Active power distribution network optimal dispatch method considering hydrogen energy utilization, unit mixed combustion and step carbon trading
By constructing a multi-energy flow operation framework and a tiered carbon trading mechanism, and optimizing the dispatch of active power distribution networks, the problems of low renewable energy absorption rate and high carbon emissions have been solved, achieving efficient absorption and low-carbon operation, and reducing total operating costs.
Patent Information
- Application Number
- CN202610293207.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-11
- Publication Date
- 2026-07-24
AI Technical Summary
Existing active power grid dispatching methods suffer from low renewable energy absorption rates, high carbon emissions, a single hydrogen energy utilization model, and insufficient carbon trading constraints, making it difficult to balance economic efficiency with the needs of low-carbon and environmental protection.
Construct a multi-energy flow operation framework, combining electrolyzers, hydrogen storage tanks, ammonia production units, hydrogen-natural gas co-firing of gas turbine units, and a tiered carbon trading mechanism, and optimize the scheduling model to minimize total operating costs, thereby achieving efficient consumption of new energy, reduction of carbon emissions, and economical operation.
Through multi-mode hydrogen energy utilization and tiered carbon trading, the renewable energy consumption rate has been increased to over 96%, carbon emissions have been reduced by over 14%, and total operating costs have been reduced by over 8%, balancing economic efficiency and environmental protection.
Smart Images
Figure CN122452968A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of active power distribution network dispatching technology. Specifically, it relates to an active power distribution network optimization dispatching method that takes into account hydrogen energy utilization, unit co-firing, and tiered carbon trading, and is applicable to the low-carbon economic operation and dispatching of active power distribution networks in scenarios with high wind and solar power penetration. Background Technology
[0002] Driven by the goals of "carbon peaking and carbon neutrality," the installed capacity and power generation share of new energy sources have continued to increase. However, large-scale wind and solar power units are characterized by strong uncertainty and randomness, resulting in the system still being highly dependent on fossil fuels, low efficiency in the use of clean energy, and prominent issues of new energy curtailment. At the same time, the operation of traditional fossil fuel units is accompanied by high carbon emissions, making it difficult to balance the economic efficiency of power distribution network operation with the requirements of low-carbon and environmental protection.
[0003] Among existing technologies, hydrogen energy storage, as a novel energy storage method, has shown advantages in mitigating the fluctuations of new energy sources and improving the absorption rate. However, current hydrogen energy utilization is mostly concentrated in the single scenario of methane reactors, and the potential value of multi-mode coordinated operation, such as hydrogen-based ammonia production and hydrogen participation in gas turbine unit combustion, has not been fully explored. Although carbon trading mechanisms have been applied to energy system optimization, traditional fixed carbon price trading is insufficient to constrain high-carbon emission units, and the emission reduction and economic benefits of carbon trading have not been deeply integrated with the demand for clean fuel substitution for units. In addition, fossil energy units lack effective clean fuel alternatives, and thermal power units and gas turbine units still mainly use coal and natural gas as fuels, resulting in high carbon emission intensity and making it difficult to adapt to the low-carbon transformation needs of the power distribution network.
[0004] In summary, existing active distribution network dispatching methods suffer from problems such as low renewable energy absorption rate, high carbon emissions, single hydrogen energy utilization mode, and insufficient carbon trading constraints. There is an urgent need for an optimized dispatching method that integrates multi-mode hydrogen energy utilization, clean co-firing of units, and tiered carbon trading to improve the economic efficiency and low-carbon level of distribution network operation. Summary of the Invention
[0005] Purpose of the Invention: The purpose of this invention is to overcome the shortcomings of the prior art and provide an active power distribution network optimization scheduling method that takes into account hydrogen energy utilization, unit co-firing, and tiered carbon trading. By constructing a multi-energy flow operation framework, a multi-mode hydrogen energy utilization model, an ammonia-blended combustion thermal power unit model, and a tiered carbon trading mechanism, combined with an optimization scheduling model that aims to minimize total operating costs, the invention achieves synergistic optimization of efficient new energy consumption, carbon emission reduction, and economic operation of the active power distribution network.
[0006] Technical solution: An active power distribution network optimization scheduling method that takes into account hydrogen energy utilization, unit co-firing, and tiered carbon trading, including the following steps: (1) Construct an active power distribution network multi-energy flow and operation framework. The framework links the electricity-heat-hydrogen-ammonia-carbon multi-energy flow through the full-chain utilization of hydrogen energy, including hydrogen production by electrolyzers, hydrogen storage tanks, hydrogen use in ammonia production units, hydrogen-natural gas co-combustion in gas turbine units, and hydrogen-carbon capture and synthesis of methane in methane reactors. Among them, the electricity load is jointly supplied by wind power, photovoltaic, thermal power units, and gas turbines, the heat load is jointly met by waste heat recovery devices of gas boilers, gas turbines, ammonia production units, and methane reactors, and the gas load is jointly met by external gas sources and natural gas generated by methane reactors. (2) Establish a hydrogen energy utilization model, which includes an electrolyzer model, a hydrogen storage tank model, an ammonia production unit model, a gas turbine unit model, and a methane reactor model; (3) Construct a model of ammonia-infused combustion in a thermal power unit, including a calculation model of energy consumption and carbon emissions of coal-ammonia co-combustion in a thermal power unit, and a calculation model of operating energy consumption and carbon capture amount of a carbon capture device. (4) Establish a tiered carbon trading mechanism model, which includes a carbon emission quota calculation model for thermal power units, gas turbines, and gas boilers, an actual carbon emission accounting model, and a tiered carbon emission trading cost calculation model based on multi-range differentiated carbon prices. With the goal of minimizing the sum of unit coal consumption cost, gas purchase cost, equipment operation and maintenance cost, renewable energy curtailment cost, carbon trading cost and carbon capture cost, an active distribution network optimization scheduling model is established by combining power balance constraints, unit operation constraints, equipment operation constraints, renewable energy output constraints, gas supply constraints, network power flow constraints, line power flow constraints and node voltage constraints. (5) The active power distribution network optimization scheduling model is solved by an optimization solver, and the output of wind power / photovoltaic power consumption, fuel input of thermal power units / gas turbine units, power regulation of hydrogen energy utilization equipment, and carbon trading revenue and expenditure are output for each time period to form an active power distribution network optimization scheduling scheme.
[0007] Furthermore, the electrolytic cell model described in step (2) satisfies:
[0008] in, for Electrical energy input of the electrolyzer during a given time period (unit: kW / MW). for Electricity output of the electrolyzer during a given time period (unit: kW / MW). These are the minimum and maximum allowable values for the electrical energy input to the electrolytic cell, used to limit the equipment's inefficient operation under low load and overload operation. These are the downward and upward ramp limits (unit: kW / h / MW / h) for the electrical energy input of the electrolyzer, used to avoid electrolyte temperature fluctuations caused by sudden power changes. The efficiency of the electrolyzer in converting electrical energy into hydrogen energy (typically ranging from 0.65 to 0.85) is determined by the type of electrolyzer (alkaline / AEL, proton exchange membrane / PEMEL).
[0009] The hydrogen storage tank model described satisfies dynamic hydrogen storage balance and operating state constraints, as follows:
[0010] in, for The remaining hydrogen amount in the hydrogen storage tank during the specified time period (unit: kWh / MWh). This is the loss factor of the hydrogen storage tank (usually ranging from 0.005 to 0.02, determined by the hydrogen storage medium and sealing technology). , They are respectively Hydrogen storage tank charging and discharging power during different time periods (unit: kW / MW). , These represent the hydrogen filling and discharging efficiency of the hydrogen storage tank (value range 0.9-0.98). , These are binary variables (0 = off, 1 = on) representing the hydrogen charging and discharging states of the hydrogen storage tank, used to limit simultaneous charging and discharging. , These represent the maximum operating power for charging and discharging hydrogen from the hydrogen storage tank. These are the minimum and maximum hydrogen storage capacities of the hydrogen storage tank (to avoid excessively low / high pressure inside the tank). , These represent the hydrogen storage levels at the beginning and end of the scheduling cycle (to ensure hydrogen storage balance within the cycle). For scheduling time intervals, This is the total scheduling period.
[0011] Furthermore, the ammonia production device model described in step (2) satisfies the constraints of hydrogen energy conversion and waste heat utilization, as follows:
[0012] in, , They are respectively Hydrogen input and output power of the ammonia production unit during a specific time period. To improve ammonia production efficiency, The heat release ratio for ammonia production equipment. for The quality of ammonia produced by the time-phase ammonia production equipment To produce the heat power released per unit mass of ammonia gas, for The heat power provided by the time-phase ammonia production equipment , These are the upper and lower limits for hydrogen input to the ammonia production equipment. This is the lower limit of the hydrogen input ramp-up for ammonia production equipment. The upper limit for hydrogen input ramp-up in the ammonia production equipment.
[0013] The gas turbine model described satisfies the constraints of hydrogen-natural gas co-firing and multi-energy output, as detailed below:
[0014] in, for Time-of-use gas turbine Energy input power, , They are Time-of-use gas turbine Hydrogen and natural gas input power, , They are Time-of-use gas turbine Gas turbine electrical and thermal power output , These refer to the energy conversion efficiency of gas turbines and gas turbine units, respectively. They are gas turbine units Upper and lower limits of input power, , They are gas turbine units Input power ramp-up limits , These are the upper and lower limits of the gas turbine output ratio, respectively, to ensure stable unit operation; The constraints of hydrogen-carbon capture for natural gas synthesis and waste heat utilization in the methane reactor model are as follows:
[0015] in, for Hydrogen input to the methane reactor during the time period, for The output of natural gas from the methane reactor during the period, It refers to the efficiency of natural gas production. The proportion of heat release from the methane reactor. To produce the heat power released per unit of natural gas, for The thermal power provided by the methane reactor during the time period for The amount of carbon dioxide consumed by the methane reactor during the specified time period. This is the ratio of carbon dioxide consumed to natural gas produced in a methane reactor. The upper and lower limits for hydrogen input to the methane reactor. The upper and lower limits for hydrogen input ramp-up in the methane reactor.
[0016] Furthermore, the ammonia-infused combustion thermal power unit model described in step (3) satisfies:
[0017] in, yes Coal consumption of thermal power units during different time periods. , and This represents the coal consumption coefficient for thermal power units. for The output power of thermal power units during the period It is the coefficient for ammonia to replace coal. for Ammonia consumption of thermal power units during different time periods , These are the lower heating values of ammonia and coal, respectively. Energy conversion efficiency of thermal power units. for Actual carbon emissions of thermal power units during the specified time period. The coefficient of carbon dioxide release per unit mass of coal combustion. For carbon capture efficiency. The flue gas split ratio of the thermal power unit. for Net output power of thermal power units during the period , These are the operating and stationary energy consumption of the carbon capture device, respectively. for Total carbon dioxide captured over a period of time Energy consumption per unit mass of carbon dioxide captured.
[0018] Furthermore, the tiered carbon trading mechanism model described in step (4) includes a carbon emission quota model, an actual carbon emission model, and a tiered carbon emission trading model, as detailed below: The carbon emission quota model is as follows:
[0019] in, , , These are carbon emission quotas for thermal power units, gas turbines, and gas-fired boilers, respectively. , Carbon emission allowances for power supply and heating units, respectively , and They are respectively The heating capacity of thermal power units, gas turbines, and gas boilers during specific time periods. This is the electro-thermal conversion factor. for Periodic gas turbine electrical energy output; The actual carbon emission model is as follows:
[0020] in, , , They are respectively Actual carbon emissions from thermal power units, gas turbines, and gas boilers during a given period. The carbon dioxide emission coefficient per unit of natural gas consumed. , They are respectively Natural gas input to gas turbines and gas boilers during specific time periods; The tiered carbon emissions trading model is as follows:
[0021] in, for The quota for participating in carbon market trading during specific time periods. for Time-based carbon trading costs The base price for carbon trading. This indicates the dividing point between different price ranges for carbon trading. This represents the percentage increase in carbon trading prices.
[0022] Furthermore, the objective function in step (5) is:
[0023] in, Total operating cost, For the unit's coal consumption cost, For gas purchase costs, For equipment operation and maintenance costs, For the cost of curtailing renewable energy, For carbon trading costs, For carbon capture costs; The power balance constraints include electrical, thermal, and gas power balance constraints, which respectively satisfy:
[0024] in, , They are Wind and solar power output during certain periods for Periodic electrical load, It refers to the power curtailment of renewable energy sources. for Periodic heat load, , These are the heating capacities of the gas turbine and the gas boiler, respectively. , These are the waste heat power of the ammonia production unit and the methane reactor, respectively. for Gas supply volume during a given time period It is the power of the natural gas produced by the methane reactor.
[0025] This invention, based on the power distribution network operation framework, integrates multiple energy flows and proposes an active power distribution network optimization scheduling method that considers hydrogen energy utilization, unit co-firing, and tiered carbon trading. On the one hand, it leverages the comprehensive utilization of hydrogen energy, such as green electricity-green hydrogen, green hydrogen-carbon capture to methane, green hydrogen-coupled nitrogen to ammonia, and green hydrogen-combined gas co-firing, to achieve combined power-heat-gas supply in the power distribution network, expanding the space for the consumption of new energy sources such as wind power and photovoltaics. On the other hand, the active power distribution network participates in carbon market trading, utilizing the carbon trading mechanism to restrict high-carbon emission units, promoting the clean fuel substitution of fossil fuel units, reducing carbon emissions, and optimizing the economics of the power distribution network. Finally, with the goal of minimizing the total day-ahead operating cost, a low-carbon optimization scheduling model for the active power distribution network is constructed, solved using the GUROBI commercial solver, and the scheduling results of different operating schemes are compared and analyzed to verify the effectiveness of the proposed scheduling method.
[0026] Beneficial effects: Compared with the prior art, the present invention has the following substantial features and significant progress: (1) Multi-mode hydrogen energy utilization to improve the consumption of new energy: By constructing a complete chain of hydrogen energy from preparation, storage to use through electrolyzers, hydrogen storage tanks, ammonia production units, gas turbine co-combustion and methane reactors, the consumption path of new energy can be broadened, the consumption rate of new energy can be increased to more than 96%, and the cost of curtailment of electricity can be significantly reduced.
[0027] (2) Clean co-firing of units to reduce carbon emissions: ammonia-coal co-firing of thermal power units and hydrogen-natural gas co-firing of gas turbine units replace part of fossil energy. Combined with carbon capture devices, carbon emissions are reduced by more than 14% compared with traditional dispatching methods, realizing low-carbon operation of the power distribution network.
[0028] (3) Tiered carbon trading enhances emission reduction incentives: The differentiated carbon price mechanism imposes strong constraints on high-carbon emission units, guiding them to prioritize the use of clean fuels. Carbon trading revenue can offset part of the operating costs, reducing total operating costs by more than 8%.
[0029] (4) Multi-energy synergy optimization to improve economic benefits: Coordinate the multi-energy flow of electricity-heat-hydrogen-ammonia-carbon, recover the waste heat from ammonia and methane production reactions to supplement the heat load, reduce the heating pressure of gas turbine units, significantly reduce gas purchase cost and coal consumption cost, and take into account both economic efficiency and environmental protection. Attached Figure Description
[0030] Figure 1 This is a diagram illustrating the multi-energy flow and operation framework of the active power distribution network described in this invention. Figure 2 Diagram of an IEEE 33-node active distribution network; Figure 3 This example illustrates the multi-energy load demand of the active distribution network. Figure 4 The figures show the predicted power output and electricity load curves for wind power and photovoltaic power at different time periods in the example. Figure 5 The scheduling results of active distribution networks under different scheduling models; Figure 6 The results of active distribution network dispatching under different carbon trading mechanisms; Figure 7 The output timing diagrams of various devices in power optimization scheduling under different schemes are shown. Figure 7 (a) is the power optimization scheduling result of Scheme 1; Figure 7 (b) is the power dispatching result of Scheme 5; Figure 8 The output timing diagrams of each device in the thermal energy optimization scheduling under different schemes are shown. Figure 8 (a) is the thermal energy optimization scheduling result of Scheme 1; Figure 8 (b) is the thermal energy optimization scheduling result of Scheme 5; Figure 9 The diagram shows the output timing of various equipment in the optimized natural gas scheduling under different schemes. Figure 9 (a) is the result of optimized natural gas dispatching under scheme 1; Figure 9 (b) is the result of the optimized natural gas dispatching under Scheme 5; Figure 10 This section compares the total operating costs and carbon emissions under different options. Detailed Implementation
[0031] To illustrate the technical solution provided by this invention in detail, a specific description is given below with reference to the accompanying drawings and examples.
[0032] First, this invention addresses the issues of renewable energy curtailment, high carbon emissions, and economic efficiency in distribution networks with high wind and solar power penetration. It comprehensively considers the combined operation of hydrogen energy utilization, clean fuel use in fossil fuel units, and carbon trading mechanisms, proposing an active distribution network optimization scheduling method that incorporates hydrogen energy utilization, unit co-firing, and tiered carbon trading. First, a mechanism analysis and model construction are conducted for hydrogen energy utilization units composed of electrolyzers, hydrogen storage tanks, hydrogen fuel cells, methane reactors, and ammonia production units, analyzing active distribution network scheduling strategies that consider multi-energy flows. Then, considering the participation of ammonia and hydrogen in fossil fuel unit combustion, a fossil fuel unit co-firing model is constructed, followed by a carbon trading cost model under a tiered carbon trading mechanism. Finally, with the objective of minimizing the sum of operating costs including coal consumption, gas purchase, and wind and solar curtailment, a low-carbon economic scheduling model for the active distribution network is established. Through setting multiple operating schemes, the effectiveness of the proposed optimization scheduling method in improving the economic efficiency and clean energy use of the distribution network is analyzed and verified.
[0033] like Figure 1 As shown, this invention first establishes a multi-energy flow and operation framework for active power distribution networks, as follows: Figure 1 As shown, various energy flows, including electricity, heat, hydrogen, ammonia, and carbon, are linked through multiple hydrogen energy utilization methods. Electricity load is supplied by a combination of wind power, photovoltaic power, thermal power units, and gas turbines; heat load is met by gas boilers and gas turbines in conjunction with other heat-releasing devices; and gas load is mainly met by gas sources combined with methane reactors. This active power grid, aggregating multiple energy flows, can better leverage the clean characteristics of new energy sources and constrain carbon emissions. On one hand, to address the balance between supply and demand for multiple energy sources, electrolyzers respond to large-scale new energy power generation on the source side, producing hydrogen to supply ammonia production units and gas turbines, achieving the conversion of heterogeneous energy sources such as electricity, hydrogen, gas, heat, and ammonia. This ensures the absorption of surplus electricity and the supply of multiple energy loads, while excess hydrogen is stored in hydrogen storage tanks, achieving smooth regulation of hydrogen production and consumption and expanding the space for new energy absorption. On the other hand, hydrogen-using equipment, mainly methane reactors and ammonia production units, absorbs hydrogen for the production of natural gas and ammonia. Ammonia can replace some coal in the combustion of thermal power units, reducing coal consumption. Gas-fired units can use hydrogen-blended combustion to reduce natural gas consumption, achieve cleaner fuel for the units, and reduce carbon emissions.
[0034] To verify the economic, clean, and low-carbon benefits of the proposed optimized scheduling method, Figure 1 The active distribution network framework shown is used as the test object, with a 24-hour scheduling cycle. A case study is conducted based on an improved IEEE 33-bus system. Figure 2As shown in the diagram, WT, PV, EC, PA, TH, MR, and GT represent wind power, photovoltaic power, electrolyzer, ammonia production unit, carbon capture unit, thermal power unit, methane reactor, and gas turbine, respectively. Wind turbines with a total installed capacity of 400MW are connected at nodes 7 and 19, wind turbines with a total installed capacity of 100MW are connected at node 31, and photovoltaic units with a total installed capacity of 400MW are connected at nodes 30 and 25. Thermal power units are installed at nodes 2 and 17, with an active power output range of 0–500MW; gas turbines are installed at node 28, with an active power output range of 0–150MW.
[0035] Multi-energy load demand and wind and solar power forecast output, such as Figure 3-4 As shown. Carbon emission quota per unit of electricity supplied by the generating unit. 0.728 kg / kW·h, unit heating carbon emission quota 0.3 kg / kW·h.
[0036] In this embodiment, the active power distribution network optimization scheduling model mentioned above was solved using MATLAB R2024a with the commercial solver GUROBI called through the Yalmip toolbox, under the computing environment of Intel Core i9-14900k CPU and 32.0 GB of memory.
[0037] To accurately analyze the effectiveness of the active power grid optimization scheduling model that considers hydrogen energy utilization, unit co-firing, and the carbon market, this embodiment sets up the following 5 models for comparative analysis: Scheme 1 is the baseline scenario, which only considers the conventional economic dispatch of the active distribution network, and involves hydrogen energy utilization, unit co-firing, and carbon market trading; Option 2 introduces devices such as electrolyzers, hydrogen storage tanks, and methane reactors into the baseline scenario to achieve hydrogen energy production, storage, and diversified utilization. Option 3 further introduces the co-firing technology between the ammonia production unit and the generator unit, thus broadening the ways to utilize hydrogen energy; Option 4 further considers waste heat recovery from the ammonia-gas production process; Option 5 is the proposed optimization, which embeds a tiered transaction mechanism into the technology of Option 4.
[0038] Various costs and results under different optimization schemes, such as Figure 5 As shown.
[0039] Compared to Option 1, Option 2, by introducing a hydrogen production-storage-utilization process, reduces the cost of renewable energy curtailment and coal consumption by 78.5% and 6.66%, respectively. Although the introduction of hydrogen energy equipment increases the operation and maintenance cost of distribution network equipment by 156.2%, the total operating cost still decreases by 8.43%, indicating that the hydrogen production-storage-utilization model can simultaneously improve the renewable energy absorption capacity and low-carbon benefits of the active distribution network. Option 3 adds an ammonia production unit and co-firing technology to Option 2, using hydrogen and ammonia to replace some fossil fuels in the unit combustion, reducing the active distribution network's dependence on fossil fuels. Therefore, the gas purchase cost and coal consumption cost are reduced by 4.61% and 4.72%, respectively, and carbon emissions are reduced by 9.24%. At the same time, the expansion of hydrogen energy application scenarios further reduces the curtailment space, further reducing the curtailment cost by 23.4%. Option 4 introduces methane and ammonia production processes to recover waste heat from the reaction and supply heat load, which not only enhances the heating flexibility of the active distribution network but also alleviates the heating pressure on gas equipment, ultimately achieving a further reduction of 3.4% in gas purchase cost. After the introduction of the tiered carbon trading mechanism in Scheme 5, carbon emission units are more inclined to use hydrocarbon-free and ammonia-free co-firing, which will further reduce the cost of curtailed electricity by 9.3% and the total operating cost will be further reduced by 2.24% compared with Scheme 4.
[0040] Overall, with the gradual optimization of the scheme, all operating costs except for equipment maintenance costs have decreased significantly (with a maximum reduction of 78.5%). At the same time, the renewable energy consumption rate has increased from 74.75% to 96.98%, and carbon emissions have decreased from 4,461 tons to 3,802 tons, fully demonstrating the dual advantages of the proposed scheduling scheme in terms of economic and environmental benefits.
[0041] To verify the effect of the tiered carbon trading mechanism on improving the absorption of clean energy and low carbon emissions in active distribution networks, three schemes were designed for analysis. The schemes are set up with the following logic: Scheme 6 is a conventional economic dispatch scheme that does not consider carbon trading costs and only considers costs such as coal consumption, gas purchase, and power curtailment; Scheme 7 introduces a fixed carbon price trading mechanism to participate in the economic dispatch of active distribution networks; Scheme 8 introduces a tiered carbon price trading mechanism to participate in the economic dispatch of active distribution networks (i.e., Scheme 5 above).
[0042] The scheduling optimization results under different carbon trading mechanisms are as follows Figure 6 As shown.
[0043] Depend on Figure 6It can be seen that, compared with Scheme 6 which does not include carbon trading costs, Schemes 7 and 8, which introduce carbon trading, have significantly reduced carbon emissions. Specifically, Scheme 7 reduces carbon emissions by 85 tons compared to Scheme 6, and Scheme 8 reduces carbon emissions by 108 tons compared to Scheme 6. Furthermore, Scheme 8 further reduces carbon emissions by 23 tons compared to Scheme 7. This indicates that the tiered carbon trading mechanism has a more prominent effect on constraining carbon emissions and can more effectively promote emission reduction and carbon reduction in active power distribution networks. Specifically, Scheme 6, which aims for minimum economic efficiency and does not involve carbon trading, cannot constrain carbon-emitting units through carbon quota trading. Its coal consumption and gas purchase costs are at a high level, resulting in the highest carbon emissions. Scheme 7, while incorporating carbon trading costs into its optimization, lacks sufficient incentive for carbon-emitting units to reduce carbon emissions through a fixed carbon price mechanism, only reducing coal consumption costs from 3.601 million yuan to 3.592 million yuan and gas purchase costs from 4.374 million yuan to 4.373 million yuan. In contrast, Scheme 8, employing a tiered carbon price mechanism, increases the carbon price as the tradable carbon quotas increase. Combined with the substitution effect of co-firing technology on fossil fuels, carbon-emitting units can maintain output while reducing fossil fuel consumption (coal consumption costs reduced to 3.532 million yuan and gas purchase costs to 4.331 million yuan) and increasing the available carbon quotas (carbon trading revenue reaching 1.903 million yuan). This reduces carbon emissions and creates better economic benefits for the active distribution network.
[0044] To further clarify the role of hydrogen energy diversification in the multi-energy dispatch plan of active distribution networks, Scheme 1 and Scheme 5 were selected for comparative analysis. The multi-energy dispatch results are as follows: Figure 7-9 As shown: exist Figure 7 In terms of power dispatch, Scheme 5 introduces multiple hydrogen energy utilization technologies compared to Scheme 1. Through the synergy of electrolyzers, hydrogen storage tanks, and hydrogen-consuming devices (ammonia production, methane production, and unit co-firing), a full-chain hydrogen energy dispatch system of "hydrogen production-hydrogen storage-multi-purpose hydrogen utilization" is constructed. During peak periods of renewable energy or off-peak periods of power load demand, surplus renewable energy on the source side is utilized through hydrogen production via water electrolysis, increasing the renewable energy grid connection capacity. The produced hydrogen can be utilized through methanation, ammonia production, and unit co-firing, reducing the consumption of fossil fuels by the active power distribution network and improving the renewable energy utilization of the active power distribution network while optimizing its economic efficiency.
[0045] exist Figure 8 At the thermal energy dispatch level, Scheme 5 introduces a waste heat recovery device to recover the reaction heat released during ammonia and methane production, which is used to supplement the heat load demand, improve the heating flexibility of the active power distribution network, and alleviate the operating pressure of heating units. Since the waste heat recovery is mainly concentrated during the peak period of new energy generation, it further confirms the role of expanding hydrogen energy utilization in promoting the consumption of new energy.
[0046] exist Figure 9At the natural gas dispatch level, Scheme 5 adopts unit co-firing technology, which supports gas turbine units to replace part of the natural gas combustion with hydrogen, reducing the dependence of the active distribution network on fossil gas sources. By comparing the gas consumption of Scheme 1 and Scheme 5, the effect of co-firing technology on reducing dependence on gas sources is intuitively demonstrated.
[0047] The coal consumption cost, gas purchase cost, carbon emissions, and renewable energy integration rate of generating units can all directly reflect the cleanliness of energy use in active power distribution networks. Lower coal consumption costs and gas purchase costs indicate reduced output from fossil fuel generating units, resulting in lower carbon emissions. To further analyze the effectiveness of co-firing technology in reducing carbon emissions and improving the utilization of clean energy in active power distribution networks, a gas-fired boiler is used as an example to analyze the heat output and natural gas demand of gas-fired boilers under different schemes.
[0048] Depend on Figure 10 It can be seen that during the peak energy consumption period from 23:00 to 3:00, as the main source of heat energy for the active distribution network, the gas-fired boilers in Scheme 2 and Scheme 3 operate at full capacity. However, the gas consumption of Scheme 3 is significantly lower than that of Scheme 2. This is because, with economic goals in mind, to reduce renewable energy curtailment, coal consumption, and gas purchase costs, fossil fuel units tend to use clean energy sources such as hydrogen and ammonia to replace natural gas and coal combustion in energy supply. Since the peak heat energy period mostly coincides with the peak wind power output period, co-firing technology can provide space for the utilization of hydrogen produced by the electrolyzer, further demonstrating the effectiveness of unit co-firing technology in promoting renewable energy utilization.
[0049] To verify the effectiveness of the optimized scheduling scheme proposed in this invention under different operating scenarios, this section takes scheme 5 as the benchmark and analyzes the operating results of the active distribution network under different new energy output and mixed combustion ratios, as shown in Table 1. The results are arranged and the units are total cost / 105 yuan, carbon emissions / 102 tons, and new energy consumption rate, respectively.
[0050] Table 1. Operation results of active power distribution networks under different renewable energy output growth coefficients and co-firing ratios.
[0051] As shown in Table 1, on the one hand, when the ratio of unit co-firing remains unchanged, as the growth coefficient of renewable energy output increases, the total cost and carbon emissions of the active power distribution network show an overall downward trend. However, the consumption of renewable energy increases first and then decreases. This is because with the increase in renewable energy output, the active power distribution network reduces its dependence on power generation devices such as thermal power units and gas turbines, and the cost of coal consumption and gas purchase decreases accordingly, and carbon emissions also gradually decrease. However, the fixed load demand and the ratio of unit co-firing cannot provide more space for renewable energy consumption. Therefore, when the growth coefficient of renewable energy output reaches a certain level, renewable energy will be subject to large-scale curtailment, and the consumption rate will decrease. On the other hand, when the output of new energy sources is fixed, as the proportion of co-firing in power units continues to rise, the total cost of active power distribution networks, carbon emissions, and the rate of new energy absorption all show an overall downward trend. This is because with the increase in the proportion of co-firing, fossil energy units can utilize more ammonia and hydrogen to participate in the combustion of the units, reducing the absorption of fossil energy while promoting the absorption of new energy, thus reducing the total cost of active power distribution networks. This demonstrates the supporting role of co-firing technology in the large-scale absorption of new energy and provides a reference for the clean and low-carbon transformation of the energy structure.
Claims
1. An active power distribution network optimization scheduling method considering hydrogen energy utilization, unit co-firing, and tiered carbon trading, characterized in that, Includes the following steps: (1) Construct an active power distribution network multi-energy flow and operation framework. The framework links the electricity-heat-hydrogen-ammonia-carbon multi-energy flow through the full-chain utilization of hydrogen energy, including hydrogen production by electrolyzers, hydrogen storage tanks, hydrogen use in ammonia production units, hydrogen-natural gas co-combustion in gas turbine units, and hydrogen-carbon capture and synthesis of methane in methane reactors. Among them, the electricity load is jointly supplied by wind power, photovoltaic, thermal power units, and gas turbines, the heat load is jointly met by waste heat recovery devices of gas boilers, gas turbines, ammonia production units, and methane reactors, and the gas load is jointly met by external gas sources and natural gas generated by methane reactors. (2) Establish a hydrogen energy utilization model, which includes an electrolyzer model, a hydrogen storage tank model, an ammonia production unit model, a gas turbine unit model, and a methane reactor model; (3) Construct a model of ammonia-infused combustion in a thermal power unit, including a calculation model of energy consumption and carbon emissions of coal-ammonia co-combustion in a thermal power unit, and a calculation model of operating energy consumption and carbon capture amount of a carbon capture device. (4) Establish a tiered carbon trading mechanism model, which includes a carbon emission quota calculation model for thermal power units, gas turbines, and gas boilers, an actual carbon emission accounting model, and a tiered carbon emission trading cost calculation model based on multi-range differentiated carbon prices. With the goal of minimizing the sum of unit coal consumption cost, gas purchase cost, equipment operation and maintenance cost, renewable energy curtailment cost, carbon trading cost and carbon capture cost, an active distribution network optimization scheduling model is established by combining power balance constraints, unit operation constraints, equipment operation constraints, renewable energy output constraints, gas supply constraints, network power flow constraints, line power flow constraints and node voltage constraints. (5) The active power distribution network optimization scheduling model is solved by an optimization solver, and the output of wind power / photovoltaic power consumption, fuel input of thermal power units / gas turbine units, power adjustment of hydrogen energy utilization equipment, and carbon trading revenue and expenditure are output for each time period to form an active power distribution network optimization scheduling scheme.
2. The active distribution network optimization scheduling method according to claim 1, characterized in that, The electrolytic cell model described in step (2) satisfies: in, for The time-phase electrolytic cell electrical energy input, for The electrical energy output of the electrolytic cell during the time period These are the upper and lower limits for the electrolytic cell input, respectively. These are the upper and lower limits of the electrolytic cell's power input ramp-up. This refers to the efficiency of the electrolyzer in converting electrical energy into hydrogen energy.
3. The active distribution network optimization scheduling method according to claim 1, characterized in that, The hydrogen storage tank model described in step (2) satisfies: in, for The amount of hydrogen remaining in the hydrogen storage tank during the specified time period. This represents the loss factor of the hydrogen storage tank. , They are respectively Hydrogen storage tank charging and discharging power during different time periods. , These refer to the hydrogen filling and discharging efficiency of the hydrogen storage tank. , These are binary variables representing the hydrogen charging and discharging states of the hydrogen storage tank, respectively. , These represent the maximum operating power for charging and discharging hydrogen from the hydrogen storage tank. These are the lower and upper limits of the hydrogen storage tank, respectively. , These represent the hydrogen storage capacity of the hydrogen storage tank at the beginning and end of the respective periods. For scheduling time intervals, This is the total scheduling period.
4. The active distribution network optimization scheduling method according to claim 1, characterized in that, The ammonia production device model described in step (2) satisfies: in, , They are respectively Hydrogen input and output of the ammonia production unit during a specific time period. To improve ammonia production efficiency, The heat release ratio for ammonia production equipment. for The quality of ammonia produced by the time-phase ammonia production equipment To produce the heat power released per unit mass of ammonia gas, for The heat power provided by the time-phase ammonia production equipment , These are the upper and lower limits for hydrogen input to the ammonia production equipment. This is the lower limit of the hydrogen input ramp-up for ammonia production equipment. The upper limit for hydrogen input ramp-up in the ammonia production equipment.
5. The active distribution network optimization scheduling method according to claim 1, characterized in that, The gas turbine model described in step (2) satisfies: in, for Time-of-use gas turbine Energy input power, , They are Time-of-use gas turbine Hydrogen and natural gas input, , They are Time-of-use gas turbine Gas turbine electrical and thermal energy output, , These refer to the energy conversion efficiency of gas turbines and gas turbine units, respectively. They are gas turbine units Upper and lower limits of input power, , They are gas turbine units Input power ramp-up limits , These represent the upper and lower limits of the gas turbine output ratio.
6. The active distribution network optimization scheduling method according to claim 1, characterized in that, The methane reactor model described in step (2) satisfies: in, for Hydrogen input to the methane reactor during the time period, for The output of natural gas from the methane reactor during the period, It refers to the efficiency of natural gas production. The proportion of heat release from the methane reactor. To produce the heat power released per unit of natural gas, for The thermal power provided by the methane reactor during the time period for The amount of carbon dioxide consumed by the methane reactor during the specified time period. This is the ratio of carbon dioxide consumed to natural gas produced in a methane reactor. The upper and lower limits for hydrogen input to the methane reactor. The upper and lower limits for hydrogen input ramp-up in the methane reactor.
7. The active distribution network optimization scheduling method according to claim 1, characterized in that, The ammonia-infused combustion thermal power unit model described in step (3) satisfies: in, yes Coal consumption of thermal power units during different time periods. , and This represents the coal consumption coefficient for thermal power units. for Output power of thermal power units during the period It is the coefficient for ammonia to replace coal. for Ammonia consumption of thermal power units during different time periods , These are the lower heating values of ammonia and coal, respectively. Energy conversion efficiency of thermal power units. for Actual carbon emissions of thermal power units during the specified time period. The coefficient of carbon dioxide release per unit mass of coal combustion. For carbon capture efficiency The flue gas split ratio of the thermal power unit. for Net output power of thermal power units during the period , These are the operating and stationary energy consumption of the carbon capture device, respectively. for Total carbon dioxide captured over a period of time. Energy consumption per unit mass of carbon dioxide captured.
8. The active distribution network optimization scheduling method according to claim 1, characterized in that, The tiered carbon trading mechanism model described in step (4) includes a carbon emission quota model, an actual carbon emission model, and a tiered carbon emission trading model, as detailed below: The carbon emission quota model is as follows: in, , , These are carbon emission quotas for thermal power units, gas turbines, and gas-fired boilers, respectively. , Carbon emission allowances for power supply and heating units, respectively , and They are respectively The heating capacity of thermal power units, gas turbines, and gas boilers during specific time periods. This is the electro-thermal conversion factor. for Periodic gas turbine electrical energy output; The actual carbon emission model is as follows: in, , , They are respectively Actual carbon emissions from thermal power units, gas turbines, and gas boilers during a given period. The carbon dioxide emission coefficient per unit of natural gas consumed. , They are respectively Natural gas input to gas turbines and gas boilers during specific time periods; The tiered carbon emissions trading model is as follows: in, for The quota for participating in carbon market trading during specific time periods. for Time-based carbon trading costs The base price for carbon trading. This indicates the dividing point between different price ranges for carbon trading. This represents the percentage increase in carbon trading prices.
9. The active distribution network optimization scheduling method according to claim 1, characterized in that, The objective function described in step (5) is: in, Total operating cost, For the unit's coal consumption cost, For gas purchase costs, For equipment operation and maintenance costs, For the cost of curtailing renewable energy, For carbon trading costs, For carbon capture costs; The power balance constraints include electrical, thermal, and gas power balance constraints, which respectively satisfy: in, , They are Wind and solar power output during certain periods for Periodic electrical load, It refers to the power curtailment of renewable energy sources. for Periodic heat load, , These are the heating capacities of the gas turbine and the gas boiler, respectively. , These are the waste heat power of the ammonia production unit and the methane reactor, respectively. for Gas supply volume during a given time period It is the power of the natural gas produced by the methane reactor.