A production-energy supply resource allocation method based on online reconstruction of production state of lithium production from salt lake

Abstract

The application discloses a production-energy supply resource allocation method based on lithium production state online reconstruction of salt lake, and belongs to the technical field of production resource allocation. The method acquires a production task, photovoltaic prediction, energy supply resource boundary and production state; determines a material processing flow feasible region according to a releasable material amount of buffer inventory, a residual accommodable material amount, a material inflow flow and a device processing boundary, and forms a production electricity demand feasible region according to a process-power coupling relationship; generates a day-ahead resource allocation plan in combination with the two types of feasible regions, the process-power coupling relationship, the production task and material and power balance constraints; recursively calculates inventory with actual measured inventory as an initial value in intraday rolling optimization, reconstructs the two types of feasible regions and rolling corrects resource allocation, executes a first period result and feeds back an updated state. The application dynamically limits a resource allocation range according to an actual production state, and realizes coordinated allocation of production resources and energy supply resources.

Description

Technical Field

[0001] This invention relates to the field of resource planning and allocation technology in industrial production management, specifically to a production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes. Background Technology

[0002] High-altitude salt lake mining areas are typically far from the main power grid, and the construction, operation, and maintenance of power grid infrastructure are constrained by geographical conditions. An off-grid energy system consisting of photovoltaic power generation, electrochemical energy storage, and diesel generator sets can utilize local solar resources to provide independent power for lithium extraction from salt lakes. However, off-grid photovoltaic-diesel-storage energy systems cannot rely on the main grid to balance power deviations. Photovoltaic power generation is affected by solar radiation and weather changes, exhibiting significant volatility and predictive uncertainty; electrochemical energy storage can quickly respond to source-load power deviations, but its continuous power supply capacity is constrained by storage capacity and state of charge; diesel generator sets can provide stable and controllable power, but their operation incurs fuel consumption, equipment maintenance costs, and carbon emissions.

[0003] Existing day-ahead and intraday multi-timescale optimization allocation methods can utilize day-ahead forecasts to formulate baseline plans and make rolling corrections to energy storage and diesel generator output based on intraday updated short-term forecasts. However, in applications targeting industrial loads, production loads are typically used as externally given load forecast sequences for energy system optimization, or divided into rigid and adjustable loads according to a preset fixed ratio. These methods do not fully utilize the operating status of the industrial production process itself, thus failing to reflect the impact of changes in production equipment status, material handling flow rates, and upstream and downstream material buffer conditions on actual electricity demand and load adjustability.

[0004] The lithium extraction process from salt lakes is characterized by continuous operation and coupling of upstream and downstream materials. A production system using salt lake brine as raw material may include brine extraction and transportation, continuous adsorption, membrane-based impurity removal and concentration, bipolar membrane electrodialysis, and mechanical vapor recompression (MVR) for lithium precipitation. Each production step employs different equipment to complete material transportation, separation, concentration, and processing, and its power consumption is closely related to the equipment's operating status, material processing flow rate, and specific process parameters. Furthermore, for adjacent steps equipped with intermediate storage, buffers, or other intermediate material buffering facilities, the upstream material outflow and downstream material processing flow rate may differ over a certain period. When intermediate material inventory is high, downstream steps can appropriately increase their material processing flow rate; when the intermediate material buffer space is large, downstream steps can temporarily reduce their material processing flow rate, with the intermediate material buffer unit absorbing some of the upstream material. Therefore, the state of intermediate materials directly affects the load regulation capacity that the production process can provide in the current and future periods.

[0005] Existing resource allocation methods typically describe lithium extraction production-side resources in salt lakes using rated power, preset load curves, or fixed adjustment ranges. These methods fail to reflect the combined impact of changes in production equipment status, material handling flow rates, process operating parameters, and intermediate material inventory on the boundaries of available production resources. Consequently, the resulting day-ahead resource allocation plan may not align with actual intraday production conditions. Furthermore, it becomes difficult to maintain coordination among production tasks, production resources, and energy supply resources after changes in photovoltaic output and production status. Summary of the Invention

[0006] Purpose of the invention: To address the shortcomings of existing technologies, this invention provides a production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes. This method solves the problem that existing methods are unable to determine the production-side resource boundaries based on the actual production status, leading to a mismatch between the day-ahead resource allocation plan and the intraday production conditions, as well as an incoordination between the allocation of production resources and energy operation resources.

[0007] Technical solution: A production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes, comprising the following steps:

[0008] S1. Obtain production tasks, photovoltaic day-ahead output forecasts, and the operating status of power supply equipment, using the given photovoltaic, energy storage, and diesel generator unit capacities as fixed resource boundaries; obtain the production status of each process in lithium extraction from salt lakes and the status of intermediate material buffer units. The production status includes equipment operating status, material processing flow rate, and process operating parameters. The status of intermediate material buffer units includes inventory, material inflow flow rate, and material outflow flow rate.

[0009] S2. Based on the inventory of the intermediate material buffer unit, the material inflow rate, and the processing boundary of the downstream equipment, determine the maximum and minimum allowable processing flow rates of the downstream process to form the feasible region of material processing flow rate; through the process-power coupling relationship between material processing flow rate, equipment operating status, process operating parameters, and power consumption, convert the feasible region of material processing flow rate into the feasible region of production power demand.

[0010] S3. Constrained by the feasible domain of material handling flow and the feasible domain of production power demand, process-power coupling relationship, production tasks, intermediate material balance, off-grid power balance and fixed resource boundary, based on the photovoltaic day-ahead output forecast and the operating status of power supply equipment, and with the goal of minimizing the expected operating cost under the photovoltaic output scenario, establish a day-ahead resource allocation model and generate a day-ahead resource allocation plan.

[0011] S4. During the intraday rolling period, the actual inventory is used as the initial value. The subsequent inventory is deduced by combining the material inflow and material outflow. The feasible domain of material handling flow and the feasible domain of production power demand are recalculated based on the actual production status. The operating resource allocation results of each process, energy storage and diesel unit are corrected based on the day-ahead resource allocation plan, combined with the photovoltaic short-term output forecast and the operating status of energy supply equipment.

[0012] S5. Execute the resource allocation results of each process, energy storage and diesel generator set in the first period of the rolling time domain, update the production status, intermediate material buffer unit status and power supply equipment operation status according to the execution feedback, and enter the next rolling time to recalculate the feasible domain of material processing flow and the feasible domain of production power demand.

[0013] The present invention also provides an electronic device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, wherein when the programs are executed by the processors, they implement the above-described production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-described production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes.

[0016] Beneficial effects:

[0017] (1) This invention obtains the production status of lithium extraction from salt lakes, determines the maximum and minimum allowable processing flow rates of downstream processes based on the material processing flow rate of each process, the inventory of intermediate material buffer units, and the material inflow flow rate, forms a feasible domain of material processing flow rate that changes with the production status of lithium extraction from salt lakes, and converts the feasible domain of material processing flow rate into a feasible domain of production power demand through process-power coupling relationship, so that the production side resource boundary can change with the actual production status of lithium extraction from salt lakes, avoids the inconsistency between the resource boundary and the actual production capacity caused by using a fixed load curve or preset adjustment range, and improves the accuracy of the production side resource boundary characterization.

[0018] (2) Within the fixed resource boundary formed by the given capacity of photovoltaic, energy storage and diesel generator sets, the present invention generates a day-ahead resource allocation plan by using the dynamically formed feasible domain of material handling flow and feasible domain of production power demand. During the day, the two types of feasible domains are recalculated based on the measured production status, short-term photovoltaic forecast and the operating status of energy supply equipment. The operation resource allocation results of the first period are corrected and executed. The subsequent resource boundary is continuously updated through execution feedback, so that the day-ahead plan can be adjusted with the changes in production conditions and energy supply conditions during the day, thereby improving the coordination between production tasks, production resources and energy supply operation resources and the executability of the resource allocation plan. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the off-grid photovoltaic-diesel-storage energy system for lithium extraction from salt lakes as described in this invention.

[0020] Figure 2 This is a schematic diagram of the production-functional resource allocation method based on online reconstruction of lithium extraction production status in salt lakes, as described in this invention.

[0021] Figure 3 This is a schematic diagram of the multi-timescale optimization framework from day to day described in this invention. Detailed Implementation

[0022] To provide a clearer understanding of the features and advantages of the technical solution of the present invention, the composition and implementation of the specific solution are described below in conjunction with the accompanying drawings.

[0023] like Figure 1 As shown, this embodiment focuses on a high-altitude salt lake lithium extraction project. The off-grid power supply system includes a photovoltaic power generation unit, an electrochemical energy storage unit, and a diesel generator set group, which supplies power to the salt lake lithium extraction production system via an off-grid AC bus. The salt lake lithium extraction production system may include brine extraction and transportation, continuous adsorption, membrane impurity removal and concentration, bipolar membrane electrodialysis, lithium precipitation mechanical vapor recompression, and auxiliary power systems; each production process is selected for connection according to the actual process configuration. The diesel generator set group consists of multiple diesel generators that can be started and stopped independently. In one embodiment, 40 diesel generators with a rated power of 2 MW can be used, so that the power supply of the group can be adjusted by the number of generators started and stopped and the output of each unit.

[0024] This invention proposes a production-energy resource allocation method based on online reconstruction of the production status of lithium extraction from salt lakes. Building upon an existing day-to-day multi-timescale optimization framework, the method incorporates the production status of lithium extraction from salt lakes into the optimization process. Based on the actual production status of each production process, it generates time-varying allowable material handling flow rates and available power ranges, which are then continuously revised based on intraday photovoltaic forecasts and production status updates. This method is executed by a resource allocation platform, which acquires the lithium extraction production tasks, production status, photovoltaic forecasts, and energy supply equipment operating status. It performs day-to-day and intraday resource allocation calculations for production resources and energy supply operating resources under a given capacity, generating and updating the operating resource allocation results for each process, energy storage, and diesel generator unit in lithium extraction from salt lakes. (Refer to...) Figure 2 This method includes the following steps:

[0025] S1. Obtain photovoltaic output forecast and energy storage and diesel generator unit operating status, and use the given photovoltaic, energy storage and diesel generator unit capacity as fixed resource boundaries; obtain the production status of each process of lithium extraction from salt lake and the status of material buffer units between processes, wherein the production status includes the equipment operating status, material processing flow rate and process operating parameters of each process.

[0026] This invention constructs an off-grid photovoltaic-diesel-storage energy system and acquires the status of the energy side and the lithium extraction production side in brine lakes. The off-grid photovoltaic-diesel-storage energy system includes a photovoltaic power generation unit, an electrochemical energy storage unit, a diesel generator set group, and a brine lake lithium extraction production system as the load side. The brine lake lithium extraction production system, according to the production process, may include brine extraction and transportation, continuous adsorption, membrane impurity removal and concentration, bipolar membrane electrodialysis (BMED), and mechanical vapor recompression (MVR) for lithium precipitation. Intermediate material buffer units can be configured between each process; these intermediate material buffer units are referred to as material buffer units. The off-grid photovoltaic-diesel-storage energy system operates in islanded mode and does not exchange power with the external main grid.

[0027] The resource allocation platform reads the production task volume and corresponding planned time period of each process from the production plan data, and reads the rated capacity of photovoltaic, rated power and rated energy of energy storage, and rated power of diesel unit from the equipment parameter records as fixed resource boundaries.

[0028] On the energy side, the day-ahead and short-time photovoltaic output forecasts, the state of charge (SoC) of electrochemical energy storage, and the start-up, shutdown, and output status of each unit in the diesel generator group are obtained as the initial state for resource allocation calculations. The day-ahead and short-time photovoltaic output forecasts can be calculated based on the power model of the photovoltaic power generation unit (as shown in Formula 15 below). The SoC of energy storage can be read from the operation monitoring data of the energy storage equipment, and the start-up, shutdown status, and actual output of the diesel generator units are read from the unit operation monitoring data. On the production side, the production status of each process in lithium extraction from the salt lake and the status of the material buffer unit are obtained. The equipment operation status and process parameters of each process can be read from the production equipment operation records and measurement data. The material processing flow rate of each process, as well as the material inflow and outflow flow rates of the intermediate material buffer unit, are obtained from the corresponding flow measurement data. Buffer inventory is obtained from inventory monitoring data; when using liquid level measurement, the inventory level is determined according to the liquid level-inventory conversion relationship.

[0029] Define the production state of the j-th production process at time t:

[0030] (1)

[0031] In the formula: j is the lithium extraction process number from the salt lake; t is the scheduling time; Let u be the production state vector of the j-th production process at time t; j,t This represents the operating status of the production process; it is set to 1 when the process is running and 0 when it is stopped. j,t θ represents the material handling flow rate in the production process. j,t This is a set of process parameters related to the power consumption of this production process. For different production processes, θ j,t The terms have different meanings: for the brine extraction and transportation process, it may include the pump head and operating efficiency; for the membrane impurity removal and concentration process, it may include the operating pressure or the pressure difference before and after the membrane; for the bipolar membrane electrodialysis process, it may include the operating voltage, operating current and feed status; for the lithium precipitation mechanical vapor recompression process, it may include the power consumption per unit material processing volume and the equipment operating status.

[0032] For cases where there is an intermediate material buffer unit between adjacent production processes, the state of the intermediate material buffer unit at time t is defined as follows:

[0033] (2)

[0034] In the formula: r is the intermediate material buffer unit number; Let B be the state of the r-th intermediate material buffer unit at time t. r,tLet be the material inventory of the r-th intermediate material buffer unit at time t. When the collected value is the liquid level, it is first converted to inventory based on the relationship between the liquid level and volume of the buffer unit; when using mass measurement, it is further converted by material density to make the dimensions of inventory consistent with those of flow rate multiplied by the time period. and These represent the material flow rates flowing into and out of the intermediate material buffer unit at the corresponding times.

[0035] S2. Based on the material processing flow rate of each process and the material inflow and outflow of the intermediate material buffer unit, determine the maximum and minimum allowable processing flow rates of the downstream processes corresponding to each intermediate material buffer unit, forming a feasible domain of material processing flow rate that varies with the lithium extraction production status in the salt lake; based on the process-power coupling relationship between process operating parameters and power consumption, convert the feasible domain of material processing flow rate into a feasible domain of production power demand.

[0036] This invention establishes a power coupling model for lithium extraction from salt lakes based on production conditions. According to the main power-consuming equipment and operating mechanisms of each production step, a mapping relationship is established between material processing flow rate, process operating parameters, and power consumption, thereby determining the allowable material processing flow rate range and the range of available power.

[0037] (1) Brine extraction and transportation process

[0038] The main adjustable loads in the brine extraction and transportation processes include the brine extraction pump and the brine transportation pump, and their power consumption is expressed as follows:

[0039] (3)

[0040] In the formula: P pump,t u represents the power consumption of the pump unit at time t. pump,t The pump unit is in operation; ρ is the density of the conveyed medium; g is the acceleration due to gravity; H t Q represents the operating head of the pump unit. t η is the material conveying flow rate of the pump unit. pump,t The overall efficiency of the pump set.

[0041] Therefore, when the head and equipment operating conditions are constant, changes in the brine flow rate can directly result in changes in the power consumption of the pump unit.

[0042] (2) Continuous adsorption process

[0043] The power requirements of the continuous adsorption process mainly include power for the feed pump, transfer pump, circulation pump, and auxiliary equipment. Its power consumption is expressed as follows:

[0044] (4)

[0045] In the formula: P ads,tN represents the total electrical power consumed in the continuous adsorption process. ads The number of pump sets associated with continuous adsorption treatment; u m,t The operating status of the m-th pump group; ρ m H represents the density of the corresponding material. m,t Let Q be the operating head of the m-th pump unit; m,t For its delivery or circulation flow; η m,t For pump set efficiency; Power consumption for other auxiliary equipment.

[0046] (3) Membrane-based impurity removal and concentration process

[0047] The main variable power consumption of the membrane-based impurity removal and concentration unit comes from the pressure-driven equipment, and its electrical power consumption is expressed as follows:

[0048] (5)

[0049] In the formula: P mem,t Power consumption for the membrane-based impurity removal and concentration process; u mem,t This refers to the operating status of the membrane treatment unit; Δp t Q represents the operating pressure differential of the membrane unit. mem,t For membrane treatment material flow rate; η mem,t To improve the efficiency of pressure-driven equipment; Power consumption for auxiliary equipment.

[0050] (4) Bipolar membrane electrodialysis process

[0051] The main power consumption of the bipolar membrane electrodialysis device is expressed as follows:

[0052] (6)

[0053] In the formula: P BMED,t Power consumption for the bipolar membrane electrodialysis process; u BMED,t This refers to the operating status of the device; U t Operating voltage; I t This is the operating current; Power consumption for auxiliary equipment such as circulating pumps.

[0054] Under given operating conditions, the current demand coefficient corresponding to the unit material handling flow rate can be further obtained based on the equipment design data or operating data.

[0055] (7)

[0056] In the formula: Q BMED,t k represents the material processing flow rate in the bipolar membrane electrodialysis process. BMED,t This is the equivalent current coefficient corresponding to the unit material processing flow rate under the current feeding and operating conditions.

[0057] Therefore, equations (6) and (7) together establish the correspondence between the material handling flow rate and the power consumption of this process.

[0058] (5) Lithium deposition mechanical vapor recompression process

[0059] The main power consumption of the lithium-ion mechanical vapor recompression unit is expressed as follows:

[0060] (8)

[0061] In the formula: P MVR,t Power consumption for the lithium deposition MVR process; u MVR,t The operating status of the MVR device; Q MVR,t For material handling flow rate in the MVR process; e MVR,t This refers to the electricity consumption per unit material flow rate under the current process conditions. Power consumption for auxiliary equipment.

[0062] For the r-th intermediate material buffer unit, the material balance relationship should be satisfied:

[0063] (9)

[0064] In the formula, B r,t Let B be the material inventory level of the r-th intermediate material buffer unit at time t, hereinafter referred to as the current inventory level; r,t Let be the material inventory of the r-th intermediate material buffer unit at time t-1. and These represent the material flow rates into and out of the intermediate material buffer unit at the corresponding times; Δt is the scheduling time interval.

[0065] Intermediate material inventory meets upper and lower limit constraints:

[0066] (10)

[0067] In the formula, and These are the minimum and maximum material inventory levels allowed for the intermediate material buffer unit, i.e., the lower and upper limits of inventory.

[0068] Based on the intermediate material status, the allowable material handling flow range for downstream production processes is further determined. The amount of material that can be released is determined by the difference between the current inventory level and the lower inventory limit of the intermediate material buffer unit. The remaining allowable material level is determined by the difference between the upper inventory level and the current inventory level. The releaseable material level is used to determine the upward adjustment space for increasing the material handling flow rate in downstream processes, and the remaining allowable material level is used to determine the downward adjustment space for decreasing the material handling flow rate in downstream processes. This, combined with the scheduling period length and material inflow flow rate, is converted into the flow boundary for downstream processes. The maximum allowable handling flow rate for downstream processes satisfies the following conditions:

[0069] (11)

[0070] Minimum allowed processing throughput must meet:

[0071] (12)

[0072] In the formula, and These represent the maximum and minimum allowable processing flow rates for downstream production processes under the current intermediate material condition; Indicates material buffer unit Related downstream production processes; and These are the maximum and minimum processing flow rates of the downstream production process, determined by equipment capacity and process requirements, i.e., the upper limit and lower limit of downstream equipment processing.

[0073] From equations (11) and (12), we can obtain:

[0074] (13)

[0075] Equation (13) describes the material buffer unit. The material output flow rate should be between the minimum and maximum allowable processing flow rates of the downstream process. Based on this, the feasible region of material processing flow rate varying with the lithium extraction production status in the brine lake can be defined as follows: .according to and Then, by using the process-power coupling relationship, the adjustable range of power consumption under the corresponding equipment operating status and process operating parameters is calculated:

[0076] (14)

[0077] in, Let be the power consumption of the j-th production process at time t. and These represent the lower and upper limits of power consumption for this process under the corresponding production conditions. The lower limit refers to the minimum power supply required to maintain continuous production of this process under the current production conditions, while the upper limit refers to the maximum permissible power consumption. Without loss of generality, [the following is a simplified explanation of the concept of power consumption]. Indicate process The equipment operating status, Indicate process Material handling flow rate Indicate process Process operating parameters, To represent the process-electric coupling relationship, the process... Power consumption Represented as: The specific process-electric coupling relationship of each step is calculated according to the corresponding model in equations (3) to (8) above.

[0078] Equation (14) describes the range of power consumption for each process related to the material handling flow rate. Based on this, the feasible region of production power demand that varies with the production status of lithium extraction from salt lakes can be defined as follows: .

[0079] S3. Taking the feasible domain of material handling flow rate and the feasible domain of production power demand, process-power coupling relationship, production tasks, intermediate material balance, off-grid power balance and fixed resource boundary as constraints, and with the goal of minimizing the expected operating cost in typical scenarios, establish a day-ahead resource allocation model and generate a day-ahead resource allocation plan.

[0080] This invention establishes operating models for photovoltaic, diesel generator sets, and energy storage, and the operating models for each device are described below.

[0081] The available power of a photovoltaic power generation unit can be expressed as:

[0082] (15)

[0083] In the formula, The rated capacity of the photovoltaic system; η PV G is the power attenuation coefficient, representing the impact of impurities on the photovoltaic panel (such as dust, snow, etc.) on the output power. T G represents solar radiation intensity. STC Solar radiation intensity under standard test conditions; T C T represents the actual temperature of the photovoltaic panel. STC The temperature of the photovoltaic panel under standard conditions; α p This represents the temperature coefficient of photovoltaic power.

[0084] The diesel generator group consists of N diesel generators that can be started and stopped independently. The total output of the group is the sum of the outputs of each online diesel generator. The model of a single diesel generator is as follows:

[0085] (16)

[0086] In the formula, f DG and P DG a0, a1, and a2 are the fuel consumption and power generation of the diesel generator, respectively; a0, a1, and a2 are the fitting coefficients. When the diesel generator is shut down, both fuel consumption and power generation are zero, and equation (16) is only applicable to the online operating state.

[0087] The state-of-charge update model for the electrochemical energy storage unit is as follows:

[0088] (17)

[0089] In the formula, The state of charge of the stored energy at time t; and These are charging power and discharging power, respectively. and These are the charging efficiency and the discharging efficiency, respectively. In equation (17), the charging power term and the discharging power term are converted into state-of-charge increments based on the scheduling time interval and the rated energy of the energy storage.

[0090] This invention establishes a day-ahead resource allocation model based on the aforementioned energy equipment operation model, the feasible domain of material handling flow and production power demand determined in step S2, and the process-power coupling relationship, combined with the following production tasks, material balance, off-grid power balance, and equipment operation constraints. The day-ahead resource allocation model is also known as the day-ahead optimization model.

[0091] First, based on the photovoltaic day-ahead power output prediction results and prediction error distribution obtained in step S1, multiple photovoltaic power output scenarios are generated using Latin hypercube sampling, and K-means clustering is used to reduce the scenarios to obtain typical photovoltaic power output scenarios and corresponding probabilities for day-ahead resource allocation.

[0092] Secondly, the current-day optimization aims to minimize the expected operating cost in typical scenarios. Simultaneously, it determines the material handling flow rate and corresponding electrical power consumption, diesel generator set start-up, shutdown, and output, as well as energy storage charging and discharging power for each production process in lithium extraction from salt lakes within permissible limits. Current-day resource allocation is planned with a 24-hour time domain and a 1-hour time interval. The objective function is constructed as follows:

[0093] (18)

[0094] In the formula, C day Let be the expected running cost of the current stochastic optimization model; s∈[1,N] s ] represents the scene number; π s Let C be the probability of scenario s occurring; fuel,sC ss,s C om,s and C emission,s These represent the diesel generator fuel cost, start-up and shutdown cost, operation and maintenance costs of each piece of equipment, and carbon emission costs under scenario s; C Q,s and C B,s These are the penalty costs for deviations in production throughput and intermediate material status from the original production plan, referred to as production unit penalty costs and buffer unit penalty costs, respectively.

[0095] The fuel cost of the diesel generator is:

[0096] (19)

[0097] In the formula, c fuel For fuel prices; f fuel,t,s Let N be the fuel consumption flow rate at time t in scenario s; Δt be the scheduling time interval; N DG This refers to the number of diesel engines.

[0098] The start-up and shutdown cost of the diesel generator is:

[0099] (20)

[0100] In the formula, c start and c shut These represent the unit start-up and shutdown costs of diesel generators; κ start,t,s and κ shut,t,s These are variables representing start-up and shutdown, κ and κ respectively. start,t,s κ represents the diesel generator starting at time t. shut,t,s The value of 1 indicates that the diesel generator stops at time t.

[0101] The operation and maintenance costs of the photovoltaic unit, the energy storage unit, and the diesel generator are as follows:

[0102] (twenty one)

[0103] In the formula, c om,PV c om,ESS and c om,DG The unit operating and maintenance costs for photovoltaic, energy storage, and diesel generators are respectively; P PV,t,s P ESS,t,s and P DG,t,s These represent the photovoltaic power output, energy storage charging and discharging power throughput, and diesel generator output at time t under scenario s, respectively.

[0104] The carbon emission cost is:

[0105] (twenty two)

[0106] In the formula, c emission Cost per unit of carbon emissions; f emission,t,s Let represent the mass of carbon dioxide (CO2) emitted at time t under scenario s.

[0107] The penalty cost for the production unit is:

[0108] (twenty three)

[0109] In the formula, c Q,j The unit penalty cost for production volume deviating from the original production plan; N p The number of production processes involved in the optimization; This represents the material handling flow rate corresponding to the original production plan. The deviation term in equation (23) is accumulated hourly within the daily planning time domain.

[0110] The penalty cost of the buffer unit is:

[0111] (twenty four)

[0112] In the formula, c B,r The unit penalty cost for the material state of the buffer unit deviating from the set value; N b This refers to the number of intermediate material buffer units. The reference material inventory for the buffer unit. The deviation term in equation (24) is accumulated hourly within the daily planning time domain.

[0113] By using the above objective function, the day-ahead optimization model can both reduce energy-side operating costs by utilizing production flexibility and avoid excessive deviation of production processes and intermediate material states from the original production plan.

[0114] The current optimized model satisfies the system's power balance constraints:

[0115] (25)

[0116] In the formula, This represents the photovoltaic output at time t. This represents the output of the diesel generator at time t. P represents the production offload power resulting from a power supply below the minimum continuous power supply for each production process; cut,t P represents the power of light discarded; load,t This represents the total power consumption of the lithium extraction system from the salt lake and other non-adjustable auxiliary loads.

[0117] The intermediate material status satisfies the material balance constraints and inventory upper and lower limit constraints as shown in formulas (9) and (10), which will not be elaborated here.

[0118] The processing capacity of each production process meets the following requirements:

[0119] (26)

[0120] And further satisfy the dynamic range determined by the state of the intermediate materials:

[0121] (27)

[0122] To ensure that flexible adjustment of production load does not reduce the production task completion volume specified within the allocation cycle, the cumulative material handling flow of each production process should meet the production task constraints:

[0123] (28)

[0124] In the formula, This represents the minimum material handling flow rate required to be completed by the j-th production process within the day-ahead planning time domain.

[0125] In addition, it is also necessary to meet the upper and lower limits of photovoltaic power output, upper and lower limits of diesel generator output, energy storage charging and discharging power, upper and lower limits of energy storage state of charge, mutual exclusion of energy storage charging and discharging, and diesel generator start-stop logic constraints; among which the upper and lower limits of equipment operation can be uniformly expressed as:

[0126] (29)

[0127] Energy storage charge / discharge mutual exclusion refers to the energy storage operating state being either charging or discharging. Diesel generator start / stop logic constraints refer to the minimum operating time and minimum downtime constraints that a diesel generator must meet.

[0128] (30)

[0129] In the formula, d start and d shut These represent the minimum running time and the minimum downtime, respectively.

[0130] Energy storage must meet the end-point state of charge recovery constraint:

[0131] (31)

[0132] Formula (31) refers to the state of charge of energy storage at time 24 in the day-ahead optimization plan being the same as that at time 1, which is usually set to 50% of the rated capacity of energy storage.

[0133] To ensure continuous lithium extraction from salt lakes, power supply reliability constraints were recently optimized using random settings.

[0134] (32)

[0135] During the recent optimization process, when the photovoltaic output is high, without exceeding production constraints, the material handling flow and corresponding power consumption of production processes with adjustable capabilities can be appropriately increased, or they can be scheduled to operate during photovoltaic surplus periods, while utilizing the remaining photovoltaic power to charge the electrochemical energy storage unit. When the photovoltaic output is low, the material handling flow and power consumption of some flexible production processes can be reduced within the range that does not disrupt production continuity and material connection, while coordinating the discharge of the electrochemical energy storage unit and increasing the number of diesel generator sets online. Throughout the process, the power deficit below the lower limit of continuous power supply for production is considered as production offload, and is constrained by power supply reliability to ensure that it does not exceed the preset allowable upper limit; in this embodiment, the preset allowable upper limit is zero.

[0136] By solving the day-ahead optimization model, the day-ahead baseline material processing flow rate, corresponding baseline power consumption plan and available power range, diesel generator set baseline start-stop and output plan, and energy storage baseline charge-discharge power and state of charge trajectory are obtained for each lithium extraction process in the salt lake. The baseline plan is then transferred to the intraday rolling optimization stage.

[0137] S4. During the intraday rolling time, based on the measured lithium extraction production status of the salt lake, the short-term output prediction of photovoltaic power generation, and the operating status of the power supply equipment, recalculate the feasible domain of material processing flow and the feasible domain of production power demand from the current time to the end of the rolling time domain, and revise the operating resource allocation results of each process, energy storage and diesel generator set based on the day-ahead resource allocation plan.

[0138] This invention constructs an intraday rolling optimization model that considers real-time production status updates. The intraday rolling optimization interval is 15 minutes, and the rolling time domain is 4 hours. At each rolling time k, the updated photovoltaic short-term forecast and the non-adjustable auxiliary load short-term forecast are obtained. At the same time, the actual operating status of each production process, the current power consumption, material handling flow rate, continuous running time, and intermediate material buffer inventory are read. The measured inventory is used as the initial value of the rolling time domain. According to the material balance relationship, combined with the material inflow and outflow flow rates of each time period in the rolling optimization, the subsequent inventory is recursively calculated. In step S2, the allowable material handling flow rate range and the available power range for each production process in the future rolling time domain are redefined.

[0139] When the production status of a certain production process changes, its allowable material handling flow rate range and available power range are updated synchronously. For example, when the intermediate material buffer margin is sufficient and the continuous operation constraint is met, the material handling flow rate of this process can be adjusted within the process allowable range, and corresponding upward or downward power adjustment capabilities can be formed through the process-power coupling relationship; when the intermediate material status is close to the allowable boundary or the production process is in a stage that must be continuously operated, the above range is correspondingly contracted. Therefore, the production load flexibility does not adopt a preset fixed ratio, but changes dynamically with the actual production status.

[0140] The objective function of the intraday rolling optimization model includes not only the operating costs in the future rolling time domain, but also a penalty term for the deviation of diesel generator sets, energy storage, and lithium extraction production load from salt lakes relative to the day-ahead baseline plan.

[0141] (33)

[0142] In the formula, ΔP ESS ΔP DG and ΔP proc These represent adjustments to the energy storage charging and discharging power, diesel generator group output, and power consumption in the lithium extraction process from salt lakes relative to the day-ahead baseline plan; δ ESS δ DG and δ proc These are the corresponding penalty coefficients. Each adjustment is a sequence within the rolling time domain, and the absolute value term in equation (33) represents the summation of the absolute deviations over each time period.

[0143] In the intraday rolling optimization model, diesel generators also need to meet the minimum running time and minimum downtime constraints.

[0144] Intraday rolling optimization continues to meet the updated production unit power boundaries, production continuity and intermediate material buffer constraints, power supply reliability constraints, and operational constraints of photovoltaic, diesel generator sets, and energy storage devices. When actual source-load deviations occur, the optimization model only utilizes process-side flexibility within the limits allowed by the production status; the remaining power deviations are coordinated and borne by energy storage and diesel generator sets.

[0145] When actual photovoltaic output exceeds the day-ahead forecast, rolling optimization can, within the limits of process allowances, increase material handling flow and power consumption in some flexible production processes, increase the charging power of electrochemical energy storage units, or reduce the number of diesel generator sets online. When actual photovoltaic output is lower than the day-ahead forecast or non-adjustable auxiliary loads increase, rolling optimization can first utilize the production-side adjustment capabilities allowed by the process to reduce short-term power deficits, and then meet the remaining power demand by rapidly discharging electrochemical energy storage units and increasing the number of diesel generator sets online. Since production-side adjustments are always constrained by actual production status, production continuity, and material balance, rigid power consumption required to ensure continuous production will not be included in the allocation as a load that can be reduced.

[0146] S5. At each rolling moment, only the first 15 minutes of control quantities of the current optimization sequence are executed, including the power consumption instructions of each adjustable production process, the start-stop and output instructions of the diesel generator group, and the energy storage charging and discharging instructions; then, the production status of lithium extraction from the salt lake, the energy storage charging status, and the operating status of the diesel generator group are updated according to the actual execution results, and the process returns to step S4 for the next rolling optimization until the full time domain optimization is completed.

[0147] Accordingly, during the daytime phase, a production and energy supply coordination baseline plan is formed by utilizing production status and photovoltaic uncertainties. During the intraday phase, the allowable material handling flow rate range and the available power range are continuously updated based on real-time production status and short-term source-load information, achieving closed-loop coordinated resource allocation between the brine lake lithium extraction production process and the off-grid photovoltaic-diesel-storage energy system. Figure 3 As shown, the day-ahead phase generates a resource allocation plan covering 24 hours and provides a baseline for the intraday phase. During the intraday phase, the measured status and short-term forecasts are updated at each rolling moment to solve for the resource allocation results for the next 4 hours, executing only the first 15-minute period; subsequently, based on the execution feedback, the rolling starting point is advanced 15 minutes to recalculate the subsequent allocation results.

[0148] The present invention also provides an electronic device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, wherein when the programs are executed by the processors, they implement the production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes as described above.

[0149] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes as described above.

[0150] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes as described above.

Claims

1. A production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes, characterized in that, Includes the following steps: S1. Obtain production tasks, photovoltaic day-ahead output forecasts, and the operating status of power supply equipment, using the given photovoltaic, energy storage, and diesel generator unit capacities as fixed resource boundaries; obtain the production status of each process in lithium extraction from salt lakes and the status of intermediate material buffer units. The production status includes equipment operating status, material processing flow rate, and process operating parameters. The status of intermediate material buffer units includes inventory, material inflow flow rate, and material outflow flow rate. S2. Based on the inventory of the intermediate material buffer unit, the material inflow rate, and the processing boundary of the downstream equipment, determine the maximum and minimum allowable processing flow rates of the downstream process to form the feasible region of material processing flow rate. By analyzing the process-power coupling relationship between material handling flow rate, equipment operating status, process operating parameters, and power consumption, the feasible domain of material handling flow rate is converted into the feasible domain of production power demand. S3. Constrained by the feasible domain of material handling flow and the feasible domain of production power demand, process-power coupling relationship, production tasks, intermediate material balance, off-grid power balance and fixed resource boundary, based on the photovoltaic day-ahead output forecast and the operating status of power supply equipment, and with the goal of minimizing the expected operating cost under the photovoltaic output scenario, establish a day-ahead resource allocation model and generate a day-ahead resource allocation plan. S4. During the intraday rolling period, the actual inventory is used as the initial value. The subsequent inventory is deduced by combining the material inflow and material outflow. The feasible domain of material handling flow and the feasible domain of production power demand are recalculated based on the actual production status. The operating resource allocation results of each process, energy storage and diesel unit are corrected based on the day-ahead resource allocation plan, combined with the photovoltaic short-term output forecast and the operating status of energy supply equipment. S5. Execute the resource allocation results of each process, energy storage and diesel generator set in the first period of the rolling time domain, update the production status, intermediate material buffer unit status and power supply equipment operation status according to the execution feedback, and enter the next rolling time to recalculate the feasible domain of material processing flow and the feasible domain of production power demand.

2. The method according to claim 1, characterized in that, In step S2, based on the inventory of the intermediate material buffer unit, the material inflow rate, and the downstream equipment processing boundary, the maximum and minimum allowable processing flow rates of the downstream process are determined, forming a feasible region for material processing flow rates, including: The current inventory is updated based on the previous inventory, material inflow rate, and material outflow rate of the intermediate material buffer unit. The amount of material that can be released is determined based on the current inventory and lower limit of the intermediate material buffer unit. The maximum allowable processing flow rate of the downstream process is determined based on the amount of material that can be released, the material inflow rate, and the upper limit of the downstream equipment. The remaining amount of material that can be accommodated is determined based on the current inventory and upper limit of the intermediate material buffer unit. The minimum allowable processing flow rate of the downstream process is determined based on the remaining amount of material that can be accommodated, the material inflow rate, and the lower limit of the downstream equipment. The flow range between the minimum allowable processing flow rate and the maximum allowable processing flow rate is defined as the feasible region for material processing flow rate.

3. The method according to claim 2, characterized in that, The formula for calculating the current inventory of the intermediate material buffer unit is as follows: in, and These represent material buffer units. The inventory at time t-1 and time t, and These represent material buffer units. The material inflow and material outflow rates at time t-1 Indicates the scheduling time interval. and These represent material buffer units. The lower and upper limits of inventory.

4. The method according to claim 2, characterized in that, The maximum allowable processing flow rate of the downstream process is determined based on the amount of releasable material, the material inflow rate, and the upper limit of downstream equipment processing capacity. The calculation formula is as follows: The minimum allowable processing flow rate for downstream processes is determined based on the remaining capacity of material, the material inflow rate, and the lower limit of downstream equipment processing capacity. The calculation formula is as follows: The feasible region for material handling flow rate is ; in, Indicates intermediate material buffer unit Related downstream processes, and These represent the upper limit and lower limit of downstream equipment processing capacity, respectively. Indicates intermediate material buffer unit The material inflow rate at time t and These represent the maximum and minimum allowable material outflow rates for the downstream process, respectively. Indicates intermediate material buffer unit The inventory at time t, Indicates the scheduling time interval. and These represent intermediate material buffer units. The lower and upper limits of inventory.

5. The method according to claim 1, characterized in that, In step S2, the feasible region of material handling flow rate is converted into the feasible region of production power demand by utilizing the process-power coupling relationship between material handling flow rate, equipment operating status, process operating parameters, and power consumption. This includes: Based on the equipment operating status and process parameters of the corresponding procedures, calculate the power consumption corresponding to the material handling flow rate within the feasible region, and determine the lower and upper limits of power consumption, expressed as follows: in, Number the production process. For scheduling time, For the first The power consumption of each production process This refers to the process-electric coupling relationship of the corresponding steps. This represents the device's operating status; the value is 1 when the device is running and 0 when it is stopped. For material processing flow rate, A set of process operating parameters, and These are the lower and upper limits of power consumption under the corresponding production conditions, respectively, with each power using a uniform power unit. The lower limit of power consumption is the minimum power supply required to maintain continuous production of the corresponding process under the current production conditions, while the upper limit of power consumption is the maximum allowable power consumption. The process-electric coupling relationship for each step is as follows: The power consumption of the brine extraction and transportation step is determined based on the pump flow rate, operating head, and operating efficiency; the power consumption of the continuous adsorption step is determined based on the online operating equipment, feed flow rate, circulation flow rate, and pump operating parameters; the power consumption of the membrane purification and concentration step is determined based on the material processing flow rate, membrane treatment operating pressure, and pressure drive equipment efficiency; the power consumption of the bipolar membrane electrodialysis step is determined based on the unit operating voltage, operating current, and material processing status; and the power consumption of the lithium precipitation mechanical vapor recompression step is determined based on the material processing flow rate and corresponding power consumption parameters. In both daily resource allocation and intraday rolling resource allocation, the material handling flow and power consumption of each process simultaneously satisfy the process-power coupling relationship.

6. The method according to claim 1, characterized in that, In step S3, a day-ahead resource allocation model is established, and a day-ahead resource allocation plan is generated, including: Based on the day-ahead photovoltaic power output forecast and forecast error, photovoltaic power output scenarios are generated and then reduced to obtain a set of photovoltaic power output scenarios. Using the material handling flow rate and equipment operating status of each process, the charging and discharging status and power of energy storage, and the start-stop status and output of the diesel generator set as variables for operating resource allocation, and with the goal of minimizing the expected operating cost under each photovoltaic output scenario, a day-ahead resource allocation model is established. The objective function is expressed as: in, The expected operating cost of the day-ahead resource allocation model. This represents a collection of photovoltaic power output scenarios. Indicates photovoltaic power output scenarios The probability of occurrence, , , and Let $\mathbf$ represent the fuel cost of diesel generators, the start-up and shutdown cost of diesel generator sets, the equipment operation and maintenance cost, and the carbon emission cost under scenario $s$. The penalty cost for material handling flow deviating from the original production plan. The penalty cost for intermediate material inventory deviating from the reference material inventory; Ensure that the cumulative material handling flow of each process within the planned timeframe meets the production task constraints: in, For the first Each process is at a certain time Material processing flow rate corresponding to the scheduling period For scheduling time intervals, For the first The minimum cumulative material handling volume required to be completed by each process within the planned timeframe; Solve the day-ahead resource allocation model to obtain the material handling flow rate of each process and the applied power, diesel generator start-up and shutdown and output, energy storage charging and discharging power and state of charge trajectory, and form a day-ahead resource allocation plan. The day-ahead operation resource allocation satisfies the feasible domain of material handling flow rate, the feasible domain of production power demand, intermediate material balance, off-grid power balance and power supply reliability constraints.

7. The method according to claim 1, characterized in that, In step S4, using the measured inventory as the initial value, the subsequent inventory is recursively calculated based on the material inflow and outflow rates. The feasible domains for material handling flow and production power demand are recalculated according to the measured production status. Combining the short-term photovoltaic output forecast and the operating status of the power supply equipment, the operating resource allocation results for each process, energy storage, and diesel generator set are revised based on the day-ahead resource allocation plan, including: The measured production status, the actual inventory of the intermediate material buffer unit, the energy storage charging status, and the start-stop and output status of the diesel generator set are used as the initial state for rolling optimization. The subsequent inventory in the rolling time domain is recursively deduced according to the material balance relationship, and the feasible domain of material handling flow and production power demand for each time period is updated accordingly. The objective is to minimize the sum of the operating cost within the rolling time domain and the power deviation cost relative to the day-ahead resource allocation plan, expressed as: in, This represents the total target cost for intraday rolling optimization; , , and These represent the fuel cost of diesel power generation, the start-up and shutdown cost of diesel generator sets, the equipment operation and maintenance cost, and the carbon emission cost in the rolling time domain, respectively. The penalty cost for material handling flow rate deviating from the original production plan within the rolling time domain. The penalty cost for intermediate material inventory deviating from the reference material inventory within the rolling time domain; , and These represent the adjustments made to the energy storage charging and discharging power, diesel generator output, and production process power consumption relative to the day-ahead resource allocation plan, respectively. , and These represent the corresponding deviation cost coefficients; Under the conditions of satisfying the updated feasible domain of material handling flow rate and feasible domain of production power demand, process-power coupling relationship, intermediate material balance, off-grid power balance, power supply reliability and equipment operation constraints, the operation resource allocation results of each process, energy storage and diesel unit are corrected.

8. The method according to claim 2, characterized in that, In step S5, the resource allocation results for each process, energy storage, and diesel generator set in the first period of the rolling time domain are executed. Based on the execution feedback, the production status, the status of the intermediate material buffer unit, and the operating status of the power supply equipment are updated. Then, the feasible domain for material handling flow and the feasible domain for production power demand are recalculated in the next rolling time period, including: Within the intraday rolling time domain, at each rolling moment, only the power consumption commands for each adjustable production process, the start / stop and output commands for diesel generator groups, and the energy storage charging and discharging commands for the first period are executed. Update the equipment operating status, material handling flow rate and process operating parameters, inventory and material inflow and outflow of intermediate material buffer units, energy storage charging status and diesel generator start-up and output status of each process based on the actual execution results. The updated actual inventory of the intermediate material buffer unit is used as the initial inventory value for the next rolling time. The amount of releaseable material, the remaining amount of material that can be accommodated, and the maximum and minimum allowable processing flow rates for downstream processes are redefined. The feasible domains for material processing flow rate and production power demand are recalculated based on the updated equipment operating status and process operating parameters.

9. An electronic device, characterized in that, It includes one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs, when executed by the processors, implement the production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the production-energy resource allocation method based on online reconfiguration of lithium extraction production status in salt lakes as described in any one of claims 1-8.

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