A scheduling scheme optimization method and a photovoltaic-thermal-electric heating coupling system

Abstract

This invention relates to a scheduling scheme optimization method and a photovoltaic-thermal-electric heating coupling system, belonging to the field of new energy power generation technology. The method includes: acquiring time-series operational data required for scheduling within a target time period; constructing an electrothermal coupling scheduling model based on the time-series operational data, the electrothermal coupling scheduling model being used to characterize the correlation between the system's time-series operational characteristics and multi-energy flow power scheduling parameters; generating multiple candidate scheduling schemes based on the electrothermal coupling scheduling model with the objectives of minimizing the input power fluctuation rate of the electrothermal coupling channel and minimizing the photovoltaic curtailment rate, the electrothermal coupling channel being the path through which surplus photovoltaic electricity is converted into heat energy by an electric heating unit and input into a thermal storage module; and selecting a target scheduling scheme from the multiple candidate scheduling schemes based on system operating data.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of new energy power generation technology, specifically to a scheduling scheme optimization method and a photovoltaic-thermal-electric heating coupling system. Background Technology

[0002] With a high proportion of photovoltaic power being integrated into the power system, the problem of power curtailment is becoming increasingly prominent. Although concentrated solar power (CSP) systems possess certain regulation capabilities and thermal storage characteristics, which can mitigate the fluctuations in renewable energy to some extent, their thermal storage units generally suffer from low utilization rates and fixed operating periods. Furthermore, thermal storage systems largely rely on solar thermal collection for supplementary energy, making it difficult to fully absorb surplus photovoltaic power. This results in limited synergy between photovoltaic and CSP operations, leading to poor system stability and low renewable energy consumption efficiency. Summary of the Invention

[0003] The purpose of the embodiments in this specification is to provide a scheduling scheme optimization method and a photovoltaic-thermal-electric heating coupling system to overcome the problems of poor system operation stability and low new energy consumption efficiency in existing methods.

[0004] To address the aforementioned technical problems, this specification provides, on the one hand, a scheduling scheme optimization method applied to a photovoltaic-thermal-electric heating coupling system; the system includes a photovoltaic power generation unit, a solar thermal power generation unit, and an electric heating unit, wherein the electric heating unit is used to convert surplus electrical energy from the photovoltaic power generation unit into thermal energy and store it in the thermal storage module of the solar thermal power generation unit; the method includes: Obtain the timing data required for scheduling within the target time period; Based on time-series operational data, an electrothermal coupling scheduling model is constructed. This model is used to characterize the relationship between the system's time-series operational characteristics and the multi-energy flow power scheduling parameters. With the goals of minimizing the input power fluctuation rate of the electrothermal coupling channel and minimizing the photovoltaic curtailment rate, multiple candidate scheduling schemes are generated based on the electrothermal coupling scheduling model. The electrothermal coupling channel is the path through which surplus photovoltaic power is converted into heat energy by the electric heating unit and input into the thermal storage module. Based on system operating data, a target scheduling scheme is selected from multiple candidate scheduling schemes.

[0005] Furthermore, obtaining the timing data required for scheduling within the target time period includes: Acquire the predicted output data of photovoltaic power generation units, the surplus photovoltaic power generation data absorbed by the powerable heating units, the grid curtailment constraint data, and the thermal storage status data of the thermal storage modules within the target time period.

[0006] Furthermore, the electrothermal coupling scheduling model includes an electrical power balance model, a thermal storage state transition model, and equipment operation constraints and thermal storage capacity constraints; The construction of the electrothermal coupling scheduling model based on time-series operational data includes: Based on the predicted output data, an electric power balance model is constructed to characterize the instantaneous equality relationship between photovoltaic power generation, grid interaction power, electric heating power and load power. Based on surplus photovoltaic power generation data, a thermal energy storage state transition model is constructed to characterize the time-series recursive relationship between the thermal energy storage in the current sub-period and the thermal energy storage, charging power, releasing power and heat loss in the previous sub-period. Based on power grid curtailment constraint data, equipment operation constraints are established, including upper and lower power limits and ramp rate constraints for photovoltaic power generation units, electric heating units, and solar thermal power generation units. Based on thermal storage status data, thermal storage capacity constraints are established, including upper and lower limits of thermal storage safety and thermal storage balance constraints at the beginning and end of scheduling.

[0007] Furthermore, the method also includes: Based on the electrothermal conversion efficiency and the actual surplus photovoltaic power consumed by the electric heating unit in each sub-period within the target period, determine the thermal power input from the electric heating unit to the thermal storage module in that sub-period. The input power fluctuation rate of the electrothermal coupling channel is determined by comparing the cumulative results of the thermal power differences between adjacent sub-time periods with the cumulative results of the thermal power across multiple sub-time periods.

[0008] Furthermore, the method also includes: Determine the surplus power within the photovoltaic forecast output that is not absorbed by the system load and the grid in each sub-period within the target period; Determine whether the remaining thermal storage capacity of the thermal storage module is zero in each sub-period. If it is not zero, the portion of surplus power that can be converted into thermal energy storage by the electric heating unit will not be included in the abandoned power, and the portion of thermal power exceeding the remaining thermal storage capacity that is converted back into electrical power will be included in the abandoned power. If it is zero, all surplus power will be included in the abandoned power. The photovoltaic curtailment rate is determined by comparing the cumulative results of curtailed power from multiple sub-periods with the cumulative results of photovoltaic predicted output.

[0009] Furthermore, with the objectives of minimizing the input power fluctuation rate of the electrothermal coupling channel and minimizing the photovoltaic curtailment rate, multiple candidate scheduling schemes are generated based on the electrothermal coupling scheduling model, including: A multi-objective optimization function is constructed with the input power fluctuation rate of the electrothermal coupling channel and the photovoltaic curtailment rate as optimization objectives. Based on the multi-objective optimization function, the electrothermal coupling scheduling model is iteratively optimized, and in each iteration, the non-dominated ranking is performed based on the Pareto dominance relationship according to the multi-objective optimization function value of each candidate scheduling scheme. Based on the Pareto front solution set that satisfies the iteration termination condition, multiple candidate scheduling schemes are determined.

[0010] Furthermore, the step of selecting a target scheduling scheme from multiple candidate scheduling schemes based on system operating condition data includes: Based on the system operating data, determine whether the remaining thermal storage capacity of the thermal storage module is zero; If the value is zero, select the scheme with the lowest input power fluctuation rate of the electrothermal coupling channel from multiple candidate scheduling schemes as the target scheduling scheme.

[0011] Furthermore, the step of selecting a target scheduling scheme from multiple candidate scheduling schemes based on system operating condition data also includes: If it is not zero, determine whether the surplus photovoltaic power generation is greater than the preset curtailment rate threshold based on the system operating data; If the rate is greater than the target rate, the scheme with the lowest photovoltaic curtailment rate will be selected from multiple candidate scheduling schemes as the target scheduling scheme.

[0012] Furthermore, the method also includes: Based on at least one of the following factors: the input power fluctuation rate of the electrothermal coupling channel, the photovoltaic curtailment rate, and the thermal storage status, the multiple candidate scheduling schemes are divided into multiple categories, and each category corresponds to an operating mode of the photovoltaic-thermal-electric heating coupling system. The step of selecting a target scheduling scheme from multiple candidate scheduling schemes based on system operating data includes: Based on the system operating data, determine the current operating mode of the photovoltaic-thermal-electric heating coupling system; Select the target scheduling scheme from the schemes that correspond to the current operating mode.

[0013] On another note, this specification provides a photovoltaic-thermal-electric heating coupling system, which includes a photovoltaic power generation unit, a solar thermal power generation unit, an electric heating unit, and a scheduling control unit. The electric heating unit is used to convert the surplus electrical energy of the photovoltaic power generation unit into thermal energy and store it in the thermal storage module of the solar thermal power generation unit. The scheduling control unit is used to execute the above-mentioned scheduling scheme optimization method.

[0014] As can be seen from the technical solutions provided in the embodiments of this specification above, the embodiments of this specification can obtain the time-series operation data required for scheduling within the target time period; based on the time-series operation data, an electrothermal coupling scheduling model is constructed, which is used to characterize the correlation between the system's time-series operation characteristics and multi-energy flow power scheduling parameters; with the goal of minimizing the input power fluctuation rate of the electrothermal coupling channel and minimizing the photovoltaic curtailment rate, multiple candidate scheduling schemes are generated based on the electrothermal coupling scheduling model, where the electrothermal coupling channel is the path for surplus photovoltaic power to be converted into heat energy by an electric heating unit and input into the thermal storage module; and based on the system operating condition data, a target scheduling scheme is selected from multiple candidate scheduling schemes. By converting surplus photovoltaic power into heat energy and storing it in the thermal storage module through electric heating units, the photovoltaic curtailment rate is effectively reduced, and the level of new energy consumption is improved. Furthermore, by acquiring time-series operating data to construct an electrothermal coupling scheduling model, and aiming to minimize the fluctuation rate of input power of the electrothermal coupling channel, the fluctuation of heat power input from the electric heating unit to the thermal storage module can be smoothed, reducing the thermal shock to the thermal storage module, extending equipment life and enhancing system operating stability. Moreover, by selecting the target scheme from multiple candidate scheduling schemes based on system operating data, the scheduling strategy can flexibly adapt to the actual operating state, further improving the operating stability of the photovoltaic-thermal-electric heating coupling system and the level of new energy consumption. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below.

[0016] Figure 1 This is a schematic diagram of the structural composition of a photovoltaic-thermal-electric heating coupling system provided in the embodiments of this specification; Figure 2 This is a schematic diagram of the energy flow of a photovoltaic-thermal-electric heating coupling system provided in the embodiments of this specification; Figure 3 This is a flowchart of a scheduling scheme optimization method provided in the embodiments of this specification; Figure 4 This is a logical schematic diagram of a scheduling scheme optimization method provided in the embodiments of this specification. Detailed Implementation

[0017] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0018] It should be noted that the terms "first," "second," etc., used in this specification, claims, and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0019] This specification provides an embodiment of a photovoltaic-thermal-electric heating coupling system, referring to... Figure 1 and Figure 2 As shown.

[0020] In some embodiments, the photovoltaic-thermal-electric heating coupling system may include a photovoltaic power generation unit, a solar thermal power generation unit, an electric heating unit, a grid interaction unit, and a dispatch control unit.

[0021] In some embodiments, the power output terminal of the photovoltaic power generation unit is connected to the power input terminal of the grid interaction unit and the electric heating unit, respectively, for converting solar energy into electrical energy and selectively supplying electrical energy to the grid interaction unit or supplying surplus electrical energy to the electric heating unit.

[0022] A photovoltaic (PV) power generation unit characterizes a device that directly converts solar radiation energy into direct current (DC) electricity. A grid interaction unit characterizes an interface device connecting the system's internal circuitry to the external public power grid, possessing functions such as energy metering, protection, and bidirectional power control. An electric heating unit characterizes a resistance or induction heating device that converts electrical energy into heat energy. During system operation, the PV power generation unit selectively feeds its generated energy into the external power grid via the grid interaction unit, or supplies surplus energy to the electric heating unit, according to dispatch instructions.

[0023] In some embodiments, the thermal energy output terminal of the electric heating unit is connected to the thermal energy input terminal of the thermal energy storage module of the solar thermal power generation unit, for converting surplus electrical energy from the photovoltaic power generation unit into thermal energy and storing it in the thermal energy storage module.

[0024] An electric heating unit characterizes an industrial heating device that generates heat using electricity as an energy source. Its components may include a heating element, a thermostat, and a heat transfer medium circulation system. A thermal storage module characterizes a thermal storage device capable of absorbing, storing, and releasing heat energy when needed; typical forms include high-temperature molten salt tanks or phase change thermal storage devices. The heat transfer medium pipeline at the outlet of the electric heating unit is directly connected to the inlet pipeline of the thermal storage module, transferring the heat generated by electric heating to the thermal storage medium.

[0025] In some embodiments, the electric heating unit is used to start when the photovoltaic power generation output exceeds the system's immediate absorption capacity, converting the surplus electrical energy into heat energy and inputting it into the thermal storage module to reduce the amount of photovoltaic power wasted.

[0026] Real-time absorption capacity is used to characterize the sum of the system's internal load consumption capacity and the grid's allowable power absorption capacity during the current period. The dispatch control unit can calculate the difference between the actual output of photovoltaic power and the real-time absorption capacity in real time. If the difference is positive and exceeds the preset start-up threshold, a start-up command is sent to the electric heating unit. The electric heating unit then converts the surplus electrical energy into heat energy and inputs it into the thermal storage module, thereby reducing the amount of photovoltaic power curtailment.

[0027] In some embodiments, the solar thermal power generation unit includes a thermal storage module and a power generation module. The thermal energy output terminal of the thermal storage module is connected to the thermal energy input terminal of the power generation module, and is used to provide thermal energy to the power generation module under the control of the dispatch control unit to realize power generation.

[0028] The thermal energy output terminal of the thermal storage module is connected to the thermal energy input terminal of the power generation module. The thermal storage module represents an adjustable heat source device consisting of a storage tank, heat exchanger, and circulating pump. The high-temperature thermal energy stored within it can be transferred to the power generation module via a heat transfer medium. The power generation module represents a power unit that converts thermal energy into electrical energy, including a steam generator, steam turbine, and generator. Under the control of the dispatch control unit, the thermal storage module releases thermal energy of a specific power to the power generation module according to dispatch instructions. The power generation module converts this into electrical energy and integrates it into the internal power grid or supplies it to external loads.

[0029] In some embodiments, the power grid interaction unit is connected between the external power grid and the internal circuit of the system to realize bidirectional power interaction between the system and the external power grid.

[0030] The grid interaction unit is used to characterize grid-connected interface cabinets or converter devices with bidirectional power metering, power direction detection, grid-connected protection, and communication functions. One side of its internal circuitry is connected to the common connection point of the external power grid, and the other side is connected to the main busbar within the system. This unit can monitor the voltage, frequency, and power curtailment commands of the external power grid in real time, and control the active and reactive power flowing into or out of the system according to the instructions of the dispatch control unit.

[0031] In some embodiments, the scheduling control unit is connected to the control terminals of the photovoltaic power generation unit, the electric heating unit, the solar thermal power generation unit, and the grid interaction unit, respectively, to collect the operating status data of each unit and issue scheduling instructions to each unit to achieve coordinated control.

[0032] The dispatch control unit characterizes an automated controller with data acquisition, status monitoring, optimization calculation, and command output functions. Its hardware components may include a central processing unit, storage modules, analog input / output modules, and digital input / output modules. The control terminal of the photovoltaic power generation unit receives start / stop commands and power regulation commands. The control terminal of the electric heating unit receives start / stop commands and power regulation commands. The control terminal of the solar thermal power generation unit receives power regulation commands from the power generation module and power charging / discharging commands from the thermal storage module. The control terminal of the grid interaction unit receives power absorption regulation commands. This dispatch control unit collects real-time operating status data of each unit via fieldbus or industrial Ethernet, including actual photovoltaic output, electric heating power feedback, thermal storage module liquid level, power generation module output power, and power at the grid interaction point. It then calculates the start / stop status and / or corresponding power target values ​​for each unit according to an embedded optimization algorithm and issues dispatch commands to each unit in a periodic manner.

[0033] In some embodiments, when the heat storage capacity of the thermal storage module reaches a preset upper limit or the photovoltaic power generation unit does not have surplus electrical energy that can be supplied to the heating unit, the scheduling control unit restricts or stops the operation of the electric heating unit.

[0034] The heat storage capacity of the thermal storage module characterizes the total amount of thermal energy stored in the medium within the storage tank, expressed as equivalent heat or liquid level. The preset upper limit characterizes the maximum heat storage capacity allowed for safe operation of the thermal storage module; exceeding this limit may lead to overpressure or medium decomposition. The surplus energy status characterizes whether the portion of the photovoltaic power generation unit's current output exceeding the system's immediate absorption capacity is greater than zero. When the current heat storage capacity of the thermal storage module reaches or exceeds the preset upper limit, or when the surplus energy of the photovoltaic power generation unit is zero, the dispatch control unit issues a restriction operation command or a complete stop command to the electric heating unit. The restriction operation command characterizes the process of gradually reducing the current power of the electric heating unit to zero, while the stop command characterizes the immediate disconnection of the power supply to the electric heating unit.

[0035] In some embodiments, the dispatch control unit prioritizes the use of thermal energy in the thermal storage module for solar thermal power generation, provided that the system's power demand is met, and dynamically adjusts the solar thermal power generation power based on the remaining thermal storage capacity.

[0036] System power demand characterizes the total electrical power required by the system's internal load during the current period. Thermal energy in the thermal storage module characterizes the available heat stored in the storage tank. The dispatch control unit first calculates the net load gap based on real-time load power and photovoltaic output. If the net load gap is positive, it prioritizes instructing the solar thermal power generation unit to extract thermal energy from the thermal storage module for power generation, rather than calling the grid to purchase electricity or starting other backup power sources. Simultaneously, the dispatch control unit dynamically adjusts the solar thermal power generation power based on the remaining capacity of the thermal storage module. Remaining capacity characterizes the difference between the maximum allowable heat storage capacity of the thermal storage module and the current heat storage capacity. When the remaining capacity is large, the solar thermal power generation unit is allowed to operate at its rated power or higher; when the remaining capacity is small, the solar thermal power generation power is reduced to preserve thermal storage space for absorbing subsequent surplus photovoltaic power. One dynamic adjustment method is as follows: at the end of each dispatch period, the dispatch control unit recalculates the remaining capacity and proportionally adjusts the solar thermal power generation power setting value for the next period.

[0037] In some embodiments, the scheduling control unit may include a microcontroller unit (MCU) or a central processing unit (CPU). Of course, the scheduling control unit may also include other devices capable of control functions, such as desktop computers, laptops, and other computer equipment. The scheduling control unit can acquire the time-series operational data required for scheduling within a target time period; based on the time-series operational data, it constructs an electrothermal coupling scheduling model, which characterizes the correlation between the system's time-series operational characteristics and multi-energy flow power scheduling parameters; with the objectives of minimizing the input power fluctuation rate of the electrothermal coupling channel and minimizing the photovoltaic curtailment rate, it generates multiple candidate scheduling schemes based on the electrothermal coupling scheduling model, where the electrothermal coupling channel is the path through which surplus photovoltaic power is converted into heat energy by an electric heating unit and input into the thermal storage module; and based on system operating data, it selects the target scheduling scheme from the multiple candidate scheduling schemes.

[0038] In some embodiments, the photovoltaic-thermal-electric heating coupling system described above is applied to industrial parks, grid-side new energy consumption stations, or high-proportion renewable energy power generation bases.

[0039] Industrial parks can be used to characterize concentrated electricity-consuming areas comprised of multiple factories and enterprises, characterized by high internal power load intensity, diverse energy demands, and peak-valley electricity price differences. In this application scenario, photovoltaic (PV) power generation units are installed on factory rooftops or vacant land, while solar thermal power generation units and thermal storage modules are located within the park's centralized energy station. Electric heating units are connected to the thermal storage modules nearby. The system interfaces with the park's microgrid management system through a dispatch control unit to obtain real-time load forecast data for each factory and power limits at grid interaction points. Industrial parks typically have large PV installed capacity, but daytime load fluctuations are significant, resulting in substantial power curtailment during peak PV output at midday and low-load periods at night. Simultaneously, the park has industrial steam or hot water demands, making traditional separate electricity and heat supply methods inefficient. By converting surplus PV electricity into heat energy and storing it in thermal storage modules, power curtailment losses can be reduced, and a low-cost heat supply can be provided to the park. The thermal storage modules and solar thermal power generation work together to achieve combined heat and power (CHP), improving the park's overall energy efficiency and power supply reliability.

[0040] Grid-side renewable energy absorption stations can be used to characterize supporting energy hub facilities built near large-scale photovoltaic power plants or wind power bases to enhance the grid connection and absorption capacity of renewable energy. In this application scenario, the system is directly connected to the collection bus of the renewable energy station, and the dispatch control unit receives power curtailment instructions and wind / solar curtailment warnings from the grid dispatch center. The photovoltaic power generation unit is the existing photovoltaic array within the station, while the electric heating unit and solar thermal power generation unit serve as flexible adjustment resources, rapidly adjusting charging and discharging power in response to grid dispatch instructions. High-proportion renewable energy integration leads to increased grid peak-shaving pressure, and traditional power curtailment measures result in resource waste, while configuring electrochemical energy storage is costly and poses safety risks. By utilizing the large-capacity, low-cost energy storage characteristics of solar thermal energy storage modules, combined with electric heating units to absorb curtailed power, the curtailed power is stored as thermal energy and fed back to the grid through solar thermal power generation when needed, providing the grid with a dispatchable and flexible power source, improving the renewable energy absorption rate while reducing energy storage investment costs.

[0041] High-proportion renewable energy power generation bases are used to characterize power generation clusters with large-scale photovoltaic (PV) and wind power plants, where renewable energy accounts for over 70% of the installed capacity. In this application scenario, the system uses concentrated solar power (CSP) units as the core regulating power source, with PV units and electric heating units serving as auxiliary regulating resources. The dispatch control unit operates collaboratively with the base's energy management system, utilizing the multi-hour, cross-period energy storage capacity of thermal storage modules to smooth out random fluctuations in PV and wind power. High-proportion renewable energy bases often lack synchronous inertia and flexible regulation capacity, leading to deterioration in power system frequency and voltage stability. By leveraging the controllable output characteristics of CSP units and the energy time-shifting capability of thermal storage modules, combined with the absorption of PV curtailment by electric heating units, the entire base acquires regulation performance and black-start capability similar to conventional thermal power plants, providing rotational inertia and frequency regulation support for high-proportion renewable energy power systems.

[0042] Corresponding to the above-described photovoltaic-thermal-electric heating coupled system, this specification provides a scheduling scheme optimization method, referring to... Figure 3 and Figure 4 As shown, the specific implementation may include the following steps: S301: Obtain the timing data required for scheduling within the target time period.

[0043] Time-series operational data is used to characterize a multivariate data sequence arranged at fixed time intervals (e.g., every hour or every 15 minutes) covering the entire target period (e.g., the next 24 hours). This time-series operational data is acquired by reading hourly forecast values ​​from the photovoltaic power forecasting system, hourly load forecast values ​​from the load forecasting system, hourly power curtailment limits from the grid dispatching platform, and thermal storage status values ​​for the initial period from the thermal storage module control system. The dispatching control unit completes the acquisition and verification of all time-series operational data before the start of the target period to ensure that the data at each time point is complete and without omissions.

[0044] In some embodiments, step S301 may specifically include: acquiring the predicted output data of the photovoltaic power generation unit, the surplus photovoltaic power generation data absorbed by the powerable heating unit, the grid curtailment constraint data, and the thermal storage status data of the thermal storage module within the target time period.

[0045] The predicted output data of photovoltaic (PV) power generation units is used to characterize the theoretically possible electrical power generation sequence of PV modules after receiving solar irradiation in each sub-period (e.g., hourly) within the target time period. This data can be generated by combining numerical weather prediction models with historical output curves of PV power plants. The surplus PV power generation data absorbed by the powerable heating units is used to characterize the estimated remaining PV power available for electric heating after deducting system load consumption and grid capacity limits. This data can be calculated by subtracting the predicted load data and grid capacity limits from the predicted output data. Grid curtailment constraint data is used to characterize the maximum power limit sequence allowed by the external grid to feed power from this system to the grid within the target time period. This data is issued by the grid dispatching department in the form of instructions. The thermal storage status data of the thermal storage modules is used to characterize the total amount of thermal energy stored in the medium of the thermal storage tank at the beginning of the target time period. This data can be expressed as equivalent heat (kWh) or standardized liquid level percentage, and can be collected and converted in real time by temperature and liquid level sensors within the thermal storage tank. The dispatching control unit completes the alignment and storage of the above four types of data within the same data acquisition cycle, ensuring that the data corresponding to each sub-period is consistent in time. By clearly defining the meaning and acquisition methods of the four types of core input data, the scheduling model can simultaneously obtain the photovoltaic resource potential, system absorption capacity, grid acceptance boundary and initial conditions of thermal storage, providing a complete and reliable data foundation for accurately constructing the electrothermal coupled scheduling model.

[0046] S302: Based on time-series operation data, construct an electrothermal coupling scheduling model. The electrothermal coupling scheduling model is used to characterize the relationship between the system's time-series operation characteristics and the multi-energy flow power scheduling parameters.

[0047] Time-series operational data is used to characterize the dataset arranged chronologically for each sub-period within the target time period, including predicted photovoltaic output, surplus power, and grid curtailment constraints. The electrothermal coupling scheduling model characterizes the system's operational variation patterns over time and the correlation between various power regulation variables. This model can consist of one or more algebraic equations, differential equations, and inequalities, and runs within the optimization engine of the scheduling control unit. The scheduling control unit can substitute the collected time-series operational data into the model to form a solvable optimization problem. By constructing the electrothermal coupling scheduling model, the system's time-series operational characteristics are correlated with power scheduling parameters, providing a quantitative computational framework for subsequent multi-objective optimization.

[0048] In some embodiments, the timing characteristics of the system may include energy flow characteristics, equipment physical characteristics, and thermal storage behavior characteristics.

[0049] Energy flow characteristics characterize the distribution, conversion, and transfer of electrical and thermal power among photovoltaic (PV) power generation units, electric heating units, grid interaction units, thermal storage modules, and loads, specifically reflecting the power balance relationship at different time periods. Equipment physical characteristics characterize the inherent technical parameters of each unit, including the maximum output power of the PV inverter, the rated power and ramp rate limit of the electric heating unit, and the minimum stable operating power and load change rate limit of the solar thermal generator unit. Thermal storage behavior characteristics characterize the dynamic changes in heat accumulation and release over time during the charging and discharging processes of the thermal storage module, including charging / discharging efficiency, heat loss coefficient, and upper and lower limits of thermal storage capacity. Traditional models often focus only on energy flow, neglecting equipment physical limitations and dynamic thermal storage behavior, resulting in theoretical solutions that are unenforceable on actual equipment. By explicitly incorporating these three types of characteristics into the model, we ensure that the model reflects energy conservation while also conforming to the actual operating boundaries of the equipment and the timing constraints of thermal storage, thus improving the engineering feasibility of the scheduling scheme.

[0050] In some embodiments, the system's multi-energy flow power scheduling parameters may include photovoltaic power generation, solar thermal power generation, charge / discharge heat generation, and electric heating power.

[0051] Photovoltaic power generation capacity characterizes the actual electrical power delivered to the system by the photovoltaic power generation unit in each sub-period within the target time period. This value is determined by the dispatch control unit and issued as an execution command to the photovoltaic inverter of the photovoltaic power generation unit. Concentrated solar power (CSP) power generation capacity characterizes the electrical power output of the CSP module in each sub-period within the target time period. This value is related to the heat release power and power generation efficiency of the thermal storage module. Charge / discharge power characterizes the rate at which the thermal storage module absorbs (charges) or releases (discharges) heat energy in each sub-period. The charging power originates from the electric heating unit or mirror field heat collection, while the discharging power supplies the power generation module. Electric heating power characterizes the excess photovoltaic power consumed by the electric heating unit in each sub-period; this value directly determines the amount of heat energy converted into electricity.

[0052] In some embodiments, the electrothermal coupling scheduling model may include an electric power balance model, a thermal storage state transition model, and equipment operation constraints and thermal storage capacity constraints.

[0053] The power balance model characterizes the algebraic sum of photovoltaic power generation, grid interaction power, electric heating power, and load power that must be satisfied at each time period. The thermal energy storage state transition model characterizes the time-varying mapping relationship of the heat storage capacity in the thermal energy storage module, linking the heat storage capacity in the current time period with the heat storage capacity in the previous time period, the charging power, the dissipation power, and the heat loss during that period. Equipment operation constraints limit the physical range of the power value of each unit and the maximum magnitude of power variation between adjacent time periods. Thermal energy storage capacity constraints limit the safe upper and lower boundaries of the heat storage capacity of the thermal energy storage module and the balance conditions at the beginning and end of the scheduling cycle.

[0054] In some embodiments, step S302 may specifically include: constructing an electric power balance model based on predicted output data to characterize the instantaneous equality relationship between photovoltaic power generation, grid interaction power, electric heating power, and load power; constructing a thermal energy storage state transition model based on surplus photovoltaic power generation data to characterize the temporal recursive relationship between the thermal energy storage in the current sub-period and the thermal energy storage, charging power, releasing power, and heat loss in the previous sub-period; establishing equipment operation constraints including upper and lower power limits and ramp rate constraints for photovoltaic power generation units, electric heating units, and solar thermal power generation units based on grid curtailment constraint data; and establishing thermal energy storage capacity constraints including upper and lower safety limits for thermal energy storage and thermal energy storage balance constraints at the beginning and end of scheduling based on thermal energy storage state data.

[0055] A power balance model can be constructed based on predicted power output data. The power balance model is used to characterize the instantaneous equality between photovoltaic power generation and grid-purchased power in any scheduling sub-period, which equals the load power, electric heating power, and grid-sold power.

[0056] A thermal energy storage state transition model can be constructed based on surplus photovoltaic power generation data. The thermal energy storage state transition model is used to characterize that the heat storage at the end of the current sub-period is equal to the heat storage at the end of the previous sub-period plus the product of the charging power and charging efficiency of this sub-period, minus the quotient of the heat release power and heat release efficiency, and then minus the heat loss of this sub-period.

[0057] Equipment operation constraints can be established based on grid curtailment data. These constraints include limiting the output power of photovoltaic power generation units to between zero and the predicted output, limiting the power of electric heating units to between zero and the rated power, limiting the power of solar thermal power generation units to between the minimum stable output and the rated power, and ensuring that the power change rate of each unit in adjacent time periods does not exceed its respective ramp-up rate limit.

[0058] Thermal storage capacity constraints can be established based on thermal storage status data. These constraints include ensuring that the thermal storage capacity of the thermal storage module does not fall below the minimum safe thermal storage capacity (to prevent the tank from drying out) or exceed the maximum allowable thermal storage capacity (to prevent overpressure) in any sub-period, and that the difference between the thermal storage capacity of the last sub-period and the thermal storage capacity of the first sub-period of the scheduling cycle is less than a set deviation limit (to achieve daily cycle balance).

[0059] In some embodiments, the above-mentioned power balance model, constructed based on predicted power output data, to characterize the instantaneous equality relationship between photovoltaic power generation, grid interaction power, electric heating power, and load power, may further include: dividing photovoltaic power generation and load power into multiple discrete state intervals, and statistically analyzing the transition frequency of each state interval between adjacent scheduling periods based on historical power time series data, to establish state transition probability matrices for photovoltaic power generation and load power respectively; within each sub-period of the target period, obtaining the actual state intervals of photovoltaic power generation and load power from the previous sub-period, and predicting the probability distribution of photovoltaic power generation and load power falling into each state interval according to the corresponding state transition probability matrices, to obtain the first and second expected values ​​of photovoltaic power generation and load power in the current sub-period; correcting the predicted power output data based on the first and second expected values ​​of each sub-period of the target period; and constructing a power balance model based on the corrected predicted power output data.

[0060] Photovoltaic power generation and load power can be divided into multiple discrete state intervals. Discrete state intervals characterize the division of a continuous power range into several sub-intervals at fixed steps (e.g., 10% of rated power), with each sub-interval representing a state level. Based on historical power time-series data, the frequency of photovoltaic power transitioning from one state interval to another between adjacent scheduling periods is statistically analyzed, forming a state transition probability matrix for photovoltaic power. Similarly, the state transition frequency of load power is statistically analyzed, forming a state transition probability matrix for load power. Within each sub-period of the target time period, the actual value of photovoltaic power generation from the previous sub-period is obtained, and its corresponding state interval is determined. Based on the photovoltaic power state transition probability matrix, the probability of photovoltaic power falling into each state interval in the current sub-period is read, and the expected value is calculated as the first expected value. The second expected value of load power is obtained using the same method. Using the first and second expected values ​​for each sub-period, the photovoltaic power and load power values ​​in the original predicted output data are corrected. Correction methods include, but are not limited to, taking a weighted average of the predicted and expected values. Based on the corrected predicted output data, a power balance model is constructed. Traditional power balance models treat photovoltaic (PV) output and load as deterministic sequences, neglecting the randomness of state transitions between adjacent time periods. This leads to severe model mismatch in scenarios with drastic weather changes or frequent load fluctuations. By introducing state transition probabilities based on historical statistics, the power balance model can reflect the time-series dependence of PV and load, improving its adaptability to uncertainty and thus generating more robust scheduling schemes.

[0061] In some embodiments, the above-mentioned thermal energy storage state transition model, constructed based on surplus photovoltaic power generation data, to characterize the temporal recursive relationship between the thermal energy storage in the current sub-period and the thermal energy storage, charging power, releasing power, and heat loss in the previous sub-period, may further include: calculating the rate of change of surplus photovoltaic power in each sub-period within the target period relative to the previous sub-period, as the volatility of that sub-period; determining the heat loss coefficient of that sub-period based on the volatility, wherein the heat loss coefficient is positively correlated with the volatility; and constructing the thermal energy storage state transition model based on the surplus photovoltaic power generation data and the corresponding volatility.

[0062] The absolute value of the rate of change of surplus photovoltaic power relative to the previous sub-period within each sub-period of the target time period can be calculated as the volatility of that sub-period. The absolute value of the rate of change characterizes the severity of fluctuations in surplus photovoltaic power between two consecutive sub-periods. One calculation formula is the absolute value of (power in the current sub-period minus power in the previous sub-period) divided by the rated power. Based on the range of the volatility value, the heat loss coefficient for that sub-period is determined. The heat loss coefficient is positively correlated with the volatility; that is, the more severe the fluctuation, the larger the heat loss coefficient, used to simulate the additional heat loss caused by frequent charge-discharge switching. A specific determination method is as follows: when the volatility is below 0.05, the heat loss coefficient takes the nominal value of 1.0; when the volatility is between 0.05 and 0.15, the heat loss coefficient takes 1.2; when the volatility is above 0.15, the heat loss coefficient takes 1.5. Of course, in specific implementations, the threshold values ​​corresponding to the volatility and the corresponding values ​​of the heat loss coefficient can be adjusted according to the actual situation. In the thermal energy storage state transition model, the original constant heat loss is multiplied by a heat loss coefficient to obtain the dynamic heat loss, which is then substituted into the time-series recursive equation. Traditional thermal energy storage state transition models assume a constant heat loss. In actual operation, drastic fluctuations in excess photovoltaic power can cause turbulent flow of the medium inside the thermal energy storage module and an increase in the temperature gradient, resulting in actual heat loss far exceeding the rated value, leading to deviations in the model's prediction of the thermal energy storage state. However, by introducing a heat loss coefficient positively correlated with the volatility, the thermal energy storage state transition model can dynamically reflect changes in actual heat loss, improving the accuracy of thermal energy storage state prediction and avoiding overcharging or underheating due to model errors.

[0063] S303: With the goals of minimizing the input power fluctuation rate of the electrothermal coupling channel and minimizing the photovoltaic curtailment rate, multiple candidate scheduling schemes are generated based on the electrothermal coupling scheduling model. The electrothermal coupling channel is the path through which surplus photovoltaic power is converted into heat energy by the electric heating unit and input into the thermal storage module.

[0064] The electrothermal coupling channel characterizes the complete energy transfer path from the photovoltaic power generation unit, through the electric heating unit (converted into heat energy), to the thermal storage module via a heat transfer medium pipeline. Input power volatility characterizes the degree of variation in thermal power within this channel during continuous scheduling periods. Lower volatility indicates more stable operation of the electric heating unit. Photovoltaic curtailment rate characterizes the proportion of photovoltaic power not absorbed by the system load, grid, or thermal storage module relative to the predicted output. The scheduling control unit combines the electrothermal coupling scheduling model with two objective functions, solving for a set of mutually independent scheduling schemes using an optimization algorithm. Traditional methods focus only on a single objective (such as minimizing curtailment rate), neglecting the stable operation requirement of the electrothermal coupling channel, leading to drastic power fluctuations in the electric heating unit, damaging equipment lifespan and causing thermal stress shocks to the thermal storage module. By simultaneously minimizing volatility and curtailment rate as optimization objectives, a set of Pareto optimal candidate schemes is generated, improving both the renewable energy absorption level and ensuring the stable operation of the electrothermal coupling channel.

[0065] In some embodiments, step S303 may specifically include: constructing a multi-objective optimization function with the input power fluctuation rate of the electrothermal coupling channel and the photovoltaic curtailment rate as optimization objectives; iteratively optimizing the electrothermal coupling scheduling model based on the multi-objective optimization function, and performing non-dominated sorting based on the Pareto dominance relationship according to the multi-objective optimization function value of each candidate scheduling scheme in each iteration; and determining multiple candidate scheduling schemes based on the Pareto front solution set that satisfies the iteration termination condition.

[0066] A multi-objective optimization function characterizes the mapping relationship between two minimizing indicators and decision variables (PV power generation, electric heating power, solar thermal power generation, etc. in different time periods), and can be represented as a vector function. Based on this multi-objective optimization function, the electrothermal coupled scheduling model is iteratively optimized, generating a set of candidate scheduling schemes in each iteration. In each iteration, based on the volatility and curtailment rates calculated for each candidate scheduling scheme, a non-dominated ranking is performed according to the Pareto dominance relation. The Pareto dominance relation defines the superiority or inferiority of schemes: if both objective values ​​of scheme A are not inferior to scheme B and at least one is strictly superior to scheme B, then A dominates B. The non-dominated ranking divides all schemes into multiple frontier layers, where schemes in the first frontier layer (non-dominated solution set) are mutually non-dominated. Iteration is repeated until a pre-set termination condition is met (e.g., reaching the maximum number of iterations or no change in the frontier layer for multiple consecutive generations). The non-dominated solution set (i.e., the Pareto frontier solution set) obtained when the iteration termination condition is met is determined as multiple candidate scheduling schemes. A single optimization can only obtain one scheme and cannot provide a space for trade-offs between multiple objectives. Through multi-objective iterative optimization and Pareto non-dominated sorting, a set of non-dominated candidate solutions is generated, providing a wealth of options for flexibly selecting target solutions based on actual operating conditions.

[0067] In some embodiments, the above-mentioned non-dominated sorting based on Pareto dominance may further include: for each candidate scheduling scheme, calculating the total deviation value of its violation of equality constraints and the total excess value of its violation of inequality constraints; determining the constraint violation degree based on the total deviation value and the total excess value; marking candidate scheduling schemes with constraint violation degrees greater than zero as infeasible solutions, sorting them in descending order of constraint violation degree, and placing the infeasible solution with the smallest constraint violation degree after the feasible solution; marking candidate scheduling schemes with constraint violation degrees equal to zero as feasible solutions, and performing Pareto non-dominated sorting based on the input power fluctuation rate of the electrothermal coupling channel and the photovoltaic curtailment rate; during the merging sorting, any feasible solution is superior to any infeasible solution; for schemes that are both feasible solutions, sorting them according to Pareto dominance; for schemes that are both infeasible solutions, sorting them in ascending order of constraint violation degree.

[0068] For each candidate scheduling scheme, the sum of the absolute values ​​of deviations from each equality constraint (e.g., power balance, thermal energy storage start-end balance) violated during all scheduling periods is calculated to obtain the total deviation value. The sum of the absolute values ​​of exceeding each inequality constraint (e.g., power upper and lower limits, ramp rate) is also calculated to obtain the total exceedance value. The total deviation value and the total exceedance value are then weighted and summed to obtain the constraint violation degree, where the weight coefficients for equality constraints are higher than those for inequality constraints. Schemes with a constraint violation degree greater than zero are marked as infeasible solutions, sorted by constraint violation degree from smallest to largest, with the infeasible solution with the smallest violation degree ranked first in its group. Schemes with a constraint violation degree equal to zero are marked as feasible solutions, and ranked according to the Pareto non-dominated order of the input power fluctuation rate of the electrothermal coupling channel and the photovoltaic curtailment rate. During the merging and ranking, any feasible solution is superior to any infeasible solution; that is, all feasible solutions are ranked before all infeasible solutions. For schemes that are both feasible solutions, the order is determined according to the Pareto dominance relationship. For solutions that are all infeasible, they are sorted in ascending order of constraint violation severity, with smaller violations appearing earlier. This sorted sequence is used for subsequent selection operations (such as tournament selection) to preserve superior individuals for the next generation. Traditional non-dominated sorting only compares objective function values ​​without distinguishing between feasible and infeasible solutions, leading to a large number of constraint-violated infeasible and feasible solutions mixed together. This interferes with the correct formation of the Pareto front and makes it difficult for the evolutionary process to converge to the feasible region. By calculating constraint violation severity and forcibly placing all feasible solutions before infeasible solutions, the algorithm guides the population to converge quickly to the feasible region; simultaneously, sorting infeasible solutions by violation severity preserves some individuals, maintaining the population's exploratory ability and improving the algorithm's optimization efficiency under complex constraints.

[0069] S304: Select the target scheduling scheme from multiple candidate scheduling schemes based on system operating data.

[0070] System operating condition data characterizes the actual operating status of the system at the current moment or near real-time, including the current heat storage capacity of the thermal storage module, the real-time output of the photovoltaic power generation unit, the grid curtailment command value, the actual load power, and the operational status of the electric heating unit. The dispatch control unit can match the collected system operating condition data with the preset applicable conditions of each candidate dispatch scheme, and select the optimal scheme under the current operating conditions as the target dispatch scheme. The target dispatch scheme characterizes the detailed power command sequence that will be issued to each execution unit in subsequent actual operation. By introducing system operating condition data as the selection criterion, the dispatch method can establish a bridge between offline optimization results and online operation, realize adaptive adjustment of the scheme, and improve the real-time applicability of dispatch commands.

[0071] In some embodiments, step S304 may specifically include: determining whether the remaining thermal storage capacity of the thermal storage module is zero based on system operating data; if it is zero, selecting the scheme with the lowest input power fluctuation rate of the electrothermal coupling channel from multiple candidate scheduling schemes as the target scheduling scheme.

[0072] The remaining thermal storage capacity characterizes the difference between the maximum allowable heat storage capacity of the thermal storage module and the current heat storage capacity. This difference reflects how much additional thermal energy the module can absorb. When the remaining thermal storage capacity is less than a preset safety buffer threshold (e.g., 2% of the maximum capacity), it is determined to be zero. If it is determined to be zero, it indicates that the thermal storage module is basically full and cannot absorb any more heat energy. In this case, the scheme with the lowest power fluctuation rate of the electrothermal coupling channel is selected as the target scheduling scheme from multiple candidate scheduling schemes. The reason for selecting the scheme with the lowest fluctuation rate is that when the thermal storage module can no longer be charged, the electric heating unit should operate as smoothly as possible or reduce its power to avoid thermal overpressure or safety valve activation due to forced start-up. When the thermal storage module is full, if the scheme is still selected according to the conventional power curtailment rate priority principle, it may cause the electric heating unit to continue to input heat into the thermal storage module that has no space, leading to a safety accident. By detecting the remaining thermal storage capacity, the scheme with the lowest fluctuation rate (usually meaning that the electric heating power is small or zero) is prioritized under full tank conditions to ensure the safe operation of the system.

[0073] In some embodiments, the selection of the scheme with the lowest input power fluctuation rate of the electrothermal coupling channel from multiple candidate scheduling schemes as the target scheduling scheme may further include: selecting a scheme from multiple candidate scheduling schemes that meets the following conditions: in each sub-period within the target time period, the heat power input by the electric heating unit to the thermal storage module does not exceed the maximum absorbable heat power corresponding to a preset safety buffer threshold; taking the selected schemes as a set of effective schemes; selecting the scheme with the lowest input power fluctuation rate of the electrothermal coupling channel from the set of effective schemes as the target scheduling scheme; if the set of effective schemes is empty, selecting the scheme with the lowest input power fluctuation rate of the electrothermal coupling channel and the smallest total electric heating heat as the target scheduling scheme.

[0074] From all candidate scheduling schemes, the following safety condition can be selected: In each sub-period within the target time period, the thermal power input from the electric heating unit to the thermal storage module does not exceed the maximum absorbable thermal power corresponding to a preset safety buffer threshold. The safety buffer threshold characterizes the allowable short-term, small-scale charging capacity of the thermal storage module when it is nearing full capacity. For example, it can be set to 0.5% to 2% of the maximum thermal storage capacity. The maximum absorbable thermal power is equal to the safety buffer threshold divided by the sub-period duration and the power value after considering thermal efficiency. Schemes that meet this condition constitute the effective scheme set. Then, in the effective scheme set, the scheme with the lowest input power fluctuation rate of the electric heating coupling channel is selected as the target scheduling scheme. If the effective scheme set is empty (i.e., no scheme can meet the safety buffer constraint in all sub-periods), the scheme with the lowest input power fluctuation rate of the electric heating coupling channel and the smallest total electric heating heat is selected as the target scheduling scheme. The minimum total electric heating heat is used to further select the scheme with the smallest total charging heat among all schemes with the lowest fluctuation rates, in order to minimize the thermal shock to the already full thermal storage module. When directly selecting the option with the lowest volatility, this option may still have a large electric heating power in some sub-periods, exceeding the safe absorption capacity under full tank conditions. By pre-screening and eliminating options that violate the safety buffer constraints, and downgrading to the option with the lowest total heat when no suitable option is found, operational stability is maintained as much as possible while ensuring safety.

[0075] In some embodiments, step S304 may further include: if it is not zero, determining whether the surplus photovoltaic power generation is greater than the preset curtailment rate threshold based on system operating data; if it is greater, selecting the scheme with the lowest photovoltaic curtailment rate from multiple candidate scheduling schemes as the target scheduling scheme.

[0076] If the remaining thermal storage capacity is not zero, the system operating data is used to determine whether the surplus photovoltaic (PV) power generation exceeds the preset curtailment rate threshold. The preset curtailment rate threshold characterizes a curtailment risk level; when the surplus PV power exceeds this threshold, it indicates that a large amount of curtailment will occur if it is not absorbed in time. The surplus PV power generation is equal to the actual PV output during the current period minus load consumption minus the grid's allowed feed-in limit. If the surplus PV power generation exceeds the preset curtailment rate threshold, the scheme with the lowest PV curtailment rate is selected as the target scheduling scheme from multiple candidate scheduling schemes. The reason for selecting the scheme with the lowest curtailment rate is that the curtailment risk is high at this point, and priority should be given to ensuring the absorption of new energy, even if it means sacrificing some stability of the electrothermal coupling channel. When there is still space for thermal storage but a large surplus PV power, prioritizing stability would miss opportunities for absorption, leading to a large amount of curtailment. By comparing the surplus power with the curtailment rate threshold, a curtailment rate-priority strategy is proactively switched to under high curtailment risk conditions to maximize the utilization rate of new energy.

[0077] In some embodiments, the selection of the photovoltaic curtailment rate as the target scheduling scheme from multiple candidate scheduling schemes may further include: selecting a scheme from multiple candidate scheduling schemes that meets the following conditions: in each sub-period within the target time period, the heat power input from the electric heating unit to the thermal storage module does not exceed the maximum allowable charging power of the thermal storage module; using the selected schemes as a set of feasible schemes; selecting the scheme with the lowest photovoltaic curtailment rate as the target scheduling scheme from the set of feasible schemes; if the set of feasible schemes is empty, selecting the scheme with the smallest peak charging power of the electric heating unit as the target scheduling scheme.

[0078] From all candidate scheduling schemes, the following equipment safety conditions can be selected: In each sub-period within the target time period, the heat power input from the electric heating unit to the thermal storage module does not exceed the maximum allowable charging power of the thermal storage module. The maximum allowable charging power characterizes the upper limit of heat that the thermal storage module can safely absorb per unit time, limited by the heat exchanger area, molten salt pump flow rate, and thermal stress; exceeding this limit will lead to equipment damage. Schemes meeting this condition constitute a set of feasible schemes. Among the feasible schemes, the scheme with the lowest photovoltaic curtailment rate is selected as the target scheduling scheme. If the set of feasible schemes is empty (i.e., in all candidate schemes, each scheme exceeds the maximum allowable charging power in at least one sub-period), then the scheme with the lowest peak charging power of the electric heating unit is selected as the target scheduling scheme. The lowest peak charging power characterizes the scheme with the lowest maximum instantaneous charging power among all schemes to avoid exceeding equipment limits. The scheme with the lowest curtailment rate may arrange for the electric heating unit to operate at extremely high power for a short period to maximize energy consumption, exceeding the maximum charging capacity of the thermal storage module, causing equipment damage or protective shutdown. By pre-screening and eliminating schemes that violate the heat charging power constraint, and downgrading to the scheme with the lowest peak power when no suitable scheme is found, a reasonable trade-off between high absorption rate and equipment safety is achieved.

[0079] In some embodiments, the above method may further include: determining the thermal power input by the electric heating unit to the thermal storage module in each sub-period based on the electrothermal conversion efficiency and the surplus photovoltaic power actually consumed by the electric heating unit in each sub-period within the target time period; and determining the input power fluctuation rate of the electrothermal coupling channel based on the comparison between the cumulative result of the thermal power difference between adjacent sub-periods and the cumulative result of the thermal power in multiple sub-periods.

[0080] Electrothermal conversion efficiency (ETC) characterizes the ratio of electrical energy to heat energy converted by an electric heating unit. ETC values ​​typically range from 0.85 to 0.98. The actual surplus photovoltaic power absorbed characterizes the power allocated to the electric heating unit in the dispatch scheme. Heat power equals electrical power multiplied by the ETC. The input power volatility of the electrothermal coupling channel can be determined by comparing the cumulative difference in heat power between adjacent sub-time periods with the cumulative heat power across multiple sub-time periods. Specifically, the total volatility is calculated by summing the absolute values ​​of the heat power differences between all adjacent sub-time periods, and the total heat power is calculated by summing the heat power across all sub-time periods. The volatility is then obtained by dividing the total volatility by the sum of the total heat power and a very small positive number.

[0081] In some embodiments, determining the thermal power input from the electric heating unit to the thermal storage module during the sub-period may further include: obtaining the inlet medium temperature of the thermal storage module and the rated design temperature of the outlet medium of the electric heating unit during the current sub-period; determining a temperature compensation coefficient based on the comparison between the inlet medium temperature and the rated design temperature to correct the electrothermal conversion efficiency; and determining the thermal power input from the electric heating unit to the thermal storage module during the sub-period based on the corrected electrothermal conversion efficiency and the actual surplus photovoltaic power consumed.

[0082] The inlet medium temperature characterizes the actual temperature of the heat transfer medium (such as heat transfer oil or molten salt) entering the heat exchanger of the electric heating unit. This temperature fluctuates with changes in the state of the thermal storage module. The rated design temperature characterizes the rated temperature of the outlet medium of the electric heating unit under calibrated operating conditions. A temperature compensation coefficient is determined based on the comparison between the inlet medium temperature and the rated design temperature. The temperature compensation coefficient corrects for changes in electrothermal conversion efficiency caused by deviations in the inlet temperature from the design value: when the inlet temperature is lower than the rated design temperature, the heat exchange temperature difference increases, the efficiency slightly improves, and the compensation coefficient is greater than 1; when the inlet temperature is higher than the rated design temperature, the heat exchange temperature difference decreases, the efficiency decreases, and the compensation coefficient is less than 1. The nominal electrothermal conversion efficiency is multiplied by the temperature compensation coefficient to obtain the corrected electrothermal conversion efficiency. Based on the corrected electrothermal conversion efficiency and the actual surplus photovoltaic power consumed, the thermal power for this sub-period is calculated. The electrothermal conversion efficiency of the electric heating unit is not constant and changes with the inlet medium temperature. Ignoring this change will lead to deviations in the thermal power calculation and affect the accuracy of thermal storage state prediction. By introducing inlet medium temperature feedback, the electrothermal conversion efficiency is corrected in real time, making the thermal power calculation closer to the actual physical process, thereby improving the accuracy of the thermal storage state transition model.

[0083] In some embodiments, the above method may further include: determining the surplus power within the photovoltaic forecast output that is not absorbed by the system load and the grid in each sub-period within the target time period; determining whether the remaining thermal storage capacity of the thermal storage module is zero in each sub-period; if it is not zero, excluding the portion of the surplus power that can be converted into thermal energy storage by the electric heating unit from the curtailed power, and including the portion of the thermal power exceeding the remaining thermal storage capacity converted back into electrical power in the curtailed power; if it is zero, including all the surplus power in the curtailed power; and determining the photovoltaic curtailment rate based on the comparison between the cumulative curtailed power of multiple sub-periods and the cumulative photovoltaic forecast output.

[0084] Surplus power is used to characterize the photovoltaic power that cannot be directly utilized during a given period. It is determined whether the remaining thermal storage capacity of the thermal storage module is zero within each sub-period. The remaining thermal storage capacity is the maximum allowable thermal storage capacity minus the current thermal storage capacity, then converted to absorbable electrical power equivalent considering the charging efficiency. If the remaining thermal storage capacity is not zero, the portion of the surplus power that can be converted into thermal energy by the electric heating unit is not included in the curtailed power. Specifically, the surplus power is multiplied by the electrothermal conversion efficiency to obtain the thermal power to be stored. This thermal power is compared with the remaining thermal storage capacity (converted to thermal capacity): if the thermal power to be stored is less than or equal to the remaining thermal storage capacity, all surplus power is not included in the curtailed power; if the thermal power to be stored is greater than the remaining thermal storage capacity, the excess thermal power is divided by the electrothermal conversion efficiency to convert back to electrical power and included in the curtailed power. If the remaining thermal storage capacity is zero, all surplus power is included in the curtailed power. The photovoltaic curtailment rate is determined based on the ratio of the cumulative curtailed power across multiple sub-periods to the cumulative predicted photovoltaic output. Traditional curtailment rate calculations treat all unabsorbed photovoltaic power as curtailed, ignoring the fact that some power stored in thermal storage modules via electric heating units has actually been absorbed. This leads to an inflated curtailment rate that fails to accurately reflect the system's absorption capacity. By distinguishing between effective storage and actual curtailment, the curtailment rate accurately reflects the system's actual renewable energy utilization level, providing a true benchmark for optimization.

[0085] In some embodiments, determining the photovoltaic curtailment rate may further include: determining the urgency of each sub-period based on the grid curtailment intensity, absorption intensity, and load intensity corresponding to each sub-period, wherein the urgency is used to characterize the cost of curtailing power in the sub-period; and determining the photovoltaic curtailment rate based on the urgency and by comparing the weighted cumulative results of multiple sub-periods with the weighted cumulative results of the photovoltaic predicted output.

[0086] Urgency is used to characterize the extent of loss caused by curtailment or the urgency of absorbing surplus photovoltaic power in a given sub-period. The higher the grid curtailment intensity (e.g., lower curtailment command power), the greater the urgency; the lower the absorption intensity (e.g., lower load forecast value), the greater the urgency; the higher the load intensity (e.g., a large proportion of critical loads), the greater the urgency. Urgency can be quantified as a weighted sum of these three factors after normalization. Based on urgency, the curtailed power across multiple sub-periods is weighted and accumulated to obtain the weighted total curtailed power; simultaneously, the predicted photovoltaic output is weighted and accumulated to obtain the weighted total predicted output. Dividing the weighted total curtailed power by the sum of the weighted total predicted output and the smallest positive number yields the weighted photovoltaic curtailment rate. Traditional curtailment rates treat all periods equally, failing to reflect the greater economic losses or power supply pressure caused by curtailment in certain periods (e.g., high electricity price periods, peak load periods). By introducing time-period urgency weighting, the curtailment rate index can distinguish the curtailment cost of different time periods, guiding the optimization algorithm to prioritize curtailment during low-cost periods, thereby improving the absorption capacity of the scheduling scheme.

[0087] In some embodiments, step S203 may further include: generating multiple candidate scheduling schemes based on the electrothermal coupling scheduling model with the objectives of minimizing the input power fluctuation rate of the electrothermal coupling channel, minimizing the photovoltaic curtailment rate, and minimizing the system loss index. The system loss index is used to characterize the degree of impact of the scheduling scheme on the system lifetime.

[0088] The system loss index characterizes the cumulative negative impact of scheduling schemes on the lifespan of system equipment. This index comprehensively considers factors such as the start-up and shutdown frequency, load variation, and thermal stress cycles of electric heating units, thermal storage modules, and solar thermal power generators. The scheduling control unit uses a three-dimensional objective vector composed of these three indices as the optimization direction and employs a multi-objective optimization algorithm (such as NSGA-III or MOEA / D) to solve the electrothermal coupling scheduling model, generating a set of Pareto optimal candidate scheduling schemes. Considering only volatility and curtailment rate as objectives ignores the long-term lifespan loss of equipment, which may lead to scheduling schemes with excellent short-term performance but premature equipment aging. By introducing the system loss index as a third optimization objective, the generated candidate scheduling schemes can achieve a three-dimensional balance between renewable energy consumption, operational stability, and equipment lifespan.

[0089] In some embodiments, the above method may further include: determining an electric heating start-stop loss index based on the number of start-stop cycles of the electric heating unit within a target time period and a preset first loss coefficient; determining an electric heating variable load loss index based on the cumulative power change rate of the electric heating unit within the target time period and a preset second loss coefficient; determining a thermal storage thermal stress loss index based on the number of switching cycles between the thermal storage module's charging power and releasing power within the target time period and a preset third loss coefficient; determining a solar thermal power generation variable load loss index based on the cumulative power change rate of the solar thermal power generation unit within the target time period and a preset fourth loss coefficient; and determining a system loss index based on the electric heating start-stop loss index, the electric heating variable load loss index, the thermal storage thermal stress loss index, and the solar thermal power generation variable load loss index.

[0090] The electric heating start-stop loss index can be determined based on the number of start-stop cycles of the electric heating unit within the target time period and a preset first loss coefficient. The number of start-stop cycles characterizes the total number of times the electric heating unit rises from zero power to positive power (start-up) and drops from positive power to zero (stop), with each start-stop cycle generating thermal cycle stress. The first loss coefficient is used to convert the number of start-stop cycles into a dimensionless loss contribution value.

[0091] The electric heating load variation loss index can be determined based on the cumulative power change rate of the electric heating unit within the target time period and a preset second loss coefficient. The cumulative power change rate is used to characterize the sum of the absolute values ​​of the electric heating power differences between adjacent time periods, reflecting the load variation amplitude. The second loss coefficient is used to convert the cumulative power change rate into a loss contribution value.

[0092] The thermal stress loss index of thermal storage can be determined based on the number of switching times between the charging and releasing power of the thermal storage module within a target time period and a preset third loss coefficient. The number of switching times characterizes the number of times the thermal storage module switches from a charging state to a releasing state or vice versa. Each switching causes a change in the temperature gradient inside the thermal storage tank, generating thermal stress. The third loss coefficient is used to convert the number of switching times into a loss contribution value.

[0093] The solar thermal power generation load variation loss index can be determined based on the cumulative power change rate of the solar thermal power generation unit within the target time period and a preset fourth loss coefficient. The calculation method for the cumulative power change rate of the solar thermal power generation unit is similar to that of the electric heating unit, but an independent fourth loss coefficient is used to reflect the sensitivity of rotating equipment such as steam turbine units to load variation.

[0094] The system loss index can be obtained by adding the electric heating start-up and shutdown loss index, the electric heating load change loss index, the thermal stress loss index of thermal storage, and the load change loss index of solar thermal power generation.

[0095] In some embodiments, determining the system loss index based on the electric heating start-up and shutdown loss index, the electric heating load change loss index, the thermal storage thermal stress loss index, and the solar thermal power generation load change loss index may further include: determining the electric heating coupling loss index based on the coupling result of the electric heating start-up and shutdown loss index and the electric heating load change loss index; determining the thermoelectric coupling loss index based on the coupling result of the thermal storage thermal stress loss index and the solar thermal power generation load change loss index; and determining the system loss index based on the electric heating coupling loss index and the thermoelectric coupling loss index.

[0096] The electric heating coupling loss index can be determined based on the coupling result between the electric heating start-up and shutdown loss index and the electric heating load change loss index. The coupling result is used to characterize the synergistic damage effect generated when the electric heating unit simultaneously starts up and shuts down and undergoes a large load change. This effect is greater than the simple sum of two independent damages. Specifically, one coupling method is to multiply the electric heating start-up and shutdown loss index by the electric heating load change loss index to obtain the electric heating coupling loss index.

[0097] Similarly, the thermoelectric coupling loss index is determined based on the coupling result of the thermal stress loss index of thermal storage and the load change loss index of solar thermal power generation. One coupling method is to multiply the thermal stress loss index of thermal storage and the load change loss index of solar thermal power generation to obtain the thermoelectric coupling loss index. This coupling term reflects the accelerated damage to the system life caused by the thermodynamic cycle when the frequent switching of the thermal storage module's charging and discharging states occurs simultaneously with the large load change of the generator set.

[0098] The system loss index is obtained by summing the loss indices for electric heating start-up and shutdown, electric heating load change, thermal storage thermal stress, solar thermal power generation load change, electric heating coupling, and thermoelectric coupling. Different weighting coefficients can be assigned to each linear and coupling term to reflect the varying importance of different damage mechanisms. The linear superposition method ignores the aggravating effect of coupling when multiple adverse operating conditions occur simultaneously, leading to an underestimation of actual lifetime damage in the system loss index. By introducing coupling terms (start-up / shutdown × load change, thermal storage switching × power generation load change), the system loss index can reflect the true damage pattern of multi-factor synergistic deterioration, thereby guiding the scheduling scheme to proactively avoid triggering multiple harmful operating conditions simultaneously, further improving the long-term reliability of equipment operation.

[0099] In some embodiments, step S303 may further include: A non-dominated sorting genetic algorithm with an elite strategy is used for multi-objective optimization. The initial population size is N, the maximum number of iterations is T, and the decision variables are the photovoltaic power generation, electric heating power, solar thermal power generation and thermal storage module charging and discharging power of each sub-period within the target period. Real number encoding is used, and each individual corresponds to a scheduling scheme. The initial population is randomly generated, and the boundary absorption strategy is used to ensure that each individual meets the upper and lower limits of the equipment power.

[0100] For each individual, the input power fluctuation rate of the electrothermal coupling channel and the photovoltaic curtailment rate are calculated; the population is non-dominated and sorted into multiple frontier levels based on the Pareto dominance relationship.

[0101] For equality constraints, a dynamic penalty function is used, incorporating the degree of violation into the objective function as a penalty term; for inequality constraints, a patching strategy is employed in variable initialization and mutation operations, performing boundary absorption or random reset on individuals that exceed the boundary.

[0102] Within the same non-dominated layer, calculate the crowding distance of each individual in the target space, and prioritize retaining individuals with higher crowding.

[0103] A tournament selection mechanism is used to select individuals from the current population to enter the mating pool; offspring populations are generated by simulating binary crossover and polynomial mutation, with the crossover probability set to Pc and the mutation probability set to Pm.

[0104] The parent and offspring generations are merged to form a temporary population of size 2N. The non-dominated sorting and crowding calculation are performed again, and the top N individuals are selected as the next generation population.

[0105] Repeat the non-dominated sorting, crowding calculation, selection, crossover, mutation and elite retention operations until the maximum number of iterations T is reached; after the iteration is completed, output the Pareto front solution set in the final population as multiple candidate scheduling schemes.

[0106] Population size N=100, maximum number of iterations T=500, crossover probability Pc=0.9, mutation probability Pm=0.1, crossover distribution index ηc=20, mutation distribution index ηm=20.

[0107] After obtaining the Pareto front solution set, the solutions are comprehensively ranked using the approximation ideal solution ranking method or the grey relational analysis method. The evaluation indicators include at least one of the following: input power fluctuation rate of the electrothermal coupling channel, photovoltaic curtailment rate, number of cycles of the thermal storage module, start-up and shutdown frequency of the electric heating unit, and effective occupancy rate of the electrothermal coupling channel. The scheduling scheme with the best comprehensive performance is selected from the Pareto front as the target scheduling scheme.

[0108] The multi-objective optimization algorithm used to generate multiple candidate scheduling schemes includes any one of the following: multi-objective particle swarm optimization algorithm, decomposition-based multi-objective evolutionary algorithm, intensity Pareto evolutionary algorithm, or hybrid optimization strategy of reinforcement learning and genetic algorithm.

[0109] Based on the system's operating status, the optimal scheduling scheme is selected from the set of scheduling schemes and output to the corresponding equipment for execution; compared with the existing scheduling operation strategy before optimization, the improvement value of each objective after optimization is output; the optimized mirror field heat collection power curve, electric heater power curve, thermal storage system charging and discharging heat power curve, heat power curve delivered to the power generation module, and thermal storage system liquid level status curve are output.

[0110] In some embodiments, step S304 may further include: classifying multiple candidate scheduling schemes into multiple categories based on at least one of the input power fluctuation rate of the electrothermal coupling channel, the photovoltaic curtailment rate, and the thermal storage status corresponding to each candidate scheduling scheme, with each category corresponding to an operating mode of the photovoltaic-thermal-electric heating coupling system; the step of selecting a target scheduling scheme from multiple candidate scheduling schemes based on system operating data includes: determining the current operating mode of the photovoltaic-thermal-electric heating coupling system based on system operating data; and selecting a target scheduling scheme from the category of schemes corresponding to the current operating mode.

[0111] Operating modes characterize the scheduling strategy tendencies that the system should follow within a specific operating range, such as high-consumption mode, stable operation mode, or thermal storage priority mode. The scheduling control unit performs unsupervised classification based on the similarity of scheme characteristics, forming several scheme categories. Based on this, the current operating mode is determined according to system operating data, specifically: collecting current thermal storage status, surplus photovoltaic power, and grid curtailment data, matching them with pre-set mode determination rules, and identifying which operating mode the system currently belongs to. Then, a target scheduling scheme is selected from the category of schemes corresponding to the current operating mode.

[0112] In some embodiments, the above-mentioned division of multiple candidate scheduling schemes into multiple schemes may further include: determining the minimum and maximum values ​​of the input power fluctuation rate of the electrothermal coupling channel in multiple candidate scheduling schemes, and dividing the interval between the two into a first preset number of fluctuation rate levels; determining the minimum and maximum values ​​of the photovoltaic curtailment rate in all candidate scheduling schemes, and dividing the interval between the two into a second preset number of curtailment rate levels; assigning each candidate scheduling scheme to the corresponding fluctuation rate level according to its fluctuation rate value, and assigning it to the corresponding curtailment rate level according to its curtailment rate value, forming a two-dimensional grid with fluctuation rate level and curtailment rate level as dimensions; and classifying all candidate scheduling schemes falling within the same two-dimensional grid into the same scheme category.

[0113] The minimum and maximum values ​​of the input power fluctuation of the electrothermal coupling channel in all candidate scheduling schemes can be determined separately, and the interval between them can be equally divided into a first preset number (e.g., 5) of fluctuation levels. Similarly, the minimum and maximum values ​​of the photovoltaic curtailment rate in all candidate scheduling schemes can be determined separately, and the interval between them can be equally divided into a second preset number (e.g., 6) of curtailment rate levels. For each candidate scheduling scheme, its fluctuation value is assigned to the corresponding fluctuation level, and its curtailment rate value is assigned to the corresponding curtailment rate level, forming a two-dimensional grid with fluctuation level as the row and curtailment rate level as the column. All candidate scheduling schemes falling within the same two-dimensional grid are classified into the same category. Each grid corresponds to an operating mode, which is defined by the range of the grid's fluctuation level and curtailment rate level. The Pareto front solution is unevenly distributed in the target space, and directly using continuous values ​​for classification lacks a unified standard, making the classification results difficult to match with operating conditions. By dividing the continuous target space into equal-interval grids, the continuous target space is discretized into a finite number of level combinations, so that each mode has a clear range of volatility and curtailment rate, which facilitates the establishment of a fast mapping rule from operating conditions to modes.

[0114] In some embodiments, the above-mentioned division of multiple candidate scheduling schemes into multiple categories may further include: for each candidate scheduling scheme, extracting the predicted heat storage of the thermal storage module in each sub-period within the target time period to form a thermal storage state time series vector; calculating the dynamic time curvature distance between the thermal storage state time series vector of each candidate scheduling scheme and the other schemes to construct a distance matrix; based on the distance matrix, using an agglomerative hierarchical clustering algorithm to cluster multiple candidate scheduling schemes into a preset number of categories, wherein the dynamic time curvature distance between any two schemes within the same category is less than a first clustering threshold, and the distance between different categories is greater than a second clustering threshold; and using the operating curves corresponding to all candidate scheduling schemes in each category as the standard features of the operating mode corresponding to that category, wherein the operating mode is marked by the change pattern of the thermal storage state, including continuously rising, continuously falling, rising then falling, and falling then rising.

[0115] For each candidate scheduling scheme, the predicted heat storage capacity of the thermal storage modules in each sub-period within the target time period is extracted to form a thermal storage state time-series vector. This vector represents the complete waveform of heat storage capacity changes over scheduling time. The dynamic time curvature distance between the thermal storage state time-series vector of each candidate scheduling scheme and the remaining schemes is calculated. This dynamic time curvature distance measures the similarity between two time series of unequal durations and can tolerate time axis shifts and scaling. A distance matrix is ​​constructed based on the pairwise distances between all schemes. An agglomerative hierarchical clustering algorithm is used, starting with each scheme forming its own cluster and gradually merging the two closest clusters until a preset number of clusters is reached. During clustering, the dynamic time curvature distance between any two schemes within the same cluster is controlled to be less than the first clustering threshold (e.g., 0.15 times the maximum distance), and the center distance between different clusters is controlled to be greater than the second clustering threshold (e.g., 0.3 times the maximum distance). The thermal storage state change curves corresponding to all candidate scheduling schemes in each category are used as the standard features of the corresponding operating mode for that category. The operating mode is characterized by the changing pattern of the thermal storage state, including continuously rising (thermal storage constantly increases), continuously falling (thermal storage constantly decreases), rising then falling (peak value in the middle), and falling then rising (trough value in the middle). Classifying solely based on volatility and curtailment rate loses information about the morphological evolution of the thermal storage state over time, leading to schemes with the same target value but vastly different thermal storage behaviors being grouped together, affecting the accuracy of operating condition matching. By using dynamic time curvature distance and hierarchical clustering, classification is based on the shape similarity of the thermal storage waveform, ensuring that each scheme corresponds to a typical thermal storage change pattern. This allows for rapid matching of the most suitable scheduling scheme based on the current trend of thermal storage state changes during real-time operation.

[0116] In some embodiments, determining the current operating mode of the photovoltaic-thermal-electric heating coupling system based on system operating condition data may further include: determining the thermal storage fullness based on the comparison between the thermal storage state of the thermal storage module in the current sub-period and the upper and lower limits of the allowable thermal storage capacity of the thermal storage module; determining the surplus ratio based on the comparison between the surplus photovoltaic power and the rated photovoltaic power in the current sub-period; pre-setting multiple mutually exclusive operating condition intervals, each operating condition interval being jointly defined by the range of thermal storage fullness and the range of surplus ratio, and each operating condition interval uniquely corresponding to one operating mode; matching the thermal storage fullness and surplus ratio corresponding to the current sub-period with each operating condition interval to determine the operating condition interval in which the current sub-period is located, and determining the operating mode corresponding to that interval as the current operating mode.

[0117] The thermal storage fullness can be determined by comparing the thermal storage status of the thermal storage module in the current sub-period with the upper and lower limits of the allowable thermal storage capacity. Thermal storage fullness characterizes the relative position of the current thermal storage capacity within the allowable range. One calculation formula is (current thermal storage capacity minus minimum allowable thermal storage capacity) divided by (maximum allowable thermal storage capacity minus minimum allowable thermal storage capacity), with the result between 0 and 1. The surplus ratio is determined by comparing the surplus photovoltaic power in the current sub-period with the rated photovoltaic power. The surplus ratio characterizes the proportion of current surplus power relative to the installed photovoltaic capacity. Multiple mutually exclusive operating condition ranges are pre-defined. Each operating condition range is jointly defined by the thermal storage fullness range (e.g., 0-0.3, 0.3-0.7, 0.7-1) and the surplus ratio range (e.g., 0-0.2, 0.2-0.5, above 0.5). Each operating condition range uniquely corresponds to one operating mode; for example, (low fullness, low surplus) corresponds to the thermal storage priority mode, and (high fullness, high surplus) corresponds to the stable operation mode. The thermal storage fullness and surplus ratio calculated in the current sub-period are matched with each operating condition interval to determine the current operating condition interval, and the operating mode corresponding to the interval is determined as the current operating mode.

[0118] In some embodiments, determining the current operating mode of the photovoltaic-thermal-electric heating coupling system based on system operating condition data may further include: constructing a current operating condition feature vector based on system operating condition data; the system operating condition data includes at least the current heat storage capacity of the thermal storage module, the current surplus photovoltaic power, the current grid curtailment constraint, and the photovoltaic predicted output change rate for the current sub-period; pre-storing the center point feature vector corresponding to each operating mode, the center point feature vector being used to characterize the historical level under that operating mode; calculating the similarity between the current operating condition feature vector and the center point feature vector corresponding to each operating mode, and selecting the operating mode with the highest similarity as the current operating mode of the combined system.

[0119] Based on system operating condition data, a current operating condition feature vector is constructed. This data includes at least the current heat storage capacity of the thermal storage module, the current surplus photovoltaic power, the current grid curtailment constraint, and the predicted photovoltaic output change rate for the current sub-period. The current heat storage capacity characterizes the thermal energy storage margin, the surplus photovoltaic power characterizes the absorption pressure, the grid curtailment constraint characterizes the external absorption capacity, and the predicted photovoltaic output change rate characterizes future fluctuation trends. A center point feature vector corresponding to each operating mode is pre-stored. This center point feature vector is the average or median of the historical typical operating condition feature vectors for that operating mode, used to characterize the typical features of that mode. The cosine similarity or the reciprocal of the Euclidean distance between the current operating condition feature vector and the center point feature vector corresponding to each operating mode is calculated as the similarity score. The operating mode with the highest similarity score is selected as the current operating mode. Through multi-dimensional feature vectors and similarity calculations, multi-dimensional operating condition information can be comprehensively utilized, making it insensitive to noise and boundary fluctuations, thus improving the accuracy and robustness of pattern recognition.

[0120] In some embodiments, the above-mentioned selection of a target scheduling scheme from a class of schemes corresponding to the current operating mode may further include: determining whether the remaining thermal storage capacity of the thermal storage module is zero based on system operating data; if it is zero, selecting the scheme with the lowest input power fluctuation rate of the electrothermal coupling channel from multiple candidate scheduling schemes as the target scheduling scheme.

[0121] Based on system operating data, it can be determined whether the remaining thermal storage capacity of the thermal storage module is zero. Zero remaining capacity indicates that the module is nearing full capacity and cannot safely absorb any more heat energy. If determined to be zero, the scheme with the lowest input power fluctuation rate of the electrothermal coupling channel is selected as the target scheduling scheme from this category (i.e., the scheme category corresponding to the current operating mode). This selection strategy prioritizes operational stability and avoids overpressure accidents caused by forced heating. Even with a defined operating mode category, a full-tank thermal storage condition may still contain multiple schemes, some of which may contain non-zero electric heating commands, requiring further screening. By superimposing a volatility-priority secondary selection rule on top of the mode definition for the full-tank condition, it is ensured that the final scheme conforms to both the characteristics of the current operating mode and meets thermal storage safety constraints.

[0122] In some embodiments, the above-mentioned selection of a target scheduling scheme from a class of schemes corresponding to the current operating mode may further include: if it is not zero, determining whether the surplus photovoltaic power generation is greater than the preset curtailment rate threshold based on system operating data; if it is greater, selecting the scheme with the lowest photovoltaic curtailment rate from multiple candidate scheduling schemes as the target scheduling scheme.

[0123] If the remaining thermal storage capacity is not zero, the system operating data is used to determine whether the surplus photovoltaic power generation exceeds the preset curtailment rate threshold. The preset curtailment rate threshold is used to identify high curtailment risk levels. If the surplus photovoltaic power generation exceeds this threshold, the scheme with the lowest photovoltaic curtailment rate is selected as the target dispatch scheme from this category. This selection strategy prioritizes the consumption of new energy and reduces curtailment losses. When there is still space for thermal storage but a large surplus power, indiscriminate selection within the mode category may miss the best consumption opportunity. By adding a secondary selection rule prioritizing curtailment rate for high curtailment risk conditions based on mode limitations, a flexible combination of mode adaptability and target priority is achieved.

[0124] As can be seen from the technical solutions provided in the embodiments of this specification above, the embodiments of this specification can obtain the time-series operation data required for scheduling within the target time period; based on the time-series operation data, an electrothermal coupling scheduling model is constructed, which is used to characterize the correlation between the system's time-series operation characteristics and multi-energy flow power scheduling parameters; with the goal of minimizing the input power fluctuation rate of the electrothermal coupling channel and minimizing the photovoltaic curtailment rate, multiple candidate scheduling schemes are generated based on the electrothermal coupling scheduling model, where the electrothermal coupling channel is the path for surplus photovoltaic power to be converted into heat energy by an electric heating unit and input into the thermal storage module; and based on the system operating condition data, a target scheduling scheme is selected from multiple candidate scheduling schemes. By converting surplus photovoltaic power into heat energy and storing it in the thermal storage module through electric heating units, the photovoltaic curtailment rate is effectively reduced, and the level of new energy consumption is improved. Furthermore, by acquiring time-series operating data to construct an electrothermal coupling scheduling model, and aiming to minimize the fluctuation rate of input power of the electrothermal coupling channel, the fluctuation of heat power input from the electric heating unit to the thermal storage module can be smoothed, reducing the thermal shock to the thermal storage module, extending equipment life and enhancing system operating stability. Moreover, by selecting the target scheme from multiple candidate scheduling schemes based on system operating data, the scheduling strategy can flexibly adapt to the actual operating state, further improving the operating stability of the photovoltaic-thermal-electric heating coupling system and the level of new energy consumption.

[0125] It should be understood that in the various embodiments of this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0126] It should also be understood that, in the embodiments of this specification, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this specification generally indicates that the preceding and following related objects have an "or" relationship.

[0127] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0128] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0129] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0130] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational tasks to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The task is a function specified in one or more boxes.

[0131] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A scheduling scheme optimization method, characterized in that, This method is applied to a photovoltaic-thermal-electric heating coupling system; the system includes a photovoltaic power generation unit, a solar thermal power generation unit, and an electric heating unit, wherein the electric heating unit is used to convert surplus electrical energy from the photovoltaic power generation unit into thermal energy and store it in the thermal storage module of the solar thermal power generation unit; the method includes: Obtain the timing data required for scheduling within the target time period; Based on time-series operational data, an electrothermal coupling scheduling model is constructed. This model is used to characterize the relationship between the system's time-series operational characteristics and the multi-energy flow power scheduling parameters. With the goals of minimizing the input power fluctuation rate of the electrothermal coupling channel and minimizing the photovoltaic curtailment rate, multiple candidate scheduling schemes are generated based on the electrothermal coupling scheduling model. The electrothermal coupling channel is the path through which surplus photovoltaic power is converted into heat energy by the electric heating unit and input into the thermal storage module. Based on system operating data, a target scheduling scheme is selected from multiple candidate scheduling schemes.

2. The method according to claim 1, characterized in that, The acquisition of the time-series runtime data required for scheduling within the target time period includes: Acquire the predicted output data of photovoltaic power generation units, the surplus photovoltaic power generation data absorbed by the powerable heating units, the grid curtailment constraint data, and the thermal storage status data of the thermal storage modules within the target time period.

3. The method according to claim 2, characterized in that, The electrothermal coupling scheduling model includes an electric power balance model, a thermal storage state transition model, and equipment operation constraints and thermal storage capacity constraints. The construction of the electrothermal coupling scheduling model based on time-series operational data includes: Based on the predicted output data, an electric power balance model is constructed to characterize the instantaneous equality relationship between photovoltaic power generation, grid interaction power, electric heating power and load power. Based on surplus photovoltaic power generation data, a thermal energy storage state transition model is constructed to characterize the time-series recursive relationship between the thermal energy storage in the current sub-period and the thermal energy storage, charging power, releasing power and heat loss in the previous sub-period. Based on power grid curtailment constraint data, equipment operation constraints are established, including upper and lower power limits and ramp rate constraints for photovoltaic power generation units, electric heating units, and solar thermal power generation units. Based on thermal storage status data, thermal storage capacity constraints are established, including upper and lower limits of thermal storage safety and thermal storage balance constraints at the beginning and end of scheduling.

4. The method according to claim 1, characterized in that, The method further includes: Based on the electrothermal conversion efficiency and the actual surplus photovoltaic power consumed by the electric heating unit in each sub-period within the target period, determine the thermal power input from the electric heating unit to the thermal storage module in that sub-period. The input power fluctuation rate of the electrothermal coupling channel is determined by comparing the cumulative results of the thermal power differences between adjacent sub-time periods with the cumulative results of the thermal power across multiple sub-time periods.

5. The method according to claim 1, characterized in that, The method further includes: Determine the surplus power within the photovoltaic forecast output that is not absorbed by the system load and the grid in each sub-period within the target period; Determine whether the remaining thermal storage capacity of the thermal storage module is zero in each sub-period. If it is not zero, the portion of surplus power that can be converted into thermal energy storage by the electric heating unit will not be included in the abandoned power, and the portion of thermal power exceeding the remaining thermal storage capacity will be converted back into electrical power and included in the abandoned power. If it is zero, all surplus power will be included in the abandoned power. The photovoltaic curtailment rate is determined by comparing the cumulative results of curtailed power from multiple sub-periods with the cumulative results of photovoltaic predicted output.

6. The method according to claim 1, characterized in that, The goal is to minimize the input power fluctuation rate of the electrothermal coupling channel and minimize the photovoltaic curtailment rate. Based on the electrothermal coupling scheduling model, multiple candidate scheduling schemes are generated, including: A multi-objective optimization function is constructed with the input power fluctuation rate of the electrothermal coupling channel and the photovoltaic curtailment rate as optimization objectives. Based on the multi-objective optimization function, the electrothermal coupling scheduling model is iteratively optimized, and in each iteration, the non-dominated ranking is performed based on the Pareto dominance relationship according to the multi-objective optimization function value of each candidate scheduling scheme. Based on the Pareto front solution set that satisfies the iteration termination condition, multiple candidate scheduling schemes are determined.

7. The method according to claim 1, characterized in that, The step of selecting a target scheduling scheme from multiple candidate scheduling schemes based on system operating data includes: Based on the system operating data, determine whether the remaining thermal storage capacity of the thermal storage module is zero; If the value is zero, select the scheme with the lowest input power fluctuation rate of the electrothermal coupling channel from multiple candidate scheduling schemes as the target scheduling scheme.

8. The method according to claim 7, characterized in that, The step of selecting a target scheduling scheme from multiple candidate scheduling schemes based on system operating data also includes: If it is not zero, determine whether the surplus photovoltaic power generation is greater than the preset curtailment rate threshold based on the system operating data; If the rate is greater than the target rate, the scheme with the lowest photovoltaic curtailment rate will be selected from multiple candidate scheduling schemes as the target scheduling scheme.

9. The method according to claim 1, characterized in that, The method further includes: Based on at least one of the following factors: the input power fluctuation rate of the electrothermal coupling channel, the photovoltaic curtailment rate, and the thermal storage status, the multiple candidate scheduling schemes are divided into multiple categories, and each category corresponds to an operating mode of the photovoltaic-thermal-electric heating coupling system. The step of selecting a target scheduling scheme from multiple candidate scheduling schemes based on system operating data includes: Based on the system operating data, determine the current operating mode of the photovoltaic-thermal-electric heating coupling system; Select the target scheduling scheme from the schemes that correspond to the current operating mode.

10. A photovoltaic-thermal-electric heating coupling system, characterized in that, The system includes a photovoltaic power generation unit, a solar thermal power generation unit, an electric heating unit, and a scheduling and control unit. The electric heating unit is used to convert the surplus electrical energy of the photovoltaic power generation unit into thermal energy and store it in the thermal storage module of the solar thermal power generation unit. The scheduling and control unit is used to execute the method as described in any one of claims 1-9.

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