A carbon emission reduction accounting method and monitoring system based on electric and hybrid power ships

Abstract

The application discloses a kind of based on carbon emission reduction accounting method and monitoring system of electric and hybrid power ship, belong to carbon emission reduction accounting technical field, the carbon emission reduction accounting method includes baseline emission calculation, project emission calculation and carbon emission reduction calculation, wherein baseline emission factor needs to be obtained according to baseline scenario fuel consumption rate, fuel net calorific value and fuel emission factor, the fuel consumption rate is calculated by constructing dynamic baseline model.Effective to overcome the calculation deviation of traditional baseline model, the application comprehensively considers the dynamic change of engine efficiency, fuel characteristics, environmental interference and transportation task, and thus establishes the dynamic baseline model of fuel consumption rate, effectively improves the baseline prediction accuracy, and provides more accurate carbon emission reduction management basis for the development of electric and hybrid power ship project.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission reduction accounting, and specifically relates to a carbon emission reduction accounting method and monitoring system based on electric and hybrid power ships. Background Art

[0002] Faced with the severe challenges of global climate change, the global shipping industry is experiencing an accelerating demand for carbon emission reduction. Electric and hybrid ships, which partially or completely replace traditional fuel with electricity, are a new type of green transportation. Compared to traditional diesel-powered ships, electric and hybrid ships offer advantages such as energy conservation, low emissions, low pollution, cleanliness, low noise, and diverse energy sources. They are an important path to achieving energy conservation and emission reduction in the shipping industry.

[0003] Voluntary greenhouse gas emissions trading is an important way to leverage market mechanisms to promote greenhouse gas emissions reductions. It plays a positive role in fostering market awareness of carbon reductions and exploring and testing carbon emissions trading procedures and regulations. A prerequisite for voluntary greenhouse gas emissions trading is the ability to accurately assess emissions reductions across various industries. For electric and hybrid ship projects, a scientific carbon reduction quantification model is urgently needed.

[0004] However, for the baseline emission scenario, the fuel consumption rate is currently mainly obtained through sampling measurements or recommended values. However, this calculation method is mainly transplanted from the road transport field and has serious adaptability defects in water transport. It ignores the nonlinear effects of ship cargo distribution, speed changes, and hydrological conditions on energy consumption, resulting in a calculated deviation of the baseline fuel consumption rate exceeding ±15%, seriously affecting the quantification accuracy of carbon emission reduction. Summary of the Invention

[0005] Purpose of the invention: In response to the above-mentioned deficiencies, the present invention provides a carbon emission reduction accounting method and monitoring system based on electric and hybrid power ships, establishes a dynamic benchmark model of fuel consumption rate based on the characteristics of ship transportation, and provides a more accurate carbon emission reduction management basis for the development of electric and hybrid power ship projects.

[0006] Technical Solution: To achieve the above objectives, the present invention provides a carbon emission reduction accounting method and monitoring system based on electric and hybrid ships, comprising the following steps:

[0007] 1) Calculate the baseline emissions of the target vessel based on the baseline emission factor and passenger and cargo turnover;

[0008] 2) Calculate the project emissions of the target vessel based on the grid emission factor, fuel emission factor, and passenger and cargo turnover;

[0009] 3) Calculate the carbon emission reduction of the target ship based on the baseline emissions and the project emissions;

[0010] The baseline emission factor is calculated based on the fuel consumption rate, fuel net calorific value and fuel emission factor under the baseline scenario. The fuel consumption rate is calculated using the following dynamic baseline model:

[0011]

[0012] Where, is the fuel consumption rate under the baseline scenario;

[0013] is the instantaneous value of the main engine power of the i-th flight segment, and n is the total number of flight segments;

[0014] is the host thermal efficiency;

[0015] It is the lower calorific value of fuel;

[0016] is the environmental correction factor;

[0017] is the cargo volume of the jth voyage, and m is the total number of voyages;

[0018] is the transportation distance of the jth voyage.

[0019] Furthermore, the host power Calculated using the following formula:

[0020]

[0021] Where, is the total resistance, is the thrust derating factor, For hull efficiency, is the relative rotation efficiency.

[0022] Furthermore, the total resistance Calculated using the following formula:

[0023]

[0024] Where, is the friction resistance, To create resistance to waves, is the air resistance, calculated according to the following formula:

[0025]

[0026]

[0027]

[0028] Where, is the water density, is the friction resistance coefficient, is the wet surface area of ​​the hull, V is the speed;

[0029] is the cargo distribution correction factor, L is the ship length, B is the ship width, T is the design draft, and Cb is the square coefficient;

[0030] is the transverse cross-sectional area, is the wind speed and θ is the wind direction.

[0031] Furthermore, the cargo distribution correction coefficient Calculated using the following formula:

[0032]

[0033] Where, For the cargo capacity of the front cargo hold;

[0034] For the cargo capacity of the rear cargo hold;

[0035] is the total cargo capacity of the cargo hold;

[0036] , that is, the coefficient of increase in wave-making resistance caused by full front loading;

[0037] , that is, the full load at the rear causes the vortex drag coefficient to increase.

[0038] Furthermore, the environmental correction factor Calculated using the following formula:

[0039]

[0040] Where Hw is the wave height, L is the ship length, Vc is the water velocity, V is the ship speed, and θ is the wind direction.

[0041] Furthermore, the fuel has a low calorific value Calculated using the following formula:

[0042]

[0043] Where, is the mass percentage of the kth hydrocarbon component;

[0044] is the standard calorific value of the kth hydrocarbon component;

[0045] is the sulfur content correction term.

[0046] Furthermore, the grid emission factor is calculated using the following model:

[0047] 2.1. Build a real-time calculation engine for regional power grid carbon emission factors:

[0048]

[0049] Where, =1~5 They represent coal power, gas power, hydropower, nuclear power, and renewable energy respectively;

[0050] Indicates the time t Class power output;

[0051] Indicates the total output of various power sources at time t;

[0052] Indicates the Life cycle emission factors for similar power sources;

[0053] 2.2. Constructing a dynamic mapping algorithm for emission factors based on ship trajectories:

[0054]

[0055] Where, represents the longitude and latitude coordinates of the ship at time t;

[0056] Indicates the charge level of the onboard battery during period t;

[0057] Indicates the charge capacity of the onboard battery within the total time period T.

[0058] In addition, the present invention also provides a carbon emission reduction monitoring system based on electric and hybrid ships, including a shipboard monitoring terminal, a cloud computing platform and a verification terminal, wherein the shipboard monitoring terminal is used to collect ship operation data in real time for the cloud computing platform to calculate the carbon emission reduction amount based on the above-mentioned carbon emission reduction accounting method, thereby uploading the collected data and calculation results to the verification terminal.

[0059] Furthermore, the shipborne monitoring terminal includes a main control unit, a sensor unit and a communication unit, wherein the main control unit realizes data collection through the sensor unit, and then realizes communication transmission with the cloud computing platform through the communication unit.

[0060] Furthermore, the cloud computing platform realizes data uploading through the blockchain evidence storage module.

[0061] Beneficial effects: The present invention comprehensively considers the dynamic changes in engine efficiency, fuel characteristics, environmental interference and transportation task volume, thereby establishing a dynamic benchmark model for fuel consumption rate, effectively improving the baseline prediction accuracy, and providing a more accurate carbon emission reduction management basis for the development of electric and hybrid ship projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a schematic diagram of a dynamic benchmark model in an embodiment of the present invention;

[0063] Figure 2 4 is an architectural diagram of a carbon emission reduction monitoring system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the objects, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0065] For electric and hybrid ship projects, the baseline scenario is the emissions generated by the ship using fossil fuels without project activities, and the project scenario is the emissions generated by the electric / hybrid ship using electricity and fossil fuels with project activities. The project emission reductions are then calculated based on the baseline emissions and the project emissions. However, the existing accounting system technology has the following defects:

[0066] 1) Baseline inaccuracy: Traditional baseline models ignore the nonlinear effects of ship cargo distribution and hydrological conditions on energy consumption, resulting in a calculation error of the baseline fuel consumption rate exceeding ±15%;

[0067] 2) Distorted emission factors: The existing system fails to reflect key factors such as fluctuations in fuel calorific value and changes in power grid dynamics, resulting in either inflated or underestimated emissions reductions.

[0068] 3) Inaccurate monitoring data: Discrete monitoring equipment has problems such as data fragmentation and insufficient accuracy, making it difficult to meet the carbon market's requirements for data traceability.

[0069] Based on this, this embodiment provides a carbon emission reduction accounting method based on electric and hybrid ships, including the following steps:

[0070] 1. Baseline emission calculation;

[0071] The baseline emissions need to be calculated based on the baseline emission factor and passenger and freight turnover. The baseline emission factor is calculated based on the fuel consumption rate, fuel net calorific value and fuel emission factor under the baseline scenario. In order to improve the prediction accuracy of the baseline model, the following dynamic baseline model (such as Figure 1Calculate fuel consumption rate:

[0072] 1.1. Multi-dimensional data collection:

[0073] a. Ship parameters: length L, breadth B, design draft T, squareness coefficient Cb;

[0074] b. Operational parameters: real-time cargo capacity W (±0.5% accuracy), ship speed V (0.1 knot resolution), main engine power P (kW);

[0075] c. Environmental parameters: water velocity Vc, wind direction θ, and wave height Hw.

[0076] 1.2. Establish a correction coefficient model for cargo distribution and resistance:

[0077]

[0078] Where, is the cargo distribution correction factor;

[0079] is the cargo capacity of the forward cargo hold (in tons);

[0080] is the cargo capacity of the rear cargo hold (tons);

[0081] is the total cargo capacity of the cargo hold (in tons);

[0082] , that is, the coefficient of increase in wave-making resistance caused by full front loading;

[0083] , that is, the full load at the rear causes the vortex drag coefficient to increase.

[0084] 1.3. Establish the relationship between main engine power and speed based on ship fluid mechanics:

[0085]

[0086] Where, is the instantaneous value of host power, is the total resistance, The thrust reduction coefficient indicates the ratio of propeller thrust reduction due to the influence of the hull, usually 0.1~0.3; Hull efficiency reflects the interaction efficiency between the hull and the propeller, which can be increased to 1.0~1.3 after optimization; The relative rotational efficiency is the ratio of the propeller efficiency in the actual hull flow field to the ideal open water efficiency, which is approximately 0.95~1.05 and is affected by the uniformity of the stern flow field. The total resistance Rt can be calculated according to the following formula:

[0087]

[0088] Specific component calculation:

[0089] Friction resistance (ITTC-1957 formula):

[0090]

[0091] Where, is the water density, is the friction coefficient, which is determined by Reynolds Re and surface roughness. is the wetted surface area of ​​the hull, i.e. the total area of ​​the hull in contact with water;

[0092] Wave resistance (modified Townsin formula):

[0093]

[0094] Air resistance:

[0095]

[0096] Where, is the transverse cross-sectional area, is the wind speed and θ is the wind direction.

[0097] 1.4. Constructing a dynamic benchmark model for fuel consumption rate:

[0098]

[0099] Where, is the fuel consumption rate under the baseline scenario;

[0100] is the instantaneous value of the main engine power in the i-th segment (kW), and n is the total number of segments (which can be divided according to the working mode of the ship engine, such as full speed, cruising, idling, etc.);

[0101] is the main engine thermal efficiency (0.45 for diesel engine and 0.52 for LNG);

[0102] The lower calorific value of the fuel (real-time detection value, unit: MJ / kg);

[0103] is the environmental correction factor, which is used to quantify the impact of the external environment on fuel consumption;

[0104] is the cargo volume (tons) of the jth voyage, and m is the total number of voyages;

[0105] is the transport distance of the jth voyage (nautical miles).

[0106] Furthermore, the environmental correction factor can be calculated according to the following formula:

[0107]

[0108] In calm sea conditions ≈1.0, and can reach 1.2~1.5 in severe sea conditions.

[0109] Furthermore, the quality of marine fuel fluctuates significantly. Actual measurements show that the difference in lower calorific value of diesel fuel between different batches can reach 1.8 MJ / kg. To further improve the accuracy of fuel consumption calculations, laser spectroscopy can be used to measure fuel components online, thereby performing real-time detection of fuel calorific value. The specific calculation formula is as follows:

[0110]

[0111] Where, is the mass percentage of the kth hydrocarbon component;

[0112] is the standard calorific value of the kth hydrocarbon component (MJ / kg);

[0113] is the sulfur content correction term.

[0114] 2. Calculation of project emissions;

[0115] Project emissions need to be calculated based on the grid emission factor, fuel emission factor, and passenger and freight turnover. To improve the prediction accuracy of the project model, the grid emission factor is obtained in real time from the temporal and spatial dimensions:

[0116] 2.1. Build a real-time calculation engine for regional power grid carbon emission factors:

[0117]

[0118] Where, =1~5 They represent coal power, gas power, hydropower, nuclear power, and renewable energy respectively;

[0119] Indicates the time t Type of power output (MW);

[0120] Indicates the total output of various power sources at time t (MW);

[0121] Indicates the Life cycle emission factors of power sources (gCO2 / kWh).

[0122] 2.2. Develop an algorithm for dynamic mapping of emission factors based on ship trajectories:

[0123]

[0124] Where, represents the longitude and latitude coordinates of the ship at time t;

[0125] Indicates the charge capacity of the onboard battery during period t (kWh);

[0126] Indicates the amount of charge onboard battery in the total time period T (kWh).

[0127] 3. Calculation of project emission reductions;

[0128] The carbon emission reduction of the target ship can be calculated based on the baseline emissions and the project emissions.

[0129] See also Figure 2 This embodiment also provides a carbon emission reduction monitoring system based on electric and hybrid ships, specifically including:

[0130] Ship-borne monitoring terminal, used to collect ship operation data in real time;

[0131] The cloud computing platform deploys a baseline model and emission factor calculation engine, thereby calculating carbon emission reductions based on the above carbon emission reduction accounting method, and uploading the collected data and calculation results to the verification terminal;

[0132] Verification terminal with data visualization and anomaly detection capabilities;

[0133] Among them, the ship-borne terminal and the cloud platform are connected through 5G / satellite dual channels, achieving data delay of ≤150ms.

[0134] Furthermore, the shipborne monitoring terminal includes a main control unit, a sensor unit and a communication unit, wherein the main control unit realizes data collection through the sensor unit, and then realizes 5G / satellite dual-channel data transmission through the communication unit and the cloud computing platform.

[0135] Specifically, the sensor unit includes:

[0136] Micro strain load cell array, used to measure load distribution and measurement error ;

[0137] Coriolis mass flowmeter for fuel metering and metering accuracy 0.25%;

[0138] Multi-frequency GNSS module for Beidou satellite positioning and positioning accuracy 0.5m, thereby realizing speed detection and track recording.

[0139] Furthermore, the cloud computing platform realizes data upload through the blockchain evidence storage module, generates a data fingerprint containing timestamp, cargo volume, speed, and energy consumption every 10 seconds, and writes it into the alliance chain after sharding encryption through the threshold signature algorithm.

[0140] Example:

[0141] Three typical ship types were selected for a 12-month field test on the Yangtze River main line. The test objects are as follows:

[0142]

[0143] Accuracy improvement data after adopting the above optimization system:

[0144] 1. Baseline calculation error:

[0145] Traditional method: Maximum error 21.4% (bulk carrier unloaded condition);

[0146] This method: the error range is ±4.7% (95% confidence level);

[0147] 2. Real-time emission factors:

[0148] Grid factor update delay reduced from 8 hours to 15 minutes;

[0149] Fuel calorific value detection response time <30 seconds;

[0150] 3. Data credibility:

[0151] The abnormal detection rate of blockchain evidence storage is 100%;

[0152] The data completeness rate is 99.8% (compared to 82.3% with traditional methods).

[0153] In summary, based on the establishment of a scientific carbon emission reduction quantitative model, this invention integrates industry pain points and technological status, and further achieves breakthroughs in the following dimensions:

[0154] 1. Dynamic baseline modeling: Develop a machine learning model that integrates cargo distribution, hydrological conditions, and vessel parameters to improve baseline prediction accuracy;

[0155] 2. High-resolution emission factors: Build a regional power grid carbon emission factor map updated minute by minute to accurately reflect dynamic changes in the power grid;

[0156] 3. Full-factor monitoring terminal: Develop an integrated ship-borne intelligent monitoring device to achieve millisecond-level simultaneous collection of cargo volume, energy consumption, and location data;

[0157] 4. Blockchain evidence storage: Establish a data traceability mechanism based on distributed ledgers to prevent tampering of monitoring data.

[0158] This invention addresses the above-mentioned technical gaps and, through innovative method architecture and hardware system design, overcomes the long-standing problems of lack of accuracy and insufficient credibility in the field of ship carbon accounting.

[0159] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A carbon emission reduction accounting method based on electric and hybrid ships, characterized in that: The following steps are involved: 1) Calculate the baseline emissions of the target vessel based on the baseline emission factor and passenger and cargo turnover; 2) Calculate the project emissions of the target vessel based on the grid emission factor, fuel emission factor, and passenger and cargo turnover; 3) Calculate the carbon emission reduction of the target ship based on the baseline emissions and the project emissions; The baseline emission factor is calculated based on the fuel consumption rate, fuel net calorific value and fuel emission factor under the baseline scenario. The fuel consumption rate is calculated using the following dynamic baseline model: Where, is the fuel consumption rate under the baseline scenario; For the i Instantaneous value of main engine power during flight segment, n is the total number of flight segments; is the host thermal efficiency; It is the lower calorific value of fuel; is the environmental correction factor; is the cargo volume of the jth voyage, m is the total number of voyages; is the transportation distance of the jth voyage; The environmental correction factor Calculated using the following formula: Where Hw is the wave height, L is the ship length, Vc is the water velocity, V is the ship speed, and θ is the wind direction.

2. The carbon emission reduction accounting method according to claim 1, characterized in that: The host power Calculated using the following formula: Where, is the total resistance, is the thrust derating factor, For hull efficiency, is the relative rotation efficiency.

3. The carbon emission reduction accounting method according to claim 2, characterized in that: The total resistance Calculated using the following formula: Where, is the friction resistance, To create resistance to waves, is the air resistance, calculated according to the following formula: Where, is the water density, is the friction resistance coefficient, is the wet surface area of ​​the hull, V is the speed; is the cargo distribution correction factor, L is the ship length, B is the ship width, T is the design draft, and Cb is the square coefficient; is the transverse cross-sectional area, is the wind speed and θ is the wind direction.

4. The carbon emission reduction accounting method according to claim 3, characterized in that: The cargo distribution correction factor Calculated using the following formula: Where, For the cargo capacity of the front cargo hold; For the rear cargo hold cargo capacity; is the total cargo capacity of the cargo hold; , that is, the coefficient of increase in wave-making resistance caused by full front loading; , that is, the full load at the rear causes the vortex drag coefficient to increase.

5. The carbon emission reduction accounting method according to claim 1, characterized in that: The lower calorific value of the fuel Calculated using the following formula: Where, is the mass percentage of the kth hydrocarbon component; is the standard calorific value of the kth hydrocarbon component; is the sulfur content correction term.

6. The carbon emission reduction accounting method according to claim 1, characterized in that: The grid emission factor is calculated using the following model: 2.

1. Build a real-time calculation engine for regional power grid carbon emission factors: Where, =1~5 They represent coal power, gas power, hydropower, nuclear power, and renewable energy respectively; Indicates the time t Class power output; Indicates the total output of various power sources at time t; Indicates the Life cycle emission factors for similar power sources; 2.

2. Constructing a dynamic mapping algorithm for emission factors based on ship trajectories: Where, represents the longitude and latitude coordinates of the ship at time t; Indicates the charge level of the onboard battery during period t; Indicates the charge capacity of the onboard battery within the total time period T.

7. A carbon emission reduction monitoring system based on electric and hybrid ships, characterized in that: The system comprises a ship-borne monitoring terminal, a cloud computing platform and a verification terminal, wherein the ship-borne monitoring terminal is used to collect ship operation data in real time, so that the cloud computing platform can calculate the carbon emission reduction amount based on the carbon emission reduction accounting method described in any one of claims 1 to 6, thereby uploading the collected data and calculation results to the verification terminal.

8. The carbon emission reduction monitoring system according to claim 7, characterized in that: The shipborne monitoring terminal includes a main control unit, a sensor unit and a communication unit, wherein the main control unit realizes data collection through the sensor unit, and then realizes communication transmission with the cloud computing platform through the communication unit.

9. The carbon emission reduction monitoring system according to claim 7, characterized in that: The cloud computing platform realizes data uploading through the blockchain evidence storage module.

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