A method for economic dispatch of virtual power plants with wind, solar and energy storage considering tiered carbon prices

Abstract

The present invention discloses an economic dispatch method for a virtual power plant containing wind, photovoltaic and storage considering a stepped carbon price, comprising the following steps: collecting historical data on wind and photovoltaic at the geographical location of the virtual power plant, and modeling the output of wind and photovoltaic units in the system, using Weibull distribution and Beta distribution to express their uncertainties, and expressing the impact of uncertainty on the virtual power plant through cost; establishing an operation model of energy storage using the state of charge, and determining the operation cost of energy storage; establishing a carbon trading system based on a stepped carbon price, and determining the carbon trading cost of the virtual power plant in the carbon trading system; obtaining a model objective function based on the cost function of the aforementioned steps, and establishing an economic dispatch model of the entire virtual power plant containing wind, photovoltaic and storage in combination with determining the system's safe operation constraints; and finally performing optimization calculations to obtain data information such as the output, line flow, carbon emissions and cost of each unit in the virtual power plant.

Description

Technical Field

[0001] The present invention relates to an economic dispatch method for a virtual power plant, and in particular to an economic dispatch method for a virtual power plant including wind, solar and energy storage taking into account a stepped carbon price. Background Art

[0002] Under the new carbon tax constraints, the demand for the power system has reached a new constraint balance. At the same time, the power system will show a new form after years of development, and the characteristics of the power system such as safety and structural characteristics will undergo new changes, among which the most prominent is the new energy power whose proportion is increasing year by year. Compared with traditional energy such as thermal power, the power system with a high proportion of distributed new energy such as wind and solar has more prominent new energy characteristics. However, due to these new changes, the economic dispatch of the entire power system requires new theories and practices.

[0003] A virtual power plant is a power coordination management system that uses advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of DERs such as DG, energy storage systems, controllable loads, and electric vehicles, so that it can participate in the power market and power grid operation as a special power plant. The addition of carbon tax will also have a corresponding impact on virtual power plants, and the characteristics of this impact need to be considered and studied. Summary of the invention

[0004] In order to solve the problems existing in the background technology, the present invention provides an economic dispatch method for a virtual power plant that is suitable for distributed energy sources including wind, solar and storage, while taking into account the certainty of conventional units and the probability of distributed resources, and taking into account the impact of step carbon prices and carbon emissions.

[0005] The technical solution adopted by the present invention to solve the above problem is: a method for economic dispatching a virtual power plant including wind, solar and energy storage taking into account a stepped carbon price, which is characterized by comprising the following steps:

[0006] Step 1: Establish a wind power and photovoltaic output model, collect local wind and solar historical data of the system, and use historical data to predict wind and solar output; use Weibull distribution to fit the uncertainty of wind speed, use Beta distribution to fit the uncertainty of light, convert the impact of uncertainty on VPP into cost, and establish a wind power and photovoltaic cost model.

[0007] Step 2: Establish an energy storage model, use the state of charge to describe the operating state of the energy storage to obtain an operating model, and express the operating cost of the energy storage.

[0008] Step 3: Establish a carbon quota, carbon emissions and carbon trading model based on a tiered carbon price for virtual power plants to determine the carbon trading costs of virtual power plants.

[0009] Step 4: Determine the objective function, based on the wind power and photovoltaic costs, energy storage costs and carbon trading costs obtained in steps 1, 2 and 3, plus the power generation costs, load reduction costs and electricity sales costs of other traditional units, to obtain the total cost function.

[0010] Step 5: Flow system modeling: Based on the total cost function obtained in step 4, with the minimum cost as the goal, combined with the unit line data of the actual system, the constraints are determined according to the system safety operation constraints, and the optimal flow economic dispatch model of the virtual power plant is established.

[0011] Step 6: Use the linear programming optimization algorithm to calculate and obtain basic data. Based on the models of steps 3 and 5, modify the carbon trading price level, use MATLAB for simulation calculations, and obtain data information such as the output, line flow, carbon emissions, and costs of each unit in the virtual power plant obtained by the system under various carbon tax levels.

[0012] The step 1 is specifically as follows:

[0013] Firstly, the historical weather data (light intensity, wind speed) of the area were obtained, and the frequency distribution statistics were performed to obtain the frequency distribution histograms of light intensity and wind speed, which were fitted using Beta distribution and Weibull distribution respectively.

[0014] Beta distribution:

[0015]

[0016] Where: L max , μ Beta , σ Beta They represent the maximum deviation, average deviation and standard deviation of solar irradiance respectively.

[0017] Two-parameter Weibull distribution:

[0018]

[0019] Where c is the scale parameter, k is the shape parameter, and v is the wind speed.

[0020] Combining the output functions of classic wind power and photovoltaic units, the probability density functions of their outputs can be obtained as follows:

[0021]

[0022]

[0023] Where: w is the wind turbine output, ρ is the air density, A is the wind turbine blade area; Q PV Output power for photovoltaic units, Q PV(max)It is the upper limit of photovoltaic output.

[0024] The over- and under-forecast costs of wind power and photovoltaic power and the carbon tax penalty costs are actually the system's rotating reserve capacity costs; when the actual wind speed or radiation intensity does not reach the predicted value, the output of the wind farm and photovoltaic power station is smaller than the planned value, and then additional thermal power units are needed to meet the power balance of the power system, which is the positive reserve cost; in the opposite case, the thermal power units need to be reduced to meet the demand, which is the negative reserve cost, manifested as a penalty caused by uncertainty.

[0025] According to the probability density distribution function of wind power and photovoltaic power obtained in step 1, the expected value of positive and negative reserve capacity can be obtained:

[0026]

[0027]

[0028] C id =K id E(F cd )

[0029] C iu =K iu E(F cu )

[0030]

[0031] Where: F cd 、F cu are positive spare capacity and negative spare capacity respectively, E is the expected value of positive and negative spare capacity, w i For wind power / photovoltaic power forecast, w r is the rated output of wind power / photovoltaic power, f w (w) is the probability density distribution function of wind power / photovoltaic power; C id , C iu are the positive and negative reserve costs of unit i, C Pwind , C Psun is the cost of wind power and photovoltaic power, C id(Pwind) , C iu(Pwind ) are respectively the positive and negative spare capacity costs of wind turbine unit i, C jd(Psun) , C ju(Psun) are the positive and negative backup capacity costs of PV unit j, K id , K iu They are the positive standby cost coefficient and negative standby cost coefficient under new energy output, NW and NP are the number of wind turbines and photovoltaic units respectively.

[0032] In addition, the simulated market auction method is used to determine the positive and negative reserve cost coefficients under the new energy output, that is, the cost coefficient of the virtual power plant from the reserve market transaction. That is, the cost at maximum output power is divided by the maximum output power to obtain the unit power generation cost coefficient K of the thermal power unit. i =C i (p i,max ) / p i,max , from K i Starting with the unit with the smallest value, the load is loaded with the maximum power of the units in turn until the sum of the power of the units is greater than the load. The K of the marginal unit i That is, it is determined as the positive standby cost coefficient under a certain load; then from K i Starting from the largest unit, the maximum power of the units is loaded in turn until the sum of the power of the units is greater than the load. The K of the marginal unit is i That is, it is determined as the negative standby cost coefficient under a certain load.

[0033] The step 2 is specifically as follows:

[0034] The amount of electricity stored in an energy storage system can generally be measured by the state of charge (SOC), which can be calculated using the following formula:

[0035] SOC(t)=SOC(t-1)-E BESS (t-1) / R BESS

[0036] Where SOC(t) is the state of charge at time period t, SOC(t-1) is the state of charge at time period t-1; E BESS (t-1) is the total discharge capacity of the battery at time t-1, R BESS is the rated capacity of the battery, and the unit of both is kW·h.

[0037] The cost can be expressed by the following formula:

[0038]

[0039] Where: is the operating cost of the ith battery storage, π BESS is the consumption cost coefficient of the battery energy storage system, They are the discharge power and charge power of the battery respectively.

[0040] The step 3 is specifically as follows:

[0041] Using the free initial carbon emission rights allocation method based on power generation, the carbon emission limit allocated to the virtual power plant is:

[0042]

[0043] Where: E q is the free carbon emission quota set by the regulatory authorities for virtual power plant operators; η is the carbon emission quota per unit of electric power produced; P b,t , P gt,t are the purchased electricity and the output of traditional units at time t respectively.

[0044] The actual carbon emissions generated by the virtual power plant during the dispatch process are determined by the following formula:

[0045]

[0046] Where: E p is the actual carbon emissions generated by the units in the VPP; a, b, and c are the actual carbon emission coefficients of the thermal power units respectively;

[0047] The transaction cost of the carbon market based on the tiered carbon price is determined by the following formula:

[0048]

[0049] Where: C carbon is the carbon trading fee that the VPP operator needs to pay; μ is the benchmark price of the carbon emission market; l is the length of the interval for each carbon emission; α is the growth rate of the step carbon trading price. For every increase in carbon emissions, the carbon trading price will increase by αμ.

[0050] The step 4 is specifically as follows:

[0051] The system objective function is the total cost of the virtual power plant, which includes the power generation cost of ordinary units (coal power, gas power, nuclear power), the cost of energy storage, the cost of overestimation of wind power and photovoltaic forecasts, the cost of controllable load reduction, and the cost of carbon trading.

[0052] 1) Power generation cost of traditional units:

[0053]

[0054]

[0055] Where: C gt is the total cost of conventional units; NG represents the number of conventional units; is the cost function of the generator set, which is related to the output of the unit; P gt,i,t represents the power generation of conventional unit i in the tth period, in MW; a i 、b i 、c i They represent the cost coefficients of the generators respectively.

[0056] 2) The energy storage cost can be expressed by the following formula:

[0057]

[0058] 3) When adjusting the controllable load, corresponding costs will be generated, which can be expressed by the following formula:

[0059]

[0060] Where: C loadcurt represents the operating cost of the controllable load; curt,t is the compensation price at time t; p curt,t is the load interruption amount at time t.

[0061] 4) The virtual power plant can buy and sell electricity with the outside world through interconnection lines, and the cost generated is C market In addition, the virtual power plant can obtain income S by supplying internal load demand load .

[0062]

[0063]

[0064] C vppprice =λC elecprice

[0065] Where: C elecprice is the market electricity price, C vppprice is the internal electricity price of VPP; P purchase is the electric power purchased from outside, P load is the load demand within the virtual power plant; it is assumed that the internal electricity price is proportional to the market electricity price, and its coefficient is λ.

[0066] 5) Total cost:

[0067] minC objective =C gt +C Pwind +C Psun +C BESS +C carbon +C loadcurt +C market -S load

[0068] Among them, C gt is the power generation cost of traditional units, C Pwind and C Psun are respectively the over- and underestimation costs of wind power and photovoltaic power, C BESS is the energy storage cost, C carbon is the carbon trading cost, C loadcurt To reduce load costs.

[0069] The step 5 is specifically as follows:

[0070] Taking into account factors such as safe operation, the operation of the economic dispatch model requires the following constraints:

[0071] 1) DC power flow constraints:

[0072] An optimal power flow model is established for the system, specifically a DC power flow model that takes network losses into account; the specific DC power flow formula is:

[0073] P=Bθ

[0074] Where P is the active power vector flowing into the node, θ is the node phase angle vector, and B is the negative number of the grid node admittance matrix during normal continuous operation.

[0075] 2) Power balance constraints:

[0076] P ij =P i -P j

[0077] P i =P i load +P i BESS -P i wind -P i sun -P i gt -P i market -P i loadcurt

[0078] Where P ij is the active power flow of the transmission line from node i to node j, P i is the active power of node i, P i load is the active load at node i, P i BESS is the active load of the energy storage output at node i, P i wind , P i sun , P i gt are the active power output of wind power, photovoltaic power and conventional power generation units at node i, P i market is the active load purchased by node i from outside, P i loadcurt is the active load reduction at node i.

[0079] 3) Node load shedding constraint: The node load shedding should not be greater than the node load, 0≤P i loadcurt ≤P i load .

[0080] 4) Transmission line capacity constraints:

[0081]

[0082] In the formula, F i are the upper and lower limits of the transmission capacity of the branch from node i to node j respectively.

[0083] 5) Unit output constraints:

[0084]

[0085] Where: and are the lower and upper limits of the output of the g-th distributed gas turbine respectively.

[0086] 6) Energy storage charging and discharging output constraints:

[0087]

[0088]

[0089] In the formula, and are the charging power and discharging power of distributed energy storage, respectively. and They are respectively the upper limit of charging power and the upper limit of discharging power of distributed energy storage.

[0090] 7) Battery SOC operating range:

[0091]

[0092] In the formula, SOC are the upper and lower limits of the energy storage charge state, respectively.

[0093] The step 6 is specifically as follows:

[0094] Use MATLAB and yalmip to call gurobi to use the linear programming algorithm to calculate the basic data. Based on the models in steps 2 and 3, set the step carbon price parameters for simulation calculations to obtain data information such as the output, energy storage capacity, line flow, carbon emissions and costs of each unit in the virtual power plant based on the step carbon price carbon trading.

[0095] Compared with the prior art, the present invention has the following advantages and effects:

[0096] 1. This economic dispatch method takes into account the certainty of conventional units in virtual power plants and the uncertainty of new energy units such as wind and solar power, and can adapt to the development of new power systems.

[0097] 2. Taking into account the impact of carbon emissions on virtual power plants in the form of a carbon trading mechanism with carbon quotas plus tiered carbon prices can adapt to the low-carbon reform of the power market and power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 is a flow chart of the method of the present invention;

[0099] Figure 2 is a schematic diagram of the structure of the 39 test nodes in an embodiment of the present invention;

[0100] Figure 3 is a diagram of basic operation results in an embodiment of the present invention;

[0101] Figure 4 It is a comparison diagram of wind speed, light, load demand and electricity price fluctuation scenarios in an embodiment of the present invention. DETAILED DESCRIPTION

[0102] The present invention will be further described in detail below with reference to the accompanying drawings and by way of examples. The following examples are intended to explain the present invention but the present invention is not limited to the following examples.

[0103] Example

[0104] like Figure 1 As shown, a method for economic dispatch of a virtual power plant with wind, solar and energy storage considering a tiered carbon price includes the following steps:

[0105] Step 1: Input the output forecast data of renewable energy such as wind power and photovoltaic power and the 24-hour forecast data of virtual power plant load, use the simulated market auction method to determine the positive and negative spinning reserve cost coefficients within the virtual power plant, and use Weibull distribution and Beta distribution to determine the spinning reserve cost expressions of wind power and photovoltaic power, respectively. The operation cost of wind power and photovoltaic power considering uncertainty is obtained comprehensively.

[0106] Step 2: Input the carbon price parameters of the carbon trading market and use the baseline method to determine the carbon emission quota of the virtual power plant for 24 hours. The cost of the virtual power plant in the carbon trading process is obtained by combining the two.

[0107] Step 3: Based on the cost functions obtained in steps 1 and 2, combined with other costs: such as conventional unit operating costs, battery operating costs, buying and selling electricity costs, internal electricity sales revenue, etc., the total cost function of the virtual power plant is obtained.

[0108] Step 4: Combined with the safety operation constraints, an economic dispatch model considering the step-by-step carbon price is established, expressed in MATLAB and yalmip languages, and the final economic dispatch result can be obtained by calling the gurobi solver.

[0109] This embodiment uses the IEEE 39-node distribution network system for testing. Figure 2 9 distributed generators, 1 wind turbine, 1 photovoltaic unit, 2 distributed energy storage, and 1 external connection line. This embodiment is simulated on the MATLAB platform and solved using the gurobi toolbox to obtain the basic calculation results in a specified carbon trading market, such as Figure 3 shown. Figure 4 This is the wind speed, light, load demand and electricity price fluctuation scenario in this embodiment.

[0110] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0111] Although the present invention has been disclosed as above by way of embodiments, it is not intended to limit the protection scope of the present invention. Any changes and modifications made by any technician familiar with the technology without departing from the concept and scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A method for economic dispatch of a virtual power plant with wind, solar and energy storage considering a tiered carbon price, characterized in that: The steps include: Step 1: Establish a wind power and photovoltaic output model, collect local wind and solar historical data of the system, and use the historical data to predict wind and solar output; use Weibull distribution to fit the uncertainty of wind speed, use Beta distribution to fit the uncertainty of light, convert the impact of uncertainty on VPP into cost, and establish a wind power and photovoltaic cost model; Step 2: Establish an energy storage model, use the state of charge to describe the operating state of the energy storage to obtain an operating model, and express the operating cost of the energy storage; Step 3: Establish a carbon trading model for virtual power plants based on carbon quotas, carbon emissions and tiered carbon prices, and determine the carbon trading costs of virtual power plants; Step 4: Determine the objective function, based on the wind power and photovoltaic costs, energy storage costs and carbon trading costs obtained in Steps 1, 2 and 3, plus the power generation costs, load reduction costs and electricity sales costs of other traditional units, to obtain the total cost function; Step 5: Flow system modeling: Based on the total cost function obtained in step 4, with the minimum cost as the goal, combined with the unit line data of the actual system, the constraints are determined according to the system safety operation constraints, and the optimal flow economic dispatch model of the virtual power plant is established; Step 6: Use the linear programming optimization algorithm to calculate and obtain basic data. Based on the models of Steps 3 and 5, modify the carbon trading price level, use MATLAB to perform simulation calculations, and obtain the output, line flow, carbon emissions and cost data information of each unit in the virtual power plant obtained by the system under various carbon tax levels; The step 1 is specifically as follows: Firstly, the historical weather data of the area was obtained, and the frequency distribution statistics were performed to obtain the frequency distribution histograms of light intensity and wind speed, which were fitted using Beta distribution and Weibull distribution respectively. Beta distribution: Where: L max , μ Beta , σ Beta Respectively represent the maximum deviation value, average deviation value and standard deviation value of solar irradiance; Two-parameter Weibull distribution: Where: c is the scale parameter, k is the shape parameter, and v is the wind speed; Combining the output functions of classic wind power and photovoltaic units, the probability density functions of their outputs are: Where: w is the wind turbine output, ρ is the air density, A is the wind turbine blade area; Q PV Output power for photovoltaic units, Q PV(max) The upper limit of photovoltaic output; The over- and under-forecast costs of wind power and photovoltaic power and the carbon tax penalty costs are actually the system's spinning reserve capacity costs. When the actual wind speed or radiation intensity does not reach the predicted value, the output of the wind farm and photovoltaic power station is less than the planned value. At this time, the thermal power units need to be increased to meet the power balance of the power system, which is the positive reserve cost. In the opposite case, the thermal power units need to be reduced to meet the power balance, which is the negative reserve cost, which is manifested as a penalty caused by uncertainty. According to the probability density distribution function of wind power and photovoltaic power obtained in step 1, the expected values ​​of positive reserve capacity and negative reserve capacity are obtained: C id =K id E(F cd ) C iu =K iu E(F cu ) Where: F cd 、F cu are positive spare capacity and negative spare capacity respectively, E is the expected value of positive and negative spare capacity, w i For wind power / photovoltaic power forecast, w r is the rated output of wind power / photovoltaic power, f w (w) is the probability density distribution function of wind power / photovoltaic power; C id , C iu are the positive standby cost and negative standby cost of unit i, respectively, C Pwind , C Psun is the cost of wind power and photovoltaic power, C id(Pwind) , C iu(Pwind) are the positive spare capacity cost and negative spare capacity cost of wind turbine unit i, respectively, C jd(Psun) , C ju(Psun) are the positive and negative backup capacity costs of PV unit j, K id , K iu They are the positive standby cost coefficient and negative standby cost coefficient under the output of new energy, NW and NP are the number of wind turbines and photovoltaic units respectively; In addition, the simulated market auction method is used to determine the positive reserve cost coefficient and negative reserve cost coefficient under the new energy output, that is, the cost coefficient of the virtual power plant from the reserve market transaction; that is, the cost at the maximum output power is divided by the maximum output power to obtain the unit power generation cost coefficient K of the thermal power unit i =C i (p i,max ) / p i,max , from K i Starting with the unit with the smallest value, the load is loaded with the maximum power of the units in turn until the sum of the power of the units is greater than the load. The K of the marginal unit i That is, it is determined as the positive standby cost coefficient under a certain load; then from K i Starting from the largest unit, the maximum power of the units is loaded in turn until the sum of the power of the units is greater than the load. The K of the marginal unit is i That is, it is determined as the negative standby cost coefficient under a certain load; The step 2 is specifically as follows: The amount of electricity stored in the energy storage system is measured by the state of charge, which is calculated using the following formula: SOC(t)=SOC(t-1)-E BESS (t-1) / R BESS Where SOC(t) is the state of charge at time period t, SOC(t-1) is the state of charge at time period t-1; E BESS (t-1) is the total discharge capacity of the battery at time t-1, R BESS is the rated capacity of the battery, in kW·h; The cost is expressed as follows: Where: is the operating cost of the ith battery storage, π BESS is the consumption cost coefficient of the battery energy storage system, are the discharge power and charge power of the battery respectively; The step 3 is specifically as follows: Using the free initial carbon emission rights allocation method based on power generation, the carbon emission limit allocated to the virtual power plant is: Where: E q is the free carbon emission quota set by the regulatory authorities for virtual power plant operators; η is the carbon emission quota per unit of electric power produced; P b,t , P gt,t are the purchased electricity and the output of the traditional unit at time t respectively; The actual carbon emissions generated by the virtual power plant during the dispatch process are determined by the following formula: Where: E p is the actual carbon emissions generated by the units in the VPP; a, b, and c are the actual carbon emission coefficients of the thermal power units respectively; The transaction cost of the carbon market based on the tiered carbon price is determined by the following formula: Where: C carbon is the carbon trading fee that the VPP operator needs to pay; μ is the benchmark price of the carbon emission market; l is the length of the interval for each carbon emission; α is the growth rate of the step carbon trading price. For every increase in carbon emissions, the carbon trading price will increase by αμ; The step 4 is specifically as follows: The system objective function is the total cost of the virtual power plant, which includes the power generation cost of ordinary units, the use cost of energy storage, the overestimation cost of wind power and photovoltaic forecasts, the controllable load reduction cost, and the carbon trading cost; 1) Power generation cost of traditional units: Where: C gt is the total cost of conventional units; NG represents the number of conventional units; is the cost function of the generator set, which is related to the output of the unit; P gt,i,t represents the power generation of conventional unit i in the tth period, in MW; a i 、b i 、c i They represent the cost coefficient of the generator respectively; 2) The energy storage cost is expressed by the following formula: 3) When adjusting the controllable load, corresponding costs will be generated, which is expressed by the following formula: Where: C loadcurt represents the operating cost of the controllable load; curt,t is the compensation price at time t; p curt,t is the load interruption amount at time t; 4) The virtual power plant buys and sells electricity with the outside world through interconnection lines, and the cost incurred is C market ; In addition, the virtual power plant can obtain income S by supplying internal load demand load ; C vppprice =λC elecprice Where: C elecprice is the market electricity price, C vppprice is the internal electricity price of VPP; P purchase is the electric power purchased from outside, P load is the load demand inside the virtual power plant; it is assumed that the internal electricity price is proportional to the market electricity price, with a coefficient of λ; 5) Total cost: minC objective =C gt +C Pwind +C Psun +C BESS +C carbon +C loadcurt +C market -S load Among them, C gt is the power generation cost of traditional units, C Pwind and C Psun are the overestimated cost and underestimated cost of wind power and photovoltaic power respectively, C BESS is the energy storage cost, C carbon is the carbon trading cost, C loadcurt To reduce load costs; The step 5 is specifically as follows: Taking into account the safety operation factors, the operation of the economic dispatch model requires the following constraints: 1) DC power flow constraints: An optimal power flow model is established for the system, specifically a DC power flow model that takes network losses into account; the specific DC power flow formula is: P=Bθ Where P is the active vector flowing into the node, θ is the node phase angle vector, and B is the negative number of the grid node admittance matrix during normal continuous operation; 2) Power balance constraints: P ij =P i -P j P i =P i load +P i BESS -P i wind -P i sun -P i gt -P i market -P i loadcurt Where P ij is the active power flow of the transmission line from node i to node j, P i is the active power of node i, P i load is the active load at node i, P i BESS is the active load of the energy storage output at node i, P i wind , P i sun , P i gt are the active power output of wind power, photovoltaic power and conventional power generation units at node i, P i market is the active load purchased by node i from outside, P i loadcurt is the active load reduced at node i; 3) Node load shedding constraint: The node load shedding should not be greater than the node load, 0≤P i loadcurt ≤P i load ; 4) Transmission line capacity constraints: In the formula, F i are the upper and lower limits of the transmission capacity of the branch from node i to node j respectively; 5) Unit output constraints: Where: and are the lower and upper limits of the output of the g-th distributed gas turbine respectively; 6) Energy storage charging and discharging output constraints: In the formula, and are the charging power and discharging power of distributed energy storage, respectively. and They are respectively the upper limit of charging power and the upper limit of discharging power of distributed energy storage; 7) Battery SOC operating range: In the formula, SOC and are the lower and upper limits of the energy storage charge state, respectively; The step 6 is specifically as follows: Use MATLAB and yalmip to call gurobi to use the linear programming algorithm to calculate the basic data. Based on the models in steps 2 and 3, set the step carbon price parameters for simulation calculations to obtain the output, energy storage capacity, line flow, carbon emissions and cost data information of each unit in the virtual power plant based on the step carbon price carbon trading.

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