Optimized scheduling method for comprehensive energy system containing gas-liquid phase change carbon dioxide energy storage

Abstract

The invention belongs to the technical field of comprehensive energy and intelligent scheduling, and provides an optimal scheduling method for a comprehensive energy system containing gas-liquid phase change carbon dioxide energy storage, which comprises the following steps: establishing a comprehensive energy system model containing gas-liquid phase change carbon dioxide energy storage; based on a comprehensive energy system model, with low carbon and lowest system cost as optimization objectives, establishing a system optimization scheduling model incorporating a stepped carbon transaction mechanism of full life cycle cost and grading pricing of the energy storage power station; and solving the system optimization scheduling model based on a robust optimization algorithm of a random robust factor, determining a system cost optimal value, and outputting an optimization scheduling scheme. According to the scheme provided by the invention, the full-life-cycle cost is included, so that the energy storage cost accounting is more accurate, a stepped carbon transaction mechanism of grading and pricing promotes the fine management of carbon emission, and a random robust optimization algorithm enhances the adaptability of the system to wind power and photovoltaic output fluctuation. And the fineness, the reliability and the dynamic scene adaptability of the scheduling link are improved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy and intelligent dispatching technology, and in particular to an optimized dispatching method for integrated energy systems containing gas-liquid phase change carbon dioxide energy storage. Background Technology

[0002] With the accelerated global energy transition, renewable energy sources such as wind and solar power are being connected to the grid on a large scale. However, the intermittency and volatility of their output pose significant challenges to the stable operation of the power system and energy consumption. Meanwhile, under the dual-carbon goals, reducing carbon emissions from the energy system and improving low-carbon efficiency have become core industry demands. Integrated energy systems can effectively alleviate these problems through multi-energy coupling and multi-device collaboration, with energy storage technology being a key support.

[0003] Currently, traditional technologies such as electrochemical energy storage suffer from drawbacks such as low energy density, limited cycle life, and high cost. Gas-liquid phase change carbon dioxide (CO2) energy storage, with its advantages of high energy density, long cycle life, and environmental friendliness, is gradually becoming a research hotspot. However, existing research on the optimal scheduling of integrated energy systems incorporating CO2 energy storage often fails to fully consider the impact of the entire lifecycle cost of energy storage power stations and lacks sufficient optimization for system robustness under fluctuations in renewable energy sources. This makes it difficult to achieve an optimal balance between low-carbon benefits and economic efficiency in scheduling schemes, and also prevents the full realization of the technological potential of CO2 energy storage.

[0004] Therefore, traditional energy system scheduling schemes suffer from technical problems such as insufficient precision and reliability, and poor adaptability to dynamic scenarios. Summary of the Invention

[0005] This invention provides an optimized scheduling method for an integrated energy system that incorporates gas-liquid phase change carbon dioxide energy storage, in order to address the shortcomings of traditional energy system scheduling schemes that are not precise or reliable enough and have poor adaptability to dynamic scenarios.

[0006] On the one hand, the present invention provides an optimized scheduling method for an integrated energy system containing gas-liquid phase change carbon dioxide energy storage, comprising: Establish a comprehensive energy system model that includes gas-liquid phase change carbon dioxide energy storage; Based on the aforementioned integrated energy system model, with low carbon emissions and minimum system cost as optimization objectives, a system optimization scheduling model is established that incorporates the full life cycle cost of energy storage power stations and a tiered carbon trading mechanism with tiered pricing. The robust optimization algorithm based on the stochastic robust factor solves the system optimization scheduling model, determines the optimal system cost, and outputs the optimized scheduling scheme.

[0007] According to the integrated energy system optimization and scheduling method for carbon dioxide energy storage with gas-liquid phase change provided by the present invention, an integrated energy system model for carbon dioxide energy storage with gas-liquid phase change is established, including: Identify the core equipment in an integrated energy system containing gas-liquid phase change carbon dioxide energy storage; Establish a mathematical model for each core piece of equipment; Based on all the mathematical models of the equipment, a comprehensive energy system model including gas-liquid phase change carbon dioxide energy storage is constructed.

[0008] According to the integrated energy system optimization and scheduling method with gas-liquid phase change carbon dioxide energy storage provided by the present invention, the core equipment includes thermal power units, cogeneration units, and gas-liquid phase change carbon dioxide energy storage equipment. Establish a mathematical model for each core device, including: Establish mathematical models of carbon capture power plants corresponding to thermal power units; Establish mathematical models of cogeneration units; Establish a carbon energy storage operation status model corresponding to the gas-liquid phase change carbon dioxide energy storage device.

[0009] According to the integrated energy system optimization scheduling method for carbon dioxide energy storage with gas-liquid phase change provided by the present invention, based on the integrated energy system model, and with low carbon emissions and minimum system cost as optimization objectives, a system optimization scheduling model is established that incorporates the full life-cycle cost of energy storage power stations and a tiered carbon trading mechanism with tiered pricing, including: Establish a system total cost function that includes the full life cycle cost of energy storage power stations and the tiered pricing cost of carbon trading; Set system constraints for power balance, joint operation of equipment, and energy storage of equipment; With low carbon emissions and minimum system cost as the optimization objectives, a system optimization scheduling model is constructed based on the total system cost function and the system constraints.

[0010] According to the integrated energy system optimization and scheduling method for carbon dioxide energy storage including gas-liquid phase change provided by the present invention, a total system cost function is established that includes the full life-cycle cost of the energy storage power station and the tiered carbon trading cost with tiered pricing, including: Determine the full life-cycle cost of energy storage power stations and the tiered carbon trading costs with differentiated pricing. The energy storage cost is determined based on the total lifecycle cost of the energy storage power station. Determine the costs of multi-source combined operation, wind and solar curtailment, and pumped storage; The total system cost function is obtained by summing the costs of tiered carbon trading, energy storage, multi-source joint operation, wind and solar curtailment, and pumped storage.

[0011] The integrated energy system optimization and scheduling method for carbon dioxide energy storage with gas-liquid phase change provided by the present invention determines the tiered carbon trading cost with differentiated pricing, including: Determine the carbon emission deviation of the integrated energy system; When the carbon emission deviation is in the deep negative carbon range, the first basic compensation term and the first linear compensation term are used to calculate the tiered carbon trading cost. When the carbon emission deviation is in the slightly negative carbon range, the second basic compensation term and the second linear compensation term are used to calculate the tiered carbon trading cost. When the carbon emission deviation is within the acceptable range, the tiered carbon trading cost is obtained by linearly calculating the carbon emission deviation based on the first compensation coefficient. When the carbon emission deviation is in the slightly excessive carbon range, the tiered carbon trading cost is directly calculated based on the carbon emission deviation. When the carbon emission deviation is in the moderately excessive carbon range, the first basic penalty term and the first linear penalty term are used to calculate the tiered carbon trading cost. When the carbon emission deviation is in the severely over-carbon range, the second basic penalty term and the second linear penalty term are used to calculate the tiered carbon trading cost.

[0012] The integrated energy system optimization and scheduling method for carbon dioxide energy storage with gas-liquid phase change provided by the present invention determines the multi-source joint operation cost, including: Determine the costs of thermal power units, natural gas, electricity-to-gas conversion, and carbon sequestration; The costs of the thermal power units, natural gas, electricity-to-gas conversion, and carbon sequestration are considered as the costs of multi-source joint operation.

[0013] According to the integrated energy system optimization scheduling method for carbon dioxide energy storage including gas-liquid phase change provided by the present invention, the energy storage cost is determined based on the full life cycle cost of the energy storage power station, including: Determine the investment cost, operation and maintenance cost, battery swapping cost, charging cost, depreciation revenue, and total on-grid electricity volume of the energy storage power station throughout its entire life cycle; The total lifecycle cost of the energy storage power station is calculated by summing the investment cost, operation and maintenance cost, battery swapping cost, charging cost, and depreciation revenue. The cost per kilowatt-hour of the energy storage power station is calculated by dividing the total life cycle cost of the energy storage power station by the total grid-connected electricity volume over the entire life cycle. The energy storage cost is obtained by multiplying the cost per kilowatt-hour by the pre-obtained discharge power at the corresponding time.

[0014] The integrated energy system optimization and scheduling method for carbon dioxide energy storage with gas-liquid phase change provided by the present invention sets system constraints for power balance, joint operation of equipment, and energy storage of equipment, including: Based on the balance between electrical load and thermal load, which are respectively equal to the corresponding energy supply and energy storage charging and discharging regulation, a constraint condition for the balance of electrical and thermal power is established. Based on the operating boundaries of each device in the integrated energy system, establish joint operation constraints for the devices; Based on the charging and discharging behavior, energy storage capacity, and output range of energy storage devices, energy storage constraints are established. The electrical and thermal power balance constraints, the equipment joint operation constraints, and the energy storage constraints are used as system constraints.

[0015] The integrated energy system optimization scheduling method for carbon dioxide energy storage with gas-liquid phase change provided by the present invention solves the system optimization scheduling model based on a robust optimization algorithm with a stochastic robust factor, determines the optimal system cost, and outputs an optimized scheduling scheme, including: Acquire the electrical and thermal load data and wind and solar power output forecast data of the integrated energy system within a set time period, and set the initial value of the stochastic robustness factor; With minimizing the total cost function of the system as the solution objective, and in conjunction with the system constraints, the cost-optimal solution and scheduling strategy corresponding to the current random robustness factor value are solved. The ratio of the difference between the system cost under the current random robust factor value and the minimum cost without robust constraints is used as the relative regret value. The value of the random robust factor is iteratively adjusted with the goal of minimizing the relative regret value, so as to obtain the cost-optimal solution and scheduling strategy under each random robust factor value. The process continues iterating until an infeasible solution is encountered. The cost-optimal solution under the previous random robust factor value of the infeasible solution is taken as the system cost-optimal value, and the scheduling strategy corresponding to the cost-optimal solution is taken as the optimized scheduling scheme.

[0016] This invention provides an optimized scheduling method for integrated energy systems incorporating gas-liquid phase-change carbon dioxide energy storage. By establishing an integrated energy system model with gas-liquid phase-change carbon dioxide energy storage, and accurately incorporating the life-cycle cost of the energy storage power station and a tiered carbon trading mechanism with differentiated pricing, an optimized scheduling model is established. This model is then solved using a robust optimization algorithm based on stochastic robust factors. This approach achieves an optimal balance between low-carbon benefits and economic efficiency while fully leveraging the high energy density and long cycle life advantages of gas-liquid phase-change carbon dioxide energy storage. On one hand, the inclusion of the life-cycle cost makes energy storage cost accounting more accurate, while the tiered carbon trading mechanism promotes refined carbon emission management, effectively reducing system carbon costs. On the other hand, the stochastic robust optimization algorithm enhances the system's adaptability to fluctuations in wind and solar power output, improves the renewable energy absorption rate, system operational robustness, and dynamic scenario adaptability. The final optimized scheduling scheme can guide the integrated energy system to achieve optimal equipment coordination in different scenarios, thus providing strong support for the low-carbon and intelligent operation of the energy system. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the integrated energy system optimization scheduling method for carbon dioxide energy storage with gas-liquid phase change provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the predicted curves for electrical and thermal loads and wind and solar power during the verification process; Figure 3 These are schematic diagrams illustrating the carbon emissions for each of the five scenarios; Figure 4 These are schematic diagrams illustrating the amount of wind and solar power curtailment in each of the five scenarios; Figure 5 This is a schematic diagram showing the power scheduling results of each generating unit; Figure 6 This is a diagram showing the maximum relative regret value and system cost corresponding to scenario 5; Figure 7 This is a schematic diagram showing the relative regret value distribution of the stochastic optimization algorithm SO and the robust optimization algorithm SPRO based on the stochastic robust factor in various scenarios. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0020] The following is combined Figures 1 to 7 This invention describes the detailed scheme of the integrated energy system optimization scheduling method with gas-liquid phase change carbon dioxide energy storage provided in the embodiments of the present invention.

[0021] like Figure 1 As shown in the figure, the integrated energy system optimization scheduling method with gas-liquid phase change carbon dioxide energy storage provided in this embodiment of the invention mainly includes the following steps: Step 110: Establish a comprehensive energy system model including gas-liquid phase change carbon dioxide energy storage.

[0022] This step mainly involves constructing a comprehensive energy system model that includes gas-liquid phase change carbon dioxide energy storage, thereby creating a physical model of the comprehensive energy system and providing a physical model basis for subsequent optimized scheduling.

[0023] Step 120: Based on the integrated energy system model, with the optimization objectives of low carbon emissions and minimum system cost, establish a system optimization scheduling model that incorporates the full life cycle cost of energy storage power stations and tiered pricing into a tiered carbon trading mechanism.

[0024] In this step, a system optimization scheduling model is constructed to clarify the optimization objective and provide a data solution basis for subsequent optimization scheduling.

[0025] Step 130: Solve the system optimization scheduling model using a robust optimization algorithm based on a stochastic robust factor, determine the optimal system cost, and output the optimized scheduling scheme.

[0026] In this step, the robust optimization algorithm of random robust factor is used to optimize the solution, which can realize the accurate transformation from goal to decision, improve the system's operational robustness and dynamic scenario adaptability, and achieve fine and reliable scheduling optimization.

[0027] In one embodiment, a comprehensive energy system model incorporating gas-liquid phase change carbon dioxide energy storage is established, specifically including: First, identify the core equipment in an integrated energy system that includes gas-liquid phase change carbon dioxide energy storage.

[0028] In this embodiment, the core equipment specifically includes thermal power units, combined heat and power units, and gas-liquid phase change carbon dioxide energy storage equipment.

[0029] Then, establish the corresponding mathematical model for each core device.

[0030] In a specific implementation, a mathematical model for each core device is established, including: On the one hand, establish mathematical models of carbon capture power plants corresponding to thermal power units.

[0031] In this embodiment, the mathematical model of the carbon capture power plant can be expressed as: (1) in, for Total output power of thermal power plants during the period; for Time period Net output power of the thermal power unit; For carbon capture Time period Energy consumption of thermal power units; Fixed energy consumption for carbon capture; Energy consumption per unit of carbon dioxide captured; for Time period The total amount of carbon dioxide captured by Taiwan's thermal power units; The mass of carbon dioxide to be processed supplied to the storage tank; For carbon capture efficiency; This refers to the flue gas split ratio; For the first Carbon emission intensity of Taiwan's thermal power units.

[0032] On the other hand, establish mathematical models of cogeneration units corresponding to cogeneration units.

[0033] In this embodiment, the mathematical model of the combined heat and power unit can be expressed as: (2) in, for Total power output of the combined heat and power unit during the period; for The electrical power output of the combined heat and power (CHP) unit connected to the grid during a given period; for The electrical power consumed by the electric gas generator during a given period; for The electrical power consumed by the combined heat and power unit during a given period; L BC,t for t The power correlation value of a combined heat and power unit under a certain reference operating condition during a certain period. L CD,t for tMinimum electrical power output correlation value of cogeneration units during specific time periods. For cogeneration units in Thermal power output during the time period; This is the maximum thermal power output of the combined heat and power unit. , These are the maximum and minimum electrical power of the combined heat and power unit, respectively. , , These are the electrothermal conversion coefficient when the output power of the cogeneration unit is at its minimum, the electrothermal conversion coefficient when the output power is at its maximum, and the linear supply slope of the thermal power and electrical power of the cogeneration unit, respectively. The point where the extension of line BC intersects the vertical axis is the intersection.

[0034] On the other hand, we need to establish a carbon energy storage operation status model for gas-liquid phase change carbon dioxide energy storage devices.

[0035] In this embodiment, the carbon energy storage operating state model can be represented as: (3) in, , They are respectively CES charging and discharging power during the specified time period; , These refer to the number of compressor stages and turbine stages, respectively. The specific heat capacity at constant pressure of carbon dioxide; , These are the efficiencies of each level of compressor and turbine; , These are the isentropic efficiencies of the compressor and turbine, respectively. , These are compression ratio and turbine ratio, respectively. The adiabatic coefficient; , For the gas temperature of the compressor and turbine; for Energy storage capacity during the CES period; for The curtailment of wind and solar power during the CES period; , They are respectively The amount of wind and solar power curtailed during a given period.

[0036] Finally, based on all the equipment mathematical models, a comprehensive energy system model including gas-liquid phase change carbon dioxide energy storage was constructed.

[0037] In one embodiment, based on a comprehensive energy system model, and with the optimization objectives of low carbon emissions and minimum system cost, a system optimization scheduling model is established that incorporates the entire life-cycle cost of energy storage power stations and tiered pricing into a tiered carbon trading mechanism. Specifically, this includes: On the one hand, a total system cost function is established that includes the full life-cycle cost of energy storage power stations and the tiered pricing of carbon trading.

[0038] Understandably, the total system cost function can characterize the overall cost of an integrated energy system, which includes the full life-cycle cost of energy storage power stations and the tiered carbon trading cost with tiered pricing.

[0039] On the other hand, system constraints are set to constrain power balance, joint operation of equipment, and energy storage of equipment.

[0040] Understandably, system constraints can define feasible operating boundaries for the system's optimized scheduling model, ensuring the rationality and feasibility of the scheduling scheme.

[0041] Finally, with low carbon emissions and minimum system cost as the optimization objectives, an optimal scheduling model for the system is constructed based on the total system cost function and system constraints.

[0042] In a specific implementation, a total system cost function is established that includes the entire lifecycle cost of the energy storage power station and the tiered pricing cost of carbon trading. Specifically, this includes: On the one hand, it is necessary to determine the full life-cycle cost of energy storage power stations and the tiered carbon trading costs with differentiated pricing.

[0043] In this embodiment, determining the tiered carbon trading cost with differentiated pricing specifically includes: First, determine the carbon emission deviation of the integrated energy system.

[0044] In the first scenario, when the carbon emission deviation is in the deep negative carbon range, the first basic compensation term and the first linear compensation term are used to calculate the tiered carbon trading cost.

[0045] In the second scenario, when the carbon emission deviation is in the slightly negative carbon range, the second basic compensation term and the second linear compensation term are used to calculate the tiered carbon trading cost.

[0046] In the third scenario, when the carbon emission deviation is within the acceptable range, the tiered carbon trading cost is obtained by linearly calculating the carbon emission deviation based on the first compensation coefficient.

[0047] In the fourth scenario, when the carbon emission deviation is in the slightly over-carbon range, the tiered carbon trading cost is directly calculated based on the carbon emission deviation.

[0048] In the fifth scenario, when the carbon emission deviation is in the moderately excessive carbon range, the first basic penalty term and the first linear penalty term are used to calculate the tiered carbon trading cost.

[0049] In the sixth scenario, when the carbon emission deviation is in the severely over-carbon range, the second basic penalty term and the second linear penalty term are used to calculate the tiered carbon trading cost.

[0050] Specifically, the cost of tiered carbon trading can be expressed as: (4) in, F T For tiered carbon trading costs, To participate in carbon trading, the unit price of carbon dioxide; for Carbon emission deviation over a given period; The price compensation rate for the negative carbon range; This represents the price growth rate of the positive carbon range; This refers to the width of the carbon trading range.

[0051] On the other hand, the cost of energy storage is determined based on the total life cycle cost of the energy storage power station.

[0052] In this embodiment, the energy storage cost is determined based on the total lifecycle cost of the energy storage power station, specifically including: First, determine the investment cost, operation and maintenance cost, battery swapping cost, charging cost, depreciation revenue, and total on-grid electricity volume of the energy storage power station throughout its entire life cycle.

[0053] Specifically, the initial investment cost of an energy storage power station can be expressed as: (5) in, F in For investment costs, The unit power investment cost of the energy storage power station; The unit capacity investment cost of an energy storage power station; This refers to the rated power of the energy storage power station; This refers to the rated capacity of the energy storage power station.

[0054] The operation and maintenance cost of an energy storage power station can be expressed as: (6) in, F om For maintenance costs, This represents the annual operation and maintenance cost coefficient for energy storage power stations. The discount rate for energy storage power stations. The years in which the energy storage power station has been in operation. This refers to the lifespan of the energy storage power station.

[0055] The battery swapping cost of an energy storage power station can be expressed as: (7) in, F rep To reduce battery swapping costs, f rep The unit battery swap cost coefficient is M, where M is the number of cycles corresponding to the number of battery swaps. E D This refers to the capacity of a single battery swap.

[0056] The charging cost of an energy storage power station can be expressed as: (8) in, F cha For charging costs, K Indicates the number of charging cycles. This refers to the annual cycle count of an energy storage power station. For the depth of charge and discharge of energy storage power stations, E This refers to the rated capacity of the energy storage power station. P c Cost per unit of charging power.

[0057] The depreciation revenue of an energy storage power station can be expressed as: (9) in, F rec For depreciation income, This represents the depreciation rate of the energy storage power station.

[0058] Then, the total life cycle cost of the energy storage power station is calculated by summing up the investment cost, operation and maintenance cost, battery swapping cost, charging cost, and depreciation revenue.

[0059] Next, the cost per kilowatt-hour of the energy storage power station is calculated by dividing the total life cycle cost of the energy storage power station by the total grid-connected electricity volume over the entire life cycle.

[0060] In this embodiment, the total electricity consumption over the entire lifecycle can be expressed as: (10) in, Total electricity consumption over the entire lifecycle. U The total number of charge-discharge cycles over the entire lifespan. For the efficiency of energy storage power stations, Annual cycle decay rate T u For the first u The time span of each cycle EThis refers to the rated capacity of the energy storage power station. This refers to the depth of charge / discharge.

[0061] Specifically, the cost per kilowatt-hour of an energy storage power station can be expressed as: (11) in, F FLC This indicates the cost per kilowatt-hour.

[0062] Finally, the energy storage cost is obtained by multiplying the cost per kilowatt-hour by the pre-obtained discharge power at the corresponding time.

[0063] In this embodiment, the energy storage cost can be expressed as: (12) in, For energy storage costs, For gas-liquid phase change carbon dioxide energy storage devices t Discharge power during a given period.

[0064] On the other hand, the costs of multi-source joint operation, wind and solar curtailment, and pumped storage should be determined.

[0065] In this embodiment, determining the cost of multi-source joint operation specifically includes: First, determine the costs of thermal power units, natural gas, electricity-to-gas conversion, and carbon sequestration.

[0066] Then, the costs of thermal power units, natural gas, electricity-to-gas conversion, and carbon sequestration are used as the costs of multi-source joint operation.

[0067] Specifically, the cost of multi-source joint operation can be expressed as: (13) in, For the cost of thermal power units, Natural gas costs For the cost of electricity to produce gas, For carbon sequestration costs, for Time period Start-up and shutdown status of thermal power units in Taiwan. This represents the number of thermal power units. , , The first The coal consumption cost coefficient of a Taiwanese thermal power unit Price per unit of natural gas V t BUY Let t represent the natural gas purchase volume during time period t. This is the operating cost coefficient for the electro-gas generation equipment. The cost of purchasing one unit of carbon dioxide, E t P2G This represents the amount of carbon dioxide consumed during the period t when converting electricity to gas. This is the carbon sequestration cost coefficient. for Total energy consumption for carbon capture during a given period for The total amount of carbon dioxide stored during a given period.

[0068] The cost of wind and solar power curtailment can be expressed as: (14) in, F A To account for the cost of wind and solar power curtailment , These are the penalty cost coefficients for wind curtailment and solar curtailment, respectively. P t WA for t Wind power output during the period P t VA for t Photovoltaic power output during a given time period.

[0069] The cost of pumped storage can be expressed as: (15) in, For pumped storage costs, and These are the costs of a single pumping and generating cycle for the pumped-storage unit; and They are respectively t The time period represents the binary variable that indicates the start-up and shutdown of the pumped storage unit.

[0070] Finally, the total system cost function is obtained by summing the costs of tiered carbon trading, energy storage, multi-source joint operation, wind and solar curtailment, and pumped storage.

[0071] In this embodiment, the total system cost function can be expressed as: (16) in, F The total system cost, For the cost of thermal power units, Natural gas costs For the cost of electricity to produce gas, For carbon sequestration costs, For tiered carbon trading costs, To account for the cost of wind and solar power curtailment For energy storage costs, This refers to the cost of pumped storage hydroelectric power.

[0072] In one embodiment, system constraints are set for power balance, joint operation of equipment, and energy storage of equipment, specifically including: On the one hand, based on the balance between electrical load and thermal load, which are respectively equal to the corresponding energy supply and energy storage charging and discharging regulation, a constraint condition for the balance of electrical and thermal power is established.

[0073] In this embodiment, the electrical and thermal power balance constraint condition can be expressed as: (17) in, For conventional power supply t Power output during a given time period For cogeneration units in t Output power during the time period They are respectively Forecasted wind and solar power for different time periods Carbon dioxide energy storage for gas-liquid phase change t Discharge power during the period For other power sources t Power output during a given time period for t Electricity load demand during different time periods For electro-gas technology in t Power consumption during a given time period For carbon capture technology in t Power consumption during a given time period Carbon dioxide energy storage for gas-liquid phase change t Charging power during the period For other electrical loads t Power consumption during a given time period For cogeneration units in t Output thermal power during the time period For gas boilers t Output thermal power during the time period This indicates that carbon dioxide storage is a form of gas-liquid phase change energy storage. t Output thermal power during the time period for t Heat load during a given time period.

[0074] On the other hand, based on the operating boundaries of each device in the integrated energy system, constraints for joint operation of the devices are established.

[0075] In this embodiment, the constraints for joint operation of the equipment can be expressed as: (18) in, , These are the upper and lower limits of the electrical output of a combined heat and power unit; , These are the upper and lower limits of the thermal output of a combined heat and power unit, respectively. For combined heat and power units, ramp-up power constraints; , These are the upper and lower limits of the output of the CCPP, respectively. Power constraints for thermal power units during ramp-up; , These are the minimum start-up and shutdown times for the unit, respectively. , respectively the unit to The time period includes consecutive power-on and power-off times; , These represent the maximum and minimum operating power of the electric gas generator, respectively.

[0076] On the other hand, energy storage constraints are established based on the charging and discharging behavior of energy storage devices, energy storage capacity, and output range.

[0077] In this embodiment, the energy storage constraint can be expressed as: (19) in, , These represent the upper and lower limits of carbon dioxide energy storage output. , These represent the charging and discharging states of carbon dioxide energy storage. This is the upper limit for energy storage. , These are the upper and lower limits of energy storage thermal power, respectively. This is the upper limit of the volume of the carbon dioxide storage tank.

[0078] Finally, the constraints on the balance of electrical and thermal power, the constraints on the joint operation of equipment, and the constraints on energy storage are used as system constraints.

[0079] In one embodiment, a robust optimization algorithm based on a stochastic robust factor is used to solve the system optimal scheduling model, determine the optimal system cost, and output an optimized scheduling scheme, specifically including: First, acquire the electrical and thermal load data and wind and solar power output forecast data of the integrated energy system within a set time period, and set the initial value of the stochastic robustness factor.

[0080] In practical applications, you can input the electric and heat load data and wind and solar power output forecast data within 24 hours, initialize a set of random scenarios covering the fluctuations of new energy sources, and set an initial value for the random robustness factor p. For example, p=0 represents deterministic optimization that does not consider robustness at all.

[0081] It is understandable that the wind power and photovoltaic output forecast data refers to the power output data of wind power stations and photovoltaic power stations that can be output in a specific future period, estimated by relevant forecasting methods under the scenario of optimized scheduling of integrated energy systems including gas-liquid phase change carbon dioxide energy storage.

[0082] Then, with minimizing the total system cost function as the solution objective and in combination with system constraints, the cost-optimal solution and scheduling strategy corresponding to the current random robustness factor value are solved.

[0083] Next, the ratio of the difference between the system cost under the current random robust factor value and the minimum cost without robust constraints is taken as the relative regret value. With minimizing the relative regret value as the adjustment objective, the value of the random robust factor is iteratively adjusted to obtain the cost-optimal solution and scheduling strategy under each random robust factor value.

[0084] Finally, the process continues iterating until an infeasible solution is found. The cost-optimal solution under the previous random robust factor value of the infeasible solution is taken as the system cost-optimal value, and the scheduling strategy corresponding to the cost-optimal solution is taken as the optimized scheduling scheme.

[0085] It is understandable that an infeasible solution refers to a scheduling scheme in which the optimization model has no constraints that satisfy all constraints at a certain value of p.

[0086] In this embodiment, the solution process can be understood as a minimization problem, and its deterministic feasible solution is: Scene set is objective function For the scene The system cost under different data, assuming a random robustness factor. For solutions with the same structure, the robustness level can be measured by the following inequality: (20) in, For the scene The cost-optimal solution in the process.

[0087] If a deterministic feasible solution For all If all of the above inequalities are satisfied, then it is said that... It is robust to p.

[0088] In the above inequality, the denominator on the left side is the relative regret value, and the numerator is the absolute regret value. In robust optimization based on stochastic robustness factors, the relative regret value is usually considered. Therefore, it can be rewritten to reflect the absolute regret or running cost, as follows: (twenty one) Integrating robust constraints into the cost-optimal solution can be expressed as: (twenty two) When the stochastic robustness factor is large enough, the problem will revert to its standard stochastic optimization form. However, by reducing the value of the stochastic robustness factor, the robustness of the system can be increased, thereby obtaining a cost-optimal solution with lower regret and higher cost.

[0089] To verify the advantages of the optimized scheduling method provided in this embodiment, a case study was conducted using a 24-hour period. The solution was performed using the Gurobi solver in the Matlab platform. The predicted curves for electricity, heat load, wind, and solar power can be found in [reference needed]. Figure 2 As shown.

[0090] In practical applications, five scenarios can be set up. Scenario 1 is the most basic scenario, where carbon capture and thermal power unit coupling are not considered, and power generation gasification operates independently. Scenario 2, based on Scenario 1, considers the impact of combined operation of thermal power unit, cogeneration unit, and power generation gasification. Building upon Scenario 2, Scenario 3 considers tiered carbon trading. Scenario 4 uses electrochemical energy storage. Scenario 5 uses gas-liquid phase change carbon dioxide as energy storage. The carbon emissions for each of the five scenarios can be found in [reference needed]. Figure 3 As shown.

[0091] Depend on Figure 3 It can be seen that scenarios 1 and 2 have higher carbon emissions due to the lack of energy storage devices. Scenario 3 has lower carbon emissions because the introduction of a tiered carbon trading mechanism gradually increases the cost of carbon emissions. Exceeding a certain emission level requires paying a higher carbon price. Under the optimization mechanism that minimizes the total system cost, the entire integrated energy system is prompted to reduce carbon emissions to lower economic costs. Scenario 5 has higher carbon emissions during peak load periods, but due to liquid carbon dioxide energy storage, the overall carbon emissions are still minimized.

[0092] The amount of wind and solar power curtailment in each of the five scenarios mentioned above can be found in [reference needed]. Figure 4 As shown, by Figure 4 It can be seen that the amount of wind and solar curtailment in Scenario 4 is higher than in other scenarios because the EES (Energy Storage System) cannot store more energy during the lowest load period in Scenario 4. Scenario 1, Scenario 2, and Scenario 3 have almost the same amount of wind and solar curtailment because there is no energy storage involved. Scenario 5 has the smallest amount of wind and solar curtailment due to the participation of energy storage. Meanwhile, after 8 hours, all wind and solar energy are absorbed by the system.

[0093] The simulation results for scenario 5 will be further analyzed in the future. Figure 5 Subplots (a) and (b) show the electrical and thermal power scheduling results for each unit, respectively. See also... Figure 5The output of thermal power units exhibits relatively regular fluctuations, mainly adjusted according to load demand, providing stable power over a relatively long period. Combined heat and power (CHP) units show a more uniform output pattern throughout the cycle. Because these units simultaneously produce heat and electricity, their output has a certain time dependence. During the 0-8 hour period, when the load is at its lowest, liquid carbon dioxide storage, electrostatic gasification, and pumped hydro storage also play important roles.

[0094] Figure 6 The maximum relative regret value and system cost corresponding to scenario 5 are shown, by Figure 6 It can be seen that as the random robustness factor p decreases, the system cost increases slightly. When p = 0.0076, an infeasible solution appears. Therefore, the last feasible solution p = 0.0077 is taken as the optimal solution. At this time, compared with pure randomness, the expected cost increases by 4.84%, and the maximum relative regret increases by 41.3%, which verifies the effectiveness of the robust optimization scheme based on the random robustness factor.

[0095] Figure 7 The distribution of relative regret values ​​is shown for various scenarios using the stochastic optimization algorithm SO and the robust optimization algorithm SPRO based on a stochastic robust factor. In the stochastic optimization algorithm, the maximum relative regret reaches a minimum in some scenarios, with profits close to the optimal solution. However, in scenarios 4, 3, 1, and 5, the maximum relative regret value is relatively high. This problem is eliminated by the robust optimization algorithm based on a stochastic robust factor. Profits in all scenarios are close to the optimal value, thus reducing the maximum relative regret value, and making the distribution of both profit and relative regret values ​​more even across scenarios.

[0096] In summary, the integrated energy system optimization scheduling method for carbon dioxide energy storage with gas-liquid phase change provided in this invention adopts a robust optimization algorithm based on a stochastic robust factor, considers the stochastic optimization situation of different operating scenarios, and optimizes scheduling decisions by changing the stochastic robust factor. It can significantly optimize economic benefits and system robustness while improving the capacity for new energy absorption and reducing carbon emissions, and has important research significance and application value.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the scheduling of a comprehensive energy system including gas-liquid phase change carbon dioxide energy storage, characterized in that, include: Establish a comprehensive energy system model that includes gas-liquid phase change carbon dioxide energy storage; Based on the aforementioned integrated energy system model, with low carbon emissions and minimum system cost as optimization objectives, a system optimization scheduling model is established that incorporates the full life cycle cost of energy storage power stations and a tiered carbon trading mechanism with tiered pricing. The robust optimization algorithm based on the stochastic robust factor solves the system optimization scheduling model, determines the optimal system cost, and outputs the optimized scheduling scheme.

2. The method for optimizing and scheduling a comprehensive energy system with gas-liquid phase change carbon dioxide energy storage according to claim 1, characterized in that, Establish a comprehensive energy system model incorporating gas-liquid phase change carbon dioxide energy storage, including: Identify the core equipment in an integrated energy system containing gas-liquid phase change carbon dioxide energy storage; Establish a mathematical model for each core piece of equipment; Based on all the mathematical models of the equipment, a comprehensive energy system model including gas-liquid phase change carbon dioxide energy storage is constructed.

3. The method for optimizing and scheduling a comprehensive energy system with gas-liquid phase change carbon dioxide energy storage according to claim 2, characterized in that, The core equipment includes thermal power units, combined heat and power units, and gas-liquid phase change carbon dioxide energy storage equipment. Establish a mathematical model for each core device, including: Establish mathematical models of carbon capture power plants corresponding to thermal power units; Establish mathematical models of cogeneration units; Establish a carbon energy storage operation status model corresponding to the gas-liquid phase change carbon dioxide energy storage device.

4. The method for optimizing and scheduling a comprehensive energy system with gas-liquid phase change carbon dioxide energy storage according to claim 1, characterized in that, Based on the aforementioned integrated energy system model, and with the optimization objectives of low carbon emissions and minimum system cost, a system optimization scheduling model is established that incorporates the full life-cycle cost of energy storage power stations and tiered pricing into a tiered carbon trading mechanism, including: Establish a system total cost function that includes the full life cycle cost of energy storage power stations and the tiered pricing cost of carbon trading; Set system constraints for power balance, joint operation of equipment, and energy storage of equipment; With low carbon emissions and minimum system cost as the optimization objectives, a system optimization scheduling model is constructed based on the total system cost function and the system constraints.

5. The method for optimizing and scheduling a comprehensive energy system with gas-liquid phase change carbon dioxide energy storage according to claim 4, characterized in that, Establish a system total cost function that includes the entire lifecycle cost of energy storage power stations and the tiered pricing cost of carbon trading, including: Determine the full life-cycle cost of energy storage power stations and the tiered carbon trading costs with differentiated pricing. The energy storage cost is determined based on the total lifecycle cost of the energy storage power station. Determine the costs of multi-source combined operation, wind and solar curtailment, and pumped storage; The total system cost function is obtained by summing the costs of tiered carbon trading, energy storage, multi-source joint operation, wind and solar curtailment, and pumped storage.

6. The method for optimizing and scheduling a comprehensive energy system with gas-liquid phase change carbon dioxide energy storage according to claim 5, characterized in that, Determine the tiered carbon trading costs with differentiated pricing, including: Determine the carbon emission deviation of the integrated energy system; When the carbon emission deviation is in the deep negative carbon range, the first basic compensation term and the first linear compensation term are used to calculate the tiered carbon trading cost. When the carbon emission deviation is in the slightly negative carbon range, the second basic compensation term and the second linear compensation term are used to calculate the tiered carbon trading cost. When the carbon emission deviation is within the acceptable range, the tiered carbon trading cost is obtained by linearly calculating the carbon emission deviation based on the first compensation coefficient. When the carbon emission deviation is in the slightly excessive carbon range, the tiered carbon trading cost is directly calculated based on the carbon emission deviation. When the carbon emission deviation is in the moderately excessive carbon range, the first basic penalty term and the first linear penalty term are used to calculate the tiered carbon trading cost. When the carbon emission deviation is in the severely over-carbon range, the second basic penalty term and the second linear penalty term are used to calculate the tiered carbon trading cost.

7. The method for optimizing the scheduling of a comprehensive energy system with gas-liquid phase change carbon dioxide energy storage according to claim 5, characterized in that, Determine the cost of multi-source joint operation, including: Determine the costs of thermal power units, natural gas, electricity-to-gas conversion, and carbon sequestration; The costs of the thermal power units, natural gas, electricity-to-gas conversion, and carbon sequestration are considered as the costs of multi-source joint operation.

8. The method for optimizing and scheduling a comprehensive energy system with gas-liquid phase change carbon dioxide energy storage according to claim 5, characterized in that, Based on the total lifecycle cost of the energy storage power station, the energy storage cost is determined, including: Determine the investment cost, operation and maintenance cost, battery swapping cost, charging cost, depreciation revenue, and total on-grid electricity volume of the energy storage power station throughout its entire life cycle; The total lifecycle cost of the energy storage power station is calculated by summing the investment cost, operation and maintenance cost, battery swapping cost, charging cost, and depreciation revenue. The cost per kilowatt-hour of the energy storage power station is calculated by dividing the total life cycle cost of the energy storage power station by the total grid-connected electricity volume over the entire life cycle. The energy storage cost is obtained by multiplying the cost per kilowatt-hour by the pre-obtained discharge power at the corresponding time.

9. The method for optimizing and scheduling a comprehensive energy system with gas-liquid phase change carbon dioxide energy storage according to claim 4, characterized in that, Define system constraints for power balance, joint operation of equipment, and energy storage of equipment, including: Based on the balance between electrical load and thermal load, which are respectively equal to the corresponding energy supply and energy storage charging and discharging regulation, a constraint condition for the balance of electrical and thermal power is established. Based on the operating boundaries of each device in the integrated energy system, establish joint operation constraints for the devices; Based on the charging and discharging behavior, energy storage capacity, and output range of energy storage devices, energy storage constraints are established. The electrical and thermal power balance constraints, the equipment joint operation constraints, and the energy storage constraints are used as system constraints.

10. The method for optimizing and scheduling a comprehensive energy system with gas-liquid phase change carbon dioxide energy storage according to claim 4, characterized in that, A robust optimization algorithm based on a stochastic robust factor is used to solve the system optimization scheduling model, determine the optimal system cost, and output an optimized scheduling scheme, including: Acquire the electrical and thermal load data and wind and solar power output forecast data of the integrated energy system within a set time period, and set the initial value of the stochastic robustness factor; With minimizing the total cost function of the system as the solution objective, and in conjunction with the system constraints, the cost-optimal solution and scheduling strategy corresponding to the current random robustness factor value are solved. The ratio of the difference between the system cost under the current random robust factor value and the minimum cost without robust constraints is used as the relative regret value. The value of the random robust factor is iteratively adjusted with the goal of minimizing the relative regret value, so as to obtain the cost-optimal solution and scheduling strategy under each random robust factor value. The process continues iterating until an infeasible solution is encountered. The cost-optimal solution under the previous random robust factor value of the infeasible solution is taken as the system cost-optimal value, and the scheduling strategy corresponding to the cost-optimal solution is taken as the optimized scheduling scheme.

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