Mine oil-to-electricity collaborative operation method and system based on energy storage and excavator leasing

Abstract

This invention relates to a method and system for collaborative operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing. The method includes: acquiring basic operational data from multiple participating entities, including demanders, equipment lessors, and energy suppliers; generating a charging and discharging scheduling strategy between power supply equipment and energy-consuming equipment based on the basic operational data and preset load timing characteristics; constructing benchmark revenue models for the participating entities in a non-cooperative state and alliance revenue models in a cooperative state; and solving for the optimal collaborative transaction pricing strategy that satisfies constraints based on a preset revenue distribution mechanism, combining the benchmark revenue model and the alliance revenue model, to maximize the revenue of all participating entities. This invention, by comprehensively considering multiple constraints such as equipment operation and maintenance costs and carbon emission reduction benefits, solves for the optimal leasing price and electricity sales price under a cooperative alliance, achieving maximum and fair distribution of revenue for all three parties.

Description

Technical Field

[0001] This invention belongs to the field of energy-saving renovation and power dispatching technology, specifically relating to a method and system for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing. Background Technology

[0002] Traditional mining operations heavily rely on fuel-intensive excavators, resulting in high operating costs, significant carbon emissions, and severe environmental pollution. With tightening environmental policies, mining areas urgently need to transition to electrification. Electric excavators offer advantages such as zero emissions, low noise, and high energy efficiency, but their widespread adoption faces two major bottlenecks: first, the weak power grid infrastructure in mining areas results in insufficient power capacity, making it difficult to support multiple high-power electric excavators operating simultaneously; second, the initial purchase cost of electric excavators is significantly higher than that of fuel-intensive excavators (a difference of approximately 400,000 yuan per unit), placing a heavy one-time investment burden on mining owners, who also need to allocate charging facilities and personnel, further increasing the conversion costs.

[0003] Currently, there are two common solutions on the market: one is for owners to directly purchase electric excavators and build their own charging facilities, but this involves high upfront investment and difficulty in guaranteeing power supply reliability; the other is for third parties to provide electric excavator rental services, but owners still need to solve the power supply problem themselves, and the rental price is difficult to determine, easily leading to conflicts of interest between the two parties. A more advanced solution is to introduce mobile energy storage vehicles as temporary or supplementary power sources, forming an "electric excavator + mobile energy storage" operation mode. However, this model involves three parties: the owner (mine operation operator), the electric excavator supplier (equipment provider), and the mobile energy storage supplier (energy provider). Each party has different interests, and independent operation often results in high rental costs, opaque energy service fees, and low overall efficiency. How to design a reasonable operation model that coordinates the interests of all three parties and achieves win-win cooperation is a pressing technical challenge that needs to be addressed.

[0004] While existing technologies include cost optimization studies for individual devices, there is a lack of tripartite collaborative optimization methods for the specific business model of "energy storage electricity sales + equipment leasing." In particular, the linkage between energy storage electricity sales prices and equipment leasing fees, as well as the revenue distribution mechanism based on cooperative game theory, are not considered, making it difficult to implement and promote this model. Summary of the Invention

[0005] To address the problems raised in the background art, a first aspect of this invention provides a method for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing, comprising: acquiring basic operational data of multiple participating entities, including demanders, equipment lessors, and energy suppliers; generating a charging and discharging scheduling strategy between power supply equipment and energy-consuming equipment based on the basic operational data and preset load timing characteristics; constructing a benchmark revenue model for the multiple participating entities in a non-cooperative state and an alliance revenue model in a cooperative state; and solving for an optimal coordinated transaction pricing strategy that satisfies constraints based on a preset revenue distribution mechanism, combined with the benchmark revenue model and the alliance revenue model, to maximize the revenue of the multiple participating entities; wherein the optimal coordinated transaction pricing strategy includes at least an equipment service fee rate and an energy transaction fee rate.

[0006] In some embodiments of the present invention, the basic operating data includes: operating parameters of energy-consuming equipment, energy efficiency parameters of power supply equipment, energy price data, and investment cost data of each participating entity.

[0007] In some embodiments of the present invention, the process of constructing the alliance revenue model includes: determining the actual electricity purchase of the power grid based on the charging and discharging scheduling strategy; comprehensively evaluating the asset depreciation factor, the operating cost factor, and the environmental benefit factor generated by electrification transformation; and constructing a joint revenue function for multiple participating entities by using the actual electricity purchase of the power grid, the asset depreciation factor, the operating cost factor, and the environmental benefit factor as input variables.

[0008] In some embodiments of the present invention, the environmental benefit factor is determined by: obtaining the fossil fuel carbon emission factor and the power grid carbon emission factor of the target area; calculating the carbon emission reduction based on the actual electricity purchase and the energy consumption benchmark of the energy-consuming equipment, and converting it into additional carbon revenue in combination with the carbon trading price.

[0009] In some embodiments of the present invention, the profit distribution mechanism includes: determining the asset investment weight, risk-bearing coefficient, and marginal contribution coefficient of each participating entity; generating a comprehensive weight based on the asset investment weight, the risk-bearing coefficient, and the marginal contribution coefficient; and using the comprehensive weight to correct a preset benchmark distribution value to obtain the profit distribution amount due to each participating entity.

[0010] In some embodiments of the present invention, the constraints include: an investment payback period constraint, which limits the principal recovery period for each participating entity to not exceed an expected threshold; and an individual rationality constraint, which limits the returns of each participating entity in the cooperative state to not be lower than their returns in the non-cooperative state.

[0011] In a second aspect, this invention provides a collaborative operation system for oil-to-electricity conversion in mining areas based on energy storage and excavator leasing, comprising: an acquisition module for acquiring basic operational data of multiple participating entities, including demanders, equipment lessors, and energy suppliers; a generation module for generating a charging and discharging scheduling strategy between power supply equipment and energy-consuming equipment based on the basic operational data and preset load timing characteristics; a construction module for constructing a benchmark revenue model for the multiple participating entities in a non-cooperative state and an alliance revenue model in a cooperative state; and a solution module for solving an optimal collaborative transaction pricing strategy that satisfies constraints based on a preset revenue distribution mechanism, combined with the benchmark revenue model and the alliance revenue model, to maximize the revenue of the multiple participating entities; wherein the optimal collaborative transaction pricing strategy includes at least an equipment service fee rate and an energy transaction fee rate.

[0012] A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing provided in the first aspect of the present invention.

[0013] In a fourth aspect, the present invention provides a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing provided in the first aspect of the present invention.

[0014] The beneficial effects of this invention are: 1. Business Model Innovation: This invention proposes a three-party collaborative operation model of "energy storage electricity sales + electric excavator leasing", which transforms the traditional equipment buying and selling relationship into a service relationship. Owners do not need to make a huge upfront investment in purchasing equipment, but only need to pay leasing fees and electricity fees, which greatly reduces the threshold for transformation. Energy storage suppliers earn the price difference by selling electricity, and excavator suppliers recover their investment through leasing, achieving a win-win situation for all parties.

[0015] 2. Power supply reliability assurance: By using mobile energy storage vehicles as flexible power sources, the problem of insufficient grid capacity in the mining area was solved. Charging during rest periods was also achieved, which enabled peak shaving and valley filling, ensuring that the excavators could work around the clock.

[0016] 3. Fair and reasonable profit distribution: Introduce a modified Shapley value distribution mechanism based on asset investment, risk-taking, and marginal contribution, so that profit distribution can better reflect the actual contributions of all parties, incentivize all parties to actively participate in cooperation, and improve the stability of the alliance.

[0017] 4. Controllable investment recovery period: By setting investment recovery period constraints, it is ensured that each party can recover its principal within a reasonable period of time, which reduces cooperation risks and improves the sustainability of the model.

[0018] 5. Significant environmental benefits: Incorporating carbon emission reduction benefits into the owners' income further incentivizes owners to carry out electrification retrofits, which meets environmental protection requirements.

[0019] 6. High scalability: The method of this invention can be extended to similar scenarios such as ports, mines, and large-scale infrastructure construction sites, and has broad application prospects. Attached Figure Description

[0020] Figure 1 This is a basic flowchart illustrating the collaborative operation method of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing in some embodiments of the present invention. Figure 2 This is a timing diagram of the operation of a mining excavator and an energy storage vehicle in some embodiments of the present invention; Figure 3 This is a schematic diagram of sensitivity analysis results in some embodiments of the present invention; Figure 4 The figures shown are Monte Carlo simulation results from some embodiments of the present invention. Figure 5 This is a daily SOC variation curve of an energy storage vehicle in some embodiments of the present invention; Figure 6 This is a schematic diagram of the structure of a mining area oil-to-electricity collaborative operation system based on energy storage and excavator leasing in some embodiments of the present invention; Figure 7 This is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. Detailed Implementation

[0021] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0022] Example 1 refer to Figure 1In a first aspect, the present invention provides a method for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing, comprising: S100. acquiring basic operational data of multiple participating entities, including demanders, equipment lessors, and energy suppliers; S200. generating a charging and discharging scheduling strategy between power supply equipment and energy-consuming equipment based on the basic operational data and preset load timing characteristics; S300. constructing a benchmark revenue model for the multiple participating entities in a non-cooperative state and an alliance revenue model in a cooperative state; S400. solving for an optimal coordinated transaction pricing strategy that satisfies constraints based on a preset revenue distribution mechanism, combined with the benchmark revenue model and the alliance revenue model, to maximize the revenue of the multiple participating entities; wherein the optimal coordinated transaction pricing strategy includes at least an equipment service fee rate and an energy transaction fee rate.

[0023] In step S100 of some embodiments of the present invention, the basic operating data includes: operating parameters of energy-consuming equipment, energy efficiency parameters of power supply equipment, energy price data, and investment cost data of each participating entity.

[0024] Specifically, the system collects operating parameters (fuel consumption, electricity consumption, purchase cost, working hours) of fuel-powered excavators and electric excavators, energy prices (diesel unit price, grid electricity price), mobile energy storage parameters (capacity, power, quantity, charging and discharging efficiency, purchase cost), charging pile parameters (quantity, unit price, lifespan), personnel configuration (quantity, wages), and equipment operation and maintenance costs (maintenance costs of fuel-powered excavators, maintenance costs of electric excavators, and operation and maintenance costs of energy storage vehicles), and obtains the grid carbon emission factor and carbon trading price of the mining area.

[0025] In step S200 of some embodiments of the present invention, a charging and discharging scheduling strategy between power supply equipment and energy consumption equipment is generated based on the basic operating data and preset load timing characteristics. The charging and discharging scheduling strategy includes: dividing the work cycle into working periods and non-working periods; controlling the power supply equipment to store energy during the non-working periods, and establishing energy coupling with the energy-consuming equipment during the working periods to provide kinetic energy support.

[0026] Specifically, a benchmark revenue model is established under non-cooperative conditions: the total cost of the owner using the fuel-powered excavator (energy cost + operation and maintenance cost) is taken as the negotiation breakdown point of the cooperative game, and the revenue of the electric excavator supplier and the mobile energy storage supplier in the absence of cooperation (usually zero or idle cost) is calculated.

[0027] In one specific implementation, the electric excavator supplier provides electric excavators to the owner on a leasing basis, charging a rental fee of R yuan / day / unit, and is responsible for the maintenance and technical support of the equipment; Mobile energy storage providers offer electricity services to owners in an electricity sales model, charging a sales price of p yuan / kWh. The energy storage vehicles charge from the grid during rest periods in the mining area and supply power to excavators during working hours, earning the purchase and sale price difference. The owner, as the core of the operation, pays the rental fee and electricity fee, while also bearing the depreciation of the charging pile and the wages of new staff, and obtains fuel savings and carbon emission reduction benefits. The charging and discharging matching scheme between electric excavators and mobile energy storage is determined based on the working sequence of the mining area to ensure that electric excavators must be connected to energy storage vehicles to work, and the impact of energy storage charging and discharging efficiency on the electricity purchased by the power grid is considered. Optionally, the working sequence of the mining area is as follows: during the 24 hours of the day, the excavator's working periods are hours 3-6, 9-12, 15-18, and 21-24, each lasting 4 hours, for a total of 16 hours of work per day; the remaining periods are rest periods, during which the energy storage vehicle is charged from the grid to ensure full power supply for the next working period.

[0028] In step S300 of some embodiments of the present invention, a baseline benefit model for the multi-party participating entities in a non-cooperative state and a coalition benefit model in a cooperative state are constructed respectively; wherein, the construction process of the coalition benefit model includes: S301. Determine the actual electricity purchase volume of the power grid based on the charging and discharging scheduling strategy; Considering the charging efficiency of energy storage Discharge efficiency The relationship between actual grid power purchases and excavator power consumption is as follows: ,in For the number of excavators, This refers to the daily power consumption of a single electric excavator.

[0029] S302. Comprehensively assess asset depreciation factors, operating cost factors, and environmental benefit factors resulting from electrification upgrades; Specifically, equipment depreciation is calculated using the straight-line method, with an economic life of 10 years, a residual value rate of 10%, and an annual working day count of 300 days.

[0030] S303. Using the actual electricity purchased by the power grid, the asset depreciation factor, the operating cost factor, and the environmental benefit factor as input variables, construct a joint benefit function for multiple participating entities.

[0031] Specifically, the risk-bearing coefficient The marginal contribution coefficient can be determined by comprehensively assessing the market risks, technological risks, and policy risks faced by each entity, and through expert scoring or the analytic hierarchy process; It can be determined by calculating the average marginal contribution of each entity in the alliance.

[0032] Furthermore, the environmental benefit factors are determined by: obtaining the fossil fuel carbon emission factors and power grid carbon emission factors for the target area; calculating carbon emission reductions based on the actual electricity purchase and the energy consumption benchmark of the energy-consuming equipment, and converting them into additional carbon revenues in conjunction with carbon trading prices.

[0033] Establish a cooperative game theory alliance payoff model: Construct the three-party payoff function: Owner benefits (total cost savings relative to fuel mode): ,in The total daily cost (including energy and maintenance) for N fuel-fired excavators. Daily depreciation of charging piles Daily wages for staff; Excavator supplier revenue: Leasing revenue minus depreciation and maintenance costs of electric excavators, i.e. ,in Daily depreciation of electric excavators Daily maintenance costs; Energy storage supplier revenue: Electricity sales revenue minus electricity purchase cost, and then minus energy storage depreciation and operation and maintenance costs, i.e. ,in Depreciation of energy storage vehicles per day For daily maintenance costs, The purchase price of electricity from the power grid; Total remaining balance after cooperation: .

[0034] Optionally, the energy storage charge / discharge efficiency is the charging efficiency. Discharge efficiency Overall efficiency Electricity purchased by the power grid .

[0035] In step S400 of some embodiments of the present invention, based on a preset revenue distribution mechanism, and in combination with the benchmark revenue model and the alliance revenue model, the optimal collaborative transaction pricing strategy that satisfies the constraints is solved to maximize the revenue of multiple participating entities; wherein, the optimal collaborative transaction pricing strategy includes at least equipment service rate and energy transaction rate.

[0036] The profit distribution mechanism includes: determining the asset investment weight, risk-bearing coefficient, and marginal contribution coefficient of each participating entity; generating a comprehensive weight based on the asset investment weight, the risk-bearing coefficient, and the marginal contribution coefficient; and using the comprehensive weight to correct a preset benchmark allocation value to obtain the profit distribution amount due to each participating entity.

[0037] The constraints include: an investment payback period constraint, which limits the principal recovery period for each participating entity to not exceed the expected threshold; and an individual rationality constraint, which limits the returns of each participating entity in the cooperative state to not be lower than their returns in the non-cooperative state.

[0038] Specifically, carbon emission reduction benefits are introduced: the carbon emission reductions achieved by property owners due to electrification are calculated, and the additional carbon revenue for property owners is obtained by combining this with carbon trading prices. This benefit does not affect the internal game among the three parties, but it can enhance the owner's willingness to cooperate; Set investment payback period constraints: Based on the equipment investment amount of each entity, set a reasonable principal recovery period. , , ,Require: Excavator Supplier: ,in D represents the incremental investment of electric excavators compared to fuel-powered excavators, and D represents the number of working days per year. Energy storage suppliers: ,in Total investment for energy storage vehicles; owner: ,in Investing in charging stations; Profit allocation based on modified Shapley value: Define a tripartite set For any subset Calculate the total revenue when this subset collaborates alone. (characteristic function), where ; Calculate the classic Shapley value: ; Introduce a correction factor to take into account the asset inputs of each entity. Risk tolerance coefficient and marginal contribution coefficient Define the overall weight ,in Let be the weighting coefficient, satisfying , Normalized asset input; Corrected Shapley value: ,in For average weights, or through normalization to make ; The adjusted Shapley value will be used as the share of the profits due to each party: ; ; ; Solving for the cooperative game equilibrium: Substitute the above allocation objective into the payoff function and solve the simultaneous equations to obtain the optimal rental fee. and the best electricity price And verify whether the investment payback period constraint and individual rationality constraint are met (the benefits of each party ≥ the benefits of non-cooperation); Output result: Optimal and As a parameter of the cooperation agreement, it guides the three parties to sign a cooperation agreement on "energy storage and electricity sales + equipment leasing" to achieve coordinated operation of "oil-to-electricity" conversion in the mining area.

[0039] Furthermore, the working sequence of the mining area is as follows: During the 24 hours of each day, the excavator's working periods are hours 3-6, 9-12, 15-18, and 21-24, each lasting 4 hours, for a total of 16 hours of work per day; the remaining time periods are rest periods, during which the energy storage vehicle charges from the grid to ensure full power supply for the next working period.

[0040] In one specific embodiment of the present invention, taking a mining area as an example, the mining area plans to replace four fuel-powered excavators with electric excavators and introduce four mobile energy storage vehicles to ensure power supply, adopting an operation model of "energy storage and power sales + electric excavator leasing". The relevant basic data is as follows: 1. Basic parameters: Fuel-fired excavator: Fuel consumption 46L / hour, diesel price 7 yuan / L, 16 hours of work per day, daily fuel consumption per unit 736L, daily fuel cost 5152 yuan. Hourly maintenance cost for fuel-fired excavators. The daily maintenance cost for a single excavator is 320 yuan per hour. The total daily cost (energy + maintenance) for 4 fuel-powered excavators is as follows: Yuan.

[0041] Electric excavator: consumes 160kW of electricity per hour, works 16 hours a day, daily power consumption per unit kWh. The purchase cost of an electric excavator is 2.2 million yuan per unit, compared to the incremental investment of a fuel-fired excavator (1.8 million yuan per unit). The cost is 10,000 yuan per unit, with a total investment of 1.6 million yuan for 4 units. The economic life is 10 years, the residual value rate is 10%, and the daily depreciation is 220 × 10⁻⁶. 4 ×0.9 / 10 / 300 = 660 yuan / unit. Hourly maintenance cost for an electric excavator. =10 yuan / hour, daily maintenance cost per unit is 160 yuan. Daily depreciation of 4 electric excavators. =4×660=2640 yuan, daily maintenance =4×160=640 yuan.

[0042] Mobile energy storage vehicle: 836kWh capacity per unit, price 0.8 yuan / Wh = 800 yuan / kWh, purchase cost per unit 668,800 yuan, total investment for 4 units. Yuan. Economic life 10 years, residual value rate 10%, daily depreciation = 668800 × 0.9 / 10 / 300 = 200.64 yuan / unit, daily depreciation for 4 units. =802.56 yuan. The operation and maintenance cost of energy storage vehicles is calculated based on the discharge volume. Yuan / kWh, daily discharge capacity of a single unit is 2560kWh, daily maintenance cost is 256 yuan, and the daily maintenance cost for 4 units is [amount missing]. Yuan. Electricity purchase price per unit from the power grid. =0.7 yuan / kWh. Charge / discharge efficiency. The overall efficiency η = 0.94 × 0.94 = 0.8836. Total electricity purchased by the power grid. kWh.

[0043] Charging station cost: 4 charging stations, each costing 100,000 yuan to purchase and install, totaling 400,000 yuan. Economic life: 10 years, residual value: 0, daily depreciation. =133.33 yuan.

[0044] Personnel costs: Two new operation and maintenance personnel will be added, with an annual salary of 100,000 yuan per person, calculated based on 300 working days per year, and a daily wage of [missing information]. =666.67 yuan.

[0045] Carbon trading parameters: Diesel carbon emission factor kgCO2 / L, carbon emission factor of power grid kgCO2 / kWh, carbon trading price =50 yuan / ton. The calculated daily carbon emission reduction for 4 excavators is 4 × (736 × 2.68). 2560 × 0.6) / 1000 = 1.746 tons, daily carbon gain π c =1.746×50=87.3 yuan (belonging to the owner).

[0046] 2. Working sequence and operation mode in the mining area: like Figure 2 As shown, in the mining area, the excavators work during the 24-hour period from hours 3-6, 9-12, 15-18, and 21-24, each lasting 4 hours, for a total of 16 hours; the remaining time is for rest. In this operation mode, each electric excavator must be connected to one energy storage vehicle to work, meaning four energy storage vehicles correspond to four excavators. The energy storage vehicles charge from the grid during rest periods and discharge to the excavators during working periods. The charging and discharging strategy must ensure that the energy storage vehicles are fully charged at the start of each working period. Considering charging and discharging efficiency, each working period requires 640 kWh from the excavator, and the energy storage vehicle needs to release approximately 640 / 0.94 ≈ 680.85 kWh, while needing to charge approximately 680.85 / 0.94 ≈ 724.3 kWh from the grid. The energy storage vehicle has a capacity of 836 kWh, which is sufficient to meet the discharge requirements for each period.

[0047] 3. Cooperative game theory model: Let the rental fee charged by the electric excavator supplier to the owner be R yuan / day / unit, and the electricity sales price charged by the mobile energy storage supplier to the owner be p yuan / kWh (charged according to the actual electricity consumption of the excavator).

[0048] The owner's total daily expenses are: ; The owner's benefit (total cost savings relative to fuel mode) is:

[0049] The excavator supplier's net profit (lease income minus depreciation and maintenance) is: ; The net profit (electricity sales revenue minus electricity purchase cost, depreciation, and operation and maintenance) of energy storage suppliers is: ; The total remaining amount from the collaboration (the sum of the benefits for all three parties) is: ; 4. Investment recovery period constraints: Set the expected investment payback period for each entity. In a year, the number of working days is D = 300 days.

[0050] Excavator Supplier: .

[0051] Energy storage suppliers: .

[0052] owner: This constraint is usually satisfied automatically.

[0053] 5. Profit allocation based on the adjusted Shapley value: First, calculate the revenue characteristic function of each subset alliance. Since excavators require energy storage to operate, no two-party alliance can achieve electrification, resulting in zero profit for both; a three-party alliance yields a profit of Π. Therefore, the classic Shapley value is... Yuan / day.

[0054] Introduce a correction factor. Set the weighting coefficients. , , Asset input normalization: .

[0055] Risk tolerance coefficient (expert score): .

[0056] Marginal contribution coefficients (tentatively assumed to be equal): .

[0057] Calculate the overall weight: ; ; ; The sum is approximately equal to 1. The total cooperative surplus is distributed according to weight: ; ; .

[0058] 6. Solve for the optimal R and p Depend on have to: Yuan / day / unit; Depend on have to: .

[0059] 7. Verify constraints Investment recovery period: Excavator: Annual profit: 2815 × 300 = 844500 Payback period: 1,600,000 / 844,500 ≈ 1.89 years < 5 years; Energy storage: Annual profit: 3720 × 300 = 1,116,000; Payback period: 2,675,200 / 1,116,000 ≈ 2.40 years < 5 years; owner: Annual savings: 1333 × 300 = 399900 The investment in charging stations can be recovered in approximately 1.00 year (400,000 / 399,900) < 5 years.

[0060] Individual rationality: The gains for all parties are far greater than 0.

[0061] The additional carbon revenue of 87.3 yuan / day for owners further enhances their willingness to cooperate.

[0062] 8. Simulation Verification and Robustness Analysis refer to Figures 3 to 5To verify the robustness and effectiveness of the method of this invention, various simulation analyses were conducted based on the above parameters, including sensitivity analysis, Monte Carlo simulation, multi-objective optimization, and SOC timing verification. Simulation results confirm that this operating model maintains good performance under price fluctuations, ensures fair revenue distribution, and demonstrates the feasibility of the power supply strategy.

[0063] 9. Results Output The optimal cooperation parameters obtained in this embodiment are: Electric excavator rental fee ; Mobile energy storage electricity sales price .

[0064] The three parties signed a cooperation agreement on "energy storage and electricity sales + equipment leasing" based on these parameters, which can achieve a total daily revenue of 7,868.16 yuan. The revenue is distributed according to the modified Shapley value, and the revenue of each party is matched with the asset investment, risk-bearing and marginal contribution, resulting in high stability of the alliance.

[0065] Example 2 refer to Figure 6 In a second aspect, the present invention provides a collaborative operation system 1 for oil-to-electricity conversion in mining areas based on energy storage and excavator leasing, comprising: an acquisition module 11 for acquiring basic operational data of multiple participating entities, including demanders, equipment lessors, and energy suppliers; a generation module 12 for generating a charging and discharging scheduling strategy between power supply equipment and energy-consuming equipment based on the basic operational data and preset load timing characteristics; a construction module 13 for constructing a benchmark revenue model for the multiple participating entities in a non-cooperative state and an alliance revenue model in a cooperative state; and a solution module 14 for solving an optimal collaborative transaction pricing strategy that satisfies constraints based on a preset revenue distribution mechanism, combined with the benchmark revenue model and the alliance revenue model, to maximize the revenue of the multiple participating entities; wherein the optimal collaborative transaction pricing strategy includes at least an equipment service fee rate and an energy transaction fee rate.

[0066] Furthermore, the construction module 13 includes: a determination unit, used to determine the actual electricity purchased by the power grid based on the charging and discharging scheduling strategy; an evaluation unit, used to comprehensively evaluate the asset depreciation factor, the operating cost factor, and the environmental benefit factor generated by electrification transformation; and a construction unit, used to construct a joint benefit function of multiple participating entities by taking the actual electricity purchased by the power grid, the asset depreciation factor, the operating cost factor, and the environmental benefit factor as input variables.

[0067] In one specific embodiment of the present invention, a collaborative operation optimization system is provided, comprising: a data acquisition module: acquiring mining operation parameters, equipment parameters, energy prices, carbon trading data, and investment cost data through sensors, database interfaces, etc.; a model building module: containing built-in sub-modules for fuel cost calculation, equipment depreciation and maintenance, charging and discharging efficiency, carbon emission reduction calculation, investment payback period constraints, and modified Shapley value allocation, used to establish a non-cooperative benchmark return model and a cooperative game alliance return model; a solution module: using linear equation solving or optimization algorithms to solve for the optimal lease fee R and mobile energy storage electricity sales price p; and an output module: displaying the optimization results in reports or a visualization interface and generating cooperative scheme suggestions. Simultaneously, the system can integrate simulation analysis functions, performing sensitivity analysis, Monte Carlo simulations, etc., providing comprehensive support for decision-making.

[0068] Example 3 refer to Figure 7 A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of the first aspect of the present invention.

[0069] Electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0070] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, hard disks; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 7 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 7 Each box shown can represent a device or multiple devices as needed.

[0071] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by a processing device 501, it performs the functions defined in the methods of embodiments of this disclosure. It should be noted that the computer-readable medium described in embodiments of this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0072] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more computer programs, which, when executed by the electronic device, cause the electronic device to: Computer program code for performing the operations of embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, C++, and Python—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0073] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A collaborative operation method for oil-to-electricity conversion in mining areas based on energy storage and excavator leasing, characterized in that, include: Acquire basic operational data from multiple participating entities, including demanders, equipment lessors, and energy suppliers; Based on the aforementioned basic operating data and preset load timing characteristics, a charging and discharging scheduling strategy is generated between the power supply equipment and the energy-consuming equipment. The baseline profit model for the multiple participating entities in a non-cooperative state and the alliance profit model in a cooperative state are constructed respectively. Based on the preset revenue distribution mechanism, and combining the benchmark revenue model and the alliance revenue model, the optimal collaborative transaction pricing strategy that satisfies the constraints is solved to maximize the revenue of multiple participating entities; wherein, the optimal collaborative transaction pricing strategy includes at least equipment service fee rate and energy transaction fee rate.

2. The method for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing as described in claim 1, characterized in that, The basic operational data includes: Operating parameters of energy-consuming equipment, energy efficiency parameters of power supply equipment, energy price data, and investment cost data of each participating entity.

3. The method for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing as described in claim 1, characterized in that, The process of constructing the alliance revenue model includes: The actual electricity purchase volume of the power grid is determined based on the aforementioned charging and discharging scheduling strategy. A comprehensive assessment was conducted, considering asset depreciation factors, operating cost factors, and environmental benefits resulting from electrification upgrades. Using the actual electricity purchased by the power grid, the asset depreciation factor, the operating cost factor, and the environmental benefit factor as input variables, a joint benefit function for multiple participating entities is constructed.

4. The method for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing as described in claim 3, characterized in that, The environmental benefit factors are determined in the following ways: Obtain the carbon emission factors of fossil fuels and the carbon emission factors of power grid electricity in the target area; Based on the actual electricity purchased and the energy consumption benchmark of the energy-consuming equipment, carbon emission reductions are calculated and converted into additional carbon revenue by combining carbon trading prices.

5. The method for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing as described in claim 1, characterized in that, The profit distribution mechanism includes: Determine the asset input weight, risk-bearing coefficient, and marginal contribution coefficient of each participating entity; A comprehensive weight is generated based on the asset investment weight, the risk-bearing coefficient, and the marginal contribution coefficient; The preset benchmark allocation value is corrected using the comprehensive weight to obtain the share of revenue that each participating entity should receive.

6. The method for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing as described in claim 1, characterized in that, The constraints include: Investment recovery period constraint is used to limit the principal recovery period for each participating entity to not exceed the expected threshold; Individual rationality constraint is used to limit the gains of each participating entity in the cooperative state to no less than their gains in the non-cooperative state.

7. A collaborative operation system for oil-to-electricity conversion in mining areas based on energy storage and excavator leasing, characterized in that, The acquisition module is used to acquire basic operational data from multiple participating entities, including demanders, equipment lessors, and energy suppliers. The generation module is used to generate a charging and discharging scheduling strategy between power supply equipment and energy consumption equipment based on the basic operating data and preset load timing characteristics. The construction module is used to construct the baseline profit model of the multi-party participants in a non-cooperative state, and the alliance profit model in a cooperative state, respectively. The solution module is used to solve for the optimal collaborative transaction pricing strategy that satisfies the constraints based on the preset revenue distribution mechanism, combined with the benchmark revenue model and the alliance revenue model, so as to maximize the revenue of multiple participating entities; wherein, the optimal collaborative transaction pricing strategy includes at least equipment service fee rate and energy transaction fee rate.

8. The mining area oil-to-electricity collaborative operation system based on energy storage and excavator leasing as described in claim 7, characterized in that, The building module includes: The determining unit is used to determine the actual electricity purchased by the power grid based on the charging and discharging scheduling strategy; The assessment unit is used to comprehensively evaluate asset depreciation factors, operating cost factors, and environmental benefit factors resulting from electrification upgrades. The construction unit is used to construct a joint benefit function for multiple participating entities by taking the actual electricity purchased by the power grid, the asset depreciation factor, the operating cost factor, and the environmental benefit factor as input variables.

9. An electronic device, comprising: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing as described in any one of claims 1 to 6.

10. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by the processor, it implements the method for coordinated operation of oil-to-electricity conversion in mining areas based on energy storage and excavator leasing as described in any one of claims 1 to 6.

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