A Low-Carbon Scheduling Method for Electrical Interconnection Systems Based on Real-Time Carbon Emission Control

Abstract

The present invention discloses a low-carbon scheduling method for an electrical interconnected system based on real-time carbon emission control, including: Step 1, establishing a zero-sum game low-carbon incentive mechanism; Step 2, constructing a carbon emission trading framework; Step 3, constructing a two-layer combined optimization model for the electrical interconnected system; Step 4, performing iterative operations on the two-layer model to obtain a low-carbon economic scheduling result considering transactions. The present invention designs a low-carbon incentive mechanism to guide the optimal allocation of low-carbon resources within the system through low-carbon incentive signals; constructs a trading framework to provide a platform for carbon trading and energy trading for multi-type and multi-energy parks; establishes a carbon emission target according to the carbon emission characteristics of the system; transforms the model into a mixed-integer linear programming problem for solution through the KKT optimality condition and the linear duality theory, and finally obtains a globally optimal pricing strategy.

Description

Technical Field

[0001] The present invention relates to the field of electrical interconnection systems, and particularly to a low-carbon scheduling method for an electrical interconnection system based on real-time carbon emission control. Background Art

[0002] With the proposal of the carbon neutrality goal, a large number of decentralized and small-scale distributed energy sources will inevitably appear in the future distribution network. At the same time, the coupling of various energy sources at the physical level will extend to the market level, and a large number of energy producers and consumers with independent decision-making capabilities will participate in the market competition in the distribution network. However, different from the wholesale market, distributed energy transactions have the characteristics of a large number of participants, small single transactions, and scattered locations. In this context, how to construct an effective distribution network trading mechanism on the basis of considering the characteristics of multi-energy collaborative operation and guide the optimal allocation of carbon emission reduction technologies and resources is an indispensable part of the research on the low-carbonization of the energy system.

[0003] Existing research has made useful explorations on the low-carbon optimal operation of integrated energy systems and the application of trading models, but there are still several deficiencies: First, existing research rarely considers optimizing the scheduling of electrical interconnection systems through real-time carbon emission control; Second, from the perspective of transactions, how to guide the optimal allocation of low-carbon resources through a low-carbon incentive mechanism is missing in existing research; Third, current research in this area still focuses on energy transactions, and there is little research on the optimal scheduling of electrical interconnection systems in the context of the coupling of energy transactions and carbon transactions; Fourth, current research in this area does not consider the actual physical constraints and energy flow constraints of the system comprehensively enough. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a low-carbon scheduling method for an electrical interconnection system based on real-time carbon emission control. It designs a low-carbon incentive mechanism to guide the optimal allocation of low-carbon resources in the system through low-carbon incentive signals; on the basis of considering actual physical constraints and power flow constraints, it constructs a trading framework to provide a platform for carbon trading and energy trading for multi-type and multi-energy parks; it establishes a carbon emission target according to the carbon emission characteristics of the system and studies the optimal scheduling technology of the integrated energy system under real-time carbon emission control; it transforms the model into a mixed-integer linear programming problem for solution through the KKT optimality condition and linear duality theory, and finally obtains a globally optimal pricing strategy.

[0005] A technical solution to achieve the above purpose is:

[0006] A low-carbon scheduling method for an electrical interconnection system based on real-time carbon emission control, comprising the following steps:

[0007] Step 1, establish a zero-sum game low-carbon incentive mechanism;

[0008] Step 2, construct a carbon emission trading framework;

[0009] Step 3, construct a two-layer combined optimization model for the electrical interconnected system. The upper-layer model considers the real-time total carbon emission control of the electrical interconnected system, and establishes an objective function with the minimum low-carbon economic operation cost of the electrical interconnected system as the goal, including the optimal economic cost and the optimal carbon emission control effect. The constraint conditions include power network constraints and natural gas network constraints. The lower-layer model aims to minimize the operation cost and environmental cost of multi-energy parks under the low-carbon incentive mechanism. The constraint conditions include power, heat, and gas power balance constraints, electrical energy storage constraints, heat storage constraints, energy conversion constraints, and carbon emission constraints of multi-energy parks;

[0010] Step 4, perform iterative operations on the two-layer model to obtain the low-carbon economic dispatch result considering the transaction.

[0011] Furthermore, the specific method for establishing the zero-sum game low-carbon incentive mechanism in Step 1 is as follows:

[0012] Under the low-carbon incentive mechanism, set the sum of carbon quotas of all multi-energy parks to a fixed value, as shown in the following formula

[0013]

[0014] In the formula, Ω EH (j) is the set of multi-energy parks; E h,t is the carbon quota of multi-energy park h at time t; is the total carbon quota of all multi-energy parks; is the carbon revenue obtained by the carbon manager; is the carbon trading price in the carbon market; S h,t is the actual carbon emission of multi-energy park h at time t; β t and Υ t are the carbon emission reward factor and the penalty factor respectively.

[0015] Furthermore, the steps for constructing the carbon emission trading framework in Step 2 are as follows:

[0016] Step 2.1, data initialization. The electrical interconnected system issues initial electricity prices, gas prices, and carbon quota information to each multi-energy park;

[0017] Step 2.2, internal process balance. The specific objective function for the internal supply and demand balance in multi-energy park h is:

[0018]

[0019] In the formula, is the cost function of multi-energy park h; P h,t represents the active power provided by the distribution network to multi-energy park h at time t; is the electricity price in the market; Gh,t Denote the gas consumption of the multi - energy park h; is the gas price; and are the demands for the purchased / sold electricity power, heat power, and carbon quota formed by the internal self - balance optimization within the multi - energy park; and are the prices for the purchased / sold electricity power, heat power, and carbon quota by the multi - energy park from other multi - energy parks respectively;

[0020] Step 2.3, each multi - energy park determines the quotation / bidding price with the goal of maximizing the expected economic benefit and minimizing the expected economic cost, and its optimal bidding objective function is:

[0021]

[0022] In the formula, Ψ e,t is the revenue function of the multi - energy park as the seller, and are the electricity, heat, and carbon quotation prices to be optimized; and ρ 、 and h 、 and s are the upper and lower limits of the electricity, heat, and carbon quotation prices respectively; and are the expected transaction electricity power, heat power, and carbon quota for the seller multi - energy park to select the buyer multi - energy park; is the electricity price and heat price in the market;

[0023] The constraint conditions to be satisfied during the bidding process are:

[0024]

[0025] Step 2.4, the social welfare ψ of the best match between the seller and the buyer e,f,t can be expressed as:

[0026]

[0027] In the formula, and are the actual transaction electricity power, heat power, and carbon quota of the buyer; are the actual transaction electricity power, heat power, and carbon quota of the seller;

[0028] The constraints for the electricity power, heat power, and carbon quota transactions are similar. The electricity power transaction constraints are as follows:

[0029]

[0030] Step 2.5, Delivery. The multi-energy park acting as the seller needs to generate electricity according to the transaction result, and the distribution network is responsible for delivering the electricity to the corresponding buyer. When there is a transaction deviation between the buyer and the seller, the distribution network shall bear the fallback liability.

[0031] Furthermore, the specific steps for constructing the bi-level combined optimization model of the electrical interconnection system in Step 3 are as follows:

[0032] Step 3.1, The upper-level optimization model considers minimizing the low-carbon economic operation cost of the electrical interconnection system, including optimal economic cost and optimal carbon emission control effect. The objective function regarding the economic cost can be expressed as:

[0033]

[0034] In the formula: The first term on the right side is the electricity purchase cost of the electrical interconnection system in the electricity market, and Ω UP (j) represents the set of superior grid nodes connected to node j, and P x,t represents the active power injected by the superior grid node to node j at time t; The second term on the right side is the gas purchase cost of the electrical interconnection system in the natural gas market, and Ω NW (m) represents the set of natural gas wells connected to node m, and G w,t represents the natural gas flow provided by natural gas well w; The third term on the right side is the revenue obtained by the distribution network from selling electric energy and natural gas to the energy integrator, and are the electricity selling price and gas selling price of the distribution network to the energy integrator respectively;

[0035] The system carbon emission control effect can be modeled using the relationship between the system's real-time carbon emission target and the actual carbon emission, and can be expressed as:

[0036]

[0037] In the formula: is the real-time low-carbon emission target value of the electrical interconnection system; is the carbon emission intensity per unit of electric power; is the carbon emission intensity per unit of natural gas;

[0038] Step 3.2, Since the electrical interconnection system consists of a distribution network and a gas distribution network, system constraints are established. The system constraints include power network constraints and gas network constraints, which are specifically as follows:

[0039] Power network constraints:

[0040]

[0041] In the formula: Ω PL represents the set of all branches in the power network; Ω OLDenote the set containing the on-load tap-changing transformer branches; P jk,t and P ij,t respectively represent the active power flowing from node j→k and node i→j at time t; r ij represents the resistance of line ij; I ij,t represents the square of the current I flowing through line ij at time t; P ij,t represents the active power load at node j at time t; Ω j,t (j), Ω wind (j), Ω PV (j), Ω GU (j), Ω P2G (j), Ω EH (j) respectively represent the sets of the superior power grid nodes, wind turbines, photovoltaic power sources, gas turbines, power-to-gas equipment, and EH connected to node j; P a,t , P b,t and P u,t respectively represent the active power injected by wind turbine a, photovoltaic b, and gas turbine u into node j at time t; P g,t represents the active power consumed by power-to-gas equipment g at time t; Ω B represents the set of all nodes in the distribution network;

[0042]

[0043] In the formula: Q jk,t and Q ij,t respectively represent the reactive power flowing from node j→k and node i→j at time t; x ij represents the reactance of line ij; Q j,t represents the reactive power load at node j at time t; Q a,t represents the reactive power absorbed by wind turbine a at time t; Q c,t represents the reactive power provided by static var compensator c at time t; Ω SVC (j) represents the set of SVC devices connected to node j.

[0044]

[0045] In the formula: v i and v j represent the squares of the voltages of nodes i and j; Z ij and S ij represent the impedance and apparent power of branch ij; represents the upper limit of the adjustable capacity of the SVC device;

[0046] Natural gas network constraint:

[0047] The energy balance constraint of natural gas node m can be expressed as:

[0048]

[0049] In the formula: Ω P2G represents the set of P2G devices connected to node m; G g′,t represents the natural gas flow provided by P2G device g′; Ω EH(m) represents the set of multi - energy parks connected to node m; G m,t represents the natural gas load of node m; Ω GU(m) represents the set of gas turbines connected to node m; G u′,t represents the gas consumption of gas turbine u′; ψ b(m) and ψ f(m) represent the sets of upstream node and downstream node connected to node m respectively; G mn′,t and G mn,t represent the natural gas flows from node n′→m and node m→n; G mn,t represents the natural gas flow in pipeline mn; Ω GC(mn) represents the set of compressors in pipeline mn; α c′ represents the fuel consumption coefficient of compressor c′; Ω GB represents the set of all natural gas nodes;

[0050] The natural gas pipeline power flow constraint can be expressed as:

[0051]

[0052] In the formula: represents the upper limit of the flow in pipeline mn.

[0053] Assume that the flow direction of the power flow in the natural gas pipeline is from node m to node n, then there is:

[0054]

[0055] G u′,t =P u,t / (η u ·I gas )

[0056] G g′,t =P g,t ·η g / I gas

[0057] In the formula: γ c′ is the compression factor of natural gas compressor c′; η u is the gas - to - electricity efficiency of GU; η g is the electricity - to - gas efficiency of P2G; I gas is the calorific value of natural gas;

[0058] Step 3.3, establish the objective function of the lower - layer optimization model

[0059] The lower - layer optimization aims to minimize the operating cost of the energy integrator and can be expressed as:

[0060]

[0061] Step 3.4, set the constraint conditions of the lower - layer optimization model;

[0062] The power balance constraints of electricity, heat, and gas in the system can be expressed as:

[0063]

[0064] In the formula: is the output of the wind turbine; is the output of the photovoltaic; represents the active power provided by the CHP unit in the EH; and represent the charging and discharging powers of the electrical energy storage device respectively; represents the electrical power consumed by the electric boiler in the EH; represents the electrical load demand of the EH;

[0065]

[0066] In the formula: and represent the thermal powers provided by the CHP unit, gas boiler, and electric boiler respectively; and represent the heat storage power and heat release power of the heat storage device; represents the heat load demand of the EH;

[0067]

[0068] In the formula: and represent the natural gas quantities required by the CHP unit and gas boiler;

[0069] Model and constraint conditions of the electrical energy storage device:

[0070]

[0071] In the formula: η EC 、η ED represent the charge - discharge efficiency of the electrical energy storage device; represents the initial and final values of the stored electricity of the electrical energy storage device;

[0072] Model and constraint conditions of the heat storage device:

[0073]

[0074] where η HC and η HD represent the heat storage and heat release efficiencies of the heat storage device; represents the initial and final values of the heat storage capacity of the heat storage device;

[0075] Model and constraint conditions of the energy conversion device:

[0076]

[0077] where: η CE and η EH and η GH respectively represent the electrical efficiency of the CHP unit, the efficiency of the electric boiler, and the efficiency of the gas boiler;

[0078] Carbon emission model and constraint conditions of the multi-energy park:

[0079]

[0080] The features and beneficial effects of the present invention are as follows:

[0081] 1) The present invention designs a low-carbon incentive mechanism to guide the optimal allocation of low-carbon resources in the system through low-carbon incentive signals.

[0082] 2) On the basis of considering actual physical constraints and power flow constraints, the present invention constructs a trading framework to provide a platform for carbon trading and energy trading for the set multi-type multi-energy parks.

[0083] 3) The present invention establishes a carbon emission target according to the carbon emission characteristics of the system and studies the optimal dispatching technology of the integrated energy system under real-time carbon emission control.

[0084] 4) The present invention transforms the model into a mixed-integer linear programming problem for solution through the KKT optimality condition and linear duality theory, and finally obtains a globally optimal pricing strategy. Description of the drawings

[0085] Figure 1 is a schematic diagram of a two-layer optimal dispatching framework of an electrical interconnected system including a multi-energy park;

[0086] Figure 2 is a schematic diagram of a multi-energy park model;

[0087] Figure 3 is a flow chart of two-layer optimal solution of an electrical interconnected system including a multi-energy park. Specific implementation manners

[0088] In order to better understand the technical solution of the present invention, the following is a detailed description through specific embodiments:

[0089] From the perspective of the safe and low-carbon dispatching operation of multi-energy systems, considering key low-carbon power technologies such as renewable energy, energy storage, and multi-energy conversion, a low-carbon dispatching method for the electrical Internet system of multi-type multi-energy parks based on real-time carbon emission control is proposed. Please refer to Figure 1 It is a schematic diagram of the two-layer optimal dispatching framework for the electrical interconnection system of multi-energy parks. Figure 2 It is a schematic diagram of the multi-energy park model for the low-carbon dispatching method of the electrical interconnection system based on real-time carbon emission control applying the present invention.

[0090] Please refer to Figure 3 , a low-carbon dispatching method for the electrical interconnection system based on real-time carbon emission control, includes the following steps:

[0091] Step 1, establish a zero-sum game low-carbon incentive mechanism;

[0092] Step 2, construct a carbon emission trading framework;

[0093] Step 3, construct a two-layer combined optimization model for the electrical interconnection system. The upper-layer model considers the total real-time carbon emission control of the electrical interconnection system, and establishes an objective function with the minimum low-carbon economic operation cost of the electrical interconnection system as the goal, including the optimal economic cost and the optimal carbon emission control effect. The constraint conditions include power network constraints and natural gas network constraints. The lower-layer model aims at the minimum operation cost and environmental cost of the multi-energy park under the low-carbon incentive mechanism. The constraint conditions include power, heat, and gas power balance constraints, electrical energy storage constraints, heat storage constraints, energy conversion constraints, and carbon emission constraints of the multi-energy park;

[0094] Step 4, perform iterative operations on the two-layer model to obtain the low-carbon economic dispatching result considering transactions.

[0095] Furthermore, the specific method for establishing the zero-sum game low-carbon incentive mechanism in Step 1 is as follows:

[0096] Under the low-carbon incentive mechanism, set the sum of the carbon quotas of all multi-energy parks to a fixed value, which is the following formula

[0097]

[0098] In the formula, Ω EH (j) is the set of multi-energy parks; E h,t is the carbon quota of multi-energy park h at time t; is the total carbon quota of all multi-energy parks; is the carbon revenue obtained by the carbon manager; is the carbon trading price in the carbon market; S h,t is the actual carbon emission of multi-energy park h at time t; β t and Υ t are the carbon emission reward factor and the penalty factor respectively.

[0099] Furthermore, the steps for constructing the carbon emission trading framework in Step 2 are as follows:

[0100] Step 2.1, data initialization, where the electrical interconnected system issues initial electricity prices, gas prices, and carbon quota information to each multi-energy park;

[0101] Step 2.2, internal process balance. The specific internal supply-demand balance objective function for multi-energy park h is:

[0102]

[0103] In the formula, is the cost function of multi-energy park h; P h,t represents the active power provided by the distribution network to multi-energy park h at time t; is the electricity price in the market; G h,t represents the gas consumption of multi-energy park h; is the gas price; and are the demands for the purchased and sold electricity power, heat power, and carbon quota formed by the internal self-balancing optimization in the multi-energy park; and are the prices for the purchased and sold electricity power, heat power, and carbon quota of the multi-energy park from other multi-energy parks respectively;

[0104] Step 2.3, each multi-energy park determines the bid / offer price with the goal of maximizing the expected economic benefit and minimizing the expected economic cost. Its optimal bidding objective function is:

[0105]

[0106] In the formula, Ψ e,t is the revenue function of the multi-energy park as the seller, and are the electricity, heat, and carbon offer prices to be optimized; and ρ , and h , and s are the upper and lower limits of the electricity, heat, and carbon offer prices respectively; and are the expected transaction electricity power, heat power, and carbon quota for the seller multi-energy park to select the buyer multi-energy park; are the electricity price and heat price in the market;

[0107] The constraint conditions to be satisfied during the bidding process are:

[0108]

[0109] Step 2.4, the social welfare ψ of the best match between the seller and the buyer e,f,t can be expressed as:

[0110]

[0111] In the formula, and are the electric power, heat power, and carbon quota actually transacted by the buyer; are the electric power, heat power, and carbon quota actually transacted by the seller;

[0112] The constraints for the trading of electric power, heat power, and carbon quota are similar. The constraints for electric power trading are as follows:

[0113]

[0114] Step 2.5, delivery. The multi - energy park acting as the seller needs to generate electricity according to the trading results, and the distribution network is responsible for delivering the electricity to the corresponding buyer. When there is a trading deviation between the buyer and the seller, the distribution network bears the fallback responsibility.

[0115] Furthermore, the specific steps for constructing the bi - level combined optimization model of the electrical interconnection system in Step 3 are as follows:

[0116] Step 3.1, the upper - layer optimization model considers minimizing the low - carbon economic operation cost of the electrical interconnection system, including optimal economic cost and optimal carbon emission control effect. The objective function regarding the economic cost can be expressed as:

[0117]

[0118] In the formula: The first term on the right - hand side is the electricity purchase cost of the electrical interconnection system in the electricity market. Ω UP (j) represents the set of superior power grid nodes connected to node j, and P x,t represents the active power injected by the superior power grid node to node j at time t. The second term on the right - hand side is the gas purchase cost of the electrical interconnection system in the natural gas market. Ω NW (m) represents the set of natural gas wells connected to node m, and G w,t represents the natural gas flow provided by natural gas well w. The third term on the right - hand side is the revenue obtained by the distribution network from selling electric energy and natural gas to the energy integrator. and are the electricity selling price and gas selling price of the distribution network to the energy integrator respectively;

[0119] The system carbon emission control effect can be modeled using the relationship between the system's real - time carbon emission target and the actual carbon emission, and can be expressed as:

[0120]

[0121] In the formula: is the real-time low-carbon emission target value for the electrical interconnection system; is the carbon emission intensity per unit of electric power; is the carbon emission intensity per unit of natural gas;

[0122] Step 3.2: Since the electrical interconnection system consists of a power distribution network and a gas distribution network, system constraints are established. The system constraints include power network constraints and gas network constraints, which are specifically as follows:

[0123] Power network constraints:

[0124]

[0125] In the formula: Ω PL represents the set of all branches in the power network; Ω OL represents the set of branches containing on-load tap-changing transformers; P jk,t and P ij,t respectively represent the active power from node j→k and node i→j at time t; r ij represents the resistance of line ij; I ij,t represents the square of the current I flowing through line ij at time t; P ij,t represents the active power load at node j at time t; Ω j,t wind (j), Ω(j), Ω PV (j), Ω GU (j), Ω P2G (j), Ω EH (j) respectively represent the sets of the superior power grid nodes, wind turbines, photovoltaic power sources, gas turbines, power-to-gas equipment, and EH connected to node j; P a,t , P b,t and P u,t respectively represent the active power injected by wind turbine a, photovoltaic power source b, and gas turbine u into node j at time t; P g,t represents the active power consumed by power-to-gas equipment g at time t; Ω B represents the set of all nodes in the power distribution network;

[0126]

[0127] In the formula: Q jk,t and Q ij,t respectively represent the reactive power from node j→k and node i→j at time t; x ij represents the reactance of line ij; Q j,t represents the reactive power load at node j at time t; Q a,t represents the reactive power absorbed by wind turbine a at time t; Q c,t represents the reactive power provided by static var compensator c at time t; Ω SVC(j) represents the set of SVC devices connected to node j.

[0128]

[0129] Where: v i and v j represent the squares of the voltages of nodes i and j; Z ij and S ij represent the impedance and apparent power of branch ij; represents the upper limit of the adjustable capacity of the SVC device;

[0130] Natural gas network constraints:

[0131] The energy balance constraint of natural gas node m can be expressed as:

[0132]

[0133] Where: Ω P2G represents the set of P2G devices connected to node m; G g′,t represents the natural gas flow provided by P2G device g′; Ω EH(m) represents the set of multi - energy parks connected to node m; G m,t represents the natural gas load of node m; Ω GU(m) represents the set of gas turbines connected to node m; G u′,t represents the gas consumption of gas turbine u′; ψ b(m) 、ψ f(m) respectively represent the sets of upstream and downstream nodes connected to node m; G mn′,t and G mn,t represent the natural gas flows from node n′→m and node m→n; G mn,t represents the natural gas flow in pipeline mn; Ω GC(mn) represents the set of compressors in pipeline mn; α c′ represents the fuel consumption coefficient of compressor c′; Ω GB represents the set of all natural gas nodes;

[0134] The natural gas pipeline power flow constraint can be expressed as:

[0135]

[0136] Where: represents the upper limit of the flow in pipeline mn.

[0137] Assuming that the direction of the power flow in the natural gas pipeline is from node m to node n, then:

[0138]

[0139] Gu′,t = P u,t / (η u · I gas )

[0140] G g′,t = P g,t · η g / I gas

[0141] Where: γ c′ is the compression factor of the natural gas compressor c'; η u is the gas-to-electricity efficiency of GU; η g is the electricity-to-gas efficiency of P2G; I gas is the calorific value of natural gas combustion;

[0142] Step 3.3, establish the objective function of the lower-level optimization model

[0143] The lower-level optimization aims to minimize the operating cost of the energy integrator, which can be expressed as:

[0144]

[0145] Step 3.4, set the constraint conditions of the lower-level optimization model;

[0146] The power balance constraints of electricity, heat, and gas in the system can be expressed as:

[0147]

[0148] Where: is the output power of the wind turbine; is the output power of the photovoltaic; represents the active power provided by the CHP unit in the EH; and represent the charging and discharging powers of the electrical energy storage device respectively; represents the electrical power consumed by the electric boiler in the EH; represents the electrical load demand of the EH;

[0149]

[0150] Where: and represent the thermal powers provided by the CHP unit, gas boiler, and electric boiler respectively; and represent the heat storage power and heat release power of the heat storage device; represents the heat load demand of the EH;

[0151]

[0152] Where: and represents the natural gas quantity required by the CHP unit and the gas boiler;

[0153] Model and constraint conditions of the electrical energy storage device:

[0154]

[0155] In the formula: η EC and η ED represent the charge and discharge efficiency of the electrical energy storage device; represents the initial and final values of the stored electrical energy of the electrical energy storage device;

[0156] Model and constraint conditions of the heat storage device:

[0157]

[0158] In the formula, η HC and η HD represent the heat storage and heat release efficiency of the heat storage device; represents the initial and final values of the stored heat quantity of the heat storage device;

[0159] Model and constraint conditions of the energy conversion device:

[0160]

[0161] In the formula: η CE and η EH and η GH respectively represent the electrical efficiency of the CHP unit, the efficiency of the electric boiler, and the efficiency of the gas boiler;

[0162] Carbon emission model and constraint conditions of the multi - energy park:

[0163]

[0164] Step 4: Finally, perform iterative operations on the double - layer model to obtain the low - carbon economic dispatch result considering transactions.

[0165] The present invention is oriented to the electrical interconnected system, and studies the multi - energy collaborative low - carbon optimal operation strategy of the system under the low - carbon incentive mechanism. Compared with the existing research, the main contributions are summarized as follows: designing a low - carbon incentive mechanism to guide the optimal allocation of low - carbon resources in the system through low - carbon incentive signals; constructing a trading framework on the basis of considering actual physical constraints and power flow constraints to provide a platform for carbon trading and energy trading for multi - type multi - energy parks; establishing a carbon emission target according to the carbon emission characteristics of the system and studying the optimal dispatching technology of the integrated energy system under real - time carbon emission control; transforming the model into a mixed - integer linear programming problem for solution through the KKT optimality condition and the linear duality theory, and finally obtaining the globally optimal pricing strategy.

[0166] Those of ordinary skill in the art should recognize that the above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. As long as they are within the scope of the spirit of the present invention, changes and modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. A low-carbon scheduling method for an electrical interconnected system based on real-time carbon emission control, characterized in that It includes the following steps: Step 1, establish a low-carbon incentive mechanism for zero-sum game; Step 2, construct a carbon emission trading framework; Step 3, construct a two-layer combined optimization model for the electrical interconnected system. The upper-layer model considers the real-time total carbon emission control of the electrical interconnected system, and establishes an objective function with the minimum low-carbon economic operation cost of the electrical interconnected system as the goal, including the optimal economic cost and the optimal carbon emission control effect. The constraint conditions include power network constraints and natural gas network constraints. The lower-layer model aims to minimize the operation cost and environmental cost of multi-energy parks under the low-carbon incentive mechanism. The constraint conditions include power, heat, and gas power balance constraints, electrical energy storage constraints, heat storage constraints, energy conversion constraints, and carbon emission constraints of multi-energy parks; Step 4, perform iterative operations on the two-layer model to obtain the low-carbon economic dispatch results considering transactions, The steps for Step 2 to construct a carbon emission trading framework are as follows: Step 2.1, data initialization. The electrical interconnected system issues initial electricity prices, gas prices, and carbon quota information to each multi-energy park; Step 2.2, internal process balance. The specific supply-demand balance objective function within multi-energy park h is: In the formula, is the cost function of the multi - energy park h; P h,t represents the active power provided by the distribution network to the multi - energy park h at time t; is the electricity price in the market; G h,t represents the gas consumption of the multi - energy park h; is the gas price; and are the demands for the purchased and sold electric power, heat power, and carbon quota formed by the internal self - balancing optimization of the multi - energy park; and are the prices of the purchased and sold electric power, heat power, and carbon quota of the multi - energy park from other multi - energy parks respectively. E h,t is the carbon quota of the multi - energy park h at time t, S h,t is the actual carbon emission of the multi - energy park h at time t, β t and Υ t are the carbon emission reward factor and penalty factor respectively; Step 2.3, each multi-energy park determines the bid / offer price with the goal of maximizing the expected economic benefit and minimizing the expected economic cost. Its optimal bidding objective function is: Where, Ψ e,t is the revenue function of the multi-energy park as the seller, and are the electricity, heat, and carbon offer prices to be optimized; and ρ, and h, and s are the upper and lower limits of the electricity, heat, and carbon offer prices respectively; and are the expected transaction electric power, heat power, and carbon quota for the seller multi-energy park to select the buyer multi-energy park; is the electricity price and heat price in the market; is the carbon trading price in the carbon market; The constraint conditions that need to be satisfied during the bidding process are: In the formula, Step 2.4, the social welfare ψ of the best match between the seller and the buyer e,f,t It is expressed as: In the formula, and are the actual electricity power, heat power, and carbon quota transacted by the buyer; are the actual electricity power, heat power, and carbon quota transacted by the seller; The constraints for the trading of electric power, heat power, and carbon quota are similar. The trading constraints for electric power are as follows: Step 2.5, delivery. The multi-energy park acting as the seller needs to generate electricity according to the transaction results, and the distribution network is responsible for delivering the electricity to the corresponding buyer. When there is a trading deviation between the buyer and the seller, the distribution network assumes the fallback responsibility.

2. The low-carbon scheduling method for an electrical interconnected system based on real-time carbon emission control according to claim 1, wherein, The specific method for Step 1 to establish a low-carbon incentive mechanism for zero-sum game is as follows: Under the low-carbon incentive mechanism, set the sum of carbon quotas of all multi-energy parks to a fixed value, as the following formula Where, Ω EH (j) is the set of multi-energy parks; E h,t is the carbon quota of multi-energy park h at time t; is the total sum of carbon quotas of all multi-energy parks; is the carbon revenue obtained by the carbon manager; is the carbon trading price in the carbon market; S h,t is the actual carbon emission of multi-energy park h at time t; β t and Υ t are the carbon emission reward factor and the penalty factor respectively.

3. A low-carbon scheduling method for an electrical interconnection system based on real-time carbon emission control according to claim 1, characterized in that, The specific steps for Step 3 to construct a two-layer combined optimization model for the electrical interconnected system are as follows: Step 3.1, the upper-layer optimization model considers the minimum low-carbon economic operation cost of the electrical interconnected system, including the optimal economic cost and the optimal carbon emission control effect. The objective function regarding the economic cost is expressed as: Where: The first term on the right side is the electricity purchase cost of the electrical interconnection system in the electricity market, Ω UP (j) represents the set of superior power grid nodes connected to node j, P x,t represents the active power injected by the superior power grid node into node j at time t; The second term on the right side is the gas purchase cost of the electrical interconnection system in the natural gas market, Ω NW (m) represents the set of natural gas wells connected to node m, G w,t represents the natural gas flow provided by natural gas well w; The third term on the right side is the revenue obtained by the distribution network from selling electricity and natural gas to the energy integrator, and are the electricity selling price and gas selling price of the distribution network to the energy integrator respectively; The system carbon emission control effect can be modeled using the relationship between the system real-time carbon emission target and the actual carbon emission, expressed as: In the formula: is the real-time low-carbon emission target value of the electrical interconnection system; is the carbon emission intensity per unit of electric power; is the carbon emission intensity per unit of natural gas; Step 3.2, according to the electrical interconnected system consisting of a distribution network and a gas distribution network, establish system constraints. The system constraints include power network constraints and natural gas network constraints, specifically as follows: Power network constraints: where: Ω PL denotes the set of all branches in the power network; Ω OL denotes the set of branches containing on-load tap-changing transformers; P jk,t and P ij,t respectively represent the active power flowing from node j→k and node i→j at time t; r ij represents the resistance of line ij; I ij,t represents the square of the current I flowing through line ij at time t; P ij,t represents the active power load at node j at time t; Ω j,t (j), Ω wind (j), Ω PV (j), Ω GU (j), Ω P2G (j), Ω EH (j) respectively represent the sets of the superior power grid nodes, wind turbines, photovoltaic panels, gas turbines, power-to-gas devices, and EHs connected to node j; P a,t , P b,t and P u,t respectively represent the active power injected by wind turbine a, photovoltaic panel b, and gas turbine u into node j at time t; P g,t represents the active power consumed by power-to-gas device g at time t; Ω B represents the set of all nodes in the distribution network; Where: Q jk,t and Q ij,t respectively represent the reactive power flowing from node j→k and node i→j at time t; x ij represents the reactance of line ij; Q j,t represents the reactive power load at node j at time t; Q a,t represents the reactive power absorbed by wind turbine a at time t; Q c,t represents the reactive power provided by static var compensator c at time t; Ω SVC (j) represents the set of SVC devices connected to node j, where: v i and v j represent the squares of the voltages of nodes i and j; Z ij and S ij represent the impedance and apparent power of branch ij; represents the upper limit of the adjustable capacity of the SVC device; Natural gas network constraints: The energy balance constraint of natural gas node m can be expressed as: Where: Ω P2G represents the set of P2G devices connected to node m; G g′,t represents the natural gas flow provided by P2G device g′; Ω EH(m) represents the set of multi - energy parks connected to node m; G m,t represents the natural gas load of node m; Ω GU(m) represents the set of gas turbines connected to node m; G u′,t represents the gas consumption of gas turbine u′; ψ b(m) 、ψ f(m) respectively represent the sets of upstream nodes and downstream nodes connected to node m; G mn′,t and G mn,t represent the natural gas flows from node n′→m and node m→n; G mn,t represents the natural gas flow in pipeline mn; Ω GC(mn) represents the set of compressors in pipeline mn; α c′ represents the fuel consumption coefficient of compressor c′; Ω GB represents the set of all natural gas nodes; The natural gas pipeline power flow constraint is expressed as: In the formula: represents the upper flow limit of pipeline mn Assume that the flow direction of the power flow in the natural gas pipeline is from node m to node n, then there is: G u′,t = P u,t / (η u · I gas ) G g′,t = P g,t · η g / I gas where: γ c′ is the compression factor of the natural gas compressor c'; η u is the gas-to-electricity conversion efficiency of GU; η g is the power-to-gas efficiency of P2G; I gas is the calorific value of natural gas combustion; Step 3.3, establish the objective function of the lower-layer optimization model The lower-layer optimization aims to minimize the operation cost of the energy integrator, expressed as: Step 3.4, set the constraint conditions of the lower-layer optimization model; The power balance constraints of electricity, heat, and gas in the system are expressed as: Where: is the output of the wind turbine; is the output of the photovoltaic; represents the active power provided by the CHP unit in the EH; and represent the charging and discharging powers of the electrical energy storage device respectively; represents the electrical power consumed by the electric boiler in the EH; represents the electrical load demand of the EH; Wherein: and respectively represent the heat powers provided by the CHP unit, gas boiler, and electric boiler; and represent the heat storage power and heat release power of the heat storage device; represents the heat load demand of EH; In the formula: and represent the natural gas quantities required by the CHP unit and the gas boiler; The model and constraint conditions of the electrical energy storage device: Where: η EC , η ED represent the charge and discharge efficiency of the electrical energy storage device; represent the initial and final values of the stored electrical energy of the electrical energy storage device; The model and constraint conditions of the heat storage device: where η HC , η HD represent the heat storage and heat release efficiencies of the heat storage device; represents the initial and final values of the heat storage capacity of the heat storage device; The model and constraint conditions of the energy conversion device: Where: η CE , η EH and η GH respectively represent the electrical efficiency of the CHP unit, the efficiency of the electric boiler, and the efficiency of the gas boiler; The carbon emission model and constraint conditions of the multi-energy park:

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