical data on load under normal weather conditions in the previous year, wind-solar-load output curves under the previous year's cold wave process, and wind-solar-load output curves under the previous year's high temperature process.

[0073] In step S20, determining the power supply operation constraints for the medium- and long-term regulation capability analysis model of the new energy power system includes:

[0074] Determine thermal power unit constraints, wherein the thermal power unit constraints include: thermal power output upper / lower limit constraints, thermal power unit start / stop constraints, and thermal power unit step-type ramp constraints;

[0075] Determine the constraints of the hydropower unit, wherein the constraints of the hydropower unit include: hydropower unit output constraints, hydropower unit power generation constraints and reservoir capacity constraints;

[0076] Determining energy storage constraints, wherein the energy storage constraints include: charge and discharge state and charge and discharge number constraints, charge and discharge power constraints, and state of charge constraints;

[0077] Determining constraints of a pumped storage power station, wherein the constraints of the pumped storage power station include: power generation constraint, pumping power constraint, water pumping and discharge state constraint, and pumped storage capacity constraint;

[0078] The determining of the system operation constraints for the medium- and long-term regulation capability analysis model of the new energy power system includes:

[0079] Determine power constraints of power supply loads, renewable energy output constraints and interconnection line constraints.

[0080] In step S30, the multi-cut Benders decomposition method is used to solve the medium- and long-term regulation capability analysis model of the new energy power system, and obtain the medium- and long-term regulation time series simulation solution of the new energy power system under the current typical major weather process, including:

[0081] The long-term regulation capability analysis model of the renewable energy power system is used as the original problem. It is decomposed into an MP model and n SP models according to a main problem and multiple sub-problems. The MP model is a mixed integer programming problem used to optimize the status of multiple types of power units. Each SP model is a small linear programming problem used to calculate the actual output of multiple types of power units.

[0082] Among them, the n SP models SP1 to SP n According to the time division, time t1 to t s is the s moments corresponding to the sub-model SP1, and the moment t (n-1)s+1 to tns For sub-model SP n The corresponding s moments, s is a positive integer, n is a positive integer and an integer multiple of s;

[0083] During the interactive iteration, the actual output of each type of generator set at s moments obtained by sub-models SP1 to SPn are returned to the MP model as Benders cuts; the states of multiple types of power units determined by the MP model are used as known quantities of each sub-model to solve the actual output of each type of generator set at s moments.

[0084] In step S40, the obtained medium- and long-term regulation time series simulation solution of the new energy power system is used to construct a plurality of medium- and long-term regulation capability analysis statistical indicators to analyze the medium- and long-term regulation capability of the new energy power system under the typical major weather process of the current year, including:

[0085] Calculate the system's up-climbing demand to characterize the up-climbing power required by the system at time t; Count the system's down-climbing demand to characterize the down-climbing power required by the system at time t;

[0086] According to the system's up-climbing demand and down-climbing demand, the system's up-climbing capacity shortfall and down-climbing capacity shortfall are calculated to represent the power gap when the system's up-climbing capacity is insufficient and the power gap when the system's down-climbing capacity is insufficient, respectively;

[0087] Calculate the upward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the downward regulation capabilities of adjustable loads, to determine the system's upward regulation capabilities; Calculate the downward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the upward regulation capabilities of adjustable loads, to determine the system's downward regulation capabilities;

[0088] Determining a maximum shortfall in the system's upward regulation capacity and a maximum shortfall in the system's downward regulation capacity based on the system's upward ramping demand and system downward ramping demand, the system's upward regulation capacity, and the system's downward regulation capacity, respectively representing the maximum power when the system's upward regulation capacity is insufficient and the maximum power when the system's downward regulation capacity is insufficient;

[0089] Collect statistics on the percentage of time when the system's upward adjustment capacity is insufficient and the percentage of time when the system's downward adjustment capacity is insufficient.

[0090] like Figure 2 As shown, the method for analyzing the regulation capability of a new energy power system under a major weather process proposed by the present invention includes the following steps S100 to S400:

[0091] S100: Acquire target power grid structure and operation data;

[0092] Specifically, the structure and operation data include: the capacity of the thermal power unit, the maximum output of the thermal power unit, the minimum output of the thermal power unit; the minimum continuous operation time of the thermal power unit, and the minimum continuous shutdown time of the thermal power unit.

[0093] Specifically, the structure and operation data also include: the capacity of the hydropower unit, the maximum output of the hydropower unit, the minimum output of the hydropower unit, and the power generation range of the hydropower unit.

[0094] Specifically, the structure and operation data also include: wind turbine capacity; photovoltaic unit capacity.

[0095] Specifically, the structure and operation data also include: the capacity of the pumped storage unit, the maximum pumping power of the pumped storage unit, the minimum pumping power of the pumped storage unit; the maximum generating power of the pumped storage unit, and the minimum generating power of the pumped storage unit.

[0096] Specifically, the structure and operation data also include: energy storage capacity, maximum or minimum charge and discharge power of energy storage, and energy storage charge and discharge efficiency;

[0097] Specifically, the structure and operation data include: the grid load that can be borne by the external power of the power source, such as the lower limit and upper limit of the transmission capacity of the tie line.

[0098] Specifically, information on various power sources in the power grid is obtained, including thermal power units, hydropower units, wind power units, photovoltaic units, pumped storage power stations, and energy storage;

[0099] Obtain the annual utilization hours of renewable energy and annual load electricity in the target power grid area in the previous year;

[0100] Obtain historical wind speed data, sunlight data, and load data for the target power grid's region under normal weather conditions over the previous year, as well as the wind and solar load output curves during the previous year's cold snap and high temperature events. Specifically, the structural and operational data includes: power grid topology; the power grid topology records the source nodes where each type of generator set is located and the load nodes where each load is located, with the source nodes and load nodes connected by tie lines.

[0101] In this way, information on various power sources such as thermal power, hydropower, wind power, photovoltaic power, pumped storage, and energy storage is determined, such as unit capacity, unit type, maximum output and minimum output of the unit.

[0102] The structure and operation data include: the annual utilization hours of new energy and the annual load electricity in the target power grid area in the previous year; the typical major weather process data of the target power grid area in the previous year and the current year; the historical wind speed data under normal weather in the previous year; the historical sunlight data under normal weather in the previous year; the historical load data under normal weather in the previous year; the wind-solar-load output curves under typical major weather processes in the previous year, such as the wind-solar-load output curves under the cold wave process in the previous year and the wind-solar-load output curves under the high temperature process in the previous year.

[0103] Specifically, the data of typical major weather processes in the current year, such as cold waves and high temperature weather processes, such as the time of occurrence, duration, maximum wind and solar power output, and maximum load. Naturally, the duration can be several hours, days, weeks or even months.

[0104] Specifically, the current year is the forecast year, the year before the current year is the current year, and the historical data is the data of the previous year.

[0105] In this way, historical operation data for the target power grid region with a time length of one year is determined. Typically, the time resolution of the data is 15 minutes, that is, a historical operation data set with a time length of one year includes obtaining multiple time series with 4 × 24 × 365 moments of data.

[0106] Specifically, for the target grid or the grid in the area where the target grid is located, grid information is collected and organized to obtain the above structure and operation data.

[0107] In some implementations, obtaining and establishing a typical scenario of wind and solar load output under a typical major weather process in the current year includes:

[0108] For the typical major weather process of cold wave, the data of the cold wave weather process of the current year (such as the degree of temperature change in a short period of time) are obtained. According to the wind and solar load output curves during the cold wave process of the previous year, with the minimum deviation of new energy utilization rate or annual load power, and with the time of occurrence, duration and maximum amplitude of wind and solar load of the cold wave as constraints, the typical scenarios of wind and solar load output during the cold wave process of the current year are predicted, including: the wind power output curve, photovoltaic output curve and load output curve during the cold wave of the current year.

[0109] It should be understood that the cold wave, a typical major weather process, has a relatively large impact on wind power, photovoltaics and loads. Therefore, it is necessary to additionally predict the corresponding typical wind, photovoltaic and load output scenarios for the cold wave process.

[0110] For the typical major weather process of high temperature, the data of the high temperature weather process of the current year (such as the degree of temperature change in a short period of time and the duration of high temperature) are obtained. According to the wind and solar load output curve under the high temperature process of the previous year, with the minimum deviation of new energy utilization rate or annual load power, and with the time of high temperature occurrence, duration and maximum value of wind and solar load as constraints, the typical scenarios of wind and solar load output under the high temperature process of the current year are predicted, including: the wind power output curve, photovoltaic output curve and load output curve during the high temperature period of the current year. Naturally, under the high temperature weather process, electricity demand increases and load power increases.

[0111] It should be understood that high temperature, a typical major weather process, has a relatively large impact on wind power, photovoltaics and loads, and it is necessary to additionally predict the corresponding typical wind, photovoltaic and load output scenarios for high temperature processes.

[0112] Specifically, based on the obtained wind power output curve, photovoltaic output curve, and load output curve, the theoretical value of the new energy output and the load before reduction can be determined.

[0113] S200: Establish a medium- and long-term regulation capability analysis model for the new energy power system, including: objective function and various operation constraints (power supply operation constraints, system operation constraints).

[0114] Specifically, under each typical major weather process, the system's medium- and long-term regulation strategy includes: reducing the number of unit starts and stops, reducing wind power curtailment, reducing solar power curtailment, and reducing load shedding scenarios. In this way, either adjusting the output of renewable energy or reducing the load. To this end, as shown in equations (1) and (2), a medium- and long-term regulation objective function is established with the goal of minimizing the regulation capacity gap (including the upper and lower regulation capacity gaps) of the renewable energy power system:

[0115]

[0116] In formula (1), is the gap in the regulation capacity of the new energy power system at time t, is the gap in the regulation capacity of the new energy power system at time t;

[0117] In formula (2), P Nf,t is the theoretical value of new energy output at time t, P N,t The actual value of renewable energy output at time t. The upper limit of renewable energy power system regulation capacity at time t is defined as the difference between the theoretical value of renewable energy output and the actual value of renewable energy output. It is usually a positive number and is used to indicate the power supply margin.

[0118] In formula (2), P df,t is the load before reduction at time t, P d,tThe load after reduction at time t is defined as the gap in the regulation capacity of the new energy power system at time t, which is the difference between the load before reduction and the load after reduction. It is usually a positive number and is used to indicate the load reduction demand.

[0119] Specifically, during the medium- and long-term adjustments of the system under typical major weather processes, various operating constraints include power supply operating constraints and system operating constraints.

[0120] As previously explained, power sources include thermal power units, hydropower units, energy storage, pumped storage power plants, wind power, and photovoltaic power. Accordingly, power source operation constraints include: thermal power unit constraints, hydropower unit constraints, energy storage constraints, and pumped storage power plant constraints.

[0121] Specifically, as shown in equations (3) to (7), the constraints of the thermal power generation units include: upper / lower limit constraints on thermal power output, start / stop constraints on the thermal power generation units, and step-by-step ramping constraints on the thermal power generation units.

[0122]

[0123] In formula (3), is the minimum output of the thermal power unit, is the maximum output of the thermal power unit; P Th,t is the output of the thermal power unit at time t, is the regulating variable; s t is the start and stop state of the thermal power unit at time t, where s t A value of 1 indicates power on, s t The value of 0 indicates shutdown, that is, the start and shutdown status of the thermal power unit at time t is 0 or 1, which is a 0-1 variable and a regulating variable.

[0124] (s t-1 -s t )(T on,t-1 -T on )≥0 (4)

[0125] (s t -s t-1 )(T off,t-1 -T off )≥0 (5)

[0126] In formula (4) and formula (5), s t-1 is the start-stop state at time t-1, s t T is the start-stop state at time t; on is the minimum continuous operation time of the thermal power unit, T off is the minimum continuous shutdown time of the thermal power unit; T on,t-1 and T off,t-1 They are the continuous operation time and continuous shutdown time of the thermal power unit at time t-1, and as of time t, the start and shutdown status s of each thermal power unit at time t-1 ist-1 After accumulation, the continuous operation time T of the thermal power unit at time t-1 is obtained on,t-1 ; Total time up to time t minus continuous running time T on,t-1 , we can get the continuous downtime T off,t-1 .

[0127] In formula (4), when time t-1 is on and time t is off, the difference between on and off is 1. In formula (5), when time t-1 is off and time t is on, the difference between off and on is 1. That is, when the on-off status at two adjacent moments is consistent (that is, continuous on or continuous off), the difference between the two is zero; when the on-off status at two adjacent moments is inconsistent (that is, the unit is switched on and off), the difference between the two is -1 or 1.

[0128] Formula (4) is used to constrain the continuous startup time of thermal power units to not exceed the preset minimum continuous operation time T on Formula (5) is used to constrain the continuous downtime of thermal power units to not exceed the preset minimum continuous downtime T off Thus, equations (4) and (5) are used to constrain the continuous on-time to not exceed a preset value or the continuous off-time to not exceed a preset value.

[0129]

[0130] In formula (6) and formula (7), P Th,t is the output of the thermal power unit at time t; P Th,t-1 is the output of the thermal power unit at time t-1; x and x+1 represent the thermal power unit climbing up, y and y+1 represent the thermal power unit climbing down, k, i, j represent different peak-shaving stages, and they satisfy 0≤k≤i≤j≤M. The peak-shaving stage from i to j is climbing up, and the peak-shaving stage from j to i is climbing down. Among them, M represents the total number of peak-shaving stages during the climbing or descending process, and R x Indicates the maximum climbing rate of the slope, R y Indicates the maximum climbing rate of the downhill climb, R i Indicates the maximum ramp rate of the i-th peak regulation stage, R j and R k are the maximum ramp rates corresponding to the jth and kth peak-shaving stages, and are the lower limits of thermal power output corresponding to the up-ramp and down-ramp of thermal power units respectively; ΔT is the duration between two adjacent moments, such as the time step.

[0131] Equation (6) shows the step-up ramp constraint. Equation (6) shows the step-up ramp constraint, and Equation (7) shows the step-down ramp constraint. In addition, the ramp rate of the thermal power unit in the deep peak-shaving phase of the system is lower than the ramp rate of the thermal power unit in the conventional peak-shaving phase of the system, and as the output of the thermal power unit decreases, the maximum ramp rate of the thermal power unit decreases in a step-by-step manner.

[0132] As described above, a step-by-step ramping constraint that conforms to the deep peak-shaving operation characteristics of thermal power units is established. Based on the specific parameters of a typical deep peak-shaving thermal power unit, the final step-by-step ramping constraint of the thermal power unit can be obtained more conveniently and quickly, and the climbing characteristics of the thermal power unit in actual operation can be more accurately characterized.

[0133] Specifically, as shown in equations (8) to (12), the constraints of the hydropower unit include: hydropower unit output constraint, hydropower unit power generation constraint and reservoir storage capacity constraint. The output P of the hydropower unit at time t can be adjusted by adjusting the inflow or outflow of the reservoir. Hy,t .

[0134]

[0135] C Hy,t =C Hy,t-1 +R in,t ΔT-R out,t ΔT (11)

[0136]

[0137] In formula (8), is the maximum output (upper limit) of the hydropower unit, is the minimum output (lower limit) of the hydropower unit, P Hy,t is the output of the hydropower unit at time t, and is the regulating variable;

[0138] In formula (9) and formula (10), is the upper limit of the hydropower generation capacity, is the lower limit of the hydropower generation; c is the set number of simulation cycles; ΔT is the time step or simulation step; Formula (9) is used to constrain that the power generation of the hydropower unit is not greater than the upper limit of the power generation of the hydropower unit in the next c consecutive cycles; Formula (10) is used to constrain that the opposite number of the power generation of the hydropower unit is not greater than the lower limit of the power generation of the hydropower unit in the next c consecutive cycles;

[0139] In formula (11), C Hy,t is the reservoir capacity at time t, C Hy,t is the reservoir capacity at time t-1, R in,t is the inflow flow at time t, R out,t is the outbound flow at time t;

[0140] In formula (12), is the maximum storage capacity limit, The minimum storage capacity limit.

[0141] Specifically, as shown in equations (13) to (18), energy storage constraints include: charge and discharge state and charge and discharge number constraints, charge and discharge power constraints, and state of charge constraints:

[0142]

[0143] Formula (13) is the charge and discharge state constraint, where is the energy storage charging state variable, is the energy storage discharge state variable; when the energy storage is in the charging state, the energy storage charging state variable The value is 1. When the energy storage is in the discharge state, the energy storage charging state variable is 0; when the energy storage is in the discharge state, the energy storage discharge state variable is When the value is 1, the energy storage is in the charging state, and the energy storage discharge state variable The value is 0; in this case, the charge and discharge state, including the energy storage charge state variable or energy storage discharge state variable is a 0-1 variable;

[0144] Formula (14) is the charge and discharge times constraint, where N 0 is the total number of charge and discharge times of energy storage; T is the total number of simulation cycles corresponding to the whole year;

[0145] Formula (15) is the charging power constraint, where is the maximum charging power of energy storage, is the minimum charging power of energy storage; P ch,t is the energy storage charging power at time t;

[0146] Formula (16) is the discharge power constraint, where P dh,t is the energy storage discharge power at time t; is the maximum discharge power of energy storage, is the minimum discharge power of energy storage;

[0147] Formula (17) is the energy storage capacity constraint, where is the energy storage capacity at time t, is the energy storage capacity at time t-1; η ch is the charging efficiency or discharging efficiency, P ch,t is the energy storage charging power at time t, P dh,t is the energy storage discharge power at time t; ΔT is the time step;

[0148] Formula (18) is the state of charge constraint, where is the energy storage capacity at time t; N Ba is the rated energy storage capacity, SOC max The upper limit of the state of charge SOC, SOC min is the lower limit of the state of charge SOC.

[0149] In this way, the charging and discharging power at time t is adjusted by adjusting the energy storage charging and discharging state.

[0150] Specifically, as shown in equations (19) to (22), the constraints of the pumped storage power station include: power generation constraint, pumping power constraint, pumping and discharge state constraint, and pumped storage capacity constraint:

[0151]

[0152] X t +Y t ≤1 (21)

[0153] W max -W 0 ≤P Hp,t ·η g -P Hps,t ·η s ≤W 0 -W min (twenty two)

[0154] Formula (19) is the power generation constraint, where P Hp,t is the power generation capacity of the pumped storage power station at time t, is the upper limit of the power generation capacity of the pumped storage power station, is the lower limit of the power generation capacity of the pumped storage power station; X t is the power generation state variable of the pumped storage power station, X t 1 means power generation, X t 0 means no power generation, X t is a 0-1 variable and a moderating variable;

[0155] Formula (20) is the pumping power constraint, where P Hps,t is the pumping power of the pumped storage power station at time t, is the upper limit of the pumping power of the pumped storage power station, Y is the lower limit of the pumping power of the pumped storage power station; t is the pumping state variable of the pumped storage power station, Y t 1 means pumping, Y t 0 means no pumping, Y t is a 0-1 variable and a moderating variable;

[0156] Formula (21) is the pumping and releasing state constraint, that is, in a pumped-storage power station, pumping and energy storage power generation are mutually exclusive; a pumped-storage power station is either in the pumping state, or in the energy storage power generation state, or in the state of neither energy storage power generation nor pumping, but it will not be in the state of both energy storage power generation and pumping.

[0157] Formula (22) is the pumped storage capacity constraint, where W 0 is the initial water volume of the pumped storage reservoir, W max is the maximum water volume of the pumped storage reservoir, W min is the minimum water volume of the pumped storage reservoir; η g is the average water conversion coefficient during power generation, η s is the average power conversion coefficient during pumping.

[0158] In this way, by adjusting the power generation state or the pumping state of the pumped-storage power station, the power generation power or the pumping power of the pumped-storage power station can be adjusted.

[0159] Specifically, as shown in Equations (23) to (25), the system operation constraints include: power load power constraints, new energy output constraints, and tie line constraints;

[0160] P Th,t +P Hy,t -P ch,t +P dh,t +P Hp,t -P Hps,t +P N,t =P d,t (twenty three)

[0161] 0≤P Nf ≤P Nf,t ,P Nf,t =P wf,t +P pvf,t (twenty four)

[0162]

[0163] Formula (23) is the power balance constraint of power supply load, where P Th,t is the output of the thermal power unit at time t; P Hy,t is the output of the hydropower unit at time t; P ch,t is the energy storage charging power at time t, P dh,t is the energy storage discharge power at time t, satisfying the aforementioned charge and discharge state constraints; P Hp,t is the power generation capacity of the pumped storage power station at time t, P Hps,t is the pumping power of the pumped storage power station at time t, satisfying the aforementioned pumping and releasing state constraints; P N,t P is the actual output of renewable energy at time t, which is usually greater than 0 and less than the theoretical value; d,tis the load after reduction at time t;

[0164] Formula (24) is the new energy output constraint, where P Nf,t Contribute to the new energy theory at time t; P Nf,t By P wf,t and P pvf,t Composition, P wf，t is the theoretical output power of wind power (determined based on wind speed and wind turbine installed capacity), P pvf，t P is the theoretical photovoltaic output power (determined based on the combined light energy resources and installed photovoltaic capacity), determined based on the wind and solar load output scenario; N,t Actual contribution to new energy at time t.

[0165] Formula (25) is the tie line constraint, where P d,t is the load after reduction at time t; is the lower limit of the transmission capacity of the tie line, It is the upper limit of the transmission capacity of the tie line.

[0166] Referring to the above description, under typical major weather processes, the system's medium- and long-term regulation strategies include: reducing the number of unit starts and stops, reducing wind power curtailment, reducing solar power curtailment, and reducing load shedding scenarios, that is, either adjusting the output of renewable energy or reducing load. In this way, in the system's medium- and long-term regulation capacity analysis model, the regulation variables include: the start and stop status s of the thermal power unit at time t t , thermal power unit peak regulation stage, energy storage charging state variables Energy storage discharge state variables Pumped storage power station power generation state variable X t , pumped storage power station pumping state variable Y t Therefore, the medium- and long-term regulation capability analysis model of the system is a large-scale mixed integer linear programming problem.

[0167] S400: Optimization and solution of the system regulation capability analysis model.

[0168] The above-mentioned system regulation capability analysis model is a large-scale mixed integer linear programming problem, which contains multiple 0-1 variables such as the start and shutdown status of thermal power units, the peak regulation stage of thermal power units, the charging and discharging status of energy storage, the power generation state variables of pumped storage power stations, and the pumping state variables of pumped storage power stations.

[0169] When performing a time-series simulation of typical wind and solar load output scenarios throughout the year (simulation time from 0 to T), the solution speed is slow due to the large number of turbine types in the model, the diverse typical wind and solar load output scenarios, the long simulation time, and the numerous constraints. Therefore, a multi-cut Benders decomposition method was used to optimize the model and perform a time-series simulation of typical wind and solar load output scenarios throughout the year, obtaining the time-series simulation solution results.

[0170] Specifically, the system medium- and long-term regulation capability analysis model determined in step S300 is used as the original problem, and the multi-cut Benders decomposition method is used to decompose it into a master problem (MP) and multiple subproblems (SP) corresponding to several scenarios, thereby obtaining an MP model and multiple SP models, i.e., a master-multiple subproblem. Figure 3 As shown in Figure 2, the MP model is a mixed integer programming problem used to optimize the status of multiple types of power units; each SP model is a small linear programming problem used to calculate the actual output of multiple types of power units and, in turn, the capacity gap, as shown in Equation (2). In this way, the solution rate can be accelerated through iterative solution of the main-multiple sub-problems, and the time series simulation solution results for typical scenarios of wind and solar load output throughout the year can be quickly determined, such as the medium- and long-term regulation strategy of the system.

[0171] Specifically, the compact formula of the original problem, that is, the system's medium- and long-term regulation capacity analysis model, is shown in Equation (26):

[0172]

[0173] In formula (26), F represents the start-up and stop status of the unit (including: thermal power units climbing up and down or peak-shaving stages, hydropower units, pumped storage units, and energy storage charging and discharging status); P represents multiple continuous variables in the actual scenario, namely, the actual output of six types of generators: thermal power units, hydropower units, pumped storage units, energy storage units, wind power, and photovoltaic new energy; A and B are predetermined coefficient values, c and d are predetermined coefficient values, and b is a predetermined coefficient value.

[0174] Specifically, one natural day can be a sub-problem, or multiple natural days can be a sub-problem. The Benders decomposition method based on multi-cut solves the main-multiple sub-problems iteratively to speed up the solution process. Figure 3 shown.

[0175] Figure 3 Among them, n SP models SP1 to SP n According to the time division, t1 to t s is the s moments corresponding to the sub-model SP1, t (n-1)s+1 to t ns For sub-model SP n The corresponding s moments. SP1 is used to solve the time from t1 to t s Actual output of various generator sets at all times, SP n To solve t (n-1)s+1 to t ns The actual output of various generator sets at the moment, among which, t ns That is, t SDuring the interactive iteration, the actual output of each type of generator set at s moments obtained by sub-models SP1 to SPn is returned to the main problem as a Benders cut. The start and stop states of the generator sets determined by the main problem are substituted into the aforementioned equations (8-12, 17-18, 22-25) as known quantities to solve the actual output of each group of six types of generator sets at s consecutive moments: thermal power units, hydropower units, pumped storage units, energy storage units, and new energy sources such as wind power and photovoltaics.

[0176] In view of the characteristics of the medium- and long-term regulation capacity analysis model, a multi-cut Benders decomposition algorithm is designed. The unit status and the actual output of the unit under multiple scenarios are set as small-scale main-multi-subproblems respectively. By iteratively solving the main-multi-subproblems, the medium- and long-term regulation capacity of the system can be calculated conveniently and efficiently.

[0177] After the system's medium- and long-term regulation capacity analysis model is solved, the medium- and long-term regulation timing simulation results of the new energy power system under the current year's typical major weather processes can be used to statistically predict indicators such as the system's net load change, climbing capability, and regulation capability under typical annual wind and solar load output scenarios. For example, indicators such as the system's net load up / down change, the maximum up / down climbing capability gap, the up / down regulation capability, the system's maximum up / down regulation capability gap, and the gap time ratio can be used. In this way, it is convenient to analyze the power supply shortage and power abandonment of the power system at each moment.

[0178] Specifically, according to formula (27), the system ramp demand at time t is calculated as Used to characterize the up-climbing power required by the system at time t; according to formula (28), calculate the down-climbing demand of the system at time t Used to characterize the downhill climbing power required by the system at time t:

[0179]

[0180] In formula (27) and formula (28), P d,t is the load value before or after system reduction at time t, that is, the net load value of the system, P d,t-1 It is the load value before or after system reduction at time t-1, that is, the net load value.

[0181] Specifically, if the net load increases between adjacent moments, the system's ramp-up demand increases; if the net load decreases between adjacent moments, the system's ramp-down demand increases. The system's ramp-up or ramp-down demand at time t, as the change in net load at that moment, affects unit startup and shutdown and the system's regulatory capabilities.

[0182] Furthermore, as shown in equations (29) to (30), the system up / down climbing capacity shortage is determined according to the system up-climbing demand and the system down-climbing demand. and They are used to represent the power gap caused by insufficient up-ramp capability and the power gap caused by insufficient down-ramp capability of the system:

[0183]

[0184]

[0185] In formula (29) and formula (30), is the system ramp rate, is the system ramp rate; The system's climbing capacity shortage, is the system's down-scaling capacity shortfall; ΔT is the time step.

[0186] Specifically, the system's ability to adjust or adjust power upwards According to formula (31),

[0187]

[0188] In formula (31), the upward regulation capability of the thermal power unit is the smaller value of the difference between the maximum technical output of the thermal power unit and the output of the thermal power unit at time t, and the increase in the upward climbing capability of the thermal power unit at time t, where: The maximum technical output of thermal power units, P Th,t is the output of the thermal power unit at time t; R x is the ramp-up rate of the thermal power unit, ΔT is the time step; is the upward regulation capability of the hydropower unit, The upward regulation capability of energy storage; It is the upper regulation capability of the pumped storage unit.

[0189] In this way, the system's adjustment capacity at time t It is determined based on the upward regulation capabilities of thermal power, hydropower, pumped storage, energy storage and other power sources at time t, taking into account factors such as the start-up and shutdown constraints, regulation speed, response time, and output range of various power sources in the power system.

[0190] Specifically, the system's ability to downregulate or downregulate power According to formula (32),

[0191]

[0192] In formula (32), the down-regulation capability of the thermal power unit is the smaller value of the difference between the output of the thermal power unit at time t and the maximum technical output of the thermal power unit and the reduction of the down-climbing capability of the thermal power unit at time t, where: is the minimum output of the thermal power unit at time t, P Th,t is the output of the thermal power unit at time t, R yis the ramp rate of the thermal power unit, ΔT is the time step; The downward regulation capability of the hydropower unit, For the down-regulation capability of energy storage, It is the downward regulation capability of the pumped storage unit.

[0193] In this way, the system's adjustment capacity at time t is It is determined based on the down-regulation capabilities of thermal power, hydropower, pumped storage, energy storage and other power sources at time t, taking into account factors such as the start and stop restrictions, regulation speed, response time, and output range of various power sources in the power system.

[0194] Furthermore, the maximum shortfall in the system's regulation capacity P max,+ , the maximum shortfall in the system's regulation capacity / P max,- Determined according to formula (33) and formula (34), they are used to characterize the maximum power when the system's upward regulation capability is insufficient or the downward regulation capability is insufficient, respectively.

[0195]

[0196] In formula (33) and formula (34), For the aforementioned system adjustment demand at time t, is the regulation demand of the system at time t; The down-regulation capability or down-regulation power of the aforementioned system; This is the aforementioned system up-regulation capability or upward-regulated power.

[0197] Furthermore, the system adjusts the capacity shortage time ratio The proportion of time when the system's adjustment capacity is insufficient Determine according to the following formula (35) and formula (36):

[0198]

[0199] In formula (35) and formula (36), is the adjustment capacity shortage time of the system at time t, is the time of the system's adjustment capacity shortage at time t, which is obtained by the time-series production simulation calculation in the aforementioned step S300 optimization solution; T is the total number of simulation cycles corresponding to the whole year.

[0200] As mentioned above, several medium- and long-term regulation capacity analysis statistical indicators have been identified, including: the up / down change in system net load, the maximum shortfall in climbing capacity, the up / down regulation capacity, the maximum shortfall in regulation capacity, and the proportion of shortfall time. These indicators can conveniently analyze the power supply shortage and power abandonment of the power system at each moment.

[0201] In summary, the present invention considers the impact of major weather processes on wind and solar power output, takes minimizing the power supply gap as the optimization goal, takes into account the system operation constraints and the operation constraints of various types of power sources, considers the step-by-step climbing constraints during the deep peak-shaving operation characteristics of thermal power units, establishes a medium- and long-term regulation capability analysis model for the new energy power system, and designs a Benders decomposition method based on multi-cut to optimize and solve the system's medium- and long-term regulation capability analysis model; constructs indicators such as the system's net load up / down change, the maximum shortfall in climbing capability, and regulation capability to characterize the system's regulation demand and regulation capability, providing a convenient and efficient analysis method for the system's medium- and long-term regulation capability analysis under major weather processes.

[0202] like Figure 4 As shown, the device for analyzing the regulation capability of a new energy power system under a major weather process according to an embodiment of the present invention includes:

[0203] a data acquisition unit, configured to acquire target grid structure and operation data, wherein the target grid structure and operation data include typical wind and solar load output scenarios under typical major weather processes of the current year, wherein the typical major weather processes include: cold waves and high temperatures;

[0204] A regulation capacity analysis model establishment unit is used to use the acquired target grid structure and operation data to determine the power supply operation constraints and system operation constraints associated with the medium- and long-term regulation capacity analysis model of the new energy power system, and to establish an objective function with the minimum regulation capacity gap of the new energy power system as the optimization goal;

[0205] A regulation capability analysis model solving unit is configured to utilize the determined power supply operation constraints, the system operation constraints, and the established objective function, and in combination with a typical scenario of wind and solar load output under a typical major weather process of the current year, employ a multi-cut Benders decomposition method to solve the medium- and long-term regulation capability analysis model of the new energy power system, thereby obtaining a simulation solution result of the medium- and long-term regulation of the new energy power system under a typical major weather process of the current year;

[0206] The regulation capability index analysis unit is used to construct a plurality of medium- and long-term regulation capability analysis statistical indicators using the obtained medium- and long-term regulation time series simulation solution results of the new energy power system to analyze the medium- and long-term regulation capability of the new energy power system under the typical major weather processes of the current year.

[0207] In some embodiments, the regulation capability analysis model solving unit is configured to solve the medium- and long-term regulation capability analysis model of the new energy power system using a multi-cut Benders decomposition method, and obtain a simulation solution result of the medium- and long-term regulation of the new energy power system under a typical major weather process of the current year, including:

[0208] The long-term regulation capability analysis model of the renewable energy power system is used as the original problem. It is decomposed into an MP model and n SP models according to a main problem and multiple sub-problems. The MP model is a mixed integer programming problem used to optimize the status of multiple types of power units. Each SP model is a small linear programming problem used to calculate the actual output of multiple types of power units.

[0209] Among them, the n SP models SP1 to SP n According to the time division, time t1 to t s is the s moments corresponding to the sub-model SP1, and the moment t (n-1)s+1 to t ns For sub-model SP n The corresponding s moments, s is a positive integer, n is a positive integer and an integer multiple of s;

[0210] During the interactive iteration, the actual output of each type of generator set at s moments obtained by sub-models SP1 to SPn are returned to the MP model as Benders cuts; the states of multiple types of power units determined by the MP model are used as known quantities of each sub-model to solve the actual output of each type of generator set at s moments.

[0211] In some embodiments, the regulation capability index analysis unit is configured to construct a plurality of medium- and long-term regulation capability analysis statistical indicators using the obtained medium- and long-term regulation time series simulation solution of the new energy power system to analyze the medium- and long-term regulation capability of the new energy power system under the current year's typical major weather process, including:

[0212] Calculate the system's up-climbing demand to characterize the up-climbing power required by the system at time t; Count the system's down-climbing demand to characterize the down-climbing power required by the system at time t;

[0213] According to the system's up-climbing demand and down-climbing demand, the system's up-climbing capacity shortfall and down-climbing capacity shortfall are calculated to represent the power gap when the system's up-climbing capacity is insufficient and the power gap when the system's down-climbing capacity is insufficient, respectively;

[0214] Calculate the upward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the downward regulation capabilities of adjustable loads, to determine the system's upward regulation capabilities; Calculate the downward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the upward regulation capabilities of adjustable loads, to determine the system's downward regulation capabilities;

[0215] Determining a maximum shortfall in the system's upward regulation capacity and a maximum shortfall in the system's downward regulation capacity based on the system's upward ramping demand and system downward ramping demand, the system's upward regulation capacity, and the system's downward regulation capacity, respectively representing the maximum power when the system's upward regulation capacity is insufficient and the maximum power when the system's downward regulation capacity is insufficient;

[0216] Collect statistics on the percentage of time when the system's upward adjustment capacity is insufficient and the percentage of time when the system's downward adjustment capacity is insufficient.

[0217] The embodiment of the present invention also provides a terminal to execute the method. Figure 5 It shows a schematic diagram of a terminal proposed in some embodiments of the present invention. Figure 5 As shown, the terminal 8 includes: a processor 800, a memory 801, a bus 802 and a communication interface 803, and the processor 800, the communication interface 803 and the memory 801 are connected via the bus 802; the memory 801 stores a computer program that can be run on the processor 800, and when the processor 800 runs the computer program, it executes the method proposed in any embodiment of the present invention.

[0218] The memory 801 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The communication connection between the device network element and at least one other network element is achieved through at least one communication interface 803 (which may be wired or wireless), and may use the Internet, a wide area network, a local area network, a metropolitan area network, etc.

[0219] The bus 802 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 801 is used to store programs. The processor 800 executes the programs upon receiving execution instructions. The method disclosed in any implementation of the embodiment of the present invention may be applied to the processor 800 or implemented by the processor 800.

[0220] Processor 800 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the method may be performed by hardware integrated logic circuits or software instructions within processor 800. Processor 800 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. It may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present invention may be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software modules may be located in storage media well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or the like. The storage medium is located in the memory 801 , and the processor 800 reads the information in the memory 801 and completes the steps of the method in combination with its hardware.

[0221] The terminal proposed in the embodiment of the present invention and the method in the embodiment of the present invention are based on the same inventive concept and have the same beneficial effects as the method adopted, operated or implemented by them.

[0222] like Figure 6 As shown, an embodiment of the present invention also provides a computer-readable storage medium corresponding to the method proposed in the aforementioned embodiment, and the computer-readable storage medium is a CD, on which a computer program (i.e., program product 900) is stored. When the computer program is run by a processor, it will execute the method proposed in any of the aforementioned embodiments.

[0223] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0224] The computer-readable storage medium proposed in the embodiment of the present invention and the method of the embodiment of the present invention are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0225] Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations fall within the scope of the claims and their equivalents, the present invention is intended to include such modifications and variations.

Claims

1. A method for analyzing the regulation capability of a new energy power system under a major weather process, characterized in that: include: Obtaining target grid structure and operation data, including typical wind and solar load output scenarios under typical major weather events in the current year, wherein the typical major weather events include: cold waves and high temperatures; Using the acquired target grid structure and operation data, the power supply operation constraints and system operation constraints associated with the medium- and long-term regulation capacity analysis model of the new energy power system are determined, and an objective function is established with the goal of minimizing the regulation capacity gap of the new energy power system. Using the determined power supply operation constraints, the system operation constraints, and the established objective function, combined with the typical wind and solar load output scenario under the typical major weather process of the current year, a multi-cut Benders decomposition method is adopted to solve the medium- and long-term regulation capability analysis model of the new energy power system, and obtain the medium- and long-term regulation time series simulation solution results of the new energy power system under the typical major weather process of the current year; Using the obtained results of the medium- and long-term regulation time series simulation of the new energy power system, a number of medium- and long-term regulation capacity analysis statistical indicators are constructed to analyze the medium- and long-term regulation capacity of the new energy power system under the typical major weather processes of the current year.

2. The method for analyzing the regulation capability of a new energy power system under a major weather process according to claim 1, wherein: The obtaining of target power grid structure and operation data includes: Obtain information about various power sources in the power grid, including thermal power units, hydropower units, wind power units, photovoltaic units, pumped storage power stations, and energy storage; Obtain the annual utilization hours of renewable energy and annual load electricity in the target power grid area in the previous year; Obtain historical data on wind speed under normal weather conditions in the previous year in the target power grid area, historical data on sunlight under normal weather conditions in the previous year, historical data on load under normal weather conditions in the previous year, wind-solar-load output curves under the previous year's cold wave process, and wind-solar-load output curves under the previous year's high temperature process.

3. The method for analyzing the regulation capability of a new energy power system under a major weather process according to claim 1, wherein: The determination of power supply operation constraints for the long-term regulation capability analysis model of the new energy power system includes: Determine thermal power unit constraints, wherein the thermal power unit constraints include: thermal power output upper / lower limit constraints, thermal power unit start / stop constraints, and thermal power unit step-type ramp constraints; Determine the constraints of the hydropower unit, wherein the constraints of the hydropower unit include: hydropower unit output constraints, hydropower unit power generation constraints and reservoir capacity constraints; Determining energy storage constraints, wherein the energy storage constraints include: charge and discharge state and charge and discharge number constraints, charge and discharge power constraints, and state of charge constraints; Determining constraints of a pumped storage power station, wherein the constraints of the pumped storage power station include: power generation constraint, pumping power constraint, water pumping and discharge state constraint, and pumped storage capacity constraint; The determining of the system operation constraints for the medium- and long-term regulation capability analysis model of the new energy power system includes: Determine power constraints of power supply loads, renewable energy output constraints and interconnection line constraints.

4. The method for analyzing the regulation capability of a new energy power system under a major weather process according to claim 1, wherein: The multi-cut Benders decomposition method is used to solve the medium- and long-term regulation capability analysis model of the new energy power system, and obtain the medium- and long-term regulation time series simulation solution of the new energy power system under the current typical major weather process, including: The long-term regulation capability analysis model of the renewable energy power system is used as the original problem. It is decomposed into an MP model and n SP models according to a main problem and multiple sub-problems. The MP model is a mixed integer programming problem used to optimize the status of multiple types of power units. Each SP model is a small linear programming problem used to calculate the actual output of multiple types of power units. The n SP models SP1 to SPn are divided according to time, time t1 to ts are s time corresponding to the sub-model SP1, time t(n-1)s+1 to tns are s time corresponding to the sub-model SPn, s is a positive integer, n is a positive integer and an integer multiple of s; During the interactive iteration, the actual output of each type of generator set at s moments obtained by sub-models SP1 to SPn are returned to the MP model as Benders cuts; the states of multiple types of power units determined by the MP model are used as known quantities of each sub-model to solve the actual output of each type of generator set at s moments.

5. The method for analyzing the regulation capability of a new energy power system under a major weather process according to claim 1, wherein: The obtained results of the time series simulation of the medium- and long-term regulation of the new energy power system are used to construct multiple medium- and long-term regulation capacity analysis statistical indicators to analyze the medium- and long-term regulation capacity of the new energy power system under the typical major weather process of the current year, including: Calculate the system's up-climbing demand to characterize the up-climbing power required by the system at time t; Count the system's down-climbing demand to characterize the down-climbing power required by the system at time t; According to the system's up-climbing demand and down-climbing demand, the system's up-climbing capacity shortfall and down-climbing capacity shortfall are calculated to represent the power gap when the system's up-climbing capacity is insufficient and the power gap when the system's down-climbing capacity is insufficient, respectively; Calculate the upward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the downward regulation capabilities of adjustable loads, to determine the system's upward regulation capabilities; Calculate the downward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the upward regulation capabilities of adjustable loads, to determine the system's downward regulation capabilities; Determining a maximum shortfall in the system's upward regulation capacity and a maximum shortfall in the system's downward regulation capacity based on the system's upward ramping demand and system downward ramping demand, the system's upward regulation capacity, and the system's downward regulation capacity, respectively representing the maximum power when the system's upward regulation capacity is insufficient and the maximum power when the system's downward regulation capacity is insufficient; Collect statistics on the percentage of time when the system's upward adjustment capacity is insufficient and the percentage of time when the system's downward adjustment capacity is insufficient.

6. A device for analyzing the regulation capability of a new energy power system under a major weather process, characterized in that: include: a data acquisition unit, configured to acquire target grid structure and operation data, wherein the target grid structure and operation data include typical wind and solar load output scenarios under typical major weather processes of the current year, wherein the typical major weather processes include: cold waves and high temperatures; A regulation capacity analysis model establishment unit is used to use the acquired target grid structure and operation data to determine the power supply operation constraints and system operation constraints associated with the medium- and long-term regulation capacity analysis model of the new energy power system, and to establish an objective function with the minimum regulation capacity gap of the new energy power system as the optimization goal; A regulation capability analysis model solving unit is configured to utilize the determined power supply operation constraints, the system operation constraints, and the established objective function, and in combination with a typical scenario of wind and solar load output under a typical major weather process of the current year, employ a multi-cut Benders decomposition method to solve the medium- and long-term regulation capability analysis model of the new energy power system, thereby obtaining a simulation solution result of the medium- and long-term regulation of the new energy power system under a typical major weather process of the current year; The regulation capability index analysis unit is used to construct a plurality of medium- and long-term regulation capability analysis statistical indicators using the obtained medium- and long-term regulation time series simulation solution results of the new energy power system to analyze the medium- and long-term regulation capability of the new energy power system under the typical major weather processes of the current year.

7. The device for analyzing the regulating capability of a new energy power system under a major weather process according to claim 6, characterized in that: The regulation capacity analysis model solving unit is used to solve the medium- and long-term regulation capacity analysis model of the new energy power system using a multi-cut Benders decomposition method, and obtain the medium- and long-term regulation time series simulation solution results of the new energy power system under the current typical major weather process, including: The long-term regulation capability analysis model of the renewable energy power system is used as the original problem. It is decomposed into an MP model and n SP models according to a main problem and multiple sub-problems. The MP model is a mixed integer programming problem used to optimize the status of multiple types of power units. Each SP model is a small linear programming problem used to calculate the actual output of multiple types of power units. The n SP models SP1 to SPn are divided according to time, time t1 to ts are s time corresponding to the sub-model SP1, time t(n-1)s+1 to tns are s time corresponding to the sub-model SPn, s is a positive integer, n is a positive integer and an integer multiple of s; During the interactive iteration, the actual output of each type of generator set at s moments obtained by sub-models SP1 to SPn are returned to the MP model as Benders cuts; the states of multiple types of power units determined by the MP model are used as known quantities of each sub-model to solve the actual output of each type of generator set at s moments.

8. The device for analyzing the regulating capability of a new energy power system under a major weather process according to claim 6, characterized in that: The regulation capability index analysis unit is used to construct a plurality of medium- and long-term regulation capability analysis statistical indicators using the obtained medium- and long-term regulation time series simulation solution of the new energy power system to analyze the medium- and long-term regulation capability of the new energy power system under the typical major weather process of the current year, including: Calculate the system's up-climbing demand to characterize the up-climbing power required by the system at time t; Count the system's down-climbing demand to characterize the down-climbing power required by the system at time t; According to the system's up-climbing demand and down-climbing demand, the system's up-climbing capacity shortfall and down-climbing capacity shortfall are calculated to represent the power gap when the system's up-climbing capacity is insufficient and the power gap when the system's down-climbing capacity is insufficient, respectively; Calculate the upward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the downward regulation capabilities of adjustable loads, to determine the system's upward regulation capabilities; Calculate the downward regulation capabilities of thermal power units, hydropower units, pumped storage power stations and energy storage, as well as the upward regulation capabilities of adjustable loads, to determine the system's downward regulation capabilities; Determining a maximum shortfall in the system's upward regulation capacity and a maximum shortfall in the system's downward regulation capacity based on the system's upward ramping demand and system downward ramping demand, the system's upward regulation capacity, and the system's downward regulation capacity, respectively representing the maximum power when the system's upward regulation capacity is insufficient and the maximum power when the system's downward regulation capacity is insufficient; Collect statistics on the percentage of time when the system's upward adjustment capacity is insufficient and the percentage of time when the system's downward adjustment capacity is insufficient.

9. A terminal comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 5.

10. A computer storage medium, characterized in that Computer-controllable instructions are stored, and the computer-controllable instructions are used to execute the method according to any one of claims 1 to 5.

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