Scheduling Method for Large-Scale New Energy Bases Based on the Uncertainty of the Power Spot Market

Abstract

The present application relates to a dispatching method for large-scale new energy bases based on the uncertainty of the electricity spot market. Specifically, the method includes collecting historical data on the nodal marginal price of the spot market and the wind and photovoltaic power generation, and generating a set of day-ahead market decision scenarios including the spot market electricity price and the output of new energy units through the Monte Carlo sampling method; establishing a two-stage stochastic optimization model for large-scale new energy bases participating in the day-ahead market considering the uncertainty of the spot market, and establishing a model for participating in the real-time market; calculating the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind turbines in combination with the set of uncertain day-ahead market decision scenarios; calculating the real-time market bidding strategy and the charge-discharge strategy of energy storage devices based on high-precision short-term price forecasts and renewable energy power forecast values, so as to maximize the profit in the market environment of large-scale new energy bases. This solves the problem of low economic efficiency in the energy storage dispatching of large-scale new energy bases in the electricity spot market.

Description

Technical Field

[0001] This application relates to the technical field of power system dispatching, and particularly to a dispatching method for large-scale new energy bases based on the uncertainty of the power spot market. Background Art

[0002] Large-scale new energy bases can centrally utilize renewable energy such as wind energy and solar energy, combined with the flexible adjustment capabilities of various resources such as thermal power and energy storage, which is an important form of the future development of new energy. At the same time, the construction process of the power market is constantly advancing, and in the future, the cost recovery and profit acquisition of large-scale new energy bases need to be realized in the market environment.

[0003] However, although renewable energy has significant advantages in terms of power generation cost, the volatility and uncertainty of its output limit their participation ability in the spot market. Existing research mainly focuses on the day-ahead market bidding method under a single settlement mechanism or the wind power bidding method considering expected electricity prices and deviation penalties. These methods focus on reducing the impact of renewable energy uncertainty on bidding deviations and ignore the dynamic profit structure under the double settlement mechanism. Therefore, there is an urgent need for energy storage dispatching in the power spot market of large-scale new energy bases to solve the above problems. Summary of the Invention

[0004] The embodiments of this application provide a dispatching method for large-scale new energy bases based on the uncertainty of the power spot market, so as to at least solve the problem of low economic efficiency in the dispatching of large-scale new energy bases in the power spot market in related technologies.

[0005] In a first aspect, the embodiments of this application provide a dispatching method for large-scale new energy bases based on the uncertainty of the power spot market, and the method includes:

[0006] According to the pre-obtained historical data of the power spot market, generate multiple market decision scenario sets through an uncertainty simulation method, and the market decision scenario sets are used to simulate the price and power generation changes under different market conditions;

[0007] Based on the market decision scenario sets, construct a two-stage optimization model for the day-ahead market. The two-stage optimization model takes maximizing the day-ahead market revenue as the objective function and follows the operating constraint conditions of different types of generating units.

[0008] Based on the two-stage optimization model, obtain the day-ahead market bidding strategy, thermal power unit start-stop plan, and wind turbine maintenance plan;

[0009] Based on the day-ahead market bidding strategy, thermal power unit start-stop plan, and wind turbine maintenance plan, construct a real-time market model. The real-time market model is used to adjust the winning bid power and deviation assessment power of the real-time market according to the day-ahead market, and takes maximizing the real-time market revenue as the objective function.

[0010] Based on the short-term price prediction and the predicted value of the renewable energy power, according to the real-time market model, obtain the charging and discharging strategies of the energy storage devices in the real-time large-scale new energy base market.

[0011] In one embodiment, before generating multiple market decision scenario sets through uncertainty simulation according to the pre-obtained historical data of the electricity spot market, the method further includes:

[0012] Obtain the historical data of the electricity spot market, where the historical data includes the nodal marginal price and the renewable energy generation power.

[0013] In one embodiment, the generating multiple market decision scenario sets through uncertainty simulation according to the pre-obtained historical data of the electricity spot market includes:

[0014] Clean and normalize the historical data of the electricity spot market;

[0015] Through the Monte Carlo sampling method, based on the processed historical data, generate multiple market decision scenario sets, where each market decision scenario set includes the price and power generation changes under different market conditions.

[0016] In one embodiment, in the first stage of the two-stage optimization model, it is used to determine the operating status, planned power, and clearance of each generating unit;

[0017] In the second stage of the two-stage optimization model, based on the operating status, planned power, and clearance of each generating unit determined in the first stage, determine the power arrangement of each generating unit in different situations.

[0018] In one embodiment, determine the day-ahead market revenue objective function through the probability of the uncertain scenario, the total number of uncertainty scenarios, the total number of periods, the nodal marginal price in the day-ahead market at a certain moment in the uncertain scenario, the planned bid volume of the power producer in the day-ahead market at a certain moment, and the operating cost in a certain time period in the uncertain scenario;

[0019] Among them, the day-ahead market revenue objective function is expressed by the following formula:

[0020]

[0021] In the formula, represents the probability of the uncertain scenario , is the total number of uncertainty scenarios, is the total number of periods, represents in the scenario at time The nodal marginal price in the market currently is the planned bid volume of power producers at time in the day-ahead market, is the scenario in the operating cost of the time period.

[0022] In one embodiment, the planned bid volume of the power producer at time in the day-ahead market is expressed by the following formula:

[0023]

[0024] In the formula, , are respectively the planned power generation volumes of thermal power units and renewable energy units at time , and are respectively the discharge power and charge power of the energy storage system at moment , , and are respectively the numbers of thermal power units, renewable energy units and energy storage systems.

[0025] In one embodiment, the operating cost includes the start-stop cost, coal consumption cost and deviation penalty cost of thermal power units. Among them, the operating cost is expressed by the following formula:

[0026]

[0027] In the formula, represents the start-up cost of thermal power unit in the time period, is the power generation cost function, which is a quadratic function of the unit output power, is the scenario in the time period for the power volume that needs deviation assessment, is the deviation assessment coefficient;

[0028] Among them, the power volume that needs deviation assessment in the scenario time period is expressed by the following formula:

[0029]

[0030] In the formula, is the actual physical delivery quantity at the time of delivery, represents the deviation assessment margin.

[0031] In one embodiment, following the operating constraint conditions of different types of generating units includes:

[0032] Following the constraint condition of the ramp rate of thermal power units: the change rate of the output power of the thermal power unit in each period does not exceed the maximum ramp rate of the thermal power unit;

[0033] Following the constraint condition of the output of wind turbines: the generated power of the wind turbine fluctuates within the predicted value range and does not exceed the maximum generating capacity of the wind turbine;

[0034] Following the constraint condition of the charge-discharge efficiency of the energy storage system: the energy loss of the energy storage system during the charge-discharge process is within a preset range.

[0035] In one embodiment, adjusting the winning bid electricity quantity and deviation assessment electricity quantity of the real-time market according to the day-ahead market, with maximizing the real-time market revenue as the objective function, includes:

[0036] Determining the objective function of maximizing the real-time market revenue through the winning bid electricity quantity of the real-time market in a certain period in the uncertain scenario, the electricity quantity that needs to be deviation-assessed in the real-time market in a certain period in the uncertain scenario, the nodal marginal price of the day-ahead market in a certain period disclosed before the start of the real-time market, and the predicted real-time electricity price of the real-time market in a certain period in the uncertain scenario;

[0037] Among them, the objective function of maximizing the real-time market revenue is expressed by the following formula:

[0038]

[0039] In the formula, is the winning bid electricity quantity of the real-time market in period in scenario , is the electricity quantity that needs to be deviation-assessed in period in scenario , is the nodal marginal price of the day-ahead market in period disclosed before the start of the real-time market, is the predicted real-time electricity price of the real-time market in period in scenario ;

[0040] Among them, the winning bid electricity quantity and deviation assessment electricity quantity of the real-time market need to meet the following conditions:

[0041]

[0042]

[0043] Among them, and respectively represent the in the scheduled sales electricity of thermal power units and new energy units in the real-time market during the and respectively represent the in the scheduled sales electricity and scheduled purchase electricity of the energy storage system in the real-time market during the is the scheduled bidding electricity of the power producer in the day-ahead market.

[0044] On the second aspect, the embodiment of the present application provides a large-scale new energy base scheduling system based on the uncertainty of the electricity spot market. The system includes a market decision scenario set module, a two-stage optimization model construction module, a day-ahead market bidding strategy and start-stop maintenance plan module, a real-time market model construction module, and a charge and discharge strategy module, where:

[0045] The market decision scenario set module is used to generate multiple market decision scenario sets through an uncertainty simulation method according to the pre-acquired historical data of the electricity spot market. The market decision scenario sets are used to simulate the price and power generation changes under different market conditions;

[0046] The two-stage optimization model construction module is used to construct a two-stage optimization model for the day-ahead market based on the market decision scenario sets. The two-stage optimization model takes maximizing the day-ahead market revenue as the objective function and follows the operating constraint conditions of different types of generating units.

[0047] The day-ahead market bidding strategy and start-stop maintenance plan module is used to obtain the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind turbines based on the two-stage optimization model;

[0048] The real-time market model construction module is used to construct a real-time market model based on the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind turbines. The real-time market model is used to adjust the winning bid electricity and deviation assessment electricity of the real-time market according to the day-ahead market, and takes maximizing the real-time market revenue as the objective function;

[0049] The charge and discharge strategy module is used to obtain the charge and discharge strategy of the energy storage device in the real-time large-scale new energy base market based on the short-term price prediction and the predicted value of renewable energy power according to the real-time market model.

[0050] In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements a large-scale new energy base scheduling method based on the uncertainty of the electricity spot market as described in the first aspect above.

[0051] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements a large-scale new energy base scheduling method based on the uncertainty of the electricity spot market as described in the first aspect above.

[0052] The large-scale new energy base scheduling method based on the uncertainty of the electricity spot market provided by the embodiment of the present application has at least the following technical effects:

[0053] By generating multiple market decision scenario sets through an uncertainty simulation method based on the pre-acquired historical data of the electricity spot market, the market decision scenario sets are used to simulate the price and power generation changes under different market conditions. Based on the market decision scenario sets, a two-stage optimization model for the day-ahead market is constructed. The two-stage optimization model takes maximizing the day-ahead market revenue as the objective function and follows the operating constraint conditions of different types of generating units. Based on the two-stage optimization model, the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind turbines are obtained. Based on the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind turbines, a real-time market model is constructed. The real-time market model is used to adjust the winning bid electricity and deviation assessment electricity of the real-time market according to the day-ahead market, with the objective function of maximizing the real-time market revenue. According to the short-term price prediction and the predicted value of renewable energy power, based on the real-time market model, the charge and discharge strategy of the energy storage device in the real-time large-scale new energy base market is obtained. The profit maximization in the large-scale new energy base market environment is realized. The problem of low economic efficiency in the energy storage scheduling of large-scale new energy bases in the related technology is solved.

[0054] The details of one or more embodiments of the present application are set forth in the following drawings and description to make the other features, objects, and advantages of the present application more comprehensible. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0056] Figure 1 It is a flowchart of a large-scale new energy base dispatching method based on the uncertainty of the electricity spot market according to an embodiment of the present application;

[0057] Figure 2 It is a schematic diagram of the day-ahead market bidding curve of a large-scale new energy base shown according to an exemplary embodiment;

[0058] Figure 3 It is a schematic diagram of the optimized operation result of a thermal power unit shown according to an exemplary embodiment;

[0059] Figure 4 It is a schematic diagram of the optimized operation result of a thermal power unit shown according to an exemplary embodiment;

[0060] Figure 5 It is a block diagram of a large-scale new energy base dispatching system based on the uncertainty of the electricity spot market shown according to an exemplary embodiment;

[0061] Figure 6 It is a block diagram of an electronic device shown according to an exemplary embodiment. Detailed implementation manners

[0062] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be described and explained below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. Based on the embodiments provided by the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0063] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, without creative efforts, the present application can also be applied to other similar scenarios based on these drawings. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in the present application, some design, manufacturing or production changes based on the technical content disclosed in the present application are only conventional technical means and should not be understood as insufficient disclosure of the content of the present application.

[0064] References to "embodiments" in this application mean that specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive of other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.

[0065] Unless otherwise defined, technical terms or scientific terms involved in this application shall have the ordinary meanings understood by those of ordinary skill in the technical field to which this application belongs. The words "a", "an", "one", "the" and similar words involved in this application do not denote a limitation of quantity and can mean singular or plural. The terms "include", "comprise", "have" and any variations thereof involved in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may further include unlisted steps or units, or may further include other steps or units inherent to these processes, methods, products or devices. The words "connected", "coupled" and similar words involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" involved in this application means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0066] In this context, it should be understood that the terms involved may be technical means for implementing a part of the present invention or other summary technical terms. For example, the terms may include:

[0067] Power spot market: Refers to a place in the power system where buyers and sellers conduct short-term power transactions through market mechanisms. Different from long-term contracts (such as futures contracts), transactions in the spot market are usually completed in a relatively short period of time, and the most common ones are the day-ahead market (DAM) and the real-time market (RTM). These markets allow power generation enterprises, power sales companies, large users and other market participants to buy and sell electricity according to the latest supply and demand conditions and price signals.

[0068] Dual settlement mechanism: That is, the time - of - use deviation electricity of the day - ahead spot trading curve and the medium - and long - term trading curve is settled according to the day - ahead spot price, and the time - of - use deviation electricity of the real - time spot trading curve and the day - ahead spot trading curve is settled according to the real - time spot price. Market participants can improve their profitability by taking advantage of the price difference between the day - ahead market and the real - time market. The dynamic of the profit structure under this mechanism is compared with that under a single settlement rule. The profit will change under the influence of the real - time market electricity price and the energy storage dispatching strategy. Ignoring this dynamic characteristic will lead to the inability to obtain the maximum profit in market strategies and energy storage dispatching.

[0069] In the first aspect, the embodiments of the present application provide a dispatching method for large - scale new - energy bases based on the uncertainty of the electricity spot market. Figure 1 It is a flowchart of the dispatching of large - scale new - energy bases based on the uncertainty of the electricity spot market, as Figure 1 shown. A dispatching method for large - scale new - energy bases based on the uncertainty of the electricity spot market includes:

[0070] Step S101: According to the pre - obtained historical data of the electricity spot market, generate multiple market decision scenario sets through an uncertainty simulation method. The market decision scenario sets are used to simulate the price and power generation changes under different market conditions.

[0071] Step S102: Based on the market decision scenario sets, construct a two - stage optimization model for the day - ahead market. The two - stage optimization model takes maximizing the day - ahead market revenue as the objective function and follows the operating constraints of different types of generating units.

[0072] Step S103: Based on the two - stage optimization model, obtain the day - ahead market bidding strategy, the start - stop plan of thermal power units, and the maintenance plan of wind turbines.

[0073] Step S104: Based on the day - ahead market bidding strategy, the start - stop plan of thermal power units, and the maintenance plan of wind turbines, construct a real - time market model. The real - time market model is used to adjust the winning bid electricity and deviation assessment electricity of the real - time market according to the day - ahead market, with maximizing the real - time market revenue as the objective function;

[0074] Step S105: According to the short - cycle price forecast and the renewable energy power forecast value, based on the real - time market model, obtain the charge - discharge strategy of the energy storage equipment in the real - time large - scale new - energy base market.

[0075] In summary, the embodiment of the present application provides a method for dispatching a large-scale new energy base based on the uncertainty of the electricity spot market. First, it is necessary to collect historical data on the marginal electricity price and wind and solar power generation of the spot market nodes, and generate a set of day-ahead market decision scenarios including the spot market electricity price and the output of new energy units through the Monte Carlo sampling method; secondly, a two-stage stochastic optimization model for large-scale new energy bases to participate in the day-ahead market considering the uncertainty of the spot market is established, and a model for participating in the real-time market is established; thirdly, the day-ahead market bidding strategy and the start-stop plan of the thermal power unit and the maintenance plan of the wind power unit are calculated in combination with the uncertain day-ahead market decision scenario set; finally, the real-time market bidding strategy and the charging and discharging strategy of the energy storage equipment are calculated based on the high-precision short-term price forecast and the renewable energy power forecast value, so as to maximize the profit in the market environment of large-scale new energy bases. The problem of low economic benefits of large-scale new energy bases in related technologies when dispatching energy storage in the electricity spot market is solved.

[0076] In one embodiment, in step S101, based on the pre-acquired historical data of the power spot market, multiple market decision scenario sets are generated by uncertainty simulation, and the market decision scenario sets are used to simulate the price and power generation changes under different market conditions. Specifically, they include:

[0077] Clean and normalize historical data of the electricity spot market;

[0078] Through the Monte Carlo sampling method, multiple market decision scenario sets are generated based on the processed historical data, where each market decision scenario set includes price and power generation changes under different market conditions.

[0079] Optionally, collect and organize historical data of the electricity spot market, including key variables such as node marginal power price (LMP), renewable energy power generation, and load demand. Clean the data to remove outliers and missing values ​​to ensure data quality and consistency. For example, use the mean or median to fill missing values ​​and remove data points with obvious abnormalities. Normalize the cleaned data to unify the numerical ranges of different variables into a standard interval (such as [0, 1]) to facilitate subsequent modeling and analysis. Based on the normalized historical data, construct the probability distribution function (PDF) of each variable. For example, use histograms or kernel density estimation (KDE) methods to estimate the probability distribution of node marginal power prices and renewable energy power generation. Use Monte Carlo sampling methods to randomly extract samples from these probability distributions (non-uniform random sampling) to generate multiple market decision scenario sets. Each scenario set covers the price and power generation changes under different market conditions to ensure the diversity and representativeness of the scenario set.

[0080] Step S101 ensures the quality and consistency of the input data by cleaning and normalizing historical data, thus improving the accuracy of uncertainty simulation. The generated market decision scenario set provides a reliable data basis for subsequent optimization models, helping to formulate more accurate bidding strategies and scheduling plans, and ultimately maximizing profits.

[0081] In one embodiment, before generating multiple market decision scenario sets by means of uncertainty simulation based on the pre-acquired historical data of the electricity spot market, the method further includes:

[0082] Obtain the historical data of the electricity spot market, where the historical data includes nodal marginal prices and renewable energy generation power.

[0083] Optionally, obtain the historical data of the electricity spot market from official electricity trading institutions or third-party data providers. The data content includes, but is not limited to, nodal marginal prices (LMP) and renewable energy generation power (such as wind power, photovoltaic power, etc.). The time range covers at least the data of the past year to ensure the sufficiency and representativeness of the data. The geographical range covers all major nodes and power generation facilities within the target area to ensure the comprehensiveness of the data. The data type, nodal marginal price (LMP), records the marginal price of each node within each time period, reflecting the price fluctuations in the electricity market. Renewable energy generation power records the actual power generation of wind power stations and photovoltaic power stations within each time period, reflecting the changes in new energy output.

[0084] By obtaining the complete historical data of the electricity spot market from official channels, the authority and accuracy of the data are ensured, providing a solid foundation for subsequent uncertainty simulation.

[0085] In one embodiment, step S102 is to construct a two-stage optimization model for the day-ahead market based on the market decision scenario set. The two-stage optimization model takes maximizing the day-ahead market revenue as the objective function and follows the operating constraints of different types of generating units.

[0086] Optionally, in the first stage of the two-stage optimization model, it is used to determine the operating status, planned power, and clearing volume of each generating unit. In the second stage of the two-stage optimization model, based on the operating status, planned power, and clearing volume of each generating unit determined in the first stage, the power arrangement of each generating unit in different situations is determined.

[0087] It should be noted that the two-stage optimization model is a modeling method considering uncertainty. The output of the first stage (bid volume and price) will be used as the parameters of the second stage. However, due to the uncertainty of other parameters in the second stage (here the electricity price), the scenario set can reflect this uncertainty. Therefore, the two-stage optimization model is a model constructed based on the scenario set.

[0088] Determine the day-ahead market revenue objective function by the probability of the uncertain scenario, the total number of uncertainty scenarios, the total number of periods, the nodal marginal price in the day-ahead market at a certain moment in the uncertain scenario, the planned bid volume of the power producer in the day-ahead market at a certain moment, and the operating cost in a certain time period in the uncertain scenario.

[0089] Objective function: The objective function of the two-stage stochastic optimization model for the day-ahead market is to maximize the expected revenue of the day-ahead market, as shown in formulas (1)-(5):

[0090] (1)

[0091] Where represents the probability of the uncertain scenario . is the total number of uncertainty scenarios. is the total number of periods. The right side of the equation represents the revenue of the large base in the day-ahead market at time in scenario , and its specific value is determined by the difference between the power sales revenue and the operating cost. represents the nodal marginal price in the day-ahead market at time in scenario . is the planned bid volume of the power producer in the day-ahead market at time . is the scenario in the time period of the operating cost.

[0092] (2)

[0093] Where, , are the planned power generation volumes of thermal power units and renewable energy units in the day-ahead market at time , respectively. and are the discharge power and charge power of the energy storage system at time , respectively. , and are the numbers of thermal power units, renewable energy units and energy storage systems, respectively.

[0094] Operating cost includes the start-up and shut-down costs, coal consumption costs, and deviation penalty costs of thermal power units:

[0095] (3)

[0096] Among them, represents the start-up cost of a thermal power unit during the time period; is the power generation cost function, which is a quadratic function of the unit output power; is the scenario in the time period, the electricity quantity that needs to be subjected to deviation assessment; is the deviation assessment coefficient.

[0097] (4)

[0098] Among them is the actual physical delivery quantity at the time of delivery, represents the deviation assessment margin.

[0099] In the stochastic optimization model constructed in this paper, the electricity delivery quantity of a large-scale new energy base in scenario in the time period is denoted as

[0100] (5)

[0101] Among them, represents the thermal power unit in scenario during the time period; represents the renewable energy in scenario during the time period; and represent the energy storage system in scenario during the time period for the discharge and charge amounts.

[0102] The operation constraint conditions followed by different types of generator sets include the constraints of thermal power units, new energy units, and energy storage systems.

[0103] Follow the constraint condition of the thermal power unit's ramp rate: the change rate of the thermal power unit's output power in each time period does not exceed the maximum ramp rate of the thermal power unit. The operation constraint conditions of the thermal power unit are shown in equations (6)-(12).

[0104] Upper and lower limit constraints of the thermal power unit's output power:

[0105] (6)

[0106] Among them, and respectively represent the thermal power unit Upper and lower limit values of power output is a 0-1 variable representing the thermal power unit at operating status during the time period, where represents that the thermal power unit is in the on state

[0107] Thermal power unit upward ramp rate constraint:

[0108] (7)

[0109] Thermal power unit downward ramp rate constraint:

[0110] (8)

[0111] where is the power output of the unit at during the time period and are the upward and downward power ramp rates of the unit respectively

[0112] Conversion condition constraint between different scenarios of thermal power units:

[0113] (9)

[0114] where represents the regulation ability of the unit respectively

[0115] Constraints on the minimum running time and shutdown time of thermal power units:

[0116] (10)

[0117] (11)

[0118] where and represent the shortest shutdown time and the shortest startup time of the unit respectively

[0119] Constraints on the startup and shutdown costs of thermal power units:

[0120] (12)

[0121] where represents the single startup cost of the unit

[0122] Follow the constraint conditions of the output of wind turbines: The generated power of wind turbines fluctuates within the predicted value range and does not exceed the maximum generating capacity of wind turbines. The constraint conditions for the operation of renewable energy units are shown in (13)-(20).

[0123] Constraint on the planned electricity sales volume:

[0124] (13)

[0125] It means that the planned electricity sales volume in each period shall not exceed the predicted maximum output of renewable energy.

[0126] Upper and lower limits of the generated electricity of renewable energy units:

[0127] (14)

[0128] Wherein, and respectively represent the upper and lower limit values of the electricity output of renewable energy units .

[0129] Constraints related to the maintenance of renewable energy units:

[0130] (15)

[0131] (16)

[0132] (17)

[0133] (18)

[0134] (19)

[0135] (20)

[0136] Wherein, is a 0-1 variable representing the operating state of the renewable energy unit at period, where represents that the renewable energy unit is in the on state. is a 0-1 variable representing the maintenance decision of the renewable energy unit at period, where represents that the renewable energy unit at period needs to be maintained. represents the maintenance requirement of the renewable energy unit . Indicates the maintenance interval of renewable energy units is the maximum value of the number of units to be maintained simultaneously.

[0137] Follow the charge-discharge efficiency constraint conditions of the energy storage system: the energy loss of the energy storage system during the charge-discharge process is within the preset range. Formulas (21)-(26) represent the constraint conditions for the operation of the energy storage system.

[0138] (21)

[0139] Among them, represents in the scenario the energy stored by the energy storage system at time periods. and are respectively the charging efficiency and discharging efficiency of the energy storage system

[0140] Charging power constraint of the energy storage system:

[0141] (22)

[0142] Discharging power constraint of the energy storage system:

[0143] (23)

[0144] Among them, and are respectively the 0-1 state variables of the energy storage system charging and discharging at in the scenario time periods. 、 、 and are respectively the limits of the charging and discharging powers of the energy storage system

[0145] Constraint that the energy storage system cannot charge and discharge simultaneously:

[0146] (24)

[0147] Energy state constraint of the energy storage system:

[0148] (25)

[0149] Among them, and are respectively the upper and lower limit values of the capacity of the energy storage system

[0150] ​​​​Constraint that the final energy of the energy storage system on the operation day is consistent with the starting state:

[0151] (26)

[0152] Step S102 constructs a two-stage optimization model for the day-ahead market, which can more accurately reflect the uncertainty of market prices and power generation. This not only improves the accuracy of the optimization results but also enables the model to make optimal decisions under various possible market conditions. Strictly follow the operating constraint conditions of various types of generating units (such as the ramp rate of thermal power units and the maximum power generation of wind turbines), ensuring the feasibility and safety of the optimization results in actual operation. This not only reduces operation risks but also improves the reliability of the system.

[0153] In one embodiment, step S103 obtains the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind turbines based on the two-stage optimization model.

[0154] Optionally, the inputs of the two-stage optimization model can be: the probabilities of each scenario and the corresponding predicted values of each scenario, specifically including the nodal marginal price of the day-ahead market; parameters related to operating costs, cost model parameters such as the start-stop cost, coal consumption cost, and deviation assessment cost of thermal power units.

[0155] The outputs of the two-stage optimization model can be:

[0156] The day-ahead market bidding strategy, including the quotations and power generation arrangements of each generating unit in each time period, ensuring maximum revenue under different market conditions;

[0157] The start-stop plan of thermal power units: specifying in detail the start and stop instructions of each thermal power unit at different time points, ensuring that while meeting the day-ahead market bidding strategy, the start-up cost is minimized;

[0158] The maintenance plan of wind turbines: specifying in detail the maintenance arrangements of each wind turbine at different time points, ensuring that the power generation efficiency is maximized without affecting the day-ahead market bidding strategy.

[0159] Step S103 effectively improves the overall economic efficiency and reduces the operating cost by maximizing the day-ahead market revenue and minimizing the start-up cost. Generating a detailed day-ahead market bidding strategy, start-stop plan, and maintenance plan provides a scientific decision-making basis for large-scale new energy bases and enhances market competitiveness. Reasonably arranging the start-stop of thermal power units and the maintenance of wind turbines improves the resource utilization efficiency and ensures that the system operates in the best state.

[0160] In one embodiment, in step S104, a real-time market model is constructed based on the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind power units. The real-time market model is used to adjust the winning bid electricity quantity and deviation assessment electricity quantity of the real-time market according to the day-ahead market, with the maximization of the real-time market revenue as the objective function. Specifically, it includes:

[0161] Constructing the real-time market model is to establish an optimization model for the real-time market. Part of the parameters of the real-time market model are the calculation results of the two-stage optimization model in step S103 above (the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind power units), and the other part is the short-term price forecast and the renewable energy power forecast value. The goal of this model is to maximize the real-time market revenue, and the output is the market bidding strategy of large-scale new energy bases in the real-time market and the charge and discharge strategies of energy storage devices. The calculation of the revenue takes into account the winning bid electricity quantity and the electricity quantity that needs to be deviation-assessed.

[0162] Based on the winning bid electricity quantity of the real-time market in a certain time period in the uncertain scenario, the electricity quantity that needs to be deviation-assessed by the real-time market in a certain time period in the uncertain scenario, the nodal marginal price of the day-ahead market disclosed before the start of the real-time market in a certain time period, and the predicted real-time electricity price of the real-time market in a certain time period in the uncertain scenario, the objective function of maximizing the real-time market revenue is determined. Specifically, large-scale new energy bases need to adjust the market decision-making of the real-time market in the th time period according to the day-ahead market clearing result and the rolling updated forecast to achieve the maximization of the revenue in the real-time market stage, and execute the market decision-making of the th time period in actual operation. The objective function of maximizing the real-time market revenue is shown in formula (27).

[0163] (27)

[0164] The right side of the equation represents the revenue of the large-scale new energy base in the real-time market in the th time period in scenario . Among them, is the winning bid electricity quantity of the real-time market in the th time period in scenario . is the electricity quantity that needs to be deviation-assessed by the real-time market in the th time period in scenario . is the nodal marginal price of the day-ahead market in the th time period disclosed before the start of the real-time market. is the predicted real-time electricity price of the real-time market in the th time period in scenario .

[0165] The winning bid electricity quantity and deviation assessment electricity quantity of the real-time market need to meet the following conditions:

[0166] (28)

[0167] (29)

[0168] Among them, and are respectively the in the scheduled sales electricity of thermal power units and new energy units in the real-time market during the and are respectively the in the scheduled sales electricity and scheduled purchase electricity of energy storage systems in the real-time market during the is the scheduled bid electricity of power producers in the day-ahead market at time

[0169] It should be noted that the operation constraints of the real-time market model are similar to those in the day-ahead market stage in Formulas (6)-(26), including the operation constraints of thermal power units, new energy units, and energy storage systems. The only difference is that the constraint period is reduced, which will not be elaborated here.

[0170] In one embodiment, in step S105, according to the short-term price forecast and the renewable energy power forecast value, based on the real-time market model, obtain the charge and discharge strategy of the energy storage device in the real-time large-scale new energy base market.

[0171] Optionally, for short-term price forecasting: collect the latest real-time electricity price data, including nodal marginal price (LMP) and other relevant market price information. Use machine learning algorithms (such as random forest, support vector machine, etc.) or physical models for short-term electricity price forecasting, with a forecasting period ranging from 5 minutes to 1 hour. Combine historical electricity price data and market trends to improve the accuracy and reliability of the forecasting. The output content is the predicted values of nodal marginal prices for the next few periods, which are used to guide real-time market operations.

[0172] For renewable energy power forecasting: collect the latest meteorological data and historical power generation data, including influencing factors such as wind speed and light intensity. Use time series analysis or deep learning models (such as LSTM) for renewable energy power generation forecasting, with a forecasting period ranging from 5 minutes to 1 hour. Combine meteorological forecasts and historical power generation data to improve the accuracy and reliability of the forecasting. The output content is the predicted values of wind power and photovoltaic power generation for the next few periods, which are used to guide real-time market operations.

[0173] Based on the latest real-time market prices and renewable energy power forecasts, the real-time market bidding strategy is dynamically adjusted according to the real-time market model to ensure maximum benefits under the latest market conditions. The output of the real-time market model includes the offers and power generation arrangements for each time period, ensuring maximum benefits under different market conditions.

[0174] The real-time market bidding strategy for large-scale new energy bases and the charge and discharge strategies of energy storage devices include:

[0175] When the electricity price is low, give priority to using low-cost electric energy to charge the energy storage device;

[0176] When the electricity price is high, give priority to discharging the energy storage device to participate in market bidding;

[0177] Ensure that the state of charge (SOC) of the energy storage device fluctuates within the preset range to avoid overcharging and over-discharging;

[0178] Consider the cycle life of the energy storage device and optimize the charge and discharge strategy to extend the service life of the device;

[0179] Ensure that when the energy storage device participates in frequency regulation ancillary services, it meets the requirements of frequency regulation capacity and frequency regulation mileage.

[0180] Step S105 improves the prediction accuracy of market prices and power generation changes through short-term price forecasting and renewable energy power forecasting methods as well as the real-time market model, enhancing the reliability of real-time decision-making. By adopting the rolling optimization algorithm, the real-time market model can flexibly respond to changes in market conditions, enhancing the adaptability and response speed of the system. Reasonably arranging the charge and discharge strategies of the energy storage device improves the resource utilization efficiency, ensures the system operates in the best state, and reduces the operating cost.

[0181] The following further explains this through specific examples.

[0182] To verify the effectiveness of the proposed model, a numerical example based on a combined wind-solar-thermal-energy storage power generation company participating in the two-stage spot market is provided, where the renewable energy power and market prices adopt real historical data.

[0183] First, basic data and parameter settings

[0184] The integrated power producer owns three thermal power units, twenty wind turbines, ten photovoltaic units, and one energy storage power station. The parameters of the thermal power units are shown in Table 1. The total installed capacity of wind power generation is 4 GW, and the total installed capacity of photovoltaic power generation is 8 GW. The parameters of the energy storage system are shown in Table 2.

[0185] Table 1: Parameters of thermal power units

[0186]

[0187] Table 2: Energy storage system parameters

[0188]

[0189] In this example, historical data of a wind farm in a certain area and historical nodal marginal price data of this area are used. The Monte Carlo sampling method is adopted to generate an uncertain day-ahead market decision scenario set of spot market electricity prices and renewable energy power generation outputs. Subsequently, using scenario reduction technology, 10 typical day-ahead market scenarios of nodal marginal electricity prices and wind power outputs are obtained.

[0190] In the real-time market stage, the prediction accuracy of nodal marginal prices and wind power outputs increases as the prediction time scale decreases. The Monte Carlo method is used to generate prediction schemes. The standard deviation of prediction errors for the prediction time range is set to 5%, and the standard deviation of prediction errors for the optimization time range is set to 10%.

[0191] Second, result analysis

[0192] Figure 2 It is a schematic diagram of the day-ahead market bidding curve of a large new energy base shown according to an exemplary embodiment, as Figure 2 shown. It can be seen that the model effectively formulates a power selling strategy for integrated energy producers to track electricity price fluctuations and renewable energy production. Among them, the sales volume is relatively high during peak electricity price periods and peak new energy power generation periods (20:00 - 23:00), while the sales volume is relatively low during off-peak electricity price periods (0:00 - 5:00) and low wind power output periods (12:00 - 14:00). The results show that the model can effectively optimize the power selling plan of the new energy base considering market electricity prices and the output of wind turbines.

[0193] According to the optimization results of the two-stage optimization model, Table 3 is the analysis of spot market revenue. As shown in Table 3, the maximum profit that the new energy base and its combined maximum profit power integrated producer can obtain in the spot market is 92.9197 million US dollars, and the day-ahead market profit and real-time market profit are 87.257 million US dollars and 5.6627 million US dollars respectively. The deviation penalty energy is only 4.16 GWh, indicating that the model effectively minimizes the deviation penalty energy, thereby increasing the profit.

[0194] Table 3: Analysis of spot market revenue

[0195]

[0196] Figure 3 It is a schematic diagram of the optimized operation result of a thermal power unit shown according to an exemplary embodiment, as Figure 3As shown, Unit 1 and Unit 2 stopped operating during low electricity price periods from 0:00 to 6:00 because the power generation costs of these units were relatively high. During periods of low electricity prices, the electricity sales revenue would be lower than the power generation costs. Therefore, shutting down these units during this period could avoid losses. Additionally, the shutdown time of thermal power units also corresponded to Figure 2 the low valley period of the declared curve in

[0197] Figure 4 It is a schematic diagram showing the optimized operation results of thermal power units according to an exemplary embodiment. As Figure 4 shown, the solid circles indicate that the units are in the operating state, and the blanks indicate that the units are under maintenance. It can be seen that each fan is maintained according to its specific maintenance requirements, and the maintenance plans among the units are staggered to ensure continuous power supply. In addition, the maintenance of wind turbines is mainly arranged during low electricity price periods such as 0:00 - 6:00 and 13:00 - 15:00. Specifically, a total of 4 units were under maintenance from 03:00 to 04:00 because the electricity price and the bidding volume were low during this period. Conducting maintenance at this time can minimize the impact on the power generation capacity and power transmission of the entire system to ensure the maximum profit of the large new energy base.

[0198] Under the framework characterized by the coexistence of a dual settlement mechanism and deviation penalties, considering the uncertainties of spot market prices and new energy generation power, a day-ahead market bidding strategy and a real-time market rolling self-scheduling strategy were formulated based on a two-stage stochastic optimization method, achieving the maximization of market revenue under the dual settlement mechanism and deviation assessment conditions. At the same time, considering the scheduling plan of thermal power units, the maintenance plan of renewable energy units, and the charge and discharge strategy of energy storage, the operation mode within the large base was optimized, enabling the large base to complete the market position curve task at a lower cost and realizing the maximization of profits under the market conditions of the large base.

[0199] In summary, the embodiment of the present application provides a scheduling method for a large-scale new energy base based on the uncertainty of the electricity spot market. First, it is necessary to collect historical data of the nodal marginal price of the spot market and the wind-solar power generation power, and generate a set of day-ahead market decision scenarios including the spot market electricity price and the output of new energy units through the Monte Carlo sampling method. Secondly, a two-stage stochastic optimization model for a large-scale new energy base participating in the day-ahead market considering the uncertainty of the spot market is established, and a real-time market participation model is established. Thirdly, in combination with the set of uncertain day-ahead market decision scenarios, calculate the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind turbines. Finally, according to the high-precision short-term price forecast and the renewable energy power forecast value, calculate the real-time market bidding strategy and the charge-discharge strategy of energy storage devices, so as to maximize the profit of the large-scale new energy base in the market environment. Solve the problem of low economic efficiency in the energy storage scheduling of large-scale new energy bases in the related technologies in the electricity spot market.

[0200] In a second aspect, the embodiment of the present application provides a scheduling system for a large-scale new energy base based on the uncertainty of the electricity spot market. Figure 5 It is a block diagram of a scheduling system for a large-scale new energy base based on the uncertainty of the electricity spot market shown according to an exemplary embodiment. As Figure 5 shown, the system includes a market decision scenario set module 510, a two-stage optimization model construction module 520, a day-ahead market bidding strategy and start-stop maintenance plan module 530, a real-time market model construction module 540, and a charge-discharge strategy module 550, where:

[0201] The market decision scenario set module 510 is used to generate multiple sets of market decision scenarios through an uncertainty simulation method according to the pre-acquired historical data of the electricity spot market. The market decision scenario set is used to simulate the price and power generation changes under different market conditions;

[0202] The two-stage optimization model construction module 520 is used to construct a two-stage optimization model for the day-ahead market based on the market decision scenario set. The two-stage optimization model takes maximizing the day-ahead market revenue as the objective function and follows the operating constraints of different types of generating units.

[0203] The day-ahead market bidding strategy and start-stop maintenance plan module 530 is used to obtain the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind turbines based on the two-stage optimization model;

[0204] The real-time market model construction module 540 is used to construct a real-time market model based on the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind turbines. The real-time market model is used to adjust the winning bid power and deviation settlement power of the real-time market according to the day-ahead market, and takes maximizing the real-time market revenue as the objective function;

[0205] The charge-discharge strategy module 550 is configured to obtain the charge-discharge strategy of the energy storage device in the real-time large-scale new energy base market based on the short-term price prediction and the renewable energy power prediction value and the real-time market model.

[0206] In summary, the large-scale new energy base dispatching system provided by the embodiment of the present application is based on the uncertainty of the electricity spot market. The system includes a market decision scenario set module 510, a two-stage optimization model construction module 520, a day-ahead market bidding strategy and start-stop maintenance plan module 530, a real-time market model construction module 540, and a charge-discharge strategy module 550, which solves the problem of low economic benefits in the energy storage dispatching of large-scale new energy bases in the related art. Specifically, first, historical data of the nodal marginal price of the spot market and the wind-solar power generation should be collected, and a day-ahead market decision scenario set including the spot market electricity price and the output of new energy units is generated by the Monte Carlo sampling method. Secondly, a two-stage stochastic optimization model for large-scale new energy bases participating in the day-ahead market considering the uncertainty of the spot market is established, and a real-time market model is established. Thirdly, the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind turbines are calculated in combination with the uncertain day-ahead market decision scenario set. Finally, according to the high-precision short-term price prediction and the renewable energy power prediction value, the real-time market bidding strategy and the charge-discharge strategy of the energy storage device are calculated to maximize the profit in the market environment of large-scale new energy bases. The problem of low economic benefits in the energy storage dispatching of large-scale new energy bases in the related art is solved.

[0207] It should be noted that the system for dispatching a large-scale new energy base based on the uncertainty of the electricity spot market provided in this embodiment is used to implement the above-mentioned implementation manners, and those that have been described will not be repeated. As used above, terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the above embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0208] In a third aspect, an embodiment of the present application provides an electronic device Figure 6 is a block diagram of an electronic device shown according to an exemplary embodiment. As Figure 6 shown, the electronic device may include a processor 61 and a memory 62 storing computer program instructions.

[0209] Specifically, the above-mentioned processor 61 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC for short), or one or more integrated circuits configured to implement the embodiments of the present application.

[0210] Among them, the memory 62 may include a mass storage for data or instructions. By way of example and not limitation, the memory 62 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In appropriate cases, the memory 62 may include removable or non-removable (or fixed) media. In appropriate cases, the memory 62 may be internal or external to the data processing device. In a particular embodiment, the memory 62 is a non-volatile memory. In a particular embodiment, the memory 62 includes a read-only memory (ROM) and a random access memory (RAM). In appropriate cases, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable read-only memory (EAROM), or a flash memory, or a combination of two or more of these. In appropriate cases, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended date out dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0211] The memory 62 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 61.

[0212] By reading and executing the computer program instructions stored in the memory 62, the processor 61 implements any one of the large-scale new energy base scheduling methods based on the uncertainty of the electricity spot market in the above embodiments.

[0213] In one embodiment, a device for scheduling a large-scale new energy base based on the uncertainty of the electricity spot market may further include a communication interface 63 and a bus 60. Among them, as Figure 6 shown, the processor 61, the memory 62, and the communication interface 63 are connected through the bus 60 and complete communication with each other.

[0214] The communication interface 63 is used to implement communication between each module, device, unit, and / or device in the embodiments of the present application. The communication port 63 can also implement data communication with other components, such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0215] Bus 60 includes hardware, software, or both, and couples components of an apparatus for scheduling a large-scale new energy base based on the uncertainty of the electricity spot market to each other. Bus 60 includes, but is not limited to, at least one of the following: Data Bus, Address Bus, Control Bus, Expansion Bus, Local Bus. By way of example and not limitation, Bus 60 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. In suitable cases, Bus 60 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0216] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a program stored thereon, which when executed by a processor, implements a method for scheduling a large-scale new energy base based on the uncertainty of the electricity spot market provided in the first aspect.

[0217] Among them, the readable storage medium may more specifically include, but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0218] In a possible implementation manner, the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps of implementing a large-scale new energy base scheduling method based on the uncertainty of the electricity spot market provided in the first aspect.

[0219] Among them, the program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, executed as an independent software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0220] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0221] The above embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A large-scale new energy base scheduling method based on the uncertainty of the power spot market, characterized in that: The method comprises: Based on the historical data of the electricity spot market acquired in advance, multiple market decision scenario sets are generated by uncertainty simulation, wherein the market decision scenario sets are used to simulate the changes in price and power generation under different market conditions; Based on the market decision scenario set, a two-stage optimization model of the day-ahead market is constructed, wherein the two-stage optimization model takes maximizing the day-ahead market revenue as an objective function and follows the operation constraints of different types of generator sets; Based on a two-stage optimization model, the day-ahead market bidding strategy, thermal power unit start-up and shutdown plan, and wind power unit maintenance plan are obtained; Based on the day-ahead market bidding strategy, the start-up and shutdown plan of the thermal power units and the maintenance plan of the wind power units, a real-time market model is constructed, wherein the real-time market model is used to adjust the winning bid power and the deviation assessment power in the real-time market according to the day-ahead market, with maximizing the real-time market revenue as the objective function; According to the short-term price forecast and the renewable energy power forecast value, based on the real-time market model, the charging and discharging strategy of the energy storage equipment in the real-time large-scale new energy base market is obtained; The method of adjusting the winning bid electricity and deviation assessment electricity in the real-time market according to the day-ahead market, with maximizing the real-time market revenue as the objective function, includes: The objective function of maximizing the real-time market revenue is determined by the winning bid electricity of the real-time market in a certain period of time in the uncertain scenario, the electricity that needs deviation assessment in the real-time market in a certain period of time in the uncertain scenario, the node marginal price of the day-ahead market in a certain period of time disclosed before the real-time market is launched, and the predicted real-time electricity price in a certain period of time in the real-time market in the uncertain scenario. The objective function of maximizing real-time market revenue is expressed by the following formula: In the formula, Represents the probability of an uncertain scenario , is the total number of uncertainty scenarios, is the total number of cycles, For the scene Real-time market in The winning bid quantity for the time period, For the scene Real-time market in The amount of electricity that needs to be evaluated for deviation in each period, is the deviation assessment coefficient, It is disclosed before the real-time market starts The node marginal price of the day-ahead market during the period, It's a scene Real-time market Predicted real-time electricity prices for time periods; Among them, the real-time market winning bid electricity and deviation assessment electricity must meet the following conditions: in, and Separate scenes Zhongwei Thermal power units in the real-time market during the period and new energy units The planned sales volume of electricity generated, and Respectively for scenes middle Energy storage system in real-time market The planned sales and purchase of electricity, Is the electricity producer in time The planned bid amount in the day-ahead market.

2. The method according to claim 1, characterized in that: Before generating a plurality of market decision scenario sets by uncertainty simulation according to the pre-acquired historical data of the power spot market, the method further comprises: Historical data of the electricity spot market is obtained, wherein the historical data includes node marginal electricity prices and renewable energy power generation power.

3. The method according to claim 1, characterized in that The method generates multiple market decision scenario sets by uncertainty simulation based on the pre-acquired historical data of the power spot market, including: Cleaning and normalizing the electricity spot market historical data; A plurality of market decision scenario sets are generated based on the processed historical data by a Monte Carlo sampling method, wherein each of the market decision scenario sets includes price and power generation changes under different market conditions.

4. The method according to claim 1, characterized in that: The first stage of the two-stage optimization model is used to determine the operating status, planned power and removal amount of each generator set; The second stage of the two-stage optimization model determines the power arrangement of each of the generator sets under different circumstances based on the operating status, planned power and removal amount of each generator set determined in the first stage.

5. The method according to claim 1, characterized in that Determine the day-ahead market revenue objective function through the probability of the uncertain scenario, the total number of uncertain scenarios, the total number of cycles, the node marginal electricity price in the day-ahead market at a certain moment in the uncertain scenario, the planned bid electricity of the power producer in the day-ahead market at a certain moment, and the operating cost in a certain period of time in the uncertain scenario; The day-ahead market return objective function is expressed by the following formula: In the formula, Represents the probability of an uncertain scenario , is the total number of uncertainty scenarios, is the total number of cycles, Indicates in the scenario In the time period The node marginal electricity price in the market today, Is the electricity producer in time The planned bidding amount in the day-ahead market, It's a scene Medium time period operating costs.

6. The method according to claim 5, characterized in that The electricity producers at the time Planned bidding volume in the day-ahead market It is expressed by the following formula: In the formula, , They are the thermal power units in the current market and renewable energy units In time Planned power generation, and They are Time storage system The discharge and charge capacity, , and They are the number of thermal power units, renewable energy units and energy storage systems respectively.

7. The method according to claim 5, characterized in that Said operating costs Including the start-up and shutdown costs, coal consumption costs and deviation penalty costs of thermal power units, among which the operating costs It is expressed by the following formula: In the formula, Representing thermal power units exist Start-up costs for the time period, is the power generation cost function, which is a quadratic function of the unit output power. For the scenario middle The amount of electricity that needs to be evaluated for deviation within the time period, is the deviation assessment coefficient; Among them, the scenario middle The amount of electricity that needs to be evaluated for deviation within the time period It is expressed by the following formula: In the formula, is the actual physical delivered power at the time of delivery, Indicates the deviation assessment margin.

8. The method according to claim 1, characterized in that The operating constraints of different types of generator sets are as follows: Comply with the constraints of the thermal power unit ramp rate: the rate of change of the output power of the thermal power unit in each time period does not exceed the maximum ramp rate of the thermal power unit; Comply with the constraints of wind turbine output: the power generation of the wind turbine fluctuates within the predicted value range and does not exceed the maximum power generation capacity of the wind turbine; Comply with the energy storage system charging and discharging efficiency constraint conditions: the energy loss of the energy storage system during the charging and discharging process is within a preset range.

9. A large-scale new energy base dispatching system based on the uncertainty of the power spot market, characterized in that: The system includes a market decision scenario set module, a two-stage optimization model building module, a day-ahead market bidding strategy and start-stop maintenance plan module, a real-time market model building module and a charge-discharge strategy module, wherein: The market decision scenario set module is used to generate multiple market decision scenario sets through uncertainty simulation based on pre-acquired historical data of the power spot market, and the market decision scenario sets are used to simulate price and power generation changes under different market conditions; The two-stage optimization model building module is used to build a two-stage optimization model for the day-ahead market based on the market decision scenario set, wherein the two-stage optimization model takes maximizing the day-ahead market revenue as an objective function and follows the operating constraints of different types of generator sets; The day-ahead market bidding strategy and start-stop maintenance plan module is used to obtain the day-ahead market bidding strategy, the start-stop plan of the thermal power unit and the maintenance plan of the wind power unit based on the two-stage optimization model; The module for constructing a real-time market model is used to construct a real-time market model based on the day-ahead market bidding strategy, the start-up and shutdown plan of the thermal power unit and the maintenance plan of the wind power unit. The real-time market model is used to adjust the winning bid electricity and the deviation assessment electricity in the real-time market according to the day-ahead market, with maximizing the real-time market revenue as the objective function; The charging and discharging strategy module is used to obtain the charging and discharging strategy of the energy storage equipment in the real-time large-scale new energy base market based on the real-time market model according to the short-term price forecast and the renewable energy power forecast value; The module for constructing a real-time market model is used to adjust the winning bid electricity and deviation assessment electricity in the real-time market according to the day-ahead market, taking maximizing the real-time market revenue as the objective function, to: The objective function of maximizing the real-time market revenue is determined by the winning bid electricity of the real-time market in a certain period of time in the uncertain scenario, the electricity that needs deviation assessment in the real-time market in a certain period of time in the uncertain scenario, the node marginal price of the day-ahead market in a certain period of time disclosed before the real-time market is launched, and the predicted real-time electricity price in a certain period of time in the real-time market in the uncertain scenario. The objective function of maximizing real-time market revenue is expressed by the following formula: In the formula, Represents the probability of an uncertain scenario , is the total number of uncertainty scenarios, is the total number of cycles, For the scene Real-time market in The winning bid quantity for the time period, For the scene Real-time market in The amount of electricity that needs to be evaluated for deviation in each period, is the deviation assessment coefficient, It is disclosed before the real-time market starts The node marginal price of the day-ahead market during the period, It's a scene Real-time market Predicted real-time electricity prices for time periods; Among them, the real-time market winning bid electricity and deviation assessment electricity must meet the following conditions: in, and Separate scenes Zhongwei Thermal power units in the real-time market during the period and new energy units The planned sales volume of electricity generated, and Respectively for scenes middle Energy storage system in real-time market The planned sales and purchase of electricity, Is the electricity producer in time The planned bid amount in the day-ahead market.

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