New energy output control method and system considering VPP providing multiple types of auxiliary services

Abstract

The application discloses a new energy output control method and system considering VPP providing multiple types of auxiliary services, relates to the technical field of power distribution network dispatching, and comprises the following steps: receiving historical data of PV and WT output power, obtaining expectation and variance of PV and WT prediction errors, and generating a fuzzy set containing moment information according to the expectation and variance of the PV and WT prediction errors; inputting the fuzzy set containing moment information into a pre-established new energy output control model considering VPP providing multiple types of auxiliary services, generating a new energy output control model considering VPP providing multiple types of auxiliary services under uncertainty based on affine theory and by selecting the safe operation constraint of the power distribution network as a distributed robust opportunity constraint; receiving various types of power commodity price data and supply-demand relationships of various types of power commodities, inputting the various types of power commodity price data and the supply-demand relationships of various types of power commodities into the new energy output control model considering VPP providing multiple types of auxiliary services under uncertainty, and outputting to obtain a multiple types of power commodity clearing result.

Description

Technical Field

[0001] This invention relates to the field of power distribution network dispatching technology, specifically to a method and system for controlling the output of new energy sources that provide multiple ancillary services to VPPs. Background Technology

[0002] In recent years, an increasing number of distributed renewable energy sources have been integrated into the distribution network, bringing a series of challenges, including deteriorating power quality, increased pressure on peak shaving and frequency regulation, and reduced reliability and stability of the power grid. Virtual Power Plants (VPPs) aggregate multiple types of distributed energy sources to participate in various electricity markets, including the energy market, reserve market, and frequency regulation market. They provide the power grid with diversified services such as energy balancing, reserve, and frequency regulation, which is crucial for improving the safe and stable operation of the system and promoting the consumption of renewable energy.

[0003] However, existing clearing methods for VPPs participating in the electricity market only apply to VPPs participating in the energy market and do not consider the uncertainty of renewable energy output. This makes it difficult for virtual power plants to better participate in the electricity market and play their supporting role in grid operation. Therefore, how to consider the uncertainty of renewable energy output and the coupling relationship between various types of electricity commodities, improve the joint trading and clearing methods and pricing strategies for VPPs of various electricity commodities, promote the participation of virtual power plants in various electricity markets, realize the local consumption and utilization of distributed renewable energy within them, and reduce the impact of distributed renewable energy fluctuations on grid stability are important issues that urgently need to be addressed. Summary of the Invention

[0004] To address the shortcomings mentioned in the background art, the present invention aims to provide a new energy output control method and system that takes into account the multiple ancillary services provided by VPPs, thereby solving the problems of coupling relationships between the various types of electricity products provided by VPPs and the impact of the uncertainty of new energy output on the clearing results.

[0005] Firstly, the objective of this invention can be achieved through the following technical solution: a new energy power output control method considering the provision of multiple ancillary services by a VPP, the method comprising the following steps:

[0006] Receive historical data on PV and WT output power, obtain the expected value and variance of PV and WT prediction errors based on the historical data on PV and WT output power, and generate a fuzzy set containing moment information based on the expected value and variance of PV and WT prediction errors.

[0007] The fuzzy set containing moment information is input into a pre-established new energy power output control model that considers multiple ancillary services provided by VPP. Based on affine theory and selecting the safe operation constraint of the distribution network as the split-Bruker chance constraint, a new energy power output control model that considers multiple ancillary services provided by VPP under uncertainty conditions is generated.

[0008] It receives price data and supply-demand relationships for various types of electricity products, inputs these data into a new energy output control model that takes into account multiple ancillary services provided by VPPs under uncertain conditions, and outputs clearing results for multiple types of electricity products.

[0009] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the pre-established new energy output control model that takes into account the operation and bidding constraints of each entity within the distribution network, and takes into account the supply and demand balance constraints and the safe operation constraints of the distribution network, and is constructed with the goal of maximizing social welfare, taking into account the cost required for the distribution network to purchase multiple types of electricity products from each entity;

[0010] This includes considerations of the operational and bidding constraints of various entities within the distribution network, while also taking into account supply and demand balance constraints and the safe operation constraints of the distribution network.

[0011] Each entity providing electrical energy, reserve, and frequency regulation electricity products is subject to the upper limit of its active power output. Each entity providing reserve, frequency regulation capacity, and frequency regulation mileage ancillary services is subject to the upper limit of its reserve capacity, frequency regulation capacity, and frequency regulation mileage. Each entity providing frequency regulation mileage is subject to frequency regulation capacity constraints. The safety operation constraints of the distribution network mainly include active power constraints on lines and node voltage constraints. It should also meet the supply and demand balance constraints of various types of electricity products.

[0012] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the constraints of the pre-established new energy output control model considering the multiple ancillary services provided by the VPP include:

[0013] MT running constraints

[0014] The three types of electricity products provided by MT (Medium-Terminal Utility) – electrical energy, reserve, and frequency regulation – are all subject to the upper limit of MT's active power output, as shown in the following formula:

[0015]

[0016] In the formula: The electrical energy, upper and lower reserves, and upper and lower frequency modulation capacity provided by MT at time t; The upper and lower limits of the active power output of MT;

[0017] The constraints for MT's provision of backup, frequency modulation capacity, and frequency modulation mileage auxiliary services are shown in the following formula:

[0018]

[0019] 0≤μ1 t +μ2 t ≤1

[0020]

[0021] 0≤μ3 t +μ4 t ≤1

[0022]

[0023] In the formula: Provides upper and lower limits for MT's upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation mileage at time t; μ1 t μ2 t μ3 t μ4 t This is a binary variable, indicating that the MT cannot simultaneously provide upper and lower backup capacity or upper and lower frequency modulation capacity at the same time;

[0024] The FM mileage provided by MT is constrained by FM capacity, as shown in the following formula:

[0025]

[0026] In the formula: k mt Let be the utilization rate of the unit frequency modulation capacity of MT at time t;

[0027] The ramp constraint for MT is shown in the following formula:

[0028]

[0029] Where: ΔP j Let t be the rate of MT's ascent at time t;

[0030] VPP Operational Bidding Constraints

[0031] The three types of electricity products provided by the VPP—electrical energy, reserve, and frequency regulation—are all subject to the upper limit of the VPP's active power output, as shown in the following formula:

[0032]

[0033] In the formula: The electrical energy, upper and lower reserves, and upper and lower frequency regulation capacity provided by VPP at time t; P i VPP The upper and lower limits of the active power output of VPP;

[0034] The constraints for VPP to provide backup, frequency modulation capacity, and frequency modulation mileage ancillary services are shown in the following formula:

[0035]

[0036] 0≤μ5 t +μ6 t ≤1

[0037]

[0038] 0≤μ7 t +μ8 t ≤1

[0039]

[0040] In the formula: Provides upper and lower limits for VPP's upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation mileage at time t; μ5 t μ6 t μ7 t μ8 t This is a binary variable, indicating that the VPP cannot provide upper and lower standby capacity or upper and lower frequency modulation capacity at the same time.

[0041] VPP provides frequency modulation mileage that is constrained by frequency modulation capacity, as shown in the following formula:

[0042]

[0043] In the formula: k vpp Let VPP be the utilization rate of its unit frequency modulation capacity at time t;

[0044] ESS running constraints

[0045] The constraints for ESS (Electrical Energy Saving) services in terms of electrical energy, backup power, frequency regulation capacity, and frequency regulation mileage are shown in the following formula:

[0046]

[0047] 0≤α dis +α ch ≤1

[0048]

[0049] 0≤μ9 t +μ10 t ≤1

[0050]

[0051] 0≤μ11 t +μ12 t ≤1

[0052]

[0053] In the formula: The charging and discharging power, upper and lower backup and upper and lower frequency modulation capacity, and frequency modulation range provided by the ESS at time t; P l ESS_Cmax P l ESS_Dmax , The upper and lower limits of the charging and discharging power, upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation range provided by the ESS at time t; α dis α ch μ9 t μ10 t μ11 t μ12 t This is a binary variable, indicating that the ESS cannot simultaneously provide charging / discharging power, upper / lower backup capacity, or upper / lower frequency modulation capacity at the same time.

[0054] ESS provides three types of electricity products: electrical energy, reserve, and frequency regulation. These are all subject to the maximum charging and discharging power constraints of the ESS, as shown in the following formula:

[0055]

[0056] ESS provides frequency modulation mileage that is constrained by frequency modulation capacity, as shown in the following formula:

[0057]

[0058] In the formula: k ESS Let be the utilization rate of the ESS unit frequency modulation capacity at time t;

[0059] The ESS charge state constraint is shown in the following equation:

[0060]

[0061] In the formula: η ch η dis E represents the charge / discharge efficiency of the ESS at time t. l,t Let be the capacity of ESS at time t; Let be the maximum and minimum capacity of ESS at time t;

[0062] PV Bidding Constraints

[0063] The three types of electricity products provided by PV—electrical energy, reserve power, and frequency regulation power—are all subject to the upper limit of PV active power output, as shown in the following formula:

[0064]

[0065] In the formula: The electrical energy, upper and lower reserves, and upper and lower frequency modulation capacity provided by PV at time t; The upper and lower limits of PV's active power output;

[0066] The constraints for PV to provide backup, frequency modulation capacity, and frequency modulation mileage ancillary services are shown in the following formula:

[0067]

[0068] 0≤μ13 t +μ14 t ≤1

[0069]

[0070] 0≤μ15 t +μ16 t ≤1

[0071]

[0072] In the formula: Provides upper and lower limits for PV's upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation mileage at time t; μ13 t μ14 t μ15 t μ16 t This is a binary variable, indicating that the PV cannot simultaneously provide upper and lower backup capacity or upper and lower frequency modulation capacity at the same time;

[0073] The FM mileage provided by PV is constrained by FM capacity, as shown in the following formula:

[0074]

[0075] In the formula: k PV Let t be the utilization rate of the PV unit frequency modulation capacity.

[0076] WT contractual constraints

[0077] The three types of electricity products provided by WT—electric energy, reserve, and frequency regulation—are all subject to the upper limit of WT's active power output, as shown in the following formula:

[0078]

[0079] In the formula: The electrical energy, upper and lower reserves, and upper and lower frequency modulation capacity provided by WT at time t; The upper and lower limits of WT's active power output;

[0080] The constraints for WT's provision of backup, frequency modulation capacity, and frequency modulation mileage ancillary services are shown in the following formula:

[0081]

[0082] 0≤μ17 t +μ18 t ≤1

[0083]

[0084] 0≤μ19 t +μ20 t ≤1

[0085]

[0086] In the formula: Provides upper and lower limits for WT's upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation mileage at time t; μ17 t μ18 t μ19 t μ20 t This is a binary variable, indicating that WT cannot provide upper and lower backup capacity or upper and lower frequency modulation capacity at the same time.

[0087] WT provides frequency modulation mileage that is constrained by frequency modulation capacity, as shown in the following formula:

[0088]

[0089] In the formula: k WT The utilization rate of the unit frequency modulation capacity of WT at time t;

[0090] Supply and demand balance constraints

[0091]

[0092] Where: N VPP N MT N ESS N PV N WT N D This refers to the number of VPPs, MTs, ESSs, PVs, WTs, and the number of distribution network nodes. P t fu P t fd F t Let t be the demand for electrical energy, upper reserve capacity, lower reserve capacity, upper frequency regulation capacity, and lower frequency regulation capacity. For time t, the electrical energy purchased by the distribution network operator from the wholesale market, the upper and lower reserve capacity, the upper and lower frequency regulation capacity, and the frequency regulation mileage;

[0093] Safety constraints for power distribution network operation

[0094]

[0095] In the formula: P ij Q ij V represents the active and reactive power on line ij; j r is the voltage amplitude at node j; ij and x ij Vij represents the resistance and reactance of line ij; V0 represents the voltage amplitude at the slack node. and It is a set of nodes and branches;

[0096] The constraints on power transmission capacity and node voltage amplitude of power lines are as follows:

[0097]

[0098] In the formula: This represents the upper limit of active power transmitted on line ij; V i , These are the lower and upper limits of the voltage amplitude at node i;

[0099] Considering the costs incurred by the distribution network in purchasing various types of electricity from VPP, MT, ESS, and the wholesale market, and with the goal of maximizing social welfare, an objective function is constructed as follows:

[0100]

[0101] In the formula: The bid price for VPP's electrical energy, upper and lower reserve capacity, upper and lower frequency regulation capacity, and frequency regulation mileage at time t; a j b j c is the cost factor for providing electrical energy to MT. The bid price for MT's standby capacity, frequency modulation capacity, and frequency modulation mileage at time t; The bid price for the ESS's charging and discharging energy, upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation mileage at time t; The transaction price for the distribution network to purchase electrical energy, upper and lower reserve capacity, upper and lower frequency regulation capacity, and frequency regulation mileage from the wholesale market at time t; The electrical energy purchased by the distribution network from the wholesale market at time t, the upper and lower reserve capacity, the upper and lower frequency regulation capacity, and the frequency regulation mileage.

[0102] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the process of obtaining the expected value and variance of the PV and WT prediction errors based on historical data of PV and WT output power.

[0103] The expected value and variance of the prediction errors for PV and WT are calculated as μ. PV μ WT , Σ PV , Σ WT The calculation formula is as follows:

[0104]

[0105] Where: N PV N WT This represents the number of samples in the historical dataset. The prediction error of PV and WT active power in the k-th sample is denoted as .

[0106] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the process of generating a fuzzy set containing moment information based on the expectation and variance of the prediction errors of PV and WT:

[0107] Using fuzzy set Ω PV Ω WT The prediction error ξ represents the active power of PV and WT. PV ξ WT The fuzzy set containing moment information is shown below:

[0108]

[0109] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the distributed bar chance constraint is as follows:

[0110]

[0111] In the formula: P is the probability distribution function of the random variable; Pr{·} represents the probability that the constraint condition is true; ε P ε U The risk probability is defined as the line power flow constraint and the node voltage amplitude constraint.

[0112] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the affine relationship between the line active power and node voltage magnitude and random variables based on affine theory is shown in the following equation:

[0113] P ij =P ij,ac +Λ ij ξ PV +Λ ij ξ WT

[0114] V i =V i,ac +S i PU ξ PV +S i PU ξ WT

[0115] In the formula: P ij,ac V i,ac Let Λ be the active power on line ij and the voltage amplitude at node i; ij S is the power transfer factor on line ij; i PU Let be the voltage sensitivity coefficient of node i;

[0116] Substituting the affine function into the split-Bruker chance constraint, as shown in the following equation:

[0117]

[0118] The general form of the split-bar chance constraint is shown in the following equation:

[0119]

[0120] In the formula: a(x) and b(x) are affine auxiliary coefficients; u and l are the upper and lower limits of the split-bar chance constraint; ε is the risk probability of the split-bar chance constraint; ξ is a random variable;

[0121] Introducing auxiliary variables y and z, and assuming the expected value of the prediction errors for PV and WT outputs is 0, i.e., μ PV =μ WT =0, the bilateral partial bar chance constraint is transformed using a second-order cone transformation, and the result is shown below:

[0122] y 2 +a(x) T Σ DG a(x)≤ε(Tz) 2

[0123] -(y+z)≤b(x)-Y≤y+z

[0124] 0≤z≤T,y≥0

[0125] In the formula: T=(ul) / 2, T=(u+l) / 2.

[0126] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the clearing result of the multiple types of electricity commodities is based on the supply and demand relationship of various types of electricity commodities. Under the condition that all parameters in the system remain unchanged, the increase in operating cost caused by each additional unit of demand for electricity products is the price of the electricity products, i.e., the price of various types of electricity products, calculated as follows:

[0127]

[0128] In the formula: L * Let the Lagrange augmented objective function of the original problem be defined under the condition that the decision variables are at the optimal solution. The price of electrical energy, upper and lower reserve capacity, upper and lower frequency regulation capacity, and frequency regulation mileage at time t.

[0129] Secondly, in order to achieve the above objectives, the present invention discloses a new energy power output control system that takes into account the provision of various ancillary services by the VPP, including:

[0130] The data processing module is used to receive historical data of PV and WT output power, obtain the expected value and variance of PV and WT prediction errors based on the historical data of PV and WT output power, and generate a fuzzy set containing moment information based on the expected value and variance of PV and WT prediction errors.

[0131] The model output module is used to input the fuzzy set containing moment information into the pre-established new energy power output control model that takes into account the multiple ancillary services provided by VPP. Based on affine theory and selecting the safe operation constraint of the distribution network as the split-Bruker chance constraint, a new energy power output control model that takes into account the multiple ancillary services provided by VPP under uncertainty conditions is generated.

[0132] The price clearing module receives price data and supply-demand relationships for various types of electricity products. It inputs these data into a new energy output control model that takes into account uncertainties and the various ancillary services provided by the VPP. The module then outputs the clearing results for various types of electricity products.

[0133] In another aspect of the present invention, in order to achieve the above-mentioned objective, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The device is characterized in that the memory stores the computer program capable of running on the processor, and when the processor loads and executes the computer program, it employs the new energy output control method as described above, which takes into account the provision of multiple auxiliary services by the VPP.

[0134] The beneficial effects of this invention are:

[0135] This invention addresses the interaction between virtual power plants and various types of electricity markets, considering the coupling relationships among multiple types of electricity commodities such as electrical energy, reserves, and frequency regulation. With the goal of maximizing the social welfare of the entire distribution network, it constructs a new energy output control model that incorporates various ancillary services provided by the virtual power plant (VPP). Based on the distributed bar chance constraint method, a fuzzy set containing moment information is used to represent the uncertainty of photovoltaic (PV) and wind turbine (WT) output prediction errors, thus constructing a new energy output control model that incorporates various ancillary services provided by the VPP. The results of this project can fully utilize the aggregation of various adjustable resources by the virtual power plant, leverage the virtual power plant's flexibility in supporting grid operation, effectively address the volatility and intermittency of new energy sources, and achieve on-site consumption and utilization of distributed new energy sources within the virtual power plant. Attached Figure Description

[0136] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0137] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0138] Figure 2 This is a schematic diagram of the overall operation mode of the present invention;

[0139] Figure 3 This is a schematic diagram of the system structure of the present invention;

[0140] Figure 4 This is a price chart for clearing out various electricity products;

[0141] Figure 5 VPP provides images of various electricity products. Detailed Implementation

[0142] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0143] Example 1:

[0144] The following is a description of the relevant terms used in the embodiments of this application:

[0145] Joint clearing refers to the allocation of resources and price determination by multiple markets or participants on a common platform to achieve optimal resource allocation and maximize efficiency. This mechanism is commonly used in fields such as electricity markets and financial markets, where centralized competition leads to a clearing outcome, ensuring the effective use of resources and maximizing social welfare.

[0146] like Figure 1 As shown, the new energy output control method considering multiple ancillary services provided by VPP includes the following steps:

[0147] S101: Receive historical data of PV and WT output power, obtain the expected value and variance of PV and WT prediction errors based on the historical data of PV and WT output power, and generate a fuzzy set containing moment information based on the expected value and variance of PV and WT prediction errors.

[0148] The process of obtaining the expected value and variance of PV and WT prediction errors based on historical data of PV and WT output power:

[0149] The expected value and variance of the prediction errors for PV and WT are calculated as μ. PV μ WT , Σ PV , Σ WT The calculation formula is as follows:

[0150]

[0151] Where: N PV N WT This represents the number of samples in the historical dataset. The prediction error of PV and WT active power in the k-th sample is denoted as .

[0152] The process of generating a fuzzy set containing moment information based on the expected value and variance of the prediction errors of PV and WT:

[0153] Using fuzzy set Ω PV Ω WT The prediction error ξ represents the active power of PV and WT. PV ξ WT The fuzzy set containing moment information is shown below:

[0154]

[0155] S102: Input the fuzzy set containing moment information into the pre-established new energy output control model that takes into account the multiple ancillary services provided by VPP. Based on affine theory and selecting the safe operation constraint of the distribution network as the multi-branch opportunity constraint, generate a new energy output control model that takes into account the multiple ancillary services provided by VPP under uncertainty conditions.

[0156] The pre-established new energy output control model, which takes into account the operation and bidding constraints of various entities within the distribution network and the supply and demand balance constraints and the safe operation constraints of the distribution network, is constructed with the goal of maximizing social welfare, taking into account the cost required for the distribution network to purchase various types of electricity products from various entities.

[0157] This includes considerations of the operational and bidding constraints of various entities within the distribution network, while also taking into account supply and demand balance constraints and the safe operation constraints of the distribution network.

[0158] Each entity providing electrical energy, reserve, and frequency regulation electricity products is subject to the upper limit of its active power output. Each entity providing reserve, frequency regulation capacity, and frequency regulation mileage ancillary services is subject to the upper limit of its reserve capacity, frequency regulation capacity, and frequency regulation mileage. Each entity providing frequency regulation mileage is subject to frequency regulation capacity constraints. The safety operation constraints of the distribution network mainly include active power constraints on lines and node voltage constraints. It should also meet the supply and demand balance constraints of various types of electricity products.

[0159] The pre-established constraints of the renewable energy output control model, which takes into account the various ancillary services provided by the VPP, include:

[0160] MT running constraints

[0161] The three types of electricity products provided by MT (Medium-Terminal Utility) – electrical energy, reserve, and frequency regulation – are all subject to the upper limit of MT's active power output, as shown in the following formula:

[0162]

[0163] In the formula: The electrical energy, upper and lower reserves, and upper and lower frequency modulation capacity provided by MT at time t; The upper and lower limits of the active power output of MT;

[0164] The constraints for MT's provision of backup, frequency modulation capacity, and frequency modulation mileage auxiliary services are shown in the following formula:

[0165]

[0166] 0≤μ1 t +μ2 t ≤1

[0167]

[0168] 0≤μ3 t +μ4 t ≤1

[0169]

[0170] In the formula: Provides upper and lower limits for MT's upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation mileage at time t; μ1 t μ2 t μ3 t μ4 t This is a binary variable, indicating that the MT cannot simultaneously provide upper and lower backup capacity or upper and lower frequency modulation capacity at the same time;

[0171] The FM mileage provided by MT is constrained by FM capacity, as shown in the following formula:

[0172]

[0173] In the formula: k mt Let be the utilization rate of the unit frequency modulation capacity of MT at time t;

[0174] The ramp constraint for MT is shown in the following formula:

[0175]

[0176] Where: ΔP j Let t be the rate of MT's ascent at time t;

[0177] VPP Operational Bidding Constraints

[0178] The three types of electricity products provided by the VPP—electrical energy, reserve, and frequency regulation—are all subject to the upper limit of the VPP's active power output, as shown in the following formula:

[0179]

[0180] In the formula: The electrical energy, upper and lower reserves, and upper and lower frequency regulation capacity provided by VPP at time t; P i VPP The upper and lower limits of the active power output of VPP;

[0181] The constraints for VPP to provide backup, frequency modulation capacity, and frequency modulation mileage ancillary services are shown in the following formula:

[0182]

[0183]

[0184] 0≤μ5 t +μ6 t ≤1

[0185]

[0186] 0≤μ7 t +μ8 t ≤1

[0187]

[0188] In the formula: Provides upper and lower limits for VPP's upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation mileage at time t; μ5 t μ6 t μ7 t μ8 t This is a binary variable, indicating that the VPP cannot provide upper and lower standby capacity or upper and lower frequency modulation capacity at the same time.

[0189] VPP provides frequency modulation mileage that is constrained by frequency modulation capacity, as shown in the following formula:

[0190]

[0191] In the formula: k vpp Let VPP be the utilization rate of its unit frequency modulation capacity at time t;

[0192] ESS running constraints

[0193] The constraints for ESS (Electrical Energy Saving) services in terms of electrical energy, backup power, frequency regulation capacity, and frequency regulation mileage are shown in the following formula:

[0194]

[0195] 0≤α dis +α ch ≤1

[0196]

[0197] 0≤μ9 t +μ10 t ≤1

[0198]

[0199] 0≤μ11 t +μ12 t ≤1

[0200]

[0201] In the formula: The charging and discharging power, upper and lower backup and upper and lower frequency modulation capacity, and frequency modulation range provided by the ESS at time t; P l ESS_Cmax P l ESS_Dmax , The upper and lower limits of the charging and discharging power, upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation range provided by the ESS at time t; α dis α ch μ9t μ10 t μ11 t μ12 t This is a binary variable, indicating that the ESS cannot simultaneously provide charging / discharging power, upper / lower backup capacity, or upper / lower frequency modulation capacity at the same time.

[0202] ESS provides three types of electricity products: electrical energy, reserve, and frequency regulation. These are all subject to the maximum charging and discharging power constraints of the ESS, as shown in the following formula:

[0203]

[0204] ESS provides frequency modulation mileage that is constrained by frequency modulation capacity, as shown in the following formula:

[0205]

[0206] In the formula: k ESS Let be the utilization rate of the ESS unit frequency modulation capacity at time t;

[0207] The ESS charge state constraint is shown in the following equation:

[0208]

[0209] In the formula: η ch η dis E represents the charge / discharge efficiency of the ESS at time t. l,t Let be the capacity of ESS at time t; Let be the maximum and minimum capacity of ESS at time t;

[0210] PV Bidding Constraints

[0211] The three types of electricity products provided by PV—electrical energy, reserve power, and frequency regulation power—are all subject to the upper limit of PV active power output, as shown in the following formula:

[0212]

[0213]

[0214] In the formula: The electrical energy, upper and lower reserves, and upper and lower frequency modulation capacity provided by PV at time t; The upper and lower limits of PV's active power output;

[0215] The constraints for PV to provide backup, frequency modulation capacity, and frequency modulation mileage ancillary services are shown in the following formula:

[0216]

[0217] 0≤μ13 t +μ14t ≤1

[0218]

[0219] 0≤μ15 t +μ16 t ≤1

[0220]

[0221] In the formula: Provides upper and lower limits for PV's upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation mileage at time t; μ13 t μ14 t μ15 t μ16 t This is a binary variable, indicating that the PV cannot simultaneously provide upper and lower backup capacity or upper and lower frequency modulation capacity at the same time;

[0222] The FM mileage provided by PV is constrained by FM capacity, as shown in the following formula:

[0223]

[0224] In the formula: k PV Let t be the utilization rate of the PV unit frequency modulation capacity.

[0225] WT contractual constraints

[0226] The three types of electricity products provided by WT—electric energy, reserve, and frequency regulation—are all subject to the upper limit of WT's active power output, as shown in the following formula:

[0227]

[0228] In the formula: The electrical energy, upper and lower reserves, and upper and lower frequency modulation capacity provided by WT at time t; The upper and lower limits of WT's active power output;

[0229] The constraints for WT's provision of backup, frequency modulation capacity, and frequency modulation mileage ancillary services are shown in the following formula:

[0230]

[0231] 0≤μ17 t +μ18 t ≤1

[0232]

[0233] 0≤μ19 t +μ20 t ≤1

[0234]

[0235] In the formula: Provides upper and lower limits for WT's upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation mileage at time t; μ17 t μ18 t μ19 t μ20 t This is a binary variable, indicating that WT cannot provide upper and lower backup capacity or upper and lower frequency modulation capacity at the same time.

[0236] WT provides frequency modulation mileage that is constrained by frequency modulation capacity, as shown in the following formula:

[0237]

[0238] In the formula: k WT The utilization rate of the unit frequency modulation capacity of WT at time t;

[0239] Supply and demand balance constraints

[0240]

[0241] Where: N VPP N MT N ESS N PV N WT N D This refers to the number of VPPs, MTs, ESSs, PVs, WTs, and the number of distribution network nodes. P t fu P t fd F t Let t be the demand for electrical energy, upper reserve capacity, lower reserve capacity, upper frequency regulation capacity, and lower frequency regulation capacity. For time t, the electrical energy purchased by the distribution network operator from the wholesale market, the upper and lower reserve capacity, the upper and lower frequency regulation capacity, and the frequency regulation mileage;

[0242] Safety constraints for power distribution network operation

[0243]

[0244] In the formula: P ij Q ij V represents the active and reactive power on line ij; j r is the voltage amplitude at node j; ij and x ij Vij represents the resistance and reactance of line ij; V0 represents the voltage amplitude at the slack node. and It is a set of nodes and branches;

[0245] The constraints on power transmission capacity and node voltage amplitude of power lines are as follows:

[0246]

[0247] In the formula: This represents the upper limit of active power transmitted on line ij; V i , These are the lower and upper limits of the voltage amplitude at node i;

[0248] Considering the costs incurred by the distribution network in purchasing various types of electricity from VPP, MT, ESS, and the wholesale market, and with the goal of maximizing social welfare, an objective function is constructed as follows:

[0249]

[0250] In the formula: The bid price for VPP's electrical energy, upper and lower reserve capacity, upper and lower frequency regulation capacity, and frequency regulation mileage at time t; a j b j c is the cost factor for providing electrical energy to MT. The bid price for MT's standby capacity, frequency modulation capacity, and frequency modulation mileage at time t; The bid price for the ESS's charging and discharging energy, upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation mileage at time t; The transaction price for the distribution network to purchase electrical energy, upper and lower reserve capacity, upper and lower frequency regulation capacity, and frequency regulation mileage from the wholesale market at time t; The electrical energy purchased by the distribution network from the wholesale market at time t, the upper and lower reserve capacity, the upper and lower frequency regulation capacity, and the frequency regulation mileage.

[0251] The opportunity constraints for the Blue bar are as follows:

[0252]

[0253] In the formula: P is the probability distribution function of the random variable; Pr{·} represents the probability that the constraint condition is true; ε P ε U The risk probability is defined as the line power flow constraint and the node voltage amplitude constraint.

[0254] Based on affine theory, the affine relationship between line active power and node voltage magnitude and random variables is shown in the following equation:

[0255] P ij =P ij,ac+Λ ij ξ PV +Λ ij ξ WT

[0256] V i =V i,ac +S i PU ξ PV +S i PU ξ WT

[0257] In the formula: P ij,ac V i,ac Let Λ be the active power on line ij and the voltage amplitude at node i; ij S is the power transfer factor on line ij; i PU Let be the voltage sensitivity coefficient of node i;

[0258] Substituting the affine function into the split-Bruker chance constraint, as shown in the following equation:

[0259]

[0260] The general form of the split-bar chance constraint is shown in the following equation:

[0261]

[0262] In the formula: a(x) and b(x) are affine auxiliary coefficients; u and l are the upper and lower limits of the split-bar chance constraint; ε is the risk probability of the split-bar chance constraint; ξ is a random variable;

[0263] Introducing auxiliary variables y and z, and assuming the expected value of the prediction errors for PV and WT outputs is 0, i.e., μ PV =μ WT =0, the bilateral partial bar chance constraint is transformed using a second-order cone transformation, and the result is shown below:

[0264] y 2 +a(x) T Σ DG a(x)≤ε(Tz) 2

[0265] -(y+z)≤b(x)-Y≤y+z

[0266] 0≤z≤T,y≥0

[0267] In the formula: T=(ul) / 2, T=(u+l) / 2.

[0268] S103: Receive price data and supply-demand relationships of various types of electricity commodities, input these data into a new energy output control model that takes into account multiple ancillary services provided by VPP under uncertain conditions, and output the clearing results of multiple types of electricity commodities.

[0269] The clearing result for various types of electricity commodities takes into account the supply and demand relationship of each type of electricity commodity. Under the condition that all parameters in the system remain unchanged, the increase in operating cost caused by each additional unit of demand for electricity products is the price of the electricity products, i.e., the price of each type of electricity product, calculated as follows:

[0270]

[0271] In the formula: L * Let the Lagrange augmented objective function of the original problem be defined under the condition that the decision variables are at the optimal solution. The price of electrical energy, upper and lower reserve capacity, upper and lower frequency regulation capacity, and frequency regulation mileage at time t.

[0272] Example 2: Second aspect, such as Figure 3 As shown, in order to achieve the above objectives, the present invention discloses a new energy power output control system that considers the provision of various ancillary services by the VPP, including:

[0273] Data processing module 11 is used to receive historical data of PV and WT output power, obtain the expected value and variance of PV and WT prediction errors based on the historical data of PV and WT output power, and generate a fuzzy set containing moment information based on the expected value and variance of PV and WT prediction errors.

[0274] Model output module 12 is used to input the fuzzy set containing moment information into the pre-established new energy power output control model that takes into account the multiple ancillary services provided by VPP. Based on affine theory and selecting the safe operation constraint of the distribution network as the split-Bruker chance constraint, a new energy power output control model that takes into account the multiple ancillary services provided by VPP under uncertainty conditions is generated.

[0275] Price clearing module 13 is used to receive price data of various types of electricity products and supply and demand relationships of various types of electricity products, input the price data of various types of electricity products and supply and demand relationships of various types of electricity products into the new energy output control model under uncertainty conditions and taking into account the multiple ancillary services provided by VPP, and output the clearing results of multiple types of electricity products.

[0276] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0277] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0278] Example 3: Simulation analysis was conducted using an IEEE 33-node distribution network system. PV, WT, and MT were configured at nodes 19, 3, and 25, respectively; six VPPs were configured at nodes 6, 12, 15, 23, 26, and 31; and two ESSs were configured at nodes 1 and 19. The variances of the active power prediction errors for PV and WT were set to 0.01 and 0.015, respectively, and the risk probability of the split-Bruker chance constraint was 0.1. All simulation examples were implemented using Matlab programming based on the Yalmip toolbox and solved using the Gurobi solver. The clearing results for various electricity commodities in the VPPs are shown below. Figure 5 As shown, the clearing prices of various electricity commodities are as follows: Figure 4 As shown.

[0279] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0280] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A new energy output control method considering VPP providing multi-type auxiliary services, characterized in that, The method comprises the following steps: receiving historical data of PV and WT output power, obtaining expectation and variance of PV and WT prediction error according to the historical data of PV and WT output power, and generating a fuzzy set containing moment information according to the expectation and variance of PV and WT prediction error; inputting the fuzzy set containing moment information into a pre-established new energy output control model considering VPP providing multiple types of auxiliary services, generating a new energy output control model considering VPP providing multiple types of auxiliary services under uncertainty based on affine theory and selecting the safe operation constraint of the power distribution network as a distribution robust opportunity constraint; the pre-established new energy output control model considering VPP providing multiple types of auxiliary services considers the operation and bid constraints of each subject in the power distribution network, and takes into account the supply and demand balance constraint and the safe operation constraint of the power distribution network, and is constructed with the maximum social welfare as the target for the cost of purchasing multiple types of power commodities from each subject in the power distribution network; wherein the operation and bid constraints of each subject in the power distribution network are considered, and the supply and demand balance constraint and the safe operation constraint of the power distribution network are taken into account, which includes: the active power output of each subject providing three types of power commodities, i.e., electric energy, reserve and frequency modulation, is jointly limited by the active power output upper limit; the reserve capacity, frequency modulation capacity and frequency modulation mileage auxiliary services provided by each subject are limited by the reserve capacity upper limit, frequency modulation capacity upper limit and frequency modulation mileage upper limit; the frequency modulation mileage provided by each subject is limited by the frequency modulation capacity and frequency modulation mileage upper limit; the safe operation constraint of the power distribution network mainly includes line active power constraint and node voltage constraint; and the supply and demand balance constraint of each type of power product should also be met; receiving price data of each type of power commodity and supply and demand relationship of each type of power commodity, inputting the price data of each type of power commodity and the supply and demand relationship of each type of power commodity into the new energy output control model considering VPP providing multiple types of auxiliary services under uncertainty, and outputting to obtain the new energy output control result in the power distribution network. 2.The new energy output control method considering VPP providing multi-class auxiliary services according to claim 1, wherein, The constraint conditions of the pre-established new energy output control model considering VPP providing multiple types of auxiliary services include: MT operation bid constraint the active power output of MT providing three types of power commodities, i.e., electric energy, reserve and frequency modulation, is jointly limited by the active power output upper limit, and the related constraint is shown in the following formula: In the formula: , , , , is the electric energy provided by the moment MT, the upper and lower reserve and the up and down frequency modulation capacity; , , is the upper and lower limit of the MT active power output; the constraint of MT providing reserve, frequency modulation capacity and frequency modulation mileage auxiliary services is shown in the following formula: In the formula: , , , , is the upper and lower limit of the up and down reserve capacity, the up and down frequency modulation capacity, and the frequency modulation mileage provided by the moment MT; , , , , is a binary variable, indicating that the same moment MT cannot simultaneously provide up and down reserve capacity or up and down frequency modulation capacity; MT provides frequency modulation mileage, which is limited by frequency modulation capacity, as shown in the following formula: In the formula: is the utilization rate of the unit frequency modulation capacity at the moment MT the ramping constraint of MT is shown in the following formula: In the formula: is the ramp rate at time MT. VPP operation bid constraint the active power output of VPP providing three types of power commodities, i.e., electric energy, reserve and frequency modulation, is jointly limited by the active power output upper limit, and the related constraint is shown in the following formula: In the formula: , , , , is the energy provided by the VPP at the moment VPP, the up and down reserve and the up and down frequency regulation capacity; , , is the upper and lower limit of the VPP active power output; the constraint of VPP providing reserve, frequency modulation capacity and frequency modulation mileage auxiliary services is shown in the following formula: In the formula: , , , , is the upper and lower limit of the up and down reserve capacity, the up and down frequency modulation capacity, and the frequency modulation mileage provided by the VPP at the moment VPP; , , , , is a binary variable, indicating that the VPP cannot simultaneously provide up and down reserve capacity or up and down frequency modulation capacity at the same time. VPP provides frequency modulation mileage, which is limited by frequency modulation capacity, as shown in the following formula: In the formula: is VPP unit frequency modulation capacity utilization rate; ESS operation bid constraint the constraint of ESS providing electric energy, reserve, frequency modulation capacity and frequency modulation mileage auxiliary services is shown in the following formula: In the formula: , , , , , , for The charging and discharging power, upper and lower backup and upper and lower frequency modulation capacity, and frequency modulation range provided by ESS at all times; , , , , , , for The charging and discharging power, upper and lower reserve capacity, upper and lower frequency modulation capacity, and upper and lower limits of frequency modulation range provided by ESS at all times; , , , , , This is a binary variable, indicating that the ESS cannot simultaneously provide charging / discharging power, upper / lower backup capacity, or upper / lower frequency modulation capacity at the same time. the active power output of ESS providing three types of power commodities, i.e., electric energy, reserve and frequency modulation, is jointly limited by the maximum charging and discharging power, and the related constraint is shown in the following formula: ESS provides frequency modulation mileage, which is limited by frequency modulation capacity, as shown in the following formula: In the formula: is the utilization rate of the ESS unit frequency modulation capacity at the moment; the state of charge constraint of ESS is shown in the following formula: In the formula: , is the charging and discharging efficiency of the ESS at the moment; is the capacity of the ESS at the moment; , is the maximum and minimum capacity of the ESS at the moment; PV bid constraint The PV provides three types of power commodities of electric energy, backup, and frequency modulation, which are jointly constrained by the upper limit of the PV active power output, and the related constraints are shown in the following formula: In the formula: , , , , for The electrical energy provided by the PV at all times, its upper and lower backup and frequency regulation capacity; , The upper and lower limits of PV's active power output; The constraints of the PV providing backup, frequency modulation capacity, and frequency modulation mileage auxiliary services are shown in the following formula: In the formula: , , , , is the upper and lower limit of the upper and lower reserve capacity, the upper and lower frequency modulation capacity, and the frequency modulation mileage provided by the PV at the moment; , , , , is a binary variable, indicating that the PV cannot simultaneously provide upper and lower reserve capacity or upper and lower frequency modulation capacity at the same time. The PV provides frequency modulation mileage, which is constrained by the frequency modulation capacity, as shown in the following formula: In the formula: is the utilization rate of the PV unit frequency modulation capacity at the moment WT bid constraints The WT provides three types of power commodities of electric energy, backup, and frequency modulation, which are jointly constrained by the upper limit of the WT active power output, and the related constraints are shown in the following formula: In the formula: , , , , for The electrical energy, upper and lower backup, and upper and lower frequency regulation capacity provided by WT at all times; , The upper and lower limits of WT's active power output; The constraints of the WT providing backup, frequency modulation capacity, and frequency modulation mileage auxiliary services are shown in the following formula: In the formula: , , , , is the upper and lower limit of the up and down reserve capacity, the up and down frequency modulation capacity, and the frequency modulation mileage provided by the WT at the moment; , , , , is a binary variable, indicating that the WT cannot simultaneously provide up and down reserve capacity or up and down frequency modulation capacity at the same time. The WT provides frequency modulation mileage, which is constrained by the frequency modulation capacity, as shown in the following formula: In the formula: is the utilization rate of the frequency modulation capacity of the time unit WT Supply and demand balance constraints In the formula: , , , , , This refers to the number of VPPs, MTs, ESSs, PVs, WTs, and the number of distribution network nodes. , , , , , for The demand for electrical energy, upper reserve capacity, lower reserve capacity, upper frequency regulation capacity, and lower frequency regulation capacity at any time; , , , , , for The electrical energy purchased by the power distribution network operator from the wholesale market at all times, the upper and lower reserve capacity, the upper and lower frequency regulation capacity, and the frequency regulation mileage; Distribution network operation safety constraints where: , is the active and reactive power on the line ; is the voltage magnitude on the node ; and are the resistance and reactance of the line ; is the balanced node voltage magnitude; and are the set of nodes and branches; The constraints of the transmission power of the power line and the node voltage amplitude are shown in the following formula: wherein: is the line upper limit on the active power transmitted over the line; , is the node lower and upper limits on the voltage magnitude at the node The cost required by the distribution network to purchase multiple types of power commodities from the VPP, the MT, the ESS, and the wholesale market is considered, and a target function is constructed with the maximization of social welfare as the target, and the target function is shown in the following formula: In the formula: , , , , , For VPP in Bidding prices for instantaneous electrical energy, upper and lower reserve capacity, upper and lower frequency regulation capacity, and frequency regulation mileage; , , The cost factor for providing electrical energy to MT , , , , For MT in Bidding prices for standby capacity, frequency modulation capacity, and frequency modulation mileage at all times; , , , , , , For ESS Bidding prices for constant charging and discharging energy, upper and lower reserve capacity, upper and lower frequency modulation capacity, and frequency modulation mileage; , , , , , For the distribution network in The transaction prices for purchasing electrical energy, standby capacity, frequency regulation capacity, and frequency regulation mileage from the wholesale market at all times; , , , , , For the distribution network in The electrical energy purchased from the wholesale market at all times, the upper and lower reserve capacity, the upper and lower frequency regulation capacity, and the frequency regulation mileage. 3.The new energy output control method considering VPP providing multi-class auxiliary services according to claim 2, wherein, The process of obtaining the expectation and variance of the PV and WT prediction error according to the historical data of the PV and WT output power: The expectation and variance of the PV, WT prediction errors are calculated as , , , , and the formulas are shown as follows: In the formula: , is the number of samples in the historical data set; , is the prediction error of the PV, WT active power in the i-th sample. is the prediction error of the PV, WT active power in the i-th sample.

4. The new energy output control method considering VPP providing multi-class auxiliary services according to claim 3, characterized in that, The process of generating a fuzzy set containing moment information according to the expectation and variance of the PV and WT prediction error: Adopting fuzzy sets , representing the prediction error of the PV, WT active power , The fuzzy set containing the information of the matrix is as follows: 。 5. The new energy output control method considering VPP providing multi-class auxiliary services according to claim 4, characterized in that, The distribution robust chance constraint is as follows: wherein: is the probability distribution function of the random variable; denotes the probability that the constraint is satisfied; , is the risk probability of the line flow constraint and the bus voltage magnitude constraint.

6. The new energy output control method considering VPP providing multi-class auxiliary services according to claim 5, characterized in that, Based on the affine theory, the affine relationship between the random variables and the line active power and the node voltage amplitude is shown in the following formula: where: is the active power on the line ; and is the voltage magnitude at the node ; and is the power rate transfer coefficient on the line ; and is the voltage sensitivity coefficient at the node The affine function is brought into the distribution robust chance constraint, as shown in the following formula: The general form of the distribution robust chance constraint is shown in the following formula: wherein: , is an affine auxiliary coefficient; , are upper and lower bounds of the distributionally robust chance constraint; is a risk probability of the distributionally robust chance constraint; is a random variable; Introducing auxiliary variables and Assume that the expectation of the prediction error of PV and WT power is zero, i.e. Using the second order cone transformation, the bi-conditional distributionally robust chance constraint is transformed as follows: In the formulae: , .

7. The new energy output control method considering VPP providing multi-class auxiliary services according to claim 6, characterized in that, The multiple types of power commodity clearing results are that the increment of the operating cost caused by the increase of the demand of each type of power product is the price of the power product, that is, the price of each type of power product, under the condition that all parameters in the system are unchanged, and the calculation is as follows: In the formula: is the Lagrange augmented objective function of the original problem under the condition that the decision variable is the optimal solution; , , , , , is the price of the moment electric energy, upper and lower reserve capacity, up and down frequency regulation capacity, frequency regulation mileage.

8. A new energy output control system considering that a VPP provides multiple types of ancillary services, characterized in that, It includes: The data processing module is used for receiving the historical data of the PV and WT output power, obtaining the expectation and variance of the PV and WT prediction error according to the historical data of the PV and WT output power, and generating a fuzzy set containing moment information according to the expectation and variance of the PV and WT prediction error; The model output module is used for inputting the fuzzy set containing moment information into a pre-established new energy output control model considering the VPP providing multiple types of auxiliary services, generating a new energy output control model considering the VPP providing multiple types of auxiliary services under uncertain conditions based on the affine theory and selecting the safe operation constraints of the distribution network as the distribution robust chance constraint; The pre-established new energy output control model considering the VPP providing multiple types of auxiliary services considers the operation and bid constraints of each subject in the distribution network, and takes into account the supply and demand balance constraints and the safe operation constraints of the distribution network, and is constructed with the maximization of social welfare as the target for the cost required by the distribution network to purchase multiple types of power commodities from each subject; The consideration of the operation and bid constraints of each subject in the distribution network, and the taking into account of the supply and demand balance constraints and the safe operation constraints of the distribution network include: The active power output of each subject is limited by the upper limit of the active power; the reserve capacity, frequency modulation capacity and frequency modulation mileage of each subject are limited by the upper limit of the reserve capacity, the upper limit of the frequency modulation capacity and the upper limit of the frequency modulation mileage; the frequency modulation mileage of each subject is limited by the frequency modulation capacity and the upper limit of the frequency modulation mileage; the safe operation of the power distribution network is mainly limited by the line active power and the node voltage; and the supply and demand balance of each type of power product should also be met; The price clearing module is used for receiving the price data of each type of power product and the supply and demand relationship of each type of power product, inputting the price data of each type of power product and the supply and demand relationship of each type of power product into the new energy output control model considering the VPP providing multiple auxiliary services under uncertainty, and outputting the clearing results of the multiple types of power products.

9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program capable of running on the processor, and the processor loads and executes the computer program. When the computer program is executed, the method for controlling the output of new energy considering the VPP providing multiple auxiliary services according to any one of claims 1 to 7 is adopted.

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