Day-ahead bidding method and system for spot transaction of power market in thermal power unit domain

Abstract

The application provides a day-ahead bidding method and system for spot transaction of a power market in a thermal power unit field, and the method comprises the following steps: determining power grid load prediction values, clearing price prediction values, new energy unit power generation load prediction values, hydropower unit power generation load prediction values, nuclear power unit power generation load prediction values, heating unit power generation load prediction values and debugging unit power generation load prediction values of each time point of a day to be bid; determining total power generation of bidding load of each thermal power unit at each time point of the day to be bid; determining power generation costs of each thermal power unit in the region at each time point of the day to be bid; and determining a bidding scheme at each time point of the day to be bid. The technical scheme provided by the application comprehensively considers the change trend of the regional market power demand, analyzes the power generation costs of all thermal power units, and provides high-quality day-ahead bidding strategies for spot transaction of the power market for the thermal power plants.

Description

Technical Field

[0001] This application relates to the field of energy utilization technology, and in particular to a day-ahead bidding method and system for spot trading in the electricity market within the thermal power unit region. Background Technology

[0002] The proportion of renewable energy sources such as wind and solar power is becoming increasingly significant. However, these renewable energy sources are characterized by intermittency and volatility, posing new challenges to the power grid. To promote the consumption of renewable energy, the electricity market is being developed through market-based mechanisms. In provinces that currently conduct spot market transactions, thermal power units submit their quantity and price quotations on the day-ahead basis and participate in the day-ahead clearing process.

[0003] The fundamental purpose of thermal power plants participating in competitive bidding in the electricity market is to ensure reasonable profits for the plants, especially in the event of a significant increase in fuel costs. Currently, the strategies employed by thermal power plants in spot market bidding often involve using basic statistical data indicators for quantity and price quotations. For example, they might use the historical average of the market clearing price as their quote, or the price of the electricity volume cleared the previous day as their current quote.

[0004] Pricing methods based on basic statistics lack flexibility. For example, clearing data for a sunny day (N-1) is not applicable to pricing for a rainy day (N). This is because when the weather changes significantly, the load on renewable energy generation in the region will change significantly, resulting in significant differences in the load on thermal power units. Summary of the Invention

[0005] This application provides a day-ahead bidding method and system for spot trading in the regional electricity market for thermal power units, in order to at least address the technical problem that the load of renewable energy generation in the region will change significantly when the weather changes significantly, resulting in significant differences in the load of thermal power units.

[0006] The first aspect of this application proposes a day-ahead bidding method for spot trading in the electricity market within a thermal power unit region, the method comprising:

[0007] Obtain the trading day type of the day to be bid, the data set corresponding to the trading day type, and the coal consumption per kilowatt-hour of each thermal power unit in the region at each time on the day to be bid. The data set includes the grid load, clearing price, power generation data of new energy units, hydropower units, nuclear power units, heating units, and commissioning units in the region at each time node of each day in the historical period.

[0008] Based on the data set corresponding to the transaction day type, determine the predicted values ​​of power grid load, clearing price, new energy unit power generation load, hydropower unit power generation load, nuclear power unit power generation load, heating unit power generation load, and commissioning unit power generation load for each time point on the day to be bid.

[0009] The total bidding load of each thermal power unit at each time point on the day to be bid is determined based on the predicted power grid load, the predicted power generation load of the new energy unit, the predicted power generation load of the hydropower unit, the predicted power generation load of the nuclear power unit, the predicted power generation load of the heating unit, and the predicted power generation load of the commissioning unit.

[0010] The power generation cost of each thermal power unit in the region at each time on the day before bidding is determined based on the coal consumption per kilowatt-hour of each thermal power unit in the region.

[0011] The bidding scheme for each time period on the day to be bid is determined based on the predicted clearing price, the total electricity volume of the bidding load, and the generation cost.

[0012] Preferably, the transaction day types include: weekday transaction types, weekend transaction types, and holiday transaction types;

[0013] The power generation data of the new energy units include: power generation data of wind turbine units and power generation data of photovoltaic units.

[0014] Preferably, the load forecast values ​​of the new energy units include: the load forecast values ​​of wind turbine units and the load forecast values ​​of photovoltaic units;

[0015] The wind turbine load forecast is determined based on the wind turbine power generation data, numerical weather forecast data for the pending bidding day, and the installed capacity of the wind turbine in the data set corresponding to the trading day type.

[0016] The photovoltaic unit load forecast value is determined based on the photovoltaic unit power generation data, numerical weather forecast data for the pending bidding day, and the installed capacity of the photovoltaic unit in the data set corresponding to the trading day type.

[0017] Preferably, the formula for calculating the total bidding load of each thermal power unit at each time point on the bidding day is as follows:

[0018] P 竞,j =P 总,j -P 新,j -P 水,j -P 核,j -P 供热,j -P 调试,j

[0019] In the formula, P 竞,jP represents the total bidding load of each thermal power unit at time j on the day of bidding. 总,j Let P be the predicted power grid load at time j on the day to be auctioned. 新,j P represents the predicted power generation load of the new energy generating units at time j on the day of the bidding. 水,j P represents the predicted power generation load of the hydropower units at time j on the day of the bidding. 核,j P represents the predicted power generation load of the nuclear power unit at time j on the day of the bidding. 供热,j P represents the predicted power generation load of the heating unit at time j on the day of the bidding. 调试,j The predicted load value of the commissioning unit at time j on the day to be auctioned.

[0020] Preferably, determining the power generation cost of each thermal power unit in the region at each time on the bidding day based on the coal consumption per kilowatt-hour of each thermal power unit in the region at each time on the bidding day includes:

[0021] Obtain the percentage of coal fuel in the power generation cost of each thermal power unit in the region at each time on the day before bidding;

[0022] The power generation cost of each thermal power unit at each time point is obtained by dividing the coal consumption per kilowatt-hour of each thermal power unit at each time point on the corresponding bidding day by the percentage of coal fuel consumption in the power generation cost of the thermal power unit at each time point on the bidding day.

[0023] Preferably, determining the bidding scheme for each time period on the bidding day based on the clearing price forecast, the total bidding load, and the generation cost includes:

[0024] If the power generation cost of the i-th thermal power unit in the region at time j on the bidding day is greater than or equal to the clearing price prediction at time j on the bidding day, then the minimum technical output of the thermal power unit will be used as the declared on-grid electricity volume of the i-th thermal power unit in the region at time j on the bidding day, and 1.1 times the power generation cost of the i-th thermal power unit in the region at time j on the bidding day will be used as the declared electricity price of the i-th thermal power unit in the region at time j on the bidding day.

[0025] If the power generation cost of the i-th thermal power unit in the region at time j on the bidding day is less than the clearing price prediction at time j on the bidding day, then the on-grid power ratio coefficient corresponding to the thermal power unit is determined, and the declared on-grid electricity volume and declared electricity price of the i-th thermal power unit in the region at time j on the bidding day are determined based on the on-grid power ratio coefficient.

[0026] Where i∈[1~I], I is the total number of wind turbines in the region, j∈[1~T], and T is the total number of moments on the day to be bid.

[0027] Furthermore, determining the on-grid power ratio coefficient corresponding to the thermal power unit, and determining the declared on-grid electricity volume and declared electricity price of the i-th thermal power unit in the region at time j on the day to be auctioned based on the on-grid power ratio coefficient, includes:

[0028] Obtain the average value of the coal consumption per kilowatt-hour of thermal power units in the region, and determine the on-grid power ratio coefficient corresponding to the i-th thermal power unit at time j on the bidding day based on the average value and the coal consumption per kilowatt-hour of the i-th thermal power unit at time j on the bidding day.

[0029] The declared on-grid power of the i-th thermal power unit at time j on the day to be bid is determined based on the on-grid power ratio coefficient of the i-th thermal power unit at time j on the day to be bid and the total power of the bidding load;

[0030] The bid price for the i-th thermal power unit at time j on the day to be bid is determined based on the on-grid power ratio coefficient, power generation cost, and clearing price forecast for the i-th thermal power unit at time j on the day to be bid.

[0031] Furthermore, the calculation formula for the declared on-grid electricity volume of the i-th thermal power unit at time j on the day to be auctioned is as follows:

[0032]

[0033] The formula for calculating the bid price of the i-th thermal power unit at time j on the day to be auctioned is as follows:

[0034] C s,i,j =C y,j,i ×K i,j (C j,c -C y,j,i )

[0035] In the formula, P i,j K represents the declared on-grid electricity volume of the i-th thermal power unit at time j on the day to be auctioned. i,j Let P be the on-grid power ratio coefficient corresponding to the i-th thermal power unit at time j on the day to be bid. im Let P be the rated power of the i-th thermal power unit. zIm P represents the total rated power of all thermal power units in the region. 竞,j C represents the total bidding load of each thermal power unit at time j on the day of bidding. s,i,j Let C be the bid price for the i-th thermal power unit at time j on the day to be auctioned. j,c Let C be the predicted clearing price of the i-th thermal power unit at time j on the day before bidding. y,j,i Let be the power generation cost of the i-th thermal power unit at time j on the day before bidding.

[0036] A second aspect of this application provides a day-ahead bidding system for spot trading in the electricity market within a thermal power unit region, the system comprising:

[0037] The acquisition module is used to acquire the transaction day type to which the bidding day belongs, the data set corresponding to the transaction day type, and the fuel coal consumption per kilowatt-hour of each thermal power unit in the region at each time on the bidding day. The data set includes the grid load, clearing price, power generation data of new energy units, hydropower units, nuclear power units, heating units, and commissioning units in the region at each time node of each day in the historical period.

[0038] The first determining module is used to determine the predicted values ​​of power grid load, clearing price, new energy unit power generation load, hydropower unit power generation load, nuclear power unit power generation load, heating unit power generation load, and commissioning unit power generation load at each time point on the day to be bid, based on the data set corresponding to the transaction day type.

[0039] The second determining module is used to determine the total bidding load of each thermal power unit at each time point on the day to be bid based on the grid load forecast, the new energy unit power generation load forecast, the hydropower unit power generation load forecast, the nuclear power unit power generation load forecast, the heating unit power generation load forecast, and the commissioning unit power generation load forecast.

[0040] The third determining module is used to determine the power generation cost of each thermal power unit in the region at each time on the bidding day based on the coal consumption per kilowatt-hour of each thermal power unit in the region at each time on the bidding day.

[0041] The fourth determining module is used to determine the bidding scheme for each time period on the day to be bid based on the clearing price forecast, the total power of the bidding load, and the power generation cost.

[0042] Preferably, the transaction day types include: weekday transaction types, weekend transaction types, and holiday transaction types;

[0043] The power generation data of the new energy units include: power generation data of wind turbine units and power generation data of photovoltaic units.

[0044] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0045] This application proposes a day-ahead bidding method and system for spot electricity trading within a thermal power unit region. The method includes: determining the grid load forecast, clearing price forecast, renewable energy unit power generation load forecast, hydropower unit power generation load forecast, nuclear power unit power generation load forecast, heating unit power generation load forecast, and commissioning unit power generation load forecast for each time point on the day to be bid; determining the total bidding load of each thermal power unit at each time point on the day to be bid; determining the power generation cost of each thermal power unit in the region at each time point on the day to be bid; and determining the bidding scheme for each time point on the day to be bid. The technical solution proposed in this application comprehensively considers the changing trend of regional market electricity demand and provides thermal power plants with a high-quality day-ahead bidding strategy for spot electricity trading by analyzing the power generation costs of all thermal power units.

[0046] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0047] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0048] Figure 1 This is a flowchart illustrating a day-ahead bidding method for spot electricity trading within a thermal power unit region, according to an embodiment of this application.

[0049] Figure 2 This is a schematic diagram illustrating the calculation of wind turbine load forecast values ​​according to an embodiment of this application;

[0050] Figure 3 This is a schematic diagram illustrating the calculation of photovoltaic unit load forecast values ​​according to an embodiment of this application;

[0051] Figure 4 This is a schematic diagram illustrating the calculation of a scaling factor according to an embodiment of this application;

[0052] Figure 5 This is a structural diagram of a day-ahead bidding system for spot trading in a thermal power unit region's electricity market, provided according to an embodiment of this application.

[0053] Figure 6 This is a structural diagram of a third determining module provided according to an embodiment of this application. Detailed Implementation

[0054] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0055] This application proposes a day-ahead bidding method and system for spot electricity trading within the region of thermal power units. The method includes: determining the grid load forecast, clearing price forecast, renewable energy unit power generation load forecast, hydropower unit power generation load forecast, nuclear power unit power generation load forecast, heating unit power generation load forecast, and commissioning unit power generation load forecast for each time point on the bidding day; determining the total bidding load of each thermal power unit at each time point on the bidding day; determining the power generation cost of each thermal power unit in the region at each time point on the bidding day; and determining the bidding scheme for each time point on the bidding day. The technical solution proposed in this application comprehensively considers the changing trends of regional market electricity demand and provides thermal power plants with a high-quality day-ahead bidding strategy for spot electricity trading by analyzing the power generation costs of all thermal power units.

[0056] The following describes, with reference to the accompanying drawings, a day-ahead bidding method and system for spot trading in the power market within the thermal power unit domain, according to embodiments of this application.

[0057] Example 1

[0058] Figure 1 This is a flowchart illustrating a day-ahead bidding method for spot electricity trading within a thermal power unit region, according to an embodiment of this application. Figure 1 As shown, the method includes:

[0059] Step 1: Obtain the trading day type of the day to be bid, the data set corresponding to the trading day type, and the fuel coal consumption per kilowatt-hour of each thermal power unit in the region at each time on the day to be bid. The data set includes the grid load, clearing price, power generation data of new energy units, hydropower units, nuclear power units, heating units, and commissioning units in the region at each time node of each day in the historical period.

[0060] It should be noted that the transaction day types include: weekday transaction types, weekend transaction types, and holiday transaction types;

[0061] The power generation data of the new energy units include: power generation data of wind turbine units and power generation data of photovoltaic units.

[0062] Step 2: Determine the predicted values ​​of power grid load, clearing price, new energy unit power generation load, hydropower unit power generation load, nuclear power unit power generation load, heating unit power generation load, and commissioning unit power generation load for each time point on the day to be bid, based on the data set corresponding to the transaction day type.

[0063] It should be noted that the power grid load forecast values ​​for each time period on the day to be auctioned are obtained from historical power grid load data obtained from the trading market. Based on the data, a long short-time neural network model is used to statistically obtain the power grid load forecast values ​​for each time period on the day to be auctioned.

[0064] The predicted clearing price for each moment on the day to be auctioned is obtained from historical clearing price data obtained from the trading market. Based on this data, a long-short-term neural network model is used to statistically analyze the predicted clearing price for each moment on the day to be auctioned.

[0065] The predicted hydropower unit power generation load at each time point on the bidding day is obtained by matching historical data, which can be the hydropower unit power generation load at each time point on similar days last year or the average value of the data of the same type in the past three days on the bidding day.

[0066] The predicted nuclear power unit load at each time on the day to be auctioned: Since nuclear power output is basically stable, the prediction is based on historical data matching, which can be the nuclear power unit load at each time on similar days last year or the average of the data of the same type in the past three days on the day to be auctioned.

[0067] The predicted power generation load of the heating units at each time point on the bidding day: Considering that the minimum technical output of the heating units does not participate in spot market trading, this part of the load needs to be calculated and listed separately, and the minimum technical output P of the heating units during the heating season needs to be statistically analyzed. 供热,j During the non-heating season, this value is zero; during the heating season, it is calculated as an average of the data from the previous year's heating season.

[0068] The predicted power generation load of the commissioning units at each time point on the bidding day: Considering that the minimum technical output of the commissioning units does not participate in the spot market transaction, this part of the load needs to be calculated and listed separately. Based on the information published by the power grid, the minimum technical output P of the commissioning units in the province on the trading day is obtained. 调试,j .

[0069] In this embodiment, the predicted load values ​​of the new energy units include: predicted load values ​​of wind turbine units and predicted load values ​​of photovoltaic units;

[0070] The wind turbine load forecast is determined based on the wind turbine power generation data, numerical weather forecast data for the pending bidding day, and the installed capacity of the wind turbine in the data set corresponding to the trading day type.

[0071] The photovoltaic unit load forecast value is determined based on the photovoltaic unit power generation data, numerical weather forecast data for the pending bidding day, and the installed capacity of the photovoltaic unit in the data set corresponding to the trading day type.

[0072] It should be noted that the renewable energy power generation load P at node j is analyzed and predicted based on the previous year's renewable energy power generation data for the entire region, wind power installed capacity, solar power installed capacity (i.e., photovoltaic unit installed capacity), and weather forecasts for the day before the bidding. 新,j The new energy power generation load is the sum of wind power load and photovoltaic load.

[0073] Furthermore, the acquisition of the wind turbine load forecast includes:

[0074] The power generation capacity of a wind turbine can be expressed as: In the formula C p Let η represent the wind turbine power coefficient, ρ be the turbine efficiency, R be the wind turbine radius, and U be the wind speed. Theoretical calculations show a positive correlation between wind turbine power generation and wind speed. Dividing the day into 96 nodes (15 minutes per node), a 24-hour medium-to-long-term time series forecast of wind power generation (i.e., wind turbine load) is required to achieve day-ahead bidding analysis for these 96 nodes. Therefore, for wind turbines within a region, the relationship between wind speed / climate and wind turbine capacity at different times can be analyzed by constructing a time series forecasting model based on a fusion of autoregression and LSTM algorithms, such as... Figure 2 As shown, the functional relationship between the wind turbine output power and the wind power installed capacity and climate is obtained.

[0075] To improve the accuracy of wind power generation prediction, this embodiment uses wind speed, wind direction, temperature, humidity, and air pressure from numerical weather prediction (NWP) data packets, as well as historical data of wind turbine generators, to construct a time series training sample set. Average Standard deviation Calculations are performed to form historical data features; the K-Means fast clustering machine learning algorithm is used to form k pattern states of wind turbine units, completing the core clustering m. x|s and clustering datasets The calculation; through the Gaussian kernel covariance matrix C x|s The calculation completes the model training and constructs the Gaussian distribution. The lightweight EM iterative algorithm is used to perform iterative calculation of Θ. in Forecast value of wind power generation Calculations are performed to achieve wind power generation prediction using an autoregressive model based on NWP data. * Training sample set through time series analysis Autoregressive model for wind power generation prediction time series sample set x * Finally, the Long Short-Term Memory (LSTM) algorithm model was used to predict the 24-hour medium-to-long-term time series of wind power generation. 风 This allows us to obtain wind turbine load forecasts that meet the required accuracy.

[0076] Furthermore, the acquisition of the photovoltaic unit load forecast includes:

[0077] For photovoltaic (PV) power generation, based on the theory of electron migration activity in PV cells, the conversion efficiency of PV cells is closely related to external environmental factors, with two important factors being light intensity and ambient temperature. Therefore, for solar power generators within a region, the relationship between light intensity, ambient temperature, and the capacity of the installed solar power units at different times can be analyzed. To improve the accuracy of solar power generation prediction, this implementation example... Figure 3 The algorithm model shown constructs a time series sample set using weather conditions (sunny, cloudy, rainy, snowy, fog), temperature, humidity, total radiation across the entire spectrum, and total visible light radiation from NWP data packets, as well as historical data from solar power generators. Average Standard deviation Calculations are performed to form historical data features; using the K-Means fast clustering machine learning algorithm, k pattern states of the solar generator set are formed, completing the core clustering m. x|s and clustering datasets The calculation; through the Gaussian kernel covariance matrix C x|s The calculation completes the model training and constructs the Gaussian distribution. The lightweight EM iterative algorithm is used to perform iterative calculation of Θ. ,

[0078] in Predicted value of solar power generation Calculations are performed to achieve an autoregressive model for predicting solar power generation based on NWP data. * Training sample set through time series analysis Autoregressive model for predicting solar power generation time series sample set x * Finally, the Long Short-Term Memory (LSTM) algorithm model is used to predict the 24-hour medium-to-long-term time series of solar power generation, thereby obtaining the photovoltaic unit load prediction value that meets the prediction accuracy requirements.

[0079] Step 3: Determine the total bidding load of each thermal power unit at each time point on the day to be bid based on the predicted grid load, the predicted power generation load of the new energy unit, the predicted power generation load of the hydropower unit, the predicted power generation load of the nuclear power unit, the predicted power generation load of the heating unit, and the predicted power generation load of the commissioning unit.

[0080] The formula for calculating the total bidding load of each thermal power unit at each time point on the bidding day is as follows:

[0081] P 竞,j =P 总,j -P 新,j -P 水,j -P 核,j -P 供热,j -P 调试,j

[0082] In the formula, P 竞,j P represents the total bidding load of each thermal power unit at time j on the day of bidding. 总,j Let P be the predicted power grid load at time j on the day to be auctioned. 新,j P represents the predicted power generation load of the new energy generating units at time j on the day of the bidding. 水,j P represents the predicted power generation load of the hydropower units at time j on the day of the bidding. 核,j P represents the predicted power generation load of the nuclear power unit at time j on the day of the bidding. 供热,j P represents the predicted power generation load of the heating unit at time j on the day of the bidding. 调试,j The predicted load value of the commissioning unit at time j on the day to be auctioned.

[0083] Step 4: Determine the power generation cost of each thermal power unit in the region at each time on the bidding day based on the coal consumption per kilowatt-hour of each thermal power unit in the region.

[0084] In this embodiment of the disclosure, step 4 specifically includes:

[0085] Step 4-1: Obtain the percentage of coal fuel in the power generation cost of each thermal power unit in the region at each time on the day before bidding;

[0086] Step 4-2: Divide the coal consumption per kilowatt-hour of each thermal power unit at each time point by the percentage of coal fuel in the power generation cost of the thermal power unit at each time point on the bidding day to obtain the power generation cost of the thermal power unit at each time point on the bidding day.

[0087] It should be noted that the power generation cost of a thermal power unit consists of fuel costs, depreciation costs, financial costs, employee salaries, material costs, and repair costs, among which fuel costs account for approximately 69%, so the percentage mentioned can be 69%.

[0088] Step 5: Determine the bidding scheme for each time period on the day to be bid based on the clearing price forecast, the total bidding load, and the generation cost.

[0089] In this embodiment of the disclosure, step 5 specifically includes:

[0090] If the power generation cost of the i-th thermal power unit in the region at time j on the bidding day is greater than or equal to the clearing price prediction at time j on the bidding day, then the minimum technical output of the thermal power unit will be used as the declared on-grid electricity volume of the i-th thermal power unit in the region at time j on the bidding day, and 1.1 times the power generation cost of the i-th thermal power unit in the region at time j on the bidding day will be used as the declared electricity price of the i-th thermal power unit in the region at time j on the bidding day.

[0091] If the power generation cost of the i-th thermal power unit in the region at time j on the bidding day is less than the clearing price prediction at time j on the bidding day, then the on-grid power ratio coefficient corresponding to the thermal power unit is determined, and the declared on-grid electricity volume and declared electricity price of the i-th thermal power unit in the region at time j on the bidding day are determined based on the on-grid power ratio coefficient.

[0092] Where i∈[1~I], I is the total number of wind turbines in the region, j∈[1~T], and T is the total number of moments on the day to be bid.

[0093] Furthermore, determining the on-grid power ratio coefficient corresponding to the thermal power unit, and determining the declared on-grid electricity volume and declared electricity price of the i-th thermal power unit in the region at time j on the day to be auctioned based on the on-grid power ratio coefficient, includes:

[0094] Obtain the average value of the coal consumption per kilowatt-hour of thermal power units in the region, and determine the on-grid power ratio coefficient corresponding to the i-th thermal power unit at time j on the bidding day based on the average value and the coal consumption per kilowatt-hour of the i-th thermal power unit at time j on the bidding day.

[0095] The declared on-grid power of the i-th thermal power unit at time j on the day to be bid is determined based on the on-grid power ratio coefficient of the i-th thermal power unit at time j on the day to be bid and the total power of the bidding load;

[0096] The bid price for the i-th thermal power unit at time j on the day to be bid is determined based on the on-grid power ratio coefficient, power generation cost, and clearing price forecast for the i-th thermal power unit at time j on the day to be bid.

[0097] The formula for calculating the declared on-grid electricity volume of the i-th thermal power unit at time j on the day to be auctioned is as follows:

[0098]

[0099] The formula for calculating the bid price of the i-th thermal power unit at time j on the day to be auctioned is as follows:

[0100] C s,i,j =C y,j,i ×K i,j (C j,c -C y,j,i )

[0101] In the formula, P i,j K represents the declared on-grid electricity volume of the i-th thermal power unit at time j on the day to be auctioned. i,j Let P be the on-grid power ratio coefficient corresponding to the i-th thermal power unit at time j on the day to be bid. im Let P be the rated power of the i-th thermal power unit. zIm P represents the total rated power of all thermal power units in the region. 竞,j C represents the total bidding load of each thermal power unit at time j on the day of bidding. s,i,j Let C be the bid price for the i-th thermal power unit at time j on the day to be auctioned. j,c Let C be the predicted clearing price of the i-th thermal power unit at time j on the day before bidding. y,j,i Let be the power generation cost of the i-th thermal power unit at time j on the day before bidding.

[0102] It should be noted that, as Figure 4 As shown, the on-grid power ratio coefficient is calculated using a quasi-standard normal distribution. Specifically, based on data published by the power grid, the distribution pattern of coal consumption levels for each thermal power unit within the region can be obtained. Then, a Z-transform is performed to obtain a standard normal distribution. Based on the normal distribution function, the coefficient is calculated to be lower than the current coal consumption of the thermal power unit by a factor of B. 当前 The corresponding probability G 当前 K = 2 - 2G 当前 The region mentioned therein can be a province.

[0103] In summary, the day-ahead bidding method for spot electricity trading within the region proposed in this embodiment takes into account the changing trends of regional market electricity demand and provides thermal power plants with a high-quality and high-precision day-ahead bidding strategy for spot electricity trading by analyzing the generation costs of all thermal power units.

[0104] Example 2

[0105] Figure 5 This is a structural diagram of a day-ahead bidding system for spot electricity trading within a thermal power unit region, according to an embodiment of this application. Figure 5 As shown, the system includes:

[0106] The acquisition module 100 is used to acquire the transaction day type to which the bidding day belongs, the data set corresponding to the transaction day type, and the fuel coal consumption per kilowatt-hour of each thermal power unit in the region at each time on the bidding day. The data set includes the grid load, clearing price, power generation data of new energy units, hydropower units, nuclear power units, heating units, and commissioning units in the region at each time node of each day in the historical period.

[0107] The first determining module 200 is used to determine the predicted values ​​of power grid load, clearing price, new energy unit power generation load, hydropower unit power generation load, nuclear power unit power generation load, heating unit power generation load, and commissioning unit power generation load at each time of the bidding day based on the data set corresponding to the trading day type.

[0108] The second determining module 300 is used to determine the total bidding load of each thermal power unit at each time on the day to be bid based on the grid load forecast, the new energy unit power generation load forecast, the hydropower unit power generation load forecast, the nuclear power unit power generation load forecast, the heating unit power generation load forecast and the commissioning unit power generation load forecast.

[0109] The third determining module 400 is used to determine the power generation cost of each thermal power unit in the region at each time on the bidding day based on the coal consumption per kilowatt-hour of each thermal power unit in the region at each time on the bidding day.

[0110] The fourth determining module 500 is used to determine the bidding scheme for each time period on the day to be bid based on the clearing price forecast, the total power of the bidding load, and the power generation cost.

[0111] In this embodiment of the disclosure, the transaction day type includes: weekday transaction type, weekend transaction type, and holiday transaction type;

[0112] The power generation data of the new energy units include: power generation data of wind turbine units and power generation data of photovoltaic units.

[0113] In this embodiment of the disclosure, the predicted load values ​​of the new energy units include: predicted load values ​​of wind turbine units and predicted load values ​​of photovoltaic units;

[0114] The wind turbine load forecast is determined based on the wind turbine power generation data, numerical weather forecast data for the pending bidding day, and the installed capacity of the wind turbine in the data set corresponding to the trading day type.

[0115] The photovoltaic unit load forecast value is determined based on the photovoltaic unit power generation data, numerical weather forecast data for the pending bidding day, and the installed capacity of the photovoltaic unit in the data set corresponding to the trading day type.

[0116] The formula for calculating the total bidding load of each thermal power unit at each time point on the bidding day is as follows:

[0117] P 竞,j =P 总,j -P 新,j -P 水,j -P 核,j -P 供热,j -P 调试,j

[0118] In the formula, P 竞,j P represents the total bidding load of each thermal power unit at time j on the day of bidding. 总,j Let P be the predicted power grid load at time j on the day to be auctioned. 新,j P represents the predicted power generation load of the new energy generating units at time j on the day of the bidding. 水,j P represents the predicted power generation load of the hydropower units at time j on the day of the bidding. 核,j P represents the predicted power generation load of the nuclear power unit at time j on the day of the bidding. 供热,j P represents the predicted power generation load of the heating unit at time j on the day of the bidding. 调试,j The predicted load value of the commissioning unit at time j on the day to be auctioned.

[0119] In the embodiments disclosed herein, such as Figure 6 As shown, the third determining module 400 includes:

[0120] The acquisition unit 401 is used to acquire the percentage of coal fuel in the power generation cost of each thermal power unit in the region at each time on the day to be bid.

[0121] The first determining unit 402 is used to divide the coal consumption per kilowatt-hour of each thermal power unit at each time by the percentage of coal fuel to the power generation cost of the thermal power unit at each time on the corresponding bidding day, so as to obtain the power generation cost of the thermal power unit at each time on the bidding day.

[0122] In this embodiment of the disclosure, the fourth determining module 500 is specifically used for:

[0123] If the power generation cost of the i-th thermal power unit in the region at time j on the bidding day is greater than or equal to the clearing price prediction at time j on the bidding day, then the minimum technical output of the thermal power unit will be used as the declared on-grid electricity volume of the i-th thermal power unit in the region at time j on the bidding day, and 1.1 times the power generation cost of the i-th thermal power unit in the region at time j on the bidding day will be used as the declared electricity price of the i-th thermal power unit in the region at time j on the bidding day.

[0124] If the power generation cost of the i-th thermal power unit in the region at time j on the bidding day is less than the clearing price prediction at time j on the bidding day, then the on-grid power ratio coefficient corresponding to the thermal power unit is determined, and the declared on-grid electricity volume and declared electricity price of the i-th thermal power unit in the region at time j on the bidding day are determined based on the on-grid power ratio coefficient.

[0125] Where i∈[1~I], I is the total number of wind turbines in the region, j∈[1~T], and T is the total number of moments on the day to be bid.

[0126] It should be noted that the step of determining the on-grid power ratio coefficient corresponding to the thermal power unit, and determining the declared on-grid electricity volume and declared electricity price of the i-th thermal power unit in the region at time j on the day to be auctioned based on the on-grid power ratio coefficient, includes:

[0127] Obtain the average value of the coal consumption per kilowatt-hour of thermal power units in the region, and determine the on-grid power ratio coefficient corresponding to the i-th thermal power unit at time j on the bidding day based on the average value and the coal consumption per kilowatt-hour of the i-th thermal power unit at time j on the bidding day.

[0128] The declared on-grid power of the i-th thermal power unit at time j on the day to be bid is determined based on the on-grid power ratio coefficient of the i-th thermal power unit at time j on the day to be bid and the total power of the bidding load;

[0129] The bid price for the i-th thermal power unit at time j on the day to be bid is determined based on the on-grid power ratio coefficient, power generation cost, and clearing price forecast for the i-th thermal power unit at time j on the day to be bid.

[0130] Furthermore, the calculation formula for the declared on-grid electricity volume of the i-th thermal power unit at time j on the day to be auctioned is as follows:

[0131]

[0132] The formula for calculating the bid price of the i-th thermal power unit at time j on the day to be auctioned is as follows:

[0133] C s,i,j =C y,j,i ×K i,j (C j,c -C y,j,i )

[0134] In the formula, P i,j K represents the declared on-grid electricity volume of the i-th thermal power unit at time j on the day to be auctioned. i,j Let P be the on-grid power ratio coefficient corresponding to the i-th thermal power unit at time j on the day to be bid. imLet P be the rated power of the i-th thermal power unit. zIm P represents the total rated power of all thermal power units in the region. 竞,j C represents the total bidding load of each thermal power unit at time j on the day of bidding. s,i,j Let C be the bid price for the i-th thermal power unit at time j on the day to be auctioned. j,c Let C be the predicted clearing price of the i-th thermal power unit at time j on the day before bidding. y,j,i Let be the power generation cost of the i-th thermal power unit at time j on the day before bidding.

[0135] In summary, the day-ahead bidding system for spot electricity trading within a thermal power unit region proposed in this embodiment takes into account the changing trends of regional market electricity demand and provides thermal power plants with high-quality and high-precision day-ahead bidding strategies for spot electricity trading by analyzing the generation costs of all thermal power units.

[0136] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0137] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0138] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A day-ahead bidding method for spot electricity trading within a thermal power unit region, characterized in that, The method includes: Obtain the trading day type of the day to be bid, the data set corresponding to the trading day type, and the coal consumption per kilowatt-hour of each thermal power unit in the region at each time on the day to be bid. The data set includes the grid load, clearing price, power generation data of new energy units, hydropower units, nuclear power units, heating units, and commissioning units in the region at each time node of each day in the historical period. Based on the data set corresponding to the transaction day type, determine the predicted values ​​of power grid load, clearing price, new energy unit power generation load, hydropower unit power generation load, nuclear power unit power generation load, heating unit power generation load, and commissioning unit power generation load for each time point on the day to be bid. The total bidding load of each thermal power unit at each time point on the day to be bid is determined based on the predicted power grid load, the predicted power generation load of the new energy unit, the predicted power generation load of the hydropower unit, the predicted power generation load of the nuclear power unit, the predicted power generation load of the heating unit, and the predicted power generation load of the commissioning unit. The power generation cost of each thermal power unit in the region at each time on the bidding day is determined based on the coal consumption per kilowatt-hour of each thermal power unit in the region at each time on the bidding day, including: obtaining the percentage of coal fuel consumption of each thermal power unit in the region at each time on the bidding day to the power generation cost of the thermal power unit. Divide the coal consumption per kilowatt-hour of each thermal power unit at each time point by the percentage of coal fuel to the power generation cost of the thermal power unit at each time point on the bidding day to obtain the power generation cost of the thermal power unit at each time point on the bidding day. The bidding scheme for each time period on the day to be bid is determined based on the predicted clearing price, the total bidding load, and the generation cost, including: If the power generation cost of the i-th thermal power unit in the region at time j on the bidding day is greater than or equal to the clearing price prediction at time j on the bidding day, then the minimum technical output of the thermal power unit will be used as the declared on-grid electricity volume of the i-th thermal power unit in the region at time j on the bidding day, and 1.1 times the power generation cost of the i-th thermal power unit in the region at time j on the bidding day will be used as the declared electricity price of the i-th thermal power unit in the region at time j on the bidding day. If the power generation cost of the i-th thermal power unit in the region at time j on the bidding day is less than the clearing price prediction at time j on the bidding day, then the on-grid power ratio coefficient corresponding to the thermal power unit is determined, and the declared on-grid electricity volume and declared electricity price of the i-th thermal power unit in the region at time j on the bidding day are determined based on the on-grid power ratio coefficient. in, , This represents the total number of wind turbines in the region. , This represents the total number of moments during the bidding period.

2. The method as described in claim 1, characterized in that, The transaction day types include: weekday transaction types, weekend transaction types, and holiday transaction types; The power generation data of the new energy units include: power generation data of wind turbine units and power generation data of photovoltaic units.

3. The method as described in claim 1, characterized in that, The predicted load values ​​for new energy units include: predicted load values ​​for wind turbine units and predicted load values ​​for photovoltaic units; The wind turbine load forecast is determined based on the wind turbine power generation data, numerical weather forecast data for the pending bidding day, and the installed capacity of the wind turbine in the data set corresponding to the trading day type. The photovoltaic unit load forecast value is determined based on the photovoltaic unit power generation data, numerical weather forecast data for the pending bidding day, and the installed capacity of the photovoltaic unit in the data set corresponding to the trading day type.

4. The method as described in claim 1, characterized in that, The formula for calculating the total bidding load of each thermal power unit at each time point on the bidding day is as follows: In the formula, The total power load of each thermal power unit at time j on the day to be auctioned is the total power consumption for bidding. The predicted power grid load at time j on the day of bidding. This represents the predicted power generation load of the new energy generating units at time j on the day of the bidding. This represents the predicted power generation load of the hydropower units at time j on the day of the bidding. This represents the predicted power generation load of the nuclear power unit at time j on the day of the bidding. The predicted power generation load of the heating unit at time j on the day to be auctioned. The predicted load value of the commissioning unit at time j on the day to be auctioned.

5. The method as described in claim 1, characterized in that, The step of determining the on-grid power ratio coefficient corresponding to the thermal power unit, and determining the declared on-grid electricity volume and declared electricity price of the i-th thermal power unit in the region at time j on the day to be auctioned based on the on-grid power ratio coefficient, includes: Obtain the average value of the coal consumption per kilowatt-hour of thermal power units in the region, and determine the on-grid power ratio coefficient corresponding to the i-th thermal power unit at time j on the bidding day based on the average value and the coal consumption per kilowatt-hour of the i-th thermal power unit at time j on the bidding day. The declared on-grid power of the i-th thermal power unit at time j on the day to be bid is determined based on the on-grid power ratio coefficient of the i-th thermal power unit at time j on the day to be bid and the total power of the bidding load; The bid price for the i-th thermal power unit at time j on the day to be bid is determined based on the on-grid power ratio coefficient, power generation cost, and clearing price forecast for the i-th thermal power unit at time j on the day to be bid.

6. The method as described in claim 5, characterized in that, The formula for calculating the declared on-grid electricity volume of the i-th thermal power unit at time j on the day to be auctioned is as follows: The formula for calculating the bid price of the i-th thermal power unit at time j on the day to be auctioned is as follows: In the formula, This refers to the declared on-grid electricity volume of the i-th thermal power unit at time j on the day before bidding. Let be the on-grid power ratio coefficient for the i-th thermal power unit at time j on the day to be auctioned. Let i be the rated power of the i-th thermal power unit. This represents the total rated power of all thermal power units within the region. The total power load of each thermal power unit at time j on the day to be auctioned is the total power consumption for bidding. Let $i$ be the bid price for the i-th thermal power unit at time $j$ on the day to be auctioned. Let be the predicted clearing price for the i-th thermal power unit at time j on the day before bidding. Let be the power generation cost of the i-th thermal power unit at time j on the day to be auctioned.

7. A day-ahead bidding system for spot electricity trading within a thermal power unit region, characterized in that, The system includes: The acquisition module is used to acquire the transaction day type to which the bidding day belongs, the data set corresponding to the transaction day type, and the fuel coal consumption per kilowatt-hour of each thermal power unit in the region at each time on the bidding day. The data set includes the grid load, clearing price, power generation data of new energy units, hydropower units, nuclear power units, heating units, and commissioning units in the region at each time node of each day in the historical period. The first determining module is used to determine the predicted values ​​of power grid load, clearing price, new energy unit power generation load, hydropower unit power generation load, nuclear power unit power generation load, heating unit power generation load, and commissioning unit power generation load at each time point on the day to be bid, based on the data set corresponding to the transaction day type. The second determining module is used to determine the total bidding load of each thermal power unit at each time point on the day to be bid based on the grid load forecast, the new energy unit power generation load forecast, the hydropower unit power generation load forecast, the nuclear power unit power generation load forecast, the heating unit power generation load forecast, and the commissioning unit power generation load forecast. The third determining module is used to determine the power generation cost of each thermal power unit in the region at each time on the bidding day based on the coal consumption per kilowatt-hour of each thermal power unit in the region at each time on the bidding day, including: obtaining the percentage of coal fuel consumption of each thermal power unit in the region at each time on the bidding day to the power generation cost of the thermal power unit. Divide the coal consumption per kilowatt-hour of each thermal power unit at each time point by the percentage of coal fuel to the power generation cost of the thermal power unit at each time point on the bidding day to obtain the power generation cost of the thermal power unit at each time point on the bidding day. The fourth determining module is used to determine the bidding scheme for each time period on the bidding day based on the predicted clearing price, the total bidding load, and the generation cost, including: If the power generation cost of the i-th thermal power unit in the region at time j on the bidding day is greater than or equal to the clearing price prediction at time j on the bidding day, then the minimum technical output of the thermal power unit will be used as the declared on-grid electricity volume of the i-th thermal power unit in the region at time j on the bidding day, and 1.1 times the power generation cost of the i-th thermal power unit in the region at time j on the bidding day will be used as the declared electricity price of the i-th thermal power unit in the region at time j on the bidding day. If the power generation cost of the i-th thermal power unit in the region at time j on the bidding day is less than the clearing price prediction at time j on the bidding day, then the on-grid power ratio coefficient corresponding to the thermal power unit is determined, and the declared on-grid electricity volume and declared electricity price of the i-th thermal power unit in the region at time j on the bidding day are determined based on the on-grid power ratio coefficient. in, , This represents the total number of wind turbines in the region. , This represents the total number of moments during the bidding period.

8. The system as described in claim 7, characterized in that, The transaction day types include: weekday transaction types, weekend transaction types, and holiday transaction types; The power generation data of the new energy units include: power generation data of wind turbine units and power generation data of photovoltaic units.

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