A layered collaborative scheduling method for a multi-energy system coupled with electricity and carbon

Abstract

The application discloses a layered collaborative scheduling method of an electric-carbon coupled multi-energy system, and comprises the following steps: S1, a multi-energy market collaborative clearing model is established, and energy demand of each period of PIES is initialized according to historical data; an upper energy operator performs joint clearing, and node energy-carbon price information of each PIES is calculated; S2, regional autonomous optimization is performed; S3, a CEA collaborative scheduling model between PIES is constructed, a CEA is traded based on a cooperative game optimization theory, and an electric-carbon coupled Nash bargaining CEA optimization benefit model is constructed; S4, a double-layer iteration solving algorithm is introduced, the regional autonomous model is taken as a bottom layer, the multi-energy market collaborative clearing model is taken as an upper layer, a double-layer distributed optimization scheduling model is formed, and the upper and lower layers are decoupled and interacted through a heterogeneous coordination operator; and S5, the value of S4 is taken as an initial value of S1, steps S1-S4 are repeated, variables are continuously interactively updated, and collaborative optimization is achieved. The application can more effectively realize full-energy optimization and carbon emission control of the system.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a hierarchical collaborative dispatching method for multi-energy systems with electric-carbon coupling. Background Technology

[0002] A multi-energy system, formed by deep coupling of various energy systems, plays a role in all aspects of energy production, transmission, distribution and conversion. It aims to closely integrate various energy resources (including electricity, natural gas, heat and other energy) and facilities in cities, economic development zones and industrial parks through multi-energy coordination and complementarity and multi-terminal energy flow interaction, so as to achieve efficient coordination and integration of energy resources.

[0003] Multi-energy systems, by fully considering the complementary characteristics of various energy systems, are of great significance for improving energy utilization efficiency and reducing the environmental impact of energy. With the maturation of new power system theories and research, the structural form of my country's power system is gradually shifting from a high-carbon power system to a low-carbon or even zero-carbon power system. Compared with traditional power systems, the integration of a high proportion of renewable energy makes the source-grid load of new power systems more complex. Simultaneously, to achieve decarbonization in all aspects of the power system, it is necessary to fully integrate carbon emission mechanism analysis, low-carbon benefit evaluation, and carbon trading markets. Multi-energy systems encompass various energy sources such as electricity, gas, and heating / cooling, exhibiting more complex energy combinations and demand characteristics. The introduction of carbon trading mechanisms makes the optimization objectives of energy systems more diversified. Therefore, how to achieve deep coupling of electricity and carbon emissions in all aspects of a multi-energy system with deep coupling of multiple energy sources such as heating, cooling, electricity, and gas remains an urgent problem to be solved.

[0004] Current research on carbon dispatch mainly focuses on introducing carbon emission-related objective functions into dispatch planning, and the power system structure is relatively simple. There has been no in-depth research on carbon trading strategies among different entities and methods for achieving coordinated and optimized dispatch in multi-energy systems. Summary of the Invention

[0005] To address the aforementioned problems, this invention aims to provide a hierarchical collaborative scheduling method for multi-energy systems with electric-carbon coupling. By fully considering the complementary characteristics of energy nodes and the energy-carbon coupling collaborative strategy of multiple energy entities, it can effectively achieve global energy optimization and carbon emission control of the system.

[0006] The technical solution of the present invention is as follows:

[0007] A hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling includes the following steps:

[0008] S1: Establish a multi-energy market collaborative clearing model and initialize the energy demand of PIES for each period based on historical data.

[0009] Upper-level energy operators according to Joint clearing is performed, and nodal energy-carbon price information for each PIES is calculated based on marginal price theory and embedded carbon flow distribution. and

[0010] S2: Establish a regional autonomous optimization model, where each PIES is based on the corresponding node energy-carbon price information. and Optimize regional autonomy;

[0011] S3: Construct a collaborative scheduling model for CEA among PIES nodes, and facilitate CEA trading among energy nodes based on cooperative game theory. Construct an energy-carbon coupled Nash bargaining CEA optimization benefit model. Based on this model, obtain the optimal CEA trading volume among PIES nodes. and the best transaction price Simultaneously update the corresponding energy requirements.

[0012] S4: Introducing a two-layer iterative solution algorithm, with the regional autonomy model as the bottom layer and the multi-energy market collaborative clearing model as the top layer, forming a two-layer distributed optimization scheduling model. The upper and lower layers are decoupled and interact through heterogeneous coordination operators.

[0013] When the iterative error of energy demand is less than a set threshold, the two-layer distributed optimization scheduling model reaches an equilibrium state, at which point the optimal CEA transaction volume is finally determined. and transaction price At the same time, operators will determine the corresponding energy demand. Determine the most reasonable node energy-carbon price and

[0014] S5: Using the value of step S4 as the initial value of step S1, repeat steps S1-S4 to continuously update variables interactively and achieve collaborative optimization.

[0015] Preferably, in step S2, the regional autonomous optimization model is:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] In the formula: These represent the upstream energy purchase cost, energy production cost, equipment operation and maintenance cost, and total carbon trading cost of the nth PIES, respectively; T represents the settlement period in days. These represent the node electricity price and node gas price received by the nth PIES, respectively; These represent the electricity and gas purchased at time t, respectively. This represents the set of corresponding devices in the nth PIES, where CU, S, WT, PV, CHP, EB, P2G, EES, HES, and GES represent the coal-fired unit, gas source, wind power, photovoltaic, CHP unit, electric boiler, P2G, electrochemical energy storage equipment, thermal energy storage equipment, and gas energy storage equipment in the corresponding devices, respectively. All are cost coefficients; These represent the output of the coal-fired unit and the gas source at time t, respectively. This indicates the unit maintenance cost of the corresponding equipment; Let represent the output of the corresponding equipment at time t, where Indicates heat output. Indicates electrical output; Let e, h, and g represent the charging and discharging power of the corresponding energy storage device at time t, respectively, where e, h, and g represent the electric, thermal, and gas energy storage devices, respectively. Let these represent the CCER cost of the nth PIES, the parent CEA transaction cost, and the inter-PIES CEA transaction cost, respectively.

[0022] The constraint set of the regional autonomous optimization model consists of the physical safety constraint set of the electricity-gas-heating network. and carbon constraint set The physical safety constraint set of the electric-gas-heat network is as follows:

[0023]

[0024]

[0025]

[0026] In the formula: This represents the set of devices connected to electrical node i, gas node j, and thermal node h, where net represents the upper-layer network. P represents the power demand of the upper-layer network at time t; ki,t P ij,t This represents the power flow of lines (k,i) and (i,j) at time t; P Di,t g Dj,t H Dh,tFor the electrical, gas, and heat loads of the corresponding nodes; Let these represent the sets of electrical nodes, gas nodes, and thermal nodes of the nth REI, respectively. This represents the natural gas demand of the upper-layer network at time t; f represents the power produced by natural gas at time P2Gt; mj,t f jn,t Represents the airflow in pipes (m,j) and (j,n); This represents the power consumption of natural gas at time CHPt; This represents the total heat source and total heat load power at the hot node h at time t; This represents the heat load power at time EB t;

[0027] The carbon constraint set It consists of quota balancing constraints and CCER offsetting constraints, which are as follows:

[0028]

[0029]

[0030] In the formula: These represent the carbon emission coefficient, unit power supply quota, and unit heat supply quota of the corresponding equipment, respectively; ω CEA ω CCER These represent the offsetting ratios of CEA and CCER, respectively. These represent the carbon allowances obtained by the CHP unit for producing one unit of electricity, the carbon allowances obtained by the CHP unit for producing one unit of heat, and the carbon allowances obtained by the CU unit for producing one unit of electricity, respectively. These represent the number of CCERs that can be obtained from a unit of electricity produced by CU and GU, respectively; κ represents the electrothermal conversion factor. This represents the amount of CEA purchased by the nth PIES from the higher-level carbon trading market; This represents the quota trading volume between the nth PIES and the other PIES; This represents the amount of CCER offset used by the nth PIES; n CU n GU These represent the number of units in CU and GU, respectively; Let represent the power of CU and GU at time t, respectively.

[0031] Preferably, the CCER cost of the nth PIES is calculated using the following formula:

[0032]

[0033] In the formula: λ CCER This indicates the price per unit of CCER; These represent the CCER values ​​for wind power and solar power, respectively.

[0034] The transaction cost of the parent CEA of the nth PIES is calculated using the following formula:

[0035]

[0036] In the formula: This represents the price at which the nth PIES purchases CEA from the higher-level carbon trading market;

[0037] The inter-PIES CEA transaction cost for the nth PIES is calculated using the following formula:

[0038]

[0039] Where: Ω DSO Represents a collection of PIES; This represents the quota trading price between the nth PIES and the other PIES.

[0040] As a preferred option, the price of CEA purchased by the nth PIES from the higher-level carbon trading market is calculated using the following formula:

[0041]

[0042] In the formula: represent or These represent the carbon price of the nodes connected to the nth PIES in the upper-level power grid and gas grid, respectively.

[0043] Preferably, in step S3, the Nash bargaining CEA optimization benefit model of electrocarbon coupling includes a total cost minimization subproblem P1 and a revenue allocation subproblem P2, wherein the total cost minimization subproblem P1 is:

[0044]

[0045] In the formula: N represents the number of REIs participating in the negotiation; st represents the conditions that need to be met; Represents the set of physical safety constraints for an electrical-gas-heating network; Represents a carbon constraint set;

[0046] The revenue allocation subproblem P2 is:

[0047]

[0048] In the formula: This represents the total cost when the nth PIES does not participate in the negotiation; This means that the nth PIES is not considered. Total cost of time optimization; and Both represent the optimal solution to the subproblem P1 that minimizes total cost; This represents the price at which the m-th PIES purchases CEA from the higher-level carbon trading market.

[0049] Preferably, both the total cost minimization subproblem P1 and the revenue allocation subproblem P2 are solved using the ADMM algorithm.

[0050] Preferably, when using the ADMM algorithm to solve the total cost minimization subproblem P1, for the nth PIES, an auxiliary variable is introduced. Construct the augmented Lagrangian function of the subproblem P1 that minimizes total cost, wherein the augmented Lagrangian function of the subproblem P1 that minimizes total cost is:

[0051]

[0052] In the formula: The augmented Lagrangian function represents the subproblem P1 that minimizes total cost; μ represents the total optimization cost of the nth PIES. 1,mn ρ 1,mn Let Lagrange multipliers and penalty factors represent the quantitative coupling constraints of the CEA, respectively. This represents the amount of CEA that the m-th PIES buys / sells to the n-th PIES; Represents the L2 norm operation;

[0053] PIES members continuously update variables through interaction. and Until convergence condition one is met;

[0054] When solving the revenue allocation subproblem P2 using the ADMM algorithm, for the nth PIES, an auxiliary variable is introduced. And substitute the optimal solution of subproblem P1 and Construct the augmented Lagrangian function of the revenue distribution subproblem P2, which is:

[0055]

[0056] In the formula: Represents the augmented Lagrangian function of the revenue distribution subproblem P2; μ 2,mn ρ 2,mn Let Lagrange multipliers and penalty factors represent the price coupling constraints, respectively. This represents the price at which the m-th PIES trades CEA to the n-th PIES;

[0057] PIES members continuously update variables through interaction. and Until convergence condition two is met.

[0058] Preferably, the first convergence condition is:

[0059] ΔE nm (k+1) ≤ε1 (19)

[0060] Where: ΔE nm (k+1) ε1 represents the dual residual of the (k+1)th iteration; ε1 represents the iteration error setting of the trading strategy.

[0061] The second convergence condition is:

[0062]

[0063] In the formula: This represents the price at which the nth REI trades CEA to the mth REI during the (k+1)th iteration. ε represents the price at which the m-th REI trades CEA to the n-th REI in the (k+1)-th iteration; ε2 represents the iteration error setting value for the transaction price.

[0064] Preferably, step S4, which involves decoupling and interaction, specifically includes the following sub-steps:

[0065] S41: For the k-th iteration, k≥2, the boundary is defined as follows:

[0066]

[0067] In the formula: Let represent the maximum and minimum electricity purchase amounts at time t in the k-th iteration, respectively; Let these represent the amount of electricity purchased at time t in the (k-2)th and (k-1)th iterations, respectively. These represent the maximum and minimum values ​​of gas purchased at time t in the k-th iteration, respectively. Let represent the gas purchase amount at time t in the (k-2)th and (k-1)th iterations, respectively;

[0068] make Determine whether convergence condition three is met at this point:

[0069] If the condition is met, the iteration stops, the energy requirement is obtained, and the process proceeds to step S5; if the condition is not met, the process proceeds to step S42.

[0070] S42: Upper-level energy operators according to Conduct joint clearing, update and Each PIES according to the current time and new boundary constraints To optimize regional autonomy and conduct carbon pricing among PIES, and update energy demand. Determine whether the third convergence condition is met at this point:

[0071] If the condition is met, the iteration stops, the energy requirement is obtained, and the process proceeds to step S5; if the condition is not met, the process proceeds to step S43.

[0072] S43: Let the iteration number k = k + 2, and repeat steps S41-S43 until the iteration error of energy demand is less than the set threshold.

[0073] Preferably, the third convergence condition is:

[0074]

[0075] In the formula: ε represents the electricity purchased in the k-th and (k-1)-th iterations, respectively; e This represents the convergence tolerance of a given electricity purchase demand; ε represents the gas purchase volume in the k-th and (k-1)-th iterations, respectively; g This represents the convergence tolerance of a given gas purchase demand.

[0076] The beneficial effects of this invention are:

[0077] This invention fully considers the complementary characteristics of energy nodes and the energy-carbon coupling and collaborative strategy of multiple energy entities, effectively achieving global energy optimization and carbon emission control. Specifically, a two-layer distributed optimization scheduling model is constructed, with the upper layer responsible for global coordination and the lower layer responsible for regional autonomy. A two-dimensional electricity-carbon coupling mechanism is introduced, taking into account the complex conversion relationships of multiple energy devices and the security constraints of the regional electricity-gas-heat coupling network to achieve bidirectional CEA collaborative optimization scheduling between PIES and between PIES and the upper-level energy-carbon market. The ADMM algorithm is used to achieve energy-carbon coordination among multiple energy nodes, ensuring the privacy and flexibility of the collaborative optimization process. A bisection iterative optimization method is introduced to improve the system's convergence, effectively avoiding oscillations and thus enhancing system stability. Attached Figure Description

[0078] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0079] Figure 1This is a schematic diagram of the framework of the two-layer distributed optimization scheduling model of the multi-energy system hierarchical collaborative scheduling method of electric carbon coupling of the present invention;

[0080] Figure 2 This is a schematic diagram of the hierarchical collaborative scheduling method for multi-energy systems with electro-carbon coupling according to the present invention. Detailed Implementation

[0081] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and technical features described in this application can be combined with each other. It should also be pointed out that, unless otherwise indicated, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terms "comprising" or "including" and similar words used in this invention refer to elements or objects preceding the word that encompass the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0082] like Figure 1-2 As shown, this invention provides a hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling, comprising the following steps:

[0083] S1: Establish a multi-energy market collaborative clearing model and initialize the energy demand of PIES for each period based on historical data.

[0084] Upper-level energy operators according to Joint clearing is performed, and nodal energy-carbon price information for each PIES is calculated based on marginal price theory and embedded carbon flow distribution. and

[0085] It should be noted that the multi-energy market collaborative clearing model and the calculation method for nodal energy-carbon price information are existing technologies, and the specific models and calculation methods will not be described in detail here.

[0086] S2: Establish a regional autonomous optimization model, where each PIES is based on the corresponding node energy-carbon price information. and Optimize regional autonomy.

[0087] In one specific embodiment, the regional autonomy optimization model is:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] In the formula: These represent the upstream energy purchase cost, energy production cost, equipment operation and maintenance cost, and total carbon trading cost of the nth PIES, respectively; T represents the settlement period in days. These represent the node electricity price and node gas price received by the nth PIES, respectively; These represent the electricity and gas purchased at time t, respectively. This represents the set of corresponding devices in the nth PIES, where CU, S, WT, PV, CHP, EB, P2G, EES, HES, and GES represent the coal-fired unit, gas source, wind power, photovoltaic, CHP unit, electric boiler, P2G, electrochemical energy storage equipment, thermal energy storage equipment, and gas energy storage equipment in the corresponding devices, respectively. All are cost coefficients; These represent the output of the coal-fired unit and the gas source at time t, respectively. This indicates the unit maintenance cost of the corresponding equipment; Let represent the output of the corresponding equipment at time t, where Indicates heat output. Indicates electrical output; Let e, h, and g represent the charging and discharging power of the corresponding energy storage device at time t, respectively, where e, h, and g represent the electric, thermal, and gas energy storage devices, respectively. Let these represent the CCER cost of the nth PIES, the parent CEA transaction cost, and the inter-PIES CEA transaction cost, respectively.

[0094] The constraint set of the regional autonomous optimization model consists of the physical safety constraint set of the electricity-gas-heating network. and carbon constraint set The physical safety constraint set of the electric-gas-heat network is as follows:

[0095]

[0096]

[0097]

[0098] In the formula: This represents the set of devices connected to electrical node i, gas node j, and thermal node h, where net represents the upper-layer network. P represents the power demand of the upper-layer network at time t; ki,t P ij,t This represents the power flow of lines (k,i) and (i,j) at time t; P Di,tg Dj,t H Dh,t For the electrical, gas, and heat loads of the corresponding nodes; Let these represent the sets of electrical nodes, gas nodes, and thermal nodes of the nth REI, respectively. This represents the natural gas demand of the upper-layer network at time t; f represents the power produced by natural gas at time P2Gt; mj,t f jn,t Represents the airflow in pipes (m,j) and (j,n); This represents the power consumption of natural gas at time CHPt; This represents the total heat source and total heat load power at the hot node h at time t; This represents the heat load power at time EB t;

[0099] The carbon constraint set It consists of quota balancing constraints and CCER offsetting constraints, which are as follows:

[0100]

[0101]

[0102] In the formula: These represent the carbon emission coefficient, unit power supply quota, and unit heat supply quota of the corresponding equipment, respectively; ω CEA ω CCER These represent the offsetting ratios of CEA and CCER, respectively. These represent the carbon allowances obtained by the CHP unit for producing one unit of electricity, the carbon allowances obtained by the CHP unit for producing one unit of heat, and the carbon allowances obtained by the CU unit for producing one unit of electricity, respectively. These represent the number of CCERs that can be obtained from a unit of electricity produced by CU and GU, respectively; κ represents the electrothermal conversion factor. This represents the amount of CEA purchased by the nth PIES from the higher-level carbon trading market; This represents the quota trading volume between the nth PIES and the other PIES; This represents the amount of CCER offset used by the nth PIES; n CU n GU These represent the number of units in CU and GU, respectively; Let represent the power of CU and GU at time t, respectively.

[0103] In the above embodiments, the first line of the quota balance constraint is the total carbon emissions of REI units and P2G carbon reduction, the second line is the amount of CEA obtained free of charge, and the third line is the amount of CEA trading and CCER offset.

[0104] In a specific embodiment, the CCER cost of the nth PIES is calculated using the following formula:

[0105]

[0106] In the formula: λ CCER This indicates the price per unit of CCER; These represent the CCER values ​​for wind power and solar power, respectively.

[0107] The transaction cost of the parent CEA of the nth PIES is calculated using the following formula:

[0108]

[0109] In the formula: This represents the price at which the nth PIES purchases CEA from the higher-level carbon trading market;

[0110] The inter-PIES CEA transaction cost for the nth PIES is calculated using the following formula:

[0111]

[0112] Where: Ω DSO Represents a collection of PIES; This represents the quota trading price between the nth PIES and the other PIES.

[0113] In one specific embodiment, the price of CEA purchased by the nth PIES from the higher-level carbon trading market is calculated using the following formula:

[0114]

[0115] In the formula: represent or These represent the carbon price of the nodes connected to the nth PIES in the upper-level power grid and gas grid, respectively.

[0116] In the above embodiments, the average node carbon price is used as the transaction price for PIES to buy and sell quotas in the upstream market, mainly because quota shortfalls are settled on a daily basis. Other methods for obtaining the transaction price for PIES to buy and sell quotas in the upstream market, as described in the prior art, are also applicable to this invention.

[0117] S3: Construct a collaborative scheduling model for CEA among PIES nodes, and facilitate CEA trading among energy nodes based on cooperative game theory. Construct an energy-carbon coupled Nash bargaining CEA optimization benefit model. Based on this model, obtain the optimal CEA trading volume among PIES nodes. and the best transaction price At the same time, update the corresponding energy requirements.

[0118] In one specific embodiment, the Nash bargaining CEA optimization benefit model of the electrocarbon coupling includes a total cost minimization subproblem P1 and a revenue allocation subproblem P2, wherein the total cost minimization subproblem P1 is:

[0119]

[0120] In the formula: N represents the number of REIs participating in the negotiation; st represents the conditions that need to be met; Represents the set of physical safety constraints for an electrical-gas-heating network; Represents a carbon constraint set;

[0121] The revenue allocation subproblem P2 is:

[0122]

[0123] In the formula: This represents the total cost when the nth PIES does not participate in the negotiation; This means that the nth PIES is not considered. Total cost of time optimization; and Both represent the optimal solution to the subproblem P1 that minimizes total cost; This represents the price at which the m-th PIES purchases CEA from the higher-level carbon trading market.

[0124] In a specific embodiment, both the total cost minimization subproblem P1 and the revenue allocation subproblem P2 are solved using the ADMM algorithm.

[0125] In a specific embodiment, when solving the total cost minimization subproblem P1 using the ADMM algorithm, for the nth PIES, an auxiliary variable is introduced. Construct the augmented Lagrangian function of the subproblem P1 that minimizes total cost, wherein the augmented Lagrangian function of the subproblem P1 that minimizes total cost is:

[0126]

[0127] In the formula: The augmented Lagrangian function represents the subproblem P1 that minimizes total cost; μ represents the total optimization cost of the nth PIES. 1,mn ρ 1,mn Let Lagrange multipliers and penalty factors represent the quantitative coupling constraints of the CEA, respectively. This represents the amount of CEA that the m-th PIES buys / sells to the n-th PIES; Represents the L2 norm operation;

[0128] PIES members continuously update variables through interaction. and Until convergence condition one is met; optionally, convergence condition one is:

[0129]

[0130] Where: ΔE nm (k+1) ε1 represents the dual residual of the (k+1)th iteration; ε1 represents the iteration error setting of the trading strategy.

[0131] The key steps in solving the ADMM algorithm include:

[0132] a. For the (k+1)th iteration, each PIES performs local calculations based on the decision information from the previous round or some of the updated strategy information to obtain a new trading strategy. As shown in the following formula:

[0133]

[0134]

[0135] b. After all PIES have completed their policy updates, update the Lagrange multiplier μ according to the following formula. nm (k+1) :

[0136]

[0137] c. Calculate the dual residual at this point using the following formula:

[0138]

[0139] In a specific embodiment, when solving the revenue allocation subproblem P2 using the ADMM algorithm, for the nth PIES, an auxiliary variable is introduced. And substitute the optimal solution of subproblem P1 and Construct the augmented Lagrangian function of the revenue distribution subproblem P2, which is:

[0140]

[0141] In the formula: Represents the augmented Lagrangian function of the revenue distribution subproblem P2; μ 2,mn ρ 2,mn Let Lagrange multipliers and penalty factors represent the price coupling constraints, respectively. This represents the price at which the m-th PIES trades CEA to the n-th PIES;

[0142] PIES members continuously update variables through interaction. and Until convergence condition two is met; optionally, convergence condition two is:

[0143]

[0144] In the formula: This represents the price at which the nth REI trades CEA to the mth REI during the (k+1)th iteration. ε represents the price at which the m-th REI trades CEA to the n-th REI in the (k+1)-th iteration; ε2 represents the iteration error setting value for the transaction price.

[0145] and The specific update steps are similar to the ADMM distributed solution process for subproblem 1, and the details will not be repeated here. By solving subproblems P1 and P2 sequentially, the optimal CEA transaction volume among PIES can be determined respectively. and the best transaction price At the same time, update the corresponding energy requirements.

[0146] S4: Introducing a two-layer iterative solution algorithm, with the regional autonomy model as the bottom layer and the multi-energy market collaborative clearing model as the top layer, forming a two-layer distributed optimization scheduling model. The upper and lower layers are decoupled and interact through heterogeneous coordination operators (energy-carbon price information, energy demand information);

[0147] When the iterative error of energy demand is less than a set threshold, the two-layer distributed optimization scheduling model reaches an equilibrium state, at which point the optimal CEA transaction volume is finally determined. and transaction price At the same time, operators will determine the corresponding energy demand. Determine the most reasonable node energy-carbon price and

[0148] In a specific embodiment, decoupling and interaction specifically includes the following sub-steps:

[0149] S41: For the k-th iteration, k≥2, the boundary is defined as follows:

[0150]

[0151] In the formula: Let represent the maximum and minimum electricity purchase amounts at time t in the k-th iteration, respectively; Let these represent the amount of electricity purchased at time t in the (k-2)th and (k-1)th iterations, respectively. These represent the maximum and minimum values ​​of gas purchased at time t in the k-th iteration, respectively. Let represent the gas purchase amount at time t in the (k-2)th and (k-1)th iterations, respectively;

[0152] make Determine whether convergence condition three is met at this point:

[0153] If the condition is met, the iteration stops, the energy requirement is obtained, and the process proceeds to step S5; if the condition is not met, the process proceeds to step S42.

[0154] S42: Upper-level energy operators according to Conduct joint clearing, update and Each PIES according to the current time and new boundary constraints To optimize regional autonomy and conduct carbon pricing among PIES, and update energy demand. Determine whether the third convergence condition is met at this time. Optionally, the third convergence condition is:

[0155]

[0156] In the formula: ε represents the electricity purchased in the k-th and (k-1)-th iterations, respectively; e This represents the convergence tolerance of a given electricity purchase demand; ε represents the gas purchase volume in the k-th and (k-1)-th iterations, respectively; g This represents the convergence tolerance of a given gas purchase demand;

[0157] If the condition is met, the iteration stops, the energy requirement is obtained, and the process proceeds to step S5; if the condition is not met, the process proceeds to step S43.

[0158] S43: Let the iteration number k = k + 2, and repeat steps S41-S43 until the iteration error of energy demand is less than the set threshold.

[0159] S5: Using the value of step S4 as the initial value of step S1, repeat steps S1-S4 to continuously update variables interactively and achieve collaborative optimization.

[0160] In the above embodiments, the transmission and distribution coordinated scheduling of the present invention further optimizes the global energy scheduling, not only to maximize energy utilization, but also to achieve a significant reduction in carbon emissions at the system level. At the same time, when the PIES system is mainly powered by electric-thermal energy complementarity, the present invention can more accurately capture its limiting effect, which provides a targeted optimization scheme for system energy allocation and carbon emission control.

[0161] By introducing an electric-carbon coupling mechanism, this invention enables PIES to adjust its electric-carbon scheduling strategy more flexibly and cost-effectively based on actual production conditions, thereby effectively reducing the overall operating costs and carbon emissions of multi-energy systems.

[0162] This invention effectively avoids oscillations and improves the convergence and stability of the system in the two-layer model solution process by introducing a bisection iterative optimization method (i.e., the two-layer iterative solution algorithm). The ADMM algorithm not only ensures the accuracy of the solution process, but also protects the privacy and security of each energy node, realizing a reliable distributed solution process.

[0163] In summary, this invention can more effectively achieve global energy optimization and carbon emission control of the system. Compared with the prior art, this invention represents a significant advancement.

[0164] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling, characterized in that, Includes the following steps: S1: Establish a multi-energy market collaborative clearing model and initialize the energy demand of PIES for each period based on historical data. Upper-level energy operators according to Joint clearing is performed, and nodal energy-carbon price information for each PIES is calculated based on marginal price theory and embedded carbon flow distribution. and S2: Establish a regional autonomous optimization model, where each PIES is based on the corresponding node energy-carbon price information. and Optimize regional autonomy; S3: Construct a collaborative scheduling model for CEA among PIES nodes, and facilitate CEA trading among energy nodes based on cooperative game theory. Construct an energy-carbon coupled Nash bargaining CEA optimization benefit model. Based on this model, obtain the optimal CEA trading volume among PIES nodes. and the best transaction price At the same time, update the corresponding energy requirements. S4: Introducing a two-layer iterative solution algorithm, with the regional autonomy model as the bottom layer and the multi-energy market collaborative clearing model as the top layer, forming a two-layer distributed optimization scheduling model. The upper and lower layers are decoupled and interact through heterogeneous coordination operators. When the iterative error of energy demand is less than a set threshold, the two-layer distributed optimization scheduling model reaches an equilibrium state, at which point the optimal CEA transaction volume is finally determined. and transaction price At the same time, operators will determine the corresponding energy demand. Determine the most reasonable node energy-carbon price and S5: Using the value of step S4 as the initial value of step S1, repeat steps S1-S4 to continuously update variables interactively and achieve collaborative optimization.

2. The hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling according to claim 1, characterized in that, In step S2, the regional autonomous optimization model is: In the formula: These represent the upstream energy purchase cost, energy production cost, equipment operation and maintenance cost, and total carbon trading cost of the nth PIES, respectively; T represents the settlement period in days. These represent the node electricity price and node gas price received by the nth PIES, respectively; These represent the electricity and gas purchased at time t, respectively. This represents the set of corresponding devices in the nth PIES, where CU, S, WT, PV, CHP, EB, P2G, EES, HES, and GES represent the coal-fired unit, gas source, wind power, photovoltaic, CHP unit, electric boiler, P2G, electrochemical energy storage equipment, thermal energy storage equipment, and gas energy storage equipment in the corresponding devices, respectively. All are cost coefficients; These represent the output of the coal-fired unit and the gas source at time t, respectively. This indicates the unit maintenance cost of the corresponding equipment; Let represent the output of the corresponding equipment at time t, where Indicates heat output. Indicates electrical output; Let e, h, and g represent the charging and discharging power of the corresponding energy storage device at time t, respectively, where e, h, and g represent the electric, thermal, and gas energy storage devices, respectively. Let these represent the CCER cost of the nth PIES, the parent CEA transaction cost, and the inter-PIES CEA transaction cost, respectively. The constraint set of the regional autonomous optimization model consists of the physical safety constraint set of the electricity-gas-heating network. and carbon constraint set The physical safety constraint set of the electric-gas-heat network is as follows: In the formula: This represents the set of devices connected to electrical node i, gas node j, and thermal node h, where net represents the upper-layer network. P represents the power demand of the upper-layer network at time t; ki,t P ij,t This represents the power flow of lines (k,i) and (i,j) at time t; P Di,t g Dj,t H Dh,t For the electrical, gas, and heat loads of the corresponding nodes; Let these represent the sets of electrical nodes, gas nodes, and thermal nodes of the nth REI, respectively. This represents the natural gas demand of the upper-layer network at time t; f represents the power produced by natural gas at time P2Gt; mj,t f jn,t Represents the airflow in pipes (m,j) and (j,n); This represents the power consumption of natural gas at time CHPt; This represents the total heat source and total heat load power at the hot node h at time t; This represents the heat load power at time EB t; The carbon constraint set It consists of quota balancing constraints and CCER offsetting constraints, which are as follows: In the formula: These represent the carbon emission coefficient, unit power supply quota, and unit heat supply quota of the corresponding equipment, respectively; ω CEA ω CCER These represent the offsetting ratios of CEA and CCER, respectively. These represent the carbon allowances obtained by the CHP unit for producing one unit of electricity, the carbon allowances obtained by the CHP unit for producing one unit of heat, and the carbon allowances obtained by the CU unit for producing one unit of electricity, respectively. These represent the number of CCERs that can be obtained from a unit of electricity produced by CU and GU, respectively; κ represents the electrothermal conversion factor. This represents the amount of CEA purchased by the nth PIES from the higher-level carbon trading market; This represents the quota trading volume between the nth PIES and the other PIES; This represents the amount of CCER offset used by the nth PIES; n CU n GU These represent the number of units in CU and GU, respectively; Let represent the power of CU and GU at time t, respectively.

3. The hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling according to claim 2, characterized in that, The CCER cost of the nth PIES is calculated using the following formula: In the formula: λ CCER This indicates the price per unit of CCER; These represent the CCER values ​​for wind power and solar power, respectively. The transaction cost of the parent CEA of the nth PIES is calculated using the following formula: In the formula: This represents the price at which the nth PIES purchases CEA from the higher-level carbon trading market; The inter-PIES CEA transaction cost for the nth PIES is calculated using the following formula: Where: Ω DSO Represents a collection of PIES; This represents the quota trading price and trading volume of the nth PIES with the other PIES.

4. The hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling according to claim 3, characterized in that, The price of CEA purchased by the nth PIES from the higher-level carbon trading market is calculated using the following formula: In the formula: represent or These represent the carbon price of the nodes connected to the nth PIES in the upper-level power grid and gas grid, respectively.

5. The hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling according to claim 3, characterized in that, In step S3, the Nash bargaining CEA optimization benefit model of the carbon-electric coupling includes a total cost minimization subproblem P1 and a benefit allocation subproblem P2. The total cost minimization subproblem P1 is as follows: In the formula: N represents the number of REIs participating in the negotiation; st represents the condition that needs to be met; Represents the set of physical safety constraints for an electrical-gas-heating network; Represents a carbon constraint set; The revenue allocation subproblem P2 is: In the formula: This represents the total cost when the nth PIES does not participate in the negotiation; This means that the nth PIES is not considered. Total cost of time optimization; and Both represent the optimal solution to the subproblem P1 that minimizes total cost; This represents the price at which the m-th PIES purchases CEA from the higher-level carbon trading market.

6. The hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling according to claim 5, characterized in that, Both the total cost minimization subproblem P1 and the revenue allocation subproblem P2 are solved using the ADMM algorithm.

7. The hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling according to claim 6, characterized in that, When solving the total cost minimization subproblem P1 using the ADMM algorithm, for the nth PIES, an auxiliary variable is introduced. Construct the augmented Lagrangian function of the subproblem P1 that minimizes total cost, wherein the augmented Lagrangian function of the subproblem P1 that minimizes total cost is: In the formula: The augmented Lagrangian function represents the subproblem P1 that minimizes total cost; μ represents the total optimization cost of the nth PIES. 1,mn ρ 1,mn Let Lagrange multipliers and penalty factors represent the quantitative coupling constraints of the CEA, respectively. This represents the amount of CEA that the m-th PIES buys / sells to the n-th PIES; Represents the L2 norm operation; PIES members continuously update variables through interaction. and Until convergence condition one is met; When solving the revenue allocation subproblem P2 using the ADMM algorithm, for the nth PIES, an auxiliary variable is introduced. And substitute the optimal solution of subproblem P1 and Construct the augmented Lagrangian function of the revenue distribution subproblem P2, which is: In the formula: Represents the augmented Lagrangian function of the revenue distribution subproblem P2; μ 2,mn ρ 2,mn Let Lagrange multipliers and penalty factors represent the price coupling constraints, respectively. This represents the price at which the m-th PIES trades CEA to the n-th PIES; PIES members continuously update variables through interaction. and Until convergence condition two is met.

8. The hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling according to claim 7, characterized in that, The first convergence condition is: ΔE nm (k+1) ≤ε1 (19) Where: ΔE nm (k+1) ε1 represents the dual residual of the (k+1)th iteration; ε1 represents the iteration error setting of the trading strategy. The second convergence condition is: In the formula: This represents the price at which the nth REI trades CEA to the mth REI during the (k+1)th iteration. ε represents the price at which the m-th REI trades CEA to the n-th REI in the (k+1)-th iteration; ε2 represents the iteration error setting value for the transaction price.

9. The hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling according to any one of claims 1-8, characterized in that, Step S4, the decoupling and interaction specifically includes the following sub-steps: S41: For the k-th iteration, k≥2, the boundary is defined as follows: In the formula: Let represent the maximum and minimum electricity purchase amounts at time t in the k-th iteration, respectively; Let these represent the amount of electricity purchased at time t in the (k-2)th and (k-1)th iterations, respectively. These represent the maximum and minimum values ​​of gas purchased at time t in the k-th iteration, respectively. Let represent the gas purchase amount at time t in the (k-2)th and (k-1)th iterations, respectively; make Determine whether convergence condition three is met at this point: If the condition is met, the iteration stops, the energy requirement is obtained, and the process proceeds to step S5; if the condition is not met, the process proceeds to step S42. S42: Upper-level energy operators according to Conduct joint clearing, update and Each PIES according to the current time and new boundary constraints To optimize regional autonomy and conduct carbon pricing among PIES, and update energy demand. Determine whether the third convergence condition is met at this point: If the condition is met, the iteration stops, the energy requirement is obtained, and the process proceeds to step S5; if the condition is not met, the process proceeds to step S43. S43: Let the iteration number k = k + 2, and repeat steps S41-S43 until the iteration error of energy demand is less than the set threshold.

10. The hierarchical cooperative scheduling method for multi-energy systems with electro-carbon coupling according to claim 9, characterized in that, The third convergence condition is: In the formula: ε represents the electricity purchased in the k-th and (k-1)-th iterations, respectively; e This represents the convergence tolerance of a given electricity purchase demand; ε represents the gas purchase volume in the k-th and (k-1)-th iterations, respectively; g This represents the convergence tolerance of a given gas purchase demand.

Images