A virtual power plant optimization scheduling method and system taking into account spinning reserve service

Abstract

The present application relates to a method and system for optimizing the scheduling of a virtual power plant taking into account rotating reserve services. The method comprises: step 1: constructing an optimized scheduling model for a virtual power plant taking into account rotating reserve services; step 2: obtaining scheduling parameters related to virtual power plant scheduling, step 3: inputting the scheduling parameters into the robust optimization scheduling model for a virtual power plant taking into account rotating reserve services constructed in step 1, performing virtual power plant scheduling analysis, and obtaining the optimal scheduling strategy for the virtual power plant. The optimal scheduling strategy for the virtual power plant includes the output power of the gas turbine in each time period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold release capacity of the cold storage pool, the interrupted load, and the total reserve capacity provided by the virtual power plant in each time period. Considering the uncertainty of photovoltaic power generation in the virtual power plant, the operation mode of the virtual power plant is optimized and scheduled, thereby giving full play to the economic potential of the virtual power plant in participating in the rotating reserve market and effectively improving the economic benefits of the virtual power plant.

Description

Technical Field

[0001] The present application relates to the technical field of power system power dispatching, and in particular to a virtual power plant optimization dispatching method and system taking spinning reserve services into account. Background Art

[0002] In recent years, the rapid development of all walks of life has led to an increasing demand for electricity. Due to the continuous reduction of fossil fuel resources, environmental pollution, and increasingly serious carbon emissions, the traditional energy supply model based on centralized power generation and long-distance transmission no longer meets the requirements of the sustainable development concept of modern society. Distributed energy has gradually been widely adopted by more and more countries for its advantages such as economy, environmental protection, and abundant reserves. As the terminal of energy supply, the explosive growth of distributed photovoltaic, energy storage, and air-conditioning capacity on the user side has gradually developed into a producer and consumer with controllable load and dual source and load. As an important subject and component of the power market, ancillary services play an important role in ensuring the safe and stable operation of my country's power system. Considering that virtual power plants participate in both the electric energy and spinning reserve markets at the same time can improve decision-making flexibility and obtain dual market benefits.

[0003] Virtual power plants aggregate multiple distributed energy sources such as renewable energy, energy storage, and demand response through advanced communication, metering, and control technologies, and participate in the operation of the power grid as a whole, which can reduce the impact of distributed energy on the public grid when connected to the grid separately and improve its market competitiveness. At present, the research on virtual power plants mainly focuses on the scheduling problem of the energy market, while ignoring its auxiliary functions, such as spinning reserve services, and has not been able to give full play to the potential functions of virtual power plants. When facing random variables such as photovoltaics, virtual power plants can use traditional uncertainty analysis methods based on probability theory such as stochastic programming and chance-constrained programming. However, these methods have problems such as large analysis volume, low analysis accuracy, and lack of security, which limits their further application and makes the resource utilization rate of virtual power plants low. Summary of the invention

[0004] Based on this, it is necessary to provide a virtual power plant optimization scheduling method and system taking into account rotating reserve services that can improve the resource utilization of virtual power plants in order to address the above technical problems.

[0005] A virtual power plant optimization scheduling method taking spinning reserve service into account, the method comprising:

[0006] Step 1: Construct a robust optimization dispatch model for virtual power plants taking spinning reserve services into account, including:

[0007] 1) constructing a virtual power plant optimization scheduling model taking into account spinning reserve services, wherein the objective function of the virtual power plant optimization scheduling model takes into account the economic benefits generated by the virtual power plant providing spinning reserve services, and the constraints of the virtual power plant optimization scheduling model include: operating power constraints, air conditioning system operating constraints, gas turbine operating constraints, energy storage power station operating constraints, interruptible load constraints, and spare capacity constraints;

[0008] 2) Performing robust optimization processing on the photovoltaic power generation power and operating power constraints in the virtual power plant optimization scheduling model, and converting the virtual power plant optimization scheduling model into a virtual power plant robust optimization scheduling model that takes into account spinning reserve services;

[0009] Step 2: Obtain scheduling parameters related to virtual power plant scheduling, the scheduling parameters including: energy market electricity price, rotating reserve market electricity price, gas turbine fuel cost, gas turbine fixed cost, gas turbine startup cost, gas turbine shutdown cost, load interruption compensation price, gas turbine power generation cost, cold storage tank capacity upper limit, cold storage tank release process energy conversion efficiency, gas turbine downward ramp rate, gas turbine upward ramp rate, gas turbine start-up time minimum, gas turbine shutdown time minimum, energy storage power station storage capacity maximum, charging efficiency, discharge efficiency and total interruption load, photovoltaic power generation predicted power, photovoltaic power generation maximum predicted deviation;

[0010] Step 3: Input the dispatching parameters into the robust optimization dispatching model of the virtual power plant taking into account the rotating reserve service constructed in step 1, conduct virtual power plant dispatching analysis, and obtain the optimal dispatching strategy of the virtual power plant. The optimal dispatching strategy of the virtual power plant includes the gas turbine output power in each time period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold release capacity of the cold storage pool, the interrupted load, and the total reserve capacity provided by the virtual power plant in each time period.

[0011] In one embodiment, the objective function of the virtual power plant optimization scheduling model taking into account spinning reserve service is:

[0012]

[0013]

[0014] Among them, t represents the time period, and T represents the scheduling period; It represents the revenue of virtual power plant participating in the electricity market, which is composed of energy market revenue and spinning reserve market revenue; is the energy market electricity price during period t; P is the market price of spinning reserve electricity in period t; t em is the bidding amount of the virtual power plant in the energy market during period t, and is the decision variable; is the bidding amount of the virtual power plant in the spinning reserve market during period t, and is the decision variable; is the gas turbine cost, is the compensation cost of interruptible load, The operating cost of the energy storage power station.

[0015] In one embodiment, the gas turbine cost includes operating cost and start-up and shutdown cost, and the gas turbine cost is obtained according to a cost calculation formula, which is:

[0016]

[0017] Among them, P t gas is the gas turbine output power during period t; k is the gas turbine fuel cost; k g is the fixed cost of gas turbine; su is the gas turbine startup cost; sd is the gas turbine stopping cost; Boolean variable μ t , They respectively indicate whether the gas turbine is working, started, or stopped during period t. If yes, it is set to 1, otherwise it is set to 0.

[0018] In one embodiment, the compensation cost of the interruptible load is:

[0019]

[0020] Among them, n m is the interrupt level number; is the mth level load interruption compensation price; It is the mth level interruption load in period t.

[0021] In one embodiment, the operating cost of the energy storage power station is linearly related to its charging and discharging power, and the expression is as follows:

[0022]

[0023] Among them, P t esc is the energy storage charging power; P t esd is the energy storage discharge power; is the first cost coefficient, is the slope of the energy storage power station operation cost function; is the second cost coefficient, which is the intersection of the energy storage power station operation cost function and the ordinate.

[0024] In one embodiment, the air conditioning operation system constraints are:

[0025]

[0026]

[0027]

[0028]

[0029] In the formula, △t represents the time interval, P t cold Indicates the power consumption of the air conditioning system; Q AC (t) represents the total cooling capacity of the air conditioner; The cooling capacity of the refrigerator; The cold storage capacity of the cold storage pool; Release cold to the cold storage pool; is the capacity of the cold storage pool during period t; is the capacity of the cold storage pool during period t-1; S c,max is the upper limit of the cold storage pool capacity; η st is the cold storage efficiency of the cold storage pool; η re is the cooling efficiency of the cold storage pool; u ch is the energy conversion efficiency of the refrigerator; u st is the energy conversion efficiency of the cold storage pool during the cold storage process; u re It is the energy conversion efficiency during the cold release process of the cold storage pool.

[0030] In one embodiment, the gas turbine operating constraints are:

[0031] P t gas ≥P gas,min μ t

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039] Where: P t gas is the gas turbine output power during period t; Spare capacity provided for gas turbines; P gas,min is the minimum output power of the gas turbine; P gas,max is the maximum output power of the gas turbine; r d is the gas turbine downward ramp rate; r u is the gas turbine upward ramp rate; t u is the minimum start-up time of the gas turbine; t d is the minimum value of the gas turbine shutdown time; Boolean variable u t Indicates whether the gas turbine is working during period t; t r For backup time; is the gas turbine output power during period t-1; Boolean variable μ t-1 Indicates whether the gas turbine is working during the t-1 period; Whether the gas turbine is started during period t; Whether the gas turbine stops during period t; t represents the time period, and T represents the scheduling cycle.

[0040] In one embodiment, the energy storage power station operation constraints are:

[0041] μ esc +μ esd ≤1

[0042]

[0043]

[0044] In the formula, The amount of electricity stored by the energy storage power station during period t; Indicates the amount of energy stored in the energy storage station during the t-1 period; Boolean variable μ esc Indicates whether the energy storage station is charged during period t, μ esd Indicates whether the energy storage power station discharges during period t, if yes, set to 1, otherwise set to 0; S esmin S is the minimum storage capacity of the energy storage power station; esmax P is the maximum storage capacity of the energy storage power station; t esc is the energy storage charging power; P t esd is the energy storage discharge power; η esc is the charging efficiency; η esd is the discharge efficiency.

[0045] In one embodiment, the interruptible load constraint is:

[0046]

[0047]

[0048]

[0049] Where n m Indicates the number of interruptible levels. It represents the interruption level coefficient of the mth level interruptible load; is the fixed electric load during period t; is the m-level interruption load in period t; P t elcurt is the total interruption load during period t; Spare capacity for virtual power plants.

[0050] In one embodiment, the operating power constraint is:

[0051] P t gas +P t pv +P t esd =P t m +P t el +P t cold -P t elcurt +P t esc

[0052] Where P t pv Output power for photovoltaic units; P t m Bidding for electricity in the energy market for virtual power plants;

[0053] In one embodiment, the spare capacity constraint is:

[0054]

[0055] In the formula, The total reserve capacity provided by the virtual power plant during period t.

[0056] In one embodiment, the photovoltaic power generation power in the virtual power plant optimization dispatch model taking into account the spinning reserve service is processed using the robust optimization principle:

[0057] In order to consider the impact of the uncertainty of photovoltaic power generation on the optimal scheduling of virtual power plants, the photovoltaic power generation in virtual power plants is robustly optimized, namely

[0058]

[0059] Where: η t is the robustness coefficient; is the uncertain expression of photovoltaic power generation; P t pv is the predicted value of photovoltaic power generation; ρ t is the estimated error coefficient, that is, it is assumed that the available power of photovoltaic power generation is [(1-ρ t ) t pv ,(1+ρ t ) t pv ] internal fluctuation, where the estimated error coefficient ρ t is 0.3.

[0060] In one embodiment, the robust optimization principle is used to process the operating power constraints in the virtual power plant optimization dispatch model taking spinning reserve service into account and transform them into robust operating constraints:

[0061] First, the operating power constraints in the virtual power plant optimization dispatch model taking into account spinning reserve services are transformed as follows:

[0062]

[0063] Where: P t elcurt is the interruptible load, P t el Fixed load;

[0064] For the convenience of description, F is used. t Represents P t gas +P t esd -P t cold +P t elcurt -P t esc -P t em -P t el ,remember:

[0065] F t =P t gas +P t esd -P t cold +P t elcurt -P t esc -P t em -P t el

[0066] Will Substituting the expression into the above formula, we can get

[0067] -P t pv -η t ρ t P t pv ≤F t

[0068] In order to ensure that the photovoltaic power generation power still meets the operation constraints when it reaches the actual prediction boundary, an auxiliary variable r is introduced. t , let r t ≥|P t pv |, then

[0069] -P t pv -η t ρ t P t pv ≤-P t pv +ρ t |P t pv |≤-P t pv +ρ t r t ≤F t

[0070] So we have the following formula:

[0071]

[0072] In order to adjust the robustness of the decision-making results and take into account economic benefits, the robust control coefficient Γ is introduced to control the conservatism of the system decision. Γ∈[0,1]. The larger the value of Γ, the more conservative the system is. The robust control coefficient Γ is used to control the conservativeness of the system decision. Maximum deviation from predicted value The operating power constraints in the virtual power plant optimal dispatch model taking into account spinning reserve services are transformed into robust operating constraints as follows:

[0073]

[0074] When the value of Γ in the virtual power plant optimization scheduling model taking into account the rotating reserve service is 0, it is equivalent to the photovoltaic power generation power taking the predicted value; when the value of Γ is 1, it is equivalent to the photovoltaic power generation power taking the predicted maximum deviation value. By adjusting the value of Γ, the optimal scheduling strategy of the virtual power plant under different degrees of conservatism can be obtained, thereby taking into account both robustness and economy.

[0075] In one of the embodiments, the photovoltaic power generation power and operating power constraints in the virtual power plant optimization scheduling model taking into account the spinning reserve service are processed according to the robust optimization principle and then converted into a virtual power plant robust optimization scheduling model taking into account the spinning reserve service.

[0076] In one embodiment, the step of inputting the dispatching parameters into the robust optimization dispatching model of the virtual power plant taking into account the spinning reserve service constructed in step 1, performing virtual power plant dispatching analysis, and obtaining the optimal dispatching strategy of the virtual power plant includes:

[0077] Input the scheduling parameters, call the optimization solver in Gams to solve, and obtain the optimal scheduling strategy of the virtual power plant, which includes the gas turbine output power in each time period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold release capacity of the cold storage pool, the interrupted load, and the total spare capacity provided by the virtual power plant in each time period.

[0078] A virtual power plant optimization scheduling system taking into account spinning reserve services using the virtual power plant optimization scheduling method, comprising a virtual power plant robust optimization scheduling model modeling module, a scheduling parameter acquisition input module, and a virtual power plant optimal scheduling strategy generation module;

[0079] The virtual power plant robust optimization scheduling model modeling module constructs a virtual power plant robust optimization scheduling model taking into account spinning reserve services, including:

[0080] 1) constructing a virtual power plant optimization scheduling model taking into account spinning reserve services, wherein the objective function of the virtual power plant optimization scheduling model takes into account the economic benefits generated by the virtual power plant providing spinning reserve services, and the constraints of the virtual power plant optimization scheduling model include: operating power constraints, air conditioning system operating constraints, gas turbine operating constraints, energy storage power station operating constraints, interruptible load constraints, and spare capacity constraints;

[0081] 2) Performing robust optimization processing on the photovoltaic power generation power and operating power constraints in the virtual power plant optimization scheduling model, and converting the virtual power plant optimization scheduling model into a virtual power plant robust optimization scheduling model that takes into account spinning reserve services;

[0082] The scheduling parameter acquisition input module obtains scheduling parameters related to virtual power plant scheduling, and the scheduling parameters include: energy market electricity price, rotating reserve market electricity price, gas turbine fuel cost, gas turbine fixed cost, gas turbine startup cost, gas turbine shutdown cost, load interruption compensation price, gas turbine power generation cost, cold storage tank capacity upper limit, cold storage tank release process energy conversion efficiency, gas turbine downward ramp rate, gas turbine upward ramp rate, gas turbine start-up time minimum, gas turbine shutdown time minimum, energy storage power station storage capacity maximum, charging efficiency, discharge efficiency and total interruption load, photovoltaic power generation predicted power, photovoltaic power generation maximum predicted deviation;

[0083] The virtual power plant optimal scheduling strategy generation module inputs the scheduling parameters into the virtual power plant robust optimization scheduling model taking into account the rotating reserve service constructed in step 1, performs virtual power plant scheduling analysis, and obtains the optimal scheduling strategy of the virtual power plant. The optimal scheduling strategy of the virtual power plant includes the gas turbine output power in each time period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold release capacity of the cold storage pool, the interrupted load, and the total reserve capacity provided by the virtual power plant in each time period.

[0084] The above-mentioned virtual power plant optimization scheduling method and system taking into account the spinning reserve service, through step 1 to construct a virtual power plant optimization scheduling model taking into account the spinning reserve service; step 2 to obtain scheduling parameters related to the virtual power plant scheduling, the scheduling parameters include: energy market electricity price, spinning reserve market electricity price, gas turbine fuel cost, gas turbine fixed cost, gas turbine startup cost, gas turbine shutdown cost, load interruption compensation price, gas turbine power generation cost, capacity upper limit of cold storage pool, energy conversion efficiency in the process of releasing cold storage pool, gas turbine downward climbing rate, gas turbine upward climbing rate, minimum gas turbine startup time, fuel oil and gas turbine fuel consumption ... The minimum value of gas turbine shutdown time, the minimum value of energy storage power station storage, the maximum value of energy storage power station storage, charging efficiency, discharge efficiency and total interruption load, photovoltaic power generation prediction power, photovoltaic power generation maximum prediction deviation; Step 3: Input the scheduling parameters into the virtual power plant robust optimization scheduling model taking into account the rotating reserve service constructed in step 1, conduct virtual power plant scheduling analysis, and obtain the optimal scheduling strategy of the virtual power plant, which includes the output power of the gas turbine in each period, the energy storage charging power, the energy storage discharge power, the cold storage capacity of the cold storage pool, the cold storage capacity, the interruption load, and the total reserve capacity provided by the virtual power plant in each period. By taking into account the uncertainty of photovoltaic power generation in the virtual power plant through the robust optimization scheduling model of the virtual power plant taking into account the rotating reserve service, the operation mode of the air conditioning system, gas turbine, energy storage power station, and interruptible load in the virtual power plant is optimized, so as to give full play to the economic potential of the virtual power plant in participating in the rotating reserve market and effectively improve the economic benefits of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 A schematic flow chart of a virtual power plant optimization dispatching method taking spinning reserve service into account in one embodiment;

[0086] Figure 2 It is the single-day fixed load demand diagram of the virtual power plant;

[0087] Figure 3 electricity price maps for the energy market and spinning reserve market;

[0088] Figure 4 The result diagram of the optimal bidding strategy of virtual power plants in the energy market under different robust control coefficients;

[0089] Figure 5 This is the optimization result diagram of the virtual power plant generator, energy storage, and cold storage pool;

[0090] Figure 6 Optimization result diagram for interrupted load and reserve capacity;

[0091] Figure 7 The profit result diagram of the virtual power plant under different robust control coefficients;

[0092] Figure 8 A schematic diagram of the structure of a virtual power plant optimization scheduling system taking spinning reserve services into account in one embodiment. DETAILED DESCRIPTION

[0093] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0094] In one embodiment, Figure 1 As shown, a virtual power plant optimization scheduling method taking spinning reserve service into account is provided, and the method is applied to a terminal as an example for explanation, including the following steps:

[0095] Step 1: Construct a robust optimization dispatch model for virtual power plants taking spinning reserve services into account, including:

[0096] 1) Construct a virtual power plant optimization dispatch model that takes into account spinning reserve services. The objective function of the virtual power plant optimization dispatch model takes into account the economic benefits generated by the virtual power plant providing spinning reserve services. The constraints of the virtual power plant optimization dispatch model include: operating power constraints, air conditioning system operating constraints, gas turbine operating constraints, energy storage power station operating constraints, interruptible load constraints, and reserve capacity constraints;

[0097] 2) Perform robust optimization processing on the photovoltaic power generation power and operating power constraints in the virtual power plant optimization scheduling model, and convert the virtual power plant optimization scheduling model into a virtual power plant robust optimization scheduling model that takes into account spinning reserve services;

[0098] Step 2: Obtain the dispatching parameters related to the virtual power plant dispatching, including: energy market electricity price, rotating reserve market electricity price, gas turbine fuel cost, gas turbine fixed cost, gas turbine startup cost, gas turbine shutdown cost, load interruption compensation price, gas turbine power generation cost, cold storage pool capacity upper limit, cold storage pool energy conversion efficiency in the process of releasing cold, gas turbine downward ramp rate, gas turbine upward ramp rate, minimum gas turbine startup time, minimum gas turbine shutdown time, minimum energy storage power station storage capacity, maximum energy storage power station storage capacity, charging efficiency, discharge efficiency and total interruption load, photovoltaic power generation forecast power, photovoltaic power generation maximum forecast deviation;

[0099] Step 3: Input the dispatch parameters into the robust optimization dispatch model of the virtual power plant taking into account the spinning reserve service constructed in Step 1, conduct virtual power plant dispatch analysis, and obtain the optimal dispatch strategy of the virtual power plant. The optimal dispatch strategy of the virtual power plant includes the gas turbine output power in each time period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold release capacity of the cold storage pool, the interrupted load, and the total reserve capacity provided by the virtual power plant in each time period.

[0100] In one embodiment, the objective function of the virtual power plant optimization dispatch model taking into account spinning reserve services is:

[0101]

[0102]

[0103] Among them, t represents the time period, and T represents the scheduling period; It represents the revenue of virtual power plant participating in the electricity market, which is composed of energy market revenue and spinning reserve market revenue; is the energy market electricity price during period t; P is the market price of spinning reserve electricity in period t; t em is the bidding amount of the virtual power plant in the energy market during period t, and is the decision variable; is the bidding amount of the virtual power plant in the spinning reserve market during period t, and is the decision variable; is the gas turbine cost, is the compensation cost of interruptible load, The operating cost of the energy storage power station.

[0104] In one embodiment, the gas turbine cost includes operating cost and start-up and shutdown cost. The gas turbine cost is obtained according to a cost calculation formula, which is:

[0105]

[0106] Among them, P t gas is the gas turbine output power during period t; k is the gas turbine fuel cost; k g is the fixed cost of gas turbine; su is the gas turbine startup cost; sd is the gas turbine stopping cost; Boolean variable μ t , They respectively indicate whether the gas turbine is working, started, or stopped during period t. If yes, it is set to 1, otherwise it is set to 0.

[0107] In one embodiment, the compensation cost of the interruptible load is:

[0108]

[0109] Among them, n m is the interrupt level number; is the mth level load interruption compensation price; It is the mth level interruption load in period t.

[0110] In one embodiment, the operating cost of the energy storage power station is linearly related to its charging and discharging power, and the expression is as follows:

[0111]

[0112] Among them, P t esc is the energy storage charging power; P t esd is the energy storage discharge power; is the first cost coefficient, is the slope of the energy storage power station operation cost function; is the second cost coefficient, which is the intersection of the energy storage power station operation cost function and the ordinate.

[0113] In one embodiment, the air conditioning operation system constraints are:

[0114]

[0115]

[0116]

[0117]

[0118] In the formula, △t represents the time interval, P t cold Indicates the power consumption of the air conditioning system; Q AC (t) represents the total cooling capacity of the air conditioner; The cooling capacity of the refrigerator; The cold storage capacity of the cold storage pool; Release cold to the cold storage pool; is the capacity of the cold storage pool during period t; is the capacity of the cold storage pool during period t-1; S c,max is the upper limit of the cold storage pool capacity; η st is the cold storage efficiency of the cold storage pool; η re is the cooling efficiency of the cold storage pool; u ch is the energy conversion efficiency of the refrigerator; u st is the energy conversion efficiency of the cold storage pool during the cold storage process; u re It is the energy conversion efficiency during the cold release process of the cold storage pool.

[0119] In one embodiment, the gas turbine operating constraints are:

[0120] P t gas ≥P gas,min μ t

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] Where: P t gas is the gas turbine output power during period t; Spare capacity provided for gas turbines; P gas,min is the minimum output power of the gas turbine; P gas,max is the maximum output power of the gas turbine; r d is the gas turbine downward ramp rate; r u is the gas turbine upward ramp rate; t u is the minimum start-up time of the gas turbine; t dis the minimum value of the gas turbine shutdown time; Boolean variable u t Indicates whether the gas turbine is working during period t; t r For backup time; is the gas turbine output power during period t-1; Boolean variable μ t-1 Indicates whether the gas turbine is working during the t-1 period; Whether the gas turbine is started during period t; Whether the gas turbine stops during period t; t represents the time period, and T represents the scheduling cycle.

[0129] In one embodiment, the energy storage power station operation constraints are:

[0130] μ esc +μ esd ≤1

[0131]

[0132]

[0133] In the formula, The amount of electricity stored by the energy storage power station during period t; Indicates the amount of energy stored in the energy storage station during the t-1 period; Boolean variable μ esc Indicates whether the energy storage station is charged during period t, μ esd Indicates whether the energy storage power station discharges during period t, if yes, set to 1, otherwise set to 0; S esmin S is the minimum storage capacity of the energy storage power station; esmax P is the maximum storage capacity of the energy storage power station; t esc is the energy storage charging power; P t esd is the energy storage discharge power; η esc is the charging efficiency; η esd is the discharge efficiency.

[0134] In one embodiment, the interruptible load constraint is:

[0135]

[0136]

[0137]

[0138] Where n m Indicates the number of interruptible levels. It represents the interruption level coefficient of the mth level interruptible load; is the fixed electric load during period t; is the m-level interruption load in period t; P telcurt is the total interruption load during period t; Spare capacity for virtual power plants.

[0139] In one embodiment, the operating power constraint is:

[0140] P t gas +P t pv +P t esd =P t m +P t el +P t cold -P t elcurt +P t esc

[0141] Where P t pv Output power for photovoltaic units; P t m Bidding for electricity in the energy market for virtual power plants;

[0142] In one embodiment, the spare capacity constraint is:

[0143]

[0144] In the formula, The total reserve capacity provided by the virtual power plant during period t.

[0145] In one embodiment, the photovoltaic power generation power in the virtual power plant optimization dispatch model taking into account the spinning reserve service is processed using the robust optimization principle:

[0146] In order to consider the impact of the uncertainty of photovoltaic power generation on the optimal scheduling of virtual power plants, the photovoltaic power generation in virtual power plants is robustly optimized, namely

[0147]

[0148] Where: η t is the robustness coefficient; is the uncertain expression of photovoltaic power generation; P t pv is the predicted value of photovoltaic power generation; ρ t is the estimated error coefficient, that is, it is assumed that the available power of photovoltaic power generation is [(1-ρ t ) t pv ,(1+ρ t )t pv ] internal fluctuation, where the estimated error coefficient ρ t is 0.3.

[0149] In one embodiment, the robust optimization principle is used to process the operating power constraints in the virtual power plant optimization dispatch model taking into account the spinning reserve service and transform them into robust operating constraints:

[0150] First, the operating power constraints in the virtual power plant optimization dispatch model taking into account spinning reserve services are transformed as follows:

[0151]

[0152] Where: P t elcurt is the interruptible load, P t el Fixed load;

[0153] For the convenience of description, F is used. t Represents P t gas +P t esd -P t cold +P t elcurt -P t esc -P t em -P t el ,remember:

[0154] F t =P t gas +P t esd -P t cold +P t elcurt -P t esc -P t em -P t el

[0155] Will Substituting the expression into the above formula, we can get

[0156] -P t pv -η t ρ t P t pv ≤F t

[0157] In order to ensure that the photovoltaic power generation power still meets the operation constraints when it reaches the actual prediction boundary, an auxiliary variable r is introduced. t , let r t ≥|P t pv |, then

[0158] -P t pv -η t ρ t P t pv ≤-P t pv +ρ t |P t pv |≤-P t pv +ρ t r t ≤F t

[0159] So we have the following formula:

[0160]

[0161] In order to adjust the robustness of the decision-making results and take into account economic benefits, the robust control coefficient Γ is introduced to control the conservatism of the system decision. Γ∈[0,1]. The larger the value of Γ, the more conservative the system is. The robust control coefficient Γ is used to control the conservativeness of the system decision. Maximum deviation from predicted value The operating power constraints in the virtual power plant optimal dispatch model taking into account spinning reserve services are transformed into robust operating constraints as follows:

[0162]

[0163] When Γ is taken as 0 in the virtual power plant optimization scheduling model taking into account spinning reserve service, it is equivalent to the photovoltaic power generation power taking the predicted value; when Γ is taken as 1, it is equivalent to the photovoltaic power generation power taking the predicted maximum deviation value. By adjusting the value of Γ, the optimal scheduling strategy of the virtual power plant under different conservatism can be obtained, thus taking into account both robustness and economy.

[0164] In one embodiment, the photovoltaic power generation power and operating power constraints in the virtual power plant optimization scheduling model taking into account spinning reserve services are processed according to the robust optimization principle and then converted into a virtual power plant robust optimization scheduling model taking into account spinning reserve services.

[0165] In one embodiment, the steps of inputting the dispatch parameters into a pre-built robust optimization dispatch model of a virtual power plant taking spinning reserve service into account, performing a virtual power plant dispatch analysis, and obtaining an optimal dispatch strategy for the virtual power plant include:

[0166] Input the scheduling parameters, call the optimization solver in Gams to obtain the optimal scheduling strategy of the virtual power plant. The optimal scheduling strategy of the virtual power plant includes the gas turbine output power in each time period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold release capacity of the cold storage pool, the interrupted load, and the total spare capacity provided by the virtual power plant in each time period.

[0167] Among them, Gams, the full name of which is General Algebraic Modeling System, is an advanced modeling system for mathematical programming and optimization. It consists of a language compiler and a stable integration of various high-performance solvers, which can well solve modeling linear, nonlinear and mixed integer optimization problems.

[0168] The above-mentioned virtual power plant optimization scheduling method taking into account the spinning reserve service, through step 1 to construct a virtual power plant optimization scheduling model taking into account the spinning reserve service; step 2 to obtain the scheduling parameters related to the virtual power plant scheduling, the scheduling parameters include: energy market electricity price, spinning reserve market electricity price, gas turbine fuel cost, gas turbine fixed cost, gas turbine startup cost, gas turbine shutdown cost, load interruption compensation price, gas turbine power generation cost, capacity upper limit of the cold storage pool, energy conversion efficiency in the cold storage pool during the release process, gas turbine downward climbing rate, gas turbine upward climbing rate, minimum gas turbine startup time, gas The minimum value of turbine shutdown time, the minimum value of energy storage power station storage, the maximum value of energy storage power station storage, charging efficiency, discharging efficiency and total interruption load, photovoltaic power generation prediction power, and photovoltaic power generation maximum prediction deviation; Step 3: Input the dispatching parameters into the virtual power plant robust optimization dispatching model taking into account the spinning reserve service constructed in step 1, conduct virtual power plant dispatching analysis, and obtain the optimal dispatching strategy of the virtual power plant. The optimal dispatching strategy of the virtual power plant includes the output power of the gas turbine in each period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold storage capacity, the interruption load, and the total reserve capacity provided by the virtual power plant in each period. Through the robust optimization dispatching model of the virtual power plant taking into account the spinning reserve service, the uncertainty of the photovoltaic power generation in the virtual power plant is considered, and the operation mode of the air conditioning system, gas turbine, energy storage power station, and interruptible load in the virtual power plant is optimized, so as to give full play to the economic potential of the virtual power plant in participating in the spinning reserve market and effectively improve the economic benefits of the virtual power plant.

[0169] Furthermore, considering that in addition to traditional generators that can provide spinning reserve services, the existence of user-side demand response can also provide reserve services to participate in the spinning reserve market, and it also has the advantages of low cost, flexibility and convenience. On the other hand, virtual power plants need to face the impact of photovoltaic output uncertainty when scheduling. Robust optimization, as a method of dealing with uncertainty, has the advantages of not needing to know the probability distribution of uncertain parameters and simple calculations, and can well deal with the impact of photovoltaic uncertainty on scheduling. Considering that virtual power plants participate in both the electric energy and spinning reserve markets at the same time can improve decision-making flexibility and obtain dual market benefits, maximizing their own benefits based on the willingness of each member to participate. Taking into account the optimized scheduling of virtual power plants with spinning reserve services enables virtual power plants to participate in the energy and auxiliary service markets at the same time, promote the consumption of renewable energy, and effectively improve the actual benefits of virtual power plants.

[0170] In one embodiment, the air conditioning system, gas turbine unit, photovoltaic unit, energy storage power station and interruptible load constitute a virtual power plant. Considering the strategy of virtual power plant participation, the scheduling period is set to 1 day, which is divided into 24 time periods.

[0171] The parameters of the air conditioning system are shown in Table 1, the specific parameters of the gas turbine are shown in Table 2, and the specific parameters of the energy storage power station are shown in Table 3. The daily fixed load demand of the virtual power plant is shown in Table 1, and the electricity prices of the energy market and the spinning reserve market are shown in Table 2. Figure 2 The photovoltaic power generation prediction curve and the maximum prediction deviation are shown in Figure 3 . Figure 4 When the robust control coefficient Γ is 0, 0.5, and 1, the virtual power plant has the optimal bidding strategy in the energy market. It can be seen that during the photovoltaic power station output time period, the virtual power plant's power purchase amount under different robust coefficients is higher than (the power sales amount is lower than) the deterministic model, and the larger the robust coefficient, the more power the virtual power plant purchases (the less power it sells) in the energy market. Figure 5 The optimal output power of the gas turbine in each time period corresponding to the virtual power plant, the optimal energy storage charging power, the optimal energy storage discharging power, the optimal cold storage capacity of the cold storage pool, and the optimal cold release capacity of the cold storage pool are given. Figure 6 The optimal interruption load and the optimal total reserve capacity provided by the virtual power plant in each period are given. Figure 5 and Figure 6 It can be seen that the virtual power plant achieves resource integration and optimization through the coordinated scheduling of various distributed resources.

[0172] Table 1 Air conditioning system parameters

[0173]

[0174]

[0175] Table 2 Gas turbine parameters

[0176]

[0177] Table 3 Energy storage power station parameters

[0178]

[0179] Figure 7 The impact of the robustness coefficient of the virtual power plant on profits is given.

[0180] The above simulation results verify the effectiveness and practicality of this application. According to the optimization scheduling results, the invention can obtain greater market benefits compared to only participating in the energy market. The use of robust optimization to process photovoltaic output can make the system take into account both robustness and economy, and improve the actual profit of the virtual power plant.

[0181] In one embodiment, Figure 8 As shown, a virtual power plant optimization scheduling system taking into account spinning reserve services using a virtual power plant optimization scheduling method is also provided, including a virtual power plant robust optimization scheduling model modeling module 310, a scheduling parameter acquisition input module 320, and a virtual power plant optimal scheduling strategy generation module 330;

[0182] The virtual power plant robust optimization dispatch model modeling module 310 constructs a virtual power plant robust optimization dispatch model taking into account spinning reserve services, including:

[0183] 1) Construct a virtual power plant optimization dispatch model that takes into account spinning reserve services. The objective function of the virtual power plant optimization dispatch model takes into account the economic benefits generated by the virtual power plant providing spinning reserve services. The constraints of the virtual power plant optimization dispatch model include: operating power constraints, air conditioning system operating constraints, gas turbine operating constraints, energy storage power station operating constraints, interruptible load constraints, and reserve capacity constraints;

[0184] 2) Perform robust optimization processing on the photovoltaic power generation power and operating power constraints in the virtual power plant optimization scheduling model, and convert the virtual power plant optimization scheduling model into a virtual power plant robust optimization scheduling model that takes into account spinning reserve services;

[0185] The dispatch parameter acquisition input module 320 obtains dispatch parameters related to virtual power plant dispatch, and the dispatch parameters include: energy market electricity price, rotating reserve market electricity price, gas turbine fuel cost, gas turbine fixed cost, gas turbine startup cost, gas turbine shutdown cost, load interruption compensation price, gas turbine power generation cost, cold storage tank capacity upper limit, energy conversion efficiency in the cold storage tank release process, gas turbine downward ramp rate, gas turbine upward ramp rate, minimum gas turbine startup time, minimum gas turbine shutdown time, minimum energy storage power station storage capacity, maximum energy storage power station storage capacity, charging efficiency, discharge efficiency and total interruption load, photovoltaic power generation predicted power, photovoltaic power generation maximum predicted deviation;

[0186] The virtual power plant optimal scheduling strategy generation module 330 inputs the scheduling parameters into the virtual power plant robust optimization scheduling model taking into account the rotating reserve service constructed in step 1, performs virtual power plant scheduling analysis, and obtains the optimal scheduling strategy of the virtual power plant. The optimal scheduling strategy of the virtual power plant includes the gas turbine output power in each time period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold release capacity of the cold storage pool, the interrupted load, and the total reserve capacity provided by the virtual power plant in each time period.

[0187] In one embodiment, the objective function of the virtual power plant optimization dispatch model taking into account spinning reserve services is:

[0188]

[0189]

[0190] Among them, t represents the time period, and T represents the scheduling period; It represents the revenue of virtual power plant participating in the electricity market, which is composed of energy market revenue and spinning reserve market revenue; is the energy market electricity price during period t; P is the market price of spinning reserve electricity in period t; t em is the bidding amount of the virtual power plant in the energy market during period t, and is the decision variable; is the bidding amount of the virtual power plant in the spinning reserve market during period t, and is the decision variable; is the gas turbine cost, is the compensation cost of interruptible load, The operating cost of the energy storage power station.

[0191] In one embodiment, the gas turbine cost includes operating cost and start-up and shutdown cost. The gas turbine cost is obtained according to a cost calculation formula, which is:

[0192] C t gas =kP tgas +k g μ t +λ su μ t su +λ sd μ t sd

[0193] Among them, P t gas is the gas turbine output power during period t; k is the gas turbine fuel cost; k g is the fixed cost of gas turbine; su is the gas turbine startup cost; sd is the gas turbine stopping cost; Boolean variable μ t , They respectively indicate whether the gas turbine is working, started, or stopped during period t. If yes, it is set to 1, otherwise it is set to 0.

[0194] In one embodiment, the compensation cost of the interruptible load is:

[0195]

[0196] Among them, n m is the interrupt level number; is the mth level load interruption compensation price; It is the mth level interruption load in period t.

[0197] In one embodiment, the operating cost of the energy storage power station is linearly related to its charging and discharging power, and the expression is as follows:

[0198]

[0199] Among them, P t esc is the energy storage charging power; P t esd is the energy storage discharge power; is the first cost coefficient, is the slope of the energy storage power station operation cost function; is the second cost coefficient, which is the intersection of the energy storage power station operation cost function and the ordinate.

[0200] In one embodiment, the air conditioning operation system constraints are:

[0201]

[0202]

[0203]

[0204]

[0205] In the formula, △t represents the time interval, P t cold Indicates the power consumption of the air conditioning system; Q AC (t) represents the total cooling capacity of the air conditioner; The cooling capacity of the refrigerator; The cold storage capacity of the cold storage pool; Release cold to the cold storage pool; is the capacity of the cold storage pool during period t; is the capacity of the cold storage pool during period t-1; S c,max is the upper limit of the cold storage pool capacity; η st is the cold storage efficiency of the cold storage pool; η re is the cooling efficiency of the cold storage pool; u ch is the energy conversion efficiency of the refrigerator; u st is the energy conversion efficiency of the cold storage pool during the cold storage process; u re It is the energy conversion efficiency during the cold release process of the cold storage pool.

[0206] In one embodiment, the gas turbine operating constraints are:

[0207] P t gas ≥P gas,min μ t

[0208] P t gas +R t gas ≤P gas,max μ t

[0209] R t gas ≤r u t r

[0210]

[0211]

[0212]

[0213]

[0214]

[0215] Where: P t gas is the gas turbine output power during period t; R t gas Spare capacity provided for gas turbines; Pgas,min is the minimum output power of the gas turbine; P gas,max is the maximum output power of the gas turbine; r d is the gas turbine downward ramp rate; r u is the gas turbine upward ramp rate; t u is the minimum start-up time of the gas turbine; t d is the minimum value of the gas turbine shutdown time; Boolean variable u t Indicates whether the gas turbine is working during period t; t r For backup time; is the gas turbine output power during period t-1; Boolean variable μ t-1 Indicates whether the gas turbine is working during the t-1 period; Whether the gas turbine is started during period t; Whether the gas turbine stops during period t; t represents the time period, and T represents the scheduling cycle.

[0216] In one embodiment, the energy storage power station operation constraints are:

[0217] μ esc +μ esd ≤1

[0218]

[0219]

[0220] In the formula, The amount of electricity stored by the energy storage power station during period t; Indicates the amount of energy stored in the energy storage station during the t-1 period; Boolean variable μ esc Indicates whether the energy storage station is charged during period t, μ esd Indicates whether the energy storage power station discharges during period t, if yes, set to 1, otherwise set to 0; S esmin S is the minimum storage capacity of the energy storage power station; esmax P is the maximum storage capacity of the energy storage power station; t esc is the energy storage charging power; P t esd is the energy storage discharge power; η esc is the charging efficiency; η esd is the discharge efficiency.

[0221] In one embodiment, the interruptible load constraint is:

[0222]

[0223]

[0224]

[0225] Where n m Indicates the number of interruptible levels. It represents the interruption level coefficient of the mth level interruptible load; is the fixed electric load during period t; is the m-level interruption load in period t; P t elcurt is the total interruption load during period t; Spare capacity for virtual power plants.

[0226] In one embodiment, the operating power constraint is:

[0227] P t gas +P t pv +P t esd =P t m +P t el +P t cold -P t elcurt +P t esc

[0228] Where P t pv Output power for photovoltaic units; P t m Bidding for electricity in the energy market for virtual power plants;

[0229] In one embodiment, the spare capacity constraint is:

[0230]

[0231] In the formula, The total reserve capacity provided by the virtual power plant during period t.

[0232] In one embodiment, the photovoltaic power generation power in the virtual power plant optimization dispatch model taking into account the spinning reserve service is processed using the robust optimization principle:

[0233] In order to consider the impact of the uncertainty of photovoltaic power generation on the optimal scheduling of virtual power plants, the photovoltaic power generation in virtual power plants is robustly optimized, namely

[0234]

[0235] Where: η t is the robustness coefficient; is the uncertain expression of photovoltaic power generation; Pt pv is the predicted value of photovoltaic power generation; ρ t is the estimated error coefficient, that is, it is assumed that the available power of photovoltaic power generation is [(1-ρ t ) t pv ,(1+ρ t ) t pv ] internal fluctuation, where the estimated error coefficient ρ t is 0.3.

[0236] In one embodiment, the robust optimization principle is used to process the operating power constraints in the virtual power plant optimization dispatch model taking into account the spinning reserve service and transform them into robust operating constraints:

[0237] First, the operating power constraints in the virtual power plant optimization dispatch model taking into account spinning reserve services are transformed as follows:

[0238]

[0239] Where: P t elcurt is the interruptible load, P t el Fixed load;

[0240] For the convenience of description, F is used. t Represents P t gas +P t esd -P t cold +P t elcurt -P t esc -P t em -P t el ,remember:

[0241] F t =P t gas +P t esd -P t cold +P t elcurt -P t esc -P t em -P t el

[0242] Will Substituting the expression into the above formula, we can get

[0243] -P t pv -η t ρ t P t pv ≤F t

[0244] In order to ensure that the photovoltaic power generation power still meets the operation constraints when it reaches the actual prediction boundary, an auxiliary variable r is introduced. t , let r t ≥|P t pv |, then

[0245]

[0246] So we have the following formula:

[0247]

[0248] In order to adjust the robustness of the decision-making results and take into account economic benefits, the robust control coefficient Γ is introduced to control the conservatism of the system decision. Γ∈[0,1]. The larger the value of Γ, the more conservative the system is. The robust control coefficient Γ is used to control the conservativeness of the system decision. Maximum deviation from predicted value Γρ t P t pv , the operating power constraints in the virtual power plant optimal dispatch model taking into account spinning reserve services are transformed into robust operating constraints as follows:

[0249]

[0250] When Γ is taken as 0 in the virtual power plant optimization scheduling model taking into account spinning reserve service, it is equivalent to the photovoltaic power generation power taking the predicted value; when Γ is taken as 1, it is equivalent to the photovoltaic power generation power taking the predicted maximum deviation value. By adjusting the value of Γ, the optimal scheduling strategy of the virtual power plant under different conservatism can be obtained, thus taking into account both robustness and economy.

[0251] In one embodiment, the photovoltaic power generation power and operating power constraints in the virtual power plant optimization scheduling model taking into account spinning reserve services are processed according to the robust optimization principle and then converted into a virtual power plant robust optimization scheduling model taking into account spinning reserve services.

[0252] In one embodiment, the step of inputting the scheduling parameters into the robust optimization scheduling model of the virtual power plant taking into account the spinning reserve service constructed in step 1, performing virtual power plant scheduling analysis, and obtaining the optimal scheduling strategy of the virtual power plant includes:

[0253] Input the scheduling parameters, call the optimization solver in Gams to obtain the optimal scheduling strategy of the virtual power plant. The optimal scheduling strategy of the virtual power plant includes the gas turbine output power in each time period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold release capacity of the cold storage pool, the interrupted load, and the total spare capacity provided by the virtual power plant in each time period.

[0254] The above-mentioned virtual power plant optimization scheduling system taking into account rotating reserve services constructs a virtual power plant optimization scheduling model taking into account rotating reserve services through a virtual power plant robust optimization scheduling model modeling module; a scheduling parameter acquisition input module obtains scheduling parameters related to virtual power plant scheduling, and the scheduling parameters include: energy market electricity price, rotating reserve market electricity price, gas turbine fuel cost, gas turbine fixed cost, gas turbine startup cost, gas turbine shutdown cost, load interruption compensation price, gas turbine power generation cost, capacity upper limit of cold storage pool, energy conversion efficiency in the process of releasing cold storage pool, gas turbine downward ramp rate, gas turbine upward ramp rate, and the minimum start-up time of gas turbine. The optimal scheduling strategy generation module of the virtual power plant inputs the scheduling parameters into the robust optimization scheduling model of the virtual power plant taking into account the spinning reserve service constructed in step 1, performs virtual power plant scheduling analysis, and obtains the optimal scheduling strategy of the virtual power plant. The optimal scheduling strategy of the virtual power plant includes the output power of the gas turbine in each period, the charging power of the energy storage, the discharging power of the energy storage, the cold storage capacity of the cold storage pool, the cold release capacity of the cold storage pool, the interruption load, and the total reserve capacity provided by the virtual power plant in each period. By taking into account the robust optimization scheduling model of the virtual power plant with spinning reserve service, the uncertainty of the photovoltaic power generation in the virtual power plant is considered, and the operation mode of the air conditioning system, gas turbine, energy storage power station, and interruptible load in the virtual power plant is optimized, so as to give full play to the economic potential of the virtual power plant in participating in the spinning reserve market and effectively improve the economic benefits of the virtual power plant.

[0255] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0256] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0257] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A virtual power plant optimization scheduling method taking into account spinning reserve services, characterized in that: The method comprises: Step 1: Construct a robust optimization dispatch model for virtual power plants taking spinning reserve services into account, including: 1) constructing a virtual power plant optimization scheduling model taking into account spinning reserve services, wherein the objective function of the virtual power plant optimization scheduling model takes into account the economic benefits generated by the virtual power plant providing spinning reserve services, and the constraints of the virtual power plant optimization scheduling model include: operating power constraints, air conditioning system operating constraints, gas turbine operating constraints, energy storage power station operating constraints, interruptible load constraints, and spare capacity constraints; 2) Performing robust optimization processing on the photovoltaic power generation power and operating power constraints in the virtual power plant optimization scheduling model, and converting the virtual power plant optimization scheduling model into a virtual power plant robust optimization scheduling model that takes into account spinning reserve services; Step 2: Obtain scheduling parameters related to virtual power plant scheduling, the scheduling parameters including: energy market electricity price, rotating reserve market electricity price, gas turbine fuel cost, gas turbine fixed cost, gas turbine startup cost, gas turbine shutdown cost, load interruption compensation price, gas turbine power generation cost, cold storage tank capacity upper limit, cold storage tank release process energy conversion efficiency, gas turbine downward ramp rate, gas turbine upward ramp rate, gas turbine start-up time minimum, gas turbine shutdown time minimum, energy storage power station storage capacity maximum, charging efficiency, discharge efficiency and total interruption load, photovoltaic power generation predicted power, photovoltaic power generation maximum predicted deviation; Step 3: Input the dispatch parameters into the robust optimization dispatch model of the virtual power plant taking into account the spinning reserve service constructed in step 1, conduct virtual power plant dispatch analysis, and obtain the optimal dispatch strategy of the virtual power plant, wherein the optimal dispatch strategy of the virtual power plant includes the gas turbine output power in each time period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold storage capacity released by the cold storage pool, the interruption load, and the total reserve capacity provided by the virtual power plant in each time period; The objective function of the virtual power plant optimization dispatch model considering spinning reserve service is: Among them, t represents the time period, and T represents the scheduling period; It represents the revenue of virtual power plant participating in the electricity market, which is composed of energy market revenue and spinning reserve market revenue; is the energy market electricity price during period t; P is the market price of spinning reserve electricity in period t; t em is the bidding amount of the virtual power plant in the energy market during period t, and is the decision variable; is the bidding amount of the virtual power plant in the spinning reserve market during period t, and is the decision variable; is the gas turbine cost, is the compensation cost of interruptible load, The operating cost of the energy storage power station; The gas turbine cost includes operating cost and start-up and shutdown cost. The gas turbine cost is obtained according to the cost calculation formula, which is: Among them, P t gas is the gas turbine output power during period t; k is the gas turbine fuel cost; k g is the fixed cost of gas turbine; su is the gas turbine startup cost; sd is the gas turbine stopping cost; Boolean variable μ t , Respectively indicate whether the gas turbine is working, started, or stopped during period t. If yes, it is set to 1, otherwise it is set to 0; The compensation cost of the interruptible load is: Among them, n m is the interrupt level number; is the mth level load interruption compensation price; is the mth level interruption load in period t; The operating cost of the energy storage power station is linearly related to its charging and discharging power, and the expression is as follows: Among them, P t esc is the energy storage charging power; P t esd is the energy storage discharge power; is the first cost coefficient, is the slope of the energy storage power station operation cost function; is the second cost coefficient, which is the intersection of the energy storage power station operation cost function and the ordinate; The air conditioning operation system constraints are: In the formula, Δt represents the time interval, P t cold Indicates the power consumption of the air conditioning system; Q AC (t) represents the total cooling capacity of the air conditioner; The cooling capacity of the refrigerator; The cold storage capacity of the cold storage pool; Release cold to the cold storage pool; is the capacity of the cold storage pool during period t; is the capacity of the cold storage pool during period t-1; S c,max is the upper limit of the capacity of the cold storage pool; η st is the cold storage efficiency of the cold storage pool; η re is the cooling efficiency of the cold storage pool; u ch is the energy conversion efficiency of the refrigerator; u st is the energy conversion efficiency of the cold storage pool during the cold storage process; u re The energy conversion efficiency of the cold storage pool during the cold release process; The gas turbine operating constraints are: P t gas ≥P gas,min μ t Where: P t gas is the gas turbine output power during period t; Spare capacity provided for gas turbines; P gas,min is the minimum output power of the gas turbine; P gas,max is the maximum output power of the gas turbine; r d is the gas turbine downward ramp rate; r u is the gas turbine upward ramp rate; t u is the minimum start-up time of the gas turbine; t d is the minimum value of the gas turbine shutdown time; Boolean variable u t Indicates whether the gas turbine is working during period t; t r For backup time; is the gas turbine output power during period t-1; Boolean variable μ t-1 Indicates whether the gas turbine is working during the t-1 period; Whether the gas turbine is started during period t; Whether the gas turbine is stopped during period t; t represents the time period, and T represents the scheduling cycle; The energy storage power station operation constraints are: m esc +m esd ≤1 In the formula, The amount of electricity stored by the energy storage power station during period t; Indicates the amount of energy stored in the energy storage station during the t-1 period; Boolean variable μ esc Indicates whether the energy storage station is charged during period t, μ esd Indicates whether the energy storage power station discharges during period t, if yes, set to 1, otherwise set to 0; S esmin S is the minimum storage capacity of the energy storage power station; esmax P is the maximum storage capacity of the energy storage power station; t esc is the energy storage charging power; P t esd is the energy storage discharge power; η esc is the charging efficiency; η esd is the discharge efficiency; The interruptible load constraint is: Where n m Indicates the number of interruptible levels. P represents the interruption level coefficient of the mth level interruptible load; t el is the fixed electric load during period t; is the m-level interruption load during period t; P t elcurt is the total interruption load during period t; spare capacity for virtual power plants; The operating power constraint is: P t gas +P t pv +P t esd =P t m +P t el +P t cold -P t elcurt +P t esc Where P t pv Output power for photovoltaic units; P t m Bidding for electricity in the energy market for virtual power plants; The spare capacity constraint is: In the formula, The total reserve capacity provided by the virtual power plant during period t; The photovoltaic power generation in the virtual power plant optimal dispatch model taking into account the spinning reserve service is processed using the robust optimization principle: In order to consider the impact of the uncertainty of photovoltaic power generation on the optimal scheduling of virtual power plants, the photovoltaic power generation in virtual power plants is robustly optimized, namely Where: η t is the robustness coefficient; is the uncertain expression of photovoltaic power generation; P t pv is the predicted value of photovoltaic power generation; ρ t is the estimated error coefficient, that is, it is assumed that the available power of photovoltaic power generation is [(1-ρ t ) t pv ,(1+ρ t ) t pv ] internal fluctuation, where the estimated error coefficient ρ t is 0.3; By using the robust optimization principle, the operating power constraints in the virtual power plant optimization dispatch model taking into account the spinning reserve service are processed and transformed into robust operating constraints: First, the operating power constraints in the virtual power plant optimization dispatch model taking into account spinning reserve services are transformed as follows: Where: P t elcurt is the interruptible load, P t el Fixed load; For the convenience of description, F is used. t Represents P t gas +P t esd -P t cold +P t elcurt -P t esc -P t em -P t el ,remember: F t =P t gas +P t esd -P t cold +P t elcurt -P t esc -P t em -P t el Will Substituting the expression into the above formula, we can get -P t pv -or t r t P t pv ≤F t In order to ensure that the photovoltaic power generation power still meets the operation constraints when it reaches the actual prediction boundary, an auxiliary variable r is introduced. t , let r t ≥|P t pv |, then -P t pv -or t r t P t pv ≤-P t pv +r t |P t pv |≤-P t pv +r t r t ≤F t So we have the following formula: In order to adjust the robustness of the decision-making results and take into account economic benefits, the robust control coefficient Γ is introduced to control the conservatism of the system decision. Γ∈[0,1]. The larger the value of Γ, the more conservative the system is. The robust control coefficient Γ is used to control the conservativeness of the system decision. Maximum deviation from predicted value Γρ t P t pv , the operating power constraints in the virtual power plant optimal dispatch model taking into account spinning reserve services are transformed into robust operating constraints as follows: When the value of Γ in the virtual power plant optimization scheduling model taking into account the rotating reserve service is 0, it is equivalent to the photovoltaic power generation power taking the predicted value; when the value of Γ is 1, it is equivalent to the photovoltaic power generation power taking the predicted maximum deviation value. By adjusting the value of Γ, the optimal scheduling strategy of the virtual power plant under different conservatism is obtained, thereby taking into account both robustness and economy.

2. The virtual power plant optimization scheduling method taking spinning reserve service into account according to claim 1 is characterized by: After the photovoltaic power generation power and operating power constraints in the virtual power plant optimization scheduling model taking into account the spinning reserve service are processed according to the robust optimization principle, it is converted into a virtual power plant robust optimization scheduling model taking into account the spinning reserve service.

3. The virtual power plant optimization scheduling method taking spinning reserve service into account according to claim 1 is characterized in that: The step of inputting the dispatching parameters into the robust optimization dispatching model of the virtual power plant taking into account the spinning reserve service constructed in step 1, performing virtual power plant dispatching analysis, and obtaining the optimal dispatching strategy of the virtual power plant includes: Input the scheduling parameters, call the optimization solver in Gams to solve, and obtain the optimal scheduling strategy of the virtual power plant, which includes the gas turbine output power in each time period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold release capacity of the cold storage pool, the interrupted load, and the total spare capacity provided by the virtual power plant in each time period.

4. A virtual power plant optimization scheduling system taking into account spinning reserve services using the virtual power plant optimization scheduling method according to any one of claims 1 to 3, comprising a virtual power plant robust optimization scheduling model modeling module, a scheduling parameter acquisition input module, and a virtual power plant optimal scheduling strategy generation module; characterized in that: The virtual power plant robust optimization scheduling model modeling module constructs a virtual power plant robust optimization scheduling model taking into account spinning reserve services, including: 1) constructing a virtual power plant optimization scheduling model taking into account spinning reserve services, wherein the objective function of the virtual power plant optimization scheduling model takes into account the economic benefits generated by the virtual power plant providing spinning reserve services, and the constraints of the virtual power plant optimization scheduling model include: operating power constraints, air conditioning system operating constraints, gas turbine operating constraints, energy storage power station operating constraints, interruptible load constraints, and spare capacity constraints; 2) Performing robust optimization processing on the photovoltaic power generation power and operating power constraints in the virtual power plant optimization scheduling model, and converting the virtual power plant optimization scheduling model into a virtual power plant robust optimization scheduling model that takes into account spinning reserve services; The scheduling parameter acquisition input module obtains scheduling parameters related to virtual power plant scheduling, and the scheduling parameters include: energy market electricity price, rotating reserve market electricity price, gas turbine fuel cost, gas turbine fixed cost, gas turbine startup cost, gas turbine shutdown cost, load interruption compensation price, gas turbine power generation cost, cold storage tank capacity upper limit, cold storage tank release process energy conversion efficiency, gas turbine downward ramp rate, gas turbine upward ramp rate, gas turbine start-up time minimum, gas turbine shutdown time minimum, energy storage power station storage capacity maximum, charging efficiency, discharge efficiency and total interruption load, photovoltaic power generation predicted power, photovoltaic power generation maximum predicted deviation; The virtual power plant optimal scheduling strategy generation module inputs the scheduling parameters into the virtual power plant robust optimization scheduling model taking into account the rotating reserve service constructed in step 1, performs virtual power plant scheduling analysis, and obtains the optimal scheduling strategy of the virtual power plant. The optimal scheduling strategy of the virtual power plant includes the gas turbine output power in each time period, the energy storage charging power, the energy storage discharging power, the cold storage capacity of the cold storage pool, the cold release capacity of the cold storage pool, the interrupted load, and the total reserve capacity provided by the virtual power plant in each time period.

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