Multi-source collaborative molten salt heat storage peak shaving system and intelligent regulation and control method

Abstract

The application discloses an intelligent regulation and control method of a multi-source cooperative molten salt heat storage peak regulation system, and the regulation and control method comprises the following steps: marking a pre-peak regulation period on a time axis; wherein the two ends of the pre-peak regulation period are locked as a reference time point and a peak regulation starting point; obtaining an initial energy storage capacity of a molten salt heat storage source at the reference time point; slidingly selecting an energy storage cutoff time in the pre-peak regulation period, and calculating a cumulative energy storage capacity containing the initial energy storage capacity at the energy storage cutoff time; slidingly selecting a peak regulation cutoff time on the time axis from the peak regulation starting point of the pre-peak regulation period, and calculating a cumulative peak regulation gap at the peak regulation cutoff time; and finding an optimal energy storage time point in the pre-peak regulation period according to the cumulative energy storage capacity and the cumulative peak regulation gap; the optimal energy storage time point is finally determined, the problem that peak regulation support is not clear in the existing energy storage scheduling strategy is solved, and the matching between energy storage capacity and peak regulation demand is realized.

Description

Technical Field

[0001] This invention relates to the field of thermal energy storage and peak shaving, specifically to an intelligent control method for a multi-source synergistic molten salt thermal energy storage and peak shaving system. Background Technology

[0002] With the increasing penetration of uncontrollable renewable energy sources such as wind power and photovoltaics in the power grid, their output fluctuations and intermittent nature pose significant challenges to the peak-shaving operation of the power system. Traditional peak-shaving methods mainly rely on controllable power sources such as thermal power for regulation, but their response speed is slow and their regulation capacity is limited, making it difficult to meet the increasingly complex needs of multi-source coordinated dispatch.

[0003] Against this backdrop, molten salt thermal energy storage systems have become a crucial technological pathway supporting renewable energy consumption and grid peak shaving due to their advantages such as high energy density, fast response speed, and large-scale application. Patent document CN119933824A discloses a method and system for extraction steam storage and molten salt thermal energy storage under deep grid peak shaving, which can achieve a multi-objective balance between deep grid peak shaving, renewable energy consumption, and system economics. However, existing molten salt thermal energy storage system control methods lack a dynamic adaptation mechanism to future power change trends in multi-source collaborative scenarios, resulting in insufficient peak shaving support capacity for energy storage dispatch.

[0004] Therefore, this invention provides an intelligent control method for a multi-source synergistic molten salt thermal storage peak-shaving system. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent control method for a multi-source collaborative molten salt thermal energy storage peak-shaving system, which solves the technical problems mentioned in the background by finding the optimal energy storage time point.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A smart control method for a multi-source synergistic molten salt thermal energy storage peak-shaving system, the control method comprising: S1. Mark the pre-peak shaving period on the time axis; where the two ends of the pre-peak shaving period are locked as the reference time point and the peak shaving start point; S2. Obtain the initial energy storage capacity of the molten salt thermal energy source at the reference time point; S3. Select the energy storage cutoff time during the pre-peak shaving period and calculate the cumulative energy storage capacity including the initial energy storage capacity when calculating the energy storage cutoff time; S4. Starting from the peak shaving start point of the pre-peak shaving period, slide to select the peak shaving end time on the time axis, and calculate the cumulative peak shaving gap at the peak shaving end time. S5. Based on the cumulative energy storage capacity and the cumulative peak-shaving gap, find the optimal energy storage time point during the pre-peak-shaving period.

[0007] In some of these embodiments, obtaining the initial energy storage capacity of the molten salt thermal energy source at a reference time point includes: S2-1. Obtain the standard output power of the controllable power supply at the reference time point, and the fluctuating output power of the uncontrollable power supply. S2-2, Define the sum of the standard output power and the fluctuating output power as the total output power of the multi-source collaborative terminal; S2-3. Obtain the load power of the demand side at the reference time point, and define the initial energy storage capacity of the molten salt thermal energy source at the reference time point based on the power redundancy of the load power and the total output power. In some embodiments, the energy storage cutoff time is slidably selected during the pre-peak shaving period, and the cumulative energy storage capacity, including the initial energy storage capacity, is calculated when the energy storage cutoff time is reached, including: S3-1. Starting from the reference time point, lock the peak-shaving start point on the time axis to form the pre-peak-shaving period; S3-2. During the pre-peak shaving period, the energy storage cutoff time is selected by sliding the slider. S3-3. Mark N energy storage time points between the reference time point and the energy storage cutoff time using a fixed time step; S3-4. Use the time-series power model to predict the fluctuating output power and load power at N energy storage time points, and determine the first power deviation at N energy storage time points based on the standard output power. S3-5. Based on the first power deviation and initial energy storage capacity at N energy storage time points, determine the cumulative energy storage capacity of the molten salt thermal energy source at the energy storage cutoff time. In some of these embodiments, calculating the cumulative peak-shaving gap when the peak-shaving deadline is reached includes: S4-1. Mark N heat release time points between the peak shaving start point and the peak shaving end time with a fixed time step; S4-2. Use the time-series power model to predict the fluctuating output power and load power at N heat release time points, and determine the second power deviation at N heat release time points based on the standard output power. S4-3. Calculate the cumulative peak-shaving gap at N heat release time points based on the second power deviation at N heat release time points; In some of these embodiments, the optimal energy storage time point is found during the pre-peak shaving period based on the cumulative energy storage capacity and the cumulative peak shaving gap, including: S5-1. Construct a thermal storage time series during the pre-peak shaving period; S5-2. Construct a time series of the shortfall outside the pre-shaving period; S5-3. Construct a peak shaving support ratio sequence based on the thermal storage time series and the deficit time series; wherein, the sequence element of the peak shaving support ratio sequence is the peak shaving support ratio; S5-4. Determine the optimal energy storage time point from the peak support ratio sequence.

[0008] In some of these embodiments, a thermal storage time series is constructed during the pre-peak shaving period, including: S5-1-1: Starting from the reference time point, slide along the time axis to select multiple possible energy storage cutoff times; S5-1-2. Real-time calculation of the cumulative energy storage capacity corresponding to the energy storage cutoff time; S5-1-3. Pair each energy storage time point with its corresponding cumulative energy storage capacity to form N thermal storage time tuples; S5-1-4. Sort the N thermal storage time tuples according to the time order of the energy storage time points to generate a thermal storage time sequence.

[0009] In some of these embodiments, a time series of the deficit is constructed outside the pre-shaving period, including: S5-2-1, Sliding peak shaving cutoff time from the peak shaving start point; S5-2-2, Calculate the cumulative peak shaving gap before the peak shaving deadline in real time; S5-2-3. Pair each heat release time point with its corresponding cumulative peak shaving gap to form N gap time tuples; S5-2-4. Sort the N missing time tuples according to the time order of the heat release time points to generate the missing time sequence.

[0010] In some embodiments, a peak-shaving support ratio sequence is constructed based on the thermal storage time series and the deficit time series, including: S5-3-1. Align the thermal storage time series and the deficit time series vertically according to their sequence positions to generate N sequence pairs; S5-3-2. For any sequence pair, calculate the peak support ratio of the cumulative energy storage capacity to the cumulative peak support gap in the sequence pair, until N peak support ratios are calculated. S5-3-3. Arrange the N peak-shaving support ratios in descending order based on their ratio values ​​to generate a peak-shaving support ratio sequence.

[0011] In some of these embodiments, determining the optimal energy storage time point within the peak support ratio sequence includes: S5-4-1. Select the first Q peak-shaving support ratios from the peak-shaving support ratio sequence; S5-4-2. Obtain the energy storage time points corresponding to the cumulative energy storage capacity in the first Q peak-shaving support ratios to get the Q energy storage time points; S5-4-3. Calculate the energy storage duration at Q energy storage time points and the Q energy storage duration at the baseline time point; S5-4-4. Select the maximum energy storage duration from the Q energy storage durations, and calculate the Q duration weights of the Q energy storage durations and the maximum energy storage duration; S5-4-5. The product of the Q duration weights and the first Q peak-shaving support ratios is determined as the Q peak-shaving indices corresponding to the Q energy storage time points. S5-4-6. Among the Q peak-shaving indices, anchor the energy storage time point corresponding to the largest peak-shaving index and define it as the optimal energy storage time point.

[0012] This invention achieves dynamic sliding of the time boundary for energy storage scheduling by setting a "pre-peak shaving period" on the time axis and establishing a time reference for the energy storage and peak shaving process based on the benchmark time point and peak shaving start point within this period. By sliding the selection of energy storage cutoff time and peak shaving cutoff time, and combining this with a time-series power model to predict and accumulate power deviations at future time nodes, the energy storage operation can be adjusted according to real-time operating status and future load change trends. Compared to traditional methods, this invention has stronger time adaptability and predictive capabilities, significantly improving the scheduling response efficiency of molten salt thermal energy storage systems in multi-source coordinated power grids, and helping to alleviate peak shaving pressure caused by wind and solar power fluctuations.

[0013] Furthermore, this invention constructs thermal storage time series and deficit time series, and further generates a peak-shaving support ratio series to quantitatively compare the cumulative energy storage capacity and the cumulative peak-shaving deficit, forming a physically meaningful evaluation index. Simultaneously, by combining the energy storage duration weight with the peak-shaving support ratio to calculate the peak-shaving index, the optimal energy storage time point is ultimately determined. This solves the problem of unclear peak-shaving support in existing energy storage scheduling strategies and achieves a match between energy storage capacity and peak-shaving demand.

[0014] Secondly, the present invention provides a multi-source synergistic molten salt thermal storage peak-shaving system, comprising: The pre-peak shaving marking unit is used to mark the pre-peak shaving period on the time axis; wherein, the two ends of the pre-peak shaving period are locked as the reference time point and the peak shaving start point; The initial capacity acquisition unit is used to acquire the initial energy storage capacity of the molten salt thermal energy source at a reference time point; The capacity calculation unit is used to slide to select the energy storage cutoff time during the pre-peak shaving period and calculate the cumulative energy storage capacity including the initial energy storage capacity when the energy storage cutoff time is reached. The gap calculation unit is used to start from the peak shaving start point of the pre-peak shaving period, slide to select the peak shaving end time on the time axis, and calculate the cumulative peak shaving gap at the peak shaving end time. The timing determination unit is used to find the optimal energy storage time point during the pre-peak shaving period based on the cumulative energy storage capacity and the cumulative peak shaving gap.

[0015] Compared with the prior art, the beneficial effects of the multi-source synergistic molten salt thermal storage peak shaving system of the present invention are the same as the beneficial effects of the intelligent control method of the multi-source synergistic molten salt thermal storage peak shaving system described above, so they will not be repeated here. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an intelligent control method for a multi-source synergistic molten salt thermal energy storage peak-shaving system according to the present invention. Figure 2 This is a schematic diagram illustrating the process of determining the optimal energy storage time point as described in this invention; Figure 3 This is a schematic diagram of a specific embodiment of the optimal energy storage time point process described in this invention; Figure 4 This is a structural block diagram of a multi-source synergistic molten salt thermal storage peak-shaving system according to the present invention. Detailed Implementation

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

[0018] Example 1: Please refer to Figure 1 - Figure 3 This invention provides an intelligent control method for a multi-source synergistic molten salt thermal energy storage peak-shaving system, comprising the following steps: A smart control method for a multi-source synergistic molten salt thermal energy storage peak-shaving system, the control method comprising: S1. Mark the pre-peak shaving period on the time axis; where the two ends of the pre-peak shaving period are locked as the reference time point and the peak shaving start point; S2. Obtain the initial energy storage capacity of the molten salt thermal energy source at the reference time point; S3. Select the energy storage cutoff time during the pre-peak shaving period and calculate the cumulative energy storage capacity including the initial energy storage capacity when calculating the energy storage cutoff time; S4. Starting from the peak shaving start point of the pre-peak shaving period, slide to select the peak shaving end time on the time axis, and calculate the cumulative peak shaving gap at the peak shaving end time. Specifically, the peak shaving deadline represents the time limit for the completion of the peak shaving task, and the position of this time limit is adaptively adjusted according to the changing needs of the peak shaving task.

[0019] S5. Based on the cumulative energy storage capacity and the cumulative peak-shaving gap, find the optimal energy storage time point during the pre-peak-shaving period.

[0020] The optimal energy storage time point represents the energy storage time point that maximizes the utilization of the cumulative energy storage capacity and minimizes the cumulative peak-shaving gap.

[0021] This embodiment constructs a pre-peak shaving period on the time axis and establishes a time reference system for the energy storage and peak shaving process based on the benchmark time point and peak shaving start point within this period, thus giving the energy dispatch of the molten salt thermal energy storage system a clear time boundary. By sliding the selection of the energy storage cutoff time and the peak shaving cutoff time, the cumulative energy storage capacity and cumulative peak shaving gap at different time points can be calculated according to the real-time operating status and future load change trends, thereby achieving a quantitative match between the energy storage process and peak shaving demand.

[0022] Based on this, the selection logic of the optimal energy storage time point is introduced, so that the energy storage operation can maximize the use of the currently available energy resources while meeting future peak shaving tasks, thereby improving the response efficiency of the thermal storage system in a multi-source collaborative power grid.

[0023] For example, step S2 specifically includes: S2-1. Obtain the standard output power of the controllable power supply at the reference time point, and the fluctuating output power of the uncontrollable power supply. Specifically, controllable power sources refer to power equipment such as thermal power units, gas turbines, and energy storage inverters that can be adjusted according to dispatch instructions; uncontrollable power sources refer to renewable energy power generation units such as wind farms and photovoltaic power plants that are greatly affected by natural conditions and whose power output has significant uncertainties.

[0024] S2-2, Define the sum of the standard output power and the fluctuating output power as the total output power of the multi-source collaborative terminal; Specifically, the total output power reflects the power output of the power grid through multi-source coordination at a reference time point.

[0025] S2-3. Obtain the load power of the demand side at the reference time point, and define the initial energy storage capacity of the molten salt thermal energy source at the reference time point based on the power redundancy of the load power and the total output power. Specifically, in order to ensure that the power redundancy at the reference time point can be effectively stored and used for future power peak shaving, when determining the standard output power of the controllable power source, the fluctuating output power of the uncontrollable power source is taken as the initial energy storage capacity of the molten salt thermal energy storage source at the reference time point; that is, the fluctuating output power at the reference time point is regarded as potential transferable energy, which is absorbed by the molten salt thermal energy storage source and converted into thermal energy for storage, for future peak shaving.

[0026] In this embodiment, the standard output power of the controllable power source and the fluctuating output power of the uncontrollable power source are obtained at a reference time point, and the two are added together to obtain the total power supply capacity of the power grid at the current moment. At the same time, the load power at that time point is obtained, and by comparing the total power supply capacity and the load power, it is determined whether there is a power surplus.

[0027] If there is a surplus, this surplus energy will be used as the initial energy storage capacity of the molten salt thermal energy source. The fluctuating output power of the uncontrollable power source is considered an absorbable energy source, converted into thermal energy and stored through the molten salt thermal energy storage system for peak shaving.

[0028] For example, step S3 specifically includes: S3-1. Starting from the reference time point, lock the peak-shaving start point on the time axis to form the pre-peak-shaving period; Specifically, the duration between the peak shaving start point and the reference time point is a fixed standard, that is, it is set according to the historical load peak distribution or the scheduling plan cycle.

[0029] S3-2. During the pre-peak shaving period, the energy storage cutoff time is selected by sliding the slider. Specifically, the energy storage cutoff time represents the maximum energy storage accumulation cutoff time that the current molten salt thermal energy source can support.

[0030] Furthermore, the purpose of the energy storage cutoff time is to dynamically adjust the termination time of the thermal storage process, which is achieved by gradually traversing all possible thermal storage termination times through a sliding window.

[0031] S3-3. Mark N energy storage time points between the reference time point and the energy storage cutoff time using a fixed time step; Specifically, each energy storage time point represents a heat storage update node of the molten salt thermal energy source, used to evaluate the changes in the heat storage state at these N energy storage time points.

[0032] S3-4. Use the time-series power model to predict the fluctuating output power and load power at N energy storage time points, and determine the first power deviation at N energy storage time points based on the standard output power. Specifically, the first power deviation refers to the difference between the total output power and the load power at each energy storage time point. If the difference is positive, it indicates that there is surplus energy available for storage; if it is negative, it indicates that the power supply capacity is insufficient and additional scheduling resources need to be introduced to supplement it.

[0033] Furthermore, the N energy storage time points are time nodes on the time axis. Therefore, the fluctuating output power and load power at these N future time nodes are both predicted values, while the standard output power is considered as a baseline power value that remains constant within the N energy storage time points. Based on this, the standard output power is added to the fluctuating output power to obtain the total output power (i.e., predicted power supply capacity) at each time point, and then compared with the load power to calculate the first power deviation at each time point.

[0034] S3-5. Based on the first power deviation and initial energy storage capacity at N energy storage time points, determine the cumulative energy storage capacity of the molten salt thermal energy source at the energy storage cutoff time. Specifically, by accumulating the positive first power deviation at each energy storage time point and combining it with the initial energy storage capacity, the maximum energy storage capacity that the molten salt thermal energy source can achieve at the energy storage cutoff time is calculated.

[0035] In this embodiment, during the pre-peak shaving period, an energy storage assessment window is defined with a reference time point as the starting point and the peak shaving start point as the ending point. By sliding to select different energy storage cutoff times, the termination node of the thermal storage process is dynamically simulated. Within this time range, multiple energy storage time points are divided with a fixed step size to evaluate the power surplus / deficit situation at each time point in segments.

[0036] Based on the time-series power model, the fluctuating output power and load power at various future energy storage time points are predicted, and the first power deviation is calculated in combination with the standard output power. If the deviation is positive, it indicates that there is a surplus of storable energy in the current period. These positive values ​​are accumulated one by one and superimposed with the initial energy storage capacity to obtain the cumulative energy storage capacity corresponding to the energy storage cutoff time.

[0037] For example, step S4 specifically includes: S4-1. Mark N heat release time points between the peak shaving start point and the peak shaving end time with a fixed time step; Specifically, each heat release time point represents a time control node in which the molten salt thermal energy storage system releases heat energy for peak power supply, and its time step should be consistent with the time step of the energy storage time point.

[0038] Furthermore, the "heat release time point" does not specifically refer to the exact physical moment when the molten salt thermal storage medium actually begins to release heat, but rather to the equivalent energy supply time point when the system responds to the dispatch command, initiates the heat release process, and ultimately drives the power generation equipment to provide peak-shaving power to the grid through the thermal energy conversion device. Considering the time delays caused by heat exchange, energy conversion, and mechanical response in the system, this time point is a dispatch reference time that integrates the entire energy release and power conversion link.

[0039] S4-2. Use the time-series power model to predict the fluctuating output power and load power at N heat release time points, and determine the second power deviation at N heat release time points based on the standard output power. Specifically, the second power deviation represents the gap between the predicted total output power and the predicted load demand at each heat release time point.

[0040] S4-3. Calculate the cumulative peak-shaving gap at N heat release time points based on the second power deviation at N heat release time points; Specifically, the cumulative peak-shaving gap is the algebraic sum of the power deficit at all heat release points.

[0041] It should be noted that the energy storage time point and heat release time point mentioned in this embodiment are all future time points. The corresponding uncontrollable power fluctuation output power and load power are not real-time measured values, but rather estimated values ​​obtained through time-series power model prediction. This model can model and predict power output and electricity demand in future time periods based on historical power grid operation data, meteorological information, and power load change patterns.

[0042] Specifically, fluctuating output power characterizes the trend of power generation capacity of uncontrollable sources (such as wind farms and photovoltaic power stations) changing with natural conditions; while load power reflects the changes in electricity demand of the power grid within a specific time interval. Both types of parameters have significant periodicity and time-series characteristics, making them suitable for modeling and prediction using time-series modeling methods.

[0043] Specifically, in this embodiment, the time-series power model can be a time-series model based on a Long Short-Term Memory (LSTM) network. By constructing a multi-layer LSTM network, the time dependence between power load and renewable energy output can be captured. Alternatively, an autoregressive integral moving average model can be used for short-term load or wind and solar power forecasting with a clear linear trend, thereby fitting load power time series or fluctuating output power series with different characteristics.

[0044] Preferably, the time-series power model is trained through the following steps: A continuous time interval is selected from historical operational data, with the start time defined as the input time point and the end time defined as the output time point. Based on the input time point, relevant operational parameters are collected and a model input feature vector is constructed. These input features include, but are not limited to, standard output power, historical power output data of wind / photovoltaic power plants, meteorological parameter sets (such as wind speed, solar intensity, ambient temperature, etc.), and timestamp features (such as hour, day of the week, and whether it is a holiday). Simultaneously, based on the output time point, corresponding system operational parameters are collected and a target output label is constructed. These output labels include, but are not limited to, the actual fluctuating output power of uncontrollable power sources, and the predicted target value of grid load power for future time periods. Utilizing the mapping relationship between the above input features and the target output, a supervised learning approach is used to iteratively train the model, continuously adjusting the model parameters to minimize prediction errors, thereby completing the modeling and learning of the time-series power characteristics of the power system. After training convergence, a time-series power prediction model with predictive capabilities is obtained and deployed to the dispatch system for predicting fluctuating output power and load power at future heat release time points.

[0045] For example, step S5 specifically includes: S5-1. Construct a thermal storage time series during the pre-peak shaving period; S5-2. Construct a time series of the shortfall outside the pre-shaving period; S5-3. Construct a peak shaving support ratio sequence based on the thermal storage time series and the deficit time series; wherein, the sequence element of the peak shaving support ratio sequence is the peak shaving support ratio; S5-4. Determine the optimal energy storage time point from the peak support ratio sequence.

[0046] This embodiment constructs a thermal energy storage time series by performing time series modeling on the energy storage capacity during the pre-peak shaving period, reflecting the energy accumulation of the molten salt thermal energy storage system at different energy storage cutoff times. Simultaneously, it predicts and models the energy gap during the peak shaving phase outside this period, forming a deficit time series.

[0047] By mapping the thermal energy storage time series to the deficit time series at each time point, the peak-shaving support ratio (PSRR) for each energy storage time point is calculated, which is the ratio of cumulative energy storage capacity to cumulative peak-shaving deficit, thus generating a PSRR sequence. This sequence identifies the time points where energy storage operations are most supportive while meeting future peak-shaving tasks, thereby determining the optimal energy storage time point.

[0048] Further, step S5-1 specifically includes: S5-1-1: Starting from the reference time point, slide along the time axis to select multiple possible energy storage cutoff times; S5-1-2. Real-time calculation of the cumulative energy storage capacity corresponding to the energy storage cutoff time; S5-1-3. Pair each energy storage time point with its corresponding cumulative energy storage capacity to form N thermal storage time tuples; S5-1-4. Sort the N thermal storage time tuples according to the time order of the energy storage time points to generate a thermal storage time sequence.

[0049] This embodiment starts from a base time point and slides along the time axis to select multiple possible energy storage cutoff times within the pre-peak shaving period, calculating the corresponding cumulative energy storage capacity for each cutoff time. Energy storage time points are paired with the energy storage capacity at those time points to form multiple thermal storage time tuples. By sorting these time tuples in chronological order, a thermal storage time series reflecting the trend of energy storage capacity changes over time is constructed, thus providing a structured data foundation for the energy storage status at different energy storage time nodes.

[0050] Further, step S5-2 specifically includes: S5-2-1, Sliding peak shaving cutoff time from the peak shaving start point; S5-2-2, Calculate the cumulative peak shaving gap before the peak shaving deadline in real time; S5-2-3. Pair each heat release time point with its corresponding cumulative peak shaving gap to form N gap time tuples; S5-2-4. Sort the N missing time tuples according to the time order of the heat release time points to generate the missing time sequence.

[0051] This embodiment starts from the peak-shaving start point, slides to select multiple possible peak-shaving end times within a time range after the pre-peak-shaving period, and calculates the corresponding cumulative peak-shaving gap for each end time. The exothermic time point is paired with the peak-shaving gap at that time point to form multiple deficit time tuples. By sorting these time tuples in chronological order, a deficit time series reflecting the changing trend of energy deficit in future peak-shaving phases is constructed.

[0052] Furthermore, step S5-3 specifically includes: S5-3-1. Align the thermal storage time series and the deficit time series vertically according to their sequence positions to generate N sequence pairs; S5-3-2. For any sequence pair, calculate the peak support ratio of the cumulative energy storage capacity to the cumulative peak support gap in the sequence pair, until N peak support ratios are calculated. S5-3-3. Arrange the N peak-shaving support ratios in descending order based on their ratio values ​​to generate a peak-shaving support ratio sequence.

[0053] This embodiment aligns the thermal energy storage time series constructed during the pre-peak shaving period with the deficit time series constructed outside the period according to time nodes, forming multiple sequence pairs. Each sequence pair contains the cumulative energy storage capacity and the corresponding cumulative peak shaving deficit at a given time point.

[0054] For each sequence pair, the ratio between them is calculated as the peak shaving support ratio, reflecting the strength of energy storage capacity's support for future peak shaving tasks at that point in time. All peak shaving support ratios are sorted in descending order of their values ​​to generate a peak shaving support ratio sequence, which is used to identify the time points when energy storage actions have the greatest peak shaving value.

[0055] Furthermore, step S5-4 specifically includes: S5-4-1. Select the first Q peak-shaving support ratios from the peak-shaving support ratio sequence; S5-4-2. Obtain the energy storage time points corresponding to the cumulative energy storage capacity in the first Q peak-shaving support ratios to get the Q energy storage time points; S5-4-3. Calculate the energy storage duration at Q energy storage time points and the Q energy storage duration at the baseline time point; S5-4-4. Select the maximum energy storage duration from the Q energy storage durations, and calculate the Q duration weights of the Q energy storage durations and the maximum energy storage duration; The duration weight is characterized as the ratio of the energy storage duration to the maximum energy storage duration. This ratio is defined as the duration weight, which is used to measure the importance of the energy storage duration corresponding to different energy storage time points.

[0056] S5-4-5. The product of the Q duration weights and the first Q peak-shaving support ratios is determined as the Q peak-shaving indices corresponding to the Q energy storage time points. Among them, the peak-shaving support ratio reflects the energy storage's ability to support peak-shaving tasks. By combining both, the optimal energy storage time point for peak-shaving response is finally determined.

[0057] S5-4-6. Among the Q peak-shaving indices, anchor the energy storage time point corresponding to the largest peak-shaving index and define it as the optimal energy storage time point.

[0058] Specifically, during the "pre-peak shaving period", all possible combinations of energy storage cutoff time and peak shaving cutoff time are iterated, and the cumulative energy storage capacity and cumulative peak shaving gap at each time point are calculated. By comparing the ratio of the cumulative energy storage capacity to the corresponding cumulative peak shaving gap, the time point that maximizes the ratio is determined.

[0059] This embodiment analyzes the top Q highest ratios in the peak shaving support ratio sequence to obtain the corresponding energy storage time points and their energy storage durations relative to the baseline time point. Each energy storage duration is compared with the maximum energy storage duration to calculate the duration weight of each time point, which characterizes the relative importance of energy storage duration in the overall scheduling strategy.

[0060] By combining the peak-shaving support ratio with the corresponding duration weight, a peak-shaving index is obtained for each energy storage time point. This index comprehensively reflects the strength of energy storage's support for peak-shaving tasks and the time effectiveness of the energy storage process. By comparing the magnitudes of the peak-shaving indices, the optimal energy storage time point is determined, which is the time node with the strongest support and the longest storage duration while meeting peak-shaving requirements.

[0061] This method quantifies the matching relationship between energy storage capacity and peak-shaving demand, and introduces energy storage duration weights to optimize and rank the results, thereby achieving time-optimal scheduling selection driven by the matching relationship between energy storage capacity and peak-shaving demand.

[0062] Example 2: See Figure 4 The technical solution of Embodiment 2 differs from Embodiment 1 in that it discloses a multi-source synergistic molten salt thermal storage peak-shaving system, including: The pre-peak shaving marking unit is used to mark the pre-peak shaving period on the time axis; wherein, the two ends of the pre-peak shaving period are locked as the reference time point and the peak shaving start point; The initial capacity acquisition unit is used to acquire the initial energy storage capacity of the molten salt thermal energy source at a reference time point; The capacity calculation unit is used to slide to select the energy storage cutoff time during the pre-peak shaving period and calculate the cumulative energy storage capacity including the initial energy storage capacity when the energy storage cutoff time is reached. The gap calculation unit is used to start from the peak shaving start point of the pre-peak shaving period, slide to select the peak shaving end time on the time axis, and calculate the cumulative peak shaving gap at the peak shaving end time. Specifically, the peak shaving deadline represents the time limit for the completion of the peak shaving task, and the position of this time limit is adaptively adjusted according to the changing needs of the peak shaving task.

[0063] The timing determination unit is used to find the optimal energy storage time point during the pre-peak shaving period based on the cumulative energy storage capacity and the cumulative peak shaving gap.

[0064] The optimal energy storage time point represents the energy storage time point that maximizes the utilization of the cumulative energy storage capacity and minimizes the cumulative peak-shaving gap.

[0065] In the above system, the pre-peak shaving period is marked by the pre-peak shaving marking unit; the initial energy storage capacity is obtained by the initial capacity acquisition unit; the cumulative energy storage capacity is calculated by the capacity calculation unit; the cumulative peak shaving gap is calculated by the gap calculation unit; and the optimal energy storage time point is determined by the time point determination unit, thus solving the problem that it is difficult to determine the optimal energy storage time point in molten salt thermal energy storage systems.

[0066] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means.

[0067] The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g.,...), etc. DVD ( ), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0068] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Furthermore, the mutual couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0069] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. An intelligent control method for a multi-source synergistic molten salt thermal energy storage peak-shaving system, characterized in that, The control method includes: S1. Mark the pre-peak shaving period on the time axis; where the two ends of the pre-peak shaving period are locked as the reference time point and the peak shaving start point; S2. Obtain the initial energy storage capacity of the molten salt thermal energy source at the reference time point; S3. Select the energy storage cutoff time during the pre-peak shaving period and calculate the cumulative energy storage capacity including the initial energy storage capacity when calculating the energy storage cutoff time; S4. Starting from the peak shaving start point of the pre-peak shaving period, slide to select the peak shaving end time on the time axis, and calculate the cumulative peak shaving gap at the peak shaving end time. S5. Based on the cumulative energy storage capacity and the cumulative peak-shaving gap, find the optimal energy storage time point during the pre-peak-shaving period; The process of finding the optimal energy storage time point during the pre-peak shaving period based on the cumulative energy storage capacity and the cumulative peak shaving gap includes: S5-1. Construct a thermal storage time series during the pre-peak shaving period; S5-2. Construct a time series of the shortfall outside the pre-shaving period; S5-3. Construct a peak shaving support ratio sequence based on the thermal storage time series and the deficit time series; wherein, the sequence element of the peak shaving support ratio sequence is the peak shaving support ratio; S5-4. Determine the optimal energy storage time point from the peak support ratio sequence; Determining the optimal energy storage time point in the peak shaving support ratio sequence includes: S5-4-1. Select the first Q peak-shaving support ratios from the peak-shaving support ratio sequence; S5-4-2. Obtain the energy storage time points corresponding to the cumulative energy storage capacity in the first Q peak-shaving support ratios to get the Q energy storage time points; S5-4-3. Calculate the energy storage duration at Q energy storage time points and the Q energy storage duration at the baseline time point; S5-4-4. Select the maximum energy storage duration from the Q energy storage durations, and calculate the Q duration weights of the Q energy storage durations and the maximum energy storage duration; S5-4-5. The product of the Q duration weights and the first Q peak-shaving support ratios is determined as the Q peak-shaving indices corresponding to the Q energy storage time points. S5-4-6. Among the Q peak-shaving indices, anchor the energy storage time point corresponding to the largest peak-shaving index and define it as the optimal energy storage time point.

2. The intelligent control method for a multi-source synergistic molten salt thermal energy storage peak-shaving system according to claim 1, characterized in that, To obtain the initial energy storage capacity of the molten salt thermal energy source at the reference time point, including: S2-1. Obtain the standard output power of the controllable power supply at the reference time point, and the fluctuating output power of the uncontrollable power supply. S2-2, Define the sum of the standard output power and the fluctuating output power as the total output power of the multi-source collaborative terminal; S2-3. Obtain the load power of the demand side at the reference time point, and define the initial energy storage capacity of the molten salt thermal energy source at the reference time point based on the power redundancy between the load power and the total output power.

3. The intelligent control method for a multi-source synergistic molten salt thermal energy storage peak-shaving system according to claim 2, characterized in that, During the pre-peak shaving period, the energy storage cutoff time is selected by sliding, and the cumulative energy storage capacity, including the initial energy storage capacity, is calculated when the energy storage cutoff time is reached. S3-1. Starting from the reference time point, lock the peak-shaving start point on the time axis to form the pre-peak-shaving period; S3-2. During the pre-peak shaving period, the energy storage cutoff time is selected by sliding the slider. S3-3. Mark N energy storage time points between the reference time point and the energy storage cutoff time using a fixed time step; S3-4. Use the time-series power model to predict the fluctuating output power and load power at N energy storage time points, and determine the first power deviation at N energy storage time points based on the standard output power. S3-5. Based on the first power deviation and initial energy storage capacity at N energy storage time points, determine the cumulative energy storage capacity of the molten salt thermal energy source at the energy storage cutoff time.

4. The intelligent control method for a multi-source synergistic molten salt thermal energy storage peak-shaving system according to claim 1, characterized in that, The cumulative peak-shaving gap at the peak-shaving deadline includes: S4-1. Mark N heat release time points between the peak shaving start point and the peak shaving end time with a fixed time step; S4-2. Use the time-series power model to predict the fluctuating output power and load power at N heat release time points, and determine the second power deviation at N heat release time points based on the standard output power. S4-3. Calculate the cumulative peak-shaving gap at N heat release time points based on the second power deviation at N heat release time points.

5. The intelligent control method for a multi-source synergistic molten salt thermal energy storage peak-shaving system according to claim 1, characterized in that, Constructing a thermal storage time series during the pre-peak shaving period includes: S5-1-1: Starting from the reference time point, slide along the time axis to select multiple possible energy storage cutoff times; S5-1-2. Real-time calculation of the cumulative energy storage capacity corresponding to the energy storage cutoff time; S5-1-3. Pair each energy storage time point with its corresponding cumulative energy storage capacity to form N thermal storage time tuples; S5-1-4. Sort the N thermal storage time tuples according to the time order of the energy storage time points to generate a thermal storage time sequence.

6. The intelligent control method for a multi-source synergistic molten salt thermal energy storage peak-shaving system according to claim 5, characterized in that, Constructing a time series of the shortfall outside the pre-shaving period, including: S5-2-1, Sliding peak shaving cutoff time from the peak shaving start point; S5-2-2, Calculate the cumulative peak shaving gap before the peak shaving deadline in real time; S5-2-3. Pair each heat release time point with its corresponding cumulative peak shaving gap to form N gap time tuples; S5-2-4. Sort the N missing time tuples according to the time order of the heat release time points to generate the missing time sequence.

7. The intelligent control method for a multi-source synergistic molten salt thermal energy storage peak-shaving system according to claim 6, characterized in that, Based on the thermal storage time series and the deficit time series, a peak-shaving support ratio series is constructed, including: S5-3-1. Align the thermal storage time series and the deficit time series vertically according to their sequence positions to generate N sequence pairs; S5-3-2. For any sequence pair, calculate the peak support ratio of the cumulative energy storage capacity to the cumulative peak support gap in the sequence pair, until N peak support ratios are calculated. S5-3-3. Arrange the N peak-shaving support ratios in descending order based on their ratio values ​​to generate a peak-shaving support ratio sequence.

8. A multi-source synergistic molten salt thermal storage peak-shaving system, characterized in that, include: The pre-peak shaving marking unit is used to mark the pre-peak shaving period on the time axis; wherein, the two ends of the pre-peak shaving period are locked as the reference time point and the peak shaving start point; The initial capacity acquisition unit is used to acquire the initial energy storage capacity of the molten salt thermal energy source at a reference time point; The capacity calculation unit is used to slide to select the energy storage cutoff time during the pre-peak shaving period and calculate the cumulative energy storage capacity including the initial energy storage capacity when the energy storage cutoff time is reached. The gap calculation unit is used to start from the peak shaving start point of the pre-peak shaving period, slide to select the peak shaving end time on the time axis, and calculate the cumulative peak shaving gap at the peak shaving end time. The timing determination unit is used to find the optimal energy storage time point during the pre-peak shaving period based on the cumulative energy storage capacity and the cumulative peak shaving gap. The process of finding the optimal energy storage time point during the pre-peak shaving period based on the cumulative energy storage capacity and the cumulative peak shaving gap includes: S5-1. Construct a thermal storage time series during the pre-peak shaving period; S5-2. Construct a time series of the shortfall outside the pre-shaving period; S5-3. Construct a peak shaving support ratio sequence based on the thermal storage time series and the deficit time series; wherein, the sequence element of the peak shaving support ratio sequence is the peak shaving support ratio; S5-4. Determine the optimal energy storage time point from the peak support ratio sequence; Determining the optimal energy storage time point in the peak shaving support ratio sequence includes: S5-4-1. Select the first Q peak-shaving support ratios from the peak-shaving support ratio sequence; S5-4-2. Obtain the energy storage time points corresponding to the cumulative energy storage capacity in the first Q peak-shaving support ratios to get the Q energy storage time points; S5-4-3. Calculate the energy storage duration at Q energy storage time points and the Q energy storage duration at the baseline time point; S5-4-4. Select the maximum energy storage duration from the Q energy storage durations, and calculate the Q duration weights of the Q energy storage durations and the maximum energy storage duration; S5-4-5. The product of the Q duration weights and the first Q peak-shaving support ratios is determined as the Q peak-shaving indices corresponding to the Q energy storage time points. S5-4-6. Among the Q peak-shaving indices, anchor the energy storage time point corresponding to the largest peak-shaving index and define it as the optimal energy storage time point.

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