A Method for Analyzing the Regulation Potential of a Virtual Power Plant in the Spot Market Model

Abstract

The present invention belongs to the technical field of virtual power plants, and particularly relates to a method for analyzing the regulation potential of virtual power plants in the spot market mode, including the following steps: establishing a classification model of three load regulation parameter types, namely translatable, transferable, and interruptible, for virtual power plant users to participate in response; constructing a typical database of external characteristics of multi-type regulation resource classification and clustering; aggregating and calculating the typical classification marginal cost of different regulation resource responses of virtual power plant users after clustering; based on the improved K-means clustering method, carrying out clustering analysis on the spot market price of a high-proportion new energy system and the peak-valley price difference interval of the classification model, and establishing an analysis model for the adjustable potential of virtual power plant users in typical scenarios of the long-cycle spot market mode, which is of great significance for improving the participation of virtual power plants in the spot market and stimulating the participation of user enterprises' regulation resources in the market.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plants, and particularly relates to a method for analyzing the regulation potential of virtual power plants in the spot market mode. Background Art

[0002] With the continuous promotion of the development of new energy, the scale of new energy is constantly expanding. On the one hand, the problem of power supply-demand balance and new energy consumption is prominent, and the regulation capacity and potential of conventional energy have been deeply explored under the large fluctuations of new energy. Especially in the face of long-term scenarios of continuous windless and sunlightless new energy or long-term scenarios of continuous strong wind of new energy, virtual power plants aggregate various types of regulation resources such as controllable industrial loads, distributed photovoltaics and energy storage, electric vehicles, and commercial buildings. Deeply tapping the potential of virtual power plants has become an important means for power supply guarantee and new energy consumption. On the other hand, the response costs of various types of regulation resources involved in virtual power plants in the spot market mode are different, and the regulation response time, regulation capacity, duration, regulation rate, regulation accuracy, etc. are complex and variable. It is necessary to conduct clustering of typical classified regulation resources and typical spot market scenarios, and conduct precise analysis in the spot market mode. Therefore, the analysis of the adjustable potential of virtual power plant users in a high-proportion new energy system under the clustering of typical market scenarios in the precise spot market mode is the basis for the operation of virtual power plants.

[0003] At present, scholars at home and abroad have carried out a large number of studies on the evaluation and analysis of the conventional regulation capabilities of virtual power plants. However, there is still a blank in the analysis of the regulation capabilities of virtual power plants under the complex spot market mode of high-proportion new energy systems. In the evaluation and analysis of the conventional regulation capabilities of virtual power plants, most methods are based on model-data joint driving, clustering estimation, and multi-model dynamic combination. On the one hand, clustering division based on label characteristics is carried out to find similar operation curves. On the other hand, based on the combined analysis of multi-modal monitoring data, the perceiving and prediction of the adjustable potential of distributed resources are realized. For example, for the prediction of the adjustable capabilities of charging and swapping stations, multi-dimensional data such as the flow of people, the heat map of electric vehicles, the historical load data of charging and swapping stations, and the working days and holiday periods need to be comprehensively analyzed. The comprehensive clustering method is used to study the load characteristics and adjustable potential of users, and for the adjustable load to participate in power grid dispatching, 5G technology integration, the use of an improved ant-lion hybrid optimization system model and regulation capability prediction, etc. However, the types of flexible resources on the user side accessed by virtual power plants are diverse, the individual capacity is small, the locations are scattered, it is difficult to collect resource equipment information and real-time monitoring data, the resource characteristics are different, there are differences in time response characteristics, and it is difficult to build an aggregation model for resources; the description system of the regulation cost model of virtual power plants and the economic benefit model are incomplete, there are large differences in regulation resources under different typical spot market modes, and there are problems that cannot be accurately analyzed and responded to. The users of virtual power plants are complex and changeable: changes in external factors such as the market environment, economic situation, policies and regulations, and the production and operation conditions of users themselves may all affect the willingness and behavior of users, resulting in difficulties in the prediction, planning, and management and control of virtual power plants. The virtual power plant market is complex and changeable: the competition is fierce, the response modes are diverse, it is difficult to calculate the regulation control cost, resulting in difficulties in price prediction and great difficulties in making trading quotation decisions for virtual power plants.

[0004] Existing research shows that by analyzing the output characteristics of high-proportion new energy, the accuracy of the analysis and evaluation of the regulation capabilities of virtual power plants under the spot market mode can be improved, and the virtual power plants can effectively participate in the actual declaration of the spot market and the adjustment of regulation responses. However, the existing research still has the following deficiencies: (1) There are relatively few studies on how to classify the regulation resource responses of virtual power plant user sides such as different typical industrial loads, distributed new energy and energy storage, and commercial buildings; (2) There is a lack of a method for analyzing the reliable response capabilities of the regulation resources of virtual power plants under complex spot market modes; (3) There is no targeted research on quantitatively describing the analysis of typical classified regulation resources of virtual power plants by using the aggregation characteristics and typical scenarios of new energy output, spot prices, and peak-valley price differences. Summary of the Invention

[0005] In view of the above technical problems in the existing evaluation of the regulation capabilities of virtual power plant users lacking adaptability to multiple scenarios of spot market operation, the present invention provides a method for analyzing the regulation potential of virtual power plants under the spot market mode.

[0006] To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0007] A method for analyzing the regulation potential of a virtual power plant in a spot market mode, comprising the following steps:

[0008] S1. For two typical demands of new energy consumption and power supply guarantee, establish three load regulation parameter classification models of translatable, transferable, and interruptible for virtual power plant users to participate in response;

[0009] S2. Considering the operation constraints of different types of regulation resources such as industrial loads, distributed energy storage, distributed photovoltaics, and industrial and commercial buildings accessed by the virtual power plant, based on the load regulation parameter classification model formed in S1, establish a typical database of the external characteristics of different types of regulation resources classified and clustered;

[0010] S3. Based on different types of regulation resources and the typical database formed in S2, calculate the typical classification marginal cost of the response of different regulation resources of virtual power plant users after clustering;

[0011] S4. For four typical combined scenarios of wind, light, large, and small, based on the improved K-means clustering method, conduct clustering analysis on the spot market price and the price difference interval of the classification model of a high-proportion new energy system;

[0012] S5. Based on the long-term prediction of the spot market price and the typical classification marginal cost, establish an analysis model for the adjustable potential of virtual power plant users in a typical scenario of the long-term spot market mode.

[0013] The method for establishing the translatable load regulation parameter classification model for virtual power plant users to participate in response in S1 is as follows:

[0014] For virtual power plant users, the requirements for steel and glass as translatable loads are that the time is uninterrupted and the power size remains unchanged at each moment before and after translation; for a certain translatable load , its power distribution vector is: (1)

[0015] Wherein, is the start time, is the continuous time period; use the 0-1 variable to represent the start state of the time period, that is, represents starting to translate from the time period; The start time period set is: ; represents that the load is not translated; and , indicating that the load is shifted to starting from and is the continuous distribution duration of the space with large output of new energy or low spot price; and the power distribution vector corresponding to is: : (2)

[0016] When , , the corresponding power distribution vector is meaningless, and all of them are set to 0, that is:

[0017] (3)

[0018] Based on the power distribution vector, the shiftable load is modeled, ;

[0019] There are only two situations for the shiftable load after participating in the response in the virtual power plant: a) not shifted; b) shifted to the time period of large output of new energy or low spot price interval, and the constraints are as follows:

[0020] (4).

[0021] The method for establishing the classification model of adjustable parameters of transferable load for virtual power plant users to participate in the response in S1 is:

[0022] For the building users of the virtual power plant, their power can be transferred, but the time requirement is not an uninterrupted requirement. It can be adjusted within a certain time period and the total power remains unchanged. The transferable load power distribution vector is: (5)

[0023] Use the 0-1 variable to represent the transfer status of the time period , that is represents transferring in the time period, represents not transferring; the power distribution vector of the load after transfer is:

[0024] (6)

[0025] The total power of the transferable load remains unchanged before and after transfer. At the moment The transferred load power is between the maximum and minimum transferred powers; to avoid the load being transferred to multiple discontinuous time periods, that is, the frequent start and stop of the switches and power levels of air conditioners, refrigeration and heating electrical equipment, it is necessary to constrain the minimum continuous operation time of the transferred load, that is:

[0026] (7)

[0027] Among them, is the start time, is the duration, and are respectively the maximum and minimum allowable transferred powers of the load at time is the minimum continuous operation time.

[0028] The method for establishing the interruptible load regulation parameter classification model for virtual power plant users to participate in response in S1 is as follows:

[0029] For the load that can be curtailed in non - continuous production enterprises such as steel and cement , as the participation response of virtual power plant users, it reduces the electricity consumption of users; use the 0 - 1 variable to represent the curtailment status of the load that can be curtailed or interrupted in time period , that is means is curtailed in time period , means is not curtailed, and the load model is:

[0030] (8)

[0031] Considering user satisfaction, it is necessary to constrain the minimum and maximum continuous curtailment times and the number of curtailments, that is:

[0032] (9)

[0033] Among them, is the curtailment coefficient of the load that can be curtailed at node at time is the minimum continuous curtailment time; is the maximum continuous curtailment time; is the maximum number of curtailments.

[0034] The operation constraints of different types of regulation resources such as industrial loads, distributed energy storage, distributed photovoltaics, and industrial and commercial buildings connected to the virtual power plant in S2 are:

[0035] Operation constraints and external characteristics description of industrial loads:

[0036] (10)

[0037] Wherein: represents the total adjustable power formed by aggregating J industrial loads of virtual power plant i at time t, 、 、 respectively represent the th industrial load's equivalent shiftable load regulation power 、transferable load 、interruptible load equivalent coefficient; represents the regulation capacity power constraint existing at time t during the operation of the th industrial load, 、 respectively represent the th industrial load's regulation equivalent coefficients corresponding to the main production load and auxiliary production load at time t during the operation;

[0038] Description of distributed energy storage operation constraints and external characteristics:

[0039] 1) During a battery charge / discharge cycle, the energy storage capacity values at each moment of the battery should be maintained within a certain range:

[0040] (11)

[0041] Wherein: is the moment's state of charge of the battery energy storage; 、 respectively correspond to the upper and lower limits of the adjustable capacity of the battery energy storage;

[0042] 2) State of charge continuity constraint of energy storage: (12)

[0043] Wherein: 、 are respectively the state of charge of the energy storage system at time t and time t - 1; 、 are respectively the charging power and discharging power of the energy storage system at time t; and respectively correspond to the charging efficiency and discharging efficiency of the battery energy storage; is the time interval ( = 1h);

[0044] 3) Energy storage charge / discharge constraint:

[0045] During the operation of the battery energy storage, the charging / discharging power per time should be controlled not to exceed its rated value, and the total discharging power should not exceed the rated energy storage capacity:

[0046] (13)

[0047] Where: 、 respectively represent the maximum charging power and the maximum discharging power of the energy storage system;

[0048] Description of the operation constraints and external characteristics of distributed photovoltaic:

[0049] The operation constraint of distributed photovoltaic is the generable power that changes with time, and it participates in the aggregated response in the form of interruptible load:

[0050] (14)

[0051] Where: 、 respectively represent the predicted values of the power generation of the distributed photovoltaic system and the maximum power generation at time t; is the equivalent interruptible load power value of distributed photovoltaic;

[0052] Description of the operation constraints and external characteristics of industrial and commercial buildings:

[0053] 1) Air conditioning system: Central air conditioners are installed in public buildings, with a load ratio of more than 40% and an adjustable potential ratio of 5% - 20%; Assuming that the central air conditioning system in the building has been in a stable operation state before the implementation of demand response, considering the comfort of the indoor environment, the adjustable load constraint model of the central air conditioner is: (15)

[0054] Where: is the maximum active power that can be reduced or increased by the central air conditioning system; is the duration of demand response; when in the heating mode, is the heating energy efficiency ratio of the central air conditioning system, and when in the cooling mode, is the cooling energy efficiency ratio of the central air conditioning system; c is the specific heat capacity of air, and the specific heat capacity at constant pressure of air at a temperature of 300K is selected, with a value of 1.005 kJ / (kg·K); S is the heating area of the building; H is the average floor height of the building; is the density of air, and the dry air density at a temperature of 300K is selected, with a value of 1.177 kg / m 3 ; is the heating temperature set by the user; is the allowable indoor temperature;

[0055] Among them, the shiftable load under different control modes of the air conditioner is equivalent to the following four scenarios:

[0056] (16)

[0057] Among them, is the response capacity of the input current percentage of the control host. From receiving the response instruction to completing the power adjustment, the response preparation time is at the minute level. is the correction parameter; is the operating power of the air conditioner host; is the rated current loading value of the host; is the operating output value of the host; is the response capacity when remotely or locally controlling the water temperature. The response preparation time is at the minute level. is the correction parameter; is the temperature adjustment value; is the response capacity for adjusting the compressor speed in the control frequency conversion mode. is the frequency change value; is the rated frequency; is the response capacity when directly controlling to shut down some air conditioners;

[0058] 2) Electric boiler:

[0059] The heat storage electric boiler is used for the domestic hot water use or heating of large public buildings such as office buildings, hotels, and shopping malls. Its load ratio can reach 30% - 40% in winter, and the adjustable potential reaches 15%; when the electric boiler has no heat storage capacity, the calculation method of its constraint model is the same as formula (15); when the electric boiler has heat storage capacity, the operation constraint is: (17)

[0060] Among them: is the heat change of the heat storage medium after t moments of response. is the temperature of the heat storage medium at the moment when the response is issued. is the specific heat capacity of the medium; is the density of the medium; V is the volume of the medium; is the temperature of the medium at t moment; in the adjustable load model constraint conditions is the maximum active power that the heat storage electric boiler can cut or increase; t′ is the demand response duration; is the efficiency of the electric boiler, is the heat exchange efficiency of the heat storage medium; is the mass flow rate of the boiler, is the water temperature at the secondary side outlet; is the water temperature at the secondary side inlet;

[0061] 3) Lighting system:

[0062] The load proportion of the lighting system is between 15% and 25%, and the adjustable potential proportion is between 3% and 10%. Its response preparation time is in the order of seconds. The response capacity comes from two aspects. One is to ensure the normal work and production of users, and the method of shunt area control is adopted to turn off a part of the lighting equipment P1. The other is that during non-working hours, the lamps P2 that could have been turned off but were not; (18)

[0063] Among them: P light is the power of the lamps originally turned on during work; λ1 is the reasonable turn-off coefficient obtained by optical measurement; P i is the lamps that can be turned off during non-working hours obtained by statistics;

[0064] 4) Elevators and cold storages:

[0065] The load proportion of the elevator and cold storage system is between 4% and 10%, and the adjustable potential proportion is between 1% and 5%. Its response preparation time is in the order of seconds. The response capacity comes from turning off some idle elevators at night and shutting down the cold storage for a short time during non-preparation hours;

[0066] (19)

[0067] Among them, and respectively represent the equivalent interruptible elevator load and the cold storage refrigeration system load of the building load.

[0068] The method for calculating the typical classification marginal cost of the responses of different regulation resources of the virtual power plant users after clustering in S3 is: (20) Among them, represents the discount rate; represents the inflation rate; represents the service life of the regulation resource; represents the unit power retrofit cost of the regulation resource j; represents the annual maintenance cost per unit power of the regulation resource j; represents the retrofit capacity of the regulation resource j; represents the operating capacity of the regulation resource j; represents the total regulation cost of aggregating a total of J regulation resources; c represents the typical regulation cost corresponding to the typical regulation power interval p.

[0069] The method for carrying out the clustering analysis of the spot market price of the high-proportion new energy system and the price difference interval of the classification model in S4 is:

[0070] For the historical data scenario set p = {x1, x2, x3,..., xn}, where there are n data samples (365 * 24) in total, each sample is m - dimensional. Assume that the n data samples are divided into K classes, and use the sample c i to represent the center point of class K i . N(K i ) represents the number of samples in class Ki, represents the distance between samples, then: (21)

[0071] Among them, r i is the class radius, which is the average value of the distances from all data within the class to the class center; λ is the ratio of class radii; C ij is the mid - point between classes, which is the point that equally divides the line connecting different class centers according to the ratio of class radii; is the class - center density, which is the number of data within the class whose distance from the class - center point is less than the class radius; is the class - edge density, representing the class - to - class dispersion, that is, among any two classes, the number of data whose distance to the mid - point between classes is less than the average value of class radii; The index is the ratio of the data density within the class to the data density between classes; is the optimal number of clusters for K - means clustering determined based on the DBI index; the spot price and peak - valley price difference formed after clustering are corresponding to the typical libraries (X K1 , X K2 , X K3 ,...., X Kbest ; C K1 , C K2 , C K3 ,...., C Kbest ).

[0072] The method for establishing the virtual - power - plant user adjustable - potential analysis model in the typical scenario of the long - cycle spot pattern in S5 is as follows: (22)

[0073] Among them, , , are the adjustable power that can be shifted, transferred, and interrupted participated by the virtual power plant respectively. For the adjustable capabilities of the shiftable and transferable types, when the spot price is higher than the cost threshold of the i th adjustable - power interval, all the adjustable capabilities of this interval can be included in the response - ability interval; for the interruptible - type adjustable ability, when the spot price is lower than its cost threshold, all its adjustable capabilities can be included in the response - ability interval.

[0074] Compared with the prior art, the beneficial effects of the present invention are:

[0075] In response to the two typical response demands of virtual power plants to participate in the consumption of new energy and ensure power supply, the present invention establishes a classification model for three load regulation parameters of translatable, transferable, and interruptible for virtual power plant users to participate in response, and gives parameter indicators for the aggregation classification of virtual power plant regulation resources; taking the operation constraints of different types of regulation resources such as industrial loads, distributed energy storage, distributed photovoltaics, and industrial and commercial buildings connected to the actual virtual power plant as the boundary, a typical database of the external characteristics of multi-type regulation resource classification and clustering is constructed; and taking the retrofit cost and operation and maintenance cost as the actual regulation resource response cost, the typical classification marginal cost of different regulation resource responses of virtual power plant users after aggregation calculation and clustering is calculated; in response to typical combined scenarios of new energy output of different scales of wind, light, large, and small, based on the improved K-means clustering method, a clustering analysis of the spot market price and the peak-valley price spread interval of the classification model of the high-proportion new energy system is carried out, and by comparing the spot price and the peak-valley price spread with the typical classification marginal cost, a model for analyzing the adjustable potential of virtual power plant users in typical scenarios of the long-term spot mode is established, which is of great significance for improving the participation of virtual power plants in the spot market and motivating user enterprises to participate in the market with regulation resources. Description of the Drawings

[0076] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can also be obtained based on the provided drawings.

[0077] The structures, ratios, sizes, etc. depicted in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limited conditions under which the present invention can be implemented. Therefore, they do not have substantial technical significance. Any modification of the structure, change in the ratio relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0078] Figure 1 It is a calculation flowchart of the present invention. Detailed Embodiments

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. These descriptions are only for further explaining the features and advantages of the present invention, rather than limiting the claims of the present invention; based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0080] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0081] A method for analyzing the regulation potential of a virtual power plant in a spot market mode, as Figure 1 shown, includes the following steps:

[0082] Step 1: For two typical demands of consuming new energy and ensuring power supply, establish a classification model of three load regulation parameters, namely translatable, transferable, and interruptible, for virtual power plant users to participate in response.

[0083] 1. Translatable load type:

[0084] For some virtual power plant users, such as steel and glass, the requirement for translatable loads is that the time is uninterrupted and the power magnitude remains unchanged at each moment before and after translation. For a certain translatable load , its power distribution vector is:

[0085] (1)

[0086] In the formula, is the start time, is the duration. Use a 0-1 variable to represent the start state of the period, that is, represents starting to translate from the period. The start period set is: represents that the load is not translated. And , represents that the load is translated to start from , is the continuous distribution duration of the space where new energy is in large generation or the spot price is low. The power distribution vector corresponding to is: (2)

[0087] When , , the corresponding power distribution vector is meaningless, and all of it is set to 0, that is:

[0088] (3)

[0089] Based on the power distribution vector, model the translatable load, 。

[0090] There are only two situations for the shiftable load after participating in the response of the virtual power plant: a) Not shifted. b) Shifted to the period when new energy is in large output or the spot price is in the low-price range. The constraints are as follows: (4)

[0091] 2. Constraints on shiftable load:

[0092] For some building users of the virtual power plant, their power can be shifted, but the time requirement is not for continuous operation. It can be adjusted within a certain period and the total power remains unchanged. The power distribution vector of the shiftable load is:

[0093] (5)

[0094] Use the 0-1 variable to represent the transfer status of the period, that is, represents transferring in the period at, and represents not transferring. The power distribution vector of the load after transfer is:

[0095] (6)

[0096] The total power of the shiftable load remains unchanged before and after transfer. At time , the transferred load power is between the maximum and minimum transfer powers. To avoid the load being transferred to multiple discontinuous periods, that is, the frequent start and stop of the switches and power sizes of air conditioners, refrigeration, heating and other electrical equipment, it is necessary to constrain the minimum continuous operation time of the transferred load, that is: (7)

[0097] In the formula, is the start time, is the duration, , are respectively the maximum and minimum allowable transfer powers of the load at time ;

[0098] 3. Constraints on interruptible load:

[0099] For the load that can be curtailed, such as non-continuous production enterprises like steel and cement , as the participation response of the virtual power plant users, the electricity consumption of the users is reduced. Use the 0-1 variable to represent the load that can be curtailed or interrupted Time period Cutting status, that is Indicates During The time period is cut, Indicates Not cut, the load model is: (8)

[0100] Considering user satisfaction, it is necessary to constrain the minimum and maximum continuous cutting times and the number of cuttings, that is:

[0101] (9)

[0102] In the formula, Is Cutting coefficient of the load that can be cut at the time node The minimum continuous cutting time. Is the minimum continuous cutting time. Is the maximum continuous cutting time. Is the maximum number of cuttings.

[0103] Step 2: Considering the operation constraints of different types of regulation resources such as industrial loads, distributed energy storage, distributed photovoltaics, and industrial and commercial buildings connected to the virtual power plant, establish a typical database of the external characteristics of multi-type regulation resource classification and clustering based on the load regulation parameter classification model formed in Step 1.

[0104] 1. Description of the operation constraints and external characteristics of industrial loads:

[0105] (10)

[0106] In the formula: Indicates the total adjustable power formed by aggregating J industrial loads of the virtual power plant i at time t, , , Respectively represent the th industrial load equivalent translatable load regulation power , transferable load , interruptible load Equivalent coefficient; Represents the th industrial load has a regulation capacity power constraint at time t during operation, , Respectively represent the th industrial load corresponds to the main production load at time t during operation And auxiliary production load Regulation equivalent coefficient.

[0107] 2. Description of Distributed Energy Storage Operation Constraints and External Characteristics:

[0108] 1) During a battery charge / discharge cycle, the energy storage capacity value of the battery at each moment should be maintained within a certain range:

[0109] (11)

[0110] In the formula: is the state of charge of the battery energy storage at the moment. and correspond to the upper and lower limits of the adjustable capacity of the battery energy storage respectively.

[0111] 2) Continuity constraint of the state of charge of energy storage:

[0112] (12)

[0113] In the formula: and are the states of charge of the energy storage system at time t and time t-1 respectively. and are the charging power and discharging power of the energy storage system at time t respectively; and correspond to the charging efficiency and discharging efficiency of the battery energy storage respectively. is the time interval ( =1h).

[0114] 3) Charging / discharging constraint of energy storage:

[0115] During operation, the battery energy storage should control the charging / discharging power each time not to exceed its rated value, and the total discharging power does not exceed the rated capacity of the energy storage:

[0116] (13)

[0117] In the formula: 、 represent the maximum charging power and maximum discharging power of the energy storage system respectively.

[0118] 3. Description of Distributed Photovoltaic Operation Constraints and External Characteristics:

[0119] The operation constraint of distributed photovoltaic is the generable power that changes with time, and it participates in the aggregated response in the form of interruptible load: (14)

[0120] In the formula: and represent the predicted values of the power generation of the distributed photovoltaic system and the maximum power generation at time t respectively; is the equivalent interruptible load power value of distributed photovoltaic.

[0121] 4. Operation constraints and external characteristics description of industrial and commercial buildings:

[0122] 1) Air conditioning system: Central air conditioners are installed in public buildings, with a load ratio of more than 40% and an adjustable potential ratio of 5% - 20%. Assuming that the central air conditioning system in the building has been operating stably before the implementation of demand response, considering the comfort of the indoor environment, the adjustable load constraint model of the central air conditioner is:

[0123] (15)

[0124] In the formula: is the maximum active power that can be reduced or increased by the central air conditioning system; is the duration of demand response; when in the heating mode, is the heating energy efficiency ratio of the central air conditioning system, and when in the cooling mode, is the cooling energy efficiency ratio of the central air conditioning system; c is the specific heat capacity of air, and the value of the specific heat capacity at constant pressure of air at a temperature of 300K is selected as 1.005 kJ / (kg·K); S is the heating area of the building; H is the average floor height of the building; is the density of air, and the density of dry air at a temperature of 300K is selected, with a value of 1.177 kg / m 3 ; is the heating temperature set by the user; is the allowable indoor temperature.

[0125] Among them, the shiftable load under different control modes of the air conditioner is equivalent to the following four scenarios: (16)

[0126] In the formula, is the response capacity of the input current percentage of the control host. From receiving the response instruction to completing the power adjustment, the response preparation time is at the minute level, is the correction parameter; is the operating power of the air conditioner host; is the host limited current loading value; is the host operating output value; is the response capacity when remotely or locally controlling the water temperature, and the response preparation time is at the minute level, is the correction parameter; is the temperature adjustment value; is

[0127] the response capacity of adjusting the compressor speed in the control frequency conversion mode, is the frequency change value; is the rated frequency; is the response capacity when directly controlling to shut down part of the air conditioners.

[0128] 2) Electric heating boiler:

[0129] The heat storage type electric heating boiler is generally used for domestic hot water use or heating in office buildings, hotels, shopping malls and other large public buildings. Its load ratio can reach 30% - 40% in winter, and the adjustable potential reaches 15%. When the electric heating boiler has no heat storage capacity, the calculation method of its constraint model is the same as formula (15). When the electric heating boiler has heat storage capacity, the operation constraints are: (17)

[0130] In the formula: is the heat change of the heat storage medium after responding for t moments, is the temperature of the heat storage medium at the moment when the response is issued, is the specific heat capacity of the medium; is the density of the medium; V is the volume of the medium; is the temperature of the medium at moment t; in the constraint conditions of the adjustable load model is the maximum active power that can be reduced or increased by the heat storage type electric boiler; t′ is the demand response duration; is the efficiency of the electric boiler, is the heat exchange efficiency of the heat storage medium; is the mass flow rate of the boiler, is the water temperature at the secondary side outlet; is the water temperature at the secondary side inlet.

[0131] 3) Lighting system:

[0132] The load ratio of the lighting system is 15% - 25%, and the adjustable potential ratio is 3% - 10%. Its response preparation time is at the second level. The response capacity comes from two aspects. One is to ensure the normal work and production of users, and the method of shunting area control is adopted to turn off part of the lighting equipment P1. The other is the lamps that could have been turned off but were not turned off during non-working hours P2:

[0133] (18)

[0134] In the formula: P light is the power of the lamps that were originally turned on during work; λ1 is the reasonable turn-off coefficient obtained by optical measurement; P i is the lamps that can be turned off during non-working hours obtained by statistics.

[0135] 4) Elevators and cold storages:

[0136] The load proportion of elevators and cold storage systems is between 4% and 10%, and the adjustable potential proportion is between 1% and 5%. Their response preparation time is at the second level, and the response capacity comes from shutting down some idle elevators at night and shutting down the cold storage for a short time during non-preparation hours:

[0137] (19)

[0138] In the formula, and respectively represent the equivalent interruptible elevator load and the cold storage refrigeration system load of building loads.

[0139] Step 3: Based on different types of regulation resources and the typical database formed in Step 2, calculate the typical classification marginal cost of the response of different regulation resources of virtual power plant users after clustering: (20)

[0140] In the formula, represents the discount rate represents the inflation rate represents the service life of the regulation resource represents the unit power retrofit cost of regulation resource j; represents the annual unit power maintenance cost of regulation resource j represents the retrofit capacity of regulation resource j represents the operating capacity of regulation resource j represents the total regulation cost of aggregating a total of J regulation resources; c represents the typical regulation cost corresponding to the typical regulation power interval p.

[0141] Step 4: For the four typical combined scenarios of wind, light, large, and small, based on the improved K-means clustering method, conduct clustering analysis on the spot market price and classification model price difference interval of the high-proportion new energy system.

[0142] For the historical data scenario set p = {x1, x2, x3,..., x n}, where there are a total of n data samples (365 * 24), each sample is m-dimensional. Assuming that the n data samples are divided into K classes, and the sample c i represents the center point of class K i , N(K i ) represents the number of samples in class Ki, represents the distance between samples, then (21)

[0143] In the formula, r i is the class radius, which is the average value of the distances from all data within the class to the class center; λ is the ratio of class radii; C ijIt is the midpoint between classes, which is the point that equally divides the connection line of different class centers according to the ratio of class radii; It is the class center density, which is the number of in-class data whose distance from the class center point is less than the class radius; It is the class edge density, representing the inter-class dispersion degree, that is, among any two classes, the number of data whose distance to the midpoint between classes is less than the average class radius; The index is the ratio of the in-class data density to the inter-class data density; It is the optimal number of clusters for K-means clustering determined based on the DBI index; the spot price and peak-valley price difference formed after clustering are the corresponding typical libraries (X K1 , X K2 , X K3 ,...., X Kbest ; C K1 , C K2 , C K3 ,...., C Kbest ).

[0144] Step Five: Based on the long-term prediction of the spot market price and the typical classification marginal cost, establish an analysis model for the adjustable potential of virtual power plant users in the typical scenario of the long-term spot model:

[0145] (22)

[0146] In the formula, , , are the adjustable power that can be shifted, transferred, and interrupted participated by the virtual power plant respectively. For the adjustable capabilities of the shiftable and transferable types, when the spot price is higher than the cost threshold of the i th adjustable power interval, all the adjustable capabilities of this interval can be included in the response capability interval; for the interruptible type of adjustable capability, when the spot price is lower than its cost threshold, all its adjustable capabilities can be included in the response capability interval.

[0147] The above only elaborates on the preferred embodiments of the present invention in detail. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those of ordinary skill in the art, various changes can be made without departing from the purpose of the present invention, and all such changes should be included within the protection scope of the present invention.

Claims

1. A method for analyzing the regulation potential of a virtual power plant in the spot market mode, characterized in that It includes the following steps: S1. For two typical demands of new energy consumption and power supply guarantee, establish a classification model of three load regulation parameter types, namely translatable, transferable, and interruptible, for virtual power plant users to participate in response; The method for establishing the classification model of translatable load regulation parameters for virtual power plant users to participate in response in S1 is as follows: For virtual power plant users, the requirements for steel and glass as shiftable loads are that the time is uninterrupted and the power magnitude remains unchanged at each moment before and after shifting; for a certain shiftable load , its power distribution vector is: （1） Among them, is the start time, is the duration period; use a 0-1 variable to represent the start state of the time period i.e., to represent starting from the time period for translation; The set of start time periods is: ; indicating that the load is not translated; and , Indicates that the load is shifted to from Start from is the continuous distribution duration of the space for large-scale new energy generation or low spot prices; and The corresponding power distribution vector is: (2) When , ,the corresponding power distribution vector is meaningless and all of them are set to 0, that is: （3） Model the translatable load based on the power distribution vector, ; There are only two cases for the shiftable load after participating in the response of the virtual power plant: a) not shifted; b) shifted to the period when new energy generation is high or the spot price is low. The constraints are as follows: (4) S2. Considering the operation constraints of different types of regulation resources such as industrial loads, distributed energy storage, distributed photovoltaic, and industrial and commercial buildings connected to the virtual power plant, based on the load regulation parameter classification model formed in S1, establish a typical database of the external characteristics of multi-type regulation resource classification and clustering; S3. Based on different types of regulation resources and the typical database formed in S2, calculate the typical classification marginal cost of the response of different regulation resources of virtual power plant users after clustering; S4. For four typical combined scenarios of wind, light, large, and small, based on the improved K-means clustering method, conduct clustering analysis on the spot market price and the price difference interval of the classification model of the high-proportion new energy system; S5. Based on the long-term prediction of the spot market price and the typical classification marginal cost, establish an analysis model of the adjustable potential of virtual power plant users under typical scenarios of the long-term spot mode.

2. The method for analyzing the regulation potential of a virtual power plant in the spot market mode according to claim 1, wherein, The method for establishing the classification model of transferable load regulation parameters for virtual power plant users to participate in response in S1 is as follows: For the building users of the virtual power plant, their power can be transferred. However, the time requirement is not for continuous operation. It can be adjusted within a certain time period while the total electricity consumption remains unchanged. This is the transferable load. The power distribution vector is: （5） Use 0-1 variables to represent the transfer status of the time period , that is to represent during the time period transfer , and to represent no transfer; the power distribution vector of the load after transfer is as follows: ​ （6） The total power remains unchanged before and after the transfer load transfer, at the moment The transfer load power is between the maximum and minimum transfer powers; to avoid the load being transferred to multiple discontinuous time periods, that is, the frequent start and stop of the switches and power sizes of air conditioners, refrigeration and heating electrical equipment, it is necessary to constrain the minimum continuous operation time of the transfer load, that is: （7） Among them, is the start time, is the duration, , are respectively the maximum and minimum allowable transfer power of the load at the moment; is the minimum continuous operation time.

3. The method for analyzing the regulation potential of a virtual power plant in a spot market mode according to claim 1, wherein The method for establishing the classification model of interruptible load regulation parameters for virtual power plant users to participate in response in S1 is as follows: For the reducible load of non - continuous production enterprises such as steel and cement , participating in the response as a virtual power plant user reduces the electricity consumption of the user; using a 0 - 1 variable to represent the reduction status of the reducible or interruptible load during the period, that is indicating being reduced during the period, indicating not being reduced, and the load model is: （8） Considering user satisfaction, it is necessary to constrain the minimum and maximum continuous reduction times and the reduction times, that is: （9） Among them, is the time node the reduction coefficient of the load that can be reduced; is the minimum continuous reduction time; is the maximum continuous reduction time; is the maximum number of reduction times.

4. A method for analyzing the regulation potential of a virtual power plant in the spot market mode according to claim 1, characterized in that, The operation constraints of different types of regulation resources such as industrial loads, distributed energy storage, distributed photovoltaic, and industrial and commercial buildings connected to the virtual power plant in S2 are as follows: Description of the operation constraints and external characteristics of industrial loads: （10） Wherein: represents the total adjustable power formed by aggregating J industrial loads of the virtual power plant i at time t, , , respectively represent the adjustable load regulation power equivalent to the th industrial load, transferable load, interruptible load equivalent coefficient; represents the regulation capacity power constraint existing at time t during the operation of the th industrial load, , respectively represent the regulation equivalent coefficients corresponding to the main production load and the auxiliary production load at time t during the operation of the th industrial load; Description of the operation constraints and external characteristics of distributed energy storage: 1) During a battery charge / discharge cycle, the energy storage capacity values of the battery at each moment should be maintained within a certain range: （11） Wherein: is the state of charge of the battery energy storage at a moment; and respectively correspond to the upper and lower limits of the dispatchable capacity of the battery energy storage; 2) Energy storage state of charge continuity constraint: (12) Wherein: and are the state of charge of the energy storage system at time t and time t-1 respectively; and are the charging power and discharging power of the energy storage system at time t respectively; and correspond to the charging efficiency and discharging efficiency of the battery energy storage respectively; is the time interval ( = 1h); 3) Energy storage charge / discharge constraint: During the operation of battery energy storage, the charge / discharge power of each time should be controlled not to exceed its rated value, and the total discharge power should not exceed the energy storage rated capacity: （13） Wherein: and respectively represent the maximum charging power and the maximum discharging power of the energy storage system; Description of the operation constraints and external characteristics of distributed photovoltaic: The operation constraint of distributed photovoltaic is the generable power that changes with time, and it participates in the aggregated response in the form of interruptible load: （14） Wherein: and respectively represent the predicted values of the power generation power and the maximum power generation power of the distributed photovoltaic system at time t; is the equivalent interruptible load power value of the distributed photovoltaic; Description of the operation constraints and external characteristics of industrial and commercial buildings: 1) Air conditioning system: Central air conditioners are installed in public buildings, with a load ratio of more than 40% and an adjustable potential ratio of 5% - 20%; assuming that the central air conditioning system in the building has been in a stable operation state before the implementation of demand response, considering the comfort of the indoor environment, the adjustable load constraint model of the central air conditioner is: （15） Wherein: is the maximum active power that the central air - conditioning system can reduce or increase; is the demand response duration; when in the heating mode, is the heating energy efficiency ratio of the central air - conditioning system, when in the cooling mode, is the cooling energy efficiency ratio of the central air - conditioning system; c is the specific heat capacity of air, and the specific heat capacity at constant pressure of air at a temperature of 300K is selected, with a value of 1.005 kJ / (kg·K); S is the heating area of the building; H is the average floor height of the building; is the density of air, and the dry air density at a temperature of 300K is selected, with a value of 1.177 kg / m 3 ; is the heating temperature set by the user; is the allowable indoor temperature; Among them, the translatable load under different control methods of the air conditioner is the equivalent of the following four scenarios: （16） Among them, is the response capacity for controlling the percentage of the input current of the host. From receiving the response instruction to completing the power adjustment, the response preparation time is at the minute level. is the correction parameter; is the operating power of the air-conditioning host; is the host limited current loading value; is the host operating output value; is the response capacity when remotely or locally controlling the water temperature. The response preparation time is at the minute level. is the correction parameter; is the temperature adjustment value; is the response capacity for controlling the compressor speed adjustment in the variable-frequency mode. is the frequency change value; is the rated frequency; is the response capacity for directly controlling the shutdown of some air conditioners; 2) Electric boiler: Regenerative electric boilers are used for domestic hot water use or heating in large public buildings such as office buildings, hotels, and shopping malls. Their load ratio can reach 30% - 40% in winter, and the adjustable potential reaches 15%; when the electric boiler has no heat storage capacity, the calculation method of its constraint model is the same as formula (15); when the electric boiler has heat storage capacity, the operation constraint is: （17） Wherein: In response to the heat change of the heat storage medium after a continuous time t, is the temperature of the heat storage medium at the response issuing moment, is the specific heat capacity of the medium; is the density of the medium; V is the volume of the medium; is the temperature of the medium at time t; In the adjustable load model constraint conditions is the maximum active power that can be reduced or increased by the heat storage electric boiler; t′ is the demand response duration; is the efficiency of the electric boiler, is the heat exchange efficiency of the heat storage medium; is the mass flow rate of the boiler, is the water temperature at the secondary side outlet; is the water temperature at the secondary side inlet; 3) Lighting system: The load ratio of the lighting system is between 15% and 25%, and the adjustable potential ratio is between 3% and 10%. Its response preparation time is at the second level. The response capacity comes from two aspects. One is to ensure the normal work and production of users. By using the method of shunt area control, a part of the lighting equipment P1 is turned off. The other is the lamps P2 that could have been turned off but were not during non-working hours. （18） Where: P light is the power of the lamp originally turned on during operation; λ1 is the reasonable turn-off coefficient obtained by optical measurement; P i is the lamps that can be turned off during non-operating periods obtained by statistics; 4) Elevators and cold storages: The load ratio of the elevator and cold storage system is between 4% and 10%, and the adjustable potential ratio is between 1% and 5%. Its response preparation time is at the second level. The response capacity comes from turning off some idle elevators at night and shutting down the cold storage for a short time during non-preparation hours. （19） Among them, and respectively represent the equivalent interruptible elevator load and cold storage refrigeration system load of building-type loads.

5. The virtual power plant regulation potential analysis method in the spot mode according to claim 1, characterized in that The method for calculating the typical classification marginal cost of the response of different regulation resources of virtual power plant users after clustering in S3 is as follows: （20） Among them, represents the discount rate; represents the inflation rate; represents the adjusted resource usage period; represents the cost of retrofitting per unit power of the adjusted resource j; represents the annual maintenance cost per unit power of the adjusted resource j; represents the retrofit capacity of the adjusted resource j; represents the operating capacity of the adjusted resource j; represents the total adjusted cost of aggregating a total of J adjusted resources; c represents the typical adjusted cost corresponding to the typical adjusted power interval p.

6. The virtual power plant regulation potential analysis method in the spot mode according to claim 1, characterized in that The method for carrying out the clustering analysis of the price difference range of the spot market of the high-proportion new energy system and the classification model in S4 is as follows: For the scenario set p = {x1, x2, x3,..., x n} of clustering classification historical data, where there are n data samples in total (365 * 24), each sample is m-dimensional. Assume that the n data samples are divided into K classes, and use the sample c i to represent the center point of class K i , N(K i ) represents the number of samples in class Ki, represents the distance between samples, then: （21） where r i is the class radius, which is the average distance from all data within the class to the class center; λ is the ratio of class radii; C ij is the midpoint between classes, which is the point that divides the line connecting different class centers according to the ratio of class radii; is the class center density, which is the number of data within the class whose distance from the class center point is less than the class radius; is the class edge density, representing the inter-class dispersion, that is, the number of data whose distance to the midpoint between classes is less than the average class radius in any two classes; The index is the ratio of the intra-class data density to the inter-class data density; is the optimal number of clusters for K-means clustering determined based on the DBI index; the spot price and peak-valley price difference formed after clustering are the corresponding typical libraries (X K1 , X K2 , X K3 ,...., X Kbest ; C K1 , C K2 , C K3 ,...., C Kbest ).

7. The method for analyzing the regulation potential of a virtual power plant in the spot market mode according to claim 1, wherein The method for establishing the adjustable potential analysis model of virtual power plant users under the typical scenario of the long-cycle spot mode in S5 is as follows: （22） Among them, , , are the adjustable power that can be shifted, transferred, and interrupted participated by the virtual power plant respectively. For the adjustable capabilities of the shiftable and transferable types, when the spot price is higher than the cost threshold of the i th adjustable power interval, all the adjustable capabilities of this interval can be included in the response capability interval; for the interruptible type of adjustable capability, when the spot price is lower than its cost threshold, all its adjustable capabilities can be included in the response capability interval.

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