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

Abstract

The invention discloses an intelligent regulation and control method for a multi-source collaborative fused salt heat storage peak regulation system. The regulation and control method comprises the steps that a peak pre-regulation time period is marked on a time axis; wherein the two ends of the pre-peak-adjusting time period are locked as a reference time point and a peak-adjusting starting point; the initial energy storage capacity of the fused salt heat storage source at the reference time point is obtained; energy storage deadline is selected in a sliding mode in the peak pre-adjustment period, and the accumulated energy storage capacity including the initial energy storage capacity at the energy storage deadline is calculated; starting from the peak regulation starting point of the peak pre-regulation time period, selecting peak regulation deadline on the time axis in a sliding manner, and calculating an accumulated peak regulation gap at the peak regulation deadline; according to the accumulative energy storage capacity and the accumulative peak regulation gap, searching an optimal energy storage time point in the peak pre-regulation period; according to the method, the optimal energy storage time point is finally determined, so that the problem of indefinite peak regulation support in the existing energy storage scheduling strategy is solved, and the matching between the energy storage capacity and the peak regulation demand is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of thermal energy storage peak shaving, in particular to an intelligent control method of a multi-source collaborative molten salt thermal energy storage peak shaving system. BACKGROUND

[0002] With the increasing penetration of uncontrollable renewable energy sources such as wind power and photovoltaic power in the power grid, the output fluctuation and intermittency of these sources have brought great challenges to the peak shaving operation of the power system. Traditional peak shaving methods mainly rely on controllable power sources such as thermal power to adjust, but the response speed is slow and the adjustment capacity is limited, which is difficult to meet the increasingly complex multi-source collaborative scheduling requirements.

[0003] Under this background, molten salt thermal energy storage systems have become one of the important technical paths to support new energy consumption and power grid peak shaving due to their high energy storage density, fast response speed, and large-scale application advantages. Patent document CN119933824A discloses a method and system for steam extraction and molten salt thermal energy storage under deep power grid peak shaving, which can achieve multi-objective balance of deep power grid peak shaving, renewable energy consumption, and system economy. However, the existing molten salt thermal energy storage system lacks a dynamic adaptation mechanism for future power change trends in multi-source collaborative scenarios, resulting in insufficient peak shaving support capacity of energy storage scheduling.

[0004] Therefore, the present application provides an intelligent control method of a multi-source collaborative molten salt thermal energy storage peak shaving system. SUMMARY

[0005] To overcome the shortcomings of the prior art, the present application provides an intelligent control method of a multi-source collaborative molten salt thermal energy storage peak shaving system, which solves the technical problems raised in the background art by finding the optimal energy storage time point.

[0006] To achieve the above purpose, the present application is implemented by the following technical solutions: An intelligent control method of a multi-source collaborative molten salt thermal energy storage peak shaving system, the control method comprising: S1, marking a pre-peak shaving period on a time axis; wherein the two ends of the pre-peak shaving period are locked as a reference time point and a peak shaving starting point; S2, obtaining the initial energy storage capacity of the molten salt thermal energy storage source at the reference time point; S3, slidingly selecting an energy storage cutoff time in the pre-peak shaving period and calculating the cumulative energy storage capacity containing the initial energy storage capacity at the energy storage cutoff time; S4, slidingly selecting a peak shaving cutoff time on the time axis from the peak shaving starting point of the pre-peak shaving period and calculating the cumulative peak shaving gap at the peak shaving cutoff time; S5, finding the optimal energy storage time point in the pre-peak shaving period according to the cumulative energy storage capacity and the cumulative peak shaving gap.

[0007] In some embodiments, the initial energy storage capacity of the molten salt heat storage source at the reference time point is obtained, comprising: S2-1, obtaining the standard output power of the controllable power source at the reference time point, and the fluctuating output power of the uncontrollable power source; S2-2, defining the total output power of the multi-source collaborative end as the power sum of the standard output power and the fluctuating output power; S2-3, obtaining the load power of the demand end at the reference time point, and defining the initial energy storage capacity of the molten salt heat storage source at the reference time point based on the power redundancy of the load power and the total output power; In some embodiments, the cumulative energy storage capacity including the initial energy storage capacity at the energy storage cutoff time in the pre-peaking period is calculated, comprising: S3-1, locking the peaking starting point on the time axis from the reference time point to form the pre-peaking period; S3-2, slidingly selecting the energy storage cutoff time in the pre-peaking period; S3-3, marking N energy storage time points between the reference time point and the energy storage cutoff time with a fixed time step; S3-4, predicting the fluctuating output power and the load power of the N energy storage time points using the time sequence power model, and determining the first power deviation of the N energy storage time points based on the standard output power; S3-5, determining the cumulative energy storage capacity of the molten salt heat storage source at the energy storage cutoff time according to the first power deviation of the N energy storage time points and the initial energy storage capacity; In some embodiments, the cumulative peak shaving gap at the peak shaving cutoff time is calculated, comprising: S4-1, marking N heat release time points between the peaking starting point and the peak shaving cutoff time with a fixed time step; S4-2, predicting the fluctuating output power and the load power of the N heat release time points using the time sequence power model, and determining the second power deviation of the N heat release time points based on the standard output power; S4-3, calculating the cumulative peak shaving gap of the N heat release time points according to the second power deviation of the N heat release time points; In some embodiments, the optimal energy storage time point is found in the pre-peaking period according to the cumulative energy storage capacity and the cumulative peak shaving gap, comprising: S5-1, constructing a heat storage time sequence in the pre-peaking period; S5-2, constructing a shortage time sequence outside the pre-peaking period; S5-3, constructing a peak shaving support ratio sequence according to the heat storage time sequence and the shortage time sequence; wherein the sequence elements of the peak shaving support ratio sequence are peak shaving support ratios; S5-4, determining the optimal energy storage time point in the peak shaving support ratio sequence.

[0008] In some embodiments, the heat storage time sequence is constructed within the pre-peak shaving period, including: S5-1-1, selecting multiple possible energy storage cutoff times along the time axis from the reference time point; S5-1-2, calculating the cumulative energy storage capacity corresponding to the energy storage cutoff time in real time; S5-1-3, pairing each energy storage time point with its corresponding cumulative energy storage capacity to form N heat storage time tuples; S5-1-4, sorting the N heat storage time tuples according to the time sequence of the energy storage time points to generate the heat storage time sequence.

[0009] In some embodiments, the shortage time sequence is constructed outside the pre-peak shaving period, including: S5-2-1, sliding the peak shaving cutoff time from the peak shaving starting point; S5-2-2, calculating the cumulative peak shaving gap of the peak shaving cutoff time in real time; S5-2-3, pairing each heat release time point with its corresponding cumulative peak shaving gap to form N shortage time tuples; S5-2-4, sorting the N shortage time tuples according to the time sequence of the heat release time points to generate the shortage time sequence.

[0010] In some embodiments, the peak shaving support ratio sequence is constructed according to the heat storage time sequence and the shortage time sequence, including: S5-3-1, aligning the heat storage time sequence and the shortage time sequence in sequence position, generating N sequence pairs; S5-3-2, for any sequence pair, calculating the peak shaving support ratio of the cumulative energy storage capacity and the cumulative peak shaving gap in the sequence pair until N peak shaving support ratios are calculated; S5-3-3, arranging the N peak shaving support ratios in descending order of ratio value to generate the peak shaving support ratio sequence.

[0011] In some embodiments, the optimal energy storage time point is determined in the peak shaving support ratio sequence, including: S5-4-1, selecting the first Q peak shaving support ratios in the peak shaving support ratio sequence; S5-4-2, obtaining the energy storage time points corresponding to the cumulative energy storage capacity in the first Q peak shaving support ratios to obtain Q energy storage time points; S5-4-3, calculating the Q energy storage durations of the Q energy storage time points and the reference time point; S5-4-4, select the maximum energy storage duration among the Q energy storage durations, and calculate Q duration weights of the Q energy storage durations and the maximum energy storage duration; S5-4-5, determine the product of the Q duration weights and the first Q peak shaving support ratios as Q peak shaving indexes corresponding to the Q energy storage time points; S5-4-6, anchor the energy storage time point corresponding to the maximum peak shaving index among the Q peak shaving indexes, and define it as the optimal energy storage time point.

[0012] The application realizes the dynamic sliding of the time boundary of energy storage scheduling by setting a "pre-peak shaving period" on the time axis and establishing a time reference of the energy storage and peak shaving process based on the reference time point and the peak shaving starting point in the period. By sliding to select the energy storage cutoff time and the peak shaving cutoff time, and combining the time sequence power model to predict and cumulatively calculate the power deviation of each future time node, the energy storage action can be adjusted according to the real-time running state and the future load change trend. Compared with the traditional method, the application has stronger time adaptability and prediction ability, significantly improves the scheduling response efficiency of the molten salt thermal storage system in the multi-source collaborative power grid, and helps to relieve the peak shaving pressure caused by wind and light fluctuations.

[0013] Further, the application quantitatively compares the cumulative energy storage capacity and the cumulative peak shaving gap by constructing a heat storage time sequence and a shortage time sequence and further generating a peak shaving support ratio sequence, forming an evaluation index with physical meaning. At the same time, the peak shaving index is calculated by combining the energy storage duration weight and the peak shaving support ratio, and the optimal energy storage time point is finally determined; the problem of unclear peak shaving support in the existing energy storage scheduling strategy is solved, and the matching between energy storage capacity and peak shaving demand is realized.

[0014] In a second aspect, the application provides a multi-source collaborative molten salt thermal storage peak shaving system, comprising: A pre-peak shaving marking unit is configured to mark a pre-peak shaving period on a time axis; wherein the two ends of the pre-peak shaving period are locked as a reference time point and a peak shaving starting point; An initial capacity acquisition unit is configured to acquire an initial energy storage capacity of the molten salt thermal storage source at the reference time point; A capacity calculation unit is configured to slide to select an energy storage cutoff time in the pre-peak shaving period, and calculate a cumulative energy storage capacity containing the initial energy storage capacity at the energy storage cutoff time; A gap calculation unit is configured to slide to select a peak shaving cutoff time on the time axis from the peak shaving starting point of the pre-peak shaving period, and calculate a cumulative peak shaving gap at the peak shaving cutoff time; A time point determination unit is configured to find an optimal energy storage time point in the pre-peak shaving period according to the cumulative energy storage capacity and the cumulative peak shaving gap.

[0015] Compared with the prior art, the multi-source collaborative molten salt heat storage peak shaving system has the same beneficial effects as the intelligent control method of the multi-source collaborative molten salt heat storage peak shaving system, and thus will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A flowchart of the intelligent control method of the multi-source collaborative molten salt heat storage peak shaving system of the present application is shown in the figure. Figure 2 A flowchart of the determination of the optimal energy storage time point is shown in the figure. Figure 3 A specific embodiment of the optimal energy storage time point flow is shown in the figure. Figure 4 A block diagram of the multi-source collaborative molten salt heat storage peak shaving system of the present application is shown in the figure. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0018] Embodiment 1: Please refer to Figure 1 - Figure 3 The present application provides an intelligent control method of a multi-source collaborative molten salt heat storage peak shaving system, comprising the following steps: An intelligent control method of a multi-source collaborative molten salt heat storage peak shaving system, the control method comprising: S1, marking a pre-peak shaving period on a time axis; wherein the two ends of the pre-peak shaving period are locked as a reference time point and a peak shaving starting point; S2, obtaining the initial energy storage capacity of the molten salt heat storage source at the reference time point; S3, slidingly selecting an energy storage cutoff time in the pre-peak shaving period, and calculating the cumulative energy storage capacity containing the initial energy storage capacity at the energy storage cutoff time; S4, slidingly selecting a peak shaving cutoff time on the time axis from the peak shaving starting point of the pre-peak shaving period, and calculating the cumulative peak shaving gap at the peak shaving cutoff time; Specifically, the peak shaving cutoff time represents the time limit for the end of the peak shaving task, and the position of the time limit is adaptively adjusted according to the demand changes of the peak shaving task.

[0019] S5, finding an optimal energy storage time point in the pre-peak shaving period according to the cumulative energy storage capacity and the cumulative peak shaving gap.

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

[0021] The embodiment builds a pre-peak shaving period on a time axis, and establishes a time reference system of the energy storage and peak shaving process based on a reference time point in the period and a peak shaving starting point, so that the energy scheduling of the molten salt thermal storage system has a clear time boundary. By sliding selection of the energy storage cutoff time and the peak shaving cutoff time, the cumulative energy storage capacity and the cumulative peak shaving gap at different time nodes can be calculated according to the real-time operation state and the future load change trend, so as to realize quantitative matching between the energy storage process and the peak shaving demand.

[0022] On this basis, the selection logic of the optimal energy storage time point is introduced, so that the energy storage action can maximize the use of the currently storable energy resources on the premise of meeting future peak shaving tasks, and improve the response efficiency of the thermal storage system in the multi-source collaborative power grid.

[0023] Exemplarily, the step S2 specifically comprises: S2-1, obtaining a standard output power of a controllable power supply at the reference time point, and a fluctuating output power of an uncontrollable power supply; Specifically, the controllable power supply represents a power supply device such as a thermal power unit, a gas turbine, and an energy storage inverter, which can be adjusted according to a scheduling instruction; the uncontrollable power supply represents a renewable energy power generation unit such as a wind farm and a photovoltaic farm, which is greatly affected by natural conditions and has significant uncertainty in power output.

[0024] S2-2, defining the sum of the standard output power and the fluctuating output power as a total output power of the multi-source collaborative end; Specifically, the total output power reflects the power output of the multi-source collaborative power supply in the power grid at the reference time point.

[0025] S2-3, obtaining a load power of a demand end at the reference time point, and defining an initial energy storage capacity of the molten salt thermal storage source at the reference time point based on a 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 supply, the fluctuating output power of the uncontrollable power supply is taken as the initial energy storage capacity of the molten salt thermal 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 storage source and converted into heat energy for storage for future peak shaving.

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

[0027] If there is a surplus, the part of the surplus energy is used as the initial energy storage capacity of the molten salt heat storage source. Among them, the fluctuating output power of the uncontrollable power supply is regarded as an energy source that can be absorbed, which is converted into heat energy storage through the molten salt heat storage system and used for peak shaving.

[0028] Exemplarily, the step S3 specifically comprises: S3-1, starting from the reference time point, locking the peak shaving starting point on the time axis to form the pre-peak shaving period; Specifically, the time length between the peak shaving starting point and the reference time point is fixed, that is, it is set according to the historical load peak value distribution or the dispatching plan cycle.

[0029] S3-2, slidingly selecting an energy storage cutoff time in the pre-peak shaving period; Specifically, the energy storage cutoff time represents the maximum energy storage accumulation cutoff time that the current molten salt heat storage source can support.

[0030] Further, the purpose of the energy storage cutoff time is to dynamically adjust the termination time of the heat storage process, which gradually traverses all possible heat storage end times through the sliding window method.

[0031] S3-3, marking N energy storage time points between the reference time point and the energy storage cutoff time at a fixed time step; Specifically, each energy storage time point represents an energy storage amount update node of the molten salt heat storage source, which is used to evaluate the change of the energy storage amount state in the N energy storage time points.

[0032] S3-4, using the time sequence power model to predict the fluctuating output power and the load power of the N energy storage time points, and determining the first power deviation of the 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 means that there is surplus energy available for storage; if it is negative, it means that the power supply capacity is insufficient, and additional dispatching resources need to be introduced to supplement.

[0033] Further, the N energy storage time points are time nodes on the time axis. Therefore, the fluctuating output power and the load power of the N future time nodes are both predicted values, and the standard output power is regarded as a reference power value that remains unchanged within the N energy storage time points. On this basis, the standard output power is added to the fluctuating output power to obtain the total output power (i.e., the predicted power supply capacity) at each time point, which is then compared with the load power to calculate the first power deviation at each time point.

[0034] S3-5, determining the cumulative energy storage capacity of the molten salt heat storage source at the energy storage cutoff time according to the first power deviation at the N energy storage time points and the initial energy storage capacity; Specifically, the maximum heat storage capacity of the molten salt heat storage source at the energy storage cutoff time is calculated by accumulating the first power deviations of the energy storage time points in the positive direction and combining the initial energy storage capacity.

[0035] In the pre-regulation peak period, the energy storage evaluation window is demarcated with the reference time point as the starting point and the peak shaving starting point as the ending point, and the termination node of the heat storage process is dynamically simulated by sliding to select different energy storage cutoff times. Within the time range, a plurality of energy storage time points are divided at a fixed step length for segmented evaluation of the power surplus or deficit at each time node.

[0036] The fluctuating output power and the load power at each energy storage time point in the future are predicted based on the time sequence power model, 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 storable energy surplus in the current period. These positive values are accumulated item by item, and the initial energy storage capacity is superimposed, to obtain the cumulative energy storage capacity corresponding to the energy storage cutoff time.

[0037] Exemplarily, the step S4 specifically includes: S4-1, marking N heat release time points between the peak shaving starting point and the peak shaving cutoff time at a fixed time step; Specifically, each heat release time point represents a time control node at which the molten salt heat storage system releases heat energy for peak shaving power supply, and the time step thereof should be consistent with the time step of the energy storage time points.

[0038] Further, the "heat release time point" does not specifically refer to the specific physical moment at which the molten salt heat storage medium actually starts to release heat, but refers to an equivalent energy supply time node at which the system responds to the dispatching instruction, starts the heat release process, and finally drives the power generation equipment to provide peak shaving power to the power grid through the heat energy conversion device. Considering the time delay caused by the links of heat exchange, energy conversion and mechanical response in the system, the time point is a dispatching reference time after the entire energy release and electrical energy conversion link.

[0039] S4-2, predicting fluctuation output power and load power of N heat release time points using a time series power model, and determining second power deviations of the N heat release time points based on 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, calculating cumulative peak regulation gaps of the N heat release time points according to the second power deviations of the N heat release time points; Specifically, the cumulative peak regulation gap is the algebraic sum of power gaps of all heat release time points.

[0041] It should be noted that the energy storage time points and the heat release time points described in the embodiment are future time points, and the fluctuation output power and the load power of the corresponding uncontrollable power sources are not real-time measurement values, but estimated values obtained by predicting through a time series power model. The model can model and predict the power output and electricity demand in the future time period based on historical data of power grid operation, meteorological information and electricity load change law.

[0042] Specifically, the fluctuation output power represents the trend of the power generation capacity of uncontrollable power sources (such as wind farms and photovoltaic power stations) changing with natural conditions, and the load power reflects the electricity demand change of the power grid in a specific time interval. Both types of parameters have significant periodicity and time sequence characteristics, and are suitable for modeling and prediction using time series modeling methods.

[0043] Specifically, in the embodiment, the time series power model can use a time series model based on a long short-term memory network (LSTM) to capture the time dependence of power load and new energy output by constructing a multi-layer LSTM network. Or for autoregressive integrated moving average model of short-term load or wind and light power prediction with obvious linear trend, so as to fit the load power time series or fluctuation output power sequence with different characteristics.

[0044] Preferably, the time series power model is trained by the following steps: In the historical operation data, a continuous time interval is selected, the starting time of the time interval is defined as the input time point, and the ending time is defined as the output time point; based on the input time point, relevant operation parameters are collected and a model input feature vector is constructed, the input features include but are not limited to: standard output power, historical output data of wind power / photovoltaic power station, meteorological parameter set (such as wind speed, light intensity, environmental temperature, etc.), time stamp features (such as hours, days, whether it is a holiday) and the like; at the same time, based on the output time point, the corresponding system operation parameters are collected and the target output label is constructed, the output label includes but is not limited to: actual fluctuation output power of uncontrollable power supply, predicted target value of power grid load power in future time period; by using the mapping relationship between the above input features and the target output, the model is iteratively trained in a supervised learning manner, and the model parameters are continuously adjusted to minimize the prediction error, thereby completing the modeling and learning of the time sequence power characteristics of the power system; after training convergence, a time sequence power prediction model with prediction capability is obtained, and is deployed into the dispatching system for predicting the fluctuation output power and load power of the future heat release time point.

[0045] Exemplarily, the step S5 specifically comprises: S5-1, constructing a heat storage time sequence in the pre-regulation peak period; S5-2, constructing a shortage time sequence outside the pre-regulation peak period; S5-3, constructing a peak regulation support ratio sequence according to the heat storage time sequence and the shortage time sequence; wherein the sequence elements of the peak regulation support ratio sequence are peak regulation support ratios; S5-4, determining an optimal energy storage time point in the peak regulation support ratio sequence.

[0046] The embodiment models the energy storage capacity in the pre-regulation peak period by time sequence, constructs a heat storage time sequence, and reflects the energy accumulation of the molten salt heat storage system under different energy storage cutoff times. At the same time, the energy gap in the peak regulation stage is predicted and modeled outside the period to form a shortage time sequence.

[0047] The heat storage time sequence and the shortage time sequence are one-to-one corresponding according to the time nodes, the peak regulation support ratio corresponding to each energy storage time point is calculated, that is, the ratio of the cumulative energy storage capacity to the cumulative peak regulation gap, and the peak regulation support ratio sequence is generated. Through the sequence, the time node with the most supporting force of energy storage action under the premise of meeting the future peak regulation task is identified, and thus the optimal energy storage time point is determined.

[0048] Further, the step S5-1 specifically comprises: S5-1-1, selecting multiple possible energy storage cutoff times along the time axis from the reference time point; S5-1-2, calculating the cumulative energy storage capacity corresponding to the energy storage cutoff time in real time; S5-1-3, pairing each energy storage time point with its corresponding cumulative energy storage capacity to form N heat storage time tuples; S5-1-4, sorting the N heat storage time tuples according to the time sequence of the energy storage time points to generate a heat storage time sequence.

[0049] This embodiment selects multiple possible energy storage cutoff times within the pre-regulation period by sliding along the time axis from the reference time point, and calculates the cumulative energy storage capacity corresponding to each cutoff time. The energy storage time point is paired with the energy storage capacity at that time point to form multiple heat storage time tuples. By sorting these time tuples in time sequence, a heat storage time sequence reflecting the trend of energy storage capacity over time is constructed, thereby providing a structured data basis for the energy storage state of different energy storage time nodes.

[0050] Further, the step S5-2 specifically includes: S5-2-1, sliding the regulation cutoff time from the regulation start point; S5-2-2, calculating the cumulative regulation gap of the regulation cutoff time in real time; S5-2-3, pairing each heat release time point with its corresponding cumulative regulation gap to form N gap time tuples; S5-2-4, sorting the N gap time tuples according to the time sequence of the heat release time points to generate a gap time sequence.

[0051] This embodiment selects multiple possible regulation cutoff times within the time range after the pre-regulation period by sliding, and calculates the cumulative regulation gap corresponding to each cutoff time. The heat release time point is paired with the regulation gap at that time point to form multiple gap time tuples. By sorting these time tuples in time sequence, a gap time sequence reflecting the trend of future regulation phase energy gap change is constructed.

[0052] Further, the step S5-3 specifically includes: S5-3-1, aligning the heat storage time sequence and the gap time sequence in sequence position up and down to generate N sequence pairs; S5-3-2, for any sequence pair, calculating the regulation support ratio of the cumulative energy storage capacity and the cumulative regulation gap in the sequence pair until N regulation support ratios are calculated; S5-3-3, arranging the N regulation support ratios in descending order based on the ratio value to generate a regulation support ratio sequence.

[0053] The embodiment forms a plurality of sequence pairs by aligning the storage time sequence constructed within the pre-regulation period and the shortage time sequence constructed outside the period according to time nodes.

[0054] For each sequence pair, the ratio between the two is calculated as a peak regulation support ratio, reflecting the support strength of the energy storage capacity at the time point to the future peak regulation task. All peak regulation support ratios are arranged in descending order of ratio value to generate a peak regulation support ratio sequence, which is used to identify the time node with the most peak regulation value of energy storage action.

[0055] Further, the step S5-4 specifically comprises: S5-4-1, selecting the first Q peak regulation support ratios in the peak regulation support ratio sequence; S5-4-2, obtaining the energy storage time points corresponding to the cumulative energy storage capacity in the first Q peak regulation support ratios to obtain Q energy storage time points; S5-4-3, calculating Q energy storage durations of the Q energy storage time points and the reference time point; S5-4-4, selecting the maximum energy storage duration in the Q energy storage durations, and calculating Q duration weights of the Q energy storage durations and the maximum energy storage duration; The duration weight is characterized as a duration ratio of the energy storage duration to the maximum energy storage duration, and the duration ratio is defined as the duration weight, which is used to measure the importance of the corresponding energy storage duration of different energy storage time points.

[0056] S5-4-5, multiplying the first Q peak regulation support ratios by the Q duration weights to determine Q peak regulation indexes corresponding to the Q energy storage time points; The peak regulation support ratio reflects the support ability of energy storage to peak regulation task. By comprehensively considering both, the energy storage time point most beneficial to peak regulation response is finally determined.

[0057] S5-4-6, anchoring the energy storage time point corresponding to the maximum peak regulation index in the first Q peak regulation indexes, and defining it as the optimal energy storage time point.

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

[0059] The embodiment obtains the energy storage duration between the corresponding energy storage time point and the reference time point by analyzing the first Q higher ratios in the peak regulation support ratio sequence. By comparing each energy storage duration with the maximum energy storage duration, the duration weight of each time point is calculated, which is used to represent the relative importance of the energy storage duration in the overall scheduling strategy.

[0060] The support ratio of peak shaving is combined with the corresponding time length weight to calculate a peak shaving index of each energy storage time point. The index comprehensively reflects the support strength of the energy storage action on the peak shaving task and the time effectiveness of the energy storage process. By comparing the sizes of the peak shaving indexes, the optimal energy storage time point is determined, that is, the time node at which the energy storage has the most support and the longest time length under the premise of meeting the peak shaving demand.

[0061] The method quantitatively evaluates the matching relationship between the energy storage capacity and the peak shaving demand, and introduces the energy storage time length weight to optimize the sorting of the results, so as to realize the time-optimal scheduling selection driven by the matching relationship between the energy storage capacity and the peak shaving demand.

[0062] Embodiment 2: see Figure 4 The technical solution of the embodiment 2 is different from that of the embodiment 1, and discloses a multi-source collaborative molten salt heat storage peak shaving system, comprising: A pre-peak shaving marking unit is configured to mark a pre-peak shaving period on a time axis; wherein two ends of the pre-peak shaving period are locked as a reference time point and a peak shaving starting point; An initial capacity acquisition unit is configured to acquire an initial energy storage capacity of the molten salt heat storage source at the reference time point; A capacity calculation unit is configured to select a energy storage cutoff time in a sliding manner in the pre-peak shaving period, and calculate a cumulative energy storage capacity containing the initial energy storage capacity at the energy storage cutoff time; A gap calculation unit is configured to select a peak shaving cutoff time in a sliding manner on the time axis from the peak shaving starting point of the pre-peak shaving period, and calculate a cumulative peak shaving gap at the peak shaving cutoff time; Specifically, the peak shaving cutoff time represents a time limit for the end of the peak shaving task, and the position of the time limit is adaptively adjusted according to the demand of the peak shaving task.

[0063] A time point determination unit is configured to find an optimal energy storage time point in the pre-peak shaving period according to the cumulative energy storage capacity and the cumulative peak shaving gap.

[0064] The optimal energy storage time point represents an energy storage time point at which the cumulative energy storage capacity is maximized and the cumulative peak shaving gap is minimized.

[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 acquired 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, thereby solving the problem that it is difficult to determine the optimal energy storage time point for the molten salt heat storage system.

[0066] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (for example, infrared, wireless, microwave, etc.) mode.

[0067] The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing a set of one or more available media. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a compact disk, a digital video disk, a Blu-ray disk, a DVD, etc.), or a semiconductor medium. The semiconductor medium can be a solid-state disk. DVD

[0068] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only illustrative, for example, a plurality of units or components can be combined or integrated into another system, or some features can be omitted or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0069] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.​

Claims

1. An intelligent control method for a multi-source collaborative molten salt heat storage peak shaving system, characterized in that, The regulation method comprises: S1, marking a pre-regulation peak period on a time axis; wherein both ends of the pre-regulation peak period are locked as a reference time point and a regulation peak starting point; S2, obtaining an initial energy storage capacity of the molten salt heat storage source at the reference time point; S3, slidingly selecting an energy storage cutoff time in the pre-regulation peak period, and calculating a cumulative energy storage capacity containing the initial energy storage capacity at the energy storage cutoff time; S4, slidingly selecting a regulation peak cutoff time on the time axis from the regulation peak starting point of the pre-regulation peak period, and calculating a cumulative regulation peak gap at the regulation peak cutoff time; S5, finding an optimal energy storage time point in the pre-regulation peak period according to the cumulative energy storage capacity and the cumulative regulation peak gap. 2.The intelligent control method of a multi-source collaborative molten salt heat storage peak shaving system according to claim 1, characterized in that, Obtaining the initial energy storage capacity of the molten salt heat storage source at the reference time point comprises: S2-1, obtaining a standard output power of a controllable power supply at the reference time point, and a fluctuating output power of an uncontrollable power supply; S2-2, defining the power sum of the standard output power and the fluctuating output power as the total output power of the multi-source collaborative end; S2-3, obtaining a load power of a demand end, and defining the initial energy storage capacity of the molten salt heat storage source at the reference time point based on the power redundancy of the load power and the total output power. 3.The intelligent control method of a multi-source collaborative molten salt heat storage peak shaving system according to claim 2, characterized in that, Slidingly selecting the energy storage cutoff time in the pre-regulation peak period, and calculating the cumulative energy storage capacity containing the initial energy storage capacity at the energy storage cutoff time, comprises: S3-1, locking the regulation peak starting point on the time axis from the reference time point, to form the pre-regulation peak period; S3-2, slidingly selecting the energy storage cutoff time in the pre-regulation peak period; S3-3, marking N energy storage time points at fixed time steps between the reference time point and the energy storage cutoff time; S3-4, predicting the fluctuating output power and the load power of the N energy storage time points using a time sequence power model, and determining a first power deviation of the N energy storage time points based on the standard output power; S3-5, determining the cumulative energy storage capacity of the molten salt heat storage source at the energy storage cutoff time according to the first power deviation of the N energy storage time points and the initial energy storage capacity. 4.The intelligent control method of a multi-source collaborative molten salt heat storage peak shaving system according to claim 1, characterized in that, Calculating the cumulative regulation peak gap at the regulation peak cutoff time comprises: S4-1, marking N heat release time points at fixed time steps between the regulation peak starting point and the regulation peak cutoff time; S4-2, predicting the fluctuating output power and the load power of the N heat release time points using a time sequence power model, and determining a second power deviation of the N heat release time points based on the standard output power; S4-3, calculating the cumulative regulation peak gap of the N heat release time points according to the second power deviation of the N heat release time points. 5.The intelligent control method of a multi-source collaborative molten salt heat storage peak shaving system according to claim 1, characterized in that, Finding the optimal energy storage time point in the pre-regulation peak period according to the cumulative energy storage capacity and the cumulative regulation peak gap comprises: S5-1, constructing a heat storage time sequence in the pre-regulation peak period; S5-2, constructing a shortage time sequence outside the pre-regulation peak period; S5-3, constructing a regulation peak support ratio sequence according to the heat storage time sequence and the shortage time sequence; wherein a sequence element of the regulation peak support ratio sequence is a regulation peak support ratio; S5-4, determining the optimal energy storage time point in the regulation peak support ratio sequence. 6.The intelligent control method of a multi-source collaborative molten salt heat storage peak shaving system according to claim 5, characterized in that, Constructing the heat storage time sequence in the pre-regulation peak period comprises: S5-1-1, slidingly selecting a plurality of possible energy storage cutoff times along the time axis from the reference time point; S5-1-2, calculate the cumulative energy storage capacity corresponding to the energy storage cutoff time in real time; S5-1-3, pair each energy storage time point with its corresponding cumulative energy storage capacity to form N heat storage time tuples; S5-1-4, sort the N heat storage time tuples according to the time sequence of the energy storage time points to generate a heat storage time sequence. 7.The intelligent control method of a multi-source collaborative molten salt heat storage peak shaving system according to claim 5, characterized in that, Construct a shortage time sequence outside the pre-regulation peak period, including: S5-2-1, slide the peak regulation cutoff time from the peak regulation starting point; S5-2-2, calculate the cumulative peak regulation gap at the peak regulation cutoff time in real time; S5-2-3, pair each heat release time point with its corresponding cumulative peak regulation gap to form N shortage time tuples; S5-2-4, sort the N shortage time tuples according to the time sequence of the heat release time points to generate a shortage time sequence. 8.The intelligent control method of a multi-source collaborative molten salt heat storage peak shaving system according to claim 5, characterized in that, According to the heat storage time sequence and the shortage time sequence, construct a peak regulation support ratio sequence, including: S5-3-1, align the heat storage time sequence and the shortage time sequence according to the sequence position to generate N sequence pairs; S5-3-2, for any sequence pair, calculate the peak regulation support ratio of the cumulative energy storage capacity and the cumulative peak regulation gap in the sequence pair until N peak regulation support ratios are calculated; S5-3-3, arrange the N peak regulation support ratios in descending order of ratio value to generate a peak regulation support ratio sequence. 9.The intelligent control method of a multi-source collaborative molten salt heat storage peak shaving system according to claim 5, characterized in that, Determine the optimal energy storage time point in the peak regulation support ratio sequence, including: S5-4-1, select the first Q peak regulation support ratios in the peak regulation support ratio sequence; S5-4-2, obtain the energy storage time points corresponding to the cumulative energy storage capacity in the first Q peak regulation support ratios to obtain Q energy storage time points; S5-4-3, calculate the Q energy storage durations of the Q energy storage time points and the reference 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, determine the Q peak regulation indices corresponding to the Q energy storage time points by multiplying the Q duration weights and the first Q peak regulation support ratios; S5-4-6, anchor the energy storage time point corresponding to the maximum peak regulation index in the Q peak regulation indices and define it as the optimal energy storage time point.

10. A multi-source collaborative molten salt heat storage peak shaving system, characterized in that, It includes: A pre-regulation peak marking unit for marking a pre-regulation peak period on a time axis; wherein the two ends of the pre-regulation peak period are locked as a reference time point and a peak regulation starting point; An initial capacity obtaining unit for obtaining the initial energy storage capacity of the molten salt heat storage source at the reference time point; A capacity calculation unit for slidingly selecting an energy storage cutoff time in the pre-regulation peak period and calculating the cumulative energy storage capacity containing the initial energy storage capacity at the energy storage cutoff time; A gap calculation unit for slidingly selecting a peak regulation cutoff time on the time axis from the peak regulation starting point of the pre-regulation peak period and calculating the cumulative peak regulation gap at the peak regulation cutoff time; A time point determination unit for finding an optimal energy storage time point in the pre-regulation peak period according to the cumulative energy storage capacity and the cumulative peak regulation gap.

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