Low-carbon optimal dispatching method for multi-energy coupling active distribution network considering green carbon interaction mechanism

Abstract

The application discloses a kind of multi-energy coupling active distribution network low-carbon optimization scheduling method considering green carbon interaction mechanism, comprising: on the basis of analyzing based on distribution network electricity, hydrogen, gas, heat and other multi-energy flow, the operation framework of distribution network considering multi-energy coupling is constructed, the scheduling strategy and low-carbon attribute value of renewable energy consumption of power grid are analyzed;Fossil energy unit ammonia and hydrogen mixing model is constructed, combined with green certificate transaction and carbon trading mechanism, the low-carbon scheduling mechanism of distribution network considering multi-energy coupling is explored;The low-carbon economic dispatching model is established with the optimal economy of distribution network as the target, to optimize the typical day optimization scheduling cost of power grid.Simulation results show that the scheduling method has higher renewable energy utilization rate, lower economic cost and carbon emission.

Description

Technical Field

[0001] This invention relates to the field of low-carbon optimization scheduling technology for multi-energy coupled active distribution networks, and in particular to a low-carbon optimization scheduling method for multi-energy coupled active distribution networks that takes into account the green carbon interaction mechanism. Background Technology

[0002] Since the Industrial Revolution, the massive combustion of fossil fuels has triggered a series of global problems, including environmental pollution, energy shortages, and the greenhouse effect. Building a green and low-carbon energy system with high renewable energy penetration has become a core direction for the energy industry. Multi-energy coupled active distribution networks combine multiple energy flows such as electricity, heat, gas, and hydrogen, enabling cascaded energy utilization. Compared to traditional single-energy distribution networks, they are better suited to energy structures with high renewable energy penetration, becoming an important vehicle for solving the problem of renewable energy consumption.

[0003] Domestic and international scholars have conducted numerous studies on hydrogen energy utilization, co-firing technology, and market trading mechanisms for the optimized operation of integrated energy systems. However, existing technologies still have significant shortcomings: First, they have not explored in depth the synergistic optimization model of the entire hydrogen energy process, from hydrogen production to hydrogen storage to methanation to ammonia production to co-firing combustion, nor have they clarified the inherent principles of its economical and low-carbon operation. Second, they have overlooked the positive role of the interaction mechanism between green certificate trading and carbon trading in promoting the consumption of clean energy and carbon emission reduction in multi-energy coupled active distribution networks. Under the traditional model, the two trading markets operate independently and cannot achieve synergistic carbon reduction. Third, they have not combined the green certificate-carbon trading interaction mechanism with unit co-firing technology, making it difficult to fully leverage the dual role of technological means and market mechanisms to achieve deep low-carbon operation of fossil energy units.

[0004] Therefore, there is an urgent need to design an active distribution network low-carbon optimization scheduling method that integrates the full-process utilization of hydrogen energy, unit co-firing technology and green carbon interaction market mechanism, so as to improve the renewable energy absorption rate, reduce the operating cost and carbon emissions of the distribution network, and achieve synergistic optimization of economic efficiency and low carbon emissions. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a low-carbon optimization scheduling method for active distribution networks that takes into account green carbon interaction mechanisms and multi-energy coupling. This method enables the synergistic utilization of hydrogen energy across all stages, the deep integration of unit co-firing technology and green carbon interaction market mechanisms, improves the renewable energy absorption rate of active distribution networks, reduces operating costs and carbon emissions, and achieves low-carbon economic scheduling of multi-energy coupled active distribution networks.

[0006] To achieve the above objectives, the present invention provides the following solution.

[0007] A low-carbon optimization scheduling method for a multi-energy coupled active distribution network that takes into account the green carbon interaction mechanism. The method uses hydrogen energy as an intermediate medium to participate in the optimization scheduling and adopts a combination of electrolyzers and hydrogen storage tanks to ensure the energy supply and demand balance of the multi-energy coupled active distribution network. The method includes generating electricity from excess renewable energy sources on the response source side, using excess electricity to electrolyze water to produce hydrogen, and supplying hydrogen-consuming equipment to produce hydrogen for storage in the hydrogen storage tank. The hydrogen-consuming equipment absorbs hydrogen for the production of ammonia and natural gas. The produced ammonia is used as a raw material for coal-fired power units. The equipment also includes a carbon capture device to absorb carbon emissions from the unit and reduce carbon emissions. This reduces the amount of natural gas produced by the coal-fired methane reactor, thereby alleviating the gas supply pressure of the multi-energy coupled active distribution network. The low-carbon optimization scheduling method includes the following steps: S1. Analyze the characteristics of electricity, hydrogen, gas, and heat multi-energy flow and supply-demand relationship in the multi-energy coupled active distribution network, and construct the operation framework of the multi-energy coupled active distribution network, including the construction of the full-link unit operation model of the source-side energy supply unit, energy conversion and storage unit, and load side. S2. Construct green certificate trading and carbon trading models, and formulate a multi-energy coupled active distribution network collaborative low-carbon dispatch mechanism. When the multi-energy coupled active distribution network participates in the carbon trading market, the carbon emission reduction behind the green certificate is used as a medium to realize the synergistic interaction between the green certificate trading mechanism and the carbon emission trading mechanism to offset part of the carbon emissions. In addition, a tiered trading mechanism is introduced to apply incentive / penalty coefficients to green certificate trading and carbon trading. S3. Establish a low-carbon economic dispatch model with the goal of optimizing the economy of multi-energy coupled active distribution network, determine the cost composition and constraints of distribution network, and optimize the total dispatch cost of the distribution network for 24 time periods on a typical day.

[0008] Furthermore, the operational framework for constructing the multi-energy coupled active distribution network is specifically as follows: the power load of the multi-energy coupled active distribution network is jointly supplied by wind power, photovoltaic, coal-ammonia co-fired units, and gas turbines; the heat load is jointly supplied by gas turbines and gas boilers; the gas load is mainly supplied by external gas sources and methane reactors; the electrolyzer and hydrogen storage tank work together to consume excess renewable energy generation from the source side and electrolyze water to produce hydrogen, which is then supplied to hydrogen-consuming equipment such as ammonia production units, methane reactors, gas turbines, and gas boilers, with excess hydrogen stored in hydrogen storage tanks; the ammonia produced by the ammonia production unit is used as raw material to replace part of the coal in the coal-ammonia co-fired units, the natural gas produced by the methane reactor alleviates the gas supply pressure on the distribution network, and the gas turbines and gas boilers use a hydrogen-gas co-fired mode to replace part of the natural gas, thus achieving a clean replacement of the raw materials for fossil energy units.

[0009] For energy supply units on the source side of multi-energy coupled active distribution networks, the peak periods for wind and solar power generation often do not match the peak periods for electricity, heat, gas, and hydrogen demand in the multi-energy coupled active distribution network, leading to frequent renewable energy reductions. This situation can be mathematically represented as: (1) In the formula, and These are the actual power generation from wind power and solar power, respectively. and These are the predicted power generation from wind power and solar power, respectively. and These are the wind curtailment volumes for wind power and solar power, respectively.

[0010] In response to the serious pollution caused by traditional coal-fired power units, which makes it difficult to ensure the clean energy utilization and low-carbon operation of active distribution networks, the clean and low-carbon operation characteristics of ammonia can be utilized. Its calorific value is equivalent to that of coal. By co-firing ammonia with coal in coal-fired power units, clean energy utilization can be effectively achieved, promoting the low-carbon development of multi-energy coupled active distribution networks.

[0011] (2)

[0012] In the formula, a , b , c These are the coal consumption characteristic parameters of coal-fired power units; and These are the coal consumption and power generation of the coal-ammonia co-fired unit, respectively. This refers to the ammonia consumption of a coal-ammonia co-fired power unit. and These are the low calorific values ​​of ammonia and coal, respectively.

[0013] Besides promoting low-carbon operation of the power distribution network through coal-ammonia co-combustion in coal-fired power units, carbon emissions can also be reduced by installing carbon capture devices to absorb the unit's carbon emissions. This invention addresses the operational framework of a multi-energy coupled active distribution network, where the coal-fired power unit reduces carbon emissions by absorbing its own carbon emissions through a carbon capture device, and models this process as follows: (3) In the formula, , These are the actual power generation of the coal-ammonia co-fired unit and the consumption of the carbon capture device, respectively. , These are the stationary and operational costs of the carbon capture unit, respectively. Carbon emissions captured from coal-ammonia co-fired power plants; The carbon capture level of the device; Carbon emission coefficient per unit of coal burned; The energy consumption required for the device to capture a unit of carbon dioxide; This represents the maximum coal consumption of a coal-ammonia co-fired power unit. This represents the actual carbon emissions of a coal-ammonia co-fired power unit. However, in the actual operation of coal-ammonia co-fired power units, in order to ensure complete combustion of ammonia and coal, and the safe and economical operation of the unit, it is necessary to constrain the ammonia blending ratio, which is defined as: (4) (5) In the formula, This represents the maximum co-combustion ratio of ammonia in a coal-ammonia co-fired power unit.

[0014] Furthermore, the multi-energy coupled active distribution network described in this invention uses a single hydrogen gas supply method. Regarding gas procurement, considering the pressure of the gas supply pipeline, the limiting condition is as follows: (6) In the formula, For the amount of gas purchased; and These are the upper and lower limits for the amount of gas that can be purchased; and These represent the lower and upper limits of the gas purchase volume ramp-up.

[0015] In the energy conversion and storage unit, the electrolyzer can consume surplus electricity to produce hydrogen, which is then supplied to the methane reactor, ammonia production unit, gas turbine, and gas boiler.

[0016] The electrolyzer uses water as a raw material to produce green hydrogen, meeting the requirements of hydrogen-using equipment. The model is as follows: (7) In the formula, and These are the electrical energy input and hydrogen output of the electrolyzer, respectively. The energy conversion efficiency of the electrolytic cell; and These are the lower and upper limits of the input power to the electrolytic cell, respectively. and These are the lower and upper limits of the ramp-up power input to the electrolytic cell, respectively.

[0017] A methane reactor synthesizes natural gas using CO2 and hydrogen, which can reduce carbon emissions and effectively alleviate China's natural gas shortage. The reaction process is as follows: (8) From equation (8), it can be seen that the molar masses of CO2, H2, and CH4 in the Sabatier reaction satisfy a ratio of 1:4:1. Since the ratio of their reaction volumes is the same under the same pressure and temperature, the relationship between the three substances should satisfy: (9) In the formula, , and These represent the input amounts of hydrogen, carbon dioxide, and natural gas in the methane reactor, respectively. It is the density of carbon dioxide; is the loss coefficient of the Sabatier reaction.

[0018] Similar to the Sabatier reaction, the molar masses of N2, H2 and NH4 in the ammonia production unit satisfy the volume ratio of 1:3:2, as shown in equation (10).

[0019] (10) (11) In the formula, , and These are the hydrogen input, nitrogen input, and ammonia output of the ammonia production unit; This is the loss coefficient for the ammonia production reaction.

[0020] Since the Sabatier and Harper reactions are exothermic, the heat released by the methane reactor and ammonia production unit can be recovered and incorporated into the heat flow of the multi-energy coupled active distribution network, as shown in the following model: (12) In the formula, and The thermal energy provided to the methane reactor and the ammonia production unit, respectively; and These are the heat release coefficients of the methane reactor and the ammonia production unit, respectively. and These represent the energy required to produce a unit of ammonia and the heat energy released from natural gas, respectively.

[0021] In multi-energy coupled active distribution networks, in order to ensure the safe and stable operation of the distribution network and the space for renewable energy consumption, this invention introduces a hydrogen storage tank to achieve smooth regulation of hydrogen production and utilization in multi-energy coupled active distribution networks.

[0022] (13)

[0023] In the formula, The remaining hydrogen in the hydrogen storage tank; and These refer to filling and releasing hydrogen from the hydrogen storage tank, respectively. and These are the hydrogen filling efficiency and hydrogen discharging efficiency of the hydrogen storage tank, respectively. Gas turbines use natural gas and hydrogen as their primary combustion fuels, enabling combined heat and power generation. The model for this is: (14) In the formula, and These are the power generation and output of the gas turbine, respectively. and These are the electrical conversion efficiency and thermal conversion efficiency of the gas turbine, respectively. This represents the input power of the gas turbine. and These are hydrogen and natural gas, respectively, input into the gas turbine; and These are the upper and lower limits of the gas turbine input power, respectively; and These are the upper and lower limits of the gas turbine ramp constraint, respectively; The hydrogen mixing ratio for the gas turbine; and These are the low calorific values ​​of hydrogen and natural gas, respectively. This represents the upper limit for the hydrogen mixture ratio in gas turbines. Similar to the gas turbine model, the gas boiler uses natural gas and hydrogen as raw materials to provide heat, and combines with the gas turbine for heating, enriching the heating flexibility of the multi-energy coupled active distribution network.

[0024] (15)

[0025] In the formula, It outputs heat for the gas-fired boiler; For the heat conversion efficiency of gas-fired boilers; Input power to the gas-fired boiler; and These are hydrogen and gas, respectively, input into the gas-fired boiler; and These are the upper and lower limits of the power input for the gas-fired boiler; and These are the upper and lower limits of the ramp constraint for gas-fired boilers, respectively. The hydrogen mixing ratio for gas-fired boilers; This is the upper limit for the hydrogen ratio in a gas-fired boiler.

[0026] Furthermore, in step S2, a green certificate trading and carbon trading model is constructed to explore a multi-energy coupled active distribution network collaborative low-carbon dispatch mechanism, specifically as follows: Traditional green certificate trading and carbon emissions trading markets operate independently, with their respective aims to promote green electricity consumption and limit carbon emissions.

[0027] The green certificate trading mechanism relies on a green electricity certificate trading platform. If the number of green certificates obtained exceeds the required quota, multi-energy coupled active distribution networks can sell the excess green certificates to generate profit. Otherwise, multi-energy coupled active distribution networks must purchase additional green certificates to meet assessment requirements. Green certificate trading costs... It can be represented as: (16) (17) In the formula, and These are the actual green certificates obtained by multi-energy coupled active distribution networks and the required green certificates; Price of green certificate for unit; For green certificate quota coefficients; For multi-energy coupled active distribution network loads; This is the conversion factor between renewable energy generation and green certificates. One green certificate corresponds to 1 MW·h of renewable energy generation.

[0028] With the official launch of the carbon emissions trading market, carbon emission rights are treated as tradable commodities, and carbon emission allowances are provided free of charge to multi-energy coupled active distribution networks, using economic means to control energy conservation and emission reduction in these networks. Carbon trading costs. It can be represented as: (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) In the formula, and These are the actual carbon emissions and quotas of the power distribution network, respectively. The base price for carbon trading; , and These are the actual carbon emissions of gas turbines, gas boilers, and coal-ammonia co-fired units, respectively. , and These are carbon emission quotas for gas turbines, gas-fired boilers, and coal-ammonia co-fired units, respectively. Carbon emission quota per unit of gas; The energy coefficient for electro-thermal conversion; and These are the carbon emission allowance coefficients for units of heating and electricity supply, respectively.

[0029] However, according to the full life-cycle carbon emission assessment of various power generation types in China's core life-cycle database, 1 kWh of renewable energy generation will reduce carbon emissions by 0.96 kg. Therefore, when multi-energy coupled active distribution networks participate in the carbon trading market, the carbon emission reductions behind green certificates can be used as a medium to achieve synergistic interaction between the green certificate trading mechanism and the carbon emission trading mechanism, thereby offsetting some carbon emissions. The amount of carbon emissions offset... It can be represented as (26) In the formula, The number of green certificates obtained for participating in green carbon interaction, Carbon emission reduction factor representing the amount of renewable energy generated by a unit.

[0030] Finally, the number of tradable green certificates and carbon emission allowances for the distribution network are shown in equations (27-28).

[0031] (27) (28) Compared to traditional fixed-price trading mechanisms, tiered trading mechanisms divide trading into multiple ranges based on trading volume, and can introduce penalty or incentive coefficients based on trading volume. , Introducing a penalty coefficient, multi-energy coupled active distribution networks require the purchase of carbon emission allowances and green certificates, which has a good restraining effect on high-carbon-emitting units. Conversely, when , By introducing an incentive coefficient, the more carbon emission allowances or green certificates sold, the higher the transaction price and the greater the benefits.

[0032] The tiered carbon trading costs for active distribution networks are as follows: (29) In the formula, and These are the compensation coefficient and penalty coefficient of the transaction mechanism, respectively; This refers to the interval length for carbon emission allowances.

[0033] Similar to the tiered carbon trading costs, the tiered green certificate trading costs for active distribution networks are as follows: (30) In the formula, The interval length for trading green certificates. and These are the carbon trading base price and the green certificate price. and These are carbon emission trading quotas and the number and volume of green certificates traded.

[0034] The method provided by this invention, through tiered carbon emission trading and green certificate trading, forces distribution networks with high carbon emissions and poor renewable energy absorption capacity to incur higher costs. This effectively encourages distribution networks to reduce carbon emissions, promotes renewable energy consumption, and shifts towards a low-carbon and environmentally friendly development model. For distribution networks with low carbon emissions and high renewable energy absorption capacity, it can improve the operational economy of the distribution network and further stimulate the vitality of the green certificate market and carbon trading market.

[0035] In summary, studying the coordination and connection between energy utilization and market mechanisms can better leverage the clean characteristics of renewable energy and constrain carbon emissions from power distribution networks. Regarding energy utilization, to improve the cleanliness of energy use, the primary considerations are coal-ammonia co-firing and gas-hydrogen co-firing in the operation of carbon-emitting units, achieving clean fuel substitution for these units. In terms of market transactions, using the carbon emission reductions behind green certificates as a medium to construct a green-carbon interaction mechanism can further leverage the low-carbon attributes of green certificates, promoting renewable energy consumption and low-carbon emissions.

[0036] Furthermore, in step 3, a low-carbon economic dispatch model was established with the goal of optimizing the economy of a multi-energy coupled active distribution network, in order to optimize the total cost of the distribution network. Specifically: This study aims to minimize the total operating cost of a multi-energy coupled active distribution network and addresses the low-carbon optimization scheduling problem within a typical day (divided into 24 time periods). Total operating cost Mainly includes coal consumption costs Equipment maintenance costs Gas purchase cost Carbon capture integration cost Carbon trading costs Green certificate transaction costs and renewable energy curtailment costs .

[0037] (31) (32) (33) (34) (35) (36) (37) In the formula, Cost per unit weight of coal; For equipment Operating power; Unit cost of natural gas; Cost per unit of carbon capture; Cost per unit of renewable energy curtailment.

[0038] The constraints of multi-energy coupled active distribution networks mainly include the supply and demand constraints of various energy sources such as electricity, heat, gas, and hydrogen, as well as the constraints of unit operation and energy storage equipment.

[0039] 1) Multi-energy supply and demand balance constraints (38) In the formula, and For multi-energy coupled active distribution network gas load and gas load; and These are the energy coefficients for hydrogen and natural gas, respectively.

[0040] 2) Operating constraints of coal-ammonia co-fired power units (39) In the formula, and These are the upper and lower limits of the power output of the coal-ammonia co-fired unit; and These are the upper and lower limits for the power output ramp-up of the coal-ammonia co-fired unit.

[0041] 3) Operating constraints of hydrogen storage tanks (40) In the formula, and These are the upper and lower limits of the capacity in the hydrogen storage tank; and These are the upper and lower limits for filling and releasing hydrogen from the hydrogen storage tank, respectively. and These represent the charging and discharging states of the hydrogen storage tank. Both cannot be 1 simultaneously, indicating that the hydrogen storage tank cannot be charged or discharged at the same time. and These represent the remaining hydrogen in the hydrogen storage tank at the beginning and end of the scheduling period, respectively. To ensure the continuity of the hydrogen storage tank scheduling cycle, these two values ​​should be equal. Attached Figure Description

[0042] Figure 1 A schematic diagram of the electric-heat-gas energy flow in an active distribution network for introducing multi-energy coupling devices; Figure 2 This is a schematic diagram of the energy flow in a multi-energy conversion and storage unit. Figure 3 Forecast information for wind power and solar power generation; Figure 4 This provides forecast information for multi-energy loads including electricity, heat, and gas. Figure 5The results of power dispatch under different schemes; Figure 6 Results of gas energy dispatch under different schemes; Figure 7 Results of thermal energy dispatch under different schemes; Figure 8 The hydrogen energy dispatch results are under scheme 6; Figure 9 The results of multi-energy coupled active distribution network operation under different renewable energy output coefficients; Figure 10 The results represent the operation of a multi-energy coupled active distribution network under the upper limit of mixed combustion of different units. Detailed Implementation

[0043] This invention first analyzes the multi-energy flows (electricity, hydrogen, gas, heat, etc.) within a multi-energy coupled active distribution network, then constructs an operational framework for the multi-energy coupled active distribution network, and analyzes the dispatch strategies for consuming renewable energy and the value of its low-carbon attributes. Specifically: Studying the coordination and connection between different energy sources can better leverage the clean characteristics of renewable energy and constrain carbon emissions from multi-energy coupled active distribution networks. In multi-energy coupled active distribution networks, the electrical load is provided by wind power, photovoltaic power, coal-ammonia co-fired units, and gas turbines. The heat load is provided by gas turbines and gas boilers. The gas load is mainly supplied by gas sources and methane reactors. On the one hand, for the energy supply and demand balance of multi-energy coupled active distribution networks, the combination of electrolyzers and hydrogen storage tanks can respond to excess renewable energy generation on the source side, using the excess electricity to electrolyze water to produce hydrogen, supplying hydrogen-consuming equipment; and storing excess hydrogen in hydrogen storage tanks to expand the consumption space of renewable energy. On the other hand, hydrogen-consuming equipment such as ammonia production units and methane reactors absorb hydrogen for ammonia and natural gas production. Ammonia can be used as a feedstock for coal-fired units, and reducing some coal consumption and natural gas production from methane reactors can effectively alleviate the gas supply pressure of multi-energy coupled active distribution networks. Simultaneously, hydrogen co-fired combustion can be applied to gas turbine units to reduce natural gas consumption and achieve cleaner feedstock consumption for carbon-emitting units.

[0044] This invention provides an implementation example to verify the rationality of the low-carbon optimized scheduling method described herein, in order to Figure 1 The multi-energy coupled active distribution network shown is used as a test object to verify the effectiveness of the model and solution algorithm proposed in this invention. This multi-energy coupled active distribution network can purchase gas energy from an external gas source, and the demand side includes electricity, heat, and gas loads. A schematic diagram of the multi-energy conversion and storage energy flow in the multi-energy coupled active distribution network is shown below. Figure 2 As shown. Renewable energy forecast data, primarily based on wind and solar power, are as follows: Figure 3 As shown. Typical electric, heat, and gas loads are as follows: Figure 4 As shown.

[0045] To verify the advantages of the proposed optimization method, the following six scheduling schemes were set up, of which scheme 6 is the scheduling method proposed in this invention.

[0046] Option 1: Conventional multi-energy coupled active distribution network optimization and dispatch model, excluding unit hybrid combustion technology and hydrogen utilization;

[0047] Option 2: Consider a multi-energy coupled active distribution network optimization scheduling model that integrates hydrogen production, hydrogen storage, and methanation.

[0048] Option 3: A multi-energy coupled active distribution network optimization scheduling model considering the unit's hybrid combustion technology.

[0049] Option 4: Based on Option 3, introduce a green certificate trading mechanism;

[0050] Option 5: Based on Option 4, introduce a green-carbon interaction mechanism;

[0051] Option 6: Based on Option 5, introduce a tiered trading mechanism and apply it to carbon trading and green certificate trading.

[0052] 1) Operation results of multi-energy coupled active distribution networks under different schemes

[0053] Table 1 shows the specific operational results of the multi-energy coupled active distribution network under different schemes.

[0054] Table 1. Operation results of multi-energy coupled active distribution networks under different schemes

[0055] As shown in Table 2, Scheme 1, compared to other schemes, does not consider technologies such as hydrogen utilization, unit co-firing, and green certificate trading. The multi-energy coupled active distribution network has the highest total cost and carbon emissions, and the worst renewable energy consumption rate. Scheme 2, based on the hydrogen production-storage-methanation process, expands the space for renewable energy consumption while alleviating the energy supply pressure from external gas sources, reducing the cost of energy curtailment and gas purchase for the multi-energy coupled active distribution network, resulting in a total cost reduction of ¥1118.1, or 11.11%.

[0056] Compared to Option 2, Option 3 reduces the total cost by 498.5 yuan, with carbon emission and renewable energy reduction costs decreasing by 19.77% and 12.70%, respectively. This is primarily because the introduction of co-firing technology allows multi-energy coupled active distribution networks to reduce carbon emissions by replacing some gas consumption in gas turbines and gas boilers, and coal consumption in coal-ammonia co-firing units, through hydrogen-ammonia blending. Simultaneously, mixing hydrogen and ammonia into the units expands the hydrogen demand of multi-energy coupled active distribution networks, further increasing the potential for renewable energy consumption.

[0057] Compared to Option 3, Option 4 introduces a green certificate trading mechanism. This multi-energy coupled active distribution network can generate profits by selling tradable green certificates. The total grid cost and further reduction costs were reduced by 9.99% and 32.70%, respectively, validating the effectiveness of the green certificate trading mechanism in promoting the consumption and economic efficiency of renewable energy in multi-energy coupled active distribution networks from a market perspective.

[0058] Compared to Scheme 4, Scheme 5 introduces a green-carbon interaction mechanism, taking into account the carbon emission reduction behind green certificates, which in turn affects the carbon emission quota of multi-energy coupled active distribution network trading. This guides the multi-energy coupled active distribution network to balance the revenue of the green certificate trading market and the carbon trading market, and can further enhance the incentive of multi-energy coupled active distribution networks to consume renewable energy and reduce carbon emissions.

[0059] Compared with Scheme 5, Scheme 6 introduces a tiered trading mechanism, which imposes stronger carbon emission and higher rewards on renewable energy consumption and carbon emissions, reducing carbon emissions of multi-energy coupled active distribution networks and renewable energy reduction costs by 19.84% and 33.82% respectively, thus achieving the goals of carbon emission reduction and clean energy utilization.

[0060] In summary, by comprehensively considering hydrogen utilization, unit hybrid combustion technology, and the interaction mechanism of green certificates and carbon emission rights, carbon emissions and renewable energy reduction can be reduced while optimizing the economics of multi-energy coupled active distribution networks, resulting in significant multi-faceted benefits.

[0061] 2) Multi-energy supply and demand balance results of multi-energy coupled active distribution networks under different schemes

[0062] In the proposed multi-energy coupled active distribution network operation framework, hydrogen utilization, unit co-combustion technology, and green-carbon interaction mechanisms are comprehensively considered for optimized scheduling. To further clarify the promoting effect of the proposed method on the multi-energy supply and demand balance of the multi-energy coupled active distribution network, the scheduling results under Scheme 1 and Scheme 6 are analyzed, and the results are as follows: Figure 5-8 As shown

[0063] analyze Figure 5The results of power supply and demand balance scheduling under different schemes show that the power load is mainly supplied by wind power, photovoltaic, coal-fired units, and gas turbine power generation. Since the optimized scheduling model proposed in this invention aims for optimal economic efficiency of the multi-energy coupled active distribution network, expanding the space for renewable energy consumption can reduce the cost of renewable energy curtailment and the carbon emissions of generating units. Therefore, after introducing hydrogen utilization in Scheme 6, the remaining renewable energy during the 23:00-2:00 period can be converted into hydrogen through an electrolyzer, and combined with a hydrogen storage tank, the spatial and temporal transfer of electricity can be achieved, promoting the consumption of renewable energy. Furthermore, hydrogen can be used to produce ammonia and natural gas, and can serve as a raw material for coal-ammonia co-fired units, gas turbines, and gas boilers, thereby reducing the use of fossil fuels and carbon emissions in the multi-energy coupled active distribution network.

[0064] like Figure 6 As shown, the natural gas sources for the multi-energy coupled active distribution network mainly include methane reactors and gas sources. Compared with Scheme 1, Scheme 6 introduces a methane reactor and hydrogen co-combustion. The multiple utilization of hydrogen alleviates the pressure on gas supply and demand balance to a certain extent, and the demand for gas sources in the multi-energy coupled active distribution network is reduced accordingly. During high renewable energy generation, surplus renewable energy hydrogen production combined with methane reactor to produce natural gas can directly participate in the natural gas flow of the multi-energy coupled active distribution network, reducing the energy supply pressure of the multi-energy coupled active distribution network.

[0065] like Figure 7 As shown, the heat supply and demand balance of the multi-energy coupled active distribution network during each scheduling period is achieved. In Scheme 1, the heat load is mainly provided by gas turbines and gas boilers. In Scheme 6, after the introduction of a methane reactor and ammonia production unit, since the chemical reactions in Equations (8) and (10) are exothermic, the multi-energy coupled active distribution network can recover excess reaction heat and participate in the heat flow, effectively alleviating the heating pressure of gas turbines and gas boilers.

[0066] like Figure 8 According to the hydrogen scheduling results of Scheme 6, hydrogen production from the electrolyzer is mainly concentrated between 17:00 and 7:00, especially between 21:00 and 3:00, which coincides with peak wind power generation and off-peak electricity load. Furthermore, the hydrogen flow in Scheme 6 involves multiple directions. Firstly, it is absorbed by the methane reactor and combines with carbon dioxide to produce natural gas. Secondly, it is absorbed by the ammonia production unit to generate ammonia, which participates in the operation of the coal-fired unit, reducing the unit's coal-ammonia usage. Thirdly, it is injected into the gas turbine and gas boiler to achieve hydrogen-gas co-combustion.

[0067] 3) Operation results of multi-energy coupled active distribution networks under different renewable energy generation growth coefficients

[0068] With the advancement of low-carbon energy structure development, the penetration rate of renewable energy will continue to increase. To verify the applicability of the proposed method in different scenarios, it is necessary to analyze the changes in the operation results of multi-energy coupled active distribution networks under different renewable energy generation growth coefficients, such as... Figure 9 As shown.

[0069] Depend on Figure 9 It can be seen that as the regeneration growth coefficient increases from 1.0 to 1.6, the total cost, coal consumption cost, gas purchase cost, and carbon emissions of multi-energy coupled active distribution networks all show a downward trend. This is because with the increase in the number of regeneration cycles, the multi-energy coupled active distribution network's dependence on carbon emission units decreases, reducing the cost of gas purchase and coal consumption. Simultaneously, as the consumption of wind and solar power by the multi-energy coupled active distribution network increases, the revenue from green certificate trading continues to rise. When the regeneration growth coefficient increases from 1.6 to 2.0, the total cost of the multi-energy coupled active distribution network and the cost of renewable energy reduction show an upward trend. This is because, under the constraint of fixed multi-load demand, the multi-energy coupled active distribution network cannot consume too much renewable energy generation, leading to an increase in the cost of curtailment. Therefore, as the renewable energy generation growth coefficient increases, the total cost of the multi-energy coupled active distribution network exhibits a V-shaped change, indicating a trade-off between the economics of the multi-energy coupled active distribution network and renewable energy growth.

[0070] 4) Operation results of multi-energy coupled active distribution networks under different upper limits of mixed combustion

[0071] Hybrid combustion technology is key to reducing carbon emissions in multi-energy coupled active distribution networks and expanding the space for renewable energy consumption; however, the aforementioned operational results all limit the blending ratio of the units to 20%. To investigate the impact of the upper limit of unit blending combustion on multi-energy coupled active distribution networks, a sensitivity analysis of the upper limit of unit blending combustion was conducted, and the results are as follows: Figure 10 As shown in the figure. However, since Scheme 6 has already reached its upper limit in terms of renewable energy consumption rate under stationary renewable energy generation, a specific sensitivity analysis cannot be performed. Therefore, the renewable energy growth coefficient is set at 180% for verification.

[0072] Figure 10 Sensitivity analysis was conducted for different co-firing limits for generating units. It can be seen that as the co-firing limit increases, the total cost of the multi-energy coupled active distribution network gradually decreases, the renewable energy absorption rate increases, and carbon emissions decrease. This is because, on the one hand, as the co-firing limit increases, the demand for hydrogen in the multi-energy coupled active distribution network increases, correspondingly expanding the space for renewable energy absorption, thus increasing the absorption rate. On the other hand, the increase in the co-firing limit reduces the coal and gas consumption of the generating units in the multi-energy coupled active distribution network, resulting in lower coal and gas purchase costs, thereby reducing the total cost and carbon emissions of the multi-energy coupled active distribution network.

Claims

1. A low-carbon optimal scheduling method for a multi-energy coupling active power distribution network considering green carbon interaction mechanism, characterized in that, This method uses hydrogen energy as an intermediate medium to participate in optimized scheduling, and adopts a combination of electrolyzers and hydrogen storage tanks to ensure the energy supply and demand balance of the multi-energy coupled active distribution network. This includes using excess renewable energy on the response source side to generate electricity, using excess electricity to electrolyze water to produce hydrogen, and supplying hydrogen-consuming equipment to produce hydrogen for storage in the hydrogen storage tank. The hydrogen-consuming equipment absorbs hydrogen for the production of ammonia and natural gas. The produced ammonia is used as a raw material for coal-fired power units. The equipment also includes a carbon capture device to absorb carbon emissions from the unit and reduce carbon emissions. This reduces the amount of natural gas produced by the coal-fired methane reactor, thereby alleviating the gas supply pressure of the multi-energy coupled active distribution network. The low-carbon optimization scheduling method includes the following steps: S1. Analyze the characteristics of electricity, hydrogen, gas, and heat multi-energy flow and supply-demand relationship in the multi-energy coupled active distribution network, and construct the operation framework of the multi-energy coupled active distribution network, including the construction of the full-link unit operation model of the source-side energy supply unit, energy conversion and storage unit, and load side. S2. Construct green certificate trading and carbon trading models, and formulate a multi-energy coupled active distribution network collaborative low-carbon dispatch mechanism. When the multi-energy coupled active distribution network participates in the carbon trading market, the carbon emission reduction behind the green certificate is used as a medium to realize the synergistic interaction between the green certificate trading mechanism and the carbon emission trading mechanism to offset part of the carbon emissions. In addition, a tiered trading mechanism is introduced to apply incentive / penalty coefficients to green certificate trading and carbon trading. S3. To establish a low-carbon economic dispatch model with the goal of optimizing the economy of multi-energy coupled active distribution network, determine the cost structure and constraints of distribution network, and optimize the total dispatch cost of distribution network for 24 time periods on a typical day. 2.The low-carbon optimal dispatching method of active power distribution network considering green carbon interaction mechanism and multi-energy coupling according to claim 1, wherein, In the operational framework of a multi-energy coupled active distribution network, the coal-fired power units reduce carbon emissions by absorbing the unit's carbon emissions through carbon capture devices, which is modeled as follows: In the formula, , These are the actual power generation of the coal-ammonia co-fired unit and the consumption of the carbon capture device, respectively. , These are the stationary and operational costs of the carbon capture unit, respectively. Carbon emissions captured from coal-ammonia co-fired power plants; The carbon capture level of the device; Carbon emission coefficient per unit of coal burned; The energy consumption required for the device to capture a unit of carbon dioxide; This represents the maximum coal consumption of a coal-ammonia co-fired power unit. This represents the actual carbon emissions of a coal-ammonia co-fired power unit. For the above coal-fired unit model, to ensure complete combustion of ammonia and coal, and the safe and economical operation of the unit, the method imposes a constraint on the ammonia blending ratio, defined as: The maximum mixed combustion ratio of ammonia in the coal-ammonia mixed combustion unit.

3. The low-carbon optimization scheduling method for multi-energy coupled active distribution networks, as described in claim 1, is characterized in that... The multi-energy coupled active distribution network uses a single hydrogen gas supply method. Considering the pressure of the gas supply pipeline, the constraints for gas procurement are as follows: In the formula, For the amount of gas purchased; and These are the upper and lower limits for the amount of gas that can be purchased; and These are the lower and upper limits for the gas purchase volume ramp-up; In the energy conversion and storage unit, the electrolyzer can consume surplus electricity to produce hydrogen, which is then supplied to the methane reactor, ammonia production unit, gas turbine, and gas boiler. The electrolyzer uses water as a raw material to produce green hydrogen, and the produced hydrogen meets the requirements of hydrogen-using equipment. The model is as follows: In the formula, and These represent the electrical energy input and hydrogen output of the electrolyzer, respectively. The energy conversion efficiency of the electrolytic cell; and These are the lower and upper limits of the input power to the electrolytic cell, respectively. and These are the lower and upper limits of the ramp-up power input to the electrolytic cell, respectively; The methane reactor satisfies the Sabatier reaction relationship, and the ammonia production unit satisfies the Harper reaction. Both reactions are exothermic. The heat released by the methane reactor and the ammonia production unit is recovered and participates in the heat flow of the multi-energy coupled active distribution network. This operating scenario is modeled as follows: In the formula, and The heat energy provided to the methane reactor and the ammonia production unit, respectively. and These are the heat release coefficients of the methane reactor and the ammonia production unit, respectively. and These represent the energy required to produce a unit of ammonia and the heat energy released from natural gas, respectively. Introducing hydrogen storage tanks to achieve stable regulation of hydrogen production and utilization in a multi-energy coupled active distribution network can be represented as: In the formula, The remaining hydrogen in the hydrogen storage tank; and These refer to filling and releasing hydrogen from the hydrogen storage tank, respectively. and These are the hydrogen filling efficiency and hydrogen discharging efficiency of the hydrogen storage tank, respectively. The gas turbine is modeled considering natural gas and hydrogen as combustion fuels, and the model is as follows: In the formula, and These are the power generation and output of the gas turbine, respectively. and These are the electrical conversion efficiency and thermal conversion efficiency of the gas turbine, respectively. This represents the input power of the gas turbine. and These are hydrogen and natural gas, respectively, input into the gas turbine; and These are the upper and lower limits of the gas turbine input power, respectively; and These are the upper and lower limits of the gas turbine ramp constraint, respectively; The hydrogen mixing ratio for the gas turbine; and These are the low calorific values ​​of hydrogen and natural gas, respectively. This represents the upper limit of the hydrogen mixture ratio for gas turbines. The gas-fired boiler uses natural gas and hydrogen as raw materials to provide heat, and is combined with a gas turbine for heating. It is modeled as follows: In the formula, It outputs heat to the gas-fired boiler; For the heat conversion efficiency of gas-fired boilers; Input power to the gas-fired boiler; and These are hydrogen and gas, respectively, input into the gas-fired boiler; and These are the upper and lower limits of the power input for the gas-fired boiler; and These are the upper and lower limits of the ramp constraint for gas-fired boilers, respectively. The hydrogen mixing ratio for gas-fired boilers; This is the upper limit for the hydrogen ratio in a gas-fired boiler.

4. The low-carbon optimization scheduling method for multi-energy coupled active distribution networks, as described in claim 1, is characterized in that... The behavioral modeling of offsetting carbon emissions trading with green certificates in step S2 is represented as follows: In the formula, represents the carbon emission amount, is the number of green certificates participating in green carbon interaction, represents the carbon emission reduction factor of unit renewable energy power generation The number of tradable green certificates and carbon emission allowances for the power distribution network are as follows: wherein, and respectively represent the number of green certificates and carbon emission allowances of the final transaction of the distribution network, and respectively represent the green certificates actually obtained and required to be obtained by the multi-energy coupling active distribution network, and respectively represent the actual carbon emissions and allowances of the distribution network.

5. The low-carbon optimization scheduling method for multi-energy coupled active distribution networks, as described in claim 4, is characterized in that... The tiered trading mechanism introduces penalty or incentive coefficients based on the number of green certificates and the trading volume of carbon emission allowances. This includes dividing green certificate trading and carbon trading into intervals based on trading volume and introducing incentive coefficients. and penalty coefficient When the actual number of green certificates is lower than the quota and the actual carbon emissions are higher than the quota, a penalty coefficient is applied, and the distribution network pays higher transaction costs; when the actual number of green certificates is higher than the quota and the actual carbon emissions are lower than the quota, an incentive coefficient is applied, and the distribution network obtains higher transaction revenue; the tiered carbon trading cost is based on the length of the carbon emission quota interval. The tiered green certificate transaction cost is calculated based on the length of the green certificate transaction interval. calculate.

6. The low-carbon optimization scheduling method for multi-energy coupled active distribution networks, as described in claim 5, is characterized in that... The tiered carbon trading costs for active distribution networks are as follows: The transaction costs for tiered green certificates in active distribution networks are as follows: In the formula, and These are the compensation coefficient and penalty coefficient of the transaction mechanism, respectively; The interval length for carbon emission allowances. The interval length for trading green certificates. and These are the carbon trading base price and the green certificate price.

7. The low-carbon optimization scheduling method for multi-energy coupled active distribution networks, as described in claim 1, is characterized in that... Step S3 aims to minimize the total operating cost of a multi-energy coupled active distribution network by determining a low-carbon optimization scheduling strategy for a typical day. In the formula, For the total time period, Represents the total cost. Indicates coal consumption cost, Indicates equipment maintenance costs, Indicates the cost of purchasing gas. Indicates the cost of carbon capture and extraction. Indicates carbon trading costs, Indicates the transaction cost of green certificates. Indicates the cost of curtailing renewable energy; The constraints include: Multi-energy supply and demand balance constraint: the total supply of electricity, heat, gas, and hydrogen must equal the total demand; Unit operating constraints: The output power, ramp rate, and blending ratio of the coal-ammonia co-fired unit, gas turbine, and gas boiler must meet the upper and lower limits of the constraints. Energy storage equipment constraints: The capacity of the hydrogen storage tank and the amount of hydrogen charged and discharged must meet the upper and lower limits, the hydrogen charging and discharging states are mutually exclusive, and the amount of hydrogen stored at the beginning and end of the scheduling cycle is equal.

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