Method and system for distributed robust low-carbon planning of multi-regional coupled regional power grids

Abstract

The present application relates to the field of multi-energy coupling regional power grid planning and operation control, and provides a multi-energy coupling regional power grid distributed robust low-carbon planning method and system. The multi-energy coupling regional power grid distributed robust low-carbon planning method comprises the following steps: based on the parameters of the multi-energy coupling regional power grid, taking the maximum of the system net present value in the planning period as the target, constructing an upper model, and then under the constraints of electric / hydrogen energy storage and thermal energy storage, solving to obtain the power scheduling sequence set and capacity configuration scheme set of the electric, thermal and hydrogen energy storage devices; taking the minimization of the total system operation cost as the target, constructing a lower model, under the system level balance constraint, network interaction constraint and device level operation constraint, using distributed robust optimization to process uncertainty, and from the power scheduling sequence set and capacity configuration scheme set of the electric, thermal and hydrogen energy storage devices, determining the optimal power scheduling sequence and optimal capacity configuration scheme of the electric, thermal and hydrogen energy storage devices. The present application realizes the economic low-carbon collaboration of system planning and operation.

Description

Technical Field

[0001] This invention relates to the field of planning and operation control of multi-energy coupled regional power grids, and particularly to a low-carbon planning method and system for multi-energy coupled regional power grids. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The inherent uncertainties in renewable energy output and load can easily lead to system power imbalances, posing a severe challenge to the stable operation of energy systems dominated by renewable energy. Achieving carbon emission reduction targets while ensuring system economics, as well as efficiently integrating diverse energy storage forms such as electricity, heat, and hydrogen and exploring their potential for coordinated dispatch, remain current research challenges.

[0004] While existing technologies have made some progress in energy storage configuration, hydrogen energy utilization, and low-carbon dispatch, the following shortcomings still exist: 1) They are mostly aimed at a single energy form or a single optimization goal, lacking a comprehensive consideration of the coupling characteristics of multiple energy flows such as electricity, heat, and hydrogen, making it difficult to form a complete planning and operation framework; moreover, the utilization of hydrogen energy is mostly limited to a single conversion path, failing to fully realize the cascade utilization of waste heat in the hydrogen conversion process, and the overall energy efficiency of the system needs to be improved; 2) The methods for dealing with the uncertainty of renewable energy and load mostly rely on preset probability distributions or limited scenarios, making it difficult to balance robustness and economy, reducing the renewable energy absorption capacity and operational stability of multi-energy coupled systems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a low-carbon planning method and system for multi-energy coupled regional power grids using distributed baffle rods. By constructing a two-layer optimization model and integrating distributed baffle rod optimization, multi-energy storage synergy, and hydrogen energy cascade utilization technology, it can achieve economical and low-carbon synergy in system planning and operation.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a low-carbon planning method for multi-energy coupled regional power grids using a distributed baffle system.

[0007] In one or more embodiments, a method for low-carbon planning of multi-energy coupled regional power grids using distributed baffles is provided, comprising: Based on the parameters of the multi-energy coupled regional power grid, with the goal of maximizing the net present value of the system during the planning period, an upper-level model is constructed. Then, under the constraints of electric / hydrogen energy storage and thermal energy storage, the power scheduling sequence set and capacity configuration scheme set of electric, thermal and hydrogen energy storage devices are obtained. With the goal of minimizing the total system operating cost, a lower-level model is constructed. Under system-level balance constraints, network interaction constraints, and device-level operating constraints, the uncertainty is handled by using bibliometric optimization. From the power scheduling sequence set and capacity configuration scheme set of electric, thermal, and hydrogen energy storage devices, the optimal power scheduling sequence and optimal capacity configuration scheme of electric, thermal, and hydrogen energy storage devices are determined.

[0008] As one implementation method, the expression for the upper-level model is:

[0009]

[0010]

[0011]

[0012] In the formula: The total operating cost of the energy storage system; For the sake of maintenance costs; For market participation costs; The investment cost of the energy storage system; For the first x Operation and maintenance cost coefficient of energy storage devices; For a moment No. x Operating power of energy storage devices; This represents the total number of energy storage system types. For the first x Market participation cost coefficient for energy storage devices; For the first x Rated power of energy storage devices; For the first x Rated capacity of energy storage devices; For the first x Rated power cost of energy storage devices; For the first x Rated capacity cost of energy storage devices; The discount rate; This refers to the equipment's lifespan.

[0013] As one implementation, the electric / hydrogen energy storage constraints and thermal energy storage constraints both include: energy state evolution constraints, power upper and lower limit constraints, charge-discharge mutual exclusion constraints, and self-discharge constraints.

[0014] As one implementation method, the expression for the lower-level model is:

[0015]

[0016]

[0017] In the formula: For regional power grid operating costs; For the operation and maintenance costs of all power generation equipment; For the cost of purchasing electricity and natural gas; For carbon trading costs; , , , , , These are the operation and maintenance cost coefficients for photovoltaic, wind turbine, combined heat and power, gas boiler, hydrogen fuel cell and electrolyzer, respectively. , , , , , Each device is in Operating power at any given time; and They are respectively The price of electricity at any given moment; and These refer to the power purchased and the power sold, respectively. for Real-time natural gas prices; and These are the thermoelectric conversion efficiencies of combined heat and power units and gas-fired boilers, respectively. This refers to the heat output power of a combined heat and power (CHP) unit.

[0018] As one implementation method, the expression for carbon trading costs is:

[0019]

[0020]

[0021] In the formula: For carbon trading costs; The benchmark carbon trading price; , , These are the initial carbon allowances for purchased electricity, combined heat and power units, and gas-fired boilers, respectively. Carbon quota coefficient per unit of electricity; Carbon quota coefficient per unit of heat; The electrothermal conversion coefficient; , , These are the actual carbon emissions from purchased electricity, combined heat and power (CHP) and gas-fired boilers, respectively. The actual carbon emission coefficient per unit of electricity; The actual carbon emission coefficient per unit of heat; For the power purchased; The heat output power of a combined heat and power unit; For cogeneration in Operating power at any given time; In order to be in The output power of the gas boiler at any given time.

[0022] As one implementation method, equipment-level operating constraints include operating constraints for combined heat and power units, operating constraints for gas-fired boilers, and constraints for electrolyzer-fuel cell coupling systems.

[0023] A second aspect of the present invention provides a low-carbon planning system for multi-energy coupled regional power grids.

[0024] In one or more embodiments, a multi-energy coupled regional power grid sub-bar low-carbon planning system includes: The upper-level model construction and solution module is used to construct an upper-level model based on the parameters of the multi-energy coupled regional power grid, with the goal of maximizing the net present value of the system during the planning period. Then, under the constraints of electric / hydrogen energy storage and thermal energy storage, it solves for the power scheduling sequence set and capacity configuration scheme set of electric, thermal and hydrogen energy storage devices. The lower-level model construction and solution module is used to construct the lower-level model with the goal of minimizing the total system operating cost. Under system-level balance constraints, network interaction constraints, and device-level operating constraints, it uses distributed bar optimization to handle uncertainties and determines the optimal power scheduling sequence and optimal capacity configuration scheme for electric, thermal, and hydrogen energy storage devices from the power scheduling sequence set and capacity configuration scheme set of electric, thermal, and hydrogen energy storage devices.

[0025] A third aspect of the present invention provides a computer-readable storage medium.

[0026] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the multi-energy coupled regional power grid sub-bar low-carbon planning method as described above.

[0027] A fourth aspect of the present invention provides an electronic device.

[0028] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the multi-energy coupled regional power grid sub-bar low-carbon planning method described above.

[0029] A fifth aspect of the present invention provides an electronic device.

[0030] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps in the multi-energy coupled regional power grid sub-bar low-carbon planning method as described above.

[0031] Compared with the prior art, the beneficial effects of the present invention are: This invention proposes a low-carbon planning method for multi-energy coupled regional power grids using a distributed bulus rod. It constructs a two-layer optimization model and integrates a multi-energy storage collaborative mechanism, a distributed bulus rod optimization method, and hydrogen energy hub cascade utilization technology to achieve economic optimization and carbon emission reduction targets for multi-energy coupled systems under uncertainty. This is of great significance for improving the renewable energy absorption capacity and ensuring the safe and stable operation of the system. Attached Figure Description

[0032] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0033] Figure 1 This is a flowchart of the low-carbon planning method for multi-energy coupled regional power grids according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a multi-energy coupled regional power grid low-carbon planning system using a distributed bristle rod according to an embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device according to an embodiment of the present invention; Figure 4 This is a diagram of the architecture of a multi-energy storage and multi-energy coupling regional power grid system according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the two-way carbon trading mechanism according to an embodiment of the present invention; Figure 6 This is a framework diagram of the two-layer sub-Bruker optimization model for multi-energy coupled regional power grids according to an embodiment of the present invention; Figure 7 This is a typical daily load and renewable energy output curve diagram according to an embodiment of the present invention; Figure 8 This is a comparison chart of the configuration cost and total carbon emissions of multi-energy storage capacity under different scenarios according to embodiments of the present invention; Figure 9(a) is a diagram showing the power energy optimization scheduling results of an embodiment of the present invention; Figure 9(b) is a diagram showing the thermal energy optimization scheduling results of an embodiment of the present invention; Figure 9(c) is a graph showing the hydrogen energy optimization scheduling results of an embodiment of the present invention; Figure 10This is a comparison chart of the electrical and thermal loads before and after the implementation of the demand response in this embodiment of the invention; Figure 11 This is a diagram showing the optimized operation results of the heat-to-power ratio of the cogeneration unit according to an embodiment of the present invention; Figure 12 This is a panoramic analysis diagram of carbon emissions from a multi-energy coupled regional power grid according to an embodiment of the present invention; Figure 13(a) is a diagram showing the optimized scheduling results of energy storage according to an embodiment of the present invention; Figure 13(b) is a diagram showing the optimized scheduling results of thermal energy storage according to an embodiment of the present invention; Figure 13(c) is a diagram showing the optimized scheduling results of hydrogen energy storage according to an embodiment of the present invention. Detailed Implementation

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0035] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0036] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0037] Figure 1 A schematic diagram of the low-carbon planning method for multi-energy coupled regional power grids according to an embodiment of the present invention is provided. Based on... Figure 1 The multi-energy coupled regional power grid low-carbon planning method of the sub-bar system in this embodiment may include the following steps S101~S102.

[0038] The specific implementation process of steps S101 to S102 is as follows: Step S101: Based on the parameters of the multi-energy coupled regional power grid, with the goal of maximizing the net present value of the system during the planning period, an upper-level model is constructed. Then, under the constraints of electric / hydrogen energy storage and thermal energy storage, the power scheduling sequence set and capacity configuration scheme set of electric, thermal and hydrogen energy storage devices are obtained. Multi-energy coupled regional power grid system architecture such as Figure 4 As shown, it comprises three core modules: energy supply, energy conversion, and energy demand, specifically consisting of the energy supply side, the conversion and storage side, and the demand side: Supply side: Includes wind power generation units and photovoltaic power generation units, providing renewable energy power to the system; Conversion and storage end: integrates cogeneration units, gas boilers, electric energy storage systems, thermal energy storage systems, and hydrogen energy storage systems (including electrolyzers, hydrogen storage tanks, and fuel cells). Among them, the hydrogen energy storage system constitutes the hydrogen energy hub, realizing the coordinated conversion and cascade utilization of electricity, hydrogen, and heat. On the demand side: This includes electrical load, thermal load, and hydrogen load, and the complementary optimization of multiple types of loads is achieved through a demand response mechanism.

[0039] The core operating logic of the system is as follows: suppressing the power fluctuations of renewable energy through a multi-energy storage system to achieve dynamic balance between source and load power; the hydrogen energy hub drives the electrolyzer to produce and store hydrogen when wind power is in surplus, and fuel cells consume hydrogen energy to generate electricity / heat when power is in short supply, while recovering the waste heat in the hydrogen conversion process for heating; the combined heat and power unit establishes the linkage of three energy flows: electricity, gas, and heat, and the gas boiler realizes the conversion of natural gas chemical energy and thermal energy, ultimately realizing the cascade utilization of heterogeneous energy such as electricity, hydrogen, and heat in terms of quality and efficient conversion in terms of quantity.

[0040] Based on the above architecture, the main equipment, energy conversion paths and information interaction relationships of the system are abstracted into nodes and edges to form a simplified topology model of a multi-energy coupled regional power grid, clarifying the functional boundaries and coupling relationships of each module.

[0041] The upper-level model is a life-cycle economic planning problem for a multi-element energy storage system. With the goal of maximizing the net present value of the system during the planning period, it determines the optimal power scheduling sequence and capacity configuration scheme for electric, thermal, and hydrogen energy storage devices.

[0042] The upper-level objective function quantifies the full-cycle economic performance of the multi-element energy storage system, and its expression is as follows: (1) (1) (3) (4) In the formula: The total operating cost of the energy storage system; For the sake of maintenance costs; For market participation costs; The investment cost of the energy storage system; For the first Operation and maintenance cost coefficient of energy storage devices; For a moment No. Operating power of energy storage devices; This represents the total number of energy storage system types. For the first Market participation cost coefficient for energy storage devices; For the first Rated power of energy storage devices; For the first Rated capacity of energy storage devices; For the first Rated power cost of energy storage devices; For the first Rated capacity cost of energy storage devices; The discount rate; This refers to the equipment's lifespan.

[0043] Constraints: The constraints for electric / hydrogen energy storage are similar, including energy state evolution constraints, power upper and lower limit constraints, charge-discharge mutual exclusion constraints, and self-discharge constraints: (5) In the formula: and They are respectively Time and The energy capacity of the instantaneous electrical energy storage system; and These represent the lower and upper limits of the energy capacity of an electric energy storage system, respectively. and for A binary variable representing the charging and discharging state at any given time; and They are respectively The charging and discharging power at any given moment; and These are the maximum charging and discharging power, respectively. This indicates the self-discharge rate of the energy storage system. and These are charging efficiency and discharging efficiency, respectively. For time intervals.

[0044] Thermal energy storage constraints include energy state evolution constraints, power upper and lower limit constraints, charge-discharge mutual exclusion constraints, and self-discharge constraints: (6) In the formula: and For the thermal storage system Time and The capacity of a given moment; and These are the lower and upper limits of thermal storage capacity, respectively. and for A binary variable that is constantly in a state of charging and discharging heat; and They are respectively The charging and discharging power at any given moment; and These are the maximum charging and discharging power of the energy storage system; and These are the heat charging efficiency and the heat dissipation efficiency, respectively. This represents the self-heating rate of the thermal storage system.

[0045] Step S102: With the goal of minimizing the total system operating cost, construct a lower-level model. Under system-level balance constraints, network interaction constraints, and device-level operating constraints, use distributed bar optimization to handle uncertainties. From the power scheduling sequence set and capacity configuration scheme set of electric, thermal, and hydrogen energy storage devices, determine the optimal power scheduling sequence and optimal capacity configuration scheme of electric, thermal, and hydrogen energy storage devices.

[0046] The lower-level model aims to minimize the total operating cost of the system, comprehensively considers the coordinated operation of multiple energy storage systems and low-carbon optimization, satisfies the balance constraints of multiple energy flows (electricity, heat, gas, and hydrogen) and the safety constraints of equipment operation, and uses bibloc bar optimization to handle uncertainties.

[0047] A fuzzy set of uncertain variables (renewable energy output and load) is constructed using the Wasserstein distance method in the probabilistic distance approach. The empirical distribution is obtained solely from historical data, without the need for a pre-defined precise probability distribution. The fuzzy set is expressed as follows: (7) (8) In the formula: For inclusion and The probability distribution space; and They are respectively distributed and random variables; Therefore and The joint distribution of marginal distributions; It is a fuzzy set of photovoltaic power output based on Wasserstein distance. Contribute to photovoltaic power; To predict photovoltaic power output; The Wasserstein distance; Represents random variables and The probability distribution characteristics are measured.

[0048] The lower-level objective function includes the operation and maintenance costs of power generation equipment, the procurement costs of electricity and natural gas, and the carbon trading costs, and its expression is: (9) In the formula: For regional power grid operating costs; For the operation and maintenance costs of all power generation equipment; For the cost of purchasing electricity and natural gas; This refers to the cost of carbon trading.

[0049] The formula for calculating operation and maintenance costs is as follows: (10) In the formula: , , , , , These are the operation and maintenance cost coefficients for photovoltaic, wind turbine, combined heat and power, gas boiler, hydrogen fuel cell and electrolyzer, respectively. , , , , These include photovoltaic, wind turbines, combined heat and power, gas-fired boilers, hydrogen fuel cells, and electrolyzers. Operating power at any given time; In order to be in The output power of the gas boiler at any given time.

[0050] The system's interaction with the external power grid and gas grid constitutes the main variable operating expenses. This cost item quantifies the costs during dispatch periods. Within this context, the net cash flow generated from electricity purchases, sales, and natural gas procurement is mathematically expressed as the product of time-varying energy prices and corresponding power.

[0051] (11) In the formula: and They are respectively The price of electricity at any given moment; and These refer to the power purchased and the power sold, respectively. for Real-time natural gas prices; and These are the thermoelectric conversion efficiencies of combined heat and power units and gas-fired boilers, respectively. This refers to the heat output power of a combined heat and power (CHP) unit.

[0052] Carbon trading costs are incorporated into the objective function as a key economic constraint. These costs reflect the scarcity of carbon emission rights resources in the system: when actual emissions exceed the free allowances, the shortfall must be purchased at market prices, resulting in a punitive cost; when there are surplus allowances, the surplus can be sold to generate revenue, thus creating an incentive.

[0053] like Figure 5 As shown, carbon trading costs are incorporated into the objective function as a key economic constraint. This cost reflects the scarcity of carbon emission rights resources in the system: when actual emissions exceed the free allowances, the shortfall must be purchased at market prices, resulting in a punitive cost; when there are surplus allowances, the surplus can be sold to generate revenue, thus creating an incentive.

[0054] Carbon emissions from the regional power grid mainly originate from purchased electricity, combined heat and power (CHP) units, and gas-fired boilers. The initial carbon allowance for each emission source is calculated by multiplying its output (electricity / heat) by the corresponding baseline coefficient, as shown in the following formula: (12) In the formula: , , These are the initial carbon allowances for purchased electricity, combined heat and power units, and gas-fired boilers, respectively. Carbon quota coefficient per unit of electricity; Carbon quota coefficient per unit of heat; The electrothermal conversion coefficient is given.

[0055] The actual carbon emissions are calculated as follows: (13) In the formula: , , These are the actual carbon emissions from purchased electricity, combined heat and power (CHP) and gas-fired boilers, respectively. The actual carbon emission coefficient per unit of electricity; This represents the actual carbon emission coefficient per unit of heat.

[0056] The total carbon trading cost of the system is determined by the product of the benchmark carbon price and net carbon emissions. Net carbon emissions are the difference between the system's actual total carbon emissions and the initial free allowances obtained.

[0057] (14) In the formula: For carbon trading costs; This serves as the benchmark carbon trading price.

[0058] The decision variables of the lower-level model must satisfy a series of physical and operational constraints, which can be summarized into three categories: system-level balance constraints, ensuring real-time supply and demand balance of electricity, heat, and hydrogen energy; network interaction constraints, limiting the power exchange limit with the upper-level power grid; and equipment-level operational constraints, including the output range, ramp-up capability, and operating efficiency of all energy supply and conversion equipment.

[0059] (15) In the formula: express The electrical load demand of the regional power grid at any given time; , , They are respectively The total heat load demand of fuel cells, electrolyzers, and systems at all times; , for Hydrogen load requirements of electrolyzers and fuel cells at all times; , for The hydrogen charging and discharging power of the hydrogen storage system at all times; for Purchased power of the wind-power-hydrogen hub.

[0060] Operating constraints of combined heat and power units: (16) In the formula: , These are the lower and upper limits of the output electrical power of a combined heat and power unit, respectively. , The rate of ascent / descent; for The unit's natural gas consumption power at specific times; , These are heat generation efficiency and gas-to-electricity conversion efficiency, respectively.

[0061] Operating constraints of gas-fired boilers: (17) In the formula: , These are the lower and upper limits of the output thermal power of the gas-fired boiler, respectively. , The rate of ascent / descent; Power consumed by natural gas; This refers to the gas-heat conversion efficiency.

[0062] Constraints of the electrolyzer-fuel cell coupling system: Hydrogen energy storage systems differ from single-function electrochemical energy storage systems in that their core advantage lies in achieving synergy and value synergy among three energy carriers: electricity, gas, and heat. They are not only flexible regulation units for the power grid, participating in power balance through hydrogen production / generation; they are also the hydrogen source for gas grids and the low-grade heat source for heating grids, realizing the cascade and full-element utilization of energy.

[0063] The system operates according to clear energy and material flow paths. In terms of energy flow, the electricity generated by the wind farm drives the electrolyzer, and the waste heat generated during the conversion process is recovered by a heat exchanger for heating. The fuel cell then converts the chemical energy of hydrogen back into electrical energy and high-quality heat energy. In terms of material flow, the hydrogen produced by the electrolyzer serves as an intermediate medium; part of it is stored for later use, and the other part is immediately transported to the fuel cell for electrochemical reactions. The generated water can be recycled. On the fuel cell side, hydrogen undergoes an electrochemical oxidation reaction at the anode, generating protons and electrons. Protons are transferred to the cathode through the electrolyte membrane, while electrons form an electric current through the external circuit to do work. Finally, at the cathode, protons, electrons, and oxygen combine to form water, releasing directly usable heat of reaction. The mathematical expression is as follows: (18) In the formula: , These are the lower and upper limits of the input electrical power of the electrolytic cell, respectively. , The upward / downward ramp rate of the electrolytic cell; , These are the lower and upper limits of the output power of the fuel cell; , For a moment Hydrogen production rate of electrolyzer and hydrogen consumption rate of fuel cell; This refers to the calorific value of hydrogen. , These are the electro-gas / electro-thermal conversion efficiencies of the electrolytic cell; , , These represent the gas-to-electricity, electricity-to-heat, and gas-to-heat conversion efficiencies of the fuel cell, respectively.

[0064] External interactive power constraints: (19) In the formula: , These are the upper limits for the power capacity to be purchased and sold; , It is a binary variable representing the state of electricity purchase and sale.

[0065] Based on characterizing the uncertainties of photovoltaics, the two-stage bibliometric optimization problem of regional power grids with multi-energy storage is decomposed into upper and lower scheduling stages. This model operates under the worst-case prediction error probability distribution. Below, by minimizing the total cost of the two stages. To obtain the optimal scheduling decision scheme and achieve a balance between risk robustness and economy.

[0066] The two-stage Brussels bar optimization matrix based on Wasserstein distance is in the form of: (20) Equation (20) can be reconstructed into Equation (21), where the outer minimization corresponds to the main problem (upper-level model), and the max-min structure represents the sub-problems (lower-level model). Decision variables are passed from the upper level to the lower level. The lower layer returns the optimal expected cost for a given decision. This forms an optimized relationship of nested coupling.

[0067] (twenty one) In the formula: For expectation operators; It is a supremum function; For the operating costs of the upper-level energy storage system; For lower-level decision variables, Its coefficient column vector; For upper-level decision variables, Its coefficient column vector; , For decision making Lower prediction error The resulting lower-level optimization costs; , , , This is the parameter matrix of the upper-level equality constraints and coupled inequality constraints; This is the photovoltaic power output prediction error matrix.

[0068] The optimization model shown in Equation (21) has significant computational obstacles, the root cause of which lies in the existence of random prediction error variables, which leads to the semi-infinite characteristics of some constraints. The resulting two-stage sub-Bruker optimization framework constitutes a semi-infinite three-level programming problem that is difficult to solve directly. Therefore, by synergistically applying strong duality theory and affine decision rules, the original model is transformed into a mixed-integer linear programming form. First, considering the lower-level decision variables... Follow prediction error The characteristics of change, for the adjustment phase Decision-making during time period Apply an affine strategy. Random prediction error at any time The affine approximation can be expressed as: (twenty two) In the formula: The affine coefficients are to be determined.

[0069] Despite affine approximation simplification, model computation remains challenging. The strong duality principle is employed, and auxiliary variables are introduced. We can obtain: (twenty three) By further transforming equation (23), the original model (21) is transformed into equation (24): (twenty four) In the formula: , These are the upper and lower bounds for photovoltaic prediction errors; For the empirical distribution of prediction errors; The dual variable introduced; It is an auxiliary variable.

[0070] Finally, by merging the lower-level min and upper-level min operations and incorporating the upper-level constraints into equation (23), the final dual bibloc optimization framework is obtained as shown in equation (24). The original bilayer model (20) of the multi-element energy storage regional power grid is thus transformed into a mixed-integer linear programming problem that can be directly solved by a commercial optimization solver.

[0071] This embodiment constructs a multi-energy storage synergistic integration mechanism of electricity, heat, and hydrogen to strengthen the multi-energy complementarity effect and improve the local consumption capacity of renewable energy and system flexibility. It adopts a bibloc bar optimization based on Wasserstein distance to handle uncertainty, without the need for preset probability distribution, relying only on historical data to improve the robustness and reliability of the scheduling strategy. It realizes the cascade utilization of waste heat in the hydrogen conversion process through the hydrogen energy hub to improve the overall energy efficiency of the system. It integrates a two-way carbon trading mechanism to achieve synergy between economic optimization and carbon emission reduction, which is in line with the goals of carbon neutrality and development. It adopts affine decision rules and strong duality theory to transform the complex model into a mixed integer linear programming problem, which is efficient and feasible to solve.

[0072] Figure 6 This paper presents a two-layer optimization framework for regional power grids with multiple energy storage systems. For regional power grids with multiple energy storage systems, a two-way carbon trading mechanism is introduced, and the Wasserstein metric is used to characterize photovoltaic uncertainty. The goal is to minimize the sum of the expected economic costs of the two layers. By combining the constraints of the upper and lower layers, and through the synergistic application of strong duality theory and affine decision rules, the sub-Bruker optimal scheduling strategy integrating two-way carbon trading is transformed into a mixed-integer linear programming model for solution.

[0073] This method has the following advantages: 1) Construct a multi-energy storage synergistic integration mechanism of electricity, heat and hydrogen to strengthen the multi-energy complementarity effect and improve the local consumption capacity and system flexibility of renewable energy; 2) The uncertainty is handled by using a bibliometric optimization based on Wasserstein distance. It does not require a preset probability distribution and only relies on historical data, which improves the robustness and reliability of the scheduling strategy. 3) Improve the overall energy efficiency of the system by utilizing the waste heat from the hydrogen conversion process in a cascade manner through the hydrogen energy hub; 4) Integrating a two-way carbon trading mechanism to achieve synergy between economic optimization and carbon emission reduction, which is in line with carbon neutrality and development goals.

[0074] Typical daily load and renewable energy forecast data for a certain region during the transition season were selected to implement a time-of-use pricing mechanism for electricity, gas, and heat. The optimization model was implemented on the MATLAB R2020a platform, with a scheduling cycle of 24 hours and a time resolution of 1 hour. The objective was to minimize the sum of the expected economic costs of the two-level system, while taking into account the balance of multiple energy flows and equipment operation constraints.

[0075] Scene design and planning results: Four optimization scenarios were designed to verify the effectiveness of the model: Scenario 1: Investigate the impact of hydrogen-blended natural gas on system operation in isolation; Scenario 2: Analyze the effects of the carbon trading mechanism in isolation; Scenario 3: Integrated hydrogen-blended natural gas and carbon trading model; Scenario 4: Demand response, hydrogen-blended natural gas and carbon trading model synergy.

[0076] Significant differences exist in the power and capacity configurations of various energy storage solutions across different scenarios: Scenario 1 has the lowest total energy storage cost but the highest carbon emissions (20917.18 kg); Scenarios 2-3 reduce emissions through carbon trading and hydrogen blending technologies, but increase energy storage costs; Scenario 4, after introducing demand response, increases thermal storage capacity to 1033.881 kW, reduces electrical energy storage capacity to 275.389 kW, and reduces carbon emissions to 16920.26 kg, achieving cost-controllable low-carbon operation.

[0077] Figure 7 The load curve and wind and solar power forecasts are displayed. Implementing time-of-use pricing strategies can effectively guide users to form sustainable energy consumption patterns. By integrating time-of-use pricing mechanisms for electricity, gas, and heat into the regional power grid framework, and by incentivizing load transfer behavior, the optimal scheduling of energy subsystems can be achieved while ensuring energy self-sufficiency.

[0078] Figure 8 The study compared the energy storage costs and carbon emissions in different scenarios: Scenario 1 had the lowest total energy storage cost but peaked carbon emissions at 20,917.18 kg; Scenarios 2-3 reduced emissions through carbon trading and hydrogen blending technologies, while increasing energy storage costs; Scenario 4 introduced demand response based on Scenarios 2-3, reducing total energy storage costs while lowering carbon emissions to 16,920.26 kg, demonstrating that synergistic optimization of demand response, hydrogen blending, and carbon trading can achieve cost-controlled low-carbon operation.

[0079] Analysis of Optimization Results in Typical Scenarios: Taking Scenario 4 (Demand Response, Hydrogen-Blended Natural Gas, and Carbon Trading Coordination) as an example, the optimization scheduling results are analyzed: 1:00–12:00: Electricity load is low, heat / hydrogen load is high. Cogeneration units bear the main electricity load, gas boilers meet high heat demand, electrolyzers produce and store hydrogen, and waste heat is utilized in stages. 12:00–17:00: Electric load increases, heat load decreases, cogeneration units increase output, gas boilers reduce load, hydrogen fuel cells participate in power / heat supply, and diversified energy storage achieves cross-period load transfer; 17:00–24:00: Electricity load is running at a high level, energy storage and hydrogen fuel cells participate in economic dispatch, gas boilers / electrolyzers increase output to meet heat demand, and hydrogen storage facilities release reserves to support hydrogen load.

[0080] The demand response mechanism enables complementary conversion of electricity and heat loads, transferring heat load to electricity during off-peak hours and shaving off peak loads during peak hours; the combined heat and power (CHP) unit dynamically adjusts the heat-to-power ratio to adapt to load changes, reducing operating costs and carbon emissions; the multi-energy storage system effectively smooths the load curve, improving the system's adaptive utilization efficiency and economy.

[0081] Figures 9(a)-9(c) illustrate the optimized electrical / thermal / hydrogen energy balance. The load optimization curves exhibit clear spatiotemporal coupling characteristics: 1:00–12:00: Electricity load decreases significantly while heat / hydrogen load surges. Cogeneration units handle the majority of the electricity load, with surplus demand met through purchased electricity. Economic charging during low-price periods enables energy storage systems to efficiently transfer load; gas-fired boilers meet high heat demand, while waste heat from cogeneration and electrolyzers is utilized in a cascade manner; hydrogen load bridges high and low demand periods—hydrogen produced by electrolyzers supports both low-carbon cogeneration operation and meets load demand; hydrogen storage tanks store surplus hydrogen during off-peak periods.

[0082] 12:00–17:00: Electricity load increases, heat load decreases, and hydrogen load stabilizes. Cogeneration units simultaneously increase electricity / heat output; gas boilers gradually reduce load under ramp-up constraints; hydrogen fuel cells partially meet electricity / heat demand; electric energy storage participates in power supply and stores heat during off-peak periods; surplus hydrogen is stored in hydrogen storage tanks during periods of low demand; diversified energy storage enables load transfer across time periods.

[0083] 17:00–24:00: Electricity load remains high, supported by combined heat and power (CHP) and purchased electricity. During peak hours of time-of-use pricing, energy storage and hydrogen fuel cells participate in economic dispatch; gas boilers / electrolyzers increase output to meet heat demand, and thermal storage systems release energy to supplement supply; after 20:00, hydrogen demand rises—electrolyzers operate at full capacity within limits, and hydrogen storage facilities utilize reserves to enhance supply capacity.

[0084] In Scenario 4, diversified energy storage effectively smooths the load curve and coordinates the supply and storage balance of electricity, heat, and hydrogen. The regional power grid significantly improves the efficiency and economy of adaptive utilization of heterogeneous energy sources through diversified energy storage solutions.

[0085] Figure 10 The electrical / thermal loads before and after the implementation of demand response were compared. In regional power grids, diverse loads exhibit coupling characteristics, necessitating the design of differentiated demand response mechanisms for heterogeneous loads to improve resource utilization efficiency and operational economy.

[0086] Demand response mechanisms enable users to adjust their energy consumption patterns based on time-of-use electricity / heat pricing. 1) Off-peak hours (4:00–5:00): Some heat load is transferred to electricity to optimize resource utilization.

[0087] 2) Periods with the lowest heat demand (9:00–12:00): Incentive electricity prices guide users to switch to electrical appliances, reducing heat load while increasing electricity demand.

[0088] 3) Peak electricity load / price periods (13:00–16:00): Demand response efficiently achieves electricity-heat load conversion and completes peak shaving and valley filling for multiple loads.

[0089] 4) Peak Hours (22:00–23:00): Guided by time-of-use electricity-gas-heat pricing and carbon trading, the system reduces electricity consumption while converting some electricity demand into heat load. This optimizes the utilization of waste heat from electrolyzers and hydrogen fuel cells, reduces energy consumption, and improves overall energy efficiency.

[0090] according to Figure 11 The study reveals that natural gas prices, electricity / heat prices, and carbon trading collectively influence system operation. The complementary coupling characteristics of electricity / heat loads constrain the operation of combined heat and power (CHP): 1) High demand periods (0:00–6:00, 21:00–24:00): Despite higher carbon costs, CHP maintains efficient operation by optimizing the heat-to-power ratio. 2) Peak electricity consumption periods (7:00–20:00): CHP adjusts the heat-to-power ratio to maintain minimum operating limits.

[0091] like Figure 12 As shown, the total system emissions are 16,920.26 kg, mainly from purchased electricity (56.26%) and natural gas combustion (23.94%): 50% of emissions are used to meet electricity load demand; the remaining emissions meet heat load demand (heat demand exceeds electricity demand). Optimizing user energy consumption behavior and converting part of the electricity load into heat load effectively reduces system-level carbon emissions.

[0092] Figures 13(a)-13(c) illustrate the capacity status and optimal charge / discharge schedule: 1) Energy Storage: During charging periods (6:00–7:00, 9:00–10:00, 17:00–19:00): the state of charge is increased; during discharging periods (12:00–15:00, 19:00–20:00): the state of charge is reduced while maintaining 24-hour stability. The charging and discharging behavior responds to electricity / gas prices, carbon trading, and hydrogen-heat load coupling, achieving peak shaving and valley filling and economic optimization through multi-mechanism coordination.

[0093] 2) Thermal storage system: Introduce low-carbon coordinated scheduling into the objective function: Low-price / low-carbon periods (13:00–14:00, 16:00–18:00, 21:00–22:00): Store low-cost, low-carbon thermal energy; Peak periods (19:00–20:00, 22:00–24:00): Efficiently release stored heat to meet demand, balancing economic development and carbon reduction.

[0094] 3) Hydrogen storage system: The optimal hydrogen storage periods are 10:00–13:00, 15:00–20:00, and 21:00–22:00 (for increasing hydrogen charge); the hydrogen release periods are 13:00–14:00, 20:00–21:00, and 22:00–23:00 (for standby use). This system serves as a crucial electricity-heat-hydrogen conversion hub: its operation is unaffected by time-of-use price fluctuations; it supplies hydrogen to cogeneration / gas-fired boilers under electricity / heat load constraints to reduce emissions; and it establishes a collaborative, low-carbon, and economical operating model by achieving seamless multi-energy conversion.

[0095] like Figure 2 As shown, the multi-energy coupled regional power grid sub-branch low-carbon planning system provided in this embodiment of the invention can be implemented in software. The multi-energy coupled regional power grid sub-branch low-carbon planning system includes the following software modules: upper-level model construction and solution module 201 and lower-level model construction and solution module 202.

[0096] The functions of each software module in the multi-energy coupled regional power grid low-carbon planning system are described below: The upper-level model construction and solution module 201 is used to construct an upper-level model based on the parameters of the multi-energy coupled regional power grid, with the goal of maximizing the net present value of the system during the planning period. Then, under the constraints of electric / hydrogen energy storage and thermal energy storage, it solves the set of power scheduling sequences and capacity configuration schemes of electric, thermal and hydrogen energy storage devices. The lower-level model construction and solution module 202 is used to construct the lower-level model with the goal of minimizing the total operating cost of the system. Under the constraints of system-level balance, network interaction, and equipment-level operation, it uses distributed bar optimization to handle uncertainties and determines the optimal power scheduling sequence and optimal capacity configuration scheme of electric, thermal, and hydrogen energy storage devices from the power scheduling sequence set and capacity configuration scheme set of electric, thermal, and hydrogen energy storage devices.

[0097] The expression for the upper-level model is:

[0098]

[0099]

[0100]

[0101] In the formula: The total operating cost of the energy storage system; For the sake of maintenance costs; For market participation costs; The investment cost of the energy storage system; For the first x Operation and maintenance cost coefficient of energy storage devices; For a moment No. x Operating power of energy storage devices; This represents the total number of energy storage system types. For the first x Market participation cost coefficient for energy storage devices; For the first x Rated power of energy storage devices; For the first x Rated capacity of energy storage devices; For the first x Rated power cost of energy storage devices; For the first x Rated capacity cost of energy storage devices; The discount rate; This refers to the equipment's lifespan.

[0102] Both electric / hydrogen energy storage constraints and thermal energy storage constraints include: energy state evolution constraints, power upper and lower limit constraints, charge-discharge mutual exclusion constraints, and self-discharge constraints.

[0103] The expression for the lower-level model is:

[0104]

[0105]

[0106] In the formula: For regional power grid operating costs; For the operation and maintenance costs of all power generation equipment; For the cost of purchasing electricity and natural gas; For carbon trading costs; , , , , , These are the operation and maintenance cost coefficients for photovoltaic, wind turbine, combined heat and power, gas boiler, hydrogen fuel cell and electrolyzer, respectively. , , , , , Each device is in Operating power at any given time; and They are respectively The price of electricity at any given moment; and These refer to the power purchased and the power sold, respectively. for Real-time natural gas prices; and These are the thermoelectric conversion efficiencies of combined heat and power units and gas-fired boilers, respectively. This refers to the heat output power of a combined heat and power (CHP) unit.

[0107] The expression for carbon trading costs is:

[0108]

[0109]

[0110] In the formula: For carbon trading costs; The benchmark carbon trading price; , , These are the initial carbon allowances for purchased electricity, combined heat and power units, and gas-fired boilers, respectively. Carbon quota coefficient per unit of electricity; Carbon quota coefficient per unit of heat; The electrothermal conversion coefficient; , , These are the actual carbon emissions from purchased electricity, combined heat and power (CHP) and gas-fired boilers, respectively. The actual carbon emission coefficient per unit of electricity; The actual carbon emission coefficient per unit of heat; For the power purchased; The heat output power of a combined heat and power unit; For cogeneration in Operating power at any given time; In order to be in The output power of the gas boiler at any given time.

[0111] Equipment-level operational constraints include operating constraints for combined heat and power units, operating constraints for gas-fired boilers, and constraints for electrolyzer-fuel cell coupling systems.

[0112] It should be noted that each module in the multi-energy coupled regional power grid sub-bar low-carbon planning system of the present invention corresponds one-to-one with each step in the multi-energy coupled regional power grid sub-bar low-carbon planning method of the above embodiments, and their specific implementation processes are the same, so they will not be repeated here.

[0113] The structure of the electronic device according to an embodiment of the present invention will be described in detail below. Figure 3 This is a schematic diagram of the composition structure of an electronic device provided in an embodiment of the present invention. It can be understood that... Figure 3 The diagram shows only an exemplary structure of the electronic device, not the entire structure. Some or all of the structures shown may be implemented as needed.

[0114] The electronic device provided in this embodiment of the invention includes: at least one processor 301, a memory 302, a user interface 303, and at least one network interface 304. Various components in the multi-energy coupled regional power grid's distributed low-carbon planning system are coupled together via a bus system 305. It can be understood that the bus system 305 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 305 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 3 The general designated all buses as Bus System 305.

[0115] The user interface 303 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0116] It is understood that memory 302 can be volatile memory or non-volatile memory, or both. In this embodiment of the invention, memory 302 is capable of storing data to support the operation of the terminal. Examples of this data include any computer programs used to operate on the terminal, such as operating systems and applications. The operating system includes various system programs, such as framework layers, core library layers, driver layers, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications.

[0117] In some embodiments, the multi-energy coupled regional power grid sub-bar low-carbon planning system provided by the present invention can be implemented using a combination of hardware and software. As an example, the multi-energy coupled regional power grid sub-bar low-carbon planning system provided by the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the multi-energy coupled regional power grid sub-bar low-carbon planning method provided by the present invention. For example, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0118] As an example, processor 301 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., wherein the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0119] As an example of the hardware implementation of the multi-energy coupled regional power grid sub-bar low-carbon planning system provided in this embodiment of the invention, the device provided in this embodiment of the invention can be directly executed by a processor 301 in the form of a hardware decoding processor. For example, it can be executed by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components to implement the multi-energy coupled regional power grid sub-bar low-carbon planning method provided in this embodiment of the invention.

[0120] The memory 302 in this embodiment of the invention is used to store various types of data to support the operation of the multi-energy coupled regional power grid's distributed low-carbon planning system, or to store data for execution. Figure 1 The program code for the method shown. Examples of this data include: any executable instructions for operation on a multi-energy coupled regional power grid sub-bar low-carbon planning system, such as executable instructions that implement the multi-energy coupled regional power grid sub-bar low-carbon planning method of the present invention can be contained in executable instructions.

[0121] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including functions for executing... Figure 1 The program code for the method shown. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by the central processing unit, it performs the various functions defined in the apparatus of this application.

[0122] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxesFigure 1 A device that provides the functions specified in one or more boxes.

[0123] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A low-carbon planning method for multi-energy coupled regional power grids using distributed baffles, characterized in that, include: Based on the parameters of the multi-energy coupled regional power grid, with the goal of maximizing the net present value of the system during the planning period, an upper-level model is constructed. Then, under the constraints of electric / hydrogen energy storage and thermal energy storage, the power scheduling sequence set and capacity configuration scheme set of electric, thermal and hydrogen energy storage devices are obtained. With the goal of minimizing the total system operating cost, a lower-level model is constructed. Under system-level balance constraints, network interaction constraints, and device-level operating constraints, the uncertainty is handled by using bibliometric optimization. From the power scheduling sequence set and capacity configuration scheme set of electric, thermal, and hydrogen energy storage devices, the optimal power scheduling sequence and optimal capacity configuration scheme of electric, thermal, and hydrogen energy storage devices are determined.

2. The low-carbon planning method for multi-energy coupled regional power grids as described in claim 1, characterized in that, The expression for the upper-level model is: In the formula: The total operating cost of the energy storage system; For the sake of maintenance costs; For market participation costs; The investment cost of the energy storage system; For the first x Operation and maintenance cost coefficient of energy storage devices; For a moment No. x Operating power of energy storage devices; This represents the total number of energy storage system types. For the first x Market participation cost coefficient for energy storage devices; For the first x Rated power of energy storage devices; For the first x Rated capacity of energy storage devices; For the first x Rated power cost of energy storage devices; For the first x Rated capacity cost of energy storage devices; The discount rate; This refers to the equipment's lifespan.

3. The low-carbon planning method for multi-energy coupled regional power grids as described in claim 1, characterized in that, The constraints on electric / hydrogen energy storage and thermal energy storage both include: energy state evolution constraints, power upper and lower limit constraints, charge-discharge mutual exclusion constraints, and self-discharge constraints.

4. The low-carbon planning method for multi-energy coupled regional power grids as described in claim 1, characterized in that, The expression for the lower-level model is: In the formula: For regional power grid operating costs; For the operation and maintenance costs of all power generation equipment; For the cost of purchasing electricity and natural gas; For carbon trading costs; , , , , , These are the operation and maintenance cost coefficients for photovoltaic, wind turbine, combined heat and power, gas boiler, hydrogen fuel cell and electrolyzer, respectively. , , , , , Each device is in Operating power at any given time; and They are respectively The price of electricity at any given moment; and These refer to the power purchased and the power sold, respectively. for Real-time natural gas prices; and These are the thermoelectric conversion efficiencies of combined heat and power units and gas-fired boilers, respectively. This refers to the heat output power of a combined heat and power (CHP) unit.

5. The low-carbon planning method for multi-energy coupled regional power grids as described in claim 1, characterized in that, The expression for carbon trading costs is: In the formula: For carbon trading costs; The benchmark carbon trading price; , , These are the initial carbon allowances for purchased electricity, combined heat and power units, and gas-fired boilers, respectively. Carbon quota coefficient per unit of electricity; Carbon quota coefficient per unit of heat; The electrothermal conversion coefficient; , , These are the actual carbon emissions from purchased electricity, combined heat and power (CHP) and gas-fired boilers, respectively. The actual carbon emission coefficient per unit of electricity; The actual carbon emission coefficient per unit of heat; For the power purchased; The heat output power of a combined heat and power unit; For cogeneration in Operating power at any given time; In order to be in The output power of the gas boiler at any given time.

6. The low-carbon planning method for multi-energy coupled regional power grids as described in claim 1, characterized in that, Equipment-level operational constraints include operating constraints for combined heat and power units, operating constraints for gas-fired boilers, and constraints for electrolyzer-fuel cell coupling systems.

7. A multi-energy coupled regional power grid sub-bar low-carbon planning system, characterized in that, The low-carbon planning method for multi-energy coupled regional power grids based on any one of claims 1-6 includes: The upper-level model construction and solution module is used to construct an upper-level model based on the parameters of the multi-energy coupled regional power grid, with the goal of maximizing the net present value of the system during the planning period. Then, under the constraints of electric / hydrogen energy storage and thermal energy storage, it solves for the power scheduling sequence set and capacity configuration scheme set of electric, thermal and hydrogen energy storage devices. The lower-level model construction and solution module is used to construct the lower-level model with the goal of minimizing the total system operating cost. Under system-level balance constraints, network interaction constraints, and device-level operating constraints, it uses distributed bar optimization to handle uncertainties and determines the optimal power scheduling sequence and optimal capacity configuration scheme for electric, thermal, and hydrogen energy storage devices from the power scheduling sequence set and capacity configuration scheme set of electric, thermal, and hydrogen energy storage devices.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the low-carbon planning method for multi-energy coupled regional power grids as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the multi-energy coupled regional power grid sub-bar low-carbon planning method as described in any one of claims 1-6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps in the low-carbon planning method for multi-energy coupled regional power grids as described in any one of claims 1-6.

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