A method for multi-objective optimization and scheduling of a wind-solar power-thermal power-energy storage-copper electrolysis integrated system

Abstract

The present application relates to the technical field of new energy system integration and optimization, in particular to a wind-solar-thermal-storage-electrolytic copper integrated system multi-objective optimization scheduling method, comprising the following steps: collecting a region's wind field wind speed and photovoltaic field light intensity database, collecting battery storage operation data, collecting a certain electrolytic copper production line thermal power unit operation data and electrolytic copper load data; analyzing the time-frequency fluctuation characteristics of wind-solar complementation, the uncertainty of electrolytic copper demand load, and the multi-time scale simulation of wind-solar-thermal-storage-electrolytic copper integrated system; based on the multi-time scale fluctuation data, establishing a wind-solar-thermal-storage-electrolytic copper integrated system multi-objective optimization scheduling objective function, and setting constraint conditions for the objective function; based on the multi-objective optimization scheduling objective function and the constraint conditions, determining the optimal scheduling scheme of electrolytic copper through the epsilon constraint method to achieve win-win economic and environmental benefits.

Description

Technical Field

[0001] This invention relates to the field of new energy system integration and optimization technology, and in particular to a multi-objective optimization scheduling method for an integrated wind, solar, thermal power, energy storage, and electrolytic copper system. Background Technology

[0002] In existing technologies, integrating low-carbon energy sources such as wind and solar power with electrolytic copper is an effective way to achieve carbon reduction at the source.

[0003] The volatility and uncertainty of wind and solar resources mean that high-proportion grid connections have a significant impact on industrial systems. Existing research mainly focuses on energy storage technology and system integration. The former increases energy storage capacity through the development of new energy storage media and the integration of various energy storage technologies. Hybrid energy storage, including electric storage, hydrogen storage, and their derivatives, has received widespread attention both domestically and internationally, and several industrial-scale installations have been built. The system integration approach primarily studies multi-energy complementarity and the rational configuration and optimized scheduling of source-storage-load systems. Several successful cases of multi-energy coupling systems exist both domestically and internationally.

[0004] Integrating wind and solar power systems into traditional thermal power-electrolytic copper systems can achieve complementary integration of new energy systems with these systems, increasing the absorption of new energy, reducing the load on thermal power, and decreasing environmental pollutant emissions. However, the uncertainties and volatility of wind and solar power mean that a high proportion of wind and solar power integration can lead to frequent start-ups and shutdowns of thermal power units, resulting in low integration of complementary systems. Therefore, it is necessary to establish an optimized scheduling mechanism for an integrated wind-solar-thermal-power-energy storage-electrolytic copper system. This mechanism should explore flexible adjustment potential from the power supply side, meeting the demand-side requirements for electrolytic copper load tracking while reducing economic operating costs, increasing the absorption of new energy, and decreasing pollutant emissions. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the prior art by providing a multi-objective optimization scheduling method for an integrated wind-solar-thermal-energy storage-electrolytic copper system. This method aims to resolve the trade-off between high-proportion and low-cost wind and solar energy consumption in the prior art, enabling large-scale, high-proportion local consumption of wind and solar energy. Simultaneously, it addresses the optimization scheduling problem of input and output uncertainties in the prior art, achieving stable operation of the integrated wind-solar-thermal-energy storage-electrolytic copper system, minimizing economic operating costs and environmental impact.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A multi-objective optimization scheduling method for an integrated wind, solar, thermal, energy storage, and electrolytic copper system, characterized by the following steps:

[0008] Step 1: Collect databases of wind speed and solar irradiance of a certain region, collect battery energy storage operation data, collect thermal power unit operation data and electrolytic copper load data of a certain electrolytic copper production line;

[0009] Step 2: Analyze the time-frequency fluctuation characteristics of wind-solar hybridization, the uncertainty of electrolytic copper demand load, and simulate the integrated wind-solar-thermal-energy storage-electrolytic copper system at multiple time scales.

[0010] Step 3: Based on the multi-timescale fluctuation data, establish a multi-objective optimization scheduling objective function for the integrated wind, solar, thermal, energy storage, and electrolytic copper system, and set constraints for the objective function; the multi-objective optimization scheduling objective function includes an economic objective function and an environmental objective function;

[0011] Step 4: Based on the multi-objective optimization scheduling objective function and the constraints, the optimal scheduling scheme for electrolytic copper that achieves a win-win situation in terms of economic and environmental benefits is determined by solving the ε-constraint method.

[0012] Preferably, in step 1, the operating costs of wind power generation, photovoltaic power generation, battery energy storage operation and maintenance, thermal power unit operation, wind and solar curtailment penalty costs, wind and solar rotating reserve penalty costs, load shedding penalty costs, as well as pollutant emission coefficients and fines are collected. The operating costs of thermal power units include coal costs, ramp-up costs, and start-up and shutdown costs.

[0013] Preferably, in step 1, the following constraints are collected for the integrated wind-solar-thermal-energy storage-electrolytic copper system: load balance constraint, wind turbine power constraint, photovoltaic power constraint, battery energy storage power constraint, thermal power unit power constraint, thermal power unit ramping constraint, thermal power unit start-stop constraint, wind and solar curtailment constraint, and system power fluctuation constraint.

[0014] Preferably, in step 3, the cost models for constructing the economic and environmental objective functions of the integrated wind-solar-thermal-energy storage-electrolytic copper system include: wind power generation operation cost model, photovoltaic power generation operation cost model, battery energy storage operation and maintenance cost model, thermal power unit operation cost model, wind and solar curtailment penalty cost model, wind and solar rotating reserve penalty cost model, load shedding penalty cost model, and pollutant emission penalty cost model.

[0015] Specifically, the cost model for constructing the economic and environmental objective functions of an integrated wind, solar, thermal, energy storage, and electrolytic copper system includes:

[0016] (1) Wind power generation operating cost model

[0017]

[0018] In the above formula, This indicates the cost per unit of wind power generation; This indicates the output power of the wind turbine during the operating period;

[0019] (2) Photovoltaic power generation operating cost model

[0020]

[0021] In the above formula, This indicates the cost per unit of electricity generated by photovoltaic power generation; This indicates the output power of the photovoltaic unit during the operating period;

[0022] (3) Battery energy storage operation and maintenance cost model

[0023]

[0024] In the above formula, This indicates the unit charge / discharge cost of a battery energy storage system. This indicates the battery's energy storage charging and discharging power during the operating period. "+" indicates battery charging, and "-" indicates battery discharging. This is the lifespan degradation factor of the battery. When the life loss factor is 1, the battery life is exhausted. This indicates the initial investment in battery energy storage;

[0025] (4) Thermal power unit operating cost model

[0026]

[0027] In the above formula, This indicates the cost of coal used by the thermal power unit during the operating period. This represents the ramp-up cost of a thermal power unit during the operating period; This indicates the start-up and shutdown costs of thermal power units during the operating period;

[0028]

[0029] In the above formula, This represents the output power of the thermal power unit during the operating period; a, b, and c represent the traditional coal-fired cost characteristic parameters of the thermal power unit; p, q, m, and n represent the coal-fired characteristic parameters of the thermal power unit under low load conditions; α represents the judgment boundary of the thermal power unit under low load conditions.

[0030]

[0031] In the above formula, This represents the ramp-up cost characteristic parameter of thermal power units; This indicates the rate of change of the output power of a thermal power unit;

[0032]

[0033] In the above formula, This indicates the start-up and shutdown cost of a single thermal power unit; This indicates the number of thermal power units started and stopped during the operating period;

[0034] (5) Wind and solar curtailment penalty cost model

[0035]

[0036] In the above formula, This indicates the amount of wind power curtailed during the operating period; This indicates the amount of solar power that was wasted during the operating period.

[0037] (6) Wind and solar power rotating reserve penalty cost model

[0038] The randomness and uncertainty of wind and solar resources lead to an increase in the system's spinning reserve capacity requirements when wind and solar power are connected to the grid. The formula for calculating the spinning reserve penalty cost for wind and solar power is as follows:

[0039]

[0040] In the above formula, This represents the characteristic parameter of the spin-off reserve penalty cost; This indicates the reliability of wind and solar power operating capacity during the operating period;

[0041] (7) Load shedding penalty cost model

[0042] To ensure the safety and stability of the wind, solar, thermal, and energy storage system on the power generation side, and to meet the grid's power requirements, and to avoid a significant deviation between the actual output power on the power generation side and the load demand on the electrolytic copper demand side, a load shedding penalty cost is introduced. The calculation formula is shown below:

[0043]

[0044] In the above formula, This represents the load shedding cost characteristic parameter; Indicates the load shedding power during the operating period;

[0045] (8) Pollutant fine cost model

[0046]

[0047] In the above formula, This indicates the penalty cost for pollutants, CO2, SO2, NO. x The penalty costs for dust are 60 yuan / t, 630 yuan / t, 630 yuan / t and 280 yuan / t, respectively.

[0048] Preferably, in step 4, the formula for calculating the economic objective function is as follows:

[0049]

[0050] In the above formula, The total operating cost of the system is represented by ; i represents the operating time period, i = 1, 2, …, N; N represents the total operating time period of the system. This indicates the operating cost of wind power generation during the operating period; This indicates the operating cost of photovoltaic power generation during the operating period; This indicates the operating and maintenance costs of battery energy storage during the operating period; This indicates the operating cost of the thermal power unit during the operating period; This indicates the penalty cost for wind and solar power curtailment during the operating period; This indicates the penalty cost for wind and solar power rotational reserve during the operating period; This represents the cost of load shedding penalty during the operating period; This indicates the cost of fines for pollutants during the operating period.

[0051] Preferably, in step 4, the calculation formula for the environmental objective function is as follows:

[0052]

[0053] In the above formula, Indicates the amount of polluting gases emitted; This represents the pollutant emission coefficients for thermal power plants, specifically CO2, SO2, and NO. x The emission coefficients for particulate matter and dust were 326.47 kg / MWh, 3.54 kg / MWh, 1.03 kg / MWh, and 1.68 kg / MWh, respectively. This indicates the output power of the thermal power unit during the operating period.

[0054] Preferably, in step 3, the constraint model for constructing the multi-objective optimization scheduling objective function constraint conditions of the integrated wind-solar-thermal-energy storage-electrolytic copper system includes: load balance constraint, thermal power unit output power constraint, thermal power unit ramping constraint, thermal power unit start-up and shutdown time constraint, wind farm output power constraint, photovoltaic power station output power constraint, battery energy storage system output power constraint, battery energy storage system power balance constraint, and power fluctuation constraint.

[0055] Specifically, the constraint model for constructing the multi-objective optimization scheduling objective function constraints of the integrated wind, solar, thermal power, energy storage, and electrolytic copper system includes:

[0056] (1) System load balance constraints

[0057] Ignoring energy losses, the total output power of wind power, solar power, thermal power, and battery energy storage at each moment is the same as the power load demand of electrolytic copper, as shown in the following formula:

[0058]

[0059] In the above formula, This indicates the power generation capacity of the battery energy storage system during the operating period; This indicates the energy storage power of the battery energy storage system during the operating period; This indicates the operating conditions of the battery energy storage system during the operating period. This indicates the power generation status of the energy storage system. This indicates the operating conditions of the energy storage system. This indicates the electrolytic copper load demand during the operating period;

[0060] (2) Output power constraints of thermal power units

[0061]

[0062] In the above formula, This indicates the minimum output of the thermal power unit; This indicates the maximum output of the thermal power unit;

[0063] (3) Climbing constraints of thermal power units

[0064]

[0065] In the above formula, This indicates the maximum upward climbing rate of the thermal power unit; This indicates the maximum downward climbing rate of the thermal power unit;

[0066] (4) Start-up and shutdown time constraints of thermal power units

[0067]

[0068] In the above formula, and These represent the continuous start-up time and continuous shutdown time of the thermal power unit, respectively. and These represent the minimum continuous start-up time and the minimum continuous shutdown time of a thermal power unit, respectively.

[0069] (5) Wind farm output power constraints

[0070]

[0071] In the above formula, This indicates the maximum output power of the wind farm;

[0072] (6) Output power constraints of photovoltaic power plants

[0073]

[0074] In the above formula, This indicates the maximum output power of the photovoltaic power station;

[0075] (7) Output power constraint of battery energy storage system

[0076]

[0077] In the above formula, This indicates the maximum discharge power of the battery energy storage system; This indicates the maximum charging power of the battery energy storage system;

[0078] (8) Battery energy storage system power balance constraints

[0079]

[0080] In the above formula, and These represent the minimum and maximum remaining charge of the battery energy storage system, respectively. and These represent the remaining battery energy storage system capacity during runtime i and runtime i+1, respectively; V represents the operating voltage of the energy storage system. This indicates the round-trip efficiency of the energy storage system during charging and discharging. This represents the time interval between a single charge and discharge cycle of the energy storage system, taken as 1 hour.

[0081] (9) Power fluctuation constraints

[0082] Electrolytic copper exhibits relatively small power load variations, resulting in a relatively stable load curve. A power fluctuation coefficient is then introduced. The power generation fluctuation constraints for wind, solar, thermal, and energy storage are shown below:

[0083]

[0084] In the above formula, and These represent the output power of the supply-side wind, solar, thermal, and energy storage system at time t and t-1, respectively.

[0085] Preferably, in step 4, based on the economic objective function, the environmental objective function, and the constraints, through... The constraint method is used to solve for the optimal scheduling scheme for electrolytic copper that achieves a win-win situation for both economic and environmental objectives, including:

[0086] Step 1: The economic objective (minimizing total system operating cost) is used as the primary objective function, while the environmental objective (minimizing pollutant emissions) is transformed into constraints. The Pareto solution is obtained by changing each objective, as shown below:

[0087]

[0088] In the above formula, x, k, and l represent the decision variables, the main objective, and other objectives in the optimization process, respectively;

[0089] Step 2: Compare the distances between each solution and the ideal solution to obtain the optimal solution. Calculate the ideal value based on the minimum normalized objective value in the Pareto solution set; the calculation formula is shown below:

[0090]

[0091] In the above formula, q represents the q-th solution in the solution set; and These represent the maximum and minimum values ​​in the solution set of the l-th objective function, respectively. This represents the q-th solution in the solution set of the l-th objective function; Represent the ideal solution; This represents the q-th solution in the solution set of the l-th objective function after normalization.

[0092] Preferably, the multi-objective optimization scheduling method for the integrated wind, solar, thermal power, energy storage, and electrolytic copper system can achieve low operating costs and low environmental emissions in the electrolytic copper industry. This method can also be extended to high-energy-consuming and high-emission industries such as electrolytic aluminum, zinc smelting, copper smelting, and nickel smelting.

[0093] The present invention has the following beneficial effects:

[0094] 1. This invention solves the trade-off between high-proportion and low-cost wind and solar energy consumption in the prior art, and realizes large-scale and high-proportion on-site consumption of wind and solar energy;

[0095] 2. This invention solves the problem of optimized scheduling due to input and output uncertainties in the prior art, and realizes the stable operation of the integrated system of proportional wind and solar power generation, thermal power generation, energy storage and electrolytic copper, so as to minimize economic operating costs and environmental impact. Attached Figure Description

[0096] Figure 1 The invention proposes an integrated system combining wind, solar, thermal power, energy storage, and electrolytic copper.

[0097] Figure 2 This is a flowchart of a multi-objective optimization scheduling method for an integrated wind, solar, thermal, energy storage, and electrolytic copper system proposed in this invention.

[0098] Figure 3 This is a graph showing the change in electrolytic copper load power in an embodiment of the present invention;

[0099] Figure 4 This is a diagram showing the photovoltaic and wind power generation capacity of an electrolytic copper plant in a certain region, as described in an embodiment of the present invention. Detailed Implementation

[0100] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0101] This invention selects a region's wind and solar resources and a large-scale electrolytic copper production line to study a multi-objective optimization scheduling method for an integrated wind-solar-thermal-energy storage-electrolytic copper system. The enterprise currently operates a 400,000-ton / year electrolytic copper production line, consuming 400 million kWh of electricity annually. The electrolytic copper load power variation is as follows: Figure 3 As shown, the maximum load utilization hours are 8760 hours, and the longest power outage time for the electrolytic copper load cannot exceed 2-3 hours. The selected power generation capacity is 300 MW for wind farms, 200 MW for photovoltaic power plants, 1350 MW for thermal power plants, and 450 MWh for battery energy storage power stations. The average power generation of wind turbines is calculated as 16.1% of the rated power, and the average power generation of photovoltaic power plants is calculated as 16.5% of the rated power of photovoltaic panels. The photovoltaic and wind power generation capacity in the area surrounding the electrolytic copper plant is as follows: Figure 4 As shown. The cost of curtailing wind power is 600 yuan / MWh, and the cost of curtailing solar power is 500 yuan / MWh. The emission coefficients of CO2, SO2, NOx, and dust per ton of standard coal are 2.9, 0.000416, 0.000476, and 0.000778, respectively, with corresponding penalty costs of 60 yuan / t, 630 yuan / t, 630 yuan / t, and 280 yuan / t.

[0102] Example 1

[0103] The study investigates multi-objective scheduling of traditional thermal power-electrolytic copper systems, where the electricity for a 400,000-ton / year electrolytic copper production line is entirely supplied by thermal power units.

[0104] Example 2

[0105] Based on Example 1, wind power and energy storage are considered to replace 50% of thermal power, and the multi-objective scheduling of the wind power-thermal power-energy storage-electrolytic copper system is studied.

[0106] Example 3

[0107] Based on Example 1, we consider adding photovoltaic power and energy storage, and study the multi-objective scheduling of the photovoltaic-thermal power-energy storage-electrolytic copper system by replacing 50% of thermal power with photovoltaic power and energy storage.

[0108] Example 4

[0109] Based on Example 1, wind power and solar power are considered to replace 50% of thermal power, and the multi-objective optimization scheduling of the wind power-solar power-thermal power-electrolytic copper system is studied.

[0110] Example 5

[0111] Based on Examples 2, 3, and 4, wind power, solar power, and energy storage are considered to be added. By replacing 50% of thermal power with wind power, solar power, and energy storage, the multi-objective optimization scheduling of the integrated wind-solar-thermal-energy-electrolytic copper system is studied.

[0112] The economic objective optimization results of Examples 1-5 are shown in Table 1:

[0113] Table 1. Operating and environmental costs of Examples 1-5

[0114]

[0115] Compared to Example 1, Examples 2-5 significantly reduce the cost of traditional thermal power-copper electrolysis systems by integrating renewable energy power to replace part of the thermal power and using it for copper electrolysis. Example 3 has the highest total cost, mainly because the limited photovoltaic capacity and output time result in the highest thermal power output, leading to higher operating and environmental costs. Example 2 uses wind power, which has a large installed capacity and output capacity, reducing thermal power output and directly reducing pollutant emissions and environmental costs. In Example 4, wind-solar hybridization provides a certain amount of load demand, while thermal power serves as the baseload power source and peak shaving. Compared to Example 4, Example 5, through the participation of battery energy storage equipment, significantly optimizes the system cost structure, resulting in the lowest total system cost. Compared to Examples 2, 3, and 4, the cost is reduced by 2.6%, 11.7%, and 0.3%, respectively.

[0116] The environmental target optimization results of Examples 1-5 are shown in Table 2:

[0117] Table 2 Environmental emissions from Examples 1-5

[0118]

[0119] Example 1 uses coal-fired power for copper electrolysis, resulting in the highest coal consumption and environmental emissions. Compared to Example 1, Examples 2-5 significantly reduce environmental emissions from the traditional coal-fired power-copper electrolysis system due to the integration of different forms of renewable energy. In particular, Example 5 uses wind-solar hybrid power generation plus energy storage for peak shaving, which greatly reduces the coal-fired power load and peak shaving, thus achieving the lowest environmental emissions.

[0120] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-objective optimization scheduling method for an integrated wind, solar, thermal, energy storage, and electrolytic copper system, characterized in that, Includes the following steps: Step 1: Collect a database of wind speed and solar irradiance of a certain region, collect battery energy storage operation data, collect thermal power unit operation data and electrolytic copper load data of a certain electrolytic copper production line; Step 2: Analyze the time-frequency fluctuation characteristics of wind-solar hybridization, the uncertainty of electrolytic copper demand load, and simulate the integrated wind-solar-thermal-energy storage-electrolytic copper system at multiple time scales. Step 3: Establish a multi-objective optimization scheduling objective function for the integrated wind, solar, thermal, energy storage, and electrolytic copper system based on multi-timescale fluctuation data, and set constraints for the objective function; the multi-objective optimization scheduling objective function includes an economic objective function and an environmental objective function; Step 4: Based on the multi-objective optimization scheduling objective function and the constraints, the optimal scheduling scheme for electrolytic copper that achieves a win-win situation in terms of economic and environmental benefits is determined by solving the ε-constraint method.

2. The multi-objective optimization scheduling method for an integrated wind, solar, thermal, energy storage, and electrolytic copper system according to claim 1, characterized in that: In step 1, the following data are collected: wind power generation operating cost, photovoltaic power generation operating cost, battery energy storage operation and maintenance cost, thermal power unit operating cost, wind and solar curtailment penalty cost, wind and solar rotating reserve penalty cost, load shedding penalty cost, and pollutant emission coefficient and fine cost. The thermal power unit operating cost includes coal cost, ramp-up cost, and start-up and shutdown cost. The following data are collected: load balance constraint, wind turbine power constraint, photovoltaic power constraint, battery energy storage power constraint, thermal power unit power constraint, thermal power unit ramp-up constraint, thermal power unit start-up and shutdown constraint, wind and solar curtailment constraint, and system power fluctuation constraint.

3. The multi-objective optimization scheduling method for an integrated wind, solar, thermal, energy storage, and electrolytic copper system according to claim 2, characterized in that: In step 3, The cost models for constructing the economic and environmental objective functions of the integrated wind, solar, thermal, energy storage, and electrolytic copper system include: wind power operation cost model, photovoltaic power operation cost model, battery energy storage operation and maintenance cost model, thermal power unit operation cost model, wind and solar curtailment penalty cost model, wind and solar spinning reserve penalty cost model, load shedding penalty cost model, and pollutant emission penalty cost model. The constraint model for constructing the multi-objective optimization scheduling objective function of the integrated wind, solar, thermal power, energy storage, and electrolytic copper system includes: load balance constraint, thermal power unit output power constraint, thermal power unit ramping constraint, thermal power unit start-up and shutdown time constraint, wind farm output power constraint, photovoltaic power station output power constraint, battery energy storage system output power constraint, battery energy storage system power balance constraint, and power fluctuation constraint.

4. The multi-objective optimization scheduling method for an integrated wind, solar, thermal, energy storage, and electrolytic copper system according to claim 3, characterized in that: In step 3, the cost model for constructing the economic and environmental objective functions of the integrated wind-solar-thermal-energy storage-electrolytic copper system includes: (1) Wind power generation operating cost model In the above formula, This indicates the cost per unit of wind power generation; This indicates the output power of the wind turbine during the operating period; (2) Photovoltaic power generation operating cost model In the above formula, This indicates the cost per unit of electricity generated by photovoltaic power generation; This indicates the output power of the photovoltaic unit during the operating period; (3) Battery energy storage operation and maintenance cost model In the above formula, This indicates the unit charge / discharge cost of a battery energy storage system. This indicates the battery's energy storage charging and discharging power during the operating period. "+" indicates battery charging, and "-" indicates battery discharging. This is the lifespan degradation factor of the battery. When the life loss factor is 1, the battery life is exhausted. This indicates the initial investment in battery energy storage; (4) Operating cost model of thermal power units In the above formula, This indicates the cost of coal used by the thermal power unit during the operating period. This represents the ramp-up cost of a thermal power unit during the operating period; This indicates the start-up and shutdown costs of thermal power units during the operating period; In the above formula, This represents the output power of the thermal power unit during the operating period; a, b, and c represent the traditional coal-fired cost characteristic parameters of the thermal power unit; p, q, m, and n represent the coal-fired characteristic parameters of the thermal power unit under low load conditions; α represents the judgment boundary of the thermal power unit under low load conditions. In the above formula, This represents the ramp-up cost characteristic parameter of thermal power units; This indicates the rate of change of the output power of a thermal power unit; In the above formula, This indicates the start-up and shutdown cost of a single thermal power unit; This indicates the number of thermal power units started and stopped during the operating period; (5) Wind and solar curtailment penalty cost model In the above formula, This indicates the amount of wind power curtailed during the operating period; This indicates the amount of solar power that was wasted during the operating period. (6) Wind and solar power rotating reserve penalty cost model The randomness and uncertainty of wind and solar resources lead to an increase in the system's spinning reserve capacity requirements when wind and solar power are connected to the grid. The formula for calculating the spinning reserve penalty cost for wind and solar power is as follows: In the above formula, This represents the characteristic parameter of the spin-off reserve penalty cost; This indicates the reliability of wind and solar power operating capacity during the operating period; (7) Load shedding penalty cost model To ensure the safety and stability of the wind, solar, thermal, and energy storage system on the power generation side, and to meet the grid's power requirements, and to avoid a significant deviation between the actual output power on the power generation side and the load demand on the electrolytic copper demand side, a load shedding penalty cost is introduced. The calculation formula is shown below: In the above formula, This represents the load shedding cost characteristic parameter; Indicates the load shedding power during the operating period; (8) Pollutant fine cost model In the above formula, This indicates the penalty cost for pollutants, CO2, SO2, NO. x The penalty costs for dust are 60 yuan / t, 630 yuan / t, 630 yuan / t and 280 yuan / t, respectively.

5. The multi-objective optimization scheduling method for an integrated wind, solar, thermal, energy storage, and electrolytic copper system according to claim 4, characterized in that: The formula for calculating the economic objective function is as follows: In the above formula, The total operating cost of the system is represented by ; i represents the operating time period, i = 1, 2, …, N; N represents the total operating time period of the system. This indicates the operating cost of wind power generation during the operating period; This indicates the operating cost of photovoltaic power generation during the operating period; This indicates the operating and maintenance costs of battery energy storage during the operating period; This indicates the operating cost of the thermal power unit during the operating period; This indicates the penalty cost for wind and solar power curtailment during the operating period; This indicates the penalty cost for wind and solar power rotational reserve during the operating period; This represents the cost of load shedding penalty during the operating period; This indicates the cost of fines for pollutants during the operating period.

6. The multi-objective optimization scheduling method for an integrated wind, solar, thermal, energy storage, and electrolytic copper system according to claim 5, characterized in that: The formula for calculating the environmental objective function is as follows: In the above formula, Indicates the amount of polluting gases emitted; This represents the pollutant emission coefficients for thermal power plants, specifically CO2, SO2, and NO. x The emission coefficients for particulate matter and dust were 326.47 kg / MWh, 3.54 kg / MWh, 1.03 kg / MWh, and 1.68 kg / MWh, respectively. This indicates the output power of the thermal power unit during the operating period.

7. A multi-objective optimization scheduling method for an integrated wind, solar, thermal, energy storage, and electrolytic copper system according to claim 6, characterized in that: In step 3, the constraint model for constructing the multi-objective optimization scheduling objective function constraint conditions of the integrated wind-solar-thermal-energy storage-electrolytic copper system includes: (1) System load balance constraints Ignoring energy losses, the total output power of wind power, solar power, thermal power, and battery energy storage at each moment is the same as the power load demand of electrolytic copper, as shown in the following formula: In the above formula, This indicates the power generation capacity of the battery energy storage system during the operating period; This indicates the energy storage power of the battery energy storage system during the operating period; This indicates the operating conditions of the battery energy storage system during the operating period. This indicates the power generation status of the energy storage system. This indicates the operating conditions of the energy storage system. This indicates the electrolytic copper load demand during the operating period; (2) Output power constraints of thermal power units In the above formula, This indicates the minimum output of the thermal power unit; This indicates the maximum output of the thermal power unit; (3) Climbing constraints of thermal power units In the above formula, This indicates the maximum upward climbing rate of the thermal power unit; This indicates the maximum downward climbing rate of the thermal power unit; (4) Start-up and shutdown time constraints of thermal power units In the above formula, and These represent the continuous start-up time and continuous shutdown time of the thermal power unit, respectively. and These represent the minimum continuous start-up time and the minimum continuous shutdown time of a thermal power unit, respectively. (5) Wind farm output power constraints In the above formula, This indicates the maximum output power of the wind farm; (6) Output power constraints of photovoltaic power plants In the above formula, This indicates the maximum output power of the photovoltaic power station; (7) Output power constraint of battery energy storage system In the above formula, This indicates the maximum discharge power of the battery energy storage system; This indicates the maximum charging power of the battery energy storage system; (8) Battery energy storage system power balance constraints In the above formula, and These represent the minimum and maximum remaining charge of the battery energy storage system, respectively. and These represent the remaining battery energy storage system capacity during runtime i and runtime i+1, respectively; V represents the operating voltage of the energy storage system. This indicates the round-trip efficiency of the energy storage system during charging and discharging. This represents the time interval between a single charge and discharge cycle of the energy storage system, taken as 1 hour. (9) Power fluctuation constraints Electrolytic copper has relatively small load power variations and a relatively stable load curve, thus introducing a power fluctuation coefficient. The power generation fluctuation constraints for wind, solar, thermal, and energy storage are shown below: In the above formula, and These represent the output power of the supply-side wind, solar, thermal, and energy storage system at time t and t-1, respectively.

8. A multi-objective optimization scheduling method for an integrated wind, solar, thermal, energy storage, and electrolytic copper system according to claim 7, characterized in that: In step 4, based on the economic objective function, the environmental objective function, and the constraints, through... The constraint method is used to solve for the optimal scheduling scheme for electrolytic copper that achieves a win-win situation for both economic and environmental objectives, including: Step 1: The economic objective is used as the primary objective function, while the environmental objective is transformed into constraints. The Pareto solution is obtained by changing each objective, as shown below: In the above formula, x, k, and l represent the decision variables, the main objective, and other objectives in the optimization process, respectively; Step 2: Compare the distances between each solution and the ideal solution to obtain the optimal solution. Calculate the ideal value based on the minimum normalized objective value in the Pareto solution set; the calculation formula is shown below: In the above formula, q represents the q-th solution in the solution set; and These represent the maximum and minimum values ​​in the solution set of the l-th objective function, respectively. This represents the q-th solution in the solution set of the l-th objective function; Represent the ideal solution; This represents the q-th solution in the solution set of the l-th objective function after normalization.

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