Double-layer dynamic game scheduling method and system considering carbon trading and equipment health perception
By employing a two-layer dynamic game scheduling method, combined with carbon trading and equipment health monitoring, the scheduling lag and equipment wear issues of the integrated energy system were resolved, achieving efficient, low-carbon, and long-life operation of the system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XI AN JIAOTONG UNIV
- Filing Date
- 2026-04-16
- Publication Date
- 2026-07-21
AI Technical Summary
Existing integrated energy systems suffer from time-scale lag in dispatch strategies, lack integration with carbon trading mechanisms, and have insufficient equipment health monitoring, resulting in severe equipment wear and energy waste.
A two-layer dynamic game scheduling method that takes into account carbon trading and equipment health perception is adopted. By constructing an upper-layer day-ahead economic optimization model and a lower-layer real-time dynamic scheduling model, and combining a dynamic Nash game objective function and health degradation penalty, the system achieves dynamic game of economic, environmental and energy efficiency objectives, and performs rolling time-domain updates.
It significantly improves the system's robustness to uncertainty, extends equipment life, reduces carbon emissions and energy waste, achieves a dynamic balance between economic efficiency and low carbon emissions, and enhances the system's stability and efficiency.
Smart Images
Figure CN122437006A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of integrated energy system optimization and scheduling, specifically involving a two-layer dynamic game scheduling method and system that takes into account carbon trading and equipment health perception. Background Technology
[0002] Currently, a new power system based on new energy sources has become an inevitable trend. However, wind and photovoltaic power generation are highly random, volatile, and intermittent, and large-scale grid connection poses a significant challenge to the safe and stable operation of the system. To solve the problem of new energy consumption, deeply coupling electrical energy with thermal and hydrogen energy to construct a comprehensive wind-solar-hydrogen-storage energy system, including wind, solar, solid oxide electrolyzers (SOEC), solid oxide fuel cells (SOFC), heat pumps, and hydrogen and thermal storage equipment, is currently recognized as a highly efficient solution.
[0003] Although existing integrated wind-solar-hydrogen-storage energy systems have achieved initial topology construction, the following technical shortcomings still urgently need to be addressed in actual engineering scheduling and underlying numerical modeling control. First, traditional scheduling strategies suffer from time-scale lag and lack deep integration with carbon trading mechanisms. Currently, the scheduling of most integrated energy systems remains at the day-ahead static planning stage. Due to significant errors in wind and solar forecasts over long time scales, static scheduling commands often lead to severe wind and solar curtailment or forced load shedding during actual execution. Simultaneously, existing scheduling models are largely driven by absolute economic costs, lacking sensitivity analysis to dynamic carbon trading market prices; when facing grid power shortages, the system tends to directly purchase electricity from the main grid or activate high-carbon emission backup heat sources, failing to achieve a dynamic trade-off between economic efficiency and low carbon emissions in real-time control. Second, real-time dynamic scheduling lacks equipment health awareness, resulting in severe damage to core hydrogen energy equipment such as SOFCs and SOECs. To cope with ultra-short-term wind and solar fluctuations, existing technologies are beginning to explore the introduction of real-time scheduling algorithms such as model predictive control. However, traditional real-time control algorithms only focus on the algebraic balance of electrothermal power, ignoring the differences in physical characteristics of different energy conversion devices. When the system encounters high-frequency transient power fluctuations, the algorithm often frequently adjusts the SOFC and SOEC, causing the equipment to operate under drastic and wide-range load variations for extended periods. This frequent power ramping can trigger severe thermal stress fatigue in the fuel cell stack, leading to a decline in stack performance and significantly shortening the service life of the SOFC and SOEC. Third, the underlying numerical modeling contains physical logic loopholes, which can easily lead to ineffective energy self-circulation within the system. In the optimization process of multi-energy-flow coupled systems, if only continuous variables are used for constraints, conventional mathematical solvers, in order to balance local thermodynamic equations or avoid penalty costs, are prone to getting caught in a cycle of simultaneously calling the electrolyzer to consume electricity for hydrogen production and the fuel cell to consume hydrogen for power generation and heat production. This simultaneous charging and discharging behavior, which violates thermodynamic principles, not only causes serious energy waste but also renders the instructions output by the scheduling algorithm unusable in actual operating conditions. Summary of the Invention
[0004] This application addresses the technical problems of existing technologies not integrating carbon trading mechanisms and lacking equipment health awareness by providing a two-layer dynamic game scheduling method and system that takes into account both carbon trading and equipment health awareness.
[0005] To achieve the above objectives, this application adopts the following technical solution: The first aspect is a two-tiered dynamic game-theoretic scheduling method that considers both carbon trading and equipment health perception, including: Real-time acquisition of operational data from integrated energy systems; Based on the operational data, an upper-level day-ahead economic optimization model is constructed. The objective function of the upper-level day-ahead economic optimization model consists of economic objectives, environmental objectives, and energy efficiency objectives. Solving the upper-level day-ahead economic optimization model generates the output benchmark trajectory and energy storage benchmark SOC curve of each device in the future scheduling cycle. Based on the aforementioned power output baseline trajectory and energy storage baseline SOC (State of Charge) curve, a lower-level real-time dynamic scheduling model is constructed. The lower-level real-time dynamic scheduling model includes a dynamic Nash game objective function, physical constraints, and health degradation penalties. Solve the lower-level real-time dynamic scheduling model to obtain the optimal equipment output sequence matrix, extract the first control step size instruction of the optimal equipment output sequence matrix and send it to each device for execution, and reacquire the operation data of the integrated energy system in real time in the next control cycle, repeat the above steps to perform rolling time-domain updates.
[0006] In some implementations, the economic objective is expressed as follows:
[0007] in, This represents the total investment cost of the system equipment. This is the capital recovery coefficient; For coal costs; For operation and maintenance costs; Costs associated with carbon emissions trading; The costs associated with curtailing wind and solar power and balancing load shedding; The environmental protection target is expressed by the following formula:
[0008] in, This represents the total coal consumption of the system. Carbon emission factors related to coal; t =1,2,3…T, where T is the total number of time steps within the scheduling period; The energy efficiency target is expressed by the following formula:
[0009] in, For electrical load power, For heat load power, To utilize wind power, To utilize photovoltaic power, This refers to the thermal power of the input coal.
[0010] In some implementations, the operational data includes predicted wind speed data, light intensity data, electrical load data, heat load data, real-time carbon price, current capacity of the hydrogen storage system, and current capacity of the thermal storage system.
[0011] In some implementations, the objective function of the dynamic Nash game is as follows:
[0012] in, To maximize the overall game benefits, , , , They represent The normalized positive benefits of the system's economy, environmental friendliness, and energy efficiency at all times. , , They represent The expected normalized positive returns of the system's economy, environmental protection, and energy efficiency at all times. , , These represent the game weights of economic efficiency, environmental friendliness, and energy efficiency, respectively.
[0013] In some implementations, the physical constraints include: electrical power balance constraints, thermal power balance constraints, equipment operating state and capacity boundary constraints, energy conversion topology constraints, and dynamic timing constraints of the energy storage system.
[0014] In some implementations, the health degradation penalty is expressed as follows:
[0015] in, and These represent the output power changes of SOEC and SOFC in adjacent control steps, respectively. and These are preset non-linear health penalty coefficients.
[0016] Secondly, a two-tiered dynamic game-theoretic scheduling system considering carbon trading and equipment health perception includes: The data acquisition module is used to acquire real-time operational data of the integrated energy system. The upper-level day-ahead economic optimization module is used to construct an upper-level day-ahead economic optimization model based on the operational data. The objective function of the upper-level day-ahead economic optimization model consists of economic objectives, environmental objectives, and energy efficiency objectives. It solves and generates the output benchmark trajectory and energy storage benchmark SOC curve of each device in the future scheduling cycle. The lower-level real-time dynamic scheduling module is used to construct a lower-level real-time dynamic scheduling model based on the output benchmark trajectory and the energy storage benchmark SOC curve. The lower-level real-time dynamic scheduling model includes a dynamic Nash game objective function, physical constraints, and health degradation penalty. The rolling execution module is used to solve the lower-level real-time dynamic scheduling model, obtain the optimal equipment output sequence matrix, extract the first control step instruction of the sequence and send it to each equipment for execution, and reacquire the operating data of the integrated energy system in real time in the next control cycle, repeating the above steps to perform rolling time-domain updates.
[0017] Thirdly, a computer device includes: a processor and a computer-readable storage medium; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the two-layer dynamic game scheduling method that takes into account carbon trading and equipment health awareness.
[0018] Fourthly, a computer-readable storage medium storing a computer program adapted to be loaded by a processor and executed by the two-layer dynamic game scheduling method considering carbon trading and device health awareness.
[0019] Fifthly, a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the aforementioned two-layer dynamic game scheduling method that considers carbon trading and equipment health perception.
[0020] Compared with the prior art, this application has the following beneficial effects: This application first acquires operational data, then constructs an upper-level day-ahead economic optimization model to generate a baseline trajectory, where the objective function consists of economic, environmental, and energy efficiency targets. Next, a lower-level real-time dynamic scheduling model is constructed, including a dynamic Nash game objective function, physical constraints, and health degradation penalties. Finally, the first control step size command is solved and issued, with rolling updates, enabling the system to balance long-term economic planning and short-term real-time response: the upper layer provides a globally optimal baseline trajectory, avoiding short-sighted real-time scheduling; the lower layer uses model predictive control for rolling corrections, effectively addressing random fluctuations in wind, solar, and load. Simultaneously, incorporating health degradation penalties into the lower-level model lays the foundation for subsequent protection of hydrogen energy equipment lifespan. Therefore, this method significantly improves the system's robustness to uncertainty while ensuring economic efficiency and low carbon emissions.
[0021] Furthermore, this application adopts a dynamic Nash game objective function, which enables the system to automatically adjust the emphasis on the three objectives of economy, environmental protection and energy efficiency according to market signals such as electricity price and carbon price during real-time operation. Compared with multi-objective optimization with fixed weights, this mechanism realizes a true adaptive game, avoids the blindness of manually setting weights, and can continuously approach the Pareto optimal frontier in uncertain environments.
[0022] Furthermore, this application introduces a health degradation penalty term, which is the square of the change in output power of SOEC and SOFC in adjacent control steps multiplied by a preset nonlinear penalty coefficient. This significantly eliminates the spikes and burrs in the power curves of SOEC and SOFC, greatly reduces thermal stress fatigue, and substantially extends the service life of the equipment. Attached Figure Description
[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0024] Figure 1 A flowchart of a two-layer dynamic game scheduling method that takes into account carbon trading and equipment health awareness, provided in an embodiment of this application; Figure 2 A schematic diagram of the structure of a two-layer dynamic game scheduling system that takes into account carbon trading and equipment health awareness, provided in an embodiment of this application; Figure 3 A schematic diagram of the integrated energy system architecture topology for wind, solar, hydrogen and storage, taking into account carbon trading and equipment health sensing, provided for an embodiment of this application; Figure 4 A schematic diagram of the real-time rolling scheduling process of model predictive control based on mixed integer quadratic programming provided in an embodiment of this application; Figure 5 A schematic diagram comparing the unoptimized scheduling method provided in the embodiments of this application with the method of the present invention on the SOEC power generation trajectory; Figure 6 A schematic diagram comparing the unoptimized scheduling method provided in the embodiments of this application with the method of the present invention on the SOFC power generation trajectory; Figure 7 A schematic diagram comparing the unoptimized scheduling method provided in the embodiments of this application with the method of the present invention in terms of cumulative carbon emissions in the system; Figure 8 A structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation
[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0026] In one embodiment of this application, such as Figure 1 and Figure 4 As shown, a two-layer dynamic game scheduling method considering carbon trading and equipment health awareness is provided, including: S1, real-time acquisition of operational data of the integrated energy system; Specifically, the operational data includes predicted wind speed data. Light intensity data Electrical load data Heat load data Real-time carbon price Current capacity of hydrogen storage system and the current capacity of the thermal storage system .
[0027] S2, Based on the operating data, construct an upper-level day-ahead economic optimization model. The objective function of the upper-level day-ahead economic optimization model consists of economic objectives, environmental objectives, and energy efficiency objectives. Solve the upper-level day-ahead economic optimization model to generate the output benchmark trajectory and energy storage benchmark SOC curve of each device in the future scheduling cycle. Specifically, the economic objective is expressed by the following formula:
[0028] in, This represents the total investment cost of the system equipment. This is the capital recovery coefficient; For coal costs; For operation and maintenance costs; Costs associated with carbon emissions trading; The costs associated with curtailing wind and solar power and balancing load shedding; The environmental protection target is expressed by the following formula:
[0029] in, This represents the total coal consumption of the system. Carbon emission factors related to coal; t =1,2,3…T, where T is the total number of time steps within the scheduling period; The energy efficiency target is expressed by the following formula:
[0030] in, For electrical load power, For heat load power, To utilize wind power, To utilize photovoltaic power, This refers to the thermal power of the input coal.
[0031] The upper-level day-ahead economic optimization model outputs the equipment output baseline trajectory and energy storage baseline SOC curve for all 24 time periods of the day every 60 minutes through the solver. S3. Based on the output benchmark trajectory and the energy storage benchmark SOC curve, a lower-level real-time dynamic scheduling model is constructed. The lower-level real-time dynamic scheduling model includes a dynamic Nash game objective function, physical constraints, and health degradation penalty. Specifically, the objective function of the dynamic Nash game is as follows:
[0032] in, To maximize the overall game benefits, , , , They represent The normalized positive benefits of the system's economy, environmental friendliness, and energy efficiency at all times. , , They represent The expected normalized positive returns of the system's economy, environmental protection, and energy efficiency at all times. , , These represent the game weights of economic efficiency, environmental friendliness, and energy efficiency, respectively.
[0033] In the lower-level real-time dynamic scheduling model, the physical constraints of the system's electro-thermal-hydrogen multi-energy flow coupling are strictly adhered to. These physical constraints include electrical power balance constraints, thermal power balance constraints, equipment operating status and capacity boundary constraints, energy conversion topology constraints, and dynamic timing constraints of the energy storage system, as detailed below: (1) Ensure that the system is in operation at any given time. The absolute balance between power supply and demand, and the power balance constraint:
[0034] In the formula: and They are time points Actual wind and solar power consumption; This refers to the power generation capacity of the SOFC. This refers to the load power actively disconnected by the system, used as a relaxation variable during extreme power outages. The electrical load required by the system; This refers to the power consumption of the SOEC. This represents the power consumption of the heat pump.
[0035] (2) Ensure that the system is in operation at any given time. Absolute balance between heat energy supply and demand, and constraints on heat power balance:
[0036] In the formula: The usable waste heat power generated during SOFC power generation; The heat output power of the heat pump; and These are the heat release power and heat charging power of the thermal storage system, respectively. The heat output of the standby coal-fired boiler; For the required heat load; The heat power that needs to be absorbed during the SOEC hydrogen production process; Wasted heat power that cannot be utilized by the system.
[0037] (3) Limit the physical output limits of each device and ensure that hydrogen production and power generation of the hydrogen energy equipment do not conflict. Constrain the operating status and capacity boundaries of the equipment:
[0038] In the formula: and These are the rated installed capacities of SOEC and SOFC, respectively. and for Boolean variables, representing SOEC and SOFC at time [time]. The running status, the sum of the two is less than or equal to Ensure that the two do not charge and discharge simultaneously.
[0039] (4) Energy conversion topological constraints:
[0040] In the formula: and These represent the real-time production rate and consumption rate of hydrogen, respectively. and These are the lower heating values of hydrogen and coal, respectively. For SOEC hydrogen production efficiency, For SOFC electrical efficiency, For heat pump performance coefficient, Thermal efficiency of coal-fired boilers; This represents the real-time coal consumption rate of a coal-fired boiler.
[0041] (5) Dynamic timing constraints of energy storage system:
[0042] In the formula: and This represents the amount of hydrogen stored at adjacent time points; and These are the calculated power consumption for filling and discharging hydrogen from the hydrogen storage tank, respectively. and This corresponds to the charge / discharge efficiency; This represents the maximum capacity of the hydrogen storage system. The timing constraints for the thermal storage system are the same as for this structure.
[0043] In the economic objective function of the lower-level real-time dynamic scheduling model, a health degradation penalty term is introduced for vulnerable equipment. The health degradation penalty term is expressed by the following formula:
[0044] in, and These represent the output power changes of SOEC and SOFC in adjacent control steps, respectively. and These are preset non-linear health penalty coefficients.
[0045] When the system detects transient power imbalance caused by high-frequency wind and solar forecast errors during real-time operation, the control system extracts the current capacity margin of the thermal storage system and adaptively optimizes under the aforementioned physical constraints. Due to the heat pump output... Without being penalized by the square term, the solver will adaptively drive the heat pump to prioritize handling high-frequency power fluctuations, thereby smoothing out the power variation of hydrogen energy equipment.
[0046] S4. Solve the lower-level real-time dynamic scheduling model to obtain the optimal equipment output sequence matrix. Extract the first control step instruction of the optimal equipment output sequence matrix and send it to each device for execution. In the next control cycle, re-acquire the operating data of the integrated energy system in real time and repeat the above steps to perform rolling time-domain updates. Specifically, a carbon price sensitivity driving mechanism is constructed based on the real-time carbon price. When the system is detected to be in a high carbon price range, the control system prioritizes the use of heat pump heating and heat storage system heat release and solid oxide fuel cell power generation for combined heat and power to suppress carbon emissions. When a sudden high-frequency wind and solar power fluctuation is detected, heat pump heating and solid oxide electrolyzers are used to absorb the curtailed wind and solar power or solid oxide fuel cell power generation to supplement energy. Specifically, at every moment Based on the aforementioned objectives and physical constraints, the solver calculates the future... The system extracts the optimal equipment output sequence matrix for each time period. It only sends the instruction for the first control step in this matrix to the underlying controllers of equipment such as wind turbines, photovoltaic systems, heat pumps, and electrolytic cells. In the next time step... Upon arrival, the latest actual physical state is collected and returned to S1 to form a rolling closed loop for model predictive control.
[0047] The method provided in this embodiment is used in a wind-solar-hydrogen-storage integrated energy system architecture, such as... Figure 3 As shown, the system mainly includes: wind turbines, photovoltaics, SOEC, SOFC, heat pumps, thermal storage tanks, hydrogen storage tanks, coal-fired boilers, thermal power plants, and user electricity and heat loads. The system uses a 15-minute rolling control step and a 1-hour day-ahead forecast step. At each moment, it collects the day-ahead forecast data from both the source and load sides and the forecast error between the 15-minute data, obtains the dynamic carbon price signal from the current carbon trading market, and reads the current energy storage status of the thermal and hydrogen storage tanks. A 24-hour cycle upper-level economic optimization model is constructed. The objective function comprehensively considers the system's annualized investment depreciation cost, operation and maintenance cost, and carbon emission trading cost. Through mixed-integer linear programming, the calculated daily equipment output baseline plan and the energy storage system reference trajectory are sent to the lower-level real-time control layer as the optimal baseline command. A lower-level real-time rolling optimization model based on model predictive control is constructed. The lower-level objective function considers the SOEC / SOFC health status and outputs the real-time equipment output. Rolling optimization is performed based on the update time.
[0048] To verify the effectiveness of the method provided in this application embodiment, this embodiment conducted a 24-hour comparative test between the unoptimized scheduling situation and the method of the present invention. Figures 5-7 The effectiveness of the lifespan protection is visually demonstrated. The results without optimization are compared with the optimized results obtained using the method of this invention. Figure 5 As shown, the SOEC power curve under conventional methods exhibits more high-frequency, large-range fluctuations that lead to thermal stress fatigue; while the SOEC power ramp-up curve of this invention is smoother. Figure 6 As shown, SOFC also exhibits the same smooth characteristics, completely avoiding stack aging caused by frequent start-stop cycles. Figure 7 As shown, the comparison of the cumulative carbon emission curves of the system demonstrates the low-carbon superiority of this invention. Compared to the results without optimization, the 24-hour carbon emissions decreased from 58,831 tons to 14,217 tons. The method of this invention significantly reduced the overall carbon footprint at the end of 24 hours, achieving an optimal solution for a multi-objective dynamic game involving economy, lifespan, and environmental protection while meeting user load requirements.
[0049] In one embodiment of this application, such as Figure 2As shown, a two-layer dynamic game scheduling system considering carbon trading and equipment health awareness is provided, including: The data acquisition module is used to acquire real-time operational data of the integrated energy system. The upper-level day-ahead economic optimization module is used to construct an upper-level day-ahead economic optimization model based on the operational data. The objective function of the upper-level day-ahead economic optimization model consists of economic objectives, environmental objectives, and energy efficiency objectives. It solves and generates the output benchmark trajectory and energy storage benchmark SOC curve of each device in the future scheduling cycle. The lower-level real-time dynamic scheduling module is used to construct a lower-level real-time dynamic scheduling model based on the output benchmark trajectory and the energy storage benchmark SOC curve. The lower-level real-time dynamic scheduling model includes a dynamic Nash game objective function, physical constraints, and health degradation penalty. The rolling execution module is used to solve the lower-level real-time dynamic scheduling model, obtain the optimal equipment output sequence matrix, extract the first control step instruction of the sequence and send it to each equipment for execution, and reacquire the operating data of the integrated energy system in real time in the next control cycle, repeating the above steps to perform rolling time-domain updates.
[0050] Specific limitations regarding the two-layer dynamic game scheduling system for carbon trading and equipment health awareness can be found in the limitations of the two-layer dynamic game scheduling method for carbon trading and equipment health awareness described above. The corresponding technical effects are equivalent and will not be repeated here. Each module in the aforementioned two-layer dynamic game scheduling system for carbon trading and equipment health awareness can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.
[0051] Figure 8 An internal structural diagram of a computer device is shown in one embodiment. This computer device may specifically be a terminal or a server. Figure 8As shown, the computer device includes a processor, memory, network interface, display, camera, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a two-layer dynamic game scheduling method for carbon trading and device health awareness. The display screen can be an LCD screen or an e-ink display screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device casing, or an external keyboard, touchpad, or mouse.
[0052] As will be understood by those skilled in the art, computer equipment Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computing device may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.
[0053] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.
[0054] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.
[0055] In summary, the two-layer dynamic game-theoretic scheduling method, system, computer equipment, and storage medium for carbon trading and equipment health awareness provided in this application address the technical shortcomings of existing technologies, such as day-ahead static scheduling lag, energy waste in SOEC and SOFC hydrogen energy equipment due to simple mathematical solutions, and severe thermal stress and performance degradation of electrochemical equipment caused by real-time high-frequency fluctuations in wind and solar resources and load conditions. It achieves optimal allocation of carbon trading and energy management through a two-layer architecture, and realizes high energy efficiency, low carbon emissions, and long lifespan operation under highly uncertain environments through a hybrid integer quadratic programming and adaptive penalty mechanism at the bottom layer. Specifically, it has the following advantages: First, it innovatively introduces an equipment health degradation penalty mechanism based on the square of the power change rate into the bottom-layer dynamic scheduling model. Through an adaptive optimization algorithm, the system enables hydrogen energy equipment to smoothly track low-frequency benchmark power. Simulation results show that this invention completely eliminates high-frequency and severe fluctuations in the power curves of SOEC and SOFC, significantly reduces the mechanical and thermal losses of electrochemical equipment, and has significant industrial application value. It also significantly extends the operating life of SOFC and SOEC hydrogen energy equipment and reduces the total life-cycle depreciation cost. Second, this invention constructs a hybrid integer quadratic programming model, namely the lower-level real-time dynamic scheduling model. By introducing 0-1 Boolean state mutually exclusive variables, it eliminates the synchronous operation of the electrolyzer and fuel cell from the underlying mathematical logic, avoiding the energy waste caused by SOEC consuming electricity to produce hydrogen while SOFC consumes hydrogen for power generation, thus maintaining the overall system energy efficiency at a high level. Third, the two-layer game architecture in this invention, consisting of the upper-level day-ahead economic optimization model and the lower-level real-time dynamic scheduling model, deeply integrates the real-time carbon trading mechanism. When carbon prices are high or carbon quotas are tight, the system automatically tilts towards environmental goals through a time-varying weight mechanism, prioritizing the use of heat release from the thermal storage system and hydrogen consumption for power generation by SOFC, thus minimizing dependence on thermal power generation and coal combustion. Empirical comparisons show that, compared with conventional scheduling methods, this method can continuously reduce the system's cumulative carbon emission curve during 24-hour continuous rolling scheduling. Fourth, this invention adopts a closed-loop rolling feedback mechanism based on model predictive control. The system continuously collects the latest physical energy storage capacity and ultra-short-term error in 15-minute control steps, and buffers this by leveraging the thermophysical inertia of the heat pump-thermal storage system and the hydrogen energy characteristics of SOFC and SOEC. Regardless of the degree of fluctuation on the source and load sides, this system can achieve absolute balance on both the electrical and thermal sides.
[0056] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.
[0057] The above-described embodiments are merely preferred embodiments of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention. Therefore, the scope of protection of this invention should be determined by the scope of the claims.
Claims
1. A two-layer dynamic game scheduling method considering carbon trading and equipment health perception, characterized in that, include: Real-time acquisition of operational data from integrated energy systems; Based on the operational data, an upper-level day-ahead economic optimization model is constructed. The objective function of the upper-level day-ahead economic optimization model consists of economic objectives, environmental objectives, and energy efficiency objectives. Solving the upper-level day-ahead economic optimization model generates the output benchmark trajectory and energy storage benchmark SOC curve of each device in the future scheduling cycle. Based on the power output baseline trajectory and the energy storage baseline SOC curve, a lower-level real-time dynamic scheduling model is constructed. The lower-level real-time dynamic scheduling model includes a dynamic Nash game objective function, physical constraints, and health degradation penalty. Solve the lower-level real-time dynamic scheduling model to obtain the optimal equipment output sequence matrix, extract the first control step size instruction of the optimal equipment output sequence matrix and send it to each device for execution, and reacquire the operation data of the integrated energy system in real time in the next control cycle, repeat the above steps to perform rolling time-domain updates.
2. The two-layer dynamic game scheduling method considering carbon trading and equipment health perception according to claim 1, characterized in that, The economic objective is expressed by the following formula: in, This represents the total investment cost of the system equipment. This is the capital recovery coefficient; For coal costs; For operation and maintenance costs; Costs associated with carbon emissions trading; The costs associated with curtailing wind and solar power and balancing load shedding; The environmental protection target is expressed by the following formula: in, This represents the total coal consumption of the system. Carbon emission factors related to coal; t =1,2,3…T, where T is the total number of time steps within the scheduling period; The energy efficiency target is expressed by the following formula: in, For electrical load power, For heat load power, To utilize wind power, To utilize photovoltaic power, This refers to the thermal power of the input coal.
3. The two-layer dynamic game scheduling method considering carbon trading and equipment health perception as described in claim 1, characterized in that, The operational data includes predicted wind speed data, light intensity data, electrical load data, heat load data, real-time carbon price, current capacity of the hydrogen storage system, and current capacity of the thermal storage system.
4. The two-layer dynamic game scheduling method considering carbon trading and equipment health perception according to claim 1, characterized in that, The objective function of the dynamic Nash game is as follows: in, To maximize the overall game benefits, , , , They represent The normalized positive benefits of the system's economy, environmental friendliness, and energy efficiency at all times. , , They represent The expected normalized positive returns of the system's economy, environmental protection, and energy efficiency at all times. , , These represent the game weights of economic efficiency, environmental friendliness, and energy efficiency, respectively.
5. The two-layer dynamic game scheduling method considering carbon trading and equipment health perception according to claim 1, characterized in that, The physical constraints include: electrical power balance constraints, thermal power balance constraints, equipment operating status and capacity boundary constraints, energy conversion topology constraints, and dynamic timing constraints of the energy storage system.
6. The two-layer dynamic game scheduling method considering carbon trading and equipment health awareness according to claim 1, characterized in that, The health degradation penalty is calculated using the following formula: in, and These represent the output power changes of SOEC and SOFC in adjacent control steps, respectively. and These are preset non-linear health penalty coefficients.
7. A two-layer dynamic game scheduling system that considers carbon trading and equipment health perception, characterized in that, include: The data acquisition module is used to acquire real-time operational data of the integrated energy system. The upper-level day-ahead economic optimization module is used to construct an upper-level day-ahead economic optimization model based on the operational data. The objective function of the upper-level day-ahead economic optimization model consists of economic objectives, environmental objectives, and energy efficiency objectives. It solves and generates the output benchmark trajectory and energy storage benchmark SOC curve of each device in the future scheduling cycle. The lower-level real-time dynamic scheduling module is used to construct a lower-level real-time dynamic scheduling model based on the output benchmark trajectory and the energy storage benchmark SOC curve. The lower-level real-time dynamic scheduling model includes a dynamic Nash game objective function, physical constraints, and health degradation penalty. The rolling execution module is used to solve the lower-level real-time dynamic scheduling model, obtain the optimal equipment output sequence matrix, extract the first control step instruction of the sequence and send it to each equipment for execution, and reacquire the operating data of the integrated energy system in real time in the next control cycle, repeating the above steps to perform rolling time-domain updates.
8. A computer device, characterized in that, include: Processor and computer-readable storage media; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the two-layer dynamic game scheduling method for carbon trading and equipment health awareness as described in any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1 to 6, which is a two-layer dynamic game scheduling method that takes into account carbon trading and equipment health awareness.
10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the two-layer dynamic game scheduling method for carbon trading and equipment health awareness as described in any one of claims 1 to 6.