Park molten salt energy storage quantitative management system and method based on distributed control

Abstract

This invention discloses a distributed control-based molten salt energy storage quantitative management system and method for industrial parks, belonging to the field of energy management technology. The system includes: a load sensing and classification module, a demand quantification analysis module, an energy storage configuration rule construction module, and a zoned configuration and scheduling module. The method acquires park load data through distributed acquisition terminals, extracts characteristics, and completes load classification; calculates the energy gap based on the classification results, and constructs a three-dimensional quantitative index system; establishes a quantitative mapping relationship between load indicators and energy storage parameters, forming a regionalized configuration rule library; calculates matching parameters according to the rules, generates configuration schemes, and realizes zoned quantitative configuration and collaborative scheduling. This invention achieves accurate load classification, demand quantification analysis, and automatic matching of energy storage parameters, improving the accuracy of molten salt energy storage configuration and the energy utilization efficiency of industrial parks, and is suitable for distributed energy management scenarios in industrial parks.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, specifically to a distributed control-based industrial park molten salt energy storage management system and method. Background Technology

[0002] As a core energy consumption scenario, industrial parks rely heavily on large-scale energy storage systems to improve clean energy utilization and optimize energy structure. Molten salt energy storage, with its advantages of long-term storage, high security, and environmental friendliness, is increasingly being used in scenarios such as combined cooling, heating, and power (CCHP) and renewable energy storage within industrial parks.

[0003] However, current molten salt energy storage systems in industrial parks generally face the technical challenge of insufficient load adaptability. Industrial parks typically encompass various load types, including industrial, commercial, and residential, with significant differences in energy intensity and temporal characteristics. Industrial loads primarily rely on stable industrial steam demand, commercial loads are concentrated in specific periods with large peak-to-valley differences, and residential loads exhibit bi-peak fluctuations during morning and evening commutes. Traditional configuration schemes fail to differentiate between load types, often employing a crude approach of summing overall load peaks or using a fixed allocation-to-storage ratio to determine molten salt energy storage capacity and rated power, lacking a precise quantitative correlation between load characteristics and energy storage parameters.

[0004] This extensive configuration often leads to redundancy or insufficiency in energy storage systems: excessive energy storage is configured for industrial loads with gentle fluctuations, resulting in a waste of equipment resources; insufficient capacity is configured for commercial loads with concentrated periods, failing to cover peak energy demand gaps; at the same time, key parameters such as the duration of energy consumption and fluctuation frequency of different loads are ignored, resulting in a mismatch between energy storage power and storage duration, limiting efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide a distributed control-based industrial park molten salt energy storage management system and method to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] The distributed control-based method for the energy-based management of molten salt storage in industrial parks includes the following steps:

[0008] S1. Obtain real-time energy consumption data of the park load within a continuous cycle through distributed acquisition terminals, and extract load demand characteristics; classify the load types based on the energy consumption time distribution and power change pattern of the park load, combined with the load demand characteristics;

[0009] S2. Based on real-time energy consumption data and characteristic parameters, conduct quantitative analysis of demand for various types of loads to obtain the maximum daily energy gap, the duration of the gap, and the frequency of fluctuation per unit time. Construct a three-dimensional quantitative index system and form a load energy demand characteristic database.

[0010] S3. Based on the independent energy consumption boundaries of each load area under distributed control, and combined with the operation and configuration requirements of molten salt energy storage, establish a quantitative mapping relationship between three-dimensional quantitative indicators and energy storage configuration parameters to form a regionalized energy storage configuration rule base;

[0011] S4. Based on the regionalized energy storage configuration rule base, calculate the molten salt energy storage configuration parameters for each region, generate a regionalized energy storage configuration scheme, and implement quantitative configuration and regional collaborative scheduling management of molten salt energy storage in the park based on the regionalized energy storage configuration scheme.

[0012] Furthermore, S1 includes the following:

[0013] According to the preset sampling time interval, the real-time energy consumption of each load node in the park is collected by the distributed acquisition terminal within the continuous statistical period to form the original energy consumption time series data; the original energy consumption time series data is smoothed by moving average to remove instantaneous disturbances and obtain smoothed energy consumption time series data.

[0014] Based on the smoothed energy consumption time-series data, load demand characteristic parameters are extracted, including daily average energy consumption, energy consumption period concentration coefficient, and energy consumption fluctuation coefficient.

[0015] The load is divided into continuous load, time-concentrated load, and random fluctuating load based on the relationship between the time-concentrated load, the energy consumption fluctuation coefficient and the corresponding threshold.

[0016] Furthermore, S2 includes the following:

[0017] For each type of load identified, based on the corresponding smoothed energy consumption time series data, the rated energy supply power of each type of load within the statistical period is statistically analyzed, and the energy consumption gap is calculated time by time. When the energy consumption gap is greater than zero, it is determined that there is an energy consumption gap.

[0018] The energy consumption gap at each time of day is statistically analyzed to obtain the maximum daily energy consumption gap; all times when energy consumption gaps occur on a single day are statistically analyzed and summed to obtain the duration of the gap; the fluctuation frequency per unit time is calculated based on the state jump of the energy consumption gap.

[0019] With the maximum daily energy shortage, the duration of the shortage, and the frequency of fluctuations per unit time as the core, a three-dimensional quantitative index system for various types of loads is constructed; the three-dimensional quantitative indexes, load demand characteristic parameters, and smoothed energy consumption power data of various types of loads are uniformly stored to form a load energy demand characteristic database.

[0020] Furthermore, S3 includes the following:

[0021] Based on the distributed control architecture, the park is divided into several independent load areas, and the independent energy consumption boundary and the three-dimensional quantitative indicators of the corresponding load type of each area are determined. The independent energy consumption boundary refers to the boundary that is independent of each load area in terms of power supply path, pipeline ownership, energy consumption characteristics and control authority. Each independent load area can be independently monitored for energy consumption, configured for energy storage and controlled for scheduling, without being coupled with other areas for energy consumption or control.

[0022] Based on the operational configuration requirements of molten salt energy storage systems, a quantitative correlation rule is established between three-dimensional quantitative indicators and energy storage configuration parameters:

[0023] The minimum effective molten salt quantity is determined based on the maximum daily energy deficit, the duration of the deficit, and the molten salt thermal storage conversion coefficient; the rated heat release power is determined based on the ratio of the maximum daily energy deficit to the duration of the deficit, the power adjustment coefficient, and the standard heat release reference duration; and the heat exchange matching coefficient is determined based on the fluctuation frequency per unit time, the reference matching coefficient, and the fluctuation attenuation coefficient.

[0024] For independent load areas containing multiple load types, the minimum effective molten salt quantity is weighted and summed using a weighted superposition rule. The maximum value of the rated heat release power and the minimum value of the heat exchange matching coefficient are taken to obtain the comprehensive energy storage configuration parameters for the area.

[0025] The aforementioned quantitative mapping relationships, weighted superposition rules, and independent energy consumption boundary constraints are integrated to form a regionalized energy storage configuration rule library.

[0026] Furthermore, S4 includes the following:

[0027] Based on the three-dimensional quantitative indicators and load type parameters corresponding to each independent load area, the energy storage configuration parameters corresponding to each single load type are calculated according to the regionalized energy storage configuration rule library; the energy storage configuration parameters include the minimum effective molten salt quantity, rated heat release power and heat exchange matching coefficient.

[0028] For an independent load area containing multiple load types, the energy storage configuration parameters corresponding to each type of load are comprehensively processed according to the weighted superposition rule to obtain the comprehensive energy storage configuration parameters of the area; the comprehensive energy storage configuration parameters include the area's comprehensive minimum effective molten salt quantity, the area's comprehensive rated heat release power, and the area's comprehensive heat exchange matching coefficient.

[0029] The comprehensive energy storage configuration parameters are converted into engineering-feasible physical configuration indicators, and regionalized energy storage configuration schemes are generated based on these physical configuration indicators.

[0030] According to the regionalized energy storage configuration scheme, the molten salt energy storage system in the park is configured in a zonal and quantitative manner under the distributed control architecture;

[0031] Based on quantitative allocation, and combined with the real-time energy consumption status, demand fluctuations, and changes in three-dimensional quantitative indicators of each independent load area, regional collaborative scheduling management is implemented to achieve complementary regulation and collaborative energy supply of energy storage resources in multiple regions.

[0032] The park molten salt energy storage quantitative management system based on distributed control includes: a load sensing and classification module, a demand quantitative analysis module, an energy storage configuration rule construction module, and a zone configuration and scheduling module.

[0033] The load sensing and classification module acquires real-time energy consumption data of the park load through distributed acquisition terminals, extracts load demand characteristics, and completes load type classification.

[0034] The demand quantification analysis module performs demand quantification analysis on various types of loads, constructs a three-dimensional quantitative index system, and forms a database of load energy demand characteristics.

[0035] The energy storage configuration rule construction module establishes a quantitative mapping relationship between three-dimensional quantitative indicators and energy storage configuration parameters based on independent energy consumption boundaries, forming a regional energy storage configuration rule library;

[0036] The partition configuration and scheduling module calculates the energy storage configuration parameters for each region based on the regional energy storage configuration rules, generates regional energy storage configuration schemes, and performs quantitative configuration and regional collaborative scheduling management.

[0037] The load sensing and classification module includes a data acquisition and processing unit and a load classification unit;

[0038] The data acquisition and processing unit collects real-time energy consumption power at a preset sampling interval, performs a moving average smoothing process on the raw power time series data, and obtains smoothed energy consumption power time series data.

[0039] The load classification unit extracts load demand characteristic parameters based on smoothed power data and classifies the load into continuous load, time-concentrated load, and random fluctuating load according to preset thresholds.

[0040] The demand quantification analysis module includes a gap calculation unit and an indicator construction unit;

[0041] The gap calculation unit counts the rated power of each type of load within the statistical period, calculates the power gap at each moment, and obtains the maximum daily power gap, the duration of the gap, and the frequency of fluctuation per unit time.

[0042] The indicator construction unit constructs a three-dimensional quantitative indicator system based on the maximum daily energy demand gap, the duration of the gap, and the frequency of fluctuations per unit time, forming a database of load energy demand characteristics.

[0043] The energy storage configuration rule construction module includes a region division unit and a mapping rule unit;

[0044] The regional division unit divides independent load areas according to the distributed control architecture and determines the energy consumption boundary and corresponding three-dimensional quantitative indicators for each area.

[0045] The mapping rule unit establishes quantitative association rules between three-dimensional quantitative indicators and minimum effective molten salt quantity, rated heat release power, and heat exchange matching coefficient. It performs weighted superposition calculations on regions with multiple load types to form a regional energy storage configuration rule library.

[0046] The partition configuration and scheduling module includes a parameter calculation unit and a scheduling execution unit;

[0047] The parameter calculation unit calculates the energy storage configuration parameters for each region based on the regional energy storage configuration rule base, converts the configuration parameters into physical configuration indicators, and generates a regional energy storage configuration scheme.

[0048] The scheduling and execution unit performs zonal quantitative configuration according to the regional energy storage configuration scheme, and performs regional collaborative scheduling in combination with real-time energy consumption status to achieve complementary regulation and collaborative energy supply of multi-regional energy storage resources.

[0049] Compared with existing technologies, the advantages of this invention are as follows: This invention acquires real-time energy consumption data of park loads through distributed acquisition terminals, and achieves refined classification by combining load demand characteristics. This allows for accurate differentiation of the energy consumption patterns and fluctuation characteristics of different loads, providing reliable data support for subsequent energy storage configuration. By constructing a three-dimensional quantitative index system consisting of the maximum daily energy gap, gap duration, and fluctuation frequency per unit time, it can comprehensively and objectively characterize the park's load demand, solving the problems of one-sided analysis and insufficient configuration basis in traditional single-indicator analysis. Based on independent energy consumption boundaries, this invention establishes a quantitative mapping relationship between three-dimensional quantitative indicators and molten salt energy storage configuration parameters, forming a standardized regional energy storage configuration rule library. This achieves precise matching from load demand to energy storage parameters, replacing the traditional experience-based configuration method and significantly improving the scientific and rational nature of molten salt energy storage system configuration. For multi-type load areas, a weighted superposition rule is used for comprehensive calculation, which can adapt to complex park energy consumption structures and ensure that energy storage configuration in each area meets actual needs. Meanwhile, this invention relies on a distributed control architecture to achieve zonal quantitative configuration and regional collaborative scheduling, which can dynamically adapt to real-time load changes, realize complementary regulation and collaborative energy supply of multi-regional energy storage resources, effectively improve the energy utilization efficiency of the park, reduce the risk of energy supply gap, enhance the economic efficiency and reliability of molten salt energy storage system operation, and is suitable for various large-scale, refined and intelligent energy management scenarios in parks. Attached Figure Description

[0050] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0051] Figure 1This is a schematic diagram of the modules of the molten salt energy storage management system for industrial parks based on distributed control, as described in this invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figure 1 The present invention provides the following technical solution:

[0054] The park molten salt energy storage quantitative management system based on distributed control includes: a load sensing and classification module, a demand quantitative analysis module, an energy storage configuration rule construction module, and a zone configuration and scheduling module.

[0055] The load sensing and classification module acquires real-time energy consumption data of the park load through distributed acquisition terminals, extracts load demand characteristics, and completes load type classification.

[0056] The demand quantification analysis module performs demand quantification analysis on various types of loads, constructs a three-dimensional quantitative index system, and forms a database of load energy demand characteristics.

[0057] The energy storage configuration rule construction module establishes a quantitative mapping relationship between three-dimensional quantitative indicators and energy storage configuration parameters based on independent energy consumption boundaries, forming a regional energy storage configuration rule library;

[0058] The partition configuration and scheduling module calculates the energy storage configuration parameters for each region based on the regional energy storage configuration rules, generates regional energy storage configuration schemes, and performs quantitative configuration and regional collaborative scheduling management.

[0059] The load sensing and classification module includes a data acquisition and processing unit and a load classification unit;

[0060] The data acquisition and processing unit collects real-time energy consumption power at a preset sampling interval, performs a moving average smoothing process on the raw power time series data, and obtains smoothed energy consumption power time series data.

[0061] The load classification unit extracts load demand characteristic parameters based on smoothed power data and classifies the load into continuous load, time-concentrated load, and random fluctuating load according to preset thresholds.

[0062] The demand quantification analysis module includes a gap calculation unit and an indicator construction unit;

[0063] The gap calculation unit counts the rated power of each type of load within the statistical period, calculates the power gap at each moment, and obtains the maximum daily power gap, the duration of the gap, and the frequency of fluctuation per unit time.

[0064] The indicator construction unit constructs a three-dimensional quantitative indicator system based on the maximum daily energy demand gap, the duration of the gap, and the frequency of fluctuations per unit time, forming a database of load energy demand characteristics.

[0065] The energy storage configuration rule construction module includes a region division unit and a mapping rule unit;

[0066] The regional division unit divides independent load areas according to the distributed control architecture and determines the energy consumption boundary and corresponding three-dimensional quantitative indicators for each area.

[0067] The mapping rule unit establishes quantitative association rules between three-dimensional quantitative indicators and minimum effective molten salt quantity, rated heat release power, and heat exchange matching coefficient. It performs weighted superposition calculations on regions with multiple load types to form a regional energy storage configuration rule library.

[0068] The partition configuration and scheduling module includes a parameter calculation unit and a scheduling execution unit;

[0069] The parameter calculation unit calculates the energy storage configuration parameters for each region based on the regional energy storage configuration rule base, converts the configuration parameters into physical configuration indicators, and generates a regional energy storage configuration scheme.

[0070] The scheduling and execution unit performs zonal quantitative configuration according to the regional energy storage configuration scheme, and performs regional collaborative scheduling in combination with real-time energy consumption status to achieve complementary regulation and collaborative energy supply of multi-regional energy storage resources.

[0071] The distributed control-based method for the energy-based management of molten salt storage in industrial parks includes the following steps:

[0072] S1. Obtain real-time energy consumption data of the park load within a continuous period through distributed acquisition terminals, and extract load demand characteristics; classify the load types based on the energy consumption time distribution and power change pattern of the park load, combined with the load demand characteristics;

[0073] S2. Based on real-time energy consumption data and characteristic parameters, conduct demand quantification analysis on various types of loads to obtain the maximum daily energy gap, gap duration and fluctuation frequency per unit time, construct a three-dimensional quantitative index system and form a load energy demand characteristic database;

[0074] S3. Based on the independent energy consumption boundaries of each load area under distributed control, and combined with the operation and configuration requirements of molten salt energy storage, establish a quantitative mapping relationship between three-dimensional quantitative indicators and energy storage configuration parameters to form a regionalized energy storage configuration rule base.

[0075] S4. Based on the regionalized energy storage configuration rule base, calculate the molten salt energy storage configuration parameters for each region, generate a regionalized energy storage configuration scheme, and implement quantitative configuration and regional collaborative scheduling management of molten salt energy storage in the park based on the regionalized energy storage configuration scheme.

[0076] S1 includes the following:

[0077] According to the preset sampling time interval, the real-time energy consumption of each load node in the park is collected by the distributed acquisition terminal within the continuous statistical period to form the original energy consumption time series data; the original energy consumption time series data is smoothed by moving average to remove instantaneous disturbances and obtain smoothed energy consumption time series data.

[0078] Based on the smoothed energy consumption time-series data, load demand characteristic parameters are extracted, including daily average energy consumption, energy consumption period concentration coefficient, and energy consumption fluctuation coefficient.

[0079] The load is divided into continuous load, time-concentrated load, and random fluctuating load based on the relationship between the time-concentrated load, the energy consumption fluctuation coefficient and the corresponding threshold.

[0080] In this embodiment, according to a preset sampling time interval Δt, the real-time energy consumption of each load node in the park is collected by a distributed acquisition terminal within a continuous statistical period to form raw energy consumption time series data; the raw energy consumption time series data is smoothed by moving average to remove instantaneous disturbances, and smoothed energy consumption time series data Ps(t) is obtained; the window width of the moving average is determined according to the load fluctuation characteristics, preferably 3-5 sampling points;

[0081] Based on the smoothed energy consumption time-series data Ps(t), load demand characteristic parameters are extracted; these parameters include daily average energy consumption, energy consumption period concentration factor, and energy consumption fluctuation factor; wherein, daily average energy consumption Pm=(1 / N)∑ i∈[1,N] Psi, N is the total number of sampling points per day, Psi is the smoothed energy consumption power of the i-th sampling point; the energy consumption period concentration coefficient γ=tu / 24, tu is the effective energy consumption duration per day, the effective energy consumption duration refers to the number of hours within a day when the load power is continuously higher than the preset benchmark value, the benchmark value can be set to 30%-50% of the daily average power; the energy consumption power fluctuation coefficient B=(Pmax-Pmin) / Pm, Pmax and Pmin are the maximum and minimum smoothed energy consumption power within the statistical period, respectively.

[0082] The preset time period concentration coefficient threshold γ0 and fluctuation coefficient threshold B0 are determined based on statistical analysis of historical load data of the park or preset functional positioning of the park. If γ≥γ0 and B≤B0, it is classified as continuous load. If γ0 / 2≤γ<γ0 and B0<B<2B0, it is classified as time period concentrated load. If γ<γ0 / 2 and B>2B0, it is classified as random fluctuation load.

[0083] S2 includes the following:

[0084] For each type of load identified, based on the corresponding smoothed energy consumption time series data, the rated energy supply power of each type of load within the statistical period is statistically analyzed, and the energy consumption gap is calculated time by time. When the energy consumption gap is greater than zero, it is determined that there is an energy consumption gap.

[0085] The energy consumption gap at each time of day is statistically analyzed to obtain the maximum daily energy consumption gap; all times when energy consumption gaps occur on a single day are statistically analyzed and summed to obtain the duration of the gap; the fluctuation frequency per unit time is calculated based on the state jump of the energy consumption gap.

[0086] With the maximum daily energy shortage, the duration of the shortage, and the frequency of fluctuations per unit time as the core, a three-dimensional quantitative index system for various types of loads is constructed; the three-dimensional quantitative indexes, load demand characteristic parameters, and smoothed energy consumption power data of various types of loads are uniformly stored to form a load energy demand characteristic database.

[0087] In this embodiment, for each type of load obtained by S1, based on its corresponding smoothed energy consumption time-series data Ps(t), the rated energy supply power Pg(t) of each type of load within the statistical period is statistically calculated, and the energy consumption gap is calculated time-by-time. The calculation formula is: ΔP(t) = Ps(t) - Pg(t), where ΔP(t) is the load energy consumption gap at time t, Ps(t) is the smoothed load energy consumption at time t, and Pg(t) is the rated energy supply power of the corresponding energy supply circuit at time t. The rated energy supply power refers to the maximum continuous power supply capacity designed for the energy supply circuit where the load is located, which is determined according to the nameplate parameters of the energy supply equipment, the capacity agreed in the power supply contract, or the 95% maximum demand statistically observed in actual operation. When ΔP(t) > 0, it indicates that there is an energy consumption gap; when ΔP(t) ≤ 0, it indicates that there is no gap.

[0088] The energy consumption gap ΔP(t) at each time point of the day is statistically analyzed to obtain the maximum daily energy consumption gap ΔPmax, where ΔPmax = max{ΔP(t)|ΔP(t)>0, t∈[1,N]}; the duration of the gap is obtained by summing all the times when the energy consumption gap occurs on a single day, where Tg = ∑ k∈[1,M]Δtk, where k is the sampling point number with energy shortage, M is the total number of sampling points with energy shortage in a single day, and Δtk is the time interval corresponding to the kth sampling point; when the sampling is at equal intervals, Tg=M×Δt; according to the state jump of the energy shortage, the fluctuation frequency fg per unit time is calculated, and fg=H / T, where H is the total number of state jumps from zero to existence or from existence to zero in the statistical period, the state jump refers to ΔP(t) of two consecutive sampling points changing from a state without shortage to a state with shortage, or from a state with shortage to a state without shortage; T is the total duration of the statistical period;

[0089] Based on the maximum daily energy shortage ΔPmax, the duration of the shortage Tg, and the frequency of fluctuation per unit time fg, a three-dimensional quantitative index system for various types of loads is constructed. The three-dimensional quantitative indexes, load demand characteristic parameters, and smoothed energy consumption power data of various types of loads are stored in a unified manner to form a load energy demand characteristic database.

[0090] S3 includes the following:

[0091] Based on the distributed control architecture, the park is divided into several independent load areas, and the independent energy consumption boundary and the three-dimensional quantitative indicators of the corresponding load type of each area are determined. The independent energy consumption boundary refers to the boundary that is independent of each load area in terms of power supply path, pipeline ownership, energy consumption characteristics and control authority. Each independent load area can be independently monitored for energy consumption, configured for energy storage and controlled for scheduling, without being coupled with other areas for energy consumption or control.

[0092] Based on the operational configuration requirements of molten salt energy storage systems, a quantitative correlation rule is established between three-dimensional quantitative indicators and energy storage configuration parameters:

[0093] The minimum effective molten salt quantity is determined based on the maximum daily energy deficit, the duration of the deficit, and the molten salt thermal storage conversion coefficient; the rated heat release power is determined based on the ratio of the maximum daily energy deficit to the duration of the deficit, the power adjustment coefficient, and the standard heat release reference duration; and the heat exchange matching coefficient is determined based on the fluctuation frequency per unit time, the reference matching coefficient, and the fluctuation attenuation coefficient.

[0094] For independent load areas containing multiple load types, the minimum effective molten salt quantity is weighted and summed using a weighted superposition rule. The maximum value of the rated heat release power and the minimum value of the heat exchange matching coefficient are taken to obtain the comprehensive energy storage configuration parameters for the area.

[0095] The aforementioned quantitative mapping relationships, weighted superposition rules, and independent energy consumption boundary constraints are integrated to form a regionalized energy storage configuration rule library.

[0096] In this embodiment, based on the distributed control architecture, the park is divided into several independent load areas according to building function, power supply circuit, pipeline zoning or physical location. The independent energy consumption boundary of each area is determined, and the load type and corresponding three-dimensional quantitative indicators of each area are clarified, including the maximum daily energy consumption gap ΔPmax, the gap duration Tg, and the fluctuation frequency fg per unit time.

[0097] Based on the operational configuration requirements of molten salt energy storage systems, key parameter dimensions required for regionalized energy storage configurations are determined, including minimum effective molten salt quantity, rated heat release power, and heat exchange matching coefficient. Furthermore, quantitative correlation rules are established between each parameter and three-dimensional quantitative indicators; specifically including:

[0098] The quantitative mapping relationship between the maximum daily energy deficit ΔPmax and the minimum effective molten salt quantity Em is established as follows:

[0099] Em = K1·ΔPmax·Tg, where K1 is the molten salt heat storage conversion coefficient, which is determined based on the molten salt operating temperature range, specific heat capacity, and overall system efficiency;

[0100] The quantitative mapping relationship between the duration of the gap Tg and the rated heat release power Pr is established as follows:

[0101] Pr = K2·(ΔPmax / Tg)·T0, where K2 is the power adjustment coefficient, preset according to the regional load response characteristics; T0 is the standard heat release reference duration, used to convert the dimension of the ratio ΔPmax / Tg into the dimension of power for easy engineering calculation;

[0102] The quantitative mapping relationship between the unit time fluctuation frequency fg and the heat exchange matching coefficient η is established as follows:

[0103] η=K3·e -λ·fg Where K3 is the baseline matching coefficient; λ is the fluctuation attenuation coefficient, which is determined based on the response characteristics of the molten salt heat exchange loop;

[0104] For situations where multiple load types are contained within the same independent load area, a weighted superposition rule is used for comprehensive configuration calculation, as shown below:

[0105] Emt=∑wj·Emj,Prt=max{Prj},ηt=min{ηj}, where wj is the energy consumption weight of the j-th type of load in the region, Emt is the minimum effective molten salt quantity of the region, Prt is the rated heat release power of the region, and ηt is the heat exchange matching coefficient of the region.

[0106] The aforementioned quantitative mapping relationships, weighted superposition rules, and independent energy consumption boundary constraints are integrated to form a regionalized energy storage configuration rule library.

[0107] S4 includes the following:

[0108] Based on the three-dimensional quantitative indicators and load type parameters corresponding to each independent load area, the energy storage configuration parameters corresponding to each single load type are calculated according to the regionalized energy storage configuration rule library; the energy storage configuration parameters include the minimum effective molten salt quantity, rated heat release power and heat exchange matching coefficient.

[0109] For an independent load area containing multiple load types, the energy storage configuration parameters corresponding to each type of load are comprehensively processed according to the weighted superposition rule to obtain the comprehensive energy storage configuration parameters of the area; the comprehensive energy storage configuration parameters include the area's comprehensive minimum effective molten salt quantity, the area's comprehensive rated heat release power, and the area's comprehensive heat exchange matching coefficient.

[0110] The comprehensive energy storage configuration parameters are converted into engineering-feasible physical configuration indicators, and regionalized energy storage configuration schemes are generated based on these physical configuration indicators.

[0111] According to the regionalized energy storage configuration scheme, the molten salt energy storage system in the park is configured in a zonal and quantitative manner under the distributed control architecture;

[0112] Based on quantitative allocation, and combined with the real-time energy consumption status, demand fluctuations, and changes in three-dimensional quantitative indicators of each independent load area, regional collaborative scheduling management is implemented to achieve complementary regulation and collaborative energy supply of energy storage resources in multiple regions.

[0113] In this embodiment, based on the three-dimensional quantitative indicators and load type parameters corresponding to each independent load area, and according to the regionalized energy storage configuration rule library constructed in S3, the energy storage configuration parameters corresponding to each single load type are calculated respectively; the energy storage configuration parameters include the minimum effective molten salt quantity, rated heat release power and heat exchange matching coefficient.

[0114] For independent load areas containing multiple load types, the energy storage configuration parameters corresponding to each type of load are comprehensively processed according to the weighted superposition rule of S3 to obtain the comprehensive energy storage configuration parameters of the area; the comprehensive energy storage configuration parameters include the area's comprehensive minimum effective molten salt quantity, the area's comprehensive rated heat release power, and the area's comprehensive heat exchange matching coefficient;

[0115] The comprehensive energy storage configuration parameters are converted into feasible physical configuration indicators for engineering projects, specifically including:

[0116] The minimum effective molten salt volume in the region is used as the thermal storage capacity configuration index to determine the effective volume of the molten salt storage tank and the amount of molten salt to fill it.

[0117] The regional comprehensive rated heat release power is used as the heat release output index to determine the power level and adjustment range of the heat exchange loop;

[0118] The regional comprehensive heat exchange matching coefficient is used as a load fluctuation adaptation index to determine the regulation response speed and control range of the heat exchange system;

[0119] Based on the above-mentioned engineering physical configuration indicators and combined with the requirements for distributed zone independent control, a regional energy storage configuration scheme is generated. The regional energy storage configuration scheme specifically includes: molten salt thermal storage capacity configuration value for each region; upper limit setting value for heat release power for each region; adaptation level and adjustment strategy of heat exchange system for each region; independent operation constraints and safety control boundaries for each zone.

[0120] According to the regionalized energy storage configuration scheme, the molten salt energy storage system in the park is configured by zone under the distributed control architecture, and the heat storage capacity allocation, heat release power setting and heat exchange parameter configuration of each region are completed.

[0121] Based on quantitative allocation, and combined with the real-time energy consumption status, demand fluctuations, and changes in three-dimensional quantitative indicators of each independent load area, regional collaborative scheduling management is implemented; specifically including:

[0122] Adjust the heat release power output of the corresponding area according to the real-time changes in the energy shortage of each area;

[0123] The heat exchange matching intensity is adaptively adjusted according to the frequency of load fluctuations in each region.

[0124] At the distributed control level, complementary regulation and coordinated energy supply of energy storage resources in multiple regions are achieved, enabling the output of the molten salt energy storage system to match the load demand of each region in real time.

[0125] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0126] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for the energy management of molten salt storage in industrial parks based on distributed control, characterized in that: The method includes the following steps: S1. Obtain real-time energy consumption data of the park load within a continuous cycle through distributed acquisition terminals, and extract load demand characteristics; classify the load types based on the energy consumption time distribution and power change pattern of the park load, combined with the load demand characteristics; S2. Based on real-time energy consumption data and characteristic parameters, conduct quantitative analysis of demand for various types of loads to obtain the maximum daily energy gap, the duration of the gap, and the frequency of fluctuation per unit time. Construct a three-dimensional quantitative index system and form a load energy demand characteristic database. S3. Based on the independent energy consumption boundaries of each load area under distributed control, and combined with the operation and configuration requirements of molten salt energy storage, establish a quantitative mapping relationship between three-dimensional quantitative indicators and energy storage configuration parameters to form a regionalized energy storage configuration rule base; S3 includes the following: Based on the distributed control architecture, the park is divided into several independent load areas, and the independent energy consumption boundary and corresponding three-dimensional quantitative indicators of the load type of each area are determined. Based on the operational configuration requirements of molten salt energy storage systems, a quantitative correlation rule is established between three-dimensional quantitative indicators and energy storage configuration parameters: The minimum effective molten salt quantity is determined based on the maximum daily energy deficit, the duration of the deficit, and the molten salt thermal storage conversion coefficient; the rated heat release power is determined based on the ratio of the maximum daily energy deficit to the duration of the deficit, the power adjustment coefficient, and the standard heat release reference duration; and the heat exchange matching coefficient is determined based on the fluctuation frequency per unit time, the reference matching coefficient, and the fluctuation attenuation coefficient. For independent load areas containing multiple load types, the minimum effective molten salt quantity is weighted and summed using a weighted superposition rule. The maximum value of the rated heat release power and the minimum value of the heat exchange matching coefficient are taken to obtain the comprehensive energy storage configuration parameters for the area. The above-mentioned quantitative mapping relationships, weighted superposition rules, and independent energy consumption boundary constraints are integrated to form a regionalized energy storage configuration rule library; S4. Based on the regionalized energy storage configuration rule base, calculate the molten salt energy storage configuration parameters for each region, generate a regionalized energy storage configuration scheme, and implement quantitative configuration and regional collaborative scheduling management of molten salt energy storage in the park based on the regionalized energy storage configuration scheme.

2. The method for energy management of molten salt storage in industrial parks based on distributed control according to claim 1, characterized in that: S1 includes the following: According to the preset sampling time interval, the real-time energy consumption of each load node in the park is collected by the distributed acquisition terminal within the continuous statistical period to form the original energy consumption time series data; the original energy consumption time series data is smoothed by moving average to remove instantaneous disturbances and obtain smoothed energy consumption time series data. Based on the smoothed energy consumption time-series data, load demand characteristic parameters are extracted, including daily average energy consumption, energy consumption period concentration coefficient, and energy consumption fluctuation coefficient. The load is divided into continuous load, time-concentrated load, and random fluctuating load based on the relationship between the time-concentrated load, the energy consumption fluctuation coefficient and the corresponding threshold.

3. The method for energy management of molten salt storage in industrial parks based on distributed control according to claim 2, characterized in that: S2 includes the following: For each type of load identified, based on the corresponding smoothed energy consumption time series data, the rated energy supply power of each type of load within the statistical period is statistically analyzed, and the energy consumption gap is calculated time by time. When the energy consumption gap is greater than zero, it is determined that there is an energy consumption gap. The energy consumption gap at each time of day is statistically analyzed to obtain the maximum daily energy consumption gap; the duration of the gap is obtained by summing up all the times when the energy consumption gap occurs on a single day. Calculate the frequency of fluctuations per unit time based on the state changes of the energy gap; With the maximum daily energy shortage, the duration of the shortage, and the frequency of fluctuations per unit time as the core, a three-dimensional quantitative index system for various types of loads is constructed; the three-dimensional quantitative indexes, load demand characteristic parameters, and smoothed energy consumption power data of various types of loads are uniformly stored to form a load energy demand characteristic database.

4. The method for energy management of molten salt storage in industrial parks based on distributed control according to claim 1, characterized in that: S4 includes the following: Based on the three-dimensional quantitative indicators and load type parameters corresponding to each independent load area, the energy storage configuration parameters corresponding to each single load type are calculated according to the regionalized energy storage configuration rule library; the energy storage configuration parameters include the minimum effective molten salt quantity, rated heat release power and heat exchange matching coefficient. For an independent load area containing multiple load types, the energy storage configuration parameters corresponding to each type of load are comprehensively processed according to the weighted superposition rule to obtain the comprehensive energy storage configuration parameters of the area; the comprehensive energy storage configuration parameters include the area's comprehensive minimum effective molten salt quantity, the area's comprehensive rated heat release power, and the area's comprehensive heat exchange matching coefficient. The comprehensive energy storage configuration parameters are converted into engineering-feasible physical configuration indicators, and regionalized energy storage configuration schemes are generated based on these physical configuration indicators. According to the regionalized energy storage configuration scheme, the molten salt energy storage system in the park is configured in a zonal and quantitative manner under the distributed control architecture; Based on quantitative allocation, and combined with the real-time energy consumption status, demand fluctuations, and changes in three-dimensional quantitative indicators of each independent load area, regional collaborative scheduling management is implemented to achieve complementary regulation and collaborative energy supply of energy storage resources in multiple regions.

5. A distributed control-based molten salt energy storage management system for industrial parks, applied to the distributed control-based molten salt energy storage management method for industrial parks as described in claim 1, characterized in that: The system includes: a load sensing and classification module, a demand quantification and analysis module, an energy storage configuration rule construction module, and a zone configuration and scheduling module; The load sensing and classification module acquires real-time energy consumption data of the park load through distributed acquisition terminals, extracts load demand characteristics, and completes load type classification. The demand quantification analysis module performs demand quantification analysis on various types of loads, constructs a three-dimensional quantitative index system, and forms a load energy demand characteristic database. The energy storage configuration rule construction module establishes a quantitative mapping relationship between three-dimensional quantitative indicators and energy storage configuration parameters based on independent energy consumption boundaries, forming a regionalized energy storage configuration rule library; The energy storage configuration rule construction module includes a region division unit and a mapping rule unit; The regional division unit divides independent load regions according to the distributed control architecture and determines the energy consumption boundary and corresponding three-dimensional quantitative indicators for each region. The mapping rule unit establishes quantitative association rules between three-dimensional quantitative indicators and minimum effective molten salt quantity, rated heat release power, and heat exchange matching coefficient. It performs weighted superposition calculations on multi-load type areas to form a regional energy storage configuration rule library. The partition configuration and scheduling module calculates the energy storage configuration parameters for each region based on the regional energy storage configuration rule base, generates regional energy storage configuration schemes, and performs quantitative configuration and regional collaborative scheduling management.

6. The industrial park molten salt energy storage management system based on distributed control according to claim 5, characterized in that: The load sensing and classification module includes a data acquisition and processing unit and a load classification unit; The data acquisition and processing unit collects real-time energy consumption power at a preset sampling interval, and performs a moving average smoothing process on the original power time series data to obtain smoothed energy consumption power time series data. The load classification unit extracts load demand characteristic parameters based on smoothed power data and classifies the load into continuous load, time-concentrated load, and random fluctuation load according to a preset threshold.

7. The industrial park molten salt energy storage management system based on distributed control according to claim 5, characterized in that: The demand quantification analysis module includes a gap calculation unit and an indicator construction unit; The gap calculation unit statistically analyzes the rated power supply of each type of load within the statistical period, calculates the power gap at each moment, and obtains the maximum daily power gap, the duration of the gap, and the frequency of fluctuation per unit time. The indicator construction unit constructs a three-dimensional quantitative indicator system based on the maximum daily energy demand gap, the duration of the gap, and the frequency of fluctuations per unit time, forming a database of load energy demand characteristics.

8. The industrial park molten salt energy storage management system based on distributed control according to claim 5, characterized in that: The partition configuration and scheduling module includes a parameter calculation unit and a scheduling execution unit; The parameter calculation unit calculates the energy storage configuration parameters for each region based on the regional energy storage configuration rule base, converts the configuration parameters into physical configuration indicators, and generates a regional energy storage configuration scheme. The scheduling execution unit performs zonal quantitative configuration according to the regional energy storage configuration scheme, and performs regional collaborative scheduling in combination with real-time energy consumption status to realize complementary regulation and collaborative energy supply of multi-regional energy storage resources.

Images