Cogeneration unit peak shaving control method and system for thermal power spot market

Abstract

The embodiment of the present disclosure provides a combined heat and power unit peak regulation control method and system for a thermal power spot market, and belongs to the technical field of thermal power spot market operation and combined heat and power unit peak regulation control. The method comprises the following steps: collecting market data, unit operation data and power grid peak regulation instructions in real time, and performing standardized preprocessing; a peak regulation income-operation cost quantitative model is constructed with the maximum of unit time total income as the target, and power generation power, heat supply load, peak regulation rate and combined heat and power decoupling equipment constraints are set; a particle swarm optimization algorithm is used to solve the target function, with power generation power, heat supply steam flow and heat storage device charging and discharging rate as the optimization variables, to obtain the optimal peak regulation strategy; real-time peak regulation instructions are generated according to the optimal strategy and are executed; the deviation of actual income and target income is calculated, and if it exceeds the threshold, feedback is given to re-solve and correct. The present disclosure combines spot market prices and peak regulation control depth, and improves peak regulation income and execution accuracy.

Description

Technical Field

[0001] The embodiments disclosed herein belong to the technical field of thermal power spot market operation and cogeneration unit peak shaving control, specifically relating to a method and system for peak shaving control of cogeneration units for thermal power spot market. Background Technology

[0002] The time-of-use electricity price and peak-shaving compensation price in the spot market for thermal power exhibit high-frequency dynamic fluctuations. However, combined heat and power (CHP) units are constrained by the inherent "heat-to-power" principle, and their existing peak-shaving strategies are mostly passive responses to grid commands, failing to deeply couple with spot market price signals. This results in the failure to maximize the peak-shaving revenue and power generation revenue of the units.

[0003] Existing peak-shaving control methods for combined heat and power (CHP) units suffer from several technical shortcomings: First, research focuses primarily on the technical aspects of load regulation, lacking a systematic and quantitative analysis of spot market revenue and unit operating costs. A collaborative optimization model integrating "market price, peak load, and heating constraints" has not been established, resulting in a disconnect between strategy formulation and market demand. Second, the control logic operates on a fixed model, unable to adapt to dynamic instructions from day-ahead and real-time spot market transactions, leading to poor adaptability. Third, the existing optimization model's solution algorithm is inefficient, failing to match the real-time fluctuations in spot market prices, resulting in delayed peak-shaving instruction generation and significant execution deviations. Fourth, it is difficult to achieve a balance between peak-shaving flexibility, heating stability, and market revenue, making it unsuitable for the trading rules and performance requirements of the thermal power spot market. Summary of the Invention

[0004] The embodiments disclosed herein aim to at least solve one of the technical problems existing in the prior art, and provide a method and system for peak shaving control of cogeneration units for the thermal power spot market.

[0005] One aspect of this disclosure provides a peak-shaving control method for cogeneration units oriented towards the thermal power spot market, the method comprising: Real-time collection of market data, unit operation data, and grid peak-shaving instructions; and standardized preprocessing of the collected multi-source data. A quantitative model of peak-shaving revenue and operating cost is constructed. The objective function is constructed with the goal of maximizing the total revenue of the unit per unit time. Constraints are set for power generation, heating load, peak-shaving rate and thermal-electric decoupling equipment. The objective function is solved using a particle swarm optimization algorithm, with power generation, steam flow rate for heating, and energy storage device charging and discharging rate as optimization variables, to obtain the optimal peak-shaving strategy for different time periods. Real-time peak shaving instructions are generated based on the optimal peak shaving strategy and sent to the unit's peak shaving equipment for peak shaving control. The system collects real-time operating data of the generating units and real-time price data of the spot market, calculates the deviation between the actual total revenue and the target total revenue, and feeds back the deviation to the particle swarm optimization algorithm to resolve the optimal peak shaving strategy when the deviation exceeds a preset threshold.

[0006] Furthermore, the market data includes day-ahead time-of-use pricing, real-time time-of-use pricing, deep peak-shaving compensation pricing, and conventional peak-shaving compensation pricing; and / or, The unit operation data includes real-time heating load demand, unit rated power, peak load range, and operating cost parameters.

[0007] Furthermore, the standardization preprocessing includes: Noisy and missing data are removed by outlier detection algorithms, incomplete data is completed, and data of different dimensions and orders of magnitude are normalized.

[0008] Furthermore, the objective function is shown in the following equation:

[0009] In the formula, For the revenue from electricity generation in the spot market, To compensate for peak shaving revenue, For unit operating costs, This incurs additional costs for thermoelectric decoupling.

[0010] Furthermore, the optimal peak-shaving strategy includes optimal power generation, optimal heating steam flow rate, and optimal energy storage device charging and discharging strategy.

[0011] Furthermore, the real-time peak-shaving commands include unit power generation commands, heating steam flow commands, and thermal storage device charging and discharging commands; The unit's peak-shaving equipment includes a steam distribution regulating valve, a thermal storage device, a back-pressure steam turbine generator set, and a boiler combustion regulating system.

[0012] Furthermore, the actual operating data of the unit includes actual power generation, actual heating load, and operating status of peak-shaving equipment.

[0013] Another aspect of this disclosure provides a peak-shaving control system for cogeneration units oriented towards the thermal power spot market, the system comprising: The data acquisition module is used to collect market data, unit operation data and power grid peak-shaving instructions in real time, and to perform standardized preprocessing on the collected multi-source data; The model building module is used to build a quantitative model of peak-shaving revenue and operating costs. It constructs an objective function with the goal of maximizing the total revenue of the unit per unit time and sets constraints on power generation, heating load, peak-shaving rate and thermal-electric decoupling equipment. The optimization solution module is used to solve the objective function using the particle swarm optimization algorithm, with power generation, steam flow rate for heating and energy storage device charging and discharging rate as optimization variables, to obtain the optimal peak shaving strategy for different time periods. The peak shaving control module is used to generate real-time peak shaving instructions based on the optimal peak shaving strategy and send them to the unit's peak shaving equipment for peak shaving control. The closed-loop feedback module is used to collect real-time operating data of the unit and real-time price data of the spot market, calculate the deviation between the actual total revenue and the target total revenue, and when the deviation exceeds a preset threshold, it is fed back to the particle swarm optimization algorithm to resolve the optimal peak shaving strategy.

[0014] Another aspect of this disclosure provides an electronic device, comprising: At least one processor; and, A memory communicatively connected to the at least one processor is used to store one or more programs, which, when executed by the at least one processor, enable the at least one processor to implement the peak-shaving control method for cogeneration units oriented towards the thermal power spot market described above.

[0015] Another aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the peak-shaving control method for cogeneration units oriented towards the thermal power spot market described above.

[0016] This disclosure discloses a method and system for peak-shaving control of cogeneration units oriented towards the thermal power spot market. By deeply integrating the price of the thermal power spot market with the unit's peak-shaving control, a quantified revenue-cost objective function is constructed, making the peak-shaving strategy conform to the trading rules and price fluctuation characteristics of the spot market, thereby improving the overall revenue of the unit's peak-shaving. A particle swarm optimization algorithm is used to solve for the optimal peak-shaving strategy. Through precise optimization of the algorithm parameters, short solution time, high accuracy, and fast speed are achieved in a single solution, adapting to the real-time fluctuation characteristics of spot market prices and enabling minute-level updates of the peak-shaving strategy to meet real-time control requirements. The established closed-loop feedback correction mechanism can identify and calculate the actual operating conditions in real time. Deviations between the execution and target strategies are eliminated through dynamic correction of peak-shaving commands, improving the accuracy of peak-shaving command execution. This maximizes the total revenue of the unit while ensuring user heating needs and grid peak-shaving requirements. No new hardware equipment is required; software modules can be developed directly on the existing unit DCS system to implement the functions. This results in low development and modification costs, strong module compatibility, and suitability for application and promotion of various cogeneration units in the thermal power spot market environment. The data acquisition and preprocessing module enables real-time updates of multi-source data, ensuring that the peak-shaving strategy is highly synchronized with market price fluctuations, grid peak-shaving commands, and unit operating status, guaranteeing the timeliness and rationality of the strategy from the data source. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart illustrating a peak-shaving control method for cogeneration units oriented towards the spot market of thermal power, according to an embodiment of this disclosure. Figure 2 This is a schematic diagram of the structure of a peak-shaving control system for a cogeneration unit oriented towards the spot market of thermal power, according to another embodiment of this disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device according to another embodiment of the present disclosure. Detailed Implementation

[0018] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this disclosure.

[0019] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0020] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0021] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of this disclosure. As used in this disclosure, the term "and / or" includes all combinations of any one and more of the associated listed items.

[0022] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing this disclosure, and therefore cannot be used to limit the scope of protection of this disclosure.

[0023] like Figure 1As shown, one embodiment of this disclosure provides a peak-shaving control method for cogeneration units oriented towards the thermal power spot market, including: Step S1: Collect market data, unit operation data and grid peak shaving instructions in real time, and perform standardized preprocessing on the collected multi-source data.

[0024] Specifically, a two-way connection is established between the dedicated network and the thermal power spot market trading platform to collect market data, unit operation data, and grid peak-shaving instructions in real time. The data collection and update frequency is ≤30 seconds to ensure that the data is synchronized with the market status and unit operation status.

[0025] The market data includes day-ahead time-of-use electricity prices, real-time time-of-use electricity prices, deep peak-shaving compensation prices, and regular peak-shaving compensation prices; the unit operation data includes core operation indicators such as real-time heating load demand, unit rated power, peak-shaving load range, and operating cost parameters.

[0026] The collected multi-source data undergoes standardized preprocessing. First, outlier detection algorithms are used to remove noisy and missing data, and incomplete data is completed. Then, data of different dimensions and orders of magnitude are normalized to form a unified standardized dataset.

[0027] Step S2: Construct a quantitative model for peak-shaving revenue operating costs. The objective function is constructed with the goal of maximizing the total revenue of the unit per unit time. Constraints are set for power generation, heating load, peak-shaving rate, and thermal-electric decoupling equipment.

[0028] Specifically, with the goal of maximizing the total revenue per unit time of the unit, the objective function is constructed as follows:

[0029] In the formula, Revenue from electricity generation in the spot market:

[0030] in, for The generator set's power output at specific times. for Time-of-use electricity pricing The time step can be 15 minutes or 30 minutes. For peak shaving compensation benefits:

[0031] in, for Peak shaving capacity of generating units at specific times; for The peak shaving compensation price is set separately for deep peak shaving and regular peak shaving. For unit operating costs:

[0032] in, The cost of coal consumption corresponding to the power generation capacity. For heating load The corresponding heating costs; Additional costs associated with thermoelectric decoupling include the operation and maintenance costs of thermal storage devices and back pressure units.

[0033] To ensure the safe operation of the generating units, meet heating demand standards, and meet grid peak-shaving requirements, the following constraints are set for the objective function: 1. Power generation constraints: , This is the unit's minimum peak-shaving power. This refers to the rated power of the unit. 2. Heating load constraints: , To meet the minimum heating load requirements of users, To meet the user's maximum heating load demand; 3. Peak shaving rate constraint: , This is the maximum allowable peak-shaving rate of the unit; 4. Constraints of thermoelectric decoupling equipment: The charging and discharging rate of the thermal storage device and the output of the back pressure unit are both within their rated range.

[0034] Step S3: The objective function is solved using the particle swarm optimization algorithm, with power generation, steam flow rate for heating and energy storage device charging and discharging rate as optimization variables, to obtain the optimal peak-shaving strategy for different time periods.

[0035] Specifically, the objective function is solved using the particle swarm optimization algorithm. Power generation at any time Heating steam flow rate Energy storage device charge / discharge rate As optimization variables, the parameters of the particle swarm optimization algorithm are set as follows: number of iterations 100~200, learning factor 1.5~2.0, and population size 40~60. Through iterative solution, the optimal power generation curve, heating steam flow curve, and energy storage device charging and discharging strategy for different time periods (day-ahead / real-time) are obtained, with a single solution time ≤5min.

[0036] Step S4: Generate real-time peak shaving instructions according to the optimal peak shaving strategy and send them to the unit's peak shaving equipment for peak shaving control.

[0037] Specifically, a hierarchical control logic is constructed, consisting of an upper-level market revenue optimization layer and a lower-level device execution layer: 1. Upper-level market revenue optimization layer: Based on the optimal peak-shaving strategy obtained in step S3, generate real-time peak-shaving instructions, including unit power generation instructions, heating steam flow instructions, and thermal storage device charging and discharging instructions; 2. Lower-level equipment execution layer: Receives peak-shaving instructions from the upper-level market revenue optimization layer, drives the unit's peak-shaving equipment to perform actions, and adjusts the unit's power generation and heating load in real time. The peak-shaving equipment includes steam distribution regulating valves, thermal storage devices, back-pressure turbine generator sets, and boiler combustion regulation systems.

[0038] Step S5: Collect real-time operating data of the unit and real-time price data of the spot market, calculate the deviation between the actual total revenue and the target total revenue, and when the deviation exceeds a preset threshold, feed it back to the particle swarm optimization algorithm to resolve the optimal peak shaving strategy.

[0039] Specifically, real-time data collection of actual unit operating data and real-time spot market price data is performed. The actual operating data includes actual power generation, actual heating load, and peak-shaving equipment operating status. The deviation between the actual total revenue and the target total revenue is calculated. If the deviation is ≥5%, the deviation signal is fed back to step S3, real-time data is re-inputted to solve for the optimal peak-shaving strategy, the peak-shaving command is corrected, and the control closed loop is achieved, controlling the peak-shaving command execution accuracy to ≤±2%.

[0040] The following example uses a 600MW supercritical cogeneration unit as an application case, applied to the spot market of thermal power in a certain province, with a time step of [missing information]. Taking 30 minutes, the peak load range of the unit is 30%~100% (180MW~600MW), and the heating load demand is 150t / h~250t / h. The maximum allowable peak shaving rate of the unit is... =2MW / min.

[0041] Spot market price parameters: Day-ahead time-of-use electricity price: peak period 1.2 yuan / kWh, flat period 0.6 yuan / kWh, valley period 0.3 yuan / kWh; deep peak shaving compensation price: 0.8 yuan / kWh (load ≤ 40%), regular peak shaving compensation price: 0.2 yuan / kWh (40% < load < 50%).

[0042] I. Implementation Preparation 1. Based on the existing DCS system of the unit, develop a spot market data acquisition and preprocessing module, and connect to the provincial thermal power spot market trading platform through a dedicated network to realize the automatic acquisition of market data and grid peak-shaving instructions, while also acquiring unit operation data, with a data update frequency of 30 seconds / time.

[0043] 2. Compile unit operating parameters and establish a coal consumption cost function for power generation. (yuan / h), heating cost function (RMB / h) Additional cost of thermoelectric decoupling =200 yuan / hour.

[0044] 3. Configure particle swarm optimization algorithm parameters: 150 iterations, learning factors c1=c2=1.8, population size 50, and optimization variables are as follows. Power generation at any time Heating steam flow rate Energy storage device charge / discharge rate .

[0045] 4. Develop control programs for the upper-level market revenue optimization layer and the lower-level equipment execution layer, embed them into the unit's DCS system, and develop a closed-loop feedback correction module to realize real-time deviation calculation and command correction.

[0046] II. Implementation Steps 1. Before 16:00 every day, the data acquisition and preprocessing module collects the day-ahead transaction data (day-ahead time-of-use electricity price, day-ahead peak-shaving compensation price) of the thermal power spot market, the next day's grid peak-shaving instructions, and the next day's heating load demand. After noise reduction, completion, and normalization preprocessing, the data is input into the peak-shaving revenue-operating cost quantification model.

[0047] 2. The particle swarm optimization algorithm is used to solve the objective function, yielding the optimal power generation curve, heating steam flow curve, and energy storage device charging and discharging strategy for every 30 minutes over the next 24 hours. Peak hours (8:00~12:00, 18:00~22:00): The generating units operate at full capacity of 600MW, and the thermal storage devices release heat to meet the heating demand, maximizing the revenue from spot power generation; During off-peak hours (0:00~6:00): the unit reduces its load to 30% (180MW) for deep peak shaving, and the thermal storage device charges and stores waste heat to obtain deep peak shaving compensation; During the off-peak period (other times): the unit load is maintained at 50%~60%, and the thermal storage device balances heating and power generation, taking into account both power generation revenue and peak shaving preparation.

[0048] 3. Based on the solution results, the upper-level market revenue optimization layer generates the peak shaving instruction for the next day and sends it to the lower-level equipment execution layer in 30-minute time steps.

[0049] 4. The lower-level equipment execution layer receives peak shaving commands, drives the steam distribution regulating valve to adjust the steam flow, the thermal storage device to switch the charging / discharging mode, the boiler combustion regulating system to adjust the coal and air volume, and the back pressure turbine generator set to adjust the output, and executes the peak shaving commands in real time.

[0050] 5. The closed-loop feedback correction mechanism collects real-time data on the actual power generation, actual heating load, equipment operating status, and real-time spot market prices of the unit. It calculates the deviation between the actual total revenue and the target total revenue. If the deviation is ≥5%, the deviation signal is fed back to the optimization model, and real-time data is re-inputted to solve and correct the peak-shaving command.

[0051] 6. At 24:00 every day, the system automatically calculates the unit's peak-shaving revenue, power generation revenue, and operating costs for the day, and generates a revenue analysis report to provide data support for subsequent peak-shaving strategy optimization.

[0052] III. Summary After adopting the peak-shaving control method for cogeneration units in the spot market of thermal power disclosed in this paper, the total daily revenue of the unit in the spot market is increased by about RMB 30,000 and the total monthly revenue is increased by about RMB 900,000 compared with the traditional peak-shaving method. The peak-shaving command execution accuracy reaches ±1.5%, and the heating load fluctuation is controlled within ±5t / h, which fully meets the user's heating demand and the grid peak-shaving requirements, without increasing the equipment operating load, and the equipment failure rate is reduced by 10%~15% compared with the traditional method.

[0053] This disclosure discloses a peak-shaving control method for cogeneration units oriented towards the thermal power spot market. By deeply integrating day-ahead / real-time price signals from the thermal power spot market with the unit's peak-shaving control, a quantified revenue-cost objective function is constructed. This addresses the industry pain point of traditional peak-shaving strategies being "technology-heavy and market-light," ensuring the peak-shaving strategy fully aligns with spot market trading rules and price fluctuation characteristics. Practical verification has shown that this method improves the overall revenue of the unit's peak-shaving efforts. The method employs a particle swarm optimization algorithm to solve for the optimal peak-shaving strategy. Through precise optimization of algorithm parameters, it achieves short solution time, high accuracy, and fast speed, adapting to the real-time fluctuation characteristics of spot market prices and enabling minute-level updates to the peak-shaving strategy to meet real-time control requirements. The method utilizes an "upper-layer market revenue optimization layer + lower-layer equipment execution layer." The hierarchical control logic focuses on optimizing strategies to maximize market returns at the upper level and on ensuring the precise and safe execution of equipment at the lower level. This effectively avoids equipment overload caused by solely pursuing market returns, balancing market returns with equipment operational safety. The established closed-loop feedback correction mechanism can identify and calculate the deviation between actual operation and the target strategy in real time. By dynamically correcting peak-shaving commands, it eliminates deviations and improves the accuracy of peak-shaving command execution. Under the premise of ensuring user heating demand and grid peak-shaving requirements, it maximizes the total revenue of the unit. No new hardware equipment is required; software modules can be developed directly on the existing unit DCS system to implement the functions. The development cost is low, the modification cost is small, and the module has strong compatibility, making it suitable for the application and promotion of various cogeneration units in the thermal power spot market environment. The data acquisition and preprocessing module realizes real-time updates of multi-source data, ensuring that the peak-shaving strategy is highly synchronized with market price fluctuations, grid peak-shaving commands, and unit operating status, ensuring the timeliness and rationality of the strategy from the data source.

[0054] like Figure 2 As shown, another embodiment of this disclosure provides a peak-shaving control system for cogeneration units oriented towards the thermal power spot market, comprising: The data acquisition module 210 is used to collect market data, unit operation data and power grid peak shaving instructions in real time, and to perform standardized preprocessing on the collected multi-source data; The model building module 220 is used to build a quantitative model of peak shaving revenue and operating cost. The objective function is built with the goal of maximizing the total revenue of the unit per unit time, and constraints are set for power generation, heating load, peak shaving rate and thermal decoupling equipment. The optimization solution module 230 is used to solve the objective function using the particle swarm optimization algorithm, with power generation, steam flow rate and energy storage device charging and discharging rate as optimization variables, to obtain the optimal peak shaving strategy for different time periods. The peak shaving control module 240 is used to generate real-time peak shaving instructions according to the optimal peak shaving strategy and send them to the unit's peak shaving equipment for peak shaving control. The closed-loop feedback module 250 is used to collect real-time operating data of the unit and real-time price data of the spot market, calculate the deviation between the actual total revenue and the target total revenue, and when the deviation exceeds a preset threshold, it is fed back to the particle swarm optimization algorithm to resolve the optimal peak shaving strategy.

[0055] Specifically, the molten salt coupled coal-fired power system heat storage and release adaptive fuzzy predictive control system according to the present disclosure is used to implement the molten salt coupled coal-fired power system heat storage and release adaptive fuzzy predictive control method described in the above embodiments. The specific implementation process has been described in detail in the above embodiments and will not be repeated here.

[0056] like Figure 3 As shown, another embodiment of this disclosure provides an electronic device, including: At least one processor 301; and a memory 302 communicatively connected to the at least one processor 301 for storing one or more programs that, when executed by the at least one processor 301, enable the at least one processor 301 to implement the peak-shaving control method for cogeneration units oriented towards the thermal power spot market described above.

[0057] The memory 302 and processor 301 are connected via a bus, which can include any number of interconnecting buses and bridges. The bus connects various circuits of one or more processors 301 and memory 302 together. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 301 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 301.

[0058] Processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 302 can be used to store data used by processor 301 during operation.

[0059] Another embodiment of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the peak-shaving control method for cogeneration units oriented towards the thermal power spot market described above.

[0060] The computer-readable storage medium may be included in the systems or electronic devices disclosed herein, or it may exist independently.

[0061] Computer-readable storage media can be any tangible medium that contains or stores a program, and can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, optical fibers, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0062] Computer-readable storage media may also include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code, specific examples of which include, but are not limited to, electromagnetic signals, optical signals, or any suitable combination thereof.

[0063] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.

Claims

1. A peak-shaving control method for cogeneration units oriented towards the thermal power spot market, characterized in that, The method includes: Real-time collection of market data, unit operation data, and grid peak-shaving instructions; and standardized preprocessing of the collected multi-source data. A quantitative model of peak-shaving revenue and operating cost is constructed. The objective function is constructed with the goal of maximizing the total revenue of the unit per unit time. Constraints are set for power generation, heating load, peak-shaving rate and thermal-electric decoupling equipment. The objective function is solved using a particle swarm optimization algorithm, with power generation, steam flow rate for heating, and energy storage device charging and discharging rate as optimization variables, to obtain the optimal peak-shaving strategy for different time periods. Real-time peak shaving instructions are generated based on the optimal peak shaving strategy and sent to the unit's peak shaving equipment for peak shaving control. The system collects real-time operating data of the generating units and real-time price data of the spot market, calculates the deviation between the actual total revenue and the target total revenue, and feeds back the deviation to the particle swarm optimization algorithm to resolve the optimal peak shaving strategy when the deviation exceeds a preset threshold.

2. The peak-shaving control method for cogeneration units oriented towards the thermal power spot market according to claim 1, characterized in that, The market data includes day-ahead time-of-use electricity prices, real-time time-of-use electricity prices, deep peak-shaving compensation prices, and conventional peak-shaving compensation prices; and / or, The unit operation data includes real-time heating load demand, unit rated power, peak load range, and operating cost parameters.

3. The peak-shaving control method for cogeneration units oriented towards the thermal power spot market according to claim 1, characterized in that, The standardized preprocessing includes: Noisy and missing data are removed by outlier detection algorithms, incomplete data is completed, and data of different dimensions and orders of magnitude are normalized.

4. The peak-shaving control method for cogeneration units oriented towards the thermal power spot market according to claim 1, characterized in that, The objective function is shown in the following equation: In the formula, For the revenue from electricity generation in the spot market, To compensate for peak shaving revenue, For unit operating costs, This incurs additional costs for thermoelectric decoupling.

5. The peak-shaving control method for cogeneration units oriented towards the thermal power spot market according to claim 1, characterized in that, The optimal peak-shaving strategy includes optimal power generation, optimal heating steam flow rate, and optimal energy storage device charging and discharging strategy.

6. The peak-shaving control method for cogeneration units oriented towards the thermal power spot market according to claim 5, characterized in that, The real-time peak-shaving commands include unit power generation commands, heating steam flow commands, and thermal storage device charging and discharging commands. The unit's peak-shaving equipment includes a steam distribution regulating valve, a thermal storage device, a back-pressure steam turbine generator set, and a boiler combustion regulating system.

7. The peak-shaving control method for cogeneration units oriented towards the thermal power spot market according to claim 5, characterized in that, The actual operating data of the unit includes actual power generation, actual heating load, and operating status of peak-shaving equipment.

8. A peak-shaving control system for cogeneration units oriented towards the thermal power spot market, characterized in that, The system includes: The data acquisition module is used to collect market data, unit operation data and power grid peak-shaving instructions in real time, and to perform standardized preprocessing on the collected multi-source data; The model building module is used to build a quantitative model of peak-shaving revenue and operating costs. It constructs an objective function with the goal of maximizing the total revenue of the unit per unit time and sets constraints on power generation, heating load, peak-shaving rate and thermal-electric decoupling equipment. The optimization solution module is used to solve the objective function using the particle swarm optimization algorithm, with power generation, steam flow rate for heating and energy storage device charging and discharging rate as optimization variables, to obtain the optimal peak shaving strategy for different time periods. The peak shaving control module is used to generate real-time peak shaving instructions based on the optimal peak shaving strategy and send them to the unit's peak shaving equipment for peak shaving control. The closed-loop feedback module is used to collect real-time operating data of the unit and real-time price data of the spot market, calculate the deviation between the actual total revenue and the target total revenue, and when the deviation exceeds a preset threshold, it is fed back to the particle swarm optimization algorithm to resolve the optimal peak shaving strategy.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor is used to store one or more programs, which, when executed by the at least one processor, enable the at least one processor to implement the peak-shaving control method for cogeneration units oriented towards the thermal power spot market as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the peak-shaving control method for cogeneration units oriented towards the thermal power spot market as described in any one of claims 1 to 7.

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