Modular implementation method of fire storage power distribution control algorithm for adaptive DCS system
Patent Information
- Application Number
- CN202611413523.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-09-11
- Publication Date
- 2026-10-30
AI Technical Summary
[0005]传统方案中对储能SOC的约束大多采用硬阈值切投方式，当SOC达到上下限时直接强制切断储能出力，极易造成功率指令的阶跃突变，引发火储联合系统的总功率出现瞬间缺口或溢出，反而触发电网的调频考核
[0018]有益效果：将全链路火储功率分配逻辑完全转化为DCS可直接组态的标准化无量纲运算模块，打破了传统方案依赖外挂独立储能控制器的技术路径依赖，无需新增第三方控制硬件、无需跨系统复杂信号对接，直接复用火电机组现有DCS的算力与通信体系，同时消除了跨平台数据延迟与时钟不同步的隐患；
Smart Images
Figure CN123019093A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power distribution, and in particular to a modular implementation method for power distribution control algorithms adapted to DCS systems. Background Technology
[0002] Guided by the "dual carbon" goals, my country's new power system construction is accelerating, with a high proportion of intermittent renewable energy sources such as wind and solar power being connected to the grid on a large scale, leading to an exponential increase in the pressure on the power grid for peak and frequency regulation. Traditional thermal power units, as the fundamental support force of the power system, are gradually transforming their operating mode from being the main source of stable power supply to playing a dual role of being the main force for flexible regulation and supporting basic loads. At the same time, the coupled application scenarios of new energy storage technologies such as molten salt energy storage and high-energy-density supercapacitors with thermal power are constantly expanding. Joint frequency regulation and peak shaving of thermal power and energy storage have become core technological paths to improve the operational flexibility of thermal power and mitigate the fluctuations of renewable energy sources.
[0003] Distributed control systems (DCS), as the core of thermal control systems for thermal power units, have long achieved integrated management and control of all equipment throughout the entire process, including boilers and turbines. All newly built large-scale thermal power units in China are equipped with mature DCS systems as standard. Natively embedding the thermal-storage joint control logic into the DCS platform, rather than adding an independent third-party energy storage control system, is the mainstream development direction in the industry for reducing system upgrade costs, improving control reliability, and achieving deep synergy between thermal and energy storage. Currently, with the continuous improvement of power ancillary service market rules, the assessment indicators for AGC regulation rate, regulation accuracy, and response time of thermal-storage joint systems are becoming increasingly stringent. These systems not only need to quickly track grid dispatch commands but also need to consider multiple objectives such as the mechanical lifespan loss of thermal power, the state-of-charge balance of energy storage, and the economic efficiency of operation throughout the entire life cycle. This places extremely high demands on the dynamic power allocation strategy between thermal and energy storage.
[0004] Against this industry backdrop, developing a thermal power-storage power allocation control algorithm that is fully compatible with the native architecture of DCS, has lightweight logic, and can be modularly configured can not only make full use of the computing power and communication resources of existing DCS and avoid the complex docking risks of multi-system collaboration, but also achieve millisecond-level collaborative response between thermal power and energy storage. This provides a low-cost and highly reliable technical implementation path for the engineering implementation of thermal power-storage joint systems, and has important engineering application value for improving the flexibility of thermal power transformation and ensuring the stable operation of a high proportion of new energy power grids.
[0005] Traditional solutions for constraining the SOC of energy storage mostly employ hard threshold switching methods. When the SOC reaches the upper or lower limit, the energy storage output is forcibly cut off, which can easily cause a sudden jump in power command, leading to a momentary power gap or overflow in the combined thermal and energy storage system, and triggering frequency regulation tests of the power grid. At the same time, the SOC feedback coefficient uses a single fixed value, which can easily lead to unnecessary small and frequent adjustments when the SOC is close to the target midpoint, and cannot quickly pull the SOC back to a reasonable range when the SOC deviates far from the midpoint. It is difficult to balance the stability of the steady-state operation of energy storage with the speed of dynamic SOC balancing.
[0006] Therefore, we propose a modular implementation method for the fire-storage power allocation control algorithm adapted to DCS systems to solve the above problems. Summary of the Invention
[0007] This invention provides a modular implementation method for the fire storage power allocation control algorithm adapted to DCS systems, which is used to adapt to DCS systems in a modular manner to achieve efficient engineering deployment.
[0008] The first aspect of this invention provides a modular implementation method for a power distribution control algorithm adapted to a DCS system. This method includes: acquiring a scheduling command from the control system, the current output of the generator set, the current output of the energy storage system, and the state of charge (SOC) signal of the energy storage system; converting the scheduling command, the current output of the generator set, the current output of the energy storage system, and the SOC signal into dimensionless values; limiting the dimensionless value of the current output of the generator set based on the ramp-up limitation of the generator set to obtain a generator set smoothing command; calculating an ideal energy storage compensation signal based on the dimensionless values of the generator set smoothing command and the scheduling command; correcting the ideal energy storage compensation signal based on the deviation between the dimensionless value of the SOC signal and a preset target value to obtain an energy storage correction command; limiting the energy storage correction command to obtain an energy storage command; converting the generator set smoothing command and the energy storage command into actual power commands, and outputting them to their respective actuators.
[0009] Optionally, in a first implementation of the first aspect of the present invention, the generator set is a thermal power unit; the control system is a distributed control system. Divide the dispatch instruction by the sum of the rated capacity of the generator set and the rated capacity of the energy storage system to obtain the dimensionless value of the dispatch instruction. Divide the current output of the generator set by the rated capacity of the generator set to obtain the dimensionless value of the current output of the generator set. Divide the current output of the energy storage system by the rated capacity of the energy storage system to obtain the dimensionless value of the current output of the energy storage system. The original proportional value of the state of charge signal is retained and used as the dimensionless value of the state of charge signal.
[0010] Optionally, in a second implementation of the first aspect of the present invention, the method includes: The maximum allowable variation is set based on the product of the ramp limit rate of the generator set and the sampling period of the control system; Determine whether the difference between the dimensionless value of the current output of the generator set and the smoothing command of the generator set in the previous cycle exceeds the maximum allowable change: If so, the generator set smoothing command of the previous cycle is added to or subtracted from the maximum allowable change amount, and used as the generator set smoothing command for the current cycle. If not, the dimensionless value of the current output of the generator set will be used as the smoothing command for the generator set in this cycle.
[0011] Optionally, in a third implementation of the first aspect of the present invention, it further includes: The maximum allowable change is dynamically adjusted based on the variation range of the dimensionless value of the scheduling instruction within a preset number of historical sampling periods. When the change exceeds the threshold, the maximum allowable change is increased. When the magnitude of the change is lower than the descent threshold, the maximum allowable change is reduced. The adjusted maximum allowable change is used to obtain the generator set smoothing command.
[0012] Optionally, in a fourth implementation of the first aspect of the present invention, the method includes: The capacity ratio of the generator set and the capacity ratio of the energy storage system are determined based on the rated capacity of the generator set and the rated capacity of the energy storage system. The difference is obtained by subtracting the product of the generator set smoothing instruction and the generator set capacity ratio from the dimensionless value of the scheduling instruction. Divide the difference by the capacity ratio of the energy storage system to obtain the ideal energy storage compensation signal.
[0013] Optionally, in a fifth implementation of the first aspect of the present invention, it further includes: Based on the deviation between the dimensionless value of the current output of the generator set and the smoothing command of the generator set, the capacity ratio of the generator set and the capacity ratio of the energy storage system are corrected: When the deviation is positive, the capacity ratio of the energy storage system is increased, and the capacity ratio of the generator set is decreased; When the deviation is negative, the capacity ratio of the energy storage system is reduced, and the capacity ratio of the generator set is increased; The corrected generator set capacity ratio and energy storage system capacity ratio are used to calculate the ideal energy storage compensation signal.
[0014] Optionally, in a sixth implementation of the first aspect of the present invention, the method includes: The deviation between the dimensionless value of the state of charge signal and the preset target value is multiplied by a feedback coefficient to obtain the state of charge correction amount. The energy storage correction command is obtained by subtracting the state of charge correction amount from the energy storage ideal compensation signal.
[0015] Optionally, in a seventh implementation of the first aspect of the invention, the feedback coefficient is determined based on the interval in which the dimensionless value of the state of charge signal lies: When the dimensionless value of the state of charge signal is within a preset narrow range close to the preset target value, the first gain coefficient is used as the feedback coefficient. When the dimensionless value of the state of charge signal exceeds the preset narrow range but does not reach the preset upper or lower limit of the state of charge, the second gain coefficient is used as the feedback coefficient. Wherein, the first gain coefficient is smaller than the second gain coefficient.
[0016] Optionally, in an eighth implementation of the first aspect of the present invention, it further includes: If the dimensionless value of the state of charge signal is lower than the charging prohibition threshold and the energy storage correction command is positive, then the energy storage correction command will be forcibly adjusted to zero. If the dimensionless value of the state of charge signal is higher than the discharge prohibition threshold and the energy storage correction command is negative, then the energy storage correction command will be forcibly adjusted to zero.
[0017] Optionally, in the ninth implementation of the first aspect of the present invention, the charging prohibition threshold and the discharging prohibition threshold correspond to preset upper and lower limits of the state of charge of the energy storage system, respectively. The process of forcibly adjusting to zero is as follows: when the dimensionless value of the state of charge signal enters a preset proximity range from the upper limit of the state of charge of the energy storage system or the lower limit of the state of charge of the energy storage system, the energy storage correction command is continuously reduced according to the remaining distance of the preset proximity range until it reaches the charging prohibition threshold or the discharging prohibition threshold and is reduced to zero.
[0018] Beneficial effects: The entire power allocation logic of the thermal power unit is completely transformed into a standardized dimensionless computing module that can be directly configured by the DCS. This breaks the technical path dependence of the traditional solution that relies on an external independent energy storage controller. There is no need to add third-party control hardware or cross-system complex signal docking. The computing power and communication system of the existing DCS of the thermal power unit can be directly reused. At the same time, the hidden dangers of cross-platform data delay and clock asynchrony are eliminated. Based on the dynamic weight correction mechanism of real-time output deviation of thermal power, and combined with the thermal power ramping limit strategy that adaptively adjusts according to the fluctuation of dispatching instructions, it avoids the problem of frequent wear of turbine control valves and combustion system caused by forced large load changes of thermal power, and also eliminates the drastic power fluctuation caused by energy storage passively undertaking all dynamic compensation tasks. Breaking away from the rigid SOC control mode of traditional hard threshold switching, it adopts a correction logic of multi-interval differentiated gain and continuous transition near the interval. When the SOC is close to the target midpoint, it uses low gain to suppress unnecessary small and frequent energy storage actions, and when the SOC deviates from the reasonable range, it uses high gain to quickly complete the power pullback. There are no command step changes throughout the process, which completely solves the grid assessment problem caused by the total power jump at the moment of SOC limit in the traditional solution. Attached Figure Description
[0019] Figure 1 This is a schematic diagram of an embodiment of the modular implementation method of the fire storage power allocation control algorithm adapted to the DCS system in this invention. Detailed Implementation
[0020] This invention provides a modular implementation method for a power distribution control algorithm adapted to a DCS system, enabling efficient engineering deployment by adapting the algorithm to the DCS system in a modular manner. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying 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 a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises 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 apparatus.
[0021] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the modular implementation method of the fire storage power allocation control algorithm adapted to the DCS system in this invention includes: 101. Obtain dispatch instructions, current output of thermal power, current output of energy storage, and energy storage status of charge signals from DCS in real time, and convert all signals into dimensionless scalar values with rated capacity as the base value.
[0022] It is understood that the executing entity of this invention can be a modular implementation device for the fire-storage power allocation control algorithm adapted to a DCS system, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be described using a server as an example.
[0023] Specifically, the dispatch command is divided by the sum of the rated capacity of thermal power and the rated capacity of energy storage, the current output of thermal power is divided by the rated capacity of thermal power, the current output of energy storage is divided by the rated capacity of energy storage, and the energy storage state of charge signal is kept at its original proportional value, thereby obtaining a unified dimensionless dispatch command, dimensionless output of thermal power, dimensionless output of energy storage, and dimensionless value of state of charge.
[0024] It should be noted that the rated capacity of a certain power plant's thermal power unit is set at 600MW, and the rated capacity of the supporting energy storage system is set at 30MW, with the sum of the two rated capacities being 630MW.
[0025] During a certain DCS real-time sampling cycle, the system collected the following real-time operating data: the grid dispatch command was 453.6MW; the actual output of the thermal power unit was 426MW; the energy storage system was currently discharging, with an actual output of 9MW; and the current state of charge of the energy storage system was 55%.
[0026] The DCS system performs the following transitions during the current cycle: The first step is to divide the received dispatch instruction of 453.6MW by the total rated capacity of 630MW to calculate the dimensionless dispatch instruction as 0.720.
[0027] The second step involves dividing the actual thermal power output of 426MW by the rated thermal power capacity of 600MW, resulting in a dimensionless thermal power output of 0.710.
[0028] The third step involves dividing the actual energy storage output of 9MW by the rated energy storage capacity of 30MW to calculate the dimensionless energy storage output as 0.300.
[0029] The fourth step is to retain 55% of the state of charge as the original proportional value, i.e., 0.55.
[0030] The above physical quantities are uniformly converted into four dimensionless scalars: 0.720, 0.710, 0.300, and 0.55.
[0031] 102. Based on the product of the thermal power plant ramp rate limit and the DCS sampling period, an incremental ramp limit is applied to the current dimensionless output of the thermal power plant to obtain a thermal power plant smoothing command.
[0032] Specifically, based on the product of the thermal power plant ramp rate limit and the DCS sampling period, a dimensionless maximum allowable change per cycle is set. The current dimensionless output of the thermal power plant is compared with this maximum allowable change. If the difference between the current dimensionless output of the thermal power plant and the thermal power plant command of the previous cycle exceeds the maximum allowable change, the thermal power plant command of the previous cycle is added to or subtracted from the maximum allowable change as the thermal power plant smoothing command for the current cycle. If it does not exceed the maximum allowable change, the current dimensionless output of the thermal power plant is directly used as the thermal power plant smoothing command for the current cycle.
[0033] Furthermore, the maximum allowable change per cycle is not a fixed value, but is dynamically adjusted according to the change amplitude of the dimensionless value of the dispatch command within a preset number of historical sampling periods: when the change amplitude exceeds the rising threshold, the maximum allowable change per cycle is increased to accelerate the thermal power tracking speed; when the change amplitude is lower than the falling threshold, the maximum allowable change per cycle is decreased to suppress unnecessary frequent adjustments of the thermal power; the adjusted maximum allowable change per cycle is used to obtain the thermal power smoothing command.
[0034] It should be noted that, assuming the DCS sampling period is 2 seconds, the system's maximum allowable change per period is set to 0.0005. The dynamic adjustment rules are as follows: the historical viewing window is 5 periods; the threshold for increase is set to 0.01, and when this threshold is met, the maximum allowable change is increased to 0.001.
[0035] The first step involved the system checking the historical buffer. It found that the dimensionless dispatch command for the power grid jumped from 0.700 to 0.720 over the past five cycles, a change of 0.020. Because this exceeded the threshold of 0.01, the system dynamically increased the maximum permissible change per cycle to 0.001 for the current cycle.
[0036] The second step involves converting the current dimensionless output of the thermal power plant in step 101 to 0.710. Assume the previous cycle's thermal power plant smoothing instruction latched in the DCS is 0.708. The system subtracts the previous instruction's 0.708 from the current output of 0.710, resulting in an increment of 0.002.
[0037] The third step involves the system comparing the increment of 0.002 with the dynamic maximum allowable change of 0.001. Because the increment exceeds the limit, the ramp limit is triggered.
[0038] The fourth step is to abandon the original output, add the maximum allowable change of 0.001 to the previous cycle command 0.708, and obtain the thermal power smoothing command of 0.709 for the current cycle, and pass it to step 103.
[0039] 103. Using the dimensionless values of the obtained thermal power smoothing command and the obtained dispatch command, calculate the ideal compensation amount required for energy storage based on the rated capacity ratio, and obtain the ideal compensation signal for energy storage.
[0040] Specifically, the thermal power capacity ratio and energy storage capacity ratio are determined based on the rated capacity of thermal power and the rated capacity of energy storage; the dimensionless value of the obtained dispatch instruction is subtracted from the product of the obtained thermal power smoothing instruction and the thermal power capacity ratio, and the difference is divided by the energy storage capacity ratio to obtain the ideal energy storage compensation signal.
[0041] Furthermore, the proportion of thermal power capacity and the proportion of energy storage capacity are not determined solely by the rated capacity, but are adjusted in real time based on the difference between the current dimensionless output of thermal power and the thermal power smoothing command: when the difference is positive, it indicates that the actual output of thermal power is higher than the smoothing command, so the proportion of energy storage capacity is increased by a preset percentage, while the proportion of thermal power capacity is decreased by the same percentage; when the difference is negative, it indicates that the actual output of thermal power is lower than the smoothing command, so the proportion of energy storage capacity is decreased by a preset percentage, while the proportion of thermal power capacity is increased by the same percentage; the adjusted proportions of thermal power capacity and energy storage capacity are used to calculate the ideal compensation signal for energy storage.
[0042] It should be noted that the aforementioned scenario of 600MW thermal power and 30MW energy storage is used. The initial thermal power capacity ratio is 600 divided by 630, which is 0.9524; the energy storage capacity ratio is 30 divided by 630, which is 0.0476. The set real-time capacity correction step size is 0.0024.
[0043] The system detected that the current dimensionless output of thermal power is 0.710, and the thermal power smoothing command obtained in step 102 is 0.709. The difference between the two is a positive 0.001, indicating that the actual output of thermal power is slightly higher than the smoothing benchmark. Therefore, the system automatically adjusts the dynamic allocation weights: the proportion of energy storage capacity is corrected to 0.0476 plus 0.0024, which equals 0.0500; the proportion of thermal power capacity is corrected to 0.9524 minus 0.0024, which equals 0.9500.
[0044] The current dispatch command is 0.720. The system uses the thermal power smoothing command 0.709 to multiply by the corrected thermal power ratio of 0.9500, calculating the thermal power allocation value to be 0.67355. The system then subtracts 0.67355 from the dispatch command 0.720, obtaining a dimensionless gap requiring energy storage compensation of 0.04645.
[0045] The system divides the dimensionless gap of 0.04645 by the corrected energy storage capacity ratio of 0.0500, and calculates and outputs the ideal energy storage compensation signal as 0.929.
[0046] 104. Subtract the deviation between the obtained dimensionless state of charge value and the target midpoint from the obtained ideal energy storage compensation signal, and multiply by a pre-set dimensionless feedback coefficient to obtain the energy storage correction command.
[0047] Specifically, the deviation between the obtained dimensionless state of charge value and the set target midpoint value is multiplied by a dimensionless feedback coefficient pre-tuned through field tests to obtain the state of charge correction amount; this state of charge correction amount is subtracted from the obtained ideal energy storage compensation signal to obtain the energy storage correction command.
[0048] Furthermore, the state of charge correction is not proportional to the deviation, but uses different gain coefficients depending on the range of the dimensionless state of charge value: when the dimensionless state of charge value is within a preset narrow range close to the target midpoint, a lower first gain coefficient is used; when the dimensionless state of charge value exceeds the narrow range but has not yet reached the upper or lower limits of the state of charge, a higher second gain coefficient is used; the first gain coefficient is less than the second gain coefficient; the state of charge correction calculated using different gain coefficients is subtracted from the ideal energy storage compensation signal to obtain the energy storage correction command.
[0049] It should be noted that the same data thread is used throughout. The system sets the target midpoint value to 0.50, with a narrow range of 0.48 to 0.52 near the midpoint. The first gain coefficient is set to 0.10, and the second gain coefficient is set to 0.40.
[0050] Subtracting the current state of charge (0.55) from the target midpoint value (0.50), the calculated deviation is negative 0.05.
[0051] The current state of charge (SOC) of 0.55 is greater than the upper limit of the narrow range (SOC) of 0.52. The range comparator control switch outputs a higher second gain coefficient, namely 0.40.
[0052] The system multiplies the deviation value of -0.05 with the gain coefficient of 0.40 to calculate the correction amount for this cycle as -0.020.
[0053] The system receives the ideal compensation signal of 0.929 obtained in step 103, subtracts the negative correction amount of 0.020 (i.e., adds 0.020), and obtains an energy storage correction command of 0.949. Due to the high charge level, the discharge command is increased from the original 0.929 to 0.949, which is consistent with the physical principle of discharging more to restore SOC.
[0054] 105. Perform continuous saturation limiting processing on the obtained energy storage correction command to obtain the energy storage command. Then, multiply the thermal power smoothing command and the energy storage command by their respective rated capacities, reverse normalize them into the actual power command, and output them to the thermal power and energy storage actuators.
[0055] Specifically, the obtained energy storage correction command is compared with the normalized upper and lower limits corresponding to the rated energy storage power. If the energy storage correction command exceeds the upper or lower limit, it is continuously and smoothly clamped to the corresponding boundary value to obtain a dimensionless energy storage command. The energy storage command and the thermal power smoothing command are multiplied by their respective rated capacities and converted into actual power commands, which are then sent as outputs to the thermal power actuator and the energy storage actuator, respectively.
[0056] It should be noted that the thermal power smoothing command obtained in step 102 of the current cycle is 0.709; the energy storage command transmitted after processing by steps 104 and 106 is 0.949.
[0057] The system's built-in continuous limiter has an upper limit of 1.0 (100% full power discharge) and a lower limit of -1.0 (100% full power charging). The current energy storage command of 0.949 is within the safe range, so the limiter will allow it directly. The dimensionless energy storage command is 0.949.
[0058] Using a multiplication block, 0.949 is multiplied by the rated energy storage capacity of 30MW to calculate the actual target energy storage power of 28.47MW. Simultaneously, the thermal power smoothing command of 0.709 is multiplied by 600MW to calculate the actual target thermal power power of 425.4MW. Both are then converted into electrical signals and transmitted.
[0059] Similarly, to demonstrate the limiting function, if the command calculated by the algorithm surges to 1.05 under extreme conditions, the limiter will forcibly clamp its action to 1.0, thereby outputting a 30MW full-load safety command. The data summary table for this period is shown in Table 1 below: Table 1
[0060] 106. Determine the polarity of the energy storage correction command based on whether the obtained dimensionless value of the state of charge is lower than the preset charging prohibition threshold or higher than the preset discharging prohibition threshold. If the state of charge is lower than the charging prohibition threshold and the energy storage correction command is positive, then the energy storage correction command is forcibly adjusted to zero. If the state of charge is higher than the discharging prohibition threshold and the energy storage correction command is negative, then the energy storage correction command is forcibly adjusted to zero. After adjustment, a direction-restricted energy storage correction command is obtained, which replaces the original energy storage correction command as the input for subsequent limiting processing.
[0061] Furthermore, the charging prohibition threshold and the discharging prohibition threshold correspond to the upper and lower limits of the energy storage state of charge, respectively. When the state of charge enters a preset proximity range from the upper or lower limit, it is forcibly adjusted to zero using a continuous transition method. That is, the corresponding polarity component of the energy storage correction command is continuously reduced according to the remaining distance of the proximity range until it reaches the threshold and becomes completely zero, thereby avoiding sudden changes in the command.
[0062] It should be noted that the charging prohibition threshold is set to 0.90, the discharging prohibition threshold is set to 0.10, and the approach range width is 0.05.
[0063] In the current continuous loop, the SOC value obtained in step 101 is 0.55, which is in the absolutely safe middle zone and has not triggered any boundary intervals. Therefore, the energy storage correction command of 0.949 calculated in step 104 is directly passed to subsequent steps as a direction-restricted energy storage correction command without any polarity reduction.
[0064] To fully illustrate the algorithm's actions in this step, assume that in another independent cycle, the SOC reaches 0.88 (within the charging range of 0.85 to 0.90), and the initial energy storage correction command input is -0.500 (charging). At this time, the system subtracts 0.88 from 0.90 to obtain a distance of 0.02, and divides this distance by the width of 0.05 to obtain a scaling factor of 0.40. The system multiplies the original command -0.500 by 0.40, smoothly reducing it to -0.200, and uses this to replace the original command output. The processing logic table for this embodiment is shown in Table 2 below: Table 2
[0065] The present invention also provides a modular implementation device for a power allocation control algorithm for a DCS system adapted to a power distribution system. The modular implementation device for a power allocation control algorithm for a DCS system adapted to a power distribution system includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the modular implementation method for a power allocation control algorithm for a DCS system adapted to a power distribution system described in the above embodiments.
[0066] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the modular implementation method of the fire and storage power allocation control algorithm adapted to the DCS system.
[0067] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0068] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0069] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A modular implementation method for a power distribution control algorithm adapted to a DCS system, characterized in that, include: The system acquires the control system's dispatching instructions, the generator set's current output, the energy storage system's current output, and the energy storage system's state of charge signal, and converts the dispatching instructions, the generator set's current output, the energy storage system's current output, and the state of charge signal into dimensionless values respectively. Based on the ramping limit of the generator set, the dimensionless value of the current output of the generator set is limited to obtain the generator set smoothing command. The ideal energy storage compensation signal is calculated based on the dimensionless values of the generator set smoothing command and the scheduling command. Based on the deviation between the dimensionless value of the state of charge signal and the preset target value, the energy storage ideal compensation signal is corrected to obtain the energy storage correction command. The energy storage correction command is subjected to amplitude limiting processing to obtain the energy storage command. The generator set smoothing command and the energy storage command are converted into actual power commands and output to the corresponding actuators respectively.
2. The modular implementation method of the fire-storage power allocation control algorithm adapted to the DCS system according to claim 1, characterized in that, The generator set is a thermal power unit; the control system is a distributed control system. Divide the dispatch instruction by the sum of the rated capacity of the generator set and the rated capacity of the energy storage system to obtain the dimensionless value of the dispatch instruction. Divide the current output of the generator set by the rated capacity of the generator set to obtain the dimensionless value of the current output of the generator set. Divide the current output of the energy storage system by the rated capacity of the energy storage system to obtain the dimensionless value of the current output of the energy storage system. The original proportional value of the state of charge signal is retained and used as the dimensionless value of the state of charge signal.
3. The modular implementation method of the fire-storage power allocation control algorithm adapted to the DCS system according to claim 1, characterized in that, include: The maximum allowable variation is set based on the product of the ramp limit rate of the generator set and the sampling period of the control system; Determine whether the difference between the dimensionless value of the current output of the generator set and the smoothing command of the generator set in the previous cycle exceeds the maximum allowable change: If so, the generator set smoothing command of the previous cycle is added to or subtracted from the maximum allowable change amount, and used as the generator set smoothing command for the current cycle. If not, the dimensionless value of the current output of the generator set will be used as the smoothing command for the generator set in this cycle.
4. The modular implementation method of the fire-storage power allocation control algorithm adapted to the DCS system according to claim 3, characterized in that, Also includes: The maximum allowable change is dynamically adjusted based on the variation range of the dimensionless value of the scheduling instruction within a preset number of historical sampling periods. When the change exceeds the threshold, the maximum allowable change is increased. When the magnitude of the change is lower than the descent threshold, the maximum allowable change is reduced. The adjusted maximum allowable change is used to obtain the generator set smoothing command.
5. The modular implementation method of the fire-storage power allocation control algorithm adapted to the DCS system according to claim 2, characterized in that, include: The capacity ratio of the generator set and the capacity ratio of the energy storage system are determined based on the rated capacity of the generator set and the rated capacity of the energy storage system. The difference is obtained by subtracting the product of the generator set smoothing instruction and the generator set capacity ratio from the dimensionless value of the scheduling instruction. Divide the difference by the capacity ratio of the energy storage system to obtain the ideal energy storage compensation signal.
6. The modular implementation method of the fire-storage power allocation control algorithm adapted to the DCS system according to claim 5, characterized in that, Also includes: Based on the deviation between the dimensionless value of the current output of the generator set and the smoothing command of the generator set, the capacity ratio of the generator set and the capacity ratio of the energy storage system are corrected: When the deviation is positive, the capacity ratio of the energy storage system is increased, and the capacity ratio of the generator set is decreased; When the deviation is negative, the capacity ratio of the energy storage system is reduced, and the capacity ratio of the generator set is increased; The corrected generator set capacity ratio and energy storage system capacity ratio are used to calculate the ideal energy storage compensation signal.
7. The modular implementation method of the fire-storage power allocation control algorithm adapted to the DCS system according to claim 1, characterized in that, include: The deviation between the dimensionless value of the state of charge signal and the preset target value is multiplied by a feedback coefficient to obtain the state of charge correction amount. The energy storage correction command is obtained by subtracting the state of charge correction amount from the energy storage ideal compensation signal.
8. The modular implementation method of the fire-storage power allocation control algorithm adapted to the DCS system according to claim 7, characterized in that, The feedback coefficient is determined based on the interval in which the dimensionless value of the state-of-charge signal lies: When the dimensionless value of the state of charge signal is within a preset narrow range close to the preset target value, the first gain coefficient is used as the feedback coefficient. When the dimensionless value of the state of charge signal exceeds the preset narrow range but does not reach the preset upper or lower limit of the state of charge, the second gain coefficient is used as the feedback coefficient. Wherein, the first gain coefficient is smaller than the second gain coefficient.
9. The modular implementation method of the fire-storage power allocation control algorithm adapted to the DCS system according to claim 1, characterized in that, Also includes: If the dimensionless value of the state of charge signal is lower than the charging prohibition threshold and the energy storage correction command is positive, then the energy storage correction command will be forcibly adjusted to zero. If the dimensionless value of the state of charge signal is higher than the discharge prohibition threshold and the energy storage correction command is negative, then the energy storage correction command will be forcibly adjusted to zero.
10. The modular implementation method of the fire-storage power allocation control algorithm adapted to the DCS system according to claim 9, characterized in that, The charging prohibition threshold and the discharging prohibition threshold correspond to the preset upper and lower limits of the state of charge of the energy storage system, respectively. The process of forcibly adjusting to zero is as follows: when the dimensionless value of the state of charge signal enters a preset proximity range from the upper limit of the state of charge of the energy storage system or the lower limit of the state of charge of the energy storage system, the energy storage correction command is continuously reduced according to the remaining distance of the preset proximity range until it reaches the charging prohibition threshold or the discharging prohibition threshold and is reduced to zero.