Multi-element electricity market and carbon market collaborative thermal power operation method and system

Abstract

The invention discloses a multi-element electricity market and carbon market collaborative thermal power operation method and system, and the method comprises the steps: predicting the average electricity price, the average auxiliary service price and the average carbon price of each month in the future one year, and taking the average electricity price, the average auxiliary service price and the average carbon price as the actual electricity price, the actual auxiliary service price and the actual carbon price of the first month in the future; predicting plan execution data of each month in the remaining months in the next year according to the actual execution data of the previous months; the average electricity price, the average auxiliary service price and the average carbon price of each day corresponding to each month in the next year are respectively used as the actual electricity price, the actual auxiliary service price and the actual carbon price of the first day of each month in the next year; and predicting plan execution data of each day in the remaining dates in the future one month according to the actual execution data of the previous dates. By using the scheme of the application, the latest data can be used for prediction, and the fluctuation of the electricity price and the carbon price can be quickly responded, so that the real-time performance of prediction and execution is improved.

Description

Technical Field

[0001] The present application generally relates to the field of power system planning technology. More specifically, the present application relates to a method and system for operating a thermal power plant in a coordinated manner with multiple power markets and carbon markets. Background Art

[0002] Driven by the goal of transforming the energy structure toward a low-carbon future, the construction of a new power system is accelerating, pushing the coordinated development of the power and carbon markets into a new stage. Thermal power companies currently face a complex operating environment characterized by multi-dimensional market coupling: on the power trading side, they must participate in a diversified trading system encompassing medium- and long-term markets, spot markets, ancillary services markets, and capacity markets. On the carbon constraint side, they must assume carbon quota compliance responsibilities and participate in carbon market transactions. The large-scale development of renewable energy power generation has led to profound changes in the competitive landscape of the power market. Thermal power units face the triple pressures of declining on-grid electricity prices, reduced annual utilization hours, and strengthened carbon emission assessments. Traditional single-market-oriented operating strategies are no longer able to meet the needs of maximizing overall benefits.

[0003] In existing technologies, thermal power companies participating in medium- and long-term electricity markets generally use a quotation method based on historical data statistics, achieving contractual power allocation through fixed curve decomposition. However, this method lacks the ability to predict and model market supply and demand changes and electricity price fluctuations. In spot market bidding, most companies still use a quotation strategy based on marginal generation costs, failing to internalize carbon market costs into the electricity trading decision-making system. Participation strategies in ancillary service markets and capacity markets often adopt isolated optimization models, and the synergies between markets are not fully explored.

[0004] However, existing technologies make independent market trading decisions, lacking a global optimization model for a multi-market coupling mechanism. Furthermore, electricity price forecasts and carbon price estimates are not integrated into strategy formulation, resulting in insufficient market risk hedging capabilities. Furthermore, cost accounting fails to integrate power generation and carbon costs, making it difficult to accurately assess comprehensive benefits. Furthermore, existing strategy optimization methods often employ static analysis frameworks, which are unable to adapt to the dynamic evolution of multi-market parameters. These factors severely constrain the competitiveness of thermal power companies in this new market environment.

[0005] In view of this, there is an urgent need to provide a thermal power operation plan that coordinates the diversified electricity market and the carbon market, so as to optimize the amount of electricity that thermal power companies participate in the diversified electricity market and the carbon market, achieve the optimal overall strategy for thermal power companies to participate in the diversified electricity market and the carbon market, and improve economic benefits. Summary of the Invention

[0006] In order to at least solve one or more of the technical problems mentioned above, this application proposes a thermal power operation plan that coordinates multiple electricity markets and carbon markets in multiple aspects.

[0007] In the first aspect, the present application provides a thermal power operation method for the coordination of multiple electricity markets and carbon markets, including: predicting the average electricity price, average ancillary service price and average carbon price for each month in the next year based on historical annual electricity price data and historical annual carbon price data, and using them as the actual electricity price, actual ancillary service price and actual carbon price for the first month in the future respectively; predicting the planned execution data for each month in the remaining months of the next year based on the actual execution data of the previous months in the remaining months of the next year based on the objective function of maximizing the annual total profit of participating in the multiple electricity markets and carbon markets and the annual constraints of participating in the multiple electricity markets and carbon markets; predicting the average electricity price, average ancillary service price and average carbon price for each day corresponding to each month in the next year based on historical monthly electricity price data and historical monthly carbon price data, and using them as the actual electricity price, actual ancillary service price and actual carbon price for the first day of each month in the future respectively; predicting the planned execution data for each day in the remaining days of the next month based on the actual execution data of the previous days in the remaining days of the next month based on the objective function of maximizing the monthly total profit of participating in the multiple electricity markets and carbon markets and the monthly constraints of participating in the multiple electricity markets and carbon markets.

[0008] In some embodiments, the method also includes: predicting the average electricity price, average ancillary service price and average carbon price for each quotation period every day in the next year based on historical daily electricity price data and historical daily carbon price data, and using them as the actual electricity price, actual ancillary service price and actual carbon price for the first quotation period of the next day respectively; based on the objective function of maximizing the daily total profit of participating in the diversified electricity market and carbon market and the daily constraints of participating in the diversified electricity market and carbon market, predicting the planned execution data for each remaining quotation period in the remaining quotation period in the future day based on the actual execution data of the previous quotation period in the remaining quotation period in the future day.

[0009] In some embodiments, the objective function expression for maximizing the total annual profit is:

[0010] Among them, FM is the maximized annual total profit, is the profit of the i-th month in the medium and long-term market, is the profit of the spot market in month i, is the profit of the ancillary service market in month i, is the income / cost of the carbon market in month i, is the revenue of the capacity market in month i; P future For medium and long-term electricity prices, is the electricity consumption in the medium and long term market in month i, CM future is the corresponding cost of electricity in the i-th month in the medium and long-term market; AM averageThe average electricity price for each month, is the electricity consumption in the spot market in month i, CM spot is the corresponding cost of electricity in the i-th month in the spot market; AM auxiliary is the average ancillary service price per month, is the electricity consumption of the ancillary service market in month i, CM auxiliary is the cost of electricity in the ancillary service market in month i; is the carbon quota for month i, is the carbon emissions in month i, AM carbon is the monthly average carbon price; is the available capacity in month i, P capacity The electricity price is compensated for capacity.

[0011] In some embodiments, the annual constraint includes a power generation capacity constraint, a medium- and long-term transaction minimum ratio constraint, and an annual carbon compliance constraint; the power generation capacity constraint is:

[0012] in, is the electricity consumption in the medium and long-term market in month i, is the electricity consumption in the spot market in month i, is the electricity consumption of the ancillary service market in month i, QM 总 is the annual total power generation capacity; the minimum proportion constraint conditions for medium- and long-term transactions are: Wherein, k is the set coefficient; the annual carbon compliance constraint conditions are: in, is the carbon quota for month i, is the amount of carbon allowances purchased / sold in month i, is the carbon emissions in month i.

[0013] In some embodiments, the carbon quota amount of the i-th month and the carbon emissions amount of the i-th month are obtained based on the total power generation of the i-th month; in the process of obtaining the carbon quota amount of the i-th month, the following steps are performed: constructing a functional relationship between the monthly total power generation and the monthly carbon quota amount based on the historical monthly total power generation and the corresponding historical monthly carbon quota amount; substituting the total power generation of the i-th month into the functional relationship between the monthly total power generation and the monthly carbon quota amount to obtain the carbon quota amount of the i-th month; in the process of obtaining the carbon emissions amount of the i-th month, the following steps are performed: constructing a functional relationship between the monthly total power generation and the monthly carbon emissions based on the historical monthly total power generation and the corresponding historical carbon emissions; substituting the total power generation of the i-th month into the functional relationship between the monthly total power generation and the monthly carbon emissions to obtain the carbon emissions amount of the i-th month; wherein, the expression of the total power generation amount of the i-th month is: QM i总 is the total power generation in month i, is the electricity consumption in the medium and long-term market in month i, is the electricity consumption in the spot market in month i, is the electricity consumption in the ancillary service market in month i.

[0014] In some embodiments, the objective function for maximizing the monthly total profit is:

[0015] Among them, TD is the total number of days in a month, FD is the maximized total monthly profit, is the profit on day t in the medium and long-term market, is the profit of the spot market on day t, is the profit of the ancillary service market on day t, is the income / cost of the carbon market on day t, is the revenue of the capacity market on day t; P future For medium and long-term electricity prices, is the electricity consumption on the tth day in the medium and long-term market, CD future is the corresponding cost of electricity on day t in the medium and long-term market; AD average The average electricity price for each month, is the electricity quantity in the spot market on day t, CD spot is the corresponding cost of electricity on the tth day in the spot market; AD auxiliary is the average ancillary service price per month, is the electricity consumption of the ancillary service market on day t, CD auxiliary is the cost of electricity in the ancillary service market on day t; is the carbon quota on day t, is the carbon emission on day t, AD carbon is the monthly average carbon price; is the available capacity on day t, P capacity The electricity price is compensated for capacity.

[0016] In some embodiments, the monthly constraint condition includes a monthly electricity constraint condition and a monthly carbon compliance constraint condition; the monthly electricity constraint condition is: Among them, TD is the total number of days in a month, is the electricity consumption on the tth day corresponding to the i-th month in the medium and long-term market, is the electricity consumption in the medium and long-term market in month i, is the electricity consumption on the tth day corresponding to the i-th month in the spot market, is the electricity consumption in the spot market in month i, is the electricity consumption on the tth day corresponding to the i-th month in the ancillary service market, is the electricity consumption of the ancillary service market in month i; the monthly carbon compliance constraint is: in, is the carbon quota on the tth day corresponding to the i-th month, is the amount of carbon allowances purchased / sold on the tth day corresponding to the i-th month, is the carbon emissions on the tth day corresponding to the i-th month, The amount of carbon allowances purchased / sold in month i.

[0017] In some embodiments, the medium- and long-term electricity prices are obtained by the following steps: respectively determining the parameters on the thermal power enterprise side and the parameters on the user side; determining the quotation range, reported quantity range on the thermal power enterprise side and the quotation range on the user side based on the parameters on the thermal power enterprise side and the parameters on the user side; randomly pairing the thermal power enterprise side and the user side, and negotiating and adjusting the quotations of the successfully paired thermal power enterprise side and the user side; taking the smaller value of the reported quantity on the thermal power enterprise side and the reported quantity on the user side after successful negotiation and adjustment as the actual transaction quantity; judging whether there is any surplus electricity after obtaining the actual transaction quantity; in response to the existence of surplus electricity, returning to the step of randomly pairing the thermal power enterprise side and the user side until there is no surplus electricity; in response to the absence of surplus electricity, taking the electricity price corresponding to the actual transaction quantity as the medium- and long-term electricity price.

[0018] In some embodiments, the execution data includes medium- and long-term market electricity, spot market electricity, ancillary service market electricity, and purchased / sold carbon quotas.

[0019] In the second aspect, the present application provides a set of thermal power operation system coordinated by multiple electricity markets and carbon markets, which adopts the thermal power operation method coordinated by multiple electricity markets and carbon markets as described in any embodiment of the first aspect to perform thermal power operation coordinated by multiple electricity markets and carbon markets, and the system includes: a first prediction module, which is used to predict the average electricity price, average ancillary service price and average carbon price of each month in the next year based on historical annual electricity price data and historical annual carbon price data, and use them as the actual electricity price, actual ancillary service price and actual carbon price of the first month in the future respectively; a monthly coordination module of multiple electricity markets and carbon markets, which is used to predict the average electricity price, average ancillary service price and average carbon price of each month in the future based on the objective function of maximizing the annual total profit of participating in the multiple electricity markets and carbon markets and the annual constraints of participating in the multiple electricity markets and carbon markets. The remaining months of the year predict the planned execution data of each month in the remaining months of the next year based on the actual execution data of the previous months; the second prediction module is used to predict the average daily electricity price, average ancillary service price and average carbon price corresponding to each month in the next year based on historical monthly electricity price data and historical monthly carbon price data, and use them as the actual electricity price, actual ancillary service price and actual carbon price on the first day of each month in the future respectively; the diversified electricity market and carbon market daily collaborative method module is used to predict the planned execution data of each day in the remaining days of the next month based on the actual execution data of the previous days based on the maximization objective function of the monthly total profit of participating in the diversified electricity market and carbon market and the monthly constraints of participating in the diversified electricity market and carbon market.

[0020] Through the thermal power operation plan coordinated by the multiple electricity market and the carbon market as provided above, the embodiment of the present application predicts monthly data based on historical annual electricity price data and historical annual carbon price data, and predicts daily data based on historical monthly electricity price data and historical monthly carbon price data, which can capture short-term price fluctuations and improve resource scheduling accuracy. At the same time, by using the forecast value of the first month / first day of each year / month as the actual data, and subsequently dynamically adjusting the remaining cycle plan based on this, it is possible to use the latest data for prediction and quickly respond to sudden fluctuations in electricity prices and carbon prices, thereby improving the real-time nature of prediction and execution. In addition, by establishing a total profit maximization objective function between the multiple electricity market and the carbon market, the revenue of the multiple electricity market and the carbon cost are balanced, avoiding the suboptimal solution of a single market decision.

[0021] Furthermore, in some embodiments, by refining forecasting and optimization across multiple daily quotation periods, it is possible to adapt to the sharp intraday price fluctuations in the electricity market and avoid the loss of revenue caused by coarse-grained forecasting. Furthermore, power generation plans can be optimized by quotation period to match real-time supply and demand changes, improving resource utilization efficiency. Furthermore, by leveraging actual data from the previous quotation period to quickly modify strategies for subsequent periods, the accumulation of forecast deviations can be reduced.

[0022] Furthermore, in some embodiments, when obtaining medium- and long-term electricity prices, parameters are set separately for the thermal power company and the user, clarifying the quoted prices and power ranges of both parties. This ensures that transactions are based on actual supply and demand capabilities and reduces information asymmetry. By using the smaller of the quoted quantities on both sides as the actual transaction volume, the performance risk caused by over-commitment is avoided, ensuring the feasibility of transaction execution. A circular matching mechanism for surplus power ensures that power generation capacity is fully aligned with power demand, reducing resource waste and improving power system utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other objects, features and advantages of the exemplary embodiments of the present application will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0024] Figure 1 An exemplary flow chart showing a method for operating a thermal power plant in collaboration with a multiple electricity market and a carbon market according to an embodiment of the present application is provided;

[0025] Figure 2 An exemplary flow chart of obtaining medium- and long-term electricity prices according to an embodiment of the present application is shown;

[0026] Figure 3 An exemplary structural block diagram of a thermal power operation system that coordinates multiple electricity markets and carbon markets according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0028] It should be understood that the terms "include" and "comprising" used in the description and claims of this application indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0029] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this specification and claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" as used in this specification and claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0030] Figure 1 An exemplary flow chart of a thermal power plant operation method 100 in coordination with multiple electricity markets and carbon markets according to an embodiment of the present application is shown.

[0031] like Figure 1 As shown, in step S110, the average electricity price, average ancillary service price and average carbon price of each month in the next year are predicted based on the historical annual electricity price data and the historical annual carbon price data, and are used as the actual electricity price, actual ancillary service price and actual carbon price of the first month in the future respectively.

[0032] In the embodiments of the present application, the historical years used can be selected from multiple years in the past according to actual needs, and this application does not impose any restrictions on this.

[0033] In an embodiment of the present application, historical annual electricity price data is obtained from the power trading center, and historical annual carbon price data is obtained from the national carbon market trading website.

[0034] In an embodiment of the present application, the historical annual electricity price data includes the historical annual electricity prices of the spot market and the historical annual electricity prices of the ancillary service market.

[0035] In some embodiments of the present application, in the process of predicting the average electricity price, average ancillary service price and average carbon price for each month in the next year based on historical annual electricity price data and historical annual carbon price data, the historical annual electricity price data and historical annual carbon price data are processed by time series method, neural network method and other methods to obtain the electricity price, ancillary service price and carbon price for the next year, and the average of the electricity price, ancillary service price and carbon price for the next year is taken to obtain the average electricity price, average ancillary service price and average carbon price for each month in the next year. In other embodiments of the present application, other methods may also be used to measure the average electricity price, average ancillary service price and average carbon price for each month in the next year, and this application does not limit this.

[0036] After executing step S110, in step S120, based on the objective function of maximizing the annual total profit of participating in the diversified electricity market and carbon market and the annual constraints of participating in the diversified electricity market and carbon market, the planned execution data of each month in the remaining months of the next year are predicted according to the actual execution data of the previous months.

[0037] In the embodiment of the present application, the objective function expression for maximizing the total annual profit is:

[0038] Among them, FM is the maximized annual total profit, is the profit of the i-th month in the medium and long-term market, is the profit of the spot market in month i, is the profit of the ancillary service market in month i, is the income / cost of the carbon market in month i, is the revenue of the capacity market in month i.

[0039] Specifically, P future For medium and long-term electricity prices, is the electricity consumption in the medium and long term market in month i, CM future is the corresponding cost of electricity in the i-th month in the medium and long-term market.

[0040] Specifically, AM average The average electricity price for each month, is the electricity consumption in the spot market in month i, CM spot is the corresponding cost of electricity in the i-th month in the spot market.

[0041] Specifically, AM auxiliary is the average ancillary service price per month, is the electricity consumption of the ancillary service market in month i, CM auxiliary is the cost of electricity in the ancillary service market in month i.

[0042] Specifically, is the carbon quota for month i, is the carbon emissions in month i, AM carbon is the monthly average carbon price.

[0043] Specifically, is the available capacity in month i, P capacity The electricity price is compensated for capacity.

[0044] In the embodiments of the present application, the foregoing annual constraint conditions include power generation capacity constraint conditions, minimum proportion constraint conditions for medium- and long-term transactions, and annual carbon compliance constraint conditions.

[0045] Specifically, the power generation capacity constraint condition is: Among them, is the electricity volume in the i-th month of the medium- and long-term market, is the electricity volume in the i-th month of the spot market, is the electricity volume in the i-th month of the ancillary service market, QM 总 is the annual total power generation capacity.

[0046] Specifically, the minimum proportion constraint condition for medium- and long-term transactions is: Among them, k is a set coefficient, and its value range is 0 < k < 1. Through the minimum proportion constraint condition for medium- and long-term transactions, the electricity volume in the long-term market in a year usually accounts for more than k times of the annual total power generation capacity.

[0047] In the embodiments of the present application, the foregoing set coefficient is set according to the province where it is located, and the present application does not limit this here. For example, in some embodiments, the set coefficient is set to 80%.

[0048] Specifically, the annual carbon compliance constraint condition is: Among them, is the carbon quota volume in the i-th month, is the carbon quota volume purchased / sold in the i-th month, is the carbon emission volume in the i-th month.

[0049] In the embodiments of the present application, the carbon quota volume in the foregoing i-th month and the carbon emission volume in the i-th month are obtained according to the total power generation volume in the i-th month.

[0050] Specifically, in the process of obtaining the carbon quota volume in the i-th month, first, a functional relationship between the monthly total power generation volume and the monthly carbon quota volume is constructed according to the historical monthly total power generation volume and the corresponding historical monthly carbon quota volume. Then, the total power generation volume in the i-th month is substituted into the functional relationship between the monthly total power generation volume and the monthly carbon quota volume to obtain the carbon quota volume in the i-th month.

[0051] Specifically, in the process of obtaining the carbon emission volume in the i-th month, first, a functional relationship between the monthly total power generation volume and the monthly carbon emission volume is constructed according to the historical monthly total power generation volume and the corresponding historical carbon emission volume. Then, the total power generation volume in the i-th month is substituted into the functional relationship between the monthly total power generation volume and the monthly carbon emission volume to obtain the carbon emission volume in the i-th month.

[0052] Specifically, the expression of the total power generation volume in the foregoing i-th month is: QM i总 is the total power generation volume in the i-th month, is the electricity consumption in the medium and long-term market in month i, is the electricity consumption in the spot market in month i, is the electricity consumption in the ancillary service market in month i.

[0053] In some embodiments of the present application, a machine learning method may be used to construct a functional relationship between the monthly total power generation and the monthly carbon quota, as well as to construct a functional relationship between the monthly total power generation and the monthly carbon emissions. In embodiments of the present application, other methods may also be used to construct a functional relationship between the monthly total power generation and the monthly carbon quota, as well as to construct a functional relationship between the monthly total power generation and the monthly carbon emissions, and this application does not limit this.

[0054] In an embodiment of the present application, during the execution of step S120, the electricity volume of the medium- and long-term market in the i-th month, the electricity volume of the spot market in the i-th month, the electricity volume of the ancillary service market in the i-th month, and the amount of carbon quota purchased / sold in the i-th month are taken as optimization variables, and the electricity volume of the medium- and long-term market in the i-th month, the electricity volume of the spot market in the i-th month, the electricity volume of the ancillary service market in the i-th month, and the amount of carbon quota purchased / sold in the i-th month are solved by maximizing the annual total profit objective function of participating in the multiple electricity market and the carbon market and the annual constraints of participating in the multiple electricity market and the carbon market. In the remaining months of the next year, the electricity volume of the long-term market, the electricity volume of the spot market, the electricity volume of the ancillary service market, and the amount of carbon quota purchased / sold in each month are predicted based on the actual electricity volume of the long-term market, the electricity volume of the spot market, the electricity volume of the ancillary service market, and the amount of carbon quota purchased / sold in the previous months.

[0055] That is, the execution data in step S120 is the electricity quantity in the long-term market, the electricity quantity in the spot market, the electricity quantity in the ancillary service market, and the purchase / sale carbon quota quantity in each month.

[0056] After executing step S120, in step S130, the daily average electricity price, average ancillary service price and average carbon price corresponding to each month in the next year are predicted based on the historical monthly electricity price data and the historical monthly carbon price data, and are used as the actual electricity price, actual ancillary service price and actual carbon price on the first day of each future month respectively.

[0057] In the embodiment of the present application, the historical months used can be selected from multiple months in the past according to actual needs, and the present application does not impose any restrictions on this.

[0058] In an embodiment of the present application, historical monthly electricity price data is obtained from the power trading center, and historical monthly carbon price data is obtained from the national carbon market trading website.

[0059] In an embodiment of the present application, the historical monthly electricity price data includes historical monthly electricity prices in the spot market and historical monthly electricity prices in the ancillary service market.

[0060] In some embodiments of the present application, in the process of predicting the daily average electricity price, average ancillary service price, and average carbon price corresponding to each month in the next year based on historical monthly electricity price data and historical monthly carbon price data, a time series method, a neural network method, or the like is used to process the historical monthly electricity price data and historical monthly carbon price data to obtain the electricity price, ancillary service price, and carbon price for each month in the next year. The average of the electricity price, ancillary service price, and carbon price for each month in the next year is taken to obtain the daily average electricity price, average ancillary service price, and average carbon price corresponding to each month in the next year. In other embodiments of the present application, other methods may also be used to measure the daily average electricity price, average ancillary service price, and average carbon price corresponding to each month in the next year, and this application does not limit this.

[0061] After executing step S130, in step S140, based on the objective function of maximizing the monthly total profit of participating in the diversified electricity market and carbon market and the monthly constraints of participating in the diversified electricity market and carbon market, the planned execution data of each day in the remaining days in the next month are predicted according to the actual execution data of the previous days.

[0062] In the embodiment of the present application, the objective function for maximizing the monthly total profit is:

[0063] Among them, TD is the total number of days in a month, FD is the maximized total monthly profit, is the profit on day t in the medium and long-term market, is the profit of the spot market on day t, is the profit of the ancillary service market on day t, is the income / cost of the carbon market on day t, is the revenue of the capacity market on day t.

[0064] Specifically, P future For medium and long-term electricity prices, is the electricity consumption on the tth day in the medium and long-term market, CD future is the corresponding cost of electricity on day t in the medium and long-term market.

[0065] Specifically, AD average The average electricity price for each month, is the electricity quantity in the spot market on day t, CD spot is the corresponding cost of electricity on the tth day in the spot market.

[0066] Specifically, AD auxiliary is the average ancillary service price per month, is the electricity consumption of the ancillary service market on day t, CD auxiliary is the corresponding cost of electricity in the ancillary service market on day t.

[0067] Specifically, is the carbon quota on day t, is the carbon emission on day t, AD carbon is the monthly average carbon price.

[0068] Specifically, is the available capacity on day t, P capacity The electricity price is compensated for capacity.

[0069] In an embodiment of the present application, the aforementioned monthly constraints include monthly electricity constraints and monthly carbon compliance constraints.

[0070] Specifically, the monthly power consumption constraint is: Among them, TD is the total number of days in a month, is the electricity consumption on the tth day corresponding to the i-th month in the medium and long-term market, is the electricity consumption in the medium and long-term market in month i, is the electricity consumption on the tth day corresponding to the i-th month in the spot market, is the electricity consumption in the spot market in month i, is the electricity consumption on the tth day corresponding to the i-th month in the ancillary service market, is the electricity consumption in the ancillary service market in month i.

[0071] Specifically, the monthly carbon compliance constraints are: in, is the carbon quota on the tth day corresponding to the i-th month, is the amount of carbon allowances purchased / sold on the tth day corresponding to the i-th month, is the carbon emissions on the tth day corresponding to the i-th month, The amount of carbon allowances purchased / sold in month i.

[0072] In an embodiment of the present application, the carbon quota amount on the tth day corresponding to the i-th month and the carbon emissions on the tth day corresponding to the i-th month are obtained based on the total power generation on the tth day corresponding to the i-th month.

[0073] Specifically, the process for obtaining the carbon quota for day t corresponding to month i is the same as the aforementioned process for obtaining the carbon quota for month i. First, a functional relationship between daily total power generation and daily carbon quota is constructed based on the historical daily total power generation and the corresponding historical daily carbon quota. Then, the total power generation for day t corresponding to month i is substituted into the functional relationship between total power generation and daily carbon quota to obtain the carbon quota for day t corresponding to month i.

[0074] Specifically, the process for obtaining carbon emissions on day t in month i is the same as the process for carbon emissions in month i. First, a functional relationship between daily total power generation and daily carbon emissions is constructed based on historical daily total power generation and the corresponding daily carbon emissions. Then, the total power generation on day t in month i is substituted into the functional relationship between total power generation and daily carbon emissions to obtain the carbon emissions on day t in month i.

[0075] Specifically, the expression for the total power generation on day t corresponding to the aforementioned month i is:

[0076] QD it总 is the total power generation on day t corresponding to month i, is the electricity consumption on the tth day corresponding to the i-th month in the medium and long-term market, is the electricity consumption on the tth day corresponding to the i-th month in the spot market, is the electricity consumption on day t corresponding to month i in the ancillary service market.

[0077] In an embodiment of the present application, during the execution of step S140, the electricity volume in the medium- and long-term market on day t, the electricity volume in the spot market on day t, the electricity volume in the ancillary service market on day t, and the amount of carbon quota purchased / sold on day t are taken as optimization variables, and the electricity volume in the medium- and long-term market on day t, the electricity volume in the spot market on day t, the electricity volume in the ancillary service market on day t, and the amount of carbon quota purchased / sold on day t are solved by maximizing the annual total profit objective function of participating in the diversified electricity market and the carbon market and the annual constraints of participating in the diversified electricity market and the carbon market, and the remaining dates in the next month are predicted based on the long-term market electricity volume, spot market electricity volume, ancillary service market electricity volume, and the amount of carbon quota purchased / sold on the previous date.

[0078] That is, the execution data in step S140 is the daily electricity quantity in the long-term market, the electricity quantity in the spot market, the electricity quantity in the ancillary service market, and the purchase / sale carbon quota quantity.

[0079] In an embodiment of the present application, after executing step S140, the following steps are further performed: First, based on the historical daily electricity price data and the historical daily carbon price data, the average electricity price, the average ancillary service price, and the average carbon price for each quotation period of each day in the next year are predicted, and the average electricity price, the actual ancillary service price, and the actual carbon price are used as the actual electricity price, the actual ancillary service price, and the actual carbon price for the first quotation period of each day in the future. Then, based on the objective function of maximizing the daily total profit of participating in the multiple electricity market and carbon market and the daily constraints of participating in the multiple electricity market and carbon market, the planned execution data for each of the remaining quotation periods in the future day are predicted based on the actual execution data of the previous quotation period in the remaining quotation periods in the future day.

[0080] In an embodiment of the present application, the specific process of predicting the planned execution data for each quotation period mentioned above adopts the same method as the specific process involved in predicting the planned execution data for each day involved in steps S130-S140, and this application will not repeat them here.

[0081] In the embodiment of the present application, the specific process of obtaining the above-mentioned medium and long-term electricity prices can be found in Figure 2 .

[0082] Figure 2 An exemplary flow chart for obtaining medium- and long-term electricity prices according to an embodiment of the present application is shown.

[0083] like Figure 2 As shown, in step S210, the thermal power company's parameters and the user's parameters are determined. In step S220, the thermal power company's quotation range, the quoted quantity range, and the user's quotation range are determined based on the thermal power company's parameters and the user's parameters. In step S230, the thermal power company and the user are randomly paired, and the quotation for the successfully paired thermal power company and user is negotiated and adjusted. In step S240, the smaller of the thermal power company's quoted quantity and the user's quoted quantity after successful negotiation and adjustment is used as the actual transaction volume. In step S250, it is determined whether there is any surplus power after the actual transaction volume is obtained. If there is any surplus power, in step S260, the process returns to step S230 and repeats steps S230-S250 until there is no surplus power. If there is no surplus power, in step S270, the electricity price corresponding to the actual transaction volume is used as the medium- and long-term electricity price.

[0084] In the embodiment of the present application, the parameters on the thermal power enterprise side include power generation costs (including coal consumption, operation and maintenance, etc.), annual total power generation capacity, minimum medium- and long-term electricity ratio, etc.

[0085] In an embodiment of the present application, the user-side parameters include the user's historical average electricity price, etc.

[0086] In an embodiment of the present application, in the process of determining the quotation range, quantity range on the thermal power enterprise side, and quotation range on the user side based on the parameters on the thermal power enterprise side and the parameters on the user side, the quotation range on the thermal power enterprise side is formed based on the power generation cost and m times the average electricity price of the previous year. The quantity range on the thermal power enterprise side is formed based on the annual power generation capacity and the annual power generation capacity multiplied by the minimum proportion of medium- and long-term electricity quantity. The quotation range on the user side is formed by floating m times up and down based on the user's historical average electricity price. Here, 0 < m < 1, which is set according to actual needs and history, and the present application does not limit it here. For example, in some embodiments, m is set to 10%.

[0087] In an embodiment of the present application, during the process of randomly pairing the thermal power enterprise side and the user side and conducting quotation negotiation and adjustment for the successfully paired thermal power enterprise side and user side, when the quotation on the thermal power enterprise side is lower than or equal to the quotation on the user side, both sides enter the transaction stage. When the quotation on the thermal power enterprise side is higher than the quotation on the user side, quotation adjustment is required. The adjustment method can be linear, that is, the thermal power enterprise side reduces the price and the user side increases the price until the quotations of both sides meet or the quotation on the user side is higher than the quotation on the thermal power enterprise side. During the adjustment process, if the quotation of any party exceeds the set quotation range (such as the quotation on the thermal power enterprise side is lower than the cost or the quotation on the user side is higher than 110% of its historical average electricity price), re-matching is required.

[0088] In summary, through the thermal power operation scheme that coordinates the multi-power market and the carbon market provided above, the embodiment of the present application predicts monthly data based on the historical annual electricity price data and historical annual carbon price data, and predicts daily data based on the historical monthly electricity price data and historical monthly carbon price data, which can capture short-term price fluctuations and improve the accuracy of resource scheduling. At the same time, by using the predicted value of the first month / first day as the actual data every year / month and dynamically adjusting the remaining cycle plan based on this, it can use the latest data for prediction and quickly respond to sudden fluctuations in electricity prices and carbon prices, thereby improving the real-time performance of prediction and execution. In addition, by establishing an objective function of maximizing the total profit between the multi-power market and the carbon market, it balances the income of the multi-power market and the carbon cost, and avoids the sub-optimal solution of single-market decision-making.

[0089] Furthermore, in some embodiments, by refining prediction and optimization to multiple quotation periods within a day, it can adapt to the剧烈日内价格波动 in the power market and avoid revenue losses caused by coarse-grained prediction. And it can optimize the power generation plan according to the quotation period, match the real-time supply and demand changes, and improve the resource utilization efficiency. At the same time, using the actual data of the previous quotation period to quickly correct the strategy of the subsequent period can reduce the accumulation of prediction deviation.

[0090] Furthermore, in some embodiments, when obtaining medium- and long-term electricity prices, parameters are set separately for the thermal power company and the user, clarifying the quoted prices and power ranges of both parties. This ensures that transactions are based on actual supply and demand capabilities and reduces information asymmetry. By using the smaller of the quoted quantities on both sides as the actual transaction volume, the performance risk caused by over-commitment is avoided, ensuring the feasibility of transaction execution. A circular matching mechanism for surplus power ensures that power generation capacity is fully aligned with power demand, reducing resource waste and improving power system utilization.

[0091] The embodiment of the present application also provides a thermal power operation system that coordinates multiple electricity markets and carbon markets. It can adopt the aforementioned thermal power operation method 100 that coordinates multiple electricity markets and carbon markets to perform thermal power operations that coordinate multiple electricity markets and carbon markets, or it can adopt other methods to perform thermal power operations that coordinate multiple electricity markets and carbon markets. This application does not limit this.

[0092] Figure 3 An exemplary structural block diagram of a thermal power operation system 300 for coordinated multi-electricity market and carbon market according to an embodiment of the present application is shown.

[0093] like Figure 3 As shown, the system 300 includes a first prediction module 310 , a multiple electricity market and carbon market monthly coordination module 320 , a second prediction module 330 and a multiple electricity market and carbon market daily coordination method module 340 .

[0094] In an embodiment of the present application, the first prediction module 310, the multiple electricity market and carbon market monthly coordination module 320, the second prediction module 330 and the multiple electricity market and carbon market daily coordination method module 340 can be separate units or integrated in the same integrated circuit, and the present application does not impose any restrictions on this.

[0095] Specifically, the first prediction module 310 is used to predict the average electricity price, average ancillary service price and average carbon price of each month in the next year based on historical annual electricity price data and historical annual carbon price data, and use them as the actual electricity price, actual ancillary service price and actual carbon price of the first month in the future respectively.

[0096] Specifically, the diversified electricity market and carbon market monthly collaboration module 320 is used to predict the planned execution data for each month in the remaining months of the next year based on the actual execution data of the previous months in the remaining months of the next year based on the objective function of maximizing the annual total profit of participating in the diversified electricity market and carbon market and the annual constraints of participating in the diversified electricity market and carbon market.

[0097] Specifically, the second prediction module 330 is used to predict the average electricity price, average ancillary service price, and average carbon price for each day of each month in the next year based on the historical monthly electricity price data and the historical monthly carbon price data, and use them as the actual electricity price, actual ancillary service price, and actual carbon price on the first day of each month in the future, respectively;

[0098] Specifically, the diversified electricity market and carbon market daily collaborative method module 340 is used to predict the planned execution data for each of the remaining days in the next month based on the actual execution data of the previous days based on the maximization objective function of the monthly total profit of participating in the diversified electricity market and carbon market and the monthly constraints of participating in the diversified electricity market and carbon market.

[0099] In an embodiment of the present application, the system 300 may further include a third prediction module 350 and a multi-electricity market and carbon market quotation period coordination method module 360 .

[0100] In an embodiment of the present application, the first prediction module 310, the multiple electricity market and carbon market monthly collaboration module 320, the second prediction module 330, the multiple electricity market and carbon market daily collaboration method module 340, the third prediction module 350 and the multiple electricity market and carbon market quotation period collaboration method module 360 can be separate units or integrated in the same integrated circuit, and the present application does not impose any restrictions on this.

[0101] Specifically, the third prediction module 350 is used to predict the average electricity price, average ancillary service price and average carbon price for each quotation period every day in the next year based on historical daily electricity price data and historical daily carbon price data, and use them as the actual electricity price, actual ancillary service price and actual carbon price for the first quotation period every day in the future.

[0102] Specifically, the multi-electricity market and carbon market quotation period collaborative method module 360 is used to predict the planned execution data of each remaining quotation period in the remaining quotation period in the next day based on the actual execution data of the previous quotation period in the remaining quotation period in the next day based on the maximization objective function of the daily total profit of participating in the multi-electricity market and carbon market and the daily constraints of participating in the multi-electricity market and carbon market.

[0103] When the system 300 adopts the aforementioned method 100 for the coordinated thermal power operation of multiple electricity markets and carbon markets to perform the coordinated thermal power operation of multiple electricity markets and carbon markets, the first prediction module 310 is used to perform the aforementioned step S110, the monthly coordination module 320 of the multiple electricity markets and carbon markets is used to perform the aforementioned step S120, the second prediction module 330 is used to perform the aforementioned step S130, the daily coordination method module 340 of the multiple electricity markets and carbon markets is used to perform the aforementioned step S140, the third prediction module is used to perform the relevant steps of predicting the average electricity price, average ancillary service price and average carbon price of each quotation period every day in the next year, and the coordination method module of the quotation period of the multiple electricity market and carbon market is used to perform the relevant steps of predicting the planned execution data of the corresponding quotation period. The specific execution process can be referred to the above text and will not be repeated here.

[0104] Although multiple embodiments of the present application have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art can conceive of many changes, modifications, and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein can be adopted. The accompanying claims are intended to define the scope of protection of the present application and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A thermal power operation method that coordinates multiple electricity markets and carbon markets, characterized by: include: Based on historical annual electricity price data and historical annual carbon price data, the average electricity price, average ancillary service price, and average carbon price for each month in the next year are predicted, and these are used as the actual electricity price, actual ancillary service price, and actual carbon price for the first month of the next month, respectively. Based on the objective function of maximizing the annual total profit of participating in the diversified electricity market and carbon market and the annual constraints of participating in the diversified electricity market and carbon market, the planned execution data of each month in the remaining months of the next year are predicted based on the actual execution data of the previous months; Based on historical monthly electricity price data and historical monthly carbon price data, the average daily electricity price, average ancillary service price, and average carbon price for each month in the next year are predicted, and these will be used as the actual electricity price, actual ancillary service price, and actual carbon price on the first day of each month in the future. Based on the objective function of maximizing the monthly total profit of participating in the diversified electricity market and carbon market and the monthly constraints of participating in the diversified electricity market and carbon market, the planned execution data for each day in the remaining days of the next month are predicted based on the actual execution data of the previous days.

2. The method for operating a thermal power plant in collaboration with multiple electricity markets and carbon markets according to claim 1, characterized in that: Also includes: Based on historical daily electricity price data and historical daily carbon price data, the average electricity price, average ancillary service price and average carbon price for each quotation period of each day in the next year are predicted, and these are used as the actual electricity price, actual ancillary service price and actual carbon price for the first quotation period of each day in the future. Based on the objective function of maximizing the daily total profit of participating in the diversified electricity market and carbon market and the daily constraints of participating in the diversified electricity market and carbon market, the planned execution data of each remaining bidding period in the remaining bidding period in the next day is predicted according to the actual execution data of the previous bidding period.

3. The method for operating a thermal power plant in collaboration with multiple electricity markets and carbon markets according to claim 1, characterized in that: The objective function expression for maximizing the total annual profit is: Among them, FM is the maximized annual total profit, is the profit of the i-th month in the medium and long-term market, is the profit of the spot market in month i, is the profit of the ancillary service market in month i, is the income / cost of the carbon market in month i, is the revenue of the capacity market in month i; P future For medium and long-term electricity prices, is the electricity consumption in the medium and long term market in month i, CM future is the corresponding cost of electricity in the i-th month in the medium and long-term market; AM average The average electricity price for each month, is the electricity consumption in the spot market in month i, CM spot is the corresponding cost of electricity in the i-th month in the spot market; AM auxiliary is the average ancillary service price per month, is the electricity consumption of the ancillary service market in month i, CM auxiliary is the cost of electricity in the ancillary service market in month i; is the carbon quota for month i, is the carbon emissions in month i, AM carbon is the monthly average carbon price; is the available capacity in month i, P capacity The electricity price is compensated for capacity.

4. The method for operating a thermal power plant in collaboration with multiple electricity markets and carbon markets according to claim 1 or 3, characterized in that: The annual constraints include power generation capacity constraints, medium- and long-term transaction minimum ratio constraints, and annual carbon compliance constraints; The power generation capacity constraint conditions are: in, is the electricity consumption in the medium and long-term market in month i, is the electricity consumption in the spot market in month i, is the electricity consumption of the ancillary service market in month i, QM 总 is the total annual power generation capacity; The minimum proportion constraints for medium and long-term transactions are: Among them, k is the setting coefficient; The annual carbon compliance constraints are: in, is the carbon quota for month i, is the amount of carbon allowances purchased / sold in month i, is the carbon emissions in month i.

5. The method for operating a thermal power plant in collaboration with multiple electricity markets and carbon markets according to claim 4, characterized in that: The carbon quota and carbon emissions for month i are obtained based on the total power generation for month i; In the process of obtaining the carbon quota amount for the i-th month, the following steps are performed: constructing a functional relationship between the monthly total power generation and the monthly carbon quota amount according to the historical monthly total power generation and the corresponding historical monthly carbon quota amount; Substitute the total power generation of the i-th month into the functional relationship between the monthly total power generation and the monthly carbon quota to obtain the carbon quota of the i-th month; In the process of obtaining the carbon emissions of the i-th month, the following steps are performed: constructing a functional relationship between the monthly total power generation and the monthly carbon emissions based on the historical monthly total power generation and the corresponding historical carbon emissions; Substitute the total power generation of the i-th month into the functional relationship between the monthly total power generation and the monthly carbon emissions to obtain the carbon emissions of the i-th month; The expression of total power generation in month i is: QM i总 is the total power generation in month i, is the electricity consumption in the medium and long-term market in month i, is the electricity consumption in the spot market in month i, is the electricity consumption in the ancillary service market in month i.

6. The method for operating a thermal power plant in collaboration with multiple electricity markets and carbon markets according to claim 1, characterized in that: The objective function for maximizing the total monthly profit is: Among them, TD is the total number of days in a month, FD is the maximized total monthly profit, is the profit on day t in the medium and long-term market, is the profit of the spot market on day t, is the profit of the ancillary service market on day t, is the income / cost of the carbon market on day t, is the revenue of the capacity market on day t; P future For medium and long-term electricity prices, is the electricity consumption on the tth day in the medium and long-term market, CD future is the corresponding cost of electricity on day t in the medium and long-term market; AD average The average electricity price for each month, is the electricity quantity in the spot market on day t, CD spot is the corresponding cost of electricity on the tth day in the spot market; AD auxiliary is the average ancillary service price per month, is the electricity consumption of the ancillary service market on day t, CD auxiliary is the cost of electricity in the ancillary service market on day t; is the carbon quota on day t, is the carbon emission on day t, AD carbon is the monthly average carbon price; is the available capacity on day t, P capacity The electricity price is compensated for capacity.

7. The method for operating a thermal power plant in collaboration with multiple electricity markets and carbon markets according to claim 1 or 5, characterized in that: The monthly constraints include monthly electricity constraints and monthly carbon compliance constraints; The monthly power consumption constraint conditions are: Among them, TD is the total number of days in a month, is the electricity consumption on the tth day corresponding to the i-th month in the medium and long-term market, is the electricity consumption in the medium and long-term market in month i, is the electricity consumption on the tth day corresponding to the i-th month in the spot market, is the electricity consumption in the spot market in month i, is the electricity consumption on the tth day corresponding to the i-th month in the ancillary service market, is the electricity consumption of the ancillary service market in month i; The monthly carbon compliance constraints are: in, is the carbon quota on the tth day corresponding to the i-th month, is the amount of carbon allowances purchased / sold on the tth day corresponding to the i-th month, is the carbon emissions on the tth day corresponding to the i-th month, The amount of carbon allowances purchased / sold in month i.

8. The method for operating a thermal power plant in collaboration with multiple electricity markets and carbon markets according to claim 3 or 6, characterized in that: The medium and long-term electricity prices are obtained through the following steps: Determine the parameters on the thermal power company side and the parameters on the user side respectively; Determine the quotation range and quantity range of the thermal power company side and the quotation range of the user side based on the parameters of the thermal power company side and the parameters of the user side; Randomly pair thermal power companies with users, and negotiate and adjust quotations for successfully matched thermal power companies and users; The smaller value between the amount reported by the thermal power company and the amount reported by the user after successful negotiation and adjustment shall be regarded as the actual transaction amount. Determine whether there is remaining power after obtaining the actual transaction power; In response to the existence of surplus power, returning to the step of randomly pairing the thermal power company side with the user side until there is no surplus power; In response to the absence of surplus electricity, the electricity price corresponding to the actual transaction electricity volume is used as the medium- and long-term electricity price.

9. The method for operating a thermal power plant in collaboration with multiple electricity markets and carbon markets according to claim 2, characterized in that: Execution data includes medium- and long-term market electricity, spot market electricity, ancillary service market electricity and purchased / sold carbon quotas.

10. A thermal power operation system that coordinates multiple electricity markets and carbon markets, characterized by: The method for operating a thermal power plant in coordination with multiple power markets and carbon markets as described in any one of claims 1 to 9 is used to operate a thermal power plant in coordination with multiple power markets and carbon markets, the system comprising: The first prediction module is used to predict the average electricity price, average ancillary service price and average carbon price of each month in the next year based on historical annual electricity price data and historical annual carbon price data, and use them as the actual electricity price, actual ancillary service price and actual carbon price of the first month of the next year respectively; A monthly collaborative module for the diversified electricity market and carbon market is used to predict the planned execution data for each month in the remaining months of the next year based on the actual execution data of the previous months, based on the objective function of maximizing the annual total profit of participating in the diversified electricity market and carbon market and the annual constraints of participating in the diversified electricity market and carbon market; The second prediction module is used to predict the average daily electricity price, average ancillary service price and average carbon price for each month in the next year based on historical monthly electricity price data and historical monthly carbon price data, and use them as the actual electricity price, actual ancillary service price and actual carbon price on the first day of each month in the future; The daily collaborative method module of the diversified electricity market and carbon market is used to predict the planned execution data of each day in the remaining days of the next month based on the actual execution data of the previous days based on the objective function of maximizing the monthly total profit of participating in the diversified electricity market and carbon market and the monthly constraints of participating in the diversified electricity market and carbon market.

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