A joint dispatching and clearing method and device for electricity market and carbon market

Abstract

The present invention relates to a method and device for joint dispatching and clearing of the electricity market and the carbon market, belonging to the field of power system technology, and solving the problem of how to reduce the operating cost of the power system, reduce carbon emissions, and thus improve the operating efficiency of the power system. The method includes: establishing an upper-level game model for renewable and conventional energy generating units; obtaining the optimal strategy for the units to participate in the electricity and carbon joint market when the electricity and carbon joint market reaches Nash equilibrium, including the quotations of renewable and conventional energy generating units in the day-ahead electricity and carbon markets respectively; establishing a lower-level joint market clearing model for the electricity and carbon markets, and performing day-ahead, real-time electricity market and carbon market clearing according to the quotations to obtain day-ahead, real-time and carbon market clearing results, that is, obtaining the output results of the generating units under the optimal operating state of the electricity market and carbon market, and feeding back to the upper-level game model. Maximizing benefits and completing the dispatching and clearing of the joint market are achieved through non-cooperative games.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and device for jointly dispatching and clearing power markets and carbon markets. Background Art

[0002] Existing research has made numerous simplifications to the two-stage electricity market and has yet to fully characterize the trading decision-making process of renewable energy generators. Renewable energy generators' trading decisions in the day-ahead market, or in the real-time market, have been proposed. Wind power and energy storage are proposed to act as active strategic agents in the day-ahead market and as price takers in the real-time market. Some researchers have also aggregated wind power and energy storage into virtual power plants to determine decision-making in both day-ahead and real-time energy markets.

[0003] The strategic behavior of renewable energy generators is also influenced by the decisions of conventional energy generators. In the day-ahead market, conventional energy generators compete with renewable energy generators in bidding for electricity. To ensure system security and stability, conventional energy generators are also required to reserve a certain percentage of spare capacity. In the real-time market, conventional energy generators use this reserved spare capacity to adjust their power output due to deviations in renewable energy output. In the carbon market, conventional energy generators will consider how to maximize their returns by using their free initial allowances and making sales and purchase decisions regarding carbon allowances. The trading strategies of conventional energy generators will have a more complex impact on the strategic behavior of renewable energy generators.

[0004] Existing research focuses on using the carbon market as a constraint for optimizing power market dispatch. However, while carbon emission costs are used as a boundary condition for optimizing power market clearing, the study examines power market equilibrium under a given carbon price. Less consideration has been given to the joint optimization of the carbon and power markets with time-varying carbon prices. Existing research proposes incorporating carbon emission costs into the supply function of generators and further explores the impact of carbon prices on generator decisions. Existing research considers a combined market for electricity and carbon, but the carbon price is fixed and fails to reflect the time-varying nature of carbon prices. Existing research attempts to establish dynamic carbon pricing mechanisms, such as employing a stepped carbon price to reflect the supply and demand relationship in the carbon market. However, this approach, which determines different carbon prices based on different carbon emission ranges and requires manual setting of the stepped ranges, is highly subjective and fails to provide a truly effective price signal for generators. A carbon emission flow theory has been proposed to study the mechanism by which demand response participates in the electricity-carbon market. Carbon pricing based on carbon flow theory calculates node carbon intensity based on a proportional sharing principle. This means nodes near high-emission units have higher carbon intensity, while nodes near renewable energy units have lower carbon intensity. This carbon market pricing model, based on a "birther-based" approach, is unfair. Continuous bidding trading, which prices floating units based on the tightness of carbon market supply and demand, can facilitate the rapid execution of carbon quota and certified voluntary emission reduction (CER) trading demands. It also releases price signals over a short timeframe, providing a clear signal of low system energy and carbon costs during periods of high wind and solar power generation, encouraging users to increase electricity consumption. This, in turn, facilitates the absorption of renewable energy.

[0005] Existing technologies consider the optimal decision-making of conventional energy generators in both the day-ahead and real-time markets under conditions of high wind power penetration. Considering the impact of the carbon market on the clearing of the electricity energy market, conventional energy generation is considered strategic behavior only, while renewable energy is treated as non-strategic. The uncertainty of renewable energy output is also not considered. Existing technologies consider the multi-market equilibrium of generators participating in the electricity, natural gas, and carbon emission markets. The carbon market has an annual trading cycle, and carbon prices cannot truly reflect the supply and demand relationship of carbon quotas. Analyzing the revenue changes of conventional energy generators participating in the electricity and carbon markets, the strategic behavior of renewable energy generators is not considered. For example, in the integrated market of the day-ahead and carbon markets, existing technologies consider the strategic behavior of conventional and renewable energy generators without considering the impact of system backup demand on the revenue of conventional energy generators or the uncertainty of renewable energy output. Summary of the Invention

[0006] In view of the above analysis, the embodiments of the present invention aim to provide a method and device for joint scheduling and clearing of the electricity market and the carbon market, so as to solve the problem of how to reduce the operating cost of the power system, reduce carbon emissions and thus optimize the operation of the power system.

[0007] On the one hand, an embodiment of the present invention provides a joint scheduling and clearing method for the electricity market and the carbon market, including: establishing an upper-level game model for renewable energy generators and conventional energy generators; based on the upper-level game model, obtaining the optimal strategy for the renewable energy generators and the conventional energy generators to participate in the electricity and carbon joint market when the electricity and carbon joint market reaches Nash equilibrium, the optimal strategy including the quotations of the renewable energy generators and the conventional energy generators in the day-ahead electricity market and the carbon market respectively, wherein the electricity market includes the day-ahead electricity market and the real-time electricity market; and establishing a lower-level joint market clearing model for the electricity market and the carbon market, clearing the day-ahead electricity market, the real-time electricity market and the carbon market according to the quotations in the day-ahead electricity market and the carbon market, so as to obtain the day-ahead electricity market clearing result, the real-time electricity market clearing result and the carbon market clearing result and feed them back to the upper-level game model.

[0008] The beneficial effects of the above technical solution are as follows: To model the clearing process of the coupled electricity and carbon markets and the decision-making process of power generators, a two-tiered decision-making model based on non-cooperative game theory is constructed. In the upper-tier model, power generators formulate optimal strategies for the two-stage electricity and carbon markets, maximizing their respective benefits in the electricity and carbon markets through non-cooperative game theory. The strategic behavior of conventional energy power generators is fully considered, including competition with renewable energy power generators in the day-ahead and real-time markets, as well as the flexible decomposition of sales / purchase quotas and initial carbon quotas in the carbon market. In the lower-tier model, the day-ahead and real-time market clearing is completed. After the real-time market in each scenario is cleared, the carbon market operator is responsible for clearing the carbon market.

[0009] Based on the further improvement of the above method, in the upper-level game model, the Nash equilibrium satisfies the following conditions:

[0010]

[0011]

[0012] in, are the optimal strategies for renewable energy generator m and conventional energy generator n to participate in the electricity and carbon joint market; are the strategies for renewable energy generator m and conventional energy generator n to participate in the electricity and carbon joint market, is the price quoted by the renewable energy generator m in the day-ahead power market, is the price quoted by the renewable energy generator m in the carbon market, is the number of certified emission reductions sold by renewable energy generator m at time t, are the quotes of conventional energy generator n in the day-ahead electricity market and carbon market, are the sales and purchase amounts of carbon quotas of conventional energy generator n at time t under scenario s, γ n,t,s is the initial carbon quota decomposition coefficient of the conventional energy generator set n at time t; argmax is a decision variable function used to maximize the benefits of the renewable energy generator set and the conventional energy generator set.

[0013] Based on further improvements of the above method, establishing an upper-level game model for renewable energy power generation groups and conventional energy power generation groups further includes: constructing an objective function for maximizing the total revenue of the renewable energy power generation group m in the power and carbon markets based on the revenue of the renewable energy power generation group in the day-ahead power market, the real-time power market revenue and the carbon market, and constructing its related constraints; and constructing an objective function for maximizing the total revenue of the conventional energy power generation group n in the power and carbon markets based on the revenue of the conventional energy power generation group in the day-ahead power market, the real-time power market revenue and the carbon market, and constructing its related constraints.

[0014] Based on the further improvement of the above method, the objective function for maximizing the total revenue of the renewable energy generator m in the electricity and carbon markets is:

[0015]

[0016]

[0017]

[0018]

[0019] The relevant constraints are:

[0020]

[0021]

[0022] τ=0.75F OM +0.25F BM Formula 9

[0023]

[0024]

[0025] Among them, the superscripts DA, RT, and CM represent the variables in the day-ahead power market, the real-time power market, and the carbon market, respectively. are the revenues of renewable energy generator m in the day-ahead electricity market, real-time electricity market and carbon market respectively. The objective function of maximizing the total revenue of renewable energy generator m in electricity and carbon markets in formula 3 represents the total revenue maximization target of renewable energy generator m in bidding decision. In the day-ahead electricity market, the objective function of maximizing the total revenue of renewable energy generator m in formula 4 is Clearing electricity prices for the day-ahead electricity market; is the day-ahead electricity market clearing quantity of renewable energy generator m at time t; in the real-time electricity market, the subscript s represents the scenario s, and π in Formula 5 s is the probability of scenario s occurring, is the clearing electricity price at time t in scenario s, is the clearing electricity of renewable energy generator m at time t under scenario s. In the carbon market, is the clearing price of the carbon market at time t in scenario s; Formulas 7 to 9 are the constraints for renewable energy generator m to participate in the carbon market. Formula 7 states that renewable energy generator m is the limit on the sale of certified voluntary emission reductions in the carbon market. is the number of certified voluntary emission reductions obtained by renewable energy generator m at time t in scenario s, τ in formula 8 is the baseline emission factor, that is, the carbon dioxide emission reduction per unit of renewable energy power, and F in formula 9 is OM is the marginal emission factor of electricity, F BM is the capacity marginal emission factor; in formula 10 is the day-ahead electricity market quotation of renewable energy generator m, are the upper and lower limits of the day-ahead electricity market bids for renewable energy generator m, respectively. is the carbon market quotation of renewable energy generator m, are the upper and lower limits of the carbon market quotation of renewable energy generator m.

[0026] Based on the further improvement of the above method, the objective function of maximizing the total revenue of the conventional energy generator n in the electricity and carbon markets is expressed by the following formula:

[0027]

[0028]

[0029]

[0030]

[0031] Relevant constraints:

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] Among them, in formula 12 are the revenues of conventional energy generator n in the day-ahead electricity market, real-time electricity market and carbon market respectively. In the day-ahead electricity market, λ in Formula 13 n,t is the marginal cost of conventional energy generator n, is the day-ahead electricity market clearing quantity of conventional energy generator n at time t. In the real-time electricity market, is the real-time electricity market clearing quantity of conventional energy n at time t under scenario s. In the carbon market, when Conventional energy generator n obtains carbon quota income, when When , conventional energy generator n needs to pay the carbon quota cost; represents the net surplus of carbon quota of conventional energy generator n under scenario s at time t; and are the purchase and sales of carbon quotas for conventional energy generators n, u i is a binary variable of 0 or 1 and M is a sufficiently large positive number, the The initial free carbon quota allocated to conventional energy generator n, γ n,t,s is the initial carbon quota decomposition coefficient; Formula 20 is the initial carbon quota decomposition coefficient constraint condition, and σ in Formula 21 n is the carbon emission coefficient of conventional energy generator set n; and are the upper and lower limits of the carbon market quotation of conventional energy power generation unit n, is the carbon market quotation of conventional energy generator n, and is the day-ahead electricity market quotation of conventional energy generator n, are the upper and lower limits of the quotations of conventional energy generator n in the day-ahead electricity market.

[0041] Based on further improvements to the above method, establishing a lower-level joint market clearing model for the electricity market and the carbon market further includes: constructing an electricity market clearing model based on the day-ahead electricity market cost and the expected cost of the real-time electricity market as the electricity market cost minimization objective function, wherein the day-ahead electricity market cost minimization objective function is constructed based on the electric energy cost and the reserve cost of the day-ahead electricity market and its related constraints are constructed; constructing the expected cost of the real-time electricity market based on the system operation adjustment cost caused by the output deviation of renewable energy under scenario s, the wind and solar power abandonment cost under scenario s, and the load shedding cost under scenario s, and its related constraints are constructed; constructing a carbon market clearing model based on the carbon market quotation of conventional energy power generation units, the carbon market sales volume decision of conventional energy power generation units, the purchase volume decision of conventional energy power generation units, the carbon market quotation of renewable energy power generation units, and the carbon market sales volume decision of renewable energy power generation units as the carbon market social welfare maximization objective function, and its related constraints are constructed.

[0042] Based on the further improvement of the above method, the electricity market cost minimization objective function is:

[0043] min C=C DA +C RT Formula 24

[0044] Where C in Formula 24 is the total cost of the electricity market, C DA is the day-ahead electricity market cost, C RT is the expected cost of the real-time electricity market; the objective function of minimizing the cost of the day-ahead electricity market is expressed by the following formula:

[0045]

[0046] are the electricity energy cost and reserve cost in the day-ahead power market respectively;

[0047]

[0048]

[0049] In formula 27 are the upper reserve and lower reserve prices of conventional energy generator set n, are the upper reserve and lower reserve capacities reserved by conventional energy generating unit n in the day-ahead power market;

[0050]

[0051] is the system operation adjustment cost caused by the output deviation of renewable energy generators in scenario s, is the cost of curtailing wind and solar power in scenario s, is the load shedding cost for scenario s;

[0052]

[0053]

[0054]

[0055] In formula 34 is the day-ahead electricity market clearing quantity of conventional energy generator n at time t, is the real-time electricity market clearing quantity of conventional energy generator n at time t under scenario s, Ultra-short-term predicted output of renewable energy generator m at time t under scenario s; is the day-ahead electricity market clearing quantity of renewable energy generator m at time t, is the carbon market quotation of conventional energy generator n, is the day-ahead electricity market quotation of conventional energy generator n, and in formula 35 is the penalty coefficient for curtailing wind and solar power, is the amount of wind and solar power curtailment by renewable energy generator m at time t; is the load loss penalty coefficient, is the load loss of the system at time t in scenario s.

[0056] Based on the further improvement of the above method, the carbon market social welfare maximization objective function is:

[0057]

[0058] Relevant constraints:

[0059]

[0060]

[0061]

[0062]

[0063]

[0064] Formula 44 is the supply and demand equilibrium constraint in the carbon market. Formulas 45 and 46 are the upper and lower limit constraints of carbon market transactions when conventional energy generator n is the seller and buyer, respectively. Formula 47 is the upper and lower limit constraints of renewable energy generator m selling certified voluntary emission reductions in the carbon market. Formula 48 represents the carbon quota clearance constraint of conventional energy generator n, which needs to ensure that the initial carbon emission quota obtained within the compliance period is The net carbon emission quota accumulated during period T Should be greater than or equal to the actual total carbon emissions during period T

[0065] On the other hand, an embodiment of the present invention provides a joint scheduling and clearing device for the electricity market and the carbon market, including: a game model construction module, used to establish an upper-level game model of renewable energy generators and conventional energy generators; an optimal strategy acquisition module, used to obtain the optimal strategy for the renewable energy generators and the conventional energy generators to participate in the electricity and carbon joint market based on the upper-level game model when the electricity and carbon joint market reaches Nash equilibrium, the optimal strategy including the quotations of the renewable energy generators and the conventional energy generators in the day-ahead electricity market and the carbon market respectively, wherein the electricity market includes the day-ahead electricity market and the real-time electricity market; and a market clearing model construction module, used to establish a lower-level joint market clearing model of the electricity market and the carbon market, clearing the day-ahead electricity market, the real-time electricity market and the carbon market according to the quotations in the day-ahead electricity market and the carbon market, so as to obtain the day-ahead electricity market clearing result, the real-time electricity market clearing result and the carbon market clearing result and feed them back to the upper-level game model.

[0066] Based on the further improvement of the above device, in the upper-level game model, the Nash equilibrium satisfies the following conditions:

[0067]

[0068]

[0069] in, are the optimal strategies for renewable energy generator m and conventional energy generator n to participate in the electricity and carbon joint market; are the strategies for renewable energy generator m and conventional energy generator n to participate in the electricity and carbon joint market, is the price quoted by the renewable energy generator m in the day-ahead power market, is the price quoted by the renewable energy generator m in the carbon market, is the number of CERs sold by renewable energy generator m at time t, are the quotes of conventional energy generator n in the day-ahead electricity market and carbon market, are the sales and purchase amounts of carbon quotas of conventional energy generator n at time t under scenario s, γ n,t,s is the initial carbon quota decomposition coefficient of the conventional energy generator set n at time t; argmax is a decision variable function used to maximize the benefits of the renewable energy generator set and the conventional energy generator set.

[0070] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0071] 1. This invention establishes a market joint dispatch and clearing model that couples electricity and carbon to analyze the strategic behavior of renewable energy units. A two-stage market framework consisting of day-ahead and real-time markets is established, integrating the joint optimization of electricity energy and reserve. Multiple scenarios are used to describe possible output scenarios of renewable energy. The carbon market adopts a continuous bidding transaction method based on a competitive model. This bidding transaction method generates the most realistic price signal based on the supply and demand tension in the carbon market. After the real-time market is cleared, the renewable energy generator units will consider the sales volume of certified voluntary emission reductions in each period. In the electricity market, physical constraints are fully considered, including the ramping constraints of the generator units and the reserve constraints of the system.

[0072] 2. To model the clearing process of the coupled electricity and carbon markets and the decision-making process of generators, a two-tiered decision-making model based on non-cooperative game theory is required. In the upper-tier model, generators formulate optimal strategies for the two-stage electricity and carbon markets, maximizing their respective revenues in both markets through non-cooperative game theory. This model fully considers the strategic behavior of conventional energy generators, including competition with renewable energy in the day-ahead and real-time markets, as well as the flexible decomposition of sales / purchase quotas and initial carbon quotas in the carbon market. In the lower-tier model, the day-ahead and real-time markets are cleared. After the real-time market in each scenario is cleared, the carbon market operator is responsible for clearing the carbon market.

[0073] 3. Because the decision-making process for the strategic behavior of renewable energy generators is essentially a two-level optimization model that cannot be solved directly, this patent uses the Karush-Kuhn-Tucker (KKT) conditions to transform the original two-level model into a single-level model. The strong duality theorem is then used to transform the nonlinear model into a mixed-integer programming problem, which is then efficiently solved using a unit industry solver. At this point, the decision-making process for each generator unit is a mixed-integer linear programming problem. A diagonalization algorithm is then used to iterate the optimal decision for each generator unit until the generator unit can achieve greater benefits by changing its own strategy, resulting in the optimal decision for both the renewable energy generator unit and the conventional energy generator unit.

[0074] 4. Regarding the decision-making behavior of generators, the reference literature does not consider the bargaining behavior between renewable energy generators (i.e., generators) and conventional energy generators, nor does it consider the flexibility and transferability of conventional energy generators' initial free carbon allowances over time. Regarding electricity market clearing models, the reference literature primarily models the electricity day-ahead market, failing to consider the potential discrepancies between renewable energy generator day-ahead and real-time forecasts.

[0075] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like parts throughout the drawings.

[0077] Figure 1 Flowchart of a joint dispatching and clearing method for the electricity market and the carbon market according to an embodiment of the present invention;

[0078] Figure 2 4 is a block diagram of a two-tier scheduling and clearing model structure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0080] A specific embodiment of the present invention, referring to Figure 1, discloses a joint dispatching and clearing method for the electricity market and the carbon market, comprising: in step S101, establishing an upper-level game model of renewable energy power generation groups and conventional energy power generation groups; in step S102, based on the upper-level game model, obtaining the optimal strategy for the renewable energy power generation groups and the conventional energy power generation groups to participate in the electricity and carbon joint market when the electricity and carbon joint market reaches a Nash equilibrium, the optimal strategy including the quotations of the renewable energy power generation groups and the conventional energy power generation groups in the day-ahead electricity market and the carbon market, respectively, wherein the electricity market includes the day-ahead electricity market and the real-time electricity market; and in step S103, establishing a lower-level joint market clearing model for the electricity market and the carbon market, clearing the day-ahead electricity market, the real-time electricity market and the carbon market according to the quotations in the day-ahead electricity market and the carbon market, so as to obtain the day-ahead electricity market clearing result, the real-time electricity market clearing result and the carbon market clearing result and feed them back to the upper-level game model.

[0081] Compared to existing technologies, the method provided in this embodiment requires the construction of a two-tiered decision-making model based on non-cooperative games to model the clearing process of the coupled electricity and carbon markets and the decision-making process of generators. In the upper-tier model, generators formulate optimal strategies for the two-stage electricity and carbon markets, maximizing their respective revenues in the electricity and carbon markets through non-cooperative games. This model fully considers the strategic behavior of conventional energy generators, including competition with renewable energy generators in the day-ahead and real-time markets, and the flexible decomposition of sales / purchase quotas and initial carbon quotas in the carbon market. In the lower-tier model, clearing of the day-ahead and real-time markets is completed. After the real-time market in each scenario is cleared, the carbon market operator is responsible for clearing the carbon market. Finally, the combined dispatch optimization results of the electricity and carbon markets are obtained.

[0082] In the following, reference Figure 1 Each step in the joint dispatching and clearing method of the electricity market and the carbon market according to an embodiment of the present invention is described in detail.

[0083] In step S101, an upper-level game model for renewable energy generators and conventional energy generators is established. Establishing the upper-level game model for renewable energy generators and conventional energy generators further includes: constructing an objective function for maximizing the total revenue of renewable energy generator m in the electricity and carbon markets based on the day-ahead electricity market revenue, real-time electricity market revenue, and carbon market revenue of the renewable energy generator, and constructing its related constraints; and constructing an objective function for maximizing the total revenue of conventional energy generator n in the electricity and carbon markets based on the day-ahead electricity market revenue, real-time electricity market revenue, and carbon market revenue of the conventional energy generator, and constructing its related constraints.

[0084] Specifically, the objective function for maximizing the total revenue of renewable energy generator m in the electricity and carbon markets is:

[0085]

[0086]

[0087]

[0088]

[0089] The relevant constraints are:

[0090]

[0091]

[0092] τ=0.75F OM +0.25F BM Formula 9

[0093]

[0094]

[0095] Among them, the superscripts DA, RT, and CM represent the variables in the day-ahead power market, the real-time power market, and the carbon market, respectively. are the revenues of renewable energy generator m in the day-ahead electricity market, real-time electricity market, and carbon market, respectively. The objective function of maximizing the total revenue of renewable energy generator m in electricity and carbon markets in Formula 3 represents the total revenue maximization goal of renewable energy generator m in bidding decision-making. In the day-ahead electricity market, the objective function of maximizing the total revenue of renewable energy generator m in Formula 4 is Clearing electricity prices for the day-ahead electricity market; is the day-ahead electricity market clearing quantity of renewable energy generator m at time t; in the real-time electricity market, the subscript s represents the scenario s, and π in Formula 5 s is the probability of scenario s occurring, is the clearing electricity price at time t in scenario s, is the clearing electricity of renewable energy generator m at time t under scenario s. In the carbon market, is the clearing price of the carbon market at time t in scenario s;

[0096] Formulas 7 to 9 are the constraints for renewable energy generator m to participate in the carbon market. Formula 7 shows that renewable energy generator m is the limit on the amount of certified voluntary emission reductions sold in the carbon market. is the number of certified voluntary emission reductions obtained by renewable energy generator m at time t in scenario s, τ in formula 8 is the baseline emission factor, that is, the carbon dioxide emission reduction per unit of renewable energy power, and F in formula 9 is OMis the marginal emission factor of electricity, F BM is the capacity marginal emission factor;

[0097] In formula 10 is the day-ahead electricity market quotation of renewable energy generator m, are the upper and lower limits of the day-ahead electricity market bids for renewable energy generator m, respectively. is the carbon market quotation of renewable energy generator m, are the upper and lower limits of the carbon market quotation of renewable energy generator m.

[0098] The objective function of maximizing the total revenue of conventional energy generator n in the electricity and carbon markets is expressed by the following formula:

[0099]

[0100]

[0101]

[0102]

[0103] Relevant constraints:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] Among them, in formula 12 are the revenues of conventional energy generator n in the day-ahead electricity market, real-time electricity market and carbon market respectively. In the day-ahead electricity market, λ in Formula 13 n,t is the marginal cost of conventional energy generator n, is the day-ahead electricity market clearing quantity of conventional energy generator n at time t. In the real-time electricity market, is the real-time electricity market clearing quantity of conventional energy generator n at time t under scenario s. In the carbon market, when Conventional energy generator n obtains carbon quota income, when When the conventional energy generator n needs to pay the carbon quota cost, represents the net surplus of carbon quota of conventional energy generator n under scenario s at time t;

[0113] In formulas 17 and 18 and are the purchase and sales of carbon quotas for conventional energy generators n, u i is a binary variable and M is a sufficiently large positive number, the formula in 19 is the net surplus of carbon quotas of conventional energy power generation units n, The initial free carbon quota allocated to conventional energy generator n; γ in Formula 20 n,t,s is the constraint condition of the initial carbon quota decomposition coefficient, σ in Formula 21 n is the carbon emission coefficient of conventional energy generator n; and

[0114] In formula 23 are the upper and lower limits of the carbon market quotation of conventional energy power generation unit n, is the carbon market quotation of conventional energy generator n, and is the day-ahead electricity market quotation of conventional energy generator n, are the upper and lower limits of the quotations of conventional energy generator n in the day-ahead electricity market.

[0115] In step S102, based on the upper-level game model, when the electricity and carbon joint market reaches Nash equilibrium, the optimal strategy for renewable energy generators and conventional energy generators to participate in the electricity and carbon joint market is obtained. The optimal strategy includes the quotations of renewable energy generators and conventional energy generators in the day-ahead electricity market and carbon market, respectively, wherein the electricity market includes the day-ahead electricity market and the real-time electricity market. The quotation in the day-ahead electricity market refers to the quotation of the generator in the day-ahead market. The real-time market clearing optimization also uses the quotation information sealed in the day-ahead market, that is, a quotation shared by the day-ahead electricity market and the real-time electricity market in the electricity market. Since the time scale of the real-time electricity market is too large, the generator does not have time to make a quotation decision. In the upper-level game model, Nash equilibrium satisfies the following conditions:

[0116]

[0117]

[0118] in, are the optimal strategies for renewable energy generator m and conventional energy generator n to participate in the electricity and carbon joint market; are the strategies for renewable energy generator m and conventional energy generator n to participate in the electricity and carbon joint market, is the bid of renewable energy generator m in the day-ahead electricity market, is the price quoted by renewable energy generator m in the carbon market, is the number of certified emission reductions sold by renewable energy generator m at time t, are the quotes of conventional energy generator n in the day-ahead electricity market and carbon market, are the sales and purchase amounts of carbon quotas of conventional energy generator n at time t under scenario s, γ n,t,s is the initial carbon quota decomposition coefficient of conventional energy generator n at time t; arg max is the decision variable function used to maximize the benefits of renewable energy generators and conventional energy generators.

[0119] In step S103, a lower-level joint market clearing model of the electricity market and the carbon market is established, and the day-ahead electricity market, real-time electricity market and carbon market are cleared according to the quotations in the electricity market and the carbon market to obtain the day-ahead electricity market clearing results, the real-time electricity market clearing results and the carbon market clearing results and feed them back to the upper-level game model. Establishing a lower-level joint market clearing model for the electricity market and the carbon market further includes: constructing an electricity market clearing model based on the day-ahead electricity market cost and the expected cost of the real-time electricity market as the electricity market cost minimization objective function, wherein the day-ahead electricity market cost minimization objective function is constructed based on the electricity energy cost and the reserve cost of the day-ahead electricity market and its related constraints are constructed; constructing the expected cost of the real-time electricity market based on the system operation adjustment cost caused by the output deviation of renewable energy under scenario s, the wind and solar power abandonment cost under scenario s, and the load shedding cost under scenario s, and its related constraints are constructed; constructing a carbon market clearing model based on the carbon market quotation of conventional energy power generation units, the carbon market sales volume decision of conventional energy power generation units, the purchase volume decision of conventional energy power generation units, the carbon market quotation of renewable energy power generation units, and the carbon market sales volume decision of renewable energy power generation units as the carbon market social welfare maximization objective function, and its related constraints are constructed.

[0120] The objective function of minimizing electricity market cost is:

[0121] min C=C DA +C RT Formula 24

[0122] Where C in Formula 24 is the total cost of the electricity market, C DAis the day-ahead electricity market cost, C RT is the expected cost of the real-time electricity market;

[0123] The objective function of minimizing the cost of the day-ahead electricity market is expressed by the following formula:

[0124]

[0125] are the electricity energy cost and reserve cost in the day-ahead power market respectively;

[0126]

[0127]

[0128] In formula 27 are the upper reserve and lower reserve prices of conventional energy generator set n, are the upper reserve and lower reserve capacities reserved by conventional energy generating unit n in the day-ahead power market;

[0129] The day-ahead market operating constraints are shown in formulas 28 to 32:

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] Constraint formula 28 represents the power balance between power generation and power consumption in the day-ahead market. is the load demand of user l at time t. Formula 29 shows that renewable energy generator m represents the power output constraint, The predicted output of renewable energy generator m at time t; are the upper and lower limits of the output of conventional energy generator set n. Formula 30 is used to express the ramp constraint of conventional energy generator set n. are the ramp-up and ramp-down rates of conventional energy generator set n, respectively. Formula 31 represents the power generation output and standby output constraints of conventional energy generator set n. Formula 32 represents the output-up and output-down capabilities of conventional units in each period, which must meet the actual system operation requirements for output-up and output-down. are the reserve requirements for the system during period t, which are adjusted upward and downward, respectively, taking 5% of the total load. Furthermore, it should be noted that the day-ahead power market decision variables are market clearing results, including the winning bid output of renewable energy generators, the winning bid output of conventional energy generators, and the upper and lower reserve outputs of conventional energy generators. is a decision variable in the upper problem, but is a known parameter in the lower problem. is the dual variable of the corresponding constraint.

[0136]

[0137] is the system operation adjustment cost caused by the output deviation of renewable energy generators in scenario s, is the cost of curtailing wind and solar power in scenario s, is the load shedding cost for scenario s;

[0138]

[0139]

[0140]

[0141] In formula 34 is the day-ahead electricity market clearing quantity of conventional energy generator n at time t, is the real-time electricity market clearing quantity of conventional energy generator n at time t under scenario s, Ultra-short-term predicted output of renewable energy generator m at time t under scenario s; is the day-ahead electricity market clearing quantity of renewable energy generator m at time t, is the carbon market quotation of conventional energy generator n, is the day-ahead electricity market quotation of conventional energy generator n, and in formula 35 is the penalty coefficient for curtailing wind and solar power, is the amount of wind and solar power curtailment by renewable energy generator m at time t; is the load loss penalty coefficient, is the load loss of the system at time t in scenario s.

[0142] The clearing constraints of the real-time market are similar to those of the day-ahead market. The most important difference is that the real-time market takes into account multiple possible output scenarios of wind power and photovoltaic power. The clearing constraints of the real-time market are shown in formulas 37 to 42.

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] Equation 37 represents the deviation between the day-ahead forecast and actual output of renewable energy generators in the real-time market, which causes adjustments in the output of conventional energy generators and user load demand. Equation 37 ensures rebalancing of power generation and consumption in the real-time market. Equation 38 calculates the real-time market output of conventional energy generator n. Equations 39-41 represent the output constraints of renewable energy generator m, the output adjustment constraints of conventional energy generator n, and the ramping constraints. Equation 42 represents the wind and solar curtailment constraints of renewable energy generator m and the load shedding constraints of users.

[0150] The objective function of maximizing social welfare in the carbon market is:

[0151]

[0152] Relevant constraints:

[0153]

[0154]

[0155]

[0156]

[0157]

[0158] Formula 44 is the supply and demand equilibrium constraint in the carbon market. Formulas 45 and 46 are the upper and lower limit constraints of carbon market transactions when conventional energy generator n is the seller and buyer, respectively. Formula 47 is the upper and lower limit constraints of renewable energy generator m selling certified voluntary emission reductions in the carbon market. Formula 48 represents the carbon quota clearance constraint of conventional energy generator n, which needs to ensure that the initial carbon emission quota obtained within the compliance period is The net carbon emission quota accumulated during the T period Should be greater than or equal to the actual total carbon emissions during period T

[0159] Hereinafter, the joint dispatching and clearing method of the electricity market and the carbon market according to an embodiment of the present invention is described in detail by way of specific examples.

[0160] This example proposes a joint dispatch and clearing model for the electricity and carbon markets. This model includes a two-stage electricity market (a day-ahead market and a real-time market), which takes into account both electricity and system reserve requirements, and a carbon market. Renewable energy generators and conventional energy generators participate in the combined electricity and carbon market. Generators maximize their profits by continuously adjusting their trading strategies. When all generators in the market are unable to maximize their market returns by changing their trading strategies, the electricity and carbon markets reach a Nash equilibrium, resulting in optimal generator output under the combined electricity and carbon market.

[0161] Electricity-Carbon Joint Market Model: The joint dispatch and clearing of electricity and carbon markets is essentially a two-level decision-making problem. In the upper-level model, it is necessary to consider the maximization of revenue for power generation entities, and then report the capacity and price information of power generation units to the lower-level model, corresponding to the day-ahead market, real-time market, and carbon market clearing respectively. The detailed structure of the two-level model is as follows: Figure 2 shown.

[0162] The generator unit makes decisions simultaneously for the day-ahead market, the real-time market, and the carbon market. Market declaration information is then transmitted to these three markets. The three lower-level markets then clear, generating a market clearing result. The ISO and the carbon market trading center then pass the results on to the upper-level decision-making optimization problem. The detailed model for the upper-level and lower-level problems is as follows.

[0163] 1. Upper model: Non-cooperative game profit maximization of power generation units.

[0164] In the electricity market, the decision variable for a generator is the market-cleared electricity volume it generates during each time period. In the carbon market, the decision variable for renewable energy generators is the amount of certified voluntary emission reductions they sell. Conventional energy generators make decisions about the initial carbon allowance decomposition, purchase, and sale of carbon allowances. In the upper-level model, each generator seeks to maximize its own profits by considering its competitors' strategies. Renewable and conventional energy generators engage in a game-playing interaction in the combined electricity and carbon market to maximize their own profits, exhibiting non-cooperative game behavior. In this game model, players aim to find a Nash equilibrium as the optimal strategy for the market game. This Nash equilibrium must satisfy Equations 1 and 2 above.

[0165] When electricity market and carbon market transactions reach Nash equilibrium, each renewable energy generator and conventional energy generator believes that no matter how other participants formulate strategies, they have no motivation to adjust their own strategies. This indicates that no generator can obtain more benefits by changing its own strategy.

[0166] (1) Renewable energy generators: The decision-making of renewable energy generators mainly considers how to maximize their own energy and environmental value. Unlike the existing technology that uses a fixed carbon price or a tiered carbon price to calculate the benefits, this patent considers the flexible sale of renewable energy in the carbon market, while taking into account the behavior of conventional energy generators in the electricity and carbon markets. In short, this model can accurately describe the behavior of renewable energy generators and incorporate it into the scheduling optimization and clearing of the two-stage electricity and carbon markets, thereby realizing a joint scheduling and clearing optimization method that is closest to the actual power system operation optimization.

[0167] Renewable energy generators primarily earn revenue from the two-stage electricity market and the carbon market. The electricity market revenue comes from the sale of electricity, while the carbon market revenue comes from the sale of certified voluntary emission reductions. In the electricity market, the variable costs of wind and photovoltaic power generation are negligible and small. The objective function for maximizing the total revenue of renewable energy generator m in the electricity and carbon markets is shown in Equations 3 to 11 above.

[0168] It should be noted that the decision variables in the upper model are the quotes of renewable energy generator m in the day-ahead market. Carbon market quotes Sales volume decisions in the carbon market The clearing amount of renewable energy generator m in the electricity market, the clearing amount in the carbon market, as well as the electricity market clearing price and carbon market clearing price are the optimization results of the lower model and are regarded as parameters in the upper model.

[0169] (2) Conventional energy generators: The revenue of conventional energy generators mainly comes from the electricity sales revenue in the two-stage electricity market and the carbon quota revenue (which may also be costs) in the carbon market. The cost of conventional energy power generation is mainly the coal consumption cost. While mainly considering the bidding decision of conventional energy generators in the electricity market and the purchase and sale decision of carbon quotas in the carbon market, this patent further considers the decomposition decision of the free initial carbon quota, further characterizing the rational behavior of conventional energy generators in the electricity-carbon market under the carbon emission constraints.

[0170] In addition, the decision variables in the upper model are the quotes of conventional energy generators n in the day-ahead market. Carbon market quotes Sales volume decisions in the carbon market Purchase quantity decision Decomposition decision of initial carbon quota γ n,t,s Similar to the optimization decision of renewable energy, the clearing amount of conventional energy generator n in the electricity market, the clearing amount in the carbon market, and the electricity market clearing price and carbon market clearing price are the optimization results of the lower model and are regarded as parameters in the upper model.

[0171] 2. Lower model: joint market clearing model.

[0172] Electricity market clearing model: The electricity market clearing model constructed in this paper mainly considers the effective coordination of two aspects. The first is the coordinated optimization of the day-ahead market and the real-time market; the second is the coordinated optimization of electric energy and reserve. Based on the coupling characteristics of the day-ahead and real-time, and electric energy and reserve in the electricity market, a day-ahead and real-time electricity market clearing model combining energy and reserve is established. This modeling approach has two advantages: on the one hand, the detailed characterization of energy value and security value can fully reflect the cost of renewable energy development and utilization; on the other hand, the coupling of the day-ahead and real-time stages can achieve effective connection between the day-ahead and real-time markets and reflect the changes in the benefits of renewable energy due to output deviations. The electricity market clearing model is based on the output uncertainty of wind power and photovoltaic renewable energy, with the minimization of electricity market cost as the optimization goal, and the objective function is formula 24.

[0173] (1) Day-ahead market: Formulas 25 to 27 represent day-ahead market costs. Day-ahead market operating constraints are shown in Formulas 28 to 32. It should be noted that the decision variables for the day-ahead electricity market are market clearing results, including the winning bid output of renewable energy generators, the winning bid output of conventional energy generators, and the upper and lower reserve output of conventional energy. is a decision variable in the upper problem, but is a known parameter in the lower problem. is the dual variable of the corresponding constraint.

[0174] (2) Real-time market: The expected cost of the real-time market is expressed as Equations 33 to 36 above. The clearing constraints of the real-time market are similar to those of the day-ahead market. The most important difference is that the real-time market considers multiple possible output scenarios for wind power and photovoltaic power. The clearing constraints of the real-time market are shown in Equations 37 to 42 above.

[0175] Equation 37 represents the deviation between the day-ahead forecast and actual output of renewable energy generators in the real-time market, which causes adjustments in the output of conventional energy generators and user load demand. Equation 37 ensures the rebalancing of power generation and consumption in the real-time market. Equation 38 calculates the real-time market output of conventional energy generator n. Equations 39 through 41 represent the output constraints of renewable energy generator m, the output adjustment constraints of conventional energy generator n, and the ramping constraints. Equation 42 represents the wind and solar curtailment constraints of renewable energy generator m and the user load shedding constraints.

[0176] Carbon Market Clearing Model: The power generation industry, as the largest contributor to carbon emissions reduction, was the first to be introduced into the national carbon market. Therefore, this patent centrally optimizes the clearing of conventional and renewable energy generators within the carbon market. The goal of carbon market optimization is to maximize social welfare, as shown in Equation 43. Related constraints are shown in Equations 44 through 48.

[0177] 3. Model solution: Equivalent transformation of the lower-level model.

[0178] When the lower-level model serves as a constraint for the upper-level model, the decision variables of the upper-level decision model do not have an explicit function expression, so it is difficult to solve directly. The solution idea of ​​the two-level transaction model designed in this embodiment is as follows:

[0179] (1) KKT conditions of the electricity market clearing model: Definition of the Lagrangian function of the electricity market clearing model

[0180]

[0181] The L1 function is respectively Find the partial derivative and set it equal to 0. The calculation process is shown in Formula 50 to Formula 59.

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191]

[0192] The complementary slack condition is

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203]

[0204]

[0205]

[0206] (2) KKT condition of the carbon market clearing model: The Lagrangian function of the carbon market clearing model is defined as

[0207]

[0208] The L2 function is respectively Find the partial derivative and set it equal to 0. The calculation process is shown in Formulas 74 to 76.

[0209]

[0210]

[0211]

[0212] The complementary slack condition is

[0213]

[0214]

[0215]

[0216]

[0217] Linearization method: After the equivalent treatment of the lower-level model described above, we transform the two-level model into a single-level model. Nonlinear terms still exist in the transformed single-level model, which can be mainly divided into two categories: (1) the product of two decision variables, such as the product of the day-ahead market clearing price and the clearing quantity, the product of the real-time market clearing price and the clearing quantity, and the product of the carbon market clearing price and the clearing quantity. (2) The complementary slack constraints generated by the KKT condition. The large M method can be used for linearization.

[0218] (1) Nonlinearity caused by multiplying two decision variables: The nonlinear terms caused by this situation mainly include And in formula 5 And in formula 13 And in formula 14

[0219] First, nonlinear As an example, we can linearize it by using the strong duality theorem and obtain the equality relationship between the objective functions of the original problem and the dual problem:

[0220]

[0221] Secondly, by using formula 51, Substituting other variables, the day-ahead market revenue of renewable energy units Can be converted into

[0222]

[0223] In order to express it more clearly, we use Represents the calculation items in formula 82, as follows

[0224]

[0225]

[0226]

[0227] for Formula 81 can be used for substitution to obtain:

[0228]

[0229] for Through the complementary relaxation condition formula 60, we can further obtain

[0230]

[0231] for Formula 55 can be used to convert Substituting other variables, we can get

[0232]

[0233] Other nonlinearities caused by the multiplication of two decision variables can also be handled using the above method.

[0234] (2) Nonlinearity caused by complementary relaxation conditions: The nonlinear terms caused by complementary relaxation conditions mainly exist in formulas 60-72 and 77-80, which can be processed using the following process. The original mutual non-relaxation condition can be written as follows

[0235] 0≤f(x)⊥g(y)≥0 Formula 89

[0236] Introducing a binary variable c and a sufficiently large number M, Formula 89 can be rewritten as

[0237] 0≤f(x)≤Mc Formula 90

[0238] 0≤g(y)≤M(1-c) Formula 91

[0239] Another specific embodiment of the present invention is a joint scheduling and clearing device for the electricity market and the carbon market, including: a game model construction module, used to establish an upper-level game model of renewable energy generators and conventional energy generators; an optimal strategy acquisition module, used to obtain the optimal strategy for renewable energy generators and conventional energy generators to participate in the electricity and carbon joint market based on the upper-level game model when the electricity and carbon joint market reaches Nash equilibrium, the optimal strategy including the quotations of renewable energy generators and conventional energy generators in the day-ahead electricity market and the carbon market respectively, wherein the electricity market includes the day-ahead electricity market and the real-time electricity market; and a market clearing model construction module, used to establish a lower-level joint market clearing model for the electricity market and the carbon market, clearing the day-ahead electricity market, the real-time electricity market and the carbon market according to the quotations in the day-ahead electricity market and the carbon market, so as to obtain the day-ahead electricity market clearing result, the real-time electricity market clearing result and the carbon market clearing result and feed them back to the upper-level game model.

[0240] In the upper-level game model, Nash equilibrium satisfies the following conditions: Formula 1 and Formula 2 above.

[0241] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0242] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A joint dispatching and clearing method for the electricity market and the carbon market, characterized in that: include: Establish an upper-level game model between renewable energy generators and conventional energy generators; Based on the upper-level game model, when the combined electricity and carbon market reaches a Nash equilibrium, obtaining optimal strategies for the renewable energy generator set and the conventional energy generator set to participate in the combined electricity and carbon market, the optimal strategies including the bids of the renewable energy generator set and the conventional energy generator set in the day-ahead electricity market and the carbon market, respectively, wherein the electricity market includes the day-ahead electricity market and the real-time electricity market, and the optimal strategies need to take into account the strategic behavior of the conventional energy generator set, including competition with the renewable energy generator set in the day-ahead electricity market and the real-time electricity market; and Establish a lower-layer joint market clearing model for the electricity market and the carbon market, clear the day-ahead electricity market, the real-time electricity market, and the carbon market based on the quotes in the day-ahead electricity market and the carbon market, obtain the day-ahead electricity market clearing results, the real-time electricity market clearing results, and the carbon market clearing results, and feed them back to the upper-layer game model. Among them, establishing the upper-level game model of renewable energy power generation units and conventional energy power generation units further includes: Construct the objective function max R of maximizing the total revenue of the renewable energy generator m in the electricity and carbon markets m And construct its related constraints, the objective function max R m Specifically: Among them, the superscripts DA, RT, and CM represent the variables in the day-ahead power market, the real-time power market, and the carbon market, respectively. are the revenues of renewable energy generator m in the day-ahead electricity market, real-time electricity market and carbon market respectively. The objective function of maximizing the total revenue of renewable energy generator m in electricity and carbon markets in formula 3 represents the maximization of the total revenue of renewable energy generator m in bidding decision. In the day-ahead electricity market, the objective function of maximizing the total revenue of renewable energy generator m in formula 4 is Clearing electricity prices for the day-ahead electricity market; is the day-ahead electricity market clearing quantity of renewable energy generator m at time t; in the real-time electricity market, the subscript s represents the scenario s, and π in Formula 5 s is the probability of scenario s occurring, is the clearing electricity price at time t in scenario s, is the clearing electricity of renewable energy generator m at time t under scenario s. In the carbon market, is the clearing price of the carbon market at time t in scenario s, is the number of CERs sold by renewable energy generator m at time t; Construct the objective function max R of maximizing the total revenue of the conventional energy generator n in the electricity and carbon markets n And construct its related constraints, the objective function max R n Specifically: Among them, in formula 12 are the revenues of conventional energy generator n in the day-ahead electricity market, real-time electricity market and carbon market respectively. In the day-ahead electricity market, λ in Formula 13 n,t is the marginal cost of conventional energy generator n, is the day-ahead electricity market clearing quantity of conventional energy generator n at time t. In the real-time electricity market, is the real-time electricity market clearing quantity of conventional energy generator n at time t under scenario s. In the carbon market, when Conventional energy generator n obtains carbon quota income, when When , conventional energy generator n needs to pay the carbon quota cost; represents the net surplus of carbon quota of conventional energy generator n under scenario s at time t; Among them, establishing the lower-level joint market clearing model of the electricity market and the carbon market further includes: constructing an electricity market clearing model based on the day-ahead electricity market cost and the expected cost of the real-time electricity market as the electricity market cost minimization objective function, wherein the day-ahead electricity market cost minimization objective function is constructed based on the electric energy cost and the reserve cost of the day-ahead electricity market and its related constraints are constructed, and the reserve cost is calculated by the upper reserve capacity, lower reserve capacity and their corresponding prices reserved by the conventional energy generator n in the day-ahead electricity market; constructing the expected cost of the real-time electricity market based on the system operation adjustment cost caused by the output deviation of renewable energy under scenario s, the wind and solar power abandonment cost under scenario s, and the load shedding cost under scenario s, and constructing its related constraints; constructing a carbon market clearing model as the carbon market social welfare maximization objective function, and constructing its related constraints, and the carbon market social welfare maximization objective function is: in, are the quotes of the renewable energy generator m and conventional energy generator n in the day-ahead power market, are the quotes of the renewable energy generator m and conventional energy generator n in the carbon market, are the sales and purchase amounts of carbon quotas of conventional energy generator n at time t under scenario s; The constraints for clearing the real-time electricity market are: Formula 37 represents the deviation between the day-ahead output forecast and actual output of renewable energy generators in the real-time power market, which causes adjustments in the output of conventional energy generators and user load demand. Formula 38 represents the output calculation of conventional energy generator n in the real-time power market. Formulas 39-41 represent the output constraints of renewable energy generator m, the output adjustment constraints of conventional energy generator n, and the ramping constraints. Formula 42 represents the wind and solar curtailment constraints of renewable energy generator m and the load shedding constraints of users. In formula 37 is the amount of wind and solar power abandoned by renewable energy generator m at time t, is the load loss of the system at time t in scenario s; are the upper and lower limits of the output of conventional energy generator set n respectively; are the upper and lower reserve capacities reserved by conventional energy generator n in the day-ahead power market; in formula 41 are the ramp-up rate and ramp-down rate of conventional energy generator set n respectively; is the load demand of user l at time t.

2. The joint dispatching and clearing method of the electricity market and the carbon market according to claim 1 is characterized in that: In the upper-level game model, the Nash equilibrium satisfies the following conditions: in, are the optimal strategies for renewable energy generator m and conventional energy generator n to participate in the electricity and carbon joint market; are the strategies for renewable energy generator m and conventional energy generator n to participate in the electricity and carbon joint market, is the price quoted by the renewable energy generator m in the day-ahead power market, is the price quoted by the renewable energy generator m in the carbon market, is the number of CERs sold by renewable energy generator m at time t, are the quotes of conventional energy generator n in the day-ahead electricity market and carbon market, are the sales and purchase amounts of carbon quotas of conventional energy generator n at time t under scenario s, γ n,t,s is the initial carbon quota decomposition coefficient of the conventional energy generator set n at time t; argmax is a decision variable function used to maximize the benefits of the renewable energy generator set and the conventional energy generator set.

3. The joint dispatching and clearing method of the electricity market and the carbon market according to claim 2 is characterized in that: The relevant constraints of the renewable energy generator set m are: τ=0.75F OM +0.25F BM Formula 9 Formulas 7 to 9 are the constraints for renewable energy generator m to participate in the carbon market. Formula 7 shows that renewable energy generator m is the limit on the amount of certified voluntary emission reductions sold in the carbon market. is the number of certified voluntary emission reductions obtained by renewable energy generator m at time t in scenario s, τ in formula 8 is the baseline emission factor, that is, the carbon dioxide emission reduction per unit of renewable energy power, and F in formula 9 is OM is the marginal emission factor of electricity, F BM is the capacity marginal emission factor; In formula 10 is the day-ahead electricity market quotation of renewable energy generator m, are the upper and lower limits of the day-ahead electricity market bids for renewable energy generator m, respectively. is the carbon market quotation of renewable energy generator m, are the upper and lower limits of the carbon market quotation of renewable energy generator m.

4. The joint dispatching and clearing method of the electricity market and the carbon market according to claim 2 is characterized in that: The relevant constraints of the conventional energy generator set are: In formulas 17 and 18 and are the purchase and sales of carbon quotas for conventional energy generators n, u i is a binary variable of 0 or 1 and M is a sufficiently large positive number, the The initial free carbon quota allocated to conventional energy generator n, γ n,t,s is the initial carbon quota decomposition coefficient; γ in Formula 20 n,t,s is the initial carbon quota decomposition coefficient constraint, σ in Formula 21 n is the carbon emission coefficient of conventional energy generator set n; as well as In formula 23 are the upper and lower limits of the carbon market quotation of conventional energy power generation unit n, is the carbon market quotation of conventional energy generator n, and is the day-ahead electricity market quotation of conventional energy generator n, are the upper and lower limits of the quotations of conventional energy generator n in the day-ahead electricity market.

5. The joint dispatching and clearing method of the electricity market and the carbon market according to claim 4 is characterized in that: Relevant constraints of the carbon market: Formula 44 is the supply and demand equilibrium constraint in the carbon market. Formulas 45 and 46 are the upper and lower limit constraints of carbon market transactions when conventional energy generator n is the seller and buyer, respectively. Formula 47 is the upper and lower limit constraints of renewable energy generator m selling certified voluntary emission reductions in the carbon market. Formula 48 represents the carbon quota clearance constraint of conventional energy generator n, which needs to ensure that the initial carbon emission quota obtained within the compliance period is The net carbon emission quota accumulated during period T Should be greater than or equal to the actual total carbon emissions during period T 6. A joint dispatching and clearing device for the electricity market and the carbon market, characterized in that: include: A game model building module is used to establish upper-level game models for renewable energy generators and conventional energy generators; an optimal strategy acquisition module, configured to acquire, based on the upper-level game model, an optimal strategy for the renewable energy generator set and the conventional energy generator set to participate in the combined electricity and carbon market when the combined electricity and carbon market reaches a Nash equilibrium, the optimal strategy including the quotes of the renewable energy generator set and the conventional energy generator set in the day-ahead electricity market and the carbon market, respectively, wherein the electricity market includes the day-ahead electricity market and the real-time electricity market, and the optimal strategy needs to take into account the strategic behavior of the conventional energy generator set, including competition with the renewable energy generator set in the day-ahead electricity market and the real-time electricity market; and The market clearing model construction module is used to establish a lower-level joint market clearing model for the electricity market and the carbon market, and to clear the day-ahead electricity market, the real-time electricity market, and the carbon market according to the quotations in the day-ahead electricity market and the carbon market, so as to obtain the day-ahead electricity market clearing results, the real-time electricity market clearing results, and the carbon market clearing results and feed them back to the upper-level game model. Among them, establishing the upper-level game model of renewable energy power generation units and conventional energy power generation units further includes: The objective function for maximizing the total revenue of the renewable energy generator m in the electricity and carbon markets is: Among them, the superscripts DA, RT, and CM represent the variables in the day-ahead power market, the real-time power market, and the carbon market, respectively. are the revenues of renewable energy generator m in the day-ahead electricity market, real-time electricity market and carbon market respectively. The objective function of maximizing the total revenue of renewable energy generator m in electricity and carbon markets in formula 3 represents the maximization of the total revenue of renewable energy generator m in bidding decision. In the day-ahead electricity market, the objective function of maximizing the total revenue of renewable energy generator m in formula 4 is Clearing electricity prices for the day-ahead electricity market; is the day-ahead electricity market clearing quantity of renewable energy generator m at time t; in the real-time electricity market, the subscript s represents the scenario s, and π in Formula 5 s is the probability of scenario s occurring, is the clearing electricity price at time t in scenario s, is the clearing electricity of renewable energy generator m at time t under scenario s. In the carbon market, is the clearing price of the carbon market at time t in scenario s, is the number of CERs sold by renewable energy generator m at time t; The objective function of maximizing the total revenue of the conventional energy generator n in the electricity and carbon markets is expressed by the following formula: Among them, in formula 12 are the revenues of conventional energy generator n in the day-ahead electricity market, real-time electricity market and carbon market respectively. In the day-ahead electricity market, λ in Formula 13 n,t is the marginal cost of conventional energy generator n, is the day-ahead electricity market clearing quantity of conventional energy generator n at time t. In the real-time electricity market, is the real-time electricity market clearing quantity of conventional energy generator n at time t under scenario s. In the carbon market, when Conventional energy generator n obtains carbon quota income, when When , conventional energy generator n needs to pay the carbon quota cost; represents the net surplus of carbon quota of conventional energy generator n under scenario s at time t; Among them, establishing the lower-level joint market clearing model of the electricity market and the carbon market further includes: constructing an electricity market clearing model based on the day-ahead electricity market cost and the expected cost of the real-time electricity market as the electricity market cost minimization objective function, wherein the day-ahead electricity market cost minimization objective function is constructed based on the electric energy cost and the reserve cost of the day-ahead electricity market and its related constraints are constructed, and the reserve cost is calculated by the upper reserve capacity, lower reserve capacity and their corresponding prices reserved by the conventional energy generator n in the day-ahead electricity market; constructing the expected cost of the real-time electricity market based on the system operation adjustment cost caused by the output deviation of renewable energy under scenario s, the wind and solar power abandonment cost under scenario s, and the load shedding cost under scenario s, and constructing its related constraints; constructing a carbon market clearing model as the carbon market social welfare maximization objective function, and constructing its related constraints; the carbon market social welfare maximization objective function is: in, is the price quoted by the renewable energy generator m in the day-ahead power market, are the quotes of conventional energy generator n in the day-ahead power market, is the price quoted by the renewable energy generator m in the carbon market, are the sales and purchase amounts of carbon quotas of conventional energy generator n at time t under scenario s; The constraints for clearing the real-time electricity market are: Formula 37 represents the deviation between the day-ahead output forecast and actual output of renewable energy generators in the real-time electricity market, which causes adjustments in the output of conventional energy generators and user load demand. Formula 38 represents the real-time market output calculation for conventional energy generator n. Formulas 39-41 represent the output constraints of renewable energy generator m, the output adjustment constraints of conventional energy generator n, and the ramping constraints. Formula 42 represents the wind and solar curtailment constraints of renewable energy generator m and the load shedding constraints of users. In formula 37 is the amount of wind and solar power abandoned by renewable energy generator m at time t, is the load loss of the system at time t in scenario s; are the upper and lower limits of the output of conventional energy generator set n respectively; are the upper and lower reserve capacities reserved by conventional energy generator n in the day-ahead power market; in formula 41 are the ramp-up rate and ramp-down rate of conventional energy generator set n respectively; is the load demand of user l at time t.

7. The combined dispatching and clearing device for the electricity market and the carbon market according to claim 6 is characterized in that: In the upper-level game model, the Nash equilibrium satisfies the following conditions: in, are the optimal strategies for renewable energy generator m and conventional energy generator n to participate in the electricity and carbon joint market; are the strategies for renewable energy generator m and conventional energy generator n to participate in the electricity and carbon joint market, is the price quoted by the renewable energy generator m in the day-ahead power market, is the price quoted by the renewable energy generator m in the carbon market, is the number of certified emission reductions sold by renewable energy generator m at time t, are the quotes of conventional energy generator n in the day-ahead electricity market and carbon market, are the sales and purchase amounts of carbon quotas of conventional energy generator n at time t under scenario s, γ n,t,s is the initial carbon quota decomposition coefficient of the conventional energy generator set n at time t; argmax is a decision variable function used to maximize the benefits of the renewable energy generator set and the conventional energy generator set.

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