Multi-virtual power plant distributed optimization method considering 5G base station and electric energy-carbon emission right-green certificate sharing

Abstract

The invention discloses a multi-virtual power plant distributed optimization method considering a 5G base station and electric energy-carbon emission right-green certificate sharing. The method comprises the following steps: 1) establishing an objective function of a multi-virtual power plant distributed optimization model considering the 5G base station and the electric energy-carbon emission right-green certificate sharing; 2) establishing constraint conditions of a multi-virtual power plant distributed optimization model considering a 5G base station and electric energy-carbon emission right-green certificate sharing, wherein the constraint conditions comprise a virtual power plant electric energy-carbon emission right-green certificate balance constraint, a carbon-containing equipment operation constraint and a carbon emission right-green certificate coupling constraint; and 3) solving the multi-virtual power plant distributed optimization model by adopting an adaptive step length alternating direction multiplier algorithm to obtain virtual power plant 5G base station scheduling and an electric energy, carbon emission right and green certificate sharing strategy.

Description

Technical Field

[0001] The present invention belongs to the field of distributed trading of virtual power plants, and in particular relates to a distributed optimization method for multiple virtual power plants taking into account 5G base stations and electricity-carbon emission rights-green certificate sharing. Background Art

[0002] At present, a large number of distributed energy sources are connected to the grid, and they are scattered and small in size. Virtual power plants can achieve energy management by aggregating distributed resources and resources such as gas turbines, user loads, and energy storage. In addition, the uncertainty of photovoltaic output causes certain revenue losses to the energy management of virtual power plants. How to quantify and measure the relationship between risk and economy will be particularly important. The intervention of virtual power plants can effectively improve energy utilization while improving the economic benefits of energy, and can also meet the electricity needs of all users while ensuring the stable operation of the power grid.

[0003] The construction of 5G base stations has achieved remarkable results, achieving full coverage of all urban areas above the prefecture level and county towns, and their service scope and depth will continue to expand in the future. Virtual power plants not only help base stations reduce energy consumption without sacrificing network performance, but also provide the power grid with the ability to shift peaks and fill valleys, thus achieving mutual benefit and complementarity between telecommunications and power systems, bringing a win-win result for both parties. This innovative model not only improves energy efficiency, but also plays a positive role in balancing supply and demand, improving power grid reliability and reducing environmental pressure. With the continuous advancement and innovation of technology, the energy management of 5G base stations will continue to be optimized to better serve the construction of a smart society.

[0004] There is a close and complex relationship between carbon trading and green certificate trading. It is of long-term significance to study how virtual power plants participate in and combine the operation model and market clearing mechanism of the electricity and carbon certificate markets. This will not only help clarify the comprehensive trading mechanism of distributed resources in the electricity and carbon markets, but also play a vital role in improving the efficiency of clean energy consumption in the power grid, promoting the development of green and low-carbon electricity, and optimizing and upgrading the energy structure. In this process, the technology and management innovation of virtual power plants can provide strong support for the grid connection of renewable energy and help establish a more fair, effective and sustainable energy market. Summary of the invention

[0005] Purpose of the invention: In order to solve the above-mentioned technical problems, the present invention proposes a distributed optimization method for multiple virtual power plants that takes into account 5G base stations and electricity-carbon emission rights-green certificate sharing. Through the shared scheduling of virtual power plant electricity, carbon emission rights, and green certificates, the rational utilization of various resources in the virtual power plant and energy conservation and emission reduction are achieved, the utilization rate of green electricity is improved, and the carbon emissions of virtual power plants are reduced.

[0006] Technical solution, in order to achieve the above-mentioned invention object, the present invention proposes a multi-virtual power plant distributed optimization method considering 5G base stations and electricity-carbon emission rights-green certificate sharing, including the following steps:

[0007] (1) Establish the objective function of the distributed optimization model of multiple virtual power plants considering 5G base stations and electricity-carbon emission rights-green certificates sharing;

[0008] (2) Establish the constraints of a distributed optimization model of multiple virtual power plants that considers 5G base stations and electricity-carbon emission rights-green certificates sharing;

[0009] (3) An adaptive step-size alternating direction multiplier algorithm is used to solve the distributed optimization model of multiple virtual power plants and obtain the virtual power plant electricity, carbon emission rights, and green certificate sharing strategy.

[0010] Furthermore, in step (1), the objective function of the multi-virtual power plant optimization model considering 5G base stations and electricity-carbon emission rights-green certificate sharing is established:

[0011]

[0012] In the formula, is the total social benefit of n virtual power plants; N is the total number of virtual power plants; n is the nth virtual power plant; N s is the total number of photovoltaic output scenarios; s is the photovoltaic scenario; N t is a purchase time cycle; t is the purchase time period; is the probability of occurrence of photovoltaic scenario s; is the electricity price in period t; is the electricity sales of the nth virtual power plant in period t; is the selling price of carbon quota in period t; is the amount of carbon quota sold by the nth virtual power plant in the carbon market during period t, The selling price of the green certificate transaction in period t; is the number of green certificates sold by the nth virtual power plant in the green certificate market during period t; They are the frequency regulation capacity electricity price and frequency regulation mileage electricity price of the 5G base station in period t respectively; They are the frequency regulation capacity and frequency regulation mileage won by the nth 5G base station virtual power plant in the frequency regulation market in the scenario s during period t; is the electricity purchase price during period t; is the amount of electricity purchased by the nth virtual power plant in period t; is the purchase cost of carbon quota in period t; It is the amount of carbon quota purchased by the nth virtual power plant in the carbon market during period t. is the purchase cost of green certificate transaction in period t; is the number of green certificates purchased by the nth virtual power plant in the green certificate market during period t; is the correlation coefficient with the gas turbine operating cost; is the power of the gas turbine in the nth 5G base station virtual power plant in the scenario s during period t, is the maintenance cost of the photovoltaic power generation unit in the nth virtual power plant; is the photovoltaic output of the nth 5G base station virtual power plant in the scenario s during period t; β n is the priority coefficient of each virtual power plant, and β n >0,λ t is the weight factor of dissatisfaction cost in period t; is the energy consumed by the user in the nth virtual power plant in period t; α n,t It is a combination of weight coefficient and preference parameter, reflecting the user’s preference weight for energy; is the battery degradation cost; They represent the charging and discharging status of the nth 5G base station virtual power plant in the scenario s during period t; is the curve fitting coefficient of battery cycle life and cycle depth; is the cycle depth; is the energy storage charge state of the nth 5G base station virtual power plant in the scenario s during period t; is the transaction cost coefficient of electricity, carbon quota and green certificate between the mth virtual power plant and the nth virtual power plant; P m,n,t 、E m,n,t , G m,n,t is the point-to-point electricity, carbon quota and green certificate trading volume between the mth virtual power plant and the nth virtual power plant in period t; c CET is the carbon market transaction price; c sto The price of storing CO2 for transmission; c GCT is the transaction price of green certificates; is the carbon cost of the gas turbine with carbon capture in the nth 5G base station virtual power plant in scenario s during period t; is the carbon emission benchmark quota of the gas turbine with carbon capture system in the nth 5G base station virtual power plant in the scenario s during period t; is the carbon emissions offset by the green certificate under the quota system in the nth 5G base station virtual power plant in the scenario s during period t; The amount of carbon dioxide transmitted and stored by the carbon storage device in the nth 5G base station virtual power plant in the scenario s during period t; It is the green certificate required by the new energy output unit in the nth 5G base station virtual power plant in the scenario s during period t to meet the quota system; It is the number of green certificates obtained by the new energy power generation unit in the nth 5G base station virtual power plant in the scenario s during period t.

[0013] Furthermore, in step (2), the constraints of the multi-virtual power plant optimization model considering 5G base stations and electricity-carbon emission rights-green certificate sharing are established, including the virtual power plant electricity-carbon emission rights-green certificate balance constraint,

[0014] (201) Establish virtual power plant electricity-carbon emission rights-green certificate balance constraints

[0015] a) Virtual power plant energy balance constraints

[0016]

[0017] In the formula, is the power consumed by the 5G base station in the nth 5G base station virtual power plant in the scenario s during period t; is the amount of abandoned light of the nth 5G base station virtual power plant in scenario s during period t; P is the power required for the carbon capture unit in the nth 5G base station virtual power plant to process CO2 per unit time in the scenario of time period s; m,n,t is the power between the mth virtual power plant and the nth virtual power plant in period t;

[0018] b) Carbon emission balance constraints of virtual power plants

[0019]

[0020] In the formula, E m,n,t is the carbon quota point-to-point transaction volume between the mth virtual power plant and the nth virtual power plant in period t; is the amount of carbon dioxide produced by the gas turbine equipped with a carbon capture unit in the nth 5G base station virtual power plant in the scenario s at time period t; is the total amount of carbon dioxide generated by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s during period t; is the total amount of carbon dioxide captured by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s during period t;

[0021] c) Green certificate balance constraints for virtual power plants

[0022]

[0023] In the formula, k is the new energy quota power of the nth 5G base station virtual power plant in scenario s during period t; PV is the new energy quota coefficient, k PV,cs The weight coefficient of the quota completion in the previous assessment cycle; θ PV The completion rate of the new energy quota in the previous assessment cycle; is the average quota completion rate in the previous assessment cycle, k is the number of green certificates obtained by the new energy power generation unit in the nth 5G base station virtual power plant in the scenario s during period t; PV,f is the impact weight of new energy prediction; PV The accuracy of the forecast of renewable energy output in the previous assessment cycle; It is the standard value of the prediction accuracy of renewable energy output in the previous assessment cycle; Δt is a calculation period.

[0024] Furthermore, in step (2), the constraints of the multi-virtual power plant optimization model considering 5G base stations and electricity-carbon emission rights-green certificate sharing are established, including the operation constraints of carbon-containing equipment;

[0025] (202) Establishing operating constraints for virtual power plant equipment, including 5G base station constraints, carbon capture unit constraints, and carbon storage equipment constraints;

[0026] a) 5G base station constraints

[0027]

[0028] In the formula, is the power consumption of the 5G base station of the nth 5G base station virtual power plant in the scenario s during period t; is the power consumption of the base station active antenna unit in the nth 5G base station virtual power plant in the scenario s during period t; is the power consumption of the baseband processing unit of the base station in the nth 5G base station virtual power plant in the scenario s during period t; is the power consumption of the transmission convergence subsystem of the base station in the nth 5G base station virtual power plant in the scenario s during period t; is the packet transmission network power consumption of the base station in the nth 5G base station virtual power plant in the scenario s during period t, and the power consumption of other auxiliary equipment is negligible. The remaining capacity of the energy storage of the nth 5G base station virtual power plant in the scenario s at time period t; is the charge and discharge efficiency; It is the charging and discharging state; is the average frequency regulation capacity of the nth 5G base station virtual power plant in scenario s during period t; is the frequency modulation-capacity ratio;

[0029] b) Carbon capture unit constraints

[0030]

[0031]

[0032] In the formula, k GT,base is the baseline emission factor; is the energy consumption of the carbon capture unit of the nth 5G base station virtual power plant in the scenario s during period t; The power consumed to capture a unit of carbon dioxide; k is the amount of carbon dioxide captured by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s during period t; capture for carbon capture efficiency; k is the amount of carbon dioxide processed by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s at time t; adjust is the regulation coefficient of the flue gas bypass system of the carbon capture unit; is the total amount of carbon dioxide generated by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s during period t; is the carbon emission intensity coefficient of the carbon capture unit; is the power generation of the carbon capture unit of the nth 5G base station virtual power plant in the scenario s at time period t;

[0033] c) Carbon storage equipment constraints

[0034]

[0035] In the formula, and is equal to the amount of carbon dioxide captured by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s at time period t; are the amounts of carbon dioxide injected and taken out by the carbon storage equipment in the nth 5G base station virtual power plant in the scenario s during period t; The amount of carbon dioxide stored in the carbon storage device of the nth 5G base station virtual power plant in the scenario s during time period t; The amount of carbon dioxide stored in the carbon storage device of the nth 5G base station virtual power plant in the scenario s during period t.

[0036] Furthermore, in step (2), the constraints of the multi-virtual power plant optimization model considering 5G base stations and electricity-carbon emission rights-green certificate sharing are established, including carbon emission rights-green certificate coupling constraints;

[0037] (203) Establish virtual power plant carbon emission rights-green certificate coupling constraints

[0038]

[0039] In the formula, is the carbon emission reduction reflected by the green certificate of the nth 5G base station virtual power plant in the scenario s during the t period; is the amount of carbon dioxide generated by the traditional fossil energy of the nth 5G base station virtual power plant in the scenario s at time t; The carbon dioxide coefficient produced per unit of power generated by photovoltaics; It is the green certificate required for the nth 5G base station virtual power plant new energy output unit to meet the quota system in the s scenario during the t period.

[0040] Furthermore, in step (3), the method of using the adaptive step-size alternating direction multiplier algorithm to solve the distributed optimization model of multiple virtual power plants is as follows:

[0041] (301) The objective function of the multi-virtual power plant optimization model considering 5G base stations and electricity-carbon emission rights-green certificate sharing is transformed into the objective function of a single virtual power plant, which is expressed as follows:

[0042]

[0043] In the formula, is the objective function value of a single virtual power plant;

[0044] (302) Establishing the augmented Lagrangian function of a single virtual power plant

[0045]

[0046] Where, L is the value of the augmented Lagrangian function; are the dual variables related to the nth 5G base station virtual power plant, power consumption and carbon quota in the scenario s at time period t; ψ, are penalty factors related to electricity and carbon quota respectively; P n,m,t 、E n,m,t is the point-to-point electricity and carbon quota trading volume between the nth virtual power plant and the mth virtual power plant in period t;

[0047] (303) Solve column (22) to obtain the distributed shared variables of electricity and carbon emission rights

[0048]

[0049] (304) Update the dual variable μ of electric energy according to equation (23): m,n,t and the dual variable of carbon emission rights m,n,t :

[0050]

[0051] (305) Update step size:

[0052]

[0053] In the formula, are the penalty factors at the kth and k+1th iterations respectively, and the values ​​of the penalty factors are dynamically adjusted according to the relationship between the residuals; are the original residual value and dual residual value at the kth iteration respectively;

[0054] (306) Calculate the original residual and dual residual of electricity and carbon quota:

[0055]

[0056] In the formula, r electric,k+1 、r carbon,k+1 are the original residual values ​​of electricity and carbon quota in distributed transactions after the k+1th iteration; s electric,k+1 、s carbon,k+1 They are the dual residual values ​​of electricity and carbon quota in distributed transactions after the k+1th iteration; is the point-to-point electricity and carbon quota trading volume between the mth virtual power plant and the nth virtual power plant in period t after the kth iteration; is the point-to-point electricity and carbon quota trading volume between the nth virtual power plant and the mth virtual power plant in period t after the kth iteration;

[0057] (307) Determine whether the primal residual and the dual residual meet the convergence conditions. If so, stop the iteration; if not, update k=k+1 and perform the next iteration. When the number of iterations is greater than the set upper limit or meets the convergence conditions of the primal residual and the dual residual, stop the iteration and output the virtual power plant electricity, carbon emission rights, and green certificate sharing strategies of each virtual power plant.

[0058] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0059] The present invention incorporates 5G base stations into virtual power plant scheduling, and improves the flexibility of virtual power plant scheduling by tapping the regulation potential of 5G base stations. At the same time, through the shared scheduling of virtual power plant electricity, carbon emission rights, and green certificates, the rational utilization of various resources in the virtual power plant and energy conservation and emission reduction are achieved, the utilization rate of green electricity is improved, and the carbon emissions of the virtual power plant are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0061] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0062] like Figure 1 As shown, the present invention designs a distributed optimization method for a virtual power plant considering the sharing of electricity, carbon emission rights and green certificates, and the specific steps are as follows:

[0063] (1) Establish the objective function of the distributed optimization model of multiple virtual power plants considering 5G base stations and electricity-carbon emission rights-green certificates sharing;

[0064] (2) Establish the constraints of a distributed optimization model of multiple virtual power plants that considers 5G base stations and electricity-carbon emission rights-green certificates sharing;

[0065] (3) An adaptive step-size alternating direction multiplier algorithm is used to solve the distributed optimization model of multiple virtual power plants and obtain the virtual power plant electricity, carbon emission rights, and green certificate sharing strategy.

[0066] Set up a multi-virtual power plant electricity carbon trading example with three virtual power plants. The resources aggregated within the virtual power plant include photovoltaics, energy storage, central air conditioning, user loads, and micro gas turbines. Among them, the generator set in virtual power plant 1 is a carbon capture unit. In the adaptive step size alternating direction multiplier algorithm, the initial dual variables of electricity and carbon quota are set to 0, and the convergence condition is less than 10 -2 , programmatically solve the above model.

[0067] In order to compare the operation of the virtual power plant alliance under different situations, six different scenarios were set up for comparative analysis.

[0068] Table 1 Information of each scene

[0069] Scene Number Participating in the carbon market Consider carbon capture systems Interaction between carbon and green certificate markets Transaction Type 1 √ √ √ P2P Trading 2 √ √ × P2P Trading 3 √ × × P2P Trading 4 × × × P2P Trading 5 √ √ √ Centralized Exchange 6 √ √ √ Single VPP transaction

[0070] Among them, scenarios 1 to 4 are point-to-point transactions between multiple virtual power plants, scenario 5 represents centralized transactions between multiple virtual power plants, and scenario 6 is a single virtual power plant transaction. The total amount of aggregated units in a single virtual power plant is the same as that of multiple virtual power plants. The information of each scenario is shown in Table 1.

[0071] After the model iteration is completed, the costs and benefits of each scenario are shown in Table 2. It can be seen from the table that under scenario 1, the total benefit of system operation is the largest. Compared with scenario 2, which reflects the current market operation, the carbon trading cost of scenario 1 has decreased. This is because the low-carbon emission reduction effect of clean energy embodied by green certificates has reduced the carbon trading cost of virtual power plants, reflecting the economic efficiency of the interaction strategy between the carbon market and the green certificate market. It can be seen from scenarios 1 and 2 that the proportion of green electricity use in scenarios 1 and 2 is relatively high, among which, under scenario 1, the proportion of green electricity is the highest. At the same time, after using the carbon capture system, the carbon capture unit can use part of the green electricity, further reducing the abandonment rate and reducing part of the gas turbine use cost.

[0072] Table 2 Costs and benefits in each scenario

[0073]

[0074]

[0075] In addition, it can be seen from the table that scenario 5 is only different from scenario 1 in terms of transaction method, but the profit is less than that under P2P transaction, which reflects that P2P transaction method is more economical. By comparing scenario 6 with the first five scenarios, it can be concluded that the single virtual power plant participates in the transaction with the least profit. By calculating the user satisfaction cost, scenario 6 is more than scenario 1 and scenario 5, indicating that when optimizing with a single virtual power plant, the load has high requirements for comfort but the matching output cannot be achieved, and there will be a high dissatisfaction cost. However, in the transaction of multiple virtual power plants, the power source and the load are divided into smaller aggregates, making the distribution of load and power output more reasonable, thereby reducing user costs. At the same time, it can be seen from Table 2 that the carbon emissions and gas turbine costs in scenario 6 are the largest, indicating that in order to balance power, the gas turbine needs to generate more power, further increasing the carbon cost.

[0076] The embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A multi-virtual power plant distributed optimization method considering 5G base stations and electricity-carbon emission rights-green certificate sharing, characterized in that: The following steps are involved: (1) Establish the objective function of the distributed optimization model of multiple virtual power plants considering 5G base stations and electricity-carbon emission rights-green certificates sharing; (2) Establish the constraints of a distributed optimization model of multiple virtual power plants that considers 5G base stations and electricity-carbon emission rights-green certificates sharing; (3) An adaptive step-size alternating direction multiplier algorithm is used to solve the distributed optimization model of multiple virtual power plants and obtain the virtual power plant electricity, carbon emission rights, and green certificate sharing strategy.

2. According to claim 1, a multi-virtual power plant distributed optimization method considering 5G base stations and electricity-carbon emission rights-green certificate sharing is characterized in that: In step (1), the objective function of the multi-virtual power plant optimization model considering 5G base stations and electricity-carbon emission rights-green certificate sharing is established: In the formula, is the total social benefit of n virtual power plants; N is the total number of virtual power plants; n is the nth virtual power plant; N s is the total number of photovoltaic output scenarios; s is the photovoltaic scenario; N t is a purchase time cycle; t is the purchase time period; is the probability of occurrence of photovoltaic scenario s; is the electricity price in period t; is the electricity sales of the nth virtual power plant in period t; is the selling price of carbon quota in period t; is the amount of carbon quota sold by the nth virtual power plant in the carbon market during period t, The selling price of the green certificate transaction in period t; is the number of green certificates sold by the nth virtual power plant in the green certificate market during period t; They are the frequency regulation capacity electricity price and frequency regulation mileage electricity price of the 5G base station in period t respectively; They are the frequency regulation capacity and frequency regulation mileage won by the nth 5G base station virtual power plant in the frequency regulation market in the scenario s during period t; is the electricity purchase price during period t; is the amount of electricity purchased by the nth virtual power plant in period t; is the purchase cost of carbon quota in period t; It is the amount of carbon quota purchased by the nth virtual power plant in the carbon market during period t. is the purchase cost of green certificate transaction in period t; is the number of green certificates purchased by the nth virtual power plant in the green certificate market during period t; is the correlation coefficient with the gas turbine operating cost; is the power of the gas turbine in the nth 5G base station virtual power plant in the scenario s during period t, is the maintenance cost of the photovoltaic power generation unit in the nth virtual power plant; is the photovoltaic output of the nth 5G base station virtual power plant in the scenario s during period t; β n is the priority coefficient of each virtual power plant, and β n >0,λ t is the weight factor of dissatisfaction cost in period t; is the energy consumed by the user in the nth virtual power plant in period t; α n,t It is a combination of weight coefficient and preference parameter, reflecting the user’s preference weight for energy; is the battery degradation cost; They represent the charging and discharging status of the nth 5G base station virtual power plant in the scenario s during period t; and is the curve fitting coefficient of battery cycle life and cycle depth; is the cycle depth; is the energy storage charge state of the nth 5G base station virtual power plant in the scenario s during period t; is the transaction cost coefficient of electricity, carbon quota and green certificate between the mth virtual power plant and the nth virtual power plant; P m,n,t 、E m,n,t , G m,n,t is the point-to-point electricity, carbon quota and green certificate trading volume between the mth virtual power plant and the nth virtual power plant in period t; c CET is the carbon market transaction price; c sto The price of storing CO2 for transmission; c GCT is the transaction price of green certificates; is the carbon cost of the gas turbine with carbon capture in the nth 5G base station virtual power plant in scenario s during period t; is the carbon emission benchmark quota of the gas turbine with carbon capture system in the nth 5G base station virtual power plant in the scenario s during period t; is the carbon emissions offset by the green certificate under the quota system in the nth 5G base station virtual power plant in the scenario s during period t; The amount of carbon dioxide transmitted and stored by the carbon storage device in the nth 5G base station virtual power plant in the scenario s during period t; It is the green certificate required by the new energy output unit in the nth 5G base station virtual power plant in the scenario s during period t to meet the quota system; It is the number of green certificates obtained by the new energy power generation unit in the nth 5G base station virtual power plant in the scenario s during period t.

3. According to claim 2, a multi-virtual power plant distributed optimization method considering 5G base stations and electricity-carbon emission rights-green certificate sharing is characterized in that: In step (2), the constraints of the multi-virtual power plant optimization model considering 5G base stations and electricity-carbon emission rights-green certificate sharing are established, including the virtual power plant electricity-carbon emission rights-green certificate balance constraint, (201) establish the virtual power plant electricity-carbon emission rights-green certificate balance constraint a) Virtual power plant energy balance constraints In the formula, is the power consumed by the 5G base station in the nth 5G base station virtual power plant in the scenario s during period t; is the amount of abandoned light of the nth 5G base station virtual power plant in scenario s during period t; P is the power required for the carbon capture unit in the nth 5G base station virtual power plant to process CO2 per unit time in the scenario of time period s; m,n,t is the power between the mth virtual power plant and the nth virtual power plant in period t; b) Carbon emission balance constraints of virtual power plants In the formula, E m,n,t is the carbon quota point-to-point transaction volume between the mth virtual power plant and the nth virtual power plant in period t; is the amount of carbon dioxide produced by the gas turbine equipped with a carbon capture unit in the nth 5G base station virtual power plant in the scenario s at time period t; is the total amount of carbon dioxide generated by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s during period t; is the total amount of carbon dioxide captured by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s during period t; c) Green certificate balance constraints for virtual power plants In the formula, k is the new energy quota power of the nth 5G base station virtual power plant in scenario s during period t; PV is the new energy quota coefficient, k PV,cs The weight coefficient of the quota completion in the previous assessment cycle; θ PV The completion rate of the new energy quota in the previous assessment cycle; is the average quota completion rate in the previous assessment cycle, k is the number of green certificates obtained by the new energy power generation unit in the nth 5G base station virtual power plant in the scenario s during period t; PV,f is the impact weight of new energy prediction; PV The accuracy of the forecast of renewable energy output in the previous assessment cycle; It is the standard value of the prediction accuracy of renewable energy output in the previous assessment cycle; Δt is a calculation period.

4. According to claim 3, a multi-virtual power plant distributed optimization method considering 5G base stations and electricity-carbon emission rights-green certificate sharing is characterized in that: In step (2), the constraints of the multi-virtual power plant optimization model considering 5G base stations and electricity-carbon emission rights-green certificates sharing are established, including the operation constraints of carbon-containing equipment; (202) Establishing operating constraints for virtual power plant equipment, including 5G base station constraints, carbon capture unit constraints, and carbon storage equipment constraints; a) 5G base station constraints In the formula, is the power consumption of the 5G base station of the nth 5G base station virtual power plant in the scenario s during period t; is the power consumption of the base station active antenna unit in the nth 5G base station virtual power plant in the scenario s during period t; is the power consumption of the baseband processing unit of the base station in the nth 5G base station virtual power plant in the scenario s during period t; is the power consumption of the transmission convergence subsystem of the base station in the nth 5G base station virtual power plant in the scenario s during period t; is the packet transmission network power consumption of the base station in the nth 5G base station virtual power plant in the scenario s during period t, and the power consumption of other auxiliary equipment is negligible. The remaining capacity of the energy storage of the nth 5G base station virtual power plant in the scenario s at time period t; is the charge and discharge efficiency; It is the charging and discharging state; is the average frequency regulation capacity of the nth 5G base station virtual power plant in scenario s during period t; is the frequency modulation-capacity ratio; b) Carbon capture unit constraints In the formula, k GT,base is the baseline emission factor; is the energy consumption of the carbon capture unit of the nth 5G base station virtual power plant in the scenario s during period t; The power consumed to capture a unit of carbon dioxide; k is the amount of carbon dioxide captured by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s during period t; capture for carbon capture efficiency; k is the amount of carbon dioxide processed by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s at time t; adjust is the regulation coefficient of the flue gas bypass system of the carbon capture unit; is the total amount of carbon dioxide generated by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s during period t; is the carbon emission intensity coefficient of the carbon capture unit; is the power generation of the carbon capture unit of the nth 5G base station virtual power plant in the scenario s at time period t; c) Carbon storage equipment constraints In the formula, and is equal to the amount of carbon dioxide captured by the carbon capture unit of the nth 5G base station virtual power plant in the scenario s at time period t; are the amounts of carbon dioxide injected and taken out by the carbon storage equipment in the nth 5G base station virtual power plant in the scenario s during period t; The amount of carbon dioxide stored in the carbon storage device of the nth 5G base station virtual power plant in the scenario s during time period t; The amount of carbon dioxide stored in the carbon storage device of the nth 5G base station virtual power plant in the scenario s during period t.

5. According to claim 4, a multi-virtual power plant distributed optimization method considering 5G base stations and electricity-carbon emission rights-green certificate sharing is characterized in that: In step (2), the constraints of the multi-virtual power plant optimization model considering 5G base stations and electricity-carbon emission rights-green certificate sharing are established, including carbon emission rights-green certificate coupling constraints; (203) Establish virtual power plant carbon emission rights-green certificate coupling constraints In the formula, is the carbon emission reduction reflected by the green certificate of the nth 5G base station virtual power plant in the scenario s during the t period; is the amount of carbon dioxide generated by the traditional fossil energy of the nth 5G base station virtual power plant in the scenario s at time t; The carbon dioxide coefficient produced per unit of power generated by photovoltaics; It is the green certificate required for the nth 5G base station virtual power plant new energy output unit to meet the quota system in the s scenario during the t period.

6. According to claim 5, a multi-virtual power plant distributed optimization method considering 5G base stations and electricity-carbon emission rights-green certificate sharing is characterized in that: In step (3), the method of using the adaptive step-size alternating direction multiplier algorithm to solve the multi-virtual power plant distributed optimization model is as follows: (301) The objective function of the multi-virtual power plant optimization model considering 5G base stations and electricity-carbon emission rights-green certificate sharing is transformed into the objective function of a single virtual power plant, which is expressed as follows: In the formula, is the objective function value of a single virtual power plant; (302) Establishing the augmented Lagrangian function of a single virtual power plant Where, L is the value of the augmented Lagrangian function; are the dual variables related to the nth 5G base station virtual power plant, power consumption and carbon quota in the scenario s at time period t; ψ, are penalty factors related to electricity and carbon quota respectively; P n,m,t 、E n,m,t is the point-to-point electricity and carbon quota trading volume between the nth virtual power plant and the mth virtual power plant in period t; (303) Solve column (22) to obtain the distributed shared variables of electricity and carbon emission rights (304) Update the dual variable μ of electric energy according to equation (23): m,n,t and the dual variable of carbon emission rights m,n,t : (305) Update step size: In the formula, are the penalty factors at the kth and k+1th iterations respectively, and the values ​​of the penalty factors are dynamically adjusted according to the relationship between the residuals; are the original residual value and dual residual value at the kth iteration respectively; (306) Calculate the original residual and dual residual of electricity and carbon quota: In the formula, r electric,k+1 、r carbon,k+1 are the original residual values ​​of electricity and carbon quota in distributed transactions after the k+1th iteration; s electric,k+1 、s carbon,k+1 They are the dual residual values ​​of electricity and carbon quota in distributed transactions after the k+1th iteration; is the point-to-point electricity and carbon quota trading volume between the mth virtual power plant and the nth virtual power plant in period t after the kth iteration; is the point-to-point electricity and carbon quota trading volume between the nth virtual power plant and the mth virtual power plant in period t after the kth iteration; (307) Determine whether the primal residual and the dual residual meet the convergence conditions. If so, stop the iteration; if not, update k=k+1 and perform the next iteration. When the number of iterations is greater than the set upper limit or meets the convergence conditions of the primal residual and the dual residual, stop the iteration and output the virtual power plant electricity, carbon emission rights, and green certificate sharing strategies of each virtual power plant.

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