Hybrid interaction dynamic simulation method and system for medium and long term electric energy market and carbon market, and medium

Abstract

The invention discloses a hybrid interaction dynamic simulation method and system for a medium and long term electric energy market and a carbon market, and a medium, and belongs to the technical field of electric-carbon market simulation. The method comprises the following steps: constructing a physical facility model based on attributes of various physical facilities; constructing a market subject model based on the physical facility model and the role of the market subject in the electricity market or the carbon market; performing spatial scale division and time scale division on the electricity market and the carbon market to respectively obtain a physical operation boundary of the electricity market and an organization mode of clearing the electricity market and the carbon market; constructing an electricity market clearing model; constructing a carbon market clearing model; and carrying out transaction simulation on the electricity market and the carbon market, and respectively solving the electricity market clearing model and the carbon market clearing model to obtain the clearing price and the clearing electric quantity of the electricity market and the clearing price and the clearing quantity of the carbon market. According to the method, the endogenous interaction and dynamic evolution of the electricity-carbon market under different boundary conditions and decision behaviors can be simulated.

Description

Technical Field

[0001] This invention belongs to the field of electricity-carbon market simulation technology, specifically involving a hybrid interactive dynamic simulation method, system, and medium for medium- and long-term electricity and carbon markets. Background Technology

[0002] Currently, the coupling trend between the electricity and carbon markets is continuously strengthening. On the one hand, the overlap of market participants in the electricity and carbon markets is increasing. As high-energy-consuming and high-emission industries gradually participate in carbon market trading, market participants will expand from the electricity supply side to the electricity consumption side. On the other hand, coupling mechanisms between the electricity and carbon markets are beginning to emerge. Green electricity certificates and green electricity consumption vouchers can serve as deduction vouchers for non-fossil energy electricity consumption when calculating indirect carbon emissions from electricity. Therefore, considering the essential differences and complex coupling relationship between the electricity and carbon markets, the importance of conducting dynamic simulations of the electricity-carbon market interaction is becoming increasingly prominent in order to ensure the efficient and stable operation of both markets and maximize market efficiency.

[0003] Existing technologies have proposed various simulation methods and platforms for the electricity market and the carbon market, and have conducted simulation studies on the electricity-carbon market based on the interaction of trading behaviors. However, these technologies fail to take a systemic view to coordinate the multi-level coupling relationship of the electricity-carbon market, and do not have the function of conducting comprehensive simulation analysis of the dynamic interaction of the electricity-carbon market. They are not only difficult to adapt to the future coupling trend of the electricity-carbon market, but their practical application effect is also limited to a certain extent. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system and medium for dynamic simulation of the hybrid interaction between the medium- and long-term electricity market and the carbon market, which can simulate the endogenous interaction and dynamic evolution of the electricity-carbon market under different boundary conditions and decision-making behaviors.

[0005] This invention provides the following technical solution:

[0006] Firstly, a hybrid dynamic simulation method for the medium- and long-term electricity market and carbon market is provided, including: constructing a physical facility model based on the attributes of various physical facilities in the energy chain of the target area obtained in advance;

[0007] Based on the physical facility model and the pre-set roles of market participants corresponding to the physical facilities in the electricity or carbon market of the target area, a market participant model is constructed, and the bidding strategy and volume reporting strategy of the electricity and carbon markets are obtained based on the market participant model.

[0008] The electricity market and carbon market are divided into spatial and temporal scales to obtain the physical operating boundary of the electricity market and the organization of the clearing of the electricity market and carbon market, respectively.

[0009] Based on the bidding strategy, quantity reporting strategy and physical operating boundary of the electricity market, an electricity market clearing model is constructed, which includes an objective function and constraints. The objective function aims to minimize the generation cost, and the constraints include node active power balance constraints, line active power flow constraints and generator output constraints.

[0010] Based on the bidding and volume strategies of the carbon market, a carbon market clearing model is constructed.

[0011] Based on the aforementioned organizational method, transaction simulations were performed on the electricity market and carbon market. The electricity market clearing model and the carbon market clearing model were solved respectively to obtain the clearing price and clearing volume of the electricity market, as well as the clearing price and clearing volume of the carbon market.

[0012] As an optional technical solution of the present invention, the step of constructing a physical facility model based on the attributes of various physical facilities in the energy chain obtained in advance includes:

[0013] The attributes of the physical facilities include energy attributes, power attributes, carbon source attributes, and carbon sink attributes;

[0014] A physical facility model is constructed based on the attributes of the physical facility, and the physical facility model includes an energy consumption model. Power generation model Electricity consumption model Carbon emission model Carbon sink model And the coupled facility model;

[0015] The coupling facility model includes an energy-electricity-carbon source-carbon sink facility model. Energy-Electricity-Carbon Source Facility Model Energy-Electricity-Carbon Sequestration Facility Model Energy-electricity facility model Electricity-carbon sink facility model and power generation and consumption facility models .

[0016] As an optional technical solution of the present invention, the step of constructing a market participant model based on a physical facility model and pre-set roles of market participants corresponding to physical facilities in the electricity market or carbon market, and obtaining bidding strategies and volume strategies for the electricity market and carbon market based on the market participant model, includes:

[0017] Based on the role of the market participants in the electricity market or carbon market, the state variables of the market participants are determined, including endogenous decision-driven state variables. and exogenous decision-driven state variables , respectively represented as:

[0018] ;

[0019] ;

[0020] in, Indicates the first Endogenous decision-driven state variables of individual market participants Indicates the first Exogenous decision-driven state variables of individual market participants Indicates the first Energy consumption model for each market entity Indicates the first Electricity generation model for each market entity Indicates the first Electricity consumption model for each market entity Indicates the first Carbon emission models for each market entity Indicates the first Carbon sink model for each market entity Indicates the first Parameters of the electricity market mechanism involving individual market participants Indicates the first Parameters of the carbon market mechanism involving individual market participants;

[0021] Based on the state variables of the market participants, a market participant model is constructed, which includes a transaction behavior model and a cost-benefit model.

[0022] The transaction behavior model is represented as follows:

[0023] ;

[0024] in, Indicates the first The transaction behavior of individual market participants Indicates the first The bidding behavior of individual market participants in the electricity market Indicates the first The reporting behavior of individual market participants in the electricity market Indicates the first The first trading moment The bidding behavior of individual market participants in the carbon market Indicates the first The first trading moment The carbon market reporting behavior of individual market participants;

[0025] The cost-benefit model is expressed as follows:

[0026] ;

[0027] in, Indicates the first Costs and benefits for individual market participants Indicates the first Fixed costs for each market entity Indicates the first Variable costs of individual market participants Indicates the first The first trading moment The benefits of individual market participants in the electricity market Indicates the first The first trading moment The benefits of individual market participants participating in the carbon market;

[0028] Based on the aforementioned trading behavior model, the proposed bid volume and bid price for the electricity market and carbon market are determined respectively, and these are input into the cost-benefit model as decision variables. The optimal bidding strategy and bid volume strategy for the electricity market and carbon market are obtained by maximizing the calculation results of the cost-benefit model as the optimization objective.

[0029] As an optional technical solution of the present invention, the step of dividing the electricity market and carbon market into spatial and temporal scales to obtain the physical operating boundaries of the electricity market and the organization of the clearing of the electricity market and carbon market respectively includes:

[0030] The electricity market and carbon market are spatially divided, and the power networks in each provincial region are equivalently aggregated into provincial nodes. The provincial nodes are connected by inter-provincial interconnection lines to form an inter-provincial interconnection network topology. Based on the pre-acquired actual power grid physical parameters, power transmission limit constraints are constructed, and the power transmission limit constraints are used as the physical operating boundary of the electricity market.

[0031] The electricity market and carbon market are divided into time scales. At the annual transaction level, the annual scale of the electricity market and carbon market is retained. At the weekly transaction level, the monthly and multi-day transactions of the electricity market are aggregated into the weekly scale, and the daily continuous transactions of the carbon market are aggregated into the weekly scale.

[0032] As an optional technical solution of the present invention, the step of constructing a power market clearing model including an objective function and constraints based on the power market's bidding strategy, quantity reporting strategy, and physical operating boundaries includes:

[0033] The objective function is expressed as:

[0034] ;

[0035] in, Indicates the first The power generation cost function of each provincial node. Indicates the first The actual output of each provincial node Indicates the total number of nodes. This indicates taking the minimum value;

[0036] The active power balance constraint at the node is expressed as:

[0037] ;

[0038] The active power flow constraint of the line is expressed as follows:

[0039] ;

[0040] The active power flow constraint of the line is expressed as follows:

[0041] ;

[0042] in, Indicates the first Load demand of each provincial node Indicates the first The upper limit of cross-sectional power flow constraints for each line. Indicates the first The injected power of the provincial node affects the first The sensitivity of each line, Indicates the first The lower limit of the actual output of each provincial node Indicates the first The actual output limit of each provincial node;

[0043] Introducing Lagrange multipliers to construct Lagrange functions , is represented as:

[0044] ;

[0045] in, Indicates the total number of lines. Denotes the Lagrange multipliers corresponding to the power balance constraint. This represents the Lagrange multiplier corresponding to the lower limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the upper limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the line transmission constraint;

[0046] Solving for the Lagrange function yields the first... Marginal price of each provincial node and optimal unit output vector These are respectively the clearing price and clearing volume in the electricity market, expressed as:

[0047] ;

[0048] ;

[0049] in, The inverse function of the nodal marginal cost function under physical constraints The projection on the surface.

[0050] As an optional technical solution of the present invention, the construction of a carbon market clearing model based on the carbon market bidding strategy and volume bidding strategy includes:

[0051] The carbon market buy bid prices are sorted from high to low and sell bid prices are sorted from low to high. With the goal of maximizing social welfare, the bid prices of both buyers and sellers in the carbon market are centrally matched to obtain several transaction pairs. In the transaction pairs, the sell bid price is lower than the buy bid price.

[0052] The last pair of all traded pairs is designated as the marginal traded pair, and the bidding price of the market participants corresponding to this marginal traded pair is taken as the clearing price of the carbon market. The calculation process is expressed as follows:

[0053] ;

[0054] in, , They represent the first The bid price and quantity of each buyer; , They represent the first The declared price and quantity of each seller;

[0055] The cumulative trading volume of all the aforementioned trading pairs will be used as the clearing volume of the carbon market.

[0056] As an optional technical solution of the present invention, the step of simulating transactions in the electricity market and carbon market based on the organizational method, solving the electricity market clearing model and the carbon market clearing model respectively, and obtaining the clearing price and clearing volume of the electricity market and the clearing price and clearing volume of the carbon market includes:

[0057] Based on the aforementioned annual scale, annual transaction simulations were performed on the electricity market and carbon market. The electricity market clearing model and carbon market clearing model were solved respectively to obtain the annual clearing price and annual clearing volume of the electricity market, as well as the annual clearing price and annual clearing volume of the carbon market.

[0058] Based on the annual trading simulation results, weekly trading simulations were conducted for the electricity market and the carbon market. The clearing models for the electricity market and the carbon market were solved respectively to obtain the weekly clearing price and weekly clearing volume of the electricity market, as well as the weekly clearing price and weekly clearing volume of the carbon market.

[0059] Secondly, a hybrid interactive dynamic simulation system for medium- and long-term electricity market and carbon market is provided, including: a physical facility model construction module, which is used to construct a physical facility model based on the attributes of various physical facilities in the energy chain of the target area obtained in advance;

[0060] The market participant model construction module is used to construct a market participant model based on the physical facility model and the pre-set roles of market participants corresponding to physical facilities in the electricity market or carbon market of the target area, and to obtain the bidding strategy and volume reporting strategy of the electricity market and carbon market based on the market participant model.

[0061] The scale division module is used to divide the electricity market and carbon market into spatial and temporal scales, respectively, to obtain the physical operating boundary of the electricity market and the organization of the clearing of the electricity market and carbon market.

[0062] The electricity market clearing model construction module is used to construct an electricity market clearing model including an objective function and constraints based on the electricity market's bidding strategy, quantity reporting strategy, and physical operating boundaries. The objective function aims to minimize generation costs, and the constraints include node active power balance constraints, line active power flow constraints, and generator output constraints.

[0063] The carbon market clearing model construction module is used to construct a carbon market clearing model based on the carbon market's bidding strategy and volume reporting strategy.

[0064] The simulation module is used to simulate transactions in the electricity market and carbon market based on the aforementioned organizational method, solve the electricity market clearing model and the carbon market clearing model respectively, and obtain the clearing price and clearing volume of the electricity market, as well as the clearing price and clearing volume of the carbon market.

[0065] As an optional technical solution of the present invention, the step of constructing a physical facility model based on the attributes of various physical facilities in the energy chain obtained in advance includes:

[0066] The attributes of the physical facilities include energy attributes, power attributes, carbon source attributes, and carbon sink attributes;

[0067] A physical facility model is constructed based on the attributes of the physical facility, and the physical facility model includes an energy consumption model. Power generation model Electricity consumption model Carbon emission model Carbon sink model And the coupled facility model;

[0068] The coupling facility model includes an energy-electricity-carbon source-carbon sink facility model. Energy-Electricity-Carbon Source Facility Model Energy-Electricity-Carbon Sequestration Facility Model Energy-electricity facility model Electricity-carbon sink facility model and power generation and consumption facility models .

[0069] As an optional technical solution of the present invention, the step of constructing a market participant model based on a physical facility model and pre-set roles of market participants corresponding to physical facilities in the electricity market or carbon market, and obtaining bidding strategies and volume strategies for the electricity market and carbon market based on the market participant model, includes:

[0070] Based on the role of the market participants in the electricity market or carbon market, the state variables of the market participants are determined, including endogenous decision-driven state variables. and exogenous decision-driven state variables , respectively represented as:

[0071] ;

[0072] ;

[0073] in, Indicates the first Endogenous decision-driven state variables of individual market participants Indicates the first Exogenous decision-driven state variables of individual market participants Indicates the first Energy consumption model for each market entity Indicates the first Electricity generation model for each market entity Indicates the first Electricity consumption model for each market entity Indicates the first Carbon emission models for each market entity Indicates the first Carbon sink model for each market entity Indicates the first Parameters of the electricity market mechanism involving individual market participants Indicates the first Parameters of the carbon market mechanism involving individual market participants;

[0074] Based on the state variables of the market participants, a market participant model is constructed, which includes a transaction behavior model and a cost-benefit model.

[0075] The transaction behavior model is represented as follows:

[0076] ;

[0077] in, Indicates the first The transaction behavior of individual market participants Indicates the first The bidding behavior of individual market participants in the electricity market Indicates the first The reporting behavior of individual market participants in the electricity market Indicates the first The first trading moment The bidding behavior of individual market participants in the carbon market Indicates the first The first trading moment The carbon market reporting behavior of individual market participants;

[0078] The cost-benefit model is expressed as follows:

[0079] ;

[0080] in, Indicates the first Costs and benefits for individual market participants Indicates the first Fixed costs for each market entity Indicates the first Variable costs of individual market participants Indicates the first The first trading moment The benefits of individual market participants in the electricity market Indicates the first The first trading moment The benefits of individual market participants participating in the carbon market;

[0081] Based on the aforementioned trading behavior model, the proposed bid volume and bid price for the electricity market and carbon market are determined respectively, and these are input into the cost-benefit model as decision variables. The optimal bidding strategy and bid volume strategy for the electricity market and carbon market are obtained by maximizing the calculation results of the cost-benefit model as the optimization objective.

[0082] As an optional technical solution of the present invention, the step of dividing the electricity market and carbon market into spatial and temporal scales to obtain the physical operating boundaries of the electricity market and the organization of the clearing of the electricity market and carbon market respectively includes:

[0083] The electricity market and carbon market are spatially divided, and the power networks in each provincial region are equivalently aggregated into provincial nodes. The provincial nodes are connected by inter-provincial interconnection lines to form an inter-provincial interconnection network topology. Based on the pre-acquired actual power grid physical parameters, power transmission limit constraints are constructed, and the power transmission limit constraints are used as the physical operating boundary of the electricity market.

[0084] The electricity market and carbon market are divided into time scales. At the annual transaction level, the annual scale of the electricity market and carbon market is retained. At the weekly transaction level, the monthly and multi-day transactions of the electricity market are aggregated into the weekly scale, and the daily continuous transactions of the carbon market are aggregated into the weekly scale.

[0085] As an optional technical solution of the present invention, the step of constructing a power market clearing model including an objective function and constraints based on the power market's bidding strategy, quantity reporting strategy, and physical operating boundaries includes:

[0086] The objective function is expressed as:

[0087] ;

[0088] in, Indicates the first The power generation cost function of each provincial node. Indicates the first The actual output of each provincial node Indicates the total number of nodes. This indicates taking the minimum value;

[0089] The active power balance constraint at the node is expressed as:

[0090] ;

[0091] The active power flow constraint of the line is expressed as follows:

[0092] ;

[0093] The active power flow constraint of the line is expressed as follows:

[0094] ;

[0095] in, Indicates the first Load demand of each provincial node Indicates the first The upper limit of cross-sectional power flow constraints for each line. Indicates the first The injected power of the provincial node affects the first The sensitivity of each line, Indicates the first The lower limit of the actual output of each provincial node Indicates the first The actual output limit of each provincial node;

[0096] Introducing Lagrange multipliers to construct Lagrange functions , is represented as:

[0097] ;

[0098] in, Indicates the total number of lines. Denotes the Lagrange multipliers corresponding to the power balance constraint. This represents the Lagrange multiplier corresponding to the lower limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the upper limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the line transmission constraint;

[0099] Solving for the Lagrange function yields the first... Marginal price of each provincial node and optimal unit output vector These are respectively the clearing price and clearing volume in the electricity market, expressed as:

[0100] ;

[0101] ;

[0102] in, The inverse function of the nodal marginal cost function under physical constraints The projection on the surface.

[0103] As an optional technical solution of the present invention, the construction of a carbon market clearing model based on the carbon market bidding strategy and volume bidding strategy includes:

[0104] The carbon market buy bid prices are sorted from high to low and sell bid prices are sorted from low to high. With the goal of maximizing social welfare, the bid prices of both buyers and sellers in the carbon market are centrally matched to obtain several transaction pairs. In the transaction pairs, the sell bid price is lower than the buy bid price.

[0105] The last pair of all traded pairs is designated as the marginal traded pair, and the bidding price of the market participants corresponding to this marginal traded pair is taken as the clearing price of the carbon market. The calculation process is expressed as follows:

[0106] ;

[0107] in, , They represent the first The bid price and quantity of each buyer; , They represent the first The declared price and quantity of each seller;

[0108] The cumulative trading volume of all the aforementioned trading pairs will be used as the clearing volume of the carbon market.

[0109] As an optional technical solution of the present invention, the step of simulating transactions in the electricity market and carbon market based on the organizational method, solving the electricity market clearing model and the carbon market clearing model respectively, and obtaining the clearing price and clearing volume of the electricity market and the clearing price and clearing volume of the carbon market includes:

[0110] Based on the aforementioned annual scale, annual transaction simulations were performed on the electricity market and carbon market. The electricity market clearing model and carbon market clearing model were solved respectively to obtain the annual clearing price and annual clearing volume of the electricity market, as well as the annual clearing price and annual clearing volume of the carbon market.

[0111] Based on the annual trading simulation results, weekly trading simulations were conducted for the electricity market and the carbon market. The clearing models for the electricity market and the carbon market were solved respectively to obtain the weekly clearing price and weekly clearing volume of the electricity market, as well as the weekly clearing price and weekly clearing volume of the carbon market.

[0112] Thirdly, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the hybrid interactive dynamic simulation method of the medium- and long-term electricity market and carbon market described in the first aspect.

[0113] Compared with the prior art, the beneficial effects of the present invention are:

[0114] This invention provides a hybrid interactive dynamic simulation method for medium- and long-term electricity and carbon markets. It focuses on the coupling relationship between physical facilities and can realize the endogenous interaction simulation of cross-provincial / intra-provincial electricity and carbon markets and the situational projection and result evaluation under different boundary conditions while balancing simulation accuracy and efficiency. It provides a simulation platform and decision support for the mechanism design of regulatory agencies and the trading and investment decisions of various market participants. Attached Figure Description

[0115] Figure 1 This is a flowchart of the dynamic simulation method for the hybrid interaction between the medium- and long-term electricity market and the carbon market in this embodiment of the invention. Detailed Implementation

[0116] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0117] Example 1

[0118] This embodiment provides a dynamic simulation method for the hybrid interaction of the medium- and long-term electricity market and the carbon market. For example... Figure 1 As shown, it includes:

[0119] Step 1: Based on the pre-acquired attributes of various physical facilities in the energy chain of the target area, construct a physical facility model. Details are as follows:

[0120] The attributes of the physical facilities include energy attributes, power attributes, carbon source attributes, and carbon sink attributes.

[0121] A physical facility model is constructed based on the attributes of the physical facility, and the physical facility model includes an energy consumption model. Power generation model Electricity consumption model Carbon emission model Carbon sink model And the coupling facility model.

[0122] The coupling facility model includes an energy-electricity-carbon source-carbon sink facility model. Energy-Electricity-Carbon Source Facility Model Energy-Electricity-Carbon Sequestration Facility Model Energy-electricity facility model Electricity-carbon sink facility model and power generation and consumption facility models .

[0123] Step Two: Based on the physical infrastructure model and the pre-defined roles of market participants corresponding to the physical infrastructure in the target region's electricity or carbon market, construct a market participant model. Based on this model, derive the bidding and volume strategies for the electricity and carbon markets. Specifically:

[0124] In this embodiment, the electricity market is an inter-provincial or intra-provincial electricity market, and the carbon market is a national carbon market.

[0125] Based on the energy-electricity-carbon attributes of physical facilities, the roles of market participants in the electricity-carbon market are determined. For energy-electricity-carbon source-carbon sink facilities, the market participants for energy-electricity-carbon source facilities are producers and consumers in the electricity market and producers and sellers in the carbon emission market. For energy-electricity-carbon sink facilities, the market participants are producers in both the electricity market and the carbon sink market. For energy-electricity facilities, the market participants are producers and consumers in the electricity market. For electricity-carbon sink facilities, the market participants are consumers in the electricity market and producers in the carbon sink market.

[0126] Based on the role of the market participants in the electricity market or carbon market, the state variables of the market participants are determined, including endogenous decision-driven state variables. and exogenous decision-driven state variables , respectively represented as:

[0127] ;

[0128] ;

[0129] in, Indicates the first Endogenous decision-driven state variables of individual market participants Indicates the first Exogenous decision-driven state variables of individual market participants Indicates the first Energy consumption model for each market entity Indicates the first Electricity generation model for each market entity Indicates the first Electricity consumption model for each market entity Indicates the first Carbon emission models for each market entity Indicates the first Carbon sink model for each market entity Indicates the first Parameters of the electricity market mechanism involving individual market participants Indicates the first Parameters of the carbon market mechanism involving individual market participants.

[0130] On the electricity market side, generating units are clustered based on physical attributes: within each provincial node, individual differences between specific units are ignored, and similar generating units are clustered into cluster-level entities according to their power generation technology type, serving as bidding and clearing units in inter-provincial and intra-provincial medium- and long-term electricity markets. These cluster-level entities are specifically categorized as follows: coal-fired power clusters of 300MW and above, coal-fired power clusters of less than 300MW, gas-fired power clusters, wind power clusters, photovoltaic power clusters, nuclear power clusters, and hydropower clusters. On the carbon market side, legal entities are clustered based on asset ownership: within each provincial node, for thermal power generating units involved in carbon emissions, their assets are aggregated according to their respective independent legal entities, forming carbon market trading entities, serving as decision-making units for quota clearing and compliance trading in the national carbon market.

[0131] Based on the state variables of the market participants, a market participant model is constructed, which includes a cost-benefit model and a transaction behavior model.

[0132] Based on the state variables of the market participants, a market participant model is constructed, which includes a transaction behavior model and a cost-benefit model.

[0133] The transaction behavior model is represented as follows:

[0134] ;

[0135] in, Indicates the first The transaction behavior of individual market participants Indicates the first The bidding behavior of individual market participants in the electricity market Indicates the first The reporting behavior of individual market participants in the electricity market Indicates the first The first trading moment The bidding behavior of individual market participants in the carbon market Indicates the first The first trading moment The reporting behavior of individual market participants in the carbon market.

[0136] The cost-benefit model is expressed as follows:

[0137] ;

[0138] in, Indicates the first Costs and benefits for individual market participants Indicates the first Fixed costs for each market entity Indicates the first Variable costs of individual market participants Indicates the first The first trading moment The benefits of individual market participants in the electricity market Indicates the first The first trading moment The benefits of market participants participating in the carbon market.

[0139] Based on the aforementioned trading behavior model, the proposed bid volume and bid price for the electricity market and carbon market are determined respectively, and these are input as decision variables into the cost-benefit model. Maximizing the calculation results of the cost-benefit model is the optimization objective, resulting in the optimal bidding strategy and bid volume strategy for the electricity market and carbon market. The bidding strategy for the electricity market is the bid price for different trading periods; the bid volume strategy for the electricity market is the bid electricity volume for different trading periods. The bidding strategy for the carbon market is the bid price for carbon allowances for different trading periods; the bid volume strategy for the carbon market is the bid quantity for carbon allowances for different trading periods.

[0140] Step 3: Divide the electricity market and carbon market into spatial and temporal scales to obtain the physical operating boundaries of the electricity market and the organization of the clearing processes in both markets. Details are as follows:

[0141] The electricity market and carbon market are spatially divided, and the power networks in each provincial region are equivalently aggregated into provincial nodes. Inter-provincial interconnection network topology is formed by connecting the provincial nodes through inter-provincial interconnection lines. Based on the pre-acquired actual power grid physical parameters, power transmission limit constraints are constructed and used as the physical operating boundary of the electricity market.

[0142] The electricity and carbon markets are divided into time scales. Considering the various trading cycles (annual, monthly, weekly, and multi-day) in the electricity market and the differences between annual quota allocation and daily continuous trading cycles in the carbon market, a unified time mapping mechanism is established, constructing a "year-week" two-layer nested simulation scale. At the annual trading level, the annual scale of both the electricity and carbon markets is retained. At the weekly trading level, monthly and multi-day transactions in the electricity market are aggregated into a weekly scale, and daily continuous transactions in the carbon market are aggregated into a weekly scale. This clarifies the organization of subsequent simulation transactions.

[0143] Step 4: Based on the electricity market's bidding strategy, quantity reporting strategy, and physical operating boundaries, construct an electricity market clearing model including an objective function and constraints. The objective function aims to minimize generation costs, and the constraints include node active power balance constraints, line active power flow constraints, and generator output constraints. Details are as follows:

[0144] The objective function is expressed as:

[0145] ;

[0146] in, Indicates the first The power generation cost function of each provincial node. Indicates the first The actual output of each provincial node Indicates the total number of nodes. This indicates taking the minimum value.

[0147] The active power balance constraint at the node is expressed as:

[0148] ;

[0149] The active power flow constraint of the line is expressed as follows:

[0150] ;

[0151] The active power flow constraint of the line is expressed as follows:

[0152] ;

[0153] in, Indicates the first Load demand of each provincial node Indicates the first The upper limit of cross-sectional power flow constraints for each line. Indicates the first The injected power of the provincial node affects the first The sensitivity of each line, Indicates the first The lower limit of the actual output of each provincial node Indicates the first The actual output limit of each provincial node.

[0154] Introducing Lagrange multipliers to construct Lagrange functions , is represented as:

[0155] ;

[0156] in, Indicates the total number of lines. Denotes the Lagrange multipliers corresponding to the power balance constraint. This represents the Lagrange multiplier corresponding to the lower limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the upper limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the line transmission constraint.

[0157] Solve for the Lagrangian function, for the decision variable (actual output). Taking the partial derivatives and setting them to zero, we obtain the following core pricing equation:

[0158] ;

[0159] Solve the above system of equations using a solver to obtain the first... Marginal price of each provincial node and optimal unit output vector These are respectively the clearing price and clearing volume in the electricity market, expressed as:

[0160] ;

[0161] ;

[0162] in, The inverse function of the nodal marginal cost function under physical constraints The projection on the surface.

[0163] Therefore, the electricity market is based on the optimal unit output vector. Issue power generation instructions to the generating units and base them on marginal prices. Each market entity settles its fees.

[0164] Step 5: Based on the bidding and volume strategies of the carbon market, construct a carbon market clearing model. Details are as follows:

[0165] The carbon market buy bid prices are sorted from high to low, and sell bid prices are sorted from low to high. With the goal of maximizing social welfare, the bid prices of both buyers and sellers in the carbon market are centrally matched to obtain several transaction pairs. In the transaction pairs, the sell bid price is lower than the buy bid price.

[0166] The last pair of all traded pairs is designated as the marginal traded pair, i.e., the last matching position where the buy order price is greater than or equal to the sell order price. The bidding price of the market participants corresponding to this marginal traded pair is used as the clearing price of the carbon market, and the calculation process is expressed as follows:

[0167] ;

[0168] in, , They represent the first The bid price and quantity of each buyer; , They represent the first The declared price and quantity of each seller;

[0169] The cumulative trading volume of all the aforementioned traded pairs is used as the clearing volume in the carbon market. Specifically, matching is performed one by one along the sorted buy and sell queues, and the clearing volume is strictly limited to the effective matching range where the buy bid price is greater than or equal to the sell bid price. From the geometric perspective of the supply and demand curves, this clearing volume corresponds to the cumulative order volume to the left of the intersection point (or price overlap cutoff point) of the demand and supply curves. Within this range, all buy and sell orders that meet the above price matching conditions are counted, and their cumulative trading volume is taken as the final market clearing volume, while orders located to the right of the intersection point are not traded.

[0170] Step Six: Based on the aforementioned organizational method, conduct transaction simulations for the electricity market and carbon market, solve the electricity market clearing model and the carbon market clearing model respectively, and obtain the clearing price and clearing volume of the electricity market, as well as the clearing price and clearing volume of the carbon market.

[0171] After obtaining the pre-set state variables, decision variables, scenario parameters, and boundary conditions involved in the electricity-carbon market interaction simulation, a multi-timescale simulation process is executed, as follows:

[0172] Based on the aforementioned annual scale, annual transaction simulations are performed on the electricity market and carbon market. The electricity market clearing model and the carbon market clearing model are solved respectively to obtain the annual clearing price and annual clearing volume of the electricity market, as well as the annual clearing price and annual clearing volume of the carbon market.

[0173] Based on the annual trading simulation results, weekly trading simulations were conducted for the electricity market and the carbon market. The clearing models for the electricity market and the carbon market were solved respectively to obtain the weekly clearing price and weekly clearing volume of the electricity market, as well as the weekly clearing price and weekly clearing volume of the carbon market.

[0174] In this embodiment, firstly, in the first month of year Y, an annual coupled simulation is initiated to simulate carbon quota allocation and annual long-term electricity contract transactions, generating the annual transaction volume, transaction price, and initial carbon quota status for year Y; then, in the j-th week of year Y (j=1, 2, ..., 52), based on the annual simulation results and the market status of the previous week, a weekly coupled simulation is initiated to simulate electricity market transactions and carbon market transactions for that week, generating the clearing volume and clearing price for week j.

[0175] Example 2

[0176] This embodiment provides a hybrid interactive dynamic simulation system for the medium- and long-term electricity market and carbon market, including:

[0177] The physical infrastructure model building module is used to construct a physical infrastructure model based on the attributes of various physical infrastructures in the energy chain of the target area, obtained in advance. It includes:

[0178] A physical facility model is constructed based on the attributes of the physical facility, and the physical facility model includes an energy consumption model. Power generation model Electricity consumption model Carbon emission model Carbon sink model And the coupling facility model.

[0179] The coupling facility model includes an energy-electricity-carbon source-carbon sink facility model. Energy-Electricity-Carbon Source Facility Model Energy-Electricity-Carbon Sequestration Facility Model Energy-electricity facility model Electricity-carbon sink facility model and power generation and consumption facility models .

[0180] The market participant model construction module is used to construct a market participant model based on the physical facility model and the pre-set roles of market participants corresponding to the physical facilities in the electricity or carbon market of the target region. Based on the market participant model, the module derives the bidding and volume strategies for the electricity and carbon markets. This includes:

[0181] Based on the role of the market participants in the electricity market or carbon market, the state variables of the market participants are determined, including endogenous decision-driven state variables. and exogenous decision-driven state variables , respectively represented as:

[0182] ;

[0183] ;

[0184] in, Indicates the first Endogenous decision-driven state variables of individual market participants Indicates the first Exogenous decision-driven state variables of individual market participants Indicates the first Energy consumption model for each market entity Indicates the first Electricity generation model for each market entity Indicates the first Electricity consumption model for each market entity Indicates the first Carbon emission models for each market entity Indicates the first Carbon sink model for each market entity Indicates the first Parameters of the electricity market mechanism involving individual market participants Indicates the first Parameters of the carbon market mechanism involving individual market participants;

[0185] Based on the state variables of the market participants, a market participant model is constructed, which includes a transaction behavior model and a cost-benefit model.

[0186] The transaction behavior model is represented as follows:

[0187] ;

[0188] in, Indicates the first The transaction behavior of individual market participants Indicates the first The bidding behavior of individual market participants in the electricity market Indicates the first The reporting behavior of individual market participants in the electricity market Indicates the first The first trading moment The bidding behavior of individual market participants in the carbon market Indicates the first The first trading moment The carbon market reporting behavior of individual market participants;

[0189] The cost-benefit model is expressed as follows:

[0190] ;

[0191] in, Indicates the first Costs and benefits for individual market participants Indicates the first Fixed costs for each market entity Indicates the first Variable costs of individual market participants Indicates the first The first trading moment The benefits of individual market participants in the electricity market Indicates the first The first trading moment The benefits of individual market participants participating in the carbon market;

[0192] Based on the aforementioned trading behavior model, the proposed bid volume and bid price for the electricity market and carbon market are determined respectively, and these are input into the cost-benefit model as decision variables. The optimal bidding strategy and bid volume strategy for the electricity market and carbon market are obtained by maximizing the calculation results of the cost-benefit model as the optimization objective.

[0193] The scaling module is used to divide the electricity market and carbon market into spatial and temporal scales, respectively, to obtain the physical operating boundaries of the electricity market and the organization of the clearing processes in both markets. This includes:

[0194] The electricity market and carbon market are spatially divided, and the power networks in each provincial region are equivalently aggregated into provincial nodes. The provincial nodes are connected by inter-provincial interconnection lines to form an inter-provincial interconnection network topology. Based on the pre-acquired actual power grid physical parameters, power transmission limit constraints are constructed, and the power transmission limit constraints are used as the physical operating boundary of the electricity market.

[0195] The electricity market and carbon market are divided into time scales. At the annual transaction level, the annual scale of the electricity market and carbon market is retained. At the weekly transaction level, the monthly and multi-day transactions of the electricity market are aggregated into the weekly scale, and the daily continuous transactions of the carbon market are aggregated into the weekly scale.

[0196] The electricity market clearing model construction module is used to construct an electricity market clearing model, including an objective function and constraints, based on the electricity market's bidding strategy, quantity reporting strategy, and physical operating boundaries. The objective function aims to minimize generation costs, and the constraints include node active power balance constraints, line active power flow constraints, and generator output constraints. (Includes:)

[0197] The objective function is expressed as:

[0198] ;

[0199] in, Indicates the first The power generation cost function of each provincial node. Indicates the first The actual output of each provincial node Indicates the total number of nodes. This indicates taking the minimum value;

[0200] The active power balance constraint at the node is expressed as:

[0201] ;

[0202] The active power flow constraint of the line is expressed as follows:

[0203] ;

[0204] The active power flow constraint of the line is expressed as follows:

[0205] ;

[0206] in, Indicates the first Load demand of each provincial node Indicates the first The upper limit of cross-sectional power flow constraints for each line. Indicates the first The injected power of the provincial node affects the first The sensitivity of each line, Indicates the first The lower limit of the actual output of each provincial node Indicates the first The actual output limit of each provincial node;

[0207] Introducing Lagrange multipliers to construct Lagrange functions , is represented as:

[0208] ;

[0209] in, Indicates the total number of lines. Denotes the Lagrange multipliers corresponding to the power balance constraint. This represents the Lagrange multiplier corresponding to the lower limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the upper limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the line transmission constraint;

[0210] Solving for the Lagrange function yields the first... Marginal price of each provincial node and optimal unit output vector These are respectively the clearing price and clearing volume in the electricity market, expressed as:

[0211] ;

[0212] ;

[0213] in, The inverse function of the nodal marginal cost function under physical constraints The projection on the surface.

[0214] The carbon market clearing model construction module is used to construct a carbon market clearing model based on the bidding and volume strategies of the carbon market. It includes:

[0215] The carbon market buy bid prices are sorted from high to low and sell bid prices are sorted from low to high. With the goal of maximizing social welfare, the bid prices of both buyers and sellers in the carbon market are centrally matched to obtain several transaction pairs. In the transaction pairs, the sell bid price is lower than the buy bid price.

[0216] The last pair of all traded pairs is designated as the marginal traded pair, and the bidding price of the market participants corresponding to this marginal traded pair is taken as the clearing price of the carbon market. The calculation process is expressed as follows:

[0217] ;

[0218] in, , They represent the first The bid price and quantity of each buyer; , They represent the first The declared price and quantity of each seller;

[0219] The cumulative trading volume of all the aforementioned trading pairs will be used as the clearing volume of the carbon market.

[0220] The simulation module is used to simulate transactions in the electricity market and carbon market based on the aforementioned organizational structure. It solves the electricity market clearing model and the carbon market clearing model respectively, obtaining the clearing price and cleared volume in the electricity market, and the clearing price and cleared volume in the carbon market. This includes:

[0221] Based on the aforementioned annual scale, annual transaction simulations were performed on the electricity market and carbon market. The electricity market clearing model and carbon market clearing model were solved respectively to obtain the annual clearing price and annual clearing volume of the electricity market, as well as the annual clearing price and annual clearing volume of the carbon market.

[0222] Based on the annual trading simulation results, weekly trading simulations were conducted for the electricity market and the carbon market. The clearing models for the electricity market and the carbon market were solved respectively to obtain the weekly clearing price and weekly clearing volume of the electricity market, as well as the weekly clearing price and weekly clearing volume of the carbon market.

[0223] Example 3

[0224] This embodiment provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the hybrid interactive dynamic simulation method of medium- and long-term electricity market and carbon market described in Embodiment 1.

[0225] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0226] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0227] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0228] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0229] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A hybrid interactive dynamic simulation method for medium- and long-term electricity market and carbon market, characterized in that, include: Based on the attributes of various physical facilities in the energy chain of the target area obtained in advance, a physical facility model is constructed; Based on the physical facility model and the pre-set roles of market participants corresponding to the physical facilities in the electricity or carbon market of the target area, a market participant model is constructed, and the bidding strategy and volume reporting strategy of the electricity and carbon markets are obtained based on the market participant model. The electricity market and carbon market are divided into spatial and temporal scales to obtain the physical operating boundary of the electricity market and the organization of the clearing of the electricity market and carbon market, respectively. Based on the bidding strategy, quantity reporting strategy and physical operating boundary of the electricity market, an electricity market clearing model is constructed, which includes an objective function and constraints. The objective function aims to minimize the generation cost, and the constraints include node active power balance constraints, line active power flow constraints and generator output constraints. Based on the bidding and volume strategies of the carbon market, a carbon market clearing model is constructed. Based on the aforementioned organizational method, transaction simulations were conducted in the electricity market and carbon market. The electricity market clearing model and the carbon market clearing model were solved respectively to obtain the clearing price and clearing volume of the electricity market, as well as the clearing price and clearing volume of the carbon market.

2. The hybrid interactive dynamic simulation method for medium- and long-term electricity market and carbon market according to claim 1, characterized in that, The construction of a physical facility model based on the attributes of various physical facilities in the pre-acquired energy chain includes: The attributes of the physical facilities include energy attributes, power attributes, carbon source attributes, and carbon sink attributes; A physical facility model is constructed based on the attributes of the physical facility, and the physical facility model includes an energy consumption model. Power generation model Electricity consumption model Carbon emission model Carbon sink model And the coupled facility model; The coupling facility model includes an energy-electricity-carbon source-carbon sink facility model. Energy-Electricity-Carbon Source Facility Model Energy-Electricity-Carbon Sequestration Facility Model Energy-electricity facility model Electricity-carbon sink facility model and power generation and consumption facility models .

3. The hybrid interactive dynamic simulation method for medium- and long-term electricity market and carbon market according to claim 1, characterized in that, The process involves constructing a market participant model based on a physical infrastructure model and pre-defined roles of market participants corresponding to these physical infrastructures in the electricity or carbon market. Based on this model, bidding and volume strategies for the electricity and carbon markets are derived, including: Based on the role of the market participants in the electricity market or carbon market, the state variables of the market participants are determined, including endogenous decision-driven state variables. and exogenous decision-driven state variables , respectively represented as: ； ； in, Indicates the first Endogenous decision-driven state variables of individual market participants Indicates the first Exogenous decision-driven state variables of individual market participants Indicates the first Energy consumption model for each market entity Indicates the first Electricity generation model for each market entity Indicates the first Electricity consumption model for each market entity Indicates the first Carbon emission models for each market entity Indicates the first Carbon sink model for each market entity Indicates the first Parameters of the electricity market mechanism involving individual market participants Indicates the first Parameters of the carbon market mechanism involving individual market participants; Based on the state variables of the market participants, a market participant model is constructed, which includes a transaction behavior model and a cost-benefit model. The transaction behavior model is represented as follows: ； in, Indicates the first The transaction behavior of individual market participants Indicates the first The bidding behavior of individual market participants in the electricity market Indicates the first The reporting behavior of individual market participants in the electricity market Indicates the first The first trading moment The bidding behavior of individual market participants in the carbon market Indicates the first The first trading moment The carbon market reporting behavior of individual market participants; The cost-benefit model is expressed as follows: ； in, Indicates the first Costs and benefits for individual market participants Indicates the first Fixed costs for each market entity Indicates the first Variable costs of individual market participants Indicates the first The first trading moment The benefits of individual market participants in the electricity market Indicates the first The first trading moment The benefits of individual market participants participating in the carbon market; Based on the aforementioned trading behavior model, the proposed bid volume and bid price for the electricity market and carbon market are determined respectively, and these are input into the cost-benefit model as decision variables. The optimal bidding strategy and bid volume strategy for the electricity market and carbon market are obtained by maximizing the calculation results of the cost-benefit model as the optimization objective.

4. The hybrid interactive dynamic simulation method for medium- and long-term electricity market and carbon market according to claim 1, characterized in that, The aforementioned spatial and temporal division of the electricity and carbon markets yields the physical operational boundaries of the electricity market and the organizational methods for clearing out the electricity and carbon markets, including: The electricity market and carbon market are spatially divided, and the power networks in each provincial region are equivalently aggregated into provincial nodes. The provincial nodes are connected by inter-provincial interconnection lines to form an inter-provincial interconnection network topology. Based on the pre-acquired actual power grid physical parameters, power transmission limit constraints are constructed, and the power transmission limit constraints are used as the physical operating boundary of the electricity market. The electricity market and carbon market are divided into time scales. At the annual transaction level, the annual scale of the electricity market and carbon market is retained. At the weekly transaction level, the monthly and multi-day transactions of the electricity market are aggregated into the weekly scale, and the daily continuous transactions of the carbon market are aggregated into the weekly scale.

5. The hybrid interactive dynamic simulation method for medium- and long-term electricity market and carbon market according to claim 4, characterized in that, The electricity market clearing model, which includes an objective function and constraints, is constructed based on the electricity market's bidding strategy, quantity reporting strategy, and physical operating boundaries. The objective function is expressed as: ； in, Indicates the first The power generation cost function of each provincial node. Indicates the first The actual output of each provincial node Indicates the total number of nodes. This indicates taking the minimum value; The active power balance constraint at the node is expressed as: ； The active power flow constraint of the line is expressed as follows: ； The active power flow constraint of the line is expressed as follows: ； in, Indicates the first Load demand of each provincial node Indicates the first The upper limit of cross-sectional power flow constraints for each line. Indicates the first The injected power of the provincial node affects the first The sensitivity of each line, Indicates the first The lower limit of the actual output of each provincial node Indicates the first The actual output limit of each provincial node; Introducing Lagrange multipliers to construct Lagrange functions , is represented as: ； in, Indicates the total number of lines. Denotes the Lagrange multipliers corresponding to the power balance constraint. This represents the Lagrange multiplier corresponding to the lower limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the upper limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the line transmission constraint; Solving for the Lagrange function yields the first... Marginal price of each provincial node and optimal unit output vector These are respectively the clearing price and clearing volume in the electricity market, expressed as: ； ； in, The inverse function of the nodal marginal cost function under physical constraints The projection on the surface.

6. The hybrid interactive dynamic simulation method for medium- and long-term electricity market and carbon market according to claim 5, characterized in that, The carbon market clearing model is constructed based on the carbon market bidding and volume strategies, including: The carbon market buy bid prices are sorted from high to low and sell bid prices are sorted from low to high. With the goal of maximizing social welfare, the bid prices of both buyers and sellers in the carbon market are centrally matched to obtain several transaction pairs. In the transaction pairs, the sell bid price is lower than the buy bid price. The last pair of all traded pairs is designated as the marginal traded pair, and the bidding price of the market participants corresponding to this marginal traded pair is taken as the clearing price of the carbon market. The calculation process is as follows: ； in, , They represent the first The bid price and quantity of each buyer; , They represent the first The declared price and quantity of each seller; The cumulative trading volume of all the aforementioned trading pairs will be used as the clearing volume of the carbon market.

7. The hybrid interactive dynamic simulation method for medium- and long-term electricity market and carbon market according to claim 6, characterized in that, The simulation of electricity and carbon market transactions based on organizational structure involves solving electricity market clearing models and carbon market clearing models respectively, yielding the clearing price and cleared volume of the electricity market, and the clearing price and cleared volume of the carbon market, including: Based on the aforementioned annual scale, annual transaction simulations were performed on the electricity market and carbon market. The electricity market clearing model and carbon market clearing model were solved respectively to obtain the annual clearing price and annual clearing volume of the electricity market, as well as the annual clearing price and annual clearing volume of the carbon market. Based on the annual trading simulation results, weekly trading simulations were conducted for the electricity market and the carbon market. The clearing models for the electricity market and the carbon market were solved respectively to obtain the weekly clearing price and weekly clearing volume of the electricity market, as well as the weekly clearing price and weekly clearing volume of the carbon market.

8. A hybrid interactive dynamic simulation system for medium- and long-term electricity market and carbon market, characterized in that, include: The physical facility model building module is used to build a physical facility model based on the attributes of various physical facilities in the energy chain of the target area obtained in advance. The market participant model construction module is used to construct a market participant model based on the physical facility model and the pre-set roles of market participants corresponding to physical facilities in the electricity market or carbon market of the target area, and to obtain the bidding strategy and volume reporting strategy of the electricity market and carbon market based on the market participant model. The scale division module is used to divide the electricity market and carbon market into spatial and temporal scales, respectively, to obtain the physical operating boundary of the electricity market and the organization of the clearing of the electricity market and carbon market. The electricity market clearing model construction module is used to construct an electricity market clearing model including an objective function and constraints based on the electricity market's bidding strategy, quantity reporting strategy, and physical operating boundaries. The objective function aims to minimize generation costs, and the constraints include node active power balance constraints, line active power flow constraints, and generator output constraints. The carbon market clearing model construction module is used to construct a carbon market clearing model based on the carbon market's bidding strategy and volume reporting strategy. The simulation module is used to simulate transactions in the electricity market and carbon market based on the aforementioned organizational method, solve the electricity market clearing model and the carbon market clearing model respectively, and obtain the clearing price and clearing volume of the electricity market, as well as the clearing price and clearing volume of the carbon market.

9. The hybrid interactive dynamic simulation system for medium- and long-term electricity market and carbon market according to claim 8, characterized in that, The construction of a physical facility model based on the attributes of various physical facilities in the pre-acquired energy chain includes: The attributes of the physical facilities include energy attributes, power attributes, carbon source attributes, and carbon sink attributes; A physical facility model is constructed based on the attributes of the physical facility, and the physical facility model includes an energy consumption model. Power generation model Electricity consumption model Carbon emission model Carbon sink model And the coupled facility model; The coupling facility model includes an energy-electricity-carbon source-carbon sink facility model. Energy-Electricity-Carbon Source Facility Model Energy-Electricity-Carbon Sequestration Facility Model Energy-electricity facility model Electricity-carbon sink facility model and power generation and consumption facility models .

10. The hybrid interactive dynamic simulation system for medium- and long-term electricity market and carbon market according to claim 8, characterized in that, The process involves constructing a market participant model based on a physical infrastructure model and pre-defined roles of market participants corresponding to these physical infrastructures in the electricity or carbon market. Based on this model, bidding and volume strategies for the electricity and carbon markets are derived, including: Based on the role of the market participants in the electricity market or carbon market, the state variables of the market participants are determined, including endogenous decision-driven state variables. and exogenous decision-driven state variables , respectively represented as: ； ； in, Indicates the first Endogenous decision-driven state variables of individual market participants Indicates the first Exogenous decision-driven state variables of individual market participants Indicates the first Energy consumption model for each market entity Indicates the first Electricity generation model for each market entity Indicates the first Electricity consumption model for each market entity Indicates the first Carbon emission models for each market entity Indicates the first Carbon sink model for each market entity Indicates the first Parameters of the electricity market mechanism involving individual market participants Indicates the first Parameters of the carbon market mechanism involving individual market participants; Based on the state variables of the market participants, a market participant model is constructed, which includes a transaction behavior model and a cost-benefit model. The transaction behavior model is represented as follows: ； in, Indicates the first The transaction behavior of individual market participants Indicates the first The bidding behavior of individual market participants in the electricity market Indicates the first The reporting behavior of individual market participants in the electricity market Indicates the first The first trading moment The bidding behavior of individual market participants in the carbon market Indicates the first The first trading moment The carbon market reporting behavior of individual market participants; The cost-benefit model is expressed as follows: ； in, Indicates the first Costs and benefits for individual market participants Indicates the first Fixed costs for each market entity Indicates the first Variable costs of individual market participants Indicates the first The first trading moment The benefits of individual market participants in the electricity market Indicates the first The first trading moment The benefits of individual market participants participating in the carbon market; Based on the aforementioned trading behavior model, the proposed bid volume and bid price for the electricity market and carbon market are determined respectively, and these are input into the cost-benefit model as decision variables. The optimal bidding strategy and bid volume strategy for the electricity market and carbon market are obtained by maximizing the calculation results of the cost-benefit model as the optimization objective.

11. The hybrid interactive dynamic simulation system for medium- and long-term electricity market and carbon market according to claim 8, characterized in that, The aforementioned spatial and temporal division of the electricity and carbon markets yields the physical operational boundaries of the electricity market and the organizational methods for clearing out the electricity and carbon markets, including: The electricity market and carbon market are spatially divided, and the power networks in each provincial region are equivalently aggregated into provincial nodes. The provincial nodes are connected by inter-provincial interconnection lines to form an inter-provincial interconnection network topology. Based on the pre-acquired actual power grid physical parameters, power transmission limit constraints are constructed, and the power transmission limit constraints are used as the physical operating boundary of the electricity market. The electricity market and carbon market are divided into time scales. At the annual transaction level, the annual scale of the electricity market and carbon market is retained. At the weekly transaction level, the monthly and multi-day transactions of the electricity market are aggregated into the weekly scale, and the daily continuous transactions of the carbon market are aggregated into the weekly scale.

12. The hybrid interactive dynamic simulation system for medium- and long-term electricity market and carbon market according to claim 11, characterized in that, The electricity market clearing model, which includes an objective function and constraints, is constructed based on the electricity market's bidding strategy, quantity reporting strategy, and physical operating boundaries. The objective function is expressed as: ； in, Indicates the first The power generation cost function of each provincial node. Indicates the first The actual output of each provincial node Indicates the total number of nodes. This indicates taking the minimum value; The active power balance constraint at the node is expressed as: ； The active power flow constraint of the line is expressed as follows: ； The active power flow constraint of the line is expressed as follows: ； in, Indicates the first Load demand of each provincial node Indicates the first The upper limit of cross-sectional power flow constraints for each line. Indicates the first The injected power of the provincial node affects the first The sensitivity of each line, Indicates the first The lower limit of the actual output of each provincial node Indicates the first The actual output limit of each provincial node; Introducing Lagrange multipliers to construct Lagrange functions , is represented as: ； in, Indicates the total number of lines. Denotes the Lagrange multipliers corresponding to the power balance constraint. This represents the Lagrange multiplier corresponding to the lower limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the upper limit of the unit output constraint. This represents the Lagrange multiplier corresponding to the line transmission constraint; Solving for the Lagrange function yields the first... Marginal price of each provincial node and optimal unit output vector These are respectively the clearing price and clearing volume in the electricity market, expressed as: ； ； in, The inverse function of the nodal marginal cost function under physical constraints The projection on the surface.

13. The hybrid interactive dynamic simulation system for the medium- and long-term electricity market and carbon market according to claim 12, characterized in that, The carbon market clearing model is constructed based on the carbon market bidding and volume strategies, including: The carbon market buy bid prices are sorted from high to low and sell bid prices are sorted from low to high. With the goal of maximizing social welfare, the bid prices of both buyers and sellers in the carbon market are centrally matched to obtain several transaction pairs. In the transaction pairs, the sell bid price is lower than the buy bid price. The last pair of all traded pairs is designated as the marginal traded pair, and the bidding price of the market participants corresponding to this marginal traded pair is taken as the clearing price of the carbon market. The calculation process is as follows: ； in, , They represent the first The bid price and quantity of each buyer; , They represent the first The declared price and quantity of each seller; The cumulative trading volume of all the aforementioned trading pairs will be used as the clearing volume of the carbon market.

14. The hybrid interactive dynamic simulation system for medium- and long-term electricity market and carbon market according to claim 13, characterized in that, The simulation of electricity and carbon market transactions based on organizational structure involves solving electricity market clearing models and carbon market clearing models respectively, yielding the clearing price and cleared volume of the electricity market, and the clearing price and cleared volume of the carbon market, including: Based on the aforementioned annual scale, annual transaction simulations were performed on the electricity market and carbon market. The electricity market clearing model and carbon market clearing model were solved respectively to obtain the annual clearing price and annual clearing volume of the electricity market, as well as the annual clearing price and annual clearing volume of the carbon market. Based on the annual trading simulation results, weekly trading simulations were conducted for the electricity market and the carbon market. The clearing models for the electricity market and the carbon market were solved respectively to obtain the weekly clearing price and weekly clearing volume of the electricity market, as well as the weekly clearing price and weekly clearing volume of the carbon market.

15. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the hybrid interactive dynamic simulation method for the medium- and long-term electricity market and carbon market as described in any one of claims 1-7.

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