Energy storage type virtual power plant frequency modulation scheduling method with participation of electric vehicles

Abstract

The invention discloses an energy storage type virtual power plant frequency modulation scheduling method with participation of an electric vehicle, and the method specifically comprises the steps: building an energy storage type virtual power plant frequency modulation resource model, and obtaining the real-time state and capacity of each frequency modulation resource; establishing a frequency modulation performance comprehensive index model, and obtaining the frequency modulation performance of each frequency modulation resource; establishing an energy storage type virtual power plant income model oriented to the frequency modulation auxiliary service market; and solving the energy storage type virtual power plant income model oriented to the frequency modulation auxiliary service market to obtain an AGC power optimization distribution result, and scheduling the virtual power plant according to the distribution result. According to the method, collaborative optimization of the frequency modulation performance and the frequency modulation cost is considered, differential scheduling is carried out on energy storage resources with different frequency modulation performance on the basis of responding to the frequency modulation instruction, advantage complementation of multi-element energy storage and long-term income maximization of the virtual power plant are achieved, and the enthusiasm and sustainability of energy storage participating in the frequency modulation auxiliary service market are improved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage-assisted frequency regulation, and more specifically, to a frequency regulation and dispatching method for an energy storage-type virtual power plant in which electric vehicles participate. Background Art

[0002] The main task of the new power system is to build a high-proportion new energy supply and consumption system. The current new energy power generation, mainly wind and solar power, has large power fluctuations and weak controllability, which seriously threatens the frequency safety 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 frequency. Energy storage virtual power plants can participate in the power frequency regulation auxiliary service market as independent entities. With the increase in energy storage types and energy storage configurations on the new energy station side, how to maximize the utilization efficiency of energy storage systems is an urgent problem to be solved today. Therefore, it is necessary to coordinate energy storage resources with different service lives, construction costs and frequency regulation performance, and reasonably construct a strategy for virtual power plants to participate in frequency regulation assistance, so as to improve the continuous regulation capabilities of virtual power plant diversified energy storage systems and the benefits of frequency regulation auxiliary services. Summary of the invention

[0004] The purpose of the present invention is to consider the further application of virtual power plant's multiple energy storage resources in the field of power system frequency regulation, propose a frequency regulation dispatching method for energy storage virtual power plants with the participation of electric vehicles, establish a frequency regulation resource model for energy storage virtual power plants, obtain the real-time status and capacity of each frequency regulation resource; establish a comprehensive index model for frequency regulation performance, obtain the frequency regulation performance of each frequency regulation resource; establish an energy storage virtual power plant revenue model for the frequency regulation auxiliary service market; solve the energy storage virtual power plant revenue model for the frequency regulation auxiliary service market to obtain the AGC power optimization allocation result, and the virtual power plant is dispatched according to the allocation result. Considering the coordinated optimization of frequency regulation performance and frequency regulation cost, differentiated dispatching of energy storage resources with different frequency regulation performance is carried out on the basis of responding to frequency regulation instructions, so as to achieve complementary advantages of multiple energy storage and maximize the long-term benefits of virtual power plants, which is conducive to enhancing the enthusiasm and sustainability of energy storage participating in the frequency regulation auxiliary service market.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a frequency regulation and dispatching method of energy storage virtual power plant in which electric vehicles participate, comprising the following steps:

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

[0007] Step S2, establishing a comprehensive frequency modulation performance index model to obtain the frequency modulation performance of each frequency modulation resource;

[0008] Step S3, establishing a revenue model of an energy storage virtual power plant for the frequency regulation auxiliary service market;

[0009] Step S4, solving the energy storage virtual power plant revenue model for the frequency regulation auxiliary service market to obtain the AGC power optimization allocation result, and the virtual power plant is dispatched according to the allocation result.

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

[0011] Step S11, establishing a frequency regulation model of a distributed generator set;

[0012] Step S12, establishing an electrochemical energy storage and phase change energy storage frequency modulation model;

[0013] Step S13, establishing a frequency modulation model for an electric vehicle charging station.

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

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

[0016]

[0017] Where: is the AGC command of the generator set at time t; P dr,i is 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 of distributed generators is expressed as:

[0019]

[0020] Where: C dg the cost of frequency regulation for distributed generators; is the unit operating cost of the i-th distributed generator set; is the actual frequency regulation mileage of the i-th distributed generator set in period t; is the penalty for wind and solar abandonment of the i-th distributed generator set. If the generator set is not wind power or photovoltaic power generation, is the power abandonment of the i-th distributed generator set in time period t; T and Δt are the total number of time periods in the scheduling cycle and the duration of each scheduling cycle.

[0021] Further: In step S12, an electrochemical energy storage and phase change energy storage frequency modulation model is established:

[0022]

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

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

[0025]

[0026] Where: C es The frequency regulation cost of electrochemical energy storage; is the average cost coefficient of initial investment in electrochemical energy storage; is the average cost coefficient of electrochemical energy storage discharge depth; P es (t) is the charge / discharge power of electrochemical energy storage at time t; D es (t) is the charge / discharge depth of electrochemical energy storage at time t.

[0027] Establish phase change energy storage frequency modulation model:

[0028]

[0029] Where: R(t+1) is the heat storage of phase change energy storage at time t+1; R(t) is the heat storage of phase change energy storage at time t; σ ps is the self-loss coefficient; are the net heat input and net heat output of phase change energy storage at time t; Δt is the time period; η hs is the conversion efficiency; They are the heat recovery from gas turbine, the heat produced by gas boiler, and the heat from upward frequency modulation at time t; They are heat consumption by heat load and downward frequency modulation heat at time t; SOC ps (t) is the heat storage state at the time t of phase change energy storage; R e is the rated capacity of phase change energy storage.

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

[0031]

[0032] Where: C psis the frequency regulation cost of phase change energy storage; ρ ps is the unit operating cost of phase change energy storage.

[0033] Further: In step S13, a frequency modulation model of an electric vehicle charging station is established:

[0034]

[0035] Where: and are the maximum charging and discharging power in charging station j during period t; ΔS j,t S is the power change caused by the change of the electric vehicle grid connection status at charging station j during period t; j,t , S j,t-1 are the power consumption of charging station j in period t and period t-1 respectively; Δt is the change period; η ch and η dis are the charging and discharging efficiency of the charging station respectively; η ref is the discharge replenishment coefficient, which is determined by the discharge loss.

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

[0037]

[0038] Where: C ev the cost of frequency regulation for electric vehicle charging stations; is the average cost coefficient of initial investment in electric vehicle charging stations; is the average cost coefficient of the discharge depth of the electric vehicle charging station; P ev (t) is the charging / discharging power of the electric vehicle charging station at time t; D ev (t) is the charge / discharge depth of the electric vehicle charging station at time t.

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

[0040] Step S21, establishing frequency modulation performance indicators;

[0041] Step S22, establishing a comprehensive index model of frequency modulation performance.

[0042] Further: the frequency modulation performance index is established in step S21:

[0043] Referring to the relevant provisions of AGC frequency regulation assessment indicators, the frequency regulation performance indicators are composed of response time, regulation rate and regulation accuracy: Response time refers to the delay time for the frequency regulation resource to reach the output direction consistent with the instruction when receiving the AGC frequency regulation instruction, that is, the duration of the frequency regulation resource crossing the regulation dead zone:

[0044]

[0045] Where: K 1,n It is the response time indicator when responding to the nth instruction; and They are the dead time in the adjustment process after receiving the nth instruction and the time from receiving to completing the instruction; K1 is the response time indicator within the settlement cycle; N is the total number of responses within the settlement cycle.

[0046] The regulation rate refers to the rate at which the frequency modulation resource reaches the output required by the instruction when receiving the AGC frequency modulation instruction. 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] Where: is the actual adjustment speed of the frequency modulation resource in the nth frequency modulation instruction; are the starting output and ending output of the nth frequency modulation instruction respectively; K2 is the response rate index within the settlement cycle; ν b A standard regulation speed pre-set for the FM market.

[0049] Regulation accuracy refers to the degree of deviation between the frequency regulation resources and the AGC frequency regulation instructions. It is calculated by the ratio of the maximum deviation and average deviation between the output required by the dispatch instruction and the actual output of the frequency regulation resources:

[0050]

[0051] Where: K3 is the adjustment accuracy index within the settlement period; They are the scheduling required output and actual output of the nth frequency modulation instruction respectively.

[0052] The step S22 establishes a frequency modulation performance comprehensive index model:

[0053] The maximum value method is used to normalize the frequency modulation performance index, and then its weighted average value is calculated:

[0054]

[0055] K=λ1k1+λ2k2+λ3k3,

[0056] Where: k i is the i-th frequency modulation performance index after normalization; K i,max , K i,min are the maximum and minimum values ​​of the i-th frequency modulation performance index respectively; λ1, λ2, λ3 are the weights of the 1st to 3rd frequency modulation performance index respectively; K is the comprehensive index of frequency modulation performance.

[0057] Further: In step S3, a revenue model of an energy storage virtual power plant for the frequency regulation auxiliary service market is established, as follows:

[0058] Step S31, establishing a frequency regulation auxiliary service market revenue model;

[0059] Step S32, establishing a frequency modulation cost model;

[0060] Step S33, establishing the energy storage virtual power plant dispatch objective function and constraints for the frequency regulation auxiliary service market.

[0061] Further: In step S31, a frequency modulation auxiliary service market revenue model is established:

[0062]

[0063] Where: R is the income from participating in the frequency modulation market; are the frequency regulation capacity price and frequency regulation mileage price in the tth period respectively; are the frequency regulation capacity and frequency regulation mileage of the virtual power plant in the tth period respectively.

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

[0065] Virtual power plants participate in frequency regulation and energy storage operation and maintenance costs:

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

[0067] Where: C m The operation and maintenance costs of energy storage for virtual power plants participating in frequency regulation.

[0068] The cost of purchasing electricity for virtual power plants to participate in frequency regulation:

[0069]

[0070] Where: C buy is the electricity purchase cost of the virtual power plant participating in frequency regulation; σ is the average loss coefficient of frequency regulation capacity; is the electricity price in the tth period.

[0071] Further: In step S33, the energy storage virtual power plant dispatch objective function and constraints of the frequency regulation auxiliary service market are established:

[0072] Objective function:

[0073] maxR-C m -C buy ,

[0074] The energy storage state should satisfy the constraints:

[0075]

[0076] Where: and The upper and lower limits of electrochemical energy storage charge, respectively; and The upper and lower limits of phase change energy and heat storage, respectively; and They are the upper and lower limits of charge at electric vehicle charging station j respectively.

[0077] The frequency modulation output should meet the constraints:

[0078]

[0079] Where: P AGC (t) is the AGC instruction at time t; P o,m (t) is the frequency regulation output of the mth frequency regulation resource in the virtual power plant at time t; M is the total number of frequency regulation resources; E b It is the upper limit of the deviation between the frequency regulation output of the virtual power plant and the AGC instruction within period T.

[0080] Further: In step S4, the energy storage virtual power plant revenue model for the frequency regulation auxiliary service market is solved to obtain the AGC power optimization allocation result, and the virtual power plant is dispatched according to the allocation result, as follows:

[0081] Based on the revenue model of energy storage virtual power plant for frequency regulation auxiliary service market, the sequential quadratic programming method of fmincon function in MATLAB optimization toolbox is used to solve the optimal AGC power allocation scheme. According to the allocation results, the virtual power plant dispatches energy storage resources with different performance and capacity in real time to achieve efficient utilization of energy storage resources and maximize the benefits.

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

[0083] The present invention proposes a frequency regulation dispatching method for energy storage virtual power plants with the participation of electric vehicles, establishes a frequency regulation resource model for energy storage virtual power plants, obtains the real-time status and capacity of each frequency regulation resource; establishes a comprehensive index model for frequency regulation performance, obtains the frequency regulation performance of each frequency regulation resource; establishes a revenue model for energy storage virtual power plants for the frequency regulation auxiliary service market; solves the revenue model for energy storage virtual power plants for the frequency regulation auxiliary service market to obtain the AGC power optimization allocation result, and the virtual power plant is dispatched according to the allocation result. Considering the coordinated optimization of frequency regulation performance and frequency regulation cost, energy storage resources with different frequency regulation performances are differentially dispatched based on the response to frequency regulation instructions, providing a frequency regulation auxiliary service market participation strategy for new virtual power plants. Realizing the complementary advantages of multiple energy storage and maximizing the long-term benefits of virtual power plants is conducive to enhancing the enthusiasm and sustainability of energy storage participating in the frequency regulation auxiliary service market. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 This is a flow chart of the frequency regulation and dispatching method of energy storage type virtual power plant involving electric vehicles proposed by the present invention. DETAILED DESCRIPTION

[0085] This section will describe in detail the specific embodiments of the present invention. The preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the accompanying drawings is to supplement the description of the text part of the specification with graphics, so that people can intuitively and vividly understand each technical feature and the overall technical solution of the present invention, but it cannot be understood as a limitation on the scope of protection of the present invention.

[0086] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0087] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other. Figure 1 , a frequency regulation dispatching method for an energy storage type virtual power plant in which electric vehicles participate, comprising the following steps: step S1, establishing a frequency regulation resource model of an energy storage type virtual power plant, and obtaining the real-time status and capacity of each frequency regulation resource;

[0088] Step S2, establishing a comprehensive frequency modulation performance index model to obtain the frequency modulation performance of each frequency modulation resource;

[0089] Step S3, establishing a revenue model of an energy storage virtual power plant for the frequency regulation auxiliary service market;

[0090] Step S4, solving the energy storage virtual power plant revenue model for the frequency regulation auxiliary service market to obtain the AGC power optimization allocation result, and the virtual power plant is dispatched according to the allocation result.

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

[0092] Step S11, establishing a frequency regulation model of a distributed generator set;

[0093] Step S12, establishing an electrochemical energy storage frequency modulation model;

[0094] Step S13, establishing an electrochemical energy storage and phase change energy storage frequency modulation model.

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

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

[0097]

[0098] Where: is the AGC command of the generator set at time t; P dr,i is 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 of distributed generators is expressed as:

[0100]

[0101] Where: C dg the cost of frequency regulation for distributed generators; is the unit operating cost of the i-th distributed generator set; is the actual frequency regulation mileage of the i-th distributed generator set in period t; is the penalty for wind and solar abandonment of the i-th distributed generator set. If the generator set is not wind power or photovoltaic power generation, is the power abandonment of the i-th distributed generator set in time period t; T and Δt are the total number of time periods in 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] Where: E(t+1) is the power at time t+1; E(t) is the power at time t; P ch (t), P dis (t) are the charge / discharge amount at time t; P down (t), P up (t) are the upward and downward frequency modulation capacities at time t; η c , η d are charge / discharge efficiency; E1 is self-discharge energy; Δt is charge / discharge period; SOC es (t) is the charge state of electrochemical energy storage at time t; E e is the rated capacity of electrochemical energy storage.

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

[0106]

[0107] Where: C es The frequency regulation cost of electrochemical energy storage; is the average cost coefficient of initial investment in electrochemical energy storage; is the average cost coefficient of electrochemical energy storage discharge depth; P es (t) is the charge / discharge power of electrochemical energy storage at time t; D es (t) is the charge / discharge depth of electrochemical energy storage at time t.

[0108] Establish phase change energy storage frequency modulation model:

[0109]

[0110] Where: R(t+1) is the heat storage of phase change energy storage at time t+1; R(t) is the heat storage of phase change energy storage at time t; σ ps is the self-loss coefficient; are the net heat input and net heat output of phase change energy storage at time t; Δt is the time period; η hs is the conversion efficiency; They are the heat recovery from the gas turbine, the heat produced by the gas boiler, and the heat of upward frequency modulation at time t; They are heat consumption by heat load and downward frequency modulation heat at time t; SOC ps (t) is the heat storage state at the time t of phase change energy storage; R e is the rated capacity of phase change energy storage.

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

[0112]

[0113] Where: C psis the frequency regulation cost of phase change energy storage; ρ ps is the unit operating cost of phase change energy storage.

[0114] Further: In step S13, a frequency modulation model of an electric vehicle charging station is established:

[0115]

[0116] Where: and are the maximum charging and discharging power in charging station j during period t; ΔS j,t S is the power change caused by the change of the electric vehicle grid connection status at charging station j during period t; j,t , S j,t-1 are the power consumption of charging station j in period t and period t-1 respectively; Δt is the change period; η ch and η dis are the charging and discharging efficiency of the charging station respectively; η ref is the discharge replenishment coefficient, which is determined by the discharge loss.

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

[0118]

[0119] Where: C ev the cost of frequency regulation for electric vehicle charging stations; is the average cost coefficient of initial investment in electric vehicle charging stations; is the average cost coefficient of the discharge depth of the electric vehicle charging station; P ev (t) is the charging / discharging power of the electric vehicle charging station at time t; D ev (t) is the charge / discharge depth of the electric vehicle charging station at time t.

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

[0121] Step S21, establishing frequency modulation performance indicators;

[0122] Step S22, establishing a comprehensive index model of frequency modulation performance.

[0123] Further: the frequency modulation performance index is established in step S21:

[0124] Referring to the relevant provisions of AGC frequency regulation assessment indicators, the frequency regulation performance indicators are composed of response time, regulation rate and regulation accuracy: Response time refers to the delay time for the frequency regulation resource to reach the output direction consistent with the instruction when receiving the AGC frequency regulation instruction, that is, the duration of the frequency regulation resource crossing the regulation dead zone:

[0125]

[0126] Where: K 1,n It is the response time indicator when responding to the nth instruction; and They are the dead time in the adjustment process after receiving the nth instruction and the time from receiving to completing the instruction; K1 is the response time indicator within the settlement cycle; N is the total number of responses within the settlement cycle.

[0127] The regulation rate refers to the rate at which the frequency modulation resource reaches the output required by the instruction when receiving the AGC frequency modulation instruction. 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] Where: is the actual adjustment speed of the frequency modulation resource in the nth frequency modulation instruction; are the starting output and ending output of the nth frequency modulation instruction respectively; K2 is the response rate index within the settlement cycle; ν b A standard regulation speed pre-set for the FM market.

[0130] Regulation accuracy refers to the degree of deviation between the frequency regulation resources and the AGC frequency regulation instructions. It is calculated by the ratio of the maximum deviation and average deviation between the output required by the dispatch instruction and the actual output of the frequency regulation resources:

[0131]

[0132] Where: K3 is the adjustment accuracy index within the settlement period; They are the scheduling required output and actual output of the nth frequency modulation instruction respectively.

[0133] The step S22 establishes a frequency modulation performance comprehensive index model:

[0134] The maximum value method is used to normalize the frequency modulation performance index, and then its weighted average value is calculated:

[0135]

[0136] K=λ1k1+λ2k2+λ3k3,

[0137] Where: k i is the i-th frequency modulation performance index after normalization; K i,max , K i,min are the maximum and minimum values ​​of the i-th frequency modulation performance index respectively; λ1, λ2, λ3 are the weights of the 1st to 3rd frequency modulation performance index respectively; K is the comprehensive index of frequency modulation performance.

[0138] Further: In step S3, a revenue model of an energy storage virtual power plant for the frequency regulation auxiliary service market is established, as follows:

[0139] Step S31, establishing a frequency regulation auxiliary service market revenue model;

[0140] Step S32, establishing a frequency modulation cost model;

[0141] Step S33, establishing the energy storage virtual power plant dispatch objective function and constraints for the frequency regulation auxiliary service market.

[0142] Further: In step S31, a frequency modulation auxiliary service market revenue model is established:

[0143]

[0144] Where: R is the income from participating in the frequency modulation market; are the frequency regulation capacity price and frequency regulation mileage price in the tth period respectively; are the frequency regulation capacity and frequency regulation mileage of the virtual power plant in the tth period respectively.

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

[0146] Virtual power plant participation in frequency regulation and energy storage operation and maintenance costs:

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

[0148] Where: C m The operation and maintenance costs of energy storage for virtual power plants participating in frequency regulation.

[0149] The cost of purchasing electricity for virtual power plants to participate in frequency regulation:

[0150]

[0151] Where: C buy is the electricity purchase cost of the virtual power plant participating in frequency regulation; σ is the average loss coefficient of frequency regulation capacity; is the electricity price in the tth period.

[0152] Further: In step S33, the energy storage virtual power plant dispatch objective function and constraints of the frequency regulation auxiliary service market are established:

[0153] Objective function:

[0154] maxR-C m -C buy ,

[0155] The energy storage state should satisfy the constraints:

[0156]

[0157] Where: and The upper and lower limits of electrochemical energy storage charge, respectively; and The upper and lower limits of phase change energy and heat storage, respectively; and They are the upper and lower limits of charge at electric vehicle charging station j respectively.

[0158] The frequency modulation output should meet the constraints:

[0159]

[0160] Where: P AGC (t) is the AGC instruction at time t; P o,m (t) is the frequency regulation output of the mth frequency regulation resource in the virtual power plant at time t; M is the total number of frequency regulation resources; E b It is the upper limit of the deviation between the frequency regulation output of the virtual power plant and the AGC instruction within period T.

[0161] Further: In step S4, the energy storage virtual power plant revenue model for the frequency regulation auxiliary service market is solved to obtain the AGC power optimization allocation result, and the virtual power plant is dispatched according to the allocation result, as follows:

[0162] Based on the revenue model of energy storage virtual power plant for frequency regulation auxiliary service market, the sequential quadratic programming method of fmincon function in MATLAB optimization toolbox is used to solve the optimal AGC power allocation scheme. According to the solved allocation results, the virtual power plant dispatches energy storage resources of different performance and capacity in real time to achieve efficient utilization of energy storage resources and maximize the benefits. Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention is described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation mode of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not deviate from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

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

Claims

1. A frequency regulation and dispatching method for energy storage virtual power plant with participation of electric vehicles, characterized in that: The following steps are involved: Step S1, establishing a frequency regulation resource model of an energy storage virtual power plant to obtain the real-time status and capacity of each frequency regulation resource; Step S2, establishing a comprehensive frequency modulation performance index model to obtain the frequency modulation performance of each frequency modulation resource; Step S3, establishing a revenue model of an energy storage virtual power plant for the frequency regulation auxiliary service market; Step S4, solving the energy storage virtual power plant revenue model for the frequency regulation auxiliary service market to obtain the AGC power optimization allocation result, and the virtual power plant is dispatched according to the allocation result. In step S1, a frequency regulation resource model of an energy storage virtual power plant is established, and the real-time status and capacity of each frequency regulation resource are obtained as follows: Step S11, establishing a frequency regulation model of distributed generator sets, Step S12, establishing an electrochemical energy storage and phase change energy storage frequency modulation model, Step S13, establishing a frequency modulation model for an electric vehicle charging station; In step S2, a frequency modulation performance comprehensive index model is established to obtain the frequency modulation performance of each frequency modulation resource, as follows: Step S21, establishing frequency modulation performance indicators, Step S22, establishing a frequency modulation performance comprehensive index model; In step S3, a revenue model of an energy storage virtual power plant for the frequency regulation auxiliary service market is established, which is specifically as follows: Step S31: Establishing a frequency modulation auxiliary service market revenue model. Step S32, establishing a frequency modulation cost model, Step S33, establishing the energy storage virtual power plant dispatch objective function and constraints for the frequency regulation auxiliary service market.

2. The frequency regulation and dispatching method of energy storage virtual power plant with participation of electric vehicles according to claim 1 is characterized in that: In step S11, a frequency regulation model of a distributed generator set is established: The generator set needs to output power according to the AGC (automatic generation control) command: Where: is the AGC command of the generator set at time t; P dr,i is the output power of the i-th distributed generator set at time t; I is the total number of distributed generator sets, The frequency regulation cost model of distributed generators is expressed as: Where: C dg the cost of frequency regulation for distributed generators; is the unit operating cost of the i-th distributed generator set; is the actual frequency regulation mileage of the i-th distributed generator set in period t; is the penalty for wind and solar abandonment of the i-th distributed generator set. If the generator set is not wind power or photovoltaic power generation, is the power abandonment of the i-th distributed generator set in time period t; T and Δt are the total number of time periods in the scheduling cycle and the duration of each scheduling cycle.

3. The frequency regulation and dispatching method of energy storage virtual power plant with participation of electric vehicles according to claim 1 is characterized in that: In step S12, an electrochemical energy storage frequency modulation model and a phase change energy storage frequency modulation model are established: Where: E(t+1) is the power at time t+1; E(t) is the power at time t; P ch (t), P dis (t) are the charge / discharge amount at time t; P down (t), P up (t) are the upward and downward frequency modulation capacities at time t, respectively; η c , η d are charge / discharge efficiency; E1 is self-discharge energy; Δt is charge / discharge period; SOC es (t) is the charge state of electrochemical energy storage at time t; E e is the rated capacity of electrochemical energy storage, The frequency regulation cost model of electrochemical energy storage is expressed as: Where: C es The frequency regulation cost of electrochemical energy storage; is the average cost coefficient of initial investment in electrochemical energy storage; is the average cost coefficient of electrochemical energy storage discharge depth; P es (t) is the charge / discharge power of electrochemical energy storage at time t; D es (t) is the charge / discharge depth of electrochemical energy storage at time t, Establish phase change energy storage frequency modulation model: Where: R(t+1) is the heat storage of phase change energy storage at time t+1; R(t) is the heat storage of phase change energy storage at time t; σ ps is the self-loss coefficient; are the net heat input and net heat output of phase change energy storage at time t; Δt is the time period; η hs is the conversion efficiency; They are the heat recovery from the gas turbine, the heat produced by the gas boiler, and the heat of upward frequency modulation at time t; They are heat consumption by heat load and downward frequency modulation heat at time t; SOC ps (t) is the heat storage state at the time t of phase change energy storage; R e is the rated capacity of phase change energy storage, and the frequency regulation cost model of phase change energy storage is expressed as: Where: C ps is the frequency regulation cost of phase change energy storage; ρ ps is the unit operating cost of phase change energy storage.

4. The frequency regulation and dispatching method of energy storage virtual power plant with participation of electric vehicles according to claim 1, characterized in that: In step S13, a frequency modulation model of an electric vehicle charging station is established: Where: and are the maximum charging and discharging power in charging station j during period t; ΔS j,t S is the power change caused by the change of the electric vehicle grid connection status at charging station j during period t; j,t , S j,t-1 are the power consumption of charging station j in period t and period t-1 respectively; Δt is the change period; η ch and η dis are the charging and discharging efficiency of the charging station respectively; η ref is the discharge replenishment coefficient, which is determined by the discharge loss. Frequency regulation cost for electric vehicle charging stations: Where: C ev the cost of frequency regulation for electric vehicle charging stations; is the average cost coefficient of initial investment in electric vehicle charging stations; is the average cost coefficient of the discharge depth of the electric vehicle charging station; P ev (t) is the charging / discharging power of the electric vehicle charging station at time t; D ev (t) is the charge / discharge depth of the electric vehicle charging station at time t.

5. The frequency regulation and dispatching method of energy storage virtual power plant with participation of electric vehicles according to claim 1, characterized in that: In step S21, a frequency modulation performance index is established: Referring to the relevant provisions of AGC frequency regulation assessment indicators, the frequency regulation performance indicators are composed of response time, regulation rate and regulation accuracy: Response time refers to the delay time for the frequency regulation resource to reach the output direction consistent with the instruction when receiving the AGC frequency regulation instruction, that is, the duration of the frequency regulation resource crossing the regulation dead zone: Where: K 1,n It is the response time indicator when responding to the nth instruction; and are the dead time in the adjustment process after receiving the nth instruction and the time from receiving to completing the instruction; K1 is the response time indicator within the settlement cycle; N is the total number of responses within the settlement cycle, The regulation rate refers to the rate at which the frequency modulation resource reaches the output required by the instruction when receiving the AGC frequency modulation instruction. It depends on the standard regulation rate set by the frequency modulation market and the actual regulation rate of the frequency modulation resource: Where: is the actual adjustment speed of the frequency modulation resource in the nth frequency modulation instruction; are the starting output and ending output of the nth frequency modulation instruction respectively; K2 is the response rate index within the settlement cycle; ν b The standard regulation speed pre-set for the frequency regulation market, Regulation accuracy refers to the degree of deviation between the frequency regulation resources and the AGC frequency regulation instructions. It is calculated by the ratio of the maximum deviation and average deviation between the output required by the dispatch instruction and the actual output of the frequency regulation resources: Where: K3 is the adjustment accuracy index within the settlement period; They are the scheduling required output and actual output of the nth frequency modulation instruction respectively.

6. The frequency regulation and dispatching method of energy storage virtual power plant involving electric vehicles according to claim 5 is characterized in that: In step S22, a frequency modulation performance comprehensive index model is established: The maximum value method is used to normalize the frequency modulation performance index, and then its weighted average value is calculated: K=λ1k1+λ2k2+λ3k3, Where: k i is the i-th frequency modulation performance index after normalization; K i,max , K i,min are the maximum and minimum values ​​of the ith frequency modulation performance index respectively; λ1, λ2, and λ3 are the weights of the first to third frequency modulation performance indicators respectively; K is the comprehensive frequency modulation performance indicator.

7. The frequency regulation and dispatching method of energy storage virtual power plant with participation of electric vehicles according to claim 1 is characterized in that: In step S31, a frequency modulation auxiliary service market revenue model is established: Where: R is the income from participating in the frequency modulation market; are the frequency regulation capacity price and frequency regulation mileage price in the tth period respectively; They are the frequency regulation capacity and frequency regulation mileage of the virtual power plant in the tth period respectively.

8. The frequency regulation and dispatching method of energy storage virtual power plant involving electric vehicles according to claim 7 is characterized in that: In step S32, a frequency modulation cost model is established: Virtual power plant participation in frequency regulation and energy storage operation and maintenance costs: C m =C dr +C es +C ps +C ev , Where: C m The operation and maintenance costs of frequency regulation and energy storage for virtual power plants, The cost of purchasing electricity for virtual power plants to participate in frequency regulation: Where: C buy is the electricity purchase cost of the virtual power plant participating in frequency regulation; σ is the average loss coefficient of frequency regulation capacity; is the electricity price in the tth period.

9. The frequency regulation and dispatching method of energy storage virtual power plant involving electric vehicles according to claim 8, characterized in that: In step S33, the energy storage virtual power plant dispatch objective function and constraints for the frequency regulation auxiliary service market are established: Objective function: maxR-C m -C buy , The energy storage state should satisfy the constraints: Where: and The upper and lower limits of electrochemical energy storage charge, respectively; and The upper and lower limits of phase change energy and heat storage, respectively; and are the upper and lower limits of the electric vehicle charging station j, The frequency modulation output should meet the constraints: Where: P AGC (t) is the AGC instruction at time t; P o,m (t) is the frequency regulation output of the mth frequency regulation resource in the virtual power plant at time t; M is the total number of frequency regulation resources; E b It is the upper limit of the deviation between the frequency regulation output of the virtual power plant and the AGC instruction within period T.

10. The frequency regulation and dispatching method of energy storage virtual power plant with participation of 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 auxiliary service market is solved to obtain the AGC power optimization allocation result, and the virtual power plant is dispatched according to the allocation result, as follows: Based on the revenue model of energy storage virtual power plant for frequency regulation auxiliary service market, the sequential quadratic programming method of fmincon function in MATLAB optimization toolbox is used to solve the optimal AGC power allocation scheme. According to the allocation results, the virtual power plant dispatches energy storage resources with different performance and capacity in real time to achieve efficient utilization of energy storage resources and maximize benefits.

Images