Optical storage optimization treatment method and device for wet smelting plant

Abstract

The invention discloses an optical storage optimization processing method and device for a wet smelting plant, and the method comprises the steps: proposing a time-phased power filling principle of an optical storage system based on the historical power failure and power limiting data of a local power system of the wet smelting plant; establishing a pre-storage mechanism of the optical storage system oriented to the power failure scene according to the current situation of frequent power failure of the power system; an energy storage power limit value is set based on the starting requirement of an asynchronous motor in a wet smelting plant and the flexible load adjusting capacity, and a continuous power supply power target value of the optical storage system after power failure is set according to the operation reliability requirement of a load in the plant, so that the configuration economy of the optical storage system is improved while the power utilization reliability of the wet smelting plant is guaranteed. According to the invention, the requirements of the wet smelting plant for supplementing commercial power vacancy for the optical storage system and ensuring continuous power supply in a power failure scene can be met, the power supply reliability of the wet smelting plant can be effectively improved, the continuity and safety of production are ensured, the economical efficiency of optical storage system configuration is also considered, and the economic benefit of the wet smelting plant is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy system processing, and in particular relates to a method and device for optimizing the processing of light storage in a hydrometallurgical plant. Background Art

[0002] The production process of a hydrometallurgical plant involves many complex links such as ore grinding, dissolution, leaching, solution purification, and metal electrolysis. The loads in the plant include ball mills, semi-autogenous mills, drainage pumps, electrolysis devices, air conditioning and lighting equipment. The start and stop of equipment such as ball mills and semi-autogenous mills will seriously affect the production continuity of the hydrometallurgical plant, so the requirements for power supply reliability are extremely high. Electrolysis devices, drainage pumps, air conditioning, lighting and other equipment are allowed to operate at limited power or be temporarily removed for a period of time. Since the load of electrolysis devices accounts for a relatively high proportion in hydrometallurgical plants, they have greater demand response flexibility and adjustment potential.

[0003] At present, in order to ensure the reliability of electricity supply in hydrometallurgical plants, it is a common solution to install backup power sources such as diesel generators in the plant area. After the diesel generator is started, it can provide stable and continuous power supply for the hydrometallurgical plants. Some hydrometallurgical plants install photovoltaic power generation equipment in the plant area and use solar energy as a supplementary power source for daily production to reduce dependence on the power grid. In view of the weather sensitivity and day-night differences of photovoltaic power generation, the photovoltaic power generation system and the energy storage battery system can be used in combination, which can smooth the power supply fluctuations to a certain extent and provide more stable power output. When determining the photovoltaic and energy storage capacity, the difference between the photovoltaic power generation in different time periods and the power demand of the factory can be used to calculate the capacity of the photovoltaic storage system with the goal of meeting the load demand during peak hours. The existing technology also often establishes a mathematical model, adopts algorithms such as linear programming or dynamic programming, and combines the operation and scheduling plan of the hydrometallurgical plant to include factors such as the capacity, initial investment, operating cost, and electricity cost savings of the photovoltaic storage system into the calculation, and optimizes the capacity of the photovoltaic storage system with the minimization of total cost as the objective function.

[0004] However, in some areas, the power grid has problems such as unreasonable power generation structure, aging system, weak network architecture, imperfect power generation and load dispatching mechanism, etc. The power supply of the power system is often limited by the external environment, load fluctuations and power grid failures, resulting in frequent power outages or power restrictions. The existing technology has significant deficiencies in improving the reliability of power supply in hydrometallurgical plants. This problem of poor power supply reliability will directly affect the normal production process of the hydrometallurgical plant, making it difficult to guarantee the continuous power supply needs of important loads such as ball mills and semi-autogenous mills, resulting in production interruptions, increased equipment losses, and even potential production safety hazards. Although diesel generators can provide power supplements for hydrometallurgical plants, the start and stop and power regulation of diesel generators are limited by internal thermal links, and their response speed is difficult to meet the high-reliability power demand of hydrometallurgical plants in areas with frequent power outages. In addition, diesel generators have problems such as high power generation costs, large carbon emissions, and serious pollution. Existing photovoltaic storage systems are mostly used in peak-shaving, energy-saving and other scenarios, and lack reliability optimization designs for the continuous production, grid-connection and off-grid connection, and asynchronous motor starting requirements of hydrometallurgical plants. When determining the capacity configuration plan of the photovoltaic storage system, the existing technology does not meet the needs of hydrometallurgical plants for the photovoltaic storage system to supplement the power shortage of the mains and ensure continuous power supply in power outage scenarios. In addition, the existing photovoltaic storage system configuration method does not take into account the demand response and flexible adjustment capabilities of equipment such as the electrolysis unit of the hydrometallurgical plant, resulting in additional cost investment. Therefore, a more efficient and stable photovoltaic storage optimization processing solution for hydrometallurgical plants is urgently needed. Summary of the invention

[0005] To this end, the present invention provides a method and device for optimizing the photovoltaic storage in a hydrometallurgical plant, which effectively improves the power supply reliability of the hydrometallurgical plant, ensures the continuity and safety of production, and takes into account the economy of the photovoltaic storage system configuration.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for optimizing the optical storage of a hydrometallurgical plant, comprising:

[0007] Obtain the grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, including electrolytic cells, ball mills, and semi-autogenous mills; obtain the historical load data, light data, and historical power outage and power restriction data of the hydrometallurgical plant; collect the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; collect the cost per kilowatt-hour of power supply from the large power grid and the photovoltaic storage system;

[0008] According to the collected grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, the historical load data and illumination data of the hydrometallurgical plant and the historical power outage and power restriction data of the local large power grid, the unit capacity investment cost of the photovoltaic, energy storage and converter equipment, and the kilowatt-hour cost of power supply from the large power grid and the photovoltaic storage system, an optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant is constructed. The constraints of the optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant include the configuration capacity of photovoltaic and energy storage, the operation constraints of photovoltaic power generation devices, the operation constraints of energy storage in the hydrometallurgical plant, the output target limit constraints of the photovoltaic storage system in different time periods and the supply and demand balance constraints during the operation of the hydrometallurgical plant;

[0009] Constructing the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model, wherein the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model is the equivalent annual value cost of the hydrometallurgical plant photovoltaic storage configuration over the entire life cycle;

[0010] According to the constraint conditions of the photovoltaic and energy storage system optimization model of the hydrometallurgical plant and the objective function of the photovoltaic and energy storage system optimization model of the hydrometallurgical plant, the photovoltaic and energy storage system optimization model of the hydrometallurgical plant is solved to obtain the optimal configuration plan of photovoltaic and energy storage of the hydrometallurgical plant.

[0011] As a preferred solution for the hydrometallurgical plant photovoltaic and energy storage optimization processing method, the configuration capacity constraint formula of the photovoltaic and energy storage system optimization configuration model of the hydrometallurgical plant is:

[0012]

[0013] In the formula, Configure power capacity for photovoltaics; The power capacity configured for the energy storage converter; Allocate capacity for energy storage; and They are the upper and lower limits of the photovoltaic configuration power capacity respectively; and They are the upper and lower limits of the power capacity configured for the energy storage converter; and are the upper and lower limits of energy storage configuration capacity respectively;

[0014] Lower limit of photovoltaic configuration power capacity The formula for the value of is:

[0015]

[0016] In the formula, is the rated load capacity of the hydrometallurgical plant; The total rated power of the ball mill and semi-autogenous mill equipment; is the rated power of the electrolyzer equipment; It is the startup factor of ball mill and semi-autogenous mill equipment; Provides a flexible adjustment factor for the electrolytic cell equipment.

[0017] As a preferred solution for the hydrometallurgical plant photovoltaic storage optimization processing method, the constraint condition formula of the operation constraint of the photovoltaic power generation device in the hydrometallurgical plant photovoltaic storage system optimization configuration model is:

[0018]

[0019] In the formula, Represents the operating power of the new energy power generation device in period t; and They represent the upper and lower allowable boundaries of the operating power of the new energy power generation device in period t; β pv is the derating factor of the photovoltaic array; is the rated power of the solar photovoltaic panel; G t is the solar radiation intensity in period t; G std is the solar radiation intensity under standard test conditions; k PT is the power temperature coefficient; T t is the average ambient temperature during period t; T std It is the ambient temperature under standard test conditions.

[0020] As a preferred solution for the hydrometallurgical plant photovoltaic energy storage optimization treatment method, the operation constraint formula of the hydrometallurgical plant photovoltaic energy storage system optimization configuration model is as follows:

[0021]

[0022] In the formula, It represents the energy value of the energy storage at the initial stage of period t; and They represent the charging and discharging power of the energy storage power in time period t respectively; and are the charging and discharging efficiencies of energy storage, respectively; and are the energy storage charging and discharging state variables, respectively, and in Indicates that the energy storage is in charging state. Indicates that the energy storage is in a discharging state; △t indicates the duration of the scheduling cycle; χ H and χ L Respectively represent the upper and lower limits of the energy storage charge state; t0 and t end They represent the start and end periods of the dispatch respectively; χ0 is the initial charge state of the energy storage;

[0023] The output target limit constraint formula of the photovoltaic storage system in different periods of the hydrometallurgical plant photovoltaic storage system optimization configuration model is:

[0024]

[0025] In the formula, is the output target limit of the PV-storage system in period t; and are the load demand and the maximum power supply of the mains in the t period of the sth sample respectively; express and 0; N s is the number of samples used;

[0026] The supply and demand balance constraint formula of the hydrometallurgical plant photovoltaic storage system optimization configuration model during the operation of the hydrometallurgical plant is:

[0027]

[0028] In the formula, and They are the power supply of the large power grid and the load power of the hydrometallurgical plant in period t respectively; It is the maximum limit of the power supply of the large power grid during the period t.

[0029] As a preferred solution for the hydrometallurgical plant photovoltaic storage optimization treatment method, the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model is:

[0030]

[0031] In the formula, and are the expected investment costs of photovoltaic and energy storage systems, respectively; Expected operation and maintenance costs for powering PV and energy storage; the expected cost of supplying electricity from the utility; Cost of lost expected benefits of load flexibility power regulation in hydrometallurgical plants; eav is the coefficient of equal annual value;

[0032] In the process of solving the optimal configuration model of the hydrometallurgical plant's photovoltaic energy storage system, a branch and bound method, a cutting plane method, a genetic algorithm, a particle swarm algorithm, a simulated annealing algorithm or a hybrid algorithm is adopted, or a commercial solver is called for solving.

[0033] The present invention also provides a hydrometallurgical plant light storage optimization processing device, comprising:

[0034] The parameter acquisition module is used to obtain the grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, including the electrolytic cell, ball mill, and semi-autogenous mill; obtain the historical load data, light data of the hydrometallurgical plant, and the historical power outage and power restriction data of the local large power grid; collect the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; collect the cost of electricity supplied by the large power grid and the photovoltaic storage system;

[0035] A module for constructing an optimal configuration model of a photovoltaic storage system of a hydrometallurgical plant is used to construct an optimal configuration model of a photovoltaic storage system of a hydrometallurgical plant based on the collected grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, the historical load data and illumination data of the hydrometallurgical plant and the historical power outage and power restriction data of the local large power grid, the unit capacity investment cost of the photovoltaic, energy storage and converter equipment, and the kilowatt-hour cost of power supply from the large power grid and the photovoltaic storage system. The constraints of the optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant include the configuration capacity of photovoltaic and energy storage, the operation constraints of the photovoltaic power generation device, the operation constraints of the energy storage in the hydrometallurgical plant, the output target limit constraints of the photovoltaic storage system in different time periods, and the supply and demand balance constraints during the operation of the hydrometallurgical plant.

[0036] An objective function construction module is used to construct an objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model, wherein the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model is an equal annual value cost within the entire life cycle of the hydrometallurgical plant photovoltaic storage configuration;

[0037] The optimization configuration scheme solving module is used to solve the optimization configuration model of the hydrometallurgical plant photovoltaic storage system according to the constraint conditions of the hydrometallurgical plant photovoltaic storage system optimization configuration model and the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model to obtain the optimization configuration scheme of photovoltaic and energy storage in the hydrometallurgical plant.

[0038] As a preferred solution for the photovoltaic and energy storage optimization processing device of a hydrometallurgical plant, in the hydrometallurgical plant photovoltaic and energy storage system optimization configuration model construction module, the photovoltaic and energy storage configuration capacity constraint formula of the hydrometallurgical plant photovoltaic and energy storage system optimization configuration model is:

[0039]

[0040] In the formula, Configure power capacity for photovoltaics; The power capacity configured for the energy storage converter; for

[0041] Energy storage configuration capacity; and They are the upper and lower limits of the photovoltaic configuration power capacity respectively; and They are the upper and lower limits of the power capacity configured for the energy storage converter; and are the upper and lower limits of energy storage configuration capacity respectively;

[0042] Lower limit of photovoltaic configuration power capacity The formula for the value of is:

[0043]

[0044] In the formula, is the rated load capacity of the hydrometallurgical plant; The total rated power of the ball mill and semi-autogenous mill equipment; is the rated power of the electrolyzer equipment; It is the startup factor of ball mill and semi-autogenous mill equipment; Provides a flexible adjustment factor for the electrolytic cell equipment.

[0045] As a preferred solution for the photovoltaic storage optimization processing device of a hydrometallurgical plant, in the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module, the constraint condition formula of the operation constraint of the photovoltaic power generation device of the hydrometallurgical plant photovoltaic storage system optimization configuration model is:

[0046]

[0047] In the formula, Represents the operating power of the new energy power generation device in period t; and They represent the upper and lower allowable boundaries of the operating power of the new energy power generation device in period t; β pv is the derating factor of the photovoltaic array; is the rated power of the solar photovoltaic panel; G t is the solar radiation intensity in period t; G std is the solar radiation intensity under standard test conditions; k PT is the power temperature coefficient; T t is the average ambient temperature during period t; T std It is the ambient temperature under standard test conditions.

[0048] As a preferred solution of the hydrometallurgical plant photovoltaic storage optimization processing device, in the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module, the hydrometallurgical plant photovoltaic storage system optimization configuration model of the hydrometallurgical plant has an operation constraint formula for the hydrometallurgical plant energy storage in the hydrometallurgical plant:

[0049]

[0050] In the formula, It represents the energy value of the energy storage at the initial stage of period t; and They represent the charging and discharging power of the energy storage power in time period t respectively; and are the charging and discharging efficiencies of energy storage, respectively; and are the energy storage charging and discharging state variables, respectively, and in Indicates that the energy storage is in charging state. Indicates that the energy storage is in a discharging state; △t indicates the duration of the scheduling cycle; χ H and χ L Respectively represent the upper and lower limits of the energy storage charge state; t0 and t end They represent the start and end periods of the dispatch respectively; χ0 is the initial charge state of the energy storage;

[0051] In the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module, the photovoltaic storage system output target limit constraint formula for different time periods of the hydrometallurgical plant photovoltaic storage system optimization configuration model is:

[0052]

[0053] In the formula, is the output target limit of the PV-storage system in period t; and are the load demand and the maximum power supply of the mains in the t period of the sth sample respectively; express and 0; N s is the number of samples used;

[0054] The supply and demand balance constraint formula of the hydrometallurgical plant photovoltaic storage system optimization configuration model during the operation of the hydrometallurgical plant is:

[0055]

[0056] In the formula, and They are the power supply of the large power grid and the load power of the hydrometallurgical plant in period t respectively; It is the maximum limit of the power supply of the large power grid during the period t.

[0057] As a preferred solution for the photovoltaic storage optimization processing device of a hydrometallurgical plant, in the objective function construction module, the objective function of the photovoltaic storage system optimization configuration model of the hydrometallurgical plant is:

[0058]

[0059] In the formula, and are the expected investment costs of photovoltaic and energy storage systems, respectively; Expected operation and maintenance costs for powering PV and energy storage; the expected cost of supplying electricity from the utility; Cost of lost expected benefits of load flexibility power regulation in hydrometallurgical plants; eav is the coefficient of equal annual value;

[0060] In the optimization configuration solution solving module, a branch and bound method, a cutting plane method, a genetic algorithm, a particle swarm algorithm, a simulated annealing algorithm or a hybrid algorithm is adopted, or a commercial solver is called for solving.

[0061] The present invention has the following advantages: based on the historical power outage and power restriction data of the local power system of the hydrometallurgical plant, a time-division power supplement principle of the photovoltaic storage system is proposed, and the output target limit of the photovoltaic storage system in different time periods is added in the configuration model in the form of constraint conditions, so as to specifically supplement the power supply gap of the city power; in view of the current situation of frequent power outages in the power system, the present invention establishes a pre-reserve mechanism of the photovoltaic storage system for power outage scenarios, and combines the typical illumination characteristics and the continuous power supply target value and power supply duration requirements of the hydrometallurgical plant in the power outage scenario to set the storage energy constraints of the energy storage in each time period in the configuration model, so as to give full play to the energy storage performance and ensure the power reliability requirements of the hydrometallurgical plant; the present invention adopts a flexible control method for part of the load in the hydrometallurgical plant, sets the energy storage power limit based on the asynchronous motor starting requirements and flexible load regulation capabilities in the hydrometallurgical plant, and sets the continuous power supply target value of the photovoltaic storage system after the power outage according to the operation reliability requirements of the load in the plant, so as to ensure the power reliability of the hydrometallurgical plant while improving the economy of the photovoltaic storage system configuration. The present invention can meet the needs of the hydrometallurgical plant for the photovoltaic storage system to supplement the shortage of municipal power and ensure continuous power supply in power outage scenarios. The photovoltaic storage configuration combined with the demand response and flexible adjustment capabilities of equipment such as the hydrometallurgical plant's electrolysis unit can avoid incurring additional cost investment, which can effectively improve the power supply reliability of the hydrometallurgical plant and ensure the continuity and safety of production, while taking into account the economy of the photovoltaic storage system configuration, thus ensuring the economic benefits of the hydrometallurgical plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0063] Figure 1 A schematic flow chart of a method for optimizing the optical storage of a hydrometallurgical plant provided in an embodiment of the present invention;

[0064] Figure 2 It is a schematic diagram of the structure of a hydrometallurgical plant targeted by the method for optimizing the optical energy storage of a hydrometallurgical plant provided in an embodiment of the present invention;

[0065] Figure 3 A typical load curve of a hydrometallurgical plant provided in an embodiment of the present invention;

[0066] Figure 4 A typical sunlight curve for the location of a hydrometallurgical plant provided in an embodiment of the present invention;

[0067] Figure 5 The specified scenario configuration result provided in the embodiment of the present invention;

[0068] Figure 6 The annual cost of a specified scenario provided in an embodiment of the present invention;

[0069] Figure 7 This is a schematic diagram of the architecture of the optical energy storage optimization processing device for a hydrometallurgical plant provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] Example 1

[0072] See also Figure 1 The embodiment of the present invention provides a method for optimizing the optical energy storage of a hydrometallurgical plant, comprising the following steps:

[0073] S1. Obtain the grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, including the electrolytic cell, ball mill, and semi-autogenous mill; obtain the historical load data, light data, and historical power outage and power restriction data of the hydrometallurgical plant; collect the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; collect the cost of electricity from the power grid and the photovoltaic storage system;

[0074] S2. Based on the collected grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, the historical load data and illumination data of the hydrometallurgical plant and the historical power outage and power restriction data of the local large power grid, the unit capacity investment cost of the photovoltaic, energy storage and converter equipment, and the kilowatt-hour cost of power supply from the large power grid and the photovoltaic storage system, an optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant is constructed. The constraints of the optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant include the configuration capacity of photovoltaic and energy storage, the operation constraints of photovoltaic power generation devices, the operation constraints of energy storage in the hydrometallurgical plant, the output target limit constraints of the photovoltaic storage system in different time periods, and the supply and demand balance constraints during the operation of the hydrometallurgical plant;

[0075] S3, constructing the objective function of the optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant, wherein the objective function of the optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant is the equivalent annual value cost of the photovoltaic storage configuration in the whole life cycle of the hydrometallurgical plant;

[0076] S4. According to the constraint conditions of the photovoltaic and energy storage system optimization configuration model of the hydrometallurgical plant and the objective function of the photovoltaic and energy storage system optimization configuration model of the hydrometallurgical plant, the photovoltaic and energy storage system optimization configuration model of the hydrometallurgical plant is solved to obtain the optimal configuration plan of photovoltaic and energy storage of the hydrometallurgical plant.

[0077] See also Figure 2 , which is a hydrometallurgical plant structure. The key loads include ball mills, semi-autogenous mills, electrolytic cells, etc. Under normal circumstances, the hydrometallurgical plant relies on the mains for power supply. The backup generator set often uses a diesel generator to provide power supplement when the power supply is insufficient due to power outages or power restrictions in the large power grid. After configuring the photovoltaic storage system, photovoltaic or energy storage can be used to supply electricity to the hydrometallurgical plant.

[0078] In this embodiment, in step S2, various types of equipment in the hydrometallurgical plant are modeled according to the parameters collected in step S1, typical load curves and illumination curves are established through clustering, and the output target limit of the photovoltaic storage system in each time period is set according to the historical power outage and power restriction data. The energy storage power limit and the continuous power supply target value of the photovoltaic storage system after the power outage are determined based on the load parameters of the electrolytic cell, ball mill, semi-autogenous mill, etc. The storage energy constraints of the energy storage in each time period are set in combination with the typical illumination characteristics and the continuous power supply target value and power supply duration requirements of the hydrometallurgical plant in the power outage scenario, and the optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant is constructed in combination with the working models and operation boundaries of various types of equipment.

[0079] The configuration capacity constraint formula of photovoltaic and energy storage in the optimal configuration model of the hydrometallurgical plant photovoltaic storage system is as follows:

[0080]

[0081] In the formula, Configure power capacity for photovoltaics; The power capacity configured for the energy storage converter; Allocate capacity for energy storage; and They are the upper and lower limits of the photovoltaic configuration power capacity respectively; and

[0082] They are the upper and lower limits of the power capacity configured for the energy storage converter; and are the upper and lower limits of energy storage configuration capacity respectively; and The value of depends on the space and capital reserves of the hydrometallurgical plant. The lower limit of the photovoltaic configuration power capacity The formula for the value of is:

[0083]

[0084] In the formula, is the rated load capacity of the hydrometallurgical plant; The total rated power of the ball mill and semi-autogenous mill equipment; is the rated power of the electrolyzer equipment; It is the starting factor of ball mill and semi-autogenous mill equipment, that is, the ratio of starting power to rated power; The electrolyzer equipment can be flexibly adjusted to adjust the ratio of the power that can quickly participate in demand response to the rated power.

[0085] In this embodiment, typical load curves and typical light curves are obtained through historical data, and methods include but are not limited to data reference method, mean method, clustering method, machine learning method, artificial intelligence method, etc. Models are built for various types of equipment in the hydrometallurgical plant, and operation constraints are established based on the equipment working model and operation boundary requirements.

[0086] Specifically, based on a typical light curve, the constraint condition formula for the operation constraint of the photovoltaic power generation device in the optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant is:

[0087]

[0088] In the formula, Represents the operating power of the new energy power generation device in period t; and They represent the upper and lower allowable boundaries of the operating power of the new energy power generation device in period t; β pv is the derating factor of the photovoltaic array; is the rated power of the solar photovoltaic panel; G t is the solar radiation intensity in period t; G stdis the solar radiation intensity under standard test conditions, generally taken as 1000W / m 2 ;k PT is the power temperature coefficient, generally taken as -0.0047 / ℃; T t is the average ambient temperature during period t; T std It is the ambient temperature under standard test conditions, generally taken as 25℃.

[0089] Among them, the operating constraint formula of the hydrometallurgical plant energy storage in the hydrometallurgical plant photovoltaic storage system optimization configuration model is:

[0090]

[0091]

[0092] In the formula, It represents the energy value of the energy storage at the initial stage of period t; and They represent the charging and discharging power of the energy storage power in time period t respectively; and are the charging and discharging efficiencies of energy storage, respectively; and are the energy storage charging and discharging state variables, respectively, and in Indicates that the energy storage is in charging state. Indicates that the energy storage is in a discharging state; △t indicates the duration of the scheduling cycle; χ H and χ L Respectively represent the upper and lower limits of the energy storage charge state; t0 and t end They represent the start and end periods of scheduling respectively; χ0 is the initial charge state of the energy storage.

[0093] The current situation of frequent power outages and power restrictions in the weak power system has resulted in the city electricity being unable to fully meet the load demand of the hydrometallurgical plant, i.e., a city electricity gap has been created. At the same time, the load of the hydrometallurgical plant has peak-to-valley fluctuation characteristics, and the power restriction value of the city electricity has statistical time-period differences. In order to specifically supplement the power supply gap of the city electricity, in this embodiment, a time-divided power gap supplementation principle for the photovoltaic storage system is established. In the configuration model, the output target limit values ​​of the photovoltaic storage system in different time periods are added in the form of constraints. The output target limit value constraint formula for the photovoltaic storage system in different time periods of the photovoltaic storage system in the optimization configuration model of the hydrometallurgical plant photovoltaic storage system is:

[0094]

[0095] In the formula, is the output target limit of the solar-storage system in time period t, which can be determined by analyzing the load demand and the city power limit value in each historical time period; and are the load demand and the maximum power supply of the mains in the t period of the sth sample respectively; express and 0; N s is the number of samples used.

[0096] In this embodiment, combined with the continuous power supply target value and power supply duration requirements in the power outage scenario, a pre-reserve mechanism for the photovoltaic storage system facing the power outage of the large power grid is proposed to ensure the reliability of electricity consumption in the hydrometallurgical plant. At the same time, based on the typical light characteristics, the energy storage limit of each time period is adjusted to give full play to the energy storage performance and avoid the increase of energy storage investment cost due to excessive conservatism. The storage energy constraints of energy storage in each time period are added to the configuration model:

[0097]

[0098] Where P suc and N suc are the target value of continuous power supply and the number of continuous power supply periods required in the power outage scenario. Considering that the loads such as the electrolytic cell and non-production line air conditioner in the hydrometallurgical plant can be operated at reduced power within a certain period of time, P suc The value can be obtained according to the following formula:

[0099]

[0100] In the formula, Flexible power adjustment for set loads in hydrometallurgical smelters.

[0101] In this embodiment, the supply and demand balance constraint formula during the operation of the hydrometallurgical plant in the hydrometallurgical plant photovoltaic storage system optimization configuration model is:

[0102]

[0103] In the formula, and They are the power supply of the large power grid and the load power of the hydrometallurgical plant in period t respectively; It is the maximum limit of the power supply of the large power grid during the period t.

[0104] In this embodiment, in step S3, the objective function of the optimization configuration model of the photovoltaic energy storage system of the hydrometallurgical plant is:

[0105]

[0106] In the formula, and are the expected investment costs of photovoltaic and energy storage systems, respectively; Expected operation and maintenance costs for powering PV and energy storage; the expected cost of supplying electricity from the utility; Cost of lost expected benefits of load flexibility power regulation in hydrometallurgical plants; eav is the equal annual value coefficient.

[0107] Specifically, the expressions of various costs and annual value coefficients are as follows:

[0108]

[0109] In the formula, and They are the investment costs per unit power of photovoltaic, per unit power of energy storage and per unit energy of energy storage; and are the power supply costs of photovoltaic, energy storage and mains electricity respectively; is the unit power adjustment cost of load flexibility in the hydrometallurgical plant; r is the discount rate; n y It is the life cycle of the photovoltaic storage system.

[0110] In this embodiment, in step S4, according to the constructed hydrometallurgical plant photovoltaic storage system optimization configuration model, the hydrometallurgical plant microgrid source grid load storage coordinated optimization scheduling scheme is solved to minimize the operation cost of the hydrometallurgical plant microgrid. The solution model is as follows:

[0111]

[0112] The above solution model belongs to mixed integer linear programming problem, which can be solved by precise methods such as branch and bound method, cutting plane method, or heuristic algorithms such as genetic algorithm, particle swarm algorithm, simulated annealing algorithm, and hybrid improved algorithms based on the above algorithms. In addition, if conditions permit, external mature commercial solvers can also be called to solve the above problems.

[0113] The embodiment of the present invention provides the following calculation examples:

[0114] For example, the rated load of a hydrometallurgical plant is about 18MW, and its typical load curve is as follows: Figure 3 The load in the hydrometallurgical plant mainly includes a 2.2MW ball mill, a 2MW semi-autogenous mill, a 10MW electrolytic cell and other auxiliary equipment. The typical light curve in the area where the hydrometallurgical plant is located is as follows Figure 4 shown.

[0115] According to the historical data of power outages and power restrictions in the local power system, the operator of the hydrometallurgical plant requires that when a random power outage occurs in the large power grid, the hydrometallurgical plant can meet the rated load operation for 5 hours without relying on diesel generators. The initial state of charge of the energy storage is set to 0.5, the upper and lower limits of the state of charge are set to 0.9 and 0.1 respectively, and the charging and discharging efficiency of the energy storage is set to 0.95. The investment costs of unit power photovoltaic, unit power energy storage and unit energy storage are respectively US$171,000 / MW, US$114,000 / MW and US$214,000 / MWh. The discount rate is 0.05, and the life cycle of the photovoltaic storage system is 13 years.

[0116] The energy storage configuration scheme proposed in the present invention takes into account the flexible control technology of the flexible load in the hydrometallurgical plant and adopts the photovoltaic storage system anti-power outage pre-reserve mechanism based on photovoltaic characteristics. In order to verify the effectiveness of the method proposed in the present invention, the example comparison settings shown in Table 1 are carried out. The corresponding factors considered in each scenario are marked as "√", and those not considered are marked as "×". The optimization results are shown in Figure 5-Figure 6 shown.

[0117] Table 1 Example comparison settings

[0118] Scenario Flexible control technology Solar storage pre-reserve mechanism Case 1 √ √ Case 2 √ × Case 3 × √ Case 4 × ×

[0119] Depend on Figure 5 , Figure 6 It can be seen that in scenarios Case 1 to Case 4, the photovoltaic and energy storage power configuration results are close to each other, but the energy storage energy configuration in Case 1 is reduced by 15.9%, 12.6%, and 24.9% respectively compared with Case 2, Case 3, and Case 4. The equivalent annual value cost of Case 1 is reduced by 3.09%, 2.17%, and 5.12% compared with Case 2, Case 3, and Case 4, respectively, which verifies the effectiveness of the method proposed in this invention.

[0120] In summary, the present invention obtains the grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, wherein the designated load equipment includes an electrolytic cell, a ball mill, and a semi-autogenous mill; obtains the historical load data, illumination data, and historical power outage and power restriction data of the local large power grid of the hydrometallurgical plant; collects the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; collects the kilowatt-hour cost of power supply from the large power grid and the photovoltaic storage system; and constructs an optimal configuration model for the photovoltaic storage system of the hydrometallurgical plant based on the collected grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, the historical load data, illumination data of the hydrometallurgical plant, and historical power outage and power restriction data of the local large power grid, the unit capacity investment cost of the photovoltaic, energy storage, and converter equipment, and the kilowatt-hour cost of power supply from the large power grid and the photovoltaic storage system. The constraints of the optimal configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant include the configuration capacity of photovoltaic and energy storage, the operation constraints of the photovoltaic power generation device, the operation constraints of the energy storage in the hydrometallurgical plant, the output target limit constraints of the photovoltaic and energy storage system in different time periods, and the supply and demand balance constraints during the operation of the hydrometallurgical plant; the objective function of the optimal configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant is constructed, and the objective function of the optimal configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant is the equal annual value cost of the photovoltaic and energy storage configuration over the entire life cycle of the hydrometallurgical plant; according to the constraints of the optimal configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant and the objective function of the optimal configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant, the optimal configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant is solved to obtain the optimal configuration plan of photovoltaic and energy storage of the hydrometallurgical plant. Based on the historical power outage and power restriction data of the local power system of the hydrometallurgical plant, the present invention proposes a time-divided power gap filling principle for the photovoltaic storage system, adds the output target limit values ​​of the photovoltaic storage system in different time periods in the configuration model in the form of constraint conditions, and specifically fills the power supply gap of the city power; in view of the current situation of frequent power outages in the power system, the present invention establishes a pre-reserve mechanism for the photovoltaic storage system for power outage scenarios, combines typical illumination characteristics and the continuous power supply target value and power supply duration requirements of the hydrometallurgical plant in the power outage scenario, and sets the storage energy constraints of the energy storage in each time period in the configuration model, so as to give full play to the energy storage performance and ensure the power reliability requirements of the hydrometallurgical plant; the present invention adopts a flexible control method for part of the load in the hydrometallurgical plant, sets the energy storage power limit value based on the asynchronous motor starting requirements and flexible load adjustment capabilities in the hydrometallurgical plant, and sets the continuous power supply target value of the photovoltaic storage system after the power outage according to the operation reliability requirements of the load in the plant, so as to ensure the power reliability of the hydrometallurgical plant while improving the economy of the photovoltaic storage system configuration. The present invention can meet the needs of the hydrometallurgical plant for the photovoltaic storage system to supplement the shortage of municipal power and ensure continuous power supply in power outage scenarios. The photovoltaic storage configuration combined with the demand response and flexible adjustment capabilities of equipment such as the hydrometallurgical plant's electrolysis unit can avoid incurring additional cost investment, which can effectively improve the power supply reliability of the hydrometallurgical plant and ensure the continuity and safety of production, while taking into account the economy of the photovoltaic storage system configuration, thus ensuring the economic benefits of the hydrometallurgical plant.

[0121] Example 2

[0122] See also Figure 7 Embodiment 2 of the present invention further provides a hydrometallurgical plant light storage optimization processing device, comprising:

[0123] The parameter acquisition module 100 is used to obtain the grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, wherein the designated load equipment includes an electrolytic cell, a ball mill, and a semi-autogenous mill; obtain the historical load data, light data, and historical power outage and power restriction data of the hydrometallurgical plant; collect the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; collect the cost of electricity supplied by the large power grid and the photovoltaic storage system;

[0124] A module 200 for constructing an optimal configuration model of a photovoltaic storage system for a hydrometallurgical plant is used to construct an optimal configuration model of a photovoltaic storage system for a hydrometallurgical plant based on the collected grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, the historical load data and illumination data of the hydrometallurgical plant and the historical power outage and power restriction data of the local large power grid, the unit capacity investment cost of the photovoltaic, energy storage and converter equipment, and the per-kilowatt-hour cost of power supply from the large power grid and the photovoltaic storage system. The constraints of the optimal configuration model of the photovoltaic storage system for a hydrometallurgical plant include the configuration capacity of photovoltaic and energy storage, the operation constraints of the photovoltaic power generation device, the operation constraints of the energy storage in the hydrometallurgical plant, the output target limit constraints of the photovoltaic storage system in different time periods, and the supply and demand balance constraints during the operation of the hydrometallurgical plant.

[0125] An objective function construction module 300 is used to construct an objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model, wherein the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model is an equal annual value cost within the entire life cycle of the hydrometallurgical plant photovoltaic storage configuration;

[0126] The optimization configuration scheme solving module 400 is used to solve the optimization configuration model of the hydrometallurgical plant photovoltaic storage system according to the constraint conditions of the hydrometallurgical plant photovoltaic storage system optimization configuration model and the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model to obtain the optimization configuration scheme of photovoltaic and energy storage of the hydrometallurgical plant.

[0127] In this embodiment, in the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module 200, the configuration capacity constraint condition formula of photovoltaic and energy storage of the hydrometallurgical plant photovoltaic storage system optimization configuration model is:

[0128]

[0129] In the formula, Configure power capacity for photovoltaics; The power capacity configured for the energy storage converter; Allocate capacity for energy storage; and They are the upper and lower limits of the photovoltaic configuration power capacity respectively; and They are the upper and lower limits of the power capacity configured for the energy storage converter; and are the upper and lower limits of energy storage configuration capacity respectively;

[0130] Lower limit of photovoltaic configuration power capacity The formula for the value of is:

[0131]

[0132] In the formula, is the rated load capacity of the hydrometallurgical plant; The total rated power of the ball mill and semi-autogenous mill equipment; is the rated power of the electrolyzer equipment; It is the startup factor of ball mill and semi-autogenous mill equipment; Provides a flexible adjustment factor for the electrolytic cell equipment.

[0133] In this embodiment, in the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module 200, the constraint condition formula of the operation constraint of the photovoltaic power generation device of the hydrometallurgical plant photovoltaic storage system optimization configuration model is:

[0134]

[0135] In the formula, Represents the operating power of the new energy power generation device in period t; and They represent the upper and lower allowable boundaries of the operating power of the new energy power generation device in period t; β pv is the derating factor of the photovoltaic array; is the rated power of the solar photovoltaic panel; G t is the solar radiation intensity in period t; G std is the solar radiation intensity under standard test conditions; k PT is the power temperature coefficient; T t is the average ambient temperature during period t; T std It is the ambient temperature under standard test conditions.

[0136] In this embodiment, in the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module 200, the hydrometallurgical plant photovoltaic storage system optimization configuration model of the hydrometallurgical plant has an operating constraint formula for the hydrometallurgical plant energy storage:

[0137]

[0138] In the formula, It represents the energy value of the energy storage at the initial stage of period t; and They represent the charging and discharging power of the energy storage power in time period t respectively; and are the charging and discharging efficiencies of energy storage, respectively; and are the energy storage charging and discharging state variables, respectively, and in Indicates that the energy storage is in charging state. Indicates that the energy storage is in a discharging state; △t indicates the duration of the scheduling cycle; χ H and χ L Respectively represent the upper and lower limits of the energy storage charge state; t0 and t end They represent the start and end periods of the dispatch respectively; χ0 is the initial charge state of the energy storage;

[0139] In the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module 200, the photovoltaic storage system output target limit constraint formula for different time periods of the hydrometallurgical plant photovoltaic storage system optimization configuration model is:

[0140]

[0141] In the formula, is the output target limit of the PV-storage system in period t; and are the load demand and the maximum power supply of the mains in the t period of the sth sample respectively; express and 0; N s is the number of samples used;

[0142] In the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module 200, the supply and demand balance constraint formula of the hydrometallurgical plant photovoltaic storage system optimization configuration model during the operation of the hydrometallurgical plant is:

[0143]

[0144] In the formula, and They are the power supply of the large power grid and the load power of the hydrometallurgical plant in period t respectively; It is the maximum limit of the power supply of the large power grid during the period t.

[0145] In this embodiment, in the objective function building module 300, the objective function of the hydrometallurgical plant photovoltaic energy storage system optimization configuration model is:

[0146]

[0147] In the formula, and are the expected investment costs of photovoltaic and energy storage systems, respectively; Expected operation and maintenance costs for powering PV and energy storage; the expected cost of supplying electricity from the utility; Cost of lost expected benefits of load flexibility power regulation in hydrometallurgical plants; eav is the coefficient of equal annual value;

[0148] In the optimization configuration solution solving module 400, a branch and bound method, a cutting plane method, a genetic algorithm, a particle swarm algorithm, a simulated annealing algorithm or a hybrid algorithm is adopted, or a commercial solver is called for solving.

[0149] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned device are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown in the previous part of the present application, and will not be repeated here.

[0150] Example 3

[0151] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code for a method for optimizing the photovoltaic energy storage in a hydrometallurgical plant is stored. The program code includes instructions for executing embodiment 1 or any possible implementation method of a method for optimizing the photovoltaic energy storage in a hydrometallurgical plant.

[0152] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0153] Example 4

[0154] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0155] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a method for optimizing the optical energy storage in a hydrometallurgical plant in accordance with Example 1 or any possible implementation thereof.

[0156] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0157] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0158] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0159] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for optimizing the optical storage of a hydrometallurgical plant, characterized in that: include: Obtaining grid structure parameters of a hydrometallurgical plant and physical parameters of designated load equipment, wherein the designated load equipment includes an electrolytic cell, a ball mill, and a semi-autogenous mill; Obtain historical load data, sunlight data of the hydrometallurgical plant and historical power outage and power restriction data of the local power grid; collect unit capacity investment costs of photovoltaic, energy storage and converter equipment; collect the cost per kilowatt-hour of power supply from the power grid and the photovoltaic storage system; According to the collected grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, the historical load data and illumination data of the hydrometallurgical plant and the historical power outage and power restriction data of the local large power grid, the unit capacity investment cost of the photovoltaic, energy storage and converter equipment, and the kilowatt-hour cost of power supply from the large power grid and the photovoltaic storage system, an optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant is constructed. The constraints of the optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant include the configuration capacity of photovoltaic and energy storage, the operation constraints of photovoltaic power generation devices, the operation constraints of energy storage in the hydrometallurgical plant, the output target limit constraints of the photovoltaic storage system in different time periods and the supply and demand balance constraints during the operation of the hydrometallurgical plant; Constructing the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model, wherein the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model is the equivalent annual value cost of the hydrometallurgical plant photovoltaic storage configuration over the entire life cycle; According to the constraint conditions of the photovoltaic and energy storage system optimization model of the hydrometallurgical plant and the objective function of the photovoltaic and energy storage system optimization model of the hydrometallurgical plant, the photovoltaic and energy storage system optimization model of the hydrometallurgical plant is solved to obtain the optimal configuration plan of photovoltaic and energy storage of the hydrometallurgical plant.

2. The method for optimizing the solar energy storage in a hydrometallurgical plant according to claim 1, characterized in that: The configuration capacity constraint formula of photovoltaic and energy storage in the optimal configuration model of the hydrometallurgical plant photovoltaic storage system is as follows: In the formula, Configure power capacity for photovoltaics; The power capacity configured for the energy storage converter; Allocate capacity for energy storage; and They are the upper and lower limits of the photovoltaic configuration power capacity respectively; and They are the upper and lower limits of the power capacity configured for the energy storage converter; and are the upper and lower limits of energy storage configuration capacity respectively; Lower limit of photovoltaic configuration power capacity The formula for the value of is: In the formula, is the rated load capacity of the hydrometallurgical plant; The total rated power of the ball mill and semi-autogenous mill equipment; is the rated power of the electrolyzer equipment; It is the startup factor of ball mill and semi-autogenous mill equipment; Provides a flexible adjustment factor for the electrolytic cell equipment.

3. The method for optimizing the solar energy storage in a hydrometallurgical plant according to claim 2, characterized in that: The constraint formula for the operation constraint of the photovoltaic power generation device in the optimal configuration model of the hydrometallurgical plant photovoltaic storage system is: In the formula, Represents the operating power of the new energy power generation device in period t; and They represent the upper and lower allowable limits of the operating power of the new energy power generation device in period t respectively; β pv is the derating factor of the photovoltaic array; is the rated power of the solar photovoltaic panel; G t is the solar radiation intensity during period t; G std is the solar radiation intensity under standard test conditions; k PT is the power temperature coefficient; T t is the average ambient temperature during period t; T std It is the ambient temperature under standard test conditions.

4. The method for optimizing the solar energy storage in a hydrometallurgical plant according to claim 3, characterized in that: The operating constraint formula of the hydrometallurgical plant energy storage in the hydrometallurgical plant photovoltaic storage system optimization configuration model is: In the formula, It represents the energy value of the energy storage at the initial stage of period t; and They represent the charging and discharging power of the energy storage power in time period t respectively; and are the charging and discharging efficiencies of energy storage, respectively; and are the energy storage charging and discharging state variables, respectively, and in Indicates that the energy storage is in charging state. Indicates that the energy storage is in a discharging state; △t indicates the duration of the scheduling cycle; χ H and χ L Respectively represent the upper and lower limits of the energy storage charge state; t0 and t end They represent the start and end periods of the dispatch respectively; χ0 is the initial charge state of the energy storage; The output target limit constraint formula of the photovoltaic storage system in different periods of the hydrometallurgical plant photovoltaic storage system optimization configuration model is: In the formula, is the output target limit of the PV-storage system in period t; and are the load demand and the maximum power supply of the mains in the t period of the sth sample respectively; express and 0; N s is the number of samples used; The supply and demand balance constraint formula of the hydrometallurgical plant photovoltaic storage system optimization configuration model during the operation of the hydrometallurgical plant is: In the formula, and They are the power supply of the large power grid and the load power of the hydrometallurgical plant in period t respectively; It is the maximum limit of the power supply of the large power grid during the period t.

5. The method for optimizing the solar energy storage in a hydrometallurgical plant according to claim 4, characterized in that: The objective function of the optimization configuration model of the photovoltaic energy storage system in the hydrometallurgical plant is: In the formula, and are the expected investment costs of photovoltaic and energy storage systems, respectively; Expected operation and maintenance costs for powering PV and energy storage; the expected cost of supplying electricity from the utility; Cost of lost expected benefits of load flexibility power regulation in hydrometallurgical plants; eav is the coefficient of equal annual value; In the process of solving the optimal configuration model of the hydrometallurgical plant's photovoltaic energy storage system, a branch and bound method, a cutting plane method, a genetic algorithm, a particle swarm algorithm, a simulated annealing algorithm or a hybrid algorithm is adopted, or a commercial solver is called for solving.

6. A hydrometallurgical plant light storage optimization processing device, characterized in that: include: The parameter acquisition module is used to obtain the grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, including the electrolytic cell, ball mill, and semi-autogenous mill; obtain the historical load data, light data of the hydrometallurgical plant, and the historical power outage and power restriction data of the local large power grid; collect the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; collect the cost of electricity supplied by the large power grid and the photovoltaic storage system; A module for constructing an optimal configuration model of a photovoltaic storage system of a hydrometallurgical plant is used to construct an optimal configuration model of a photovoltaic storage system of a hydrometallurgical plant based on the collected grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, the historical load data and illumination data of the hydrometallurgical plant and the historical power outage and power restriction data of the local large power grid, the unit capacity investment cost of the photovoltaic, energy storage and converter equipment, and the kilowatt-hour cost of power supply from the large power grid and the photovoltaic storage system. The constraints of the optimal configuration model of the photovoltaic storage system of the hydrometallurgical plant include the configuration capacity of photovoltaic and energy storage, the operation constraints of the photovoltaic power generation device, the operation constraints of the energy storage in the hydrometallurgical plant, the output target limit constraints of the photovoltaic storage system in different time periods, and the supply and demand balance constraints during the operation of the hydrometallurgical plant. An objective function construction module is used to construct an objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model, wherein the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model is an equal annual value cost within the entire life cycle of the hydrometallurgical plant photovoltaic storage configuration; The optimization configuration scheme solving module is used to solve the optimization configuration model of the hydrometallurgical plant photovoltaic storage system according to the constraint conditions of the hydrometallurgical plant photovoltaic storage system optimization configuration model and the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model to obtain the optimization configuration scheme of photovoltaic and energy storage in the hydrometallurgical plant.

7. The optical energy storage optimization processing device for a hydrometallurgical plant according to claim 6 is characterized in that: In the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module, the photovoltaic and energy storage configuration capacity constraint formula of the hydrometallurgical plant photovoltaic storage system optimization configuration model is: In the formula, Configure power capacity for photovoltaics; The power capacity configured for the energy storage converter; Allocate capacity for energy storage; and They are the upper and lower limits of the photovoltaic configuration power capacity respectively; and They are the upper and lower limits of the power capacity configured for the energy storage converter; and are the upper and lower limits of energy storage configuration capacity respectively; Lower limit of photovoltaic configuration power capacity The formula for the value of is: In the formula, is the rated load capacity of the hydrometallurgical plant; The total rated power of the ball mill and semi-autogenous mill equipment; is the rated power of the electrolyzer equipment; It is the startup factor of ball mill and semi-autogenous mill equipment; Provides a flexible adjustment factor for the electrolytic cell equipment.

8. The device for optimizing the solar energy storage in a hydrometallurgical plant according to claim 7, characterized in that: In the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module, the constraint condition formula of the operation constraint of the photovoltaic power generation device of the hydrometallurgical plant photovoltaic storage system optimization configuration model is: In the formula, Represents the operating power of the new energy power generation device in period t; and They represent the upper and lower allowable limits of the operating power of the new energy power generation device in period t respectively; β pv is the derating factor of the photovoltaic array; is the rated power of the solar photovoltaic panel; G t is the solar radiation intensity during period t; G std is the solar radiation intensity under standard test conditions; k PT is the power temperature coefficient; T t is the average ambient temperature during period t; T std It is the ambient temperature under standard test conditions.

9. The optical energy storage optimization processing device for a hydrometallurgical plant according to claim 8 is characterized in that: In the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module, the hydrometallurgical plant photovoltaic storage system optimization configuration model of the hydrometallurgical plant internal energy storage operation constraint formula is: In the formula, It represents the energy value of the energy storage at the initial stage of period t; and They represent the charging and discharging power of the energy storage power in time period t respectively; and are the charging and discharging efficiencies of energy storage, respectively; and are the energy storage charging and discharging state variables, respectively, and in Indicates that the energy storage is in charging state. Indicates that the energy storage is in a discharging state; △t indicates the duration of the scheduling cycle; χ H and χ L Respectively represent the upper and lower limits of the energy storage charge state; t0 and t end They represent the start and end periods of the dispatch respectively; χ0 is the initial charge state of the energy storage; In the hydrometallurgical plant photovoltaic storage system optimization configuration model construction module, the photovoltaic storage system output target limit constraint formula for different time periods of the hydrometallurgical plant photovoltaic storage system optimization configuration model is: In the formula, is the output target limit of the PV-storage system in period t; and are the load demand and the maximum power supply of the mains in the t period of the sth sample respectively; express and 0; N s is the number of samples used; The supply and demand balance constraint formula of the hydrometallurgical plant photovoltaic storage system optimization configuration model during the operation of the hydrometallurgical plant is: In the formula, and They are the power supply of the large power grid and the load power of the hydrometallurgical plant in period t respectively; It is the maximum limit of the power supply of the large power grid during the period t.

10. The optical energy storage optimization processing device for a hydrometallurgical plant according to claim 9, characterized in that: In the objective function construction module, the objective function of the hydrometallurgical plant photovoltaic storage system optimization configuration model is: In the formula, and are the expected investment costs of photovoltaic and energy storage systems, respectively; Expected operation and maintenance costs for powering PV and energy storage; the expected cost of supplying electricity from the utility; Cost of lost expected benefits of load flexibility power regulation in hydrometallurgical plants; eav is the coefficient of equal annual value; In the optimization configuration solution solving module, a branch and bound method, a cutting plane method, a genetic algorithm, a particle swarm algorithm, a simulated annealing algorithm or a hybrid algorithm is adopted, or a commercial solver is called for solving.

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