A frequency regulation and dispatch method for energy storage virtual power plants involving electric vehicles

Abstract

This invention discloses a frequency regulation scheduling method for energy storage-based virtual power plants involving electric vehicles. Specifically, it involves: establishing a frequency regulation resource model for the energy storage-based virtual power plant to obtain the real-time status and capacity of each frequency regulation resource; establishing a comprehensive frequency regulation performance index model to obtain the frequency regulation performance of each resource; establishing a revenue model for the energy storage-based virtual power plant oriented towards the frequency regulation ancillary services market; solving the revenue model for the energy storage-based virtual power plant oriented towards the frequency regulation ancillary services market to obtain the AGC power optimization allocation result; and scheduling the virtual power plant according to the allocation result. This method considers the synergistic optimization of frequency regulation performance and cost, and differentiates the scheduling of energy storage resources with different frequency regulation performance based on the response to frequency regulation commands. This achieves complementary advantages of diverse energy storage and maximizes the long-term revenue of the virtual power plant, which is conducive to enhancing the enthusiasm and sustainability of energy storage participating in the frequency regulation ancillary services market.

Description

Technical Field

[0001] This invention relates to the field of energy storage-assisted frequency regulation, and more specifically, to a frequency regulation and scheduling method for an energy storage virtual power plant involving electric vehicles. Background Technology

[0002] The main task of the new power system is to build a high-proportion renewable energy supply and consumption system. Currently, the large fluctuations and weak controllability of renewable energy power generation, mainly wind and solar, seriously threaten the frequency security of the power system.

[0003] As an advanced energy solution, energy storage virtual power plants provide strong support for the precise regulation, stable control, and safe operation of power grid frequencies. Energy storage virtual power plants can participate in the power frequency regulation ancillary services market as independent entities. With the increasing variety of energy storage types and the deployment of energy storage at renewable energy power plants, maximizing the utilization efficiency of energy storage systems is a pressing issue. Therefore, it is necessary to coordinate energy storage resources with different lifespans, construction costs, and frequency regulation performance, and rationally construct strategies for virtual power plants to participate in frequency regulation ancillary services, thereby improving the continuous regulation capabilities of multi-energy storage systems in virtual power plants and the revenue from frequency regulation ancillary services. Summary of the Invention

[0004] The purpose of this invention is to further consider the application of multi-energy storage resources in the field of power system frequency regulation in virtual power plants. It proposes a frequency regulation scheduling method for energy storage-based virtual power plants involving electric vehicles. This method involves establishing a frequency regulation resource model for energy storage-based virtual power plants to obtain the real-time status and capacity of each resource; establishing a comprehensive frequency regulation performance index model to obtain the frequency regulation performance of each resource; establishing a revenue model for energy storage-based virtual power plants oriented towards the frequency regulation ancillary services market; solving the revenue model for energy storage-based virtual power plants oriented towards the frequency regulation ancillary services market to obtain the AGC power optimization allocation result; and then scheduling the virtual power plant according to the allocation result. Considering the synergistic optimization of frequency regulation performance and cost, the method differentiates the scheduling of energy storage resources with different frequency regulation performance based on the response to frequency regulation commands, achieving complementary advantages of multi-energy storage and maximizing the long-term revenue of the virtual power plant. This is beneficial to enhancing the enthusiasm and sustainability of energy storage in participating in the frequency regulation ancillary services market.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a frequency regulation and dispatching method for an energy storage virtual power plant involving electric vehicles, comprising the following steps:

[0006] Step S1: Establish a frequency regulation resource model for an energy storage virtual power plant and obtain the real-time status and capacity of each frequency regulation resource;

[0007] Step S2: Establish a comprehensive frequency modulation performance index model and obtain the frequency modulation performance of each frequency modulation resource;

[0008] Step S3: Establish a revenue model for energy storage virtual power plants oriented towards the frequency regulation ancillary services market;

[0009] Step S4: Solve the revenue model of the energy storage virtual power plant for the frequency regulation ancillary service market to obtain the AGC power optimization allocation result, and the virtual power plant performs scheduling according to the allocation result.

[0010] Further: In step S1, the process of establishing a frequency regulation resource model for an energy storage virtual power plant and obtaining the real-time status and capacity of each frequency regulation resource is as follows:

[0011] Step S11: Establish a frequency regulation model for distributed generator sets;

[0012] Step S12: Establish frequency regulation models for electrochemical energy storage and phase change energy storage;

[0013] Step S13: Establish a frequency regulation model for electric vehicle charging stations.

[0014] Furthermore: In step S11, a distributed generator set frequency regulation model is established:

[0015] The generator set needs to output power according to AGC (automatic generation control) commands:

[0016]

[0017] In the formula: The generator set AGC command at time t; P dr,i Let be the output power of the i-th distributed generator set at time t; I is the total number of distributed generator sets.

[0018] The frequency regulation cost model for distributed generator sets is expressed as follows:

[0019]

[0020] In the formula: C dg Frequency regulation cost of distributed generator sets; The unit operating cost of the i-th distributed generator set; The actual frequency regulation mileage of the i-th distributed generator unit during time period t; The penalty for wind and solar curtailment is applied to the i-th distributed generator unit, if the generator unit is not a wind or solar power generator. Let t be the power curtailed by the i-th distributed generator unit during time period t; T and Δt are the total number of time periods within the scheduling cycle and the duration of each scheduling cycle.

[0021] Further: In step S12, establish frequency modulation models for electrochemical energy storage and phase change energy storage.

[0022]

[0023] In the formula: E(t+1) is the charge at time t+1; E(t) is the charge at time t; P ch (t), P dis (t) represents the charge / discharge amount at time t; P down (t), P up (t) represents the upward and downward frequency modulation capacities at time t; η c η d These represent the charge / discharge efficiency; E1 is the self-discharge energy; Δt is the charge / discharge period; and SOC is the state of charge / discharge. es (t) represents the state of charge of the electrochemical energy storage at time t; E e This refers to the rated capacity of electrochemical energy storage.

[0024] The frequency regulation cost model for electrochemical energy storage is expressed as follows:

[0025]

[0026] In the formula: C es Frequency regulation costs for electrochemical energy storage; This represents the average initial investment cost coefficient for electrochemical energy storage. P represents the average cost coefficient for the depth of discharge in electrochemical energy storage. es (t) represents the charge / discharge power of the electrochemical energy storage at time t; D es (t) represents the depth of charge / discharge at time t in electrochemical energy storage.

[0027] Establish a phase change energy storage frequency regulation model:

[0028]

[0029] In the formula: R(t+1) is the heat stored in phase change energy at time t+1; R(t) is the heat stored in phase change energy at time t; σ ps This is the self-loss coefficient; These represent the net thermal input and net thermal output of phase change energy storage at time t, respectively; Δt is the time period; η hs For conversion efficiency; These represent the heat recovery from the gas turbine, the heat generated by the gas boiler, and the heat generated by the upward frequency regulation at time t, respectively. These represent the heat consumption at time t and the heat generated by downward frequency modulation, respectively; SOC ps (t) represents the thermal storage state at time t in phase change energy storage; R e This is the rated capacity of phase change energy storage.

[0030] The frequency regulation cost model for phase change energy storage is expressed as follows:

[0031]

[0032] In the formula: C psFrequency regulation cost for phase change energy storage; ρ ps The unit operating cost of phase change energy storage.

[0033] Further: In step S13, a frequency regulation model for electric vehicle charging stations is established:

[0034]

[0035] In the formula: and These represent the maximum charging and discharging power of charging station j during time period t; ΔS j,t The change in electricity consumption at charging station j due to the change in the grid-connected status of electric vehicles during time period t; S j,t 、S j,t-1 η represents the electricity consumption of charging station j during time period t and time period t-1, respectively; Δt represents the time period of change; η ch and η dis These represent the charging and discharging efficiencies of the charging station, respectively; η ref This is the discharge compensation coefficient, determined by the discharge loss.

[0036] Frequency regulation costs for electric vehicle charging stations:

[0037]

[0038] In the formula: C ev Frequency regulation costs for electric vehicle charging stations; The average cost coefficient for the initial investment of electric vehicle charging stations; P represents the average cost coefficient for the depth of discharge of electric vehicle charging stations. ev (t) represents the charging / discharging power of the electric vehicle charging station at time t; D ev (t) represents the charging / discharging depth of the electric vehicle charging station at time t.

[0039] Furthermore: In step S2, a comprehensive frequency modulation performance index model is established to obtain the frequency modulation performance of each frequency modulation resource, as detailed below:

[0040] Step S21: Establish frequency modulation performance indicators;

[0041] Step S22: Establish a comprehensive index model for frequency modulation performance.

[0042] Furthermore: In step S21, frequency modulation performance indicators are established:

[0043] Referring to the relevant regulations on AGC frequency modulation assessment indicators, the frequency modulation performance indicators consist of response time, regulation rate, and regulation accuracy: Response time refers to the delay time for the frequency modulation resource to reach the same output direction as required by the AGC frequency modulation command, that is, the time for the frequency modulation resource to cross the regulation dead zone.

[0044]

[0045] In the formula: K 1,n This is the response time metric for the nth response command; and These represent the dead time during the adjustment process after receiving the nth instruction, the time from receiving the instruction to completing it, respectively; K1 is the response time index within the settlement cycle; and N is the total number of responses within the settlement cycle.

[0046] The regulation rate refers to the rate at which a frequency modulation resource reaches the required output when it receives an AGC frequency modulation command. It depends on the standard regulation rate set by the frequency modulation market and the actual regulation rate of the frequency modulation resource.

[0047]

[0048] In the formula: The actual adjustment speed of the frequency modulation resource in the nth frequency modulation command; These represent the starting and ending outputs of the nth frequency modulation command, respectively; K2 is the response rate index within this settlement cycle; ν b The standard adjustment speed pre-set for the FM market.

[0049] Regulation accuracy refers to the degree of deviation between the frequency modulation resources and the AGC frequency modulation commands. It is calculated as the ratio of the maximum deviation and the average deviation between the output required by the scheduling command and the actual output of the frequency modulation resources.

[0050]

[0051] In the formula: K3 is the adjustment accuracy index within the settlement period; These represent the required output and actual output for the nth frequency modulation command, respectively.

[0052] Step S22 establishes a comprehensive index model for frequency modulation performance:

[0053] The frequency modulation performance index is normalized using the extreme value method, and then its weighted average value is calculated:

[0054]

[0055] K = λ1k1 + λ2k2 + λ3k3,

[0056] In the formula: k i K represents the i-th frequency modulation performance index after normalization. i,max K i,min λ1, λ2, and λ3 are the maximum and minimum values ​​of the i-th frequency modulation performance index, respectively; λ1, λ2, and λ3 are the weights of the first to third frequency modulation performance indexes, respectively; K is the comprehensive frequency modulation performance index.

[0057] Furthermore: In step S3, a revenue model for energy storage virtual power plants oriented towards the frequency regulation ancillary services market is established, as follows:

[0058] Step S31: Establish a revenue model for the frequency modulation ancillary services market;

[0059] Step S32: Establish a frequency modulation cost model;

[0060] Step S33: Establish the objective function and constraints for scheduling energy storage virtual power plants in the frequency regulation ancillary service market.

[0061] Furthermore: In step S31, a revenue model for the frequency modulation ancillary service market is established:

[0062]

[0063] In the formula: R represents the revenue from participating in the FM market; These are the frequency modulation capacity price and frequency modulation mileage price for the t-th time period, respectively; These represent the frequency regulation capacity and frequency regulation mileage of the virtual power plant in the t-th time period, respectively.

[0064] Furthermore: In step S32, a frequency modulation cost model is established:

[0065] Operation and maintenance costs of virtual power plants participating in frequency regulation and energy storage:

[0066] C m =C dr +C es +C ps +C ev ,

[0067] In the formula: C m Cost of operation and maintenance for virtual power plants participating in frequency regulation and energy storage.

[0068] Cost of virtual power plants participating in frequency regulation power purchase:

[0069]

[0070] In the formula: C buy The cost of virtual power plants participating in frequency regulation power purchase; σ is the average loss coefficient of frequency regulation capacity; Let t be the electricity price for the t-th time period.

[0071] Furthermore: In step S33, the objective function and constraints for scheduling energy storage virtual power plants in the frequency regulation ancillary service market are established:

[0072] Objective function:

[0073] maxR-C m -C buy ,

[0074] The energy storage state should meet the following constraints:

[0075]

[0076] In the formula: and The upper and lower limits of charge in electrochemical energy storage, respectively; and Upper and lower limits for phase change energy storage and thermal storage, respectively; and These represent the upper and lower limits of the charge level for electric vehicle charging stations, respectively.

[0077] Frequency modulation output should meet the following constraints:

[0078]

[0079] In the formula: P AGC (t) represents the AGC command at time t; P o,m (t) represents the frequency regulation output of the m-th frequency regulation resource in the virtual power plant at time t; M is the total number of frequency regulation resources; E b This represents the upper limit of the deviation between the frequency regulation output of the virtual power plant and the AGC command within period T.

[0080] Furthermore: In step S4, the revenue model of the energy storage virtual power plant oriented towards the frequency regulation ancillary service market is solved to obtain the AGC power optimization allocation result. The virtual power plant is then scheduled according to the allocation result, as follows:

[0081] Based on a revenue model for energy storage-based virtual power plants targeting the frequency regulation ancillary services market, the optimal AGC power allocation scheme is solved using the sequential quadratic programming method of the fmincon function in the MATLAB optimization toolbox. According to the allocation results, the virtual power plant schedules energy storage resources with different performance and capacity in real time, achieving efficient utilization of energy storage resources and maximizing revenue.

[0082] Compared with the prior art, the present invention has the following beneficial effects:

[0083] This invention proposes a frequency regulation scheduling method for energy storage-based virtual power plants involving electric vehicles. It establishes a frequency regulation resource model for energy storage-based virtual power plants to obtain the real-time status and capacity of each resource; establishes a comprehensive frequency regulation performance index model to obtain the frequency regulation performance of each resource; establishes a revenue model for energy storage-based virtual power plants oriented towards the frequency regulation ancillary services market; solves the revenue model for energy storage-based virtual power plants oriented towards the frequency regulation ancillary services market to obtain the AGC power optimization allocation result, and the virtual power plant performs scheduling based on the allocation result. Considering the synergistic optimization of frequency regulation performance and cost, it differentiates the scheduling of energy storage resources with different frequency regulation performances based on the response to frequency regulation commands, providing a strategy for new virtual power plants to participate in the frequency regulation ancillary services market. This achieves complementary advantages of diversified energy storage and maximizes the long-term revenue of virtual power plants, which is conducive to improving the enthusiasm and sustainability of energy storage in participating in the frequency regulation ancillary services market. Attached Figure Description

[0084] Figure 1 This is a flowchart of the frequency regulation and scheduling method for an energy storage virtual power plant involving electric vehicles proposed in this invention. Detailed Implementation

[0085] This section will describe in detail specific embodiments of the present invention. Preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the drawings is to supplement the textual description with graphics, so that people can intuitively and vividly understand each technical feature and overall technical solution of the present invention, but they should not be construed as limiting the scope of protection of the present invention.

[0086] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0087] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. Embodiment 1: See [link to embodiment]. Figure 1 A frequency regulation scheduling method for an energy storage virtual power plant involving electric vehicles includes the following steps: Step S1, establishing a frequency regulation resource model for an energy storage virtual power plant and obtaining the real-time status and capacity of each frequency regulation resource;

[0088] Step S2: Establish a comprehensive frequency modulation performance index model and obtain the frequency modulation performance of each frequency modulation resource;

[0089] Step S3: Establish a revenue model for energy storage virtual power plants oriented towards the frequency regulation ancillary services market;

[0090] Step S4: Solve the revenue model of the energy storage virtual power plant for the frequency regulation ancillary service market to obtain the AGC power optimization allocation result, and the virtual power plant performs scheduling according to the allocation result.

[0091] Further: In step S1, the process of establishing a frequency regulation resource model for an energy storage virtual power plant and obtaining the real-time status and capacity of each frequency regulation resource is as follows:

[0092] Step S11: Establish a frequency regulation model for distributed generator sets;

[0093] Step S12: Establish an electrochemical energy storage frequency modulation model;

[0094] Step S13: Establish frequency regulation models for electrochemical energy storage and phase change energy storage.

[0095] Furthermore: In step S11, a distributed generator set frequency regulation model is established:

[0096] The generator set needs to output power according to AGC (automatic generation control) commands:

[0097]

[0098] In the formula: The generator set AGC command at time t; P dr,i Let be the output power of the i-th distributed generator set at time t; I is the total number of distributed generator sets.

[0099] The frequency regulation cost model for distributed generator sets is expressed as follows:

[0100]

[0101] In the formula: C dg Frequency regulation cost of distributed generator sets; The unit operating cost of the i-th distributed generator set; The actual frequency regulation mileage of the i-th distributed generator unit during time period t; The penalty for wind and solar curtailment is applied to the i-th distributed generator unit, if the generator unit is not a wind or solar power generator. Let t be the power curtailed by the i-th distributed generator unit during time period t; T and Δt are the total number of time periods within the scheduling cycle and the duration of each scheduling cycle.

[0102] Further: In step S12, an electrochemical energy storage frequency modulation model is established:

[0103]

[0104] In the formula: E(t+1) is the charge at time t+1; E(t) is the charge at time t; P ch (t), P dis (t) represents the charge / discharge amount at time t; P down (t), P up (t) represents the upward and downward frequency modulation capacities at time t; η c η d These represent the charge / discharge efficiency; E1 is the self-discharge energy; Δt is the charge / discharge period; and SOC is the state of charge / discharge. es (t) represents the state of charge of the electrochemical energy storage at time t; E e This refers to the rated capacity of electrochemical energy storage.

[0105] The frequency regulation cost model for electrochemical energy storage is expressed as follows:

[0106]

[0107] In the formula: C es Frequency regulation costs for electrochemical energy storage; This represents the average initial investment cost coefficient for electrochemical energy storage. P represents the average cost coefficient for the depth of discharge in electrochemical energy storage. es (t) represents the charge / discharge power of the electrochemical energy storage at time t; D es (t) represents the depth of charge / discharge at time t in electrochemical energy storage.

[0108] Establish a phase change energy storage frequency regulation model:

[0109]

[0110] In the formula: R(t+1) is the heat stored in phase change energy at time t+1; R(t) is the heat stored in phase change energy at time t; σ ps This is the self-loss coefficient; These represent the net thermal input and net thermal output of phase change energy storage at time t, respectively; Δt is the time period; η hs For conversion efficiency; These represent the heat recovery from the gas turbine, the heat generated by the gas boiler, and the heat generated by the upward frequency regulation at time t, respectively. These represent the heat consumption at time t and the heat generated by downward frequency modulation, respectively; SOC ps (t) represents the thermal storage state at time t in phase change energy storage; R e This is the rated capacity of phase change energy storage.

[0111] The frequency regulation cost model for phase change energy storage is expressed as follows:

[0112]

[0113] In the formula: C psFrequency regulation cost for phase change energy storage; ρ ps The unit operating cost of phase change energy storage.

[0114] Further: In step S13, a frequency regulation model for electric vehicle charging stations is established:

[0115]

[0116] In the formula: and These represent the maximum charging and discharging power of charging station j during time period t; ΔS j,t The change in electricity consumption at charging station j due to the change in the grid-connected status of electric vehicles during time period t; S j,t 、S j,t-1 η represents the electricity consumption of charging station j during time period t and time period t-1, respectively; Δt represents the time period of change; η ch and η dis These represent the charging and discharging efficiencies of the charging station, respectively; η ref This is the discharge compensation coefficient, determined by the discharge loss.

[0117] Frequency regulation costs for electric vehicle charging stations:

[0118]

[0119] In the formula: C ev Frequency regulation costs for electric vehicle charging stations; The average cost coefficient for the initial investment of electric vehicle charging stations; P represents the average cost coefficient for the depth of discharge of electric vehicle charging stations. ev (t) represents the charging / discharging power of the electric vehicle charging station at time t; D ev (t) represents the charging / discharging depth of the electric vehicle charging station at time t.

[0120] Furthermore: In step S2, a comprehensive frequency modulation performance index model is established to obtain the frequency modulation performance of each frequency modulation resource, as detailed below:

[0121] Step S21: Establish frequency modulation performance indicators;

[0122] Step S22: Establish a comprehensive index model for frequency modulation performance.

[0123] Furthermore: In step S21, frequency modulation performance indicators are established:

[0124] Referring to the relevant regulations on AGC frequency modulation assessment indicators, the frequency modulation performance indicators consist of response time, regulation rate, and regulation accuracy: Response time refers to the delay time for the frequency modulation resource to reach the same output direction as required by the AGC frequency modulation command, that is, the time for the frequency modulation resource to cross the regulation dead zone.

[0125]

[0126] In the formula: K 1,n This is the response time metric for the nth response command; and These represent the dead time during the adjustment process after receiving the nth instruction, the time from receiving the instruction to completing it, respectively; K1 is the response time index within the settlement cycle; and N is the total number of responses within the settlement cycle.

[0127] The regulation rate refers to the rate at which a frequency modulation resource reaches the required output when it receives an AGC frequency modulation command. It depends on the standard regulation rate set by the frequency modulation market and the actual regulation rate of the frequency modulation resource.

[0128]

[0129] In the formula: The actual adjustment speed of the frequency modulation resource in the nth frequency modulation command; These represent the starting and ending outputs of the nth frequency modulation command, respectively; K2 is the response rate index within this settlement cycle; ν b The standard adjustment speed pre-set for the FM market.

[0130] Regulation accuracy refers to the degree of deviation between the frequency modulation resources and the AGC frequency modulation commands. It is calculated as the ratio of the maximum deviation and the average deviation between the output required by the scheduling command and the actual output of the frequency modulation resources.

[0131]

[0132] In the formula: K3 is the adjustment accuracy index within the settlement period; These represent the required output and actual output for the nth frequency modulation command, respectively.

[0133] Step S22 establishes a comprehensive index model for frequency modulation performance:

[0134] The frequency modulation performance index is normalized using the extreme value method, and then its weighted average value is calculated:

[0135]

[0136] K = λ1k1 + λ2k2 + λ3k3,

[0137] In the formula: k i K represents the i-th frequency modulation performance index after normalization. i,max K i,min λ1, λ2, and λ3 are the maximum and minimum values ​​of the i-th frequency modulation performance index, respectively; λ1, λ2, and λ3 are the weights of the first to third frequency modulation performance indexes, respectively; K is the comprehensive frequency modulation performance index.

[0138] Furthermore: In step S3, a revenue model for energy storage virtual power plants oriented towards the frequency regulation ancillary services market is established, as follows:

[0139] Step S31: Establish a revenue model for the frequency modulation ancillary services market;

[0140] Step S32: Establish a frequency modulation cost model;

[0141] Step S33: Establish the objective function and constraints for scheduling energy storage virtual power plants in the frequency regulation ancillary service market.

[0142] Furthermore: In step S31, a revenue model for the frequency modulation ancillary service market is established:

[0143]

[0144] In the formula: R represents the revenue from participating in the FM market; These are the frequency modulation capacity price and frequency modulation mileage price for the t-th time period, respectively; These represent the frequency regulation capacity and frequency regulation mileage of the virtual power plant in the t-th time period, respectively.

[0145] Furthermore: In step S32, a frequency modulation cost model is established:

[0146] Operation and maintenance costs of virtual power plants participating in frequency regulation and energy storage:

[0147] C m =C dr +C es +C ps +C ev ,

[0148] In the formula: C m Cost of operation and maintenance for virtual power plants participating in frequency regulation and energy storage.

[0149] Cost of virtual power plants participating in frequency regulation power purchase:

[0150]

[0151] In the formula: C buy The cost of virtual power plants participating in frequency regulation power purchase; σ is the average loss coefficient of frequency regulation capacity; Let t be the electricity price for the t-th time period.

[0152] Furthermore: In step S33, the objective function and constraints for scheduling energy storage virtual power plants in the frequency regulation ancillary service market are established:

[0153] Objective function:

[0154] maxR-C m -C buy ,

[0155] The energy storage state should meet the following constraints:

[0156]

[0157] In the formula: and The upper and lower limits of charge in electrochemical energy storage, respectively; and Upper and lower limits for phase change energy storage and thermal storage, respectively; and These represent the upper and lower limits of the charge level for electric vehicle charging stations, respectively.

[0158] Frequency modulation output should meet the following constraints:

[0159]

[0160] In the formula: P AGC (t) represents the AGC command at time t; P o,m (t) represents the frequency regulation output of the m-th frequency regulation resource in the virtual power plant at time t; M is the total number of frequency regulation resources; E b This represents the upper limit of the deviation between the frequency regulation output of the virtual power plant and the AGC command within period T.

[0161] Furthermore: In step S4, the revenue model of the energy storage virtual power plant oriented towards the frequency regulation ancillary service market is solved to obtain the AGC power optimization allocation result. The virtual power plant is then scheduled according to the allocation result, as follows:

[0162] Based on the revenue model of a storage-based virtual power plant for the frequency regulation ancillary services market, the optimal AGC power allocation scheme is solved using the sequential quadratic programming method of the fmincon function in the MATLAB optimization toolbox. According to the allocation results, the virtual power plant schedules energy storage resources with different performance and capacity in real time, achieving efficient utilization of energy storage resources and maximizing revenue. Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

[0163] It should be noted that the above embodiments are not intended to limit the scope of protection of the present invention. Equivalent transformations or substitutions made based on the above technical solutions all fall within the scope of protection of the claims of the present invention.

Claims

1. A frequency regulation and dispatching method for an energy storage virtual power plant involving electric vehicles, characterized in that, Includes the following steps: Step S1: Establish a frequency regulation resource model for an energy storage virtual power plant and obtain the real-time status and capacity of each frequency regulation resource; Step S2: Establish a comprehensive frequency modulation performance index model and obtain the frequency modulation performance of each frequency modulation resource; Step S3: Establish a revenue model for energy storage virtual power plants oriented towards the frequency regulation ancillary services market; Step S4: Solve the revenue model of the energy storage virtual power plant for the frequency regulation ancillary service market to obtain the AGC power optimization allocation result. The virtual power plant then performs scheduling based on the allocation result. In step S1, the process of establishing a frequency regulation resource model for an energy storage virtual power plant and obtaining the real-time status and capacity of each frequency regulation resource is as follows: Step S11: Establish a distributed generator set frequency regulation model. Step S12: Establish frequency regulation models for electrochemical energy storage and phase change energy storage. Step S13: Establish a frequency regulation model for electric vehicle charging stations; In step S2, a comprehensive frequency modulation performance index model is established to obtain the frequency modulation performance of each frequency modulation resource, as detailed below: Step S21, establish frequency modulation performance indicators, Step S22: Establish a comprehensive index model for frequency modulation performance; In step S3, a revenue model for energy storage virtual power plants oriented towards the frequency regulation ancillary services market is established, as follows: Step S31: Establish a revenue model for the frequency modulation ancillary services market. Step S32: Establish a frequency modulation cost model. Step S33: Establish the objective function and constraints for scheduling energy storage virtual power plants in the frequency regulation ancillary services market; In step S11, a distributed generator frequency regulation model is established: The generator set needs to output power according to AGC (automatic generation control) commands: ， In the formula: The generator set AGC command at time t; Let be the output power of the i-th distributed generator set at time t; This represents the total number of distributed generator sets. The frequency regulation cost model for distributed generator sets is expressed as follows: ， In the formula: Frequency regulation cost of distributed generator sets; The unit operating cost of the i-th distributed generator set; The actual frequency regulation mileage of the i-th distributed generator unit during time period t; The penalty for wind and solar curtailment is applied to the i-th distributed generator unit, if the generator unit is not a wind or solar power generator. ; Let be the power curtailed by the i-th distributed generator unit during time period t; and This represents the total number of time periods within a scheduling cycle and the duration of each scheduling cycle. In step S12, an electrochemical energy storage frequency modulation model and a phase change energy storage frequency modulation model are established: ， ， In the formula: The charge level at time t+1; Let t be the charge at time t; , These represent the charge / discharge amounts at time t; , These represent the upward and downward frequency modulation capacities at time t, respectively. , These represent charge / discharge efficiencies, respectively. This is the energy generated by self-discharge. This is the charging / discharging period; The state of charge at time t for electrochemical energy storage; For the rated capacity of electrochemical energy storage, The frequency regulation cost model for electrochemical energy storage is expressed as follows: ， In the formula: Frequency regulation costs for electrochemical energy storage; This represents the average initial investment cost coefficient for electrochemical energy storage. This represents the average cost coefficient for the depth of discharge in electrochemical energy storage. The charge / discharge power at time t for electrochemical energy storage; The depth of charge / discharge at time t for electrochemical energy storage. Establish a phase change energy storage frequency regulation model: ， ， In the formula: The heat stored at time t+1 for phase change energy storage; The heat stored at time t for phase change energy storage; This is the self-loss coefficient; , These represent the net heat input and net heat output of phase change energy storage at time t, respectively. For time period; For conversion efficiency; , , These represent the heat recovery from the gas turbine, the heat generated by the gas boiler, and the heat generated by the upward frequency regulation at time t, respectively. , These represent the heat consumption at time t and the heat generated by the downward frequency adjustment, respectively. The thermal storage state at time t represents phase change energy storage. This is the rated capacity of phase change energy storage. The frequency regulation cost model for phase change energy storage is expressed as follows: ， In the formula: Frequency regulation costs for phase change energy storage; The unit operating cost of phase change energy storage.

2. The frequency regulation and dispatching method for energy storage virtual power plants involving electric vehicles according to claim 1, characterized in that, In step S13, a frequency regulation model for electric vehicle charging stations is established: In the formula: and These represent the maximum charging and discharging power of charging station j during time period t; The change in electricity consumption at charging station j due to the change in the grid connection status of electric vehicles during time period t; , These represent the electricity consumption of charging station j during time period t and time period t-1, respectively. For periods of change; and These refer to the charging and discharging efficiencies of the charging station, respectively. The discharge compensation factor is determined by the discharge loss. Frequency regulation costs for electric vehicle charging stations: ， In the formula: Frequency regulation costs for electric vehicle charging stations; The average cost coefficient for the initial investment of electric vehicle charging stations; The average cost coefficient for the depth of discharge of electric vehicle charging stations; The charging / discharging power of the electric vehicle charging station at time t; The depth of charge / discharge at time t for electric vehicle charging stations.

3. The frequency regulation and dispatching method for energy storage virtual power plants involving electric vehicles according to claim 1, characterized in that, In step S21, frequency modulation performance indicators are established: According to the relevant regulations on frequency modulation performance indicators for AGC, the frequency modulation performance indicators consist of response time, adjustment rate, and adjustment accuracy: Response time refers to the delay time for the frequency modulation resource to reach the same output direction as required by the AGC frequency modulation command, i.e., the time it takes for the frequency modulation resource to cross the regulation dead zone. ， ， In the formula: This is the response time metric for the nth response command; and These are the dead time and the duration from receiving the nth instruction to completing the adjustment process, respectively. This refers to the response time metric within the settlement period. This represents the total number of responses within the settlement period. The regulation rate refers to the rate at which a frequency modulation resource reaches the required output when it receives an AGC frequency modulation command. It depends on the standard regulation rate set by the frequency modulation market and the actual regulation rate of the frequency modulation resource. ， ， In the formula: The actual adjustment speed of the frequency modulation resource in the nth frequency modulation command; , These represent the starting and ending outputs of the nth frequency modulation command, respectively. This refers to the response rate metric within the settlement period. The standard adjustment speed pre-set for the FM market, Regulation accuracy refers to the degree of deviation between the frequency modulation resources and the AGC frequency modulation commands. It is calculated as the ratio of the maximum deviation and the average deviation between the output required by the scheduling command and the actual output of the frequency modulation resources. ， In the formula: This refers to the adjustment accuracy index within the settlement period. , These represent the required output and actual output for the nth frequency modulation command, respectively.

4. The frequency regulation and dispatching method for energy storage virtual power plants involving electric vehicles according to claim 3, characterized in that, In step S22, a comprehensive index model for frequency modulation performance is established: The frequency modulation performance index is normalized using the extreme value method, and then its weighted average value is calculated: ， ， In the formula: This represents the i-th frequency modulation performance index after normalization. , These are the maximum and minimum values ​​of the i-th frequency modulation performance index, respectively; , , These are the weights for the first to third frequency modulation performance indicators, respectively. This is a comprehensive indicator of frequency modulation performance.

5. The frequency regulation and dispatching method for energy storage virtual power plants involving electric vehicles according to claim 2, characterized in that, In step S31, a revenue model for the frequency modulation ancillary service market is established: ， In the formula: To participate in the FM market for profit; , These are the frequency modulation capacity price and frequency modulation mileage price for the t-th time period, respectively; , These represent the frequency regulation capacity and frequency regulation mileage of the virtual power plant in the t-th time period, respectively.

6. The frequency regulation and dispatching method for energy storage virtual power plants involving electric vehicles according to claim 5, characterized in that, In step S32, a frequency modulation cost model is established: Operation and maintenance costs of virtual power plants participating in frequency regulation and energy storage: ， In the formula: To reduce the operation and maintenance costs of virtual power plants participating in frequency regulation and energy storage, Cost of virtual power plants participating in frequency regulation power purchase: ， In the formula: Cost of virtual power plants participating in frequency regulation power purchase; This represents the average loss coefficient for frequency modulation capacity. Let t be the electricity price for the t-th time period.

7. The frequency regulation and dispatching method for energy storage virtual power plants involving electric vehicles according to claim 6, characterized in that, In step S33, the objective function and constraints for scheduling energy storage virtual power plants in the frequency regulation ancillary service market are established: Objective function: ， The energy storage state should meet the following constraints: ， ， ， In the formula: and The upper and lower limits of charge in electrochemical energy storage, respectively; and Upper and lower limits for phase change energy storage and thermal storage, respectively; and These represent the upper and lower limits of the charge level for electric vehicle charging stations. Frequency modulation output should meet the following constraints: ， In the formula: The AGC instruction at time t; The frequency regulation output of the m-th frequency regulation resource in the virtual power plant at time t; This represents the total number of frequency modulation resources. This represents the upper limit of the deviation between the frequency regulation output of the virtual power plant and the AGC command within period T.

8. The frequency regulation and dispatching method for energy storage virtual power plants involving electric vehicles according to claim 1, characterized in that, In step S4, the revenue model of the energy storage virtual power plant for the frequency regulation ancillary service market is solved to obtain the AGC power optimization allocation result. The virtual power plant is then scheduled according to the allocation result, as follows: Based on the revenue model of energy storage virtual power plants for the frequency regulation ancillary services market, the optimal AGC power allocation scheme is solved using the sequential quadratic programming method of the fmincon function in the MATLAB optimization toolbox. According to the allocation results, the virtual power plant schedules energy storage resources with different performance and capacity in real time to achieve efficient utilization of energy storage resources and maximize revenue.

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