A Virtual Power Plant Optimal Scheduling Method for Aggregating Electric Vehicles to Participate in Carbon Trading

Abstract

The present invention belongs to the technical field of virtual power plants, and particularly relates to an optimized scheduling method for a virtual power plant that aggregates electric vehicles to participate in carbon trading. Aiming at the deficiencies of the low enthusiasm and degree of electric vehicles participating in the operation of existing virtual power plants, the present invention adopts the following technical solutions: An optimized scheduling method for a virtual power plant that aggregates electric vehicles to participate in carbon trading, including: Step 1, the virtual power plant aggregates electric vehicles to participate in demand response and acts as an agent for electric vehicles to participate in carbon trading; Step 2, the virtual power plant obtains the predicted output values of each distributed power source and obtains the charging strategies of electric vehicles; Step 3, calculates the various revenues and expenditures of the virtual power plant, and establishes an optimized scheduling model with the maximum profit of the virtual power plant itself as the optimization goal; Step 4, adjusts the output of the gas turbine and the charging strategies of electric vehicles according to the actual output of photovoltaic and wind turbines. The beneficial effect of the present invention is: to achieve a win-win situation for VPP and EV to participate in the carbon market.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plants, and particularly relates to an optimized dispatching method for a virtual power plant that aggregates electric vehicles to participate in carbon trading. Background Art

[0002] Under the national dual-carbon goal, distributed energy resources (DER) mainly based on new energy sources such as wind and light have developed rapidly. However, due to high investment costs, large output fluctuations, and strong randomness, it is at a disadvantage in the market competition with thermal power. To promote the development of new energy, China has implemented benchmark electricity prices nationwide, and power grid companies have given corresponding subsidies to new energy, but there are still a series of problems such as high costs, delayed subsidy payments, and large gaps, which affect the enthusiasm of the power grid to absorb new energy.

[0003] The emergence of virtual power plants (VPP) provides a new idea for solving the above problems. VPP can realize the combination of multiple small power sources, integrate DER by using reasonable control means and management mechanisms, and then output a relatively stable and larger output. At the same time, due to the global low-carbon emission reduction requirements, the emission rights of greenhouse gases are given the attribute of valuable substances and can be freely traded in the market, thus forming a carbon trading market. Since the DER that makes up VPP contains more renewable resources, participating in carbon trading can enable VPP to obtain more profits.

[0004] With the gradual popularization of electric vehicles (EV) and the gradual maturity of the orderly charging management mechanism for EV, the characteristics of high flexibility and high price sensitivity during EV charging can make it a special controllable load, better realizing the coordination and complementarity among various new energy sources and overall optimization.

[0005] The Chinese patent application with the publication number CN109523052A discloses an optimized dispatching method for a virtual power plant considering demand response and carbon trading, including the following steps: counting the number of distributed power sources in the virtual power plant and the number of controllable loads of electric vehicles and water-cooled air-conditioning systems participating in demand response; predicting the output of wind power and photovoltaic units in the virtual power plant and the rigid loads in the power system for the next day; the energy management system of the virtual power plant receiving the day-ahead declaration information of electric vehicle owners; the energy management system of the virtual power plant establishing an optimized dispatching model for the virtual power plant to participate in both the power market and the carbon trading market according to the day-ahead prediction information, electric vehicle declaration information, and price information of the power market and the carbon trading market; solving the optimized dispatching model to obtain the day-ahead optimized dispatching plan for the virtual power plant.

[0006] The proposed solution for this invention patent application can give full play to the advantages of demand response resources in aspects such as peak shaving and valley filling. Additionally, it can effectively reduce the carbon emissions of the power system. However, this solution only treats electric vehicles as a tool similar to water-cooled energy storage air conditioners, and its function is limited to peak shaving and valley filling, without analyzing how electric vehicles specifically participate in carbon trading. Moreover, currently, the carbon emission trading volume of EVs is small and difficult to centrally manage. At the same time, the statistical calculation of CERs is also a complex task. The existence of these problems has led to low enthusiasm among EV owners to participate in the carbon market trading of CERs. Summary of the Invention

[0007] In view of the deficiency that the enthusiasm and degree of electric vehicles participating in the operation of existing virtual power plants are relatively low, the present invention provides an optimized dispatching method for a virtual power plant that aggregates electric vehicles to participate in carbon trading. It develops electric vehicles as controllable loads and energy storage devices, integrates distributed energy within the distribution network through the virtual power plant, and guides electric vehicles to connect to the grid in an orderly manner to achieve the coordination of various distributed energy sources, improve the economic benefits of the virtual power plant participating in the electricity energy market, and enhance the stability of the overall output of the virtual power plant. At the same time, the virtual power plant participates in the certified emission reduction market on behalf of electric vehicles, which not only solves the problem that it is relatively difficult for electric vehicle owners to participate in carbon market accounting for carbon emissions alone but also enables the virtual power plant operator to obtain greater benefits.

[0008] To achieve the above objectives, the present invention adopts the following technical solutions: An optimized dispatching method for a virtual power plant that aggregates electric vehicles to participate in carbon trading, the optimized dispatching method for a virtual power plant that aggregates electric vehicles to participate in carbon trading includes:

[0009] Step 1, the virtual power plant aggregates electric vehicles to participate in demand response and acts on behalf of electric vehicles to participate in carbon trading;

[0010] Step 2, the virtual power plant obtains the predicted output values of each distributed power source and obtains the charging strategy of electric vehicles. The distributed power sources include gas turbines, photovoltaics, and wind turbines;

[0011] Step 3, calculate the various revenues and expenditures of the virtual power plant, and establish an optimized dispatching model with the maximum profit of the virtual power plant itself as the optimization objective;

[0012] Step 4, adjust the output of the gas turbine and the charging strategy of electric vehicles according to the actual output of photovoltaics and wind turbines.

[0013] The virtual power plant optimization scheduling method for aggregating electric vehicles to participate in carbon trading in the present invention utilizes a VPP to aggregate different types of power generation resources and EVs, coordinates the scheduling of a gas turbine (MT) and EVs as positive / negative power compensation, and reduces the risk cost brought by the volatility of new energy. In the VPP optimal operation model, a reasonable interaction mechanism and pricing strategy between the VPP and EVs are designed to achieve a win-win situation for the VPP and EVs to participate in the carbon market. As a price taker, the VPP realizes load peak shaving and valley filling by reasonably guiding the orderly charging and discharging of EVs and MT power compensation, and improves power supply reliability.

[0014] As an improvement, in step 1, the electric vehicle owner authorizes the virtual power plant with the certified voluntary emission reduction amount within a certain period. The virtual power plant pools it and sells it on the carbon trading market. The virtual power plant sells electricity to the electric vehicle owner at a charging price lower than the market price. The EV owner transfers the emission reduction amount within a certain period to the VPP operator, enabling the operator to pool the limited carbon emission reduction amounts of individual EVs and sell them on the carbon trading market. The obtained income can be reflected in the electricity selling price given by the VPP, thereby reducing the charging cost of the EV owner and enhancing the enthusiasm of the EV owner to participate in the optimal operation of the VPP.

[0015] As an improvement, in step S2, the virtual power plant collects the output prediction values of photovoltaic and wind turbines and the output declaration power curve of the gas turbine; the virtual power plant generates the charging strategy of the electric vehicle according to the travel arrangement and charging amount submitted by the electric vehicle owner.

[0016] As an improvement, in step S3, the income items of the virtual power plant include electricity selling income in the power market, initial carbon quota income, income from selling electricity to electric vehicles, and income from acting on behalf of electric vehicles to sell certified voluntary emission reduction amounts; the cost items of the virtual power plant include the operation cost of the virtual power plant and the risk cost of the virtual power plant. Due to the uncertainty of the output of WT (wind power) and PV (photovoltaic), the VPP will bear corresponding uncertainty risks when formulating the power purchase and sale strategy based on the predicted power. Therefore, it is necessary to reasonably evaluate the impact of this risk cost on the VPP strategy formulation.

[0017] As an improvement, in step 3, the operation cost Q of the virtual power plant t is the sum of the operation costs of different types of distributed power sources, expressed by the formula:

[0018] Q t = Q PV,t + Q WT,t + Q MT,t

[0019] In the formula, Q PV, is the power generation cost of PV, which is proportional to the output; Q WT,t is the power generation cost of WT, which is proportional to the output;

[0020] The operating cost Q of a gas turbine MT , including the power generation cost Q of the gas turbine MT,G,t and the carbon emission cost Q of the gas turbine MT,C,t , is expressed by the formula as follows:

[0021] Q MT,t =Q MT,G,t +Q MT,C,t

[0022]

[0023] Q MT,C,t =I MT θ C P MT,t

[0024] In the formula, a MT , b MT , c MT are the relevant parameters of the MT power generation cost respectively; I MT is the carbon emission coefficient of the MT; θ C is the selling price of the unit CO2 emission right in the carbon market.

[0025] As an improvement, in step 3, the calculation process of the risk cost of the virtual power plant is as follows:

[0026] The error of wind power at time t satisfies a normal distribution N(0,σ WT,t 2 ) with an expectation of 0 and a variance of σ WT,t 2 ; the error of photovoltaic power at time t satisfies a normal distribution N(0,σ PV,t 2 ) with an expectation of 0 and a variance of σ PV,t 2 ;

[0027] Combined with Monte Carlo sampling, L sets of output scenarios {P PV,t,s , P WT,t,s} are generated, and for each scenario s, the risk cost risk t,s of the virtual power plant is calculated respectively, and the arithmetic mean risk t is taken as the risk cost of the virtual power plant and included in the virtual power plant optimal scheduling model. It is expressed by the formula as follows:

[0028]

[0029] risk t,s =risk MT,t,s +risk unb,t,s

[0030] risk MT,t,s = |Q MT,t (P MT,t,s ) - Q MT,t (P MT,t,s + ΔP MT,t,s )|

[0031] risk unb,t,s = π + P t,s+ - π - P t,s-

[0032] In the formula, risk t,s is the risk cost of the VPP in scenario s at time t, which includes the MT adjustment risk cost risk MT,t,s and the unbalanced power trading risk cost risk unb,t,s ; ΔP MT,t,s is the MT power adjustment amount at time t in scenario s; π + and π － are the penalty electricity prices for unbalanced power trading respectively; P t,s+ and P t,s- are the unbalanced powers of the VPP under the output scenario s at time t. When the actual power exceeds the predicted power, i.e., ΔP t,s ≥ 0, P t,s- = 0. When the actual power is insufficient, i.e., ΔP t,s ≤ 0, P t,s+ = 0.

[0033] As an improvement, in step S3, based on the electricity selling declaration strategy of the time-of-use electricity price model, an optimal scheduling model is established;

[0034] Before the start of scheduling, the next scheduling period is divided into T sub-periods. For each sub-period, the VPP declares its expected electricity selling volume P G,t to the superior market respectively. That is, the power purchase and selling strategy of the VPP in a scheduling period can be expressed as P G = {P G,1 , P G,2 , …, P G,T};

[0035] The revenue R t of the VPP includes the electricity market revenue R G,t , the carbon market revenue R C,t , the revenue R EV,t from selling electricity to EVs, and the revenue R CER,t from acting as an agent to sell CERs for EVs. It is expressed by the formula as follows:

[0036] R t = R G,t + R C,t + R EV,t + RCER,t

[0037] R G,t = π G,t P G,t

[0038] R C,t = θ C C G,t

[0039]

[0040]

[0041] where: t = 1, 2, …, T; π G,t is the price at which the VPP sells electricity to the power grid, which is determined by the superior market according to the historical load peak-valley distribution before the day-ahead market clearing; P G,t is the electricity quantity sold by the VPP to the superior market; θ C is the selling price of a unit of CO2 emission rights in the carbon market; i represents different EVs; π EV,t is the EV charging price set by the VPP at time t, μ i,t characterizes whether the EV can be charged at time t, μ i,t = 1 indicates that the i-th EV can be connected to the grid for charging at time t, μ i,t = 0 indicates that the i-th EV does not charge at time t; P EV,i,t is the charging power of the i-th EV at time t.

[0042] As an improvement, in step S3, the optimal scheduling model is expressed as:

[0043]

[0044] As an improvement, the constraint conditions of the optimal scheduling model include power balance constraint, charging rate constraint, and capacity constraint.

[0045] As an improvement, the power balance constraint is expressed as:

[0046]

[0047] where j is different types of generating units; P j,t is the power generation of unit j;

[0048] The charging rate constraint is expressed as:

[0049] P EV,i,min ≤ P EV,i,t ≤ P EV,i,max

[0050] where P EV,i,max and PEV,i,min They are the upper and lower limits of the charging rate of the i-th EV respectively;

[0051] Capacity 约束 It is expressed as:

[0052] SOC i,min ≤SOC i,t ≤SOC i,max

[0053] In the formula, SOC i,min and SOC i,max are the upper and lower limits of the state of charge allowed for the i-th EV respectively.

[0054] As an improvement, it further includes step 5. After the scheduling is completed, sell the unused carbon quota and the CER transferred by the EV in the carbon trading market.

[0055] The beneficial effects of the virtual power plant optimal scheduling method for aggregating electric vehicles to participate in carbon trading according to the present invention are as follows: By using the VPP to aggregate different types of power generation resources and EVs, coordinating the scheduling of MT and EVs as positive / negative power compensation, the risk cost brought by the volatility of new energy is reduced; In the VPP optimal operation model, a reasonable interaction mechanism and pricing strategy between the VPP and the EV are designed to achieve a win-win situation for the VPP and the EV to participate in the carbon market; As a price taker, the VPP realizes load peak shaving and valley filling by reasonably guiding the orderly charging and discharging of the EV and the power compensation of the MT, and improves the power supply reliability. Description of the Drawings

[0056] Figure 1 is the flowchart of the virtual power plant optimal scheduling method of the embodiment of the present invention.

[0057] Figure 2 is the schematic diagram of the response characteristics of the EV grid connection adopted in the embodiment of the present invention;

[0058] Figure 3 is the schematic diagram of the composition of the income and cost of the VPP adopted in the embodiment of the present invention. Detailed Embodiments

[0059] The following combines the drawings of the embodiments of the present invention to explain and illustrate the technical solutions of the embodiments of the present invention. However, the following embodiments are only the preferred embodiments of the present invention and not all of them. Based on the embodiments in the embodiments, other embodiments obtained by those skilled in the art without creative efforts all belong to the protection scope of the present invention.

[0060] Refer to Figures 1 to 3 , a virtual power plant optimal scheduling method for aggregating electric vehicles to participate in carbon trading according to the present invention, the virtual power plant optimal scheduling method for aggregating electric vehicles to participate in carbon trading includes:

[0061] Step 1: The virtual power plant aggregates electric vehicles to participate in demand response and acts as an agent for electric vehicles to participate in carbon trading;

[0062] Step 2: The virtual power plant obtains the output prediction values of each distributed power source and the charging strategies of electric vehicles. The distributed power sources include gas turbines, photovoltaics, and wind turbines;

[0063] Step 3: Calculate the various revenues and expenditures of the virtual power plant, and establish an optimal scheduling model with the maximum profit of the virtual power plant itself as the optimization goal;

[0064] Step 4: Adjust the output of the gas turbine and the charging strategies of electric vehicles according to the actual output of photovoltaics and wind turbines.

[0065] The optimal scheduling method of the virtual power plant that aggregates electric vehicles to participate in carbon trading according to the present invention aggregates different types of power generation resources and electric vehicles by using the VPP, coordinates the scheduling of the MT and electric vehicles as positive / negative power compensation, and reduces the risk cost brought by the volatility of new energy; in the VPP optimal operation model, a reasonable interaction mechanism and pricing strategy between the VPP and electric vehicles are designed to achieve a win-win situation for the VPP and electric vehicles to participate in the carbon market; as a price taker, the VPP realizes load peak shaving and valley filling by reasonably guiding the orderly charging and discharging of electric vehicles and MT power compensation, and improves the power supply reliability.

[0066] Embodiment 1

[0067] See Figures 1 to 3 , an optimal scheduling method of a virtual power plant that aggregates electric vehicles to participate in carbon trading according to the present invention, the optimal scheduling method of the virtual power plant that aggregates electric vehicles to participate in carbon trading includes:

[0068] Step 1: The virtual power plant aggregates electric vehicles to participate in demand response and acts as an agent for electric vehicles to participate in carbon trading;

[0069] Step 2: The virtual power plant obtains the output prediction values of each distributed power source and the charging strategies of electric vehicles. The distributed power sources include gas turbines, photovoltaics, and wind turbines;

[0070] Step 3: Calculate the various revenues and expenditures of the virtual power plant, and establish an optimal scheduling model with the maximum profit of the virtual power plant itself as the optimization goal;

[0071] Step 4: Adjust the output of the gas turbine and the charging strategies of electric vehicles according to the actual output of photovoltaics and wind turbines.

[0072] In this embodiment, in step 1, the electric vehicle owner authorizes the certified voluntary emission reduction amount within a certain period to the virtual power plant. The virtual power plant aggregates them and sells them on the carbon trading market. The virtual power plant sells electricity to the electric vehicle owner at a charging price lower than the market price. The EV owner transfers the emission reduction amount within a certain period to the VPP operator, enabling the operator to aggregate the limited carbon emission reduction amounts of individual EVs and sell them on the carbon trading market. The obtained income can be reflected in the electricity selling price given by the VPP, thereby reducing the charging cost of the EV owner and improving the enthusiasm of the EV owner to participate in the optimal operation of the VPP. Due to the disadvantages of small carbon emission trading volume and difficult centralized control of electric vehicles, the enthusiasm of electric vehicle owners to participate in carbon market trading is not high. Therefore, it is highly feasible for electric vehicles participating in the optimal operation of the virtual power plant to entrust the virtual power plant operator to act as an agent for the carbon market trading of electric vehicles. The virtual power plant formulates a charging price lower than the market electricity price to attract electric vehicles to charge and transfer the corresponding certified voluntary emission reduction amounts. Then the electric vehicle submits the charging amount and selects the time available for charging. While charging the electric vehicle, the virtual power plant can use the remaining capacity of the electric vehicle battery for energy storage to adjust the actual output of the virtual power plant.

[0073] In this embodiment, in step S2, the virtual power plant collects the output prediction values of photovoltaic and wind turbines and the output declaration power curve of the gas turbine; the virtual power plant generates a charging strategy for the electric vehicle according to the travel arrangement and charging amount submitted by the electric vehicle owner.

[0074] In this embodiment, in step S3, the income items of the virtual power plant include electricity selling income in the electricity market, initial carbon quota income, electricity selling income to electric vehicles, and income from acting as an agent to sell certified voluntary emission reduction amounts of electric vehicles; the cost items of the virtual power plant include the operation cost of the virtual power plant and the risk cost of the virtual power plant. Due to the uncertainty of the output of WT and PV, the VPP will bear corresponding uncertainty risks when formulating the power purchase and selling strategy based on the predicted power. Therefore, it is necessary to reasonably evaluate the impact of this risk cost on the VPP strategy formulation.

[0075] In this embodiment, in step 3, the operation cost Q of the virtual power plant t is the sum of the operation costs of different types of distributed power sources, which is expressed by the formula:

[0076] Q t = Q PV,t + Q WT,t + Q MT,t

[0077] In the formula, Q PV,t is the power generation cost of PV, which is proportional to the output; Q WT,t is the power generation cost of WT, which is proportional to the output;

[0078] Gas turbine operating cost Q MT , including the power generation cost Q of the gas turbine MT,G,t and the carbon emission cost Q of the gas turbine MT,C,t , which is expressed by the formula as:

[0079] Q MT,t = Q MT,G,t + Q MT,C,t

[0080]

[0081] Q MT,C,t = I MT θ C P MT,t

[0082] In the formula, a MT , b MT , c MT are the relevant parameters of the MT power generation cost respectively; I MT is the carbon emission coefficient of the MT; θ C is the selling price of the unit CO2 emission right in the carbon market.

[0083] The main characteristics of the two power generation resources of WT and PV are that they do not generate carbon emissions, and their outputs are both uncertain. The MT has strong controllability and can be used as an auxiliary power supply for DER in the VPP to suppress the randomness and volatility of the wind power and photovoltaic outputs. At the same time, the MT is also the main carbon emission source of the VPP, and the output of the MT should be minimized within the operating boundary.

[0084] In this embodiment, in step 3, the calculation process of the risk cost of the virtual power plant is as follows:

[0085] The error of the wind power at time t satisfies a normal distribution N(0,σ WT,t 2 ) with an expected value of 0 and a variance of σ WT,t 2 ; the error of the photovoltaic at time t satisfies a normal distribution N(0,σ PV,t 2 ) with an expected value of 0 and a variance of σ PV,t 2 );

[0086] Combined with Monte Carlo sampling, generate L sets of output scenarios {P PV,t,s , P WT,t,s}, and calculate the risk cost risk t,s of the virtual power plant for each scenario s respectively, and obtain the arithmetic mean risk t as the risk cost of the virtual power plant and incorporate it into the virtual power plant optimal scheduling model, which is expressed by the formula as:

[0087]

[0088] risk t,s = risk MT,t,s + risk inb,t,s

[0089] risk MT,t,s = |Q MT,t (P MT,t,s ) - Q MT,t (P MT,t,s + ΔP MT,t,s )|

[0090] risk unb,t,s = π + p t,s+ - π - P t,s-

[0091] Where risk t,s is the risk cost of the VPP in scenario s at time t, which includes the MT adjustment risk cost risk MT,t,s and the unbalanced power trading risk cost risk unb,t,s ; ΔP MT,t,s is the MT power adjustment amount in scenario s at time t; π + and π － are the penalty electricity prices for unbalanced power trading respectively; P t,s+ and P t,s- are the unbalanced powers of the VPP under the output scenario s at time t. When the actual power exceeds the predicted power, i.e., ΔP t,s ≥ 0, P t,s- = 0. When the actual power is insufficient, i.e., ΔP t,s ≤ 0, P t,s+ = 0.

[0092] EV participates in the optimal scheduling of the VPP as a controllable load. The VPP can guide the EV to charge during corresponding periods according to the PV and WT outputs to reduce the penalty for wind and light curtailment. The VPP scheduling the EV to participate in the optimal operation of the carbon market can also improve user economy. Attached Figure 2 is the response characteristic when the EV is connected to the grid. The area within the solid line box is the feasible region for EV charging and discharging. Among them, t s and t e are the start and end times of EV charging and discharging respectively, SOC s and SOC e are the state of charge when the EV connects to and disconnects from the grid, and SOC max and SOC min are the upper and lower limits of the state of charge allowed for the EV respectively.

[0093] In this embodiment, in step S3, an optimal dispatching model is established based on the electricity selling declaration strategy of the time-of-use electricity price model.

[0094] Before the dispatching starts, the next dispatching period is divided into T sub-periods. For each sub-period, the VPP declares its expected electricity selling volume P to the superior market. G,t That is, the electricity purchase and selling strategy of the VPP in a dispatching period can be expressed as P. G ={P G,1 , P G,2 , …, P G,T};

[0095] The revenue R of the VPP t includes the electricity market revenue R G,t , the carbon market revenue R C,t , the revenue R EV,t from selling electricity to EVs, and the revenue R CER,t from acting as an agent to sell CERs for EVs. It is expressed by the formula as follows:

[0096] R t =R G,t +R C,t +R EV, t+R CER,t

[0097] R G,t = G,t P G,t

[0098] R C,t = C C G,t

[0099]

[0100]

[0101] In the formula: t = 1, 2, …, T; π G,t is the price at which the VPP sells electricity to the power grid. This price is formulated by the superior market according to the historical peak and valley load distribution before the day-ahead market clearing; P G,t is the electricity volume sold by the VPP to the superior market; θ C is the selling price of a unit of CO2 emission right in the carbon market; i represents different EVs; π EV,t is the EV charging price set by the VPP at time t, and μ i,t characterizes whether the EV can be charged at time t. μ i,t =1 indicates that the i-th EV can be connected to the grid for charging at time t, and μ i,t =0 indicates that the i-th EV does not charge at time t; P EV,i,t is the charging power of the i-th EV at time t.

[0102] In this embodiment, in step S3, the optimized scheduling model is expressed as:

[0103]

[0104] In this embodiment, the constraint conditions of the optimized scheduling model include power balance constraint, charging rate constraint and capacity constraint.

[0105] In this embodiment, the power balance constraint is expressed as:

[0106]

[0107] In the formula, j is different types of generating units; P j,t is the power generation of unit j;

[0108] The charging rate constraint is expressed as:

[0109] P EV,i,min ≤P EV,i,t ≤P EV,i,max

[0110] In the formula, P EV,i,max and P EV,i,min are respectively the upper and lower limits of the charging rate of the i-th EV;

[0111] Capacity 约束 is expressed as:

[0112] SOC i,min ≤SOC i,t ≤SOC i,max

[0113] In the formula, SOC i,min and SOC i,max are respectively the upper and lower limits of the allowable state of charge of the i-th EV.

[0114] In this embodiment, it further includes step 5. After the scheduling is completed, the unused carbon quotas and the CERs transferred by EVs are sold in the carbon trading market.

[0115] The beneficial effects of the virtual power plant optimal scheduling method for aggregating electric vehicles to participate in carbon trading in the present invention are as follows: By using the VPP to aggregate different types of power generation resources and EVs, and coordinating the scheduling of MT and EVs as positive / negative power compensation, the risk cost brought by the volatility of new energy is reduced; In the VPP optimal operation model, a reasonable interaction mechanism and pricing strategy between the VPP and EVs are designed to achieve a win-win situation for the VPP and EVs to participate in the carbon market; As a price taker, the VPP realizes load peak shaving and valley filling and improves power supply reliability by reasonably guiding the orderly charging and discharging of EVs and MT power compensation; In carbon market trading, the virtual power plant acts as an agent for electric vehicles to participate in carbon market trading, and electric vehicles can obtain a lower charging price, which improves the enthusiasm of electric vehicles to participate in the carbon market.

[0116] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that the present invention includes but is not limited to the content described in the above specific implementation manner. Any modification that does not deviate from the functional and structural principles of the present invention will be included in the scope of the claims.

Claims

1. A virtual power plant optimal scheduling method for aggregating electric vehicles to participate in carbon trading, characterized in that: The virtual power plant optimization scheduling method for aggregating electric vehicles to participate in carbon trading includes: Step 1, the virtual power plant aggregates electric vehicles to participate in demand response and agents electric vehicles to participate in carbon trading; Step 2, the virtual power plant obtains the output prediction values of each distributed power source and the charging strategies of electric vehicles. The distributed power sources include gas turbines, photovoltaics, and wind turbines; Step 3, calculate the various revenues and expenditures of the virtual power plant, and establish an optimization scheduling model with the maximum profit of the virtual power plant itself as the optimization goal; Step 4, adjust the output of the gas turbine and the charging strategies of electric vehicles according to the actual output of photovoltaics and wind turbines; In Step 1, the electric vehicle owners authorize the virtual power plant with the certified voluntary emission reduction amount within a period of time. The virtual power plant pools and sells it on the carbon trading market. The virtual power plant sells electricity to the electric vehicle owners at a charging price lower than the market price; In Step 3, the optimization scheduling model is expressed as: ； Among them, based on the electricity selling declaration strategy of the time-of-use electricity price mode, an optimization scheduling model is established; Before the start of scheduling, the next scheduling period is divided into T sub-periods. For each sub-period, the virtual power plant separately declares its expected electricity sales volume to the superior market P G,t , that is, the power purchase and sales strategy of the virtual power plant in a scheduling period can be expressed as P G ={ P G,1 , P G,2 ,…, P G,T}; Revenue of Virtual Power Plant R t including electricity market revenue R G,t , carbon market revenue R C,t , revenue from selling electricity to EVs R EV,t and revenue from acting as an agent to sell CERs on behalf of EVs R CER,t , which is expressed by the formula as follows: Wherein: t = 1, 2,..., T ; π G,t is the price at which the virtual power plant sells electricity to the power grid, which is formulated by the superior market according to the historical load peak-valley distribution before the day-ahead market clearing; P G,t is the electricity quantity sold by the virtual power plant to the superior market; θ C is the selling price of a unit of CO2 emission rights in the carbon market; i represents different EVs; π EV,t is the EV charging price at time t formulated by the virtual power plant, μ i,t characterizes whether the EV can be charged at time t, μ i,t = 1 indicates that the i th EV can be connected to the grid for charging at t time, μ i,t = 0 indicates that the i th EV does not charge at t time; P EV,i,t is the charging power of the i th EV at t time; Operating cost of virtual power plant Q t It is the sum of the operating costs of different types of distributed power sources, expressed by the formula: wherein, is the power generation cost of PV, which is proportional to the output; is the power generation cost of WT, which is proportional to the output, is the operating cost of the gas turbine; Risk costs of virtual power plants The calculation process is as follows: The error of wind power at t satisfies a normal distribution with an expected value of 0 and a variance of σ WT,t 2 ; The error of photovoltaic power at N (0, σ WT,t 2 ) satisfies a normal distribution with an expected value of 0 and a variance of t σ PV,t 2 ; The error of photovoltaic power at N (0, σ PV,t 2 ); Generate, in combination with Monte Carlo sampling, L a set of output scenarios { P PV,t , P WT,t}, and calculate the virtual power plant risk cost for each scenario respectively , and obtain the arithmetic mean to be used as the virtual power plant risk cost and incorporated into the virtual power plant optimal scheduling model.

2. The virtual power plant optimal scheduling method for aggregating electric vehicles to participate in carbon trading according to claim 1, characterized in that: In Step 2, the virtual power plant collects the output prediction values of photovoltaics and wind turbines and the output declaration power curve of the gas turbine. The virtual power plant generates the charging strategies of electric vehicles according to the travel arrangements and charging amounts submitted by the electric vehicle owners.

3. The virtual power plant optimal scheduling method for aggregating electric vehicles to participate in carbon trading according to claim 1, wherein: In Step 3, the revenue items of the virtual power plant include electricity selling revenue in the electricity market, initial carbon quota revenue, revenue from selling electricity to electric vehicles, and revenue from agent electric vehicles selling certified voluntary emission reduction amounts. The cost items of the virtual power plant include the operation cost of the virtual power plant and the risk cost of the virtual power plant.

4. A virtual power plant optimal scheduling method for aggregating electric vehicles to participate in carbon trading according to claim 1, characterized in that: Operating cost of gas turbine including the power generation cost of gas turbine and the carbon emission cost of gas turbine , which is expressed by the formula as follows: In the formula, , , are the relevant parameters of the power generation cost of the gas turbine respectively; is the carbon emission coefficient of the gas turbine; θ C is the selling price of the unit CO2 emission right in the carbon market.

5. A virtual power plant optimal scheduling method for aggregating electric vehicles to participate in carbon trading according to claim 1, characterized in that: Risk cost of virtual power plant It is expressed by the formula as follows: In the formula, risk t, is t the risk cost of the virtual power plant in the scenario at time , which includes the risk cost of gas turbine adjustment risk unb,t, ; Δ is t the adjustment amount of the gas turbine power in the scenario at time π ＋ and π － are respectively the penalty electricity prices for unbalanced power trading; P t,+ and P t,- are the unbalanced power of the virtual power plant under the output scenario at time t . When the actual power exceeds the predicted power, i.e., Δ P t, ≥0, P t,- =0; when the actual power is insufficient, i.e., Δ P t, ≤0, P t,+ =0.

6. A virtual power plant optimal scheduling method for aggregating electric vehicles to participate in carbon trading according to claim 1, characterized in that: The constraint conditions of the optimization scheduling model include power balance constraint, charging rate constraint, and capacity constraint. The power balance constraint is expressed as: In the formula, j are different types of generator sets; P j,t is the unit j electricity generation; The charging rate constraint is expressed as: In the formula, P EV,i,max and P EV,i,min are respectively the upper and lower limits of the EV charging rate of the i th unit; The capacity constraint is expressed as: In the formula, and are respectively the upper and lower limits of the state of charge allowed for the i th EV.

7. A virtual power plant optimal scheduling method for aggregating electric vehicles to participate in carbon trading according to claim 1, characterized in that: It also includes Step 5: After the scheduling is completed, sell the unused carbon quotas and CERs transferred by EVs on the carbon trading market.

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