Zero-carbon energy system and method for optimizing configuration thereof

Abstract

This invention provides a zero-carbon energy system and its configuration optimization method, relating to the field of new energy technology. It involves acquiring regional data of the target area and technical assumptions about the zero-carbon energy system; processing the regional data to determine the power generation factor and electricity demand; optimizing the objective function to minimize the total system cost based on the power generation factor, electricity demand, and technical assumptions; obtaining optimized values ​​for the installed capacity and hourly dispatch of multiple devices; and configuring these devices based on these optimized values. By using the minimization of the total system cost as the objective function, the installed capacity and hourly dispatch of each device are collaboratively optimized to configure multiple devices according to these optimized values. This achieves a deep coupling and dispatch mechanism between nuclear thermal energy, electrical energy storage, and renewable energy in the zero-carbon energy system, improving the overall economic efficiency and reliability of the system.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and in particular to zero-carbon energy systems and their configuration optimization methods. Background Technology

[0002] Building a safe, reliable, and economical deeply decarbonized power system requires not only a large-scale expansion of variable renewable energy generation capacity but also the urgent development of diversified energy storage and dispatchable zero-carbon power sources. Among these, battery energy storage offers rapid response and plays a significant role in mitigating intraday fluctuations; hydrogen-based energy storage is considered a potential solution for cross-weekly and even seasonal regulation. Regarding dispatchable zero-carbon power sources, nuclear power, with its high capacity factor and stable output, continues to attract attention amidst growing electricity demand. However, traditional large-scale nuclear power plants face high investment costs, long construction periods, and relatively rigid operating modes, making them difficult to adapt to the flexible dispatch requirements of high-proportion renewable energy systems. Small Modular Reactors (SMRs), with their lower capital threshold, enhanced safety features, flexible deployment potential, and multi-purpose output capabilities, have emerged as a new dispatchable zero-carbon option for future distributed energy systems. Crucially, the modular design and thermal output nature of SMRs make them easy to efficiently couple with thermal energy storage (TES) systems. By ensuring constant and efficient reactor operation, combined with TES storage of thermal energy and on-demand power generation, nuclear power can be transformed from a baseload power source into a resource with flexible adjustment capabilities.

[0003] In existing technological solutions, common zero-carbon energy systems mainly include single renewable energy systems (such as wind and solar) or nuclear energy systems, with some systems incorporating battery energy storage for short-term regulation. Furthermore, existing solutions often employ independent modeling or simple overlay, lacking synergistic optimization of multiple energy forms, particularly lacking deep coupling and dispatch mechanisms between nuclear thermal, hydrogen energy storage, and renewable energy sources, making it difficult to achieve the optimal balance between overall system economy and reliability. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a zero-carbon energy system and its configuration optimization method to alleviate the above-mentioned technical problems.

[0005] In a first aspect, embodiments of the present invention provide a configuration optimization method for a zero-carbon energy system, the zero-carbon energy system comprising: a nuclear energy subsystem, a renewable energy subsystem, an electric energy storage subsystem, and a power grid subsystem; the method comprising: acquiring regional data of a target area and technical assumption information of the zero-carbon energy system; wherein, the regional data includes electricity demand data and meteorological data, and the technical assumption information includes cost parameter assumption information and performance parameter assumption information; processing the regional data to determine the power generation factor and electricity demand; optimizing an objective function to minimize the total system cost based on the power generation factor, electricity demand, and technical assumption information, to obtain optimized values ​​for the installed capacity and hourly dispatch quantity of multiple devices; wherein, the multiple devices include: a reactor, a thermal storage tank, and a steam generator of the nuclear energy subsystem; a wind power generation unit and a solar power generation unit of the renewable energy subsystem; and a battery, an electrolyzer, a hydrogen storage tank, and a fuel cell of the electric energy storage subsystem; and configuring the multiple devices according to the optimized values ​​for the installed capacity and hourly dispatch quantity.

[0006] Optionally, the power energy storage subsystem includes a hydrogen energy storage subsystem; the objective function is configured with constraints; wherein the constraints include: thermal energy balance constraints after nuclear-thermal decoupling in the nuclear energy subsystem and state constraints of the thermal storage tank, state of charge constraints of battery energy storage and material balance constraints of the hydrogen energy storage subsystem.

[0007] Optionally, the regional data is processed to determine the power generation factor and electricity demand, including: normalizing the electricity demand data and determining the electricity demand based on the normalized electricity demand data; and determining the power generation factor based on meteorological data.

[0008] Optionally, the meteorological data includes the total horizontal radiation and ambient temperature of the target area, and the power generation factor includes the solar power generation factor; the power generation factor is determined based on the meteorological data, including: determining the solar power generation factor based on the total horizontal radiation and ambient temperature.

[0009] Optionally, the meteorological data includes the preset height wind speed corresponding to the preset height in the target area, and the power generation factor includes the wind power generation factor; the power generation factor is determined based on the meteorological data, including: determining the wind power generation factor based on the preset height wind speed and the preset wind speed threshold.

[0010] Optionally, the preset wind speed threshold includes a preset cut-in wind speed and a preset rated wind speed; determining the wind power generation factor based on the preset height wind speed and the preset wind speed threshold includes: if the preset height wind speed is less than the preset rated wind speed but not less than the preset cut-in wind speed, determining the wind power generation factor based on the preset height wind speed and the preset rated wind speed.

[0011] Optionally, the preset wind speed threshold also includes a preset cut-out wind speed; determining the wind power generation factor based on the preset height wind speed and the preset wind speed threshold further includes: if the preset height wind speed is less than the preset cut-in wind speed, or if the preset height wind speed is greater than the preset cut-out wind speed, determining the wind power generation factor to be 0; if the preset height wind speed is not greater than the preset cut-out wind speed and not less than the preset rated wind speed, determining the wind power generation factor to be 1.

[0012] Secondly, embodiments of the present invention also provide a zero-carbon energy system, including: a controller, a nuclear energy subsystem, a renewable energy subsystem, an electric energy storage subsystem, and a power grid subsystem; wherein the controller is used to optimize the configuration of the zero-carbon energy system using the method described in the first aspect.

[0013] Optionally, the nuclear energy subsystem includes a reactor, a thermal storage tank, and a steam generator; the renewable energy subsystem includes a wind power generation unit and a solar power generation unit; the power storage subsystem includes a battery energy storage subsystem and a hydrogen energy storage subsystem; wherein the battery energy storage subsystem includes a battery, and the hydrogen energy storage subsystem includes an electrolyzer, a hydrogen storage tank, and a fuel cell.

[0014] Thirdly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method described in the first aspect.

[0015] The embodiments of the present invention bring the following beneficial effects: This invention provides a zero-carbon energy system and its configuration optimization method. First, regional data of the target area and technical assumptions of the zero-carbon energy system are acquired. Then, the regional data is processed to determine the power generation factor and electricity demand. Finally, based on the power generation factor, electricity demand, and technical assumptions, the objective function of minimizing the total system cost is optimized to obtain optimized values ​​for the installed capacity and hourly dispatch of multiple devices. These optimized values ​​are then used to configure the multiple devices. This method, using the minimization of the total system cost as the objective function, collaboratively optimizes the installed capacity and hourly dispatch of each device. This allows for the configuration of multiple devices based on the optimized values, thereby achieving a deep coupling and dispatch mechanism between nuclear thermal energy, electrical energy storage, and renewable energy in the zero-carbon energy system, improving the overall economic efficiency and reliability of the system.

[0016] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a configuration optimization method for a zero-carbon energy system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a zero-carbon energy system provided in an embodiment of the present invention; Figure 3 Flowchart of another configuration optimization method for a zero-carbon energy system provided in an embodiment of the present invention; Figure 4 A schematic diagram of a normalized annual electricity demand curve provided for an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the system cost after optimizing four operating schemes, as provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] To facilitate understanding of this embodiment, the embodiments of the present invention will be described in detail below.

[0022] This invention provides a configuration optimization method for a zero-carbon energy system, wherein the zero-carbon energy system includes: a nuclear energy subsystem, a renewable energy subsystem, an energy storage subsystem, and a power grid subsystem. Based on this zero-carbon energy system, such as... Figure 1 As shown, the configuration optimization method for a zero-carbon energy system provided in this embodiment of the invention includes the following steps: Step S102: Obtain regional data of the target area and technical assumptions about the zero-carbon energy system.

[0023] The regional data includes electricity demand data and meteorological data, such as hourly electricity demand data sequences and meteorological data sequences for the target area. Technical assumptions include cost parameter assumptions and performance parameter assumptions; in this embodiment, the cost parameter refers to the levelized cost of energy technology, which consists of capital cost, fixed operation and maintenance cost, and variable operation and maintenance cost, specifically set according to the devices in the zero-carbon energy system; the performance parameter assumptions include round-trip efficiency, self-discharge rate, and charge / discharge duration limits, etc., and the specific technical assumptions can be set according to actual conditions.

[0024] Specifically, the performance parameters described above are based on assumptions made for energy storage devices (such as batteries) in zero-carbon energy systems. Round-trip efficiency is defined as the ratio of discharged energy to charged input energy during a single charge-discharge cycle. In practical applications, a higher round-trip efficiency indicates less energy conversion loss between storage and release.

[0025] Step S104: Process the regional data to determine the power generation factor and electricity demand.

[0026] Step S106: Based on the information of power generation factor, power demand and technical assumptions, optimize the objective function to minimize the total system cost, and obtain the optimized values ​​of the installed capacity and hourly dispatch quantity of multiple devices.

[0027] Specifically, in this embodiment of the invention, the zero-carbon energy system is a nuclear, wind, and solar zero-carbon energy system that includes thermal storage and hydrogen storage, such as... Figure 2 As shown, the system includes a nuclear energy subsystem, a renewable energy subsystem, an energy storage subsystem, and a power grid subsystem. Each subsystem is connected by a power node S, and several constraints exist between them. Furthermore, the zero-carbon energy system includes multiple devices, specifically: the nuclear energy subsystem includes a reactor, a thermal storage tank, and a steam generator; the renewable energy subsystem includes wind power generation units and solar power generation units; and the energy storage subsystem includes batteries and a hydrogen energy storage subsystem, where the hydrogen energy storage subsystem includes an electrolyzer, a hydrogen storage tank, and a fuel cell. The specific devices can be configured according to the zero-carbon energy system requirements.

[0028] Step S108: Configure multiple devices according to the optimized values ​​of installed capacity and hourly scheduling quantity.

[0029] The configuration optimization method for a zero-carbon energy system provided in this invention takes minimizing the total system cost as the objective function and performs collaborative optimization on the installed capacity and hourly scheduling of each device. This allows for the configuration of multiple devices based on the optimized installed capacity and hourly scheduling values, thereby realizing a deep coupling and scheduling mechanism between nuclear thermal, electrical energy storage and renewable energy in the zero-carbon energy system, and improving the overall economy and reliability of the system.

[0030] In one implementation, the regional data is processed to determine the power generation factor and the power demand, including: (A1) normalizing the power demand data and determining the power demand based on the normalized power demand data; and (A2) determining the power generation factor based on meteorological data.

[0031] Specifically, in (A1), the electricity demand data for the target area is normalized by dividing the original electricity demand data by the annual average electricity demand to obtain the normalized electricity demand data, and the value of the normalized electricity demand data at time t is taken as the electricity demand at time t. .

[0032] Furthermore, in (A2), the power generation factor is determined based on meteorological data. This power generation factor characterizes the potential power generation from renewable resources, including the solar power generation factor and the wind power generation factor. The calculation process for the solar power generation factor and the wind power generation factor is explained in detail below.

[0033] The meteorological data includes the total horizontal radiation (GHI) and ambient temperature of the target area. T amb At this point, based on the total horizontal radiation GHI and ambient temperature T amb Determine the solar power generation factor S cf Specifically, solar power generation factor S cf The calculation expression is as follows: (1) in, Represents normalized irradiance. This represents the reference irradiance, expressed in W / m². 2 In this embodiment of the invention, 100W / m is preferred. 2 ; This represents the estimated panel temperature, in °C. T amb This indicates ambient temperature, expressed in °C. The free heat flux is expressed in °C·m² / W, and is preferably 0.035 °C·m² / W in this embodiment of the invention. This represents the reference temperature correction term, in °C, which is preferably 25 °C in this embodiment of the invention. Specifically, k i ( i =1~6) represents a fixed fitting coefficient, where, k 1 and k 2 is a dimensionless coefficient. k 3. k4 and k The unit of 5 is ℃ - ¹, k The unit of 6 is ℃ - ², The specific values ​​of each coefficient in the implementation of this invention are as follows: k 1 is -0.017162. k 2 is -0.040289. k 3 is -0.004681. k 4 is 0.000148. k 5 is 0.000169. k 6 is 0.000005.

[0034] In addition, the meteorological data also includes the wind speed at a preset altitude in the target area. u The preset height here is preferably the height of the wind turbine hub. Assuming the wind turbine hub height is 100m, the preset height wind speed is... u The wind speed at a height of 100 meters; then based on the preset wind speed at that height. u Determine the wind power generation factor with a preset wind speed threshold. W cf The preset wind speed threshold includes the preset cut-in wind speed. u ci (like u ci =3m / s), preset rated wind speed u r (like u r =12m / s) and preset cut-out wind speed u co (like u co =25m / s), specifically u ci , u r and u co The value can be adjusted adaptively according to the actual situation.

[0035] Among them, wind power generation factor W cf The calculation formula is as follows: (2) Specifically, if the wind speed at the preset altitude is less than the preset rated wind speed but not less than the preset cut-in wind speed, the wind power generation factor is determined based on the wind speed at the preset altitude and the preset rated wind speed. That is, when... u ci ≤ u < u r At that time, according to u andu r Determine wind power generation factor W cf Furthermore, if the wind speed at the preset height is less than the preset cut-in wind speed, or if the wind speed at the preset height is greater than the preset cut-out wind speed, the wind power generation factor is determined to be 0; that is, when... u < u ci or u > u co At that time, W cf =0. Also, if the wind speed at the preset height is not greater than the preset cut-out wind speed and not less than the preset rated wind speed, the wind power generation factor is determined to be 1, i.e., when... u r ≤ u < u co At that time, W cf =1.

[0036] Therefore, the solar power generation factor is determined based on meteorological data. S cf and wind power generation factor W cf This is used to optimize the subsequent objective function, thereby coordinating the optimization of the installed capacity and hourly scheduling of each device, and configuring multiple devices according to the optimized values ​​of installed capacity and hourly scheduling. This realizes a deep coupling and scheduling mechanism between nuclear thermal, electric energy storage and renewable energy in the zero-carbon energy system, improving the overall economy and reliability of the system.

[0037] In one implementation, the objective function is configured with constraints; wherein the constraints include: thermal energy balance constraints and thermal storage tank state constraints after nuclear-thermal decoupling in the nuclear energy subsystem, state of charge constraints for battery energy storage, and material balance constraints for the hydrogen energy storage subsystem.

[0038] To facilitate understanding, we will use various subsystems as examples to illustrate the objective function and its constraints. Specifically: (1) Nuclear energy subsystem; consisting of a reactor, a thermal storage tank and a steam generator coupled together, used to realize the generation, storage and conversion of thermal energy respectively; wherein, the reactor is preferably a small modular reactor (SMR).

[0039] In practical applications, the SMR reactor serves as the main heat source for the nuclear energy subsystem, continuously outputting high-temperature thermal energy through the fission reaction of nuclear fuel. ;in, The thermal power output of the SMR at time t is expressed in kW. This refers to the reactor's installed capacity, measured in kW. Let be the power generation factor of the SMR at time t, typically 1 (full load operation). When the nuclear subsystem is operating, as a baseload heat source, its output thermal power is limited by the installed capacity, specifically... .

[0040] Thermal energy storage tanks (TES) are used to realize the time-shifting utilization of thermal energy; that is, when the thermal power output of the reactor exceeds the immediate demand, the excess heat is transferred to the TES for storage; conversely, when the reactor's power supply is insufficient, the TES releases the stored heat energy to supplement it. For example, at time t+1, the heat stored in the TES depends on the heat at the previous time t, the heat loss during the time interval from t to t+1, and the heat input or output from external heat sources, specifically satisfying the following expression: (3) in, and These are the heat storage capacity of the thermal storage tank at time t and t+1, respectively, in kWh. The round-trip efficiency of the heat storage pipe is %. This represents the heat dissipation rate, 1 / h, with a default value of 1.00 × 10⁻⁶. -5 / h; Δt is the time difference between time t+1 and time t, in hours; This indicates the charging power of the thermal storage tank, expressed in kW. This indicates the heat release capacity of the thermal storage tank, measured in kW.

[0041] Furthermore, the state of the thermal storage tank is constrained by energy balance, including: ① the stored heat capacity does not exceed the installed capacity. Taking the stored heat capacity at time t as an example, it satisfies... ;in, ① The installed capacity of the thermal storage tank is expressed in kWh. ② The charging and discharging power has upper and lower limits: , ;in, CT tes and DT tes These represent the charging time and the releasing time, respectively, in hours (h).

[0042] Therefore, in the nuclear energy subsystem, the high-temperature thermal energy output from the SMR reactor and TES storage tank is transferred to the steam generator, which converts the thermal energy into electrical energy, thus achieving stable and reliable zero-carbon power output. The electrical power output of the steam generator is as follows: ;in, The final electrical power output of the steam generator, measured in kW; This represents the thermoelectric conversion efficiency of the steam generator, expressed as a percentage, with a default setting of 37%. It should be noted that electrical power and thermal power are converted using this thermoelectric conversion efficiency.

[0043] Furthermore, traditional nuclear power plants and most SMR systems are typically designed to operate continuously at rated power (base load operation), making it difficult to respond quickly to load fluctuations and lacking flexibility. Based on this, this invention introduces a thermal storage tank to achieve nuclear-thermal decoupling between the SMR and the steam generator, enabling the nuclear energy subsystem to flexibly adjust its power output, thus achieving "nuclear-thermal decoupling" and improving the load-following capability of the nuclear energy subsystem.

[0044] (2) Renewable energy subsystem; including wind power generation unit and solar power generation unit, used to provide clean electricity, which is volatile and uncertain. The maximum output of the renewable energy subsystem is limited by resource conditions, namely the power generation factor, as follows: , ;in, and These are the output power of the wind power generation unit and the output power of the solar power generation unit at time t, respectively, in kW; and These are wind power installed capacity and solar power installed capacity, respectively, in kW; and The wind power generation factor and solar power generation factor at time t are calculated according to formula (2) and formula (1), respectively.

[0045] In practical applications, wind power generation units and solar power generation units exhibit significant intermittent and seasonal fluctuations. To mitigate these fluctuations, existing technologies require large-scale battery energy storage subsystems, resulting in high system costs and low efficiency. Therefore, this invention determines wind power generation factors and solar power generation factors using meteorological data and optimizes the objective function based on these factors. This eliminates the need for additional large-scale battery energy storage subsystems, thereby reducing system costs and improving configuration efficiency.

[0046] (3) Power storage subsystem; composed of battery storage subsystem and hydrogen storage subsystem. The battery storage subsystem includes batteries. In practical applications, batteries have a fast response speed and are suitable for short-term power regulation. For example, at time t+1, the amount of electricity stored in the battery depends on the amount of electricity at the previous time t, the electrical losses during the time period, and the amount of electricity input or output from the outside, specifically satisfying the following expression: (4) in, and Δt represents the battery's energy storage at time t+1 and time t, respectively, in kWh; Δt represents the time difference between time t+1 and time t, in hours. The energy dissipation rate is 1 / h, and the default setting is 2.60 × 10⁻⁶. 4 / h; The battery charge / discharge round-trip efficiency, % (default setting is 90%). and These are the battery's charging power and discharging power, respectively, in kW.

[0047] Furthermore, the battery's state is constrained by energy balance, including: ① the stored energy does not exceed the installed capacity. Taking the stored heat at time t as an example, it satisfies: ;in, ① Battery installed capacity, unit: kWh. ② Charge and discharge power has upper and lower limits: , ; CT batt Indicates charging time. DT batt The values ​​represent the discharge time, all in hours (h).

[0048] Furthermore, the hydrogen energy storage subsystem achieves electricity-hydrogen-electricity conversion through an electrolyzer, hydrogen storage tank, and fuel cell, making it suitable for long-term energy storage. Specifically, the electrolyzer converts excess electrical energy into hydrogen gas. ; The power required to produce hydrogen, expressed in kW; Electrolysis efficiency, expressed as a percentage, default is 70%; This represents the electrical power input to the electrolyzer, measured in kW. The hydrogen storage tank is used to store hydrogen gas. For example, at time t+1, the hydrogen energy stored in the tank depends on the hydrogen energy at the previous time t, the hydrogen energy loss during the time interval, and the hydrogen energy input or output from external sources, specifically satisfying the following expression: (5) in, and Δt represents the hydrogen storage energy of the hydrogen storage tank at time t+1 and time t, respectively, in kWh; Δt is the time difference between time t+1 and time t, in hours. This represents the hydrogen dissipation rate, 1 / h, with a default value of 1.14 × 10⁻⁶. -8 / h; and These represent the power consumption required for hydrogen production in the electrolyzer and the power consumption required for hydrogen consumption in the fuel cell, respectively, in kW. It should be noted that the hydrogen storage tank has a capacity constraint, meaning the stored hydrogen cannot exceed the installed capacity. Taking time t as an example, this satisfies: ;in, The hydrogen storage tank capacity is expressed in kWh. Furthermore, for electrolyzers and fuel cells, this embodiment of the invention does not directly model the physical unit of hydrogen quantity, but rather treats hydrogen as an equivalent energy carrier in the form of power; therefore, the unit is kWh.

[0049] Fuel cells are used to convert hydrogen into electrical energy, that is... ;in, The power required for a fuel cell to consume hydrogen, expressed in kW; This represents the hydrogen-to-electricity conversion efficiency, expressed as a percentage, with a default value of 70%. This refers to the electrical power output of the fuel cell, measured in kW.

[0050] (4) Power grid subsystem; mainly includes electricity demand. System abandoned electricity and system power loss The system consists of three parts, with each physical quantity measured in kW. Among them, the system abandoned power refers to the surplus power generated by the system that exceeds the real-time power demand. This part of the power is not subject to cost, meaning it is treated as free. The system lost power refers to the shortfall in power that the system's power supply cannot meet the real-time power demand. This part of the power will be included in the penalty cost at a standard of $10 / kWh.

[0051] (5) Power node S; used to balance the instantaneous power of the zero-carbon energy system; specifically, when the total power output of the power generation section exceeds the real-time demand of the grid, the excess power is stored by the power storage subsystem to improve the regulation capability and power supply reliability of the zero-carbon energy system, that is, the power supply and power consumption must be equal in real time, and the specific expression is as follows: (6) in, This represents the electrical power output of the steam generator at time t. This represents the output power of the wind power generation unit at time t. This represents the output power of the solar power generation unit at time t. This represents the battery's discharge power at time t. This represents the output electrical power of the fuel cell at time t. This represents the system charge loss at time t. This represents the electricity demand at time t. This represents the charging power of the battery at time t. This represents the electrical power input to the electrolytic cell at time t. This represents the amount of electricity wasted by the system at time t.

[0052] Furthermore, while satisfying all constraints, minimize the total system cost. C total The objective function can be obtained by including the fixed and variable maintenance costs of each device. ;in, C fix To fix maintenance costs, Cvar For variable operation and maintenance costs.

[0053] Among them, fixed operation and maintenance costs C fix The expression is as follows: (7) in, N This represents the total number of time steps, set to 1 year (8760h); Δ t Indicates the time step, set to 1 hour; FC This represents the fixed cost per unit capacity of the corresponding device.

[0054] Similarly, variable operation and maintenance costs C var The expression is: ;in, M The expression is as follows: (8) in, VC This represents the variable operation and maintenance cost per unit energy of the corresponding device. In particular, the objective function also considers the penalties for power outages and curtailments in the power grid subsystem, further enhancing the reliability of the configured zero-carbon energy system.

[0055] It should be noted that only nuclear energy considers variable operation and maintenance costs; other technologies do not because they do not incur fuel costs. Therefore, in practical applications, wind power generation units and solar power generation units only consider capital costs and fixed operation and maintenance costs, as their marginal fuel cost for power generation is zero; reactors consider capital costs, fixed operation and maintenance costs, and variable operation and maintenance costs simultaneously; and power storage subsystems, in addition to capital costs, also consider performance parameter assumptions, such as round-trip efficiency, self-discharge rate, and charging and discharging time limitations.

[0056] Therefore, the present invention takes minimizing the total system cost as the objective function and combines it with constraints, including but not limited to the thermal energy balance constraints after nuclear-thermal decoupling in the nuclear energy subsystem (i.e., the above formula (3)) and the state constraints of the thermal storage tank (including energy balance constraints and upper and lower limits of charging and discharging power), the state of charge constraints of battery energy storage (i.e., the above formula (4)) and the material balance constraints of the hydrogen energy storage subsystem (i.e., the above formula (5)). The installed capacity and hourly scheduling of each device are optimized in a coordinated manner so that multiple devices can be configured according to the optimized installed capacity value and the optimized hourly scheduling value. This realizes the deep coupling and scheduling mechanism between nuclear-thermal energy, electric energy storage and renewable energy in the zero-carbon energy system, and improves the overall economy and reliability of the system.

[0057] In summary, the configuration optimization method for a zero-carbon energy system provided in this embodiment of the invention is as follows: Figure 3 As shown, the process first acquires the input regional data and technical assumptions. The regional data consists of hourly electricity demand and meteorological data sequences for the target region, while the technical assumptions include cost and performance parameter assumptions. Then, data processing is performed, including generation factor calculation and average normalization. Next, using the installed capacity and hourly dispatch volume (i.e., hourly dispatch volume) of each device as decision variables, and under constraints, minimizing the total system cost is used as the objective function. Through model simulation and the Gurobi optimizer, optimized values ​​for the installed capacity and hourly dispatch volume of multiple devices are obtained. Finally, the multiple devices are configured based on these optimized values.

[0058] To make it easier to understand, an example is given here. First, regional data is obtained, including the national average total horizontal radiation (GHI) and wind speed at 100 meters. u And electricity demand time series data. Next, technical assumption information is collected, including cost parameter assumptions and performance parameter assumptions. The cost parameter assumptions include the fixed and variable operation and maintenance costs of each unit, in units of $ / kWh (energy storage technology) or $ / kW (generation technology). For example, the cost parameter assumptions for each unit are shown in Table 1 below: Table 1

[0059] In addition, the performance parameter assumptions include, but are not limited to, round-trip efficiency, self-discharge rate, self-loss rate, and charging time. Taking round-trip efficiency, self-loss rate (monthly), and charging time (h) as examples, the performance parameter assumptions for each device are shown in Table 2 below: Table 2

[0060] Then, the regional data was processed, and the solar power generation factor was calculated to be 0.41, and the wind power generation factor to be 0.27; and the electricity demand time series data was normalized to obtain the following results: Figure 4 The normalized annual electricity demand curve is shown below; the horizontal axis represents time in hours, and the vertical axis represents the normalized electricity demand.

[0061] Next, we set the decision variables and objective function for the optimization problem, as shown in Table 3 below: Table 3

[0062] Finally, the Gurobi optimizer is used to solve the above optimization problem, and the optimized values ​​of the installed capacity and hourly scheduling amount of each device are obtained. The devices are then configured according to the optimized values ​​of the installed capacity and hourly scheduling amount. For example, the optimized value of the installed capacity can be directly used as the installed capacity of each device, or the value between 0 and the optimized value of the installed capacity can be used as the installed capacity of each device.

[0063] For example, when the objective function, i.e., the total system cost, reaches its minimum value of $727.08, that is, the average cost per kilowatt-hour is $0.083 / kWh, where the average cost per kilowatt-hour = total cost / total annual electricity consumption, and since the normalized average power demand is 1kW, the total annual electricity consumption is defined as 8760kWh; at this time, the optimized installed capacity of the solar power generation unit is 1.13kW, the optimized installed capacity of the wind power generation unit is 1.87kW, the optimized installed capacity of the reactor is 0.23kW, the optimized installed capacity of the thermal storage tank is 4.40kWh, the optimized installed capacity of the steam generator is 0.27kW, the optimized installed capacity of the battery is 0.00kWh, the optimized installed capacity of the electrolyzer is 0.16kW, the optimized installed capacity of the hydrogen storage tank is 407.40kWh, and the optimized installed capacity of the fuel cell is 0.44kW. The optimal configuration of the zero-carbon energy system at this time is as follows: the installed capacity of the solar power generation unit is 1.13kW, the installed capacity of the wind power generation unit is 1.87kW, the installed capacity of the reactor is 0.23kW, the installed capacity of the thermal storage tank is 4.40kWh, the installed capacity of the steam generator is 0.27kW, the installed capacity of the battery is 0.00kWh, the installed capacity of the electrolyzer is 0.16kW, the installed capacity of the hydrogen storage tank is 407.40kWh, and the installed capacity of the fuel cell is 0.44kW.

[0064] It should be noted that for each device, if it is used for power generation technology, its installed capacity is in kW; if it is used for energy storage technology, its installed capacity is in kWh. Furthermore, energy = power × time. Since the time resolution in this embodiment is 1 hour, the power unit (kW) and energy unit (kWh) per hour satisfy the conservation principle.

[0065] Furthermore, most existing optimization models neglect the synergistic effect of thermal energy storage and hydrogen energy storage at different time scales. Battery energy storage is suitable for short-term regulation, but it cannot effectively solve the problem of power supply and demand imbalance on a weekly / monthly scale caused by extreme weather or seasonal changes. Based on this, the embodiments of the present invention achieve continuous and stable operation of the reactor as a baseload power source by optimizing the hourly scheduling values ​​of each device, enabling the thermal storage tank to achieve time-shifted regulation of nuclear thermal power, allowing the nuclear energy subsystem to flexibly respond to load changes; the hydrogen energy storage subsystem plays an important role in seasonal load fluctuations, effectively solving the problem of seasonal supply and demand imbalance of renewable energy, and realizing the synergistic optimization of multiple energy sources, thereby ensuring the energy balance of multiple energy sources in various forms such as heat, electricity, and hydrogen.

[0066] Furthermore, the optimization scheme provided in this embodiment of the invention can obtain the operating costs under different energy subsystems through simulation calculations. To verify the superiority of the optimization scheme provided in this embodiment of the invention, four comparative schemes are set up for operational optimization. Specifically, the configurations of the four schemes are shown in Table 4 below: Table 4

[0067] In this embodiment, Case 1 represents the optimized solution provided by the present invention; Case 2 represents a conventional nuclear-wind-solar-storage energy system without a thermal storage tank; Case 3 represents a wind-solar-storage energy system without nuclear power; and Case 4 represents a nuclear-wind-solar energy system without hydrogen energy. The average levelized cost of electricity (LCOE) after optimization of the four solutions is as follows: Figure 5 As shown, the system cost of Case 1 is $0.083 / kWh, the system cost of Case 2 is $0.084 / kWh, the system cost of Case 3 is $0.084 / kWh, and the system cost of Case 4 is $0.097 / kWh. Therefore, Case 1 is the optimal cost system among the four schemes, which is 1.2%, 1.2%, and 16.9% lower than Case 2, Case 3, and Case 4, respectively.

[0068] Therefore, the configuration optimization method for a zero-carbon energy system provided in this embodiment of the invention takes minimizing the total system cost as the objective function and performs coordinated optimization on the installed capacity and hourly scheduling of each device. At the same time, it integrates battery energy storage for short-term regulation and hydrogen energy storage for long-term seasonal regulation, forming a multi-timescale energy storage collaborative architecture, realizing the coordinated optimization of short-term and seasonal scheduling. This achieves a deep coupling and scheduling mechanism between nuclear thermal, electrical energy storage and renewable energy in a zero-carbon energy system, significantly improving the economy and reliability of the energy system, and has outstanding technological progress and practical value.

[0069] Furthermore, embodiments of the present invention also provide a zero-carbon energy system, including: a controller, a nuclear energy subsystem, a renewable energy subsystem, an electric energy storage subsystem, and a power grid subsystem; wherein, the controller is used to optimize the configuration of the zero-carbon energy system using the above-described method embodiments.

[0070] In practical applications, the nuclear energy subsystem includes a reactor, a thermal storage tank, and a steam generator; the renewable energy subsystem includes a wind power generation unit and a solar power generation unit; the power storage subsystem includes a battery energy storage subsystem and a hydrogen energy storage subsystem; wherein, the battery energy storage subsystem includes a battery, and the hydrogen energy storage subsystem includes an electrolyzer, a hydrogen storage tank, and a fuel cell. The specific structure of the zero-carbon energy system can be referred to the foregoing embodiments, and the embodiments of the present invention will not be described in detail here.

[0071] The zero-carbon energy system provided in this embodiment of the invention has the same technical features as the configuration optimization method of the zero-carbon energy system provided in the above embodiments, so it can also solve the same technical problems and achieve the same technical effects.

[0072] This embodiment also provides a computer-readable storage medium storing a computer program, which is executed by a processor to perform the above-described configuration optimization method for a zero-carbon energy system.

[0073] The computer program product of the zero-carbon energy system and its configuration optimization method provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0074] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0075] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0076] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0078] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, 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, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the configuration of a zero-carbon energy system, the zero-carbon energy system comprising: The system comprises a nuclear energy subsystem, a renewable energy subsystem, an electric energy storage subsystem, and a power grid subsystem; characterized in that the method includes: Acquire regional data of the target area and technical assumptions of the zero-carbon energy system; wherein, the regional data includes electricity demand data and meteorological data, and the technical assumptions include cost parameter assumptions and performance parameter assumptions; The regional data is processed to determine the power generation factor and electricity demand. Based on the power generation factor, the power demand, and the technical assumptions, the objective function aimed at minimizing the total system cost is optimized to obtain optimized values ​​for the installed capacity and hourly scheduling of multiple devices. These multiple devices include: the reactor, thermal storage tank, and steam generator of the nuclear energy subsystem; the wind power generation unit and solar power generation unit of the renewable energy subsystem; and the battery, electrolyzer, hydrogen storage tank, and fuel cell of the power storage subsystem. Multiple devices are configured based on the optimized installed capacity value and the optimized hourly scheduling value.

2. The method according to claim 1, characterized in that, The power energy storage subsystem includes a hydrogen energy storage subsystem; the objective function is configured with constraints; wherein the constraints include: thermal energy balance constraints and thermal storage tank state constraints after nuclear-thermal decoupling in the nuclear energy subsystem, state of charge constraints for battery energy storage, and material balance constraints for the hydrogen energy storage subsystem.

3. The method according to claim 1, characterized in that, The process of processing the regional data to determine the power generation factor and electricity demand includes: The electricity demand data is normalized, and the electricity demand is determined based on the normalized electricity demand data. The power generation factor is determined based on the meteorological data.

4. The method according to claim 3, characterized in that, The meteorological data includes the total horizontal radiation and ambient temperature of the target area, and the power generation factor includes the solar power generation factor. The step of determining the power generation factor based on the meteorological data includes: The solar power generation factor is determined based on the total horizontal radiation and the ambient temperature.

5. The method according to claim 3, characterized in that, The meteorological data includes the wind speed at a preset altitude corresponding to a preset altitude in the target area, and the power generation factor includes the wind power generation factor; The step of determining the power generation factor based on the meteorological data includes: The wind power generation factor is determined based on the preset wind speed at the preset altitude and the preset wind speed threshold.

6. The method according to claim 5, characterized in that, The preset wind speed threshold includes the preset cut-in wind speed and the preset rated wind speed; The step of determining the wind power generation factor based on the preset height wind speed and the preset wind speed threshold includes: If the preset height wind speed is less than the preset rated wind speed and not less than the preset cut-in wind speed, the wind power generation factor is determined based on the preset height wind speed and the preset rated wind speed.

7. The method according to claim 6, characterized in that, The preset wind speed threshold also includes a preset cut-out wind speed; The step of determining the wind power generation factor based on the preset height wind speed and the preset wind speed threshold also includes: If the preset wind speed at the height is less than the preset cut-in wind speed, or if the preset wind speed at the height is greater than the preset cut-out wind speed, the wind power generation factor is determined to be 0. If the wind speed at the preset height is not greater than the preset cut-out wind speed and not less than the preset rated wind speed, the wind power generation factor is determined to be 1.

8. A zero-carbon energy system, characterized in that, include: The system comprises a controller, a nuclear energy subsystem, a renewable energy subsystem, an energy storage subsystem, and a power grid subsystem; wherein the controller is used to optimize the configuration of the zero-carbon energy system using the method described in any one of claims 1-7.

9. The system according to claim 8, characterized in that, The nuclear energy subsystem includes a reactor, a thermal storage tank, and a steam generator; the renewable energy subsystem includes a wind power generation unit and a solar power generation unit; the power storage subsystem includes a battery storage subsystem and a hydrogen storage subsystem; wherein the battery storage subsystem includes a battery, and the hydrogen storage subsystem includes an electrolyzer, a hydrogen storage tank, and a fuel cell.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the method described in any one of claims 1-7.

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