Microgrid group low-carbon scheduling method based on green certificate excitation and demand response

Abstract

The invention provides a micro-grid group low-carbon scheduling method based on green certificate excitation and demand response, and the method comprises the steps: firstly, adding power-to-gas and carbon capture equipment in a micro-grid, and constructing a comprehensive energy multi-micro-grid model fusing a carbon capture system (CCS) and power-to-gas (P2G); secondly, introducing a demand response mechanism, a carbon transaction model and a green certificate transaction model; establishing a comprehensive energy multi-microgrid low-carbon economic dispatching optimization model by taking minimization of the total operation cost, the carbon transaction cost and the green certificate transaction cost of the system as a target; and finally, a comprehensive energy multi-microgrid distributed scheduling model based on the ADMM is constructed, the provided model is solved through iterative optimization, the distributed strategy can converge to the optimal solution more quickly, and lower system operation cost is achieved. According to the invention, the carbon dioxide emission in the operation process of the micro-grid system can be obviously reduced, the adjustment capability of energy scheduling management of the micro-grid system is improved, and the low-carbon operation of the system is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated energy low-carbon scheduling, and particularly relates to a micro-grid group low-carbon scheduling method based on green certificate incentive and demand response. BACKGROUND

[0002] Under the background of carbon neutrality and frequent energy supply shortage, an integrated energy system integrates power systems, natural gas pipelines, heat networks and other various energy systems, can improve energy utilization efficiency, and help achieve net zero carbon emissions. With the rapid development of integrated energy systems, integrated energy microgrids typified by combined heat and power microgrids have also been rapidly developed and widely applied.

[0003] In order to achieve the double carbon goal, the low-carbon transformation of integrated energy microgrid is imminent. At present, many domestic and foreign scholars have carried out research on low-carbon dispatching and operation of power system. The application of carbon capture technology (CCS) and power to gas (P2G) in the power industry not only can more effectively reduce the carbon emissions of the system, but also can improve the utilization efficiency of energy, so installing carbon capture devices in traditional conventional units will become the development trend in the future. Literature [Dong, W.; Lu, Z.; He, L.; Geng, L.; Guo, X.; Zhang, J. Low-carbon optimal planning of an integrated energy station considering combined power-to-gas and gas-fired units equipped with carbon capture systems. Int. J. Electr. Power Energy Syst. 2022, 138, 107966.] establishes a low-carbon economic dispatching model of integrated energy considering carbon capture system, which reduces the carbon dioxide emissions of the system. Literature [Zhou, R.; Li, Y.; Sun, J.; Zhang, H.; Liu, D. Low-carbon economic dispatch considering carbon capture unit and demand response under carbon trading. IEEE APPEEC. 2016, 1435-1439] explores the low-carbon performance of the system and improves the economic benefit through demand response and carbon capture power plant. While carbon trading and green certificate trading mechanism provides a new way to improve the consumption rate of renewable energy generation and reduce carbon emissions.The literature [Guo, W.; Xu, X. Comprehensive energy demand response optimization dispatch method based on carbon trading. Energies. 2022, 15, 3128] analyzes the carbon dioxide emission characteristics of different power generation resources, considers carbon capture technology and demand response, incorporates carbon trading costs into the objective function, and establishes a low-carbon economic dispatch model; the literature [Yang, D.; Wang, M. Optimal operation of an integrated energy system by considering the multi energy coupling, AC-DC topology and demand responses. Int. J. Electr. Power Energy Syst. 2021, 129, 106826] introduces a tiered carbon trading mechanism, which sets tiered carbon prices based on the carbon emission range, further limiting the system's carbon emissions. The literature [Yang, D.; Xu, Y.; Liu, X.; Jiang, C.; Nie, F.; Ran, Z. Economic-emission dispatch problem in integrated electricity and heat system considering multi-energy demand response and carbon capture technologies. Energy. 2022, 253, 124153] proposes a multi-objective dynamic economic emission dispatch model based on a tradable green certificate mechanism for wind, solar, and hydropower to promote the development and utilization of renewable energy, effectively promoting the absorption of renewable energy. Therefore, it is necessary to introduce carbon trading and green certificate trading mechanisms into integrated energy microgrid systems. User-side demand response, as a flexible adjustment means participating in system optimization scheduling, can achieve peak shaving and valley filling, improve the renewable energy absorption capacity, and reduce system operating costs.References [Li, X.; Yang, J.; Du, D.; Zhou, Z.; Li, K.; Wu, L. Resilient distributed economic dispatch for cyber–physical power systems considering carbon emissions trading and false data injection attacks. Energy . 2025, 332, 136881] construct a virtual power plant optimal scheduling model that considers demand response resources and participates in carbon emission trading, improving the system's power supply reliability and carbon emission reduction capabilities. Under the network architecture of integrated energy systems, traditional power energy demand response will gradually transform into integrated demand response. By adjusting different energy demands, the demand among multiple loads can be met on the user side. References [Shao, C.; Ding, Y.; Siano, P.; Song, Y. Optimal scheduling of the integrated electricity and natural gas systems considering the integrated demand response of energyhubs. IEEE Syst. J. 2021, 15, 4545-4553] establish an optimization model for a campus integrated energy microgrid that considers the integrated demand response of multiple loads for electricity and heat. The results show that, compared with single electricity demand response, integrated demand response has significant advantages in terms of economy, environmental protection and energy utilization.

[0004] However, with the continuous growth of electricity demand, the increasing promotion of distributed energy technologies, and the diversification of energy demands, individual microgrids have exposed shortcomings such as insufficient anti-interference capabilities and energy shortages, making them unable to meet complex system dispatch objectives. To address this situation, adjacent microgrids need to form multi-microgrid systems, achieving energy complementarity and sharing among microgrids and improving the power supply reliability and stability of the system. The literature [Du, Y.; Li, F. A hierarchical real-time balancing market considering multi-microgrids with distributed sustainable resources. IEEE Trans. Sustain. 2020, 11, 72-83] proposes a hierarchical market structure that allows multiple microgrids to participate in the transmission layer real-time balancing market and provide ancillary services to the grid. At the distribution level, local microgrids with distributed sustainable resources are economically dispatched by the distribution system operator. A two-level optimization model is developed by combining these two problems into a mathematical programming problem with complementary constraints. The literature [Nikmehr, N.; Najafi-Ravadanegh, S. Probabilistic optimal power dispatch in multi-microgrids using heuristical algorithms. In Proc. 2014 Smart Grid Conf. (SGC), Tehran, Iran. 2014, 1-6] uses particle swarm optimization to solve for multi-microgrid systems with the objective of minimizing total operating costs. While the centralized optimization method mentioned in the above literature has achieved some success in energy management and dispatch of multi-microgrids, it also presents some significant problems and challenges. Centralized optimization requires substantial communication infrastructure to support information collection and command issuance, and may also raise issues of information security and privacy protection. Furthermore, centralized optimization algorithms require accurate modeling of the entire system; the uncertainties and volatility of wind and solar power generation increase the complexity and difficulty of the model. Summary of the Invention

[0005] To address the shortcomings of the aforementioned technologies, this invention proposes a low-carbon scheduling method for microgrid clusters based on green certificate incentives and demand response. It employs a distributed algorithm based on the Alternating Directional Multiplier Method (ADMM) to manage multi-microgrid systems with a high proportion of distributed energy access. Compared to traditional centralized methods, this distributed strategy can converge to the optimal solution faster and achieve lower system operating costs.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0007] A low-carbon dispatching method for microgrid groups based on green certificate incentives and demand response, comprising the following steps:

[0008] Step 1: Electricity-to-gas (EPG) and carbon capture (C2G) devices were added to the microgrid to construct a comprehensive energy multi-microgrid model that integrates the CCS carbon capture system and P2G.

[0009] Step 2: Taking into account the coupling characteristics of the electro-thermal multi-energy system, establish a comprehensive demand response mechanism for electrothermal load;

[0010] Step 3: Establish carbon trading model and green certificate trading model; with the goal of minimizing the total system operating cost, carbon trading cost and green certificate trading cost, establish a comprehensive energy multi-microgrid low-carbon economic dispatch optimization model;

[0011] Step 4: Construct a distributed scheduling model for integrated energy multi-microgrid based on ADMM, and iteratively optimize and solve the proposed model.

[0012] Preferably, the expression for the integrated energy multi-microgrid model established in step one is:

[0013] ;

[0014] in, The conversion efficiency of P2G electrical energy to gas energy. The conversion factor of the amount of electricity consumed by CCS to capture carbon dioxide. The carbon dioxide production intensity coefficient. This is the lower limit of the generating capacity of the CHP unit. This is the upper limit of the generating capacity of the CHP unit. For CHP units in Heating power at any given time This represents the heating capacity corresponding to the minimum power generation capacity of the CHP unit. The microgrid provides electrical power to the CHP cogeneration unit at time t. Let t be the gas production power of P2G. The electrothermal conversion coefficient of the CHP unit corresponding to the minimum output power. The electrothermal conversion coefficient of the CHP unit corresponding to the maximum output power. The linear supply slope for the power generation and heating power of the CHP unit.

[0015] Preferably, the electrothermal characteristic equation of the CHP unit is:

[0016] ;

[0017] The power generation constraint for CHP units is:

[0018] ;

[0019] The heating power constraint for CHP units is:

[0020] ;

[0021] in, This is the lower limit of the heating capacity of the CHP unit. These are the lower and upper limits of the heating capacity of the CHP unit.

[0022] Preferably, in the integrated energy multi-microgrid model, the electrical energy generated by the CHP unit is divided into three parts, as follows:

[0023] ;

[0024] in, Let be the electrical power consumed by the electro-gas P2G device at time t. Let t be the electrical power consumed by the carbon capture system (CCS).

[0025] The relationship between the power generated by P2G natural gas and the power supplied by consuming CHP is as follows:

[0026] ;

[0027] The relationship between the carbon dioxide required for P2G to produce natural gas is:

[0028] ;

[0029] In the formula: Let t be the amount of carbon dioxide required to consume electrical energy by P2G;

[0030] The conversion relationship between CCS carbon dioxide capture capacity and operating power consumption is as follows:

[0031] ;

[0032] In the formula: The conversion factor of the amount of CCS power consumed to capture carbon dioxide;

[0033] The power constraints for CCS and P2G are as follows:

[0034] ;

[0035] In the formula: This represents the lower limit of the electrical power consumed by the CCS. This represents the upper limit of the electrical power consumed by the CCS. This represents the lower limit of the power consumption of P2G. This represents the upper limit of the electrical power consumed by P2G.

[0036] Preferably, the comprehensive demand response mechanism for the electric heating load includes a transferable load model and a load reduction model;

[0037] Transferable load model:

[0038] Transferable load refers to load that is adjusted according to a preset plan and whose total electricity consumption remains unchanged during the dispatch cycle; based on transferable load The load distribution vector before scheduling is represented as:

[0039] ;

[0040] In the formula: k is the electrical or thermal load; when k is e, it represents the electrical load; when k is h, it represents the thermal load. For portable electrical or thermal loads, t s Let t be the start time. d Duration;

[0041] The movable time interval is [t] sh- , t sh+ ], The start time and duration are represented by 0-1 variables. express During a certain period of time The translation state, when When =1, it means from The period begins; when =0 indicates If the load does not shift, then the set of initial time periods is [t]. sh- , t sh+ -t d +1]; if =t s This indicates that the load has not changed; if [t sh- , t sh+ -t d +1] and =t s ,express From the start time t s Translate to the starting time of The power distribution vector is:

[0042] ;

[0043] The compensation cost incurred after a user migration can be expressed as:

[0044] ;

[0045] In the formula: To compensate for the cost of transferable load, The unit price is for transferable load compensation. The sum of transferable loads;

[0046] Load reduction model:

[0047] Introducing 0-1 variables Characterizes load reduction During the period Reduction state: when =0 indicates During the period Not reduced; when When =1, it means From time period The load was reduced; accordingly, the load participated in the subsequent scheduling period. The power is expressed as:

[0048] ;

[0049] In the formula: for Load reduction factor for the time period [0,1]; Before load reduction can participate in scheduling Power during a given time period;

[0050] The compensation to users after the reduction is as follows:

[0051] ;

[0052] In the formula: In order to reduce load compensation costs, This is to reduce the unit price of load compensation.

[0053] Preferably, the carbon trading model is as follows:

[0054] Carbon emission quotas for integrated energy microgrids Represented as:

[0055] ;

[0056] In the formula: Let i be the renewable energy generation capacity of the i-th microgrid during time period t. , These are the carbon emission allowances for each unit of electricity produced by the microgrid and the carbon emission allowances for each unit of electricity purchased.

[0057] Actual carbon emissions of integrated energy microgrids :

[0058] ;

[0059] In the formula: , The carbon emission coefficient of a combined heat and power (CHP) unit. The carbon emission coefficient of a gas-fired boiler. The carbon dioxide emission factor for the amount of electricity purchased. The amount of carbon dioxide reduced by the system after the introduction of carbon capture and electro-gas conversion equipment;

[0060] The computational cost reflecting the advantages of tiered carbon trading is expressed as follows:

[0061] ;

[0062] In the formula: Let i be the carbon trading cost of the i-th microgrid. This refers to the carbon trading price on the market; The length of the carbon emission range; , These are the penalty coefficient and the reward coefficient, respectively.

[0063] The green certificate trading model is as follows:

[0064] ;

[0065] ;

[0066] ;

[0067] In the formula: The green certificate transaction cost for the i-th micronet is... The price at which green certificates are traded; To punish prices; The proportion of renewable energy generation quota; This is the penalty coefficient; This refers to the proportion of actual renewable energy in the total electricity generated by grid connection; This refers to the total electricity generated by power generation companies and fed into the grid.

[0068] Preferably, the integrated energy multi-microgrid low-carbon economic dispatch optimization model is as follows:

[0069] ;

[0070] ;

[0071] ;

[0072] in, The operating costs of cogeneration units containing carbon capture and power-to-gas conversion equipment, The operation and maintenance cost of the i-th microgrid energy storage device is... To reduce the external interaction costs of integrated energy microgrids, Cost of responding to demand; The network transmission cost incurred by the power interaction between the i-th micronet and other micronets. For network transmission parameters, This represents the power of interaction between the i-th microgrid and other microgrids. A positive value indicates the output power, and a negative value indicates the input power.

[0073] Preferably, the operating cost of the combined heat and power unit containing carbon capture and power-to-gas conversion equipment is... for:

[0074] ;

[0075] In the formula: , , These are the operating cost coefficients and constants for combined heat and power units, respectively. , These are the operating cost coefficients for carbon capture and power-to-gas conversion equipment, respectively, and T is the total scheduling period.

[0076] The operation and maintenance cost of energy storage devices is:

[0077] ;

[0078] In the formula: The operation and maintenance cost of the i-th microgrid energy storage device is... and Let i be the charging and discharging power of the i-th microgrid energy storage device. This represents the operation and maintenance cost coefficient for energy storage devices.

[0079] External interaction costs of integrated energy microgrids The related costs, including the electricity purchase and sale costs from the power grid and the gas purchase costs, are expressed as follows:

[0080] ;

[0081] In the formula: , The electricity purchase and sale prices for time period t are respectively. The purchase price of natural gas. , For the purchase and sale of electrical power, Let be the gas purchase volume of the i-th microgrid CHP system during time period t;

[0082] Demand response cost:

[0083] ;

[0084] In the formula: , These are the costs of relocatable and reducible electrical load compensation. It is a compensation cost that can be reduced due to heat load.

[0085] Preferably, the constraints of the integrated energy multi-microgrid low-carbon economic dispatch optimization model include:

[0086] Electric power balance constraints:

[0087] ;

[0088] Thermal power balance constraint:

[0089] ;

[0090] Gas power balance constraints:

[0091] ;

[0092] CCS-P2G-CHP power upper and lower limits constraints:

[0093] ;

[0094] ;

[0095] Renewable energy power constraints:

[0096] ;

[0097] In the formula: Let be the predicted renewable energy power of the i-th microgrid during time period t;

[0098] Constraints of energy storage devices:

[0099] ;

[0100] Green certificate quota constraints:

[0101] ;

[0102] Demand response constraints:

[0103] ;

[0104] Power interaction constraints between microgrid and main grid:

[0105] .

[0106] Preferably, the iterative process of distributed optimization in ADMM is as follows:

[0107] ;

[0108] ;

[0109] ;

[0110] In the formula: , , , These are 0-1 variables used to characterize the load scheduling status. It is the penalty coefficient. For the first The next Lagrange multiplier, For the number of iterations, For the first The interaction power between microgrids and the power grid;

[0111] Based on the principles of the ADMM algorithm, the original residuals are used. and dual residuals As the basis for convergence, their convergence criteria are expressed as follows:

[0112] ;

[0113] ;

[0114] In the formula: and It is the expected convergence error of the parameters at the end of the iteration.

[0115] The beneficial effects of this invention are:

[0116] 1) The proposed integrated energy microgrid low-carbon dispatch model with CCS-P2G improves conventional cogeneration units by introducing carbon capture and power-to-gas conversion equipment and forming a CCS-P2G-CHP integrated coupled operation. This model can significantly reduce carbon dioxide emissions during the operation of the microgrid system, improve the regulation capability of the microgrid system's energy dispatch management, and effectively enhance the low-carbon operation of the system.

[0117] 2) Combining the energy sharing framework of the integrated energy multi-microgrid system, a distributed energy management strategy for multi-microgrids is proposed. Energy interaction between microgrids is carried out. Compared with the independent operation of a single microgrid, the operating costs and carbon dioxide emissions of each microgrid are significantly reduced. Moreover, it promotes the absorption rate of renewable energy and improves the economic efficiency of the system.

[0118] 3) Establish a distributed model of integrated energy multi-microgrid based on ADMM. The operating cost of the integrated energy multi-microgrid system is solved iteratively by using the alternating multiplier method. Only a few dozen iterations are needed to obtain the optimized scheduling scheme, which can quickly achieve the convergence effect, realize distributed scheduling, and effectively protect the transaction information of each participating microgrid. Attached Figure Description

[0119] 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.

[0120] Figure 1 This is a schematic diagram of the integrated energy microgrid operation framework of the present invention.

[0121] Figure 2 This is a diagram illustrating the electrothermal coupling characteristics of the cogeneration system of the present invention.

[0122] Figure 3 This is a flowchart of the multi-micronet optimization scheduling solution of the present invention.

[0123] Figure 4 This is a curve showing the change in power generation from renewable energy sources within the microgrid.

[0124] Figure 5 This is the power variation curve of the electrical load data within the microgrid.

[0125] Figure 6 This is the power variation curve of the heat load data within the microgrid.

[0126] Figure 7 The cost iteration curve for integrated energy microgrids.

[0127] Figure 8 The results of the power output and power balance optimization scheduling of each integrated energy microgrid are as follows: (a) internal optimization of power supply in microgrid 1, (b) internal optimization of power supply in microgrid 2, and (c) internal optimization of power supply in microgrid 3.

[0128] Figure 9The results of the power output and heat power balance optimization scheduling of each integrated energy microgrid are shown; among them, (a) internal optimization of heat energy of microgrid 1, (b) internal optimization of heat energy of microgrid 2, and (c) internal optimization of heat energy of microgrid 3.

[0129] Figure 10 This is a curve showing the change in power exchange between microgrids. Detailed Implementation

[0130] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0131] This invention provides a low-carbon dispatching method for microgrid clusters based on green certificate incentives and demand response. First, electricity-to-gas (E2G) and carbon capture and storage (CCS) devices are added to the microgrid, constructing a comprehensive energy microgrid model integrating P2G and CCS technologies. This model effectively enhances the system's low-carbon regulation capabilities. Second, to effectively incentivize user participation in system optimization, a demand response mechanism and a tiered green certificate trading model are introduced. Based on this, a low-carbon economic dispatching model (MM-IES) is established with the goal of minimizing total system operating costs, carbon trading costs, and green certificate trading costs. To protect the privacy of each microgrid and achieve efficient collaboration, an ADMM-based distributed dispatching model for multiple microgrids is constructed, and the proposed model is iteratively optimized. The proposed strategy effectively reduces system operating costs and carbon emissions.

[0132] For a single microgrid, a multi-energy microgrid system operation framework is established, comprising equipment such as cogeneration units, gas-fired boilers, energy storage devices, and renewable energy power generation devices, using an electric-gas-heat coupling system. During the entire system operation, the cogeneration unit is the primary source of carbon dioxide emissions. The carbon capture device efficiently utilizes electricity to capture carbon dioxide from the cogeneration unit, and the power-to-gas (EPG) equipment can use carbon dioxide as a feedstock to produce natural gas, effectively reducing system carbon emissions, minimizing energy waste, and enhancing system flexibility. Therefore, this invention incorporates EPG and carbon capture equipment into the microgrid, while simultaneously constructing a carbon trading and green certificate trading market, effectively balancing economic efficiency and low-carbon footprint. Within the system, each microgrid can purchase electricity from or sell electricity to other microgrids. To promote energy consumption within the microgrid, this invention assumes that the price of electricity purchased between microgrids is lower than the price the microgrid purchases from the main grid, and that microgrids prioritize purchasing electricity from other microgrids. Only when the purchased amount is insufficient to support the system's load demand will the microgrid purchase electricity from the main grid. Each microgrid is equipped with a microgrid energy management system (MGEMS). MGEMS utilizes information and communication technologies to enable energy exchange and information interaction between adjacent microgrids, improving energy utilization efficiency and economic and environmental benefits across multiple microgrids. Based on the above, a low-carbon operation model for a multi-energy microgrid system is constructed as follows: Figure 1 .

[0133] Gas turbines are the core equipment for coupling multiple energy forms such as electricity and heat. They generate electricity by burning natural gas, and the gas consumption is proportional to the power generation, as shown in the model below.

[0134] (1)

[0135] In the formula: It is a gas turbine unit Power generation at any given moment It refers to the power generation efficiency of the gas turbine unit. It is the calorific value of natural gas. It is a gas turbine unit Gas consumption at any given moment.

[0136] Gas-fired boilers, as efficient heat supply devices, can generate high-temperature hot water or steam by burning fuels such as natural gas, providing heat energy to users in microgrids. They also facilitate energy complementarity with other power generation devices in the microgrid (such as gas turbines, photovoltaics, and wind power). In situations of insufficient or excessive power supply, gas-fired boilers can adjust the energy structure by regulating the heat supply, ensuring the stable operation of the microgrid. The mathematical model of a gas-fired boiler is shown in the equation.

[0137] (2)

[0138] In the formula, It is a gas-fired boiler. Heat production capacity at any given time It is a gas turbine unit Gas consumption at any time, This refers to the heat production efficiency of a gas-fired boiler. Because the combined heat and power (CHP) power of a gas-fired unit is constrained by the principle of "heat-driven power generation," its electrical and thermal outputs are mutually restrictive and coupled. Its electrothermal characteristic equation is shown in the figure.

[0139] (3)

[0140] In the formula, , The lower and upper limits of the CHP unit's generating capacity. This represents the heating capacity corresponding to the minimum power generation capacity of the CHP unit. For CHP units in Power generation at any given moment For CHP units in Heating power at any given time , The CHP unit's electrothermal conversion coefficients are the minimum and maximum output power, respectively. The linear supply slope for the power generation and heating capacity of CHP.

[0141] The power generation constraint of CHP units is

[0142] (4)

[0143] In the formula: and These are the lower and upper limits of the CHP unit's generating capacity, respectively.

[0144] The heating power limit of CHP units

[0145] (5)

[0146] In the formula, and These are the lower and upper limits of the heating capacity of the CHP unit, respectively.

[0147] Cogeneration (CHP) systems significantly improve overall energy efficiency by simultaneously supplying electricity and heat, and provide the necessary power for carbon capture (CCA) and power-to-gas (EPG) systems. The CCA system captures carbon dioxide produced by the CHP unit through a carbon dioxide absorption tower, reducing the overall carbon footprint. The regeneration tower then releases carbon dioxide to supply the EPG system, which uses electricity from the CHP to electrolyze water in an electrolyzer to produce hydrogen and oxygen. The carbon dioxide collected by the CCA is then used to generate methane. This process not only balances grid load and efficiently absorbs intermittent renewable energy, but also feeds the generated natural gas back to the CHP system as an energy storage medium or clean energy source, thus forming a circular economy operation. The combination of these three technologies enhances the power regulation capability of the CHP system and weakens its inherent electro-thermal coupling characteristics.

[0148] In the CCS-P2G-CHP coupled model, the electrical energy generated by the CHP unit is divided into three parts, represented as follows:

[0149] (6)

[0150] in, The microgrid supplies electrical power to the CHP cogeneration unit at time t. Let be the electrical power consumed by the electro-gas P2G device at time t. Let t be the electrical power consumed by the carbon capture system (CCS).

[0151] The relationship between the power generated by P2G natural gas and the power supplied by consuming CHP is as follows:

[0152] (7)

[0153] In the formula: Let t be the gas production power of P2G. The conversion efficiency of P2G electrical energy to gas energy.

[0154] The relationship between the carbon dioxide required for P2G to produce natural gas is:

[0155] (8)

[0156] In the formula: Let t be the amount of carbon dioxide required to consume electrical energy by P2G; This is the carbon dioxide intensity coefficient for gas production (the amount of carbon dioxide required per unit of natural gas production).

[0157] The conversion relationship between CCS carbon dioxide capture capacity and operating power consumption is as follows:

[0158] (9)

[0159] In the formula: The conversion factor of the amount of CCS power consumed to capture carbon dioxide.

[0160] The power constraints for CCS and P2G are as follows:

[0161] (10)

[0162] In the formula: This represents the lower limit of the electrical power consumed by the CCS. This represents the upper limit of the electrical power consumed by the CCS. This represents the lower limit of the power consumption of P2G. This represents the upper limit of the electrical power consumed by P2G.

[0163] Substituting equation (6) into equation (3), we can obtain the thermoelectric coupling characteristic equation of the cogeneration system containing the electro-gas conversion and carbon capture equipment, as shown in equation (11).

[0164] (11)

[0165] The combined power consumption range of P2G and CCS is shown in Equation (12).

[0166] (12)

[0167] Substituting equation (11) into equation (12), we obtain the new thermoelectric coupling characteristics of the cogeneration system with electricity-to-gas conversion and carbon capture, as shown in equation (13).

[0168] (13)

[0169] Based on formulas (3), (11), and (12), the feasible range of electrical and thermal power before and after adding CCS and P2G to the CHP unit can be derived as follows: Figure 2 ABCD and EFGCD.

[0170] like Figure 2 As shown, after the introduction of CCS and P2G equipment, the minimum power generation of CHP changes from point A to point D, expanding the output range of CHP power generation. When the heating capacity of the CCS and P2G cogeneration units is constant, the power generation adjustment range of CHP expands from segment KL to segment KO. Therefore, the coordinated operation of CCS-P2G-CHP effectively enhances the power generation flexibility of the CHP unit and weakens its inherent strong electro-thermal coupling characteristics.

[0171] Substituting formula (7) into (10), we can derive formula (14) to obtain the upper and lower limits of P2G gas production power.

[0172] (14)

[0173] Substituting equations (7), (8), and (9) into equation (12), we can obtain the power supply, gas production, and heating power relationship of the CCS-P2G-CHP joint operation model as shown in equation (15).

[0174] (15)

[0175] Integrated demand response has become an important alternative to the traditional energy system operation paradigm. In the traditional model, user-side loads are usually considered rigid, and the system mainly meets electricity demand by adjusting the output of the generation side. However, this approach not only makes it difficult to maintain the real-time energy balance of the power grid but may also jeopardize the safe and stable operation of the system. Against this backdrop, demand-side resources have shown great potential. Data shows that during typical peak electricity consumption periods for urban residents in China, the available load resources on the demand side account for approximately 15% to 20% of the total grid load. Therefore, guiding residents to change their energy consumption patterns and incentivizing their active participation in system scheduling is crucial. By fully exploring and utilizing the flexible load resources on the demand side, refined coordination between supply and demand can be achieved, thereby ensuring the safe, efficient, and stable operation of the system. Based on the above analysis, this invention takes into account the coupling characteristics of the electric-thermal multi-energy system, models the demand response of electric and thermal loads separately, and integrates them into an optimized scheduling model. This model is based on load classification, mainly considering transferable and reducible loads, realizing the horizontal shift of multiple loads in the time dimension and the vertical substitution in the energy dimension. On this basis, an integrated demand response model is established according to the load response period.

[0176] Transferable load model:

[0177] Transferable load refers to load that can be adjusted according to a preset plan, while maintaining a constant total electricity consumption within the scheduling cycle; its characteristic is that it is not limited to a specific time period, and the scheduling cycle of this invention is set to 1 hour. Based on transferable load... The load distribution vector before scheduling is represented as:

[0178] (16)

[0179] In the formula: k is the electrical or thermal load; when k is e, it represents the electrical load; when k is h, it represents the thermal load. For portable electrical or thermal loads, t s Let t be the start time. d For duration.

[0180] The movable time interval is [t] sh- , t sh+ Because it is necessary to consider the overall translation constraints of the load, it is necessary to consider... The start time and duration are represented by 0-1 variables. express During a certain period of time The translation state, when When =1, it means from The period begins; when =0 indicates If the load does not shift, then the set of initial time periods is [t]. sh- ,t sh+ -t d +1]. If =t s This indicates that the load has not changed; if [t sh- , t sh+ -t d +1] and =t s ,express From the start time t s Translate to the starting time of The power distribution vector is:

[0181] (17)

[0182] The compensation cost incurred after a user migration can be expressed as:

[0183] (18)

[0184] In the formula: To compensate for the cost of transferable load, The unit price is for transferable load compensation. This is the sum of transferable loads.

[0185] Load reduction model:

[0186] Unlike transferable loads, which do not change the overall electricity consumption characteristics of a user, load shedding directly reduces the user's electricity consumption. (Introducing 0-1 variables) Characterizes load reduction During the period Reduction state: when =0 indicates During the period Not reduced; when When =1, it means From time period The load was reduced; accordingly, the load participated in the subsequent scheduling period. The power is expressed as:

[0187] (19)

[0188] In the formula: for Load reduction factor for the time period [0,1]; Before load reduction can participate in scheduling Power during a given time period.

[0189] The compensation to users after the reduction is as follows:

[0190] (20)

[0191] In the formula: In order to reduce load compensation costs, This is to reduce the unit price of load compensation.

[0192] The economic dispatch objective of integrated energy microgrids is to minimize the total system operating cost by formulating optimal output plans for each device, while ensuring the stable operation of cogeneration units, distributed power sources, and other equipment. To achieve this objective and fully utilize controllable resources, the dispatch objective function of the i-th microgrid is expressed as follows:

[0193] (twenty one)

[0194] (twenty two)

[0195] (twenty three)

[0196] In the formula: This includes the controllable power generation cost of the i-th microgrid system, the operating cost of energy storage devices, the external interaction cost, the carbon trading and green certificate trading cost, and the demand response cost. The network transmission cost incurred by the power interaction between the i-th micronet and other micronets. For network transmission parameters, This represents the power of interaction between the i-th microgrid and other microgrids. A positive value indicates the output power, and a negative value indicates the input power.

[0197] This invention studies multiple interconnected microgrids forming a comprehensive energy multi-microgrid system, where adjacent microgrids exchange information and power. This system minimizes operating costs and improves energy efficiency by formulating an optimal power dispatch scheme. The objective function for the optimal dispatch of this comprehensive energy multi-microgrid system (composed of N microgrids) is expressed as follows:

[0198] (twenty four)

[0199] Operating costs of cogeneration units containing carbon capture and power-to-gas conversion equipment for:

[0200] (25)

[0201] In the formula: , , These are the operating cost coefficients and constants for combined heat and power units, respectively. , These are the operating cost coefficients for carbon capture and power-to-gas conversion equipment, respectively, and T is the total scheduling period.

[0202] The operation and maintenance cost of energy storage devices is:

[0203] (26)

[0204] In the formula: The operation and maintenance cost of the i-th microgrid energy storage device is... and Let i be the charging and discharging power of the i-th microgrid energy storage device. This represents the operation and maintenance cost coefficient for energy storage devices.

[0205] External interaction costs of integrated energy microgrids The related costs, including the electricity purchase and sale costs from the power grid and the gas purchase costs, are expressed as follows:

[0206] (27)

[0207] In the formula: , The electricity purchase and sale prices for time period t are respectively. The purchase price of natural gas. , For the purchase and sale of electrical power, Let represent the gas purchase volume of the i-th microgrid CHP system during time period t.

[0208] The carbon trading model is as follows:

[0209] Carbon emission quotas for integrated energy microgrids Represented as:

[0210] (28)

[0211] In the formula: Let i be the renewable energy generation capacity of the i-th microgrid during time period t. , These are the carbon emission allowances for each unit of electricity produced by the microgrid and the carbon emission allowances for each unit of electricity purchased.

[0212] Actual carbon emissions of integrated energy microgrids :

[0213] (29)

[0214] In the formula: , The carbon emission coefficient of a combined heat and power (CHP) unit. The carbon emission coefficient of a gas-fired boiler. The carbon dioxide emission factor for the amount of electricity purchased. The amount of carbon dioxide reduced by the system after the introduction of carbon capture and electro-gas conversion equipment;

[0215] The computational cost reflecting the advantages of tiered carbon trading is expressed as follows:

[0216] (30)

[0217] In the formula: Let i be the carbon trading cost of the i-th microgrid. This refers to the carbon trading price on the market; The length of the carbon emission range; , These are the penalty coefficient and the reward coefficient, respectively.

[0218] Demand response cost:

[0219] (31)

[0220] In the formula: , These are the costs of relocatable and reducible electrical load compensation. It is a compensation cost that can be reduced due to heat load.

[0221] The green certificate trading model is as follows:

[0222] (32)

[0223] (33)

[0224] (34)

[0225] In the formula: The green certificate transaction cost for the i-th micronet is... The price at which green certificates are traded; To punish prices; The proportion of renewable energy generation quota; This is the penalty coefficient; This refers to the proportion of actual renewable energy in the total electricity generated by grid connection; This refers to the total electricity generated by power generation companies and fed into the grid.

[0226] Balance and Constraint

[0227] Electric power balance constraints:

[0228] (35)

[0229] Thermal power balance constraint:

[0230] (36)

[0231] Gas power balance constraints:

[0232] (37)

[0233] CCS-P2G-CHP power upper and lower limits constraints:

[0234]

[0235] (38)

[0236] Renewable energy power constraints:

[0237] (39)

[0238] In the formula: Let be the predicted renewable energy power of the i-th microgrid during time period t.

[0239] Constraints of energy storage devices:

[0240] (40)

[0241] Green certificate quota constraints:

[0242] (41)

[0243] Demand response constraints:

[0244] (42)

[0245] Power interaction constraints between microgrid and main grid:

[0246] (43)

[0247] The Alternating Direction Multiplier Method (ADMM) is a widely used algorithm for solving separable convex optimization problems. Due to its simplicity, efficiency, robustness, and good convergence properties, this algorithm is particularly effective in large-scale distributed computing and optimization tasks.

[0248] The standard form of ADMM is:

[0249] (44)

[0250] In the formula: , , , , The objective function is decomposed into two parts: and Both are about variables and A convex function, when the function yes ,function yes When the function is convex, the algorithm guarantees convergence and finds the optimal solution. Because the function... and The value can be 1000. Its characteristics enable it to characterize specific optimization objectives and equality or inequality constraints: if a variable satisfies the constraint, the corresponding function takes the value 0; when a variable does not satisfy the constraint, the corresponding function takes the value... .

[0251] The iterative process of the ADMM algorithm is carried out by solving subproblems sequentially. In each iteration, the solution of the previous subproblem is substituted into the subsequent subproblems for optimization, and the Lagrange multipliers are updated after all subproblems have been iterated. Based on formula (24) and the basic principle of ADMM, its distributed optimization iterative process is described by formulas (45) and (46).

[0252] (45)

[0253] (46)

[0254] (47)

[0255] In the formula: Primarily used as a (0-1) variable to characterize the load scheduling status. It is the penalty coefficient. For the first The next Lagrange multiplier, For the number of iterations, For the first The interaction power between microgrids and the power grid.

[0256] The power of each power generation device, energy storage device, and interaction between each microgrid and the grid can be obtained from equation (45). Based on the above results, the microgrid energy management system can update the transmission power between adjacent microgrids and between the microgrid and the grid using equation (46), and then adjust the magnitude of the expected interaction power through continuous iterative updates. This expected interaction power will be used as a Lagrange multiplier and iteratively updated according to formula (47). This solution process does not require global information exchange, and there is no need for information reporting between microgrids, which significantly reduces communication costs and ensures the privacy of the operating data of each microgrid. According to the principle of ADMM algorithm, the original residual is used. and dual residuals As the basis for convergence, their convergence criteria are expressed as follows:

[0257] (48)

[0258] (49)

[0259] In the formula: and It is the expected convergence error of the parameters at the end of the iteration.

[0260] like Figure 3 As shown, the overall specific process of the multi-micronet distributed scheduling algorithm based on ADMM proposed in this invention is as follows:

[0261] (1) Input the operating parameters of the equipment, including the cogeneration unit, carbon capture system, power-to-gas device and energy storage equipment, and input the output data of each microgrid distributed power source and the corresponding load data, and initialize the original data such as Lagrange multipliers and penalty coefficients.

[0262] (2) The microgrid energy management system analyzes the past data uploaded by each microgrid and formulates a preliminary scheduling plan for the microgrid.

[0263] (3) Each microgrid exchanges its adjustable power and load demand information through the energy management system. The energy management system obtains the optimal scheduling strategy and expected interactive power for each microgrid through distributed solution iteration. Since the microgrids only exchange information related to power purchase and sale plans, they do not need to share specific data on internal power generation output, thus ensuring the internal privacy of each microgrid.

[0264] (4) The MGEMS of each microgrid form an integrated energy multi-microgrid energy management system to meet the load demand of each microgrid and achieve the minimum operating cost and efficient use of energy of the integrated energy multi-microgrid.

[0265] This invention focuses on low-carbon dispatching research for a system consisting of three integrated energy microgrids. Microgrid 1 and Microgrid 3 are equipped with conventional combined heat and power (CHP) units, while Microgrid 2 employs a CHP unit model integrating carbon capture and power-to-gas (HPC) technologies. System parameters for each part of the integrated energy microgrid are shown in Table 1, and the electricity purchase and sale prices between the microgrid and the main grid, as well as the natural gas purchase prices, are shown in Table 2. The predicted power generation of renewable energy within the microgrid is as follows: Figure 4 As shown, the electrical and thermal load data are respectively shown in... Figure 5 and Figure 6 .

[0266] Table 1. Integrated Energy Microgrid Parameters

[0267]

[0268] Table 2. Electricity Purchase and Sale Prices and Natural Gas Prices

[0269]

[0270] The ADMM algorithm is used to solve the integrated energy microgrid in a distributed manner. The convergence of the multi-microgrid cost iteration is as follows: Figure 7 As shown.

[0271] Table 3. Comparison of Operation Results of Integrated Energy Multi-Microgrid System under Different Schemes

[0272]

[0273] This invention employs the ADMM algorithm for distributed solution of integrated energy multi-microgrid systems. The iterative convergence process of the total cost of the multi-microgrid systems is as follows: Figure 7 As shown, the algorithm converged after 69 iterations, with a total computation time of 390 seconds. During the iteration process, the integrated energy microgrid system continuously updates the multiplier parameters and gradually increases the penalty term to explore new optimal solutions, aiming to minimize operating costs. The combination of iterative process and coordinated computation jointly ensured the convergence of the algorithm. The results show that the distributed optimization algorithm based on the alternating multiplier method proposed in this invention not only has excellent convergence performance and computational efficiency, but also effectively maintains data privacy, meeting the application requirements of day-ahead optimization scheduling.

[0274] The optimized scheduling results of the output, electrical power, and thermal power balance of each integrated energy microgrid are as follows: Figure 8 and Figure 9 As shown. By Figure 8 It can be seen that the cogeneration units of the three microgrids operated continuously throughout the entire daytime dispatch cycle. Figure 8(a) shows that microgrid 1 has a significantly higher wind power output, and the system balances its power deficit by exchanging power with other microgrids. Specifically, during the periods of 01:00–08:00 and 21:00–24:00, microgrid 1 operates in a multi-source power supply mode, supplying power to the other two microgrids to generate revenue; conversely, during the period of 10:00–15:00, microgrid 1's own power generation resources cannot meet the load demand, and it needs to exchange power with other microgrids to ensure power supply during this period.

[0275] like Figure 8 As shown in (b), photovoltaic power generation in microgrid 2 is concentrated between 07:00 and 17:00, but its output level is relatively low. Due to the configuration of carbon capture and power-to-gas (HPC) systems, the cogeneration units of this microgrid increase their power generation output between 01:00 and 09:00 and between 18:00 and 24:00. Under these circumstances, microgrid 2 experiences a power deficit and needs to receive electricity from microgrid 1 and purchase electricity from the main grid. During the high-price period from 20:00 to 22:00, the energy storage system discharges to reduce external electricity purchases, thereby reducing operating costs. During other scheduling periods, only a small amount of electricity is exchanged between microgrid 2 and microgrid 1. The power load of microgrid 2 achieves system power balance and optimized scheduling through the synergistic effect of various methods, including photovoltaic power generation, HPC units, carbon capture and power-to-gas (HPC) consumption, energy storage charging and discharging, and external electricity purchases.

[0276] like Figure 8 As shown in (c), microgrid 3 operates during the same photovoltaic (PV) power generation period as microgrid 2, but generates more power. The system maintains power balance and does not experience curtailment. Since microgrid 3 is equipped with a conventional combined heat and power (CHP) unit, its power demand is lower than that of microgrid 2. This results in microgrid 3 needing to purchase a small amount of electricity from the main grid, while microgrid 1 also provides a small amount of power support to microgrid 3. The energy storage system discharges between 19:00 and 21:00. Furthermore, during periods of excess PV power generation, the system fully absorbs renewable energy and exchanges power with microgrid 1.

[0277] In integrated energy microgrid systems, the heat load is shared by combined heat and power (CHP) units and gas-fired boilers. When the CHP unit's heating output cannot meet the system's heat load demand, the gas-fired boiler will supplement the heating supply. However, the heating capacity of the gas-fired boiler is constrained by the gas turbine output and the fluctuation characteristics of the system's heat load.

[0278] Table 4. Operation results of integrated energy microgrids under different schemes

[0279]

[0280] The results of the current-day optimization phase for the power interaction between microgrids are as follows: Figure 10 As shown.

[0281] according toFigure 10 During the periods of 01:00-08:00 and 18:00-24:00, microgrid 1 experiences low load demand and surplus wind power output. Therefore, the system transfers the surplus electricity to microgrids 2 and 3. Conversely, during the period of 09:00-17:00, microgrid 1 experiences increased load demand and is in a power shortage state. Microgrids 2 and 3, with relatively low overall load demand, transfer their surplus electricity to microgrid 1 to meet its load requirements. Therefore, the proposed distributed energy management strategy enhances the system's adaptability and responsiveness to changes in energy demand by promoting energy complementarity and sharing among multiple microgrids, effectively improving the overall operating efficiency and economy of the microgrid system.

[0282] This invention designs four different schemes and verifies the rationality of the proposed model by analyzing the differences in microgrid scheduling results under each scheme. Table 4 summarizes the integrated energy microgrid system operation results corresponding to the four schemes.

[0283] As shown in Table 3, taking microgrid 1 as an example, compared with scheme 4, scheme 1 reduces its operating costs by 54.2%, carbon emissions by 40.9%, and the proportion of renewable energy generation by 45%. Integrated energy microgrids further optimize system dispatch by implementing integrated demand response and energy sharing strategies: these measures reduce the amount of electricity purchased from the external grid, increase the proportion of renewable energy consumption, and reduce carbon dioxide emissions, thereby bringing carbon benefits to the system, reducing operating costs, and ultimately improving the economic and environmental performance of the microgrid system.

[0284] Comparing Scheme 2 and Scheme 4, operating costs were reduced by 16.7%, carbon emissions by 7.6%, and the proportion of renewable energy generation by 1.2%. After the microgrid system incorporated energy sharing, the integrated energy microgrids achieved efficient energy utilization and regulation through energy sharing, increased renewable energy output, reduced energy procurement costs and carbon emissions, and further increased carbon revenue from the carbon market.

[0285] Comparing Schemes 3 and 4, operating costs are reduced by 42%, carbon emissions by 27.4%, and the proportion of renewable energy generation by 6.5%. The integration of a comprehensive load demand response mechanism into the integrated energy microgrid allows the demand side to adjust load demand in real time by deeply exploring and fully utilizing the system's optimization potential, effectively promoting a balance between energy supply and demand. This approach not only avoids energy waste and shortages but also significantly reduces the burden of system operation and scheduling, thereby significantly improving the stability and reliability of the entire energy system. This lays a solid foundation for ensuring the continuity and efficiency of energy supply.

[0286] Finally, through calculation, it was found that before participating in energy sharing, the carbon dioxide emissions of microgrid 2 before and after adding carbon capture and power-to-gas conversion devices were 37718.2 kg and 32398.9 kg, respectively; after participating in energy sharing, the carbon dioxide emissions before and after adding carbon capture and power-to-gas conversion devices were 24027.7 kg and 23351.2 kg, respectively. It can be seen that carbon capture and power-to-gas conversion equipment have a significant effect on system carbon emission reduction, can effectively reduce carbon dioxide emissions, promote low-carbon operation of the system, and help the power industry achieve dual carbon targets.

[0287] In summary, the effectiveness of the scheduling strategy proposed in this invention has been demonstrated. In the integrated energy multi-microgrid system, each microgrid significantly reduces carbon dioxide emissions, improves the absorption of renewable energy, reduces operating costs, and enhances the economic and environmental performance of the integrated energy multi-microgrid.

[0288] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A low-carbon dispatching method for microgrid groups based on green certificate incentives and demand response, characterized in that, The steps are as follows: Step 1: Electricity-to-gas (EPG) and carbon capture (C2G) devices were added to the microgrid to construct a comprehensive energy multi-microgrid model that integrates the CCS carbon capture system and P2G. Step 2: Taking into account the coupling characteristics of the electro-thermal multi-energy system, establish a comprehensive demand response mechanism for electrothermal load; Step 3: Establish carbon trading model and green certificate trading model; with the goal of minimizing the total system operating cost, carbon trading cost and green certificate trading cost, establish a comprehensive energy multi-microgrid low-carbon economic dispatch optimization model; Step 4: Construct a distributed scheduling model for integrated energy multi-microgrid based on ADMM, and iteratively optimize and solve the proposed model.

2. The low-carbon dispatching method for microgrid groups based on green certificate incentives and demand response as described in claim 1, characterized in that, The expression for the integrated energy multi-microgrid model established in step one is: ； in, The conversion efficiency of P2G electrical energy to gas energy. The conversion factor of the amount of electricity consumed by CCS to capture carbon dioxide. The carbon dioxide production intensity coefficient. This is the lower limit of the generating capacity of the CHP unit. This is the upper limit of the generating capacity of the CHP unit. For CHP units in Heating power at any given time This represents the heating capacity corresponding to the minimum power generation capacity of the CHP unit. The microgrid provides electrical power to the CHP cogeneration unit at time t. Let t be the gas production power of P2G. The electrothermal conversion coefficient of the CHP unit corresponding to the minimum output power. The electrothermal conversion coefficient of the CHP unit corresponding to the maximum output power. The linear supply slope for the power generation and heating power of the CHP unit.

3. The microgrid group low-carbon dispatching method based on green certificate incentives and demand response as described in claim 2, characterized in that, The electrothermal characteristic equation of the CHP unit is: ； The power generation constraint for CHP units is: ； The heating power constraint for CHP units is: ； in, This is the lower limit of the heating capacity of the CHP unit. These are the lower and upper limits of the heating capacity of the CHP unit.

4. The low-carbon dispatching method for microgrid groups based on green certificate incentives and demand response as described in claim 2 or 3, characterized in that, In the integrated energy multi-microgrid model, the electrical energy generated by the CHP unit is divided into three parts, represented as follows: ； in, Let be the electrical power consumed by the electro-gas P2G device at time t. Let t be the electrical power consumed by the carbon capture system (CCS). The relationship between the power generated by P2G natural gas and the power supplied by consuming CHP is as follows: ； The relationship between the carbon dioxide required for P2G to produce natural gas is: ； In the formula: Let t be the amount of carbon dioxide required to consume electrical energy by P2G; The conversion relationship between CCS carbon dioxide capture capacity and operating power consumption is as follows: ； In the formula: The conversion factor of the amount of CCS power consumed to capture carbon dioxide; The power constraints for CCS and P2G are as follows: ； In the formula: This represents the lower limit of the electrical power consumed by the CCS. This represents the upper limit of the electrical power consumed by the CCS. This represents the lower limit of the power consumption of P2G. This represents the upper limit of the electrical power consumed by P2G.

5. The low-carbon dispatching method for microgrid groups based on green certificate incentives and demand response as described in claim 2, characterized in that, The comprehensive demand response mechanism for the electric heating load includes a transferable load model and a load reduction model; Transferable load model: Transferable load refers to load that is adjusted according to a preset plan and whose total electricity consumption remains unchanged during the dispatch cycle; based on transferable load The load distribution vector before scheduling is represented as: ； In the formula: k is the electrical or thermal load; when k is e, it represents the electrical load; when k is h, it represents the thermal load. For portable electrical or thermal loads, t s Let t be the start time. d Duration; The movable time interval is [t] sh- , t sh+ ], The start time and duration are represented by 0-1 variables. express During a certain period of time The translation state, when When =1, it means from The period begins; when =0 indicates If the load does not shift, then the set of initial time periods is [t]. sh- , t sh+ -t d +1]; if =t s This indicates that the load has not changed; if [t sh- , t sh+ -t d +1] and =t s ,express From the start time t s Translation to the starting time is of The power distribution vector is: ； The compensation cost incurred after a user migration can be expressed as: ； In the formula: To compensate for the cost of transferable load, The unit price for transferable load compensation, The sum of transferable loads; Load reduction model: Introducing 0-1 variables Characterizes load reduction During the period Reduction state: when =0 indicates During the period Not reduced; when When =1, it means From time period The number of people involved was reduced. Therefore, this load participates in the subsequent scheduling period. The power is expressed as: ； In the formula: for Load reduction factor for the time period [0,1]; Before load reduction can participate in scheduling Power during a given time period; The compensation to users after the reduction is as follows: ； In the formula: In order to reduce load compensation costs, This is to reduce the unit price of load compensation.

6. The low-carbon dispatching method for microgrid groups based on green certificate incentives and demand response as described in claim 5, characterized in that, The carbon trading model is as follows: Carbon emission quotas for integrated energy microgrids Represented as: ； In the formula: Let i be the renewable energy generation capacity of the i-th microgrid during time period t. , These are the carbon emission allowances for each unit of electricity produced by the microgrid and the carbon emission allowances for each unit of electricity purchased. Actual carbon emissions of integrated energy microgrids : ； In the formula: , The carbon emission coefficient of a combined heat and power (CHP) unit. The carbon emission coefficient of a gas-fired boiler. The carbon dioxide emission factor for the purchased electricity volume. The amount of carbon dioxide reduced by the system after the introduction of carbon capture and electro-gas conversion equipment; The computational cost reflecting the advantages of tiered carbon trading is expressed as follows: ； In the formula: Let i be the carbon trading cost of the i-th microgrid. This refers to the carbon trading price on the market; The length of the carbon emission range; , These are the penalty coefficient and the reward coefficient, respectively. The green certificate trading model is as follows: ； ； ； In the formula: The green certificate transaction cost for the i-th micronet is... The price at which green certificates are traded; To punish prices; The proportion of renewable energy generation quota; This is the penalty coefficient; This refers to the proportion of actual renewable energy in the total electricity generated by grid connection; This refers to the total electricity generated by power generation companies and fed into the grid.

7. The low-carbon dispatching method for microgrid groups based on green certificate incentives and demand response as described in claim 6, characterized in that, The integrated energy multi-microgrid low-carbon economic dispatch optimization model is as follows: ； ； ； in, The operating costs of cogeneration units containing carbon capture and power-to-gas conversion equipment, The operation and maintenance cost of the i-th microgrid energy storage device is... To reduce the external interaction costs of integrated energy microgrids, Cost of responding to demand; The network transmission cost incurred by the power interaction between the i-th micronet and other micronets. For network transmission parameters, This represents the power of interaction between the i-th microgrid and other microgrids. A positive value indicates the output power, and a negative value indicates the input power.

8. The low-carbon dispatching method for microgrid groups based on green certificate incentives and demand response as described in claim 7, characterized in that, Operating costs of the combined heat and power unit containing carbon capture and power-to-gas conversion equipment for: ； In the formula: , , These are the operating cost coefficients and constants for combined heat and power units, respectively. , These are the operating cost coefficients for carbon capture and power-to-gas conversion equipment, respectively, and T is the total scheduling period. The operation and maintenance cost of energy storage devices is: ； In the formula: The operation and maintenance cost of the i-th microgrid energy storage device is... and Let i be the charging and discharging power of the i-th microgrid energy storage device. This represents the operation and maintenance cost coefficient for energy storage devices. External interaction costs of integrated energy microgrids The related costs, including the electricity purchase and sale costs from the power grid and the gas purchase costs, are expressed as follows: ； In the formula: , The electricity purchase and sale prices for time period t are respectively. The purchase price of natural gas. , For the purchase and sale of electrical power, Let be the gas purchase volume of the i-th microgrid CHP system during time period t; Demand response cost: ； In the formula: , These are the costs of relocatable and reducible electrical load compensation. It is a compensation cost that can be reduced due to heat load.

9. The low-carbon dispatching method for microgrid groups based on green certificate incentives and demand response as described in claim 8, characterized in that, The constraints of the integrated energy multi-microgrid low-carbon economic dispatch optimization model include: Electric power balance constraints: ； Thermal power balance constraint: ； Gas power balance constraints: ； CCS-P2G-CHP power upper and lower limits constraints: ； ； Renewable energy power constraints: ； In the formula: Let be the predicted renewable energy power of the i-th microgrid during time period t; Constraints of energy storage devices: ； Green certificate quota constraints: ； Demand response constraints: ； Power interaction constraints between microgrid and main grid: 。 10. The low-carbon dispatching method for microgrid groups based on green certificate incentives and demand response as described in claim 1, characterized in that, The iterative process of distributed optimization in ADMM is as follows: ； ； ； In the formula: , , , These are 0-1 variables used to characterize the load scheduling status. It is the penalty coefficient. For the first The next Lagrange multiplier, For the number of iterations, For the first The interaction power between microgrids and the power grid; Based on the principles of the ADMM algorithm, the original residuals are used. and dual residuals As the basis for convergence, their convergence criteria are expressed as follows: ； ； In the formula: and It is the expected convergence error of the parameters at the end of the iteration.

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