Robust Optimal Scheduling Method for Virtual Power Plant Considering Uncertainty and Demand Response

Abstract

The present invention discloses a robust optimal scheduling method for a virtual power plant considering uncertainty and demand response, including: importing the collected virtual power plant information into a basic model for optimal scheduling; wherein, based on the virtual power plant information, multiple costs and revenues of the virtual power plant are respectively obtained, and a net profit economic optimal scheduling objective function of the virtual power plant is constructed, and constraint conditions for the safe operation of the virtual power plant are set for the optimal scheduling objective function; according to the robust optimal scheduling model, the economic optimal scheduling objective function is optimized and solved, and the virtual power plant information after optimal scheduling is output; the present invention fully considers the uncertainty factors of wind power and photovoltaic on the power supply side of the virtual power plant and the demand response factors of electric vehicles and interrupted loads on the power consumption side, provides a reference for the fine-grained modeling of the uncertainty and demand response of the virtual power plant, helps to avoid the net profit risk, and further ensures the reliability of economic benefits.

Description

Technical Field

[0001] The present invention belongs to the technical field of fine-grained modeling and uncertainty optimal scheduling of virtual power plants, and particularly relates to a robust optimal scheduling method for virtual power plants considering uncertainty and demand response. Background Art

[0002] The optimal scheduling of virtual power plants mainly uses advanced communication technologies and control strategies to aggregate distributed flexible resources inside. When meeting various system network and physical constraint conditions, it adjusts their output to participate in the operation of the electricity market, energy market or ancillary service market. Its goal is to fully utilize clean distributed power sources such as wind power and photovoltaic power in the virtual power plant on the premise of meeting the user load demand, and then use the economically operating units in the virtual power plant to meet the load demand, so as to achieve the optimal operation goals of maximum power generation revenue, minimum operating cost, minimum pollutants, carbon emissions, etc.

[0003] The distributed adjustable resources in the virtual power plant include a large number of renewable energy sources. The randomness of these resources themselves leads to a certain degree of uncertainty in the output of the virtual power plant, which is also a major feature that differentiates virtual power plants from traditional power plants. Specifically manifested as: when the superior dispatching issues a power generation order, the renewable energy power generation units that should have executed the corresponding plan fail to complete the corresponding power generation plan on time due to irresistible environmental and other factors, resulting in the virtual power plant being unable to output stable and reliable electric energy to the outside. Therefore, the factors affecting the optimal scheduling effect of the virtual power plant mainly come from the randomness of the output of renewable energy sources. With the large-scale access of electric vehicles to the virtual power plant and the increasing participation of interruptible loads, the demand response strategies on the user side become more complex, and the virtual power plant system operator's demand for a fine-grained model of electric vehicle and interruptible load demand response is more urgent, and it is necessary to further explore the adjustment potential on the user side. Summary of the Invention

[0004] Object of the Invention: In order to overcome the deficiencies in the prior art, the present invention provides a robust optimal scheduling method for virtual power plants considering uncertainty and demand response. It fully considers the uncertainty factors of wind power and photovoltaic power on the power supply side of the virtual power plant and the demand response factors of electric vehicles and interruptible loads on the power consumption side. Based on the robust optimal scheduling model of information distance decision theory, a fine-grained model under the uncertainty factors and demand response factors of the virtual power plant is proposed to help the virtual power plant system operator avoid the net profit risk and thus ensure the reliability of economic benefits, further explore the flexibility potential of electric vehicles and interruptible loads participating in the optimal scheduling in the virtual power plant, and further improve the enthusiasm of demand-side response, so as to provide reference and guidance for the fine-grained modeling and uncertainty optimal scheduling of virtual power plants.

[0005] Technical solution: In the first aspect, the present invention provides a robust optimal scheduling method for a virtual power plant considering uncertainty and demand response, including: collecting virtual power plant information, importing the virtual power plant information into a basic model, and outputting the optimized virtual power plant information after scheduling;

[0006] Among them, the virtual power plant information includes: photovoltaic power generation system information, wind turbine power generation system information, energy storage system information, electric vehicle cluster information, grid interface information, and load information;

[0007] Based on the virtual power plant information, the revenue from selling electricity to the grid by the virtual power plant, the revenue from selling electricity to electric vehicles, the revenue from selling electricity to conventional power loads, the cost of the energy storage system, the cost of interrupting load demand response, and the cost of electric vehicle demand response are respectively obtained;

[0008] According to the revenue from selling electricity to the grid by the virtual power plant, the revenue from selling electricity to electric vehicles, the revenue from selling electricity to conventional power loads, the cost of the energy storage system, the cost of interrupting load demand response, and the cost of electric vehicle demand response, an economic optimal scheduling objective function is constructed, and constraint conditions for the safe operation of the virtual power plant are set for the optimal scheduling objective function;

[0009] The constraint conditions for the safe operation of the virtual power plant are imported into the robust optimal scheduling model to optimize and solve the economic optimal scheduling objective function. The basic model updates the coefficients in the basic model in real time according to the optimized economic optimal scheduling objective function, and outputs the optimized virtual power plant information.

[0010] In a further embodiment, the basic model includes: a deterministic model, an uncertainty model, and a demand response model;

[0011] Among them, importing the virtual power plant information into the basic model includes: importing the load information and the energy storage system information into the deterministic model for calculation, respectively obtaining the output parameters of the energy storage system model and the output parameters of the conventional power load model, importing the photovoltaic power generation system information and the wind turbine power generation system information into the uncertainty model for calculation, respectively obtaining the output parameters of the photovoltaic power generation output model and the output parameters of the wind turbine power generation model, and importing the electric vehicle cluster information and the load information into the demand response model for calculation, respectively obtaining the output parameters of the electric vehicle model and the output parameters of the interrupted load model;

[0012] The photovoltaic power generation system information includes: photovoltaic power output prediction information and robust coefficient information;

[0013] The wind turbine power generation system information includes: wind power output prediction information and robust coefficient information;

[0014] The energy storage system information includes: installed capacity information, charge and discharge power limit information, state of charge limit information, and charge and discharge cycle limit information;

[0015] The electric vehicle cluster information includes: the maximum charging time information, the rated charging power information, the habitual charging start time information, and the charging electricity price information of each electric vehicle;

[0016] The grid interface information includes: the on-grid electricity price information and the on-grid power limit information; the load information includes: the conventional power load information and the interrupted load information.

[0017] In a further embodiment, the expression of the energy storage system model is:

[0018]

[0019] In the formula: are the stored electricity amounts of the energy storage system at time t and at time t + 1 respectively; σ es is the self-discharge rate of the energy storage system; are the discharge power and the charging power of the energy storage system at time t respectively; η es,cha and η es,dis are the charging efficiency and the discharge efficiency of the energy storage system respectively; Δt is the time step set for the virtual power plant simulation;

[0020] The expression of the conventional power load model is:

[0021] P t Ltrad = ξ L (P t Ltrad )

[0022] In the formula: P t Ltrad is the conventional power load demand at time t; ξ L (·) is the load research and statistics sample.

[0023] In a further embodiment, the expression of the photovoltaic power generation output model is:

[0024]

[0025] In the formula: P t pva and P t pvf are the actual output and the predicted output of the photovoltaic power generation at time t respectively; τ pv is the fluctuation range of the photovoltaic power generation uncertainty variable P t pva , that is, the robust coefficient related to the photovoltaic power generation; Ω(τ pv , P t pvf ) is the set relationship of P t pva ;

[0026] The expression of the model for the wind turbine power generation output is as follows:

[0027]

[0028] In the formula: P t wpa , P t wpf are respectively the actual output of the wind turbine power generation and the predicted output of the wind turbine power generation at time period t; τ wp is the fluctuation range of the wind turbine power generation uncertainty variable P t pva , that is, the robust coefficient related to the wind turbine power generation; Ω(τ wp , P t wpf ) is the set relationship of P t wpa .

[0029] In a further embodiment, the output parameters of the electric vehicle model include: the output parameters of the electric vehicle demand response model, the output parameters of the charging load transfer response model, and the output parameters of the electric vehicle response incentive model;

[0030] The expression of the electric vehicle demand response model is as follows:

[0031] P t after = P t before + P t in - P t out

[0032] In the formula: P t after , P t before are respectively the electric vehicle charging load after the demand response and the electric vehicle charging load before the demand response at time period t; P t in , P t out are respectively the electric vehicle charging load transferred in and the electric vehicle charging load transferred out at time period t;

[0033] The expression of the charging load transfer response model is as follows:

[0034]

[0035] In the formula: P t in , P t outare the charging load transferred in by electric vehicles and the charging load transferred out by electric vehicles during time period t, respectively; is the rated charging power of a single electric vehicle; k is the type of electric vehicle; are the number of electric vehicles of type k starting to charge during time period t and the number of electric vehicles of type k ending charging during time period t, respectively; is the number of electric vehicles of type k ending charging during time period t - d; d max is the longest charging time required for each type of electric vehicle; T is the optimization scheduling period of the virtual power plant;

[0036] The electric vehicle response incentive model includes an availability model, a feasibility model, a response satisfaction model, and a coupling relationship model between response incentive and response effect;

[0037] The expression of the availability model is:

[0038]

[0039] In the formula: is the availability of the electric vehicle user during time period t; are the demand-side response incentive subsidy price paid by the system operator during time period t and the electricity selling price of the system operator to electricity users, respectively;

[0040] The expression of the feasibility model is:

[0041]

[0042] In the formula: is the feasibility of the electric vehicle user during time period t; t before 、t after are the charging start time before the demand-side response of the electric vehicle user and the charging start time after the demand-side response, respectively; T is the optimization scheduling period of the virtual power plant;

[0043] The expression of the response satisfaction model is:

[0044]

[0045] In the formula: is the response satisfaction of the electric vehicle user during time period t; θ is the importance factor of the electric vehicle user for availability; is the availability of the electric vehicle user during time period t; is the feasibility of the electric vehicle user during time period t;

[0046] The expression of the coupling relationship model between response incentive and response effect is:

[0047]

[0048] In the formula: are respectively the number of electric vehicles of the k-th type charging during the transfer period t; is a Boolean variable indicating whether the i-th electric vehicle in the k-th type of electric vehicles participates in demand-side response during period t. When the value is 1, it means that the electric vehicle participates in the demand response during period t, and when the value is 0, it means that the electric vehicle does not participate in the demand response during period t; N i is the total number of electric vehicles of the k-th type; is the response satisfaction of electric vehicle users during period t; is the response satisfaction threshold of the i-th electric vehicle in the k-th type of electric vehicles.

[0049] In a further embodiment, the expression of the interruptible load model is:

[0050]

[0051] In the formula: is the response cost of the virtual power plant for interruptible load at time t; Ω m is the interrupt level; is the compensation price factor for the m-th level of interrupt level; is the interruptible load volume at the m-th level of interrupt level at time t; P t Lcurt is the interruptible load volume at all levels of interrupt levels at time t.

[0052] In a further embodiment, the expression of the economic optimal dispatch objective function is:

[0053] max f vpp = C profit - C cost

[0054] In the formula: f vpp is the net profit during the optimal dispatch period of the virtual power plant; C profit is the electricity sales revenue during the optimal dispatch period of the virtual power plant; C cost is the normal operation basic cost during the optimal dispatch period of the virtual power plant;

[0055] The electricity sales revenue C during the optimal dispatch period of the virtual power plant profit has the following expression:

[0056]

[0057] In the formula: C profit is the electricity sales revenue during the optimal dispatch period of the virtual power plant; is the electricity price at which the virtual power plant sells electricity to the power grid during period t; P t sell is the electricity selling power of the virtual power plant to the power grid during period t; is the electricity selling price from the system operator to electricity users during period t; is the equivalent total electricity load demand of the virtual power plant during period t excluding the electric vehicle charging load; P t after is the electric vehicle charging load after demand response during period t; T is the optimization scheduling period of the virtual power plant; Δt is the time step of the virtual power plant simulation setting;

[0058] The normal operation basic cost C within the optimization scheduling period of the virtual power plant cost has the following expression:

[0059] C cost = C cult + C ES + C DR

[0060]

[0061] In the formula: C cost is the normal operation basic cost within the optimization scheduling period of the virtual power plant; C cult , C ES , C DR are respectively the interruption load demand response cost, energy storage system cost, and electric vehicle demand response cost within the optimization scheduling period of the virtual power plant; is the response cost of the virtual power plant for interrupted load at time t; are respectively the cost loss coefficients for single charging and single discharging of the energy storage system; is a 0-1 variable of the charging state of the energy storage system at time t, taking the value of 1 when in the charging state, otherwise taking the value of 0; is a 0-1 variable of the discharging state of the energy storage system at time t, taking the value of 1 when in the discharging state, otherwise taking the value of 0; N i is the total number of the kth type of electric vehicles; is the demand response incentive subsidy price paid by the system operator during period t; is the number of the kth type of electric vehicles transferred to charge during period t; is the rated charging power of a single electric vehicle; T is the optimization scheduling period of the virtual power plant.

[0062] In a further embodiment, the constraint conditions for safe operation include: the constraint conditions for the safe operation of the energy storage system, the constraint conditions for the safe operation of the electric power balance, the constraint conditions for the safe operation of the load, and the constraint conditions for the safe operation of electric vehicles;

[0063] Among them, the expression of the constraint conditions for the safe operation of the energy storage system is:

[0064]

[0065] In the formula: are the discharge power and charging power of the energy storage system at time period t, respectively; are the upper limits of the discharge power and charging power of the energy storage system, respectively; is a 0-1 variable of the state of charge of the energy storage system at time t. It takes the value of 1 when in the charging state, otherwise it takes the value of 0; is a 0-1 variable of the discharge state of the energy storage system at time t. It takes the value of 1 when in the discharge state, otherwise it takes the value of 0; is the stored electricity of the energy storage system at time period t; is the stored electricity of the energy storage system at time period t+T; are the upper limit coefficient and lower limit coefficient of the real-time stored electricity of the energy storage system, respectively; is the rated capacity of the energy storage system; are the initial stored electricity and the final stored electricity of the energy storage system, respectively; N ES is the threshold of the charge and discharge times of the energy storage system within the optimal dispatching cycle of the virtual power plant; T is the optimal dispatching cycle of the virtual power plant;

[0066] The expression of the power balance and safe operation constraint condition is:

[0067]

[0068] In the formula: P t pva is the actual output of photovoltaic power generation at time period t; P t wpa is the actual output of wind power generation at time period t; are the discharge power and charging power of the energy storage system at time period t, respectively; P t after is the electric vehicle charging load after demand response at time period t; P t sell is the power sold by the virtual power plant to the power grid at time period t; is the equivalent total power load demand inside the virtual power plant except for the electric vehicle charging load at time period t; P t Ltrad is the conventional power load demand at time period t; P t Lcurt is the interrupted load amount at all levels of interruption levels at time t;

[0069] The expression of the load safe operation constraint condition is:

[0070]

[0071] In the formula: is the interrupted load amount at the mth level of interruption level at time t; is the interruption level coefficient at the m-th level; P t Ltrad is the conventional power load demand during period t; P t Lcurt is the interrupted load amount at all levels of interruption at time t; Ω m is the interruption level; P t sell is the power selling power of the virtual power plant to the power grid during period t; is the threshold of the power selling power of the virtual power plant to the power grid during period t;

[0072] The expression of the constraint condition for the safe operation of electric vehicles is:

[0073]

[0074] In the formula: k is the type of electric vehicle; is the number of electric vehicles of the k-th type charging during transfer period t and charging during transfer period t-d; d max is the longest charging time required for each type of electric vehicle; N i is the total number of electric vehicles of the k-th type; T is the optimization scheduling period of the virtual power plant.

[0075] In a further embodiment, the method of importing the constraint conditions for the safe operation of the virtual power plant into the robust optimization scheduling model to optimize and solve the economic optimization scheduling objective function, and the basic model updates the coefficients in the basic model in real time according to the optimized economic optimization scheduling objective function and outputs the information of the optimized virtual power plant is:

[0076] Substitute the constraint conditions for the safe operation of the energy storage system, the constraint conditions for the safe operation of the power balance, the constraint conditions for the safe operation of the load, and the constraint conditions for the safe operation of the electric vehicle into the robust optimization scheduling model to optimize and solve the economic optimization scheduling objective function, and select whether to adjust the deviation coefficient in the robust optimization scheduling model according to the solution result;

[0077] If the deviation coefficient in the robust optimization scheduling model is not adjusted, the basic model directly outputs the information of the optimized virtual power plant;

[0078] If the deviation coefficient in the robust optimization scheduling model needs to be adjusted, substitute the specific actual data of the actual application scenario into the corresponding calculation model for analysis to obtain the parameter information of each application scenario;

[0079] Re-calibrate the deviation coefficient in the robust optimization scheduling model according to the parameter information of each application scenario, and judge whether the scenario of the virtual power plant optimization scheduling has been adjusted. According to the judgment result, select whether to re-collect the information of the adjusted virtual power plant, optimize the coefficients of the basic model, and output the information of the optimized virtual power plant.

[0080] In a further embodiment, the model expression of the robust optimization scheduling method is as follows:

[0081] maxζ vpp =π RM τ pv +(1 - π RM )τ wp

[0082]

[0083] Where: ζ vpp is the fluctuation range of the comprehensive uncertain variable; π RM is the uncertainty weight coefficient, and its value is obtained from scheduling statistics; τ pv is the fluctuation range of the photovoltaic power generation uncertain variable P t pva , that is, the robust coefficient related to photovoltaic power generation; τ wp is the fluctuation range of the wind power generation uncertain variable P t pva , that is, the robust coefficient related to wind power generation; f vpp is the net profit within the optimization scheduling period of the virtual power plant; is the minimum expected net profit target value acceptable to the virtual power plant operator according to uncertainty factors; Δ0 is the deviation degree between the expected net profit target value and the optimal net profit value, called the deviation coefficient; is the optimal net profit value, that is, the deterministic optimization result without considering uncertainty factors; h(·) represents all equality constraint relationships; g(·) represents all inequality constraint relationships; U represents all input variables; d represents all decision variables; P t pva and P t pvf are the actual output and predicted output of wind power generation at time period t, respectively; Ω(τ pv , P t pvf ) represents the set relationship of P t pva ; P t wpa and P t wpf are the actual output and predicted output of photovoltaic power generation at time period t, respectively; Ω(τ wp , P t wpf ) represents the set relationship of P t wpa .

[0084] Beneficial effects: The present invention has the following advantages compared with the prior art:

[0085] (1) The present invention fully takes into account the uncertainty factors of wind power and photovoltaic power on the power supply side of the virtual power plant, which helps to further improve the reliability and accuracy of the system's optimal operation and can make up for the deficiencies of deterministic optimal dispatching;

[0086] (2) The present invention fully takes into account the demand response factors of electric vehicles and interrupted loads on the power consumption side, constructs a refined demand response model, which helps to further explore the flexibility potential of electric vehicles and interrupted loads in the virtual power plant to participate in optimal dispatching, and can further improve the enthusiasm of demand-side response;

[0087] (3) The present invention constructs a robust optimal dispatching model based on the information distance decision theory, which helps the virtual power plant system operator to avoid the net profit risk and thus ensure the reliability of economic benefits;

[0088] (4) The present invention comprehensively considers the uncertainty factors and demand response factors in the virtual power plant, and can provide reference and guidance for the fine-grained modeling and uncertainty optimal dispatching of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 is the architecture diagram of the robust optimal dispatching method for the virtual power plant considering uncertainty and demand response of the present invention;

[0090] Figure 2 is the example structure diagram of the robust optimal dispatching method for the virtual power plant considering uncertainty and demand response of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0091] In order to more fully understand the technical content of the present invention, the technical solution of the present invention will be further introduced and described below in conjunction with specific embodiments, but not limited thereto.

[0092] Example 1:

[0093] Collect the information of the virtual power plant, import the information of the virtual power plant into the basic model, and output the information of the virtual power plant after optimal dispatching; wherein the information of the virtual power plant includes: photovoltaic power generation system information, wind turbine power generation system information, energy storage system information, electric vehicle cluster information, power grid interface information, load information;

[0094] The photovoltaic power generation system information includes: photovoltaic power output prediction information, robust coefficient information;

[0095] The wind turbine power generation system information includes: wind power output prediction information, robust coefficient information;

[0096] The energy storage system information includes: installed capacity information, charge and discharge power limit information, state of charge limit information, charge and discharge times limit information;

[0097] The information of the electric vehicle cluster includes: the maximum charging time information, the rated charging power information, the habitual charging start time information, and the charging electricity price information of each electric vehicle;

[0098] The grid interface information includes: the on-grid electricity price information and the on-grid power limit information;

[0099] The load information includes: the conventional power load information and the interrupted load information.

[0100] Importing the virtual power plant information into the basic model includes: importing the load information and the energy storage system information into the deterministic model for calculation, respectively obtaining the output parameters of the energy storage system model and the output parameters of the conventional power load model, importing the photovoltaic power generation system information and the wind turbine power generation system information into the uncertainty model for calculation, respectively obtaining the output parameters of the photovoltaic power generation output model and the output parameters of the wind turbine power generation output model, and importing the electric vehicle cluster information and the load information into the demand response model for calculation, respectively obtaining the output parameters of the electric vehicle model and the output parameters of the interrupted load model;

[0101] Among them, the expression of the energy storage system model is:

[0102]

[0103] In the formula: are the stored electricity of the energy storage system at time t and at time t + 1 respectively; σ es is the self-discharge rate of the energy storage system; are the discharge power and the charging power of the energy storage system at time t respectively; η es,cha 、η es,dis are the charging efficiency and the discharge efficiency of the energy storage system respectively; Δt is the time step set for the virtual power plant simulation;

[0104] The expression of the conventional power load model is:

[0105] P t Ltrad =ξ L (P t Ltrad )

[0106] In the formula: P t Ltrad is the conventional power load demand at time t; ξ L (·) is the load research and statistics sample.

[0107] The expression of the photovoltaic power generation output model is:

[0108]

[0109] In the formula: P t pva 、Pt pvf are the actual output and the predicted output of photovoltaic power generation at time period t, respectively; τ pv is the uncertainty variable P of photovoltaic power generation t pva 's fluctuation range, that is, the robust coefficient related to photovoltaic power generation; Ω(τ pv , P t pvf ) is the set relationship of P t pva ;

[0110] The expression of the model of the wind turbine power output is:

[0111]

[0112] In the formula: P t wpa , P t wpf are the actual output and the predicted output of the wind turbine power generation at time period t, respectively; τ wp is the uncertainty variable P of the wind turbine power generation t pva 's fluctuation range, that is, the robust coefficient related to the wind turbine power generation; Ω(τ wp , P t wpf ) is the set relationship of P t wpa ;

[0113] The output parameters of the electric vehicle model include: the output parameters of the electric vehicle demand-side response model, the output parameters of the charging load transfer response model, and the output parameters of the electric vehicle response incentive model;

[0114] The expression of the electric vehicle demand-side response model is:

[0115] P t after = P t before + P t in - P t out

[0116] In the formula: P t after , P t before are the electric vehicle charging load after the demand-side response and the electric vehicle charging load before the demand-side response at time period t, respectively; P t in , P t outThey are the charging load when electric vehicles transfer in and the charging load when electric vehicles transfer out during time period t, respectively.

[0117] The expression of the charging load transfer response model is:

[0118]

[0119] In the formula: P t in and P t out They are the charging load when electric vehicles transfer in and the charging load when electric vehicles transfer out during time period t, respectively. is the rated charging power of a single electric vehicle; k is the type of electric vehicle; They are the number of electric vehicles of the k-th type starting to charge during time period t and the number of electric vehicles of the k-th type ending charging during time period t, respectively. is the number of electric vehicles of the k-th type ending charging at time period t - d; d max is the longest charging time required for each type of electric vehicle; T is the optimization scheduling period of the virtual power plant;

[0120] The electric vehicle response incentive model includes an availability model, a feasibility model, a response satisfaction model, and a coupling relationship model between response incentive and response effect;

[0121] The expression of the availability model is:

[0122]

[0123] In the formula: is the availability of electric vehicle users during time period t; They are the demand-side response incentive subsidy price paid by the system operator during time period t and the electricity selling price of the system operator to electricity users, respectively.

[0124] The expression of the feasibility model is:

[0125]

[0126] In the formula: is the feasibility of electric vehicle users during time period t; t before and t after They are the charging start time before the demand-side response of electric vehicle users and the charging start time after the demand-side response, respectively; T is the optimization scheduling period of the virtual power plant;

[0127] The expression of the response satisfaction model is:

[0128]

[0129] In the formula: is the response satisfaction degree of electric vehicle users at time period t; θ is the importance factor of electric vehicle users for availability; is the availability of electric vehicle users at time period t; is the feasibility of electric vehicle users at time period t;

[0130] The expression of the coupling relationship model between response incentive and response effect is:

[0131]

[0132] In the formula: are respectively the number of electric vehicles of the k-th type transferred to charge at time period t; is a Boolean variable indicating whether the i-th electric vehicle in the k-th type of electric vehicle participates in demand-side response at time period t. When the value is 1, it means that the electric vehicle participates in the demand response at time period t. When the value is 0, it means that the electric vehicle does not participate in the demand response at time period t; N i is the total number of the k-th type of electric vehicles; is the response satisfaction degree of electric vehicle users at time period t; is the response satisfaction threshold of the i-th electric vehicle in the k-th type of electric vehicles.

[0133] The expression of the interrupted load model is:

[0134]

[0135] In the formula: is the response cost of the virtual power plant for interrupted load at time t; Ω m is the interruption level; is the compensation price factor for the m-th level of interruption level; is the interrupted load volume at the m-th level of interruption level at time t; P t Lcurt is the interrupted load volume at all levels of interruption levels at time t.

[0136] Based on the collected information of the virtual power plant, the revenue from selling electricity to the power grid, the revenue from selling electricity to electric vehicles, the revenue from selling electricity to conventional power loads, the cost of the energy storage system, the cost of interrupted load demand response, and the cost of electric vehicle demand response are respectively obtained;

[0137] Based on the revenue from selling electricity to the power grid, the revenue from selling electricity to electric vehicles, the revenue from selling electricity to conventional power loads, the cost of the energy storage system, the cost of interrupted load demand response, and the cost of electric vehicle demand response, an economic optimal scheduling objective function is constructed, and constraint conditions for the safe operation of the virtual power plant are set for the optimal scheduling objective function;

[0138] Among them, the expression of the economic optimal scheduling objective function is:

[0139] max f vpp = C profit - C cost

[0140] where: f vpp is the net profit during the optimal dispatching period of the virtual power plant; C profit is the electricity sales revenue during the optimal dispatching period of the virtual power plant; C cost is the basic cost of normal operation during the optimal dispatching period of the virtual power plant;

[0141] The expression for the electricity sales revenue C profit during the optimal dispatching period of the virtual power plant is:

[0142]

[0143] where: C profit is the electricity sales revenue during the optimal dispatching period of the virtual power plant; is the electricity price at which the virtual power plant sells electricity to the power grid at time t; P t sell is the electric power sold by the virtual power plant to the power grid at time t; is the electricity price at which the system operator sells electricity to power users at time t; is the equivalent total power load demand inside the virtual power plant except for the electric vehicle charging load at time t; P t after is the electric vehicle charging load after demand response at time t; T is the optimal dispatching period of the virtual power plant; Δt is the time step of the virtual power plant simulation setting;

[0144] The expression for the basic cost of normal operation C cost during the optimal dispatching period of the virtual power plant is:

[0145] C cost = C cult + C ES + C DR

[0146]

[0147] where: C cost is the basic cost of normal operation during the optimal dispatching period of the virtual power plant; C cult , C ES , C DR are respectively the interruption load demand response cost, energy storage system cost, and electric vehicle demand response cost during the optimal dispatching period of the virtual power plant; is the response cost of the interruption load of the virtual power plant at time t; are respectively the cost loss coefficients of single charge and single discharge of the energy storage system; The state-of-charge 0-1 variable of the energy storage system at time t, which takes the value of 1 when in the state of charge and 0 otherwise; The state-of-discharge 0-1 variable of the energy storage system at time t, which takes the value of 1 when in the state of discharge and 0 otherwise; N i The total number of electric vehicles of the k-th type; The demand-side response incentive subsidy price paid by the system operator during period t; The number of electric vehicles of the k-th type transferred to charging during period t; The rated charging power of a single electric vehicle; T is the optimization scheduling period of the virtual power plant.

[0148] Among them, the constraints for safe operation include: the constraints for the safe operation of the energy storage system, the constraints for the safe operation of the electric power balance, the constraints for the safe operation of the load, and the constraints for the safe operation of the electric vehicles;

[0149] The expression for the constraints for the safe operation of the energy storage system is:

[0150]

[0151] In the formula: The discharge power and charging power of the energy storage system during period t, respectively; The upper limit of the discharge power and the upper limit of the charging power of the energy storage system, respectively; The state-of-charge 0-1 variable of the energy storage system at time t, which takes the value of 1 when in the state of charge and 0 otherwise; The state-of-discharge 0-1 variable of the energy storage system at time t, which takes the value of 1 when in the state of discharge and 0 otherwise; The stored energy of the energy storage system during period t; The stored energy of the energy storage system at time t+T; The upper limit coefficient and the lower limit coefficient of the real-time stored energy of the energy storage system, respectively; The rated capacity of the energy storage system; The stored energy at the initial time and the stored energy at the end time of the energy storage system, respectively; N ES The threshold value of the charge and discharge times of the energy storage system within the optimization scheduling period of the virtual power plant; T is the optimization scheduling period of the virtual power plant;

[0152] The expression for the constraints for the safe operation of the electric power balance is:

[0153]

[0154] In the formula: P t pva The actual output of the photovoltaic power generation during period t; P t wpa The actual output of the wind power generation during period t; are the discharge power and charging power of the energy storage system at time period t; P t after is the electric vehicle charging load after demand response at time period t; P t sell is the power sold by the virtual power plant to the power grid at time period t; is the equivalent total power load demand inside the virtual power plant except for the electric vehicle charging load at time period t; P t Ltrad is the conventional power load demand at time period t; P t Lcurt is the amount of interrupted load at all levels of interruption at time t;

[0155] The expression of the load safe operation constraint condition is:

[0156]

[0157] In the formula: is the amount of interrupted load at the mth level of interruption at time t; is the coefficient of the mth level of interruption; P t Ltrad is the conventional power load demand at time period t; P t Lcurt is the amount of interrupted load at all levels of interruption at time t; Ω m is the level of interruption; P t sell is the power sold by the virtual power plant to the power grid at time period t; is the threshold of the power sold by the virtual power plant to the power grid at time period t;

[0158] The expression of the electric vehicle safe operation constraint condition is:

[0159]

[0160] In the formula: k is the type of electric vehicle; are the numbers of the kth type of electric vehicle charging at time period t and charging at time period t - d; d max is the longest charging time required for each type of electric vehicle; N i is the total number of the kth type of electric vehicle; T is the optimization scheduling period of the virtual power plant.

[0161] The method of importing the constraint conditions of the virtual power plant safe operation into the robust optimization scheduling model to optimize and solve the economic optimization scheduling objective function, and the basic model updates the coefficients in the basic model in real time according to the optimized economic optimization scheduling objective function and outputs the information of the virtual power plant after optimized scheduling is:

[0162] Substitute the safety operation constraint conditions of the energy storage system, the safety operation constraint conditions of the electric power balance, the safety operation constraint conditions of the load, and the safety operation constraint conditions of the electric vehicles into the robust optimization scheduling model to optimize and solve the economic optimization scheduling objective function, and select whether to adjust the deviation coefficient in the robust optimization scheduling model according to the solution results;

[0163] If the deviation coefficient in the robust optimization scheduling model is not adjusted, the basic model directly outputs the virtual power plant information after optimized scheduling;

[0164] If the deviation coefficient in the robust optimization scheduling model needs to be adjusted, substitute the specific actual data of the actual application scenario into the corresponding calculation model for analysis to obtain the parameter information of each application scenario;

[0165] Re-check the deviation coefficient in the robust optimization scheduling model according to the parameter information of each application scenario, and judge whether the scenario of the virtual power plant optimized scheduling has been adjusted. According to the judgment result, select whether to re-collect the adjusted virtual power plant information, optimize the coefficients of the basic model, and output the virtual power plant information after optimized scheduling.

[0166] The model expression of the robust optimization scheduling method is:

[0167] maxζ vpp =π RM τ pv +(1-π RM )τ wp

[0168]

[0169] In the formula: ζ vpp is the fluctuation range of the comprehensive uncertain variable; π RM is the uncertainty weight coefficient, and its value is obtained from the scheduling statistics; τ pv is the fluctuation range of the photovoltaic power generation uncertain variable P t pva , which is also the robust coefficient related to photovoltaic power generation; τ wp is the fluctuation range of the wind power generation uncertain variable P t pva , which is also the robust coefficient related to wind power generation; f vpp is the net profit within the optimized scheduling period of the virtual power plant; is the minimum expected net profit target value acceptable to the virtual power plant operator according to the uncertainty factors; Δ0 is the deviation degree between the expected net profit target value and the optimal net profit value, which is called the deviation coefficient; is the optimal net profit value, that is, the deterministic optimization result without considering uncertainty factors; h(·) represents all equality constraint relationships; g(·) represents all inequality constraint relationships; U represents all input variables; d represents all decision variables; P t pva , P t pvf are respectively the actual output and the predicted output of wind power generation at time period t; Ω(τ pv , P t pvf ) represents the set relationship of P t pva ; P t wpa , P t wpf are respectively the actual output and the predicted output of photovoltaic power generation at time period t; Ω(τ wp , P t wpf ) represents the set relationship of P t wpa .

[0170] The schematic diagram of the architecture of an embodiment of a robust optimal scheduling method for a virtual power plant considering uncertainty and demand response in the present invention is as Figure 2 shown.

[0171] Embodiment description: In Figure 2 , power generation resources are aggregated inside the virtual power plant, including an energy storage system, a photovoltaic power generation system, and a wind power generation system; at the same time, power consumption resources are aggregated, including a conventional power load, an interrupted power load, and an electric vehicle cluster charging load; the direction of power flow between internal resources is as Figure 2 indicated by the arrows. The virtual power plant is connected to the large power grid through a grid interface, coordinates and cooperates with the large power grid, and the virtual power plant sells electricity to the large power grid through the grid interface.

[0172] Specific implementation process: Combining Figure 1 and Figure 2 to illustrate the basic process steps of the embodiment:

[0173] Step 1: Collect the internal resource information of the virtual power plant. Among them, the information of the photovoltaic power generation system includes the predicted photovoltaic power output information, the robust coefficient information to be set, the information of the wind turbine power generation system includes the predicted wind power output information, the robust coefficient information; among them, the information of the energy storage system includes the installed capacity information, the charge and discharge power limit information, the state of charge limit information, the charge and discharge times limit information; among them, the information of the electric vehicle cluster includes the maximum charging time information of each electric vehicle, the rated charging power information, the habitual charging start time information, the charging electricity price information, and approximate the maximum charging time to an integer; among them, the grid interface information includes the on-grid electricity price information, the on-grid power limit information; among them, the dispatching data set information includes the ideal economic cost information, the tolerable loss economic degree information, the single charge and discharge loss information of the energy storage system; among them, the load information includes the conventional power load information, the interrupted load information.

[0174] Step 2: Substitute the energy storage system information and the conventional power load information into the established deterministic model, substitute the photovoltaic power generation output information and the wind turbine power generation output information into the established uncertainty model, substitute the electric vehicle information and the interrupted load information into the established demand response model, and complete the data entry.

[0175] Step 3: Substitute the parameter information of each part of the virtual power plant's electricity sales revenue to the grid, electricity sales revenue to electric vehicles, electricity sales revenue to conventional power loads, energy storage system cost, interrupted load demand response cost, and electric vehicle demand response cost into the constructed economic optimal dispatching objective function.

[0176] Step 4: Set and check the safety operation constraint conditions of the energy storage system, the safety operation constraint conditions of the electric power balance, the safety operation constraint conditions of the load, and the safety operation constraint conditions of the electric vehicles one by one, and confirm that the set parameters can meet the safety operation requirements of the virtual power plant.

[0177] Step 5: Perform optimal dispatching according to the proposed robust optimal dispatching model based on the information distance decision theory, output the optimal dispatching result of the virtual power plant, and judge the deviation coefficient in the robust optimal dispatching model. Among them, according to the judgment result of the deviation coefficient of the robust optimal dispatching model in Step 5, determine whether it is a new application scenario. If it is a new application scenario, repeat Steps 1 to 5 in a loop.

[0178] Through the description and analysis of the specific implementation process of this embodiment, it can be seen that the robust optimal scheduling method for virtual power plants considering uncertainty and demand response proposed by the present invention is effective, easy to operate, widely applicable, and reasonable, which helps to further improve the reliability and accuracy of the optimal operation of the system; is conducive to making up for the deficiencies of deterministic optimal scheduling; helps to further explore the flexibility potential of electric vehicles and interrupted loads participating in the optimal scheduling in virtual power plants, and can further improve the enthusiasm of demand-side response; helps the virtual power plant system operator avoid the net profit risk and thus ensure the reliability of economic benefits; and can provide reference and guidance for the fine-grained modeling and uncertainty robust optimal scheduling of virtual power plants considering uncertainty factors and demand response factors.

[0179] Embodiments of the present application may be provided as a method, a system, or a computer program product. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0180] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0181] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0182] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing in the processFigure 1 one process or multiple processes and / or boxes Figure 1 steps of functions specified in one box or multiple boxes

[0183] The above is only the preferred embodiment of the present invention. Without departing from the technical principle of the present invention, several improvements and deformations can be made, and these improvements and deformations should also be regarded as the protection scope of the present invention.

Claims

1. A robust optimal scheduling method for a virtual power plant considering uncertainty and demand response, characterized in that Including: Collecting virtual power plant information, importing the virtual power plant information into the basic model, and outputting the optimized and scheduled virtual power plant information; Among them, the virtual power plant information includes: photovoltaic power generation system information, wind turbine power generation system information, energy storage system information, electric vehicle cluster information, grid interface information, and load information; Based on the virtual power plant information, respectively obtaining the revenue from selling electricity to the grid by the virtual power plant, the revenue from selling electricity to electric vehicles, the revenue from selling electricity to conventional power loads, the cost of the energy storage system, the cost of interruptible load demand response, and the cost of electric vehicle demand response; Constructing an economic optimal scheduling objective function according to the revenue from selling electricity to the grid by the virtual power plant, the revenue from selling electricity to electric vehicles, the revenue from selling electricity to conventional power loads, the cost of the energy storage system, the cost of interruptible load demand response, and the cost of electric vehicle demand response, and setting the constraint conditions for the safe operation of the virtual power plant for the optimal scheduling objective function; Importing the constraint conditions for the safe operation of the virtual power plant into the robust optimal scheduling model to optimize and solve the economic optimal scheduling objective function, and the basic model updates the coefficients in the basic model in real time according to the optimized economic optimal scheduling objective function and outputs the optimized and scheduled virtual power plant information; The basic model includes: a deterministic model, an uncertainty model, and a demand response model; Among them, importing the virtual power plant information into the basic model includes: importing the load information and the energy storage system information into the deterministic model for calculation, respectively obtaining the output parameters of the energy storage system model and the output parameters of the conventional power load model, importing the photovoltaic power generation system information and the wind turbine power generation system information into the uncertainty model for calculation, respectively obtaining the output parameters of the photovoltaic power generation output model and the output parameters of the wind turbine power generation output model, and importing the electric vehicle cluster information and the load information into the demand response model for calculation, respectively obtaining the output parameters of the electric vehicle model and the output parameters of the interruptible load model; The photovoltaic power generation system information includes: photovoltaic power output prediction information and robust coefficient information; The wind turbine power generation system information includes: wind power output prediction information and robust coefficient information; The energy storage system information includes: installed capacity information, charge and discharge power limit information, state of charge limit information, and charge and discharge cycle limit information; The electric vehicle cluster information includes: the maximum charging time information of each electric vehicle, the rated charging power information, the habitual charging start time information, and the charging electricity price information; The grid interface information includes: on-grid electricity price information and on-grid power limit information; The load information includes: conventional power load information and interruptible load information; The expression of the photovoltaic power generation output model is: ； Wherein: and are respectively the actual output and the predicted output of photovoltaic power generation in the time period ; is the uncertainty variable of photovoltaic power generation , that is, the robustness coefficient related to photovoltaic power generation; is of the set relationship; The expression of the wind turbine power generation output model is: ； In the formula: and are respectively the actual output and the predicted output of the wind turbine power generation during the time period ; is the uncertainty variable of the wind turbine power generation , that is, the robustness coefficient related to the wind turbine power generation; is the set relationship of; The output parameters of the electric vehicle model include: the output parameters of the electric vehicle demand-side response model, the output parameters of the charging load transfer response model, and the output parameters of the electric vehicle response incentive model; The expression of the electric vehicle demand-side response model is: ； Wherein: , are respectively the electric vehicle charging load after demand - side response and the electric vehicle charging load before demand - side response during the time period ; , are respectively the charging load when electric vehicles transfer in and the charging load when electric vehicles transfer out during the time period . The expression of the charging load transfer response model is: ； Wherein: and are the charging load transferred in and the charging load transferred out of electric vehicles during the time period respectively; is the rated charging power of a single electric vehicle; is the type of electric vehicle; and are the number of electric vehicles of the th type charging during the transfer-in time period and the number of electric vehicles of the th type charging during the transfer-out time period respectively; is the number of electric vehicles of the th type charging during the transfer-out time period ; is the longest charging time required for each type of electric vehicle; is the optimal scheduling period of the virtual power plant; The electric vehicle response incentive model includes an availability model, a feasibility model, a response satisfaction model, and a coupling relationship model between response incentive and response effect; The expression of the availability model is: ； Wherein: is the availability of electric vehicle users during time period ; , are respectively the demand response incentive subsidy price paid by the system operator during time period and the electricity selling price of the system operator to electricity users; The expression of the feasibility model is: ； Wherein: is the feasibility of electric vehicle users during time period ; , are respectively the charging start time before the demand response of electric vehicle users and the charging start time after the demand response; is the optimal scheduling period of the virtual power plant; The expression of the response satisfaction model is: ； Wherein: is the response satisfaction of electric vehicle users during the time period ; is the importance factor of electric vehicle users for availability; is the availability of electric vehicle users during the time period ; is the feasibility of electric vehicle users during the time period ; The expression of the coupling relationship model between response excitation and response effect is as follows: ； where: are respectively the number of Class electric vehicles transferred in during the charging period; is the Boolean variable indicating whether the th electric vehicle in Class participates in demand response during the period . When the value is 1, it means that the electric vehicle participates in the demand response during the period , and when the value is 0, it means that the electric vehicle does not participate in the demand response during the period is the total number of Class electric vehicles; is the response satisfaction of electric vehicle users during the period is the response satisfaction threshold of the th electric vehicle in Class The expression of the interruptible load model is as follows: ； Wherein: is the response cost of the virtual power plant for the interrupted load at time ; is the interruption level; is the compensation price factor for the -level interruption level; is the interrupted load volume at the -level interruption level at time ; is the interrupted load volume of all levels of interruption levels at time . The expression of the economic optimal dispatch objective function is as follows: ； Wherein: is the net profit during the optimal scheduling period of the virtual power plant; is the electricity sales revenue during the optimal scheduling period of the virtual power plant; is the basic cost of normal operation during the optimal scheduling period of the virtual power plant; The electricity sales revenue within the optimization scheduling period of the virtual power plant The expression is as follows: ； Wherein: is the electricity sales revenue during the optimization scheduling period of the virtual power plant; is the electricity price at which the virtual power plant sells electricity to the power grid during time period ; is the electricity sales power at which the virtual power plant sells electricity to the power grid during time period ; is the electricity sales price at which the system operator sells electricity to power users during time period ; is the equivalent total power load demand other than the electric vehicle charging load inside the virtual power plant during time period ; is the electric vehicle charging load after demand-side response during time period ; is the optimization scheduling period of the virtual power plant; is the time step of the virtual power plant simulation setting; The normal operation basic cost within the optimization scheduling period of the virtual power plant The expression is as follows: ； Wherein: is the normal operation basic cost within the virtual power plant optimization scheduling period; , , are respectively the interruption load demand response cost, energy storage system cost, and electric vehicle demand response cost within the virtual power plant optimization scheduling period; is the response cost of the virtual power plant for the interruption load at time ; , are respectively the single - charge and single - discharge cost loss coefficients of the energy storage system; is the 0 - 1 variable of the charging state of the energy storage system at time , taking the value of 1 when in the charging state, otherwise taking the value of 0; is the 0 - 1 variable of the discharging state of the energy storage system at time , taking the value of 1 when in the discharging state, otherwise taking the value of 0; is the total number of electric vehicles of the th type; is the demand - side response incentive subsidy price paid by the system operator during the time period ; is the number of electric vehicles of the th type transferred to charge during the time period ; is the rated charging power of a single electric vehicle; is the virtual power plant optimization scheduling period; The constraint conditions for safe operation include: the constraint conditions for the safe operation of the energy storage system, the constraint conditions for the safe operation of the electric power balance, the constraint conditions for the safe operation of the load, and the constraint conditions for the safe operation of electric vehicles; Among them, the expression of the constraint conditions for the safe operation of the energy storage system is as follows: ； Wherein: and are the discharge power and charge power of the energy storage system during the time period respectively; and are the upper limit of the discharge power and the upper limit of the charge power of the energy storage system respectively; is a 0-1 variable of the charge state of the energy storage system at time taking the value of 1 when in the charge state and 0 otherwise; is a 0-1 variable of the discharge state of the energy storage system at time taking the value of 1 when in the discharge state and 0 otherwise; is the stored electricity of the energy storage system during the time period ; is the stored electricity of the energy storage system during the time period ; and are the upper limit coefficient and lower limit coefficient of the real-time stored electricity of the energy storage system respectively; is the rated capacity of the energy storage system; and are the initial stored electricity and the final stored electricity of the energy storage system respectively; is the threshold value of the charge and discharge times of the energy storage system within the optimal dispatching period of the virtual power plant; is the optimal dispatching period of the virtual power plant; The expression of the constraint conditions for the safe operation of the electric power balance is as follows: ； Wherein: is the actual output of photovoltaic power generation in time period ; is the actual output of wind power generation in time period ; , are respectively the discharge power and charge power of the energy storage system in time period ; is the electric vehicle charging load after demand-side response in time period ; is the power sold by the virtual power plant to the power grid in time period ; is the equivalent total power load demand inside the virtual power plant except for the electric vehicle charging load in time period ; is the conventional power load demand in time period ; is the interruption load amount at all levels of interruption levels at time ; The expression of the constraint conditions for the safe operation of the load is as follows: ； Wherein: is the interruption load at the th level interruption level at time is the th level interruption level coefficient; is the conventional power load demand during the time period ; is the interruption load of all level interruption levels at time ; is the interruption level; is the power selling rate of the virtual power plant to the power grid during the time period ; is the threshold of the power selling rate of the virtual power plant to the power grid during the time period ; The expression of the constraint conditions for the safe operation of electric vehicles is as follows: ； Wherein: is the category of electric vehicles; , is the transfer-out period of the category of electric vehicles charging and transfer-out period the quantity of charging; is the longest charging time required for each category of electric vehicles; is the total quantity of the category of electric vehicles; is the optimization scheduling period of the virtual power plant; The expression of the robust optimal dispatch model is as follows: ； In the formula: is the fluctuation range of the comprehensive uncertain variable; is the uncertainty weight coefficient, and its value is obtained by dispatching statistics; is the uncertain variable of photovoltaic power generation The fluctuation range, that is, the robust coefficient related to photovoltaic power generation; is the uncertain variable of wind power generation The fluctuation range, that is, the robust coefficient related to wind power generation; is the net profit within the optimization dispatching period of the virtual power plant; is the minimum expected net profit target value acceptable to the virtual power plant operator according to uncertain factors; is the deviation degree between the expected net profit target value and the optimal net profit value, called the deviation coefficient; is the optimal net profit value, that is, the deterministic optimization result without considering uncertain factors; is all equality constraint relations; is all inequality constraint relations; is all input variables; is all decision variables; 、 are the actual output and the predicted output of wind power generation at time period respectively; is The set relationship of; 、 are the actual output and the predicted output of photovoltaic power generation at time period respectively; is The set relationship of.

2. The robust optimal scheduling method for a virtual power plant considering uncertainty and demand response according to claim 1, characterized in that The expression of the energy storage system model is as follows: ； Wherein: , are respectively the stored electricity of the energy storage system in time period , and in time period ; is the self-discharge rate of the energy storage system; , are respectively the discharge power and charge power of the energy storage system in time period ; , are respectively the charge efficiency and discharge efficiency of the energy storage system; is the time step set for the virtual power plant simulation; The expression of the conventional power load model is as follows: ； In the formula: is the conventional power load demand during the time period ; is the load research and statistics sample.

3. The robust optimal scheduling method for a virtual power plant considering uncertainty and demand response according to claim 1, characterized in that, The method of importing the constraint conditions for the safe operation of the virtual power plant into the robust optimal dispatch model to optimize and solve the economic optimal dispatch objective function, and the basic model updates the coefficients in the basic model in real time according to the optimized economic optimal dispatch objective function and outputs the information of the virtual power plant after optimal dispatch is as follows: Substitute the constraint conditions for the safe operation of the energy storage system, the constraint conditions for the safe operation of the electric power balance, the constraint conditions for the safe operation of the load, and the constraint conditions for the safe operation of electric vehicles into the robust optimal dispatch model to optimize and solve the economic optimal dispatch objective function, and select whether to adjust the deviation coefficient in the robust optimal dispatch model according to the solution result; If the deviation coefficient in the robust optimal dispatch model is not adjusted, the basic model directly outputs the information of the virtual power plant after optimal dispatch; If the deviation coefficient in the robust optimal dispatch model needs to be adjusted, substitute the specific actual data of the actual application scenario into the corresponding calculation model for analysis to obtain the parameter information of each application scenario; Recheck the deviation coefficient in the robust optimal dispatch model according to the parameter information of each application scenario, judge whether the scenario of the virtual power plant optimal dispatch has been adjusted, select whether to re-collect the information of the virtual power plant after adjustment according to the judgment result, optimize the coefficients of the basic model, and output the information of the virtual power plant after optimal dispatch.

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