Hydrometallurgical plant light storage optimization processing method and device

Abstract

The application discloses a hydrometallurgical plant light storage optimization processing method and device, based on the local power system historical power failure and power limiting data of the hydrometallurgical plant, the time-sharing power compensation principle of the light storage system is proposed; the pre-reserve mechanism of the light storage system for the power failure scene is established according to the frequent power failure situation of the power system; the energy storage power limit value is set based on the asynchronous motor starting demand and the flexible load adjustment capacity in the hydrometallurgical plant, and the continuous power supply power target value of the light storage system after power failure is set according to the operation reliability demand of the load in the plant, so that the reliability of the power supply of the hydrometallurgical plant is ensured, and the economy of the light storage system configuration is improved. The application can meet the demand of the hydrometallurgical plant for the light storage system to compensate for the power shortage of the municipal power supply and ensure continuous power supply under the power failure scene, can effectively improve the power supply reliability of the hydrometallurgical plant, ensure the continuity and safety of production, and at the same time, the economy of the light storage system configuration is considered, and the economic benefit of the hydrometallurgical plant is ensured.

Description

Technical Field

[0001] This invention belongs to the field of energy system processing technology, specifically relating to a method and apparatus for optimizing photovoltaic-storage processing in a hydrometallurgical plant. Background Technology

[0002] The production process of a hydrometallurgical plant involves multiple complex stages, including ore grinding, dissolution, leaching, solution purification, and metal electrowinning. The plant's load includes equipment such as ball mills, semi-autogenous mills, drainage pumps, electrolysis units, air conditioning, and lighting. The start-up and shutdown of equipment such as ball mills and semi-autogenous mills significantly impact the continuity of hydrometallurgical plant production, thus requiring extremely high power supply reliability. Equipment such as electrolysis units, drainage pumps, air conditioning, and lighting are allowed to operate at limited power or be temporarily disconnected for a period of time. Since the load of the electrolysis unit accounts for a significant portion of the hydrometallurgical plant's load, it possesses considerable demand response flexibility and adjustment potential.

[0003] Currently, to ensure the reliability of power supply in hydrometallurgical plants, installing diesel generators or other backup power sources on-site is a common solution. Once started, these generators provide a stable and continuous power supply to the hydrometallurgical plant. Some hydrometallurgical plants also install photovoltaic (PV) power generation equipment on-site, using solar energy as a supplementary power source for daily production to reduce reliance on the power grid. Considering the weather sensitivity and diurnal variations of PV power generation, combining PV power generation systems with energy storage battery systems can smooth out power fluctuations and provide a more stable power output. When determining the capacity of PV and energy storage, the difference between PV power generation at different times and the plant's electricity demand can be used to calculate the PV-storage system capacity with the goal of meeting peak load demand. Existing technologies also often use mathematical models and algorithms such as linear programming or dynamic programming, combined with the hydrometallurgical plant's operation and scheduling scheme, to incorporate factors such as the capacity of the PV-storage system, initial investment, operating costs, and electricity savings into the calculations, optimizing the PV-storage system capacity with the objective function of minimizing total cost.

[0004] However, some regional power grids suffer from problems such as unreasonable power generation structure, aging and disrepaired systems, weak network architecture, and imperfect power generation and load dispatch mechanisms. Power supply is often constrained by external environment, load fluctuations, and grid faults, leading to frequent power outages or curtailments. Existing technologies have significant shortcomings in improving the reliability of power supply to hydrometallurgical plants. This poor power supply reliability directly affects the normal production process of hydrometallurgical plants, making it difficult to guarantee the continuous power supply needs of critical loads such as ball mills and semi-autogenous mills, resulting in production interruptions, increased equipment wear, and even potential safety hazards. Although diesel generators can provide supplemental power to hydrometallurgical plants, their start-up, shutdown, and power regulation are limited by internal thermal processes, and their response speed cannot meet the high reliability requirements of hydrometallurgical plants in areas with frequent power outages. Furthermore, diesel generators suffer from high generation costs, large carbon emissions, and severe pollution. Existing photovoltaic-storage systems are mostly used for peak shaving and energy conservation, lacking reliability optimization designs for continuous production, grid connection, and asynchronous motor starting requirements in hydrometallurgical plants. When determining the capacity configuration scheme of photovoltaic-storage systems, existing technologies do not meet the needs of hydrometallurgical plants for supplementing the mains power shortage and ensuring continuous power supply during power outages. Furthermore, existing photovoltaic-storage system configuration methods do not consider the demand response and flexible adjustment capabilities of equipment such as electrolysis units in hydrometallurgical plants, resulting in additional cost investments. Therefore, a more efficient and stable optimized photovoltaic-storage solution for hydrometallurgical plants is urgently needed. Summary of the Invention

[0005] Therefore, the present invention provides a method and apparatus for optimizing the photovoltaic and energy storage system in a hydrometallurgical plant, which effectively improves the power supply reliability of the hydrometallurgical plant, ensures the continuity and safety of production, and at the same time takes into account the economic efficiency of the photovoltaic and energy storage system configuration.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the photovoltaic and energy storage processes in 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 historical load data, solar radiation data, and historical power outage and curtailment data of the local power grid; collect the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; and collect the cost per kilowatt-hour of power supplied by the power grid and the photovoltaic-storage system.

[0008] Based on the collected grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, historical load data, solar radiation data, and historical power outage and curtailment data of the local power grid, the unit capacity investment cost of the photovoltaic, energy storage, and converter equipment, and the cost per kilowatt-hour of power supplied by the power grid and the photovoltaic-storage system, an optimal configuration model for the photovoltaic-storage system of the hydrometallurgical plant is constructed. The constraints of the optimal configuration model for 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 within the hydrometallurgical plant, the output target limit constraints of the photovoltaic-storage system at different times, and the supply and demand balance constraints during the operation of the hydrometallurgical plant.

[0009] The objective function of the optimized configuration model of the photovoltaic-storage system of the hydrometallurgical plant is constructed. The objective function of the optimized configuration model of the photovoltaic-storage system of the hydrometallurgical plant is the annual cost of the photovoltaic-storage configuration over the entire life cycle.

[0010] Based on the constraints and objective function of the optimized configuration model of the photovoltaic and energy storage system in the hydrometallurgical plant, the optimized configuration scheme of the photovoltaic and energy storage system in the hydrometallurgical plant is solved to obtain the optimized configuration scheme of photovoltaic and energy storage in the hydrometallurgical plant.

[0011] As a preferred scheme for the photovoltaic-storage optimization treatment method in hydrometallurgical plants, the configuration capacity constraint formulas for the photovoltaic and energy storage systems in the optimized configuration model of the hydrometallurgical plant photovoltaic-storage system are as follows:

[0012]

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

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

[0015]

[0016] In the formula, Rated load capacity of hydrometallurgical plant; The total rated power for ball mills and semi-autogenous mills; This refers to the rated power of the electrolytic cell equipment. The starting coefficient for ball mills and semi-autogenous mills; The adjustment coefficient is for flexible use of electrolytic cell equipment.

[0017] As a preferred scheme for the photovoltaic-storage optimization treatment method in hydrometallurgical plants, the constraint condition formula for the operation constraint of the photovoltaic power generation device in the optimized configuration model of the photovoltaic-storage system in the hydrometallurgical plant is as follows:

[0018]

[0019] In the formula, This represents the operating power of the new energy power generation device during time period t; and β represents the upper and lower allowable limits of the operating power of the new energy power generation device during time period t; pv This represents the derating factor for the photovoltaic array; Rated power of the solar photovoltaic panel; G t G represents the solar radiation intensity during time period t; std Solar radiation intensity under standard test conditions; k PT T is the power temperature coefficient; t T represents the average ambient temperature during the time period t; std The ambient temperature is the temperature under standard test conditions.

[0020] As a preferred scheme for the optimized photovoltaic-storage system treatment method in hydrometallurgical plants, the operational constraint formula for energy storage within the hydrometallurgical plant in the optimized configuration model of the photovoltaic-storage system is as follows:

[0021]

[0022] In the formula, This represents the energy value of the energy storage at the initial stage of time period t; and These represent the charging and discharging power of the energy storage power during time period t, respectively. and These are the charging and discharging efficiencies of energy storage, respectively. and These are the state variables for energy storage charging and discharging, respectively. in This indicates that the energy storage is in a charging state. This indicates that the energy storage is in a discharging state; Δt represents the duration of the dispatch cycle; χ H and χ L These represent the upper and lower limits of the energy storage state of charge, respectively; t0 and t end These represent the start and end times of the scheduling process, respectively; χ0 represents the initial state of charge of the energy storage.

[0023] The formula for the output limit constraint of the photovoltaic-storage system at different time periods in the optimal configuration model of the photovoltaic-storage system of the hydrometallurgical plant is as follows:

[0024]

[0025] In the formula, The target output limit of the photovoltaic-storage system within time period t; and These represent the load demand and the maximum power supply from the mains during time period t in the s-th sample, respectively. express The maximum value among 0 and N; s The number of samples used;

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

[0027]

[0028] In the formula, and These represent the power supply from the main power grid and the power consumption of the hydrometallurgical plant during time period t, respectively. This represents the maximum power supply capacity of the large power grid within time period t.

[0029] As a preferred scheme for the photovoltaic-storage optimization treatment method in hydrometallurgical plants, the objective function of the optimized configuration model of the photovoltaic-storage system in hydrometallurgical plants is:

[0030]

[0031] In the formula, and These are the expected investment costs for photovoltaic and energy storage systems, respectively. The expected operation and maintenance costs for powering photovoltaic and energy storage systems; The expected cost of supplying electricity from the mains; The expected benefit loss cost of load flexibility power regulation in hydrometallurgical plants; eav This is the annual value coefficient;

[0032] In solving the optimal configuration model of the photovoltaic-storage system of the hydrometallurgical plant, the branch-and-bound method, the cutting plane method, the genetic algorithm, the particle swarm optimization algorithm, the simulated annealing algorithm, or a hybrid algorithm are used, or a commercial solver is called.

[0033] The present invention also provides a photovoltaic-storage optimization treatment device for hydrometallurgical plants, comprising:

[0034] The parameter acquisition module is used to acquire the grid structure parameters of the hydrometallurgical plant and the physical parameters of the specified load equipment, including electrolytic cells, ball mills, and semi-autogenous mills; acquire historical load data, solar radiation data, and historical power outage and curtailment data of the local power grid; collect the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; and collect the cost per kilowatt-hour of power supplied by the power grid and the photovoltaic-storage system.

[0035] The module for constructing an optimized configuration model of the photovoltaic-storage system in a hydrometallurgical plant is used to construct an optimized configuration model of the photovoltaic-storage system based on the collected grid structure parameters of the hydrometallurgical plant, the physical parameters of the specified load equipment, the historical load data, solar radiation data, and historical power outage and curtailment data of the local power grid, the unit capacity investment cost of the photovoltaic, energy storage, and converter equipment, and the cost per kilowatt-hour of power supplied by the power grid and the photovoltaic-storage system. The constraints of the optimized configuration model of the photovoltaic-storage system in a 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 within the hydrometallurgical plant, the output target limit constraints of the photovoltaic-storage system at different times, and the supply and demand balance constraints during the operation of the hydrometallurgical plant.

[0036] The objective function construction module is used to construct the objective function of the optimal configuration model of the photovoltaic-storage system of the hydrometallurgical plant. The objective function of the optimal configuration model of the photovoltaic-storage system of the hydrometallurgical plant is the annual cost of the photovoltaic-storage configuration over the entire life cycle.

[0037] The optimization configuration solution module is used to solve the optimization configuration model of the hydrometallurgical photovoltaic and energy storage system based on the constraints and objective function of the optimization configuration model of the hydrometallurgical photovoltaic and energy storage system, so as to obtain the optimization configuration scheme of photovoltaic and energy storage in the hydrometallurgical plant.

[0038] As a preferred solution for the photovoltaic-storage optimization treatment device in a hydrometallurgical plant, the configuration capacity constraint formulas for the photovoltaic and energy storage systems in the optimized configuration model construction module of the hydrometallurgical plant photovoltaic-storage system are as follows:

[0039]

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

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

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

[0043]

[0044] In the formula, Rated load capacity of hydrometallurgical plant; The total rated power for ball mills and semi-autogenous mills; This refers to the rated power of the electrolytic cell equipment. The starting coefficient for ball mills and semi-autogenous mills; The adjustment coefficient is for flexible use of electrolytic cell equipment.

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

[0046]

[0047] In the formula, This represents the operating power of the new energy power generation device during time period t; and β represents the upper and lower allowable limits of the operating power of the new energy power generation device during time period t; pv This represents the derating factor for the photovoltaic array; Rated power of the solar photovoltaic panel; G t G represents the solar radiation intensity during time period t; std Solar radiation intensity under standard test conditions; k PT T is the power temperature coefficient; t T represents the average ambient temperature during the time period t; std The ambient temperature is the temperature under standard test conditions.

[0048] As a preferred option for the photovoltaic-storage optimization treatment device in a hydrometallurgical plant, the operational constraint formula for energy storage within the hydrometallurgical plant in the photovoltaic-storage system optimization configuration model construction module is as follows:

[0049]

[0050] In the formula, This represents the energy value of the energy storage at the initial stage of time period t; and These represent the charging and discharging power of the energy storage power during time period t, respectively. and These are the charging and discharging efficiencies of energy storage, respectively. and These are the state variables for energy storage charging and discharging, respectively. in This indicates that the energy storage is in a charging state. This indicates that the energy storage is in a discharging state; Δt represents the duration of the dispatch cycle; χ H and χ L These represent the upper and lower limits of the energy storage state of charge, respectively; t0 and t end These represent the start and end times of the scheduling process, respectively; χ0 represents the initial state of charge of the energy storage.

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

[0052]

[0053] In the formula, The target output limit of the photovoltaic-storage system within time period t; and These represent the load demand and the maximum power supply from the mains during time period t in the s-th sample, respectively. express The maximum value among 0 and N; s The number of samples used;

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

[0055]

[0056] In the formula, and These represent the power supply from the main power grid and the power consumption of the hydrometallurgical plant during time period t, respectively. This represents the maximum power supply capacity of the large power grid within time period t.

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

[0058]

[0059] In the formula, and These are the expected investment costs for photovoltaic and energy storage systems, respectively. The expected operation and maintenance costs for powering photovoltaic and energy storage systems; The expected cost of supplying electricity from the mains; The expected benefit loss cost of load flexibility power regulation in hydrometallurgical plants; eav This is the annual value coefficient;

[0060] The optimization configuration solution solution module employs branch and bound method, cutting plane method, genetic algorithm, particle swarm algorithm, simulated annealing algorithm or hybrid algorithm, or calls commercial solvers for solution.

[0061] This invention has the following advantages: Based on historical power outage and curtailment data of the local power system of the hydrometallurgical plant, it proposes a time-of-use power compensation principle for the photovoltaic-storage system. In the configuration model, it adds target output limits for the photovoltaic-storage system at different times as constraints, specifically supplementing the power supply gap of the mains power. Addressing the frequent power outages in the power system, this invention establishes a pre-reserve mechanism for the photovoltaic-storage system oriented towards outage scenarios. Combining typical solar characteristics and the target continuous power supply and duration requirements of the hydrometallurgical plant under outage scenarios, it sets energy storage constraints for each time period in the configuration model, thereby fully utilizing energy storage performance and ensuring the reliability of power supply to the hydrometallurgical plant. This invention adopts a flexible control method for some loads in the hydrometallurgical plant. Based on the starting requirements of asynchronous motors and the flexible load adjustment capability within the hydrometallurgical plant, it sets energy storage power limits and sets the target continuous power supply of the photovoltaic-storage system after a power outage according to the operational reliability requirements of the plant's loads. This improves the economic efficiency of the photovoltaic-storage system configuration while ensuring the reliability of power supply to the hydrometallurgical plant. This invention can meet the needs of hydrometallurgical plants for supplementing the mains power shortage and ensuring continuous power supply in the event of a power outage. The combination of photovoltaic and energy storage configuration with the demand response and flexible adjustment capabilities of equipment such as electrolysis units in hydrometallurgical plants can avoid additional cost investment. It can effectively improve the power supply reliability of hydrometallurgical plants, ensure the continuity and safety of production, and at the same time take into account the economics of photovoltaic and energy storage system configuration, thus ensuring the economic benefits of hydrometallurgical plants. Attached Figure Description

[0062] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0063] Figure 1 This is a schematic diagram of the process flow of the photovoltaic-storage optimization treatment method for hydrometallurgical plants provided in this embodiment of the invention;

[0064] Figure 2 This is a schematic diagram of the structure of the hydrometallurgical plant targeted by the photovoltaic-storage optimization treatment method for hydrometallurgical plants provided in this embodiment of the invention.

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

[0066] Figure 4 This is a typical solar radiation curve for the location of a hydrometallurgical plant provided in this embodiment of the invention;

[0067] Figure 5 This refers to the configuration result for a specific scenario provided in the embodiments of the present invention;

[0068] Figure 6 The annual value cost for a specified scenario provided in this embodiment of the invention;

[0069] Figure 7 This is a schematic diagram of the architecture of the photovoltaic-storage optimization treatment device for a hydrometallurgical plant provided in an embodiment of the present invention. Detailed Implementation

[0070] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] Example 1

[0072] See Figure 1 This invention provides a method for optimizing the photovoltaic and energy storage processes in 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 electrolytic cells, ball mills, and semi-autogenous mills; obtain historical load data, solar radiation data, and historical power outage and curtailment data of the local power grid; collect the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; collect the cost per kilowatt-hour of power supplied by 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, solar radiation data, and historical power outage and curtailment data of the hydrometallurgical plant and the local power grid, the unit capacity investment cost of the photovoltaic, energy storage, and converter equipment, and the cost per kilowatt-hour of power supplied by the power grid and the photovoltaic-energy storage system, an optimal configuration model for the photovoltaic-energy storage system of the hydrometallurgical plant is constructed. The constraints of the optimal configuration model for the photovoltaic-energy 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 within the hydrometallurgical plant, the output target limit constraints of the photovoltaic-energy storage system at different times, and the supply and demand balance constraints during the operation of the hydrometallurgical plant.

[0075] S3. Construct the objective function of the optimized configuration model of the photovoltaic-storage system of the hydrometallurgical plant. The objective function of the optimized configuration model of the photovoltaic-storage system of the hydrometallurgical plant is the annual cost of the photovoltaic-storage configuration throughout the entire life cycle of the hydrometallurgical plant.

[0076] S4. Based on the constraints and objective function of the optimal configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant, solve the optimal configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant to obtain the optimal configuration scheme of photovoltaic and energy storage in the hydrometallurgical plant.

[0077] See Figure 2 The structure of a hydrometallurgical plant typically includes critical loads such as ball mills, semi-autogenous mills, and electrolytic cells. Under normal circumstances, hydrometallurgical plants rely on mains power, with diesel generators often serving as backup generators to supplement power supply during grid outages or rationing. By configuring a photovoltaic (PV) and energy storage system, the plant can utilize either solar power or energy storage to provide electricity.

[0078] In this embodiment, in step S2, various types of equipment in the hydrometallurgical plant are modeled based on the parameters collected in step S1. Typical load curves and illumination curves are established through clustering. The output target limits of the photovoltaic-storage system are set for each time period based on 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 power outage are determined based on load parameters such as electrolytic cells, ball mills, and semi-autogenous mills. The energy storage constraints for each time period are set by combining typical illumination characteristics and the continuous power supply target value and power supply duration requirements of the hydrometallurgical plant under power outage scenarios. The optimized configuration model of the photovoltaic-storage system of the hydrometallurgical plant is constructed by combining the working models and operating boundaries of various equipment.

[0079] The configuration capacity constraint formulas for the photovoltaic and energy storage systems in the optimized configuration model of the hydrometallurgical plant are as follows:

[0080]

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

[0082] These are the upper and lower limits of the power capacity configured for the energy storage converter, respectively. and These are the upper and lower limits of the energy storage configuration capacity, respectively. and The value depends on limitations such as space and capital reserves in the hydrometallurgical plant, which constitute the lower limit of the photovoltaic power capacity. The formula for determining the value of is:

[0083]

[0084] In the formula, Rated load capacity of hydrometallurgical plant; The total rated power for ball mills and semi-autogenous mills; This refers to the rated power of the electrolytic cell equipment. This is the starting coefficient for ball mills and semi-autogenous mills, which is the ratio of starting power to rated power. The adjustable coefficient for electrolytic cell equipment is the ratio of the power required to quickly respond to demand to the rated power.

[0085] In this embodiment, typical load curves and typical illumination curves are obtained through historical data. Methods include, but are not limited to, literature review, mean averaging, clustering, machine learning, and artificial intelligence. Various types of equipment within the hydrometallurgical plant are modeled, and operational constraints are established based on the equipment's working model and operational boundary requirements.

[0086] Specifically, based on typical illumination curves, the constraint formulas for the operation constraints of the photovoltaic power generation device in the optimal configuration model of the hydrometallurgical plant's photovoltaic-storage system are as follows:

[0087]

[0088] In the formula, This represents the operating power of the new energy power generation device during time period t; and β represents the upper and lower allowable limits of the operating power of the new energy power generation device during time period t; pv This represents the derating factor for the photovoltaic array; Rated power of the solar photovoltaic panel; G t G represents the solar radiation intensity during time period t; stdThe solar radiation intensity under standard test conditions is typically taken as 1000 W / m². 2 ;k PT The power temperature coefficient is typically taken as -0.0047 / ℃; T t T represents the average ambient temperature during the time period t; std The ambient temperature under standard test conditions is typically taken as 25℃.

[0089] The operational constraint formula for energy storage within the hydrometallurgical plant in the optimized configuration model of the photovoltaic-storage system is as follows:

[0090]

[0091]

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

[0093] The frequent power outages and rationing in the weak power system result in the grid power supply being unable to fully meet the load demand of the hydrometallurgical plant, creating a grid power shortage. Simultaneously, the hydrometallurgical plant's load exhibits peak-valley fluctuations, and the grid power rationing values ​​show statistically significant time-of-day differences. To specifically supplement the grid power supply gap, this embodiment establishes a time-of-day power compensation principle for the photovoltaic-storage system. In the configuration model, target output limits for the photovoltaic-storage system at different times are added as constraints. The constraint formula for the target output limits of the photovoltaic-storage system at different times in the optimized configuration model of the hydrometallurgical plant's photovoltaic-storage system is as follows:

[0094]

[0095] In the formula, The target output limit of the photovoltaic-storage system within time period t can be determined by analyzing the load demand and grid power curtailment values ​​of historical time periods. and These represent the load demand and the maximum power supply from the mains during time period t in the s-th sample, respectively. express The maximum value among 0 and N; s The number of samples used.

[0096] In this embodiment, a pre-reserve mechanism for photovoltaic-storage systems under power outage scenarios is proposed, taking into account the target value of continuous power supply and the required power supply duration. This mechanism aims to ensure the reliability of power supply for hydrometallurgical plants during power outages. Simultaneously, the energy storage limits for each time period are adjusted based on typical solar illumination characteristics to fully utilize energy storage performance and avoid increased investment costs due to excessive conservatism. Energy storage constraints for each time period are added to the configuration model.

[0097]

[0098] In the formula, P suc and N suc These represent the target continuous power supply capacity and the number of continuous power supply periods required under power outage scenarios. Considering that loads such as electrolytic cells and non-production line air conditioners in hydrometallurgical plants can operate at reduced power for a certain period, P... suc The value can be obtained using the following formula:

[0099]

[0100] In the formula, The power can be flexibly adjusted according to the load set in the hydrometallurgical plant.

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

[0102]

[0103] In the formula, and These represent the power supply from the main power grid and the power consumption of the hydrometallurgical plant during time period t, respectively. This represents the maximum power supply capacity of the large power grid within time period t.

[0104] In this embodiment, in step S3, the objective function of the optimal configuration model for the hydrometallurgical plant's photovoltaic-storage system is:

[0105]

[0106] In the formula, and These are the expected investment costs for photovoltaic and energy storage systems, respectively. The expected operation and maintenance costs for powering photovoltaic and energy storage systems; The expected cost of supplying electricity from the mains; The expected benefit loss cost of load flexibility power regulation in hydrometallurgical plants; eav This is the annual value coefficient.

[0107] Specifically, the expressions for each cost and its equivalent annual value coefficient are as follows:

[0108]

[0109] In the formula, and These are the investment costs per unit power of photovoltaic, per unit power of energy storage, and per unit energy of energy storage, respectively. and These are the power supply costs for photovoltaic, energy storage, and grid power, respectively. The unit power adjustment cost for load flexibility in a hydrometallurgical plant; r is the discount rate; n y This refers to the lifecycle years of the photovoltaic-storage system.

[0110] In this embodiment, in step S4, based on the constructed optimal configuration model of the hydrometallurgical plant's photovoltaic-storage system, the source-grid-load-storage coordinated optimal scheduling scheme of the hydrometallurgical plant's microgrid is solved to minimize the operating cost of the hydrometallurgical plant's microgrid. The solution model is shown below:

[0111]

[0112] The above-described solution model belongs to the category of mixed-integer linear programming problems. It can be solved using precise methods such as branch and bound, or cutting plane methods, or heuristic algorithms such as genetic algorithms, particle swarm optimization, or simulated annealing, as well as hybrid improved algorithms based on these methods. Furthermore, if resources permit, external mature commercial solvers can also be used to solve this problem.

[0113] The following examples are provided in the embodiments of the present invention:

[0114] The example hydrometallurgical plant has a rated load of approximately 18MW, and its typical load curve is as follows: Figure 3 As shown. The load within the hydrometallurgical plant mainly includes a 2.2MW ball mill, a 2MW semi-autogenous mill, a 10MW electrolytic cell, and other auxiliary equipment. A typical illumination curve for the region where the hydrometallurgical plant is located is shown below. Figure 4 As shown.

[0115] Based on historical power outage and curtailment data from the local power system, the hydrometallurgical plant operator requires that, in the event of a random power outage in the main grid, the hydrometallurgical plant can operate at rated load for 5 hours without relying on diesel generators. The initial state of charge (SOC) of the energy storage is set at 0.5, with upper and lower SOC limits set at 0.9 and 0.1 respectively, and the charge / discharge efficiency set at 0.95. The investment costs per unit power of photovoltaic (PV), per unit power of energy storage, and per unit energy of energy storage are taken as US$171,000 / MW, US$114,000 / MW, and US$214,000 / MWh, respectively. The discount rate is taken as 0.05, and the lifespan of the PV-energy storage system is taken as 13 years.

[0116] The energy storage configuration scheme proposed in this invention considers flexible control technology for flexible loads within hydrometallurgical plants and employs a photovoltaic-storage system with a pre-storage mechanism to prevent power outages based on photovoltaic characteristics. To verify the effectiveness of the proposed method, comparative calculation examples are set as shown in Table 1. Each scenario considering the corresponding factors is marked with "√", and those not considered are marked with "×". The optimization results are as follows: Figures 5-6 As shown.

[0117] Table 1 Comparison Settings for the Case Studies

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

[0119] Depend on Figure 5 , Figure 6 As can be seen, in Cases 1 to 4, the photovoltaic and energy storage power configurations are very close to each other. However, in Case 1, the energy storage configuration is reduced by 15.9%, 12.6%, and 24.9% compared to Cases 2, 3, and 4, respectively. The annualized cost of Case 1 is reduced by 3.09%, 2.17%, and 5.12% compared to Cases 2, 3, and 4, respectively, thus verifying the effectiveness of the method proposed in this invention.

[0120] In summary, this invention obtains the grid structure parameters and physical parameters of designated load equipment in a hydrometallurgical plant, including electrolytic cells, ball mills, and semi-autogenous mills; acquires historical load data, solar radiation data, and historical power outage and curtailment data from the local power grid; collects the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; collects the levelized cost of electricity supplied by the power grid and the photovoltaic-storage system; and, based on the collected grid structure parameters and physical parameters of the designated load equipment, the historical load data, solar radiation data, and historical power outage and curtailment data from the local power grid, the unit capacity investment cost of the photovoltaic, energy storage, and converter equipment, and the levelized cost of electricity supplied by the power grid and the photovoltaic-storage system, constructs an optimized configuration model for the photovoltaic-storage system of the hydrometallurgical plant. The constraints of the optimal configuration model for the photovoltaic and energy storage system in the hydrometallurgical plant include the configuration capacity of photovoltaic and energy storage, the operational constraints of the photovoltaic power generation device, the operational constraints of the energy storage within the hydrometallurgical plant, the output target limit constraints of the photovoltaic and energy storage system at different times, and the supply and demand balance constraints during the operation of the hydrometallurgical plant. An objective function for the optimal configuration model of the photovoltaic and energy storage system in the hydrometallurgical plant is constructed, which is the equivalent annual cost over the entire life cycle of the photovoltaic and energy storage configuration. Based on the constraints and objective function of the optimal configuration model, the optimal configuration model of the photovoltaic and energy storage system in the hydrometallurgical plant is solved to obtain the optimal configuration scheme for photovoltaic and energy storage in the hydrometallurgical plant. This invention, based on historical power outage and curtailment data of the local power system for hydrometallurgical plants, proposes a time-of-use power compensation principle for photovoltaic (PV) and energy storage (ESS) systems. It adds target output limits for the PV and ESS systems at different times as constraints in the configuration model to specifically supplement the power supply gap in the mains. Furthermore, this invention establishes a pre-reserve mechanism for PV and ESS systems in response to frequent power outages. Combining typical solar characteristics with the continuous power supply target and duration requirements of the hydrometallurgical plant during outages, it sets energy storage constraints for each time period in the configuration model, thereby fully utilizing energy storage performance and ensuring the reliability of power supply to the hydrometallurgical plant. Finally, this invention adopts a flexible control method for some loads in the hydrometallurgical plant. It sets energy storage power limits based on the asynchronous motor starting requirements and flexible load adjustment capabilities within the plant, and sets the continuous power supply target value of the PV and ESS systems after a power outage based on the plant's load operation reliability requirements. This ensures the reliability of power supply to the hydrometallurgical plant while improving the economic efficiency of PV and ESS system configuration. This invention can meet the needs of hydrometallurgical plants for supplementing the mains power shortage and ensuring continuous power supply in the event of a power outage. The combination of photovoltaic and energy storage configuration with the demand response and flexible adjustment capabilities of equipment such as electrolysis units in hydrometallurgical plants can avoid additional cost investment. It can effectively improve the power supply reliability of hydrometallurgical plants, ensure the continuity and safety of production, and at the same time take into account the economics of photovoltaic and energy storage system configuration, thus ensuring the economic benefits of hydrometallurgical plants.

[0121] Example 2

[0122] See Figure 7 Embodiment 2 of the present invention also provides a photovoltaic-storage optimization treatment device for hydrometallurgical plants, comprising:

[0123] The parameter acquisition module 100 is used to acquire 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; acquire historical load data, solar radiation data, and historical power outage and curtailment data of the local power grid; collect the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; and collect the cost per kilowatt-hour of power supplied by the power grid and the photovoltaic-storage system.

[0124] The module 200 for constructing an optimized configuration model of the photovoltaic-storage system in a hydrometallurgical plant is used to construct an optimized configuration model of the photovoltaic-storage system in a hydrometallurgical plant based on the collected grid structure parameters of the hydrometallurgical plant and the physical parameters of the specified load equipment, historical load data, solar radiation data and historical power outage and curtailment data of the hydrometallurgical plant and the local power grid, the unit capacity investment cost of the photovoltaic, energy storage and converter equipment, and the cost per kilowatt-hour of power supplied by the power grid and the photovoltaic-storage system. The constraints of the optimized configuration model of the photovoltaic-storage system in a 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 at different time periods, and the supply and demand balance constraints during the operation of the hydrometallurgical plant.

[0125] The objective function construction module 300 is used to construct the objective function of the optimal configuration model of the photovoltaic-storage system of the hydrometallurgical plant. The objective function of the optimal configuration model of the photovoltaic-storage system of the hydrometallurgical plant is the annual cost of the photovoltaic-storage configuration throughout the entire life cycle of the hydrometallurgical plant.

[0126] The optimization configuration solution module 400 is used to solve the optimization configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant according to the constraints and objective function of the optimization configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant, so as 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 formulas for the photovoltaic and energy storage systems in the hydrometallurgical plant photovoltaic-storage system optimization configuration model are as follows:

[0128]

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

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

[0131]

[0132] In the formula, Rated load capacity of hydrometallurgical plant; The total rated power for ball mills and semi-autogenous mills; This refers to the rated power of the electrolytic cell equipment. The starting coefficient for ball mills and semi-autogenous mills; The adjustment coefficient is for flexible use of electrolytic cell equipment.

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

[0134]

[0135] In the formula, This represents the operating power of the new energy power generation device during time period t; and β represents the upper and lower allowable limits of the operating power of the new energy power generation device during time period t; pv This represents the derating factor for the photovoltaic array; Rated power of the solar photovoltaic panel; G t G represents the solar radiation intensity during time period t; std Solar radiation intensity under standard test conditions; k PT T is the power temperature coefficient; t T represents the average ambient temperature during the time period t; std The ambient temperature is the temperature under standard test conditions.

[0136] In this embodiment, in the hydrometallurgical plant photovoltaic-storage system optimization configuration model construction module 200, the operating constraint formula for energy storage within the hydrometallurgical plant in the hydrometallurgical plant photovoltaic-storage system optimization configuration model is as follows:

[0137]

[0138] In the formula, This represents the energy value of the energy storage at the initial stage of time period t; and These represent the charging and discharging power of the energy storage power during time period t, respectively. and These are the charging and discharging efficiencies of energy storage, respectively. and These are the state variables for energy storage charging and discharging, respectively. in This indicates that the energy storage is in a charging state. This indicates that the energy storage is in a discharging state; Δt represents the duration of the dispatch cycle; χ H and χ L These represent the upper and lower limits of the energy storage state of charge, respectively; t0 and t end These represent the start and end times of the scheduling process, respectively; χ0 represents the initial state of charge of the energy storage.

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

[0140]

[0141] In the formula, The target output limit of the photovoltaic-storage system within time period t; and These represent the load demand and the maximum power supply from the mains during time period t in the s-th sample, respectively. express The maximum value among 0 and N; s 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 for the hydrometallurgical plant photovoltaic-storage system optimization configuration model during hydrometallurgical plant operation is:

[0143]

[0144] In the formula, and These represent the power supply from the main power grid and the power consumption of the hydrometallurgical plant during time period t, respectively. This represents the maximum power supply capacity of the large power grid within time period t.

[0145] In this embodiment, the objective function of the optimized configuration model of the hydrometallurgical plant's photovoltaic-storage system in the objective function construction module 300 is:

[0146]

[0147] In the formula, and These are the expected investment costs for photovoltaic and energy storage systems, respectively. The expected operation and maintenance costs for powering photovoltaic and energy storage systems; The expected cost of supplying electricity from the mains; The expected benefit loss cost of load flexibility power regulation in hydrometallurgical plants; eav This is the annual value coefficient;

[0148] The optimization configuration solution solution module 400 employs branch and bound method, cutting plane method, genetic algorithm, particle swarm algorithm, simulated annealing algorithm or hybrid algorithm, or calls a commercial solver for solution.

[0149] It should be noted that the information interaction and execution process between the modules of the above-mentioned device are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0150] Example 3

[0151] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium storing program code for a method of optimizing the photovoltaic and energy storage in a hydrometallurgical plant. The program code includes instructions for executing the method of optimizing the photovoltaic and energy storage in a hydrometallurgical plant according to Embodiment 1 or any possible implementation thereof.

[0152] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives, SSDs).

[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 can execute the photovoltaic-storage optimization processing method for a hydrometallurgical plant by calling the program instructions.

[0156] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0157] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as 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, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0158] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0159] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for optimizing the photovoltaic and energy storage processes in a hydrometallurgical plant, characterized in that, include: Obtain the grid structure parameters of the hydrometallurgical plant and the physical parameters of the specified load equipment, including electrolytic cells, ball mills, and semi-autogenous mills; Acquire historical load data, solar radiation data, and historical power outage and curtailment data of the local power grid for hydrometallurgical plants; collect unit capacity investment costs of photovoltaic, energy storage, and converter equipment; collect the cost per kilowatt-hour of power supplied by the power grid and the photovoltaic-storage system. Based on the collected grid structure parameters of the hydrometallurgical plant and the physical parameters of the designated load equipment, historical load data, solar radiation data, and historical power outage and curtailment data of the local power grid, the unit capacity investment cost of the photovoltaic, energy storage, and converter equipment, and the cost per kilowatt-hour of power supplied by the power grid and the photovoltaic-storage system, an optimal configuration model for the photovoltaic-storage system of the hydrometallurgical plant is constructed. The constraints of the optimal configuration model for 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 within the hydrometallurgical plant, the output target limit constraints of the photovoltaic-storage system at different times, and the supply and demand balance constraints during the operation of the hydrometallurgical plant. The objective function of the optimized configuration model of the photovoltaic-storage system of the hydrometallurgical plant is constructed. The objective function of the optimized configuration model of the photovoltaic-storage system of the hydrometallurgical plant is the annual cost of the photovoltaic-storage configuration over the entire life cycle. Based on the constraints and objective function of the optimized configuration model of the photovoltaic and energy storage system in the hydrometallurgical plant, the optimized configuration model of the photovoltaic and energy storage system in the hydrometallurgical plant is solved to obtain the optimized configuration scheme of photovoltaic and energy storage in the hydrometallurgical plant. The formulas for the configuration capacity constraints of photovoltaic and energy storage in the optimal configuration model of the hydrometallurgical plant photovoltaic-energy storage system are as follows: ； ； ； In the formula, Configure power capacity for photovoltaics; Power capacity configured for the energy storage converter; Configure capacity for energy storage; and These are the upper and lower limits of the photovoltaic configuration power capacity, respectively. and These are the upper and lower limits of the power capacity configured for the energy storage converter, respectively. and These are the upper and lower limits of the energy storage configuration capacity, respectively. Lower limit of photovoltaic configuration power capacity The formula for determining the value of is: ； In the formula, Rated load capacity of hydrometallurgical plant; The total rated power for ball mills and semi-autogenous mills; This refers to the rated power of the electrolytic cell equipment. The starting coefficient for ball mills and semi-autogenous mills; The adjustment coefficient is for flexible use of electrolytic cell equipment.

2. The method for optimizing the photovoltaic and energy storage processes in a hydrometallurgical plant according to claim 1, characterized in that, The constraint 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 as follows: ； ； In the formula, This represents the operating power of the new energy power generation device during time period t; and These represent the upper and lower allowable boundaries of the operating power of the new energy power generation device during time period t; This represents the derating factor for the photovoltaic array; Rated power of the solar photovoltaic panel; G represents the solar radiation intensity during time period t; std Solar radiation intensity under standard test conditions; k PT T is the power temperature coefficient; t T represents the average ambient temperature during the time period t; std The ambient temperature is the temperature under standard test conditions.

3. The method for optimizing the photovoltaic and energy storage processes in a hydrometallurgical plant according to claim 2, characterized in that, The operational constraint formula for energy storage within the hydrometallurgical plant in the optimized configuration model of the photovoltaic-storage system is as follows: ； ； ； ； ； ； In the formula, This represents the energy value of the energy storage at the initial stage of time period t; and These represent the charging and discharging power of the energy storage power during time period t, respectively. and These are the charging and discharging efficiencies of energy storage, respectively. and These are the state variables for energy storage charging and discharging, respectively. ,in This indicates that the energy storage is in a charging state. This indicates that the energy storage is in a discharging state; Indicates the duration of the scheduling cycle; and These represent the upper and lower limits of the energy storage state of charge, respectively; t0 and t end These represent the start and end time periods of the scheduling process; This represents the initial state of charge of the energy storage. The formula for the output limit constraint of the photovoltaic-storage system at different time periods in the optimal configuration model of the photovoltaic-storage system of the hydrometallurgical plant is as follows: ； ； In the formula, The target output limit of the photovoltaic-storage system within time period t; and These represent the load demand and the maximum power supply from the mains during time period t in the s-th sample, respectively. express The maximum value among 0; The number of samples used; The supply and demand balance constraint formula for the hydrometallurgical-storage system optimization configuration model during the operation of the hydrometallurgical plant is as follows: ； ； In the formula, and These represent the power supply from the main power grid and the power consumption of the hydrometallurgical plant during time period t, respectively. This represents the maximum power supply capacity of the large power grid within time period t.

4. The method for optimizing the photovoltaic and energy storage processes in a hydrometallurgical plant according to claim 3, characterized in that, The objective function of the optimal configuration model for the photovoltaic-storage system in the hydrometallurgical plant is: ； In the formula, and These are the expected investment costs for photovoltaic and energy storage systems, respectively. , The expected operation and maintenance costs for powering photovoltaic and energy storage systems; The expected cost of supplying electricity from the mains; The cost of lost benefits from load flexibility power regulation in hydrometallurgical plants; This is the annual value coefficient; In solving the optimal configuration model of the photovoltaic-storage system of the hydrometallurgical plant, the branch-and-bound method, the cutting plane method, the genetic algorithm, the particle swarm optimization algorithm, the simulated annealing algorithm, or a hybrid algorithm are used, or a commercial solver is called.

5. A photovoltaic-storage optimization treatment device for a hydrometallurgical plant, characterized in that, include: The parameter acquisition module is used to acquire the grid structure parameters of the hydrometallurgical plant and the physical parameters of the specified load equipment, including electrolytic cells, ball mills, and semi-autogenous mills; acquire historical load data, solar radiation data, and historical power outage and curtailment data of the local power grid; collect the unit capacity investment cost of photovoltaic, energy storage, and converter equipment; and collect the cost per kilowatt-hour of power supplied by the power grid and the photovoltaic-storage system. The module for constructing an optimized configuration model of the photovoltaic-storage system in a hydrometallurgical plant is used to construct an optimized configuration model of the photovoltaic-storage system based on the collected grid structure parameters of the hydrometallurgical plant, the physical parameters of the specified load equipment, the historical load data, solar radiation data, and historical power outage and curtailment data of the local power grid, the unit capacity investment cost of the photovoltaic, energy storage, and converter equipment, and the cost per kilowatt-hour of power supplied by the power grid and the photovoltaic-storage system. The constraints of the optimized configuration model of the photovoltaic-storage system in a 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 within the hydrometallurgical plant, the output target limit constraints of the photovoltaic-storage system at different times, and the supply and demand balance constraints during the operation of the hydrometallurgical plant. The objective function construction module is used to construct the objective function of the optimal configuration model of the photovoltaic-storage system of the hydrometallurgical plant. The objective function of the optimal configuration model of the photovoltaic-storage system of the hydrometallurgical plant is the annual cost of the photovoltaic-storage configuration over the entire life cycle. The optimization configuration solution module is used to solve the optimization configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant according to the constraints and objective function of the optimization configuration model of the photovoltaic and energy storage system of the hydrometallurgical plant, so as to obtain the optimization configuration scheme of photovoltaic and energy storage of the hydrometallurgical plant. In the construction module of the photovoltaic-storage system optimization configuration model for the hydrometallurgical plant, the configuration capacity constraint formulas for the photovoltaic and energy storage systems in the optimization configuration model are as follows: ； ； ； In the formula, Configure power capacity for photovoltaics; Power capacity configured for the energy storage converter; Configure capacity for energy storage; and These are the upper and lower limits of the photovoltaic configuration power capacity, respectively. and These are the upper and lower limits of the power capacity configured for the energy storage converter, respectively. and These are the upper and lower limits of the energy storage configuration capacity, respectively. Lower limit of photovoltaic configuration power capacity The formula for determining the value of is: ； In the formula, Rated load capacity of hydrometallurgical plant; The total rated power for ball mills and semi-autogenous mills; This refers to the rated power of the electrolytic cell equipment. The starting coefficient for ball mills and semi-autogenous mills; The adjustment coefficient is for flexible use of electrolytic cell equipment.

6. The photovoltaic-storage optimization treatment device for a hydrometallurgical plant according to claim 5, characterized in that, In the construction module of the photovoltaic-storage system optimization configuration model for the hydrometallurgical plant, the constraint condition formula for the operation constraint of the photovoltaic power generation device in the optimization configuration model of the photovoltaic-storage system for the hydrometallurgical plant is as follows: ； ； In the formula, This represents the operating power of the new energy power generation device during time period t; and These represent the upper and lower allowable boundaries of the operating power of the new energy power generation device during time period t; This represents the derating factor for the photovoltaic array; Rated power of the solar photovoltaic panel; G represents the solar radiation intensity during time period t; std Solar radiation intensity under standard test conditions; k PT T is the power temperature coefficient; t T represents the average ambient temperature during the time period t; std The ambient temperature is the temperature under standard test conditions.

7. The photovoltaic-storage optimization treatment device for a hydrometallurgical plant according to claim 6, characterized in that, In the module for constructing the optimal configuration model of the photovoltaic-storage system in the hydrometallurgical plant, the operational constraint formula for energy storage within the hydrometallurgical plant in the optimal configuration model of the photovoltaic-storage system is as follows: ； ； ； ； ； ； In the formula, This represents the energy value of the energy storage at the initial stage of time period t; and These represent the charging and discharging power of the energy storage power during time period t, respectively. and These are the charging and discharging efficiencies of energy storage, respectively. and These are the state variables for energy storage charging and discharging, respectively. ,in This indicates that the energy storage is in a charging state. This indicates that the energy storage is in a discharging state; Indicates the duration of the scheduling cycle; and These represent the upper and lower limits of the energy storage state of charge, respectively; t0 and t end These represent the start and end time periods of the scheduling process; This represents the initial state of charge of the energy storage. In the construction module of the photovoltaic-storage system optimization configuration model for the hydrometallurgical plant, the constraint formula for the output target limit of the photovoltaic-storage system at different time periods in the optimization configuration model of the hydrometallurgical plant photovoltaic-storage system is as follows: ； ； In the formula, The target output limit of the photovoltaic-storage system within time period t; and These represent the load demand and the maximum power supply from the mains during time period t in the s-th sample, respectively. express The maximum value among 0; The number of samples used; The supply and demand balance constraint formula for the hydrometallurgical-storage system optimization configuration model during the operation of the hydrometallurgical plant is as follows: ； ； In the formula, and These represent the power supply from the main power grid and the power consumption of the hydrometallurgical plant during time period t, respectively. This represents the maximum power supply capacity of the large power grid within time period t.

8. The photovoltaic-storage optimization treatment device for a hydrometallurgical plant according to claim 7, characterized in that, In the objective function construction module, the objective function of the optimal configuration model for the photovoltaic-storage system of the hydrometallurgical plant is: ； In the formula, and These are the expected investment costs for photovoltaic and energy storage systems, respectively. , The expected operation and maintenance costs for powering photovoltaic and energy storage systems; The expected cost of supplying electricity from the mains; The cost of lost benefits from load flexibility power regulation in hydrometallurgical plants; This is the annual value coefficient; The optimization configuration solution solution module employs branch and bound method, cutting plane method, genetic algorithm, particle swarm algorithm, simulated annealing algorithm or hybrid algorithm, or calls commercial solvers for solution.

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