Virtual power plant and distributed resource control method and system based on bilateral matching

Abstract

The invention discloses a bilateral matching-based virtual power plant and distributed resource control method and system, and the method comprises the steps: determining an optimal combination of virtual power plants based on an aggregation income model according to the income of each distributed resource and each virtual power plant during aggregation, and carrying out the control of the distributed resources through employing the total number Um of the virtual power plants in the optimal combination of the virtual power plants, sequentially defining the preference degree of adding each distributed resource into the virtual power plant queue as # imgabs0 # in the determined virtual power plant optimal combination, and taking each distributed resource corresponding to the corrected aggregation income maximum value as a distributed resource optimal combination matched with each virtual power plant in the virtual power plant optimal combination; and based on a bilateral matching method, determining a bilateral matching combination of the virtual power plant and the distributed resources by taking the maximum corrected aggregation profit maximum value and the minimum difference between the corrected cooperation tendency indexes as an optimization target, and controlling the virtual power plant and the distributed resources based on a bivariant matching combination result. And rapid selection and stable matching aggregation of the virtual power plant and the distributed resources are realized.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a collaborative control method and system for a virtual power plant and distributed resources based on a bilateral matching method. Background Art

[0002] With the access of a large number of distributed energy resources (DER) such as renewable energy, energy storage, and controllable loads to the power grid, giving full play to their regulation potential has become the key to building a new power system. In this context, virtual power plants (VPPs) aggregate multiple types of DERs through advanced communication, regulation, and metering technologies to achieve stable and reliable overall output, which is a flexible way to manage DERs. Among them, the aggregation strategy of VPPs and existing DERs is the key to promoting the development of VPPs.

[0003] In the prior art, the VPP resource portfolio optimization model considering the uncertainty of demand-side response analyzes the impact of load response willingness on VPP return risk based on the mean-variance model; based on the enterprise comprehensive utility evaluation method of game cross-efficiency, a member selection model of VPP for thermal-wind-storage-high-load energy enterprises is constructed; DERs with similar electrical distances should be aggregated first, and VPP aggregation is achieved with the goal of minimizing voltage deviation and power fluctuation; in the construction of an electricity package design optimization model considering the limited rationality of DER, the selection method of demand-side response resources for aggregators providing different electricity packages is analyzed; considering the load information interaction efficiency and user participation willingness, a random selection model of DER for VPP is constructed; however, the prior art is based on the one-way selection of VPP to DER or DER to VPP. However, in reality, both VPP and DER are rational entities, and both parties to the aggregation have the right to choose and refuse, that is, the aggregation of VPP and DER is a bilateral matching problem.

[0004] In addition, in a market with incomplete information, some VPPs and DERs do not have the ability to search for information and negotiate (such as ordinary electricity users), resulting in spontaneous bilateral matching between VPPs and DERs that is often difficult to aggregate due to high transaction costs. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a control method and system for a virtual power plant and distributed resources based on bilateral matching. The optimal combination of virtual power plants and the optimal combination of distributed resources are first determined based on the principle of two-way selection and maximization of aggregated benefits. Then, within the range of the optimal combination of virtual power plants and the optimal combination of distributed resources, a bilateral matching combination that meets the bilateral matching requirements is determined. The virtual power plant and distributed resources are controlled based on the dual-variable matching combination result.

[0006] The present invention adopts the following technical solution.

[0007] The present invention proposes a control method for a virtual power plant and distributed resources based on bilateral matching, comprising:

[0008] Collect the operating parameters of virtual power plants and distributed resources, and establish an aggregated revenue model when any virtual power plant and all distributed resources are aggregated;

[0009] Based on the aggregation revenue model, the optimal combination of virtual power plants is determined according to the revenue when each distributed resource is aggregated with each virtual power plant. The virtual power plants in the optimal combination of virtual power plants are sorted in ascending order according to the maximum revenue when each virtual power plant in the optimal combination of distributed resources and virtual power plants is aggregated, and a virtual power plant queue is obtained. The total number of virtual power plants in the optimal combination of virtual power plants U is used m , and define the preference of each distributed resource to join the virtual power plant queue as In the determined optimal combination of virtual power plants, the variables of each virtual power plant's selection of each distributed resource and the preference of each distributed resource for joining the virtual power plant queue are set to form the cooperation tendency index of each distributed resource to each virtual power plant; the correction coefficient of the cooperation tendency index is obtained by using the entropy weight method and the AHP weighting method; the maximum value of the aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource is corrected by using the cooperation tendency index and the correction coefficient of the cooperation tendency index, and the distributed resources corresponding to the corrected maximum value of the aggregated benefit are used as the optimal combination of distributed resources matching each virtual power plant in the optimal combination of virtual power plants;

[0010] For each virtual power plant in the optimal combination of virtual power plants and each distributed resource in the optimal combination of distributed resources, based on the bilateral matching method, the bilateral matching combination of virtual power plants and distributed resources is determined, and the virtual power plants and distributed resources in the bilateral matching combination are controlled, with the optimization goal of maximizing the maximum value of the corrected aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource and minimizing the difference between the cooperation tendency indicators corrected by the correction coefficient of each distributed resource to each virtual power plant.

[0011] Preferably, the operating parameters of the virtual power plant and the operating parameters of the distributed resources are collected, and an aggregated benefit model when any virtual power plant and all distributed resources are aggregated is established, including:

[0012] Collect the operating parameters of the virtual power plant and build a market revenue model for the virtual power plant. The market revenue model includes an objective function and constraints.

[0013] The operating parameters of distributed resources are collected, and based on the new virtual power plant obtained by aggregating any virtual power plant and all distributed resources, an aggregation profit model of the new virtual power plant is constructed.

[0014] Preferably, the market revenue model of the virtual power plant satisfies the following relationship:

[0015] max(R M -C IL -C PU )(1)

[0016]

[0017] In the formula, max(R M -C IL -C PU ) is the objective function, R M , C IL , C PU are the electricity spot market revenue, interruptible load adjustment cost and deviation power assessment fee respectively; T is the total number of daily dispatch periods, Δt is the length of the dispatch period, Φ s is the set of uncertain scenarios of wind, solar, hydro output and market electricity prices, π s is the probability of scene s, is the market electricity price at time period t under scenario s, P t M is the output of the virtual power plant in the day-ahead market during period t; is the penalty electricity price in time period t under scenario s; IL k is the interruptible load adjustment cost; pos and k neg They are respectively the positive and negative deviation electricity assessment cost coefficients; They are the interruptible load reduction output, over-generation and under-generation of the virtual power plant in period t under scenario s, respectively.

[0018] Preferably, the constraints of the market revenue model of the virtual power plant include:

[0019] 5) Power balance constraints:

[0020]

[0021] In the formula, and are the outputs of virtual power plant, wind power, photovoltaic power and hydropower in time period t under scenario s respectively; and are the charging and discharging power of the energy storage in time period t under scenario s respectively; is the load demand when the interruptible load is not called in time period t under scenario s;

[0022] 6) Hydropower balance constraints:

[0023]

[0024] V s,t+1 =V s,t +ΔV s,t (9)

[0025]

[0026] V min ≤V s,t ≤V max (11)

[0027] Where P HV,min and P HV,max are the minimum and maximum output of hydropower respectively; and are the water intake, water consumption and water abandonment of the reservoir-type hydropower station in time period t under scenario s; V s,t+1 and V s,t are the reservoir water storage in time period t+1 and time period t under scenario s; ΔV s,t are the changes in reservoir water volume in time period t under scenario s; V min and V max are the minimum and maximum water storage capacity of the reservoir respectively; η HV for hydropower generation efficiency;

[0028] 7) Energy storage constraints:

[0029]

[0030] Where P ESS and E ESS They are the maximum charging and discharging power and the maximum energy storage capacity of the energy storage respectively; and are the energy storage charge states at time period t and time period t-1 under scenario s respectively; S ESS,min and S ESS,max are the minimum and maximum charge states of energy storage, respectively; η ch and η dis are the charging and discharging efficiency of energy storage respectively;

[0031] 8) Interruptible load constraints:

[0032]

[0033] Where P IL,max is the maximum interruptible load capacity.

[0034] Preferably, {n, K} is defined to represent a new virtual power plant formed by aggregating the nth virtual power plant and the distributed resource set K, where K is the distributed resource set participating in the aggregation. Then, the aggregation revenue model of the new virtual power plant satisfies the following relationship:

[0035]

[0036] In the formula, is the aggregated revenue of the new virtual power plant formed by aggregating the nth virtual power plant and the distributed resource set K; are the market revenues of the nth virtual power plant before and after aggregation, respectively; is the revenue when the mth distributed resource is not added to the virtual power plant; The aggregation cost includes the installation cost of the communication, control, and metering equipment involved in the process of aggregating various resources. The installation cost of the communication, control, and metering equipment installed for the same type of distributed resources is the same; c w 、c PV 、c ESS 、c IL are the aggregate cost coefficients of wind power, photovoltaic power, energy storage and interruptible load respectively; are the number of wind power, photovoltaic, energy storage and interruptible load resources in the distributed resource set K respectively.

[0037] Preferably, based on the aggregated revenue model, the optimal combination of virtual power plants is determined according to the revenue when each distributed resource and each virtual power plant are aggregated, including:

[0038] Based on the aggregated benefit model, the aggregated benefit after the mth distributed resource and the nth virtual power plant are aggregated is determined; when the aggregated benefit is not less than the benefit threshold, the mth distributed resource is determined to join the nth virtual power plant; all virtual power plants joined by the mth distributed resource constitute the optimal combination of virtual power plants;

[0039] The profit threshold is 70% of the average value of the aggregated profit after the mth distributed resource is aggregated with different virtual power plants.

[0040] Preferably, define the mth distributed resource to join the NU outside the virtual power plant queue m The preference degree of each virtual power plant is 0, and N is the total number of virtual power plants;

[0041] Based on the degree of preference, the aggregated benefit generated by the aggregation of the mth distributed resource and the nth virtual power plant satisfies the following relationship:

[0042]

[0043] In the formula, is the aggregated revenue generated by aggregating the mth distributed resource and the nth virtual power plant; is the aggregate cost of the mth distributed resource; are the market revenues of the nth virtual power plant before and after aggregation with the mth distributed resource, is the revenue when the mth distributed resource is not added to the virtual power plant, h n,m The preference for the mth distributed resource to join the nth virtual power plant.

[0044] Preferably, the cooperation tendency index satisfies the following relationship:

[0045]

[0046] In the formula, is the distributed resource set K in M n The cooperation tendency index between the distributed resources and the nth virtual power plant; x n,m The variables for selecting distributed resources for each virtual power plant in the optimal combination of virtual power plants, x n,m =1 means that the nth virtual power plant selects the mth distributed resource, x n,m =0 means that the nth virtual power plant does not select the mth distributed resource; h n,m The preference for joining the nth virtual power plant for the mth distributed resource;

[0047] The modified entropy weight-AHP weighting method is used to obtain the correction coefficient of the cooperation tendency index, which satisfies the following relationship:

[0048]

[0049] In the formula, N1 is the total number of indicators; are the objective weight of the j-th cooperation tendency index calculated by the entropy weight method and the subjective weight of the j-th cooperation tendency index calculated by the AHP weighting method; w Ent,QX and w AHP,QX They are the calculation coefficients of the entropy weight method and the AHPAHP weighting method respectively;

[0050] The maximum value of the aggregated benefit generated by the aggregation of the nth virtual power plant and each distributed resource is corrected by using the cooperation tendency index and the correction coefficient of the cooperation tendency index to satisfy the following relationship:

[0051]

[0052] In the formula, is the maximum aggregated benefit generated by the aggregation of the nth virtual power plant and the distributed resources in the distributed resource set K, It is the aggregated revenue of the new virtual power plant formed by aggregating the nth virtual power plant and the distributed resource set K.

[0053] Preferably, within the range of the optimal combination of virtual power plants and the optimal combination of distributed resources, the maximum value of the corrected aggregated benefits generated by the aggregation of each virtual power plant and each distributed resource is taken as the satisfaction of the bilateral subjects. One goal of bilateral matching is to maximize the satisfaction of the bilateral subjects. The difference between the cooperation tendency indicators corrected by the correction coefficients of each distributed resource on each virtual power plant is taken as the difference between the bilateral subjects. Another goal of bilateral matching is to minimize the difference between the bilateral subjects. The combination of virtual power plants and distributed resources that achieves both goals is a bilateral matching combination.

[0054] The present invention also proposes a control system for a virtual power plant and distributed resources based on bilateral matching, comprising:

[0055] A model building module is used to collect the operating parameters of the virtual power plant and the operating parameters of the distributed resources, and to establish an aggregated benefit model when any virtual power plant and all distributed resources are aggregated;

[0056] The optimal combination determination module is used to determine the optimal combination of virtual power plants based on the aggregated benefit model according to the benefits of each distributed resource and each virtual power plant when aggregated, and to sort the virtual power plants in the optimal combination of virtual power plants in the order of the maximum benefits of each virtual power plant when aggregated in the optimal combination of each distributed resource and virtual power plant from small to large to obtain a virtual power plant queue; the total number of virtual power plants in the optimal combination of virtual power plants U m , and define the preference of each distributed resource to join the virtual power plant queue as In the determined optimal combination of virtual power plants, the variables of each virtual power plant's selection of each distributed resource and the preference of each distributed resource for joining the virtual power plant queue are set to form the cooperation tendency index of each distributed resource to each virtual power plant; the correction coefficient of the cooperation tendency index is obtained by using the entropy weight method and the AHP weighting method; the maximum value of the aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource is corrected by using the cooperation tendency index and the correction coefficient of the cooperation tendency index, and the distributed resources corresponding to the corrected maximum value of the aggregated benefit are used as the optimal combination of distributed resources matching each virtual power plant in the optimal combination of virtual power plants;

[0057] The bilateral matching combination module is used to determine the bilateral matching combination of virtual power plants and distributed resources based on the bilateral matching method, and control the virtual power plants and distributed resources in the bilateral matching combination, with the maximum value of the corrected aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource and the difference between the cooperation tendency indicators corrected by the correction coefficient of each distributed resource to each virtual power plant as the optimization goal.

[0058] The beneficial effect of the present invention is that, compared with the prior art, at least the method proposed in the present invention realizes the optimal bilateral matching of VPP and DER under the condition of maximizing the aggregation benefit and minimizing the difference between the cooperation tendency indicators, so as to realize the aggregation of rapid selection and stable matching of virtual power plants and distributed resources, and first determines the optimal combination of virtual power plants and the optimal combination of distributed resources before performing bilateral matching, so as to realize that the matching parties first select satisfactory cooperation objects but do not match immediately, thereby allowing multiple satisfactory cooperation objects to be selected and compared in the actual operation process and finally perform the optimal matching, which is conducive to improving the operation efficiency and stability after aggregation; and the difference between the cooperation tendency indicators is minimized, which avoids the problem that each market player simply pursues the maximization of its own benefits and thus makes it difficult to form a stable match, and also avoids the problem that some resources cannot be matched because they do not have the ability to search for information and negotiate, and also avoids the formation of a matching combination with low system efficiency and high transaction costs due to asymmetric and incomplete information. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of a control method of a virtual power plant and distributed resources based on bilateral matching proposed by the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0061] The present invention proposes a control method for virtual power plants and distributed resources based on bilateral matching, such as Figure 1 As shown, including:

[0062] Step 1: Collect the operating parameters of the virtual power plant and the operating parameters of the distributed resources, and establish an aggregated profit model when any virtual power plant and all distributed resources are aggregated.

[0063] Specifically, a typical library of regulation time response characteristics and spatiotemporal complementary characteristics of distributed resources (DER) is established, and the regulation characteristics of distributed resources in different seasons, at different times, under different demand responses and market mechanisms are considered. A virtual power plant (VPP) market revenue model is constructed according to the different regulation characteristics of wind-solar-hydro-storage-load in the electricity spot market environment. Based on the virtual power plant market revenue model, an aggregated revenue model of VPP and DER is constructed.

[0064] Specifically, step 1 includes:

[0065] Step 1.1, collect the operating parameters of the virtual power plant and build a market revenue model for the virtual power plant. The market revenue model includes an objective function and constraints.

[0066] A VPP market revenue model integrating wind, solar, water, storage and load is constructed for the resources in a certain region of China. Since the resources are located in the same area, in order to simplify the model, the present invention integrates similar resources and only considers the difference in resource capacity. This simplification does not affect the universality of the model. VPP organically combines internal resources and acts as an agent to participate in the electricity market. According to the trading rules of the electricity spot market, VPP declares output curves in the day-ahead market and participates in the day-ahead market as a price acceptor. In real-time operation, due to the strong uncertainty of wind power and photovoltaic output, the deviation between the actual output of VPP and the cleared electricity will face assessment. Therefore, when VPP declares output curves in the day-ahead market, in addition to considering the uncertainty of market electricity prices, it is also necessary to fully consider the volatility of various renewable energy power generation outputs.

[0067] Assume that the uncertainty scenario set of wind, solar, hydropower output and market electricity price is Φ s In the VPP market revenue model, the objective function is to maximize the operating revenue in the electricity spot market, satisfying the following relationship:

[0068] max(R M -C IL -C PU )(1)

[0069]

[0070] In the formula, max(R M -C IL -C PU ) is the objective function, R M , C IL , C PU are the electricity spot market revenue, interruptible load adjustment cost and deviation power assessment fee respectively; T is the total number of daily dispatch periods, Δt is the length of the dispatch period, Φ s is the set of uncertain scenarios of wind, solar, hydro output and market electricity prices, π s is the probability of scene s, is the market electricity price at time period t under scenario s, P t M is the output of VPP in the day-ahead market during period t; is the penalty electricity price in time period t under scenario s; IL k is the interruptible load adjustment cost; pos and k neg They are respectively the positive and negative deviation electricity assessment cost coefficients; They are the interruptible load reduction output, VPP over-generation and under-generation in period t under scenario s, respectively.

[0071] The constraints of the VPP market revenue model include:

[0072] 9) Power balance constraints:

[0073]

[0074] In the formula, and are the outputs of VPP, wind power, photovoltaic power, and hydropower in time period t under scenario s, respectively; and are the charging and discharging power of the energy storage in time period t under scenario s respectively; is the load demand when the interruptible load is not called in period t under scenario s.

[0075] 10) Hydropower balance constraints:

[0076]

[0077] V s,t+1 =V s,t +ΔV s,t (9)

[0078]

[0079] V min ≤V s,t ≤V max (11)

[0080] Where P HV,min and P HV,max are the minimum and maximum output of hydropower respectively; and are the water intake, water consumption and water abandonment of the reservoir-type hydropower station in time period t under scenario s; V s,t+1 and V s,t are the reservoir water storage in time period t+1 and time period t under scenario s; ΔV s,t are the changes in reservoir water volume in time period t under scenario s; V min and V max are the minimum and maximum water storage capacity of the reservoir respectively; η HV For hydropower generation efficiency.

[0081] 11) Energy storage constraints:

[0082]

[0083] Where P ESS and E ESSThey are the maximum charging and discharging power and the maximum energy storage capacity of the energy storage respectively; and are the energy storage charge states at time period t and time period t-1 under scenario s respectively; S ESS,min and S ESS,max are the minimum and maximum charge states of energy storage, respectively; η ch and η dis are the charging and discharging efficiency of energy storage respectively.

[0084] 12) Interruptible load constraints:

[0085]

[0086] Where P IL,max is the maximum interruptible load capacity.

[0087] Step 1.2, collect the operating parameters of distributed resources, and build an aggregated revenue model of a new virtual power plant based on the new virtual power plant obtained by aggregating any virtual power plant and all distributed resources.

[0088] Specifically, {n, K} is defined to represent the new VPP formed by aggregating the nth VPP and the DER set K, where K is the DER set participating in the aggregation. The aggregation revenue model of the new virtual power plant satisfies the following relationship:

[0089]

[0090]

[0091] In the formula, is the aggregated revenue of the new VPP formed by aggregating the nth VPP and the DER set K; are the market returns of the nth VPP before and after aggregation, respectively; is the revenue of the mth DER when it does not join the VPP; Aggregation cost, which is mainly generated by the installation cost of communication, control, and metering equipment involved in the process of aggregating various resources. The installation cost of communication, control, and metering equipment installed for the same type of DER is the same; c w 、c PV 、c ESS 、c IL are the aggregate cost coefficients of wind power, photovoltaic power, energy storage and interruptible load respectively; are the number of wind power, photovoltaic, energy storage and interruptible load resources in DER set K respectively.

[0092] Step 2: Based on the aggregated revenue model, the optimal combination of virtual power plants is determined according to the revenue when each distributed resource is aggregated with each virtual power plant. The virtual power plants in the optimal combination of virtual power plants are sorted in ascending order according to the maximum revenue when each virtual power plant in the optimal combination of distributed resources and virtual power plants is aggregated, and a virtual power plant queue is obtained. The total number of virtual power plants in the optimal combination of virtual power plants U m , and define the preference of each distributed resource to join the virtual power plant queue as Within the determined optimal combination of virtual power plants, the variables for each virtual power plant to select each distributed resource and the preference degree of each distributed resource to join the virtual power plant queue are set to form the cooperation tendency index of each distributed resource towards each virtual power plant; the entropy weight method and the AHP weighting method are used to obtain the correction coefficient of the cooperation tendency index; the cooperation tendency index and the correction coefficient of the cooperation tendency index are used to correct the maximum value of the aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource, and the distributed resources corresponding to the corrected maximum value of the aggregated benefit are used as the optimal combination of distributed resources matching each virtual power plant within the optimal combination of virtual power plants.

[0093] Specifically, considering the aggregation of VPP and DER is a process of mutual selection, the selection strategies of both parties for cooperation objects need to be fully considered in the matching process. Since the volume of DER is usually small and considering the control problem, only one VPP can be selected as a whole and joined, while VPP can choose to cooperate with multiple DERs at the same time. According to the fact that there are N independent VPPs and M independent DERs in the region, and each of them selects cooperation objects with the goal of maximizing its own utility under their own constraints, a VPP and DER cooperation object selection decision model is constructed. The present invention proposes the optimal combination scheme for matching related distributed resources based on the aggregation benefit model, as well as the judgment indicators and cooperation tendency indicators for each distributed resource and virtual power plant combination.

[0094] Specifically, step 2 includes:

[0095] Step 2.1, based on the aggregated revenue model, determine the optimal combination of virtual power plants according to the revenue when each distributed resource and each virtual power plant are aggregated, sort the virtual power plants in the optimal combination of virtual power plants in the order of the maximum revenue when each virtual power plant in the optimal combination of each distributed resource and virtual power plant is aggregated from small to large, and obtain the virtual power plant queue; use the total number of virtual power plants in the optimal combination of virtual power plants U m , and define the preference of each distributed resource to join the virtual power plant queue as

[0096] Specifically, based on the aggregated benefit model, the aggregated benefit after the mth distributed resource and the nth virtual power plant are aggregated is determined; when the aggregated benefit is not less than the benefit threshold, the mth distributed resource is determined to join the nth virtual power plant; and all virtual power plants joined by the mth distributed resource constitute the optimal combination of virtual power plants.

[0097] In the two-way selection process between DER and VPP, there is a possibility that VPP will refuse to cooperate with DER. When rejected by the best partner, DER can only settle for the second best. However, DER has a tolerance limit for reduced profits, that is, DER chooses the VPP to cooperate with according to the bottom line of profit requirements.

[0098] Specifically, the profit threshold is 70% of the average value of the aggregated profit after the mth distributed resource is aggregated with different virtual power plants.

[0099] The virtual power plants in the optimal combination of virtual power plants are sorted in ascending order according to the maximum value of the revenue when each virtual power plant in the optimal combination of distributed resources and virtual power plants is aggregated, to obtain a virtual power plant queue.

[0100] The total number of virtual power plants in the optimal combination of virtual power plants U m , and define the preference of each distributed resource to join the virtual power plant queue as The greater the preference, the greater the aggregate benefit after the mth distributed resource joins the corresponding virtual power plant; define the mth distributed resource to join the NU outside the virtual power plant queue m The preference degree of each virtual power plant is 0, and N is the total number of virtual power plants; therefore, the preference degree of the mth distributed resource joining all virtual power plants satisfies the following relationship:

[0101]

[0102] In the formula, h n,m Indicates the preference degree of the mth distributed resource to join the nth virtual power plant.

[0103] Based on the degree of preference, the aggregated benefit generated by the aggregation of the mth distributed resource and the nth virtual power plant satisfies the following relationship:

[0104]

[0105] In the formula, is the aggregated revenue generated by aggregating the mth distributed resource and the nth virtual power plant; is the aggregate cost of the mth distributed resource; are the market revenue of the nth virtual power plant before and after aggregation with the mth distributed resource, h n,mThe preference for the mth distributed resource to join the nth virtual power plant.

[0106] There is a mapping relationship between the preference degree and the aggregated benefit of the mth distributed resource joining each virtual power plant, satisfying the following relationship:

[0107]

[0108] In the formula, are the aggregated benefits generated by the aggregation of the mth distributed resource and the 1st to Nth virtual power plants; h 1,m 、h 2,m ,……,h N,m The preference levels for adding the 1st to Nth virtual power plants to the mth distributed resource respectively.

[0109] In the method proposed in the present invention, the total number of virtual power plants that distributed resources are willing to join is determined when the aggregated revenue after aggregating any distributed resource with different virtual power plants is not less than the revenue threshold, and the optimal combination of virtual power plants is obtained. Then, the preference degree of any distributed resource for each virtual power plant is ranked according to the maximum value of the aggregated revenue, and finally the virtual power plant queue matching each distributed resource is determined, so that the virtual power plant selected by the distributed resource can not only ensure a certain aggregated revenue, but also meet the requirements of bilateral matching between distributed resources and virtual power plants from the perspective of distributed resources.

[0110] Step 2.2, within the determined optimal combination of virtual power plants, set the variables for each virtual power plant to select each distributed resource, and the preference degree of each distributed resource to join the virtual power plant queue, to form the cooperation tendency index of each distributed resource towards each virtual power plant; use the entropy weight method and AHP weighting method to obtain the correction coefficient of the cooperation tendency index; use the cooperation tendency index and the correction coefficient of the cooperation tendency index to correct the maximum value of the aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource, and use the distributed resources corresponding to the corrected maximum value of the aggregated benefit as the optimal combination of distributed resources matching each virtual power plant in the optimal combination of virtual power plants.

[0111] In the process of selecting the optimal DER combination plan, VPP not only takes economic benefits as the main goal, but also needs to consider the willingness of each DER to cooperate, which is directly related to the stability of the final cooperation. For DERs with a lower degree of preference, even if cooperation is finally achieved, the stability of the cooperative relationship is poor. Therefore, a cooperation tendency indicator is defined.

[0112] Assume that there are M in the DER set K n DERs are willing to cooperate with the nth VPP, x n,m (m=1,2,…,M n) is a 0-1 variable for each virtual power plant to select each distributed resource in the optimal combination of virtual power plants. Each DER willing to cooperate with the nth VPP corresponds to an x n,m , so we have M n x n,m (m=1,2,…,M n ), x n,m =1 means that the nth VPP selects the mth DER as its partner, x n,m =0 means that the nth VPP does not select the mth DER as a cooperation partner. Cooperation tendency index It represents the average value of the preference of each DER in the DER set K for the nth VPP, satisfying the following relationship:

[0113]

[0114] In the formula, It is an indicator of cooperation tendency;

[0115] The larger the cooperation tendency index is, the more satisfied each DER in the DER set K is with the aggregated benefits after joining the nth VPP, and the more stable the cooperation relationship is.

[0116] In the process of the nth VPP selecting the optimal DER combination, usually the smaller the difference in the degree of preference of each DER for the nth VPP, the smaller the difference in the cooperation tendency indicators between different DER combination schemes, and the lower the importance the nth VPP attaches to the cooperation tendency indicators. Therefore, it is necessary to introduce subjective and objective weights to further highlight the degree of differentiation of the cooperation tendency indicators between different DER combination schemes.

[0117] The objective weight of the cooperation tendency index is calculated by the entropy weight method, and the subjective weight of the cooperation tendency index is calculated by the analytic hierarchy process;

[0118] The subjective and objective weighting method is a weighting method based on all data, that is, all indicators must be calculated before weighting can be performed. For the nth VPP, the number of DER combination solutions that can be selected is 2M n , it is difficult to directly calculate the indicators of all schemes. However, although the number of DER combination schemes is growing exponentially, for VPPs, the differences between the schemes mainly lie in the capacity and cooperation tendency indicators of various resources. For the nth VPP, it consists of M n The solution matrix X formed by the combination of 0-1 variables n It can be transformed into a weight matrix W through linear transformation n , satisfying the following relationship:

[0119]

[0120] In the formula, and They are wind power installed capacity, photovoltaic installed capacity, energy storage power, energy storage capacity and interruptible load capacity in DER set K1; and They are wind power installed capacity, photovoltaic installed capacity, energy storage power, energy storage capacity and interruptible load capacity in DER set K2 respectively; and so on. and They are DER sets K n China's wind power installed capacity, photovoltaic installed capacity, energy storage power, energy storage capacity and interruptible load capacity; They are DER sets K1, K2, ..., K n The corresponding cooperation tendency indicator.

[0121] Through the linear transformation of formula (25), the present invention uses K-means clustering to reduce the number of combination schemes, thereby reducing the computational complexity of the entropy weight-AHP method and achieving algorithm improvement. Assume that K-means clustering converts the weight matrix W n Divide into L clusters, the number of elements in each cluster is The aggregated benefit and cooperation tendency index of the distributed resource and virtual power plant aggregation scheme closest to the centroid in each cluster are calculated and used as the aggregated benefit and cooperation tendency index of all combination schemes in the cluster.

[0122] The entropy weight method and AHP weighting method are used to obtain the correction coefficient of the cooperation tendency index. Satisfies the following relationship:

[0123]

[0124] In the formula, N1 is the total number of indicators; are the objective weight of the j-th cooperation tendency index calculated by the entropy weight method and the subjective weight of the j-th cooperation tendency index calculated by the AHP weighting method; w Ent,QX and w AHP,QX They are the calculation coefficients of the entropy weight method and the AHPAHP weighting method respectively.

[0125]

[0126]

[0127] In the formula, A ji In order to reflect the importance variables of the jth cooperation tendency index and the ith cooperation tendency index through expert scoring, a relative importance judgment matrix satisfying consistency is constructed by using the 1-9 scaling method; E j 、E iare the information entropy of the j-th cooperation tendency index and the i-th cooperation tendency index respectively; is the jth cooperation tendency index of the lth combination scheme after normalization; r lj is the weight value of the cooperation tendency index.

[0128] The entropy weight method is a weighting method that can effectively measure the differences in indicator data. To this end, the present invention uses the entropy weight method to objectively weight the cooperation tendency indicators. At the same time, in order to maintain the subjective importance of aggregated benefits in the VPP aggregation benefit evaluation, the analytic hierarchy process (AHP) is often used to determine the subjective weight. In summary, the present invention uses the improved entropy weight-AHP subjective and objective weighting method to determine the correction coefficient To a certain extent, it can reflect the differences between different DER combination schemes. degree of differentiation.

[0129] While VPP attaches importance to cooperation benefits, it also considers the stability of cooperation. Therefore, the maximum value of the aggregated benefits generated by the aggregation of the nth virtual power plant and various distributed resources is corrected by using the cooperation tendency index and the correction coefficient of the cooperation tendency index to satisfy the following relationship:

[0130]

[0131] In the formula, is the maximum aggregated benefit generated by the aggregation of the nth virtual power plant and the distributed resources in the distributed resource set K, It is the aggregated revenue of the new virtual power plant formed by aggregating the nth virtual power plant and the distributed resource set K.

[0132] The distributed resources corresponding to the maximum value of the corrected aggregated profit are taken as the optimal combination of distributed resources matching each virtual power plant within the optimal combination of virtual power plants.

[0133] Step 3, for each virtual power plant in the optimal combination of virtual power plants and each distributed resource in the optimal combination of distributed resources, based on the bilateral matching method, the bilateral matching combination of virtual power plants and distributed resources is determined, with the optimization goal of maximizing the maximum value of the corrected aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource and minimizing the difference between the cooperation tendency indicators corrected by the correction coefficient of each distributed resource to each virtual power plant, and the virtual power plants and distributed resources in the bilateral matching combination are controlled.

[0134] Considering that both VPP and DER have the right to accept or refuse cooperation in the process of aggregation, it is usually difficult to form a stable matching result when all market players pursue their own benefits. In addition, some resources do not have the ability to search for information and negotiate. Information asymmetry and incompleteness may reduce system efficiency and increase transaction costs. Therefore, it is necessary to build a well-designed market mechanism to match the two parties and try to ensure the stability of cooperation and the total aggregation benefits. The aggregation of VPP and DER is a typical "one-to-many" matching problem, that is, one VPP aggregates multiple DERs. The bilateral matching algorithm is a market mechanism that can quickly find a stable matching solution to this problem. The algorithm divides the matching parties into a selector and an acceptor. It is believed that the acceptor will not immediately accept the satisfactory cooperation object but choose to keep it. The bilateral satisfaction and the minimum bilateral subject difference in the bilateral matching algorithm are expressed by the modified maximum aggregation benefit and the cooperation tendency index. The matching model between massive distributed flexible resources and virtual power plants can be constructed and solved.

[0135] In the embodiment, within the optimal combination of virtual power plants and distributed resources, the maximum value of the aggregated benefits after correction generated by the aggregation of each virtual power plant and each distributed resource is taken as the satisfaction of the bilateral subject, and one goal of bilateral matching is to maximize the satisfaction of the bilateral subject; the difference between the cooperation tendency indicators after correction of the correction coefficients of each distributed resource to each virtual power plant is taken as the difference of the bilateral subject, and another goal of bilateral matching is to minimize the difference of the bilateral subject. The combination of virtual power plants and distributed resources that achieves both goals is a bilateral matching combination.

[0136] The present invention also proposes a control system for a virtual power plant and distributed resources based on bilateral matching, comprising:

[0137] A model building module is used to collect the operating parameters of the virtual power plant and the operating parameters of the distributed resources, and to establish an aggregated benefit model when any virtual power plant and all distributed resources are aggregated;

[0138] The optimal combination determination module is used to determine the optimal combination of virtual power plants based on the aggregated benefit model according to the benefits of each distributed resource and each virtual power plant when aggregated, and to sort the virtual power plants in the optimal combination of virtual power plants in the order of the maximum benefits of each virtual power plant when aggregated in the optimal combination of each distributed resource and virtual power plant from small to large to obtain a virtual power plant queue; the total number of virtual power plants in the optimal combination of virtual power plants U m , and define the preference of each distributed resource to join the virtual power plant queue as In the determined optimal combination of virtual power plants, the variables of each virtual power plant's selection of each distributed resource and the preference of each distributed resource for joining the virtual power plant queue are set to form the cooperation tendency index of each distributed resource to each virtual power plant; the correction coefficient of the cooperation tendency index is obtained by using the entropy weight method and the AHP weighting method; the maximum value of the aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource is corrected by using the cooperation tendency index and the correction coefficient of the cooperation tendency index, and the distributed resources corresponding to the corrected maximum value of the aggregated benefit are used as the optimal combination of distributed resources matching each virtual power plant in the optimal combination of virtual power plants;

[0139] The bilateral matching combination module is used to determine the bilateral matching combination of virtual power plants and distributed resources based on the bilateral matching method, and control the virtual power plants and distributed resources in the bilateral matching combination, with the maximum value of the corrected aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource and the difference between the cooperation tendency indicators corrected by the correction coefficient of each distributed resource to each virtual power plant as the optimization goal.

[0140] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0141] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0142] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0143] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A control method for virtual power plants and distributed resources based on bilateral matching, characterized in that: include: Collect the operating parameters of virtual power plants and distributed resources, and establish an aggregated revenue model when any virtual power plant and all distributed resources are aggregated; Based on the aggregation revenue model, the optimal combination of virtual power plants is determined according to the revenue when each distributed resource is aggregated with each virtual power plant. The virtual power plants in the optimal combination of virtual power plants are sorted in ascending order according to the maximum revenue when each virtual power plant in the optimal combination of distributed resources and virtual power plants is aggregated, and a virtual power plant queue is obtained. The total number of virtual power plants in the optimal combination of virtual power plants U is used m , and define the preference of each distributed resource to join the virtual power plant queue as Within the determined optimal combination of virtual power plants, the variables of each virtual power plant's selection of each distributed resource and the degree of preference of each distributed resource in joining the virtual power plant queue are set to form the cooperation tendency index of each distributed resource towards each virtual power plant; the entropy weight method and AHP weighting method are used to obtain the correction coefficient of the cooperation tendency index; The maximum value of the aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource is corrected by using the cooperation tendency index and the correction coefficient of the cooperation tendency index, and each distributed resource corresponding to the corrected maximum value of the aggregated benefit is used as the optimal combination of distributed resources matching each virtual power plant in the optimal combination of virtual power plants; For each virtual power plant in the optimal combination of virtual power plants and each distributed resource in the optimal combination of distributed resources, based on the bilateral matching method, the bilateral matching combination of virtual power plants and distributed resources is determined, and the virtual power plants and distributed resources in the bilateral matching combination are controlled, with the optimization goal of maximizing the maximum value of the corrected aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource and minimizing the difference between the cooperation tendency indicators corrected by the correction coefficient of each distributed resource to each virtual power plant.

2. The control method of virtual power plant and distributed resources based on bilateral matching according to claim 1 is characterized in that: Collect the operating parameters of the virtual power plant and the operating parameters of the distributed resources, and establish an aggregated profit model when any virtual power plant and all distributed resources are aggregated, including: Collect the operating parameters of the virtual power plant and build a market revenue model for the virtual power plant. The market revenue model includes an objective function and constraints. The operating parameters of distributed resources are collected, and based on the new virtual power plant obtained by aggregating any virtual power plant and all distributed resources, an aggregation profit model of the new virtual power plant is constructed.

3. The control method of virtual power plant and distributed resources based on bilateral matching according to claim 2 is characterized in that: The market revenue model of the virtual power plant satisfies the following relationship: max(R M -C IL -C PU )(1) In the formula, max(R M -C IL -C PU ) is the objective function, R M , C IL , C PU are the electricity spot market revenue, interruptible load adjustment cost and deviation power assessment fee respectively; T is the total number of daily dispatch periods, Δt is the length of the dispatch period, Φ s is the set of uncertain scenarios of wind, solar, hydro output and market electricity prices, π s is the probability of scene s, is the market electricity price at time period t under scenario s, P t M is the output of the virtual power plant in the day-ahead market during period t; is the penalty electricity price in time period t under scenario s; IL k is the interruptible load adjustment cost; pos and k neg They are respectively the positive and negative deviation electricity assessment cost coefficients; They are the interruptible load reduction output, over-generation and under-generation of the virtual power plant in period t under scenario s, respectively.

4. The control method of virtual power plant and distributed resources based on bilateral matching according to claim 3 is characterized in that: The constraints of the market revenue model of virtual power plants include: 1) Power balance constraints: In the formula, and are the outputs of virtual power plant, wind power, photovoltaic power and hydropower in time period t under scenario s respectively; and are the charging and discharging power of the energy storage in time period t under scenario s respectively; is the load demand when the interruptible load is not called in time period t under scenario s; 2) Hydropower balance constraints: V s,t+1 =V s,t +ΔV s,t (9) In min ≤V s,t ≤V max (11) Where P HV,min and P HV,max are the minimum and maximum output of hydropower respectively; and are the water intake, water consumption and water abandonment of the reservoir-type hydropower station in time period t under scenario s; V s,t+1 and V s,t are the reservoir water storage in time period t+1 and time period t under scenario s; ΔV s,t are the changes in reservoir water volume in time period t under scenario s; V min and V max are the minimum and maximum water storage capacity of the reservoir respectively; η HV for hydropower generation efficiency; 3) Energy storage constraints: Where P ESS and E ESS They are the maximum charging and discharging power and the maximum energy storage capacity of the energy storage respectively; and are the energy storage charge states at time period t and time period t-1 under scenario s respectively; S ESS,min and S ESS,max are the minimum and maximum charge states of energy storage, respectively; η ch and η dis are the charging and discharging efficiency of energy storage respectively; 4) Interruptible load constraints: Where P IL,max is the maximum interruptible load capacity.

5. The control method of virtual power plant and distributed resources based on bilateral matching according to claim 4 is characterized in that: Define {n, K} to represent the new virtual power plant formed by aggregating the nth virtual power plant and the distributed resource set K, where K is the distributed resource set participating in the aggregation. The aggregation revenue model of the new virtual power plant satisfies the following relationship: In the formula, is the aggregated revenue of the new virtual power plant formed by aggregating the nth virtual power plant and the distributed resource set K; are the market revenues of the nth virtual power plant before and after aggregation, respectively; is the revenue when the mth distributed resource is not added to the virtual power plant; The aggregation cost includes the installation cost of the communication, control, and metering equipment involved in the process of aggregating various resources. The installation cost of the communication, control, and metering equipment installed for the same type of distributed resources is the same; c w 、c PV 、c ESS 、c IL are the aggregate cost coefficients of wind power, photovoltaic power, energy storage and interruptible load respectively; are the number of wind power, photovoltaic, energy storage and interruptible load resources in the distributed resource set K respectively.

6. The control method of virtual power plant and distributed resources based on bilateral matching according to claim 1, characterized in that: Based on the aggregated revenue model, the optimal combination of virtual power plants is determined according to the revenue when each distributed resource is aggregated with each virtual power plant, including: Based on the aggregated benefit model, the aggregated benefit after the mth distributed resource and the nth virtual power plant are aggregated is determined; when the aggregated benefit is not less than the benefit threshold, the mth distributed resource is determined to join the nth virtual power plant; all virtual power plants joined by the mth distributed resource constitute the optimal combination of virtual power plants; The profit threshold is 70% of the average value of the aggregated profit after the mth distributed resource is aggregated with different virtual power plants.

7. The control method of virtual power plant and distributed resources based on bilateral matching according to claim 6 is characterized in that: Define the mth distributed resource to join the virtual power plant queue outside the NU m The preference degree of each virtual power plant is 0, and N is the total number of virtual power plants; Based on the degree of preference, the aggregated benefit generated by the aggregation of the mth distributed resource and the nth virtual power plant satisfies the following relationship: In the formula, is the aggregated revenue generated by aggregating the mth distributed resource and the nth virtual power plant; is the aggregate cost of the mth distributed resource; are the market revenues of the nth virtual power plant before and after aggregation with the mth distributed resource, is the revenue when the mth distributed resource is not added to the virtual power plant, h n,m The preference for the mth distributed resource to join the nth virtual power plant.

8. The control method of virtual power plant and distributed resources based on bilateral matching according to claim 6, characterized in that: The cooperation tendency index satisfies the following relationship: In the formula, is the distributed resource set K in M n The cooperation tendency index between the distributed resources and the nth virtual power plant; x n,m The variables for selecting distributed resources for each virtual power plant in the optimal combination of virtual power plants, x n,m =1 means that the nth virtual power plant selects the mth distributed resource, x n,m =0 means that the nth virtual power plant does not select the mth distributed resource; h n,m The preference for joining the nth virtual power plant for the mth distributed resource; The modified entropy weight-AHP weighting method is used to obtain the correction coefficient of the cooperation tendency index, which satisfies the following relationship: In the formula, N1 is the total number of indicators; are the objective weight of the j-th cooperation tendency index calculated by the entropy weight method and the subjective weight of the j-th cooperation tendency index calculated by the AHP weighting method; w Ent,QX and w AHP,QX They are the calculation coefficients of the entropy weight method and the AHPAHP weighting method respectively; The maximum value of the aggregated benefit generated by the aggregation of the nth virtual power plant and each distributed resource is corrected by using the cooperation tendency index and the correction coefficient of the cooperation tendency index to satisfy the following relationship: In the formula, is the maximum aggregated benefit generated by the aggregation of the nth virtual power plant and the distributed resources in the distributed resource set K, It is the aggregated revenue of the new virtual power plant formed by aggregating the nth virtual power plant and the distributed resource set K.

9. The control method of virtual power plant and distributed resources based on bilateral matching according to claim 1, characterized in that: Within the optimal combination of virtual power plants and distributed resources, the maximum value of the corrected aggregated benefits generated by the aggregation of each virtual power plant and each distributed resource is the satisfaction of the bilateral subjects. One goal of bilateral matching is to maximize the satisfaction of the bilateral subjects. The difference between the cooperation tendency indicators corrected by the correction coefficients of each distributed resource on each virtual power plant is the difference between the bilateral subjects. Another goal of bilateral matching is to minimize the difference between the bilateral subjects. The combination of virtual power plants and distributed resources that achieves both goals is a bilateral matching combination.

10. A control system for a virtual power plant and distributed resources based on bilateral matching, characterized in that: include: A model building module is used to collect the operating parameters of the virtual power plant and the operating parameters of the distributed resources, and to establish an aggregated benefit model when any virtual power plant and all distributed resources are aggregated; The optimal combination determination module is used to determine the optimal combination of virtual power plants based on the aggregated benefit model according to the benefits of each distributed resource and each virtual power plant when aggregated, and to sort the virtual power plants in the optimal combination of virtual power plants in the order of the maximum benefits of each virtual power plant when aggregated in the optimal combination of each distributed resource and virtual power plant from small to large to obtain a virtual power plant queue; the total number of virtual power plants in the optimal combination of virtual power plants U m , and define the preference of each distributed resource to join the virtual power plant queue as Within the determined optimal combination of virtual power plants, the variables of each virtual power plant's selection of each distributed resource and the degree of preference of each distributed resource in joining the virtual power plant queue are set to form the cooperation tendency index of each distributed resource towards each virtual power plant; the entropy weight method and AHP weighting method are used to obtain the correction coefficient of the cooperation tendency index; The maximum value of the aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource is corrected by using the cooperation tendency index and the correction coefficient of the cooperation tendency index, and each distributed resource corresponding to the corrected maximum value of the aggregated benefit is used as the optimal combination of distributed resources matching each virtual power plant in the optimal combination of virtual power plants; The bilateral matching combination module is used to determine the bilateral matching combination of virtual power plants and distributed resources based on the bilateral matching method, and control the virtual power plants and distributed resources in the bilateral matching combination, with the maximum value of the corrected aggregated benefit generated by the aggregation of each virtual power plant and each distributed resource and the difference between the cooperation tendency indicators corrected by the correction coefficient of each distributed resource to each virtual power plant as the optimization goal.

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