Multi-energy complementary peak shaving heat supply method and device based on chemical heat storage

Abstract

The present application relates to the technical field of chemical heat storage, and particularly relates to a multi-energy complementary peak shaving heat supply method and device based on chemical heat storage. The method comprises the following steps: firstly, collecting real-time weather information and calendar information of a region where a heat supply pipe network is located; based on the two types of information, a first heat supply energy consumption curve, i.e. an actual heat supply demand curve, of the current region is generated through feature fusion and a prediction model; the first heat supply energy consumption curve is compared with a preset second heat supply energy consumption curve, i.e. an inherent heat supply capability curve of a traditional energy supply unit, to calculate a heat supply energy consumption fluctuation curve reflecting a supply-demand gap; finally, the opening and closing states and opening degrees of valves and the output of the traditional energy supply unit are dynamically regulated according to the fluctuation curve, multi-energy collaborative scheduling is realized, through this mechanism, the heat supply demand and energy supply can be accurately matched, the energy utilization efficiency can be significantly improved, the green electricity consumption can be maximized, and the operation cost can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of chemical heat storage, in particular to a multi-energy complementary peak shaving heat supply method and device based on chemical heat storage. BACKGROUND

[0002] With the increasing demand for clean energy worldwide, the proportion of renewable energy such as solar energy and wind energy in the energy structure continues to rise. However, renewable energy has the characteristics of intermittency and instability, which brings great challenges to the stable operation of the power grid. For example, during the night or in windless weather, the power of solar photovoltaic power generation and wind power generation will decrease significantly or even stop. At the same time, in the field of heat supply, traditional heat supply methods such as coal-fired heat supply not only have low energy utilization efficiency, but also produce a large amount of pollutants, causing serious pollution to the environment.

[0003] Chemical heat storage, as an efficient energy storage method, has the advantages of high energy storage density and long energy storage time, and can effectively solve the intermittency problem of renewable energy and the drawbacks of traditional heat supply in theory. However, in actual application, chemical heat storage lacks effective control strategies and cannot achieve good collaborative optimization with other energy, limiting its large-scale promotion and application.

[0004] Therefore, the present application provides a multi-energy complementary peak shaving heat supply method and device based on chemical heat storage to solve the above technical problems. SUMMARY

[0005] The present application describes a multi-energy complementary peak shaving heat supply method and device based on chemical heat storage, which can improve the heat supply utilization rate of energy.

[0006] According to a first aspect, the present application provides a multi-energy complementary peak shaving heat supply method based on chemical heat storage, which is applied to a controller of a multi-energy complementary peak shaving heat supply system based on chemical heat storage. The system includes a main chemical heat storage unit, a backup chemical heat storage unit, a renewable energy power generation unit, a traditional energy power supply unit, a heat supply pipe network, and the controller. The heat supply pipe network is connected in sequence with the traditional energy power supply unit, the backup chemical heat storage unit, the main chemical heat storage unit, and the renewable energy power generation unit through a first valve, a second valve, a third valve, and a fifth valve. The backup chemical heat storage unit is connected with the traditional energy power supply unit. The chemical heat storage unit is connected with the renewable energy power generation unit through a fourth valve. The controller is electrically connected with the first valve, the second valve, the third valve, the fourth valve, the fifth valve, and the traditional energy power supply unit. The method includes:

[0007] Obtaining weather information and calendar information of the area where the heat supply pipe network is located;

[0008] determine a first heating energy consumption curve of a region where the heating pipe network is currently located based on the weather information and the calendar information;

[0009] determine a heating energy consumption fluctuation curve based on the first heating energy consumption curve and a preset second heating energy consumption curve; wherein the preset second heating energy consumption curve is a heating energy consumption curve of the traditional energy power supply unit;

[0010] control the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the traditional energy power supply unit based on the heating energy consumption fluctuation curve.

[0011] According to a second aspect, the present application provides a multi-energy complementary peak regulation heating device based on chemical heat storage, which is applied to a controller of a multi-energy complementary peak regulation heating system based on chemical heat storage. The system includes a main chemical heat storage unit, a backup chemical heat storage unit, a renewable energy power generation unit, a traditional energy power supply unit, a heating pipe network and the controller. The heating pipe network is connected with the traditional energy power supply unit, the backup chemical heat storage unit, the main chemical heat storage unit and the renewable energy power generation unit in sequence through a first valve, a second valve, a third valve and a fifth valve. The backup chemical heat storage unit is connected with the traditional energy power supply unit. The chemical heat storage unit is connected with the renewable energy power generation unit through a fourth valve. The controller is electrically connected with the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the traditional energy power supply unit respectively. The device includes:

[0012] an acquisition unit configured to acquire weather information and calendar information of a region where the heating pipe network is currently located;

[0013] a first data processing unit configured to determine a first heating energy consumption curve of a region where the heating pipe network is currently located based on the weather information and the calendar information;

[0014] a second data processing unit configured to determine a heating energy consumption fluctuation curve based on the first heating energy consumption curve and a preset second heating energy consumption curve; wherein the preset second heating energy consumption curve is a heating energy consumption curve of the traditional energy power supply unit;

[0015] a third data processing unit configured to control the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the traditional energy power supply unit based on the heating energy consumption fluctuation curve.

[0016] In a third aspect, the embodiments of the present specification also provide an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method of any of the embodiments of the present specification.

[0017] In a fourth aspect, the embodiments of the present specification also provide a multi-energy complementary peak shaving heat supply system based on chemical heat storage, comprising a main chemical heat storage unit, a backup chemical heat storage unit, a renewable energy power generation unit, a traditional energy power supply unit, a heat supply pipe network, and a controller, wherein the heat supply pipe network is connected with the traditional energy power supply unit, the backup chemical heat storage unit, the main chemical heat storage unit, and the renewable energy power generation unit in sequence through a first valve, a second valve, a third valve, and a fifth valve, the backup chemical heat storage unit is connected with the traditional energy power supply unit, the chemical heat storage unit is connected with the renewable energy power generation unit through a fourth valve, and the controller is electrically connected with the first valve, the second valve, the third valve, the fourth valve, the fifth valve, the main chemical heat storage unit, and the traditional energy power supply unit, and is configured to execute the method of any of the embodiments of the present specification.

[0018] According to the multi-energy complementary peak shaving heat supply method and device based on chemical heat storage provided by the present application, first, real-time weather information (covering environmental parameters such as temperature, humidity, and wind force) and calendar information (including time characteristics such as workday / holiday attributes and seasonal cycles) of the heat supply pipe network coverage area are collected; then, according to the weather information and the calendar information, a first heat supply energy consumption curve of the current area is determined, which dynamically depicts the trend of the actual heat supply demand changing with time, providing an accurate demand benchmark for subsequent scheduling. On this basis, the first heat supply energy consumption curve is compared with a preset second heat supply energy consumption curve (i.e., the inherent heat supply capability curve of the traditional energy power supply unit) hour by hour, and a heat supply energy consumption fluctuation curve is generated through difference operation. This curve directly quantifies the energy supply and demand imbalance state in different periods: the positive value segment represents the gap between the demand and the traditional energy supply capacity, and the negative value segment reflects the redundant amount of the traditional energy supply surplus. Finally, taking the fluctuation curve as the core decision basis, the opening and closing states and the opening degree of each valve are real-time regulated, and the output of the traditional energy power supply unit is coordinated and adjusted, forming a closed-loop control of multi-energy collaborative scheduling. In this way, the present application can dynamically balance the heat supply demand and the energy supply, accurately meet the user's heat demand, maximize the consumption of green power such as solar energy and wind energy, significantly improve the overall energy utilization efficiency, effectively reduce the system operation cost, and have both economic and environmental benefits. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0020] Figure 1 A flowchart of a multi-energy complementary peak shaving heat supply method based on chemical heat storage according to an embodiment is shown.

[0021] Figure 2 A schematic block diagram of a multi-energy complementary peak shaving heat supply device based on chemical heat storage according to an embodiment is shown.

[0022] Figure 3 A schematic block diagram of a multi-energy complementary peak shaving heat supply system based on chemical heat storage according to an embodiment is shown. DETAILED DESCRIPTION

[0023] The schemes provided by the present application will be described below in combination with the drawings.

[0024] Figure 1 A flowchart of a multi-energy complementary peak shaving heat supply method based on chemical heat storage according to an embodiment is shown. It can be understood that the method can be executed by any device, equipment, platform, device cluster with computing and processing capabilities. The multi-energy complementary peak shaving heat supply method based on chemical heat storage is applied to a controller of a multi-energy complementary peak shaving heat supply system based on chemical heat storage. The system includes a main chemical heat storage unit, a backup chemical heat storage unit, a renewable energy power generation unit, a traditional energy power supply unit, a heat supply pipe network, and a controller. The heat supply pipe network is connected with the traditional energy power supply unit, the backup chemical heat storage unit, the main chemical heat storage unit, and the renewable energy power generation unit in sequence through a first valve, a second valve, a third valve, and a fifth valve. The backup chemical heat storage unit is connected with the traditional energy power supply unit. The chemical heat storage unit is connected with the renewable energy power generation unit through a fourth valve. The controller is electrically connected with the first valve, the second valve, the third valve, the fourth valve, the fifth valve, and the traditional energy power supply unit, as shown in Figure 1 The method includes the following steps.

[0025] Step 100, obtaining weather information and calendar information of a region where a current heat supply pipe network is located;

[0026] Step 102, determining a first heat supply energy consumption curve of the region where the current heat supply pipe network is located based on the weather information and the calendar information;

[0027] In step 104, a heating energy consumption fluctuation curve is determined based on the first heating energy consumption curve and a preset second heating energy consumption curve; the preset second heating energy consumption curve is a heating energy consumption curve of the traditional energy supply unit;

[0028] In step 106, the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the traditional energy supply unit are controlled based on the heating energy consumption fluctuation curve.

[0029] In the embodiment, first, real-time weather information (covering environmental parameters such as temperature, humidity, wind power, etc.) and calendar information (including time characteristics such as workday / holiday attributes, seasonal cycles, etc.) of a heating pipe network coverage area are collected; then, a first heating energy consumption curve of the current area is determined according to the weather information and the calendar information, which dynamically depicts the trend of actual heating demand changing with time, providing an accurate demand benchmark for subsequent scheduling. On this basis, the first heating energy consumption curve is compared with a preset second heating energy consumption curve (i.e., the inherent heating capacity curve of the traditional energy supply unit) hour by hour, and a heating energy consumption fluctuation curve is generated through difference operation. This curve directly quantifies the energy supply and demand imbalance state in different periods: the positive value segment represents the gap between demand and traditional energy supply capacity, and the negative value segment reflects the redundant amount of traditional energy supply surplus. Finally, taking the fluctuation curve as the core decision basis, the opening and closing states and the opening degree of each valve are real-time regulated, and the output of the traditional energy supply unit is coordinated to form a closed-loop control of multi-energy collaborative scheduling. In this way, the present application can dynamically balance heating demand and energy supply, accurately meet user heat demand while maximizing the consumption of solar energy, wind energy and other green power, significantly improving overall energy utilization efficiency, effectively reducing system operation cost, and having both economic and environmental benefits.

[0030] In an embodiment of the present application, based on weather information and calendar information, a first heating energy consumption curve of a region where a current heating pipe network is located is determined, including:

[0031] The weather information and the calendar information are respectively subjected to feature extraction to obtain weather features and calendar features;

[0032] The weather features and the calendar features are fused according to corresponding feature confidence proportions to obtain fused features; the feature confidence proportions include a confidence weight of the weather features and a confidence weight of the calendar features;

[0033] The fused features are input into a preset heating energy consumption curve prediction model to obtain the first heating energy consumption curve;

[0034] The preset heating energy consumption curve prediction model is obtained by taking known fused features and a heating energy consumption curve corresponding to the known fused features as samples to train a classifier.

[0035] In this embodiment, the collected weather information and calendar information are first subjected to feature extraction respectively. For weather information, key features such as temperature variation trend, humidity fluctuation, wind force level, etc. are extracted; for calendar information, core features such as working day / holiday attribute, seasonal cycle, special holiday, etc. are sorted out, thus forming independent weather features and calendar features. Then feature fusion is performed. According to the dynamically calculated feature confidence ratio, the weather features and calendar features are weighted and integrated into fusion features. The confidence ratio here includes the confidence weight of weather features and the confidence weight of calendar features, which cooperate with each other, not only embodying the correlation strength of the two types of features in historical data with heating demand, but also being able to flexibly adjust the proportion according to real-time prediction error. Finally, the fusion features are input into a preset heating energy consumption curve prediction model. The model takes known fusion features in history and their corresponding actual heating energy consumption curves as training samples, is optimized by training a classifier, and can accurately output a first heating energy consumption curve reflecting the actual heating demand change of the current region. This step-by-step feature processing and fusion mode enables the model to fully absorb the effective value in weather and calendar information, improves the accuracy of heating demand prediction, and provides a reliable basis for subsequent energy scheduling.

[0036] In this embodiment, the standby chemical heat storage unit and the main chemical heat storage unit: adopt heat storage materials with reversible chemical reactions, such as metal hydride, hydrate, etc. The unit is provided with a heat storage reaction cavity, the heat storage material is filled in the reaction cavity, the reaction cavity is connected with a heat supply pipeline and a gas delivery pipeline, the heat supply pipeline is connected with a heat supply pipe network, and is used for delivering the heat generated by the heat storage material in the heat release reaction to the heat supply pipe network; the gas delivery pipeline is used for delivering the gas reactant in the heat storage reaction, and delivering the high-temperature and high-pressure gas generated in the heat release reaction to a steam turbine. The renewable energy power generation unit: includes solar photovoltaic panels, wind turbines, etc., and is used for converting solar energy and wind energy into electric energy, and the output end is connected with the main chemical heat storage unit and the heat supply pipe network, so as to provide electric energy for the chemical heat storage unit. The conventional energy power generation unit: such as a gas turbine, the output end of which is connected with the heat supply pipe network, and the waste heat output end of which is connected with the standby chemical heat storage unit, so that the waste heat can be used to drive the heat storage reaction of the standby chemical heat storage unit. The heat supply pipe network: is used for receiving the heat provided by the main chemical heat storage unit and other heat sources, and supplying heat to users.

[0037] In an embodiment of the present application, based on the first heating energy consumption curve and the preset second heating energy consumption curve, a heating energy consumption fluctuation curve is determined, comprising:

[0038] The first heating energy consumption curve and the preset second heating energy consumption curve are subtracted to obtain the heating energy consumption fluctuation curve;

[0039] Among them, the first heating energy consumption curve, the preset second heating energy consumption curve and the heating energy consumption fluctuation curve are all two-dimensional curves with time as the horizontal axis and heating energy consumption as the vertical axis.

[0040] In the embodiment, the fluctuation curve of the heating energy consumption is obtained by calculating the difference between the first heating energy consumption curve (reflecting the actual dynamic change of the regional heating demand with time) and the preset second heating energy consumption curve (the preset distribution rule of the inherent heating capacity of the traditional energy supply unit with time) at each time point. The three curves have time as the horizontal axis and heating energy consumption as the vertical axis, and the value of the fluctuation curve directly reflects the supply-demand relationship at different time points: the positive value segment represents the gap between the actual heating demand and the traditional energy supply capacity, the negative value segment reflects the redundant amount of the traditional energy supply surplus, and the zero point indicates that the supply and demand are in balance. The difference operation accurately quantifies the dynamic imbalance state of energy supply and demand, and provides a clear decision basis for subsequent multi-energy collaborative scheduling.

[0041] In an embodiment of the application, based on the fluctuation curve of the heating energy consumption, the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the traditional energy supply unit are controlled, including:

[0042] The fluctuation curve of the heating energy consumption is divided according to the positive and negative of the heating energy consumption, to obtain a plurality of positive fluctuation curves of the heating energy consumption and a plurality of negative fluctuation curves of the heating energy consumption;

[0043] When the current period is in the negative fluctuation curve of the heating energy consumption, the second valve, the third valve and the fifth valve are controlled to be closed, and the first valve is controlled to reduce the first opening degree and the traditional energy supply unit is controlled to reduce the first heating energy consumption; wherein the first opening degree and the first heating energy consumption are positively correlated with the area surrounded by the negative fluctuation curve of the heating energy consumption and the coordinate axis;

[0044] When the current period is in the positive fluctuation curve of the heating energy consumption, the fifth valve is controlled to be opened, the third valve is controlled to be opened to the second opening degree, the opening degree of the first valve is controlled to return to the preset opening degree, the traditional energy supply unit is controlled to heat according to the preset second heating energy consumption curve, and the fourth valve is controlled to be closed; wherein the second opening degree of the third valve is positively correlated with the area surrounded by the positive fluctuation curve of the heating energy consumption and the coordinate axis and the amount of electricity provided by the renewable energy power generation unit.

[0045] In the embodiment, firstly, the heat supply energy consumption fluctuation curve is divided according to the positive and negative values of the energy consumption values, to obtain a plurality of positive sub fluctuation curves (corresponding to the demand over-supply period) and negative sub fluctuation curves (corresponding to the excess supply period). When the current period is in the negative sub fluctuation curve interval (i.e. the traditional energy supply is in excess), the second valve (cutting off the standby heat storage output), the third valve (suspending the main heat storage heat release) and the fifth valve (stopping the direct heat supply of renewable energy) are closed; at the same time, the opening degree of the first valve is reduced to the first opening degree, and the heat supply energy consumption of the traditional energy supply unit is reduced to the first heat supply energy consumption. The reduction range of the first opening degree and the reduction amount of the first heat supply energy consumption are positively correlated with the area surrounded by the negative sub fluctuation curve and the coordinate axis. The greater the area, the more the excess amount, and the adjustment range is correspondingly increased to reduce energy waste. When the current period is in the positive sub fluctuation curve interval (i.e. the demand exceeds the supply capacity of the traditional energy), the fifth valve (introducing renewable energy to directly supplement heat supply) is opened, the third valve is adjusted to the second opening degree (enabling the main heat storage unit to release heat), at the same time, the first valve is restored to the preset opening degree, the traditional energy supply unit is normally operated according to the preset second heat supply energy consumption curve, and the fourth valve (stopping the energy input of renewable energy to the heat storage unit) is closed. Here, the second opening degree of the third valve is positively correlated with the area (reflecting the demand gap size) surrounded by the positive sub fluctuation curve and the coordinate axis and the real-time power supply amount of the renewable energy power generation unit. The greater the gap and the more sufficient the renewable energy supply, the higher the heat release output of the main heat storage unit, so as to quickly fill the supply-demand gap. The dynamic regulation and control logic realizes efficient cooperation of multiple energies in different supply-demand scenarios by accurately matching the characteristic parameters of the fluctuation curve and the equipment operating state, avoids energy waste, and guarantees stable supply during the demand peak.

[0046] In an embodiment of the present application, when the second opening degree is equal to the maximum preset opening degree, the fourth valve is controlled to be opened;

[0047] When the energy storage of the main chemical heat storage unit is greater than the first preset energy storage, the fifth valve is controlled to be closed;

[0048] When the energy storage of the main chemical heat storage unit is less than the second preset energy storage, the fifth valve is controlled to be opened; wherein the first preset energy storage is greater than the second preset energy storage.

[0049] In this embodiment, when the second opening degree of the third valve reaches the maximum preset opening degree (i.e., the main chemical heat storage unit is in a full load heat release state), the fourth valve is opened synchronously, and the electric energy generated by the renewable energy power generation unit is preferentially input to the main chemical heat storage unit for energy storage, reserving energy for subsequent possible demand peaks. When the energy storage level of the main chemical heat storage unit is higher than the first preset energy storage (i.e., the heat storage is close to saturation), the fifth valve is closed, and the direct heating of the renewable energy power generation unit to the heat supply pipe network is suspended to avoid energy redundancy; and when the energy storage of the main chemical heat storage unit is lower than the second preset energy storage (i.e., the heat storage is insufficient and needs to be supplemented), the fifth valve is opened to allow the renewable energy power generation unit to directly heat the pipe network, while reducing the dependence on the heat storage unit. It should be noted that the threshold value of the first preset energy storage is set to be higher than the second preset energy storage, forming a "high limit protection" and a "low limit warning" dual interval control of the heat storage state, so that the heat storage unit is always maintained in an energy interval with high efficiency. This set of supplementary control logic further refines the energy distribution strategy under different working conditions, realizes the dynamic balance between energy storage and immediate supply through the linkage of heat storage state and valve action, avoids overcharging or undercharging of the heat storage unit, and maximizes the immediate utilization value of renewable energy.

[0050] In one embodiment of the application, the feature confidence ratio is determined by the following formula:

[0051]

[0052] ω CF,t =1-ω WF,t

[0053]

[0054] In the formula, ω WF,t is the confidence weight of the weather feature, ρ WF,t is the sliding window coefficient of the weather feature and the heating demand, ρ CF,t is the sliding window coefficient of the calendar feature and the heating demand, ω CF,t is the confidence weight of the calendar feature, α t is the adaptive adjustment coefficient, ∈ WF,t-1 is the prediction error of the weather feature, ∈ CF,t-1 is the prediction error of the calendar feature, α0 is the initial adjustment sensitivity, β is the decay rate parameter, is the prediction loss function, WF t is the weather feature, CF t is the calendar feature, D t is the actual heating demand, λ is the regularization coefficient, and t is the discrete time step.

[0055] In the embodiment, the core innovation of the equation set is the deep integration of dynamic adaptability and system robustness, which is embodied in three aspects: first, a "double driving" weight updating mechanism is constructed. The initial weight is allocated based on the dynamic correlation strength of the features and the heating demand, ensuring that the decision conforms to the long-term regularity, and real-time error feedback adjustment is embedded, which dynamically adjusts the weight proportion based on the previous period prediction deviation, so that the confidence of weather and calendar features can respond to short-term fluctuations in real time, realizing the collaborative decision of historical experience and real-time data. Second, an adaptive attenuation adjustment strategy is designed. The adaptive adjustment coefficient decays exponentially with the cumulative error of the system, which can quickly adapt to the scene characteristics in the early stage through a large adjustment amplitude, accelerating the convergence of the model; as the prediction accuracy improves, the adjustment amplitude automatically narrows, avoiding the oscillation of the mature system due to excessive correction, which not only ensures the learning efficiency in the early stage, but also enhances the long-term running stability. Third, an innovative multi-layer constraint optimization framework is designed. The normalization constraint ensures the integrity of the weight allocation (the sum is always 1), the boundary constraint avoids the dominance of a single feature (the weight is limited to the interval of 0.1-0.9), and the optimization objective of L2 regularization suppresses extreme weights while minimizing prediction loss, preventing model overfitting, and finally achieving precise and robust adaptation of confidence ratio under complex conditions, balancing prediction accuracy and system generalization ability.

[0056] In an embodiment of the present application, the preset heating energy consumption curve prediction model is a long-time sequence neural network model.

[0057] In the embodiment, the preset heating energy consumption curve prediction model adopts a long-time sequence neural network model. This model has the ability to capture long-term dependencies in time series, can deeply mine the dynamic correlation of weather features and calendar features over time, and accurately learn the periodic and trend regularities hidden in historical heating energy consumption data. Compared with traditional time series models, it effectively alleviates the gradient vanishing problem in long sequence training through multi-layer gating mechanism, and can make longer and more detailed time series prediction of heating energy consumption curve, providing reliable model support for forward-looking scheduling of multi-energy complementary peak shaving and peak regulation heating system.

[0058] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different than the order in which they are recited in the embodiments and still achieve the desired result. In addition, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous or possible.

[0059] According to another aspect, embodiments of the present application provide a multi-energy complementary peak shaving and peak regulation heating device based on chemical heat storage. Figure 2A schematic block diagram of a chemical heat storage based multi-energy complementary peak shaving heat supply device according to an embodiment is shown. It can be understood that the device can be implemented by any device, equipment, platform and equipment cluster with computing and processing capabilities. The device is applied to a controller of a chemical heat storage based multi-energy complementary peak shaving heat supply system, the system comprising a main chemical heat storage unit, a backup chemical heat storage unit, a renewable energy power generation unit, a traditional energy power supply unit, a heat supply pipe network and the controller, the heat supply pipe network being connected with the traditional energy power supply unit, the backup chemical heat storage unit, the main chemical heat storage unit and the renewable energy power generation unit in sequence through a first valve, a second valve, a third valve and a fifth valve, the backup chemical heat storage unit being connected with the traditional energy power supply unit, the chemical heat storage unit being connected with the renewable energy power generation unit through a fourth valve, and the controller being electrically connected with the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the traditional energy power supply unit respectively, as shown in Figure 2 The device comprises an acquisition unit 200, a first data processing unit 202, a second data processing unit 204 and a third data processing unit 206. The main functions of each component unit are as follows:

[0060] The acquisition unit 200 is configured to acquire weather information and calendar information of a region where the heat supply pipe network is located.

[0061] The first data processing unit 202 is configured to determine a first heat supply energy consumption curve of the region where the heat supply pipe network is located based on the weather information and the calendar information.

[0062] The second data processing unit 204 is configured to determine a heat supply energy consumption fluctuation curve based on the first heat supply energy consumption curve and a preset second heat supply energy consumption curve, wherein the preset second heat supply energy consumption curve is a heat supply energy consumption curve of the traditional energy power supply unit.

[0063] The third data processing unit 206 is configured to control the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the traditional energy power supply unit based on the heat supply energy consumption fluctuation curve.

[0064] In an embodiment of the present application, the first data processing unit 202 is configured to perform the following operations:

[0065] The weather information and the calendar information are respectively subjected to feature extraction to obtain weather features and calendar features.

[0066] The weather feature and the calendar feature are fused according to corresponding feature confidence ratios to obtain a fused feature; wherein the feature confidence ratios include a confidence weight of the weather feature and a confidence weight of the calendar feature.

[0067] The fused feature is input into a preset heating energy consumption curve prediction model to obtain the first heating energy consumption curve.

[0068] The preset heating energy consumption curve prediction model is trained by using a known fused feature and a heating energy consumption curve corresponding to the known fused feature as samples to obtain a classifier.

[0069] In an embodiment of the present application, the second data processing unit 204 is configured to perform the following operations:

[0070] The first heating energy consumption curve and a preset second heating energy consumption curve are subtracted to obtain a heating energy consumption fluctuation curve.

[0071] The first heating energy consumption curve, the preset second heating energy consumption curve and the heating energy consumption fluctuation curve are all two-dimensional curves with a time axis as the horizontal axis and a heating energy consumption axis as the vertical axis.

[0072] In an embodiment of the present application, the third data processing unit 206 is configured to perform the following operations:

[0073] The heating energy consumption fluctuation curve is divided according to the positive and negative of the heating energy consumption to obtain a plurality of positive heating energy consumption fluctuation curves and a plurality of negative heating energy consumption fluctuation curves.

[0074] When the current period is in the negative heating energy consumption fluctuation curve, the second valve, the third valve and the fifth valve are controlled to be closed, the first valve is controlled to reduce a first opening degree, and the conventional energy supply unit is controlled to reduce a first heating energy consumption; wherein the first opening degree and the first heating energy consumption are positively correlated with an area enclosed by the negative heating energy consumption fluctuation curve and the coordinate axis on which the negative heating energy consumption fluctuation curve is located.

[0075] When the current period is in the positive heating energy consumption fluctuation curve, the fifth valve is controlled to be opened, the third valve is controlled to be opened to a second opening degree, the opening degree of the first valve is controlled to return to a preset opening degree, the conventional energy supply unit is controlled to supply heat according to the preset second heating energy consumption curve, and the fourth valve is controlled to be closed; wherein the second opening degree of the third valve is positively correlated with an area enclosed by the positive heating energy consumption fluctuation curve and the coordinate axis on which the positive heating energy consumption fluctuation curve is located and an amount of electricity provided by the renewable energy power generation unit.

[0076] In an embodiment of the present application, the system further comprises a fourth data processing unit, which is configured to perform the following operations:

[0077] when the second opening degree is equal to the maximum preset opening degree, controlling the fourth valve to open;

[0078] when the energy storage of the main chemical heat storage unit is greater than a first preset energy storage, controlling the fifth valve to close;

[0079] when the energy storage of the main chemical heat storage unit is less than a second preset energy storage, controlling the fifth valve to open; wherein the first preset energy storage is greater than the second preset energy storage.

[0080] In an embodiment of the present application, the feature confidence ratio is determined by the following formula:

[0081]

[0082] ω CF,t =1-ω WF,t

[0083]

[0084] In the formula, ω WF,t is the confidence weight of the weather feature, ρ WF,t is the sliding window coefficient of the weather feature and the heating demand, ρ CF,t is the sliding window coefficient of the calendar feature and the heating demand, ω CF,t is the confidence weight of the calendar feature, α t is the adaptive adjustment coefficient, ∈ WF,t-1 is the prediction error of the weather feature, ∈ CF,t-1 is the prediction error of the calendar feature, α0 is the initial adjustment sensitivity, β is the decay rate parameter, is the prediction loss function, WF t is the weather feature, CF t is the calendar feature, D t is the actual heating demand, λ is the regularization coefficient, and t is the discrete time step.

[0085] In an embodiment of the present application, the preset heating energy consumption curve prediction model is a long-time sequence neural network model.

[0086] As Figure 3As shown, according to the embodiment of still another aspect, a multi-energy complementary peak shaving heat supply system based on chemical heat storage is also provided, which comprises a main chemical heat storage unit, a backup chemical heat storage unit, a renewable energy power generation unit, a traditional energy power supply unit, a heat supply pipe network and a controller, the heat supply pipe network is connected with the traditional energy power supply unit, the backup chemical heat storage unit, the main chemical heat storage unit and the renewable energy power generation unit in sequence through a first valve, a second valve, a third valve and a fifth valve, the backup chemical heat storage unit is connected with the traditional energy power supply unit, the chemical heat storage unit is connected with the renewable energy power generation unit through a fourth valve, the controller is electrically connected with the first valve, the second valve, the third valve, the fourth valve, the fifth valve, the main chemical heat storage unit and the traditional energy power supply unit respectively, and when the controller executes the executable code, the method combining Figure 1 the method is realized.

[0087] According to the embodiment of still another aspect, an electronic device is also provided, which comprises a memory and a processor, the memory stores executable code, and when the processor executes the executable code, the method combining Figure 1 the method is realized.

[0088] Each of the embodiments in the present application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment mainly explains the difference from other embodiments. Especially, for the device embodiments, since they are basically similar to the method embodiments, they are described more simply, and the related parts can be referred to the part of the method embodiments.

[0089] Those skilled in the art should realize that in one or more of the above examples, the functions described in the present application can be realized by hardware, software, firmware or any combination thereof. When realized by software, these functions can be stored in a computer readable medium or transmitted as one or more instructions or codes on a computer readable medium.

[0090] The above specific embodiments further explain the purpose, technical solutions and beneficial effects of the present application, and it should be understood that the above description is only the specific embodiments of the present application, and is not used to limit the protection scope of the present application, and any modification, equivalent replacement, improvement, etc. made on the basis of the technical solutions of the present application should be included in the protection scope of the present application.

Claims

1. A multi-energy complementary peak shaving heat supply method based on chemical heat storage, characterized in that, The method is applied to a controller of a multi-energy complementary peak shaving heat supply system based on chemical heat storage, the system comprising a main chemical heat storage unit, a backup chemical heat storage unit, a renewable energy power generation unit, a traditional energy power supply unit, a heat supply pipe network and the controller, the heat supply pipe network being connected with the traditional energy power supply unit, the backup chemical heat storage unit, the main chemical heat storage unit and the renewable energy power generation unit in sequence through a first valve, a second valve, a third valve and a fifth valve, the backup chemical heat storage unit being connected with the traditional energy power supply unit, the main chemical heat storage unit being connected with the renewable energy power generation unit through a fourth valve, the controller being electrically connected with the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the traditional energy power supply unit respectively, and the method comprising: obtaining weather information and calendar information of a region where the heat supply pipe network is located; determining a first heat supply energy consumption curve of the region where the heat supply pipe network is located based on the weather information and the calendar information; determining a heat supply energy consumption fluctuation curve based on the first heat supply energy consumption curve and a preset second heat supply energy consumption curve, wherein the preset second heat supply energy consumption curve is a heat supply energy consumption curve of the traditional energy power supply unit; controlling the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the traditional energy power supply unit based on the heat supply energy consumption fluctuation curve; the determination of the first heat supply energy consumption curve of the region where the heat supply pipe network is located based on the weather information and the calendar information comprises: performing feature extraction on the weather information and the calendar information respectively to obtain weather features and calendar features; fusing the weather features and the calendar features according to corresponding feature confidence ratios to obtain fused features, wherein the feature confidence ratios comprise a confidence weight of the weather features and a confidence weight of the calendar features; inputting the fused features into a preset heat supply energy consumption curve prediction model to obtain the first heat supply energy consumption curve; wherein the preset heat supply energy consumption curve prediction model takes known fused features and a heat supply energy consumption curve corresponding to the known fused features as samples to train a classifier; the feature confidence ratios are determined by the following formula: wherein, is a confidence weight for the weather feature, is a sliding window coefficient for the weather feature and heating demand, is a sliding window coefficient for the calendar feature and heating demand, is a confidence weight for the calendar feature, is an adaptive adjustment coefficient, is a prediction error for the weather feature, is a prediction error for the calendar feature, is an initial adjustment sensitivity, is a decay rate parameter, is a prediction loss function, is the weather feature, is the calendar feature, is the actual heating demand, is a regularization coefficient, t is a discrete time step.

2. The method of claim 1, wherein, the determination of the heat supply energy consumption fluctuation curve based on the first heat supply energy consumption curve and the preset second heat supply energy consumption curve comprises: subtracting the first heat supply energy consumption curve from the preset second heat supply energy consumption curve to obtain the heat supply energy consumption fluctuation curve; wherein the first heat supply energy consumption curve, the preset second heat supply energy consumption curve and the heat supply energy consumption fluctuation curve are all two-dimensional curves with time as the horizontal axis and heat supply energy consumption as the vertical axis.

3. The method of claim 2, wherein, the control of the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the traditional energy power supply unit based on the heat supply energy consumption fluctuation curve comprises: The heating energy consumption fluctuation curve is divided into a plurality of positive heating energy consumption fluctuation curves and a plurality of negative heating energy consumption fluctuation curves according to the positive and negative of the heating energy consumption; When the current period is in the negative heating energy consumption fluctuation curve, the second valve, the third valve and the fifth valve are controlled to be closed, the first valve is controlled to reduce the first opening degree, and the traditional energy supply unit is controlled to reduce the first heating energy consumption; wherein the first opening degree and the first heating energy consumption are positively correlated with the area surrounded by the negative heating energy consumption fluctuation curve and the coordinate axis; When the current period is in the positive heating energy consumption fluctuation curve, the fifth valve is controlled to be opened, the third valve is controlled to be opened to the second opening degree, the opening degree of the first valve is controlled to return to the preset opening degree, the traditional energy supply unit is controlled to heat according to the preset second heating energy consumption curve, and the fourth valve is controlled to be closed; wherein the second opening degree of the third valve is positively correlated with the area surrounded by the positive heating energy consumption fluctuation curve and the coordinate axis and the amount of electricity provided by the renewable energy power generation unit.

4. The method of claim 3, wherein, Also includes; When the second opening degree is equal to the maximum preset opening degree, the fourth valve is controlled to be opened; When the energy storage of the main chemical heat storage unit is greater than the first preset energy storage, the fifth valve is controlled to be closed; When the energy storage of the main chemical heat storage unit is less than the second preset energy storage, the fifth valve is controlled to be opened; wherein the first preset energy storage is greater than the second preset energy storage.

5. The method of claim 1, wherein, The preset heating energy consumption curve prediction model is a long-time neural network model.

6. A multi-energy complementary peak shaving heat supply device based on chemical heat storage, characterized in that, The device is applied to a controller of a multi-energy complementary peak shaving and heating system based on chemical heat storage, the system comprising a main chemical heat storage unit, a standby chemical heat storage unit, a renewable energy power generation unit, a traditional energy supply unit, a heating pipe network and the controller, the heating pipe network being connected with the traditional energy supply unit, the standby chemical heat storage unit, the main chemical heat storage unit and the renewable energy power generation unit in sequence through a first valve, a second valve, a third valve and a fifth valve, the standby chemical heat storage unit being connected with the traditional energy supply unit, the main chemical heat storage unit being connected with the renewable energy power generation unit through a fourth valve, the controller being electrically connected with the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the traditional energy supply unit, the controller being used to execute the method in any one of claims 1-5, and the device comprising: an acquisition unit configured to acquire weather information and calendar information of a region where the heating pipe network is located; a first data processing unit configured to determine a first heating energy consumption curve of the region where the heating pipe network is located based on the weather information and the calendar information; a second data processing unit configured to determine a heating energy consumption fluctuation curve based on the first heating energy consumption curve and a preset second heating energy consumption curve; wherein the preset second heating energy consumption curve is a heating energy consumption curve of the traditional energy supply unit. A third data processing unit configured to control the first valve, the second valve, the third valve, the fourth valve, the fifth valve and the conventional energy supply unit based on the heat supply energy consumption fluctuation curve.

7. An electronic device, comprising: A computer program product comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1-5.

8. A multi-energy complementary peak shaving heat supply system based on chemical heat storage, characterized in that, The system comprises a main chemical heat storage unit, a backup chemical heat storage unit, a renewable energy power generation unit, a conventional energy supply unit, a heat supply pipe network and a controller, the heat supply pipe network is sequentially connected with the conventional energy supply unit, the backup chemical heat storage unit, the main chemical heat storage unit and the renewable energy power generation unit through the first valve, the second valve, the third valve and the fifth valve, the backup chemical heat storage unit is connected with the conventional energy supply unit, the main chemical heat storage unit is connected with the renewable energy power generation unit through the fourth valve, the controller is electrically connected with the first valve, the second valve, the third valve, the fourth valve, the fifth valve, the main chemical heat storage unit and the conventional energy supply unit respectively, and the controller is used to execute the method according to any one of claims 1-5.

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