Large-scale new energy base scheduling method based on uncertainty of electric power spot market

Abstract

The invention relates to a large-scale new energy base scheduling method based on uncertainty of an electric power spot market, and the method comprises the steps: collecting historical data of spot market node marginal electricity price and wind-solar power generation power, and generating a day-ahead market decision scene set comprising the spot market electricity price and new energy unit output through a Monte Carlo sampling method; establishing a two-stage stochastic optimization model for the large new energy base to participate in the day-ahead market by considering the uncertainty of the spot market, and establishing a model for participating in the real-time market; calculating a day-ahead market bidding strategy, a thermal power generating unit start-stop plan and a wind turbine generator maintenance plan in combination with the uncertain day-ahead market decision scene set; and according to high-precision short-period price prediction and renewable energy power prediction values, a real-time market bidding strategy and a charging and discharging strategy of energy storage equipment are calculated, and profit maximization in a large-scale new energy base market environment is realized. The problem of low economic benefit during energy storage scheduling of a large-scale new energy base in an electric power spot market is solved.

Description

Technical Field

[0001] The present application relates to the technical field of power system dispatching, and in particular to a dispatching method for a large-scale new energy base 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 and solar energy, and combine the flexible adjustment capabilities of multiple resources such as thermal power and energy storage, which is an important form of future development of new energy. At the same time, the construction of the power market is constantly advancing, and the cost recovery and profit acquisition of large-scale new energy bases in the future need to be achieved in the market environment.

[0003] However, despite the significant advantages of renewable energy in terms of power generation costs, the volatility and uncertainty of their output limit their ability to participate in the spot market. Existing research mainly focuses on the day-ahead market bidding method under a single settlement mechanism or the wind bidding method considering expected electricity prices and deviation penalties. These methods focus on mitigating the impact of renewable energy uncertainty on bidding deviations and ignore the profit structure dynamics under the dual settlement mechanism. Therefore, there is an urgent need for energy storage scheduling in the power spot market based on large-scale renewable energy bases to solve the above problems. Summary of the invention

[0004] 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, so as to at least solve the problem of low economic benefits of large-scale new energy bases in related technologies when dispatching in the electricity spot market.

[0005] In a first aspect, an embodiment of the present application provides a large-scale new energy base scheduling method based on the uncertainty of the power spot market, the method comprising: 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. 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 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.

[0006] In one embodiment, before generating a plurality of market decision scenario sets by uncertainty simulation based on pre-acquired electricity spot market historical data, the method further includes: Historical data of the electricity spot market is obtained, wherein the historical data includes node marginal electricity prices and renewable energy power generation power.

[0007] In one embodiment, the method of generating multiple market decision scenario sets by uncertainty simulation based on the pre-acquired electricity spot market historical data includes: 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.

[0008] In one embodiment, 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.

[0009] In one embodiment, the day-ahead market revenue objective function is determined by 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 time period in the uncertain scenario; The day-ahead market return objective function is expressed by the following formula:

[0010] 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 time 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.

[0011] In one embodiment, the power producer Planned bidding volume in the day-ahead market It is expressed by the following formula:

[0012] 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 Momentary energy storage system The discharge and charge capacity, , and They are the number of thermal power units, renewable energy units and energy storage systems respectively.

[0013] In one embodiment, the operating cost 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:

[0014] 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:

[0015] In the formula, is the actual physical quantity delivered at the time of delivery, Indicates the deviation assessment margin.

[0016] In one embodiment, complying with the operating constraints of different types of generator sets includes: 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.

[0017] In one embodiment, adjusting 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, 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:

[0018] In the formula, 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, It is disclosed before the real-time market launch 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:

[0019] in, and They are The planned sales volume of electricity generated by thermal power units and new energy units in the real-time market during the period, and They are The planned sales and purchase electricity of the energy storage system in the real-time market during the period.

[0020] In the second aspect, the embodiment of the present application provides a large-scale new energy base dispatching system based on the uncertainty of the power 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 charging and discharging 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. 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.

[0021] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein 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.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, 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.

[0023] The large-scale new energy base scheduling method based on the uncertainty of the power spot market provided by the embodiment of the present application has at least the following technical effects: Based on the historical data of the electricity spot market acquired in advance, multiple market decision scenario sets are generated through uncertainty simulation. The market decision scenario sets are used to simulate the changes in prices and power generation 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 constraints of different types of generators. 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 turbine units are obtained. Based on the day-ahead market bidding strategy, the start-stop plan of thermal power units, and the maintenance plan of wind turbine units, a real-time market model is constructed. The real-time market model is used to adjust the winning bid power and 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. Based on the short-term price forecast and the renewable energy power forecast value, the charging and discharging strategy of the energy storage equipment in the real-time large-scale new energy base market is obtained based on the real-time market model. Maximization of profits in the market environment of large-scale new energy bases is achieved. The problem of low economic benefits of large-scale new energy bases in related technologies when energy storage is dispatched in the electricity spot market is solved.

[0024] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] 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 of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a flow chart of a large-scale new energy base scheduling method based on uncertainty in the electricity spot market according to an embodiment of the present application; Figure 2 is a schematic diagram of a day-ahead market bidding curve for a large-scale new energy base according to an exemplary embodiment; Figure 3 is a schematic diagram showing an optimized operation result of a thermal power unit according to an exemplary embodiment; Figure 4 is a schematic diagram showing an optimized operation result of a thermal power unit according to an exemplary embodiment; Figure 5 is a block diagram of a large-scale new energy base dispatching system based on power spot market uncertainty according to an exemplary embodiment; Figure 6 is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with 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 intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0027] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0028] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0029] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantity limitation, and may indicate the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; 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 also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the 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 ordering of the objects.

[0030] In this document, 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: Electricity spot market: refers to the place in the power system where buyers and sellers conduct short-term electricity transactions through market mechanisms. Unlike long-term contracts (such as futures contracts), transactions in the spot market are usually completed in a shorter period of time. The most common ones are the Day-Ahead Market (DAM) and the Real-Time Market (RTM). These markets allow power generation companies, power sales companies, large users and other market participants to buy and sell electricity based on the latest supply and demand conditions and price signals.

[0031] Dual settlement mechanism: that is, the time-sharing deviation 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-sharing deviation 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 use the price difference between the day-ahead market and the real-time market to improve profitability. The profit structure dynamics under this mechanism is compared with the single settlement rule, where profits will change under the influence of real-time market electricity prices and energy storage scheduling strategies. Ignoring this dynamic characteristic will result in the inability to obtain the maximum profit in market strategies and energy storage scheduling.

[0032] In a first aspect, the embodiments of the present application provide a large-scale new energy base scheduling method based on the uncertainty of the power spot market. Figure 1 It is a flow chart of large-scale renewable energy base dispatch based on the uncertainty of electricity spot market, such as Figure 1 As shown, a large-scale renewable energy base dispatching method based on the uncertainty of the electricity spot market includes: Step S101: Generate multiple market decision scenario sets through uncertainty simulation based on pre-acquired historical data of the power spot market. The market decision scenario sets are used to simulate price and power generation changes under different market conditions.

[0033] Step S102: Based on the market decision scenario set, a two-stage optimization model of 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 constraints of different types of generator sets.

[0034] Step S103: Based on the two-stage optimization model, the day-ahead market bidding strategy, the start-stop plan of the thermal power unit and the maintenance plan of the wind power unit are obtained.

[0035] Step S104: 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, a real-time market model is constructed. 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; Step S105: 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.

[0036] 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.

[0037] 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: Clean and normalize historical data of the electricity spot market; 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.

[0038] 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.

[0039] Step S101 ensures the quality and consistency of input data by cleaning and normalizing historical data, thereby improving the accuracy of uncertainty simulation. The generated market decision scenario set provides a reliable data foundation for subsequent optimization models, helping to formulate more accurate bidding strategies and scheduling plans, and ultimately achieving profit maximization.

[0040] In one embodiment, before generating a plurality of market decision scenario sets by uncertainty simulation based on pre-acquired electricity spot market historical data, the method further includes: Obtain historical data on the electricity spot market, including node marginal electricity prices and renewable energy generation power.

[0041] Optionally, historical electricity spot market data is obtained from official power trading institutions or third-party data providers. The data content includes but is not limited to node marginal power price (LMP) and renewable energy power generation (such as wind power, photovoltaic, etc.). Time range, at least covering the past year's data to ensure the adequacy and representativeness of the data. Geographical scope, covering all major nodes and power generation facilities in the target area to ensure the comprehensiveness of the data. Data type, node marginal power price (LMP), records the marginal power price of each node in each time period, reflecting the price fluctuations in the electricity market. Renewable energy power generation, records the actual power generation of wind power stations and photovoltaic power stations in each time period, reflecting the changes in new energy output.

[0042] By obtaining 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.

[0043] In one embodiment, step S102, based on the market decision scenario set, a two-stage optimization model of 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 constraints of different types of generator sets.

[0044] Optionally, 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 generator set under different circumstances based on the operating status, planned power and removal amount of each generator set determined in the first stage.

[0045] It is worth noting that the two-stage optimization model is a modeling method that takes uncertainty into account. The output of the first stage (bid quantity and price) will be used as the parameter of the second stage. However, since other parameters of the second stage (here is the electricity price) are uncertain, the scenario set can reflect this uncertainty. Therefore, the two-stage optimization model is a model built based on the scenario set.

[0046] The objective function of the day-ahead market revenue is determined by the probability of the uncertain scenarios, 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 scenarios, the planned bid electricity of the power producers in the day-ahead market at a certain moment, and the operating cost in a certain period of time in the uncertain scenarios.

[0047] Objective function: The objective function of the two-stage stochastic optimization model of the day-ahead market is to maximize the expected return of the day-ahead market, as shown in formula (1) to formula (5):

[0048] in Represents the probability of an uncertain scenario . is the total number of uncertainty scenarios. is the total number of cycles. The right side of the equation represents the In the time of the big base The specific value of revenue in the day-ahead market is determined by the difference between electricity sales revenue and operating costs. Indicates in the scenario in time The nodal marginal electricity price in the day-ahead market. Is the electricity producer in time The planned bid amount in the day-ahead market. It's a scene Medium time period operating costs.

[0049] in, , 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 capacity and charge capacity. , and They are the number of thermal power units, renewable energy units and energy storage systems respectively.

[0050] Running costs Including the start-up and shutdown costs, coal consumption costs, and deviation penalty costs of thermal power units:

[0051] in, 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 during the time period; is the deviation assessment coefficient.

[0052] in is the actual physical quantity delivered at the time of delivery, Indicates the deviation assessment margin.

[0053] In the stochastic optimization model constructed in this paper, large-scale new energy bases are middle The amount of electricity delivered during the time period is recorded as

[0054] in, Indicates the situation China Thermal Power Plant exist The amount of electricity generated during the period; Indicates the situation Renewable Energy exist The amount of electricity generated during the period; and Indicates the situation Energy Storage System exist The discharge and charge amounts during the time period.

[0055] The operating constraints of different types of generating units include constraints on the operation of thermal power units, new energy units and energy storage systems.

[0056] Comply with the constraints of the thermal power unit ramp rate: the rate of change of the thermal power unit output power in each period does not exceed the maximum ramp rate of the thermal power unit. The constraints of the operation of the thermal power unit are shown in equations (6)-(12).

[0057] Upper and lower limit constraints on thermal power unit output power:

[0058] in, and Respectively represent thermal power units The upper and lower limits of power output. Is a 0-1 variable, indicating the thermal power unit exist The running status of the time period, where Represents thermal power unit Is turned on.

[0059] Constraints on the upward climbing rate of thermal power units:

[0060] Constraints on the downward ramp rate of thermal power units:

[0061] in, It's the crew exist Power output during the period. and The units are Up and down power ramp speeds.

[0062] The transition condition constraints between different scenarios of thermal power units:

[0063] in, Indicates the unit adjustment capabilities.

[0064] Constraints on the minimum operating time and downtime of thermal power units:

[0065] in, and They represent the shortest closing time and the shortest opening time of the unit respectively.

[0066] Startup and shutdown cost constraints of thermal power units:

[0067] in, Indicates the cost of a single startup of the unit.

[0068] 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. The constraints of renewable energy unit operation are shown in (13)-(20).

[0069] Planned electricity sales constraints:

[0070] Indicates that the planned electricity sales in each period must not exceed the predicted maximum output of renewable energy Upper and lower limits on renewable energy generation:

[0071] in, and Represents renewable energy units The upper and lower limits of power output.

[0072] Maintenance-related constraints for renewable energy units:

[0073] in, is a 0-1 variable representing the renewable energy unit exist The running status of the time period, where Renewable energy units Is in the on state. is a 0-1 variable representing the renewable energy unit exist Maintenance decision for each period, where Renewable energy units exist Maintenance is required during this period. Represents renewable energy units maintenance needs. Renewable energy units maintenance interval. It is the maximum number of units that can be overhauled simultaneously.

[0074] 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 the preset range. Formulas (21)-(26) represent the constraints for the operation of the energy storage system.

[0075] in, Indicates in the scene Energy storage system exist The energy stored in the time period. and Energy storage system charging efficiency and discharging efficiency.

[0076] Charging power constraints of energy storage systems:

[0077] Discharge power constraints of energy storage systems:

[0078] in, and Energy storage system In the scene middle 0-1 state variable for period charging and discharging. , , and Energy storage system Charging and discharging power limitations.

[0079] The energy storage system cannot be charged and discharged at the same time:

[0080] Energy state constraints of energy storage system:

[0081] in, and Energy storage system The upper and lower capacity limits.

[0082] The final energy of the energy storage system on the operation day is consistent with the initial state constraint:

[0083] 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 a variety of possible market conditions. Strictly follow the operating constraints of various types of generator sets (such as the ramp rate of thermal power units, the maximum power generation of wind turbines, etc.) to ensure the feasibility and safety of the optimization results in actual operations. This not only reduces operational risks, but also improves the reliability of the system.

[0084] In one embodiment, step S103 obtains 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 a two-stage optimization model.

[0085] Optionally, the input of the two-stage optimization model can be: the probability of each scenario and the corresponding predicted value of each scenario, specifically including the node marginal electricity price in the day-ahead market; operating cost related parameters, cost model parameters such as the start-up and shutdown cost of thermal power units, coal consumption cost, and deviation assessment cost.

[0086] The output of the two-stage optimization model can be: Day-ahead market bidding strategies, including the bids and power generation arrangements of each power generation unit in different time periods, to ensure maximum revenue under different market conditions; Thermal power unit start and stop plan: Detailed provisions for the start and stop instructions of each thermal power unit at different time points to ensure that the start-up cost is minimized while meeting the day-ahead market bidding strategy; Wind turbine maintenance plan: Detailed provisions for the maintenance arrangements of each wind turbine at different time points to ensure that power generation efficiency is maximized without affecting the day-ahead market bidding strategy.

[0087] Step S103 effectively improves the overall economic benefits and reduces the operating costs by maximizing the day-ahead market revenue and minimizing the startup costs. Generates detailed day-ahead market bidding strategies, start-up and shutdown plans, and maintenance plans, providing scientific decision-making basis for large-scale new energy bases and enhancing market competitiveness. Reasonable arrangements for the start-up and shutdown of thermal power units and the maintenance of wind turbines improve resource utilization efficiency and ensure that the system operates in the best state.

[0088] In one embodiment, step S104, based on the day-ahead market bidding strategy, the start-stop plan of the thermal power unit and the maintenance plan of the wind power unit, a real-time market model is constructed, and 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. Specifically, it includes: Constructing a real-time market model is to establish an optimization model for the real-time market. One part of the parameters of the real-time market model is the calculation result of the two-stage optimization model in step S103 (the day-ahead market bidding strategy, the start-up and shutdown 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. The output is the real-time market bidding strategy for large-scale new energy bases and the charging and discharging strategy of energy storage equipment. The calculation of the revenue takes into account the winning electricity and the electricity that needs deviation assessment.

[0089] The objective function of maximizing the real-time market revenue is determined by the winning bid volume of the real-time market in a certain period of time in the uncertain scenario, the volume of the real-time market that needs deviation assessment 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 uncertain scenario. Specifically, large-scale new energy bases need to adjust the real-time market first according to their day-ahead market clearing results and rolling updated forecasts. market decisions in the time period to maximize the benefits of the real-time market stage and implement the The objective function of maximizing the real-time market return is shown in formula (27).

[0090] The right side of the equation represents the scenario Medium and large-scale new energy bases are The real-time market income during the period. For the scene Real-time market in The winning bid electricity amount in the time period. For the scene Real-time market in The amount of electricity that needs to be evaluated during the time period. It is disclosed before the real-time market launch The nodal marginal price in the day-ahead market during the period. It's a scene Real-time market Predicted real-time electricity prices for the time period.

[0091] The real-time market bidding quantity and deviation assessment quantity must meet the following conditions:

[0092] in, and They are The planned sales volume of electricity generated by thermal power units and new energy units in the real-time market during the period, and They are The planned sales and purchase electricity of the energy storage system in the real-time market during the period.

[0093] It is worth noting that the operating constraints of the real-time market model are similar to those of the day-ahead market stage in formulas (6)-(26), including the operating constraints of thermal power units, the operating constraints of new energy units, and the operating constraints of energy storage systems. The only difference is that the constraint period is reduced, which will not be repeated here.

[0094] In one embodiment, step S105, according to the short-term price forecast and the renewable energy power forecast value, based on the real-time market model, the real-time charging and discharging strategy of the energy storage equipment in the large-scale new energy base market is obtained.

[0095] Optionally, short-term price forecasting: Collect the latest real-time electricity price data, including node marginal power price (LMP) and other relevant market price information. Use machine learning algorithms (such as random forests, support vector machines, etc.) or physical models to make short-term electricity price forecasts, with forecast periods ranging from 5 minutes to 1 hour. Combine historical electricity price data and market trends to improve the accuracy and reliability of forecasts. The output is the predicted node marginal power price in the next few time periods, which is used to guide real-time market operations.

[0096] Renewable energy power forecast: 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) to forecast renewable energy power generation, with a forecast period ranging from 5 minutes to 1 hour. Combine meteorological forecasts and historical power generation data to improve the accuracy and reliability of forecasts. The output is the predicted values ​​of wind power and photovoltaic power generation in the next few time periods, which is used to guide real-time market operations.

[0097] According to the latest real-time market prices and renewable energy power forecasts, based on the real-time market model, the real-time market bidding strategy is dynamically adjusted to ensure maximum benefits under the latest market conditions. The output of the real-time market model includes the quotation and power generation arrangement for each period to ensure maximum benefits under different market conditions.

[0098] Real-time large-scale new energy base market bidding strategies and energy storage equipment charging and discharging strategies include: When the electricity price is low, low-priced electricity is used to charge energy storage devices first; When electricity prices are high, energy storage equipment is used to discharge electricity first and participate in market bidding; Ensure that the state of charge (SOC) of the energy storage device fluctuates within a preset range to avoid overcharging and discharging; Consider the cycle life of energy storage equipment and optimize the charging and discharging strategy to extend the service life of the equipment; Ensure that energy storage equipment meets the requirements of frequency regulation capacity and frequency regulation mileage when participating in frequency regulation auxiliary services.

[0099] Step S105 improves the prediction accuracy of market price and power generation changes and enhances the reliability of real-time decision-making through short-term price prediction, renewable energy power prediction method and real-time market model. The rolling optimization algorithm enables the real-time market model to flexibly respond to changes in market conditions, enhancing the adaptability and response speed of the system. The charging and discharging strategy of energy storage equipment is reasonably arranged to improve resource utilization efficiency, ensure that the system operates in the best state, and reduce operating costs.

[0100] Please further explain this with reference to specific examples below.

[0101] In order to verify the effectiveness of the proposed model, a numerical example based on the participation of wind, solar, thermal and energy storage combined power producers in a two-stage spot market is provided, in which the renewable energy power and market price adopt real historical data.

[0102] First, basic data and parameter settings The integrated power producer has three thermal power units, twenty wind power units, 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.

[0103] Table 1: Thermal power unit parameters

[0104] Table 2: Energy storage system parameters

[0105] This example uses the historical data of wind farms in a certain region and the historical node marginal price data of the region, and uses the Monte Carlo sampling method to generate a set of uncertain day-ahead market decision scenarios for spot market electricity prices and renewable energy power output. Subsequently, using scenario reduction technology, 10 typical day-ahead market scenarios for node marginal electricity prices and wind power output are obtained.

[0106] In the real-time market stage, the prediction accuracy of node marginal price and wind power output increases as the prediction time scale decreases. Monte Carlo method is used to generate prediction schemes, and the standard deviation of prediction error is set to 5% for the prediction time range and 10% for the optimization time range.

[0107] Second, results analysis Figure 2 is a schematic diagram of a day-ahead market bidding curve for a large-scale new energy base according to an exemplary embodiment. Figure 2 As shown in the figure, it can be seen that the model effectively develops a power sales strategy for integrated energy producers to track electricity price fluctuations and renewable energy production. Among them, sales are higher during peak electricity price periods and peak renewable energy generation periods (20:00-23:00), while sales are lower during non-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 sales plan of the renewable energy base while comprehensively considering the market electricity price and wind turbine output.

[0108] According to the optimization results of the two-stage optimization model, Table 3 is the spot market revenue analysis. As shown in Table 3, the maximum profit that the new energy base and its combination of integrated power producers 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 profits.

[0109] Table 3: Spot market revenue analysis

[0110] Figure 3 is a schematic diagram showing the optimized operation result of a thermal power unit according to an exemplary embodiment, such as Figure 3 As shown in the figure, Unit 1 and Unit 2 are shut down during the period of low electricity prices, such as 0:00-6:00. The reason is that the power generation costs of these units are high. During the period of low electricity prices, the power sales revenue will be lower than the power generation costs. Therefore, shutting down these units during this period can avoid losses. In addition, the downtime of thermal power units also corresponds to Figure 2 The risk of being penalized for over-generation is avoided during the trough period of the declared curve.

[0111] Figure 4 is a schematic diagram showing the optimized operation result of a thermal power unit according to an exemplary embodiment, such as Figure 4As shown, the solid circle indicates that the unit is in operation, and the blank indicates that the unit is under maintenance. It can be seen that each wind turbine is maintained according to its specific maintenance requirements, and the maintenance plans between the units are staggered to ensure continuous power supply. In addition, the maintenance of wind turbines is mainly scheduled during the time periods of 0:00- 6:00 and 13:00- 15:00 when the electricity price is low. Specifically, a total of 4 units were overhauled from 03:00 to 04:00 because the electricity price was low and the bid volume was low during this period. Maintenance at this time can minimize the impact on the power generation capacity and power delivery of the entire system to ensure the maximum profit of the large new energy base.

[0112] In a framework characterized by the coexistence of dual settlement mechanisms and deviation penalties, the uncertainty of spot market prices and renewable energy power generation is taken into account, and a day-ahead market bidding strategy and a real-time market rolling self-dispatching strategy are formulated based on a two-stage stochastic optimization method, which maximizes market returns under the dual settlement mechanism and deviation assessment conditions. At the same time, the dispatch plan of thermal power units, the maintenance plan of renewable energy units, and the charging and discharging strategy of energy storage are taken into account, and the internal operation mode of large bases is optimized, so that large bases can complete market position curve tasks at a lower cost and maximize profits under large base market conditions.

[0113] 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.

[0114] In a second aspect, an embodiment of the present application provides a large-scale new energy base dispatching system based on the uncertainty of the electricity spot market. Figure 5 FIG. 1 is a block diagram of a large-scale new energy base dispatching system based on the uncertainty of the power spot market according to an exemplary embodiment. Figure 5As shown, the system includes a market decision scenario set module 510, a two-stage optimization model building module 520, a day-ahead market bidding strategy and start-stop maintenance plan module 530, a real-time market model building module 540 and a charge-discharge strategy module 550, wherein: The market decision scenario set module 510 is used to generate multiple market decision scenario sets by uncertainty simulation based on the pre-acquired historical data of the power spot market, and the market decision scenario sets are used to simulate the changes in price and power generation under different market conditions; The two-stage optimization model building module 520 is used to build 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 generator sets. 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 the thermal power unit and the maintenance plan of the wind power unit based on the two-stage optimization model; A real-time market model building module 540 is used to build a real-time market model based on the day-ahead market bidding strategy, the start-stop 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 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; The charging and discharging strategy module 550 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 short-term price forecast and the renewable energy power forecast value and the real-time market model.

[0115] In summary, the present application embodiment provides a large-scale new energy base dispatching system based on the uncertainty of the power 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 charging and discharging strategy module 550, which solves the problem of low economic benefits of large-scale new energy bases in the power spot market energy storage dispatching in the related technology. Specifically, first, it is necessary to collect historical data of the marginal electricity price and wind and solar power generation power of the spot market node, and generate a day-ahead market decision scenario set including the spot market electricity price and the output of new energy units through the Monte Carlo sampling method; secondly, a two-stage random 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 real-time market model 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, according to the high-precision short-term price forecast and the renewable energy power forecast value, the real-time market bidding strategy and the charging and discharging strategy of the energy storage equipment are calculated to achieve profit maximization in the market environment of large-scale new energy bases. Solve the problem of low economic benefits of large-scale new energy bases in related technologies when energy storage is dispatched in the electricity spot market.

[0116] It should be noted that the system for dispatching a large-scale renewable energy base based on the uncertainty of the electricity spot market provided in this embodiment is used to implement the above-mentioned implementation mode, and the descriptions that have been made will not be repeated. As used above, the terms "module", "unit", "subunit", etc. can implement a combination of software and / or hardware for predetermined functions. Although the apparatus described in the above embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0117] In a third aspect, an embodiment of the present application provides an electronic device, Figure 6 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 6 As shown, the electronic device may include a processor 61 and a memory 62 storing computer program instructions.

[0118] Specifically, the processor 61 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0119] Among them, the memory 62 may include a large-capacity memory for data or instructions. For example, but 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 disk, a magneto-optical disk, 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 a removable or non-removable (or fixed) medium. In appropriate cases, the memory 62 may be inside or outside the data processing device. In a specific embodiment, the memory 62 is a non-volatile memory. In a specific embodiment, the memory 62 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0120] The memory 62 may 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 .

[0121] The processor 61 reads and executes computer program instructions stored in the memory 62 to implement any one of the large-scale new energy base scheduling methods based on the uncertainty of the electricity spot market in the above embodiments.

[0122] In one embodiment, a device for scheduling a large-scale renewable energy base based on uncertainty in the electricity spot market may further include a communication interface 63 and a bus 60. Figure 6 As shown, the processor 61, the memory 62, and the communication interface 63 are connected via a bus 60 and communicate with each other.

[0123] The communication interface 63 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. The communication port 63 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0124] The bus 60 includes hardware, software or both, and couples the components of a device for large-scale new energy base scheduling based on uncertainty in the power spot market to each other. The bus 60 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a 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. Where appropriate, bus 60 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0125] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a large-scale new energy base scheduling method based on the uncertainty of the electricity spot market provided in the first aspect is implemented.

[0126] The readable storage medium may 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.

[0127] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of a large-scale new energy base scheduling method based on the uncertainty of the electricity spot market provided in the first aspect.

[0128] Among them, the program code for executing the present invention can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.

[0129] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0130] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached 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.

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 Momentary energy 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 quantity delivered 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. The method according to claim 1, characterized in that: 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, 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, It is disclosed before the real-time market launch 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 They are The planned sales volume of electricity generated by thermal power units and new energy units in the real-time market during the period, and They are The planned sales and purchase electricity of the energy storage system in the real-time market during the period.

10. 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.

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