Dynamic resource aggregation scheduling method and system for virtual power plant and medium

Abstract

The invention relates to a dynamic resource aggregation scheduling method and system for a virtual power plant and a medium, and the method comprises the following steps: S1, building a virtual power plant (VPP) system model; s2, power system requirements and technical constraints are determined; s3, performing preliminary scheduling on various energy sources in the virtual power plant by using a random mixed integer linear programming model; s4, quantifying the system contribution of the distributed resources by using a VCG (Vickrey-Clarke-Grove) mechanism in a cooperative game theory; and S5, according to the quantitative result and the real-time data, dynamically adjusting scheduling schemes of various energy sources in the virtual power plant with economic benefit maximization. According to the method, the aggregation efficiency of virtual power plant resources can be effectively improved, and the overall energy utilization efficiency and the operation stability of a power grid are improved.

Description

Technical Field

[0001] The present invention relates to the field of power system management, and in particular to a method, system and medium for dynamic resource aggregation scheduling of a virtual power plant. Background Art

[0002] Virtual power plant (VPP) is an innovative power system concept that aims to achieve efficient aggregation and management of distributed energy resources (DERs) through information technology and advanced dispatch strategies. The energy in traditional power systems mainly comes from large centralized power plants, while distributed energy resources such as wind power, solar power plants, electric vehicles and commercial HVAC systems (heating-ventilation-air conditioning) are scattered across the country. Through technology integration, virtual power plants simulate the functions of large power plants, optimize resource allocation, and enhance the reliability and flexibility of the power grid. However, existing technologies have limitations in resource scheduling optimization, especially when faced with highly variable energy supply and demand and complex market environments.

[0003] In the prior art, the application number is "202311760512.3", and the name is "Virtual Power Plant Scheduling Method, Computer Equipment and Storage Medium". A virtual power plant scheduling method is proposed, which collects target data about various energy generation units and combines constraints to calculate and optimize expected profits and downside risks, thereby determining the optimal power plant operation scheduling plan. The main focus is on a static virtual power plant scheduling method, which focuses on obtaining target data within the virtual power plant and solving constraints, and optimizing the scheduling plan by calculating expected profits and downside risks.

[0004] In this way, in the face of high demand fluctuations and changes in electricity prices, how to dispatch to ensure the stable operation of the power system while achieving the best economic benefits is the current problem. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A virtual power plant dynamic resource aggregation scheduling method comprises the following steps:

[0008] Step S1: Establish a virtual power plant system model, and describe in detail the various distributed energy types involved, including the location, capacity, and operating parameters of wind, solar, electric vehicles, and commercial heating-ventilation-air conditioning (HVAC) systems;

[0009] Step S2: Determine the power system requirements and technical constraints, including factors such as power loss, voltage support, and line congestion;

[0010] Step S3: Using a random mixed integer linear programming model, preliminarily dispatch various energy sources in the virtual power plant to maximize cost-effectiveness;

[0011] Step S4: Apply the VCG (Vickrey-Clarke-Grove) mechanism in cooperative game theory to quantify the system contribution of distributed resources and reasonably distribute economic benefits;

[0012] Step S5: Based on the quantitative results and real-time data, dynamically adjust the scheduling plans of various energy sources in the virtual power plant to maximize economic benefits.

[0013] In step S1, the profit of the virtual power plant (VPP) is maximized, which consists of two parts: the income P from selling electricity to commercial users and the PSC and the operating cost of VPP C TVPP , taking into account technical and economic constraints:

[0014] Max:∑(P PSC -C VPP ) (1)

[0015]

[0016] Where: s / Ω s Indicates scene index / collection; h / Ω h Indicates time index / collection; k / Ω k {r,c,id∈n} represents load index / set; HVAC / Ω HVAC Represents HVAC index / collection; ev / Ω ev Represents an electric vehicle index / collection; represents the time-of-use electricity price, $ / MWh; ρ s represents the probability of scene s occurring; P k,s,h Indicates daily load power; Indicates the charging power of electric vehicles; Indicates HVAC power.

[0017] The operating cost C VPP The decomposition is shown in formula (3), including the generation fee and maintenance fee paid to the distributed generation owned by the user:

[0018]

[0019] Where: Ω g Represents the index set of the generator group g; Indicates the market Index collection; O OC,g represents the unit energy production cost; Indicates the sale of power from the grid; represents the distributed generation power; Indicates the discharge power of electric vehicles; P Penalty For penalty items; represents the day-ahead market electricity price; represents the charging cost of electric vehicles; Ω ω Represents the operating mode ω index set.

[0020] The penalty item is the charging of electric vehicles (P Penalty,ev ) or to start the HVAC system (P Penalty,HVAC ) If the VPP is not activated, no such fee will be incurred.

[0021] P Penalty =P Penalty,ev +P Penalty,HVAC (4)

[0022]

[0023] Where: Indicates the charging power of electric vehicles; Represents the charge and discharge cycle cost of electric vehicles; Indicates the discharge power of electric vehicles.

[0024] The reason for the penalty is that the VPP should compensate the customer for the remaining cost. This simulates the difference between the customer's optimal cost and the HVAC cycle cost, and can also be understood as an incentive to participate in VPP energy scheduling:

[0025]

[0026] Where: represents the active power of the HVAC system, and the optimal cost of HVAC is P Penalty,HVAC .

[0027] In step S2:

[0028] (1) Maximum charging and discharging rate constraints for electric vehicles:

[0029]

[0030] Where: represents the binary variable of charge and discharge; Indicates the charge and discharge limit power.

[0031] (2) State of charge (SOC) constraints of electric vehicles:

[0032]

[0033] Where: Indicates the energy storage limit; μ ev represents the scaling factor. Inequality (11) ensures that the SOC is always within the allowed range. Equation (12) sets the initial SOC and requires the EV to return to the initial SOC at the end of the operation period.

[0034] (3) Active and reactive power flow constraints:

[0035]

[0036] Where: Ω l represents the line l index set; P l,s,h and Q l,s,h represent the active power flow and reactive power flow in the line respectively, and and Represent the active and reactive power demands at the node respectively; and They represent the active power loss and reactive power loss in the line respectively.

[0037] In step S3: the large M method is used to linearize the nonlinear constraints in the virtual power plant system model, and the active and reactive power flow constraints are obtained as follows:

[0038]

[0039]

[0040] Where: g l ,b l Represents the conductance and reactance of each branch l; M P,l ,M Q,l Indicates the large M parameter related to the active and reactive power flow of each branch; ΔV n,s,h Indicates the voltage difference amplitude at node n; ΔV m,s,h Represents the voltage difference amplitude on node m; V nom Indicates rated voltage; θ l,s,h Refers to the angle difference θ n,s,h -θ m,s,h , where n and m are nodes corresponding to the same line.

[0041] The maximum power flow of the lines and the active power loss and reactive power loss of each line are:

[0042]

[0043] Where: represents the flow limit of each branch l; Rl ,X l Indicates resistance and reactance; l,h Represents a binary variable of line status.

[0044] In step S4: the VCG mechanism in cooperative game theory is applied to quantify the system contribution of distributed resources, and a set of real valuations submitted by participants can be obtained (the set of participant valuations is ), which ensures that the distributed energy owner i obtains benefits that match the real marginal contribution of distributed energy to the system. Formally, the transfer payment t received by participant i i The description is as follows:

[0045]

[0046] Where: represents the transfer payment to participant i; is the set of valuations of all participants, where is the value estimated by participant i for the resources or services it provides; Among all participants The optimal benefit that the system can achieve when providing resources or services; represents the contribution of other owners to the total system revenue when participant i is included; is the set of valuations of all owners except participant i; It represents the optimal profit that the system can achieve after removing participant i.

[0047] In step S5, the real-time data includes power demand and weather conditions.

[0048] A virtual power plant dynamic resource aggregation scheduling system, comprising:

[0049] Model module, used to build a virtual power plant system model;

[0050] A constraint module, used to determine the constraints of the virtual power plant system model;

[0051] A preliminary scheduling module is used to perform preliminary scheduling of various energy sources in the virtual power plant based on the virtual power plant system model and constraints by using a random mixed integer linear programming model to solve;

[0052] The quantification module is used to quantify the contribution of various energy sources in the preliminary scheduling by applying the VCG mechanism in cooperative game theory;

[0053] The dynamic scheduling module is used to dynamically adjust the scheduling plans of various energy sources in the virtual power plant based on quantitative results and real-time data to maximize economic benefits.

[0054] A computer-readable storage medium stores a computer program, which, when executed, implements any of the methods for dynamic resource aggregation and scheduling of virtual power plants.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. The virtual power plant model proposed in this paper uses electric vehicles and HVAC systems as dynamic response resources, effectively enhancing the system's adaptability to high demand fluctuations and electricity price changes. Through intelligent scheduling strategies, the system's flexibility and load management capabilities are enhanced. This strategy not only reduces energy costs, but also improves the grid's adaptability to the intermittency and uncertainty of renewable energy.

[0057] 2. The present invention introduces the VCG mechanism to efficiently distribute the system benefits brought by electric vehicles and other distributed resources. This resource aggregation and optimized scheduling strategy not only improves the operational efficiency of the power grid, but also optimizes energy consumption, reduces energy losses caused by transmission distance, thereby reducing overall operating costs and significantly improving the economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of the method of the present invention;

[0059] Figure 2 is a single line diagram of a test system of the present invention;

[0060] Figure 3 It is the energy operation of Case 4 of the present invention;

[0061] Figure 4 is the electric vehicle aggregate V2G power curve of the present invention;

[0062] Figure 5 It is the electric vehicle aggregate charging demand curve of the present invention. DETAILED DESCRIPTION

[0063] The technical solution of the present invention will be more clearly and completely explained below through description of preferred embodiments of the present invention in combination with the accompanying drawings.

[0064] like Figure 1 As shown, the present invention includes:

[0065] Step S1: Establish a virtual power plant system model, and describe in detail the location, capacity, and operating parameters of various distributed energy types involved, including wind, solar, electric vehicles, and commercial heating-ventilation-air conditioning (HVAC) systems; this provides the necessary basic data and model framework for subsequent scheduling and optimization, and is a prerequisite for effective scheduling.

[0066] Step S2: Determine the power system demands and technical constraints, including factors such as power losses, voltage support, and line congestion; the model in step S1 needs to operate under these determined demands and constraints.

[0067] Step S3: Use a random mixed integer linear programming model to preliminarily dispatch various energy sources within the virtual power plant to maximize cost-effectiveness; consider the model in step S1 and the technical constraints in step S2 to generate a scheduling plan.

[0068] Step S4: Apply the VCG (Vickrey-Clarke-Grove) mechanism in cooperative game theory to quantify the system contribution of distributed resources and reasonably distribute economic benefits.

[0069] Based on the dispatch results of step S3, the participants are further optimized and motivated, ensuring the economic sustainability of the dispatch scheme and the enthusiasm of the participants, which is also the key to the long-term operation and continuous optimization of the system.

[0070] Step S5: Based on the quantitative results and real-time data, dynamically adjust the scheduling plans of various energy sources in the virtual power plant to maximize economic benefits.

[0071] The dynamic adjustment scheme in the present invention is:

[0072] First, the VCG mechanism is used to quantify the marginal contribution of each distributed resource (such as wind power, solar energy, electric vehicles, HVAC systems, etc.) to the stability and economy of the system. Based on the marginal contribution of the resource, the transfer payment that each resource should receive, that is, the incentive payment, is calculated to encourage the resource owner to maximize its positive impact on the system. Then real-time data, including power demand, resource availability, weather conditions, etc., are collected to affect the operation of the system and the availability of resources. Based on real-time data, the output or input of the resource is adjusted, such as adjusting the charging and discharging schedule, power generation, etc.

[0073] Based on the quantitative results and real-time data, the preliminary dispatch plan of various energy sources within the virtual power plant is adjusted, including increasing reliance on certain resources, reducing the use of other resources, and adjusting the schedule of power generation and load to optimize system performance and cost efficiency.

[0074] Re-optimize the dispatch plan to ensure that while meeting power demand, operating costs are minimized and economic returns are maximized. Consider factors such as market electricity prices, resource costs, and maintenance costs, and reconfigure resources to achieve the best economic benefits.

[0075] The present invention introduces a dynamic resource aggregation scheduling method, which not only includes the static scheduling of virtual power plants, but also adds the VCG mechanism in cooperative game theory to reasonably distribute economic benefits, and uses a random mixed integer linear programming model to preliminarily schedule various energy sources in the power plant. This method emphasizes dynamic adjustment and optimization, including real-time quantification of the contribution of distributed resources, and can also dynamically adjust power distribution and pricing strategies based on real-time data.

[0076] The virtual power plant in the present invention dynamically adjusts the output of renewable resources such as solar and wind energy through the power generation of these resources. Use real-time data to adjust the charging and discharging plans of electric vehicles (EVs). The virtual power plant management system can guide electric vehicles to charge during off-peak hours and discharge during peak hours through vehicle-to-grid (V2G) technology to support the needs of the grid based on the demand and electricity prices of the grid. Through real-time data analysis, the virtual power plant can dynamically adjust the electricity consumption of industrial and residential users. For example, when the power supply is tight, the energy consumption of large industrial users can be reduced through demand-side management, or the operation of the HVAC system of commercial buildings can be dynamically adjusted to reduce the overall power demand.

[0077] According to the peak and trough periods of electricity demand, electricity prices are adjusted dynamically. This not only encourages users to use more electricity when electricity prices are low (such as charging electric vehicles at night), but also suppresses electricity consumption through high electricity prices during peak electricity demand periods, thereby optimizing the load balance of the power grid. Virtual power plants can operate according to real-time market electricity prices. When electricity prices suddenly rise, they can quickly dispatch dispatchable resources such as energy storage systems to discharge to capture high market prices and increase operating revenue. At the same time, when market electricity prices are low, increase the charging of energy storage equipment to reduce operating costs. While implementing a dynamic electricity price strategy, virtual power plant operators can also encourage users to participate in demand response programs by providing incentives (such as electricity bill discounts, discounts, etc.). This not only improves user participation, but also enhances the regulation capacity of the power grid and the economic benefits of the system.

[0078] The present invention is specifically as follows:

[0079] The present invention maximizes the profit of the virtual power plant (VPP), which consists of two parts: the income P from selling electricity to commercial users and the PSC and the operating cost of VPP C VPP , taking into account technical and economic constraints:

[0080] Max:∑(P PSC -C VPP ) (1)

[0081]

[0082] Where: Ω sRepresents the scene s index set; Ω h Represents the time h index set; Ω k represents the load k index set; Ω HVAC Represents the HVAC index set; Ω ev Represents the electric vehicle ev index collection; represents the time-of-use electricity price; ρ s represents the probability of scene s occurring; P k,s,h Indicates daily load power; Indicates the charging power of electric vehicles; Indicates HVAC power.

[0083] The operating cost C VPP The decomposition is shown in formula (3), including the generation fee and maintenance fee paid to the distributed generation owned by the user:

[0084]

[0085] Where: Ω g Represents the index set of the generator group g; Indicates the market Index collection; O OC,g represents the unit energy production cost; Indicates the sale of power from the grid; represents the distributed generation power; Indicates the discharge power of electric vehicles; P Penalty For penalty items; represents the day-ahead market electricity price; represents the charging cost of electric vehicles; Ω ω Represents the operating mode ω index set.

[0086] The penalty item is the charging of electric vehicles (P Penalty,ev ) or to start the HVAC system (P Penalty,HVAC ) If the VPP is not activated, no such fee will be incurred.

[0087] in,

[0088] P Penalty =P Penalty,ev +P Penalty,HVAC (4)

[0089]

[0090] Where: Indicates the charging power of electric vehicles; Represents the charge and discharge cycle cost of electric vehicles; Indicates the discharge power of electric vehicles.

[0091] The reason for the penalty is that the VPP should compensate the customer for the remaining cost. This simulates the difference between the customer's optimal cost and the HVAC cycle cost, and can also be understood as an incentive to participate in VPP energy scheduling:

[0092]

[0093] Where: represents the active power of the HVAC system. The optimal cost of HVAC is P Penalty,HVAC .

[0094] The present invention provides constraints on the objective function:

[0095] (1) Maximum charging and discharging rate constraints for electric vehicles:

[0096]

[0097] Where: represents the binary variable of charge and discharge; Indicates the charge and discharge limit power.

[0098] (2) State of charge (SOC) constraints of electric vehicles:

[0099]

[0100] Where: Indicates the energy storage limit; μ ev represents the scaling factor. Inequality (11) ensures that the SOC is always within the allowed range. Equation (12) sets the initial SOC and requires the EV to return to the initial SOC at the end of the operation period.

[0101] (3) Active and reactive power flow constraints:

[0102]

[0103] Where: Ω l represents the line l index set; P l,s,h and Q l,s,h represent the active power flow and reactive power flow in the line respectively, and and Represent the active and reactive power demands at the node respectively; and They represent the active power loss and reactive power loss in the line respectively.

[0104] The present invention adopts the big M method to linearize the nonlinear constraints in the virtual power plant system model, and obtains the active and reactive power flow constraints as follows:

[0105]

[0106] Where: g l ,b l Represents the conductance and reactance of each branch l; M P,l ,M Q,l Indicates the large M parameter related to the active and reactive power flow of each branch; ΔV n,s,h Indicates the voltage difference amplitude at node n; ΔV m,s,h Represents the voltage difference amplitude on node m; V nom Indicates rated voltage; θ l,s,h Refers to the angle difference θ n,s,h -θ m,s,h , where n and m are nodes corresponding to the same line.

[0107] The big M method is introduced to handle the nonlinear constraints in the model and linearize them to adapt to the linear programming solution method. Formulas (17) and (18) are the constraints of active and reactive power flows after the linearized big M method.

[0108] The big M method was introduced for linearization to obtain the random mixed integer linear programming model. The solution process was to build the model in matlab and use CPLEX 12.0 solver to solve it.

[0109] The maximum power flow of the lines and the active power loss and reactive power loss of each line are:

[0110]

[0111] Where: represents the flow limit of each branch l; R l ,X l Indicates resistance and reactance; l,h Represents a binary variable of line status.

[0112] The present invention applies the VCG mechanism in cooperative game theory to quantify the system contribution of distributed resources, and obtains a set of real valuations submitted by participants (the set of participant valuations is ), which ensures that the distributed energy owner i obtains benefits that match the real marginal contribution of distributed energy to the system. Formally, the transfer payment t received by participant i i The description is as follows:

[0113]

[0114] Where: represents the transfer payment to participant i; is the set of valuations of all participants, where is the value estimated by participant i for the resources or services it provides; Among all participants The optimal benefit that the system can achieve when resources or services are provided; represents the contribution of other owners to the total system revenue when participant i is included; is the set of valuations of all owners except participant i; It represents the optimal profit that the system can achieve after removing participant i.

[0115] The specific process of quantification in the present invention is:

[0116] First, the total revenue of the virtual power plant with the participation of all distributed energy owners is calculated. It represents the best performance that the system can achieve when all distributed energy owners provide their resources (such as power output, regulation services, etc.). This function depends on the valuation submitted by the owner. Each of these This may include costs, expected benefits or other relevant parameters.

[0117] Then, to calculate the marginal contribution of each energy owner to the system, we need to evaluate how the system performs if a particular energy owner i is removed. This is done by solving a new optimization problem, in, represents the set of valuations of all owners except participant i. This is to understand the best performance that the system can achieve without participant i.

[0118] The core of the VCG mechanism is to ensure that the compensation received by each participant matches its marginal contribution to the total system revenue. It is calculated based on the difference in its impact on the total revenue of the system. By calculating in this way, each participant's transfer payment ensures that they are compensated according to the true value provided to the system. This strategy not only encourages honest bidding, but also optimizes the allocation and use of resources, because each participant knows that they will receive corresponding benefits based on their true marginal contribution.

[0119] A virtual power plant dynamic resource aggregation scheduling system, comprising:

[0120] Model module, used to build a virtual power plant system model;

[0121] A constraint module, used to determine the constraints of the virtual power plant system model;

[0122] A preliminary scheduling module is used to perform preliminary scheduling of various energy sources in the virtual power plant based on the virtual power plant system model and constraints by using a random mixed integer linear programming model to solve;

[0123] The quantification module is used to quantify the contribution of various energy sources in the preliminary scheduling by applying the VCG mechanism in cooperative game theory;

[0124] The dynamic scheduling module is used to dynamically adjust the scheduling plans of various energy sources in the virtual power plant based on quantitative results and real-time data to maximize economic benefits.

[0125] A computer-readable storage medium stores a computer program, which, when executed, implements any of the methods for dynamic resource aggregation and scheduling of virtual power plants.

[0126] The present invention uses Figure 2 The test system shown is used for case analysis. The rated voltage of the system is 10 kV, the demand is 22.7 MW and 17.04 MVar. Two types of distributed energy are considered: wind and solar, with an installed capacity of 1 MW. The initial value of SOC is 45% or 62%. When generating the scenario, the correlation between solar irradiation, wind speed and demand is taken into account to ensure that the generated scenario is closely related to the real conditions. The correlation coefficient between solar irradiance and wind speed is -0.3. The correlation coefficient between wind speed and demand is 0.28, and the correlation coefficient between solar irradiance and demand is 0.5.

[0127] In addition to the above, the following assumptions and system data are also considered: the time span is hours, the voltage deviation of each node is ±5%. The reference node is the substation, the voltage amplitude is set to 1p.u., and the angle is 0; the power factor of the distributed generator set is 0.95, and the power factor of the substation is 0.8; the charging and discharging efficiency of electric vehicles is 90%; the operating cost of electric vehicles during charging and discharging is 5$ / MWh; the discharge of electric vehicles cannot be less than 40%; the operating cost of solar units is 18.24$ / MWh; the operating cost of wind turbines is 13.2$ / MWh.

[0128] The present invention sets four cases for analysis. Case 1 uses an external power grid to meet system requirements and is a benchmark case for comparing various impacts of VPP.

[0129] Case 2 incorporates renewable energy (photovoltaic and wind) based on Case 1 to meet part of the demand outside the external grid. In addition, the impact of demand response is analyzed through time-of-use electricity prices.

[0130] Case 3 adds electric vehicles and commercial HVAC systems to Case 1, but does not include renewable energy, and studies the impact of electric vehicles and commercial HVAC systems on system flexibility. The possibility of electric vehicles as mobile energy storage systems to assist the grid operation when renewable energy generation suddenly decreases is studied. Electricity purchased from the market, electricity aggregation from electric vehicles, the use of HVAC in commercial buildings, and demand response flexibility in the absence of renewable energy generation are considered.

[0131] Case 4 is an extension of Case 3, incorporating renewable energy generation, electric vehicles, and HVAC systems to analyze the combined impact of these resources on the proposed approach. Electricity purchased from the market, distributed energy resources, and electricity aggregation from various sources, such as demand response flexibility provided by electric vehicle V2G interface technology, commercial building HVAC use, and time-of-use electricity prices, are considered. Changes in the temperature range of commercial buildings are also evaluated.

[0132] The model was built using MATLAB and solved using CPLEX 12.0. The simulation was performed on a platform equipped with two 3.1GHz E5-2687W processors and 256GB of memory.

[0133] Table 1 shows the revenue from selling electricity to users, the operating costs of the VPP, and the final profit of the VPP in all cases.

[0134] Table 1 Comparison of revenue costs in different cases

[0135] Case income cost profit 1 44091.62 28849.79 15241.83 2 44517.26 14992.79 29524.47 3 44143.67 16989.28 27154.38 4 44783.89 12757.65 32026.23

[0136] Table 1 shows that when distributed energy resources are added to the system (Case 2), the operating cost is reduced by 48% compared to Case 1. When electric vehicles and HVAC equipment are added to the distributed energy resources (Case 4), the cost is reduced by 56% relative to the base case. In terms of profit, Case 2 increases its profit by 94% compared to Case 1. When all distributed energy resources are included (Case 4), the profit increases to 32026.23. The energy mix of Case 4 is as follows Figure 3 shown. Figure 3 The figure shows the share of distributed energy (wind, solar and V2G), as well as the charging requirements for electric vehicles. Figure 3 As can be seen in the graph, the amount of electric vehicle charging is larger in the early morning hours, and the amount of charging increases slightly when the solar photovoltaic power generation device is generating electricity. The amount of electric vehicle charging decreases during the evening peak hours. Importantly, the implementation of time-of-use electricity prices ensures that the load is reduced during peak hours.

[0137] The present invention takes into account the technical constraints of the grid, including the capacity of each line in the system. Table 2 shows the cases where the line load was exceeded in Case 1, and the cases where the line load was reduced or eliminated in the other cases. In Case 1, there were 19 cases where the load exceeded the rated line capacity, 3 cases in Case 2, 15 cases in Case 3, and no cases in Case 4 where the rated line capacity was exceeded. In Case 3, only the electric vehicles and HVAC equipment were in operation. Due to the capacity of the electric vehicle battery, its ability to provide power compensation is limited, which is why the number of times the line capacity was exceeded was relatively high.

[0138] Table 2 Line congestion

[0139] line Case 1 (MVA) Case 2 (MVA) Case 3 (MVA) Case 4 (MVA) 1 35.03 0.00 9.42 0.00 30 27.66 0.00 0.00 0.00 41 30.56 0.00 0.00 0.00 57 10.98 0.00 10.98 0.00 62 42.12 0.45 29.79 0.00 63 62.65 0.00 44.14 0.00 66 53.16 0.00 53.16 0.00 67 82.35 22.35 82.35 0.00 68 41.09 0.00 41.09 0.00 69 41.09 0.00 41.09 0.00 77 9.44 0.00 0.00 0.00 88 44.86 0.00 0.00 0.00 99 48.94 0.00 8.47 0.00 101 17.02 0.00 17.02 0.00 102 57.45 0.00 57.45 0.00 105 75.64 0.00 75.64 0.00 106 0.63 0.63 0.63 0.00 107 0.63 0.00 0.63 0.00 109 44.24 0.00 25.71 0.00

[0140] The present invention incorporates electric vehicles and HVAC equipment in commercial and service buildings in Case 3 and Case 4. Electric vehicles have limited power compensation capabilities and therefore have less impact on voltage deviations. They can charge and discharge according to system demand and time-of-use electricity prices. Figure 4 The active power discharge of the EV aggregate is shown. The impact of time-of-use electricity prices is obvious, with EVs providing a large amount of V2G power during the evening peak period (19:00 to 21:00). Figure 5 The total charging demand of the EV aggregates is shown. As can be seen from the figure, there is little demand for charging during the peak time of time-of-use electricity prices from 10:00 to 20:00. Only EV aggregate 1 has a large demand for charging, because this aggregate is located in a hospital and has the highest demand for EV charging throughout the day.

[0141] This paper establishes a VPP model that takes into account the technical limitations of the power grid and evaluates the relevant impact of technical constraints. The method optimizes the dispatch of distributed energy resources to maximize profits. Through optimization, system flexibility is improved, VPP participation is increased, renewable energy generation is increased, and line losses are reduced by nearly 70%. The results show that the proposed method reduces operating costs and increases system energy sales revenue, with costs reduced by up to 56% compared to the base case.

[0142] The technical means disclosed in the scheme of the present invention are not limited to the technical means disclosed in the above-mentioned implementation mode, but also include technical schemes composed of any combination of the above-mentioned technical features. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also regarded as the protection scope of the present invention.

Claims

1. A virtual power plant dynamic resource aggregation scheduling method, characterized by: The following steps are involved: Step S1: Establish a virtual power plant system model; Step S2: determining constraints of the virtual power plant system model; Step S3: Based on the virtual power plant system model and constraints, a random mixed integer linear programming model is used to solve and preliminarily dispatch various energy sources in the virtual power plant; Step S4: Apply the VCG mechanism in cooperative game theory to quantify the contribution of various energy sources in the preliminary scheduling; Step S5: Based on the quantitative results and real-time data, dynamically adjust the scheduling plans of various energy sources in the virtual power plant to maximize economic benefits.

2. The method for dynamic resource aggregation and scheduling of a virtual power plant according to claim 1, characterized in that: The virtual power plant model includes the location, capacity and operating parameters of wind, solar, electric vehicles and commercial HVAC systems.

3. The method for dynamic resource aggregation and scheduling of a virtual power plant according to claim 1, characterized in that: The objective function of the virtual power plant model is: Max:∑(P PSC -C VPP ) (1) Where: P PSC For the income from selling electricity to commercial users, C VPP It is the operating cost of VPP.

4. The method for dynamic resource aggregation and scheduling of a virtual power plant according to claim 3, characterized in that: in, Where: Ω s Represents the scene s index set; Ω h Represents the time h index set; Ω k represents the load k index set; Ω HVAC Represents the HVAC index set; Ω ev Represents the electric vehicle ev index collection; represents the time-of-use electricity price; ρ s represents the probability of scene s occurring; P k,s,h Indicates daily load power; Indicates the charging power of electric vehicles; Indicates HVAC power.

5. The method for dynamic resource aggregation and scheduling of a virtual power plant according to claim 3, characterized in that: Operating it, Where: Ω g Represents the index set of the generator group g; Indicates the market Index collection; O OC,g represents the unit energy production cost; Indicates the sale of power from the grid; represents the distributed generation power; Indicates the discharge power of electric vehicles; P Penalty For penalty items; represents the day-ahead market electricity price; represents the charging cost of electric vehicles; Ω ω Represents the operating mode ω index set.

6. The method for dynamic resource aggregation and scheduling of a virtual power plant according to claim 5, characterized in that: Operating it, The penalties are for charging an electric vehicle or for starting an HVAC system, as follows: P Penalty =P Penalty,ev +P Penalty,HVAC (4) Where: P Penalty,ev represents the cost of charging an electric vehicle, Indicates the charging power of electric vehicles; Represents the charge and discharge cycle cost of electric vehicles; Indicates the discharge power of electric vehicles; Where: Represents the active power of the HVAC system, P Penalty,HVAC Best cost for HVAC systems.

7. The method for dynamic resource aggregation and scheduling of a virtual power plant according to claim 1, characterized in that: Operating it, The constraints of the virtual power plant system model include maximum charging rate and discharging rate constraints of electric vehicles, state of charge constraints of electric vehicles, and active and reactive power flow constraints.

8. The method for dynamic resource aggregation and scheduling of a virtual power plant according to claim 7, characterized in that: The maximum charging rate and discharging rate constraints of the electric vehicle include: Where: represents the binary variable of charge and discharge; Indicates the charge and discharge limit power; The state of charge constraints of the electric vehicle include: Where: Indicates the energy storage limit; μ ev represents the scaling factor; The active and reactive power flow constraints include: Where: Ω l represents the line l index set; P l,s,h and Q l,s,h represent the active power flow and reactive power flow in the line respectively, and and Represent the active and reactive power demands at the node respectively; and They represent the active power loss and reactive power loss in the line respectively.

9. The method for dynamic resource aggregation and scheduling of a virtual power plant according to claim 1, characterized in that: In step S3: the large M method is used to linearize the nonlinear constraints in the virtual power plant system model, and the active and reactive power flow constraints are obtained as follows: Where: g l ,b l Represents the conductance and reactance of each branch l; M P,l ,M Q,l Indicates the large M parameter related to the active and reactive power flow of each branch; ΔV n,s,h Indicates the voltage difference amplitude at node n; ΔV m,s,h Represents the voltage difference amplitude on node m; V nom Indicates rated voltage; θ l,s,h Represents θ n,s,h -θ m,s,h , where n and m are nodes corresponding to the same line.

10. The method for dynamic resource aggregation and scheduling of a virtual power plant according to claim 9, characterized in that: The maximum power flow of the line and the active power loss and reactive power loss of each line are: Where: represents the flow limit of each branch l; R l ,X l Indicates resistance and reactance; l,h Represents a binary variable of line status.

11. The method for dynamic resource aggregation and scheduling of a virtual power plant according to claim 1, characterized in that: In step S4, the VCG mechanism in cooperative game theory is applied to quantify the system contribution of distributed resources. Formally, the transfer payment t received by participant i i as follows: Where: represents the transfer payment to participant i; is the set of valuations of all participants, where is the value estimated by participant i for the resources or services it provides; Among all participants The optimal benefit that the system can achieve when resources or services are provided; represents the contribution of other owners to the total system revenue when participant i is included; is the set of valuations of all owners except participant i; It represents the optimal profit that the system can achieve after removing participant i.

12. The method for dynamic resource aggregation and scheduling of a virtual power plant according to claim 1, characterized in that: In step S5, the real-time data includes power demand and weather conditions.

13. A virtual power plant dynamic resource aggregation scheduling system, characterized by: include: Model module, used to build a virtual power plant system model; A constraint module, used to determine the constraints of the virtual power plant system model; A preliminary scheduling module is used to perform preliminary scheduling of various energy sources in the virtual power plant based on the virtual power plant system model and constraints by using a random mixed integer linear programming model to solve; The quantification module is used to quantify the contribution of various energy sources in the preliminary scheduling by applying the VCG mechanism in cooperative game theory; The dynamic scheduling module is used to dynamically adjust the scheduling plans of various energy sources in the virtual power plant based on quantitative results and real-time data to maximize economic benefits.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed, the virtual power plant dynamic resource aggregation scheduling method described in any one of claims 1-12 is implemented.

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