A method for optimizing collaborative operation of a coupling energy storage system of a thermal power unit in a cold region

Abstract

The present application belongs to the technical field of energy storage, and relates to a kind of severe cold area thermal power unit coupling energy storage system's collaborative operation optimization method. Including: obtaining historical meteorological data, historical load data and the operation state data of thermal power unit, based on data to determine environmental temperature variation curve, electrical load demand curve and heat load demand curve;According to the curve and operation state data, with the lowest total operation cost of thermal power unit as the goal, determine the output plan of thermal power unit and the energy storage system charging and discharging plan;Solve the plan to obtain the output plan curve of thermal power unit and the energy storage system charging and discharging plan curve;According to actual environmental temperature, electrical load and heat load, generate the actual dispatching instruction of current time. The present application realizes the safe and economic collaborative operation of thermal power unit and energy storage system under the complex working condition of thermal power coupling, significantly improves the response ability of the system to the rapid fluctuation of meteorology and load in severe cold area, effectively guarantees the heating reliability and overall operation efficiency.

Description

Technical Field

[0001] This invention belongs to the field of energy storage technology and relates to a method for optimizing the coordinated operation of a thermal power unit coupled with an energy storage system in extremely cold regions. Background Technology

[0002] With the transformation of the energy structure, the installed capacity of new energy sources such as wind power and photovoltaic power generation is rapidly increasing in frigid regions. However, affected by severe weather conditions such as extreme cold and snowstorms in winter, the output of new energy sources often fluctuates significantly and even risks grid disconnection, posing a serious challenge to the supply and demand balance of regional power grids. Against this backdrop, thermal power units, as the cornerstone of ensuring the stable operation of the power system, still need to undertake the main tasks of peak shaving and heating. At the same time, energy storage systems, due to their rapid response and bidirectional regulation capabilities, are widely introduced to improve system flexibility and the level of new energy absorption.

[0003] However, existing optimization methods for the coordinated operation of thermal power units and energy storage systems are mostly designed for conventional climatic conditions and are difficult to apply directly to the complex operating environment unique to frigid regions. First, the extremely low and drastically fluctuating ambient temperatures in frigid regions not only lead to a strong coupling between heat load demand and electrical load demand but also pose a threat to the equipment safety of thermal power units. If the units operate at excessively low loads for extended periods, the risks of boiler water circulation stagnation and pipe freezing and cracking increase significantly, and traditional minimum output constraints are no longer sufficient to meet anti-freezing requirements. Second, the performance of energy storage systems degrades severely in low-temperature environments, and the additional insulation energy required to maintain their normal operating temperature significantly increases the system's operating costs and scheduling complexity.

[0004] In addition, existing scheduling methods are usually based on day-ahead forecasts to formulate plans. However, in frigid regions, the rapid and irregular changes in meteorological conditions and load demand significantly increase forecast errors, resulting in large deviations between day-ahead plans and actual operating conditions. This leads to system response lag, which not only affects the reliability of heating supply but also exacerbates fuel consumption and equipment wear of thermal power units. Summary of the Invention

[0005] To address the problems in existing technologies, this invention provides a method for optimizing the coordinated operation of thermal power units coupled with energy storage systems in frigid regions. This method enables safe and economical coordinated operation of thermal power units and energy storage systems under complex thermoelectric coupling conditions, significantly improves the system's response to rapid fluctuations in weather and load in frigid regions, and effectively ensures heating reliability and overall operating efficiency.

[0006] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a method for optimizing the coordinated operation of a thermal power unit coupled with an energy storage system in frigid regions, comprising the following steps: Historical meteorological data, historical load data, and operating status data of thermal power units are acquired. Based on the historical meteorological data, an ambient temperature change curve is determined, and based on the historical load data, an electrical load demand curve and a heat load demand curve are determined. Based on the ambient temperature change curve, the electricity load demand curve, the heat load demand curve, and the operating status data, with the goal of minimizing the total operating cost of the thermal power unit, the operating relationship between the thermal power unit and the energy storage system is determined, and the output plan of the thermal power unit and the charging and discharging plan of the energy storage system are formed. The power output plan and energy storage plan are optimized and solved to obtain the power output plan curve of the thermal power unit and the energy storage system energy storage plan curve; Obtain the current ambient temperature, actual electrical load, and actual thermal load; based on the actual ambient temperature, actual electrical load, and actual thermal load, generate the current actual dispatch command according to the output plan curve and the charge / discharge plan curve.

[0007] Preferably, the historical meteorological data includes historical ambient temperature, historical wind speed, and historical solar radiation. Determining the environmental temperature change curve based on the aforementioned historical meteorological data includes: The historical ambient temperature data is fitted to a time series to generate a basic ambient temperature curve; the basic ambient temperature curve is then corrected based on the historical wind speed and historical solar radiation to obtain the ambient temperature change curve.

[0008] Preferably, the step of determining the operational relationship between the thermal power unit and the energy storage system with the goal of minimizing the total operating cost of the thermal power unit, and forming a power output plan for the thermal power unit and a charging and discharging plan for the energy storage system, includes: Establish an objective function that minimizes the sum of fuel cost, start-up and shutdown cost of thermal power units, and charging and discharging loss cost of energy storage systems; Set constraints on the output of thermal power units, the charging and discharging power of energy storage systems, and the operating constraints of equipment in extremely cold environments; Under the objective function and constraints, the optimal power output allocation between the thermal power unit and the energy storage system is solved to form the power output plan of the thermal power unit and the energy storage system charging and discharging plan. The expression for the objective function is:

[0009] In the formula, Minimize the total operating cost of the system; This represents the total number of time periods; This refers to the number of thermal power units. For the first Taiwan thermal power units during the period fuel costs; For the first Taiwan thermal power units during the period Start-up and shutdown costs; For the first Taiwan thermal power units during the period Maintenance costs; For energy storage systems during time periods The cost of charging and discharging losses; The expression for the output constraint of the thermal power unit is:

[0010] In the formula, For the first Taiwan thermal power units during the period contribution; For the first Minimum output of a thermal power unit; For the first The maximum output of the thermal power unit; For the first Start-up and shutdown status of the thermal power units in Taiwan; The expression for the charging and discharging power constraint of the energy storage system is:

[0011]

[0012] In the formula, For energy storage systems during time periods The charging power; This is the maximum charging power of the energy storage system. For energy storage systems during time periods The discharge power; This represents the maximum discharge power of the energy storage system. This is a variable indicating the charging status.

[0013] Preferably, the equipment operation constraints in the severe cold environment include the minimum output constraint for antifreeze thermal power units and the insulation energy consumption constraint for energy storage systems; The expression for the minimum output constraint for freeze protection of the thermal power unit is as follows:

[0014] In the formula, Minimum output for freeze protection; The expression for the thermal insulation energy consumption constraint of the energy storage system is:

[0015]

[0016] In the formula, For energy storage systems during time periods Power consumption for heat preservation; The comprehensive insulation heat loss coefficient of the energy storage system; The minimum set temperature required for the normal operation of the energy storage system; For the time period Ambient temperature; For energy storage systems during time periods The state of charge; For charging efficiency; This refers to the discharge efficiency.

[0017] Preferably, the power output plan and energy storage charge / discharge plan are optimized and solved to obtain the power output plan curve of the thermal power unit and the energy storage system charge / discharge plan curve, including: Based on the aforementioned ambient temperature change curve, electrical load demand curve, and heat load demand curve, a multi-time-scale optimization scheduling model is constructed. Solving the optimized scheduling model yields the planned output curves of thermal power units and the planned charging and discharging of energy storage systems within a preset time period.

[0018] Preferably, solving the optimized scheduling model includes: Based on the aforementioned ambient temperature change curve, electricity load demand curve, and heat load demand curve, the optimal scheduling model is decomposed into sub-problems at multiple time scales. A mixed-integer linear programming algorithm is used to iteratively solve the subproblems at each time scale; In each iteration, the boundary conditions of the current time scale are updated based on the solution results of the previous time scale until the convergence condition is met, thus obtaining the power output plan curve of the thermal power unit and the energy storage system charge and discharge plan curve.

[0019] Preferably, generating the actual dispatch instruction for the current moment based on the output plan curve and the charge / discharge plan curve includes: Obtain the current ambient temperature, actual electrical load, and actual heat load; The actual ambient temperature, actual electrical load, and actual heat load are compared with the predicted values ​​at the corresponding times in the power output plan curve and the energy charge / discharge plan curve, respectively, and the ambient temperature deviation, electrical load deviation, and heat load deviation are calculated. Based on the ambient temperature deviation, electrical load deviation, and thermal load deviation, the planned output value at the current moment in the output plan curve and the planned charge / discharge value at the current moment in the charge / discharge plan curve are corrected to generate the actual dispatch command at the current moment.

[0020] Secondly, the present invention provides a collaborative operation optimization system for a thermal power unit coupled with energy storage system in frigid regions, comprising: Curve fitting module: used to acquire historical meteorological data, historical load data and operating status data of thermal power units, determine the ambient temperature change curve based on the historical meteorological data, and determine the electricity load demand curve and heat load demand curve based on the historical load data; Collaborative optimization planning module: Based on the ambient temperature change curve, electricity load demand curve, heat load demand curve and the operating status data, with the goal of minimizing the total operating cost of thermal power units, it determines the operating relationship between thermal power units and energy storage systems, and forms the output plan of thermal power units and the charging and discharging plan of energy storage systems. Optimization and solution module: used to optimize and solve the output plan and energy charge / discharge plan to obtain the output plan curve of the thermal power unit and the energy storage system charge / discharge plan curve; The scheduling instruction generation module is used to obtain the actual ambient temperature, actual electrical load, and actual thermal load at the current moment; based on the actual ambient temperature, actual electrical load, and actual thermal load, and according to the power output plan curve and the energy charge / discharge plan curve, it generates the actual scheduling instruction for the current moment.

[0021] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a method for optimizing the coordinated operation of a thermal power unit coupled with an energy storage system in frigid regions.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of a method for optimizing the coordinated operation of a thermal power unit coupled with an energy storage system in frigid regions are implemented.

[0023] Compared with the prior art, the present invention has the following beneficial effects: By acquiring historical meteorological and load data and determining the ambient temperature change curve and the electricity and heat load demand curves, the unique meteorological fluctuations and thermoelectric coupling constraints of frigid regions were characterized. Based on the above curves and unit operating status data, the operating relationship between thermal power units and energy storage systems was determined with the goal of minimizing the total operating cost of thermal power units, and output plans and energy charging and discharging plans were formed, realizing a coordinated operation strategy that takes into account both economy and safety. The output plan curve and energy charging and discharging plan curve were obtained by optimizing the plan. Through mathematical solution and curve output, the quantitative conversion from the plan scheme to executable dispatch instructions was realized. By acquiring the actual ambient temperature, electricity load and heat load at the current moment, actual dispatch instructions were generated according to the plan curves. Thus, while coping with the rapid fluctuations in load and weather in frigid regions, the system's response speed, control accuracy and heating reliability were significantly improved, fuel consumption and equipment wear were comprehensively reduced, and the safe, stable and economical coordinated operation of thermal power units and energy storage systems under extreme climatic conditions was ensured. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0026] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0027] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0028] The first objective of this invention is to provide a method for optimizing the coordinated operation of a thermal power unit coupled with an energy storage system in frigid regions, such as... Figure 1 As shown, it includes the following steps: Historical meteorological data, historical load data, and operating status data of thermal power units are acquired. Based on the historical meteorological data, an ambient temperature change curve that reflects the temperature fluctuation characteristics of frigid regions is determined. Based on the historical load data, an electrical load demand curve and a heat load demand curve are determined respectively, thereby constructing a complex constraint boundary that takes into account both heating and power supply needs. Based on the ambient temperature change curve, the electricity load demand curve, the heat load demand curve, and the operating status data, with the goal of minimizing the total operating cost of the thermal power unit, the operating relationship between the thermal power unit and the energy storage system is determined, and the output plan of the thermal power unit and the charging and discharging plan of the energy storage system are formed. The power output plan and energy storage plan are optimized and solved to obtain the power output plan curve of the thermal power unit and the energy storage system energy storage plan curve for a future preset time period. These curves can guide the coordinated cooperation between the unit and the energy storage at different time scales. Obtain the current ambient temperature, actual electrical load, and actual thermal load; based on the actual ambient temperature, actual electrical load, and actual thermal load, generate the current actual dispatch command according to the output plan curve and the charge / discharge plan curve.

[0029] This method addresses the unique operating environment of frigid regions, characterized by extremely low winter temperatures, close thermoelectric coupling, and drastic load fluctuations. It constructs a constraint boundary that balances both heating and power supply needs using historical meteorological and load data. Based on this boundary, it optimizes the solution with the goal of minimizing total operating cost, thereby generating a coordinated output plan and real-time correction commands for thermal power units and energy storage systems. This method not only significantly reduces the cost of thermal power units and minimizes charging and discharging losses in energy storage systems, improving the overall economic efficiency and operational effectiveness of the system, but also effectively addresses the uncertainties of equipment operation constraints and load fluctuations in frigid environments. It enhances the system's flexible adjustment capabilities and heating reliability, ultimately achieving safe, stable, and efficient coordinated operation of thermal power units and energy storage systems under extreme climatic conditions. For example, the historical meteorological data includes historical ambient temperature, historical wind speed, and historical solar radiation. Determining the environmental temperature change curve based on the aforementioned historical meteorological data includes: The historical ambient temperature data is fitted to a time series to generate a basic ambient temperature curve that reflects seasonal changes and diurnal fluctuations. Since the actual ambient temperature in frigid regions is significantly affected by wind speed and solar radiation, increased wind speed will exacerbate surface convection heat transfer, leading to a decrease in perceived temperature and actual operating temperature of equipment. Conversely, solar radiation will increase the temperature of the ground surface and equipment surfaces. Therefore, it is necessary to calculate a wind chill effect correction coefficient based on historical wind speed data and a solar radiation warming correction coefficient based on historical solar radiation. The two are then superimposed to correct the basic ambient temperature curve point by point, resulting in an ambient temperature change curve that more closely reflects the actual physical environment. This curve can accurately reflect the actual operating environment of thermal equipment and energy storage systems under complex meteorological conditions in frigid regions, providing precise boundary conditions for subsequent load forecasting and optimized scheduling.

[0030] This invention significantly improves the accuracy of ambient temperature forecasting by refining historical meteorological data and incorporating wind speed and sunshine corrections. This allows thermal power units and energy storage systems to fully consider factors such as accelerated heat dissipation of equipment in frigid environments, fluctuations in heating demand, and changes in the output of new energy sources such as photovoltaics when formulating power output plans. This effectively avoids insufficient heating or energy waste caused by temperature forecast deviations, and improves the reliability and economy of system operation.

[0031] For example, the step of determining the operational relationship between the thermal power unit and the energy storage system with the goal of minimizing the total operating cost of the thermal power unit, and forming a power output plan for the thermal power unit and a charging and discharging plan for the energy storage system, includes: Establish an objective function that minimizes the sum of fuel cost, start-up and shutdown cost, and charging and discharging loss cost of the energy storage system. Fuel cost is usually expressed as a quadratic function or piecewise linear function of unit output. Start-up and shutdown cost is calculated based on changes in the start-up and shutdown state of the unit. Energy storage loss cost is related to charging and discharging power and the number of cycles, and can be expressed as a linear function of charging and discharging power using a linearized model. Set constraints on the output of thermal power units, the charging and discharging power of energy storage systems, and the operating constraints of equipment in extremely cold environments; Under the objective function and constraints, the optimal power output allocation between the thermal power unit and the energy storage system is solved to form the power output plan of the thermal power unit and the energy storage system charging and discharging plan.

[0032] The expression for the objective function is:

[0033] In the formula, Minimize the total operating cost of the system; This represents the total number of time periods; This refers to the number of thermal power units. For the first Taiwan thermal power units during the period fuel costs; For the first Taiwan thermal power units during the period Start-up and shutdown costs; For the first Taiwan thermal power units during the period Maintenance costs; For energy storage systems during time periods The cost of charging and discharging losses.

[0034] The expression for the output constraint of the thermal power unit is:

[0035] In the formula, For the first Taiwan thermal power units during the period contribution; For the first Minimum output of a thermal power unit; For the first The maximum output of the thermal power unit; For the first The start-up and shutdown status of the thermal power unit (0-1 variable, 1 indicates running, 0 indicates shutdown).

[0036] The expression for the charging and discharging power constraint of the energy storage system is:

[0037]

[0038] In the formula, For energy storage systems during time periods The charging power; This is the maximum charging power of the energy storage system. For energy storage systems during time periods The discharge power; This represents the maximum discharge power of the energy storage system. This is a charging status indicator variable (a 0-1 variable, where 1 indicates charging and 0 indicates non-charging). This variable ensures that only charging or discharging can occur at any given time.

[0039] This method introduces a refined model that covers heat load balance, energy storage dynamics, and special constraints of frigid environments, making the optimization results more consistent with actual operating conditions. It can not only effectively reduce system fuel consumption and start-up and shutdown costs and reduce energy storage losses, but also ensure the safe and stable operation of units and energy storage in extremely cold weather, avoid reliability risks caused by insufficient heating or equipment performance degradation, and significantly improve the economy and flexibility of cogeneration systems in frigid regions.

[0040] For example, the equipment operation constraints in the extreme cold environment include the minimum output constraint for antifreeze thermal power units and the insulation energy consumption constraint for energy storage systems. The expression for the minimum output constraint for freeze protection of the thermal power unit is as follows:

[0041] In the formula, This is the minimum output for freeze protection, a higher value than the minimum output for conventional technology, and is usually related to the ambient temperature. Relatedly, when the ambient temperature decreases, in order to prevent the boiler water circulation from stagnating and the economizer or water-cooled wall from freezing and being damaged, a higher minimum output must be maintained to ensure sufficient heat load flows through the heating surface. Therefore, this value can be expressed as a decreasing function of the ambient temperature, for example, by using a piecewise linear function or by fitting it based on historical operating data.

[0042] The expression for the thermal insulation energy consumption constraint of the energy storage system is:

[0043]

[0044] In the formula, For energy storage systems during time periods Power consumption for heat preservation; The overall thermal loss coefficient of the energy storage system depends on the insulation material, structural dimensions and surface heat exchange conditions of the energy storage system. The minimum set temperature required for the normal operation of the energy storage system; For the time period When the ambient temperature is lower than the set temperature, the insulation system will automatically start and consume electrical or thermal energy for heating. This energy consumption needs to be deducted from the net charge and discharge of the energy storage system, thus affecting its actual state of charge. For energy storage systems during time periods The state of charge; For charging efficiency; This refers to the discharge efficiency.

[0045] By introducing the aforementioned constraints specific to extremely cold environments into the optimization model, the output plans of thermal power units and the charging and discharging plans of energy storage systems can more realistically reflect the operating costs and limitations under extremely cold conditions. On the one hand, this effectively prevents the risk of equipment freezing damage caused by low-load operation and ensures the safety of power generation and heating. On the other hand, it accurately quantifies the self-sustaining energy consumption of energy storage systems and avoids insufficient actual available capacity or failure of dispatch commands due to neglecting insulation requirements. This significantly improves the feasibility and economy of optimized scheduling of cogeneration systems in extremely cold regions.

[0046] For example, the power output plan and energy storage charge / discharge plan are optimized and solved to obtain the power output plan curve of the thermal power unit and the energy storage system charge / discharge plan curve, including: Based on the aforementioned ambient temperature change curve, electrical load demand curve, and heat load demand curve, a multi-time-scale optimization scheduling model is constructed. Solving the optimized scheduling model yields the planned output curves of thermal power units and the planned charging and discharging of energy storage systems within a preset time period.

[0047] Specifically, the optimized scheduling model is usually divided into a day-ahead planning layer and an intraday rolling correction layer. The day-ahead planning layer uses an hourly time resolution to globally optimize the start-up and shutdown status of the units and the output base value, as well as the charging and discharging periods of energy storage, for the next 24 to 72 hours. The intraday rolling correction layer uses a 15-minute or shorter time resolution to make local adjustments to the day-ahead plan based on the latest meteorological and load forecast data in order to cope with the rapid fluctuations in load and temperature in frigid regions.

[0048] For example, solving the optimized scheduling model includes: Based on the aforementioned ambient temperature change curve, electricity load demand curve, and heat load demand curve, the optimized scheduling model is decomposed into sub-problems at multiple time scales. For example, the model is decomposed into a day-ahead planning layer sub-problem and an intraday rolling correction layer sub-problem. The day-ahead planning layer has an hourly resolution and focuses on determining the start-up and shutdown status of thermal power units and the daily charging and discharging strategy of the energy storage system. The intraday rolling correction layer has a minute-level resolution and focuses on finely adjusting the unit output and energy storage power to track the fluctuations of actual load and temperature, provided that the start-up and shutdown status of the units is fixed. A mixed-integer linear programming algorithm is used to iteratively solve the subproblems at each time scale. Specifically, when solving the day-ahead planning subproblem, the initial values ​​of the unit start-up and shutdown scheme and the energy storage state of charge are first obtained. Then, these results are used as boundary conditions to pass to the intraday rolling correction subproblem. After the intraday layer optimization is completed, the actual operation feedback information (such as changes in the actual available capacity of energy storage) is substituted back into the day-ahead layer for verification and fine-tuning. In each iteration, the boundary conditions of the current time scale are updated based on the solution results of the previous time scale until the convergence condition is met. For example, the unit combination variables of the intraday layer are fixed based on the unit start-up and shutdown status determined by the day-ahead layer, and the load prediction deviation of the day-ahead layer is corrected based on the optimization results of the intraday layer. This process is repeated until the preset convergence condition is met, such as the change in unit output between two adjacent iterations being less than a given threshold or the change in the objective function value being less than the set accuracy. Finally, the planned output curve of the thermal power unit and the planned charging and discharging curve of the energy storage system are obtained.

[0049] For example, generating the actual dispatch instruction for the current moment based on the output plan curve and the charge / discharge plan curve includes: Obtain the current ambient temperature, actual electrical load, and actual heat load; The actual ambient temperature, actual electrical load, and actual heat load are compared with the predicted values ​​at the corresponding times in the power output plan curve and the energy charge / discharge plan curve, respectively, and the ambient temperature deviation, electrical load deviation, and heat load deviation are calculated. Based on the ambient temperature deviation, electrical load deviation, and thermal load deviation, the planned output value at the current moment in the output plan curve and the planned charge / discharge value at the current moment in the charge / discharge plan curve are corrected to generate the actual dispatch command at the current moment.

[0050] Specifically, the planned output value at the current moment in the output plan curve and the planned charge / discharge value at the current moment in the charge / discharge plan curve are corrected, and the expression is as follows:

[0051] In the formula, The corrected charge / discharge energy values; Planned charge / discharge values ​​for the current moment; This refers to the deviation of ambient temperature. This refers to the electrical load deviation. This refers to the deviation in heat load. , and All of these are adjustment coefficients, which can be identified through the unit's consumption characteristic curve or historical operating data. At the same time, it is necessary to ensure that the corrected output value meets the unit's ramp rate constraints, output upper and lower limit constraints, and the energy storage system's state of charge constraints and charging and discharging power constraints. After the above corrections, the actual dispatch instructions that can be issued and executed at the current moment are finally generated.

[0052] This method effectively compensates for the lag and error accumulation problems of the current plan when dealing with rapid changes in weather and load in frigid regions by introducing a real-time correction mechanism based on measured deviations. This enables dispatching instructions to closely track actual operating conditions and significantly improves the system's response speed and control accuracy.

[0053] The second objective of this invention is to provide a collaborative operation optimization system for thermal power units coupled with energy storage systems in frigid regions, comprising: Curve fitting module: used to acquire historical meteorological data, historical load data and operating status data of thermal power units, determine the ambient temperature change curve based on the historical meteorological data, and determine the electricity load demand curve and heat load demand curve based on the historical load data; Collaborative optimization planning module: Based on the ambient temperature change curve, electricity load demand curve, heat load demand curve and the operating status data, with the goal of minimizing the total operating cost of thermal power units, it determines the operating relationship between thermal power units and energy storage systems, and forms the output plan of thermal power units and the charging and discharging plan of energy storage systems. Optimization and solution module: used to optimize and solve the output plan and energy charge / discharge plan to obtain the output plan curve of the thermal power unit and the energy storage system charge / discharge plan curve; The scheduling instruction generation module is used to obtain the actual ambient temperature, actual electrical load, and actual thermal load at the current moment; based on the actual ambient temperature, actual electrical load, and actual thermal load, and according to the power output plan curve and the energy charge / discharge plan curve, it generates the actual scheduling instruction for the current moment.

[0054] The curve fitting module accurately constructs a data foundation reflecting the characteristics of severe cold climate and fluctuations in thermal and power loads, providing reliable boundary conditions for subsequent optimization. The collaborative optimization planning module, aiming to minimize total operating costs, comprehensively considers the operating characteristics of thermal power units and energy storage systems, as well as the special constraints of severe cold environments, generating output and charging / discharging plans that balance economy and safety. The optimization solution module employs a multi-timescale progressive solution strategy, ensuring global optimality while improving computational efficiency and plan adaptability. The dispatch instruction generation module, through real-time data acquisition and deviation correction, accurately transforms the day-ahead plan into executable dispatch instructions, effectively addressing rapid changes in weather and load in severely cold regions. This invention not only significantly reduces system fuel consumption and equipment wear, improving operational economy, but also ensures safe and reliable operation of equipment under extreme climatic conditions by meticulously considering constraints such as minimum output for freeze protection and insulation energy consumption, while simultaneously enhancing the system's responsiveness to thermal and power load fluctuations and its heating guarantee level.

[0055] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a collaborative operation optimization method for thermal power units coupled with energy storage systems in frigid regions.

[0056] This invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the coordinated operation optimization method for coupled energy storage systems of thermal power units in frigid regions described in the above embodiments.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps 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 steps of the function specified in one or more boxes.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the cooperative operation of a cold region thermal power unit coupled with an energy storage system, characterized in that, Includes the following steps: Historical meteorological data, historical load data, and operating status data of thermal power units are acquired. Based on the historical meteorological data, an ambient temperature change curve is determined, and based on the historical load data, an electrical load demand curve and a heat load demand curve are determined. Based on the ambient temperature change curve, the electricity load demand curve, the heat load demand curve, and the operating status data, with the goal of minimizing the total operating cost of the thermal power unit, the operating relationship between the thermal power unit and the energy storage system is determined, and the output plan of the thermal power unit and the charging and discharging plan of the energy storage system are formed. The power output plan and energy storage plan are optimized and solved to obtain the power output plan curve of the thermal power unit and the energy storage system energy storage plan curve; Obtain the current ambient temperature, actual electrical load, and actual heat load; Based on the actual ambient temperature, actual electrical load, and actual thermal load, the actual dispatch command for the current moment is generated according to the power output plan curve and the energy charge / discharge plan curve.

2. The method for optimizing the coordinated operation of a coupled energy storage system for thermal power units in frigid regions according to claim 1, characterized in that, The historical meteorological data includes historical ambient temperature, historical wind speed, and historical solar radiation. Determining the environmental temperature change curve based on the aforementioned historical meteorological data includes: The historical ambient temperature data is fitted according to the time series to generate a basic ambient temperature curve; The baseline ambient temperature curve is corrected based on the historical wind speed and historical solar radiation to obtain the ambient temperature change curve.

3. The method for optimizing the coordinated operation of a coupled energy storage system for thermal power units in frigid regions according to claim 1, characterized in that, The goal is to minimize the total operating cost of thermal power units, determine the operational relationship between thermal power units and energy storage systems, and formulate power output plans for thermal power units and energy storage system charging and discharging plans, including: Establish an objective function that minimizes the sum of fuel cost, start-up and shutdown cost of thermal power units, and charging and discharging loss cost of energy storage systems; Set constraints on the output of thermal power units, the charging and discharging power of energy storage systems, and the operating constraints of equipment in extremely cold environments; Under the objective function and constraints, the optimal power output allocation between the thermal power unit and the energy storage system is solved to form the power output plan of the thermal power unit and the energy storage system charging and discharging plan. The expression for the objective function is: In the formula, Minimize the total operating cost of the system; This represents the total number of time periods; This refers to the number of thermal power units. For the first Taiwan thermal power units during the period fuel costs; For the first Taiwan thermal power units during the period Start-up and shutdown costs; For the first Taiwan thermal power units during the period Maintenance costs; For energy storage systems during time periods The cost of charging and discharging losses; The expression for the output constraint of the thermal power unit is: In the formula, For the first Taiwan thermal power units during the period contribution; For the first Minimum output of a thermal power unit; For the first The maximum output of the thermal power unit; For the first Start-up and shutdown status of the thermal power units; The expression for the charging and discharging power constraint of the energy storage system is: In the formula, For energy storage systems during time periods The charging power; This is the maximum charging power of the energy storage system. For energy storage systems during time periods The discharge power; This represents the maximum discharge power of the energy storage system. This is a variable indicating the charging status.

4. The method for optimizing the coordinated operation of a coupled energy storage system for thermal power units in frigid regions according to claim 3, characterized in that, The equipment operation constraints in the severe cold environment include the minimum output constraint for antifreeze thermal power units and the heat preservation energy consumption constraint for energy storage systems. The expression for the minimum output constraint for freeze protection of the thermal power unit is as follows: In the formula, Minimum output for freeze protection; The expression for the thermal insulation energy consumption constraint of the energy storage system is: In the formula, For energy storage systems during time periods Power consumption for heat preservation; The comprehensive insulation heat loss coefficient of the energy storage system; The minimum set temperature required for the normal operation of the energy storage system; For the time period Ambient temperature; For energy storage systems during time periods The state of charge; For charging efficiency; This refers to the discharge efficiency.

5. The method for optimizing the coordinated operation of a coupled energy storage system for thermal power units in frigid regions according to claim 1, characterized in that, The power output plan and energy storage charge / discharge plan are optimized and solved to obtain the power output plan curve of the thermal power unit and the energy storage system charge / discharge plan curve, including: Based on the aforementioned ambient temperature change curve, electrical load demand curve, and heat load demand curve, a multi-time-scale optimization scheduling model is constructed. Solving the optimized scheduling model yields the planned output curves of thermal power units and the planned charging and discharging of energy storage systems within a preset time period.

6. The method for optimizing the coordinated operation of a coupled energy storage system for thermal power units in frigid regions according to claim 5, characterized in that, Solving the optimized scheduling model includes: Based on the aforementioned ambient temperature change curve, electricity load demand curve, and heat load demand curve, the optimal scheduling model is decomposed into sub-problems at multiple time scales. A mixed-integer linear programming algorithm is used to iteratively solve the subproblems at each time scale; In each iteration, the boundary conditions of the current time scale are updated based on the solution results of the previous time scale until the convergence condition is met, thus obtaining the power output plan curve of the thermal power unit and the energy storage system charge and discharge plan curve.

7. The method for optimizing the coordinated operation of a coupled energy storage system for thermal power units in frigid regions according to claim 1, characterized in that, The step of generating the actual dispatch instruction for the current moment based on the output plan curve and the charge / discharge plan curve includes: Obtain the current ambient temperature, actual electrical load, and actual heat load; The actual ambient temperature, actual electrical load, and actual heat load are compared with the predicted values ​​at the corresponding times in the power output plan curve and the energy charge / discharge plan curve, respectively, and the ambient temperature deviation, electrical load deviation, and heat load deviation are calculated. Based on the ambient temperature deviation, electrical load deviation, and thermal load deviation, the planned output value at the current moment in the output plan curve and the planned charge / discharge value at the current moment in the charge / discharge plan curve are corrected to generate the actual dispatch command at the current moment.

8. A collaborative operation optimization system for a coupled energy storage system of a thermal power unit in frigid regions, characterized in that, include: Curve fitting module: used to acquire historical meteorological data, historical load data and operating status data of thermal power units, determine the ambient temperature change curve based on the historical meteorological data, and determine the electricity load demand curve and heat load demand curve based on the historical load data; Collaborative optimization planning module: Based on the ambient temperature change curve, electricity load demand curve, heat load demand curve and the operating status data, with the goal of minimizing the total operating cost of thermal power units, it determines the operating relationship between thermal power units and energy storage systems, and forms the output plan of thermal power units and the charging and discharging plan of energy storage systems. Optimization and solution module: used to optimize and solve the output plan and energy charge / discharge plan to obtain the output plan curve of the thermal power unit and the energy storage system charge / discharge plan curve; The scheduling instruction generation module is used to obtain the current ambient temperature, actual electrical load, and actual thermal load. Based on the actual ambient temperature, actual electrical load, and actual thermal load, the actual dispatch command for the current moment is generated according to the power output plan curve and the energy charge / discharge plan curve.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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