A biogas power generation management method and system based on time-of-use pricing

Abstract

The present application belongs to the technical field of power generation management, and provides a biogas power generation management method and system based on time-of-use electricity price, comprising: step one: obtaining time-of-use electricity price information in a future preset time interval, dividing the preset time interval into a plurality of continuous electricity price periods according to the time-of-use electricity price information, the electricity price periods including peak periods and valley periods, and determining the start time, end time and duration of each electricity price period, while predicting the operating parameters of the biogas power generation system; step two: for the valley periods, calculating the stoppage duration according to the operating parameters of the biogas power generation system before the start of the valley periods. Through the complete technical path of "risk prediction-reverse recursion-linkage optimization-rolling update", the gas storage capacity constraint of the valley periods and the power generation income target of the peak periods are optimized across time periods, the power generation income of the peak periods is maximized under the premise of ensuring the upper limit of the safety capacity of the gas tank, and the benefit of the biogas power generation project is improved.

Description

Technical Field

[0001] This invention belongs to the field of power generation management technology, specifically a biogas power generation management method and system based on time-of-use pricing. Background Technology

[0002] Biogas power generation technology is an important way to utilize biomass energy, and it is widely used in large-scale farms, sewage treatment plants, landfills, and agricultural waste treatment centers. A typical biogas power generation system includes an anaerobic digester, a gas storage tank, a generator set, and a supporting heat recovery device. With the deepening of the power market reform, the time-of-use pricing mechanism has been widely implemented in most parts of my country. By setting differentiated electricity prices for different periods such as peak, flat, and off-peak hours, it guides users to smooth out peak and valley usage and optimize electricity consumption behavior. For biogas power generation projects, time-of-use pricing brings significant economic incentives, allowing for higher electricity sales revenue during peak hours. However, it also places higher demands on its operation and dispatch. Compared with renewable energy sources such as wind power and photovoltaics, whose output is uncontrollable, biogas power generation has the unique advantages of storable biogas and adjustable gas production rate, giving it natural flexibility in responding to time-of-use pricing. Therefore, how to utilize time-of-use pricing signals to optimize the operation and management of biogas power generation has become the key to improving the economic benefits of the project. However, most existing biogas power generation management methods are based on traditional continuous operation modes or simple peak-valley manual adjustment, lacking a systematic consideration of cross-period coordinated scheduling under time-of-use pricing. In actual operation, due to the physical upper limit of gas holder capacity and the relatively stable biogas production rate, when multiple low-price periods occur consecutively, if a strategy of shutting down and storing gas is adopted in each low-price period, the gas holder may reach its capacity limit before the previous low-price period ends, leaving nowhere to store the subsequently generated biogas. This forces the generator unit to start generating electricity during low-price periods, resulting in economic losses from "selling electricity at low prices." In severe cases, it may even be necessary to flare and burn excess biogas, which not only wastes energy but also increases the environmental risks caused by methane escape. In addition, if only a single low-price period is considered in isolation and its relationship with the preceding peak period is ignored, it often leads to conflicts between the power generation plan during the peak period and the gas storage demand during the low-price period, making it impossible to maximize overall benefits. In some cases, improper scheduling may even cause operational safety problems such as gas holder overpressure and frequent start-up and shutdown of the unit, thus restricting the effective realization of the advantages of the time-of-use pricing mechanism in the field of biogas power generation. Therefore, the present invention provides a biogas power generation management method and system based on time-of-use pricing. Summary of the Invention

[0003] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0004] The technical solution adopted by this invention to solve its technical problem is: a biogas power generation management method based on time-of-use pricing, comprising: Step 1: Obtain time-of-use electricity price information for the future preset time interval, divide the preset time interval into multiple consecutive electricity price periods based on the time-of-use electricity price information, including peak periods and off-peak periods, and determine the start time, end time and duration of each electricity price period, while predicting the operating parameters of the biogas power generation system; Step 2: For off-peak periods, calculate the downtime based on the operating parameters of the biogas power generation system before the start of the off-peak period, compare the duration of the off-peak period with the downtime to determine the period to be intervened; Step 3: Starting from the period to be intervened, proceed backward along the time axis to the current time, and determine the upper limit of the safe capacity of the gas holder at the end of each previous electricity price period. Taking the real-time remaining capacity of the gas holder at the current time as the starting point, the upper limit of the safe capacity of the gas holder at the end of each electricity price period as the constraint, and the goal of maximizing the power generation revenue during peak periods, optimize and determine the generator unit operation plan for each electricity price period. Step 4: Control the operation of the generator set during each electricity price period according to the generator set operation plan, and monitor the changes in the remaining capacity of the gas holder and the biogas production rate in real time. When the deviation between the actual monitored parameters and the predicted values ​​exceeds the preset value, update the generator set operation plan for subsequent periods on a rolling basis.

[0005] A biogas power generation management system based on time-of-use pricing, the system comprising: Time Period Division and Parameter Acquisition Module: Acquires time-of-use electricity price information within a future preset time interval, divides the preset time interval into multiple consecutive electricity price periods based on the time-of-use electricity price information, including peak periods and off-peak periods, and determines the start time, end time and duration of each electricity price period, while predicting the operating parameters of the biogas power generation system; Off-peak period risk assessment module: For off-peak periods, the module calculates the downtime based on the operating parameters of the biogas power generation system before the start of the off-peak period, compares the duration of the off-peak period with the downtime to determine the period to be intervened. Cross-time period linkage optimization module: Starting from the period to be intervened, it backtracks along the time axis to the current time, and determines the upper limit of the safe capacity of the gas holder at the end of each previous electricity price period. Taking the real-time remaining capacity of the gas holder at the current time as the starting point, the upper limit of the safe capacity of the gas holder at the end of each electricity price period as the constraint, and the goal of maximizing the power generation revenue during peak periods, it optimizes and determines the generator unit operation plan for each electricity price period. The scheduling execution and rolling update module controls the operation of the generator set during each electricity price period according to the generator set operation plan, and monitors the changes in the remaining capacity of the gas holder and the biogas production rate in real time. When the deviation between the actual parameters and the predicted values ​​exceeds the preset value, the generator set operation plan for subsequent periods is updated on a rolling basis.

[0006] The beneficial effects of this invention are as follows: This invention uses a complete technical path of "risk prediction - reverse deduction - linkage optimization - rolling update" to link and optimize the gas storage capacity constraint during off-peak hours with the power generation revenue target during peak hours across time periods. It maximizes the power generation revenue during peak hours while ensuring the upper limit of the safe capacity of the gas holder, thereby improving the efficiency of biogas power generation projects. Meanwhile, the real-time monitoring and rolling update mechanism enhances the adaptability to gas production fluctuations, reduces unit losses by limiting the number of start-ups and shutdowns, and reduces methane escape by avoiding overpressure release from the gas holder. This achieves multiple benefits in terms of safety, economy, and environmental protection, and fundamentally solves problems such as gas holder capacity overruns, losses from low-price electricity sales, and poor adaptability to gas production fluctuations caused by isolated dispatch. It has outstanding substantive features and progress. Attached Figure Description

[0007] The invention will now be further described with reference to the accompanying drawings.

[0008] Figure 1 This is a flowchart of the steps of a biogas power generation management method based on time-of-use pricing according to the present invention; Figure 2 This is a block diagram of a biogas power generation management system based on time-of-use pricing according to the present invention. Detailed Implementation

[0009] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0010] Example 1 Please see Figure 1 As shown in the embodiment of the present invention, a biogas power generation management method based on time-of-use pricing includes the following steps: Step 1: Obtain time-of-use electricity price information for the future preset time interval, divide the preset time interval into multiple consecutive electricity price periods based on the time-of-use electricity price information, including peak periods and off-peak periods, and determine the start time, end time and duration of each electricity price period, while predicting the operating parameters of the biogas power generation system; The operating parameters include: the real-time remaining capacity of the gas holder, the biogas production rate, the basic biogas consumption rate excluding power generation, the minimum sustainable operating gas consumption rate of the generator set, and the maximum power generation gas consumption rate. In step one, the time-of-use electricity price information for the future preset time interval is obtained through any of the following methods: If the region where the project is located has implemented electricity spot market transactions, the time-of-use electricity price forecast data for the next 24 hours can be obtained daily through the API interface provided by the electricity market trading platform. The data includes the start time, end time, time period type and forecast electricity price for each trading period. If the area where the project is located implements a fixed time-of-use electricity price policy, the fixed time period division information, including the specific start and end times and corresponding electricity prices for peak hours, flat hours, and off-peak hours, should be manually entered or pre-stored through the official time-of-use electricity price announcement issued by the power grid company. The preset time interval is preferably the next 24 hours, but can also be extended to the next 48 hours or 72 hours to support longer-term scheduling planning. Based on the acquired time-of-use electricity price information, the preset time interval is divided into multiple consecutive and non-overlapping electricity price periods in chronological order, forming an electricity price period sequence. The attributes of each electricity price period are recorded, including: Start time, end time, duration, type of electricity period, and corresponding electricity price; Furthermore, time periods include: peak hours, average hours, and off-peak hours; When two adjacent electricity price periods have the same time period type, they can be merged into a single consecutive electricity price period. Based on the above merging process, the computational complexity of subsequent optimizations can be reduced; The current operating parameters of the biogas power generation system are collected in real time through a sensor network, including the following: The process of obtaining the real-time remaining capacity of the gas holder: When a dual-membrane gas holder is used, the membrane height is obtained by a membrane position sensor or laser rangefinder installed on the top of the inner membrane of the gas holder, and the remaining capacity is calculated by combining the geometric parameters of the gas holder; when a steel wet gas holder is used, the floating hood height is obtained by a floating hood position sensor, and the remaining capacity is calculated by combining the cross-sectional area of ​​the gas holder; the sampling frequency is preferably once every 10 seconds, and the average value within the most recent minute is taken as the current value; The process of obtaining the current biogas production rate: Install a thermal gas mass flow meter or vortex flow meter on the gas holder inlet pipe to continuously monitor the instantaneous flow rate of biogas entering the gas holder; take the average flow rate within the last 15 minutes as the current biogas production rate; The process for obtaining the basic biogas consumption rate (excluding power generation): Collect data on all currently operating gas-consuming equipment, excluding generator sets, including but not limited to: fermentation tank heating boilers, flare lamps, biogas purification devices, and domestic gas consumption; obtain the rate by summing the gas flow meters on each branch line, or by installing a flow meter on the main gas supply line and subtracting the gas consumption of the generator set; if some equipment does not have flow meters installed, the basic consumption rate can be estimated based on the equipment's rated gas consumption and operating status (on / off); The process of obtaining generator set operating parameters is as follows: the minimum sustainable operating load rate and rated load rate of the generator set are read from the generator set control system; the minimum sustainable operating gas consumption rate and the maximum power generation gas consumption rate are calculated in combination with the rated gas consumption of the generator set; for systems equipped with multiple generator sets, the parameters of each generator set are recorded separately and summarized to form a system-level combined operating range. By combining historical data, current status, and operational plans, predict the average operating parameters for each electricity price period in the future; Using a time series forecasting model, with historical gas production rate data from the past 24 hours as input, the average gas production rate for each future period is predicted. Among them, time series forecasting models include, but are not limited to, ARIMA models, LSTM neural networks, or forecasting methods based on exponential smoothing; An empirical model based on material balance is adopted: the theoretical gas production rate is calculated based on the effective volume of the fermenter, the feed organic load, the empirical coefficient of gas production rate, and the feed plan for each future period. Preferably, the two methods described above are combined, using an empirical model as a basis and historical data to correct the model parameters to achieve adaptive prediction. The forecast results are updated every hour to account for disturbances such as changes in feed and temperature fluctuations. For gas-consuming equipment that is greatly affected by ambient temperature (such as boilers for heating fermenters), the heat load required to maintain the fermentation temperature is calculated based on the predicted ambient temperature values ​​for each future period, and then converted into the biogas consumption rate. For gas-consuming equipment related to the production plan, determine the equipment operating status for each time period based on the scheduled production plan, and calculate the biogas consumption rate accordingly. For continuously operating equipment (such as torch lamps), the current measured value is directly used as the predicted value; The reasonableness of the predicted parameters is verified to determine whether they are within the normal fluctuation range of historical statistical values. If a parameter exceeds the reasonable range or data is missing, one of the following alternative methods shall be used: use the historical average of the previous period, use the average of the same period of the same type in the same period last year, or use the default value set manually. Then, an abnormal parameter warning message shall be generated to prompt the operator to check the relevant equipment and prediction model. Based on the above, those skilled in the art should understand that: When a biogas power generation system is equipped with multiple gas holders, the real-time remaining capacity of the gas holders is the sum of the remaining capacities of all gas holders. The gas holders are connected by connecting pipes to maintain pressure balance. The volume of the connecting pipes must be taken into account when calculating. When a biogas power generation system is equipped with multiple generator sets, the generator set parameters are the aggregate parameters of each set. The generator set operation plan should be decomposed into individual sets, including specific instructions such as start-up and shutdown times and load distribution for each set.

[0011] Step 2: For each off-peak period, calculate the downtime based on the operating parameters of the biogas power generation system before the start of the off-peak period, compare the duration of the off-peak period with the downtime to determine the period to be intervened in. The downtime represents the time required for the gas holder to grow from its current remaining capacity to its maximum capacity without starting the generator set during off-peak hours. In step two: For any off-peak period, obtain the system state parameters at the start of the off-peak period, including: remaining capacity of the gas holder; average biogas production rate; and predicted biogas basic consumption rate excluding power generation. The remaining capacity of the gas holder at the start of the off-peak period is the actual value collected by real-time sensors. If the off-peak period is not the first period, the remaining capacity at the start is determined by the scheduling result of the previous period. Specifically, for the first off-peak period after the current period, the current remaining capacity of the gas holder collected in real time is used; for off-peak periods further away, the estimated remaining capacity obtained by reverse recursion is used. It should be noted that the downtime is the time required for the gas holder to increase from its current remaining capacity to its maximum capacity without starting the generator set. The calculation method for the downtime is as follows: determine the net growth rate of the gas holder during the off-peak period. The net growth rate is equal to the biogas production rate during that period minus the basic biogas consumption rate. If the gas production rate is greater than the basic consumption rate, the gas holder inventory will continue to increase; if the gas production rate is less than or equal to the basic consumption rate, the gas holder will not increase towards its capacity limit, and the downtime can be considered infinite. Calculate the remaining space between the current remaining capacity of the gas holder and the upper limit of the capacity, i.e., the upper limit of the capacity minus the current remaining capacity; Divide the remaining space by the net growth rate to get the downtime. If the net growth rate is less than or equal to zero, the downtime is directly determined to be infinite, meaning there is no risk of capacity overflow during off-peak periods. Compare the duration of the off-peak period with the corresponding downtime; If the duration of the off-peak period is less than or equal to the downtime, it indicates that even if the generator set is not started during the entire off-peak period, the gas holder will not reach its capacity limit, and this period is marked as a safe period. If the duration of the off-peak period is longer than the allowable downtime, it indicates that if no power is generated during the off-peak period, the gas holder will reach its capacity limit before the end of the off-peak period, and the biogas produced thereafter will have nowhere to be stored. This period is marked as a period requiring intervention. Extract all the low periods marked as requiring intervention in chronological order to form a list of periods requiring intervention.

[0012] It should be noted that the following situations also need to be considered during actual operation; If the remaining capacity of the gas holder before the start of a low period has reached or exceeded the safety threshold of the capacity limit (e.g., 90% of the capacity limit), then regardless of the calculated downtime, the period will be directly marked as an intervention period to prevent capacity overrun. If there are flat or peak periods between several consecutive low periods, and the scheduling plans for these intermediate periods may cause significant changes in the remaining capacity of the gas holder before entering the next low period, then the results of step three of Xia Su should be considered when making a judgment, rather than being calculated in isolation.

[0013] Based on the above, those skilled in the art should understand that: When the system is configured with multiple gas holders, the upper limit of the capacity is the sum of the rated capacity of all gas holders, the remaining capacity of the gas holders is the sum of the remaining capacity of each gas holder, and the net growth rate of the gas holders needs to be calculated based on the total gas volume. When the biogas production rate or basic consumption rate fluctuates significantly during the off-peak period, the off-peak period can be further divided into smaller time segments, and the net growth in each time segment can be calculated separately. The downtime can be accurately calculated using an integral method to improve the accuracy of the judgment. If the calculated downtime is much longer than the duration of off-peak periods (e.g., more than 10 times), the capacity risk during that period can be ignored in actual scheduling, and no further intervention is required.

[0014] Step 3: For the intervention period, starting from the intervention period, reverse the time axis to the current time and determine the upper limit of the gas holder's safe capacity at the end of each previous electricity price period. Taking the current real-time remaining capacity of the gas holder as the starting point, the upper limit of the gas holder's safe capacity at the end of each electricity price period as the constraint, and the goal of maximizing power generation revenue during peak hours, optimize and determine the generator unit operation plan for each electricity price period. The generator set operation plan shall include at least the start-up and shutdown status and the gas consumption rate for power generation during each electricity price period; In step three, for any intervention period, it is determined that the gas holder should not exceed the upper limit of the safe capacity at the end of the intervention period; The upper limit of the safe capacity is determined by taking the upper limit of the gas holder capacity at the end of the off-peak period as the boundary condition, that is, the gas holder inventory at the end of the off-peak period must not exceed the upper limit of the rated capacity of the gas holder. For each intervention period, starting from the end of the low period, the safe capacity limit that the gas holder should meet is calculated by recursively calculating each period in the past along the time axis. The reverse recursive calculation process is as follows: For any given electricity price period, the gas storage capacity at the end of the electricity price period is related to the gas storage capacity at the beginning of the electricity price period as follows: The gas storage capacity at the end of the electricity price period is: the gas storage capacity at the beginning of the electricity price period, plus the total amount of biogas generated during the electricity price period, minus the total basic biogas consumption during the electricity price period excluding power generation, and then minus the total amount of biogas consumed by the generator set during the electricity price period. When performing reverse calculations, since the operating plan of the generator units during the electricity price period has not yet been determined, it is necessary to first determine the upper limit of the stock that should be met at the beginning of the electricity price period when the generator units are not operating or are operating at the minimum power generation rate during the electricity price period. Specifically, for any given electricity price period, if the upper limit of the inventory at the end of the electricity price period is known, then subtracting the net increase in gas volume during the electricity price period will yield the upper limit of the inventory at the beginning of the electricity price period; where the net increase in gas volume is equal to the biogas production during the electricity price period minus the basic biogas consumption. If power generation is planned during the electricity price period, the amount of biogas consumed by power generation will further reduce the requirement for the upper limit of the stock at the beginning of the electricity price period. That is, the more biogas consumed by power generation, the higher the upper limit of the stock at the beginning of the electricity price period. Because when performing the reverse recursion, it is assumed that the generating units do not operate during the electricity price period, and the basic stock limit value is calculated. Based on the type of electricity price period, it is determined whether the above basic stock limit value can be relaxed by scheduling power generation. For peak periods, since power generation can generate higher electricity sales revenue, power generation should be scheduled as much as possible, thus allowing a higher stock limit value at the beginning of the peak period. For flat periods, it is decided whether to schedule power generation based on the actual situation. For off-peak periods, power generation should be avoided in principle, so the stock limit value is calculated as if no power generation is performed. By recursively calculating backwards from the end of the off-peak period to the current time, the upper limit of the gas storage safe capacity corresponding to the end of each preceding electricity price period is obtained. When there are multiple off-peak periods to be intervened, the above backward calculation process is performed on each off-peak period to obtain multiple sets of backward calculation results. For the end of each electricity price period, the minimum value among the multiple sets of backward calculation results is taken as the final upper limit of the gas storage safe capacity at the end time. After obtaining the final safe capacity limit of the gas holder at the end of each electricity price period, the generator unit operation plan for each electricity price period is optimized and determined, starting from the real-time remaining capacity of the gas holder at the current moment, constrained by the safe capacity limit of the gas holder at the end of each electricity price period, and with the goal of maximizing power generation revenue during peak periods. The generator set operation plan should include at least the start-up and shutdown status and the gas consumption rate for power generation during each electricity price period. The optimization objective is to maximize the total power generation revenue during all peak periods. For each peak period, the power generation revenue is equal to the power generation during the peak period multiplied by the electricity price corresponding to that peak period. The amount of electricity generated is determined by the average rate of gas consumption for power generation during the peak period and the duration of the peak period. During peak and off-peak periods, the electricity price is low and the power generation revenue is insufficient to cover the wear and maintenance costs of the generating units. Therefore, the power generation revenue during these periods is not calculated in the optimization objective, or it is set to zero. The optimization process must meet the following constraints: First, there is a constraint on the gas holder's inventory. At the end of each electricity price period, the actual inventory of the gas holder must not exceed the upper limit of the gas holder's final safe capacity at that time, nor should it be lower than the minimum safe inventory of the gas holder, in order to prevent damage from negative pressure in the gas holder. Second, the gas holder inventory is recursively constrained. The gas holder inventory at the end of each electricity price period must be equal to the gas holder inventory at the beginning of that electricity price period, plus the predicted total biogas production during that electricity price period, minus the predicted total biogas basic consumption during that electricity price period, and then minus the total biogas consumption of the generator units during that electricity price period. The total biogas production and the total biogas basic consumption are both predicted values ​​from step one. Third, generator set operation constraints: the average gas consumption rate for power generation during each electricity price period must be between the minimum sustainable gas consumption rate and the maximum gas consumption rate for power generation; if the generator set is shut down during that electricity price period, the gas consumption rate for power generation is zero. For systems equipped with multiple generator sets, the operating parameter limitations of each generator set and the load distribution balance requirements among the units must also be met. Fourth, start-stop count constraint: the total number of start-stops of the generator set within the preset time interval shall not exceed the maximum allowed number of start-stops; the counting method for start-stop count is: the generator set changing from a stopped state to a running state is counted as one start, and changing from a running state to a stopped state is counted as one stop. The optimization model is composed of the upper limit of the safe capacity of the gas holder at the end of each electricity price period, the set optimization objective, and the set constraints. For cases where the number of electricity price periods and the number of generator sets are small, linear programming or integer programming methods are used to solve the problem precisely, obtaining the optimal power generation and gas consumption rate and start-up and shutdown status for each electricity price period; for cases where the number of electricity price periods or the number of generator sets are large, heuristic algorithms are used to solve the problem approximatingly. Specifically, for cases where the number of electricity price periods does not exceed 24 and the number of generator sets does not exceed 3, linear programming or integer programming methods are used to solve the problem precisely, obtaining the optimal power generation and gas consumption rate and start-up and shutdown status for each electricity price period; for cases where the number of electricity price periods exceeds 24 or the number of generator sets exceeds 3, heuristic algorithms are used to solve the problem approximately. Furthermore, heuristic algorithms include genetic algorithms, particle swarm optimization, or simulated annealing. After the solution is completed, the generator set operation plan is output. The generator set operation plan should include at least: the start-up and shutdown status of each electricity price period, the average power generation and gas consumption rate of each electricity price period, and for multi-unit systems, the start-up and shutdown time and load distribution ratio of each generator set should also be broken down. The feasibility of the obtained generator set operation plan is verified to check whether all constraints are met. If the calculated gas holder inventory at the end of a certain electricity price period exceeds the upper limit of the gas holder's safe capacity, or the number of start-ups and shutdowns exceeds the allowable value, then the operation plan is partially adjusted, including: Prioritize power generation during peak hours when electricity prices are highest, and appropriately reduce power generation during peak hours when electricity prices are lower. If the number of start-stop cycles is limited and insufficient power generation cannot be scheduled during peak hours, the start-stop cycle limit should be appropriately relaxed, or a "minimum start-stop cycle priority" strategy should be adopted to prioritize power generation during the most favorable peak hours.

[0015] Based on the above, those skilled in the art should understand that: When the system is configured with multiple gas holders and the gas holders are connected by connecting pipes, the upper limit of the safe capacity of the gas holders should take into account the individual differences of each gas holder, take the gas holder most likely to exceed the limit as the control object, or allocate the stock constraint according to the capacity ratio of each gas holder when recursively calculating in reverse. In the reverse recursion process, for complex situations where the current time is far away and there are multiple intervention periods in between, an iterative approach can be used to recursively calculate multiple times until the upper limit of the safe capacity of the gas holder at the end of each electricity price period converges and stabilizes.

[0016] Step 4: Control the operation of the generator set during each electricity price period according to the generator set operation plan, and monitor the changes in the remaining capacity of the gas holder and the biogas production rate in real time during the operation. When the deviation between the actual monitored parameters and the predicted values ​​exceeds the preset value, update the generator set operation plan for subsequent periods on a rolling basis. In step four, the generator set operation plan optimized in step three is sent to the generator set control system. The generator set control system automatically controls the generator set's start-up, shutdown, and load adjustment based on the start-up and shutdown status and power generation gas consumption rate instructions for each electricity price period in the operation plan. For a single generator set, the execution method is as follows: at the planned start-up time, the control system issues a start-up command, the generator set starts running and carries the load according to the planned power generation and gas consumption rate; at the planned shutdown time, the control system issues a shutdown command, the generator set gradually reduces the load and then shuts down. For multiple generator sets, the execution method is as follows: the control system controls the start-up, shutdown and load adjustment of each generator set according to the start-up and shutdown time and load distribution ratio allocated to each generator set in the operation plan; during operation, the control system coordinates the output of each unit in real time to ensure that the total power generation and gas consumption rate is consistent with the operation plan. When the generator set control system supports automatic control, the operation plan is directly written into the generator set controller through the automation interface; when the generator set control system does not support automatic control, the operation plan is converted into a list of operation instructions and presented to the operator in a visual manner for manual execution. During the operation of the generator set, key operating parameters of the biogas power generation system are continuously monitored in real time through a sensor network, with a monitoring frequency of no less than once every 10 seconds. The monitored parameters include: The real-time remaining capacity of the gas holder is continuously collected by the gas holder membrane level sensor or radar level gauge to determine the deviation between the actual gas holder inventory and the predicted value. The real-time biogas production rate is continuously collected by a gas flow meter installed on the gas holder inlet pipe and used to determine the deviation between the actual gas production rate and the predicted value. The actual gas consumption rate of the generator set is continuously collected by a gas flow meter installed on the gas supply pipeline of the generator set to confirm whether the unit is operating according to the plan. The real-time pressure of the gas holder is continuously collected by a gas holder pressure sensor for safety monitoring. The actual parameters obtained from real-time monitoring are compared with the parameter values ​​predicted in step one above for the corresponding time period, and the deviation is calculated. For the remaining capacity of the gas holder, calculate the difference between the actual remaining capacity of the gas holder and the predicted remaining capacity of the gas holder at the corresponding time in the operation plan. When the absolute value of the difference exceeds the set percentage of the rated capacity of the gas holder, it is judged as an excessive deviation. The preferred percentage is 5%, which can be adjusted according to actual operating conditions. For biogas production rate, calculate the difference between the real-time biogas production rate and the average production rate for the corresponding time period predicted in step one. When the difference exceeds the set percentage of the predicted value, it is determined that the deviation exceeds the limit. The preferred percentage is 10%, which can be adjusted based on historical fluctuations. When any parameter is determined to have exceeded the deviation limit, the rolling update process is triggered. The rolling update process includes: taking the current moment as the new starting point, re-executing the aforementioned steps one to three to generate updated generator operation plans for each electricity price period within the remaining time period from the current moment to the end of the original preset time interval; During the rolling update process, the time-of-use electricity price information in step one remains unchanged, but it is necessary to re-acquire the real-time operating parameters at the current moment as the new initial state, and re-predict the biogas production rate and biogas basic consumption rate in the remaining period. The execution frequency of rolling updates is determined based on the degree of deviation: a rolling update is executed immediately when the deviation exceeds a preset threshold; at the same time, in order to maintain the timeliness of the plan, a rolling update is actively executed every fixed time interval (e.g., 1 hour), even if the deviation does not exceed the preset threshold. After the rolling update is completed, the updated generator set operation plan will be sent to the generator set control system to replace the original plan and continue to be executed. During operation, the following data will be continuously recorded and stored in the database: actual start-up and shutdown time for each electricity price period, actual gas consumption rate for power generation, actual power generation, actual gas holder inventory change curve, actual gas production rate change curve, triggering reasons and update time for each rolling update.

[0017] Based on the above, those skilled in the art should understand that: When the system is configured with multiple gas holders, the deviation detection of the remaining capacity of the gas holders should be based on the total remaining capacity of all gas holders, while also paying attention to the balance between each gas holder. If the remaining capacity of a certain gas holder deviates too much from the average value, it is necessary to check whether the connecting pipeline is unobstructed. The calculation time for rolling updates should be kept within an acceptable range, such as no more than 5 minutes, to ensure that the updated plan can be executed in a timely manner.

[0018] This embodiment has at least the following effects: By establishing a cross-period linkage optimization model of "peak-valley", the gas storage capacity constraint of the valley period is transmitted in reverse to the preceding peak period. With the goal of maximizing the power generation revenue during the peak period, the operation plan of the generator unit for each electricity price period is optimized and determined. This can make full use of the gas storage capacity of the gas holder, prioritize the storage of biogas during the valley period, and concentrate power generation during the peak period when the electricity price is the highest, thereby maximizing the electricity sales revenue during the peak period. At the same time, it avoids the economic loss of "selling electricity at low price" caused by the generator unit being forced to start during the valley period. By calculating and comparing the downtime for each off-peak period, intervention periods with capacity overflow risks can be identified in advance. The upper limit of the safe capacity of the gas holder for each preceding period can be determined by reverse recursion. Safety constraints are integrated throughout the entire scheduling optimization process, which can effectively prevent the risk of gas holder overpressure caused by gas storage during multiple consecutive off-peak periods. This avoids safety accidents such as equipment damage, biogas leakage, or even explosion caused by gas holder capacity exceeding the limit. At the same time, by setting a minimum safe storage limit for the gas holder, negative pressure damage to the gas holder can also be prevented, thus comprehensively ensuring the safe operation of the biogas power generation system. By using the number of start-stop cycles as an optimization constraint, priority is given to power generation during peak periods with the highest electricity prices, while power generation during peak periods with lower electricity prices is appropriately reduced. This achieves maximum revenue with the fewest start-stop cycles, effectively reducing start-stop wear on generator sets, extending equipment maintenance cycles and service life, and reducing operation and maintenance costs. By monitoring the changes in the remaining capacity of the gas holder and the biogas production rate in real time, when the deviation between the actual parameters and the predicted values ​​exceeds the preset threshold, the rolling update process is automatically triggered. The generator set operation plan for subsequent periods is re-optimized with the current moment as a new starting point. This can respond in a timely manner to disturbances such as fluctuations in the fermentation process, changes in feed, and changes in ambient temperature, and dynamically adjust the power generation plan to ensure that the scheduling plan is always consistent with the actual operating status, thereby improving the reliability and execution effect of the scheduling plan. By scientifically scheduling biogas storage tanks to avoid exceeding capacity limits, the situation where excess biogas is forced to be released and burned through flares due to full storage tanks is fundamentally reduced, making full use of biogas resources and reducing the escape emissions of methane, a strong greenhouse gas. This not only improves economic benefits but also achieves environmental benefits, meeting the requirements of the "dual carbon" target and the development of a circular economy. It takes into account the capacity superposition and balance of each gas holder when multiple gas holders are configured, as well as the combined operation and load distribution of each generator unit when multiple generator units are configured. It can be flexibly applied to biogas power generation projects of various scales and configurations, including complex scenarios such as large-scale farms, sewage treatment plants, landfills and multi-energy complementary systems in industrial parks. It has good universality and promotion value.

[0019] Example 2 Based on the same inventive concept as the biogas power generation management method based on time-of-use pricing in the foregoing embodiments, such as Figure 2 As shown, this application provides a biogas power generation management system based on time-of-use pricing, wherein the system specifically includes: Time Period Division and Parameter Acquisition Module: Acquires time-of-use electricity price information within a future preset time interval, divides the preset time interval into multiple consecutive electricity price periods based on the time-of-use electricity price information, including peak periods and off-peak periods, and determines the start time, end time and duration of each electricity price period, while predicting the operating parameters of the biogas power generation system; Off-peak period risk assessment module: For each off-peak period, the system calculates the downtime based on the operating parameters of the biogas power generation system before the start of the off-peak period, compares the duration of the off-peak period with the downtime to determine the period to be intervened. Cross-time period linkage optimization module: For the period to be intervened, starting from the period to be intervened, the time axis is reversed to the current time, and the upper limit of the safe capacity of the gas holder at the end of each previous electricity price period is determined in turn. Taking the real-time remaining capacity of the gas holder at the current time as the starting point, the upper limit of the safe capacity of the gas holder at the end of each electricity price period as the constraint, and the goal of maximizing the power generation revenue during the peak period, the generator unit operation plan for each electricity price period is optimized and determined. The scheduling execution and rolling update module controls the operation of the generator set during each electricity price period according to the generator set operation plan. During the operation, it monitors the changes in the remaining capacity of the gas holder and the biogas production rate in real time. When the deviation between the actual parameters and the predicted values ​​exceeds the preset value, it updates the generator set operation plan for subsequent periods.

[0020] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A biogas power generation management method based on time-of-use pricing, characterized in that: include: Step 1: Obtain time-of-use electricity price information for the future preset time interval, divide the preset time interval into multiple consecutive electricity price periods based on the time-of-use electricity price information, including peak periods and off-peak periods, and determine the start time, end time and duration of each electricity price period, while predicting the operating parameters of the biogas power generation system; Step 2: For off-peak periods, calculate the downtime based on the operating parameters of the biogas power generation system before the start of the off-peak period, compare the duration of the off-peak period with the downtime to determine the period to be intervened; Step 3: Starting from the period to be intervened, proceed backward along the time axis to the current time, and determine the upper limit of the safe capacity of the gas holder at the end of each previous electricity price period. Taking the real-time remaining capacity of the gas holder at the current time as the starting point, the upper limit of the safe capacity of the gas holder at the end of each electricity price period as the constraint, and the goal of maximizing the power generation revenue during peak periods, optimize and determine the generator unit operation plan for each electricity price period. Step 4: Control the operation of the generator set during each electricity price period according to the generator set operation plan, and monitor the changes in the remaining capacity of the gas holder and the biogas production rate in real time. When the deviation between the actual monitored parameters and the predicted values ​​exceeds the preset value, update the generator set operation plan for subsequent periods on a rolling basis.

2. The biogas power generation management method based on time-of-use pricing according to claim 1, characterized in that: Step one specifically includes: Obtain time-of-use electricity price information, divide the preset time interval into multiple electricity price periods in chronological order, and record the start time, end time, duration, period type, and corresponding electricity price; Real-time acquisition of current operating parameters of the biogas power generation system, including: real-time remaining capacity of the gas holder, current biogas production rate, basic biogas consumption rate other than power generation, minimum sustainable operating gas consumption rate of the generator set, and maximum gas consumption rate for power generation; By combining historical data, current status, and operational plans, a time series forecasting model is used to predict the average biogas production rate and average biogas base consumption rate for each electricity price period in the future.

3. The biogas power generation management method based on time-of-use pricing according to claim 1, characterized in that: The process of calculating the downtime is as follows: For any off-peak period, obtain the remaining capacity of the gas holder at the start of the off-peak period, the predicted average biogas production rate and the average biogas basal consumption rate during the off-peak period. Calculate the net growth rate of the gas holder during the off-peak period, which is the biogas production rate minus the basic biogas consumption rate. If the net growth rate is greater than zero, the downtime can be calculated by dividing the remaining space between the current remaining capacity of the gas holder and the upper limit of the capacity by the net growth rate; if the net growth rate is less than or equal to zero, the downtime can be considered infinite.

4. The biogas power generation management method based on time-of-use pricing according to claim 1, characterized in that: The process of extracting the intervention period is as follows: If the duration of a low point period exceeds the allowable downtime, it is marked as a period requiring intervention.

5. A biogas power generation management method based on time-of-use pricing according to claim 1, characterized in that: The process of determining the upper limit of the safe capacity of the gas holder at the end of each preceding electricity price period is as follows: For any off-peak period to be intervened, the upper limit of the rated capacity of the gas holder at the end of the off-peak period to be intervened is used as the boundary condition. Starting from the end of the low point period to be intervened, the process is recursively pushed backward along the time axis. For any electricity price period, the upper limit of the stock at the end of the known electricity price period is subtracted from the net increase in gas volume during the electricity price period to obtain the upper limit of the stock at the beginning of the electricity price period. The net increase in gas volume is equal to the biogas production during the electricity price period minus the basic biogas consumption. For peak periods, it is permissible to increase the initial stock limit by scheduling power generation; For peak periods, the decision to schedule power generation is made based on the actual situation; for off-peak periods, the upper limit of the stock is calculated based on the assumption that no power generation is performed. Starting from the end of the off-peak period to be intervened, the upper limit of the safe capacity of the gas holder is obtained by recursively pushing back to the current time for each previous electricity price period. When there are multiple low-temperature periods to be intervened, reverse recursion is performed for each low-temperature period to be intervened, and the minimum value among multiple sets of results is taken as the final upper limit of the gas holder's safe capacity.

6. The biogas power generation management method based on time-of-use pricing according to claim 1, characterized in that: The process of optimizing and determining the generator unit operation plan for each electricity price period is as follows: The optimization objective is to maximize the total power generation revenue during all peak periods. Power generation revenue is equal to the power generation during the peak period multiplied by the electricity price corresponding to that peak period. The optimization process must meet the following constraints: the actual gas storage at the end of each electricity price period must not exceed the final safe capacity limit of the gas storage at that time, and must not be lower than the minimum safe storage of the gas storage; the gas storage at the end of each electricity price period is equal to the gas storage at the beginning of that electricity price period plus the total biogas production during that electricity price period, minus the total basic biogas consumption, and then minus the total biogas consumed by the generator set. The average gas consumption rate for power generation of the generator set during each electricity price period is between the minimum sustainable operating gas consumption rate and the maximum power generation gas consumption rate; The total number of start-stop operations of the generator set within the preset time interval shall not exceed the maximum allowable number of start-stop operations; The optimization model is constructed by combining the upper limit of the safe capacity of the gas holder at the end of each electricity price period, the set optimization objectives, and the set constraints, and then the generator set operation plan is output.

7. A biogas power generation management method based on time-of-use pricing according to claim 6, characterized in that: The methods for solving the optimization model include: Choose the solution method based on the electricity price period and the number of generator sets; After the solution is completed, the feasibility of the obtained generator set operation plan is verified. If it is found that the calculated value of the gas holder inventory at the end of a certain electricity price period exceeds the upper limit of the gas holder's safe capacity, or the number of start-ups and shutdowns exceeds the allowable value, the operation plan will be adjusted.

8. A biogas power generation management method based on time-of-use pricing according to claim 1, characterized in that: Step four includes: The generator set operation plan is sent to the generator set control system. Real-time monitoring of key operating parameters, including real-time remaining capacity of the gas holder, real-time biogas production rate, actual gas consumption rate of the generator set, and real-time pressure of the gas holder; When the deviation of the remaining capacity of the gas holder exceeds the set percentage of the rated capacity of the gas holder, or the deviation of the biogas production rate exceeds the set percentage of the predicted value, the rolling update process is triggered. The rolling update process is as follows: taking the current moment as the new starting point, an updated generator set operation plan is generated for the remaining time period from the current moment to the end of the original preset time interval, and the updated generator set operation plan is sent to the generator set control system to replace the original plan and continue to be executed.

9. A biogas power generation management method based on time-of-use pricing according to claim 8, characterized in that: Step four also includes: The execution frequency of rolling updates is determined based on the degree of deviation: a rolling update is executed immediately when the deviation exceeds a preset threshold; at the same time, a rolling update is actively executed at fixed time intervals, even if the deviation does not exceed the preset threshold. The calculation time for rolling updates is controlled within a preset time range to ensure that the updated plan can be executed.

10. A biogas power generation management system based on time-of-use pricing, characterized in that, The system is used to perform the method according to any one of claims 1-9, the system comprising: Time Period Division and Parameter Acquisition Module: Acquires time-of-use electricity price information within a future preset time interval, divides the preset time interval into multiple consecutive electricity price periods based on the time-of-use electricity price information, including peak periods and off-peak periods, and determines the start time, end time and duration of each electricity price period, while predicting the operating parameters of the biogas power generation system; Off-peak period risk assessment module: For off-peak periods, the module calculates the downtime based on the operating parameters of the biogas power generation system before the start of the off-peak period, compares the duration of the off-peak period with the downtime to determine the period to be intervened. Cross-time period linkage optimization module: Starting from the period to be intervened, it backtracks along the time axis to the current time, and determines the upper limit of the safe capacity of the gas holder at the end of each previous electricity price period. Taking the real-time remaining capacity of the gas holder at the current time as the starting point, the upper limit of the safe capacity of the gas holder at the end of each electricity price period as the constraint, and the goal of maximizing the power generation revenue during peak periods, it optimizes and determines the generator unit operation plan for each electricity price period. The scheduling execution and rolling update module controls the operation of the generator set during each electricity price period according to the generator set operation plan, and monitors the changes in the remaining capacity of the gas holder and the biogas production rate in real time. When the deviation between the actual parameters and the predicted values ​​exceeds the preset value, the generator set operation plan for subsequent periods is updated on a rolling basis.

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