Energy storage distribution control method and device of multi-energy complementary system

Abstract

The invention provides an energy storage distribution control method and device for a multi-energy complementary system, and relates to the field of steel energy, and the method comprises the steps: carrying out the self-adaptive variational mode decomposition of an active power data sample of a gateway incoming line of a steel enterprise before energy storage configuration, and determining the energy storage type and maximum energy storage output power required by the steel enterprise; solving an optimal solution of an energy storage optimal configuration objective function according to a preset energy storage rate and the maximum energy storage output power, and determining an energy storage configuration capacity; after energy storage configuration is carried out according to the energy storage type and the energy storage configuration capacity, an active power prediction model is utilized to predict enterprise gateway active power and green power active power in a preset future time period; and performing real-time control on the multi-energy complementary system according to the enterprise gateway active power and the green power active power. The problems that the multi-energy utilization rate of iron and steel enterprises is low, and the proportion of self-generating electricity and green electricity is limited can be solved, and the purposes of reducing the electricity demand of the enterprises, clipping peaks and filling valleys and improving the energy utilization rate are achieved.

Description

Technical Field

[0001] The present application relates to the field of steel energy, and specifically to an energy storage and control method and device for a multi-energy complementary system. Background Art

[0002] At present, the self-generation rate of large and medium-sized steel enterprises can generally exceed 60%. They mainly utilize the waste gas (coke oven, blast furnace, converter gas, etc.), waste heat (multiple links), waste energy (blast furnace gas waste pressure turbine-TRT power generation, etc.) and photovoltaic power generation generated in the production process to form a multi-energy system. Through energy storage and its control system, the peak and valley electricity consumption and demand of the above-mentioned energy sources can be adjusted, and the characteristics of different response times, processes, and electricity usage habits of various energy sources can be fully utilized to achieve multi-energy complementarity and improve the utilization rate of the company's self-generation, the proportion of green electricity access and energy efficiency.

[0003] However, the existing energy storage technology has not proposed an analysis of the load characteristics of large industrial users such as steel enterprises, nor has it proposed a suitable type of energy storage, capacity configuration plan and control method. On the one hand, the existing technology has not proposed a method for energy storage (batteries, other such as flywheels, supercapacitors, hydrogen energy, etc.) to "complement" with various energy generation in steel enterprises from the perspective of time and space characteristics (such as continuous discharge time, response speed, cycle life, etc.) to improve energy utilization; on the other hand, the existing technology is mainly divided into two directions: system-side centralized energy storage and user-side energy storage. For user-side energy storage, the configuration and peak-valley electricity fee benefit model are mainly given from different perspectives, and some constraints and benefit models are given from the demand perspective, but the problem of the simultaneity of energy storage "valley charging-peak discharge" and large industrial electricity consumption is not fully considered, and it is only applicable to situations where the load fluctuation is not large, the frequency is low, and it is relatively regular. The optimal initial configuration plan and control strategy for steel enterprises have not been proposed, resulting in excessive or too small storage capacity and failure of control strategy.

[0004] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No admission is made that the description herein is prior art by inclusion in this section. Summary of the invention

[0005] In response to the problems in the prior art, the present application provides a method and device for energy storage and control of a multi-energy complementary system, which can solve the problems of low multi-energy utilization, limited proportion of self-generation and green electricity in steel enterprises, and achieve the goal of reducing the enterprise's electricity demand, shaving peaks and filling valleys, and improving energy utilization.

[0006] In order to solve the above technical problems, this application provides the following technical solutions:

[0007] In a first aspect, the present application provides an energy storage control method for a multi-energy complementary system, comprising:

[0008] Adaptive variational mode decomposition is performed on the active power data samples of the tie line between the steel enterprise and the external power grid before energy storage configuration to determine the energy storage type and maximum storage output power required by the steel enterprise;

[0009] Obtaining the optimal solution of the energy storage optimization configuration objective function according to the preset energy storage energy ratio and the maximum energy storage output power, and determining the energy storage configuration capacity;

[0010] After energy storage configuration is performed based on the energy storage type and the energy storage configuration capacity, a pre-trained active power prediction model is used to predict the enterprise's gateway active power and green power active power in a preset future period;

[0011] The multi-energy complementary system is controlled in real time according to the enterprise gateway active power and green electricity active power.

[0012] Furthermore, the active power data samples of the steel enterprise gateway incoming line before the energy storage configuration are subjected to adaptive variational mode decomposition to determine the energy storage type and maximum energy storage output power required by the steel enterprise, including:

[0013] Performing adaptive variational modal decomposition on the active power data sample to obtain an active power modal number and an active power modal component of the active power data sample;

[0014] Determining the energy storage response time and the maximum energy storage output power according to the active power modal number and the active power modal component;

[0015] The energy storage type is determined according to the energy storage response time.

[0016] Furthermore, obtaining the optimal solution of the energy storage optimization configuration objective function according to the preset energy storage energy ratio and the maximum energy storage output power to determine the energy storage configuration capacity includes:

[0017] Constructing a maximum required energy storage capacity constraint condition according to the energy storage energy ratio and the maximum energy storage output power, and iterating and obtaining the energy storage optional capacity based on the maximum required energy storage capacity constraint condition with a variable step size;

[0018] The energy storage life span is determined based on the number of equivalent full cycles in the energy storage life cycle, the number of days per year participating in peak-valley arbitrage, the number of days per year participating in demand regulation, and the shallow discharge and shallow charging loss coefficient;

[0019] Determine the peak-valley income based on the number of days of peak-valley arbitrage participation per year, the duration of valley charging and peak discharge, the duration of flat charging and peak discharge, the exchange power and the life of the energy storage over the entire life cycle;

[0020] Determine the demand revenue based on the annual reduced demand months and demand unit price;

[0021] Determine the initial construction cost based on the optional capacity of the energy storage;

[0022] Determine the operation and maintenance cost according to the optional capacity of the energy storage and the life of the energy storage throughout the life cycle;

[0023] The energy storage optimization configuration objective function is generated according to the initial construction cost, the operation and maintenance cost, the peak-valley income, and the demand income, and the energy storage optional capacity when the energy storage optimization configuration objective function reaches the optimal solution is determined as the energy storage configuration capacity.

[0024] Furthermore, after energy storage configuration is performed based on the energy storage type and the energy storage configuration capacity, a pre-trained active power prediction model is used to predict the enterprise's gateway active power and green power active power in a preset future period, including:

[0025] After energy storage configuration is performed according to the energy storage type and the energy storage configuration capacity, a load impact factor is obtained; wherein the load impact factor includes a load prediction impact factor and a green electricity prediction impact factor;

[0026] Performing correlation analysis and differentiation processing on the load influencing factors to obtain preprocessing results;

[0027] The preprocessing result is input into the active power prediction model to obtain the enterprise gateway active power and green electricity active power; wherein the active power prediction model is a fusion model of long short-term memory neural network and support vector machine.

[0028] Furthermore, the real-time control of the multi-energy complementary system according to the enterprise gateway active power and the green electricity active power includes:

[0029] Performing slip calculation on the active power data samples to obtain the maximum active power demand;

[0030] Adjusting the control strategy of the multi-energy complementary system according to the maximum active power demand, the enterprise gateway active power, the green power active power and the energy storage configuration capacity;

[0031] The multi-energy complementary system is controlled in real time according to the control strategy.

[0032] In a second aspect, the present application provides an energy storage control device for a multi-energy complementary system, comprising:

[0033] The energy storage type and capacity determination unit is used to perform adaptive variational mode decomposition on the active power data samples of the steel enterprise gateway before energy storage configuration to determine the energy storage type and maximum energy storage output power required by the steel enterprise;

[0034] A configuration capacity determination unit, used to obtain an optimal solution of the energy storage optimization configuration objective function according to a preset energy storage energy ratio and the maximum energy storage output power, and determine the energy storage configuration capacity;

[0035] An active power prediction unit, configured to predict the enterprise's gateway active power and green power active power in a preset future period by using a pre-trained active power prediction model after energy storage configuration based on the energy storage type and the energy storage configuration capacity;

[0036] The energy storage control unit is used to perform real-time control of the multi-energy complementary system according to the enterprise gateway active power and the green electricity active power.

[0037] Furthermore, the energy storage type capacity determination unit includes:

[0038] A modal decomposition module, used for performing adaptive variational modal decomposition on the active power data sample to obtain the active power modal number and active power modal component of the active power data sample;

[0039] A time capacity determination module, used to determine the energy storage response time and the maximum energy storage output power according to the active power modal number and the active power modal component;

[0040] An energy storage type determination module is used to determine the energy storage type according to the energy storage response time.

[0041] Furthermore, the configuration capacity determination unit includes:

[0042] An optional capacity determination module is used to construct a maximum required energy storage capacity constraint condition according to the energy storage energy ratio and the maximum energy storage output power, and iterate and obtain the energy storage optional capacity based on the maximum required energy storage capacity constraint condition with a variable step size;

[0043] The energy storage life determination module is used to determine the energy storage life of the entire life cycle according to the number of equivalent full cycles of the energy storage life cycle, the number of days participating in peak-valley arbitrage per year, the number of days participating in demand regulation per year, and the shallow discharge and shallow charging loss coefficient;

[0044] A peak-valley income determination module is used to determine the peak-valley income based on the number of days participating in peak-valley arbitrage in a year, the duration of valley charging and peak discharge, the duration of flat charging and peak discharge, the exchange power and the life of the energy storage in the entire life cycle;

[0045] Demand revenue determination module, used to determine demand revenue based on annual reduced demand months and demand unit price;

[0046] An initial construction cost determination module, used to determine the initial construction cost according to the optional capacity of the energy storage;

[0047] An operation and maintenance cost determination module, used to determine the operation and maintenance cost according to the energy storage optional capacity and the full life cycle energy storage life;

[0048] A configuration capacity determination module is used to generate the energy storage optimization configuration objective function according to the initial construction cost, the operation and maintenance cost, the peak-valley income, and the demand income, and determine the energy storage optional capacity when the energy storage optimization configuration objective function reaches the optimal solution as the energy storage configuration capacity.

[0049] Furthermore, the active power prediction unit comprises:

[0050] An influence factor acquisition module, used to acquire a load influence factor after performing energy storage configuration according to the energy storage type and the energy storage configuration capacity; wherein the load influence factor includes a load prediction influence factor and a green electricity prediction influence factor;

[0051] A factor analysis processing module is used to perform correlation analysis and differentiation processing on the load influencing factors to obtain preprocessing results;

[0052] The active power calculation module is used to input the preprocessing results into the active power prediction model to obtain the enterprise gateway active power and green electricity active power; wherein the active power prediction model is a fusion model of long short-term memory neural network and support vector machine.

[0053] Furthermore, the complementary system control unit comprises:

[0054] A power demand calculation module, used for performing slip calculation on the active power data sample to obtain the maximum active power demand;

[0055] An energy storage control module, used to adjust the control strategy of the multi-energy complementary system according to the maximum active power demand, the enterprise gateway active power, the green power active power and the energy storage configuration capacity;

[0056] A real-time control module is used to perform real-time control on the multi-energy complementary system according to the control strategy.

[0057] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the energy storage control method of the multi-energy complementary system when executing the program.

[0058] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the energy storage control method of the multi-energy complementary system.

[0059] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the energy storage control method of the multi-energy complementary system.

[0060] In response to the problems in the prior art, the energy storage control method and device of the multi-energy complementary system provided by the present application can solve the problems of multi-energy complementary storage type selection and capacity configuration optimization from a mechanism perspective, targeting the multi-energy complementary characteristics of steel enterprises with a high proportion of impact loads and multi-energy power generation, thereby avoiding over- or under-configuration of energy storage capacity and reducing energy storage life loss; thereby solving the problem that enterprises are unable to further increase the proportion of self-generation due to impact load fluctuations, and the increase in demand or inability to operate at full load due to the access of large-proportion fluctuating green electricity, resulting in the inability to fully utilize the company's surplus energy or green electricity and wasting energy; among them, the adaptive The variational modal decomposition-Hilbert marginal spectrum method can obtain the results with the lowest modal aliasing and false components, thereby overcoming the over-decomposition or under-decomposition problems caused by the subjective setting of the decomposition scale in the existing algorithms; the maximum demand discharge method proposed by it is used to establish an enterprise energy storage optimization configuration model, which solves the problems of excessive demand, low return on investment and rapid battery life loss caused by only considering the peak and valley electricity tariff benefits and unreasonable demand management targets; in addition, it also constructs a set of steel enterprise energy storage control devices that include integrated acquisition, calculation and control. It has low hardware requirements and the calculation and control links can be implemented using ordinary personal computers. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 This is the power curve of the day when the maximum demand of one 220kV incoming line of a certain enterprise occurs in the embodiment of the present application;

[0063] Figure 2 The demand curve of the day of the maximum demand month calculated by the sliding difference method in the embodiment of the present application;

[0064] Figure 3 The original data and the power curve of each modal component in the embodiment of the present application;

[0065] Figure 4 The IMF1 modal component power and Hilbert marginal spectrum curve in the embodiment of the present application;

[0066] Figure 5The IMF2 modal component power and Hilbert marginal spectrum curve in the embodiment of the present application;

[0067] Figure 6 The IMF3 modal component power and Hilbert marginal spectrum curve in the embodiment of the present application;

[0068] Figure 7 The IMF4 modal component power and Hilbert marginal spectrum curve in the embodiment of the present application;

[0069] Figure 8 The IMF5 modal component power and Hilbert marginal spectrum curve in the embodiment of the present application;

[0070] Fig. 9 This is a flow chart of the energy storage control method of the multi-energy complementary system in the embodiment of the present application;

[0071] Fig.10 A flow chart for determining the energy storage type and maximum energy storage output power required by a steel enterprise in an embodiment of the present application;

[0072] Fig.11 A flowchart for determining energy storage configuration capacity in an embodiment of the present application;

[0073] Fig.12 This is a flow chart of preset enterprise threshold active power and green power active power in the embodiment of the present application;

[0074] Fig.13 A flow chart for adjusting the energy storage configuration of the multi-energy complementary system in an embodiment of the present application;

[0075] Fig.14 This is a structural diagram of the energy storage control device of the multi-energy complementary system in the embodiment of the present application;

[0076] Fig.15 This is a structural diagram of the energy storage type capacity determination unit in an embodiment of the present application;

[0077] Fig.16 This is a structural diagram of a configuration capacity determination unit in an embodiment of the present application;

[0078] Fig.17 This is a structural diagram of an active power prediction unit in an embodiment of the present application;

[0079] Fig.18 This is a structural diagram of a complementary system control unit in an embodiment of the present application;

[0080] Fig.19 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0081] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0082] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0083] Provide users with corresponding operation entrances for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0084] In one embodiment, see Fig. 9 In order to solve the problems of low multi-energy utilization rate, limited proportion of self-generation and green electricity in steel enterprises, and to achieve the goals of reducing enterprise electricity demand, peak load shifting and valley filling, and improving energy utilization rate, this application provides a storage control method for a multi-energy complementary system, including:

[0085] S101: performing adaptive variational mode decomposition on active power data samples of the steel enterprise’s gateway incoming line before energy storage configuration to determine the energy storage type and maximum energy storage output power required by the steel enterprise;

[0086] S102: Obtaining an optimal solution of an energy storage optimization configuration objective function according to a preset energy storage energy ratio and the maximum energy storage output power, and determining an energy storage configuration capacity;

[0087] S103: After energy storage configuration is performed based on the energy storage type and the energy storage configuration capacity, a pre-trained active power prediction model is used to predict the enterprise's gateway active power and green power active power in a preset future period;

[0088] S104: Real-time control of the multi-energy complementary system is performed according to the enterprise gateway active power and the green electricity active power (it can also be understood as adjusting the control strategy of the multi-energy complementary system according to the enterprise gateway active power and the green electricity active power).

[0089] It is understandable that the self-generation rate of large and medium-sized steel enterprises can generally exceed 60%, mainly using the waste gas (coke oven, blast furnace, converter gas, etc.), waste heat (multiple links), waste energy (blast furnace gas waste pressure turbine-TRT power generation, etc.) and photovoltaic power generation generated in the production process. Since the power grid of steel enterprises has a large proportion of high-power impact loads such as rolling mills and LF furnaces, the power off the grid shows a large-scale, frequent, and weak regularity of fluctuation characteristics. It is necessary to efficiently use a variety of "energy + energy storage" technologies to achieve "multi-energy complementarity" to improve the energy utilization rate of enterprises. In addition, enterprises have successively connected large-capacity solar and wind power generation to the enterprise power grid. Green electricity has developed from distributed small-capacity rooftop photovoltaics to large-scale centralized photovoltaic and wind power generation (hundreds of megawatts). The access of large-scale green electricity has introduced more instability and randomness to corporate electricity consumption.

[0090] The present invention proposes an energy storage configuration scheme, control method and device for the multi-energy complementary system of steel enterprises, which is used to solve the problems of low multi-energy utilization rate, limited self-generation and green electricity ratio in steel enterprises, and achieve the goal of reducing the enterprise's electricity demand, shaving peaks and filling valleys, and improving energy utilization. The method provided in this application is applicable to the full life cycle solution of energy storage projects, which can be divided into configuration links and operation links.

[0091] To this end, the energy storage control method for the multi-energy complementary system of steel enterprises proposed in this application mainly includes the following two links:

[0092] First, the configuration link. The amplitude-frequency characteristics of the steel enterprise's key load data are decomposed to match the appropriate energy storage method, that is, the energy storage type and maximum energy storage output power required by the enterprise are determined through analysis and calculation, so as to "complement" the power generation of various energy sources such as surplus gas, surplus heat, and surplus energy in the enterprise, and the power generation of various energy sources is "replaced at the right time" through reasonably configured energy storage. Comprehensive consideration is given to reducing demand, peak shaving and valley filling, initial construction and operation and maintenance costs, and energy storage life cycle losses. An enterprise energy storage optimization configuration model is established to determine the optimal energy storage configuration capacity, and solve the problem of low energy utilization of enterprises caused by excessive or too small energy storage configuration.

[0093] Second, the operation link. With the goal of optimizing the energy utilization rate of the joint operation of multi-energy power generation and energy storage, short-term data forecasts are conducted on the enterprise's key loads and self-provided power generation, and the forecast results are decomposed into amplitude-frequency characteristics to generate future energy storage-multi-energy power generation power control strategies, thereby reducing electricity demand, achieving efficient utilization of multi-energy complementarity through peak shaving and valley filling, and extending the service life of energy storage batteries to solve problems such as excessive demand, insufficient peak-valley regulation, and rapid battery life loss.

[0094] From the above description, it can be seen that the energy storage control method and device of the multi-energy complementary system provided by the present application can solve the problem of multi-energy complementary storage type selection and capacity configuration optimization from a mechanism perspective, aiming at the multi-energy complementary characteristics of steel enterprises containing a high proportion of impact loads and multi-energy power generation, thereby avoiding excessive or too small configuration of energy storage capacity and reducing energy storage life loss; thereby solving the problem that enterprises are unable to further increase the proportion of self-generation due to impact load fluctuations, and the increase in demand or inability to operate at full load due to the access of large-proportion fluctuating green electricity, resulting in the inability to fully utilize the company's surplus energy or green electricity and wasting energy; among them, the adaptive variable involved The modal decomposition-Hilbert marginal spectrum method can obtain the results with the lowest modal aliasing and false components, thereby overcoming the over-decomposition or under-decomposition problems caused by the subjective setting of the decomposition scale in the existing algorithms; the maximum demand discharge method proposed by it is used to establish an enterprise energy storage optimization configuration model, which solves the problems of excessive demand, low return on investment and rapid battery life loss caused by only considering the peak and valley electricity tariff benefits and unreasonable demand management targets; in addition, it also constructs a set of steel enterprise energy storage control devices that include integrated acquisition-computation-control, which has low hardware requirements, and the calculation and control links can be implemented using ordinary personal computers.

[0095] In one embodiment, see Fig.10 , the adaptive variational modal decomposition of active power data samples of the steel enterprise gateway before energy storage configuration to determine the energy storage type and maximum energy storage output power required by the steel enterprise includes:

[0096] S201: performing adaptive variational modal decomposition on the active power data sample to obtain an active power modal number and an active power modal component of the active power data sample;

[0097] S202: Determine the energy storage response time and the maximum energy storage output power according to the active power modal number and the active power modal component;

[0098] S203: Determine the energy storage type according to the energy storage response time.

[0099] It can be understood that steps S201 to S203 are implemented through the following two processes.

[0100] Process 1: Obtain the active power of the connection line between the steel enterprise and the external power grid before allocation and storage (also called the active power of the steel enterprise's gateway incoming line) as a data sample sequence. The demand is the 15-minute average of the active power, and the data granularity is 1 minute for greater accuracy, that is, p2-p1=1min.

[0101] For accuracy considerations, the data requirements are as follows: The sample period should include at least one full-load production cycle, and the monthly active power data with the maximum demand in the previous year [p1,...,pt ], the total data points t = 30 × 24 × 60 = 43200.

[0102] The sliding difference method is used to calculate the 15-minute demand data sample [P1,...,P r ], determine the maximum active power demand The maximum active power demand is also used as basic data in the calculation from step S501 to step S502.

[0103] Process 2: Adaptive variational mode decomposition-Hilbert marginal spectrum method (VHT, VMD-Hilbert) is used to analyze the data sample [p1,...,p t ] It can adaptively determine the modal number k1 of the active power decomposition of the data sample and the active power components of each modal In order to obtain the lowest modal aliasing and false component of the data samples, the over-decomposition or under-decomposition problem caused by the subjective setting of the decomposition scale in the existing VMD (Variational Mode Decomposition) algorithm is overcome.

[0104] Specifically, in order to reduce the degree of modal aliasing and avoid under-decomposition, the embodiment of the present application introduces the frequency interval constraint condition of the middle value area of ​​the frequency domain envelope: in, is the modal component IMF i The lower limit of the Hilbert marginal spectrum (the frequency band corresponding to the middle value), is the modal component IMF i Hilbert marginal spectrum (frequency band corresponding to the middle value) upper limit, △f i,i+1 is the difference between the lower limit and the upper limit, It represents the decomposition mode number k1 with the most dispersed distribution of modal components.

[0105] Specifically, in order to reduce the degree of false components and avoid over-decomposition, the embodiment of the present application introduces the synthetic difference constraint condition: k1 is 1…7. Among them, the active power division point is And the maximum power value of this frequency interval It can be used as a basis for configuring self-generation within the enterprise and selecting the type of energy storage. For details, please see the "Response and power constraints of self-generation and energy storage selection" below.

[0106] Response and power constraints of self-generation and energy storage selection:

[0107] where t react is the response time, P b is the rated power, k xFor all matching modes (1,…,k1), is the kxth active power division frequency point, is the maximum power value of the kxth frequency interval.

[0108] According to the above formula, the energy storage response time t described in step S202 can be determined react and the maximum energy storage output power P b . Afterwards, due to different response times t react Corresponding to different energy storage types, it can be based on the energy storage response time t react Determine the energy storage type. For example, if the active power response time t react If it is at the minute level, it matches the characteristics of the factory's self-provided generating sets and is suitable for adjustment by them; if it is at the second level, it matches the electrochemical energy storage battery and is suitable for adjustment by them; if it is at the millisecond to second level, it matches the flywheel energy storage or supercapacitor, and considering the output capacity if it is below the MW level, it is suitable for adjustment by them.

[0109] From the above description, it can be seen that the energy storage control method of the multi-energy complementary system provided in this application can perform adaptive variational modal decomposition on the active power data samples of the steel enterprise's gateway incoming line before energy storage configuration, and determine the energy storage type and maximum energy storage output power required by the steel enterprise.

[0110] In one embodiment, see Fig.11 , obtaining the optimal solution of the energy storage optimization configuration objective function according to the preset energy storage energy ratio and the maximum energy storage output power, and determining the energy storage configuration capacity, includes:

[0111] S301: constructing a maximum required energy storage capacity constraint condition according to the energy storage energy ratio and the maximum energy storage output power, and iterating and obtaining the energy storage optional capacity based on the maximum required energy storage capacity constraint condition with a variable step size;

[0112] S302: Determine the full life cycle energy storage life according to the number of equivalent full cycles of the full life cycle of the energy storage, the number of days participating in peak-valley arbitrage per year, the number of days participating in demand regulation per year, and the shallow discharge and shallow charge loss coefficient;

[0113] S303: Determine the peak-valley income according to the annual number of days participating in peak-valley arbitrage, the duration of valley charging and peak discharge, the duration of flat charging and peak discharge, the exchange power and the life of the energy storage in the whole life cycle;

[0114] S304: Determine the demand revenue according to the annual reduced demand month and the demand unit price;

[0115] S305: Determine the initial construction cost according to the optional capacity of the energy storage;

[0116] S306: Determine operation and maintenance costs according to the optional capacity of the energy storage and the full life cycle energy storage life;

[0117] S307: Generate the energy storage optimization configuration objective function according to the initial construction cost, the operation and maintenance cost, the peak-valley income, and the demand income, and determine the energy storage optional capacity when the energy storage optimization configuration objective function reaches the optimal solution as the energy storage configuration capacity.

[0118] It can be understood that steps S301 to S307 correspond to the process of establishing an enterprise energy storage optimization configuration model using the maximum demand method (also referred to as process three).

[0119] ① Objective function of enterprise energy storage optimization configuration model:

[0120] maxf1=R1+R2-C b -C m ;

[0121] Among them, maxf1 represents the maximum total benefit of energy storage throughout its life cycle; R1 is the peak and valley electricity fee benefit; R2 is the demand benefit; C b is the initial construction cost; C m For operation and maintenance costs.

[0122] In order to obtain the maximum benefit, the battery capacity E should obtain the benefits of peak-valley arbitrage and demand reduction at the same time. Since the demand appears randomly and has poor fault tolerance, the battery capacity E needs to meet the peak-valley arbitrage capacity E1 twice a day, and reserve enough capacity E2 to ensure the monthly demand P' m＝112 Control at the monthly demand adjustment target value P' max Below, the optimal capacity of the battery is determined based on the maximum demand requirement.

[0123] ② Energy storage rate constraints:

[0124] Where E is the energy storage capacity, η is the energy storage rate (0.5C, 1C, etc.), P b The energy storage energy ratio is an important indicator for evaluating the reliability and efficiency of energy storage systems. It is specifically defined as the ratio of the output power of the energy storage system to the rated capacity, which reflects the ability of the energy storage system to store and release energy.

[0125] ③ Maximum required battery capacity constraints:

[0126]

[0127] Among them, E1 is the energy storage capacity participating in peak-valley regulation, and E2 is the energy storage capacity participating in demand regulation. The energy storage capacity is required to be no less than the monthly demand regulation target power and the duration of participation in regulation per unit day, P maxis the maximum required active power value before the aforementioned allocation and storage, P' max is the monthly demand regulation target value active power, a is [P1,...,P r ] is greater than P' max Number of occurrences, t c is the duration of the corresponding cumulative occurrences per day. Based on different η, the optimal energy storage configuration scheme can be obtained through variable step size iteration;

[0128] For example, in the iterative process of solving E1 and E2 for 0.5C and 1C respectively, each iteration can be calculated according to ΔP1=1%P b and ΔP2 = 10%P b Two step sizes are used to solve the objective function, with △P being the step size. If the objective function with a large step size is better than the objective function with a small step size, the result with the large step size is selected as the initial value for the next iteration, and the iteration is still performed with two step sizes; if the result with a small step size is better, the result with the small step size is selected as the initial value for the next iteration, and △P1=1%P is used. b and ΔP2 = 5%P b At the same time, the objective function is solved for comparison.

[0129] It should be noted that the demand target P' max The lower the demand, the lower the value (P max -P' max ) is lower, the demand benefit is higher, but the number of times a and the adjustment time t required to participate in the adjustment are c The longer it is, the shorter the battery life will be, which will affect the overall benefit. At the same time, if the battery capacity is enlarged, the part that exceeds the demand can only participate in peak and valley regulation, which will also affect the objective function result.

[0130] The embodiment of the present application proposes to limit the total equivalent cycle number within one day based on the equivalent full cycle number of two deep discharges and deep charges and a shallow discharges and shallow charges per day.

[0131] This constraint condition corresponds to the iterative determination of the optional energy storage capacity in step S301. The P obtained in each iteration b When a certain energy storage optional capacity satisfies other constraints and can make the "objective function of the enterprise energy storage optimization configuration model" reach the optimal solution, the energy storage optional capacity can be determined as the "energy storage configuration capacity" in step S307.

[0132] ④Full life cycle energy storage life constraints;

[0133]

[0134] Among them, A is the energy storage life of the whole life cycle; N is the equivalent full cycle number of energy storage life cycle; N R1N is the number of days per year for peak-to-valley arbitrage (a given value preset according to actual working conditions); R2 is the number of days participating in demand regulation in a year (a given value preset according to actual working conditions); λ(a) is the loss coefficient for a shallow discharge and shallow filling.

[0135] ⑤ The peak-valley electricity fee income R1 in the objective function is expressed as:

[0136] Among them, A is the energy storage life of the whole life cycle, T year is the number of days participating in peak-to-valley arbitrage per year, s peak 、s vally 、s flat The unit prices of electricity in peak, valley and flat periods are respectively, t peak ,t flat They are the duration of valley charge-peak discharge and flat charge-peak discharge, ρ i , j The exchange power of each section is in turn, where i and j represent the date.

[0137] ⑥ Valley charging and peak discharge, flat charging and peak discharge duration and switching power constraints of each section:

[0138]

[0139] ⑦ The demand electricity fee income R2 in the objective function is expressed as:

[0140] R2=AT month s n (P m ' ax -P max ), where A is the energy storage life of the entire life cycle, T month is the annual demand reduction month, s n The unit price of demand electricity.

[0141] ⑧ Initial construction cost C in the objective function b It is expressed as:

[0142] C b =E×s b , where s b It is the unit price of construction per unit capacity.

[0143] ⑨Operation and maintenance cost C in the objective function m It is expressed as:

[0144] C m =A×E×s m , where s m It is the annual maintenance price per unit capacity.

[0145] The same symbols in each formula have the same meaning and will not be repeated here.

[0146] From the above description, it can be seen that the energy storage configuration control method of the multi-energy complementary system provided in the present application can obtain the optimal solution of the energy storage optimization configuration objective function according to the preset energy storage energy ratio and the maximum energy storage output power, and determine the energy storage configuration capacity.

[0147] In one embodiment, see Fig.12 After the energy storage configuration is performed based on the energy storage type and the energy storage configuration capacity, the pre-trained active power prediction model is used to predict the enterprise's gateway active power and green power active power in a preset future period, including:

[0148] S401: After performing energy storage configuration according to the energy storage type and the energy storage configuration capacity, a load impact factor is obtained; wherein the load impact factor includes a load prediction impact factor and a green electricity prediction impact factor;

[0149] S402: performing correlation analysis and differentiation processing on the load influencing factors to obtain preprocessing results;

[0150] S403: Input the preprocessing result into the active power prediction model to obtain the enterprise gateway active power and green electricity active power; wherein the active power prediction model is a fusion model of long short-term memory neural network and support vector machine.

[0151] It can be understood that steps S401 to S403 are the operating links of the embodiment of the present application, which specifically correspond to the following two steps.

[0152] Step 1: Real-time monitoring of the enterprise's active power value, green power active power value and system topology. The monitoring topology is used to determine the corresponding relationship between controlled energy storage, self-generated power generation and load, and green power, and the sampling frequency is no longer than 1 minute.

[0153] Step 2: Load forecast the green power active power value and enterprise threshold load value in the preset future time period, determine the power value and time of energy storage, self-generation, and green power input (for example, in the next 15 minutes), and formulate energy storage control strategy in real time.

[0154] The long short-term memory neural network model (Adam-LSTM) of the adaptive learning algorithm (Adam) is used to make short-term predictions of the electricity load of steel enterprises. The covariance and Pearson algorithms are used to perform correlation analysis and differentiation processing on the load influencing factors respectively to obtain the preprocessing results. Six attributes, including historical threshold load, single impact equipment load, date, steel price, electricity price, and iron ore price, are selected as load forecasting influencing factors, and four attributes, including date, time period, temperature, and wind speed, are selected as green electricity forecasting influencing factors. LSTM and SVM (Support Vector Machine) are integrated through fuzzy weight logic to obtain the final green electricity and load forecast results for the next 15 minutes.

[0155] From the above description, it can be seen that the energy storage control method of the multi-energy complementary system provided in the present application can configure energy storage based on the energy storage type and the energy storage configuration capacity, and then use the pre-trained active power prediction model to predict the enterprise's gateway active power and green electricity active power in a preset future time period.

[0156] In one embodiment, see Fig.13 , the real-time control of the multi-energy complementary system according to the enterprise gateway active power and the green electricity active power includes:

[0157] S501: performing slip calculation on the active power data sample to obtain the maximum active power demand; wherein, the slip calculation process refers to the description in the aforementioned “Process 1”;

[0158] S502: adjusting a control strategy of a multi-energy complementary system according to the maximum active power demand, the enterprise gateway active power, the green power active power and the energy storage configuration capacity;

[0159] S503: Control the multi-energy complementary system in real time according to the control strategy.

[0160] It can be understood that according to the prediction results of steps S401 to S403, by analyzing the characteristics of future loads and green electricity based on the adaptive variational mode decomposition-Hilbert marginal spectrum method (VHT, VMD-Hilbert), the above method can obtain the response time and maximum output power value suitable for energy storage and self-generation regulation.

[0161] By adopting the method in the aforementioned configuration link, according to the maximum demand method (see the description in the aforementioned steps S301 to S307), the energy storage and self-generation output power values ​​obtained based on the optimal control target in the next 15 minutes are predicted in real time, so as to correct the control strategy to obtain the optimal multi-energy complementary control result.

[0162] Among them, the control strategy refers to: adjusting the value of energy storage and the active power output of the self-generating set.

[0163] It can be seen from the above description that the energy storage control method of the multi-energy complementary system provided in the present application can perform real-time control of the multi-energy complementary system according to the enterprise gateway active power and the green electricity active power.

[0164] In order to verify the effectiveness and rationality of the energy storage configuration scheme, control system and device for the multi-energy complementary system for steel enterprises described in the present invention, a large and medium-sized steel plant is taken as an example to analyze its energy storage configuration scheme.

[0165] The company has two 220kV main step-down substations and four 220kV incoming lines. For one of the 220kV incoming lines, an energy storage configuration plan is planned according to the method proposed in this patent.

[0166] Step 1: Obtain the maximum demand month of active power of one of the enterprise’s 220 kV incoming lines before allocation and storage as the planning data sample sequence [p1,...,p t ], Figure 1 This is the power curve for the day of the month with maximum demand. The horizontal axis unit is min and the vertical axis unit is MW.

[0167] The sliding difference method is used to calculate the 15-minute demand data sample [P1,...,P r ], determine the maximum demand Figure 2 This is the demand curve for the day of the calculated maximum demand month, with the horizontal axis in units and the vertical axis in MW.

[0168] Step 2: Adaptive variational mode decomposition-Hilbert marginal spectrum method (VHT, VMD-Hilbert) is used to analyze the data sample [p1,...,p t ], it is determined that k1 = 5 is the optimal solution,

[0169] The IMF curves and Hilbert marginal spectrum distributions of each modal component are as follows: Figures 3 to 8 shown.

[0170] The corresponding modal components, frequency division intervals, response times and maximum amplitudes are shown in Table 1.

[0171] Table 1: Modal components, frequency division intervals, response time and maximum amplitude when k1=5

[0172]

[0173]

[0174] Low frequency components IMF4 and 5 (crossover point f 3,4The corresponding response time is 16 minutes, and the demand control time should be less than 15 minutes). The required energy is the largest and the response time is the slowest. It can be adjusted by self-generated power. The required energy of IMF1-3 is small, t react = 2min, it is possible to consider using relatively low-cost electrochemical battery energy storage for regulation, and the maximum power reference value of the energy storage installation is P b ≤17.77MW.

[0175] Step 3: Use the maximum demand discharge method to establish an enterprise energy storage optimization configuration model;

[0176] First, set the maximum demand control target P' after storage allocation max , statistics[P1,...,P r ]Daily number of exceedances a and cumulative daily duration t c , obtain the energy storage participation demand benefit part capacity E2 and the full life cycle energy storage life A;

[0177] The peak and valley distribution of electricity consumption in the region where the enterprise is located and the unit prices of various types of electricity charges are shown in Table 2.

[0178] Table 2: Distribution of peak and valley periods of electricity consumption and unit prices of various types of electricity charges

[0179]

[0180] η is the energy storage rate, and 0.5C and 1C are taken for comparative analysis;

[0181] According to the objective function of the enterprise energy storage optimization configuration model, maxf1=R1+R2-C b -C m , after optimization calculation, P is determined b =15MW, η=0.5C, the total benefit is the highest, and the results are shown in Table 3.

[0182] Table 3: Energy storage configuration optimization results

[0183]

[0184]

[0185] Based on the same inventive concept, the embodiment of the present application also provides an energy storage distribution and control device for a multi-energy complementary system, which can be used to implement the method described in the above embodiment, as described in the following embodiment. Since the principle of solving the problem by the energy storage distribution and control device of the multi-energy complementary system is similar to the energy storage distribution and control method of the multi-energy complementary system, the implementation of the energy storage distribution and control device of the multi-energy complementary system can refer to the implementation of the method based on the software performance benchmark determination, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0186] In one embodiment, see Fig.14 In order to solve the problems of low multi-energy utilization rate, limited proportion of self-generation and green electricity in steel enterprises, and to achieve the goals of reducing enterprise electricity demand, peak load shifting and valley filling, and improving energy utilization rate, this application provides a storage control device for a multi-energy complementary system, including:

[0187] The energy storage type and capacity determination unit 1401 is used to perform adaptive variational mode decomposition on the active power data samples of the steel enterprise gateway before the energy storage configuration to determine the energy storage type and maximum energy storage output power required by the steel enterprise;

[0188] A configuration capacity determination unit 1402 is used to obtain an optimal solution of the energy storage optimization configuration objective function according to a preset energy storage energy ratio and the maximum energy storage output power, and determine the energy storage configuration capacity;

[0189] The active power prediction unit 1403 is used to predict the enterprise's gateway active power and green power active power in a preset future period by using a pre-trained active power prediction model after performing energy storage configuration based on the energy storage type and the energy storage configuration capacity;

[0190] The complementary system control unit 1404 is used to perform real-time control on the multi-energy complementary system according to the enterprise gateway active power and the green electricity active power.

[0191] In one embodiment, see Fig.15 The energy storage type capacity determination unit 1401 includes:

[0192] A modal decomposition module 1501 is used to perform adaptive variational modal decomposition on the active power data sample to obtain an active power modal number and an active power modal component of the active power data sample;

[0193] A time capacity determination module 1502, configured to determine the energy storage response time and the maximum energy storage output power according to the active power modal number and the active power modal component;

[0194] The energy storage type determination module 1503 is used to determine the energy storage type according to the energy storage response time.

[0195] In one embodiment, see Fig.16 , the configuration capacity determining unit 1402 includes:

[0196] The optional capacity determination module 1601 is used to construct a maximum required energy storage capacity constraint condition according to the energy storage energy ratio and the maximum energy storage output power, and iterate and obtain the energy storage optional capacity based on the maximum required energy storage capacity constraint condition with a variable step size;

[0197] The energy storage life determination module 1602 is used to determine the energy storage life of the entire life cycle according to the number of equivalent full cycles of the energy storage life cycle, the number of days participating in peak-valley arbitrage per year, the number of days participating in demand regulation per year, and the shallow discharge and shallow charge loss coefficient;

[0198] The peak-valley income determination module 1603 is used to determine the peak-valley income according to the annual number of days participating in peak-valley arbitrage, the duration of valley charging and peak discharge, the duration of flat charging and peak discharge, the exchange power and the life of the energy storage in the whole life cycle;

[0199] The demand benefit determination module 1604 is used to determine the demand benefit according to the annual reduced demand month and the demand unit price;

[0200] An initial construction cost determination module 1605 is used to determine the initial construction cost according to the energy storage optional capacity;

[0201] An operation and maintenance cost determination module 1606 is used to determine the operation and maintenance cost according to the energy storage optional capacity and the full life cycle energy storage life;

[0202] The configuration capacity determination module 1607 is used to generate the energy storage optimization configuration objective function according to the initial construction cost, the operation and maintenance cost, the peak-valley income, and the demand income, and determine the energy storage optional capacity when the energy storage optimization configuration objective function reaches the optimal solution as the energy storage configuration capacity.

[0203] In one embodiment, see Fig.17 , the active power prediction unit 1403 comprises:

[0204] The impact factor acquisition module 1701 is used to acquire the load impact factor after performing energy storage configuration according to the energy storage type and the energy storage configuration capacity; wherein the load impact factor includes a load prediction impact factor and a green power prediction impact factor;

[0205] The factor analysis processing module 1702 is used to perform correlation analysis and differentiation processing on the load influencing factors to obtain preprocessing results;

[0206] The active power calculation module 1703 is used to input the preprocessing result into the active power prediction model to obtain the enterprise gateway active power and green electricity active power; wherein the active power prediction model is a fusion model of long short-term memory neural network and support vector machine.

[0207] In one embodiment, see Fig.18 , the complementary system control unit 1404 comprises:

[0208] The power demand calculation module 1801 is used to perform slip calculation on the active power data sample to obtain the maximum active power demand;

[0209] The energy storage control module 1802 is used to adjust the control strategy of the multi-energy complementary system according to the maximum active power demand, the enterprise gateway active power, the green power active power and the energy storage configuration capacity;

[0210] The real-time control module 1803 is used to perform real-time control on the multi-energy complementary system according to the control strategy.

[0211] From the hardware level, in order to solve the problems of low multi-energy utilization rate, limited proportion of self-generation and green electricity in steel enterprises, and achieve the goals of reducing enterprise electricity demand, peak load shifting and improving energy utilization rate, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the energy storage control method of the multi-energy complementary system, and the electronic device specifically includes the following contents:

[0212] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the energy storage distribution and control device of the multi-energy complementary system and related equipment such as the core business system, user terminal and related database; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the energy storage distribution and control method of the multi-energy complementary system in the embodiment, and the embodiment of the energy storage distribution and control device of the multi-energy complementary system, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0213] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0214] In practical applications, part of the energy storage control method of the multi-energy complementary system can be executed on the electronic device side as described above, or all operations can be completed in the client device. The specific selection can be based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0215] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0216] Fig.19 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig.19 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig.19 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0217] In one embodiment, the energy storage control method function of the multi-energy complementary system can be integrated into the central processor 9100. The central processor 9100 can be configured to perform the following control:

[0218] S101: performing adaptive variational mode decomposition on active power data samples of the steel enterprise’s gateway incoming line before energy storage configuration to determine the energy storage type and maximum energy storage output power required by the steel enterprise;

[0219] S102: Obtaining an optimal solution of an energy storage optimization configuration objective function according to a preset energy storage energy ratio and the maximum energy storage output power, and determining an energy storage configuration capacity;

[0220] S103: After energy storage configuration is performed based on the energy storage type and the energy storage configuration capacity, a pre-trained active power prediction model is used to predict the enterprise's gateway active power and green power active power in a preset future period;

[0221] S104: Real-time control of the multi-energy complementary system is performed according to the enterprise gateway active power and the green electricity active power.

[0222] From the above description, it can be seen that the energy storage control method and device of the multi-energy complementary system provided by the present application can solve the problem of multi-energy complementary storage type selection and capacity configuration optimization from a mechanism perspective, aiming at the multi-energy complementary characteristics of steel enterprises containing a high proportion of impact loads and multi-energy power generation, thereby avoiding excessive or too small configuration of energy storage capacity and reducing energy storage life loss; thereby solving the problem that enterprises are unable to further increase the proportion of self-generation due to impact load fluctuations, and the increase in demand or inability to operate at full load due to the access of large-proportion fluctuating green electricity, resulting in the inability to fully utilize the company's surplus energy or green electricity and wasting energy; among them, the adaptive variable involved The modal decomposition-Hilbert marginal spectrum method can obtain the results with the lowest modal aliasing and false components, thereby overcoming the over-decomposition or under-decomposition problems caused by the subjective setting of the decomposition scale in the existing algorithms; the maximum demand discharge method proposed by it is used to establish an enterprise energy storage optimization configuration model, which solves the problems of excessive demand, low return on investment and rapid battery life loss caused by only considering the peak and valley electricity tariff benefits and unreasonable demand management targets; in addition, it also constructs a set of steel enterprise energy storage control devices that include integrated acquisition-computation-control, which has low hardware requirements, and the calculation and control links can be implemented using ordinary personal computers.

[0223] In another embodiment, the energy storage and control device of the multi-energy complementary system can be configured separately from the central processor 9100. For example, the energy storage and control device of the multi-energy complementary system of the data composite transmission device can be configured as a chip connected to the central processor 9100, and the functions of the energy storage and control method of the multi-energy complementary system are realized through the control of the central processor.

[0224] like Fig.19 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig.19 In addition, the electronic device 9600 may also include Fig.19 For components not shown, reference may be made to the prior art.

[0225] like Fig.19 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0226] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0227] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0228] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0229] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0230] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0231] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0232] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the energy storage control method for a multi-energy complementary system in the above embodiments, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all the steps of the energy storage control method for a multi-energy complementary system in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0233] S101: performing adaptive variational mode decomposition on active power data samples of the steel enterprise’s gateway incoming line before energy storage configuration to determine the energy storage type and maximum energy storage output power required by the steel enterprise;

[0234] S102: Obtaining an optimal solution of an energy storage optimization configuration objective function according to a preset energy storage energy ratio and the maximum energy storage output power, and determining an energy storage configuration capacity;

[0235] S103: After energy storage configuration is performed based on the energy storage type and the energy storage configuration capacity, a pre-trained active power prediction model is used to predict the enterprise's gateway active power and green power active power in a preset future period;

[0236] S104: Real-time control of the multi-energy complementary system is performed according to the enterprise gateway active power and the green electricity active power.

[0237] From the above description, it can be seen that the energy storage control method and device of the multi-energy complementary system provided by the present application can solve the problem of multi-energy complementary storage type selection and capacity configuration optimization from a mechanism perspective, aiming at the multi-energy complementary characteristics of steel enterprises containing a high proportion of impact loads and multi-energy power generation, thereby avoiding excessive or too small configuration of energy storage capacity and reducing energy storage life loss; thereby solving the problem that enterprises are unable to further increase the proportion of self-generation due to impact load fluctuations, and the increase in demand or inability to operate at full load due to the access of large-proportion fluctuating green electricity, resulting in the inability to fully utilize the company's surplus energy or green electricity and wasting energy; among them, the adaptive variable involved The modal decomposition-Hilbert marginal spectrum method can obtain the results with the lowest modal aliasing and false components, thereby overcoming the over-decomposition or under-decomposition problems caused by the subjective setting of the decomposition scale in the existing algorithms; the maximum demand discharge method proposed by it is used to establish an enterprise energy storage optimization configuration model, which solves the problems of excessive demand, low return on investment and rapid battery life loss caused by only considering the peak and valley electricity tariff benefits and unreasonable demand management targets; in addition, it also constructs a set of steel enterprise energy storage control devices that include integrated acquisition-computation-control, which has low hardware requirements, and the calculation and control links can be implemented using ordinary personal computers.

[0238] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0239] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0240] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0241] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0242] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for energy storage control of a multi-energy complementary system, characterized in that: include: Adaptive variational mode decomposition is performed on the active power data samples of the steel enterprise’s gateway before energy storage configuration to determine the energy storage type and maximum energy storage output power required by the steel enterprise; Obtaining the optimal solution of the energy storage optimization configuration objective function according to the preset energy storage energy ratio and the maximum energy storage output power, and determining the energy storage configuration capacity; After energy storage configuration is performed based on the energy storage type and the energy storage configuration capacity, a pre-trained active power prediction model is used to predict the enterprise's gateway active power and green power active power in a preset future period; The multi-energy complementary system is controlled in real time according to the enterprise gateway active power and green electricity active power.

2. The energy storage control method of the multi-energy complementary system according to claim 1, characterized in that: The method of performing adaptive variational mode decomposition on active power data samples of the steel enterprise gateway before energy storage configuration to determine the energy storage type and maximum energy storage output power required by the steel enterprise includes: Performing adaptive variational modal decomposition on the active power data sample to obtain an active power modal number and an active power modal component of the active power data sample; Determining the energy storage response time and the maximum energy storage output power according to the active power modal number and the active power modal component; The energy storage type is determined according to the energy storage response time.

3. The energy storage control method of the multi-energy complementary system according to claim 1, characterized in that: The step of obtaining an optimal solution of the energy storage optimization configuration objective function according to the preset energy storage energy ratio and the maximum energy storage output power, and determining the energy storage configuration capacity, comprises: Constructing a maximum required energy storage capacity constraint condition according to the energy storage energy ratio and the maximum energy storage output power, and iterating and obtaining the energy storage optional capacity based on the maximum required energy storage capacity constraint condition with a variable step size; The energy storage life span is determined based on the number of equivalent full cycles in the energy storage life cycle, the number of days per year participating in peak-valley arbitrage, the number of days per year participating in demand regulation, and the shallow discharge and shallow charging loss coefficient; Determine the peak-valley income based on the number of days of peak-valley arbitrage participation per year, the duration of valley charging and peak discharge, the duration of flat charging and peak discharge, the exchange power and the life of the energy storage over the entire life cycle; Determine the demand revenue based on the annual reduced demand months and demand unit price; Determine the initial construction cost based on the optional capacity of the energy storage; Determine the operation and maintenance cost according to the optional capacity of the energy storage and the life of the energy storage throughout the life cycle; The energy storage optimization configuration objective function is generated according to the initial construction cost, the operation and maintenance cost, the peak-valley income, and the demand income, and the energy storage optional capacity when the energy storage optimization configuration objective function reaches the optimal solution is determined as the energy storage configuration capacity.

4. The energy storage control method of the multi-energy complementary system according to claim 1, characterized in that: After energy storage configuration is performed based on the energy storage type and the energy storage configuration capacity, a pre-trained active power prediction model is used to predict the enterprise's gateway active power and green power active power in a preset future period, including: After energy storage configuration is performed according to the energy storage type and the energy storage configuration capacity, a load impact factor is obtained; wherein the load impact factor includes a load prediction impact factor and a green electricity prediction impact factor; Performing correlation analysis and differentiation processing on the load influencing factors to obtain preprocessing results; The preprocessing result is input into the active power prediction model to obtain the enterprise gateway active power and green electricity active power; wherein the active power prediction model is a fusion model of long short-term memory neural network and support vector machine.

5. The energy storage control method of the multi-energy complementary system according to claim 1, characterized in that: The real-time control of the multi-energy complementary system according to the enterprise gateway active power and the green electricity active power includes: Performing slip calculation on the active power data samples to obtain the maximum active power demand; Adjusting the control strategy of the multi-energy complementary system according to the maximum active power demand, the enterprise gateway active power, the green power active power and the energy storage configuration capacity; The multi-energy complementary system is controlled in real time according to the control strategy.

6. An energy storage control device for a multi-energy complementary system, characterized in that: include: The energy storage type and capacity determination unit is used to perform adaptive variational mode decomposition on the active power data samples of the steel enterprise gateway before energy storage configuration to determine the energy storage type and maximum energy storage output power required by the steel enterprise; A configuration capacity determination unit, used to obtain an optimal solution of the energy storage optimization configuration objective function according to a preset energy storage energy ratio and the maximum energy storage output power, and determine the energy storage configuration capacity; An active power prediction unit, configured to predict the enterprise's gateway active power and green power active power in a preset future period by using a pre-trained active power prediction model after energy storage configuration is performed based on the energy storage type and the energy storage configuration capacity; The complementary system control unit is used to perform real-time control of the multi-energy complementary system according to the enterprise gateway active power and the green electricity active power.

7. The energy storage control device of the multi-energy complementary system according to claim 6, characterized in that: The energy storage type capacity determination unit includes: A modal decomposition module, used for performing adaptive variational modal decomposition on the active power data sample to obtain the active power modal number and active power modal component of the active power data sample; A time capacity determination module, used to determine the energy storage response time and the maximum energy storage output power according to the active power modal number and the active power modal component; An energy storage type determination module is used to determine the energy storage type according to the energy storage response time.

8. The energy storage control device of the multi-energy complementary system according to claim 6, characterized in that: The configuration capacity determination unit includes: An optional capacity determination module is used to construct a maximum required energy storage capacity constraint condition according to the energy storage energy ratio and the maximum energy storage output power, and iterate and obtain the energy storage optional capacity based on the maximum required energy storage capacity constraint condition with a variable step size; The energy storage life determination module is used to determine the energy storage life of the entire life cycle according to the number of equivalent full cycles of the energy storage life cycle, the number of days participating in peak-valley arbitrage per year, the number of days participating in demand regulation per year, and the shallow discharge and shallow charging loss coefficient; A peak-valley income determination module is used to determine the peak-valley income based on the number of days participating in peak-valley arbitrage in a year, the duration of valley charging and peak discharge, the duration of flat charging and peak discharge, the exchange power and the life of the energy storage in the entire life cycle; Demand revenue determination module, used to determine demand revenue based on annual reduced demand months and demand unit price; An initial construction cost determination module, used to determine the initial construction cost according to the optional capacity of the energy storage; An operation and maintenance cost determination module, used to determine the operation and maintenance cost according to the energy storage optional capacity and the full life cycle energy storage life; A configuration capacity determination module is used to generate the energy storage optimization configuration objective function according to the initial construction cost, the operation and maintenance cost, the peak-valley income, and the demand income, and determine the energy storage optional capacity when the energy storage optimization configuration objective function reaches the optimal solution as the energy storage configuration capacity.

9. The energy storage control device of the multi-energy complementary system according to claim 6, characterized in that: The active power prediction unit comprises: An influence factor acquisition module, used to acquire a load influence factor after performing energy storage configuration according to the energy storage type and the energy storage configuration capacity; wherein the load influence factor includes a load prediction influence factor and a green electricity prediction influence factor; A factor analysis processing module is used to perform correlation analysis and differentiation processing on the load influencing factors to obtain preprocessing results; The active power calculation module is used to input the preprocessing results into the active power prediction model to obtain the enterprise gateway active power and green electricity active power; wherein the active power prediction model is a fusion model of long short-term memory neural network and support vector machine.

10. The energy storage control device of the multi-energy complementary system according to claim 6, characterized in that: The complementary system control unit comprises: A power demand calculation module, used for performing slip calculation on the active power data sample to obtain the maximum active power demand; An energy storage control module, used to adjust the control strategy of the multi-energy complementary system according to the maximum active power demand, the enterprise gateway active power, the green power active power and the energy storage configuration capacity; A real-time control module is used to perform real-time control on the multi-energy complementary system according to the control strategy.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the energy storage control method for a multi-energy complementary system according to any one of claims 1 to 5 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the energy storage control method for a multi-energy complementary system described in any one of claims 1 to 5 are implemented.

13. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the energy storage control method for a multi-energy complementary system described in any one of claims 1 to 5 are implemented.

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