A virtual power plant optimization power supply method based on consistency theory

Abstract

The application relates to the technical field of power systems, and particularly discloses a virtual power plant optimization power supply method based on consistency theory, which comprises the following steps: constructing a power supply decision framework; generating a normalized load curve according to new energy output prediction, net load fluctuation and power supply demand; taking the adjustment cost as a consistency variable between virtual power plants VPPs according to the consistency theory; constructing a double-layer scheduling model based on the power supply decision framework, wherein the upper-layer model takes the minimum adjustment cost of the virtual power plants VPPs under the power supply scene and the maximum incentive of the curve as the target, adopts a consistency algorithm to coordinate the adjustment cost of each virtual power plant VPP, and determines the total adjustment amount of each virtual power plant; the lower-layer model takes the minimum scheduling cost of various flexible resources in the virtual power plant VPP as the target, and realizes the reconstruction of the load curve; and the double-layer scheduling model is solved to obtain an optimized power supply scheme. The application has the advantage that the power supply capacity can be significantly improved under the premise of meeting the safety constraints of the power grid.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a virtual power plant optimization and supply guarantee method based on consistency theory. Background Technology

[0002] Against the backdrop of new power system construction, the high proportion of renewable energy grid connection has profoundly changed the operating mechanism and control methods of the power system. Coupled with the disturbances of extreme weather events, spatiotemporal mismatches between the load side and the source side have become more frequent, significantly increasing the pressure on power supply. In addition to power source flexibility upgrades, deeply exploring the observability and controllability of distributed adjustable resources on the user side and constructing a rapid, reliable, and scalable demand-side response system has become one of the key paths to alleviate supply contradictions. However, distributed resources on the user side are characterized by "large scale, small individual units, scattered distribution, and strong heterogeneity," making it difficult for them to directly participate in the grid company's interactive regulation. Virtual power plants (VPPs), as a new intelligent management model for distributed resources, can effectively aggregate massive distributed demand response resources to support the flexible regulation of the power system.

[0003] Existing technologies mainly employ centralized optimization methods to construct virtual power plant scheduling models. A central controller centrally aggregates global information and issues control commands to achieve resource coordination and demand response optimization. This method often integrates auction mechanisms or multi-timescale optimization to ensure that VPP aggregates DERs participate in power supply security. Furthermore, some schemes introduce privacy-preserving auction mechanisms to achieve energy sharing and pricing coordination, supporting reliable operation of VPPs in high-penetration renewable energy scenarios. Existing literature [1-3] uses hierarchical centralized coordination optimization to achieve energy mutual assistance among VPP clusters. Centralized coordination pricing and flexibility mutual assistance mechanisms improve the flexible operation of the system. However, in high-penetration distributed resource scenarios, these methods are prone to problems such as increased single-point failure risk, prominent communication and computing bottlenecks, and insufficient scalability and real-time performance.

[0004] Traditional demand response (DR) methods mainly focus on the deviation of user load from the baseline, emphasizing the assessment of the scale and potential of time-of-use power regulation, and achieving peak-valley regulation and grid balance through time-period response capability calculation. Existing literature mainly divides DR into two categories: price-based and incentive-based, and conducts research on their large-scale implementation and system security. Price-based DR mainly includes peak-valley time-of-use pricing, dynamic real-time pricing, etc. Existing literature [4] transforms network security constraints into load node interaction response constraints to ensure that the system flow does not exceed limits during the implementation of price-based DR. Existing literature [5] fully considers the safety operation constraints in the study of DR based on real-time pricing participating in system economic dispatch, but does not include the impact of the uncertainty of flexible load response on system security. Existing literature [6] incentive-based DR assists the grid in peak shaving and valley filling and eliminates flow exceeding limits through load interruption, direct load control (existing literature [7]), and emergency power DR (existing literature [8]), and provides incentive compensation based on the change of user load relative to the baseline load. While existing literature [9] models the uncertainty in incentive-based large-scale adjustable load optimization scheduling, it does not analyze the impact of this uncertainty on system security.

[0005] Existing literature:

[0006] [1] Wang Qi, Dong Hongda, He Ziqian, et al. Optimization scheduling strategy for virtual power plant clusters considering flexibility and resource mutual assistance [J]. Journal of Northeast Electric Power University, 2024, 44(05):101-111.

[0007] [2] Liu Fang, Xu Yaojie, Yang Xiu, et al. Multi-timescale coordinated operation strategy of virtual power plant cluster considering power interaction and sharing [J]. Power System Technology, 2022, 46(02):642-656.

[0008] [3] Yu Yongjin, Liu Ximing. Two-stage optimization decision model for park-type virtual power plant clusters considering multiple time scales [J]. Journal of Shandong University of Science and Technology (Natural Science Edition), 2024, 43(05):120-130.

[0009] [4] Zeng Dan, Yao Jianguo, Yang Shengchun, et al. Modeling of price-based demand response optimization scheduling based on security constraints in wind power consumption [J]. Proceedings of the CSEE, 2014, 34(31): 5571-5578.

[0010] [5] Shi Wenchao, Lü Lin, Gao Hongjun, et al. Active distribution network economic dispatch considering demand response and electric vehicle participation [J]. Automation of Electric Power Systems, 2020, 44(11): 41-51.

[0011] [6] AALAMI HA, MOGHADDAM MP, YOUSEFI G R. Demand response modeling considering interruptible / curtailable loads and capacity market programs[J]. Applied Energy, 2010, 87(1): 243-250.

[0012] [7] Fan Shuai, Jia Kunqi, Guo Bingqing, et al. Collaborative optimization operation strategy for distributed electric heating load [J]. Automation of Electric Power Systems, 2017, 41(19): 20-29.

[0013] [8] Wang Luping, Li Haozhi, Xie Xiaorong. Emergency demand response distributed coordinated control method to improve short-term frequency stability [J]. Proceedings of the CSEE, 2020, 40(11): 3462-3470.

[0014] [9] Liu Dunnan, Wang Lingxiang, Wang Weiye, et al. Optimal scheduling of large-scale electric vehicle charging and swapping loads based on deep reinforcement learning [J]. Automation of Electric Power Systems, 2022, 46(4): 36-46. Summary of the Invention

[0015] The purpose of this invention is to overcome the shortcomings of the prior art and provide a virtual power plant optimization and supply guarantee method based on consistency theory.

[0016] The objective of this invention is achieved through the following technical solution: a virtual power plant optimization and supply guarantee method based on consistency theory, the method comprising,

[0017] Construct a supply guarantee decision-making framework; generate a standardized load baseline based on new energy output forecasts, net load fluctuations, and supply guarantee demand; and use adjustment costs as a consistency variable among virtual power plants (VPPs) based on consistency theory.

[0018] A two-layer scheduling model is constructed based on the supply guarantee decision-making framework. The upper-layer model aims to minimize the adjustment cost of virtual power plant (VPP) and maximize the guideline incentive under the supply guarantee scenario. It uses a consensus algorithm to coordinate the adjustment costs of each virtual power plant VPP and determine the total adjustment amount of each virtual power plant. The lower-layer model aims to minimize the scheduling cost of various flexible resources within the virtual power plant VPP and realize the reconstruction of the load curve.

[0019] Specifically, the construction steps of the supply guarantee decision-making framework are as follows:

[0020] S1. Initialize the guideline template, defining a standardized guideline based on historical data:

[0021] (1)

[0022] In the formula, Baseline load curve, ;

[0023] S2. Introducing uncertainty in new energy output, using a normal distribution to simulate photovoltaic / wind power deviation:

[0024] (2)

[0025] In the formula, Indicates the output of photovoltaic / wind power. t Timing deviation; It is a normal distribution function with parameters of mean 0 and variance. This indicates that the deviation is symmetrically distributed around 0, and there is no system bias.

[0026] S3, Update the alignment:

[0027] (3)

[0028] In the formula, After the update, at any time t The reference line; As a regulating factor;

[0029] S4. Similarity Calculation and Incentive Optimization: The similarity metric is expanded to weighted Euclidean distance.

[0030] (4)

[0031] (5)

[0032] (6)

[0033] In the formula, The time period weight represents the weight at time. t Time period weighting; For VPP at time t The per-unit value of the load curve; As a reference line at time t The per-unit value; I Let be the excitation function; This is the incentive coefficient; This is the maximum allowed distance; This refers to the total adjustment amount; It is a Lagrange function; For Lagrange multipliers; The threshold for fairness constraints, This represents the upper limit of the maximum allowed distance.

[0034] S5. Real-time verification, iteratively updating the guideline every time period, and adjusting it based on demand response signals. Repeat steps S2-S3 until convergence.

[0035] S6. Execution and Feedback: The internal resource response baseline of the Virtual Power Plant (VPP) is automatically executed through smart contracts, and the actual curve deviation is fed back to the power grid.

[0036] S7. Specific incentive mechanism for the quasi-linear VPP:

[0037] (7)

[0038] (8)

[0039] (8)

[0040] In the formula, Incentives obtained by the virtual power plant (VPP); This is the incentive coefficient; The similarity index between the load curve and the load baseline of a virtual power plant (VPP); The total adjustable capacity declared for virtual power plant (VPP); The similarity coefficient set for the power grid company; This measures the distance between the actual load profile and the load baseline of a virtual power plant (VPP). For VPP in t Per-unit value of load at any given time; for t The load baseline value at any given time; The time scale is 24;

[0041] Specifically, the objective function of the upper-level model is:

[0042] (9)

[0043] (10)

[0044] (11)

[0045] In the formula, The objective function of the upper-level model; The operating cost of a virtual power plant (VPP); The excitation obtained for the load guideline of the virtual power plant (VPP); For the first A virtual power plant (VPP) in t The unit cost of action at any given moment; For Virtual Power Plant (VPP) t Active power that is constantly adjusted. Incentives for the virtual power plant (VPP) The number of virtual power plants (VPPs) within the distribution network; For time scale.

[0046] Specifically, the constraints of the upper-level model include:

[0047] Power flow constraints in distribution networks:

[0048] (12)

[0049] (13)

[0050] (14)

[0051] (15)

[0052] (16)

[0053] (17)

[0054] In the formula, For nodes The set of the starting nodes of a line that is the terminal node; For nodes The set of terminal nodes of a line whose starting node is a node; and They are nodes i exist t The active and reactive power injected at all times; , , , These are the injection node lines. and outflow node lines Resistance and reactance; , They are respectively in Timetable The active and reactive power flowing upstream; It is a line Apparent power flowing upstream, It equals the sum of the squares of active power and reactive power; for t Time Node Net active power; and express t Time Node The active and reactive power injected into the photovoltaic system; and express t Time Node Active and reactive power under load; express t Time VPP node Active power; For the line The current flowing through it; and express Time Node The sum of the squares of the voltages of the circuit The square of the current flowing through it;

[0055] Voltage deviation constraint:

[0056] (18)

[0057] In the formula, Nominal voltage; and These represent the upper and lower limits of the voltage deviation rate allowed by the national standard, respectively.

[0058] Line current constraints:

[0059] (19)

[0060] In the formula, For the line The maximum current that it can carry;

[0061] Photovoltaic power constraints:

[0062] (20)

[0063] (twenty one)

[0064] In the formula, and The first The maximum and minimum power factors of each photovoltaic inverter; For the first Photovoltaic installation capacity at each node; for t The proportion of photovoltaic power output to maximum output during a given period;

[0065] Transformer operating capacity constraints:

[0066] (twenty two)

[0067] (twenty three)

[0068] (twenty four)

[0069] In the formula, , , and These are the minimum and maximum active and reactive power that the transformer node can transmit; The maximum active power that a transformer node can transmit during the power supply period; and express The active and reactive power input to the transformer node at any given time; A collection of periods of normal power supply; A collection of periods for ensuring supply;

[0070] VPP regulation power constraint:

[0071] (25)

[0072] (26)

[0073] (27)

[0074] In the formula, and The first i The maximum and minimum adjustable values ​​of each VPP; for t Time of the first i The adjustment amount of each VPP.

[0075] Specifically, the objective function of the lower-level model is:

[0076] (28)

[0077] (29)

[0078] (30)

[0079] (31)

[0080] (32)

[0081] (33)

[0082] (34)

[0083] (35)

[0084] In the formula, , , , , , and These are, respectively, industrial load transfer costs, residential load transfer costs, commercial load transfer costs, electric vehicle operating costs, energy storage operating costs, solar curtailment costs, and wind curtailment costs; , , , , , and These are, respectively, the unit industrial load transfer cost, the unit residential load transfer cost, the unit commercial load transfer cost, the unit electric vehicle operating cost, the unit energy storage operating cost, the unit solar curtailment cost, and the unit wind curtailment cost; , They are respectively t The amount of industrial and residential load transferred within the VPP at any given time; for t Commercial load transfer volume within a given time period; and These are the charging / discharging power of the EV, respectively. and These represent the discharge and charging power of the energy storage, respectively. and They are respectively t The output of photovoltaic and wind power during different time periods; and These refer to the installed capacity of photovoltaic and wind power, respectively. and Solar and wind power respectively t The proportion of output during a given period to the maximum output.

[0085] Specifically, the constraints of the lower-level model include:

[0086] Industrial load transfer constraints:

[0087] (36)

[0088] (37)

[0089] (38)

[0090] In the formula, , and They are respectively tThe industrial load after transfer, the industrial load before transfer, and the transfer amount within the virtual power plant (VPP) at any given moment; To assess the responsiveness of industrial loads; and These are the maximum output and input power of the industrial load, respectively;

[0091] Resident transfer load constraints:

[0092] (39)

[0093] (40)

[0094] (41)

[0095] In the formula, , and They are respectively t Resident load after transfer within VPP, resident load before transfer, and transfer amount; To assess the responsiveness of residents to their load; and These are the maximum transferable and transferable power of residential load, respectively;

[0096] Commercial load transfer constraints:

[0097] (42)

[0098] (43)

[0099] (44)

[0100] In the formula, , and They are respectively t Commercial load after transfer within the VPP at any given time, commercial load before transfer, and transfer amount; To assess the responsiveness of commercial load; and These are the maximum power that can be transferred out and the maximum power that can be transferred in from commercial loads, respectively.

[0101] Energy storage constraints:

[0102] (45)

[0103] (46)

[0104] (47)

[0105] (48)

[0106] (49)

[0107] (50)

[0108] In the formula, and These represent the maximum discharge and charging power of the energy storage, respectively. and Energy storage The state of discharge and charge at any given time; Indicates the initial charge of the stored energy; This indicates the charge level of the energy storage at the end of the daily cycle; Indicates energy storage Charge at any given moment; and Indicates the minimum and maximum charge of the stored energy; and Indicates the discharge and charging efficiency of energy storage;

[0109] Electric vehicle constraints:

[0110] Assuming the travel mileage distribution of EVs is similar to that of gasoline vehicles, the time probability density function for the initial arrival time at a charging station of an EV is as follows:

[0111] (51)

[0112] In the formula, Take 22.5, Take 6.8;

[0113] The probability density function for the EV's departure time is as follows:

[0114] (52)

[0115] In the formula, Take 12.5. Take 3.8;

[0116] The probability density of daily mileage for electric vehicles is:

[0117] (53)

[0118] In the formula, Daily mileage; Take 2.98; Take 1.14; to obtain the electric vehicle m The expected power consumption is:

[0119] (54)

[0120] In the formula, Electricity consumption per kilometer for an EV; Indicates energy conversion efficiency; Indicates electric vehicles m Expected battery capacity reserved for driving needs;

[0121] After considering the responsiveness of EV demand, the constraints for electric vehicles are as follows:

[0122] (55)

[0123] (56)

[0124] (57)

[0125] (58)

[0126] (59)

[0127] (60)

[0128] In the formula, yes t The number of EVs that remain within the VPP during the time period; , They are in t The number of EVs entering and leaving the virtual power plant (VPP) during a given time period; Is t The total capacity of EVs within the virtual power plant (VPP) during the specified time period; and These represent the expected capacities of each EV when it enters and exits the vehicle, respectively, and both follow a normal distribution. and ; This is the battery capacity of an EV; and The charging and discharging efficiencies of a single EV are shown below. and The maximum charging and discharging power of an EV; The responsiveness of EV users; and These are the charging / discharging power of the EV, respectively.

[0129] Wind power and photovoltaic power generation constraints:

[0130] (61)

[0131] (62)

[0132] Power balance constraints:

[0133] (63).

[0134] Specifically, the consensus algorithm is as follows:

[0135] (64)

[0136] (65)

[0137] (66)

[0138] (67)

[0139] (68)

[0140] (69)

[0141] In the formula, For the first i A virtual power plant (VPP) in t Adjust costs constantly; For the first i A virtual power plant (VPP) in t The unit adjustment cost at any given moment; This is the adjustment factor for power error. ; For the first j Each VPP at time t and iteration k The cost of virtual consensus adjustment at that time; To solve for the total power regulation; The sum of the regulating power of all virtual power plant VPPs and Deviation between; For row random matrix In the k The first iteration i OK j Column elements, The Middle i The line represents the first i The collaborative contribution ratio of each virtual power plant (VPP), with each column element representing its collaborative contribution ratio. During the iteration of the consensus algorithm, the first... j The regulation of the virtual power plant VPP is allocated to the first i The proportion of virtual power plant VPPs; It is the negative value of the adjacency weight, representing the node i andj Differences between them are the driving force; The weighted adjacency matrix of the distribution network topology G The i OK j Column element values, , representing a node i and j The weights between them; To adjust the sum of cost differences, it is used to quantify the degree of cost consistency among VPPs and serve as a global metric for convergence judgment; and They represent the first i The and the first j Each VPP at time t and iteration k Adjustment costs at that time.

[0142] Specifically, embedding the consensus algorithm into the two-layer scheduling model includes:

[0143] Initialization and guideline broadcasting, defining state set S:

[0144] (70)

[0145] In the formula, For the first n Each VPP at time t The energy storage state of charge is typically in the range of 0-1; For the first n Each VPP at time t The wind power output; For the first n Each VPP at time t The load power; For a moment t Power grid-related factors;

[0146] Power Grid Release Guidelines Local initialization cost for each virtual power plant (VPP) and Lagrange multipliers ;

[0147] Upper-level model consistency iteration:

[0148] (71)

[0149] (72)

[0150] In the formula, For at any time t The deviation of the directrix; For at any time tThe time period weighting is used to emphasize the non-negative coefficients of key periods such as peaks and troughs; Let VPP be the per-unit value of the load curve at time t; As a reference line at time t The per-unit value; For the first k Each agent is iterating. t+1 The updated power at that time represents the power allocation value for the next round; This is an adjustment factor or bias coefficient used to control the impact of the directrix deviation on power updates;

[0151] Incremental cost of inter-VPP exchange in a virtual power plant Update power: ,in, It is a Laplace matrix; Let be the learning rate, and be a positive constant. In iteration t Incremental cost vector at time; introduction of directrix deviation bias: ,Adjustment ;

[0152] Lagrange decomposition subproblems, relaxing the coupling constraints to:

[0153] (73)

[0154] In the formula, For the first n Each VPP at time t The summation of these cost items represents the total local operating costs. For the first n Each VPP at time t The Lagrange multipliers are used to relax the nonnegative coefficients of the coupling constraints and are iteratively updated in the dual optimization. The power sold by the VPP to external sources or the power grid. For at any time t VPP purchase power;

[0155] Each virtual power plant's VPP is solved independently:

[0156] (74)

[0157] In the formula, For the first n The grid-related costs of a VPP represent the expenses incurred in interacting with the grid (such as purchasing and selling electricity); For the first n The clustering or control cost of a VPP represents the cost of internal resource coordination or aggregation. For the first n The operation and maintenance costs of a VPP; For the first n Each VPP at time t The deviation variable is used for penalty calculation;

[0158] QGA solution of subproblems:

[0159] (75)

[0160] In the formula, The quantum state of the decision variable components is and Linear superposition; yes The amplitude, with probability of , These are polar angle parameters; yes The amplitude, Introducing phase, For the azimuth angle; in the VPP supply subproblem, QGA is used to solve the Lagrange daily function. In order to deal with uncertainty;

[0161] Convergence and verification:

[0162] (76)

[0163] (77)

[0164] In the formula, The dual gap is used to quantify the relative difference between the solutions to the primal problem and the dual problem, serving as a criterion for convergence judgment, and is usually dimensionless. It is the absolute value of the difference between the maximum and minimum values ​​of the Lagrange function; This is the convergence threshold; For the first n Each VPP at time t and iteration k+1 The update multiplier at time represents the Lagrange value for the next round. For the first n Each VPP at time t and iteration k The current multiplier represents the Lagrange value for this round; This is the subgradient step size; Let be the subgradient at iteration k, and let be the gradient direction representing the degree of constraint violation, used for dual updates.

[0165] The present invention has the following advantages:

[0166] 1. The distributed two-layer scheduling strategy of this invention achieves rapid consensus and fair allocation of adjustment costs among VPPs. Through the consensus algorithm of the distribution network operator layer, only local information exchange between neighboring VPPs is required, such as adjustment cost and power deviation, to converge to the globally optimal adjustment amount within a finite number of iterations, avoiding single point of failure and communication bottlenecks of centralized methods. This significantly improves the robustness and real-time performance of the system in high-penetration distributed resource scenarios, because low-cost VPPs can contribute more adjustment share, balancing the burden of high-cost VPPs, thereby reducing the overall supply guarantee risk.

[0167] 2. The introduction of a load baseline incentive mechanism guides VPP load curve reconstruction, improving peak shaving and valley filling effects and renewable energy consumption levels in supply guarantee scenarios. Traditional time-of-use pricing is prone to load rebound, while CDL encourages VPPs to shift peak demand to off-peak periods through full-time similarity measurement. The derivation process involves minimizing internal resource costs at the lower level and combining this with the overall regulation target at the upper level to ensure improved curve smoothness.

[0168] 3. This invention significantly improves the fairness of regulation and the enthusiasm of user participation while meeting the security constraints of the distribution network. The consensus algorithm makes the regulation costs of VPPs tend to be consistent, avoiding excessive burden on high-response-cost VPPs, such as the cost surge caused by industrial VPPs transferring loads alone. By optimizing the incentive coefficient and similarity index, the contributions of different types of VPPs are fairly compensated. Simulation verification shows that the fairness index is better than the non-belief scheme. The derivation lies in the fact that distributed interaction protects privacy and reduces the impact of uncertainty, ultimately reducing the system operating cost and providing a replicable path for large-scale VPP promotion. Attached Figure Description

[0169] Figure 1 This is a schematic diagram of the supply guarantee decision-making framework of the present invention;

[0170] Figure 2 This is a schematic diagram of the two-layer scheduling model structure of the present invention;

[0171] Figure 3 This is a flowchart of the solution process for the two-level scheduling model of the present invention. Detailed Implementation

[0172] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0173] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0174] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0175] The present invention will be further described below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0176] like Figures 1 to 3 As shown, a virtual power plant optimization and supply guarantee method based on consistency theory is proposed. This method includes:

[0177] Construct a supply guarantee decision-making framework; generate a standardized load baseline based on new energy output forecasts, net load fluctuations, and supply guarantee demand; and use adjustment costs as a consistency variable among virtual power plants (VPPs) based on consistency theory.

[0178] Load curves are typically normalized to a 0-1 range to guide demand-side resources, such as virtual power plants (VPPs), to adjust their load curves to achieve peak-valley regulation and energy transfer throughout the day.

[0179] The steps for constructing the supply guarantee decision-making framework are as follows:

[0180] S1. Initialize the guideline template, defining a standardized guideline based on historical data:

[0181] (1)

[0182] In the formula, Baseline load curve, ;

[0183] S2. Uncertainty modeling: Introducing uncertainty in new energy output, and using a normal distribution to simulate photovoltaic / wind power deviations:

[0184] (2)

[0185] In the formula, Power output for photovoltaic / wind power t The deviation at time, which follows a normal distribution, is used to simulate uncertainty. It is a normal distribution function with parameters of mean 0 and variance. This indicates that the deviation is symmetrically distributed around 0, and there is no system bias.

[0186] S3, Update the alignment:

[0187] (3)

[0188] In the formula, After the update, at any time t The reference line; As a regulating factor;

[0189] S4. Similarity Calculation and Excitation Optimization, VPP Adjustment Load Curve Using a matching guideline, the similarity metric is expanded to a weighted Euclidean distance by minimizing distance and maximizing incentive through multi-objective optimization.

[0190] (4)

[0191] (5)

[0192] (6)

[0193] In the formula, For a moment t The time period weighting is higher during peak periods to emphasize key time periods; For VPP at time t The per-unit value of the load curve; As a reference line at time t The per-unit value; I Let be the excitation function; This is the incentive coefficient; This is the maximum allowed distance, a reference value used to normalize the similarity distance; This refers to the total adjustment amount; For example, the Lagrangian function is used to handle objective functions in constrained optimization. These are Lagrange multipliers, which are iteratively updated during optimization. The threshold for fairness constraints, Indicates the upper limit of the maximum allowed distance;

[0194] S5. Real-time verification, iteratively updating the guideline every time period, and adjusting it based on demand response signals. Repeat steps S2-S3 until convergence.

[0195] S6. Execution and Feedback: The internal resource response baseline of the Virtual Power Plant (VPP) is automatically executed through smart contracts, and the actual curve deviation is fed back to the power grid.

[0196] S7. Specific incentive mechanism for the quasi-linear VPP:

[0197] (7)

[0198] (8)

[0199] (8)

[0200] In the formula, Incentives obtained by the virtual power plant (VPP); This is the incentive coefficient; The similarity index between the load curve and the load baseline of a virtual power plant (VPP); The total adjustable capacity declared for virtual power plant (VPP); The similarity coefficient set for the power grid company; This measures the distance between the actual load profile and the load baseline of a virtual power plant (VPP). For VPP in t Per-unit value of load at any given time; for t The load baseline value at any given time; The time scale is 24.

[0201] Because the formula uses per-unit curves, the power grid company does not need to provide specific adjustment targets for each virtual power plant (VPP). It only needs to ensure that the load curve shape is similar to the load baseline, which can significantly reduce the workload. In the scenario of ensuring power supply, the load baseline is lowered as much as possible during the power supply period, guiding the VPP to transfer electricity demand to other periods of low load, thus ensuring the power supply needs of the load.

[0202] like Figure 1As shown, the power supply guarantee decision-making framework mainly consists of two parts: The first part is the load baseline specified and released by the power grid company based on the day-ahead renewable energy output and net load information of the distribution network operator in the region, combined with the power supply guarantee demand. The second part is the response load baseline of virtual power plants (VPPs) within the distribution network to meet the power supply guarantee demand. The flexible resources within the VPPs mainly include renewable energy units such as wind power and photovoltaics, energy storage, electric vehicles, and residential and industrial flexible loads with DR (Dynamic Reduction) response capabilities. According to the consistency theory, using regulation cost as a consistency variable among VPPs can ensure the efficiency of regulation. Therefore, VPPs with lower response regulation costs will bear a larger share of power.

[0203] A two-layer scheduling model is constructed based on the supply guarantee decision-making framework. The upper-layer model aims to minimize the adjustment cost of virtual power plant (VPP) and maximize the baseline incentive under the supply guarantee scenario. It guides VPP to transfer load during the supply guarantee period and uses a consensus algorithm to coordinate the adjustment costs of each virtual power plant VPP to reach consistency, thereby determining the total adjustment amount of each virtual power plant. The lower-layer model aims to minimize the scheduling cost of various flexible resources within the virtual power plant VPP, realizes the reconstruction of the load curve, and works together to achieve the virtual power plant VPP adjustment power target transmitted by the upper-layer model.

[0204] The objective function of the upper-level model is:

[0205] (9)

[0206] (10)

[0207] (11)

[0208] In the formula, The objective function of the upper-level model; The operating cost of a virtual power plant (VPP); The excitation obtained for the load guideline of the virtual power plant (VPP); For the first A virtual power plant (VPP) in t The unit cost of action at any given moment; For Virtual Power Plant (VPP) t Active power that is constantly adjusted. Incentives for the virtual power plant (VPP) The number of virtual power plants (VPPs) within the distribution network; For time scale.

[0209] The constraints of the upper-level model include:

[0210] Power flow constraints in distribution networks:

[0211] (12)

[0212] (13)

[0213] (14)

[0214] (15)

[0215] (16)

[0216] (17)

[0217] In the formula, For nodes The set of the starting nodes of a line that is the terminal node; For nodes The set of terminal nodes of a line whose starting node is a node; and They are nodes i exist t The active and reactive power injected at all times; , , , These are the injection node lines. and outflow node lines Resistance and reactance; , Indicates in Timetable The active and reactive power flowing upstream; It is a line Apparent power flowing upstream, It equals the sum of the squares of active power and reactive power; for t Time Node Net active power; and express t Time Node The active and reactive power injected into the photovoltaic system; and express t Time Node Active and reactive power under load; express t Time VPP node Active power; For the line The current flowing through it; and express Time Node The sum of the squares of the voltages of the circuit The square of the current flowing through it;

[0218] Voltage deviation constraint:

[0219] (18)

[0220] In the formula, Nominal voltage; and These represent the upper and lower limits of the voltage deviation rate allowed by the national standard, respectively.

[0221] Line current constraints:

[0222] (19)

[0223] In the formula, For the line The maximum current that it can carry;

[0224] Photovoltaic power constraints:

[0225] (20)

[0226] (twenty one)

[0227] In the formula, and They are respectively the first one The maximum and minimum power factor of the photovoltaic inverter; For the first Photovoltaic installation capacity at each node; for t The proportion of photovoltaic power output to maximum output during a given period;

[0228] During periods of guaranteed power supply, the amount of electricity transmitted from the upstream network to the distribution network is limited, and the operating capacity of transformers is constrained.

[0229] (twenty two)

[0230] (twenty three)

[0231] (twenty four)

[0232] In the formula, , , and These are the minimum and maximum active and reactive power that the transformer node can transmit; The maximum active power that a transformer node can transmit during the power supply period; and express The active and reactive power input to the transformer node at any given time; A collection of periods of normal power supply; A collection of periods for ensuring supply;

[0233] VPP regulation power constraint:

[0234] (25)

[0235] (26)

[0236] (27)

[0237] In the formula, and The first i The maximum and minimum adjustable values ​​of each VPP; for t Time of the first i The adjustment amount of each VPP.

[0238] The objective function of the lower-level model is:

[0239] (28)

[0240] (29)

[0241] (30)

[0242] (31)

[0243] (32)

[0244] (33)

[0245] (34)

[0246] (35)

[0247] In the formula, , , , , , and These are, respectively, industrial load transfer costs, residential load transfer costs, commercial load transfer costs, electric vehicle operating costs, energy storage operating costs, solar curtailment costs, and wind curtailment costs; , , , , , and These are, respectively, the unit industrial load transfer cost, the unit residential load transfer cost, the unit commercial load transfer cost, the unit electric vehicle operating cost, the unit energy storage operating cost, the unit solar curtailment cost, and the unit wind curtailment cost; , They are respectively t The amount of industrial and residential load transferred within the VPP at any given time; for t Commercial load transfer volume within a given time period; and These are the charging / discharging power of the EV, respectively. and These represent the discharge and charging power of the energy storage, respectively. and They are respectively t The output of photovoltaic and wind power during different time periods; and These refer to the installed capacity of photovoltaic and wind power, respectively. and Solar and wind power respectively t The proportion of output during a given period to the maximum output.

[0248] Furthermore, the constraints of the lower-level model include:

[0249] Industrial load transfer constraints:

[0250] (36)

[0251] (37)

[0252] (38)

[0253] In the formula, , and They are respectively t The industrial load after transfer, the industrial load before transfer, and the transfer amount within the virtual power plant (VPP) at any given moment; To assess the responsiveness of industrial loads; and These are the maximum output and input power of the industrial load, respectively;

[0254] Resident transfer load constraints:

[0255] (39)

[0256] (40)

[0257] (41)

[0258] In the formula, , and They are respectively t Resident load after transfer within VPP, resident load before transfer, and transfer amount; To assess the responsiveness of residents to their load; and These are the maximum transferable and transferable power of residential load, respectively;

[0259] Commercial load transfer constraints:

[0260] (42)

[0261] (43)

[0262] (44)

[0263] In the formula, , and They are respectively t Commercial load after transfer within the VPP at any given time, commercial load before transfer, and transfer amount; To assess the responsiveness of commercial load; and These are the maximum power that can be transferred out and the maximum power that can be transferred in from commercial loads, respectively.

[0264] Energy storage constraints:

[0265] (45)

[0266] (46)

[0267] (47)

[0268] (48)

[0269] (49)

[0270] (50)

[0271] In the formula, and These represent the maximum discharge and charging power of the energy storage, respectively. and Energy storage The state of discharge and charge at any given time; Indicates the initial charge of the stored energy; This indicates the charge level of the energy storage at the end of the daily cycle; Indicates energy storage Charge at any given moment; and Indicates the minimum and maximum charge of the stored energy; and Indicates the discharge and charging efficiency of energy storage;

[0272] Electric vehicle constraints:

[0273] Assuming the travel mileage distribution of EVs is similar to that of gasoline vehicles, the time probability density function for the initial arrival time at a charging station of an EV is as follows:

[0274] (51)

[0275] In the formula, Take 22.5, Take 6.8;

[0276] The probability density function for the EV's departure time is as follows:

[0277] (52)

[0278] In the formula, Take 12.5. Take 3.8;

[0279] The probability density of daily mileage for electric vehicles is:

[0280] (53)

[0281] In the formula, Daily mileage; Take 2.98; Take 1.14; to obtain the electric vehicle m The expected power consumption is:

[0282] (54)

[0283] In the formula, Electricity consumption per kilometer for an EV; Indicates energy conversion efficiency; Indicates electric vehicles m Expected battery capacity reserved for driving needs;

[0284] After considering the responsiveness of EV demand, the constraints for electric vehicles are as follows:

[0285] (55)

[0286] (56)

[0287] (57)

[0288] (58)

[0289] (59)

[0290] (60)

[0291] In the formula, yes t The number of EVs that remain within the VPP during the time period; , They are in t The number of EVs entering and leaving the virtual power plant (VPP) during a given time period; and The charging and discharging efficiencies of a single EV are shown below. Is t The total capacity of EVs within the virtual power plant (VPP) during the specified time period; and These are the expected capacities of each EV when it enters and when it leaves, and both follow a normal distribution. and ; This is the battery capacity of an EV; and The maximum charging and discharging power of an EV; The responsiveness of EV users; and These are the charging / discharging power of the EV, respectively.

[0292] Wind power and photovoltaic power generation constraints:

[0293] (61)

[0294] (62)

[0295] Power balance constraints:

[0296] (63).

[0297] The goal of consensus algorithms is to enable each Virtual Private Cloud (VPP) to negotiate adjustment amounts and reach consensus on response costs, thus making adjustments more equitable. Each VPP can update its own state in real time by communicating with neighboring VPPs. Considering the communication latency between VPPs, a first-order consensus algorithm for discrete-time systems is chosen, expressed as:

[0298] (64)

[0299] (65)

[0300] (66)

[0301] (67)

[0302] (68)

[0303] (69)

[0304] In the formula, For the first i A virtual power plant (VPP) in t Adjust costs constantly; For the first i A virtual power plant (VPP) in t The unit adjustment cost at any given moment; This is the adjustment factor for power error. ; Indicates the first j Each VPP at time t and iteration k The cost of virtual consensus adjustment at that time; To solve for the total power regulation; The sum of the regulating power of all virtual power plant VPPs and Deviation between; For row random matrix In the k The first iteration i OK j Column elements, The Middle i The line represents the first i The collaborative contribution ratio of each virtual power plant (VPP), with each column element representing its collaborative contribution ratio. During the iteration of the consensus algorithm, the first... j The regulation of the virtual power plant VPP is allocated to the first i The proportion of virtual power plant VPPs; It is the negative value of the adjacency weight, representing the node i and j Differences between them are the driving force; The weighted adjacency matrix of the distribution network topology G The i OK j Column element values, , representing a node i and j The weights between them; It represents the sum of adjustment cost differences, used to quantify the degree of cost consistency among VPPs, and serves as a global metric for convergence judgment; and They represent the first i The and the first j Each VPP at time t and iteration k The adjustment cost at that time; the update pattern of the virtual power plant (VPP) in each iteration is as follows: when At that time, if This indicates that the VPP regulation power is not up to standard. This needs to be added; the same applies to other cases. The convergence condition of the consensus algorithm is: the Manhattan distance of the adjustment costs among virtual power plant (VPP) is less than a given threshold. .

[0305] The process of combining the consensus principle with the load profile involves embedding a distributed consensus algorithm into a two-layer optimization framework of VPPs. This enables coordinated supply assurance in response to the load profile among VPPs. In the upper-layer model, the consensus algorithm uses adjustment cost as the consensus variable. Through local communication among neighboring VPPs, it aims to make the marginal costs of each VPP consistent, while simultaneously optimizing the total adjustment to match the load profile pattern published by the power grid. The lower layer, based on the objectives transmitted from the upper layer, schedules internal resources, such as energy storage and EVs, to reconstruct the load curve and calculates similarity indices to maximize incentive benefits. Specifically, this includes:

[0306] Initialization and guideline broadcasting, defining state set S:

[0307] (70)

[0308] In the formula, For the first n Each VPP at time t The energy storage state of charge is typically in the range of 0-1; For the first n Each VPP at time t The wind power output; For the first n Each VPP at time t The load power; Indicates time t Power grid-related factors;

[0309] Power Grid Release Guidelines Local initialization cost for each virtual power plant (VPP) and Lagrange multipliers ;

[0310] Upper-level model consistency iteration:

[0311] (71)

[0312] (72)

[0313] In the formula, For at any time t The deviation of the directrix; Indicates at time t The time period weighting is used to emphasize the non-negative coefficients of key periods such as peaks and troughs; For VPP at time t The per-unit value of the load curve; As a reference line at time t The per-unit value; For the first k Each agent is iterating. t+1 The updated power at that time represents the power allocation value for the next round; This represents the adjustment factor or bias coefficient, used to control the impact of the directrix deviation on power updates;

[0314] Incremental cost of inter-VPP exchange in a virtual power plant Update power: ,in, It is a Laplace matrix; Let be the learning rate, and be a positive constant. In iteration t Incremental cost vector at time; introduction of directrix deviation bias: ,Adjustment ;

[0315] Lagrange decomposition subproblems, relaxing the coupling constraints to:

[0316] (73)

[0317] In the formula, For the first n Each VPP at time t The summation of these cost items represents the total local operating costs. For the first n Each VPP at time t The Lagrange multipliers are used to relax the nonnegative coefficients of the coupling constraints and are iteratively updated in the dual optimization. The power sold by the VPP to external sources or the power grid. For at any time t VPP purchase power;

[0318] Each virtual power plant's VPP is solved independently:

[0319] (74)

[0320] In the formula, For the first n The grid-related costs of a VPP represent the expenses incurred in interacting with the grid (such as purchasing and selling electricity); For the first n The clustering or control cost of a VPP represents the cost of internal resource coordination or aggregation. For the first n The operation and maintenance costs of a VPP; For the first n Each VPP at time t The deviation variable is used for penalty calculation;

[0321] QGA solution of subproblems:

[0322] (75)

[0323] In the formula, The quantum state of the decision variable components is and Linear superposition; yes The amplitude, with probability of , These are polar angle parameters; yes The amplitude, Introducing phase, For the azimuth angle; in the VPP supply subproblem, QGA is used to solve the Lagrange daily function. In order to deal with uncertainty;

[0324] Convergence and verification:

[0325] (76)

[0326] (77)

[0327] In the formula, The dual gap is used to quantify the relative difference between the solutions to the primal problem and the dual problem, serving as a criterion for convergence judgment, and is usually dimensionless. It is the absolute value of the difference between the maximum and minimum values ​​of the Lagrange function; This is the convergence threshold; For the first n Each VPP at time tand iteration k+1 The update multiplier at time represents the Lagrange value for the next round. For the first n Each VPP at time t and iteration k The current multiplier represents the Lagrange value for this round; This is the subgradient step size; In iteration k The subgradient at time t represents the gradient direction indicating the degree of constraint violation, and is used for dual updates.

[0328] The model transformation and solution process involves a mixed-integer non-convex and nonlinear programming model. This invention employs mathematical optimization to transform the nonlinear and non-convex constraints in the model into linear and convex constraints, and then uses the CPLEX solver to solve the problem. Some of the constraints are non-convex and nonlinear, particularly those containing squared terms. and Introducing auxiliary variables and Remove the squared terms. For equality constraints, even with the introduction of auxiliary variables, the constraint remains non-convex and nonlinear. We use second-order cone relaxation to transform it into a second-order cone convex constraint, as shown in the following equation:

[0329] (78)

[0330] (79)

[0331] (80)

[0332] (81)

[0333] The per-unit value of VPP load is measured using Euclidean distance. With load guideline The similarity is used as an incentive in the objective function, exhibiting high nonlinearity. Under the optimization of minimizing the objective function, and Since the values ​​are close, the Euclidean distance can be simplified to the Manhattan distance, as shown in the following formula:

[0334] (82)

[0335] Linearization can be achieved by using the following methods to handle absolute values:

[0336] (83)

[0337] In the formula: The auxiliary variables introduced are similar to the other linearization methods.

[0338] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any person skilled in the art can make many possible variations and modifications to the technical solution of the present invention, or modify it into equivalent embodiments, without departing from the scope of the present invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technology of the present invention without departing from the scope of the present invention are within the protection scope of the present invention.

Claims

1. A virtual power plant optimization and supply guarantee method based on consistency theory, characterized in that: The method includes, Construct a supply guarantee decision-making framework; generate a standardized load baseline based on new energy output forecasts, net load fluctuations, and supply guarantee demand; and use adjustment costs as a consistency variable among virtual power plants (VPPs) based on consistency theory. A two-layer scheduling model is constructed based on the supply guarantee decision-making framework. The upper-layer model aims to minimize the adjustment cost of virtual power plant (VPP) and maximize the guideline incentive under the supply guarantee scenario. It uses a consensus algorithm to coordinate the adjustment costs of each virtual power plant VPP and determine the total adjustment amount of each virtual power plant. The lower-layer model aims to minimize the scheduling cost of various flexible resources within the virtual power plant VPP and realize the reconstruction of the load curve. The consensus algorithm is as follows: （64） （65） （66） （67） （68） （69） In the formula, The adjustment cost of the i-th virtual power plant VPP at time t; Let VPP be the unit adjustment cost of the i-th virtual power plant at time t; This is the adjustment factor for power error. ; The virtual consensus adjustment cost for the j-th VPP at time t and iteration k; To solve for the total power regulation; The sum of the regulating power of all virtual power plant VPPs and Deviation between; For row random matrix The element in the i-th row and j-th column at the k-th iteration. The i-th row represents the collaborative contribution ratio of the i-th virtual power plant (VPP), and each column contains elements... The proportion of the adjustment amount of the j-th virtual power plant VPP allocated to the i-th virtual power plant VPP during the consensus algorithm iteration; The value is the negative of the adjacency weight; The weighted adjacency matrix of the distribution network topology G The value of the element in the i-th row and j-th column. , representing the weight between nodes i and j; To adjust the sum of cost differences; and Let $i$ be the adjustment costs of the i-th and j-th VPPs at time $t$ and iteration $k$, respectively. Embedding the consensus algorithm into a two-layer scheduling model specifically includes: Initialization and guideline broadcasting, defining state set S: （70） In the formula, The energy storage state of charge of the nth VPP at time t; Let be the wind power output of the nth VPP at time t; Let be the load power of the nth VPP at time t; For time t, the relevant factors of the power grid; the power grid release guideline. Local initialization cost for each virtual power plant (VPP) and Lagrange multipliers ; Upper-level model consistency iteration: （71） （72） In the formula, The deviation of the directrix at time t is the offset; The time interval weight at time t; Let VPP be the per-unit value of the load curve at time t; The per-unit value of the reference guideline at time t; The update power of the k-th agent at iteration t+1; This is an adjustment factor or bias coefficient; Incremental cost of inter-VPP exchange in a virtual power plant Update power: ,in, It is a Laplace matrix; The learning rate; Let be the incremental cost vector at iteration t; introduce a directrix offset: ,Adjustment ; Lagrange decomposition subproblems, relaxing the coupling constraints to: （73） In the formula, This is the cost term for the nth VPP at time t; Let n be the Lagrange multiplier of the nth VPP at time t; The power sold by the VPP to the external environment or the power grid; Purchase power for VPP at time t; Each virtual power plant's VPP is solved independently: （74） In the formula, The grid-related costs for the nth VPP; The clustering or control cost for the nth VPP; The operation and maintenance cost of the nth VPP; Let be the deviation variable of the nth VPP at time t; QGA solution of subproblems: （75） In the formula, The quantum state of the decision variable components is and Linear superposition; for The amplitude, with probability of , These are polar angle parameters; for The amplitude, Introducing phase, It is the azimuth angle; Convergence and verification: （76） （77） In the formula, The dual gap is used to quantify the relative difference between the solutions to the primal problem and the dual problem, serving as a criterion for convergence judgment. It is the absolute value of the difference between the maximum and minimum values ​​of the Lagrange function; This is the convergence threshold; This is the update multiplier for the nth VPP at time t and iteration k+1; The current multiplier of the nth VPP at time t and iteration k; This is the subgradient step size; Let be the subgradient at iteration k; An optimized supply guarantee scheme is obtained by solving the two-level scheduling model.

2. The virtual power plant optimization and supply guarantee method based on consistency theory according to claim 1, characterized in that: The steps for constructing the supply guarantee decision-making framework are as follows: S1. Initialize the guideline template, defining a standardized guideline based on historical data: （1） In the formula, Baseline load curve, ; S2. Introducing uncertainty in new energy output, using a normal distribution to simulate photovoltaic / wind power deviation: （2） In the formula, The deviation of photovoltaic / wind power output at time t is given, and the deviation follows a normal distribution to simulate uncertainty. It is a normal distribution function with parameters of mean 0 and variance. This indicates that the deviation is symmetrically distributed around 0, and there is no system bias. S3, Update the alignment: （3） In the formula, The updated reference guideline at time t; As a regulating factor; S4. Similarity Calculation and Incentive Optimization: The similarity metric is expanded to weighted Euclidean distance. （4） （5） （6） In the formula, The time period weight is t; Let VPP be the per-unit value of the load curve at time t; I is the per-unit value of the reference guideline at time t; I is the excitation function; Indicates the incentive coefficient; Indicates the maximum permissible distance; represents the total adjustment amount. It is a Lagrange function; These are Lagrange multipliers, which are iteratively updated during optimization. The threshold for fairness constraints, Indicates the upper limit of the maximum allowed distance; S5. Real-time verification, iteratively updating the guideline every time period, and adjusting it based on demand response signals. Repeat steps S2-S3 until convergence; S6. Execution and Feedback: The internal resource response baseline of the Virtual Power Plant (VPP) is automatically executed through smart contracts, and the actual curve deviation is fed back to the power grid. S7. Specific incentive mechanism for the quasi-linear VPP: （7） （8） （8） In the formula, Incentives obtained by the virtual power plant (VPP); This is the incentive coefficient; The similarity index between the load curve and the load baseline of a virtual power plant (VPP); The total adjustable capacity declared for virtual power plant (VPP); The similarity coefficient set for the power grid company; This measures the distance between the actual load profile and the load baseline of a virtual power plant (VPP). Let VPP be the per-unit value of the load at time t; Let be the load baseline value at time t; The time scale is 24.

3. The virtual power plant optimization and supply guarantee method based on consistency theory according to claim 1, characterized in that: The objective function of the upper-level model is: （9） （10） （11） In the formula, The objective function of the upper-level model; The operating cost of a virtual power plant (VPP); The excitation obtained for the load baseline of the virtual power plant (VPP) response; For the first The unit action cost of a virtual power plant (VPP) at time t; The active power adjusted by the virtual power plant VPP at time t; Incentives for the virtual power plant (VPP); The number of virtual power plants (VPPs) within the distribution network; For time scale.

4. The virtual power plant optimization and supply guarantee method based on consistency theory according to claim 3, characterized in that: The constraints of the upper-level model include power flow constraints, voltage deviation constraints, line current constraints, photovoltaic power constraints, transformer operating capacity constraints, and virtual power plant (VPP) regulation power constraints.

5. The virtual power plant optimization and supply guarantee method based on consistency theory according to claim 1, characterized in that: The objective function of the lower-level model is: （28） （29） （30） （31） （32） （33） （34） （35） In the formula, , , , , , and These are, respectively, industrial load transfer costs, residential load transfer costs, commercial load transfer costs, electric vehicle operating costs, energy storage operating costs, solar curtailment costs, and wind curtailment costs; , , , , , and These are the unit industrial load transfer cost, unit residential load transfer cost, unit commercial load transfer cost, unit electric vehicle operating cost, unit energy storage operating cost, unit solar curtailment cost, and unit wind curtailment cost, respectively. , These represent the transfer amounts of industrial and residential loads within the VPP at time t, respectively. Let t be the amount of commercial load transfer within time t; and These are the charging / discharging power of the EV, respectively. and These represent the discharge and charging power of the energy storage, respectively. and These represent the output of photovoltaic and wind power respectively during time period t; and These refer to the installed capacity of photovoltaic and wind power, respectively. and These represent the proportions of photovoltaic and wind power output to their maximum output during time period t.

6. The virtual power plant optimization and supply guarantee method based on consistency theory according to claim 5, characterized in that: The constraints of the lower-level model include industrial load transfer constraints, residential load transfer constraints, commercial load transfer constraints, energy storage constraints, electric vehicle constraints, wind power and photovoltaic power generation constraints, and power balance constraints.

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